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Updated: Oct 1, 2025

Vegetated Treatment Systems for Removing Contaminants Associated with Surface Water Toxicity in Agriculture and Urban Runoff

Vegetated Treatment Systems for Removing Contaminants Associated with Surface Water Toxicity in Agriculture and Urban Runoff

Published on: May 15, 2017

[Identification and Optimization Method of Rainfall-Runoff Pollution Risk Level].

Xiao-Tian Qi^1,2, Zhi-Ming Zhang^1,2, Xin Zhao^1,2

¹School of Environment and Energy Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China.

Huan Jing Ke Xue= Huanjing Kexue

|March 8, 2022

Summary

This summary is machine-generated.

Identifying urban runoff pollution risk is crucial for water protection. This study developed a method combining landscape patterns and processes to pinpoint high-risk river areas, guiding targeted pollution control efforts.

Keywords:

landscape index landscape pattern landscape process minimum cumulative resistance model rainfall-runoff pollution

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Design and Construction of an Urban Runoff Research Facility

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Area of Science:

Environmental Science
Urban Hydrology
Ecology

Background:

Urban runoff pollution poses significant challenges to water environment protection.
Effective identification of runoff pollution risk is key to enhancing control efficiency.

Purpose of the Study:

To propose integrated methods for identifying and optimizing river rainfall-runoff pollution risk.
To analyze pollution patterns, processes, and comprehensive risk indices in major river reaches.

Main Methods:

Combined landscape patterns and processes with landscape pattern index and minimum cumulative resistance model.
Calculated rainfall-runoff pollution patterns, processes, and comprehensive risk indices.
Identified risk paths from cultivated land, urban construction land, and traffic industrial/mining land.

Main Results:

A ten-level pollution risk index identified high-risk rivers in central and southern regions.
Fragmentation and dominance landscape factors posed higher risks than aggregation factors.
High-risk rivers were concentrated in central urban areas, decreasing towards the periphery.
Cultivated land had the widest pollution range, while traffic/industrial/mining land had the highest intensity.
Middle and southeastern river sections showed significantly higher pollution risk, with 13 Level 1 and 11 Level 2 risk sections identified.

Conclusions:

The developed integrated method effectively identifies river rainfall-runoff pollution risk.
Landscape pattern optimization strategies are proposed based on identified risk factors.
Targeted pollution control should focus on identified high-risk river reaches, particularly in central and southeastern areas.