TY - JOUR
T1 - Mapping global hotspots and trends of water quality (1992–2010): a data driven approach
AU - Desbureaux, Sebastien
AU - Mortier, Frederic
AU - Zaveri, Esha
AU - Vliet, Michelle T H van
AU - Russ, Jason
AU - Rodella, Aude Sophie
AU - Damania, Richard
N1 - Funding Information:
For helpful comments and support, we are grateful to the seminar participants at CEE-M, IRD (Espace-Dev), Environmental Justice Programme—Georgetown, the editor and two excellent anonymous reviewers. This paper was part of the ‘Quality Unknown’ project funded by the World Bank. MvV was financially supported by a VIDI Grant (Project No. VI.Vidi.193.019) of the Netherlands Scientific Organization (NWO). This research was undertaken as part of the Quality Unknown: The Invisible Water Crisis project within the World Bank’s Water Global Practice. MvV was financially supported by a VIDI Grant (Project No. VI.Vidi.193.019) of the Netherlands Scientific Organization (NWO).The authors are very grateful for comments from seminar participants at Eco-Publique, CEE-M, Espace-Dev and Georgetown EJP. The findings, interpretations, and conclusions are entirely those of the authors.
Publisher Copyright:
© 2022 The Author(s). Published by IOP Publishing Ltd.
PY - 2022/11/11
Y1 - 2022/11/11
N2 - Clean water is key for sustainable development. However, large gaps in monitoring data limit our understanding of global hotspots of poor water quality and their evolution over time. We demonstrate the value added of a data-driven approach (here, random forest) to provide accurate high-frequency estimates of surface water quality worldwide over the period 1992-2010. We assess water quality for six indicators (temperature, dissolved oxygen, pH, salinity, nitrate-nitrite, phosphorus) relevant for the sustainable development goals. The performance of our modeling approach compares well to, or exceeds, the performance of recently published process-based models. The model’s outputs indicate that poor water quality is a global problem that impacts low-, middle- and high-income countries but with different pollutants. When countries become richer, water pollution does not disappear but evolves. Water quality exhibited a signif icant change between 1992 and 2010 with a higher percentage of grid cells where water quality shows a statistically significant deterioration (30%) compared to where water quality improved (22%).
AB - Clean water is key for sustainable development. However, large gaps in monitoring data limit our understanding of global hotspots of poor water quality and their evolution over time. We demonstrate the value added of a data-driven approach (here, random forest) to provide accurate high-frequency estimates of surface water quality worldwide over the period 1992-2010. We assess water quality for six indicators (temperature, dissolved oxygen, pH, salinity, nitrate-nitrite, phosphorus) relevant for the sustainable development goals. The performance of our modeling approach compares well to, or exceeds, the performance of recently published process-based models. The model’s outputs indicate that poor water quality is a global problem that impacts low-, middle- and high-income countries but with different pollutants. When countries become richer, water pollution does not disappear but evolves. Water quality exhibited a signif icant change between 1992 and 2010 with a higher percentage of grid cells where water quality shows a statistically significant deterioration (30%) compared to where water quality improved (22%).
KW - Data-driven modelling
KW - Random forest
KW - Sustainable development goals
KW - Water quality
UR - http://www.scopus.com/inward/record.url?scp=85141926301&partnerID=8YFLogxK
U2 - 10.1088/1748-9326/ac9cf6
DO - 10.1088/1748-9326/ac9cf6
M3 - Article
SN - 1748-9326
VL - 17
SP - 1
EP - 9
JO - Environmental Research Letters
JF - Environmental Research Letters
IS - 11
M1 - 114048
ER -