economic development in bangladesh case study

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An Empirical Analysis of Population Growth on Economic Development: The Case Study of Bangladesh

2015, International Journal of Economics, Finance and Management Sciences

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JISR management and social sciences & economics

Samia Awais

Pakistan has one of the highest population growth rates in the world. Rapid population growth is the bone of contention as well as the crux of the problems. 80 million people live below the poverty line. Macroeconomic instability, rising absolute and relative poverty and worsening income inequalities are the direct as well as indirect result of the high population growth. Population pressures are threatening land, forests and water resources. Improvement in operation and implementation of the family planning programme, increase in female literacy rate and enhancement of women social status can help reduce population growth and poverty. The demographic window argument is far-fetched and irrelevant to Pakistan. A redistribution of agriculture land through radical changes in land tenure coupled with corresponding changes in rural institutions that direct production are expected to help reduce poverty significantly and also improve socio-economic scene in the rural areas. A sound popula...

Mahtab Uddin

Review of Politics and Public Policy in Emerging Economies

Abdul Nawaz

Purpose: Population growth and its impact on economic development have been a subject of major concern among demographers, economists, and social scientists. Some believe that rapid population growth is a significant problem, while others consider it just a hoax. Previous studies have discovered the negative as well as the positive and profound impact of rapid population growth on economic development. The present study analyses population growth and its impact on economic development in the context of Pakistan. Design/Methodology/Approach: A Descriptive Methodology is found suitable to analyze the population growth and its impact on economic development in the context of Pakistan. In this regard, 50 articles from google scholar and statistical reports from World Bank are studied. The data is taken from World Bank Indicators and presented in the form of charts. Findings: It is found that poor family planning, high fertility, low status of women in society, and illiteracy are the mai...

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Shamarukh Mohiuddin

Syed Al-Helal Uddin

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SSRN Electronic Journal

Dale Honeck

Binayak Sen

This paper belongs to a series of 14 country case studies spanning Africa, Asia, Latin America and Eastern Europe. The series is part of the ‘Operationalising Pro-Poor Growth (OPPG)’ work programme, a joint initiative of AFD, BMZ (GTZ, KfW Entwicklungsbank), DFID, and the World Bank. The OPPG work programme aims to provide better advice to governments on policies that facilitate the participation of poor people in the growth process. Other outputs of the OPPG initiative include a joint synthesis report, a note on methodological approaches to analysing the distributional impact of growth, cross-country econometric work, literature reviews, and six synthesis papers on: macroeconomics and structural policies, institutions, labour markets, agriculture and rural development, pro-poor spending, and gender. The country case studies and synthesis papers will be disseminated in 2005.

Business Ethics in Bangladesh

George Friedrich

Business and ethics are uncontroversial notions while however its integration as “business ethics” is then again a widely controversial notion. Is “the organized effort (…) to produce and sell, for a profit, the goods and services that satisfy society’s needs” (Pride, et al. 2014) contradictory to what is right and what is wrong – a “collective illusion”, where individuals think it is something it really is not (Ruse 1986: 257)? The prevalence of scandals, fraud, and various forms of executive misconduct in corporations has spurred the watchful eye of the public (Kuratko 2009: 87). Moreover, Multinational corporations (MNCs) are considered suspect in developing countries (LDCs) by their own populations and by others, especially in those countries perceived as corrupt (Demuijnck and Ngnodjom 2011: 253). Although scholarship has examined multinational corporations (MNCs) economic and human rights impact on less developed countries (LDCs), to the best knowledge of the author, no conceptualized framework summarizing the relationship between business ethics, corporate social responsibility (CSR) and multinational corporations (MNCs) economic and human rights impact on less developed countries (LDCs), human rights and human rights protection has been provided. The objective of this dissertation is thus to provide a systematic report on the literature concerned with business ethics, corporate social responsibility (CSR), multinational corporations (MNCs) economic and human rights impact on less developed countries (LDCs), human rights as well as human rights protection on the example of business ethics in Bangladesh, including an in-depth interview with an international student from Bangladesh. Based on a comparative country analysis, the author found that poverty rates of Bangladesh, with regard to its vast international attention, exceed the majority of those of Sub Saharan African countries. In this argument, the author concludes that business ethics in Bangladesh is nonexistent as multinational corporations (MNCs), whose presence is indisputable in Bangladesh, do more economic and political harm and perpetuate poverty.

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Bangladesh economy: A case of 'development miracle'

Bangladesh has become the fastest-growing brand nation in the world, making it an example of unprecedented economic development and an inspiring model of poverty eradication in recent years.

Harvard University professor Joseph Noe defined soft power as a nation's power deriving from economic, political and cultural influence in contrast to military power. Recently, UK-based accounting firm Brand Finance released the Global Soft Power Index 2023 report – which said the brand value of Bangladesh increased by 37% from $371 billion in 2022 to $508 billion in 2023. 

Bangladesh has become the fastest-growing brand nation in the world. That is, the brand value of Bangladesh increased the most last year. Bangladesh is followed by Uzbekistan (32%), Azerbaijan (30%), United Arab Emirates (24%) and Georgia (23%). 

Bangladesh has the second highest brand value in South Asia, right after India, which is more than twice that of Pakistan and 10 times that of Sri Lanka. The report specifically states that Bangladesh is an example of unprecedented economic development and an inspiring model of poverty eradication as declared by the World Bank.

Keep updated, follow The Business Standard's Google news channel

A few days ago, Boston Consultative Group, one of America's largest management consulting firms, published a special report on Bangladesh under the title "The Trillion Dollar Prize." During 2016-2021, Bangladesh's average GDP growth was 6.4%. This growth rate is twice the average of lower-middle-income countries and much higher than the world average (2.9%). 

The key drivers for the economic growth of Bangladesh are increasing consumer spending, an emerging young workforce, high economic resilience and digital momentum, increasing government spending, rapid private sector investment growth, etc. After independence, government expenditure accounted for 85% of total investment, while private sector investment accounted for only 15%. 

When the Eighth Five-Year Plan was drafted, private sector investment was 87% and public investment was 17%. One thing is clear – the economy of Bangladesh is one of the most sustainable economies in the world. During the global financial crisis of 2007-2009, Bangladesh's annual growth rate was 5.5%, which was higher than the average growth rate of India, Indonesia, the Philippines, Thailand and the world average. 

During the Covid-19 pandemic, the growth rate of Bangladesh was 3.4%, whereas this rate was negative in most of the countries including the major economies of the world. The Center for Economics and Business Research (CEBR) projects the economy of different countries of the world every year, which is known as the "World Economic League Table". 

And, according to their 2023 report, Bangladesh's GDP in 2037 will be $1628 billion (more than one and a half trillion), which means Bangladesh will be the 20th largest economy in 2037. In 2022 Bangladesh's position was 34th.

Even a decade ago, Bangladesh's success was considered "accidental." Despite being a country prone to natural disasters, unfavourable conditions and corruption at almost all levels, the recent economic and various social progress of Bangladesh is now considered a "Development Miracle" or "Development Labyrinth." 

A similar opinion is found outside the academicians that despite being a poor country, Bangladesh is able to achieve progress in social indicators compared to other similar countries. 

Since the second decade of the 21st century, qualitative changes in planning have played a major role in poverty alleviation and employment of the people of Bangladesh. The national five-year plan shifted its focus from the agriculture-based economy to the industrial and service sectors. 

After independence, more than half of the country's GDP came from agriculture. Now agriculture contributes only about 12% of its GDP. On the other hand, the contribution of the industry increased from 8% to 35%. 

In the last decade and a half, the government has consistently given top priority to agriculture. As a result, Bangladesh is now self-sufficient in food grains. Rice production almost quadrupled in the past 50 years despite the decline in cultivable land. Not only food grains but also fish, meat, eggs, milk and fruit production has increased to a large extent. 

As a result of the government's agriculture-friendly policies, Bangladesh made outstanding contributions in the fields of agricultural mechanisation, agricultural machinery subsidies, agricultural research and innovation. Moreover, the entrepreneurial spirit at the grassroots level and the ability to adapt to the adverse environment have greatly accelerated the socio-economic development of Bangladesh. 

