Integrating source apportionment and landscape patterns to capture nutrient variability across a typical urbanized watershed

Jin Liu; Tiezhu Yan; Jianwen Bai; Zhenyao Shen

doi:10.1016/j.jenvman.2022.116559

Integrating source apportionment and landscape patterns to capture nutrient variability across a typical urbanized watershed

J Environ Manage. 2023 Jan 1;325(Pt B):116559. doi: 10.1016/j.jenvman.2022.116559. Epub 2022 Oct 22.

Authors

Jin Liu¹, Tiezhu Yan², Jianwen Bai³, Zhenyao Shen⁴

Affiliations

¹ State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing, 100875, PR China; Hebei Key Laboratory of Environmental Change and Ecological Construction, Hebei Technology Innovation Center for Remote Sensing Identification of Environmental Change, School of Geographical Sciences, Hebei Normal University, Shijiazhuang, 050024, China.
² State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing, 100875, PR China; Technical Centre for Soil, Agricultural and Rural Ecology and Environment, Ministry of Ecology and Environment, Beijing, 100012, China.
³ College of Engineering, Jilin Normal University, Siping, 136000, China.
⁴ State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing, 100875, PR China. Electronic address: zyshen@bnu.edu.cn.

PMID: 36283170
DOI: 10.1016/j.jenvman.2022.116559

Abstract

Effective integrated watershed management requires models that can characterize the sources and transport processes of pollutants at the watershed with multiple landscape patterns. However, few studies have investigated the influence of landscape spatial configuration on pollutant transport processes. In this study, the SPARROW_TN and SPARROW_TP models were constructed by combining direct pollution source data and landscape pattern data to investigate the source composition and nutrient transport processes and to reveal the influence of landscape patterns on nutrient transport in the urbanized Beiyun River Watershed. The introduction of landscape metrics significantly improved the simulation results of both models, with R² increasing from 0.89 to 0.85 to 0.93 and 0.91, respectively. Spatial variations existed in TN and TP loads and yields, as well as the source compositions. Pollution hotspots were effectively identified. Source apportionment showed that for the entire watershed, TN came from atmospheric nitrogen deposition (35.25%), untreated sewage (28.23%), agricultural sources (22.60%), and treated sewage (13.92%). In comparison, TP came from untreated sewage (44.94%), agricultural sources (40.22%), and treated sewage (11.51%). In addition, the largest patch index of grassland correlated positively with both TN and TP, whereas the largest shape index of buildup land and interspersion and juxtaposition index of forest were negatively correlated with TN and TP, respectively. The results of this study will provide insight into effective nutrient control measures that consider spatially varying nutrient sources and associated nutrient transport processes.

Keywords: Landscape pattern; Nutrient; SPARROW model; Urban; Water quality.

MeSH terms

China
Environmental Monitoring*
Nitrogen / analysis
Nutrients
Phosphorus / analysis
Rivers
Sewage
Water Pollutants, Chemical* / analysis

Substances

Phosphorus
Water Pollutants, Chemical
Sewage
Nitrogen