Unveiling microbial nitrogen metabolism in rivers using a machine learning approach

Jia, Yuying; Hu, Xiangang; Kang, Weilu; Dong, Xu

doi:10.1021/acs.est.3c09653

Cited by 111Wastewater Treatment and Nitrogen RemovalSoil and Water Nutrient DynamicsMicrobial Community Ecology and Physiology

Unveiling microbial nitrogen metabolism in rivers using a machine learning approach

Jia, Yuying, Hu, Xiangang, Kang, Weilu, Dong, Xu·2024·Environmental Science & Technology

DOI 10.1021/acs.est.3c09653

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Abstract

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 NO 3 – -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.

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Peng, M., Yang, Z., Liu, Z., Han, W., Wang, Q., Liu, F., Zhou, Y., Ma, H., Bai, J., & Cheng, H. (2024) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2024.177095 · Cited by 12 · see citing works
Carbon and water fluxes characteristics and differential regulation mechanisms in dryland ecosystems
Zhang, S., Duan, L., Hao, L., Bao, Y., Tong, X., Singh, V. P., & Liu, T. (2025) · Journal of Hydrology · doi:10.1016/j.jhydrol.2025.133735 · Cited by 11 · see citing works
Differentiated landscape pattern transformation strategies drive territorial space carbon neutrality: A path exploration based on mixed land use units
Fan, H., Jin, X., Zhu, J., Zhang, Z., Hu, C., Hu, H., Du, X., & Zhou, Y. (2025) · Sustainable Cities and Society · doi:10.1016/j.scs.2025.106865 · Cited by 11 · see citing works
Decoding soil health constraints in regional agroecosystems: Machine learning reveals microbial enzymatic thresholds and drivers
Xu, X., Zhang, K., Zhu, Z., Wei, L., Wang, S., Zhang, W., Li, L., Kuzyakov, Y. V., Chen, J., & Ge, T. (2025) · Soil and Tillage Research · doi:10.1016/j.still.2025.107000 · Cited by 11 · see citing works
A comprehensive OBD data analysis framework: Identification and factor analysis of high-emission heavy-duty vehicles
Cao, Z., Shi, K., Qin, H., Xu, Z., Zhao, X., Yin, J., Jia, Z., Zhang, Y., Liu, H., Zhang, Q., et al. (2025) · Environmental Pollution · doi:10.1016/j.envpol.2025.125751 · Cited by 11 · see citing works
Common issues of data science on the eco-environmental risks of emerging contaminants
Hu, X., Xu, D., & Wang, Z. (2025) · Environment International · doi:10.1016/j.envint.2025.109301 · Cited by 11 · see citing works
Microbial biomass stoichiometry and proportion of Fe organic complexes separately shape the heterogeneity of mixotrophic denitrification and net N2O sinks in iron-carbon amended ecological ditch
Jiang, B., Zhang, Y., Wang, Y., Liu, H., Zhang, Z., Yang, Y.-J., & Song, H. (2024) · Water Research · doi:10.1016/j.watres.2024.122945 · Cited by 10 · see citing works
Unraveling drivers of land use efficiency in rapidly urbanizing areas: A hybrid SBM-DDF and explainable machine learning framework
Zhu, J., Wenxin, L. I. U., Zheng, S., & Sun, Y. (2025) · Habitat International · doi:10.1016/j.habitatint.2025.103518 · Cited by 10 · see citing works
Predicting and interpreting nitrogen removal performance and functional microbial abundance of single-stage partial nitrification and anammox system using machine learning methods
Mu, X., Jia, F., Qiu, S., Li, Y., Mei, N., Zhao, X., Han, B., Han, X., Zhang, J., & Yao, H. (2025) · Bioresource Technology · doi:10.1016/j.biortech.2025.133119 · Cited by 10 · see citing works
Unraveling the overlooked dissolved methane and nitrous oxide concentration hotspots and their causes in higher carbon level environments
Li, W., Zhu, Z., Lu, L., Gao, X., Peng, S., Xie, Z., Li, S., Li, Y., & Liang, J. (2025) · Water Research · doi:10.1016/j.watres.2025.124096 · Cited by 9 · see citing works