The last decade can be called the golden age of Bangladesh's development. Bangladesh managed to achieve most of the Millennium Development Goals. As a result, Bangladesh was awarded more than a dozen international prestigious awards. 

The country was promoted to a lower-middle income country and fulfilled the criteria for becoming a developing country twice in 2018 and 2021 from Least Developed Country status. 

The government's macroeconomic policies played a key role in Bangladesh's transformation. Investment continues to grow as the economy remains stable in the long term. Bangladesh has the highest average gross domestic product or GDP in the last decade after China due to its stable economy. 

Moreover, the government has taken up several important infrastructure-based mega projects to improve the business environment. Due to the provision of road connections and electricity facilities in almost every upazila, the growth centres and markets are now connected and the economic activities have become dynamic. 

Recently, the Bloomberg news agency praised Prime Minister Sheikh Hasina for her timely reform measures to counter the negative impact of the global economic crisis and maintain economic stability. 

Digital transformation is now underway in the country and there are 177 million mobile users. Internet user rate has increased by 70% in the last 10 years. The size of the digital economy grew from $1.7 billion in 2019 to $3.5 billion in 2022. 

The government is also creating an enabling environment for the economy by tripling its spending in the last decade. Bangladesh is now the ninth-largest mobile market in the world. Bikash is the world's leading mobile financial service provider. With more than 650,000 freelancers, Bangladesh is the world's second-largest provider of online labour (15% of the world's total freelancers). 

Under the guidance of the Prime Minister, the unprecedented Centenary Delta Plan 2100 is formulated to deal with climate change and manage agriculture and water resources. The National Social Security Strategy was formulated in the middle of the second decade of the 21st century with a desire to make the social security system more effective and dynamic. 

The government is working to increase investment, create employment and alleviate poverty with the aim of making the country a developed and smart Bangladesh. Necessary physical and social infrastructure is rapidly developing to keep pace with the plan.

According to the Labor Force Survey 2016-17, 85% of our total employment is in the informal sector. In the case of women, this rate is about 92%. Entrepreneurship is now being created in villages and rural areas, and, as a result, small and medium industries are developing. 

The expansion of the banking sector as well as microcredit has led to greater participation of people in economic activities. The latest Household Income and Expenditure Survey revealed that the participation of women in the workplace has increased from 36.3% to 42.68% in 2022-23. 

The unemployment problem has come down from 4.2% to 3.6%. People are now risk-tolerant and investment-oriented due to the expansion of growth centres in rural areas and the modernisation of agriculture and infrastructure facilities. Multipurpose agricultural activities have expanded. 

Along with economic progress, increasing social mobility is playing a controlling role in the development of Bangladesh. If there is no major social or political unrest in near future, this development will continue at an incredible pace.

Indrajit Kumar is an educator, columnist, international affairs analyst and freelance researcher. 

Disclaimer: The views and opinions expressed in this article are those of the authors and do not necessarily reflect the opinions and views of The Business Standard.

Bangladesh / Economy

Bangladesh / economic development

While most comments will be posted if they are on-topic and not abusive, moderation decisions are subjective. Published comments are readers’ own views and The Business Standard does not endorse any of the readers’ comments.

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• DOI: 10.3844/JSSP.2006.54.60
• Corpus ID: 55549444

Landlessness and its Impact on Economic Development: A Case Study on Bangladesh

• M. Rahman , Somprawin Manprasert
• Published 1 March 2006

37 Citations

Changing pattern of rural income diversification and its impacts on household economy: a study on a selected village in jashore district, bangladesh, horizontal expansion of housing in rural areas of bangladesh: does population growth matter, an analysis of poverty situation of landless peasants: evidence from sindh pakistan, determinants and decomposition of poverty of rural india: glimpses from the purulia district of west bengal, dependence level of rural landless households on allocated communal land: evidences from kilte awlaelo and atsbi wenberta districts, tigrai region northern ethiopia, increasing landlessness and its impact on food, livelihood options of landless households and land contracts in north-west ethiopia, mitigation of land scarcity situation through tenure practices: a study on two selected villages in jashore district of bangladesh, rationalizing the choice of housing on cultivable land: is cash-return the only determinant, 30 references, rural poverty reduction strategy for south asia, landlessness amidst abundance, working paper c1. migration as a livelihood strategy of the poor: the bangladesh case, surviving on their feet: charting the mobile livelihoods of the poor in rural bangladesh., issues in employment and poverty discussion paper 6 bangladesh: bringing poverty focus in rural infrastructure development, the people's republic of bangladesh, impact of land utilization systems on agricultural productivity.

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South Africa's Agrarian Question: Extreme and Exceptional?

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Pakistan and Bangladesh: Comparative Economic Analysis (1971-2020)

26 Pages Posted: 13 Oct 2021

College of Economics & Social Development - Institute of Business Management

Date Written: September 9, 2021

The aim of this paper is to examine and conduct a comparative economic analysis between Pakistan and Bangladesh based on different economic indicators from 1971 to 2020. The methodology used in this study is primarily qualitative, with data mainly obtained from secondary sources. The findings of the study suggested that in terms of economic and social development, Bangladesh clearly surpassed Pakistan in all the domains. Bangladesh’s progressive and consistent economic approach with emphasis on economic empowerment of their population was the key towards Bangladesh’s success. Bangladesh dominated the following economic indicators: Current account balance, GDP growth rate, GDP per capita growth rate, and social development. In contrast, Pakistan lacked behind due to immersive political and economic instability caused by inconsistent political regimes and economic policies. The findings revealed that Pakistan’s: Exports % of GDP, Trade balance, Net income, Current account balance, GDP annual growth rate, GDP per capita annual growth rate, portrayed better performance in the first economic phase (1971-2000) than in the second economic phase (2000-2020).

Keywords: Pakistan, Bangladesh, GDP, Growth Rate, Social Development, Comparison

Suggested Citation: Suggested Citation

Syed Saif Ur Rehman Shah (Contact Author)

College of economics & social development - institute of business management ( email ).

Korangi Creek Karachi Sindh, 75190 Pakistan

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Education System and Economic Development in Rural Bangladesh

DOI link for Education System and Economic Development in Rural Bangladesh

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This study focuses on how rural people in Bangladesh utilised their school education career or certificates in their life courses through a comparative case study of two rural villages in the country. Educational development in contemporary Bangladesh has raised the nation’s level of education and has upgraded compared to that of previous generations. Furthermore, it seems that many people have obtained various certificates including Primary to Higher education. This tendency was evident even in remote rural areas. To identify and consider educational development in rural areas, the study conducted a comparative study comprising ten years of longitudinal research in two villages: one in a remote rural setting and the other in a semi-urban remote setting. Based on the results of the case studies, the following conclusions were drawn regarding the acceptance of the school education system in rural Bangladesh. Commonalities of both villages were found. One is that spurred by cash income pressure, the villagers prioritised that their children obtain education careers or certificates. Clear differences were observed between the remote rural setting, where it is difficult to utilise education career or certificates for market access, and the semi-urban rural setting, where this is comparatively easy.

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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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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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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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Bangladesh Receives $900 million World Bank Financing to Increase Economic and Urban Resilience for Sustainable Growth

WASHINGTON DC, June 21, 2024  — The World Bank’s Board of Executive Directors today approved two projects totaling $900 million to help Bangladesh strengthen fiscal and financial sector policies and improve urban infrastructure and management to ensure sustainable and climate-resilient growth.

“Decisive reforms will help Bangladesh sustain growth and strengthen resilience to climate change and other shocks,”   said Abdoulaye Seck, World Bank Country Director for Bangladesh and Bhutan .  “These new financing operations will help Bangladesh in two critical areas – the financial sector and urban management — to achieve its vision of upper middle-income status.”

The  Second Recovery and Resilience Development Policy Credit ($500 million)— the last in a series of two   credits — supports fiscal and financial sector reforms to accelerate sustainable growth and build resilience to future shocks, including climate change.

