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Globally elevated greenhouse gas emissions from polluted urban rivers

Abstract

Cities are at the heart of global anthropogenic greenhouse gas (GHG) emissions, with rivers embedded in urban landscapes as a potentially large yet uncharacterized GHG source. Urban rivers emit GHGs due to excess carbon and nitrogen inputs from urban environments and their watersheds. Here relying on a compiled urban river GHG dataset and robust modelling, we estimated that globally urban rivers emitted annually 1.1, 42.3 and 0.021 Tg CH4, CO2 and N2O, totalling 78.1 ± 3.5 Tg CO2-equivalent (CO2-eq) emissions. Predicted GHG emissions were nearly twofold those from non-urban rivers (~815 versus 414 mmol CO2-eq m−2 d−1) and similar to scope-1 urban emissions in intensity (1,058 mmol CO2-eq m−2 d−1), with particularly higher CH4 and N2O emissions linked to widespread eutrophication and altered carbon and nutrient cycling in urban rivers. Globally, the emissions varied with national income levels with the highest emissions happening in lower–middle-income countries where river pollution control is deficient. These findings highlight the importance of pollution controls in mitigating urban river GHG emissions and ensuring urban sustainability.

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Fig. 1: GHG concentrations and fluxes and associated physico-chemical measurements from global urban rivers.
Fig. 2: Standardized linear regression coefficients.
Fig. 3: Geographical distribution of predicted GHG concentrations and fluxes in urban rivers of world’s cities.
Fig. 4: Global urban river GHG emissions in CO2-eq emissions.

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Data availability

The global morphological urban areas product is available from https://data.mendeley.com/datasets/v3p8gk5724/1. The Global Reach-scale A priori Discharge Estimates for SWOT (GRADES) dataset is available from https://www.reachhydro.org/home/records/grades. The Global River Methane Database (GriMeDB) is available from https://doi.org/10.6073/pasta/f48cdb77282598052349e969920356ef. The temperature and precipitation are available from WorldClim (version 2) (https://www.worldclim.org/data/worldclim21.html). The elevation and slope are available from Global Multi-resolution Terrain Elevation Dataset (https://www.earthenv.org/topography). The GDP and GDP per capita are available from Gridded global datasets for Gross Domestic Product and Human Development Index over 1990–2015 via Dryad at https://doi.org/10.5061/dryad.dk1j0 (ref. 57). The population density is available from Gridded Population of the World (version 4) (https://beta.sedac.ciesin.columbia.edu/data/set/gpw-v4-population-density). The MODIS gross and net primary productivity data are available from https://www.umt.edu/numerical-terradynamic-simulation-group/project/modis/mod17.php. The soil respiration rates data are available from http://cse.ffpri.affrc.go.jp/shojih/data/index.html. Detailed information on an array of spatially explicit geospatial datasets used in this analysis is summarized in Supplementary Table 4. The dataset of urban river GHG concentrations and fluxes and related physico-chemical properties is available via figshare at https://doi.org/10.6084/m9.figshare.24233902 (ref. 58). Source data are provided with this paper.

Code availability

All data processing and analysis were performed using Microsoft Excel (version 2021), OriginPro (version 2023), randomForest package in R (version 4.2.1) and ArcGIS (version 10.8). The code used in this study is available via figshare at https://doi.org/10.6084/m9.figshare.24233902 (ref. 58).

References

  1. World Cities Report 2022: Envisaging the Future of Cities (UN-Habitat, 2022).

  2. Urban Development (World Bank, 2023).

  3. Urban Climate Action—The Urban Content of the NDCs: Global Review 2022 (UN-Habitat, 2022).

  4. Liu, Z., He, C., Zhou, Y. & Wu, J. How much of the world’s land has been urbanized, really? A hierarchical framework for avoiding confusion. Landscape Ecol. 29, 763–771 (2014).

    Article  Google Scholar 

  5. Creutzig, F. et al. Global typology of urban energy use and potentials for an urbanization mitigation wedge. Proc. Natl Acad. Sci. USA 112, 6283–6288 (2015).

