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Urban Studies and Planning Dissertations and Theses

Theses/dissertations from 2024 2024.

Embodied Urban Political Ecology of Oil: Social Reproduction in Oil ‎Geographies Case Study: Ahwaz, Khuzestan, Iran , Maryam Amiri (Dissertation)

Three Essays on Communicative Planning: From the Perspective of East Asians , Minji Cho (Dissertation)

Participatory Democracy: The Potential for Civic Transformation Understanding Participants' Learning in the Milwaukie Citizens Jury , Amanda Grace Hudson (Dissertation)

An Analysis of Citizen Participation in Spatial Plan Preparation, Case Study of Gaborone City, Botswana , Bongani Tshidiso Ikaneng (Thesis)

The Central Role of Perceived Safety in Connecting Crash Risk Factors and Walking Behavior , Kyu Ri Kim (Dissertation)

The Effects of Physical Accessibility and Subjective Accessibility on Grocery Shopping Behaviors in Oakland, California , Huijun Tan (Dissertation)

Theses/Dissertations from 2023 2023

E Hui me ke Kaiāulu: To Connect with the Community , Heather Kayleen Bartlett (Thesis)

The Affective Discourses of Eviction: Right to Counsel in New York City , Hadley Savana Bates (Thesis)

A Just Futures Framework: Insurgent Roller-Skating in Portland, Oregon , Célia Camile Beauchamp (Thesis)

Factors Affecting Community Rating System Participation in the National Flood Insurance Program: A Case Study of Texas , Ryan David Eddings (Dissertation)

LEED Buildings and Green Gentrification: Portland as a Case Study , Jordan Macintosh (Thesis)

Wasted Space , Ryan Martyn (Thesis)

The Use and Influence of Health Indicators in Municipal Transportation Plans , Kelly Christine Rodgers (Dissertation)

Uncovering the Nuance and Complexity of Gentrification in Asian Immigrant Communities: A Case Study of Koreatown, Los Angeles , Seyoung Sung (Dissertation)

Defining Dementia-Friendly Communities From the Perspective of Those Affected , Iris Alexandra Wernher (Dissertation)

Theses/Dissertations from 2022 2022

Heat, Wildfire and Energy Demand: An Examination of Residential Buildings and Community Equity , Chrissi Argyro Antonopoulos (Dissertation)

The Connections Between Innovation, Culture, and Expertise in Water Infrastructure Organizations , Alice Brawley-Chesworth (Dissertation)

The New Shiny Penny? Regenerative Agriculture Beliefs and Practices Among Portland's Urban Agriculturalists , Melia Ann Chase (Thesis)

Fortunate People in a Fortunate Land: Dwelling and Residential Alienation in Santa Monica's Rent-Controlled Housing , Lauren E.M. Everett (Dissertation)

In Favor of Bringing Game Theory into Urban Studies and Planning Curriculum: Reintroducing an Underused Method for the Next Generation of Urban Scholars , Brian McDonald Gardner (Thesis)

Transportation Mode Choice Behavior in the Era of Autonomous Vehicles: The Application of Discrete Choice Modeling and Machine Learning , Sangwan Lee (Dissertation)

An Analysis of the Strengths and Weaknesses of the Tulsa Remote Program, As an Effective Economic Development Strategy , Kristen J. Padilla (Thesis)

Geographies of Urban Unsafety: Homeless Women, Mental Maps, and Isolation , Jan Radle Roberson (Dissertation)

The Impact of New Light Rail Service on Employment Growth in Portland, Oregon , Lahar Santra (Thesis)

Examining Emergency Citizen Response to the COVID-19 Pandemic: Emergent Groups Addressing Food Insecurity in Portland, Oregon , Aliza Ruth Tuttle (Thesis)

Theses/Dissertations from 2021 2021

Nature-Based Solutions in Environmental Planning: Ecosystem-Based Adaptations, Green Infrastructures, and Ecosystem Services to Promote Diversity in Urban Landscapes , Lorena Alves Carvalho Nascimento (Dissertation)

Gas Stations and the Wealth Divide: Analyzing Spatial Correlations Between Wealth and Fuel Branding , Jean-Carl Ende (Thesis)

'There are No Bathrooms Available!': How Older Adults Experiencing Houselessness Manage their Daily Activities , Ellis Jourdan Hews (Thesis)

The Mode Less Traveled: Exploring Bicyclist Identity in Portland, OR , Christopher Johnson (Thesis)

The Soniferous Experience of Public Space: A Soundscape Approach , Kenya DuBois Williams (Dissertation)

Short-term and Long-term Effects of New Light Rail Transit Service on Transit Ridership and Traffic Congestion at Two Geographical Levels , Huajie Yang (Dissertation)

Theses/Dissertations from 2020 2020

Waste Management in the Global South: an Inquiry on the Patterns of Plastic and Waste Material Flows in Colombo, Sri Lanka , Katie Ann Conlon (Dissertation)

Unpacking the Process and Outcomes of Ethical Markets: a Focus on Certified B Corporations , Renée Bogin Curtis (Dissertation)

The Persistence of Indigenous Markets in Mexico's 'Supermarket Revolution' , Diana Christina Denham (Dissertation)

The Electronic Hardware Music Subculture in Portland, Oregon , James Andrew Hickey (Thesis)

"I Should Have Moved Somewhere Else": the Impacts of Gentrification on Transportation and Social Support for Black Working-Poor Families in Portland, Oregon , Steven Anthony Howland (Dissertation)

The Impacts of the Bicycle Network on Bicycling Activity: a Longitudinal Multi-City Approach , Wei Shi (Dissertation)

Theses/Dissertations from 2019 2019

"Poverty Wages Are Not Fresh, Local, or Sustainable": Building Worker Power by Organizing Around (Re)production in Portland's "Sustainable" Food Industry , Amy Katherine Rose Coplen (Dissertation)

Manufacturing in Place: Industrial Preservation in the US , Jamaal William Green (Dissertation)

Can Churches Change a Neighborhood? A Census Tract, Multilevel Analysis of Churches and Neighborhood Change , David E. Kresta (Dissertation)

An Examination of Non-waged Labor and Local Food Movement Growth in the Southern Appalachians , Amy Kathryn Marion (Thesis)

Making Imaginaries: Identity, Value, and Place in the Maker Movement in Detroit and Portland , Stephen Joseph Marotta (Dissertation)

Recognizing and Addressing Risk Ambiguity in Sea Level Rise Adaptation Planning: a Case Study of Miami-Dade County, Florida , Mary Ann Rozance (Dissertation)

The Impact of Implementing Different Cordon Size Designs on Land Use Patterns in Portland, OR , Asia Spilotros (Dissertation)

Gentrification and Student Achievement: a Quantitative Analysis of Student Performance on Standardized Tests in Portland's Gentrifying Neighborhoods , Justin Joseph Ward (Thesis)

Theses/Dissertations from 2018 2018

Environmental Justice in Natural Disaster Mitigation Policy and Planning: a Case Study of Flood Risk Management in Johnson Creek, Portland, Oregon , Seong Yun Cho (Dissertation)

Our Town: Articulating Place Meanings and Attachments in St. Johns Using Resident-Employed Photography , Lauren Elizabeth Morrow Everett (Thesis)

Millennial Perceptions on Homeownership and Financial Planning Decisions , Margaret Ann Greenfield (Thesis)

Utilitarian Skateboarding: Insight into an Emergent Mode of Mobility , Michael Joseph Harpool (Thesis)

Consciousness Against Commodifcation: the Potential for a Radical Housing Movement in the Cully Neighborhood , Cameron Hart Herrington (Thesis)

News Work: the Impact of Corporate Newsroom Culture on News Workers & Community Reporting , Carey Lynne Higgins-Dobney (Dissertation)

Recent Advances in Activity-Based Travel Demand Models for Greater Flexibility , Kihong Kim (Dissertation)

An Analysis of the BizX Commercial Trade Exchange: the Attitudes and Motivations Behind Its Use , Ján André Montoya (Thesis)

Between a Rock and a Hot Place: Economic Development and Climate Change Adaptation in Vietnam , Khanh Katherine Pham (Thesis)

Neighborhood Economic Impacts of Contemporary Art Centers , Steve Van Eck (Closed Thesis)

Urban Geocomputation: Two Studies on Urban Form and its Role in Altering Climate , Jackson Lee Voelkel (Thesis)

Theses/Dissertations from 2017 2017

Explaining Unequal Transportation Outcomes in a Gentrifying City: the Example of Portland, Oregon , Eugenio Arriaga Cordero (Dissertation)

Identifying Clusters of Non-Farm Activity within Exclusive Farm Use Zones in the Northern Willamette Valley , Nicholas Chun (Thesis)

Drivers' Attitudes and Behaviors Toward Bicyclists: Intermodal Interactions and Implications for Road Safety , Tara Beth Goddard (Dissertation)

Grassroots Resistance in the Sustainable City: Portland Harbor Superfund Site Contamination, Cleanup, and Collective Action , Erin Katherine Goodling (Dissertation)

Responsible Pet Ownership: Dog Parks and Demographic Change in Portland, Oregon , Matthew Harris (Thesis)

The Tension between Technocratic and Social Values in Environmental Decision-making: An'Yang Stream Restoration in South Korea , Chang-Yu Hong (Dissertation)

Regulating Pavement Dwellers: the Politics of the Visibly Poor in Public Space , Lauren Marie Larin (Dissertation)

Making Software, Making Regions: Labor Market Dualization, Segmentation, and Feminization in Austin, Portland and Seattle , Dillon Mahmoudi (Dissertation)

Knowing Nature in the City: Comparative Analysis of Knowledge Systems Challenges Along the 'Eco-Techno' Spectrum of Green Infrastructure in Portland & Baltimore , Annie Marissa Matsler (Dissertation)

Assessing the Impact of Land Use and Travel on Carbon Dioxide Emissions in Portland, Oregon , Zakari Mumuni (Thesis)

Trade-offs: the Production of Sustainability in Households , Kirstin Marie Elizabeth Munro (Dissertation)

Theses/Dissertations from 2016 2016

The Kazaks of Istanbul: A Case of Social Cohesion, Economic Breakdown and the Search for a Moral Economy , Daniel Marc Auger (Thesis)

Citizen-led Urban Agriculture and the Politics of Spatial Reappropriation in Montreal, Quebec , Claire Emmanuelle Bach (Thesis)

Travel Mode Choice Framework Incorporating Realistic Bike and Walk Routes , Joseph Broach (Dissertation)

Cyclist Path Choices Through Shared Space Intersections in England , Allison Boyce Duncan (Dissertation)

Star Academics: Do They Garner Increasing Returns? , James Jeffrey Kline (Dissertation)

Configuring the Urban Smart Grid: Transitions, Experimentation, and Governance , Anthony Michael Levenda (Dissertation)

The Effects of Frequency of Social Interaction, Social Cohesion, Age, and the Built Environment on Walking , Gretchen Allison Luhr (Dissertation)

The Village Market: New Columbia Goes Shopping for Food Justice , Jane Therese Waddell (Dissertation)

Theses/Dissertations from 2015 2015

Developing Key Sustainability Competencies through Real-World Learning Experiences: Evaluating Community Environmental Services , Erin Lorene Anderson (Thesis)

Beyond Fruit: Examining Community in a Community Orchard , Emily Jane Becker (Thesis)

Challenges, Experiences, and Future Directions of Senior Centers Serving the Portland Metropolitan Area , Melissa Lynn Cannon (Dissertation)

Building Social Sustainability from the Ground Up: The Contested Social Dimension of Sustainability in Neighborhood-Scale Urban Regeneration in Portland, Copenhagen, and Nagoya , Jacklyn Nicole Kohon (Dissertation)

The Effects of Urban Containment Policies on Commuting Patterns , Sung Moon Kwon (Dissertation)

Energy Efficiency and Conservation Attitudes: An Exploration of a Landscape of Choices , Mersiha Spahic McClaren (Dissertation)

The Impact of Communication Impairments on the Social Relationships of Older Adults , Andrew Demetrius Palmer (Dissertation)

The Scales and Shapes of Queer Women's Geographies: Mapping Private, Public and Cyber Spaces in Portland, OR , Paola Renata Saldaña (Thesis)

Caring for the Land, Serving People: Creating a Multicultural Forest Service in the Civil Rights Era , Donna Lynn Sinclair (Dissertation)

Theses/Dissertations from 2014 2014

Determinants of Recent Mover Non-work Travel Mode Choice , Arlie Steven Adkins (Dissertation)

Changing the Face of the Earth: The Morrison-­Knudsen Corporation as Partner to the U.S. Federal Government , Christopher S. Blanchard (Dissertation)

Participation, Information, Values, and Community Interests Within Health Impact Assessments , Nicole Iroz-Elardo (Dissertation)

The Objective vs. the Perceived Environment: What Matters for Active Travel , Liang Ma (Dissertation)

Implications of Local and Regional Food Systems: Toward a New Food Economy in Portland, Oregon , Michael Mercer Mertens (Dissertation)

Spirituality and Religion in Women's Leadership for Sustainable Development in Crisis Conditions: The Case of Burma , Phyusin Myo Kyaw Myint (Dissertation)

Street Level Food Networks: Understanding Ethnic Food Cart Supply Chains in Eastern Portland, OR , Alexander G. Novie (Thesis)

Theses/Dissertations from 2013 2013

Diffusion of Energy Efficient Technology in Commercial Buildings: An Analysis of the Commercial Building Partnerships Program , Chrissi Argyro Antonopoulos (Thesis)

Faulty Measurements and Shaky Tools: An Exploration into Hazus and the Seismic Vulnerabilities of Portland, OR , Brittany Ann Brannon (Thesis)

Sustainable, Affordable Housing for Older Adults: A Case Study of Factors that Affect Development in Portland, Oregon , Alan Kenneth DeLaTorre (Dissertation)

The Historical, Political, Social, and Individual Factors That Have Influenced the Development of Aging and Disability Resource Centers and Options Counseling , Sheryl DeJoy Elliott (Thesis)

Neighborhood Identity and Sustainability: A Comparison Study of Two Neighborhoods in Portland, Oregon , Zachary Lawrence Hathaway (Thesis)

Neighborhood Commercial Corridor Change: Portland, Oregon 1990-2010 , Kelly Ann Howsley-Glover (Dissertation)

Public Space and Urban Life: A Spatial Ethnography of a Portland Plaza , Katrina Leigh Johnston (Thesis)

Green Mind Gray Yard: Micro Scale Assessment of Ecosystem Services , Erin Jolene Kirkpatrick (Thesis)

The Impacts of Urban Renewal: The Residents' Experiences in Qianmen, Beijing, China , Yongxia Kou (Dissertation)

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• Published: 27 April 2021

Urbanization: an increasing source of multiple pollutants to rivers in the 21st century

• Maryna Strokal   ORCID: orcid.org/0000-0002-8063-7743 1 ,
• Zhaohai Bai   ORCID: orcid.org/0000-0001-7685-5441 2 ,
• Wietse Franssen 1 ,
• Nynke Hofstra 1 ,
• Albert A. Koelmans 3 ,
• Fulco Ludwig 1 ,
• Lin Ma   ORCID: orcid.org/0000-0003-1761-0158 2 ,
• Peter van Puijenbroek   ORCID: orcid.org/0000-0001-6370-2411 4 ,
• J. Emiel Spanier 1 ,
• Lucie C. Vermeulen   ORCID: orcid.org/0000-0002-8403-2442 5 ,
• Michelle T. H. van Vliet   ORCID: orcid.org/0000-0002-2597-8422 6 ,
• Jikke van Wijnen 7 &
• Carolien Kroeze 1  

npj Urban Sustainability volume  1 , Article number:  24 ( 2021 ) Cite this article

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Most of the global population will live in urban areas in the 21st century. We study impacts of urbanization on future river pollution taking a multi-pollutant approach. We quantify combined point-source inputs of nutrients, microplastics, a chemical (triclosan) and a pathogen ( Cryptosporidium ) to 10,226 rivers in 2010, 2050 and 2100, and show how pollutants are related. Our scenarios consider socio-economic developments and varying rates of urbanization and wastewater treatment. Today, river pollution in Europe, South-East Asia and North America is severe. In the future, around 80% of the global population is projected to live in sub-basins with multi-pollutant problems in our high urbanization scenarios. In Africa, future river pollution is projected to be 11–18 times higher than in 2010, making it difficult to meet Sustainable Development Goals. Avoiding future pollution is technically possible with advanced wastewater treatment in many regions. In Africa, however, clean water availability is projected to remain challenging. Our multi-pollutant approach could support effective water pollution assessment in urban areas.

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Current wastewater treatment targets are insufficient to protect surface water quality

Chemical pollution imposes limitations to the ecological status of European surface waters

Domestic waste emissions to European waters in the 2010s

Introduction.

Urban areas currently accommodate more than half of the global population 1 and generate over two-thirds of the world gross domestic products (GDP) 2 , 3 . In 2050, more than two-thirds of the global population will live in cities 1 , 4 , 5 . Rapid urbanization creates opportunities for economic developments 6 , but may also increase the use of freshwater resources 4 , 6 , 7 , 8 , 9 . This will increase competition for water between cities and agriculture 4 . More urban waste is likely to result in contamination of water with multiple pollutants such as nutrients 10 and pathogens 11 , 12 from human excretion, plastics 13 , 14 , 15 , 16 , 17 , 18 , and chemicals 19 , 20 from personal care products. River pollution poses a threat to the availability of clean water in large parts of the world 7 , 21 , challenging the achievement of Sustainable Development Goal 6 (SDG, clean water for all) and 11 (sustainable cities). Recent studies on impacts of rapid urbanization on water stress or water scarcity worldwide exist 4 , but often ignore water quality 7 .

Previous global studies likely underestimate the impact of urbanization on water pollution because of their strong focus on single pollutants 10 , 16 , 20 , 22 , 23 , 24 (Fig. 1 ). Urbanization (e.g., sewer connections in cities) is, however, often a common, point source of multiple pollutants in rivers, contributing to multiple impacts. Examples are eutrophication problems caused by nitrogen (N) and phosphorus (P) in many world regions 25 , 26 , and diarrhea caused by pathogens (e.g., Cryptosporidium ) especially in developing countries 11 , 27 . A multi-pollutant approach is, thus, urgently needed to account for interactions between drivers of urbanization (e.g., population, economy) and pressures such as emissions of different pollutants 21 . This can help to identify effective solutions accounting for synergies and trade-offs in pollution control. Furthermore, reducing multiple pollutants in rivers from urban-related sources might be easier (e.g., improved wastewater treatment) than from diffuse sources such as agricultural runoff (e.g., delay effects of reduction options due to accumulation of substances in soils). This may have a positive effect on the overall water quality status depending on diffuse sources.

The figure shows a difference between single-pollutant approaches (most existing studies) and a multi-pollutant approach (this study) to assess the impacts of the rapid urbanization on future global river quality. We take N (nitrogen), P (phosphorus), pathogens and plastics as examples. Advances of the multi-pollutant approach are discussed in the main text.

In this paper, we study the impacts of urbanization on river pollution in the 21st century, taking a multi-pollutant perspective. We define multi-pollutant problems as increasing levels of more than one pollutant to rivers in future decades. We analyze, simultaneously, the following groups of pollutants: nutrients (N and P), pathogens (such as Cryptosporidium ), microplastics and chemicals (such as triclosan). These pollutants are selected because of their increasing pollution in many rivers worldwide 18 , 20 , 23 , 28 , 29 , 30 . Yet, these pollutants have common urban sources such as sewer systems (worldwide) and open defecation. We quantify point-source inputs of the pollutants to 10,226 rivers for 2010, 2050 and 2100 associated with urbanization: sewer systems and open defecation. For this, we use a global model of Strokal et al. 31 that takes the sub-basin scale modelling approach of Strokal et al. 32 for nutrients and integrates modelling approaches for other pollutants 18 , 20 , 23 (Supplementary Tables 1 , 2 and 3 ). We develop this model further for multiple-pollutants and future analyses based on evaluated, modelling approaches (see the “Methods” section).

To assess the impacts of urbanization, we develop five scenarios with different levels of urbanization and wastewater treatment rates (Fig. 2 ). The storylines are interpretations of the five Shared Socio-economic Pathways (SSPs) 33 , 34 , 35 , 36 (Supplementary Tables 4 , 5 and 6 ). These SSPs are five pathways with different levels of socio-economic challenges for mitigation and adaptation 33 , 34 , 35 , 36 . SSP1 is a Green Road pathway with low socio-economic challenges (e.g., low population growth), but with high economic and urbanization development. It is largely oriented towards achieving sustainable goals (see Supplementary Tables 4 , 5 and 6 ). SSP2 is a middle of the road pathway with medium challenges to mitigation and adaptation. Future trends will not be very different from historical trends. SSP3 is a Rocky Road pathway with high challenges to mitigation and adaptation. It is a world with difficulties to control the population growth and has low economic and urbanization development (see Supplementary Tables 4 , 5 and 6 ). SSP4 is a Road Divided pathway with high challenges to mitigation and low to adaptation. It has a large gap between urban and rural development with the high urbanization rates especially in urban areas. SSP5 is a taking the highway pathway with high challenges to mitigate, but low challenges to adapt. It is a word with priorities towards economy (see Supplementary Tables 4 , 5 and 6 ).

Low, moderate and high urbanization is defined here as the increasing number of urban people and total people with sewer connections (see a and b panels and Supplementary Tables 4 – 6 ). The number of people opens defecating directly to water is assumed to decrease with sewer connection. Higher sewer connections imply that more wastewater treatment plants will be constructed to maintain the increasing volumes of the waste (see the “Methods” section). Low, moderate and high wastewater treatment levels refer here to a shirt (low, moderate, high) towards a next treatment type: e.g., from primary to secondary to tertiary ( a , b , Supplementary Tables 4 – 6 ). This implies the low, moderate and high ambitions to improve wastewater treatment ( b ). Future years are 2050 and 2100. Supplementary Tables 1 – 6 give quantitative interpretations of the storylines for our multi-pollutant model (see also the “Methods” section). GDP is the gross domestic product. Sources for the technologies are in the main text and in Supplementary Table 3 .

Our five scenarios incorporate socio-economic pathways of SSPs, but with quantitative interpretations of aspects related to urbanization and wastewater treatment (see the “Methods” section). Our scenarios aim to show the impact of urbanization on multiple pollutants in rivers. Thus, the names of our five scenarios correspond to the different levels of urbanization and wastewater treatment: from low urbanization and low wastewater treatment rates towards high urbanization and high wastewater treatment rates. This results in the following scenarios: low urbanization and low wastewater treatment rates (Low urb –Low wwt , based on SSP3), moderate urbanization and moderate wastewater treatment rates (Mod urb –Mod wwt , based on SSP2), high urbanization and low wastewater treatment rates (High urb –Low wwt , based on SSP4), high urbanization and moderate wastewater treatment rates (High urb –Mod wwt , based on SSP5), and high urbanization and high wastewater treatment rates (High urb –High wwt , based on SSP1) (Fig. 2 ). The five scenarios consider interactions between global change (socio-economic pathways), urbanization, sanitation and wastewater treatment.