The program supports the transition from trade taxes to consumption and income taxes, which will help Bangladesh strengthen competitiveness and prepare for LDC graduation. It will help institutionalize the public procurement authority responsible for the electronic government procurement (e-GP system), which will cut down the average e-GP procurement lead time from 70 days to 55 days. It supports stronger banking sector oversight and improved management of National Savings Certificates. It will also help improve the efficiency and targeting of cash-based social protection programs and scale up public and private climate adaptation and mitigation investments.

“A well-functioning financial sector is critical for Bangladesh to increase investment and improve access to finance for those left out of formal banking systems ,”  said  Bernard Haven, World Bank   Senior   Economist and Task Team Leader for the program .  “The government has adopted strong macroeconomic reforms to address external imbalances and a new legislative framework to strengthen the financial sector.”

The financing will help streamline the bank recovery framework, implementing a prompt corrective action framework to address undercapitalized banks. It will also bolster social protection programs, safeguarding the most vulnerable during economic downturns and natural disasters.

The Resilient Urban and Territorial Development Project ($400 million) will help improve climate-resilient and gender-responsive urban infrastructure and urban management capacities in seven city clusters along the economic corridor covering over 950 kilometers of the highway from Cox Bazaar in the south to Panchagarh in the north of Bangladesh.

In 2021, about 38% of the country’s population lived in urban areas. This number is projected to rise to 60% by 2050. The trend of urbanization has largely been driven by the rapid growth of manufacturing sector jobs in the Dhaka metro area, making Dhaka one of the most densely populated cities in the world. Climate resilient development in secondary cities will help absorb climate migrants while helping reduce congestion in Dhaka. The project prioritizes coordinated investment in infrastructure and planning across these cities, ensuring a holistic approach compared to individual city plans.

The project will help provide investments to support the development of strategically important corridors and city clusters. It will help create jobs, enhance rural-urban linkages and transition, and strengthen food supply chains, and link to domestic markets and global value chains. It will improve connectivity between the selected cities and surrounding Union Parishads by improving roads and bus terminals and also create new economic opportunities by enhancing facilities for tourism, open spaces, and others. The infrastructure designs will incorporate the needs of female users and green building features including rainwater harvesting.

“This will be the first in a series of projects helping build resilience to climate change and create new opportunities and jobs in secondary cities through spatially targeted investments ,” said  Kwabena Amankwah-Ayeh, World Bank   Senior   Urban Development Specialist and Task Team Leader for the project . “ Developing secondary cities as growth hubs will be critical for the country’s sustainable growth .”

Selected municipalities and city corporations will receive subgrants for investing in climate-resilient and gender responsive infrastructure. The selected cities are expected to increase revenues by 20%. They will form Town-Level Coordinating Committees, where at least one-third of the members will be women.

The World Bank was among the first development partners to support Bangladesh following its independence. Since then, the World Bank has committed about $41 billion in grants and interest-free credits to Bangladesh. In recent years, Bangladesh has been among the largest recipients of the World Bank’s interest-free credits.

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Resource assessment and techno-economic analysis of solar pv integrated hybrid off-grid power generation system: a case study of Krishnanagar, India

• Published: 24 June 2024

Cite this article

• Dipankar Pramanick 1 ,
• Jitendra Kumar 1 ,
• Pankaj Kumar 1 &
• Himanshu Sharma 1  

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Integrating renewable energy resources with conventional sources offers a viable option for supplying electricity to remote regions of India, addressing the challenge of inconsistent grid power availability. The study intends to assess the efficacy of solar PV array by estimating several performance metrics, demonstrating the potential for deploying solar PV technology at Krishnanagar located in the eastern part of India and designing a solar PV integrated power generation system (IPGS) by carrying out a comprehensive techno-economic analysis specific to the region. Under the climatic conditions of the aforementioned region, the solar PV system exhibits an annual average Performance-ratio (PR) of 77.50% and a Capacity-factor (CF) of 16.78%. The design and optimization of the IPGS are conducted employing the HOMER Pro application. The obtained result depicts that combining a 12.4 kW PV system, a 6.3 kW diesel generator (DG), a 19 kWh battery energy storage system (BESS) and a 4.83 kW bi-directional converter system (BCS) for a load of 30.39 kWh/d provides the best outcome in terms of least cost-of-energy (COE) and net-present-cost (NPC), while minimising carbon emissions and attaining the maximum renewable fraction (RF). The COE and NPC of the optimal IPGS design are obtained as 0.248 $/kWh and $35,627.85 respectively, with a RF of 96.66%. The carbon emissions obtained from the proposed system is only about 2.5% compared to the DG only configuration. Moreover, the NPC and COE get reduced by 76.4% when compared with DG-only systems. Finally, to further validate the suitability of the proposed system at the considered location, a unique multi-dimensional sensitivity analysis is carried out depicting the variation in COE by varying various parameters that influence power generation and economics.

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Data availability.

The data are available from the corresponding author upon request.

Abbreviations

Solar PV rated capacity in kW

Solar PV derating factor in percentage

Solar PV output power

Power temperature co-efficient in per ◦C

Cell temperature of solar PV at standard test condition in ◦C

Cell temperature of solar PV in ◦C

Solar radiation incident at STC in kW/m 2

Solar radiation incident in kW/m 2

Efficiency of solar PV in percentage

Transmittance of solar PV in percentage

Co-efficient of heat transfer to the surroundings in kW/m 2 /°C

Absorptance of PV array in percentage

Solar radiation at NOCT in kW/m 2

Solar PV AC output in kWh

Monthly solar AC output in kWh

Solar PV DC output in kWh

Monthly solar PV DC output in kWh

Solar PV nominal operating cell temperature in °C

Ambient temperature in ◦C

Ambient temperature under nominal operating cell temperature in °C

Bi-directional converter efficiency in %

Solar PV Array yield in h/d

Solar PV Reference yield in h/d

Solar PV final yield expressed in h/d

System loss in h/d

Array capture loss in h/d

Total power generated from whole system in kWh

Renewable energy power in kWh

No. of observation

Predicted value

Obtained value

System’s overall annualized cost in $/yr

Actual interest rate in percentage

Project life time in year

Annual electrical load served in kWh/yr

Daily in-plane solar irradiance in kWh/m 2

Capital recovery factor

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Pramanick, D., Kumar, J., Kumar, P. et al. Resource assessment and techno-economic analysis of solar pv integrated hybrid off-grid power generation system: a case study of Krishnanagar, India. Environ Dev Sustain (2024). https://doi.org/10.1007/s10668-024-05140-0

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RFQ 2024/009 - Data Visualization Training (Closing: July 12, 2024)

Description of requirements.

The United States government, represented by the U.S. Agency for International Development (USAID), Regional Development Mission Asia (RDMA) invites qualified companies/individuals to submit proposals of the services specified below. This is to support the operation of USAID/RDMA, Bangkok Thailand.

Proposal submission and questions regarding this Request for Proposal (RFP) shall be ONLY via email to [email protected] by the time/date specified above.

The award of a contract hereunder is subject to the availability of funds. Issuance of this RFP does not constitute an award or commitment on the part of the U.S. Government, nor does it commit the U.S. Government to pay for costs incurred in the preparation and submission of a quotation. Please be advised that all interested parties are required to be registered in Dun and Bradstreet and are subject to SAM registration before an award can be made.  Information on obtaining the Data Universal Numbering System (DUNS), can be found at this website: http://fedgov.dnb.com/webform . Offerors are also required to register their business on the U.S. Government’s System for Award Management (SAM) at www.sam.gov . Information on this process for foreign vendors is available here . Prospective offerors are encouraged to register in SAM prior to the submittal of proposals.

Data Visualization Statement of Work

I. introduction.

The Asia Regional Training Center (ARTC) of the U. S. Agency for International Development/ Regional Development Mission for Asia (USAID/RDMA) is considering a training service contract for a Data Visualization course in December 2024. The course is a 4-day onsite session in Bangkok, Thailand

II. Background

USAID Regional Development Mission Asia (RDMA) is a leading U.S. government agency that works to end extreme global poverty and enable resilient, democratic societies in Asia. RDMA implements regional programs that address cross-border issues such as climate change, economic development, and health security. It also provides thought leadership, management, and technical support to other USAID missions in Asia which include Thailand, Burma, Cambodia, Laos, Vietnam, Philippines, Indonesia, India, Bangladesh, Sri Lanka, Nepal, Pakistan, Central Asia, and many more.

