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Source identification and prediction of nitrogen and phosphorus pollution of Lake Taihu by an ensemble machine learning technique

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Abstract

Effective control of lake eutrophication necessitates a full understanding of the complicated nitrogen and phosphorus pollution sources, for which mathematical modeling is commonly adopted. In contrast to the conventional knowledge-based models that usually perform poorly due to insufficient knowledge of pollutant geochemical cycling, we employed an ensemble machine learning (ML) model to identify the key nitrogen and phosphorus sources of lakes. Six ML models were developed based on 13 years of historical data of Lake Taihu’s water quality, environmental input, and meteorological conditions, among which the XGBoost model stood out as the best model for total nitrogen (TN) and total phosphorus (TP) prediction. The results suggest that the lake TN is mainly affected by the endogenous load and inflow river water quality, while the lake TP is predominantly from endogenous sources. The prediction of the lake TN and TP concentration changes in response to these key feature variations suggests that endogenous source control is a highly desirable option for lake eutrophication control. Finally, one-month-ahead prediction of lake TN and TP concentrations (R2 of 0.85 and 0.95, respectively) was achieved based on this model with sliding time window lengths of 9 and 6 months, respectively. Our work demonstrates the great potential of using ensemble ML models for lake pollution source tracking and prediction, which may provide valuable references for early warning and rational control of lake eutrophication.

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Abbreviations

DO:

Dissolved oxygen

COD:

Chemical oxygen demand

BOD:

Biochemical oxygen demand

SS:

Suspended solids

NH3-N:

Ammonia-nitrogen

TN:

Total nitrogen

TP:

Total phosphorus

CNA:

Concentrated nitric acid

SA:

Synthetic ammonia

CF:

Chemical fertilizers

CP:

Chemical pesticides

SD:

Synthetic detergents

NF:

Nitrogen fertilizer

PF:

Phosphate fertilizer

RH_M:

Relative humidity_Meteorology

AP_M:

Air pressure_Meteorology

T_M:

Temperature_Meteorology

E_M:

Evaporation_Meteorology

P_M:

Precipitation_Meteorology

WS_M:

Wind speed_Meteorology

LT:

Lake Taihu

JSIR:

Jiangsu inflow river

ZJIR:

Zhejiang inflow river

WD:

WWTP discharge

JSIP:

Output of Jiangsu industrial products

ZJIP:

Output of Zhejiang industrial products

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Acknowledgements

The authors thank the National Natural Science Foundation of China (Nos. 52192681, U21A20160, and 51821006) for supporting this work. We thank Professor Boqiang Qin from the Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences for providing monitoring data from the Lake Taihu Water Quality Monitoring Station (China).

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Author notes

  1. These authors contributed equally to this work.

Authors and Affiliations

  1. CAS Key Laboratory of Urban Pollutant Conversion, Department of Environmental Science and Engineering, University of Science and Technology of China, Hefei, 230026, China

    Yirong Hu, Cheng Yang & Wenwei Li

  2. School of Software Engineering, University of Science and Technology of China, Hefei, 230026, China

    Wenjie Du & Yang Wang

  3. USTC-CityU Joint Advanced Research Center, Suzhou Institute for Advanced Research of USTC, Suzhou, 215123, China

    Yirong Hu, Wenjie Du & Wenwei Li

  4. National and Local Joint Engineering Laboratory for Municipal Sewage Resource Utilization Technology, Suzhou University of Science and Technology, Suzhou, 215009, China

    Tianyin Huang & Xiaoyi Xu

Authors
  1. Yirong Hu
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  2. Wenjie Du
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  3. Cheng Yang
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  4. Yang Wang
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  5. Tianyin Huang
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  6. Xiaoyi Xu
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  7. Wenwei Li
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Corresponding authors

Correspondence to Yang Wang or Wenwei Li.

Additional information

Highlights

• A machine learning model was used to identify lake nutrient pollution sources.

• XGBoost model showed the best performance for lake water quality prediction.

• Model feature size was reduced by screening the key features with the MIC method.

• TN and TP concentrations of Lake Taihu are mainly affected by endogenous sources.

• Next-month lake TN and TP concentrations were predicted accurately.

Special Issue—Artificial Intelligence/Machine Learning on Environmental Science & Engineering (Responsible Editors: Yongsheng Chen, Xiaonan Wang, Joe F. Bozeman III & Shouliang Yi)

Data Accessibility Statement

By repository: The code applied in this study are openly available in GitHub at the website of github.com/huyirong-97/FESE_TN-TP-prediction-code. The data used in this study were obtained from other research institutions and are not allowed for disclosure. So, we don’t have the right to share it to the public database.

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Hu, Y., Du, W., Yang, C. et al. Source identification and prediction of nitrogen and phosphorus pollution of Lake Taihu by an ensemble machine learning technique. Front. Environ. Sci. Eng. 17, 55 (2023). https://doi.org/10.1007/s11783-023-1655-7

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  • DOI: https://doi.org/10.1007/s11783-023-1655-7

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