Low, moderate and high urbanization reflect different levels of increases in urban population, and, indirectly, people with sewer connections between 2010 and future years (see the “Methods” section). As a net effect, the number of people practicing open defecation (direct inputs of human waste to rivers) may decrease. Increasing sewer connections assume higher capacities of treatment plants to manage increasing volumes of the wastewater. Low, moderate and high rates of wastewater treatment are defined based on a shift towards a next treatment type: e.g., from primary (technologies with <10% removal rates 10 , 18 , 20 , low) to secondary (50% removal rates 10 , 18 , 20 , 37 , moderate) or to tertiary (>75% removal rates 10 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , high, see the “Methods” section). The differences between the Low urb –Low wwt, and High urb –Low wwt scenarios indicate the impact of urbanization in terms of increasing numbers of people with sewer connections with low ambitions to improve the wastewater treatment under different socio-economic developments. The Mod urb –Mod wwt scenario could be considered business as usual. The differences between the High urb –Low wwt , High urb –Mod wwt and High urb –High wwt scenarios indicate the impact of improving the wastewater treatment in highly urbanized areas. Details are given in the “Methods” section on qualitative and quantitative descriptions of the five urbanization scenarios.

River pollution today

River pollution in Europe, South-East Asia and North America is already severe today. For these regions, we calculate high inputs of N (>50 kg km −2  year −1 ), P (>30 kg km −2  year −1 ), triclosan (>10 g km −2  year −1 ), microplastics (>5 kg km −2  year −1 ) and Cryptosporidium (>100 × 10 17 oocysts km −2 year −1 ) to many rivers in 2010 (Fig. 3 ). These regions experience severe water pollution problems 9 , 16 , 21 , 25 , 45 , contributing to negative impacts 21 such as eutrophication 45 and waterborne diseases (South-East Asian countries). For African sub-basins, pollution levels are not as high as in those regions (Fig. 3 ). However, some impacts of polluted water on children’s health are already indicated 21 . Globally, 9.5 Tg of N, 1.6 Tg of P, 0.45 Tg of microplastics, 0.72 kton of triclosan and 1.6 × 10 17 oocysts of Cryptosporidium entered rivers in 2010 (Fig. 4 , Supplementary Table 7 ). More than half of these inputs are to rivers in South-East Asia. Most of the pollutants in rivers are from sewer systems (see details in Supplementary Figs. from 1 to 29 ). Exceptions are some sub-basins in Africa and South-East Asia where open defecation contributes to over 20% of N, P and Cryptosporidium to their rivers. Existing assessments 9 , 10 , 13 , 20 , 23 reveal similar global estimates, but with diverse spatial scales. Our consistent spatial and temporal scales increase the robustness of our comparisons between multiple pollutants worldwide (e.g., Fig. 4 ).

Units are kg km −2 of sub-basin area year −1 for nitrogen (N), phosphorus (P) and microplastics (MP), g km −2 of sub-basin area year −1 for triclosan (TCS) and 10 17 oocysts km −2 of sub-basin area year −1 for Cryptosporidium . Source: the global multi-pollutant model (model description is provided in the “Methods” section, and in Supplementary Tables 1 – 6 , model inputs are in Supplementary Figs. 1 – 14 ). Model uncertainties are discussed in the “Methods” section.

a – e Future trends for individual pollutants. Pies show the shares of the surface areas by region as % of the global surface area. Spatially explicit results are shown in Fig. 3 for 2010 and Fig. 5 for the future. The description of the scenarios is in Fig. 2 , in the “Methods” section and Supplementary Tables 1 – 6 . Source: the global multi-pollutant model (model description is provided in the “Methods” section, and in Supplementary Tables 1 – 6 , model inputs are in Supplementary Figs. 1 – 14 ). Model uncertainties are discussed in the “Methods” section.

High pollution levels result from the net effect of population densities, sewer connection rates (Supplementary Figs. 1 , 2 and 3 ), production of pollutants in human waste (Supplementary Figs. 4 , 5 , 6 , 7 and 8 for individual pollutants) and wastewater treatment efficiencies (Supplementary Figs. 9 , 10 , 11 , 12 and 13 for individual pollutants) in countries (Supplementary Figs. 14 and 15 ). For South-East Asia, high pollution levels are driven by high population densities (Supplementary Figs. 3 and 16 ). This region accommodates approximately half of the global population (3 billion people, Supplementary Fig. 1 ) on 12% of the global surface area (Fig. 4 ). For comparison, sub-basins of Europe (excluding Russia) and North America accommodate around 10% of the global population (0.8 billion people, Supplementary Fig. 1 ) on 20% of the global surface area (Fig. 4 ). Approximately 20% of the total population in 2010 was connected to sewer systems (Supplementary Fig. 1 ) with relatively low wastewater treatment efficiencies (removal levels <50% for most pollutants, Supplementary Figs. 9 – 13 ). For Europe and North America, the high pollution levels per km 2 of sub-basins are driven by high connection rates to sewer systems especially in urban areas. Here, over two-thirds of the population live in urban areas and are largely connected to sewer systems with removal efficiencies above 50% for the studied pollutants (Supplementary Figs. 9 – 13 ). Supplementary Fig. 17 shows the results of the sensitivity analysis indicating the importance of wastewater treatment and human development in river pollution (see the “Discussion” section).

Future river pollution globally

In the future, ~80% of the global population is projected to live in sub-basins with multi-pollutant problems (Figs. 5 and 6 ). These sub-basins cover over half of the global surface area (Fig. 6 ) for which inputs of more than one pollutant will increase at least 30% (Fig. 5 ) between 2010 and 2050 or 2100. This is for all scenarios, except for High urb –High wwt . In the scenario assuming low urbanization and low wastewater treatment (Low urb –Low wwt ), global inputs of most pollutants will less than double between 2010 and 2050 (Fig. 4 ). In this scenario, the population growth is high, and almost doubles between 2010 and 2100 (Supplementary Fig. 3 ). Approximately one-third of the total population globally will be connected to sewer systems. This number is much lower than in the other scenarios in 2100 (Supplementary Fig. 3 ). As a net effect of the low sewer connection (Supplementary Fig. 3 ) and low wastewater treatment (Supplementary Figs. 9 – 13 ), future inputs of pollutants to rivers from sewage are lower in the Low urb –Low wwt scenario than in the others (Fig. 3 ). However, as a trade-off, more nutrients and Cryptosporidium are projected to enter rivers from open defecation, mainly in developing countries (see Supplementary Figs. 14 and 15 ) compared to the other scenarios.

Maps show changes in inputs of pollutants to rivers during the periods of 2010–2050, 2010–2100 and 2050–2100 according to the five scenarios. We classify sub-basins based on the number of pollutants for which the increases are higher or lower than 30% (Note: 30% is arbitrary; see Supplementary Figs. 18 and 20 for results based on 10 and 50% thresholds). The pollutants include Cryptosporidium , microplastic, triclosan, nitrogen and phosphorus. More information is available in Supplementary Figs. 18 – 29 . The description of the five scenarios is in Fig. 2 , in the “Methods” section and Supplementary Tables 1 – 6 . Results for 2010 are in Fig. 3 . Source: the global multi-pollutant model (model description is provided in the “Methods” section, and in Supplementary Tables 1 – 6 , model inputs are in Supplementary Figs. 1 – 14 ). Model uncertainties are discussed in the “Methods” section.

Sub-basins are classified based on the number of pollutants for which the increases are higher or lower than 30% during the periods of 2010–2050, 2010–2100 and 2050–2100 according to the five scenarios. Graphs show the number of sub-basins ( a ), sub-basin areas ( b ), total population ( c ) and urban population ( d ) for the sub-basins with the increases of higher or lower than 30% (Note: 30% is arbitrary; see Supplementary Figs. 19 and 21 for results based on 10% and 50% thresholds). More information is available in Supplementary Figs. 18 – 29 . See Fig. 5 for the changes in inputs of pollutants during the periods of 2010–2050, 2010–2100 and 2050–2100. The description of the scenarios is in Fig. 2 , in the “Methods” section and Supplementary Tables 1 – 6 . Results for 2010 are in Fig. 3 . Source: the global multi-pollutant model (model description is provided in the “Methods” section, and in Supplementary Tables 1 – 6 , model inputs are in Supplementary Figs. 1 – 14 ). Model uncertainties are discussed in the “Methods” section.

The future inputs of most pollutants to rivers are projected to be higher in the scenarios with moderate (Mod urb –Mod wwt ) and high urbanization (High urb –Low wwt , High urb –Mod wwt , Fig. 4 ). The population grows not as fast as in the Low urb –Low wwt scenario, but the rate of urbanization is much higher, especially in the High urb –Low wwt and High urb –Mod wwt scenarios (Supplementary Tables 4 – 6 ). As a result, over two-thirds of the global population is projected to be connected to sewer systems in 2100 (Supplementary Fig. 3 ). Wastewater treatment efficiency is slightly improved (Mod urb –Mod wwt , High urb –Mod wwt ) depending on the economic development (Supplementary Figs. 9 – 13 ). As a net effect, the High urb –Low wwt and High urb –Mod wwt scenarios project, generally, higher inputs of most pollutants to rivers than the Low urb –Low wwt and Mod urb –Mod wwt scenarios (Fig. 4 ).

Pollutants differ in their future trends. For example, High urb –Low wwt projects the highest inputs of Cryptosporidium , microplastics and triclosan globally in 2100 compared to the other pollutants and scenarios (Fig. 4 ). For N and P, High urb -Low wwt and High urb -Mod wwt project somewhat similar amounts globally (Fig. 4 ). All these differences between pollutants and scenarios are a net effect of three important factors: socio-economic development (e.g., population, GDP), urbanization rates (population connected to sewer systems) and treatment efficiencies. For example, higher GDP results generally in higher N and P excretion rates per capita because of changes towards protein-rich diets 31 , 46 (Supplementary Figs. 4 – 5 ). Developed countries (Human Developing Index, HDI > 0.785) have generally lower infection rates, leading to less per capita excretion of Cryptosporidium 23 (Supplementary Fig. 8 ), but may lead to higher production of microplastics from car tyres 31 (Supplementary Fig. 7 ) as a result of industrialization. All these interactions are considered together with different trends in the population growth (Supplementary Fig. 3 ), urbanization rates (Supplementary Figs. 1 and 2 ) and treatment levels (Supplementary Figs. 9 – 13 ) among scenarios and regions.

Future river pollution in Africa

Future river pollution is projected to be 11–18 times higher than in 2010 in the scenario with high urbanization and low wastewater improvements (High urb –Low wwt ). This range is for increasing inputs of the five pollutants by at least 30% during the period of 2010–2100 (Fig. 5 ). Africa may become a major contributor to river pollution in the world (Fig. 4 ). For example, by 2100, up to half of the global inputs of multiple pollutants are projected in Africa in High urb –Low wwt (Fig. 4 ). For comparison: in 2010 the contribution of African rivers to the global river pollution was <5% (Fig. 4 ). All scenarios project increasing river pollution in the future for Africa (Figs. 5 and 6 ). This is largely associated with the projected population growth and assumed wastewater treatment. The African population is projected to more than double in many sub-basins during 2010–2100 in all scenarios (Supplementary Fig. 3 ). Many people will live in urban areas (High urb –Low wwt and High urb –Mod wwt , Supplementary Figs. 1 – 3 ). More people will inevitably generate more waste, and this may not be treated effectively enough (e.g., High urb –Low wwt ). This all explains the large future increases in river pollution in Africa (Fig. 5 ). In the low urbanization scenario (Low urb –Low wwt ), less people will live in urban areas, and a lower percentage of people will be connected to sewer systems. Thus, open defecation may continue in Low urb –Low wwt especially by 2050. This is an important source of nutrients and Cryptosporidium to African rivers in this scenario. Supplementary Figs. 18 , 19 , 20 and 21 show results for increasing inputs of the five pollutants by at least 10% and 50% during the period of 2010–2100. Supplementary Figs. 22 , 23 , 24 , 25 and 26 show future trends in river pollution by individual pollutants. Supplementary Figs. 27 , 28 and 29 show scenarios and sub-basins where open defecation is an important source of P, N and Cryptosporidium in rivers.

Future river pollution in Asia

Future river pollution is projected to be 2–3 times higher than in 2010 in the scenario with high urbanization and low wastewater improvements (High urb –Low wwt ). This range is for at least 30% increases in inputs of the five pollutants for the period 2010–2100 (Fig. 5 ). Exceptions are rivers in sub-basins of China (Fig. 5 ). These rivers are projected to be cleaner in 2100 than in 2050, but inputs of the pollutants may still be higher in 2100 than in 2010 in the urbanized scenarios with the low (High urb –Low wwt and Low urb –Low wwt ) and moderate (Mod urb –Mod wwt and High urb –Mod wwt ) wastewater treatment improvements (Fig. 5 ). The Chinese population is projected to decrease in the future in all scenarios (Supplementary Fig. 3 ). However, with the rapid urbanization (Supplementary Figs. 1 – 2 ), the wastewater treatment (Supplementary Figs. 9 – 13 ) may not keep up with the pollution loads. This explains higher river pollution levels. This is different for some other Asian countries such as India and Pakistan. By 2050, the total population of India and Pakistan will have increased (Supplementary Fig. 3 ). By 2100, the total population will have decreased or increased depending on the socio-economic development in the scenarios (Supplementary Fig. 3 , Supplementary Tables 4 – 6 for the scenario description). However, the wastewater treatment is poorer or absent compared to the Chinese sub-basins (Supplementary Figs. 9 – 13 ), resulting in more pollutants in rivers (Fig. 5 , Supplementary Figs. 18 – 21 ).

Future river pollution in Europe and North America

Many rivers in Europe and North America may be cleaner in the future. European rivers (Western, Northern and Southern) may get cleaner in the future because of high removal efficiencies to treat wastewater (Supplementary Figs. 9 – 13 ). However, in the High urb –Mod wwt scenario, high wastewater treatment efficiencies (>50% for all pollutants) may not be enough to reduce future pollution to the level below 2010. For American rivers, future trends differ largely between South and North in the scenarios with the low (Low urb –Low wwt ) and high (High urb –Low wwt ) urbanization trends. In the Low urb –Low wwt scenario, lower increases (<30%) in inputs of pollutants are projected for many Northern rivers whereas higher increases (>30%) for most Southern rivers (Fig. 5 , Supplementary Figs. 18 – 21 ). This difference can be explained by the higher population growth (Supplementary Figs. 1 – 3 ) and less efficient wastewater treatment (Supplementary Figs. 9 – 13 ) in South America compared to North America. In the High urb –Low wwt scenario, higher increases in river pollution are projected for South America by 2050, but lower by 2100. This is associated with the decreased population (Supplementary Fig. 3 ) and with the increased efficiencies of wastewater treatment between 2050 and 2100 (Supplementary Figs. 9 – 13 ). Rivers in Australia may be more polluted in the future (Fig. 5 ). Exceptions are the Low urb –Low wwt and High urb –Low wwt scenarios with less pollution in 2100 than in 2050. This is largely associated with the decreasing population during 2050–2100 (Supplementary Figs. 1 – 13 , 18 – 21 ).

Reducing future river pollution

Advanced wastewater treatment can reduce future river pollution in many world regions, but not in Africa (High urb –High wwt ). In High urb –High wwt, all developed countries (HDI > 0.785) will shift completely towards tertiary treatment with enough capacities and high efficiencies to remove pollutants from the wastewater (>75% for all pollutants, Supplementary Figs. 1 – 14 ). Examples of such technologies are annomox 47 for N, calcium precipitation for P 48 , disinfection by Ultraviolet radiation for Cryptosporidium 42 , reverse osmosis for nutrients 41 and microplastics 49 . Developing countries (HDI < 0.785) will also shift towards tertiary technologies, but in combination with secondary technologies 10 , 46 (Supplementary Figs. 1 – 14 ). Open defecation will stop by 2100. Thus, High urb –High wwt shows the technical potential of advanced technologies with enough treatment capacities to reduce future pollution from highly urbanized areas.

It will be difficult to reduce future river pollution in Africa to the level of 2010, even with advanced technologies (High urb –High wwt , Fig. 5 ). Inputs of most pollutants to many African rivers are projected to increase by at least 30% during 2010–2100 in High urb –High wwt (Fig. 5 ). The main reason is an increase in the total population, which is much higher (>doubling) than in other world regions (Supplementary Fig. 3 ). As a result, implementing advanced technologies in 2100 may help to reduce inputs of most pollutants to the level of 2050, but not to the level of 2010. For many other world’s rivers, advanced technologies with enough treatment capacities are projected to lower future inputs of pollutants in High urb –High wwt (Fig. 5 , Supplementary Fig. 20 ). This may have a positive impact on the overall pollution status depending also on the contribution of diffuse sources from agriculture. However, for some rivers in Asia (e.g. India, Pakistan), inputs of most pollutants from point sources will still increase by 2050, but may be lower by 2100 in High urb –High wwt (Fig. 5 ). Some rivers in North America, Middle Asia and Australia are projected to have higher inputs of pollutants in 2100 than in 2050, but lower than in 2010 (Fig. 5 , Supplementary Fig. 20 ). These trends are the net effect of the population growth, urbanization and wastewater treatment in High urb –High wwt (Figs. 2 , 5 and 6 ).

Scenario analyses are widely used to explore possible futures 1 , 34 , 36 , 50 , 51 , 52 . Our five scenarios are a combination of possible trends in urbanization, socio-economic development (existing SSPs 1 , 36 , 53 ) and our assumptions on sanitation, wastewater treatment capacities and removal efficiencies of pollutants. Our assumptions may, however, seem ambitious (Supplementary Tables 5 and 6 ). For example, we assume the full implementation of advanced technologies with enough treatment capacities in High urb –High wwt for all developed countries. We did this to show the effects of sustainable practices in urban areas on increasing the availability of clean water for people and nature. This assumption, however, might be ambitious to achieve. In our scenarios, we reflect a relation between urbanization (e.g., more urban people) and sewer connections (see High urb –Low wwt, High urb –Med wwt ) with sustainable urbanization practices (see High urb –High wwt ). This relation may, however, not emerge everywhere in the world. On the other hand, we explore possible futures; we do not state how likely or desirable these futures are. Our scenarios aim to identify impacts of future urbanization (e.g., differences between Low urb –Low wwt and High urb –Low wwt ) and the technical potentials of proven wastewater treatment technologies to reduce future river pollution from point sources (e.g., differences between High urb –Low wwt and High urb –High wwt ). Our insights may contribute to the formulation of sustainable urbanization practices where wastewater treatment is effective enough to reduce pollutants in the urban waste (e.g., SDG11) and thus to increase the availability of clean water in the future (e.g., SDG6).

Our global multi-pollutant model quantifies, simultaneously, five pollutants in rivers with consistent datasets in space and time. However, uncertainties exist. The model is developed based on existing, evaluated models for pollutants 11 , 18 , 20 , 23 , 29 , 32 (e.g., comparisons with observed concentrations and sensitivity analyses). We further evaluate our combined model using five approaches 54 (see the “Methods” section). First, we compare our model outputs with existing studies (see the “Methods” section, Supplementary Table 7 ), showing a good agreement for the five pollutants. Second, we compare the spatial pattern of pollution problems with existing models 8 , 9 , 10 , 11 , 12 , 16 , 55 , 56 , indicating the river pollution in densely populated and highly urbanized areas (Figs. 3 – 5 , Supplementary Tables 7 and 8 ). However, existing studies did not focus on a simultaneous reduction of the five pollutants from urbanized activities in the 21st century, which is a multi-pollutant perspective of our study. Third, we performed a sensitivity analysis for pollution hotspots. We define multi-pollutant hotspots as places with >30% increases in two or more pollutants between 2010 and future years (Fig. 5 ). This is an elegant way to combine the five pollutants. We realize that the 30% threshold is arbitrary. The results should, therefore, be interpreted as warning signals of future river pollution. In the sensitivity analysis, we changed the 30% threshold to 10% (Supplementary Figs. 18 – 19 ) and 50% (Supplementary Figs. 20 – 21 ). The results confirm the robustness of our main messages about future multi-pollutant hotspots. Fourth, we performed a sensitivity analysis for all important model inputs underlying the calculations (Supplementary Tables 9 , 10 , 11 and 12 , Supplementary Fig. 17 ). In total, 25 model inputs are changed with ±10%, resulting in 50 model runs for 10,226 sub-basins and five pollutants. The results show that the model is not very sensitive to changes in most model inputs. For most sub-basins, the model outputs are relatively sensitive to changes in <5 model inputs. These inputs are related to HDI, wastewater treatment types and removal efficiencies. The 10% changes in these inputs, resulted in up to 5% change in model output for sub-basins covering over two-thirds of the global surface area (see details in the “Methods” section for all sub-basins). Fifth, we compare model inputs with independent datasets (Supplementary Table 8 , Supplementary Figs. 15 and 16 ). All this gives trust in the model performance (see the “Methods” section).

Our results are future oriented. We focus on trends in future hotspots of multi-pollutant problems in the world. We believe that not all model uncertainties affect our main messages about trends. We also realize that our results are relatively sensitive to the assumptions on future HDI and wastewater treatment (see Approach 4 in the “Methods” section and sensitivity analysis). For HDI, we assumed an increase of 0, 10 and 20% between 2010 and 2050 and further increase by 2100 depending on scenario (Supplementary Tables 5 – 6 ). For wastewater treatment rates, we assumed a shift towards a next treatment type between 2010 and future years (e.g., 0–50% shift depending on scenario). To increase trust in our assumptions for future trends, we compared our model inputs with other independent studies. We did this for our five scenarios (Supplementary Table 8 , Supplementary Fig. 15 ). For example, future trends in our HDI between 2010 and future years are strongly in line with an independent study 57 ( R 2 above 0.88, see Supplementary Fig. 15 ). Crespo Cuaresma and Lutz 57 took into account differences in human development and their socio-economic wealth in projecting future HDI. Our wastewater treatment types in 2050 are also well compared with an independent study 10 (Supplementary Table 8 ).

Another potential source of uncertainties relates to the local variation in pollution levels. For example, sewage overflows may happen under heavy rain events, causing local peaks in water pollution. Such events are time dependent and may also contribute to global pollution levels 58 . We do not account for such local events in our model. We, however, believe that such omissions of events do not affect our messages for the multi-pollutants worldwide. This is because we explore future trends in the multi-pollutant hotspots worldwide that are influenced by global change, urbanization and wastewater treatment. Local analyses should, however, account for the impact of local events on local water quality (e.g., cities).

Our study aims to analyze the impact of the socio-economic drivers (e.g., GDP) and urbanization on future inputs of pollutants to rivers from point sources worldwide. However, we do not consider the transport of pollutants to rivers from agricultural fields, nor the impact of climate change on future river pollution. Next steps could be to further develop our global multi-pollutant model by calculating inputs of pollutants from agricultural fields and associated river export of pollutants. This will allow to explicitly combine the impact of both climate change and of socio-economic developments.