Machine-Learning-Based Prediction of Algal Density Using Algal Volatile Organic Compounds for Bloom Early Warning
Guo, J., Yu, C., Qi, W., Qu, J., Duan, Y., & Liu, H. (2025) · Environmental Science & Technology · doi:10.1021/acs.est.5c04879 · Cited by 9 · see citing works
Artificial Intelligence in Surface Water Quality Research and Management: Recent Progress and Future Directions
Li, Y., Han, F., & Zheng, Y. (2025) · Ecosystem Health and Sustainability · doi:10.34133/ehs.0474 · Cited by 9 · see citing works
Mechanistic insights into sulfamethoxazole removal in activated sludge systems through machine learning and microcosm experiments
Chen, J., Li, L., Lv, X., Liu, T., Zhang, J., Luo, L., & Tian, Y. H. (2026) · Water Research · doi:10.1016/j.watres.2026.125331 · Cited by 9 · see citing works
Predicting photosynthetic bacteria-derived protein synthesis from wastewater using machine learning and causal inference
Hou, P., Liu, S., Hu, D., Zhang, J., Liang, J., Liu, H., Zhang, G., & Zhang, G. (2024) · Bioresource Technology · doi:10.1016/j.biortech.2024.131638 · Cited by 8 · see citing works
Linking fluorescence spectral to machine learning predicts the emissions fates of greenhouse gas during composting
Bai, B., Liu, H., Liang, A., Wang, L., & Wang, A. (2025) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2025.110430 · Cited by 8 · see citing works
Data-driven assessment of nitrogen and phosphorus buffering capacity across 460 Chinese watersheds: Spatial patterns, drivers, and future projections
Cao, Y., Yao, H., Zhang, C., Gu, C., Yang, L., Hao, R., Li, X. K., Wang, Z., Xiao, H., Wang, J., et al. (2026) · Water Research · doi:10.1016/j.watres.2026.125424 · Cited by 8 · see citing works
A two-step structural equation modeling and explainable machine learning framework for understanding university students’ adoption of generative AI: balancing intrinsic motivations and perceived risks
Zeng, D., Xu, X., Zhu, T., Li, Y., & Li, Q. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1743722 · Cited by 7 · see citing works
Developing Physiologically Compatible Electron Donors for Reductive Dechlorination by Dissimilatory Iron-Reducing Bacteria Using Machine Learning
Yu, Y., Li, J., Xing, D., Zhou, C., Meng, J., & Li, A. (2025) · ACS ES&T Engineering · doi:10.1021/acsestengg.4c00848 · Cited by 7 · see citing works
Pan-Arctic riverine carbon and nitrogen exports dominated by hydrologic factors
Zhao, Y., Zhang, Y., Kang, S., Liu, H., & Semiletov, I. (2025) · Environmental Research · doi:10.1016/j.envres.2025.122017 · Cited by 7 · see citing works
Interpretable machine learning unveils nonlinear drivers of global energy risk spillovers: A TVP-VAR approach
Zhang, D., Tang, P., Tang, C., & Lai, X. (2025) · Economic Modelling · doi:10.1016/j.econmod.2025.107178 · Cited by 7 · see citing works
Identifying Priority Nutrients for Achieving Water Quality Improvement and Climate Change Mitigation
Li, W., Liang, J., Xiong, W., Li, S., Zhu, Z., Gao, X., Lu, L., Peng, S., Li, Y., Zhang, C., et al. (2025) · Environmental Science & Technology · doi:10.1021/acs.est.5c05476 · Cited by 7 · see citing works
Novel soil health assessment framework for legume-based rotation farmland by interpretable machine learning with causal inference
Xu, X., Liu, Q., Liu, Y., Li, Y., Chen, Y., Lei, T., Kuzyakov, Y. V., Zhang, W., Chen, J., & Ge, T. (2025) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2025.111011 · Cited by 5 · see citing works
Machine Learning Integrated with a Causal Pathway Framework Unravels Differential Mechanisms of Biochar-Driven Soil Organic Carbon Dynamics under Cadmium Stress
