Quantitative assessment of background pollutants using a modified method in data-poor regions.

Quantitative assessment of background pollutants using a modified method in data-poor regions.

Duan, Maoqing;Du, Xia;Peng, Wenqi;Jiang, Cuiling;Zhang, Shijie;Ding, Yang;
Environmental monitoring and assessment 2020 Vol. 192 pp. 160
197
duan2020quantitativeenvironmental

Abstract

Heavy background pollutant loads pose a difficult problem for the assessment and management of regional water quality, especially in areas where surface water quality is less affected by anthropogenic pollution. Deducting background values from those derived from water quality monitoring is a new method for evaluating surface water environments in areas with heavy background loads. In this study, river source reserves in Heilongjiang province were evaluated with an export coefficient model (ECM) that considers the rainfall influence factor, has an improved timescale, and is based on synchronous rainfall monitoring data and concentrations. Moreover, the ECM was combined with a mechanism model. The chemical oxygen demand, ammonia nitrogen, and other water quality indices are affected by background environment, and therefore, suitable export coefficients for the study area were determined and a regression equation between the rainfall influence factor and precipitation was established. By combining the ECM and mechanism model, the concentrations entering the river during eight rainfall events in 2018 were predicted, and the background value was calculated to evaluate surface water quality. The predicted values were found to approximate the monitored values. Therefore, this study is of great significance for water quality assessment and management in areas with heavy background pollutant loads.

Citation

ID: 91551
Ref Key: duan2020quantitativeenvironmental
Use this key to autocite in SciMatic or Thesis Manager

References

Blockchain Verification

Account:
NFT Contract Address:
0x95644003c57E6F55A65596E3D9Eac6813e3566dA
Article ID:
91551
Unique Identifier:
10.1007/s10661-020-8122-8
Network:
Scimatic Chain (ID: 481)
Loading...
Blockchain Readiness Checklist
Authors
Abstract
Journal Name
Year
Title
5/5
Creates 1,000,000 NFT tokens for this article
Token Features:
  • ERC-1155 Standard NFT
  • 1 Million Supply per Article
  • Transferable via MetaMask
  • Permanent Blockchain Record
Blockchain QR Code
Scan with Saymatik Web3.0 Wallet

Saymatik Web3.0 Wallet