IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i10p8325-d1151409.html
   My bibliography  Save this article

Role of Landscape and Land-Use Transformation on Nonpoint Source Pollution and Runoff Distribution in the Dongsheng Basin, China

Author

Listed:
  • Nametso Matomela

    (School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China
    Beijing Key Laboratory of Resource-Oriented Treatment of Industrial Pollutants, Beijing 100083, China)

  • Tianxin Li

    (School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China
    Beijing Key Laboratory of Resource-Oriented Treatment of Industrial Pollutants, Beijing 100083, China)

  • Peng Zhang

    (Environmental Protection Key Laboratory of Quality Control in Environmental Monitoring, China National Environmental Monitoring Centre, Beijing 100012, China)

  • Harrison Odion Ikhumhen

    (College of Environment and Ecology, Xiamen University, Xiamen 361102, China)

  • Namir Domingos Raimundo Lopes

    (School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China
    Beijing Key Laboratory of Resource-Oriented Treatment of Industrial Pollutants, Beijing 100083, China)

Abstract

Non-point source pollution (NSP) and runoff intensities and distribution are primarily affected by landscape structure and composition. Multiple causalities hinder our ability to determine significant variables that influence NSP. Therefore, we developed an approach that integrates the Soil and Water Assessment Tool (SWAT), random forest regression model, redundancy analysis, and correlation coefficient to assess the role of landscape structure on runoff and NSP in the Dongsheng basin. We used R to calculate landscape metrics and the SWAT to simulate NSP loads from 1990 to 2019. redundancy analysis (RDA), random forest, and Pearson correlation were used to analyze the relationships among landscape metrics and NSP variables. The largest patch index (LPI) shows a significant negative correlation with NSP, with an R2 of −0.58 for TP and TN and −0.62 for sediment load. The findings indicate that landscapes with larger patch sizes, a high number of patches, and aggregation of patches largely influence pollution distribution. Overall, the results suggest that the role of landscape patterns on NSP outweighs that of runoff. Moreover, the findings infer that the aggregation and connectivity of forest patches contribute to the decline in NSP load and vice versa for cropland cover. Thus, for sustainable watershed management, it is crucial to encourage unfragmented landscapes, especially pollutant-intercepting landcovers such as forests.