Sun, X., Du, Z., Ding, J., 郑向群, Yao, Y., Wu, L., Chen, H., An, Y., & Luo, Y. (2026) · Environmental Science & Technology · doi:10.1021/acs.est.5c12567 · Cited by 5 · see citing works
Hydraulic and thermal performance trigger the deterministic assembly of water microbiomes: From biogeographical homogenization to machine learning model
Wu, G., Zhang, H., Huang, T., Song, Y., Liu, X., Liu, X., Wang, X., Pei, T., Xu, G., & Wang, Z. (2025) · Water Research · doi:10.1016/j.watres.2025.123626 · Cited by 5 · see citing works
Reconstructing missing NOx emissions in heavy-duty diesel vehicle OBD data: A machine learning approach
Cao, Z., Wang, Y., Zhao, X., Yin, J., Jia, Z., Zhan, Y., Liu, Y., Zhang, Q., & Mao, H. (2025) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2025.138619 · Cited by 4 · see citing works
Unveiling modified montmorillonite enhancing the remediation ability of biochar for phthalate ester and Cd co-contaminated soil using an explainable machine learning approach
Li, W., Chen, X., Ling, W., Qin, C., & Gao, Y. (2025) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2025.140810 · Cited by 4 · see citing works
Global mapping of pharmaceutical ecological risk in rivers using machine learning: drivers, hotspots, and compounded water stress
Chen, J., Zhao, L., Zhu, L., Chen, S., Gao, M., Yu, G., & Cai, Z. (2025) · Water Research · doi:10.1016/j.watres.2025.125184 · Cited by 4 · see citing works
Revealing the key factors of synergistic transport of microplastics and heavy metals in groundwater: Batch experiments, density functional theory calculations, molecular dynamics simulations, and machine learning modeling
Yu, F., Ji, Y., Han, Z., Lyu, H., Lyu, H., Dong, T., & Li, Z. (2026) · Chemical Engineering Journal · doi:10.1016/j.cej.2026.175301 · Cited by 4 · see citing works
Customizing interpretable machine learning strategy to unravel causal mechanisms in organic micropollutant rejection by polyamide membranes
Wang, H., Ma, B., Li, Y., Wu, Z., Dai, R., & Wang, Z. (2025) · Journal of Membrane Science · doi:10.1016/j.memsci.2025.124805 · Cited by 3 · see citing works
Engineering optimization of food waste treatment via anaerobic digestion: Building causal machine learning networks for comprehensive analysis
Wang, F., Zhang, Y., Peng, J., Fu, Y., Wan, H., He, J., Su, J., Lu, Y., & Li, Y. (2026) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2026.147564 · Cited by 3 · see citing works
Warming Modulates Microplastic Impacts on Coastal Nitrogen Cycling by Synergistically Amplifying Sediment Hypoxia and Restructuring the Denitrifying Microbiome
Chen, C., Zhang, Y. S., Yin, G., Sun, D., Li, Y., Hou, L., & Liu, M. (2026) · Environmental Science & Technology · doi:10.1021/acs.est.5c08930 · Cited by 3 · see citing works
Unveiling Hydrogen Fluoride Emission Mechanisms in Municipal Solid Waste Incineration Using a Machine Learning Approach
Feng, X., Liu, L., Li, J., Ye, M., Mašek, O., Gouda, S. G., Ali, I. M. E., Chang, K., Wang, X., & Huang, Q. (2026) · Environmental Science & Technology · doi:10.1021/acs.est.6c00686 · Cited by 3 · see citing works
Where Machine Learning Fails in Predicting Emerging Contaminant Adsorption: A Decision-Oriented Framework for Model Credibility and Transferability
Fan, Y., Li, J., Ren, Y., Liu, C., & Zhang, W. (2026) · Environmental Science & Technology · doi:10.1021/acs.est.6c00882 · Cited by 3 · see citing works
Bacterial insights into river health: Predicting water quality indicators and micropollutants with explainable machine learning