A multi-pollutant approach supports the search for effective solutions. A multi-pollutant approach might be more effective in reducing river pollution than a single-pollutant approach (Fig. 1 ). For example, reducing one pollutant may reduce (synergies) or increase (trade-offs) another pollutant. Our study serves as an illustrative example for the five pollutants. For example, increasing sewer connections may increase inputs of the five pollutants to rivers, but decrease inputs of N, P and Cryptosporidium from open defecation (Low urb –Low wwt ; trade-off). Higher economic developments may lead to less excreted Cryptosporidium per capita because of lower infection risks in developed countries 11 , 23 (Supplementary Fig. 8 ), but may generate more N and P in human excreta (Supplementary Figs. 4 – 5 ) as a result of protein-rich food consumption 10 , 46 (trade-off). Synergies also exist. For example, increasing sewer connections with advanced technologies and sufficient wastewater treatment capacities is projected to decrease the inputs of all five pollutants to many rivers in the future (High urb –High wwt ). This is also associated with synergies in treatment technologies to remove multiple pollutants. Some technologies are developed to target specific pollutants (e.g., N 47 , P 48 , Cryptosporidium 42 ). This implies that implementing technologies for one pollutant may not strongly influence another pollutant. However, technologies exist to treat more than one pollutant (e.g., 10 , 38 , 39 , 40 , 42 , 59 ). For example, secondary treatment with removal efficiencies of around 40–50% (assumed in Mod urb –Mod wwt and High urb –Mod wwt ) converts organic N into inorganic and gas, removing N from the waste 10 . They can also facilitate the biodegradation of triclosan 59 . Microplastics can host microorganisms (e.g., Cryptosporidium ) and serve as vectors for chemicals 15 , 49 , 60 . As a result, biofilms and flocs can form in, for example, activated sludge ponds and then settle down 49 . Triclosan can sorb to large particles and also settle down with other pollutants 38 , 39 , 59 . Advanced technologies (assumed in High urb –High wwt ) such as efficient ultrafiltration methods can reduce Cryptosporidium 42 and microplastics 49 , and reverse osmosis can recover nutrients 41 and reduce microplastics 49 . Nature-based solutions such as stabilization ponds and constructed wetlands are largely effective to reduce Cryptosporidium 42 and nutrients 61 . Accounting for synergies and trade-offs is essential to identify effective solutions for multiple pollutants. This can support the achievement of SDG11 for sustainable cities and SDG6 for clean water.

Our results can support policy assessment of water pollution in urban areas, and form the basis for actionable and region-specific solutions. We identify hotspots of urban-related river pollution and show possible effects of future urbanization on river quality under global change. This could help to prioritize short-term actions to avoid river pollution in the 21st century. Improving wastewater treatment is important to avoid multi-pollutant problems in an urbanized world (Fig. 5 , differences between High urb –High wwt and High urb –Low wwt ). Our sensitivity analysis indicates where improved wastewater treatment could have a larger impact (Supplementary Fig. 17 ). Our model indicates that water pollution is related to human development (expressed as human development index). This is important to realize when reducing Cryptosporidium and microplastics. Some countries in the world already introduced policies such as a ban of detergents and triclosan in products. Combing such policies with improved wastewater treatment may contribute to synergetic solutions for achieving SDGs and reducing river pollution from urban waste. For Africa, improving wastewater treatment may not be enough. Controlling the African population growth to reduce waste production in the future may be needed in urban and water policy assessments.

Our study quantifies future trends in inputs of five pollutants to rivers for five scenarios. We argue that a multi-pollutant perspective is needed in quantitative analyses of future trends in global change, urbanization, sanitation and wastewater treatment. We analyzed multiple pollutants simultaneously in a consistent way. We did this for 10,226 sub-basins for 2010, 2050 and 2100. Our insights are in how future trends differ between pollutants, sub-basins and how hotspots of multi-pollutant problems change in the 21st century. Our study provides an example of multi-pollutant problems from urban point sources. We show that future inputs of pollutants are projected to increase with increasing urbanization. We also show that it is technically possible to avoid these increases with advanced proven technologies to treat wastewater, except in Africa. In Africa, clean water availability is projected to remain a challenge because of the fast increasing population. This will consequently challenge the achievement of SDGs 6 and 11 in Africa. Our model may serve as an example for multi-pollutant modelling of diffuse sources such as agricultural runoff and other pollutants, such as pesticides 62 , antibiotics 24 and antimicrobial resistance. Another opportunity is to analyze the economic (e.g., costs), societal, institutional and political feasibilities of future pollution reduction options. This is important to identify region-specific solutions. Our long-term projections can help to increase the awareness of society and decision makers about pollution hotspots in the 21st century. This can facilitate short-term actions in different regions to avoid pollution in the future and contribute to achieve SDGs 6 and 11.

Model description and inputs

We used a model of Strokal et al. 31 that takes the sub-basin scale modelling approach of Strokal et al. 32 for nutrients and integrates modelling approaches for other pollutants 18 , 20 , 23 . We developed it further for future analyses of point-source inputs of pollutants to rivers (Supplementary Table 1 ). Our model quantifies inputs of five pollutants to 10,226 rivers: nitrogen (N), phosphorus (P), microplastics, triclosan and Cryptosporidium for 2010, 2050 and 2100. The model of Strokal et al. 31 was developed for 2010 taking the sub-basin modelling approach of Strokal, et al. 32 for N 29 , 32 , P 29 , 32 and integrating the existing modelling approaches for microplastics 18 , triclsan 20 and Cryptosporidium 23 . We developed the model for the years 2050 and 2100 based on the urbanization storylines of the SSPs and our assumptions. Our multi-pollutant model quantifies simultaneously annual inputs of the five pollutants to rivers at the sub-basin scale using the consistent spatial and temporal dataset for model inputs for 2010, 2050 and 2100. The model quantifies inputs of the five pollutants from sewer systems and open defecation. These are the point sources of the pollutants in rivers. Sewer systems discharge five pollutants to rivers. Open defecation is a point source of N, P and Cryptosporidium in our model. Model evaluation is presented below after the scenario descriptions.

Inputs of the pollutants to rivers from open defecation are quantified as a function of the population that is open defecating and the excretion or consumption rates of pollutants per person per year (Supplementary Tables 1 and 2 ). Inputs of pollutants from sewer systems are quantified as a function of the population that is connected to sewer systems, the excretion or consumption rates of pollutants per person per year and removal efficiencies of pollutants during treatment. We quantified inputs of the pollutants at 0.5° grid and then aggregate the results to 10,226 river sub-basins (Supplementary Table 1 ). Model inputs for 2010 are directly from Strokal, et al. 31 . Model inputs for 2050 and 2100 are based on the SSPs with different trends in urbanization and wastewater treatment (see scenario descriptions below).

Below, we explain how model inputs were derived (Supplementary Tables 1 – 6 ). Population for 2010, 2050 and 2100 are aggregated to 0.5° grid from the global, 0.125 degree cell database of Jones and O’Neill 53 . The number of people with sewer connections and open defecation are quantified at 0.5° grid using the population map of 0.5° grid and the fraction of people with sewer connections or open defecation. For 2010, the fraction of urban and rural people with sewer systems and open defecation were available by country from the Joint Monitoring Program (see details in Strokal et al. 31 and Hofstra and Vermeulen 11 ). We assigned the national values to grids of 0.5° grid. Then, we multiplied the number of people per grid (aggregated from Jones and O’Neill 53 ) with the fraction of people connected to sewer systems or open defecating (based on Hofstra and Vermeulen 11 ). For 2050 and 2100, we made assumptions for the fractions of people connected to sewer systems and with open defecation. These assumptions were based on storylines of SSPs for economy, population and urbanization (Fig. 2 , Supplementary Tables 4 – 6 ). Our assumptions differ among urban and rural people, and among developing and developed countries (see scenario descriptions below).

Excretion or consumption rates of pollutants were largely derived based on existing, evaluated approaches and sources. Excretion rates of N and P in human waste per person are quantified as a function of GDP (gross domestic product) at purchasing power parity, following the approach of Van Drecht et al. 46 , but adjusted to the unit of 2005 (see details in Strokal et al. 31 , Supplementary Tables 1 – 6 ). For 2010, 2050 and 2100, GDP at 0.5° grid was derived from the global SSP database with the projections from the International Institute for Applied Systems Analysis (IIASA, 63 ). P in detergents was from Van Drecht et al. 46 for the world regions (Supplementary Tables 1 – 6 ).

Excretion rates of Cryptosporidium were quantified based on the infection rate in developed (5%) and developing (10%) countries and the excretion rate per ill person (10 9 oocysts) according to Hofstra et al. 23 . For 2010, developed and developing countries were defined based on the Human Development Index (HDI), following the approach of Hofstra et al. 23 : HDI > 0.785 (developed) and HDI < 0.785 (developing). For 2050 and 2100, we made assumptions for HDI for countries depending on SSP storylines for the economy, population growth and urbanization (see scenario descriptions below and Supplementary Tables 4 – 6 ).

Consumption rates of microplastics per person per year were derived directly from Siegfried et al. 18 , but with some modifications (details are in Strokal et al. 31 ). Microplastics in sewer systems result from car tyres, PCPs (personal care products), household dusts and laundry. For PCPs, dust and laundry, consumption rates are 0.071, 0.08 and 0.12 kg of microplastics per person per year according to Siegfried, et al. 18 . We assumed that these values do not change over time. For tyres, this is different. Strokal et al. 31 assumed that developed countries will contribute more microplastics to sewage from car tyres as a side-effect of economic and infrastructural developments. Thus, we assigned 0.18 kg of microplastics from tyres per person for developed countries (HDI > 0.785) and 0.018 kg of microplastics from tyres per person for developing countries (HDI < 0.785) according to Strokal et al. 31 . We assumed changes in HDI by country in the future based on the SSPs storylines (see scenario descriptions below and Supplementary Tables 1 – 6 ).

Consumption rates of triclosan per person in the world were directly taken van Wijnen et al. 20 (0.5 kg per person per year for 2010). We assumed that the consumption rate will not change largely in the future and thus will remain as in 2010.

Removal efficiencies of pollutants during treatment were derived based on the existing studies. For N, P and Cryptosporidium , removal efficiencies were quantified by country using the national distribution of wastewater treatment types (primary, secondary, tertiary, no treatment) and their treatment efficiencies for pollutants, following the approaches of 11 , 23 , 46 (see Supplementary Tables 1 – 6 , Supplementary Figs. 1 – 14 ). The quantified national removal efficiencies were then assigned to corresponding grids of 0.5°. For 2010, national distributions of wastewater treatment types were derived from Hofstra and Vermeulen 11 with a few corrections for countries with missing data (details are in Strokal et al. 31 ). For 2050 and 2100, we assumed changes (low, moderate, high) in the distribution of the treatment types depending on the storylines of SSPs (see scenario descriptions below). These changes imply a shift towards a next treatment type: e.g., from primary to secondary to tertiary (Supplementary Tables 1 – 6 ). Removal efficiencies of pollutants for different treatment types were taken directly from literature (see Supplementary Table 3 ) and do not vary among years.

For triclosan and microplastics, removal efficiencies were quantified based on the approaches of van Wijnen et al. 20 and Siegfried et al. 18 (details are in Strokal et al. 31 ). We used the known removal rate of phosphorus to assume the removal of triclosan and microplastics. For our assumptions, we used data about the removal of triclosan and microplastics from literature 39 , 59 , 64 , 65 , 66 . Based on these data, we related average phosphorus removal in a watershed to triclosan removal. We formulated three classes of triclosan removal (0, 60 or 90%) and related these to known phosphorus removal in each sub-basin (details are in van Wijnen et al. 20 ). A similar approach was carried out for microplastics. We formulated four microplastics removal classes based on literature and related those to the known average phosphorus removal in each sub-basin 18 , 30 . These classes represent an average microplastics removal in each sub-basin. Microplastic removal depends on the size and density of the microplastics. Therefore, the removal at each individual WWTP will be dependent on these and other characteristics. In our study, on a global scale, we chose to assume average removal for each sub-basin.

Scenario description

Storylines of the five scenarios are summarized in Fig. 2 , Supplementary Tables 1 – 6 and Supplementary Figs. 1 – 14 . Our five scenarios are with low urbanization and low wastewater treatment rates (Low urb –Low wwt ), moderate urbanization and moderate wastewater treatment rates (Mod urb –Mod wwt ), high urbanization and low wastewater treatment rates (High urb –Low wwt ), high urbanization and moderate wastewater treatment rates (High urb –Mod wwt ), and high urbanization and high wastewater treatment rates (High urb –High wwt ) (Fig. 2 ). These scenarios follow future trends in the socio-economic development based on the existing SSPs 1 , 63 , combined with our assumptions for population with sewer connections, open defecation and for wastewater treatment capacities and technologies (Supplementary Tables 4 – 6 ). Below, we describe each scenario. Quantitative interpretations of the scenario assumptions are presented in Supplementary Tables 4 – 6 for 2050 and 2100, and inputs are given in Supplementary Figs. 1 – 14 .

The Low urb -Low wwt scenario is based on SSP3 projections for the socio-economic development (Fig. 2 , Supplementary Tables 4 – 6 ). The scenario assumes a fragmented world with difficulties to control population growth. In this world, It is projected a low economic development with low urbanization rates and high population growth. For example, a global population of approximately 12 billion people is projected for 2100, of which 58% will be urban (Supplementary Figs. 1 – 3 ). Low economic developments will not allow to develop technologies largely. For 2050, HDI is assumed to stay as in 2010 and increase by 10% between 2050 and 2100 on a county level (Supplementary Tables 4 – 6 ). The society will not focus on reducing or avoiding future river pollution. As a result, the fraction of the population with sewer connections (around one-third of the global population) and the treatment efficiencies of wastewater (e.g., 14–18% globally depending on pollutant) will remain in 2050 as in 2010 (Supplementary Figs. 3 , 9 – 13 ). The same holds for the wastewater treatment capacities. However, by 2100 more people may be connected to sewer systems (above one-third of the global population). This will result in higher capacities of the wastewater treatment plants with slightly improved treatment technologies (e.g., 21–24% of removal efficiencies globally depending on pollutant). However, future wastewater treatment efficiencies vary largely among world countries: e.g., 0–96% in 2100 depending on region and pollutant. In general, higher wastewater treatment efficiencies are projected for Europe, North America and Australia (Supplementary Figs. 9 – 13 ),

The Mod urb -Mod wwt scenario is based on SSP2 projections of the middle of the road for the socio-economic development (Fig. 2 , Supplementary Tables 4 – 6 ). The scenario assumes a moderate economic development, moderate urbanization rates and moderate population growth compared to the other scenarios. For example, 9 billion people are projected globally for 2100 and 80% will be urban (Supplementary Figs. 1 – 3 ). From 2010, HDI is assumed to increase by 10% by 2050 and further increase by 10% by 2100 on a county level (Supplementary Tables 4 – 6 ). Technological development follows the business as usual trends. As a result, more people will be connected to sewer systems than today (45% in 2050 and 68% in 2100 globally, Supplementary Fig. 3 ). A number of wastewater treatment plants will be constructed to maintain the increasing volume of the wastewater from connected population to sewer systems. The amount of waste that is collected will be treated with slightly improved wastewater treatment. For example, on average, 33–42% of removal efficiencies globally are projected for 2100. This range is for the five pollutants. The removal efficiencies vary largely among regions (0–97% depending on region and pollutant, Supplementary Figs. 9 – 13 ). The number of people connected to sewer systems will be larger for urban (over two-thirds) than for rural (less than one-third) population. Some people may still experience open defecation in 2050. By 2100, all people who opened defecated in 2050 will become connected to sewer systems.

The High urb -Low wwt scenario is based on SSP4 projections for the socio-economic development (Fig. 2 , Supplementary Tables 4 – 6 ). The scenario assumes a large gap between urban and rural developments. The economic development is projected to be moderate compared to the other scenarios. HDI is projected to increase as in the Mod urb –Mod wwt scenario (Supplementary Tables 1 – 3 , Supplementary Fig. 14 ). The population is projected to increase in the future, but not largely: e.g., around 30% between 2010 and 2100 globally. By 2100, the global population is projected to reach 9.3 billion people (Supplementary Fig. 3a ). However, the urban population will develop faster than the rural. Urbanization will be high: e.g., 76% and 90% of the global population will be urban in 2050 and 2100, respectively. As a result, the connection rate of the population to sewer systems will increase in the future for urban areas. For example, 80% of urban and 11% of rural population globally is projected to be connected to sewer systems in 2100 (Supplementary Figs. 1 – 3 ). Wastewater treatment capacities will be enough to maintain the waste from sewer systems and treatment will be improved as in the Mod urb -Mod wwt scenario. For rural areas, the fraction of people connected to sewer systems in 2050 may remain the same as in the Low urb -Low wwt scenario and will be improved by 2100 (Supplementary Tables 4 – 6 ). By 2050, some rural people may still open defecate. By 2100, all rural people who opened defecated in 2050 will become connected to sewer systems with better treatment.

The High urb –Mod wwt scenario is based on SSP5 projections for the socio-economic development (Supplementary Tables 4 – 6 , Fig. 2 ). The scenario assumes a high economic development with high urbanization and low population growth (Fig. 2 , Supplementary Table 4 ). For example, the total population globally is projected to increase by less than 10% between 2010 and 2100, reaching 7.4 billion people in 2100 (Supplementary Fig. 3a ). However, more than 90% of the global population will be urban in 2100. From 2010, HDI is assumed to increase by 20% by 2050 and further increase by 20% by 2100. The technological development is relatively high compared to the Mod urb -Mod wwt scenario. This will lead to a higher population with sewer connections. More than half of the global population will be connected to sewer systems in 2050. For 2100, this number is over two-thirds of the global population (Supplementary Figs. 1 – 3 ). The capacities of the wastewater treatment plants will be enough to manage the amount of waste from sewer systems. However, people will invest less in improving wastewater treatment. People will focus more on the economy rather than on reducing river pollution. As a result, wastewater treatment may follow the business as usual trends. For example, on average, 34–44% of the wastewater treatment efficiencies are projected globally for 2100. However, these efficiencies vary largely among regions (0–97% depending on area and pollutant, Supplementary Figs. 9 – 13 ). Furthermore, some people may still open defecate in nearby water systems in the future. By 2100, all people who opened defecated in 2050 will become connected to sewer systems.

The High urb –High wwt scenario is based on SSP1 projections for the socio-economic development (Fig. 2 , Supplementary Tables 4 – 6 ). The society will develop fast with high urbanization rates as comparable to the High urb –Mod wwt scenario. The global population is projected to reach 6.9 billion people in 2100 (Supplementary Fig. 3a ). The share of urban people globally is projected to be 77% in 2050 and 92% in 2100 (Supplementary Figs. 1 – 3 ). The share of the total connected people to sewer systems is projected to be 55% in 2050 and 82% in 2100. HDI is projected to increase in the same rate as in the High urb –Mod wwt scenario. However, in this world, a strong focus is on reducing or avoiding river pollution by using the best available advanced technologies in all areas. Technological development is high because of the high economic development. People will invest in improving technologies to treat wastewater with multiple pollutants. There will be opportunities to develop technologies for multiple pollutants and combine them with nature-based solutions. As a result, the wastewater treatment is assumed to be improved largely with high removal efficiencies (60–98% depending on year, area and pollutant, Supplementary Figs. 9 – 13 ).

Model evaluation

We evaluated the uncertainties in our model using four approaches following a building trust circle method 54 . This method has been applied in several water quality studies 32 , 67 , 68 . First, we compare model outputs with existing studies. Second, we compare the spatial pattern of the pollution problems with existing models for individual pollutants. Third, we perform a sensitivity analysis for pollution hotspots. Fourth, we perform a comprehensive sensitivity analysis for all important model inputs underlying the calculations. Fifth, we compare model inputs with independent datasets. Model validation against observed concentrations is, unfortunately, challenging. This is because our model does not quantify concentrations. Some of the existing global models calculate concentrations and were evaluated against observations (Supplementary Tables 7 – 8 ). Thus, we used those models to compare their results with ours for individual pollutants. Below, we elaborate on these five approaches. Details are in Supplementary Tables 7 – 12 and Supplementary Figs. 15 , 17 .

Approach 1: evaluating model outputs by comparing them with other models and studies for individual pollutants. This comparison is presented in Supplementary Table 7 . The results show that our model outputs for global inputs of nitrogen, phosphorus, microplastics, triclosan and Cryptosporidium are generally in line with other models and studies. For example, our model quantified 9.5 Tg of nitrogen to rivers from point sources in 2010. Other models quantified 6.4–10.4 Tg of nitrogen to rivers from points sources during 2000–2010 10 , 46 , 69 (Supplementary Table 7 ). For phosphorus, we quantified 1.6 Tg in 2010 whereas the other models quantified 1.0–1.5 Tg for the period of 2000–2010 10 , 46 , 69 . For 2050, we quantified 5.4–21.0 Tg of nitrogen and 0.6–3.5 Tg of phosphorus in 2050 (ranges for the five scenarios). van Puijenbroek et al. 10 quantified 13.5–17.9 Tg of nitrogen and 1.6–2.4 Tg of phosphorus in 2050 under the five SSPs. For Cryptosporidium , our model quantified 1.6 × 10 9 oocysts in 2010 which is 1.1–1.4 × 10 9 oocysts in another model in 2000–2010 11 , 23 (Supplementary Table 7 ). For 2050, our model quantified 0.4–2.9 × 10 9 oocysts (range for the five scenarios). For the Low urb -Low wwt scenario, this value is 2.44 × 10 9 oocysts, which is comparable with 2.28 × 10 9 oocysts from the other model 11 , 23 . To our knowledge, van Wijnen, et al. 20 is the only study quantifying triclosan export by rivers. Our estimates for Danube, Zhujiang and Ganges are comparable with estimates of van Wijnen et al. 20 (Supplementary Table 7 ). For microplastics, our model quantified 0.45 Tg entering rivers globally in 2010. Best 9 indicated loads of 0.41–4.00 Tg of plastics in 32 world’s rivers. This is higher than our estimate because Best 9 accounts for macro- and microplastics whereas we only consider microplastics. Avio et al. 13 indicated 0.27 Tg of plastics to oceans in some regions in the world. This is lower than our estimate because we quantify inputs of plastics to rivers and not to the oceans. The other reasons for the differences between our model and other studies are in data inputs and the spatial level of detail. We focus on sub-basin analyses with the consistent model inputs for multiple pollutants (Supplementary Table 7 , Supplementary Figs. 1 – 13 ).