The Asia Regional Training Center (ARTC) was established in 2011 to provide cost-effective, high-quality training to Federal employees in the Asia region and beyond. ARTC offers a wide range of courses, including core professional skills training, advanced technical training, and leadership development training. ARTC has conducted more than 1,800 courses, workshops, seminars, and special events, involving more than 41,000 Federal employees and partners.

In today's data-driven world, the ability to effectively communicate is essential. As a response to the growing importance of data-driven communication, ARTC is planning to provide training in Data Visualization to USAID staff. This course serves as an introduction to the powerful combination of data visualization and data presentation, equipping participants with the tools and techniques to convey complex information in a compelling and impactful manner. Participants will gain a solid understanding of data visualization and presentation concepts and the ability to apply these techniques creatively. The objective is to enhance the staff's capacity to present data effectively in their USAID work.

III. Services Required

• Learner-centered design: Puts the learner's needs and goals at the forefront of the training.
• Variety of learning modalities: Offers different ways to learn, such as interactive exercises, case studies, and simulations.
• Personalized reflection and feedback: Creates opportunities for learners to reflect on their learning and receive tailored feedback.
• USAID DEIA lens: Creating a space where everyone feels valued and respected. Reference: https://www.usaid.gov/about-us/diversity-equity-inclusion  
• Training Contents

The training must include but not limited to the following topics:

• Best practices for designing impactful data visualizations.
• Choosing the right chart types for different data sets.
• Understanding the importance of color in data visualization. Providing resources for finding colorblind-friendly palettes online.
• Best practices for delivering impactful data.
• Tailoring the complexity of the data and the tone of the presentation to best engage with the audience.
• Presenting the compelling data visualizations with clarity, confidence, and conviction.
• Interactive Exercises : Participants will actively participate in individual and group exercises designed to develop their data visualization skills using Google Sheets or Excel.  They will develop their own data visualization which they will then present to the class.
• Instructor Feedback : Participants will receive individual and group feedback on their data visualization and presentation efforts throughout the workshop.
• Real-world Example : Showcase an example where charts and color choices impact the interpretation of a visualization.
• Online Tools and Websites : Explore popular online tools and websites designed to streamline data visualization creation.

Bidders are invited to suggest additional learning topics or innovative approaches that would enhance the learning experience and meet the needs of the learners. Topics should be engaging, current, and relevant to the field of Data Visualization.         

• Training Deliverables
• Hold a virtual meeting with ARTC within one week of contract award to discuss training requirements and expectations. The meeting must be conducted during ARTC business hours (8 am-4 pm, Thailand time).
• Gather sample data sets from participants and tailor the course content to meet their specific requirements.
• Submit training materials including presentation slides and evaluation forms (criteria: training content quality, materials quality, instructor expertise, comments and suggestions) to ARTC for final review.
• Onsite Learning Approach : o/a December 17-20, 2024, 8:30 am-4:30 pm, ARTC training center, Bangkok, Thailand
• Audience : 25 USAID staff with good spreadsheet skills and a beginner-to-intermediate understanding of data visualization. They aim to improve their ability to communicate complex data through impactful visualizations for both technical and non-technical audiences.
• Language : English
• Optional for additional session: To facilitate more participants and increase program accessibility, an additional optional session can be added. This additional session may be scheduled at another date and time. The content and structure of the new session would be comparable to the current program, encompassing the same training modules and objectives. To assure the quality and efficacy of the additional session, it is recommended that the participant-to-trainer ratio be comparable to that of the previous session. This will allow for sufficient interaction, engagement, and personalized attention during the training.
• Timeline - Submit the report within two weeks of onsite training completion. 
• Achieve at least 80% participant response rate for the training evaluation survey.
• Attach a copy of the raw data from the survey in Excel format or a similar format.
• Include an overall training rating and individual ratings for course content, training materials, and instructor expertise (use a 5-point scale or percentage).
• Summarize participant comments by evaluation criteria.
• Include instructor observations and recommendations for improvement.

IV. Proposal Instructions

• Submit a detailed agenda with learning objectives, learning modules, and suggested class activities with descriptions.
• Illustrate core concepts and highlight distinctive features of the training.
• Submit a resume(s) of the proposed instructor(s) that describes their expertise in conducting the subject training with the offeror within the past five years.
• Provide a video clip(s) of the proposed instructor(s) introducing or delivering the subject training (3-5 minutes, viewable online).
• Summarize your experience organizing the subject training or similar training to international audiences within the past five years.
• Provide three references from clients who have received the subject training or similar training within the past five years (contact details, email addresses, name of training, and period of training).

Failure to provide documentation and information as instructed may result in disqualification.

• Price Proposal
• Total fixed-price quotation - be sure to consider all costs associated with the delivery of this training course including but not limited to instructor’s travel costs, lodging cost, training materials, delivery of training materials and other miscellaneous costs (if any).
• Separate fixed-price quotation for additional delivery - be sure to consider all costs associated with the additional optional session.  Repetitive costs such as course design should be deducted.

The cost of the training venue should not be included in the quotation. ARTC will provide the training room with landline internet access as well as a projector for presentations. To connect to the ARTC audio/visual system, the offeror must bring their own laptop.

V.  Selection Criteria

• Course Content                   40%
• Instructor Expertise           40%
• Price Proposal                     10%
• Past Performance              10%

Total                                         100%

The full payment will be processed after acceptance of the deliverables and within 30 days of valid invoice receipt. Invoices should be sent by email to the USAID/RDMA Financial Office.

USAID intends to make a contract award that represents the best value to the U.S. Government. “Best value” is defined as the offer that results in the most advantageous solution for the U.S. Government, in consideration of technical, cost/price, and other factors. All evaluation factors other than cost or price, when combined, are significantly more important than cost or price. When the Contracting Officer determines that competing technical proposals are essentially equal, cost/price factors may become the determining factor in source selection. Upon successful negotiations with the offeror, a fixed-price Purchase Order will be issued to procure the services.

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Analysis of the heterogeneous coordination between urban development levels and the ecological environment in the chinese grassland region (2000–2020): a case study of the inner mongolia autonomous region, share and cite.

Wang, Y.; Yang, Y. Analysis of the Heterogeneous Coordination between Urban Development Levels and the Ecological Environment in the Chinese Grassland Region (2000–2020): A Case Study of the Inner Mongolia Autonomous Region. Land 2024 , 13 , 951. https://doi.org/10.3390/land13070951

Wang Y, Yang Y. Analysis of the Heterogeneous Coordination between Urban Development Levels and the Ecological Environment in the Chinese Grassland Region (2000–2020): A Case Study of the Inner Mongolia Autonomous Region. Land . 2024; 13(7):951. https://doi.org/10.3390/land13070951

Wang, Yue, and Yongchun Yang. 2024. "Analysis of the Heterogeneous Coordination between Urban Development Levels and the Ecological Environment in the Chinese Grassland Region (2000–2020): A Case Study of the Inner Mongolia Autonomous Region" Land 13, no. 7: 951. https://doi.org/10.3390/land13070951

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Generalized anxiety disorder (GAD) is a devastating mental health condition characterized by constant, uncontrolled worrying. Recent hypotheses indicate that pro-inflammatory cytokines and chemokines are potential contributors to the pathogenesis of GAD. Here, we aimed to assess the role of interleukin-2 (IL-2) and interleukin-10 (IL-10) in the pathophysiology and development of GAD.

This study recruited 50 GAD patients diagnosed according to the DSM-5 criteria and 38 age-sex-matched healthy controls (HCs). A qualified psychiatrist evaluated all study subjects. The socio-demographic and clinical characteristics of the study population were determined using pre-structured questionnaires or interviews, and cytokine serum levels were estimated using commercially available ELISA kits.

