Heavy pollution episodes, transport pathways and potential sources of PM2.5 during the winter of 2013 in Chengdu (China)

Liao, Tingting; Wang, Shan; Ai, Jie; Gui, Ke; Duan, Bolong; Zhao, Qi; Zhang, Xiao; Jiang, Wanting; Sun, Yifei

doi:10.1016/j.scitotenv.2017.01.160

Cited by 177 publications

(68 citation statements)

References 37 publications

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Section: Literature Reviewmentioning

confidence: 99%

“…Based on eight monitoring locations in Chengdu and meteorological data over three months, Liao et al (2017) analyzed the spatiotemporal characteristics and sources of PM 2.5 in Chengdu. The results reveal that the major potential sources of PM 2.5 in Chengdu are located along the western margin of the Sichuan Basin and in the southeastern cities [26]. Xin et al (2016) adopted daily average PM 10 concentration data and Global Data Assimilation System (GDAS) data to study the transport trajectories that significantly influence PM 10 in Xining and found that atmospheric pollution is easily affected by inland trajectories [27].…”

Section: Literature Reviewmentioning

confidence: 99%

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Transport Pathways and Potential Source Region Contributions of PM2.5 in Weifang: Seasonal Variations

Dai

Liu

et al. 2020

Applied Sciences

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As air pollution becomes progressively more serious, accurate identification of urban air pollution characteristics and associated pollutant transport mechanisms helps to effectively control and alleviate air pollution. This paper investigates the pollution characteristics, transport pathways, and potential sources of PM2.5 in Weifang based on PM2.5 monitoring data from 2015 to 2016 using three methods: Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT), the potential source contribution function (PSCF), and concentration weighted trajectory (CWT). The results show the following: (1) Air pollution in Weifang was severe from 2015 to 2016, and the annual average PM2.5 concentration was more than twice the national air quality second-level standard (35 μg/m3). (2) Seasonal transport pathways of PM2.5 vary significantly: in winter, spring and autumn, airflow from the northwest and north directions accounts for a large proportion; in contrast, in summer, warm-humid airflows from the ocean in the southeastern direction dominate with scattered characteristics. (3) The PSCF and CWT results share generally similar characteristics in the seasonal distributions of source areas, which demonstrate the credibility and accuracy of the analysis results. (4) More attention should be paid to short-distance transport from the surrounding areas of Weifang, and a joint pollution prevention and control mechanism is critical for controlling regional pollution.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Transport Pathways and Potential Source Region Contributions of PM2.5 in Weifang: Seasonal Variations

Dai

Liu

et al. 2020

Applied Sciences

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“…In this study, in order to reduce the uncertainty caused by inconsistency of spatiotemporal between AOT and meteorological data, we firstly incorporated MAIAC CWV into PM 2.5 estimation model based on widely used LMEM method [34]; and the model is implemented to urban agglomeration of Chengdu Plain, which is one of the most heavily polluted in China in 2015 [54][55][56]; then, we assessed model performance via different ground measurements of PM 2.5 datasets and MAIAC AOT datasets; the spatiotemporal change of PM 2.5 level in urban agglomeration of Chengdu Plain was also analyzed. A final table with acronyms is attached at the end of the manuscript, please see Appendix A.…”

Section: Introductionmentioning

confidence: 99%

Satellite-Based Estimation of Daily Ground-Level PM2.5 Concentrations over Urban Agglomeration of Chengdu Plain