Wang, X., Wang, C., Bai, Y., Mao, J., Meng, F., Qiu, C., & Wang, S. (2025) · Process Safety and Environmental Protection · doi:10.1016/j.psep.2025.107763 · Cited by 3 · see citing works
Interpretable ensemble learning for modeling phosphorus dynamics and mitigating eutrophication in riverine ecosystems
Kheimi, M., & Zounemat‐Kermani, M. (2025) · Ecohydrology & Hydrobiology · doi:10.1016/j.ecohyd.2025.100697 · Cited by 2 · see citing works
The microbial community metabolic regime adapts to hydraulic disturbance in river–lake systems with high–frequency regulation
Ding, J., Yang, W., Li, X., Liu, X., Zhao, J., Sun, T., & Liu, H. (2025) · Environmental Microbiome · doi:10.1186/s40793-025-00821-3 · Cited by 2 · see citing works
Interpretable machine learning analysis of key factors influencing PFAS adsorption−desorption in sediment−water systems and risk priority ranking in a large shallow lake
Zhang, Y., Wang, H., Wang, Z., Qian, X., Qu, X., & Li, H. (2025) · Journal of environmental chemical engineering · doi:10.1016/j.jece.2025.120931 · Cited by 2 · see citing works
Integrating machine learning and prioritization to decipher the partitioning and risk of tire-derived chemicals in highly populated coastal regions
Wu, N.-N., Liu, Y., Liu, S., Chen, J.-X., Zhou, Q.-G., Xu, X., Yang, Q.-S., Ling, J., & Zhao, J. (2026) · Water Research · doi:10.1016/j.watres.2026.125343 · Cited by 2 · see citing works
Emission and control of nitrous oxide from biological denitrification treatment of wastewater
Zhang, Z., Wang, Z., Wang, K., Chen, J., Wu, L., & Li, X. (2026) · Environmental Research · doi:10.1016/j.envres.2026.124097 · Cited by 2 · see citing works
Global Coastal Hotspots and the Cascading Effect of Microplastic Burden in Marine Fish
Liu, C., Mu, L., Hu, X., Liu, M., Wang, R., Qu, Q., Wang, R., Deng, P., & Wang, Z. (2026) · Environmental Science & Technology · doi:10.1021/acs.est.5c17619 · Cited by 2 · see citing works
Machine learning models unravel contrasting enrichment of uranium and arsenic in groundwater from the Songnen Basin, China
Sun, H., Gao, Z., Cao, W., Su, X., & Guo, H. (2026) · Journal of Hydrology · doi:10.1016/j.jhydrol.2026.135582 · Cited by 2 · see citing works
Machine learning-driven nitrogen management in mariculture wastewater: A critical review
Yang, T., Wang, Q., Li, Y., Zeng, Y., Liu, R., Zhou, G., 李羿盈, Su, Z., & 李锋民 (2026) · Environmental Research · doi:10.1016/j.envres.2026.124723 · Cited by 2 · see citing works
Targeted prevention strategy: Exploring the interaction effect of environmental and social factors on infectious diseases
Wang, S., Zhou, X., Zhang, T., Li, Z., Cao, J., & Hong, J. (2024) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2024.178218 · Cited by 2 · see citing works
Evapotranspiration components and its driving mechanism in a sandy shrubland
Bao, Y., Liu, T., Duan, L., Zhang, W., Liu, H., & Singh, V. P. (2025) · Ecological Indicators · doi:10.1016/j.ecolind.2025.114322 · Cited by 1 · see citing works
Discrepancies in microbial nitrogen cycling among diverse karst hydrological systems: enhanced nitrate reduction potential in karst conduits
Zhu, D., Li, Y., Niu, L., Zhou, C., Fan, L., Lu, H., & Lin, L. (2026) · CATENA · doi:10.1016/j.catena.2026.109795 · Cited by 1 · see citing works
Stage-wise environmental recovery patterns and cross-domain interactions of planktonic bacterial communities in Zhanjiang Bay following typhoon disturbance
Huang, H., Wu, J., Zeng, F., Hu, Z., Putri, S. C. D., & Zhang, Y. (2026) · Environmental Research · doi:10.1016/j.envres.2026.123872 · Cited by 1 · see citing works
Reducing the Discrepancy in Quantifying the Temperature Dependence of Global Wetland Methane Emission