Approach 2: evaluating model outputs by comparing the spatial variability in pollution hotspots with other studies. We reviewed the literature on pollution hotspots in the world for individual pollutants 8 , 9 , 10 , 11 , 12 , 16 , 55 , 56 , 70 . Our pollution hotspots for multiple pollutants are in line with the existing studies for individual pollutants. For example, most pollution often happens in densely populated and highly urbanized areas 8 , 9 , 10 , 11 , 12 , 16 , 55 , 56 . For example, Best 9 indicated over 80% of large transboundary rivers in the world with multiple pollutants. For many large cities in polluted regions, the demand for water already exceeds its availability. For example, water scarcity (ratio between the water demand and availability) has been already reported for cities in countries such as China (e.g., Shanghai, Beijing), India (e.g., Delhi, Kolkata, Bangalore, Hyderabad), Mexico, North America (e.g., Los Angeles) 70 . In the future, river pollution will further decrease the availability of clean water in many urban regions 4 , 7 , 8 , 71 . We show that it is technically possible to increase the availability of clean water with implementing advanced technologies (High urb _High wwt , Figs. 3 – 6 ). However, future analyses for multi-pollutant hotspots are lacking in the existing literature. A few global models performed future analysis for individual pollutants 10 , 11 , 18 , 20 where urbanization was taken into account by 2050. Their results indicate pollution hotspots where human activities are most intensive, which is in line with our study. However, studies exploring trends in multi-pollutant hotspots by 2100 do not exist. We explore trends in pollution hotspots for multi-pollutant problems covering the entire 21st century under the five scenarios with different socio-economic developments and levels of wastewater treatment.

Approach 3: evaluating model outputs for pollution hotspots by sensitivity analysis. In Fig. 5 , we showed multi-pollutant hotspots. These hotspots were defined as at least a 30% increase in inputs of more than one pollutant to rivers during 2010–2050, 2010–2100 and 2050–2100. This definition is modest and easier to understand and interpret. We checked if the pollution hotspots remain the same by changing a 30% increase to 10% (Supplementary Figs. 18 – 19 ) and 50% (Supplementary Fig. 20 – 21 ). Results of this sensitivity analysis indicate that our main messages stay the same: Africa will become a hotspot region with multiple pollutants in rivers in the 21st century and advanced technologies may help to reduce pollution in many rivers of the world.

Approach 4: evaluating model inputs by a sensitivity analysis. We performed a comprehensive sensitivity analysis for all important model inputs underlying the calculations. In total, there are 25 model input parameters included in this analysis. Every model input was changed by +10% and −10%. As a result, we did 50 runs of the model for the year 2010. We analyzed the results of the 50 runs for 10,226 sub-basins and five pollutants: Cryptosporidium , nitrogen, phosphorus, triclosan and microplastics. Details can be found in Supplementary Tables 9 – 12 and Supplementary Fig. 17 .

In general, increasing the model inputs (13 out of 25) that are responsible for excretion or consumption rates of pollutants in urban waste lead to more pollutants in rivers (Supplementary Tables 10 – 12 ). The opposite is observed when these model inputs are decreased. An exception is HDI for Cryptosporidium and microplastics. Model inputs that are responsible for wastewater treatments (6 out of 25) have the following effect on the model outputs: increases in these inputs lead to less pollutants in rivers and vice versa. Model inputs (6 out of 25) that are responsible for the number of people (urban and rural) connected to sewage systems have the following effect on the model outputs: increases in these inputs lead to more pollutants in rivers and vice versa (Supplementary Tables 10 – 12 ).

We find that model outputs are most sensitive to changes in 2–5 out of the 25 model inputs. The sensitivities vary among sub-basins and pollutants. These model inputs are HDI (sensitive for Cryptosporidium and microplastics), the fractions of secondary (sensitive for triclosan and microplastics) and tertiary (sensitive for all five pollutants) treatment, and the removal efficiencies of secondary (sensitive for triclosan and microplastics) and tertiary (sensitive for all five pollutants) treatment. We analyze model outputs for 10,226 sub-basins that are sensitive to changes in those 2–5 model inputs. Supplementary Tables 11 – 12 show the percentages of the sub-basin areas where model outputs for the five pollutants change by: <5%, 5–10%, 10–50% and >50%. Supplementary Fig. 17 shows the location of the sub-basins for which model outputs are sensitive to one or more model inputs.

The model results for sub-basins covering over two-thirds of the global surface area changed by less than 5% (Supplementary Tables 11 – 12 ). For ≤13% of the global surface area the model outputs changed between 5–10%. This is for all pollutants. For ≤8% of the global area, the changes are between 10–50% in the model outputs. Exceptions are Cryptosporidium and microplastics, which are relatively sensitive for HDI. In one-third of the sub-basin area the model output for Cryptosporidium changed 10–50% as a result of changes in HDI. For microplastic, the changes may be even higher. However, the number of basins with changes above 50% is small. These results show that HDI is an important model input for Cryptosporidium and microplastics (see Supplementary Tables 1 , 9 – 12 ).

Approach 5: evaluating model inputs by comparing them with independent datasets. We provide this comparison in Supplementary Table 8 , Supplementary Figs. 15 and 16 . Comparison results build trust in our model inputs. We compared the following important model inputs for 2010 and 2050 scenarios: total population, population with sewer connections, distribution of treatment types, removal efficiencies of pollutants, nutrients in human excretion (Supplementary Table 8 ). We compared these inputs with van Puijenbroek et al. 10 who recently published global analyses of nutrient inputs to rivers from point sources. We also compared our population from Jones and O’Neill 53 with another global dataset from Kc and Lutz 34 (Supplementary Fig. 16 ). Our model inputs are well compared with the mentioned studies. Furthermore, we compared our HDI index for 2010 and 2050 with the HDI index from Crespo Cuaresma and Lutz 57 (Supplementary Fig. 15 ). HDI is an important input in our model to quantify the excretion of Cryptosporidium . HDI influences the treatment developments and consumption of microplastics associated with the use of car tyres. Our values for HDI under the five scenarios are well compared with the values of Crespo Cuaresma and Lutz 57 ( R 2  > 0.88 for the five scenarios).

Results of these five approaches give us trust in using our multi-pollutant model to explore future trends in inputs of multiple pollutants to rivers from urbanization activities. All data are available in Strokal et al. 72 and Strokal et al. 73 .

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

All the datasets generated and analysed during this study are publicly available in the Data Archiving and Networked Services (DANS Easy) repository: https://doi.org/10.17026/dans-zyx-jce3 73 . The data will be available for download from 01–04–2021. The data supporting the findings of this study are described in the following metadata record: https://doi.org/10.6084/m9.figshare.13333796 72 .

Code availability

All equations to the model are provided in the supplementary information files of this study and in the Data Archiving and Networked Services (DANS Easy) repository: https://doi.org/10.17026/dans-zyx-jce3 . The data will be available for download from 01–04–2021.

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Acknowledgements

M.S. (the corresponding author) was financially supported by a Veni-grant (0.16.Veni.198.001) and a KNAW-MOST SURE + project (5160957392).

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Water Systems and Global Change group, Wageningen University & Research, Wageningen, the Netherlands

Maryna Strokal, Wietse Franssen, Nynke Hofstra, Fulco Ludwig, J. Emiel Spanier & Carolien Kroeze

Key Laboratory of Agricultural Water Resources, Hebei Key Laboratory of Soil Ecology, Center for Agricultural Resources Research, Institute of Genetic and Developmental Biology, The Chinese Academy of Sciences, Hebei, China

Zhaohai Bai & Lin Ma

Aquatic Ecology and Water Quality Management group, Wageningen University & Research, Wageningen, the Netherlands

Albert A. Koelmans

PBL Netherlands Environmental Assessment Agency, the Hague, the Netherlands

Peter van Puijenbroek

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M.S. led this manuscript. M.S. was responsible for designing the manuscript, developing a multi-pollutant model, and analyzing and writing the manuscript. C.K. substantially assisted in designing the manuscript, developing the model and analyzing the results. Z.B., W.F., N.H., A.A.K., L.V., M.T.H.V., J.E.S. and J.W., contributed largely in developing the global multi-pollutant model that was used in the manuscript for future analyses of the impact of urbanization on river pollution. They and other authors provided information to the manuscript and advised on the analyses. All authors assisted the interpretations of the Shared Socio-economic Pathways. These pathways are used in the manuscript for multiple pollutants. All authors read and commented on the text. All authors approved the final version and were involved in the accountability for all aspects of the manuscript.

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Strokal, M., Bai, Z., Franssen, W. et al. Urbanization: an increasing source of multiple pollutants to rivers in the 21st century. npj Urban Sustain 1 , 24 (2021). https://doi.org/10.1038/s42949-021-00026-w

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The study of slums as social and physical constructs: challenges and emerging research opportunities

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Background and theory, the challenge of modelling, monitoring and analyzing slums, opportunities, additional information.

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Over 1 billion people currently live in slums, with the number of slum dwellers only expected to grow in the coming decades. The vast majority of slums are located in and around urban centres in the less economically developed countries, which are also experiencing greater rates of urbanization compared with more developed countries. This rapid rate of urbanization is cause for significant concern given that many of these countries often lack the ability to provide the infrastructure (e.g., roads and affordable housing) and basic services (e.g., water and sanitation) to provide adequately for the increasing influx of people into cities. While research on slums has been ongoing, such work has mainly focused on one of three constructs: exploring the socio-economic and policy issues; exploring the physical characteristics; and, lastly, those modelling slums. This paper reviews these lines of research and argues that while each is valuable, there is a need for a more holistic approach for studying slums to truly understand them. By synthesizing the social and physical constructs, this paper provides a more holistic synthesis of the problem, which can potentially lead to a deeper understanding and, consequently, better approaches for tackling the challenge of slums at the local, national and regional scales.

• informal settlements
• socio-economic
• remote sensing
• crowdsourced information

Jel classification:

Over the last century there has been tremendous growth in the urban population. This growth, however, has not been uniform. Growth in urban areas in less developed regions of the world has been especially rapid, increasing at an average rate of 2% annually compared with 0.5% in more developed regions. This trend is expected to continue with most of the less developed countries faced with the challenge of absorbing the majority of the future population growth (United Nations, Citation 2015a ). Increased urbanization is a major concern for less developed countries since they often lack the infrastructure and basic services (e.g., water, sanitation and healthcare) necessary to absorb the increasing number of people (Cohen, Citation 2006 ; Montgomery, Citation 2008 ). Unable to adequately meet the demands of the growing population, slums have emerged and continue to proliferate in many less developed countries. Currently, about 1 billion people live in slums, with most slum dwellers located in less developed countries, which accounts for about 30% of their urban population (United Nations, Citation 2015a ). The number of slum dwellers is projected to increase to 2 billion by 2030 and to 3 billion by 2050 if current trends persist (UN-Habitat, Citation 2010 ). The presence of slums has regional and global implications, impacting areas such as education, health and child mortality, and political and social exclusion, among many other things (UN-Habitat, Citation 2003 ).

Although widely studied, most work on slums Footnote 1 has focused on one of three main lines of enquiry: socio-economic and policy (e.g., Omole, Citation 2010 ; Patel, Koizumi, & Crooks, Citation 2014 ; Sola, Citation 2013 ); physical characteristics using approaches such as remote sensing (e.g., Filho & Sobreira, Citation 2005 ; Kit, Lüdeke, & Reckien, Citation 2012 ; Kohli, Sliuzas, Kerle, & Stein, Citation 2012 ); and, more recently, modelling using approaches such as cellular automata (CA) and agent-based models (ABMs) (e.g., Augustijn-Beckers, Flacke, & Retsios, Citation 2011 ; Jokar Arsanjani, Helbich, Kainz, & Darvishi Boloorani, Citation 2013 ; Patel, Crooks, & Koizumi, Citation 2012 ; Sietchiping, Citation 2004 ). Each line of enquiry is often characterized by its own set of assumptions and interpretations, leading only to a partial view of slums. However, these different themes are all interrelated at various levels. For instance, modelling slums without considering the social constructs that reflect the choice to migrate to a slum or ignoring the physical layout of the slum can lead to misrepresentation and misinterpretation of the model results. In order to grapple with the complexities of slums one must consider both their social and their physical constructs. Arguably, this must take into account the drivers that cause people to live in slums, along with their physical location, so that appropriate policies can be developed and explored in order to improve the well-being of slum dwellers. This approach requires one to consider slums as multifaceted, with various social and physical constructs at play for each slum. Consequently, slums should be examined using a interdisciplinary approach, ensuring a more holistic and systematic assessment.

Figure 1. Framework for studying and understanding slums.

In the light of the framework put forward, the main contribution of this paper is twofold: first, it identifies the most pressing issues surrounding slums based on current understandings, which ranges from theory to practice; and second, it puts forward a future integrated research agenda for developing a deeper understanding of the fundamental underlying processes that define and shape slums. The framework depicted in Figure 1 should be regarded as evolutional rather than static in order to capture and understand the dynamic nature of slums and their complex interaction in the urban environment.

However, before moving onto the various aspects of the framework, a caveat is required. While a more detailed discussion for each component in Figure 1 is possible, this is beyond the scope of this paper. For example, the modelling of slums is a large and extensive area of research, which to cover comprehensively would require its own paper. To overcome this issue, we include some representative studies and reviews that provide a gateway to a more detailed description of each topic (e.g., see Roy, Lees, Palavalli, Pfeffer, & Sloot, Citation 2014 , for an overview of modelling).

Slums and informal settlements

Slums have been in existence since the time of cities and their presence has long been documented in the literature (e.g., Booth, Citation 1903 ). The word ‘slum’ has mainly been used to describe people living under substandard conditions and squalor. For example, Cities Alliance ( Citation 2006 ) describes slums as ‘neglected parts of cities where housing and living conditions are appallingly poor’. Often the terms ‘slum’ and ‘informal settlements’ are used interchangeably in the literature (UN Habitat, Citation 2012 ). However, some would argue that there is a distinct difference between slum and informal settlement (e.g., Hurskainen, Citation 2004 ; Rashid, Citation 2009 ). (Readers interested in this debate are referred to Gilbert ( Citation 2007 ) for a more thorough discussion). This paper adopts the view that slums and informal settlements are one in the same and, therefore, can be treated as one. Usually, as argued by UN-Habitat ( Citation 2003 ), most characteristics of these environments, whether slum or informal settlement, are not found in isolation, leading to multiple levels of deprivation for slum dwellers. Whether slums and informal settlements are viewed separately or as a single entity, they represent disadvantaged communities continuing to have large impact on the physical and economic landscapes, as will be discussed below.

The vast majority of the slums are located in less developed countries, and most of the growth in slum populations is expected to occur in such countries. However, just as the term ‘slum’ can have multiple connotations, so does the term ‘developing country’. For the purpose of this paper, we adopt the United Nation’s definitions of ‘less developed country’ and ‘developing country’ for a country where the majority of the population makes significantly less income and has weaker social indicators than the population in highly industrialized countries (UN-OHRLLS, Citation 2016 ). Furthermore, in such countries there is lack of basic public services. Although alternative terms have been used to describe less developed countries (e.g., ‘emergent nations’), much of the literature on slums that is reviewed here has used the terms ‘less developed’ and ‘developing country’ to refer to areas most impacted by slums.

The impact of slums

The growth of slums is expected to have major consequences on both humans and the environment, which are inextricably linked. On the one hand, the conditions that exist in slums such as living under physical threat from natural and manmade disasters and improper housing have direct impacts on their residents (Napier, Citation 2007 ). This is mainly due to the low capacity of slums dwellers to recover from disasters, such as floods and earthquakes, compared with more formal communities (Ajibade & McBean, Citation 2014 ; Ebert, Kerle, & Stein, Citation 2009 ). On the other hand, slum residents themselves can impact their environment due to lack of basic services, which results in contaminated soil and polluted air and waterways. This results in a perpetuated cycle of decline for both slum dwellers and the environment (Ali & Sulaiman, Citation 2006 ), with the possibility of impacts extending to communities beyond the slums (e.g., flooding). The growth and expansion of slums can therefore threaten sustainable urban development at local, national and regional scales (Patel, Citation 2012 ).

Growth and expansion of slums can also impact the national and regional economy, both negatively and positively. For example, the negative aspect of slums relates to the fact that they are often burdened with high transaction costs, including increases in transportation expenditures because of inadequate infrastructure, and disease burden on urban dwellers because of the large number of people living in slums (Fox, Citation 2008 ). In addition, ambiguous property and tenancy rights can reduce the efficiency of urban land and housing markets, and may discourage investment or reduce participation in urban labour markets (Marx, Stoker, & Suri, Citation 2013a ). This may potentially impact a country or a region’s overall economic outlook in the global economy, making it less competitive in the worldwide arena (Gambo, Idowu, & Anyakora, Citation 2012 ).

Less often mentioned in the literature is the economic good provided by slum dwellers. Slum dwellers subsidize the middle classes and the business world by providing a comparatively cheap source of labour (Brugmann, Citation 2010 ; Malecki & Ewers, Citation 2007 ). Furthermore, it has been argued that their low economic standing and general lack of education forces them to accept low-paying jobs that may not be readily accepted by middle- and upper-class people (Pawar & Mane, Citation 2013 ). Slum dwellers often find jobs in the informal sector, which is usually linked very closely to the needs of surrounding formal communities (Opeyemi, Olabode, Olalekan, & Omolola, Citation 2012 ). In some cases, this informal economy has positively impacted the country’s gross domestic product (GDP). Slums in Dharavi, India, for example, employ as many as 70% of its residents (Gruber et al., Citation 2005 ) with current economic output estimates of US$700 million annually (Chege & Mwisukha, Citation 2013 ). Other places such as Delhi, Bangalore and Kolkata, all in India, also depend heavily on the informal sector for supporting much of their day-to-day manufacturing activities (Benjamin, Citation 2004 ). Some researchers (e.g., Brugmann, Citation 2013 ; Saglio-Yatzimirsky, Citation 2013 ) suggest that if such economic benefits were to be considered alone, places such as Dharavi would no longer be considered a slum.

Lastly, the poor conditions that exist in slums and daily struggles that slum dwellers face continues to be recognized as a global and ethical challenge. Many governments, regional and international non-government organizations (NGOs) have put in place legislation or systems recognizing the need to protect and improve the lives of slum dwellers (e.g., the Rajiv Awas Yojana programme in India; MHUPA, Citation 2013 ). Likewise, Target 7D of the United Nations Millennium Development Goals (UN MDG) aims to improve significantly the lives of 100 million slum dwellers worldwide by 2020 (United Nations, Citation 2015a ). This target was further advanced with Goal 1 of the UN’s Sustainable Development Goals, the MDG’s successor. Goal 1 aims to reduce at least by half the population of all people living in poverty in all its dimensions according to national definitions by 2030 (United Nations, Citation 2015b ). Failure to take part in such global initiatives may have negative impacts on countries that depend on others for financial and political support. These impacts highlight the critical need for managing slum populations worldwide. To achieve this goal, it is necessary to understand the factors that have led to the emergence and persistence of slums over time. We would argue that such issues must be dealt with for any future intervention to be successful.

Factors influencing the growth of slums

Many factors have been linked to the growth of slums. This section highlights several of these factors, specifically those relating to locational choice factors, rural-to-urban migration, poor urban governance and ill-designed policies. Those factors chosen for discussion have been identified as those most often discussed by others for the growth and persistence of slums based on an extensive review of the literature.

Locational choice factors

The emergence of slums has been linked to a number of socio-cultural, economic and physical factors. For example, Lall, Lundberg, and Shalizi ( Citation 2008 ) examined the residential locational decisions made by slum dwellers in Pune, India, and found that locational decisions were made around theories relating to socio-cultural and economic factors such as commuting costs (e.g., Alonso, Citation 1964 ), access to local public goods (e.g., Tiebout, Citation 1956 ) and individual preferences for community or neighbourhood composition (e.g., Schelling, Citation 1978 ). In addition to these classical models, they found that the locational choices made by slum dwellers was also guided by housing quality and neighbourhood amenities. Similar findings have been reported in other studies (e.g., Abramo, Citation 2009 ; Takeuchi, Cropper, & Bento, Citation 2006 ). Social ties related to common culture, language and similar income-generating activities have also been found associated with the locational choices of new immigrants (e.g., Barnhardt, Field, & Pande, Citation 2014 ; Kombe, Citation 2005 ). It has also been found that people born within or in close proximity to slums tend to remain in them or move to them (UN-Habitat, Citation 2003 ), while some slum dwellers are of the belief that places outside of slums are out of their reach (e.g., Ahmed, Brookins, & Ali, Citation 2011 ). This suggests that any efforts to address the issue of slums must also consider their surroundings and their social structures.

With respect to physical factors impacting the location of slum dwellers, the availability of land has been highly cited in the literature. It has been shown that slum dwellers typically inhabit marginal locations such as riverbanks, steep slopes or dumping grounds (e.g., Sietchiping, Citation 2004 ). This is mainly due to the low purchasing power of slum dwellers in formal land markets when compared with high-income groups (Ajibade & McBean, Citation 2014 ; Praharaj, Citation 2013 ). Slums in South America, for example, Rio de Janiero in Brazil and Lima in Peru, are notoriously known for building on steep slopes (Fernandes et al., Citation 2004 ). Such locations are usually unsafe, posing a risk to human life because of the occurrence of natural disasters such as landslides. Furthermore, slums are also found both within and on the outskirts of cities (UN-Habitat, Citation 2003 ). These unique physical characteristics of slums and the complex interplay with the various socio-economic factors pertaining to their growth and persistence represent a challenging task that has yet to be holistically addressed.

Rural-to-urban migration

Within the literature, rural-to-urban migration not only has been the driver for the growth of cities, but also it has been identified as one of the primary drivers for the growth of slums. In less developed countries the current rapid and immense net movement of the rural population to urban spaces has intensified (Malecki & Ewers, Citation 2007 ; Srivastava & Singh, Citation 1996 ). This is due to several factors: those pertaining to the pulling of rural people to cities and push factors driving population away from rural areas. Often cited in the literature with respect to pull factors include the relative perception of better economic opportunities (e.g., Glaeser, Citation 2011 ) and provision of basic services such as education and healthcare within cities (e.g., Khan, Kraemer, & Kraemer, Citation 2014 ), or the freedom from restrictive social or cultural norms often found in rural areas (e.g., Opeyemi et al., Citation 2012 ; Ullah, Citation 2004 ). Push factors include rural poverty (e.g., Khan et al., Citation 2014 ), excess agricultural labour supply (e.g., Oberai, Citation 1993 ), environmental hardships (e.g., Ishtiaque & Mahmud, Citation 2011 ), threat from natural disasters or conflict and war (e.g., Black, Bennett, Thomas, & Beddington, Citation 2011 ). While such events have pushed rural dwellers to urban areas, there has been a lack of economic growth in many of these countries and when combined has been touted for the growth and persistence of slums, especially in African countries (Fox, Citation 2012 ). Whether a pull or a push factor, the net result of rural-to-urban migration is overwhelming to the urban centres in less developed countries, which are not equipped to support the additional population, especially when combined with the socio-economic factors discussed above. Faced with nowhere else to go, people turn to slums to meet their most basic housing needs (Vasudevan, Citation 2015 ).

Poor urban governance

Other factors relating to the upward growth of slums in many less developed countries is the use of rigid and often outdated urban planning regulations, which are typically bypassed by slum dwellers to meet their housing needs. This is a reflection of poor urban governance (Chiodelli & Moroni, Citation 2014 ). Many local and national governments are also unable to enforce planning regulations due to lack of resources (Tsenkova, Potsiou, & Badyina, Citation 2009 ). In Cote d’Ivoire, for example, high infrastructure cost and rigid building standards have led to a slow pace of development, which resulted in unaffordable housing for many large segments of the population (Mayo & Angel, Citation 1993 ). In Nigeria, official fees involved in the registration process to acquire land can be as much as 27% of the property value (World Bank, Citation 2005 ), presenting a challenge for slum dwellers with limited finances. These circumstances ultimately lead to high rates of insecurity of tenure and provide conducive conditions that continue to drive the growth of slums due to poorly functioning land markets.