    Article  CAS  Google Scholar 

  6. Ramaswami, A. et al. Carbon analytics for net-zero emissions sustainable cities. Nat. Sustain. 4, 460–463 (2021).

    Article  Google Scholar 

  7. Perini, K. & Sabbion, P. Urban Sustainability and River Restoration: Green and Blue Infrastructure (John Wiley & Sons, 2016).

  8. Gurnell, A., Lee, M. & Souch, C. Urban rivers: hydrology, geomorphology, ecology and opportunities for change. Geogr. Compass 1, 1118–1137 (2007).

    Article  Google Scholar 

  9. Strokal, M. et al. Urbanization: an increasing source of multiple pollutants to rivers in the 21st century. npj Urban Sustain. 1, 24 (2021).

    Article  Google Scholar 

  10. Peng, S. S. et al. Surface urban heat island across 419 global big cities. Environ. Sci. Technol. 46, 696–703 (2012).

    Article  CAS  Google Scholar 

  11. Wang, G. Q. et al. Intense methane ebullition from urban inland waters and its significant contribution to greenhouse gas emissions. Water Res. 189, 116654 (2021).

    Article  CAS  Google Scholar 

  12. Wang, X. F. et al. pCO2 and CO2 fluxes of the metropolitan river network in relation to the urbanization of Chongqing, China. J. Geophys. Res. 122, 470–486 (2017).

    Article  CAS  Google Scholar 

  13. Song, K. et al. Distinctive microbial processes and controlling factors related to indirect N2O emission from agricultural and urban rivers in Taihu watershed. Environ. Sci. Technol. 56, 4642–4654 (2022).

    Article  CAS  Google Scholar 

  14. Wang, G. Q. et al. Unexpected low CO2 emission from highly disturbed urban inland waters. Environ. Res. 235, 116689 (2023).

    Article  CAS  Google Scholar 

  15. Zhang, W. S. et al. Urban rivers are hotspots of riverine greenhouse gas (N2O, CH4, CO2) emissions in the mixed-landscape Chaohu Lake basin. Water Res. 189, 116624 (2021).

    Article  CAS  Google Scholar 

  16. Yu, Z. J. et al. Carbon dioxide and methane dynamics in a human-dominated lowland coastal river network (Shanghai, China). J. Geophys. Res. 122, 1738–1758 (2017).

    Article  CAS  Google Scholar 

  17. Taubenböck, H. et al. A new ranking of the world’s largest cities—do administrative units obscure morphological realities? Remote Sens. Environ. 232, 111353 (2019).

    Article  Google Scholar 

  18. Stanley, E. H. et al. GriMeDB: the global river database of methane concentrations and fluxes. Earth Syst. Sci. Data 15, 2879–2926 (2022).

    Article  Google Scholar 

  19. Ran, L. S. et al. Substantial decrease in CO2 emissions from Chinese inland waters due to global change. Nat. Commun. 12, 1730 (2021).

    Article  CAS  Google Scholar 

  20. Gómez-Gener, L. et al. Global carbon dioxide efflux from rivers enhanced by high nocturnal emissions. Nat. Geosci. 14, 289–294 (2021).

    Article  Google Scholar 

  21. Garnier, J. et al. Nitrous oxide (N2O) in the Seine River and basin: observations and budgets. Agric. Ecosyst. Environ. 133, 223–233 (2009).

    Article  CAS  Google Scholar 

  22. Yvon-Durocher, G. et al. Methane fluxes show consistent temperature dependence across microbial to ecosystem scales. Nature 507, 488–491 (2014).

    Article  CAS  Google Scholar 

  23. Velthuis, M. & Veraart, A. J. Temperature sensitivity of freshwater denitrification and N2O emission—a meta-analysis. Glob. Biogeochem. Cycles 36, e2022GB007339 (2022).