Hu, H., Xue, K., Sun, Y., Zhu, Q., Carlson, H. K., Hu, R., Tan, R. X., Qian, C., Huang, W., Zhou, J. Z., et al. (2026) · Global Change Biology · doi:10.1111/gcb.70748 · Cited by 1 · see citing works
“…Additive exPlanations (SHAP) analysis with permutation-based feature-importance assessment (Huang et al. 2024) and partial dependence modeling (Jia et al. 2024) to determine the critical predictors controlling the adherence of wetland CH 4 emission-temperature relationships to the Arrhenius…”
Influential
Identification of critical soil moisture thresholds and the water–energy regulation mechanisms in semi-arid sand dune–meadow ecosystems
Zhang, S., Duan, L., Bao, Y., Hao, L., Tong, X., Singh, V. P., & Liu, T. (2026) · Agricultural Water Management · doi:10.1016/j.agwat.2026.110235 · Cited by 1 · see citing works
Tracking vegetation dynamics in the Upper Yellow River Basin: An analysis using kNDVI and multi-model trajectory detection
Su, W., Jin, X., Jin, Y., Fu, D., Li, X., Li, Z., & Yang, J. (2026) · Ecological Informatics · doi:10.1016/j.ecoinf.2026.103708 · Cited by 1 · see citing works
Disclosing microbial nitrogen metabolism in groundwater via automated machine learning-based analysis: A fluorescence information-based approach
Wang, L., Yang, Y., Qian, C., Wang, Z., Lin, L., & Li, Y. (2026) · Journal of Environmental Management · doi:10.1016/j.jenvman.2026.129638 · Cited by 1 · see citing works
Interpretable causal machine learning elucidates performance regulation via atomic Fe‑S synergistic pathways in Sulfidated zero‑valent Iron
Yan, Z., Wang, R., Li, Z., Huang, T., Zu, Y., Zeng, X., 曹娣, Wu, B., Huang, J., Ho, S., et al. (2026) · Water Research · doi:10.1016/j.watres.2026.126070 · Cited by 1 · see citing works
Unveiling the role of soil microorganisms in indicating paddy soil health via metagenomics combined with machine learning
Zheng, Y.-L., Guo, Y.-S., Ren, X.-Y., Wang, Y.-F., Cui, H., Zhang, L., Ding, L., & Zhu, Y. (2026) · ISME Communications · doi:10.1093/ismeco/ycag133 · Cited by 1 · see citing works
“Hyperparameters were optimized using the GridSearchCV function [ 37], and model performance was evaluated based on the coefficient of determination ( R 2 ), root mean square error ( RMSE ), and mean absolute error ( MAE ) [ 38].”
Environmental determinants and spatial patterns of soil antibiotic resistance gene diversity across Europe using interpretable geographically weighted machine learning
Zhang, S., Pan, X., Li, Y., Liu, X., Xiao, H., Fan, Y., Qian, X., & Zhang, C. (2026) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2026.148579 · Cited by 1 · see citing works
Closing Knowledge Gaps on Global Budget of Nitrogen Oxides ( NO x ) by Quantifying Indirect Emissions From Anthropogenic Nitrogen
Wang, Y., Yin, Y., Gong, C., Jain, N., Alfaro, M. A., Bernoux, M., Drewer, J., Wang, C., Sanz-Cobeña, A., Nyawira, S. S., et al. (2026) · Global Change Biology · doi:10.1111/gcb.71048 · Cited by 1 · see citing works
“We set SHAP values to zero when the threshold for a factor's influence on R NO ∕ N 2 O is reached, following Jia et al. (2024).”
Spatial patterns of soil heavy metal(loid)s outside the boundaries of China’s iron and steel enterprises during 2015–2025: Environmental drivers, mechanistic insights, and policy implications
Xu, X., Zhang, Z., Ma, Z., Wang, P., Zhang, X., & Bo, X. (2026) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2026.143639 · Cited by 1 · see citing works
Picoplankton Groups and Their Responses to Environmental Factors in Small Cascade Hydropower Stations
Li, P., Luo, Z., Zhu, X., Dang, Z., Zhang, D., & Sui, X. (2025) · Water · doi:10.3390/w17060903 · Cited by 1 · see citing works
“N concentrations [70].”