Another issue in many less developed countries is the failure of governments to incorporate slum dwellers as part of the overall planning process (Cities Alliance, Citation 2014 ). This is often due to the inability of many governments to keep abreast with urbanization because of the lack of resources and corrupt governments (Fekade, Citation 2000 ). Some governments also refuse to provide urban services to slum dwellers in fear that this will only escalate the issue of slums (Opeyemi et al., Citation 2012 ), or lead to more established property rights, which many governments are unwilling to provide (Fox, Citation 2014 ). As discussed by Werlin ( Citation 1999 ), without more established property rights for slum dwellers, governments lack the ability to force slum residents into paying for public services and improving their homes. It is also the case that some governments simply lack the political will to address these issues (e.g., Rashid, Citation 2009 ). Overall, one could argue that not enough attention has been given to the plight of slum dwellers on the part of government, further enabling the continued growth of slums.

Ill-designed policies

Closely related to poor urban governance are the inappropriate policies developed by local and national governments, and regional led organizations to curb the growth of slums. During the 1950s and 1960s, a time when urbanization was viewed as a positive, with large labour forces needed to fuel economic growth in cities (Fox, Citation 2014 ), macro-policy intervention with respect to slums was largely ignored, leading to the widespread growth of slums. To tackle this issue, a sites and services approach was introduced in the 1970s. This policy provided infrastructure at new sites (e.g., roads and electricity) and slum dwellers were relocated to these sites (Fekade, Citation 2000 ). Existing slums were demolished and slum dwellers were expected to pay for the newly developed serviced land as well as all home construction costs (Linden, Citation 1986 ). With limited finances, and the added challenge of competing with the greater demand by higher income groups for the same available land, slums continued to grow (Arimah, Citation 2001 ).

The failure of the sites and services approach led to a new wave of thinking during the 1980s, in which slums were upgraded with improved communal infrastructure and services. This avoided the unnecessary demolition of settlements (Gilbert, Citation 2014 ) while lowering costs in many instances (e.g., Werlin, Citation 1999 ). However, the slum-upgrading approach has been criticized at four major levels: failed financial commitment, negative socio-economic impacts, non-replicability of best practices and insecurity of tenure (Fekade, Citation 2000 ). Subsequently, an enabling approach through security of tenure was introduced in the 1990s. Legal occupational status was given to slum dwellers in the hope that they would be more willing improve their own circumstances (de Soto, Citation 2002 ). This approach relied heavily on the assumption that most slum dwellers owned the dwelling in which they lived. However, this was not the case, as most slum dwellers did not do so (e.g., Gulyani, Talukdar, & Jack, Citation 2010 ; Marx, Stoker, & Suri, Citation 2013b ), and this remains the plight of many slum dwellers today. Further, some would argue (e.g., Desai & Loftus, Citation 2013 ) that security of land tenure adds an additional burden on slum dwellers since landlords are more likely to increase rental prices and evict those slum dwellers unable to pay. Moreover, as de Souza ( Citation 2001 ) argues, perceptions of security of tenure may vary widely from household to household. Specifically, their study showed that slum dwellers were more willing to invest in improving their housing conditions irrespective of land tenure status. Such findings further challenge the notion that security of land tenure alone would be enough to motivate slum dwellers to improve their own living conditions.

The most recent approach, at least from a macro-policy viewpoint, to reduce the growth of slums is the Cities Without Slums action plan, introduced during the 2000s. This approach forms part of the MDG to improve the lives of 100 million slum dwellers by 2020 (United Nations, Citation 2015a ). Central to this idea is the understanding that both poverty and slums are interrelated, with any attempt to fix one having also to consider the other (Arimah, Citation 2001 ). However, several issues have been identified with the Cities Without Slums action plan. These include little attention being given to other important poverty-related variables (e.g., political instability and natural disasters) and the non-comparability of metrics because of issues with data in many countries and the unique characteristics of slums (Saith, Citation 2006 ). Furthermore, as no metrics are available for monitoring newly emerging slums, it has been suggested that the goal of improving the lives of at least 100 million slum dwellers by 2020 is far too modest to generate a meaningful reduction in slums globally (Sietchiping, Citation 2004 ). Finally, the key terms used for measuring the improvement of slums (e.g., ‘access’, ‘improved’ and ‘sufficient’) are far too abstract for capturing and monitoring change rigorously (Cohen, Citation 2014 ).

As this section has discussed, the continued failure of implemented slum policies has, in reality, facilitated the propagation of slums. One could argue that this is due to the inability of governments to understand fully the needs of slum dwellers and incorporate their needs when developing appropriate policies (Fekade, Citation 2000 ). In addition, the locational decisions of slum dwellers, rural-to-urban migration and poor urban governance in general are all interrelated factors that have contributed to the growth of slums. Taken holistically, these various factors are all important for designing more appropriate slum policies. Failure to do so will only lead to the continued growth and persistence of slums. However, to grapple fully with the problems discussed above, we would argue that one needs also to understand the various operational challenges encountered when trying to address the issue of slums, which we turn to next.

As discussed above, slums continue to persist, although many attempts have been made to curb their growth over the past several decades. Some of the main reasons identified for their continued persistence include locational choice factors, rural-to-urban migration, poor urban governance and ill-designed policies. This section will argue that in order to investigate fully these various factors more holistically, two broad but interrelated challenges must first be addressed. The first issue relates to arriving at a consensus with respect to what is a slum. Once a definition has been derived, a set of measures can then be formulated in order to quantify the various aspects that characterize slums. Based on these measures, it is then possible to define the types and characteristics of the data required to analyze and monitor slums. This second issue is discussed.

Towards an operational definition of slum

The first step in developing a more structured approach to the studying of slums is to define what they are. However, there continues to be no agreed-upon definition for what is a slum (e.g., Gilbert, Citation 2011 ; Richter, Miscione, De, & Pfeffer, Citation 2011 ), nor at what scale with which to study them. For example, Smith ( Citation 2013 ) states that as many as 12 different definitions for slums are used by the Affordable Housing Institute. Slums’ definitions can vary based on location, official and unofficial descriptions, or differences on issues covered (e.g., temporary nature, land legality and health). Furthermore, some places (e.g., Nairobi in Kenya and Mexico City in Mexico) have no formal definition in place for identifying slums, adding yet another level of subjectivity in defining slums (UN-Habitat, Citation 2003 ).

In an attempt to enable a better accounting and monitoring of slums, the UN in 2003 formulated one of the first operational definitions for slums. Specifically, a slum was defined as a group of households lacking one or more of the following: (1) durable housing of a permanent nature that protects against extreme climate conditions; (2) inadequate living space; (3) easy access to safe water in sufficient amounts at an affordable price; (4) adequate access to sanitation in the form of a private or public toilet shared by a reasonable number of people; and (5) security of tenure that prevents against forced evictions (UN-Habitat, Citation 2003 ). This definition for a slum was later revised by UN-Habitat ( Citation 2006 ) to the individual household level (i.e., a group of individuals living under the same roof). These definitions are based on two major tenants: physical and legal characteristics of slums. Physical characteristics look at the circumstances that surround slum life, depicting the deficiencies and poor conditions that occur within slums. Legal tenure status, other the other hand, often depends on de facto or de jure rights, or a lack of them. This measure looks at the denial and violation of housing rights, as well the progressive fulfilment of these rights. The UN-Habitat ( Citation 2006 ) definition, however, does not capture the full degree of deprivation faced by slum households, nor does it capture information on how the extent and type of deprivations change over time. As McFarlane ( Citation 2012 ) points out, the type of informality experienced by slum dwellers is far from being static, and instead, it is in a dynamic state that changes over time. Consequently, it is important to monitor continuously whether implemented policies and other interventions are changing the conditions within slums.

An enhanced definition for slums proposed by UN-Habitat ( Citation 2008 ) groups slums into broad categories of moderately deprived (one-shelter deprivation), severely deprived (two-shelter deprivations) and extremely deprived (three- or more-shelter deprivations). The main issues with this enhanced definition, as with those previously mentioned, relate to the absence of a social dimension, and the difficulty in capturing information as it relates to security of tenure within slums. These factors are therefore not present in any global comparison of slums (United Nations, Citation 2012 ). In conjunction with this, some would argue that risk posed to human life has been highly underplayed in previous definitions for slums (e.g., Khalifa, Citation 2011 ). Without incorporating risk factors, it is difficult to provide adequate resources to mitigate risk for individual slums, as each slum is unique with its own set of associated risk factors (Jankowska, Weeks, & Engstrom, Citation 2012 ).

The variety of definitions and the lack of consensus on the appropriate scale at which slums should be studied poses serious challenges for monitoring and reducing their populations worldwide. This has led to a variety of area-specific slum-mapping approaches in different areas. From a policy perspective, the enumeration of slum dwellers may mean that national governments need to allocate more resources for welfare programmes (Patel, Citation 2012 ). Likewise, it is difficult to compare slum populations using different definitions from a research point of view. Multiple definitions further result in different estimates of slum populations (e.g., Patel et al., Citation 2014 ) and variations in the types and quality of data being collected on slums. Moreover, without a common slum definition in place, issues in monitoring the growth of slums and determining the overall success of slum policies will prove challenging. Consequently, without a common slum definition, efforts to estimate the magnitude and extent of slums as a global issue will be hindered. At the same time, a common definition alone without adequate data for identifying and monitoring slums will be of limited benefit from a practical viewpoint. Some of these data challenges are identified next.

Despite a lack of consensus on how to define what represents a slum, researchers have been collecting data on these environments for some time. However, we would argue, this has been piecemeal at best given that most data are collected for specific slums and at different spatial and temporal scales. Traditional methods for detecting slums have often inferred their presence using census data. These approaches collect information though surveys as a basis of deprivation or poverty mapping. Although more commonly available for most countries when compared with other forms of data (e.g., high-resolution remotely sensed imagery), census data present several issues when used to detect slums. One major issue is that there are long temporal gaps between collection events and the release of statistics (Martínez, Citation 2009 ), usually 5–10 years (Hall, Malcolm, & Piwowar, Citation 2001 ). Given the dynamic spatial and temporal behaviour of slums (e.g., a growth rate of more than 1000 people per hour in Dhaka, Bangladesh; Kotkin, Citation 2014 ), the spatial information mapped by surveying may already be obsolete when consolidated.

Another identified issue with census data is that they are made available to users at coarse spatial scales, leading to a loss of spatial heterogeneity of landscape features, which may mask the presence of slums (depending on the specific definition of slums used). It is further difficult to link survey data collected on small populations (e.g., ethnographic information and dwelling attributes) with aggregated statistics collected from census. Such data integration is increasingly becoming important because of growing research and interest on marginalized populations (Galeon, Citation 2008 ; Gibson-Graham, Citation 2008 ). Moreover, census campaigns are expensive and labour intensive, which some governments are unable to afford, especially with increasing public sector cutbacks in many less developed countries (Leete, Citation 2003 ). Other issues reported in the literature for census-related studies include safety issues for field workers and respondents being absent from their dwellings during data collection (Marx et al., Citation 2013b ). This may lead to skewed results in statistics generated from such census data. Given these issues, reliance on census data, at least on their own, is unable to provide the spatial and temporally disaggregated information needed to inform more comprehensive slum assessments.

Another major issue with slum data is their absence in some cases. For example, Makoko, one of Nigeria’s oldest slums, was not included in any census until 2007 (Babalola, Citation 2009 ). Whereas for large and well-known slums such as Kibera in Nairobi, Kenya, a wealth of information exists. This is mainly due to Kibera’s large footprint on the landscape and its international presence from a humanitarian and socio-political point of view. Others institutions such as national and regional governments may do little to remove the presence of slums given the large voting population that exists within them (Marx et al., Citation 2013b ). For example, in some slums in Mumbai, India, while residents may not have the legal rights to reside within them (Chatterjee, Citation 2004 ), local government institutions often use the distribution of toilets to sway slum residents’ votes (McFarlane, Citation 2008 ). These issues altogether add another layer of complexity with respect to collecting and obtaining reliable data on slums, which eventually can lead to neglect and/or having limited assistance entirely.

Even when data on slums are available, this information may be fragmented across different organizations and with different levels of quality. There may also be significant restrictions when trying to access such information (e.g., Killemsetty, Citation 2013 ; Sen, Hobson, & Joshi, Citation 2003 ). Many less developed countries also do not implement any quality control during data collection, leading to low confidence in the use of these datasets (Henderson, Storeygard, & Weil, Citation 2012 ). Moreover, most less developed countries lack the information infrastructure necessary for cataloguing slum information. Failure to continue without such systems in place will hinder the potential for advancing the research on slums at the global context, and, by extension, impact our capacity to address the challenges associated with them.

While the above section has identified challenges with respect to defining, identifying and monitoring of slums, current advances in technology present several new-found opportunities with respect to addressing the challenges of slums. Specifically, the availability to acquire and analyze remotely sensed imagery, the emergence of crowdsourced information and advances in computational modelling are providing new opportunities to monitor, analyze and model slums.

Remote sensing

While remote sensing is not a new technology, the current and continued advancement of remote sensing makes it a sustainable source of information on slums and, therefore, an important opportunity to improve our ability to map and monitor slums. The most notable application of remote sensing as it relates to slums has been the capturing of their different surface characteristics, represented in the form of geospatial images. These images are then used to detect, identify and monitor slums in both space and time, allowing for greater understanding of their physical manifestations. In addition, remote sensing provides an unobtrusive method for collecting physical data on slums, and in some cases provides the only source of available information for some slums.

As discussed above, data on slums often suffer from a variety of issues. These include, among others, poor spatial and temporal coverage, prohibitive cost, and limited availability when using traditional modes of data collection (e.g., surveys). Compared with traditional methods, remote sensing offers several advantages. Remote sensing systems can capture physical information on slums at higher temporal resolutions that can range from continuous coverage to daily, weekly or monthly coverage. This provides us with the ability to better monitor and track the growth of slums towards a better understanding of their dynamics. Moreover, such timely information is important for slum upgrading (Augustijn-Beckers et al., Citation 2011 ), as well as in the implementation of required services (e.g., water and healthcare facilities) in slums given the differences in settlement growth stages (Fekade, Citation 2000 ). Also, studies have shown how remote sensing can be used as a proxy for deriving population estimates for slums (e.g., Veljanovski, Kanjir, Pehani, Otir, & Kovai, Citation 2012 ). Thus, compared with the collection of census data once every several years, if done at all (as discussed), remote sensing provides an opportunity to capture population estimates in addition to the physical characteristics of slums.

Remote sensing systems also enables the collection of disaggregate information at a range of different spatial resolutions and for large geographic areas, allowing for a cost-effective collection of information on slums (Taubenböck et al., Citation 2009 ) compared with traditional survey-based methods. The range of spatial resolutions offered by remote sensing platforms can be used to map slums to varying levels of analysis. For example, data can be collected at a fine spatial resolution (e.g., sub-metre), capturing information on individual dwellings, or at a more coarse resolution (e.g., 30 m), providing information for settlements-level analysis. Even finer spatial resolution can usually be obtained using airborne and unmanned aerial vehicle platforms (Meier, Citation 2015 ), with the additional flexibility of capturing information at user-defined times. Higher spatial resolution data on slums can also lead to a greater settlement characterization. The reason for this is their better ability to capture the physical and structural heterogeneity, which occurs in such environments, a property generally not captured in census data (Weeks, Hill, Stow, Getis, & Fugate, Citation 2007 ), as discussed previously.

Another important characteristic of newer remote sensing systems is the provision of higher spectral resolution data. Such information collected on slums has been used in numerous studies to discriminate them from their surroundings because of the differences in spectral characteristics, such as roof colour and the surface properties of roads (e.g., Hofmann, Citation 2001 ; Veljanovski et al., Citation 2012 ).

However, several challenges have been identified with the use of remote sensing, at least on its own, for detecting and mapping slums. One limitation of optical space-borne remote sensing relates to significant and persistent cloud cover over many areas with large slum populations (Kuffer, Pfeffer, & Sliuzas, Citation 2016 ). To overcome this issue, cloud-covered parts of images must be removed and combined with other images captured within a small temporal window to avoid problems of seasonal effects. This can be a difficult task at times since some locations close to the equator have very few cloud-free images available annually (e.g., Asner, Citation 2001 ). Such a requirement impacts the cost of obtaining remote sensing data, which can quickly escalate given the requirement for frequent and large-scale monitoring of slums. While other remote sensing technologies such as radar have the potential to overcome cloud-coverage issues, very few studies have utilized radar for the study of slums (e.g., Lisini, Gamba, & Dell’Acqua, Citation 2012 ; Stasolla & Gamba, Citation 2008 ). This could be attributed to the limited availability of radar data, as well as the contributed complexity of processing such data.

Another limitation of remote sensing is that it only captures the physical properties of slums. While this may be adequate for identifying the physical characteristics of slums, such as irregular and crowded patterns of dwellings and the location of slums at hazardous places, this information alone may be insufficient for identifying slums. As discussed, slums are multifaceted, for which several types of information not related to physical properties (e.g., lack of tenure security) are required for their identification. For such data integration to occur, social data collected on slums with similar spatial and temporal coverage as the remote sensing imagery data must be available. However, this is a long-standing problem with census data due to their infrequent collection in many developing countries, as previously discussed. Other sources of social data that have the potential to provide information on slums are therefore required to overcome some of the challenges. One of these sources is crowdsourced spatial data.

Crowdsourcing and spatial data infrastructures

As discussed above, often the collection of slum data was mainly carried out in a top-down manner. However, with advancement in technology (e.g., geographic information systems (GISs), global positioning systems (GPSs), mobile computing and web services), alternative methods for the collection of slum information have emerged. One such approach is that of crowdsourced information gathered from experts and non-experts alike (Goodchild, Citation 2007 ). Much of this new stream of information has been enabled by open-source initiatives such as OpenStreetMap, and, with respect to slums, the Map Kibera initiative. Prior to this project, little to no spatial information existed on the Kibera slum in Nairobi. In this project, members of the Kibera community actively took part in the collection of information on their slum, including data on the location of water, sanitation and health facilities, and these data were made available on OpenStreetMap. Although OpenStreetMap has been widely used for the collection of slum information, other mapping platforms also exist, e.g., Wikimapia and Google’s Mapmaker to name just a few. These initiatives have reversed the top-down approach typical of most traditional data-collection methods to a bottom-up approach.

In conjunction with the emergence of crowdsourced data, centrally collected spatial data are increasingly being made openly available. In some developed countries open data initiatives are well on their way by governments (e.g., OpenData.gov in the United States and Data.gov.uk in the UK), NGOs (e.g., Data.worldbank.org by the World Bank), and industry (e.g., google.com/mapmaker by Google), making such data freely available to the general public. Currently, there are few such examples in less developed countries (discussed below). Therefore, any global initiative to map slums should consider not only the role of crowdsourced information but also how other open sources of information (along with remote sensing) can be utilized for the identification and mapping of slums. This could potentially allow for a suite of slum indicators to emerge in a similar vain to other urban indicators more generally applied and used in developed countries (Kitchin, Lauriault, & McArdle, Citation 2015 ).

As alternative and complementing sources of information are being relied upon and harnessed to supplement existing data on slums, it is becoming increasingly necessary to have systems in place for adequately storing and managing these data. One such approach is the use a spatial data infrastructure (SDI) which can be used systematically to store, catalogue and distribute geographical information (United Nations, Citation 2000 ). In the case of slums, this approach can be applied to provide data to a multitude of users, at different spatial and temporal resolutions. Such datasets can then be used in a variety of other applications such as planning infrastructure (Fekade, Citation 2000 ), growth modelling (Dubovyk, Sliuzas, & Flacke, Citation 2011 ) and disaster management (Ebert et al., Citation 2009 ). With the advancement of web technologies, distributed computing and open data standards, SDI systems are increasingly moving towards a service-oriented architecture approach and cloud services (Crooks, Hudson-Smith, Croitoru, & Stefanidis, Citation 2014 ). In this approach, greater focus is placed on the generation of knowledge from data (Davis & Alves, Citation 2005 ). Many governments worldwide (especially in developed countries) have begun placing substantial investments in SDIs or similar initiatives (e.g., WebGIS). One such initiative in a less developed country is Virtual Kenya ( Citation 2015 ), a web-based geospatial portal providing information on Kenya, including mapped locations of slums. Such systems have benefited substantially from the increased availability of open-source hardware and software programs, compared with the previous paradigm of either investing in commercial software or developing one’s own applications, thus lowering the cost of participation (Reades, Citation 2014 ).

While there have many advances with respect the collection of data and mapping of slums, little attention has been paid to modelling their growth trajectories. Modelling of urban growth has received a large amount of attention in the developed world (e.g., Batty, Citation 2009 ), but little has been done with respect to modelling the growth of slums in the less developed world (Patel, Citation 2012 ). Previous models such as those developed by the Chicago School (e.g., Burgess, Citation 1925 ) are based on rigid and outdated assumptions that do not fit today’s rapidly changing slums. These models further do not take into account factors such as advancement in transportation, rural-to-urban migration and the transformation in the global economy, all of which impact the growth of slums. While newer models based on post-modern theory such as social area analysis (Shevky & Bell, Citation 1955 ) have made remarkable strides in poverty detection and characterization, they still continue to assume a static system. More adaptive approaches are therefore needed to understand better how slums grow and evolve over time.

According to Jacobs ( Citation 1961 ), cities are complex, self-organizing systems made up of many different parts that dynamically interact with each other, both competing and engaging in mutual relationships. Taking such notions from complexity science, attempts have been made to model slums as dynamic systems. Approaches such as cellular automata (CA) and agent-based models (ABMs) have been used to develop dynamic models to simulate and explore the growth and evolution of slums (Roy et al., Citation 2014 ). Such modelling approaches offer a different lens with which to view and understand slums from the bottom-up. These models further have the added benefit of being able to work with both quantitative and qualitative data in a single environment (O’Sullivan, Citation 2008 ). Various studies have used CA to develop models of slums. Sietchiping ( Citation 2004 ), for example, integrated GIS and CA to study the dynamic growth of slums in Yaounde, Cameroon. Similarly, Arsanjani et al. ( Citation 2013 ) used CA, combining it with logistic regression and Markov chain modelling techniques to create a new hybrid model for a better understanding of slums in Tehran, Iran.

Compared with CA models, ABMs focus not only on the environment but also on individual entities (i.e., agents) within the environment, and it has been argued that ABMs more closely resemble the real world (Crooks & Heppenstall, Citation 2012 ). With respect to slums, there has been several ABM attempts ranging from the theoretical formation of slums in Latin American cities (Barros, Citation 2012 ) to the growth and shape of slums based on residential preferences and the availability of land in Tanzania (e.g., Augustijn-Beckers et al., Citation 2011 ; Young & Flacke, Citation 2010 ). Patel et al. ( Citation 2012 ) explored the spatial and temporal dynamics of slum formation in India, incorporating slum dwellings, developers and politicians, which unlike previous ABMs captured the cross-scale dynamics of several actors known to influence the formation and persistence of slum settlements. Other tools exist for understanding complex systems, such as interacting state machines, genetic and particle swarm approaches; however, little work has been done using these approaches to study slums. Such approaches are expected to have a large impact in slum research, as such a shift will focus on slums being seen not as static systems but rather as dynamic and complex entities in their own right.

