Tracking the optimal watershed landscape pattern for driving pollutant transport: Insights from the integration of mechanistic models and data-driven approaches

Yuexin Zheng; Chong Li; Jingshan Yu; Qianyang Wang; Qimeng Yue

doi:10.1016/j.jenvman.2024.123939

Tracking the optimal watershed landscape pattern for driving pollutant transport: Insights from the integration of mechanistic models and data-driven approaches

J Environ Manage. 2025 Jan 2:373:123939. doi: 10.1016/j.jenvman.2024.123939. Online ahead of print.

Authors

Yuexin Zheng¹, Chong Li², Jingshan Yu³, Qianyang Wang⁴, Qimeng Yue²

Affiliations

¹ College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China; College of Water Sciences, Beijing Normal University, Beijing, 100875, China.
² College of Water Sciences, Beijing Normal University, Beijing, 100875, China.
³ College of Water Sciences, Beijing Normal University, Beijing, 100875, China. Electronic address: jingshan_bnu@hotmail.com.
⁴ Faculty of Engineering, University of Alberta, Edmonton, T6G 2R3, Canada.

PMID: 39754799
DOI: 10.1016/j.jenvman.2024.123939

Abstract

Identifying landscape patterns conducive to pollutant transport control is of vitally importance for water quality protection. However, it remains unclear which landscape patterns can weaken the transport capacity of pollutants entering water bodies. To fill this gap, this study proposes a new framework. This framework quantifies the contribution of landscape patterns to pollutant migration; it also identifies the optimal landscape patterns capable of reducing pollutants entering rivers. Furthermore, it analyzes the impact pathways of landscape patterns on pollutant migration by integrating mechanism models, machine learning techniques, and structural equation models (SEM). The results showed that on cultivated land and urban land, when the slope reached 35%, the terrestrial transport intensity of NH₃-N peaked at 34 kg/km² and 45 kg/km² respectively, with more pollutants entering the receiving water bodies. Meanwhile, in the forest with a DEM of 900 m, the terrestrial transport intensity of NH₃-N was the highest (50 kg/km²). The complexity of the landscape boundary shape in areas dominated by cultivated land and forest was verified to have a significant impact on the terrestrial migration intensity of NH₃-N, with a contribution rate of over 65%. From the comparison results of multiple land use combinations, it can be seen that the combination of woodland and grassland indirectly weakens the transport capacity of pollutants entering water bodies by directly influencing the connectivity among landscape units. In particular, when the proportion of woodland and grassland reaches 75%, it has a positive effect on improving river pollution and is the optimal landscape combination pattern for reducing the pollution load of the river. The outcomes can be used to develop more efficacious optimization and regulation tactics for landscape patterns and offer a decision - making foundation for the control of pollutant transport in large basins.

Keywords: Coupling effect; Landscape pattern elements; Mechanism model; Pollutant transport; Random forest.