Sedimentary recorded nitrogen-transforming microbes reflect climate fluctuations in a glacial lake on the Qinghai-Tibetan Plateau over a millennium timescale
Wang, J., Zhang, H., Huo, S., Liu, Y., & Zhang, J. (2025) · Journal of Great Lakes Research · doi:10.1016/j.jglr.2025.102579
Exploring Sediment-Water Partitioning Behavior and Key Influencing Factors of Per- and Polyfluoroalkyl Substances (Pfas) in a Large Shallow Lake Using Interpretable Machine LearningPreprint
Zhang, Y., Wang, H., Wang, Z., Qian, X., Qu, X., & Li, H. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5400305
Enhancing urban river self-purification through riverbed substrates configuration: A nature-based solution for nutrient removal and restoration planning
Xin, Y., Liu, L., Chen, S., Zhao, Q.-B., & Zheng, Y.-M. (2026) · Journal of Environmental Sciences · doi:10.1016/j.jes.2026.01.038
Linking Microbial Communities and Nitrogen Cycling in Agricultural Groundwater of a Semiarid Region
Hu, C., Gao, X., Li, L., Xia, L., Xu, Q., Qin, M., & Wang, J. (2026) · ACS ES&T Water · doi:10.1021/acsestwater.5c01282
Investigating River Bacterial Community Structure and Nitrogen Metabolism at Continental Scale: An Interpretable Machine Learning Approach with Causal InferencePreprint
Kang, L., Wang, P., Hu, M., Shu, W., Ding, M., Nie, M., Huang, G., & Zhang, H. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6189959
A review of microplastics stress on nitrogen conversion and nitrous oxide emissions from biological wastewater treatment: Efficiency, mechanism and prospects
He, Y., Chen, J., Liang, B., Wu, L., Liu, S., Chen, S., Lu, Y., Huang, Z., & Su, C. (2026) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2026.181606
Graph-based molecular embedding and machine learning for accurate prediction of CO2 absorption in amine solutions
Wang, L., Aierken, A., Dai, Q., Zhao, H., Wu, J., Liu, X., Wang, Y., Zhang, T., Li, Q., & Yu, G. (2026) · Separation and Purification Technology · doi:10.1016/j.seppur.2026.137404
Tracing the impact of metal mixtures on primary membranous nephropathy: An integrative exposome-machine learning approach
Zhou, A., Zhang, L., Chen, H., Peng, Y., Meng, R., Qin, X., Liu, S., Zou, Y., & Wang, M. (2026) · Environmental Chemistry and Ecotoxicology · doi:10.1016/j.enceco.2026.02.023
Temperature-driven regime shifts in riverine bacterial communities: Machine learning identifies key thresholds and seasonal management implications
Liu, R., Wang, P., Shu, W., Ding, M., Nie, M., Huang, G., Zhang, H., Chen, Q., & Yang, W. (2026) · Journal of Environmental Sciences · doi:10.1016/j.jes.2026.02.049
Serum Element-Driven Gut Microbial Disturbances and Their Predictive Implications for Gestational Dyslipidemia: A Prospective Birth Cohort Study
Zhou, A., Jin, B., Lu, J., Chen, J., Zhang, Y., Dong, C., Guan, Q., Zou, Y., Peng, Y., & Xia, Y. (2026) · Environmental Science & Technology · doi:10.1021/acs.est.5c14364
Development of an interpretable data-driven model for direct greenhouse gas emissions in the lab-scale papermaking wastewater biochemical treatment system
Niu, G., Liu, Y., Mukhamatjonovich, N. A., Fan, X., Liu, R., Qin, J., Shen, W., & Huang, M. (2026) · Chemical Engineering Science · doi:10.1016/j.ces.2026.123727
Integrating target, nontarget analysis with machine learning to illuminate PFAS characteristics and health risks in Chinese cosmetics
