globalchange  > 气候变化与战略
DOI: 10.1016/j.atmosenv.2019.117157
A refined source apportionment study of atmospheric PM2.5 during winter heating period in Shijiazhuang, China, using a receptor model coupled with a source-oriented model
Author: Zhang W.; Liu B.; Zhang Y.; Li Y.; Sun X.; Gu Y.; Dai C.; Li N.; Song C.; Dai Q.; Han Y.; Feng Y.
Source Publication: Atmospheric Environment
ISSN: 1352-2310
Publishing Year: 2020
Volume: 222
Language: 英语
Keyword: Air pollution ; Air pollution control ; Coal dust ; Heating ; Industrial emissions ; Odor control ; Organic carbon ; Roads and streets ; Vehicles ; Architectural material ; CALPUFF modeling ; Emission inventories ; PM2.5 ; Residential combustion ; Shijiazhuang , China ; Source apportionment ; Source directional apportionment ; Coal combustion ; carbon ; coal ; nitrate ; organic carbon ; biomass burning ; coal combustion ; elemental carbon ; emission inventory ; haze ; heating ; particulate matter ; pollutant source ; source apportionment ; spatiotemporal analysis ; winter ; air pollution ; Article ; biomass ; China ; combustion ; concentration (parameter) ; diesel engine ; dust ; haze ; heating ; industry ; model ; particle size ; particulate matter ; priority journal ; traffic ; winter ; China ; Hebei ; Shijiazhuang
Subject Area: CALPUFF model ; PM2.5 ; PMF ; Refined source apportionment ; Source directional apportionment
Abstract: With the intensification of Chinese source control of air pollution, there is an urgent need for refined and rapid source apportionment techniques. A refined source apportionment method was constructed based on an off-line sampling dataset using a receptor model coupled with a source-oriented model, and the method was implemented in Shijiazhuang during the heating period. The refined results for source apportionment mainly included temporal, spatial, and source-category refinement data. The results indicated that the mean concentration of PM2.5 during the heating period was 96 μg/m3. Organic carbon (OC) and NO3− were found to be the dominant species of PM2.5 during the study. A high correlation was detected between elemental carbon (EC) and NO3− on polluted days, which was suggestive of the stagnant condition that accumulates EC and nitrate simultaneously. Secondary particle formation greatly promoted the occurrence of haze events. Secondary sources (34.9%), vehicle exhaust (18.6%), coal combustion (20.0%), industrial emissions (9.2%), crustal dust (9.7%), and biomass burning (7.6%) were the major sources during the heating period. The contributions of secondary sources and vehicle exhaust increased on polluted days, while those of coal combustion, industrial emissions and crustal dust decreased significantly. The contribution percentage of secondary sources from the southeast direction was basically the highest, while those of vehicle exhaust from the northwest or southeast directions were relatively higher as well, likely due to the distribution of traffic arteries. Based on the refined results for the source-category assessment, we found that the heating boilers (17.0%), non-road mobile (13.8%), diesel vehicles (10.4%), residential combustion (6.7%), road dust (5.5%), and architectural material industry (4.9%) were the major contributors to PM2.5. There was some uncertainty in the distribution proportions of the refined results, which were derived based on the emission inventory and the results of CALPUFF model. © 2019 Elsevier Ltd
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Document Type: 期刊论文
Appears in Collections:气候变化与战略

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Affiliation: State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control & Center for Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China; Shijiazhuang Environmental Monitoring Center of Hebei Province, Shijiazhuang, Hebei 050022, China; School of Environment, Tsinghua University, Beijing, 100084, China

Recommended Citation:
Zhang W.,Liu B.,Zhang Y.,et al. A refined source apportionment study of atmospheric PM2.5 during winter heating period in Shijiazhuang, China, using a receptor model coupled with a source-oriented model[J]. Atmospheric Environment,2020-01-01,222
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