    Article  CAS  Google Scholar 

  24. Liu, S. D. et al. The importance of hydrology in routing terrestrial carbon to the atmosphere via global streams and rivers. Proc. Natl Acad. Sci. USA 119, e2106322119 (2022).

    Article  CAS  Google Scholar 

  25. Rocher-Ros, G. et al. Global methane emissions from rivers and streams. Nature 621, 530–535 (2023).

    Article  CAS  Google Scholar 

  26. Smith, R. M. et al. Influence of infrastructure on water quality and greenhouse gas dynamics in urban streams. Biogeosciences 14, 2831–2849 (2017).

    Article  CAS  Google Scholar 

  27. Tang, W. et al. Land use and hydrological factors control concentrations and diffusive fluxes of riverine dissolved carbon dioxide and methane in low-order streams. Water Res. 231, 119615 (2023).

    Article  CAS  Google Scholar 

  28. Da Costa, F. P., Grinfeld, M. & Wattis, J. A. D. A hierarchical cluster system based on Horton–Strahler rules for river networks. Stud. Appl. Math. 109, 163–204 (2002).

    Article  Google Scholar 

  29. Wang, X. F. et al. Methane and nitrous oxide concentrations and fluxes from heavily polluted urban streams: comprehensive influence of pollution and restoration. Environ. Pollut. 313, 120098 (2022).

    Article  CAS  Google Scholar 

  30. Wang, J. L. et al. Ecological restoration effectively mitigated pCO2 and CO2 evasions from severely polluted urban rivers. J. Geophys. Res. 128, e2023JG007531 (2023).

    Article  CAS  Google Scholar 

  31. Yan, R. F. et al. Pollution abatement reducing the river N2O emissions although it is partially offset by a warming climate: insights from an urbanized watershed study. Water Res. 236, 119934 (2023).

    Article  CAS  Google Scholar 

  32. Zhang, X. et al. Managing nitrogen for sustainable development. Nature 528, 51–59 (2015).

    Article  CAS  Google Scholar 

  33. Zhou, W. L. et al. Water environment protection and sustainable development in townlet of China: a case study in Taicang. J. Environ. Sci. 110, 129–139 (2021).

    Article  Google Scholar 

  34. Yao, H. Z. et al. Increased global nitrous oxide emissions from streams and rivers in the Anthropocene. Nat. Clim. Change 10, 138–142 (2020).

    Article  CAS  Google Scholar 

  35. Lucas, R. W. et al. Long-term declines in stream and river inorganic nitrogen export correspond to forest change. Ecol. Appl. 26, 545–556 (2016).

    Article  Google Scholar 

  36. Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).

    Article  Google Scholar 

  37. Lauerwald, R. et al. Inland water greenhouse gas budgets for RECCAP2: 2. regionalization and homogenization of estimates. Glob. Biogeochem. Cycles 37, e2022GB007658 (2023).

    Article  CAS  Google Scholar 

  38. Grossman, G. M. & Krueger, A. Economic growth and the environment. Q. J. Econ. 110, 353–377 (1995).

    Article  Google Scholar 

  39. Xu, Z. X. et al. Urban river pollution control in developing countries. Nat. Sustain. 2, 158–160 (2019).

    Article  Google Scholar 

  40. Ma, T. et al. China’s improving inland surface water quality since 2003. Sci. Adv. 6, eaau3798 (2020).

    Article  CAS  Google Scholar 

  41. Denfeld, B. A. et al. A synthesis of carbon dioxide and methane dynamics during the ice-covered period of northern lakes. Limnol. Oceanogr. Lett. 3, 117–131 (2018).

    Article  CAS  Google Scholar 

  42. Huo, D. et al. Carbon monitor cities near-real-time daily estimates of CO2 emissions from 1,500 cities worldwide. Sci. Data 9, 533 (2022).

    Article  CAS  Google Scholar 

  43. Wu, W. X. et al. Agricultural ditches are hotspots of greenhouse gas emissions controlled by nutrient input. Water Res. 242, 120271 (2023).

