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Calculation of city total maximum allocated load of total nitrogen for jurisdictions in Qingdao, China: A water quality-based modeling approach.

Authors :
Wang Y
Song D
Li K
Su Y
Liang S
Li Y
Wang X
Source :
The Science of the total environment [Sci Total Environ] 2019 Feb 20; Vol. 652, pp. 455-470. Date of Electronic Publication: 2018 Oct 09.
Publication Year :
2019

Abstract

This study aims to provide a quantitative basis for the precision emission reduction of land-based total nitrogen (TN) pollutants in Qingdao, China. The total maximum allocated load (TMAL) of TN pollutants within jurisdictions in Qingdao was calculated by using a 3D hydrodynamic-water quality model and a linear programming model. The TMAL includes emission TMAL, point-source and nonpoint source TMAL, TMAL removed by municipal sewage treatment system (MSTS), and soil-retained and water-retained TMAL, which were calculated after the division of source units, establishment of the land-based TN load matrix, simulation of the concentration response matrix, setting of a dissolved inorganic nitrogen (DIN) concentration control standard in the Qingdao coastal area, and calculation of TMAL. In the reliability analysis, a concentration under TMAL was considered to indicate satisfactory water quality criteria (relative standard deviation = 17%, Kappa = 0.55). The results showed that the emission TMAL density of nitrogen pollutants in source units was 1.8 ton/km <superscript>2</superscript> /a. Nonpoint and point source-produced TMAL densities were 17.0 ton/km <superscript>2</superscript> /a and 5.2 ton/km <superscript>2</superscript> /a, respectively. MSTS-removed TMAL density was 12.2 ton/km <superscript>2</superscript> /a. Soil- and water-retained TMAL were 4.0 ton/km <superscript>2</superscript> /a and 0.7 ton/km <superscript>2</superscript> /a, respectively. The summed F <subscript>(D)</subscript> <superscript>∗</superscript> proportions of 10 districts were, in descending order, Huangdao (22%), Laoshan (21%), Jimo (18%), Shibei (13%), Licang (7%), Pingdu (7%), Jiaozhou (4%), Shinan (3%), Laixi (3%) and Chengyan (2%).<br /> (Copyright © 2018 Elsevier B.V. All rights reserved.)

Details

Language :
English
ISSN :
1879-1026
Volume :
652
Database :
MEDLINE
Journal :
The Science of the total environment
Publication Type :
Academic Journal
Accession number :
30368176
Full Text :
https://doi.org/10.1016/j.scitotenv.2018.10.113