Literature DB >> 30041324

Assessment of nitrogen reduction by constructed wetland based on InVEST: A case study of the Jiulong River Watershed, China.

Yiyun Yan1, Qingsong Guan1, Min Wang1, Xiaoling Su1, Gaojie Wu1, Penchi Chiang2, Wenzhi Cao3.   

Abstract

The Jiulong River watershed (JRW) in southeast China includes livestock breeding and agriculture, leading to large amounts of non-point source pollution. Nitrogen (N) reductions were simulated and mapped using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) under scenarios that were built considered both constructed wetlands (CWs) and climate change, which are not common in the literature on ecosystem services assessments. The results showed that the amount of N exported from non-point sources within the JRW was 12,569 t·yr-1. The areal N load was relatively higher in the north, while more N exported in the southeast. Constructed riparian wetlands can intercept and reduce the N loads that enter water bodies, but climate change may be a factor driving the deterioration of water quality. The methodology can be generalized to reduce other contaminants, and provides a tool for decision-makers to weigh the costs and benefits of urbanization and conservation.
Copyright © 2018 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Climate change; Constructed wetland; InVEST; Nitrogen reduction

Mesh:

Substances:

Year:  2018        PMID: 30041324     DOI: 10.1016/j.marpolbul.2018.05.050

Source DB:  PubMed          Journal:  Mar Pollut Bull        ISSN: 0025-326X            Impact factor:   5.553


  2 in total

1.  Water quantity and quality changes from forested riparian buffer in Beijing.

Authors:  Kang Xu; Lichun Mo; Zhenming Zhang; Mingxiang Zhang
Journal:  Environ Sci Pollut Res Int       Date:  2019-08-07       Impact factor: 4.223

2.  Identifying Spatial Patterns and Ecosystem Service Delivery of Nature-Based Solutions.

Authors:  Paulina Guerrero; Dagmar Haase; Christian Albert
Journal:  Environ Manage       Date:  2022-03-09       Impact factor: 3.644

  2 in total

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