We observed reduced serum IL-10 levels in GAD patients compared to HCs (33.69 ± 1.37 pg/ml vs. 44.12 ± 3.16 pg/ml). Also, we observed a significant negative correlation between altered IL-10 levels and GAD-7 scores ( r =-0.315, p  = 0.039). Moreover, IL-10 serum measurement exhibited good predictive value in receiver operating characteristics (ROC) analysis with an area under the curve (AUC) value of 0.793 ( p  < 0.001) with 80.65% sensitivity and 62.79% specificity at a cutoff value of 33.93 pg/ml. Conversely, we noticed elevated serum IL-2 levels in GAD patients than in HCs (14.81 ± 2.88 pg/ml vs. 8.08 ± 1.1 pg/ml); however, it failed to maintain any significant association with GAD-7 scores, implying that IL-2 might not be involved in GAD pathogenesis. The lower AUC value (0.640; p  > 0.05) exhibited by IL-2 serum measurement in ROC analysis further supported that IL-2 might not be associated with GAD.

This study provides new insights into the complex interplay between anti-inflammatory cytokines and GAD pathogenesis. Based on the present findings, we can assume that IL-10 but not IL-2 may be associated with the pathophysiology and development of GAD. However, further research with a larger population size and longitudinal design is required to confirm the potential diagnostic efficacy of IL-10.

Peer Review reports

Generalized anxiety disorder (GAD) is a chronic neuropsychiatric disorder characterized by persistent and excessive uncontrollable fear or worry (occurs for at least 6 months) about various aspects/activities of daily life, affecting the educational, occupational, or social lives of the affected people [ 1 ]. If a person is excessively worried about anything for most days over at least 6 months, he/she is considered to have GAD. Though currently the prevalence rate of GAD is 3–6% worldwide [ 1 , 2 , 3 ], the prevalence is increasing day by day due to the complexity of modern lifestyles and thus warrants attention from national and international authorities to take interventions for mitigating and managing this disorder properly. If it remains undiagnosed or untreated, the uncontrollable and persistently intense anxiety can lead to a marked reduction in cognitive functions or a reduced capacity to work properly in all spheres of life, including educational, family, social, and individual routine work. As such, chronic GAD leads to a reduced quality of life and thereby poses a significant mental health concern globally.

Despite its high prevalence, significant morbidity, and socioeconomic burden, GAD remains poorly characterized in terms of its pathophysiology or effective treatment options. Though the precise cause and mechanism of pathogenesis are still unknown, evidence suggests that multiple factors, including disrupted serotonergic, dopaminergic, and GABAergic neurotransmission and excessive glutamatergic neurotransmission in the brain, genetic factors, family or environmental stress, chronic diseases, hyperthyroidism, childhood trauma, and special personality traits, are linked to GAD. Alterations in monoaminergic neurotransmissions in limbic systems (cingulate gyrus, hippocampus, amygdala, thalamus, and hypothalamus) due to the lower synaptic availability of serotonin, norepinephrine, and dopamine are thought to be associated with anxiety symptoms. Besides, decreased GABA-mediated inhibitory neurotransmission in the amygdala or excessive activation of excitatory glutamatergic neurotransmission are also suggested to be involved in GAD pathology.

Currently, available pharmacotherapies for GAD include selective serotonin reuptake inhibitors (SSRIs), serotonin and norepinephrine reuptake inhibitors (SNRIs), pregabalin, and benzodiazepines, which act by reversing these altered monoaminergic neurotransmitter systems. Alongside these drug treatments, non-pharmacological therapies such as several psychological interventions, including cognitive-behavioral therapy, and the acquisition and application of stress management skills, including relaxation and mindfulness skills are also widely used for the management of GAD. However, currently, available pharmacotherapies (SSRIs, SNRIs, pregabalin, and benzodiazepines) have failed to demonstrate the required efficacy in treating anxiety disorders, as 50% of patients failed to respond to these drugs, and at least in 30% of cases, there is a recurrence of the disease following the pharmacological treatment [ 1 , 4 , 5 ]. Moreover, studies reported a higher rate of discontinuity from these pharmacotherapies with low patient adherence or compliance due to the adverse effects, including sexual dysfunction for SSRIs and SNRIs, nausea and dizziness for pregabalin, demonstrating an urgent need for searching for novel anxiolytics [ 3 ]. These findings raised questions about the validity of the currently available mechanism of pathogenesis and suggested that the altered monoaminergic neurotransmitter system might not fully explain the molecular mechanism of GAD development, suggesting other pathophysiological factors might be involved in GAD. Recently, dysregulated immune systems have attracted great interest as an important pathophysiological factor for the development of GAD [ 4 , 6 , 7 , 8 ]. Several clinical and preclinical studies suggest a link between the altered immune system and GAD pathology. Preclinical studies in mice also demonstrated that administration of pro-inflammatory cytokines (including IL-1β, TNF-α, and IL-6) in mice resulted in anxiety-like behaviors that were attenuated or normalized after injecting either anti-inflammatory cytokines or antagonists for the concerned cytokines [ 9 , 10 , 11 , 12 , 13 ]. A recent prospective cohort study conducted by Hou et al., (2019) demonstrated that administration of selective serotonin reuptake inhibitors (escitalopram or sertraline) resulted in a significant reduction in peripheral pro-inflammatory cytokines, and the authors suggested that the anxiolytic effects of these SSRIs might partly be based on their acute anti-inflammatory activities [ 14 ], implicating a significant association between dysregulated peripheral immune systems and GAD development. The development of anxiety-like symptoms in IL-4 gene knock-out mice, reduced levels of IL-4 in anxious mice, and the significant attenuation of anxiety-like behaviors following IL-4 injection demonstrated a positive association between anti-inflammatory cytokines, IL-4 levels, and anxiety pathology [ 15 , 16 , 17 , 18 ]. This immune hypothesis of GAD development is further potentiated by findings from several clinical studies that reported that GAD patients showed significantly higher levels of pro-inflammatory cytokines ( IL-1Ra, IL-1, IL-6, TNF-α, etc.) compared to healthy controls (HCs) [ 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ] along with decreased levels of anti-inflammatory cytokines, including IL-4 and IL-10 [ 25 ]. Besides, pro-inflammatory cytokines such as TNF-α, and IL-6 were significantly associated with anxiety scores [ 29 ]. Consistent with this, a randomized clinical trial in humans demonstrated that LPS administration resulted in enhanced anxiety scores, and the authors suggested a significant correlation between pro-inflammatory cytokine levels and anxiety severity [ 30 ]. LPS-mediated microglia activation causes enhanced release of excessive pro-inflammatory cytokines in the basolateral amygdala, which ultimately leads to neuroinflammation in mice, resulting in the development of anxiety and depression-like behaviors by modulating neuronal plasticity. The authors found that anxiety pathogenesis was due to the excessive release of excitatory neurotransmitter glutamate from presynaptic axonal terminals of the prefrontal cortex, leading to neuroplasticity [ 31 ]. However, some studies reported either no significant variation in pro-inflammatory or anti-inflammatory cytokine serum levels between GAD patients and HCs [ 32 ] or that pro-inflammatory cytokines including IL-1, IL-2, and IL-6 were significantly reduced in GAD patients than HCs [ 33 , 34 ]. This discrepancy in altered levels of inflammatory cytokines across clinical studies necessitates a further examination of the role of these cytokines in GAD pathophysiology.

Interleukin-2 (IL-2) is one of the major pro-inflammatory cytokines implicated in T cell activation, proliferation, and differentiation and is thus linked to excessive neuro-inflammatory processes [ 35 ]. IL-2 has been shown to impair synaptic plasticity and cause neuroinflammation, which ultimately leads to neuronal damage in neurocircuits associated with fear and anxiety signal transduction. IL-2 was also reported to act as a potent modulator of NMDA and kainite-mediated excitability in mesolimbic or mesostriatal systems [ 36 , 37 , 38 ] and thus affect neuroplasticity. As IL-2 was found to be positively associated with major depressive disorder [ 38 , 39 ], probably, IL-2 might also be correlated with anxiety disorders like GAD, as MDD and GAD are highly co-morbid themselves and thus might share common pathophysiological factors. Recently, a preclinical study conducted by Gilio et al., (2022) observed that IL-2 administration in experimentally healthy mice triggered marked anxiety and depression-like behaviors, and the authors suggested that inhibition of GABA-mediated synaptic inhibitory neurotransmission was involved in the pathology of anxiety [ 40 ].