However, while dynamic models continue to provide valuable insights into slums and the people who live in them, several key challenges must be addressed when interpreting the results from such models. One consideration is that the output of any model is only as good as its data sources and the theory upon which it is grounded. However, without a common definition of what a slum is, building such models will continue to prove challenging. Moreover, as is common in many modelling approaches, including CA and ABMs, the models created tend to be specifically tailored to the data types and characteristics of the specific place being modelled (Crooks, Castle, & Batty, Citation 2008 ). Given the uniqueness of different slums, knowledge gained from models built for slums in one location may not be easily transferred to slums in another location. This is especially problematic when the scale of specific variables (e.g., population density) being incorporated into the model for one location is different for another location. Such issues are common in many less developing countries because of lack of standards and systems in place for collecting data and making these data available to users, as previously discussed. The high variability in the characteristics of slums, as well as in the way that data are collected, may also present challenges with respect to the calibration, verification and validation of such models. In particular, such variability in the data-collection process and data coverage may lead to inconsistent results, which cannot necessarily be attributed to the model itself. Moreover, because models such as CA and ABMs try to model complex behaviour, rich datasets spanning the micro-preferences of individuals and their behaviours, and more macro-data to evaluate emergent patterns are required for such models (Heppenstall, Malleson, & Crooks, Citation 2016 ). This poses a significant challenge with respect to slums as such data are often unavailable. This could explain in part why only a few studies have used dynamic models to study slums.

For the first time in human history more people live in cities than in rural areas. Cities have long been revered as the economic centres of the world, attracting a multitude of people for different reasons such as better jobs and economic opportunities. While for others cities provide an opportunity to escape social norms typical of many rural areas. Usually, urban growth and economic development must go hand in hand: as cities grow, so does the economy in order to adequately meet and provide the infrastructure and services necessary to support the increasing number of people. However, as history has shown, this is not always the case in many less developed countries. Unable to support the increased urban population, slums have emerged and continue to grow and persist in many less developed countries and, particularly, in rapidly urbanizing regions in the Global South.

In this paper we have presented a framework for understanding and studying slums, bringing together some of the most pressing issues surrounding slums identified in the literature. These issues are yet to be holistically addressed from both a research and an implementation viewpoint. Therefore, it is hoped that the information contained here not only will provide valuable insight for future research but also will help inform governments, NGOs and the public at large towards improving our understanding of slums. Towards this objective, we discussed the impacts of slums in terms of both the negative (e.g., burden on taxpayers for improving conditions in slums) and positive effects on society (e.g., contributions made to country GDP). However, most of the literature reviewed view slums as an issue that will continue to grow and persist for the foreseeable future. As all the fastest growing slums are found in less developed countries that suffer from a general shortage of resources, a key challenge is striking a balance between improving the conditions in slums and using the resources slums require to assist their economy.

While there has been a substantial amount of work on slums, such work has largely focused on only one of the constructs (e.g., social or physical). This review has shown that slums are a multidimensional issue, with a wide range of factors attributed to their occurrence. Social factors such as rural-to-urban migration, poor urban governance and policies that fail to address the needs of slum dwellers, along with various locational choice factors, have led to the present state of slums today. Some slums also have very unique social and physical characteristics that set them apart from other slums. These characteristics, along with inconsistent definitions of what a slum is and a general lack of reliable data, make the quantification and comparison of slums a challenging undertaking both within and among countries, and across world regions.

Several attempts have been made to explain the emergence of slums. However, these assume a static system with rigid assumptions and do not take into account the diverse nature of slums and their multi-tiered and interrelated connections within society and the environment. Although newer methods have used dynamic approaches such as CA and ABMs to understand better the complex nature of slums, few such studies have been carried out, signalling a greater need for more research in these areas.

While the presence of slums has different implications not only for individual cities, countries or regions around the world, ultimately slums are a global issue. To grapple totally with their complexities, one needs to take a multifaceted and multidisciplinary perspective on slums. This is not a new concept and has been applied to other real-world societal problems such as climate change. However, in the context of slums, this first requires a common definition for slums followed by standardized procedures for the collection of slum-related data. We would argue that such data need to be collected at adequate spatial and temporal resolutions to accommodate a greater number of users’ diverse needs, along with approaches towards managing and distributing data in general. Also, emerging sources of data that can provide spatial, temporal and social insights about slums (e.g., crowdsourced information and open data) should be further explored. These new information sources should be used to complement traditional sources of data collected on slums (e.g., census surveys and remote sensing). Moreover, there is a need to explore how traditional sources could be used to validate these newer streams of information. Furthermore, open-data initiatives should be investigated as a means of reducing the cost of acquiring data and support data-quality management.

Figure 2. Conceptual model for integrating social and physical constructs to monitor, analyze and model slums.

1. The terms ‘slums’ and ‘informal settlements’ are often used interchangeably in the literature. However, UN-Habitat ( Citation 2003 ) views an informal settlement as one type of slum, specifically where there is insecurity of tenure (this will be further discussed below).

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• Letter to the Editor
• Open access
• Published: 02 January 2020

Urbanization: a problem for the rich and the poor?

• Md Abdul Kuddus 1 , 2 , 4 ,
• Elizabeth Tynan 3 &
• Emma McBryde 1 , 2  

Public Health Reviews volume  41 , Article number:  1 ( 2020 ) Cite this article

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Urbanization has long been associated with human development and progress, but recent studies have shown that urban settings can also lead to significant inequalities and health problems. This paper is concerned with the adverse impact of urbanization on both developed and developing nations and both wealthy and poor populations within those nations, addressing issues associated with public health problems in urban areas. The discussion in this paper will be of interest to policy makers. The paper advocates policies that improve the socio-economic conditions of the urban poor and promote their better health. Further, this discussion encourages wealthy people and nations to become better informed about the challenges that may arise when urbanization occurs in their regions without the required social supports and infrastructure.

Urbanization refers to the mass movement of populations from rural to urban settings and the consequent physical changes to urban settings. In 2019, the United Nations estimated that more than half the world’s population (4.2 billion people) now live in urban area and by 2041, this figure will increase to 6 billion people [ 1 ].

Cities are known to play multifaceted functions in all societies. They are the heart of technological development and economic growth of many nations, while at the same time serving as a breeding ground for poverty, inequality, environmental hazards, and communicable diseases [ 2 ]. When large numbers of people congregate in cities, many problems result, particularly for the poor. For example, many rural migrants who settle in an urban slum area bring their families and their domesticated animals—both pets and livestock—with them. This influx of humans and animals leads to vulnerability of all migrants to circulating communicable diseases and the potential to establish an urban transmission cycle. Further, most urban poor live in slums that are unregulated, have congested conditions, are overcrowded, are positioned near open sewers, and restricted to geographically dangerous areas such as hillsides, riverbanks, and water basins subject to landslides, flooding, or industrial hazards. All of these factors lead to the spread of communicable and non-communicable diseases, pollution, poor nutrition, road traffic, and so on [ 3 , 4 , 5 ]. The problems faced by the poor spill over to other city dwellers. As the trend to urbanization continues, this spillover effect increases and takes on a global dimension as more and more of the world’s populations are affected [ 3 ].

Some of the major health problems resulting from urbanization include poor nutrition, pollution-related health conditions and communicable diseases, poor sanitation and housing conditions, and related health conditions. These have direct impacts on individual quality of life, while straining public health systems and resources [ 6 ].

Urbanization has a major negative impact on the nutritional health of poor populations. Because they have limited financial resources and the cost of food is higher in cities, the urban poor lack nutritious diets and this leads to illness, which contributes to loss of appetite and poor absorption of nutrients among those affected. Furthermore, environmental contamination also contributes to undernutrition; street food is often prepared in unhygienic conditions, leading to outbreaks of food-borne illnesses (e.g., botulism, salmonellosis, and shigellosis) [ 6 ]. Urban dwellers also suffer from overnutrition and obesity, a growing global public health problem. Obesity and other lifestyle conditions contribute to chronic diseases (such as cancers, diabetes, and heart diseases). Although obesity is most common among the wealthy, international agencies have noted the emergence of increased weight among the middle class and poor in recent years [ 7 ].

Populations in poor nations that suffer from protein-energy malnutrition [ 8 ] have increased susceptibility to infection [ 9 ] through the impact of micronutrient deficiency on immune system development and function [ 10 ]. Around 168 million children under 5 are estimated to be malnourished and 76% of these children live in Asia [ 11 ]. At the same time, the World Health Organization is concerned that there is an emerging pandemic of obesity in poor countries that leads to non-communicable diseases such as diabetes, cardiovascular disease, cancer, hypertension, and stroke [ 12 ].

Obesity is caused by increased caloric intake and decreased physical activity [ 13 ], something historically associated with wealth. However, people in urbanized areas of developing countries are also now vulnerable to obesity due to lack of physical space, continually sitting in workplaces, and excessive energy intake and low energy expenditure. In these areas, infrastructure is often lacking, including sufficient space for recreational activities. Further, in developing countries, as in developed countries, large employers frequently place head offices in urban capitals and work is increasingly sedentary in nature [ 14 ]. Another culprit associated with the risk of developing obesity is the change in food intake that has led to the so-called nutrition transition (increased the consumption of animal-source foods, sugar, fats and oils, refined grains, and processed foods) in urban areas. For instance, in China, dietary patterns have changed concomitantly with urbanization in the past 30 years, leading to increased obesity [ 15 ]. In 2003, the World Health Organization estimated that more than 300 million adults were affected, the majority in developed and highly urbanized countries [ 16 ]. Since then, the prevalence of obesity has increased. For example, in Australia, around 28% of adults were obese in 2014–2015 [ 17 ].

Pollution is another major contributor to poor health in urban environments. For instance, the World Health Organization estimated that 6.5 million people died (11.6% of all global deaths) as a consequence of indoor and outdoor air pollution and nearly 90% of air-pollution-related deaths occurred in low- and middle-income countries [ 18 ]. Poor nutrition and pollution both contribute to a third major challenge for urban populations: communicable diseases. The poor live in congested conditions, near open sewers and stagnant water, and are therefore constantly exposed to unhealthy waste [ 6 ]. Inadequate sanitation can lead to the transmission of helminths and other intestinal parasites. Pollution (e.g., from CO 2 emission) from congested urban areas contributes to localized and global climate change and direct health problems, such as respiratory illnesses, cardiovascular diseases, and cancer for both the rich and the poor.

In addition to human-to-human transmission, animals and insects serve as efficient vectors for diseases within urban settings and do not discriminate between the rich and poor. The prevalence and impact of communicable diseases in urban settings, such as tuberculosis (TB), malaria, cholera, dengue, and others, is well established and of global concern.

National and international researchers and policy makers have explored various strategies to address such problems, yet the problems remain. For example, research on solutions for megacities has been ongoing since the early 1990s [ 19 , 20 ]. These studies have concluded that pollution, unreliable electricity, and non-functioning infrastructure are priority initiatives; nevertheless, air pollution, quality of water in cities, congestion, disaster management issues, and infrastructure are not being systematically addressed [ 19 , 20 ].

The impact of inner city transportation on health, such as road traffic, is emerging as a serious problem. Statistics show that a minimum of 10 people die every day on the railways in the city of Mumbai, India [ 21 ]. Vietnam is another example of a country that has seen a remarkable increase in road traffic accidents [ 22 ]. Improvements to the country’s infrastructure have not been able to meet the increasing growth of vehicular and human traffic on the street. Vietnam reportedly has a population of 95 million and more than 18 million motorbikes on its roads. A deliberate policy is needed to reduce accidents [ 21 ].

Although urbanization has become an irreversible phenomenon, some have argued that to resolve the problems of the city, we must tackle the root causes of the problem, such as improving the socio-economic situation of the urban poor.

Until the conditions in rural areas improve, populations will continue to migrate to urban settings. Given the challenges that rural development poses, the root causes are unlikely to be addressed in the near future. Therefore, governments and development agencies should concentrate on adapting to the challenges of urbanization, while seeking to reduce unplanned urbanization.

Some examples of policies and practices that should be considered include (i) policies that consider whole-of-life journeys, incorporating accessible employment, community participation, mobility/migration and social transition, to break generational poverty cycles; (ii) policies addressing urban environmental issues, such as planned urban space and taxes on the use of vehicles to reduce use or to encourage vehicles that use less fuel as well as encourage bicycle use, walking, and other forms of human transportation; (iii) greater cooperative planning between rural and urban regions to improve food security (e.g., subsidies for farmers providing locally produced, unprocessed and low cost food to urban centers); (iv) social protection and universal health coverage to reduce wealth disparity among urban dwellers; including introduction of programs and services for health, for example by establishing primary healthcare clinics accessible and affordable for all including those living in urban slums [ 23 ].

Availability of data and materials

Not applicable

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Acknowledgements

The authors would like to thank the editor for his/her thoughtful comments and efforts towards improving the manuscript.

This work was conducted as a part of a PhD programme of the first authors and funded by the College of Medicine and Dentistry at the James Cook University, Australia (JCU-QLD-933347).

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MAK planned the study, analyzed, and prepared the manuscript. ET and EM helped in the preparation of the manuscript. All authors read and approved the final manuscript.

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Kuddus, M.A., Tynan, E. & McBryde, E. Urbanization: a problem for the rich and the poor?. Public Health Rev 41 , 1 (2020). https://doi.org/10.1186/s40985-019-0116-0

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15 Architecture Thesis Topics for Urban Architecture

Urban Architecture has consistently been a trending architecture thesis topic among the students. And before we go deep into the variety of topics that can be used we must understand what exactly is Urban Architecture?

One could say urban architecture refers to any building type that establishes an appreciable relationship with its surrounding context, the built environment , and the community itself. It comprises buildings that are mostly located in urban areas, are accessible, and are meant to serve the public at large. Its purpose hence would be to make society better. Indeed, people are indeed strongly affected by building forms and facades. According to research, the main cause of ‘social stress’ in urban environments is often the absence of social bonding and interconnection in city landscapes . Design that stimulates social and urban cohesion is hence, very important for good community living. This is where urban architecture comes in; a holistic approach to the subject may result in projects like iconic skyscrapers or even residential developments . However, the focus revolves around enhancing the experience of people who are connected to the architecture.

When choosing to do a architecture thesis project on the subject of urban architecture, one needs to understand the platitude of areas and scopes encompassed by the field. There are indeed endless possibilities and avenues to explore that intend to serve the interests of the public, and also make community life better.

Before you delve into the list of topics of urban architecture to choose from, make that:

• You understand the subject thoroughly. Choose a topic relevantly and appealing to your interests, especially prospects, masters, or a job.
• You discuss it with your thesis advisor so that he can comprehend your intent and help you through the course of the project .
• The topic does not necessarily have to be unique. It also should not be something that has been tried and tested far too many times.it is because your work is what would represent you. Make sure, it speaks of who you are and what you want to do.

Here are a few options for viable architecture thesis topics that you could choose to look at.

1. Low-cost housing | Architecture Thesis

As more and more people are moving to dense urban cities like New York , in search of a better quality of living and opportunities, the city population is on the rise. As is the cost of living, making low-cost housing a dire need of societies, as low-income residents have limited choices for affordable living. When affordable housing complexes were being constructed ever since the mid-20 th century, these projects were often seen as monumental solutions to provide economical living spaces to large groups of people. Hence, even with the best of intentions of the designers, the imposing towers often turned out to be negligent of human scale, and were often more inhospitable and discouraging for communities, leaving them feeling more isolated and unwelcome.

However, a rising interest in the area since recent years has seen a rise in alternative solutions to the outdated models. Low-cost, affordable housing is not seen as merely buildings creating decent spaces for living, but also using sustainable building features to reduce costs, maintenance and to help improve the quality of life and belongingness for residents, allowing them to feel more connected to not just the resources, but also to communities and the spaces outside.

2. Art and Heritage museum

To design a building that is important not only for the preservation of the history of the community but to also integrate members of the community and to what they share. This topic uses a method that looks at the study not only qualitatively, but also based on a theoretical foundation, with the acute understanding that comes from familiarizing oneself with concepts and standards of museums, exhibition spaces, contextualism, and exhibit care and preservation.

The project should not only focus on respecting the importance of the historical context, but also ensure that it avoids the damage of pieces of its past. It should shed light on the concept of the museum itself, the types of functions and activities it would encourage, the form and physicality of the building, and the interconnectivity between different elements of the museum . The journey of a user and the enriching experience that the museum provides, concerning its displays but to communal spaces of social interaction and discussion should also be of high value when taking this topic.

3. Airport of Urban Architecture Thesis

Many countries in the world, including the USA, are suffering from outdated aviation infrastructure, with most airports being more than 40 years old, and a lot of money being spent on the revamp, expansion or construction to meet the challenging new needs of today. Design-wise, architects need to not only provide solutions for the necessary functioning and program of the airport , but also to enhance the experience of travel for the visitors, which includes interesting features for wayfinding, atriums for nature incorporation and natural light, state-of-the-art visual elements, and huge spaces for sightseeing and rest, as well cultural experiences which encapsulate the context of the airport, gardens, and desert landscapes. The project area also has a lot of potential for experimentation with physical form and modelmaking, which could induce a sense of awe for the public at large.

The functional aspects, of course, include catering to huge parking spaces, checking and security posts, luggage management areas, lobby areas, airport maintenance spaces, airplane ramps, and cargos, and many others, as well as allowing for the potential for future expansion. Thus, airports not only present an interesting challenge for a thesis topic but are also one that provides extensive avenues to understand the flexibility of a space which is in fact the cardinal space a visitor comes into contact with when entering a new city or a country. Hence, holding great social importance. The change seen in recent airport designs does indeed seem like a promising area to work in.

4. Cinema and theatre architecture

Cinemas and theatres are interesting places, where the anticipation to experience is just as important as the actual film or performance itself. This is why the design and nature of the building hold such great importance.  It should in some way, either reflect the magnitude of the experience that it would showcase, or subdue itself against the marvel of the performance . Either way, it should be taken as a work of art, as architectural icons as done so in the past, which communicate the spirit of the times through the design.

The building requires a careful understanding of the program; it features their relationships with one another, the type of circulation from one space to another, and the allowance of gathering spaces with technical ones as well. The seating arrangement, sound buffering, technical knowledge must be handled as meticulously as possible, as close attention to the sound, visuals, and theatrics are what greatly enhance the experience of the performance. This is why this is also a very fascinating topic, for a building that integrates different groups of society and brings them together to experience a shared feature.

5. Skyscraper design | Architecture Thesis

Living in a time when the competition to rise, to go higher, and to reach greater heights resonates with the fact that there is an ever-increasing desire to build very tall buildings. By definition, a skyscraper is a building that exceeds 330 feet in height. Yet the contemporary approach is not only to reach unattainable heights in construction, but it is also to rejuvenate thinking abilities, and present inventions with cutting-edge designs, that also meet the function of the building with elegance and pride. From encompassing different architectural movements like art deco and modernism, skyscraper designs also look at the intensive technical understanding of how high-rise work, the relationship of functionality between different floors, structural knowledge, and the municipalities that come with handling such delicate tasks.

6. Suburban housing community

Suburban homes provide an avenue to understand a huge sector of society without directly destroying existing structures. They should be able to cater to the needs of the ever-changing dynamic of the public, to provide a potential for future expansion, and to provide an environment of ownership that allows for a comforting feeling of belongingness that leads to greater social integration.

The nature of the task often involves dealing with multiple stakeholders that are directly associated with such regions, including developers and the municipal government. Therefore, this subject involves a meticulous understanding of the way rules and regulations work, sizing, areas and appropriate zoning, transportation, and also a critical comprehension of the associated infrastructure required to cater to the needs of residential living, and of course, the quality of life.

7. Marine park design of Urban Architecture

Projects paying attention to marine life can help bring new life into waterfront areas and can also provide a point of interest for the entire region itself. There exists in our society an absence of awareness regarding marine ecosystems, especially informal sectors, which has resulted in a lack of opportunities, care, and resources available for marine life. Thus, a thesis project on this topic would not be addressing the administrative concerns related to marine life, but could also cater to providing a recreational public space , where visitors can appreciate and interact with marine life along with exhibition spaces intended to create awareness for the general public.

Whilst taking the project a step ahead, a proper research institute could also be designed to further the knowledge available of the oceans and the organisms that inhabit them. These institutes with research facilities and equipment could provide areas for analysis, experimentation, and research for discovery. Thus, this project would not only help educate the public at large, but help generate revenue as a popular tourist attraction, and plant seeds for much-needed research of marine life.

8. Convention center of Urban Architecture

A convention center is a public building of urban architecture meant to convey ideas and knowledge. It is also perceived to be more like the expansion of a town hall, where people having shared interests, goals, though, religion, or professions, could gather to interact, communicate, learn, and make decisions regarding the public realm. Hence, it is a space that caters to large groups of people, providing them with communal spaces that encourage different uses as well as appropriate exhibition spaces. 

Furthermore, since a convention center is meant to act as a medium for discourse, the first thing to consider is to develop a concept that would intend to attract people. It should have easy accessibility, be welcoming and fascinating and its spaces should be able to provide the necessary means for it to function efficiently and effectively. 

9. Library of Urban Architecture Thesis

In the modern age of digitalization, the internet and technology have greatly transformed the manner in which we consume information. With this rapidly changing paradigm, the traditional function of a library is put on a pedestal and called to question. While it is true that the physical collection of books in a certain environment as compared to quick access to data using the internet does question the sustainability of a public library and the resources it offers, we must also keep in mind that a library also functions as a flexible space, that can be transformed to an active social space, agent for interaction and societal growth.

It must not only be considered to be a space that allows access to information, but also an environment that encourages discourse, communication, and exchange of meaningful ideas between people from different ages and social groups. With this in mind, a public library must be considered as one of the most democratic building types available, and one that has huge potential to add value to community development, growth, resource, and service. Therefore, with the sensitivity that comes with designing a library comes great responsibility, and this must be looked at as an area with the potential to be explored as a vital public asset.

10. School of art and design

Projects that are practical solutions to community needs also have greater impacts on communities socially as well as economically. A thesis of urban architecture at a School for Art and Design could immensely help in this regard. It would only provide a platform for artists, architects, students, and citizens from various fields and social groups to gather and interact, share ideas and learn through conventional as well as modern ways and activities. This center would also enable these artists to share and exhibit their work and experiences through exhibition spaces, seminars, events, and conferences with members of their own community and the wider world through event halls, conference rooms, and libraries for research and learning.

