Tailor-made ammonia nitrogen risk management with machine learning models for aquatic environments in the Mainland of China

Zitong Liao; Yun Lu; Dongbin Wei; Ren Ding; Yinhu Wu; Huanan Gao; Anran Liao; Yingcai Tang; Hongwei Xu; Zhuo Chen; Hong-Ying Hu

doi:10.1016/j.jhazmat.2024.135726

Tailor-made ammonia nitrogen risk management with machine learning models for aquatic environments in the Mainland of China

J Hazard Mater. 2024 Nov 5:479:135726. doi: 10.1016/j.jhazmat.2024.135726. Epub 2024 Sep 3.

Authors

Zitong Liao¹, Yun Lu², Dongbin Wei³, Ren Ding¹, Yinhu Wu², Huanan Gao¹, Anran Liao¹, Yingcai Tang¹, Hongwei Xu¹, Zhuo Chen⁴, Hong-Ying Hu²

Affiliations

¹ Environmental Simulation and Pollution Control State Key Joint Laboratory, Key Laboratory of Microorganism Application and Risk Control (SMARC) of Ministry of Ecology and Environment, School of Environment, Tsinghua University, Beijing 100084, PR China.
² Environmental Simulation and Pollution Control State Key Joint Laboratory, Key Laboratory of Microorganism Application and Risk Control (SMARC) of Ministry of Ecology and Environment, School of Environment, Tsinghua University, Beijing 100084, PR China; Beijing Laboratory for Environmental Frontier Technologies, School of Environment, Tsinghua University, Beijing 100084, PR China.
³ Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China.
⁴ Environmental Simulation and Pollution Control State Key Joint Laboratory, Key Laboratory of Microorganism Application and Risk Control (SMARC) of Ministry of Ecology and Environment, School of Environment, Tsinghua University, Beijing 100084, PR China; Beijing Laboratory for Environmental Frontier Technologies, School of Environment, Tsinghua University, Beijing 100084, PR China. Electronic address: zhuochen@mail.tsinghua.edu.cn.

PMID: 39241361
DOI: 10.1016/j.jhazmat.2024.135726

Abstract

Efficient management of pollutant risks in water bodies is crucial for public health and aquatic ecosystem sustainability. However, the toxicities of pollutants, such as ammonia nitrogen (NH₃-N), are often affected by multiple water quality factors, including the pH and water temperature. Extensive spatial and temporal variability in these factors hinders tailor-made management of risk. This study used high-frequency monitoring data collected over 1 year to evaluate the long-term NH₃-N risk in China's aquatic ecosystems. High accuracy and interpretability were achieved by decomposing NH₃-N risk into the contributions of key influencing factors using random forest models and Shapley Additive Explanations. Two distinct types of NH₃-N risk hotspots were identified across 18 cities: 15 cities with high NH₃-N concentrations and 3 cities with low environmental carrying capacity due to high pH levels or elevated water temperatures. For the former, rapid NH₃-N abatement measures are necessary to bring NH₃-N concentrations back below the environmental capacity. For the latter, it is recommended that NH₃-N related industries are relocated to regions with high environmental capacities because fragile environments are not suitable for such industries. Importantly, this study investigated methods for attributing pollutant risks in the context of non-linear influencing factors, and the risk of NH₃-N was predicted to increase by 6.1 % by the end of 2100 in the context of increasing temperatures under the SSP 2-4.5 scenario. The methodology is also adaptable and suitable for integration into global ecosystem risk management efforts to balance development and aquatic ecological sustainability.

Keywords: Ammonia nitrogen; Aquatic ecosystems; City-level risk categorization; Risk management; Tailored management standards.