    Article  CAS  Google Scholar 

  44. Soued, C. et al. Reservoir CO2 and CH4 emissions and their climate impact over the period 1900–2060. Nat. Geosci. 15, 700–705 (2022).

    Article  CAS  Google Scholar 

  45. Yu, C. Q. et al. Managing nitrogen to restore water quality in China. Nature 567, 516–520 (2019).

    Article  CAS  Google Scholar 

  46. Lund, N. S. V. et al. Integrated stormwater inflow control for sewers and green structures in urban landscapes. Nat. Sustain. 2, 1003–1010 (2019).

    Article  Google Scholar 

  47. Campos, J. L. et al. Greenhouse gases emissions from wastewater treatment plants: minimization, treatment, and prevention. J. Chem. 2016, 3796352 (2016).

    Article  Google Scholar 

  48. Keller, J. & Hartley, K. Greenhouse gas production in wastewater treatment: process selection is the major factor. Water Sci. Technol. 47, 43–48 (2003).

    Article  CAS  Google Scholar 

  49. Montgomery, M. R. The urban transformation of the developing world. Science 319, 761–764 (2008).

    Article  CAS  Google Scholar 

  50. Esch, T. et al. Where we live—a summary of the achievements and planned evolution of the global urban footprint. Remote Sens. 10, 895 (2018).

    Article  Google Scholar 

  51. Lin, P. R. et al. Global reconstruction of naturalized river flows at 2.94 million reaches. Water Resour. Res. 55, 6499–6516 (2019).

    Article  Google Scholar 

  52. World Development Indicators (World Bank, 2021).

  53. Schielzeth, H. Simple means to improve the interpretability of regression coefficients. Methods Ecol. Evol. 1, 103–113 (2010).

    Article  Google Scholar 

  54. Cavaliere, E. & Baulch, H. M. Denitrification under lake ice. Biogeochemistry 137, 285–295 (2018).

    Article  CAS  Google Scholar 

  55. Forster, P. et al. in Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) 923–1054 (Cambridge Univ. Press, 2021).

  56. Allen, G. H. & Pavelsky, T. M. Global extent of rivers and streams. Science 361, 585–588 (2018).

    Article  CAS  Google Scholar 

  57. Kummu, M., Maija, T. & Guillaume, J. H. A. Data from: gridded global datasets for gross domestic product and human development index over 1990–2015. Dryad https://doi.org/10.5061/dryad.dk1j0 (2020).

  58. Xu, W. H. et al. Urban river GHG dataset. figshare https://doi.org/10.6084/m9.figshare.24233902 (2024).

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (T2261129474 (X.X.), 52388101 (Z.Y.), 52039001 (X.X.) and 52379057 (S.L.)), the National Key Research and Development Project of China (2021YFC3200401 (S.L.)) and the US National Science Foundation (2215300 (W.H.M.)).

Author information

Authors and Affiliations

Authors

Contributions

X.X. and S.L. designed the study. W.X. and G.W. compiled and analysed the data. J.W. aided in the analysis and compilation of the dataset. X.X., S.L., W.X., G.W. and J.W. interpreted the results and wrote the first draft of the paper. W.H.M., K.H., P.A.R. and Z.Y. provided insights and revised the paper. All authors contributed to the discussion and revision of the paper.

Corresponding authors

Correspondence to Shaoda Liu or Xinghui Xia.

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The authors declare no competing interests.

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Nature Sustainability thanks Alberto Borges and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Comparing measured GHG emission fluxes between urban rivers and other river types from the compiled urban river GHG dataset.

a, CH4 flux. b, CO2 flux. c, N2O flux. In each plot, box spans the 25th and 75th percentiles. Solid line denotes the median and the whiskers represent 1.5 × the interquartile range. Statistical significance between groups was tested with the two-sided Wilcoxon rank-sum test, using urban river as the reference group. Number in parentheses indicates number of measurements.