Han

Tong

2019

Atmosphere

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Monitoring particulate matter with aerodynamic diameters of less than 2.5 µm (PM 2.5 ) is of great importance to assess its adverse effects on human health, especially densely populated regions. In this paper, an improved linear mixed effect model (LMEM) was developed. The model introduced meteorological variable, column water vapor (CWV), which has as the same resolution as satellite-derived aerosol optical thickness (AOT), to enhance PM 2.5 estimation accuracy by considering spatiotemporal consistency of CWV and AOT. The model was implemented to urban agglomeration of Chengdu Plain during 2015. The results show that model accuracy has been improved significantly compared to linear regression model (R 2 = 0.49), with R 2 of 0.81 and root mean squared prediction error (RMSPE) of 15.47 µg/m 3 , mean prediction error (MPE) of 11.09 µg/m 3 , and effectively revealed the characteristics of spatiotemporal variations PM 2.5 level across the study area: The PM 2.5 level is higher in the central and southern areas with dense population, while it is lower in the northwest and southwest mountain areas; and the PM 2.5 level is higher during autumn and winter, while it is lower during spring and summer. The product data in this paper are valuable for local government pollution monitoring, public health research, and urban air quality control. widely used for AOT retrieval, e.g., advanced very high-resolution radiometer (AVHRR), total ozone mapping spectrometer (TOMS), moderate-resolution imaging spectroradiometer (MODIS), multi-angle imaging spectroradiometer (MISR), sea-viewing wide field-of-view sensor (SeaWifFS) [16], and visible infrared imaging radiometer suite (VIIRS) [17], continuously measuring AOT from the late 1990s to the most recent.Satellite data are able to monitor large/urban scale air quality through adding synoptic and spatial distribution information to ground-based air quality measurements and modeling [18,19]. In recent decades, numerous researchers have developed many algorithms for calibrating satellite-derived AOT to ground-level PM 2.5 concentrations. Early studies primarily established simple linear regression models to relate PM 2.5 and AOT [19][20][21]. Then, meteorological data were incorporated in the multiple regression analysis and improved the PM 2.5 -AOT correlation [22]; advanced statistical models, such as artificial neural networks (ARN) [23], generalized additive model (GAM) [24,25], land use regression model (LUR) [26][27][28], and geographically weighted regression (GWR) [29][30][31][32][33] were developed to analyze the variability of PM 2.5 -AOT relationship correlated with meteorology or land use data. Although such advanced models gained higher estimation accuracy for large regions such as the northeastern and southeastern US, Europe, and China, etc., auxiliary parameters increased the complexity of these models and introduced other errors. Lee firstly incorporated time-varying parameters (day-to-day) in a statistical model to calibrate MODIS AOT and obtained good estimation accurac...

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“…In China, particulate contamination is a serious environmental problem that affects air quality, regional and global climate, and human health (Han, Zhou, & Li, 2016;Liao, Shan, & Jie, 2017;Xu, Ho, & Cao, 2017). Which BTH region is China's PM2.5 pollution of the heavy disaster area, it formed a large area of continuous PM2.5 serious pollution area.…”

Section: Introductionmentioning

confidence: 99%

“…At present, most studies focused on the cause of the pollution of PM2.5, source, and spatiotemporal distribution and the simulation of regional single pollution processes (Chen, Li, & Ge, 2015a;Liao et al, 2017;Luo, Du, & Samat, 2017). Constrained by PM2.5 monitoring data, the existing research in China has analysed the Spatiotemporal distribution pattern of PM2.5 in the national space range from the year, quarter, and month scale the temporal pattern of PM2.5 and the spatial agglomeration characteristics in the Short -term or a short time analysis of a city or region (Chen, Cai, & Gao, 2017;Hua, Cheng, & Wang, 2015;Li, Ren, & Yu, 2016;Li, Zhang, & Zhang, 2015a;Wang et al, 2015;Zhang, Wang, & Zhang, 2016).…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal patterns of PM2.5 in the Beijing–Tianjin–Hebei region during 2013–2016

Qiongfei

Yang

Luo

2017

Geology, Ecology, and Landscapes

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Air pollution is a major environmental problem in China. Especially, the Beijing-Tianjin-Hebei (BTH) region experienced most serious airborne pollution. In the paper, the spatial data statistical model was employed to analysis the spatiotemporal variability of PM2.5 concentration and spatial agglomeration law within the BTH region. Thus, it can provide considerable benefits when developing environmental policies. Our results indicate that PM2.5 annual average concentrations decreased annually from 2014 to 2016, the days of reaching the standards increased annually and the days of serious pollution decreased annually; the concentration of PM2.5 in BTH region showed significant spatial differentiation, the overall performance from north-west to south-east gradually increased, north and south very different spatial pattern; showing a significant assemble law, hot spots are mainly concentrated in the vast majority of Baoding, Shijiazhuang, Hengshui, Xingtai,Handan and a few of Changzhou county-level cities. Cold spots are mainly concentrated in the part of the county city of Zhangjiakou, Qinhuangdao, Chengde, and Beijing. Compared with 2014, the high concentration of county-level cities in 2016 was slightly narrowed, and the level of agglomeration was reduced.

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Heavy pollution episodes, transport pathways and potential sources of PM2.5 during the winter of 2013 in Chengdu (China)

Cited by 177 publications

References 37 publications

Transport Pathways and Potential Source Region Contributions of PM2.5 in Weifang: Seasonal Variations

Transport Pathways and Potential Source Region Contributions of PM2.5 in Weifang: Seasonal Variations

Satellite-Based Estimation of Daily Ground-Level PM2.5 Concentrations over Urban Agglomeration of Chengdu Plain

Spatiotemporal patterns of PM2.5 in the Beijing–Tianjin–Hebei region during 2013–2016

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