Interleukin-10 (IL-10) is one of the major anti-inflammatory cytokines that is secreted from Treg cells, Th2 cells, CD4 + T cells, CD8 + T cells, monocytes, macrophages, dendritic cells, B cells, neutrophils in the peripheral nervous system, and from microglia, astrocytes in the central nervous system (CNS) [ 41 ]. IL-10 signaling triggers anti-inflammatory, immunosuppressive, and immunoregulatory activities, including downregulating the production and secretion of pro-inflammatory cytokines and chemokines from activated macrophages, neutrophils, mast cells, Th1 cells, and DCS, decreasing the expression of MHC class II and co-stimulatory molecules on macrophages, and thereby suppressing the antigen presentation capacity of APCS [ 42 , 43 , 44 , 45 , 46 ]. In the CNS, it inhibits the production of such cytokines and chemokines by activated microglia and thereby counteracts cellular and tissue damage in response to excessive neuroinflammation [ 47 , 48 ]. IL-10 has also been shown to stimulate axonal regeneration and activate wound healing through tissue repair [ 48 ]. Research also indicates its role as an inhibitor for microglial hyperactivation in response to LPS-induced inflammatory stimulus [ 49 ]. Based on its anti-inflammatory and immunoregulatory functions, researchers suggested an intricate role for IL-10 in several auto-immune and neuropsychiatric disorders. For example, Mesquita et al., (2008) observed that IL-10 KO mice developed markedly enhanced depressive-like behavior, which was attenuated after IL-10 administration, and that overexpression of IL-10 resulted in reduced depressive behaviors in mice [ 50 ]. Moreover, administration of IL-10 into rats attenuated the pro-inflammatory cytokine IL-1β-induced anxiety-like symptoms in male rats [ 10 ], demonstrating that IL-10 possesses anxiolytic activities. Preclinical research using an experimental animal model also suggests that the observed anxiolytic effect of several anti-anxiety drugs, including 3’-deoxyadenosine (3’-dA), imipramine, fluoxetine, and chlordiazepoxide, stems from their ability to upregulate anti-inflammatory cytokine (IL-4, IL-10) expression in the prefrontal cortex and locus coeruleus and simultaneous down-regulation of proinflammatory cytokine gene expression, leading to a correction of the imbalance between proinflammatory and anti-inflammatory states [ 51 , 52 ]. Though several preclinical studies suggest a potential link between IL-10 levels and anxiety disorder, there is a scarcity of clinical studies aimed at evaluating such an association between IL-10 and GAD development [ 10 ].

Currently, there is no objective and cost-effective diagnostic or prognostic biomarker for GAD, which poses challenges in early diagnosis or risk prediction and leads to misdiagnosis or underdiagnosis, hampering the proper management of the disease. Currently available diagnostic tools, including self-reported symptoms and scoring severity based on the patient’s response to the 7-item questionnaire (GAD-7 scores), are subjective. Though neuroimaging techniques such as positron emission tomography (PET) and functional MRI can be used for the proper and objective diagnosis of GAD, due to their high cost and sophistication or complexities, these diagnostic tools are not suitable for either mass-level screening or are not easy to conduct multiple times to monitor the disease progression or therapeutic drug response. As such, the investigation of cost-effective objective biomarkers for GAD is one of the major focuses of current research on GAD. Finding a suitable biomarker is essential for early diagnosis and initiating psychotherapy and pharmacotherapy as early as possible [ 3 ]. Several studies were performed investigating the potential association between altered pro-inflammatory cytokines or anti-inflammatory cytokines and the pathogenesis of GAD. However, the actual role of inflammatory cytokines in GAD patients is not well explained. Therefore, the present study aims to explore the role of pro-inflammatory cytokines (IL-2) and anti-inflammatory cytokines (IL-10) in the pathophysiology and development of GAD. Also, we aim to find the potential associations of IL-2 and IL-10 with the severity of GAD patients. We believe the present study results would help to understand the pathophysiology and development of GAD.

Study population

We recruited 88 participants for this case-control study (50 GAD patients and 38 HCs matched by age and sex). Patients were collected from the Department of Psychiatry, Bangabandhu Sheikh Mujib Medical University Hospital, Dhaka, Bangladesh, and HCs from nearby areas of Dhaka city. A professional psychiatrist diagnosed patients and evaluated HCs based on DSM-5 criteria. We applied a 7-item GAD scale to assess the severity of anxiety symptoms [ 53 ]. The total scores range from 0 to 21, and it classifies the anxiety severity into four categories: minimal anxiety (0–4 scores), mild anxiety (5–9 scores), moderate anxiety (10–14 scores), and severe anxiety (15–21 scores). We excluded subjects with a co-morbidity of other psychiatric disorders, such as MDD, panic disorder, post-traumatic stress disorder, and social phobia, from the study. Additional exclusion criteria for participants were chronic liver and kidney diseases, infectious diseases, and alcohol or substance abuse. We also excluded patients who were exposed to anxiolytics or antidepressant medications within at least two weeks prior to the study that might have an impact on cytokine levels. We recorded the sociodemographic profile of the study population using a pre-designed questionnaire. The objectives of the study were explained to each participant, and informed written consent was obtained from them before their participation in this study. The study was conducted in accordance with the Declaration of Helsinki.

Blood sample collection and serum isolation

A 5 ml blood sample was collected from the cephalic vein of each participant. The blood samples were kept at room temperature for 1 hour to ensure coagulation and were then subjected to centrifugation at 3000 rpm for 15 minutes at room temperature to collect serum samples. The collected serum was then placed in the Eppendorf tube and stored at -80 °C until further analysis.

Estimation of serum cytokine levels

We estimated the serum levels of IL-2 and IL-10 by ELISA methods (Boster Bio, USA). We followed the manufacturer’s protocol for the ELISA assays. At first, we added 100 µl of standard cytokine solution, samples, and controls to each well of a pre-coated 96-well microplate. The microplates were covered with a plate sealer and incubated for 90 min at 37⁰C. After that, the cover was removed, and the liquid in each well was discarded. Subsequently, 100 µl of biotinylated anti-IL-2 antibody or anti-IL-10 antibody was incorporated into each well and incubated for 60 min at 37⁰C. After discarding the liquid from each well and washing it three times with 300 µl of wash buffer, 100 µl of avidin-biotin-peroxidase complex was added to each well, and the microplate was then again incubated for 30 min at 37⁰C. After the incubation period, the liquid was again discarded, and the plate was washed again with 300 µl of wash buffer five times. Following the addition of 90 µl color-developing reagent (TMB) into each well, the plate was incubated in a dark place for 30 min at RT, followed by the addition of 90 µl of stop solution to each well to stop the reaction process. We measured the absorbance with a microplate reader at 450 nm. We calculated the cytokine levels using standard curves and expressed them as pg/ml.

Data presentation and statistical analysis

GraphPad Prism (version 8.0.1) and Statistical Package for the Social Sciences (version 24.0) were used for data analysis. We used descriptive statistics to find the variations in sociodemographic profiles and clinical characteristics between the groups. A T-test and a Chi-square test were employed to determine the statistical level of significance between the mean differences for variables across patients versus HC groups in the case of continuous variables and categorical variables, respectively. We used boxplot graphs for comparisons of analyzed cytokines between patients and HCs. We also generated scatter plot graphs for several clinical variables in GAD patients to show the correlations among the clinical parameters. A correlation analysis was performed to assess the potential association between several demographic and clinical variables in GAD patients. Receiver operating characteristics (ROC) analysis was conducted to determine the diagnostic efficacy of serum IL-2 or IL-10 levels in discriminating GAD patients from HCs. In all cases, statistical significance was considered at p  < 0.05.

Sociodemographic characteristics of the study population

The sociodemographic characteristics of the study population are presented in Table  1 . The GAD patients and HCs were similar in terms of their age, sex, and BMI. Among the participants, about 60% were male and from urban areas. The majority of patients (60.00%) and HCs (68.42%) were unmarried. There was no significant variation between patients and HCs for their education level, occupation, economic status, or smoking status. In contrast, there was a difference between patients and HCs for their family history and previous history of the disease. In GAD patients, 20.00% had a family history, and 40.00% had a previous history of the disease.