Source data

Extended Data Fig. 2 Relationships between urban river GHG concentrations and fluxes and catchment environmental and socioeconomic variables.

Dots represent individual data points, solid lines represent regression fits, shaded areas are the 95% confidence intervals, number is the standardized regression coefficient ± standard error. The P values were estimated with a two-sided F-test. No adjustments for multiple comparisons were made.

Source data

Extended Data Fig. 3 Variable importance and model performance of GHG concentrations and fluxes.

Mean decrease in accuracy (increase in mean squared error, %IncMSE) estimated from RF models and the model performance on the 15% testing sub-dataset. a,b, CH4. c,d, CO2. e,f, N2O. Dashed line represents the 1:1 line. R2 is the coefficient of determination of the linear regression and RMSE is the root mean square error.

Source data

Extended Data Fig. 4 Spatial variation of urban river GHG concentrations and fluxes across four income levels of temperate cities.

a, Geographical distributions of temperate cities classified by four income levels. b–g, Boxplots show urban river GHG concentrations (b–d) and fluxes (e–g) in urban rivers of temperate cities. In each plot in b–g, box spans the 25th and 75th percentiles. Solid line denotes the median and the whiskers represent 1.5 × the interquartile range. Statistical significance between groups was tested with the two-sided Wilcoxon rank-sum test, using lower-middle as the reference group. Number in parentheses indicates number of measurements. Basemap in a from GADM (https://gadm.org/).

Source data

Extended Data Fig. 5 The effects of ice and ice-melt corrections on the magnitude of GHG emissions from global urban rivers on a latitudinal basis.

a, Annual ice-free days of urban rivers. b–d, The effects of ice and ice-melt correction on CH4, CO2, and N2O emissions, respectively. Basemap in a from GADM (https://gadm.org/).

Source data

Extended Data Fig. 6 Monthly variations of global urban river GHG emissions.

a, CH4. b, CO2. c, N2O. d, GHG in CO2-equivalent. Color-coded columns show the magnitude of changes in monthly emissions after applying the ice and ice-melt corrections.

Source data

Extended Data Fig. 7 Major climatic and socioeconomic conditions of cities covered by the compiled urban river GHG dataset.

a, Mean annual temperature. b, Mean annual precipitation. c, Log10 population. d, Log10 GDP per capita. Number in each subplot indicates the percentage of global MUAs covered by a corresponding parameter range from the urban river GHG dataset.

Source data

Extended Data Fig. 8 Map showing national income level.

Countries were classified into four groups based on gross national income per capita in 2021: low income countries (< $1,085), lower-middle income countries ($1,086–$4,255), upper-middle income countries ($4,256–$13,205), and high income countries (> $13,205). Basemap from GADM (https://gadm.org/).

Source data

Extended Data Fig. 9 Model performance in separate seasons.

af, Comparisons between measured and predicted urban river GHG concentrations (a, c, e) and fluxes (b, d, f) values obtained from 15% testing sub-set of RF model in separate seasons. a,b, CH4. c,d, CO2. e,f, N2O. Dashed line represents the 1:1 line. R2 is the coefficient of determination of the linear regression and RMSE is the root mean square error.

Source data

Extended Data Fig. 10 Source of error for urban river GHG emission estimates.

a–c, Flux errors from the RF modeling, which were determined by fitting the model residuals to a normal distribution and calculate the error at one standard deviation (1δ). d, Error associated with river surface area estimate in this analysis, which was determined by comparing with those from the Global River Width from Landsat (GRWL) Database.

Source data

Supplementary information

Supplementary Information

Supplementary Tables 1–4.

Reporting Summary

Supplementary Data 1

Urban river GHG concentrations and fluxes and associated water physico-chemical properties.

Supplementary Code 1

Code for data processing and modelling.

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Xu, W., Wang, G., Liu, S. et al. Globally elevated greenhouse gas emissions from polluted urban rivers. Nat Sustain 7, 938–948 (2024). https://doi.org/10.1038/s41893-024-01358-y

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