With a learning institute as part of the program, the center would also allow aspiring artists to develop skills through formal training as well as informal activities. Thus, this institute would help create inclusivity in society but integrating different groups of people with a shared interest throughout the day and hence, year. It would also act as a viable magnet for social interaction between professionals, beneficial for the community and the campus. This, in turn, would enhance and regenerate the urban fabric, add depth to the context of the city and help drive the society forward in a positive direction. A thesis conducted on this topic, therefore, would allow you to look at art as a potential field to a group and bring communities together to appreciate the marvel that is an art and its ability to create change in the contemporary world.

11. Bus terminal cum commercial complex

Transit facilities are indeed one of the most important and vital functions of a city itself. They constitute some of the most important goals of the city and its government by inviting a large number of people to the city, merges different groups of crows, and bring in opportunities of work and living for the masses, thus building the scope of urban architecture. Therefore, smooth and better transit provides ground for future development and helps the urban fabric to grow incredibly. Transit not only improves the urban squares and nodes, and provides a push to less developed areas to allow them to be at par with the rest of the city.

Understanding the scope of development associated with a bus terminal with a commercial complex attached as an additional function thus presents itself as an interesting topic to pursue. It would not only group different travelers with one another but also with the locals, allowing them to appreciate and value local culture and tradition, as well as activities that integrate the urban living community.

12. Sports stadium of Urban Architecture

A stadium is one of the building typologies that have the power to shape the city or town it is located in. it not only helps put the city on the maps but also establishes an identity for the community and provides a tourist attraction and a focal point in its landscape. It is thus, a huge actor of theatrics that represents the output of a sport, and has a significant role for the city with regards to politics, geography, as well as socio-economics.

Thus, a sports stadium should not be looked at as a revenue-generating machine, but a building type that should be sustainable, iconic in design, with strong structural understanding for it to be considered a marvel in civic urban architecture. It requires a comprehensive understanding of various issues related to planning and design, which also cater to increased interaction and ease of access to its activities, and the environment is contained and encouraged.

13. Resort design | Architecture Thesis

A resort is a place that caters to accommodation, leisure, and recreation. It provides for a variety of activities and luxury in scenic areas and is able to house different groups of people together. Some facilities provided include rooms or huts, swimming pools , sports grounds, gyms, fine dining areas, halls for events, and many others.

Resort tourism is an area that is rapidly gaining popularity. It has a lot of municipalities involved that are often delicate in nature so as to provide high levels of comfort for its users. Therefore, it often talks about large scales, an attractive form that is meant to attract the general public, and advanced equipment and management strategies. It is indeed an interesting topic to consider when one wants to work on an area that not only deals with program efficiency but also the psychological impacts of effective design strategies. 

14. Religious buildings

An architecture thesis of urban architecture on religious buildings is a fascinating area to work on. It provides an avenue to create places with identity and an environment that awakens the senses and the emotions, enhances the experience, and provides a platform for spiritual practice. It should be kept in mind that the metaphysical concerns and experiences can largely be enhanced using effective space strategies that will come with a keen understanding of spatial and urban architecture.

Thus, space aims to heighten the experience of religion and spirituality and tends to cater to the tangible and intangible aspects of architecture, that involve senses. It is, therefore, a great challenge for architects to design spaces for religious activities, but also one that provides that greater amount of emotional appraisal. The modern religious building not only functions as only a religious center but also provides opportunities for people to come together and engage in communal activities. This is another aspect that architects need to consider when designing religious centers for contemporary times.

15. Educational Institute for rural children

With the understanding that urban architecture paves the way for enhancing the educational process with effective plan strategies and expression of detail, the topic provides an opportunity to explore this area with the development of an educational institute for rural children. This would not only emphasize the importance of education for all sectors of society but would allow meaningful involvement of the community for development projects meant to improve the quality of life for the rural sectors.

The planning involved would recognize the basic functions needed to run a school, especially in a rural setting with a standard of quality education kept in mind. There is an urgent need for developers to look at this area in society, as existing schools do not meet the typical standard, which in turn affects the educational lives of its students, making them unable to perform effectively to become important assets for their society. Thus, this topic for social responsibility helps to integrate schools and the community, with the building serving as a reflection of ideas of both its place and time through its design, concept, and function.

An Architect by profession, a writer, artist, and baker by interest, Amna Pervaiz sees Architecture and Urban Planning as a multifaceted avenue allowing her to explore a plethora of disciplinary elements. She sees the field as an untapped canvas; a journey she hopes would one day lead her towards social responsibility and welfare.

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• Published: 11 November 2022

Urbanization, economic development, and environmental changes in transitional economies in the global south: a case of Yangon

• Peilei Fan   ORCID: orcid.org/0000-0003-4448-4281 1 ,
• Jiquan Chen 2 ,
• Cadi Fung 3 ,
• Zaw Naing 4 ,
• Zutao Ouyang 5 ,
• Khaing Moe Nyunt 6 ,
• Zin Nwe Myint 7 ,
• Jiaguo Qi 2 ,
• Joseph P. Messina 8 ,
• Soe W. Myint 9 &
• Brad G. Peter 10  

Ecological Processes volume  11 , Article number:  65 ( 2022 ) Cite this article

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Metrics details

Transitional economies in Southeast Asia—a distinct group of developing countries—have experienced rapid urbanization in the past several decades due to the economic transition that fundamentally changed the function of their economies, societies and the environment. Myanmar, one of the least developed transitional economies in Southeast Asia, increased urbanization substantially from 25% in 1990 to 31% in 2019. However, major knowledge gaps exist in understanding the changes in urban land use and land cover and environment and their drivers in its cities.

We studied Yangon, the largest city in Myanmar, for the urbanization, environmental changes, and the underlying driving forces in a radically transitioned economy in the developing world. Based on satellite imagery and historic land use maps, we quantified the expansion of urban built-up land and constructed the land conversion matrix from 1990 through 2020. We also used three air pollutants to illustrate the changes in environmental conditions. We analyzed the coupled dynamics among urbanization, economic development, and environmental changes. Through conducting a workshop with 20 local experts, we further analyzed the influence of human systems and natural systems on Yangon’s urbanization and sustainability.

The city of Yangon expanded urban built-up land rapidly from 1990 to 2000, slowed down from 2000 to 2010, but gained momentum again from 2010 to 2020, with most newly added urban built-up land appearing to be converted from farmland and green land in both 1990–2000 and 2010–2020. Furthermore, the air pollutant concentration of CO decreased, but that of NO 2 and PM 2.5 increased in recent years. A positive correlation exists between population and economic development and the concentration of PM 2.5 is highly associated with population, the economy, and the number of vehicles. Finally, the expert panel also identified other potential drivers for urbanization, including the extreme climate event of Cyclone Nargis, capital relocation, and globalization.

Conclusions

Our research highlights the dramatic expansion of urban land and degradation of urban environment measured by air pollutants and interdependent changes between urbanization, economic development, and environmental changes.

Introduction

Urbanization has been viewed as one of the five ways that the world will change radically during the twenty-first century (Wolchover 2011 ), with developing countries being the main force to the increasingly urban world. Cohen ( 2006 ) estimated that virtually all population growth will be concentrated in urban areas of the developing world in the next three decades. Transitional economies in Southeast Asia—a distinct group of developing countries—have experienced rapid urbanization in the past several decades due to the economic transition that fundamentally changed the function of their economies, societies and the environment. Yet, there remain several major knowledge gaps in understanding on how state and market together have affected the urbanization process throughout the transition (Fan et al. 2017a , b ; Fan 2022 ; Sýkora and Bouzarovski 2012 ), particularly on the changes in urban land cover and environment that had been directly and indirectly shaped by the multiple external drivers, such as extreme events, globalization, and institutional shifts (e.g., relocation of the capital city).

The Republic of the Union of Myanmar, for example, is one of the least developed transitional economies in Southeast Asia, albeit the urbanization ratio increased significantly since the early 1990s. Its urbanization was at 22% in 1968, 25% in 1990, and 31% in 2019 (World Bank 2021 ). This rapid urbanization, spurred by the country’s economic liberalization beginning in the late 1980s, has been accompanied by a dramatic societal change in the most recent decade, which has featured privatization and the democratic transition in the political sphere in 2011. Myanmar initiated economic liberalization in 1988, focusing on marketization of the agricultural sector by allowing state enterprises to operate as market-oriented firms. In addition to this marketization, foreign investment was permitted through the Foreign Investment Law in 1988 that encouraged the benefits of tax exemptions for 3 years (Rigg 2004 ). Liberalization was evaluated as having improved the living conditions of farmers with a rise in income and other available agricultural technologies (Okamoto 2007 ). However, critics argued that economic liberalization negatively affected the livelihood of the people. For example, environmental degradation has been coupled with resource extraction by foreign investors, elevated unemployment rates of farmers seemed to be related to the confiscation of their land for irrigation projects, and the uneven distribution of benefits among the agricultural sectors tended to skew toward large-scale agribusiness (Skidmore and Wilson 2007 ; McCarthy 2000 ; Hudson-Rodd and Htay 2008 ).

As the largest city in Myanmar, Yangon has been the epicenter of the nation’s socioeconomic and political transformations. However, due to the country’s isolation and the lack of data accessible to foreign scholars, few studies have succeeded in exploring urbanization in Myanmar, related spatiotemporal changes and the underlying socioeconomic and natural forces, except the followings. Nwe ( 1998 ) examined the demographic and area expansion of Yangon when three new towns were constructed in the late 1980s. Khaing ( 2015 ) described the demography of major cities in Myanmar and highlighted several major socioeconomic and environmental issues related to urban landscape changes. Myint ( 1998 ) studied the urban growth of Yangon and found that the expansion in early 1990s was largely due to the establishment of many new towns. The newly established town of Dagon alone was larger than the whole Yangon in the 1980s. Wang et al. ( 2018 ) evaluated the urban expansions of Yangon and the new capital city (Nay Pyi Taw) and their environmental consequences with five variables obtained from remote sensing data, including land surface temperature (LST), percent tree cover (PTC), evapotranspiration (ET), terrestrial ecosystem net primary productivity (NPP), and aerosol optical depth (AOD) from 2000 to 2013.

While being informative, these studies do not provide a comprehensive analysis of spatiotemporal change of cities in Myanmar, their major urban environmental impacts, and the major drivers since the beginning of the economic liberalization. To that end, we used Yangon as a case to reveal the urbanization pattern, impacts, and drivers in a radically transitioned economy in the developing world. Although it may not be a typical city in Myanmar, Yangon is selected due to its extremely important economic position in Myanmar and dramatic urban and environmental transformations after economic liberalization. In addition, Yangon’s urban development may have been affected by some other forces, such as both the capital relocation from Yangon to Nay Pyi Taw in 2005 and the occurrence of the extreme climate event of Nargis in 2008, in addition to serving as the primary gateway connecting Myanmar with the outside world, thus making it an ideal case to evaluate the impact of these external influences. We aim to answer the following research questions related to urbanization and environmental changes:

What spatial and temporal changes in urban development have occurred in Yangon during the last three decades?

What major environmental changes has Yangon experienced?

How have the urbanization, economic development, and environmental changes co-evolved over time?

How have extreme climate events, globalization, and the capital’s relocation to Nay Pyi Taw affected urban transformation in Yangon?

This paper illustrates the spatial and temporal changes of urbanization and reveals major environmental challenges experienced by Yangon. In addition, we highlighted the co-evolved relationship among urbanization, economic development, and environmental changes. Our approaches of using both quantitative and qualitative analysis to understand the urbanization process of a mega city in a transitional economies have great implications for other similar research.

Yangon (16° 51′ N, 96° 11′ E), formerly known as “Rangoon,” is located at the convergence of the Yangon and Bago rivers from the Gulf of Martaban (Fig.  1 ). It has a tropical monsoon climate with a rainy season from May through October and a dry season from November through April. The annual mean temperature is 27.4 °C (Myint 1998 ). With a metropolitan population of 7.4 million (5.2 million urban population) according to the 2014 census, it is the largest city in Myanmar and the economic and transportation center for the nation. Founded as a small fishing village in the eleventh century by the Mon people, Yangon did not have any significant urban development until the colonial period when the British turned the city into a commercial and political hub and later the capital for British Burma. The colonial city soon became known as “the garden city of the East” due to the spectacular urban landscape featured by parks and lakes, colonial buildings, traditional wooden architecture, and high-quality public infrastructure and urban services. Under the colonial government, Yangon expanded from 72.5 km 2 in 1901 to 86.45 km 2 in 1921 (Myint 1998 ). After Myanmar’s independence from the British in 1948, Yangon started to expand, with new satellite towns of South Okkalapa, North Okkalapa, and Tharkayta all built in 1958. Yangon has rapidly urbanized, since the country began its economic liberalization in 1988. Large new towns, such as Dagon, Hlaingtharyar, and Shwepyithar, as well as many small new towns, such as Weibergi, Shwepaukkan, Pale, and Padamyar, were established in 1988. The city expanded its area from 123 km 2 in 1953 to 166 km 2 in 1962, 209 km 2 in 1974, 346 km 2 in 1988, and 679 km 2 in 1995 (Myint 1998 ).

Location of the Yangon Region in Myanmar in Southeast Asia. The red-dotted lines mark the boundaries of the Yangon Region that is equivalent to a province or a state in many countries. The city of Yangon is located at the confluence of the Bago and Yangon rivers (blue lines)

Data and analysis

We assessed the spatiotemporal changes of urban land expansion, the restructuring of urban land, and the environmental changes from for the last three to four decades based on satellite imagery, historic land use maps, and expert opinions. We analyzed the coupled dynamics among urbanization, economic development, and environmental changes of the city. We further discussed the influence of human systems (i.e., privatization, capital relocation, and globalization by FDI and tourism industry) and natural systems (e.g., extreme climate event of Cyclone Nargis) on Yangon’s urbanization and sustainability.

Data processing

Urban built-up land data and processing.

Land use data were classified from Landsat TM images in four periods (i.e., 1990, 2000, 2010, and 2020) to examine spatiotemporal changes. All Landsat TM images were downloaded from the United States Geological Survey (USGS) ( https://www.usgs.gov/ ) Level-1 geo-referenced product, which were then converted to reflectance using the calibration function built-in ENVI 4.8. For each image, we classified it into five classes: urban built-up land, farmland, green land, water body, and bare land, using an object-oriented method as described in Ouyang et al. ( 2016 ), except that we manually identified bare land. For the detailed accuracy assessment method, please see Appendix 2 .

Environmental data and processing

Due to lack of ground-station-based air pollution data, we derived surface air pollution data of fine particulate matter (PM 2.5 ), NO 2 , and CO from 1997 through 2021 based on remote sensing products. Surface PM 2.5 and NO 2 mixing ratios were accessed from the Atmospheric Composition Analysis Group ( https://sites.wustl.edu/acag/ ). The annual mean surface PM 2.5 at 0.01 × 0.01 degrees resolution was estimated by combining aerosol optical depth retrievals from the NASA MODIS, MISR, and SeaWIFS instruments with the GEOS-Chem chemical transport model, and subsequently calibrated to global ground-based observations of PM 2.5 using geographically weighted regression (van Donkelaar et al. 2016 ). The annual mean surface NO 2 mixing ratio at 0.1 × 0.1 degrees resolution was also inferred from the GOME, SCIAMACHY, and GOME-2 satellite instruments (Lamsal et al. 2008 ). MOPITT data at 1 × 1 degrees resolution were accessed for processing surface CO mixing ratio (Deeter et al. 2003 , 2013 ). We downloaded Version 6 Level 3 monthly CO data and then aggregated into annual means. To extract data for Yangon, we used the administrative area for the whole Yangon region and computed the spatial mean value. These data sets were obtained and exported from the Google Earth Engine data catalog and aggregated into annual means.

Socioeconomic and population data

We collected data on population and economic development, such as gross domestic product (GDP), GDP per capita (GDPpc), percentages of primary, secondary, and tertiary industries of GDP. The major sources of the demographic and socioeconomic data are from the Census of Myanmar and the Statistical Yearbook of Myanmar (Myanmar Government 2011 , 2015 ). We also collected transportation-related data of Yangon, such as on number of vehicles and average speeds of private cars and buses at peak hours (Myanmar Government 2015 ). To understand the economic development and institutional mechanisms, we collected reports on topics such as the development of the industrial zones, documents, and news related to land regulation and urban development/planning policies.

Analysis on co-evolved relationships between urbanization, economic development, and environmental changes

We studied the co-evolved relationships between urbanization, economic development (measured by GDPpc) and environmental changes (measured by concentration of three air pollutants) by correlations and regressions. We complemented the quantitative analysis with qualitative methods of interviews and an expert panel. We have conducted multiple field trips to Yangon and interviewed eight local experts in urban development to understand major socioeconomic drivers and impacts of extreme events, globalization, and capital relocation on urbanization, economic development, and environment changes. We also organized a 1-day workshop on Yangon’s urban development with additional 20 local experts from Myanmar, mostly based on Yangon, in summer 2015. The experts are government officers, planners, and university professors in urban planning, economic development, and environment. They were divided into three groups and each group identified developmental stages and drivers of urbanization of Yangon after 1988.

Extreme event, globalization, and capital relocation as drivers for urbanization

To understand the impact of the extreme event of Cyclone Nargis in 2008, we derived a map of flooded croplands and examined the migration from severely impacted areas to Yangon. We used the Moderate Resolution Imaging Spectroradiometer (MODIS) products to help identify and delineate flooded croplands during Cyclone Nargis given required data quality, temporal frequency and spatial resolution. Original data set was preprocessed using MRTools and ArcGIS for Desktop, including format conversion, reprojection (WGS84), study area extraction and reflectance calculation. Cloud mask was created to mitigate effect of cloud contamination after Nargis hit the low-lying Irrawaddy River Delta. Usually, cloud has a high reflectance in blue band compared to other objects, thus we could define an appropriate threshold to help mask out such atmosphere contamination. In this case, pixels with a blue band reflectance higher than 0.2 was classified into cloud. In addition, this threshold was in well with a previous study (Xiao et al. 2006 ). Meanwhile, we depicted croplands distribution over the entire delta using land cover classification information in an annual MOD12Q1 layer. The Normalized Difference Vegetation Index (NDVI) is a uniformed indicator for observing, measuring and understanding terrestrial vegetation activities from space. In addition, the Enhanced Vegetation Index (EVI) is designed to optimize vegetation signals, especially in high biomass regions, and improve vegetation monitoring through a de-coupling of the canopy background signal and atmosphere inferences (Huete et al. 2002 ). Both indicators can be employed to detect and investigate seasonal changes in vegetation growth. Here, we applied them to determine and evaluate pixels, where Nargis devastated croplands. Late April and early May is a good time for harvesting paddy rice in the delta; consequently, both NDVI and EVI values remain high in Yangon, Myaungmya, Pyapon and surrounding neighbors. However, heavy rainfall, strong winds and high tide surges brought on by Nargis flooded and destroyed mature crops and caused both vegetation indices to decrease sharply. Therefore, we assumed that any cropland pixel with lower NDVI and EVI values on May 5, 2008 after Nargis left the crop-rich regions were victims of the disaster. To understand the degree of globalization of Yangon, we collected data on topics, such as foreign direct investment (FDI), overseas development aid (ODA), foreign passengers and freight passage through Yangon. To evaluate the impacts of the capital relocation, we collected data on population in the resettlement area of Nay Pyi Taw and the pre-existing towns around Nay Pyi Taw from Department of Human Settlement and Housing Development (DHSHD), as well as compared the population of Yangon before and after the relocation.

Urban expansion and urban transformation

Yangon expanded its urban built-up land rapidly from 1990 to 2000, slowed down from 2000 to 2010, and gained momentum again during 2010–2020 (Fig.  2 ). The urban built-up area increased by 79% from 161 km 2 in 1990 to 289 km 2 in 2000, 104% to 329 km 2 in 2010, and 225% to 739 km 2 in 2020. Urban built-up land was mostly converted from farmlands and green land in both 1990–2000 and 2010–2020 (Table 1 ). In fact, 128 km 2 (2%) of farmland was converted to urban built-up area during 1990–2000, contributing to 44% of urban land in 2000. During this period, the majority of new urban land expanded into the northeast/northwest, whereas little change was observed to the south of the Yangon River. Although a new town was established in the southern part of Yangon (i.e., Dala Township), urban land developed slowly due to the difficulties of water supply and commuting problems as there is no bridge to cross the Yangon River. Urban expansion continued into 2020 with an additional 328 km 2 of farmland and 112 km 2 of green land being converted to urban built-up land for the period of 2010–2020, more than doubling urban built-up area.

Urban expansion of Yangon, Myanmar during 1990–2020. The urban built-up area expanded from 161 km 2 in 1990 to 289 km 2 in 2000, 329 km 2 in 2010, and 739 km 2 in 2020

Environmental changes

We used three air pollutant concentrations to illustrate the changes in environmental conditions in Yangon (Fig.  3 ). While the concentrations of CO generally decreased over the years, from 287 ppb in 2001 to 262 ppb in 2021, the concentrations of NO 2  increased from 0.18 ppb to 2.29 ppb and the concentration of PM 2.5 had steadily increased from 13 μg/m 3 in 1998 to 23 μg/m 3 in 2019. PM 2.5 of the whole period exceeds the annual mean of PM 2.5 set by the National Ambient Air Quality Standards (NAAQS) of the United States Environmental Protection Agency (USEPA) at 12 μg/m 3 , but annual mean NO 2 appeared lower than the NAAQS of 53 ppb. As for the CO concentration, the maximum concentration of 287 ppb (0.287 ppm) is above the global background concentration of CO (0.05–0.12 ppm) (WHO 2000 ).

Changes in PM 2.5 , NO 2 , and CO concentrations in Yangon from 1997 to 2021. While the concentrations of CO decreased from the late 1990s, the concentrations of NO 2 and PM 2.5 increased from the late 1990s to 2019

Coupled changes with economic development

Based on the data analysis from 1997 to 2014, Tthe empirical relationship between population and socioeconomic development is apparent (Fig.  4 ), with a correlation coefficient of 0.89 between them (Table 2 ). PM 2.5 was positively correlated with population and GDPpc, with correlation coefficient of 0.95 and 0.89, respectively (Table 2 , Fig.  5 ). In contrast, NO 2 and CO had negative correlations with population and economic development (− 0.65 to − 0.19), showing a decline with population increase and economy development. It is worth noting that the number of vehicles has very high correlation with population and GDPpc (0.97 and 0.97, respectively), with PM 2.5 and NO 2 (0.78 and 0.64, respectively), but a negative correlation with CO (− 0.32). While automobile emissions have been recognized as one of the major sources for all three of these air pollutants, our analysis shows that the increase in vehicle number is only associated with increasing PM 2.5 for Yangon (Table 2 ).

