Characteristics of PM2.5 and Assessing Effects of Emission-Reduction Measures in the Heavy Polluted City of Shijiazhuang, before, during, and after the Ceremonial Parade 2015

Gang, Wang; Cheng, Shuiyuan; Lang, Jianlei; Yang, Xudong; Wang, Xiaoqi; Chen, Guolei; Liu, Xiaoyu; Zhang, Hanyu

doi:10.4209/aaqr.2016.05.0181

Cited by 13 publications

(9 citation statements)

References 36 publications

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“…In recent decades, haze frequently occurred in China because of rapid urbanization and industrialization without an efficient emission control (Huang et al, 2014;Wu et al, 2018;Zhao et al, 2018). Previous studies have demonstrated that both unfavorable diffusion conditions and anthropogenic sources emissions play key roles in the formation of haze pollution (Chen et al, 2017;Wang et al, 2017). Haze events have gained considerable attention because of its significant impact on visibility, air quality, human health, and globle climate change (Kanakidou et al, 2004;Wang et al, 2016;Wang et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Molecular Compositions and Sources of Organic Aerosols from Urban Atmosphere in the North China Plain during the Wintertime of 2017

Meng

Hou

et al. 2019

Aerosol Air Qual. Res.

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PM 2.5 samples were collected from Liaocheng, a typical city in the North China Plain, during a winter haze episode around 2017 Chinese Spring Festival (Lunar New Year, LNY) to investigate the impact of firework on organic aerosols. A comparison of PM 2.5 concentrations during different periods, with different air mass origins, and under different pollution situations was done. Organic compounds including normal alkanes (n-alkanes), polycylic aromatic hydrocarbons (PAHs), saccharides, and organic acids in PM 2.5 aerosols were determined by GC/MS. Sources were analyzed by diagnostics ratios and principal component analysis/multiple linear regression (PCA/MLR) model. The results showed that fireworks burning has significant impacts on fine particle pollution. During the haze period, a sharp increase in n-alkanes, PAHs, saccharides, and fatty acids were observed, but the influence of fireworks burning on n-alkanes concentration is minor. The concentrations of carcinogenic PAHs during haze and LNY periods were more than three times higher than those in the clean period, indicating that PAHs were more carcinogenic during the two periods. In addition, the estimated ILCR for children and adults were both about three times higher than those in the clean periods, suggesting a moderate potential carcinogenic risk in Liaocheng. The higher concentration and the dominance of levoglucoan in the total saccharides suggested that the biomass burning is the predominance source of saccharides. Both the ratios of C 18:1 /C 18:0 and BaP/BeP were the highest during the haze period, indicating that aerosols in the haze period were more oxidized. According to the source precise molecular tracers and the PCA-MLR model, coal combustion, biomass burning, and vehicle emissions were the major sources of organic compounds in PM 2.5 aerosols during the winter in Liaocheng, cooking activity and firework burning had impact on organic aerosols obviously during LNY. Our data provided first analysis of the molecular distributions and sources of organic aerosols during Chinese Spring Festival in Liaocheng and their potential effects on human health.

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Section: Introductionmentioning

confidence: 99%

Molecular Compositions and Sources of Organic Aerosols from Urban Atmosphere in the North China Plain during the Wintertime of 2017

Meng

Hou

et al. 2019

Aerosol Air Qual. Res.

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“…He et al (2016) built a statistical model by analyzing the relationship between concentrations of atmospheric pollutants and several factors including potential temperature decline rate, stable energy, Froude number, gradient Richardson number, and so on. Constructing meteorological conditions for air pollution and studying the feasibility of index forecasting are also belong to the conventional statistical forecasting method (Wang et al, 2013;Chen et al, 2015;Yang et al, 2015). The dynamic-statistical forecasting method are based on Chemical Transport Models (CTMs) and linear or nonlinear statistical methods to build a statistical model using the observed or modeled concentrations of pollutants and meteorological elements.…”

Section: Introductionmentioning

confidence: 99%

“…The Yangtze River Delta (YRD) is a special region with a well-developed economy, dense population and high degree of urbanization. Serious heavy haze occurred in China since 2013 (Chen et al, 2017;Wang et al, 2017b;Zhang et al, 2017), especially in North China and the YRD Region (Lang et al, 2017;Wang et al, 2017a). The increase in anthropogenic emissions and disadvantageous meteorological conditions have caused frequent regional air pollution and greatly restricted the accuracy of air quality forecast in this region (Jia et al, 2017;Zhang et al, 2008;Parrish et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Regional Air Quality Forecast Using a Machine Learning Method and the WRF Model over the Yangtze River Delta, East China

Jia

Cheng

Zhao

et al. 2019

Aerosol Air Qual. Res.