Suggested Citation

  • Nametso Matomela & Tianxin Li & Peng Zhang & Harrison Odion Ikhumhen & Namir Domingos Raimundo Lopes, 2023. "Role of Landscape and Land-Use Transformation on Nonpoint Source Pollution and Runoff Distribution in the Dongsheng Basin, China," Sustainability, MDPI, vol. 15(10), pages 1-19, May.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:10:p:8325-:d:1151409
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/10/8325/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/10/8325/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gérard Biau & Erwan Scornet, 2016. "Rejoinder on: A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 264-268, June.
    2. Yu Song & Xiaodong Song & Guofan Shao, 2020. "Response of Water Quality to Landscape Patterns in an Urbanized Watershed in Hangzhou, China," Sustainability, MDPI, vol. 12(14), pages 1-17, July.
    3. Gérard Biau & Erwan Scornet, 2016. "A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 197-227, June.
    4. Peixuan Cheng & Fansheng Meng & Yeyao Wang & Lingsong Zhang & Qi Yang & Mingcen Jiang, 2018. "The Impacts of Land Use Patterns on Water Quality in a Trans-Boundary River Basin in Northeast China Based on Eco-Functional Regionalization," IJERPH, MDPI, vol. 15(9), pages 1-29, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hou, Lei & Elsworth, Derek & Zhang, Fengshou & Wang, Zhiyuan & Zhang, Jianbo, 2023. "Evaluation of proppant injection based on a data-driven approach integrating numerical and ensemble learning models," Energy, Elsevier, vol. 264(C).
    2. Ma, Zhikai & Huo, Qian & Wang, Wei & Zhang, Tao, 2023. "Voltage-temperature aware thermal runaway alarming framework for electric vehicles via deep learning with attention mechanism in time-frequency domain," Energy, Elsevier, vol. 278(C).
    3. Patrick Krennmair & Timo Schmid, 2022. "Flexible domain prediction using mixed effects random forests," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1865-1894, November.
    4. Jie Shi & Arno P. J. M. Siebes & Siamak Mehrkanoon, 2023. "TransCORALNet: A Two-Stream Transformer CORAL Networks for Supply Chain Credit Assessment Cold Start," Papers 2311.18749, arXiv.org.
    5. Bourdouxhe, Axel & Wibail, Lionel & Claessens, Hugues & Dufrêne, Marc, 2023. "Modeling potential natural vegetation: A new light on an old concept to guide nature conservation in fragmented and degraded landscapes," Ecological Modelling, Elsevier, vol. 481(C).
    6. Manuel J. García Rodríguez & Vicente Rodríguez Montequín & Francisco Ortega Fernández & Joaquín M. Villanueva Balsera, 2019. "Public Procurement Announcements in Spain: Regulations, Data Analysis, and Award Price Estimator Using Machine Learning," Complexity, Hindawi, vol. 2019, pages 1-20, November.
    7. Borup, Daniel & Christensen, Bent Jesper & Mühlbach, Nicolaj Søndergaard & Nielsen, Mikkel Slot, 2023. "Targeting predictors in random forest regression," International Journal of Forecasting, Elsevier, vol. 39(2), pages 841-868.
    8. Yiyi Huo & Yingying Fan & Fang Han, 2023. "On the adaptation of causal forests to manifold data," Papers 2311.16486, arXiv.org, revised Dec 2023.
    9. Akshita Bassi & Aditya Manchanda & Rajwinder Singh & Mahesh Patel, 2023. "A comparative study of machine learning algorithms for the prediction of compressive strength of rice husk ash-based concrete," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 118(1), pages 209-238, August.
    10. Sachin Kumar & Zairu Nisha & Jagvinder Singh & Anuj Kumar Sharma, 2022. "Sensor network driven novel hybrid model based on feature selection and SVR to predict indoor temperature for energy consumption optimisation in smart buildings," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(6), pages 3048-3061, December.
    11. Yong-Chao Su & Cheng-Yu Wu & Cheng-Hong Yang & Bo-Sheng Li & Sin-Hua Moi & Yu-Da Lin, 2021. "Machine Learning Data Imputation and Prediction of Foraging Group Size in a Kleptoparasitic Spider," Mathematics, MDPI, vol. 9(4), pages 1-16, February.
    12. Diogenis A. Kiziridis & Anna Mastrogianni & Magdalini Pleniou & Elpida Karadimou & Spyros Tsiftsis & Fotios Xystrakis & Ioannis Tsiripidis, 2022. "Acceleration and Relocation of Abandonment in a Mediterranean Mountainous Landscape: Drivers, Consequences, and Management Implications," Land, MDPI, vol. 11(3), pages 1-23, March.
    13. Escribano, Álvaro & Wang, Dandan, 2021. "Mixed random forest, cointegration, and forecasting gasoline prices," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1442-1462.
    14. Hunish Bansal & Basavraj Chinagundi & Prashant Singh Rana & Neeraj Kumar, 2022. "An Ensemble Machine Learning Technique for Detection of Abnormalities in Knee Movement Sustainability," Sustainability, MDPI, vol. 14(20), pages 1-16, October.
    15. Yigit Aydede & Jan Ditzen, 2022. "Identifying the regional drivers of influenza-like illness in Nova Scotia with dominance analysis," Papers 2212.06684, arXiv.org.
    16. Siyoon Kwon & Hyoseob Noh & Il Won Seo & Sung Hyun Jung & Donghae Baek, 2021. "Identification Framework of Contaminant Spill in Rivers Using Machine Learning with Breakthrough Curve Analysis," IJERPH, MDPI, vol. 18(3), pages 1-26, January.
    17. Sylwester Bejger, 2024. "Machine Learning in Cartel Screening—The Case of Parallel Pricing in a Fuel Wholesale Market," Energies, MDPI, vol. 17(16), pages 1-17, August.
    18. Lotfi Boudabsa & Damir Filipovi'c, 2022. "Ensemble learning for portfolio valuation and risk management," Papers 2204.05926, arXiv.org.
    19. Yan, Ran & Wang, Shuaian & Du, Yuquan, 2020. "Development of a two-stage ship fuel consumption prediction and reduction model for a dry bulk ship," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 138(C).
    20. Daniel Boller & Michael Lechner & Gabriel Okasa, 2021. "The Effect of Sport in Online Dating: Evidence from Causal Machine Learning," Papers 2104.04601, arXiv.org.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:10:p:8325-:d:1151409. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.