Changes in population and GDP per capita (GDPpc) in Yangon from 1990 to 2014. Note: Two external events and the growth of population and GDPpc of Yangon, capital relocation in 2005 and Cyclone Nargis in 2008. These two events seem do not have obvious impacts on city’s population or economic development level

Changes in concentrations of three air pollutants (PM 2.5 , NO 2 , and CO) with three socioeconomic variables (population, GDPpc, and # of vehicles) in Yangon. PM 2.5 was positively associated with all three socioeconomic variables; NO 2 decreased and increased with population and GDPpc, respectively; and CO declined with population and GDPpc

Urban development stages identified by the workshop

In addition to interviews, we organized a 1-day workshop with 20 local experts in summer 2015 to understand the urban development of Yangon. Expert discussions in our workshop highlighted different sets of drivers of urbanization in Yangon in three distinct periods after 1988, i.e., 1988–1992, 1992–2005, and 2005–2015. It should be noted that these periods do not correspond to the land use data we used to analyze urban expansion of Yangon for 1990, 2000, 2010, and 2020. Rather, urban development in these three periods were driven by different major forces. For the first period (1988–1992), the policies for the construction of new towns played a major role in the urban expansion of the city, resulting in the construction of a new town area that was equivalent to the city area in 1983. In 1988, the military government took over the socialist government and implemented urban development policies focusing on new town development, including New Dagon, Mingalardon, and Shwe Pauk Kan northeast of the inner city, and Shwe Pyi Thar and Hlaing Thar Yar northwest of the inner city.

For the second period (1992–2005), the private sector and capital markets gradually became the major forces for development. Land speculation started in 1991 as people lost trust in the strength of local currency and felt safer investing in land. The city expanded without any urban planning or proper urban infrastructure development. As a result, many low standard and unhygienic areas were developed lacking proper drainage, sewage, and garbage collection systems.

For the third period (2005–2015), the private sector continued playing a strong role but three new major drivers appeared: (1) relocation of the capital from Yangon to Nay Pyi Taw in 2005, (2) the Cyclone Nargis in 2008, which caused a large inflow of migrants to Yangon, and (3) elevated foreign investment, especially in the real estate market, after Myanmar transitioned from military to civilian government in 2010. For example, a Vietnam-based real estate developer constructed the Myanmar Plaza, located in the inner city near Inya Lake. Several Chinese development firms also invested in the real estate in Yangon and Mandalay, starting a new wave of city building in Yangon. In 2015, the National League for Democracy (NLD), led by Aung San Suu Kyi, won the majority of the seats in both chambers of the national parliament, leading to the change from the “civilian government” (note that ~ 90% of the government cabinet members were ex-military officers) to the democratic government. Therefore, the momentum further escalated as more large-scale real estate development projects appeared.

The workshop also identified four major environmental challenges: (1) urban service provision and distribution, (2) traffic congestion, (3) urban flooding, and (4) lack of green space. Yangon had faced mounting pressure to improve urban service provision and distribution, especially on garbage collection, drainage systems, and water supply. The city generated ~ 1600 tons/day of garbage for its landfill facility. However, some outer areas lacked services for garbage collection and sewage management. Amongst many consequences was the blockage of drainage systems. Relying primarily on four surrounding reservoirs and underground water, Yangon has the capacity to supply 160 million gallons of water to its 5.2 million inhabitants (i.e., 30 gallon per person per day). However, only 65% of Yangon’s population was served, whereas four towns (Dagon Seikan, Hlaing Thar Yar, Shwe Pyi Thar, and Seikkyi-Kanaungto) are poorly served. Due to leakage and accounting problems, it is estimated that more than 30% of water is either lost to leakage or not otherwise accounted. This challenge in urban service provisioning reflects the thesis of urban environmental transition (McGranahan et al. 1996 ), which states that for cities with low economic development, environmental actions should focus on local scale and intermediate issues, such as garbage collection, wastewater, and water supply.

Yangon has faced serious traffic jams due to a combination of rapid population growth, poorly designed road network, increased wealth, the relaxed policies on importation of vehicles. Since 2010, the government has allowed the importation of cars without much restriction, leading to a significant increase in private vehicles (Fig.  6 ). From 2007 to 2014, the number of vehicles in Yangon doubled within 7 years, from around 18,000 in 2007 to more than 37,000 in 2014. The total number of vehicles increased quickly, whereas the average speed of either private cars or buses at peak travel hours decreased accordingly. Average speed of private cars and buses both declined, with private cars decreasing more dramatically in 2010 when the restriction on car imports was lifted. The average speed of private vehicles in peak hours was only 18 km/h in 2014, less than one-third of the speed in 2007 (62 km/h) in 2007.

Number of vehicles and average speed for private cars and buses at peak hours in Yangon, Myanmar. The number of vehicles doubled from 2007 to 2014. Meanwhile, average speed of buses and private cars at peak hours both decrease dramatically, leading to the speeds decreased to less than 1/2 or 1/3 of the 2007’s levels for buses and private cars

Flooding represents another serious challenge because of the geophysical and natural setting of Yangon, i.e., located in the river delta and in the Asian monsoon region. Due to the lack of adequate urban infrastructure including drainage systems, and the blockages from uncollected garbage mentioned previously, Yangon easily and frequently floods during the rainy seasons. Because of the serious repercussions of urban flooding, the city paid 1 million USD in the 2016–2017 fiscal year to private contractors to drain storm water. Moreover, Yangon’s mayor announced in 2017 that the city would use US $77 million, a significant part of the US $124 million that World Bank loan intended for public infrastructure, to fund projects to end the perennial flooding problems in Yangon (Win 2017 ).

Green space provision was identified as a major urban environmental problem and one that has been a contentious topic in Yangon. While the colonial city of Yangon has been praised as “the garden of the East” due in part to its beautiful lakes and park spaces, the expanded city under the socialist era did not consider green space in their urban designs and developments, except through the allocation of small portions of the land at the corners of a neighborhood as playgrounds or sport fields. This resulted in extremely low coverage of green space (38%) and low green space per capita (21 m 2 ), compared with other regional cities in Southeast Asia (48% and 68 m 2 ) (Richards et al. 2017 ). More importantly, green space was further reduced when the military-controlled government allowed commercial development on these already limited public spaces. Despite protests from the residents, much land in and around parks and lakes has been auctioned away for commercial development in the past two decades. When enough in-situ ground data can be collected, further research can be conducted on how green space may affect the spatiotemporal changes in air pollution in tropical urban climates, such as Yangon, as illustrated by research on other cities with sub-tropical climates in Asia (Chen et al. 2016 ).

Economic and environmental changes during urbanization

Urbanization, economic development, and environmental changes have complex relationships and pose great challenges for Yangon (Table 2 , Figs. 4 and 5 ). It was not surprising to see the strong correlation between population and economic development, although it is interesting to witness population coupled with rapid growth of GDPpc (Fig.  4 ). This implies that the productivity gain due to the agglomeration effects of the economy may outweigh the downsides of increased population concentration, such as congestion (Batty 2008 ). As for the relationships between air pollution and GDPpc, while the concentration of PM 2.5 increased as population increased and the economy developed (Fig.  5 ), the concentrations of two other air pollutants (NO 2 and CO), especially NO 2 , did not have strong correlations with either population or economic development (Table 2 ). Indeed, the trend lines indicated a “U” relationship between NO 2 concentration and population or economic development. As Yangon has not yet transformed into an industrial city, we suspect that the strong relationships between air pollutants and economic development at the early stage of industrialization may not be apparent. A similar relationship had been reported in Ulaanbaatar and large cities in Vietnam (Fan et al. 2016 , 2019 ; Fan 2022 ). Furthermore, the differing relationship between air pollutants and the number of vehicles not only implies the multi-source nature of air pollution but also illustrates that industrial emissions may not be as important as automobile emissions as the major source of air pollution in Yangon. Our findings have highlighted the needs for a more nuanced understanding of drivers of urbanization and environmental changes in the developing world.

Major external drivers not captured by current data analysis

Although our expert panelists highlighted the importance of the capital relocation, the extreme event of Cyclone Nargis, and globalization as external drivers for the most recent period of urban development in Yangon, we were not able to detect their impacts from the changes in demography and economic development (Fig.  4 ). No significant changes in the population and GDPpc were found for 2005 or 2009. This phenomenon highlighted the need to place our study in the context of locals through obtaining opinions of local experts. Due to the insignificant amount of relocated population and the inability of obtaining official statistics on rural–urban migrants, the impacts of this extreme event and the capital relocations were not reflected in the statistical data of the population. Thus, relying only on what seems-to-be official data can be misleading. Similarly, the impact of globalization is complex and hard to measure. More efforts are needed to untangle the impacts of these external drivers (see Appendix 1 for details).

Limitations and future research

While our research is limited mainly due to the unavailability and unreliability of official statistics, we partially resolved the issue through using other sources such as satellite images to derive urban land and air quality data. However, it is beyond a single research team’s ability to collect long-term, large-scale socioeconomic data. With the current unstable political regime in Myanmar, this remains a challenge for any comprehensive scientific investigations on the city, as well as the country. Myanmar’s first census was conducted in 2014 with technical advice and funding from international organizations and donors. However, it has received many criticisms since then. Other data issues include the impact on our analysis due to an artificial foreign currency exchange rate. For example, using the official statistics, we found that FDI as a percentage of GDP remains low (< 1%), indicating that Myanmar’s economy is still quite closed-off from the global economic system. However, a closer look by correcting the artificial exchange rate to the real exchange rate illustrated that FDI in Myanmar is quite variable, with FDI as a percentage of GDP ranging normally between 1% and 3%, but with some high peaks of 17% in 1997, 25% in 2005, and 50% in 2010.

Our expert panel highlighted a distinctly different set of forcing mechanisms that had affected the urbanization processes for 1988–1992, 1992–2005, and 2005–2015, as well as for the major environmental challenges of the city: urban service provision, traffic congestion, flooding, and green space. The expert panel identified the capital relocation, Cyclone Nargis, and globalization as the three most significant drivers of urbanization in the most recent period of urban development. However, their impacts cannot be validated through the official demographic or economic data. Clearly, to conduct a critical analysis, future attention needs to be placed on this mismatch among official data, remote sensing, and the reflections of local experts. Currently, the spatial resolution of air pollution (0.01°, ~ 1.11 km) is too coarse to conduct a meaningful spatial analysis on air pollutants with urban built-up land use data which has a spatial resolution of 30 m. In future, if in-situ observation data from local air quality monitor stations are available, more fine scale (currently air pollution map has 0.01 degree resolution, about 1.1 km) spatial maps of three air pollutant concentrations at higher resolution may also be generated. The spatial analysis about the relationship between the concentration of different air pollutants and urban built-up land (30 m resolution) can be conducted to reveal how urbanization has affected patterns of air pollution.

Implications of the research

Myanmar’s isolation from international investment and its weakened economy from sanctions following a 1988 military coup further exacerbated the country’s economy. While the transition to civilian administration in 2011 provoked hopes of positive transformations, the military launched another horrible coup in 2021, arresting opposition leaders, including de facto leader Aung San Suu Kyi, civil society activists, and officially elected members of the congress (Thein and Gillan 2021 ). These difficult social, ecological, and political situations make the use of remote sensing a critical tool for tracking land use and changes. In combination with a human–environment systems approach and other secondary socio-economic data, this study investigated the complexity and dynamics of land change system transitions, and quantified the economic value of key services in connection to air pollution. It can be expected that there will be an extraordinary level of economic and social crises and an exceptional uncertainty in the country’s near future.

We used Yangon as a case to reveal the urbanization, environmental changes, and the underlying driving forces in a radically transitioned economy in the developing world. We assessed the spatiotemporal changes of urban land, the environmental changes in air pollutants, and major drivers of urbanization from satellite imagery, historic land use maps and expert panels. We found that the city expanded urban built-up land rapidly from 1990 to 2000, slowed down from 2000 to 2010, and regained momentum again for 2010–2020, with most newly added urban built-up lands appearing to be converted from farmland and green land in both 1990–2000 and 2010–2020. Furthermore, the air pollutant concentration of CO decreased, but that of NO 2 and PM 2.5 increased in recent years. We further analyzed the interdependent changes between urbanization, economic development, and environmental changes. A positive correlation exists between population and economic development. It is worth noting that the concentration of PM 2.5 is highly associated with population, the economy, and the number of the vehicles. Our research illustrates how the complex relationships between urbanization, economic development, and environmental changes can be untangled and analyzed. It confirmed the strong correlation between urban population growth and economic development level indicated GDPpc. Our finding implies that industrial emissions may not be as important as automobile emissions as the major source of air pollution in Yangon. As Yangon has not yet transformed into an industrial city, the strong relationships between air pollutants and economic development at the early stage of industrialization may not be apparent. Finally, the expert panel also identified other potential drivers for urbanization, i.e., the extreme climate event of Cyclone Nargis, capital relocation, and globalization. However, the impacts of these drivers were not captured by any quantitative data sources. Our research thus underlies the need to combine both quantitative data derived from satellite images and official statistics and qualitative data provided local experts to conduct a critical analysis, especially for cities in the developing world. The most recent military takeover remains an uncertain driver for Yangon’s future, highlighting the use of remote sensing as a critical tool for tracking land use and changes and environmental changes in certain part of the developing world.

Availability of data and materials

The data sets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Land surface temperature

Percent tree cover

Evapotranspiration

Terrestrial ecosystem net primary productivity

Aerosol optical depth

United States Geological Survey

Gross domestic product

GDP per capita

Moderate Resolution Imaging Spectroradiometer

Normalized Difference Vegetation Index

Enhanced Vegetation Index

Foreign direct investment

Overseas development aid

Department of Human Settlement and Housing Development

National Ambient Air Quality Standards

United States Environmental Protection Agency

National League for Democracy

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Acknowledgements

We thank Zihan Lin for processing the Cyclone Nargis data, Tanni Sarker for processing part of the air pollution data, and Connor Crank for editing the manuscript. We appreciate the constructive comments provided by anonymous reviewers to this paper. Any opinions, findings, and conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of NASA.

This work was supported by the National Aeronautics and Space Administration (NASA) (Grant numbers: NNX15AD51G, 80NSSC20K0740).

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Mandalay Technology, Yangon, Myanmar

Earth System Science, Stanford University, Stanford, CA, 94305, USA

Zutao Ouyang

Yangon City Development Committee, Yangon, Myanmar

Khaing Moe Nyunt

Yangon University, Yangon, Myanmar

Zin Nwe Myint

College of Arts and Sciences, University of Alabama, Tuscaloosa, AL, 35487, USA

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Contributions

PF and JC designed and supervised the research, PF, CF, and ZO collected and analyzed data, PF, JC, and ZN organized expert panel, KMN, ZNM, JQ attended the expert panel, PF, JC, CF, ZO, ZN, KMN, ZNM, JQ, JPM, SWM, and BGP wrote different sections of the paper and PF, JC, CF, ZO, and JPM edited the paper. All authors read and approved the final manuscript.

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Appendix 1. Major external drivers un-captured by current data analysis

Extreme event of cyclone nargis.

The changes in global climate have profoundly affected Monsoon Asia, such as more frequent floods and intense droughts on agricultural and aquatic ecosystems that directly affect the livelihood of farmers, particularly the “subsistence” or “smallholder” farmers (e.g., Chotamonsak et al. 2011 ; Morton 2007 ; Zhai and Zhuang 2012 ). Unfortunately, the literature is scant on the effects of extreme climate events on urban processes, even though they are considered important drivers of urbanization. For example, urban processes can provide a push for rural–urban migration. Cyclone Nargis on May 2, 2008 was the worst natural disaster in the recorded history of Myanmar when at least 138,000 causalities were reported. The Yangon Region and the Ayeyarwady Region were affected by Cyclone Nargis, with a total damage estimate of at least US $10 billion (Toronto Star 2008 ). They were also the two most affected regions in terms of flooded croplands (Fig.  7 ). The aftermath caused a huge rural-to-urban migration from the rural areas of both states to Yangon for urban-related employment. An anecdotal estimate showed that > 90% of the rural–urban migrants who work in the garment or construction sectors are from Ayeyarwady. Many of these migrants lost their rural livelihoods due to Cyclone Nargis. These migrant workers usually settled down in informal settlements in the southern part of Yangon, where poor services were provided by the city. Unfortunately, official statistics on migration following Cyclone Nargis do not exist. Thus, although our experts have identified the extreme event as a significant driver for the rural–urban migration, no official statistics can provide such evidence.

Impact of the extreme event—Cyclone Nargis: A1a (top figure). Croplands suffered from Cyclone Nargis (2008) in the low-lying delta of Myanmar; A1b (bottom figure) Regional proportion of flooded croplands

Yangon, Pyapon and Myaungmya suffered the most from a huge agricultural economic loss due to Cyclone Nargis, with a total area of 73.74 km 2 of affected croplands. Around 61% of the entire Pyapon experienced a paddy rice loss, followed by 41% in southern Yangon and 28% in Myanmar (Fig.  7 b).

• Globalization

Globalization, the international integration process from economic, cultural, and political influences, and information and technology, has affected the development of various regions including transitional economies, particularly through economic globalization—the international integration of the world production system through trade, investment, and production (IMF 2000 ; Sassen, 1991 ). In urban studies, globalization has been identified as an important driver causing divergent development of cities based on their positions in the hierarchical networks of world cities, but affected by different kinds of flows of capital, technology, and labor (Beaverstock et al. 1999 ; Castells 2011 ; Sassen 1999). Recent studies have also examined how globalization has affected urbanization and urban environments in transitional economies (e.g., Fan et al. 2017a , b ). Relevant data for Yangon are unfortunately not available; thus, we used data for Myanmar, including trade, foreign direct investment (FDI), and international tourists, as proxies to assess how Yangon is connected to the global economy. Myanmar was well integrated with the world economy, as indicated by trade, foreign direct investment, and visits by foreign tourists. For example, 20% of the national GDP was from trade during the 1990s and the 2000s, with exports increasing more than 18 times from US $476.5 million in 1990 to US $8861.0 million in 2010, and from US $888.6 million in 1990 to US $6412.7 million in 2010 (i.e., an increase of more than 7 times). FDI also increased significantly in Myanmar since 1990 with the number of foreign companies and branches increasing by 15 times from 82 in 1990 to 1283 in 2010. Myanmar steadily increased its foreign tourism and started to catch up with its neighbors in Mainland Southeast Asia. However, political instability still presents a major threat to foreign direct investment and tourism, especially when comparing Myanmar with its neighboring countries.

As the largest city in Myanmar, Yangon was exposed to the force of globalization as early as 1853 when it became the colonial capital of British Burma, well as the whole country was largely shunned by the rest of the world, particularly beginning from the inception of military rule in 1962. It was not until 2011 when the military junta dissolved itself and transitioned the power to the civil government that Myanmar started to open up to the world again. The brief period of reconnecting to the world in recent years, however, has been intense and diverse and has dramatically increased flows of trade, foreign investment, overseas aid, foreign tourism, among additional matters that were revealed by our findings. For example, while less than 0.8 million foreign tourists visited Myanmar in 2009, in 2015, it welcomed six times more for a total of 4.7 million foreign tourists, almost the same as Cambodia (4.8 million) (Fig.  8 ).

Foreign tourists in Vietnam, Myanmar, and Cambodia (1995–2015). Myanmar’s tourists increased rapidly in the early 2010s, but it is still far behind its neighboring countries of Vietnam and Cambodia

• Capital relocation

In November 2005, Myanmar officially relocated its capital from Yangon to Nay Pyi Taw (NPT). Almost all the government agencies, along with their employees, moved to the new capital. It is estimated that over 500,000 population of NPT, 65,000 government workers of the ~ 80,000 total were relocated from Yangon to NPT. However, the population relocation from Yangon to NPT was not detrimental to Yangon as it only accounted for ~ 1% of Yangon’s population. Interestingly, these government employees did not really relocate in the sense that their families still resided in Yangon due to the superior education, medical and cultural amenities of the city; commuting is widely practiced between Yangon and NPT. The real impact of the capital relocation is that the buildings and land that used to belong to the government now were leased mostly for commercial development. Due to skyrocketing land prices in Yangon, the government can reap huge profits by leasing their buildings, which usually have high historic and cultural values, to the private sector, despite the protests from Yangon residents who advocated for preserving these valuable cultural assets. The impact of privatization of the land and buildings with high historic, environmental, and cultural values remains to be evaluated.

Capital relocation is not uncommon. Historically, developed nations, such as the United States, Canada, and Australia, have relocated their capitals to either a small town or constructed a new city for its national capital. Several countries from the developing world have also relocated their capitals in recent decades, such as Brazil (from Rio de Janeiro to Brasilia in 1961), Malawi (from Zomba to Lilongwe, initiated in 1975 and completed in 1994), Nigeria (from Lagos to Abuja in 1991) and most recently Myanmar (from Yangon to Nay Pyi Taw in 2006), etc. There are multiple reasons for these relocations, such as to balance different regional/ethnicity interests (US, Canada, Australia, and Nigeria), to enhance linkages with neighboring countries (Russia), to decongest the original capital (Nigeria, Kazakhstan), to stimulate development in underdeveloped regions (Cote d’Ivoire), or security concerns (Myanmar). Relocations unavoidably affect the original capital city and the new capital. Yet, limited literature has examined how external shocks such as capital relocation have affected both the source and destinations. Some evidence shows that the original capital cities may be not impacted, especially those large cities in either developed or developing nations due to the failure of the new capital to provide counter-attraction against the old capital such as in the case of Malawi (Potts 1985 ). It was also noted that strong government commitment can facilitate the transition. Although the rationale of the movement was that the central location of NPT may provide easy access to all parts of the country, other speculation has existed, including the fear of foreign attack and greater control over the ethnic minority of the border region. Capital relocation had significantly changed NPT’s urban development with the dramatically increased urban built-up land and population in either the planned or unplanned periphery zone of NPT. The relocated population settled in both new resettlement areas in Nay Pyi Taw and the three pre-existing towns around Nay Payi Taw, Pyinmana, Leway and Tatkon, which were incorporated within National Union Territory in 2011 (Table 3 ). In 2007, the total population density of the three pre-existing towns grew from 113 persons/ha in 2004 to 142 persons/ha in 2007 due to the relocation.

Appendix 2. Land classification accuracy assessment

We conducted an accuracy assessment by first creating a stratified random sample of 200 points for each of the urban land layers for 2010 and 2020. Each sample was verified using very high-resolution Google Earth imagery (TerraMetrics 2022 ), as well as Landsat 7/8 RGB imagery (30-m spatial resolution) for 2010 and 2020, respectively; the median across all images for each year (2010 and 2020) was used for the verification layers. Landsat 7/8 imagery is courtesy of the U.S. Geological Survey and was accessed and processed in Google Earth Engine (Gorelick et al. 2017 ). For 2010, the overall accuracy came to 76.0%, and for 2020, the overall accuracy came to 72.5%. The 2020 urban land classification was also compared to a recently distributed 10-m spatial resolution ESA WorldCover data set (Zanaga et al. 2021 ). This data set was resampled to 30-m spatial resolution to match the Landsat classifications. After resampling, the number of urban pixels within the study area was 588,237 (an area approximately 503 km 2 ). Of the ESA WorldCover urban area, the Landsat-based classification used here covered approximately 56.9% of the area. Of the Landsat-based classification, 65.4% was also characterized as urban by the ESA WorldCover layer.

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Fan, P., Chen, J., Fung, C. et al. Urbanization, economic development, and environmental changes in transitional economies in the global south: a case of Yangon. Ecol Process 11 , 65 (2022). https://doi.org/10.1186/s13717-022-00409-6

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