A PHP Error was encountered

Severity: Warning

Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests

Filename: helpers/my_audit_helper.php

Line Number: 176

Backtrace:

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3122
Function: getPubMedXML

File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global

File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword

File: /var/www/html/index.php
Line: 316
Function: require_once

Source and sectoral contribution analysis of PM based on efficient response surface modeling technique over Pearl River Delta Region of China. | LitMetric

Source and sectoral contribution analysis of PM based on efficient response surface modeling technique over Pearl River Delta Region of China.

Sci Total Environ

Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control, School of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China.

Published: October 2020

Identifying and quantifying source contributions of pollutant emissions are crucial for an effective control strategy to break through the bottleneck in reducing ambient PM levels over the Pearl River Delta (PRD) region of China. In this study, an innovative response surface modeling technique with differential method (RSM-DM) has been developed and applied to investigate the PM contributions from multiple regions, sectors, and pollutants over the PRD region in 2015. The new differential method, with the ability to reproduce the nonlinear response surface of PM to precursor emissions by dissecting the emission changes into a series of small intervals, has shown to overcome the issue of the traditional brute force method in overestimating the accumulative contribution of precursor emissions to PM. The results of this case study showed that PM in the PRD region was generally dominated by local emission sources (39-64%). Among the contributions of PM from various sectors and pollutants, the primary PM emissions from fugitive dust source contributed most (25-42%) to PM levels. The contributions of agriculture NH emissions (6-13%) could also play a significant role compared to other sectoral precursor emissions. Among the NO sectors, the emissions control of stationary combustion source could be most effective in reducing PM levels over the PRD region.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.scitotenv.2020.139655DOI Listing

Publication Analysis

Top Keywords

prd region
16
response surface
12
precursor emissions
12
surface modeling
8
modeling technique
8
pearl river
8
river delta
8
region china
8
differential method
8
sectors pollutants
8

Similar Publications

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!