Zhao, L., Chen, J., Sun, Y., Li, Y., Li, H., & Wu, Q. (2026) · Environmental Pollution · doi:10.1016/j.envpol.2026.127933
Microbial community steady state driven by metabolic integration under combined stress: A case study of moving bed biofilm reactor
Chen, Z., Li, Q., Jin, L., Fang, X., Ren, H., & Huang, H. (2026) · Journal of Environmental Sciences · doi:10.1016/j.jes.2026.03.019
A data-mechanism integrated framework for elucidating the migration and attenuation of light non-aqueous phase liquids in the vadose zone
Xue, Z., Deng, Y., Zuo, R., Lu, H., Wu, Z., Liu, J., Wang, H., Zhang, Y., Liang, X., & Wang, J. (2026) · Water Research · doi:10.1016/j.watres.2026.125755
Turning points of subtropical forest carbon sinks in southern China: spatiotemporal dynamics and management insights
Shao, Z., Wu, Z., Li, Y., Zeng, H., Zheng, G., Liu, G., Gao, Y., & Gong, J. (2026) · Carbon Balance and Management · doi:10.1186/s13021-026-00530-5
Heterogeneous impacts of modern Yellow River dynamics on groundwater arsenic distribution in China’s Hetao Basin
Wu, N., Yin, S., Yang, L., Guo, Z., & Wei, B. (2026) · Journal of Environmental Sciences · doi:10.1016/j.jes.2026.03.070
Response of nitrogen cycling to hydrological conditions and nitrogen pollution sources in the Weihe River Basin
Zhang, Y., Cheng, W., Jia, Z., Wang, M. J., Ren, J., & Singhal, N. (2026) · Journal of environmental chemical engineering · doi:10.1016/j.jece.2026.122698
Headwater streams influence downstream sedimentary nitrogen cycling via microbial community shifts on the eastern Qinghai-Tibetan Plateau
Liu, X., Zhong, B., Naying, L., Wu, W., Xiaofeng, W., Mei, X., Abdul, R., Shaoliang, Y., & He, Y. (2026) · Journal of Environmental Sciences · doi:10.1016/j.jes.2026.04.044
Identify emission-abnormal heavy-duty diesel vehicles via on-board diagnostic urea analysis
Cao, Z., Guo, L., Yu, Z., Zhao, X., Yin, J., Jia, Z., Wang, J., Tang, M., Zhang, Q., & Mao, H. (2026) · Journal of Environmental Sciences · doi:10.1016/j.jes.2026.05.005
Initial Stormwater Runoff Drives Co-Variation of Pollutants and Microbial Communities at the Sediment–Water Interface in Reclaimed Water-Receiving Rivers
Xue, C., Liang, M., Tan, X., Zhao, Y., Ren, Y., Liu, X., Zhao, F., & Li, H. (2026) · Applied Sciences · doi:10.3390/app16115442
“Microbial communities and their metabolic activities predominantly drive riverine N-cycling, regulating transformation rates across multiple pathways and influencing water self-purification capacity [46].”
Microeukaryotes are more vulnerable than picoeukaryotes to reservoir salinization: Evidence from multi-year high-frequency monitoring
Shi, W., Mo, Y., Zhang, H., Jeppesen, E., Yan, X., Chen, H., Zhang, H., & Yang, J. R. (2026) · Environmental Research · doi:10.1016/j.envres.2026.125098
Continuing Decrease in Nitrogen Fixation in the North Atlantic Ocean Associated with Dissolved Inorganic Carbon
Li, J., Hu, X., Xu, D., & Liu, H. (2026) · ACS ES&T Water · doi:10.1021/acsestwater.6c00403
From chaos to order: cross-kingdom coordination buffers microbial communities against typhoon impacts
Huang, H., Wu, J., Hu, Z., Wu, H., & Zhang, Y. S. (2026) · BMC Microbiology · doi:10.1186/s12866-026-05387-9
“These models were used to capture key ecological associations from high-dimensional bacterial and environmental datasets to predict phytoplankton diversity dynamics [30].”