Clinical characteristics and laboratory findings

Clinical characteristics and laboratory analysis results are presented in Table  2 . GAD patients displayed markedly higher serum levels of IL-2 (14.81 ± 2.88 pg/ml) compared to HCs (8.08 ± 1.10 pg/ml), and the difference reached the statistically significant level ( p  = 0.037, two-tailed unpaired t-test) (Table  2 ; Fig.  1 ). Though male GAD patients exhibited markedly higher levels of IL-2 compared to male HCs ( p  = 0.048), there was no significant variation in IL-2 levels between female patients and female HCs ( p  > 0.05) (Fig.  1 ). Though some 1.8-fold higher IL-2 serum levels were observed in male GAD patients compared to female GAD patients, the difference did not reach the statistical significance level ( p  = 0.198, two-tailed unpaired t-test). In contrast to the results obtained for IL-2, IL-10 showed a statistically significant ( p  < 0.001) reduction in GAD patients (33.69 ± 1.37 pg/ml) compared to HCs (44.12 ± 3.16 pg/ml) (Fig.  1 ). Similar to the results obtained for IL-2, IL-10 levels showed a statistically significant difference between patients versus HCs when male people were considered (Fig.  1 ). In contrast, there was no significant variation in IL-10 levels between female GAD patients and female HCs ( p  > 0.05).

Distribution of serum IL-2 ( a i ) and IL-10 ( b i ) levels in GAD patients and healthy controls. Comparison of IL-2 and IL-10 levels between GAD patients and their counterparts in control subjects are showed in a i and b i . Comparison of IL-2 and IL-10 levels between male or female GAD patients and their counterparts in control subjects are presented in a ii and b ii

Correlation analysis among different study parameters

We then performed a series of correlation analyses to investigate the association of altered cytokine serum levels with several demographic and clinical variables, such as age, BMI, DSM-5, and GAD-7 scores (Table  3 ). Serum IL-2 levels did not show any positive or negative association with either DSM-5 or GAD-7 scores ( p  > 0.05), suggesting that despite its significant enhancement in GAD patients compared to HCs, IL-2 may not associate with GAD pathophysiology. We also observed no significant association between the ages of the patients and IL-2 serum levels. In contrast, the IL-2 levels of GAD patients maintained a significant and positive correlation with BMI levels of patients ( r  = 0.390, p  < 0.05) which is consistent with the intricate relationship between body mass and enhanced pro-inflammatory responses. Contrary to the results obtained for IL-2, reduced serum IL-10 levels maintained a significant but negative association with both DSM-5 scores ( r =-0.300, p  = 0.045) and GAD-7 scores ( r =-0.315, p  = 0.039), implicating that altered IL-10 levels are linked to GAD development or pathogenesis. However, the age and BMI levels of GAD patients failed to show any positive or negative association with IL-10 serum levels. Analysis also showed a significant and strong positive association between IL-2 and IL-10 serum levels ( r  = 0.471, p  = 0.011) in GAD patients, which might be due to the compensatory enhancement of anti-inflammatory cytokine, IL-10 in response to elevated pro-inflammatory cytokine, IL-2 levels. Also, we displayed these correlations among several clinical variables of GAD patients by scatter plot graphs (Fig.  2 ).

Scatter plot graphs for several clinical variables of GAD patients showing existence or absence of correlation between the clinical parameters. Scatter plot for serum IL-2 levels versus GAD-7 scores ( a ) or DSM-5 scores ( b ) expressing no significant association between IL-2 and both clinical parameters. Scatter plot graphs showing significant association between IL-2 levels and BMI ( c ), IL-10 levels and GAD-7 scores ( d ), IL-10 levels and DSM-5 scores and IL-10 and IL-2 levels ( f )

Receiver operating characteristic curve analysis

Serum IL-10 measurement showed a good performance in differentiating GAD patients from HCs, which was evidenced by its significantly higher area under the curve (AUC) value of 0.793 ( p  < 0.001) with 80.65% sensitivity and 62.79% specificity at a cut-off value of 33.93 pg/ml, in which the cytokine levels below this point indicate disease states (Table  4 ; Fig.  3 ). ROC analysis of serum IL-2 levels failed to discriminate GAD patients from HCs as the AUC value was below the acceptable range (AUC: 0.640; p  = 0.108) with 54.17% sensitivity and 68.18% specificity at a cut-off value of 8.83 pg/ml) (Fig.  3 ; Table  4 ).

Receiver operating characteristic curve (ROC) for serum IL-2 ( a ) and IL-10 levels ( b )

To the best of our knowledge, this is the first case-control study to investigate the potential association between the pathophysiology of GAD and the pro-inflammatory cytokine, IL-2, and the anti-inflammatory cytokine, IL-10, among the Bangladeshi population. We observed that IL-10 serum levels were significantly lower in GAD patients than in HCs, and this reduction was found to be significantly but negatively associated with both DSM-5 scores and GAD-7 scores, demonstrating potential involvement of this anti-inflammatory cytokine in disease severity and symptoms. Our results of a significant reduction in IL-10 levels in GAD patients are in good agreement with those observed in other studies [ 23 , 25 ]. In contrast, our results diverge from those reported by others [ 33 , 54 ] who either reported no significant variation in IL-10 levels between GAD patients and HCs or that IL-10 levels were enhanced in GAD patients compared to HCs. ROC analysis also demonstrated the good predictive value of IL-10 serum measurement in discriminating diseased patients from HCs, suggesting that IL-10 serum level might be a potential biomarker for diagnosis, anti-anxiety drug response monitoring, or disease progression monitoring. Recently, Hou et al. (2019) demonstrated that peripheral serum levels of the pro-inflammatory cytokine IL-6 could be used to monitor the treatment response of SSRIs in GAD [ 14 ]. Similarly, IL-10 might be used as a marker for therapeutic drug monitoring in GAD. However, further longitudinal studies are required to find any causal relationship between IL-10 and disease severity or pathogenesis. On the other hand, serum IL-2 levels were significantly elevated in GAD patients compared to HCs, but they failed to demonstrate any significant association with either DSM-5 scores or GAD-7 scores in Pearson correlation analysis, implying that IL-2 levels might not be associated with the pathophysiology and development of GAD. Consistent with this, ROC analysis showed that IL-2 levels have no significant diagnostic efficacy in differentiating GAD patients from HCs. Further analysis with a larger population size is required to explore the role of IL-2 in the context of GAD severity. Our results are consistent with those reported by Tang et al. (2018), who also observed that GAD patients exhibited significantly higher serum levels of IL-2 compared to HCs [ 19 ]. However, our results are not in agreement with those reported by others who observed either no significant variation in IL-2 levels [ 54 ] or a significant reduction in GAD patients compared to HCs [ 25 , 33 , 34 , 55 ]. We also observed a significant positive correlation between IL-2 and IL-10 levels in GAD patients, which indicates a compensatory mechanism [ 56 ].

Our study provides some valuable insights into the complex and intricate relationship between the dysregulated immune system and GAD. The observed reduction in IL-10 levels in GAD patients in our study suggests a potential immunoregulatory imbalance in GAD, with IL-10 playing a role in modulating anxiety severity. The lack of a significant association between IL-2 serum levels and anxiety severity highlights the nuanced nature of immune dysregulation in GAD, warranting further exploration into the specific mechanisms involved. Elevated levels of pro-inflammatory cytokine, IL-2, and decreased levels of anti-inflammatory cytokine, IL-10, in GAD patients compared to HCs indicate that GAD individuals of the Bangladeshi cohort are characterized by heightened inflammatory responses derived from the imbalance between pro-inflammatory and anti-inflammatory states. Our study finding provides further support for the cytokine hypothesis of anxiety disorder, which proposes that pro-inflammatory cytokine-mediated neuroinflammatory processes can lead to anxiety symptoms or behaviors by downregulating serotonin biosynthesis or enhancing the reuptake of serotonin, resulting in an altered serotonergic neurotransmitter system in the CNS [ 15 ]. The observed significant negative correlation between IL-10 and DSM-5 scores or GAD-7 scores suggests that lowering IL-10 levels might be involved in the pathogenesis of GAD. One of the major implications of our study findings is that IL-10 signaling might be targeted to explore potential novel immunological/immunomodulatory therapies against GAD. The diminished IL-10 levels and their negative correlation with GAD severity suggest a potential avenue for therapeutic intervention. IL-10 might also be used as an anti-inflammatory adjunctive therapy with other pharmacotherapies including SSRIs/SNRIs. However, at this moment, we don’t know the exact mechanism by which lowered levels of IL-10 are linked to higher anxiety severity in GAD patients.

