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Nitrate concentrations predominantly driven by human, climate, and soil properties in US rivers
Kayalvizhi Sadayappan1, Devon Kerins1, Chaopeng Shen1
1Department of Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA, USA.
Water Research
|November 2, 2022
Summary
Nitrate pollution is widespread, driven by human activities, climate, and soil. Machine learning models predict concentrations, identifying key drivers like nitrogen rates, urban areas, precipitation, temperature, and sand content.
Area of Science:
- Environmental Science
- Hydrology
- Water Quality
Background:
- Nitrate is a pervasive pollutant impacting water bodies globally.
- Understanding the primary drivers of nitrate dynamics across diverse environments remains a challenge.
Purpose of the Study:
- To develop predictive models for long-term mean nitrate concentrations in US rivers.
- To identify and quantify the key drivers influencing spatial variations in riverine nitrate.
Main Methods:
- Collected nitrate data from 2061 rivers across the contiguous United States (CONUS).
- Utilized machine learning models incorporating 32 watershed characteristic indexes.
- Validated model performance for predicting nitrate in chemically-ungauged locations.
Main Results:
- Machine learning models accurately predict nitrate concentrations (~70% accuracy).
- Five key drivers explain ~70% of spatial nitrate variations: nitrogen application rates (Nrate), urban area percentage (Aurban%), mean annual precipitation, mean annual temperature, and sand percentage (Sand%).
- Nitrate dynamics are influenced by a combination of human inputs, climate factors, and soil properties.
Conclusions:
- Human activities (Nrate, Aurban%) and environmental factors (climate, soil) interact to determine riverine nitrate concentrations.
- Nitrate levels may increase with population growth and climate change, even without direct human input.
- These findings provide crucial insights for effective water resource management under changing environmental conditions.
Keywords:
AgricultureBiogeochemical cyclesClimate changeLand useMachine learningNutrient contaminationUrbanizationWater QualityMore Related Videos
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