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• W3008663397 endingPage "117346" @default.
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• W3008663397 abstract "Air pollution has recently become a regional problem in the North China Plain, which is heavily populated with industrialized city clusters. The local air quality is frequently affected by air pollutant transport in this region, as well as other meteorological conditions. This study aims to reveal the roles of air pollutant transport and atmospheric boundary layer variation in the development of air pollution episodes. Two cities (Dezhou and Cangzhou in Shandong and Hebei Provinces, respectively) near the centre of the North China Plain are chosen, and PM2.5 (particulate matter with an aerodynamic diameter less than 2.5 μm) concentrations over two winters (January 2016 and December 2017 to January 2018) are evaluated. Both meteorological and air quality observational data are used to construct hourly wind and PM2.5 concentration fields. High-quality atmospheric boundary layer soundings provide detailed information on boundary layer height and its diurnal evolution. A simple box model is employed to simulate variation in PM2.5 levels in these two cities by using the spatial gradients and temporal variation rates in the diagnostic fields of wind and PM2.5 concentrations. Two prominent factors affecting PM2.5 concentration variation are identified by comparing the simulated and observed diurnal variation in PM2.5 concentration. The first one is the entrainment of upper-layer air pollutants into the boundary layer in the morning. And the second one is the temporary accumulation of PM2.5 during the day-to-evening period. After incorporating these two effects, the simulated PM2.5 is comparable with the observed data. The correlation coefficients between the simulation and observation are 0.84 and 0.77 (P < 0.01) for Cangzhou and Dezhou, respectively, and the normalized mean biases are −0.04 and −0.09, respectively. The relative contributions of different processes affecting PM2.5 concentrations during air pollution episodes are assessed with the model simulations. The percentage contributions for local emissions, deposition, horizontal advection, temporary accumulation, and boundary layer height variation are from 27% to 45%, −28% to −20%, 46% to 76%, 13% to 40%, and −6% to 22%, respectively. Local emission and regional air pollutant transport are the main causes of these episodes. Temporary accumulation of PM2.5 during the day-to-evening period is also an important contributor to the episodes. In addition, the entrainment shows varying contributions to PM2.5 increase with a maximum rate of 30% during the PM2.5 pollution episodes in the North China Plain." @default.
• W3008663397 created "2020-03-06" @default.
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• W3008663397 date "2020-03-01" @default.
• W3008663397 modified "2023-10-16" @default.
• W3008663397 title "Diagnostic analysis of wintertime PM2.5 pollution in the North China Plain: The impacts of regional transport and atmospheric boundary layer variation" @default.
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• W3008663397 cites W1862113676 @default.
• W3008663397 cites W1973127847 @default.
• W3008663397 cites W1977670273 @default.
• W3008663397 cites W1977927381 @default.
• W3008663397 cites W1979628724 @default.
• W3008663397 cites W1986259363 @default.
• W3008663397 cites W1992170152 @default.
• W3008663397 cites W1994225046 @default.
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• W3008663397 cites W2009009889 @default.
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• W3008663397 cites W2011460032 @default.
• W3008663397 cites W2017542038 @default.
• W3008663397 cites W2024355140 @default.
• W3008663397 cites W2028933240 @default.
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• W3008663397 cites W2154567020 @default.
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• W3008663397 doi "https://doi.org/10.1016/j.atmosenv.2020.117346" @default.
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