Treated wastewater and weak removal mechanisms enhance nitrate pollution in metropolitan rivers

Zhao, Guanglei; Sun, Taihu; Wang, Dongqi; Chen, Shu; Ding, Yan; Li, Yilan; Shi, Guitao; Sun, Hechen; Wu, Shengnan; Li, Yizhe; Wu, Chenyang; Li, Yufang; Yu, Zhongjie; Chen, Zhenlou

doi:10.1016/j.envres.2023.116182

Cited by 8 publications

(1 citation statement)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…River ecosystems link matter exchange processes among atmospheric, terrestrial and marine ecosystems, and rivers are important sources and sinks of nitrogen pollution. − Human activities such as fossil fuel consumption, vehicle exhaust emission, fertilizer application, and leguminous crop production have greatly accelerated nitrogen pollution and greenhouse gas (GHG), such as N 2 O, emission in rivers, resulting in a 10-fold increase in reactive nitrogen fluxes over the last century. − Excess nitrogen enters rivers, causing eutrophication, harmful algal blooms, hypoxia, and water quality deterioration. , Currently, regulating nitrogen pollution and N 2 O emissions in rivers remains an enormous challenge worldwide.…”

Section: Introductionmentioning

confidence: 99%

Unveiling Microbial Nitrogen Metabolism in Rivers using a Machine Learning Approach

Jia,

Hu,

Kang

et al. 2024

Environ. Sci. Technol.

View full text Add to dashboard Cite

Microbial nitrogen metabolism is a complicated and key process in mediating environmental pollution and greenhouse gas emissions in rivers. However, the interactive drivers of microbial nitrogen metabolism in rivers have not been identified. Here, we analyze the microbial nitrogen metabolism patterns in 105 rivers in China driven by 26 environmental and socioeconomic factors using an interpretable causal machine learning (ICML) framework. ICML better recognizes the complex relationships between factors and microbial nitrogen metabolism than traditional linear regression models. Furthermore, tipping points and concentration windows were proposed to precisely regulate microbial nitrogen metabolism. For example, concentrations of dissolved organic carbon (DOC) below tipping points of 6.2 and 4.2 mg/L easily reduce bacterial denitrification and nitrification, respectively. The concentration windows for NO3 –-N (15.9–18.0 mg/L) and DOC (9.1–10.8 mg/L) enabled the highest abundance of denitrifying bacteria on a national scale. The integration of ICML models and field data clarifies the important drivers of microbial nitrogen metabolism, supporting the precise regulation of nitrogen pollution and river ecological management.

show abstract