Mapping global hotspots and trends of water quality (1992–2010): a data driven approach
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 est...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2022-01-01
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| Series: | Environmental Research Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1748-9326/ac9cf6 |
| _version_ | 1797747215237644288 |
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| author | Sebastien Desbureaux Frederic Mortier Esha Zaveri Michelle T H van Vliet Jason Russ Aude Sophie Rodella Richard Damania |
| author_facet | Sebastien Desbureaux Frederic Mortier Esha Zaveri Michelle T H van Vliet Jason Russ Aude Sophie Rodella Richard Damania |
| author_sort | Sebastien Desbureaux |
| collection | DOAJ |
| description | 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%). |
| first_indexed | 2024-03-12T15:48:36Z |
| format | Article |
| id | doaj.art-14b58328a42f49869f5c71615b79617c |
| institution | Directory Open Access Journal |
| issn | 1748-9326 |
| language | English |
| last_indexed | 2024-03-12T15:48:36Z |
| publishDate | 2022-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Environmental Research Letters |
| spelling | doaj.art-14b58328a42f49869f5c71615b79617c2023-08-09T15:18:57ZengIOP PublishingEnvironmental Research Letters1748-93262022-01-01171111404810.1088/1748-9326/ac9cf6Mapping global hotspots and trends of water quality (1992–2010): a data driven approachSebastien Desbureaux0https://orcid.org/0000-0001-5489-4917Frederic Mortier1https://orcid.org/0000-0001-5473-709XEsha Zaveri2Michelle T H van Vliet3https://orcid.org/0000-0002-2597-8422Jason Russ4Aude Sophie Rodella5Richard Damania6Center for Environmental Economics—Montpellier, University Montpellier, CNRS, INRA, SupAgro , Montpellier, FranceEnvironmental Justice Program—Georgetown, Georgetown University , Washington DC, United States of America; Forêts et Sociétés, Univ. Montpellier, CIRAD , Montpellier, FranceThe World Bank , Washington DC, United States of AmericaDepartment of Physical Geography, Utrecht University , Utrecht, The NetherlandsThe World Bank , Washington DC, United States of AmericaThe World Bank , Washington DC, United States of AmericaThe World Bank , Washington DC, United States of AmericaClean 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%).https://doi.org/10.1088/1748-9326/ac9cf6water qualitysustainable development goalsrandom forestdata-driven modelling |
| spellingShingle | Sebastien Desbureaux Frederic Mortier Esha Zaveri Michelle T H van Vliet Jason Russ Aude Sophie Rodella Richard Damania Mapping global hotspots and trends of water quality (1992–2010): a data driven approach Environmental Research Letters water quality sustainable development goals random forest data-driven modelling |
| title | Mapping global hotspots and trends of water quality (1992–2010): a data driven approach |
| title_full | Mapping global hotspots and trends of water quality (1992–2010): a data driven approach |
| title_fullStr | Mapping global hotspots and trends of water quality (1992–2010): a data driven approach |
| title_full_unstemmed | Mapping global hotspots and trends of water quality (1992–2010): a data driven approach |
| title_short | Mapping global hotspots and trends of water quality (1992–2010): a data driven approach |
| title_sort | mapping global hotspots and trends of water quality 1992 2010 a data driven approach |
| topic | water quality sustainable development goals random forest data-driven modelling |
| url | https://doi.org/10.1088/1748-9326/ac9cf6 |
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