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A statistical forecasting method of air quality based on meteorological elements with high spatiotemporal resolution simulated by the Weather Research and Forecasting (WRF) model and a back-propagation (BP) neural network was established to predict 72 h PM 2.5 mass concentrations over the Yangtze River Delta (YRD) region of eastern China. Shortterm statistical forecasting of air quality in 25 major cities in the YRD region was conducted and the PM 2.5 forecast was validated using the corresponding surface PM 2.5 observational data in this study. Results indicate that the short-term air quality forecasting system has a ability to accurately forecast PM 2.5 concentration in the major cities in the YRD region. The average index of agreement (IA) between PM 2.5 forecasts and observations in the four seasons ranges from 74% to 77%, and the root mean square error (RMSE) fall between 15.2 µg m -3 and 33.0 µg m -3 . The data with PM 2.5 concentration greater than 115 µg m -3 are selected to establish the EXP-Polluted model and then used to predict PM 2.5 concentration during heavy haze periods in 2017. The RMSEs of PM 2.5 forecasts during severe haze periods are improved by 44.1%, which compared to predictions using the EXP-All Time model constructed by the full-year data.

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“…To ensure good air quality during major events, such as the 70th anniversary of the Anti-Japanese War victory and the Asia-Pacific Economic Cooperation, emission controls have been implemented in these regions (the Beijing, Tianjin, Hebei, and Shaanxi provinces). Those measures were implemented successfully, and the pollution levels were low [15,16]. These practices provided opportunities to experiment and explore the atmospheric chemical mechanism in China.…”

Section: Introductionmentioning

confidence: 99%

The Aerosol-Radiation Interaction Effects of Different Particulate Matter Components during Heavy Pollution Periods in China

Wang

Ma²,

Guo

et al. 2020

Atmosphere

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The Beijing-Tianjin-Hebei (BTH) region experienced heavy air pollution in December 2015, which provided a good opportunity to explore the aerosol-radiation interaction (ARI) effects of different particulate matter (PM) components (sulfate, nitrate, and black carbon (BC)). In this study, five tests were conducted by the Weather Research and Forecasting-Chemistry (WRF-Chem) model. The tests included scenario 1 simulation with ARI turned on, scenario 2 simulation with ARI turned off, scenario3 simulation without NO x /NO 3 − emissions and with ARI turned on, scenario 4 simulation without SO 2 /SO 4 2− emissions and with ARI turned on, and scenario 5 simulation without BC emissions and with ARI turned on. The ARI decreased the downward shortwave radiation (SWDOWN) and the temperature at 2 m (T2), reduced the planetary boundary layer (PBL) height (PBLH), and increased the relative humidity (RH) at 2 m in the region. These factors also contribute to pollution accumulation. The results revealed that BC aerosols have a stronger effect on the reduction in SWDOWN than sulfate (SO 4 2− ) and nitrate (NO 3 − ). BC aerosols produce both cooling and heating effects, while SO 4 2− aerosols produce only cooling effects. The PBL decreased and RH2 increased due to the aerosol feedback effect of sulfate, nitrate, and BC. The ARI effect on meteorological factors during the nonheavy pollution period was much smaller than that during the pollution period.

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Characteristics of PM2.5 and Assessing Effects of Emission-Reduction Measures in the Heavy Polluted City of Shijiazhuang, before, during, and after the Ceremonial Parade 2015

Cited by 13 publications

References 36 publications

Molecular Compositions and Sources of Organic Aerosols from Urban Atmosphere in the North China Plain during the Wintertime of 2017

Molecular Compositions and Sources of Organic Aerosols from Urban Atmosphere in the North China Plain during the Wintertime of 2017

Regional Air Quality Forecast Using a Machine Learning Method and the WRF Model over the Yangtze River Delta, East China

The Aerosol-Radiation Interaction Effects of Different Particulate Matter Components during Heavy Pollution Periods in China

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