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An improved risk-explicit interval linear programming model for pollution load allocation for watershed management.

Bisheng Xia1, Xin Qian2, Hong Yao1,3

  • 1State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, 210023, China.

Environmental Science and Pollution Research International
|September 19, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces an improved risk-explicit interval linear programming (REILP) model to address equity issues in pollution load allocation. The enhanced model ensures balanced development by proportionally linking pollution reduction to pollutant load.

Keywords:
Equity problemLoad reductionOptimization modelPollution load allocationRisk-explicit interval linear programmingSimulation–optimization

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Area of Science:

  • Environmental Science
  • Operations Research
  • Water Resource Management

Background:

  • The risk-explicit interval linear programming (REILP) model provides interval solutions but suffers from allocation inequity.
  • Unbalanced allocation can negatively impact regional economic development and environmental justice.

Purpose of the Study:

  • To propose an improved REILP model that incorporates an equity objective function.
  • To address the inequity problem in pollution load allocation and ensure balanced regional development.

Main Methods:

  • An enhanced REILP model was developed by adding an equity objective function and three new constraints.
  • The model was applied to a transboundary watershed for pollution load allocation.

Main Results:

  • The improved model achieved equitable distribution of pollutant loads between upstream and downstream regions.
  • It resolved the issue of prioritizing pollution reduction from sources closest to the control section.

Conclusions:

  • The enhanced REILP model offers a more equitable and balanced approach to pollution load allocation.
  • This method supports sustainable development in transboundary watersheds by ensuring fairness in environmental management.