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Updated: Oct 21, 2025

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds
Published on: September 26, 2017
Water environmental pressure assessment in agricultural systems in Central Asia based on an Integrated Excess
Wei Feng1, Hongwei Lu2, Tianci Yao1
1Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China.
Abstract:
Agricultural runoff is the main source of water pollution in Central Asia. Excessive nitrogen (N) inputs from overuse of chemical fertilizers are threatening regional water resources. However, the scarcity of quantitative data and simplified empirical models limit the reliability of grey water footprint (GWF), particularly in undeveloped regions. In this study, we developed an Integrated Excess Nitrogen Load Model (IENLM) to calculate excess N load and evaluate its potential water environmental pressure in Central Asia. The model optimized the biological N fixation and atmospheric N deposition modules by involving more environmental variables and human activities. Results showed that N fertilizer application contributed over 60% to total N input and was mainly responsible for 42.9% increase of total GWF from 101.5 to 145.0 billion m3 during 1992 - 2018. Water pollution level (WPL) increased from 0.55 in 1992 to 2.41 in 2018 and the pollution assimilation capacity of water systems has been fully consumed just by N load from agriculture since 2005. GWF intensity and grey water pollution - efficiency types in all Central Asian countries have improved in recent years except for Turkmenistan. N fertilizer application and agricultural economy development were the main driving factors induced N pollution. Results were validated by riverine nitrate concentrations and the estimates from prior studies. In future, combining the N fertilizer reduction with other farm management practices were projected to effectively improve the WPL. The modeling framework is favorable for N pollution research in data-scarce regions and provides a scientific basis for decision-making for agriculture and water resource managements.
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