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1.
J Environ Manage ; 325(Pt A): 116534, 2023 Jan 01.
Artigo em Inglês | MEDLINE | ID: mdl-36419282

RESUMO

Long-term continuous hourly measurements of ambient volatile organic compounds (VOCs) are scarce at the regional scale. In this study, a one-year hourly measurement campaign of VOCs was performed in Lvliang, Linfen, and Yuncheng in the heavily polluted Fenhe Plain region in China. The VOC average (±standard deviation, std) concentrations in Lvliang, Linfen, and Yuncheng were 44.4 ± 24.9, 45.7 ± 24.9, and 37.5 ± 25.0 ppbv, respectively. Compared to published data from the past two decades in China, the observed VOCs were at high concentration levels. VOCs in the Fenhe Plain cities were significantly impacted by industrial sources according to calculated emission ratios but were less affected by liquefied petroleum gas and natural gas (LPG/NG) and traffic emissions than those in megacities abroad. The emission inventories and observation data were combined for verification and identification of the key VOC species and sources controlling ozone (O3). Industrial emissions were the largest source of VOCs, accounting for 65%-79% of the total VOC emissions, while the coking industry accounted for 45.2%-66.0%. The emission inventories significantly underestimated oxygenated VOC (OVOC) emissions through the verification of VOC emission ratios. O3 control scenarios were analyzed by changing VOC/NOX reduction ratios through a photochemical box model. O3 control strategies were formulated considering local pollution control plans, emission inventories, and O3 formation regimes. The O3 reduction of reactivity-control measures was comparable with emission-control measures, ranging from 16% to 41%, which was contrary to the general perception that ozone formation potential (OFP)-based measures were more efficient for O3 reduction. Sources with high VOC emissions are accompanied by high OFP on the Fenhe Plain, indicating that the control of high-emission sources can effectively mitigate O3 pollution on this region.


Assuntos
Ozônio , Compostos Orgânicos Voláteis , Cidades , China , Poluição Ambiental
2.
Chemosphere ; 243: 125267, 2020 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-31734594

RESUMO

In 2015, comprehensive observations were carried out in Chengdu, Sichuan Province, China, to elucidate the seasonal variation characteristics of the concentrations, chemical compositions, and the sources of PM2.5 pollution. The meteorological parameters, gaseous pollutants and chemical compositions of PM2.5 were measured. The annual average concentration of PM2.5 in Chengdu was 67.44 ±â€¯48.78 µg/m3. The highest seasonal PM2.5 mass concentration occurred in winter with an average of 103.04 ±â€¯66.76 µg/m3, followed by spring, autumn, and summer, and the wind speed had an important impact on the diffusion of PM2.5. The seasonal variation characteristics of chemical components in PM2.5 were analysed. The contribution and chemical conversion ability of secondary aerosols increased with increasing of PM2.5 concentration. Source appointment of positive matrix factorization (PMF) shows that the main sources of PM2.5 were secondary aerosols, coal combustion, biomass burning, vehicle emissions, dust and industrial sources, which have more obvious seasonal differences than other sources, and secondary aerosols and coal combustion were the major sources. Conditional probability function (CPF) analysis showed that the local sources of high PM2.5 concentrations were mainly from the eastern and southeastern areas of Chengdu. Potential source contribution function (PSCF), concentration weighted trajectory (CWT) and backward trajectory cluster analyses indicated that the southern, southeast and eastern parts of the Sichuan Basin were the most likely potential sources of PM2.5, and the unique geographical and topographical factors in Chengdu play important roles in the transport and diffusion of pollutants in this region.


Assuntos
Poluentes Atmosféricos/análise , Monitoramento Ambiental , Material Particulado/análise , Aerossóis/análise , Biomassa , China , Clima , Carvão Mineral/análise , Poeira/análise , Poluição Ambiental/análise , Gases/análise , Estações do Ano , Emissões de Veículos/análise
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