Microplastic Pollution Is Associated with Fragmentation and Environmental Sensitivity of Marine Planktonic Microbial Communities
Ma, R., Guo, G., Liu, C., Deng, P., Xu, D., Mu, L., Qu, Q., & Hu, X. (2026) · Environmental Science & Technology · doi:10.1021/acs.est.6c06158
Augmented causal ensemble learning decodes and tailors waste-derived biochar's electron accepting/donating capacity for remediating redox‑sensitive contaminants
Jiang, H., Tang, L., Zhao, F., Alessi, D. S., Chen, H., & Zhou, B. (2026) · Journal of Analytical and Applied Pyrolysis · doi:10.1016/j.jaap.2026.108017
Integrating network analysis and machine learning to assess urban ecological resilience for zoning and governance
Chen, R., Wang, P., & Liu, X. (2026) · Journal of Geographical Sciences · doi:10.1007/s11442-026-2511-z
“Furthermore, to quantify the nonlinear response curves and identify specific critical thresholds of these driving factors, we incorporate the Generalized Additive Model into the analytical framework (Jia et al. , 2024; Fan et al. , 2025).”

Investigating River Bacterial Community Structure and Nitrogen Metabolism at Continental Scale: An Interpretable Machine Learning Approach with Causal InferencePreprint
Kang, L., Wang, P., Hu, M., Shu, W., Ding, M., Nie, M., Huang, G., & Zhang, H. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6189959
The structure and nitrogen metabolism potential of river microbial communities based on water conservancy project construction
Song, W., Wei, X., & Tian, M. (2025) · Journal of Water Process Engineering · doi:10.1016/j.jwpe.2025.109048 · Cited by 1 · see citing works
Systematic tracking of nitrogen sources in complex river catchments: Machine learning approach based on microbial metagenomics
Zhu, Z., Ding, J., Du, R., Zhang, Z., Guo, J., Li, X., Jiang, L., Chen, G., Bu, Q., Tang, N., et al. (2024) · Water Research · doi:10.1016/j.watres.2024.121255 · Cited by 45 · see citing works
Response of nitrogen cycling to hydrological conditions and nitrogen pollution sources in the Weihe River Basin
Zhang, Y., Cheng, W., Jia, Z., Wang, M. J., Ren, J., & Singhal, N. (2026) · Journal of environmental chemical engineering · doi:10.1016/j.jece.2026.122698
Metagenomic insights into comparative study of nitrogen metabolic potential and microbial community between primitive and urban river sediments
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Authors: Jia, Y., Hu, X., Kang, W., & Dong, X.
Affiliations: Nankai University (CN)
Corresponding author: Hu, Xiangang
ORCID: Jia, Yuying
Hu, Xiangang
Kang, Weilu
Dong, Xu
Venue: Environmental Science & Technology
Publisher: American Chemical Society (ACS)
Published: 2024-04-03
Volume: 58
Issue: 15
Pages: 6605-6615
ISSN: 0013-936X (ISSN-L)
1520-5851
DOI: 10.1021/acs.est.3c09653
OpenAlex: W4393902196
Semantic Scholar: 7deafd94de5f8a0025557b0a29d82a9e851f1c6c
Cited by: 111
References: 66
Citations by year: 2026: 60 · 2025: 39 · 2024: 12
FWCI: 11.04
Type: journal article
Language: en
Open access: closed
Licence: doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
doi.org/10.15223/policy-045 (stm-asf)
Locations: Environmental Science & Technology (publishedVersion)
PubMed (publishedVersion)
Funders: National Natural Science Foundation of China (U22A20615)
Topics: Physical Sciences › Environmental Science › Pollution › Wastewater Treatment and Nitrogen Removal
Physical Sciences › Environmental Science › Environmental Chemistry › Soil and Water Nutrient Dynamics
Physical Sciences › Environmental Science › Ecology › Microbial Community Ecology and Physiology
Keywords: microbial nitrogen metabolism, rivers, bacterial denitrification, nitrification, dissolved organic carbon, nitrate, tipping points, nitrogen pollution, socio-economic drivers
MeSH terms: Bacteria, China, Nitrogen, Rivers, Denitrification, Nitrification

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Unveiling microbial nitrogen metabolism in rivers using a machine learning approach

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