As IL-10 has anti-inflammatory and immunoregulatory activities such as suppression of production of pro-inflammatory cytokines (IL-1β, IL-6, and TNF-α) from microglia and astrocytes, reduction in IL-10 levels in GAD patients in our study led to an imbalance between pro-inflammatory and anti-inflammatory states and resulted in enhanced pro-inflammatory responses, which might be the cause of enhanced anxiety symptoms as inflammatory cytokine-mediated neuroinflammation was reported to be linked with disrupted monoaminergic neurotransmission in the brain. Besides, elevated levels of IL-10 were shown to attenuate anxiety-like behaviors by modulating GABAergic neurotransmission in the amygdala (Patel et al., 2021). IL-10 was also reported to display some neuroprotective activities and has been shown to inhibit neuronal apoptosis and promote neurite outgrowth, axonal outgrowth, and synapse formation in the brain by the JAK1-STAT3 signaling pathway [ 57 ]. In a preclinical study, IL-4 has been shown to cause the shifting of microglia and macrophages from pro-inflammatory to anti-inflammatory neuroprotective phenotypes characterized by excessive production of arginase-1 and PPARγ receptor expression in microglia and macrophage and thereby attenuating brain-injury-mediated anxiety by inhibiting neuronal loss and nerve tracts in the limbic system [ 58 ]. A similar mechanism might be involved in IL-10-mediated anxiety symptom improvement in GAD patients. Further research is required to unravel the exact mechanisms of IL-10-mediated anxiety symptom attenuation in GAD patients.

In terms of diagnostic marker development, as IL-10 serum level measurement demonstrated good performance in discriminating GAD patients from HCs and as IL-10 levels maintained a significant and negative correlation with disease severity, IL-10 serum level raised the possibility of being an objective biomarker for GAD. However, the diagnostic efficacy of this cytokine should be investigated thoroughly using a range of longitudinal studies. Despite this, at this time we can conclude that IL-10 might be used as a risk indicator for assessment of susceptibility to anxiety disorder, resulting in early detection of the disease and prompting the initiation of intervention strategies. This early detection will reduce treatment costs and decrease the prevalence and morbidity associated with this chronic disorder.

The strength of our study is that we designed a set of inclusion and exclusion criteria for the recruitment of participants and followed those criteria in such a way that homogenous population data could be obtained. The strict study design helped us enormously to minimize the potential impact of several confounding variables, including age, sex, BMI, co-morbid diseases, and immunomodulatory drugs, on cytokine levels. However, our study also has some limitations that should be acknowledged. The major limitation of this study is the smaller sample size. We recruited 50 patients and 38 HCs, which does not represent the whole Bangladeshi demographic. It would be better if we could enroll an equal number of cases and controls. For example, we observed that cytokine levels maintained a statistically significant difference between male GAD patients and male HCs. In contrast, no significant variation in cytokine levels was observed when female data were considered. As we have included more male participants (60%) than female participants (40%), the lower sample size of female participants might generate a higher background noise, resulting in lower statistical power, warranting further studies recruiting a larger population size to investigate sex-specific differences in cytokine levels in GAD patients. Our case-control study design is inherently correlational and thus not able to evaluate the causal relationship between altered cytokine levels and GAD. So, at this moment, we cannot conclude whether the altered levels of serum cytokines are the causes of anxiety development or just the outcome of pathophysiological changes.

Longitudinal studies are required to investigate whether altered cytokine levels precede GAD or if it’s just a mere reflection of GAD pathology. Though we have restricted the impacts of several co-variates, other confounding variables, including genetic polymorphism in cytokine genes, the effect of lifestyle or xenobiotics, and dietary habits, were not considered, which might have modulatory effects on serum cytokine levels.

The study provides valuable insights for understanding the pathogenesis of GAD. Despite having elevated IL-2 levels in GAD patients compared to HCs, it failed to demonstrate a significant association with anxiety severity as assessed by GAD-7 scores. In contrast, serum IL-10 levels were significantly reduced in GAD patients compared to HCs and showed a significant negative correlation with anxiety severity, implicating a potential link with the GAD pathophysiology. Our results support the immune hypothesis of GAD development. Our study findings also suggest that IL-10 serum level measurement might offer an objective blood-based biomarker or risk assessment indicator for GAD. We recommend further research employing a larger population size and homogenous data from different areas of Bangladesh to confirm our study findings.

Data availability

All the relevant data and information will be available from the corresponding author upon reasonable request.

Abbreviations

Body mass index

Chronic energy deficiency

Confidence interval

Central nervous system

Diagnostic and statistical manual for mental disorders, 5th edition

Enzyme-linked immunosorbent assay

• Generalized anxiety disorder

Generalized anxiety disorder 7-item scores

Healthy control

• Interleukin-2
• Interleukin-10

Receiver operating characteristic

Standard error mean

Statistical package for social science

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Acknowledgements

The authors are thankful to all the participants of this study. They are also thankful to the staff and physicians at the Department of Psychiatry, BSMMU, for their technical and administrative support. The authors are also thankful for the laboratory support provided by the Department of Pharmacy, University of Asia Pacific, Dhaka Bangladesh.

This research received no specific grant from any funding agency. However, we received partial funding from University of Dhaka, Bangladesh (Centennial Research grant (2nd Phase) for the year of 2020–2021, project title: “Investigation of peripheral pro-inflammatory and anti-inflammatory cytokines and immune balance in Bangladeshi patients with Generalized Anxiety Disorder”).

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Nisat Sarmin, A. S. M. Roknuzzaman and Rapty Sarker contributed equally to this work.

Authors and Affiliations

Department of Clinical Pharmacy and Pharmacology, Faculty of Pharmacy, University of Dhaka, Dhaka, 1000, Bangladesh

Nisat Sarmin, Rapty Sarker, Mamun -or-Rashid & Zobaer Al Mahmud

Department of Pharmacy, University of Asia Pacific, Dhaka, 1205, Bangladesh

A. S. M. Roknuzzaman

Department of Psychiatry, Bangabandhu Sheikh Mujib Medical University, Shahabagh, Dhaka, 1000, Bangladesh

MMA Shalahuddin Qusar

Department of Pharmacy, Faculty of Pharmacy, University of Dhaka, Dhaka, 1000, Bangladesh

Sitesh Chandra Bachar

School of Pharmacy, BRAC University, Kha 224 Bir Uttam Rafiqul Islam Avenue, Merul Badda, Dhaka, 1212, Bangladesh

Eva Rahman Kabir & Md. Rabiul Islam

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Contributions

NS, ASMR, RS, MRI, and ZAM: Conceptualization, Data curation, Investigation, Writing – original draft. MR, MMASQ, SCB, and ZAM: Funding acquisition, Project administration, Validation. ERK, MRI, and ZAM: Conceptualization, Formal analysis, Methodology, Supervision, Writing – review & editing.

Corresponding authors

Correspondence to Md. Rabiul Islam or Zobaer Al Mahmud .

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The research protocol was approved by the Research Ethics Committee (REC) of the University of Asia Pacific, Dhaka, Bangladesh (Ref: UAP/REC/2023/202-S). We briefed the objectives of the study to the participants, and informed consent was obtained from each of them. We conducted this investigation following the Helsinki Declaration’s guiding principles.

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Sarmin, N., Roknuzzaman, A.S.M., Sarker, R. et al. Association of interleukin-2 and interleukin-10 with the pathophysiology and development of generalized anxiety disorder: a case-control study. BMC Psychiatry 24 , 462 (2024). https://doi.org/10.1186/s12888-024-05911-z

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Received : 31 December 2023

Accepted : 13 June 2024

Published : 20 June 2024

DOI : https://doi.org/10.1186/s12888-024-05911-z

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