Spatial and temporal variability of PM2.5 and PM10 over the North China Plain and the Yangtze River Delta, China

Hu, Jianlin; Wang, Yungang; Ying, Qi; Zhang, Hongliang

doi:10.1016/j.atmosenv.2014.07.019

Cited by 386 publications

(189 citation statements)

References 60 publications

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“…Around 25% PM 2.5 mass concentrations exceeded the second grade of new issued National Ambient Air Quality Standard (NAAQS, GB 3095-2012) of China, with daily average value of 75 μg m -3 . In addition, the mean ratio of PM 2.5 /PM 10 was estimated about 0.67, suggesting that fine particles contributed a large part of PM 10 , 10 which is consistent with the previous studies in the Pearl River Delta region, North China Plan and Yangtze River Delta region (Wang et al, 2006;Sun et al, 2004;Hu et al, 2014).…”

Section: Concentrations Of Pm 25 and Pm 10 And Mass Closure Analysissupporting

confidence: 79%

High-resolution sampling and analysis of ambient particulate matter in the Pearl River Delta region of Southern China: source apportionment and health risk implications

Zhou¹,

Davy²,

Huang³

et al. 2017

Preprint

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Abstract. Hazardous air pollutants, such as trace elements in particulate matter (PM), are known or highly suspected to cause detrimental effects on human health. To understand the sources and associated risks of PM to human health, hourly time-integrated major trace elements in size-segregated coarse (PM 2.5-10 ) and fine (PM 2.5 ) particulate matter were collected at the industrial city of Foshan in the Pearl River Delta region, China. Receptor modeling of the dataset by positive matrix factorization (PMF) 25 was used to identify six sources contributing to PM 2.5 and PM 10 concentrations at the site. Dominant sources included industrial coal combustion, secondary inorganic aerosol, motor vehicles and construction dust along with two intermittent sources (biomass combustion and marine aerosol). The biomass combustion source was found to be a significant contributor to peak PM 2.5 episodes along with 2 motor vehicles and industrial coal combustion. Conditional probability function (CPF) was applied to estimate the source locations using the PMF-resolved source contribution coupled with the surface wind direction data. Health exposure risk of hazardous trace elements (Pb, As, Si, Cr, Mn and Ni) and sourcespecific values were estimated. The total hazard quotient (HQ) of PM 2.5 was 2.09, higher than the acceptable limit (HQ = 1). The total carcinogenic risk (CR) was 3.3710 -3 for PM 2.5 , which was three 5 times higher than the most tolerable limit (1.010 -4 ). Among the selected trace elements, As and Pb posed the highest non-carcinogenic and carcinogenic risks to human health, respectively. In addition, our results showed that industrial coal combustion source was the dominant non-carcinogenic and carcinogenic risks contributor, highlighting the need for stringent control of this source. This study provides new insight for policy makers to prioritize sources in air quality management and health risk 10 reduction.Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2017-807 Manuscript under review for journal Atmos. Chem. Phys.

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Section: Concentrations Of Pm 25 and Pm 10 And Mass Closure Analysissupporting

confidence: 79%

High-resolution sampling and analysis of ambient particulate matter in the Pearl River Delta region of Southern China: source apportionment and health risk implications

Zhou¹,

Davy²,

Huang³

et al. 2017

Preprint

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“…Observations at multiple sites in one city were averaged to calculate the average concentrations of the city. Detailed quality control of the data can be found in previous studies (Hu et al, 2014a. Statistical matrix of mean normalized bias (MNB), mean normalized error (MNE), mean fractional bias (MFB), and mean fractional error (MFE) were calculated using Eqs.…”

Section: Model Evaluationmentioning

confidence: 99%

Ensemble prediction of air quality using the WRF/CMAQ model system for health effect studies in China

Huang

et al. 2017

Atmos. Chem. Phys.

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Abstract. Accurate exposure estimates are required for health effect analyses of severe air pollution in China. Chemical transport models (CTMs) are widely used to provide spatial distribution, chemical composition, particle size fractions, and source origins of air pollutants. The accuracy of air quality predictions in China is greatly affected by the uncertainties of emission inventories. The Community Multiscale Air Quality (CMAQ) model with meteorological inputs from the Weather Research and Forecasting (WRF) model were used in this study to simulate air pollutants in China in 2013. Four simulations were conducted with four different anthropogenic emission inventories, including the Multi-resolution Emission Inventory for China (MEIC), the Emission Inventory for China by School of Environment at Tsinghua University (SOE), the Emissions Database for Global Atmospheric Research (EDGAR), and the Regional Emission inventory in Asia version 2 (REAS2). Model performance of each simulation was evaluated against available observation data from 422 sites in 60 cities across China. Model predictions of O 3 and PM 2.5 generally meet the model performance criteria, but performance differences exist in different regions, for different pollutants, and among inventories. Ensemble predictions were calculated by linearly combining the results from different inventories to minimize the sum of the squared errors between the ensemble results and the observations in all cities. The ensemble concentrations show improved agreement with observations in most cities. The mean fractional bias (MFB) and mean fractional errors (MFEs) of the ensemble annual PM 2.5 in the 60 cities are −0.11 and 0.24, respectively, which are better than the MFB (−0.25 to −0.16) and MFE (0.26-0.31) of individual simulations. The ensemble annual daily maximum 1 h O 3 (O 3 -1h) concentrations are also improved, with mean normalized bias (MNB) of 0.03 and mean normalized errors (MNE) of 0.14, compared to MNB of 0.06-0.19 and MNE of 0.16-0.22 of the individual predictions. The ensemble predictions agree better with observations with daily, monthly, and annual averaging times in all regions of China for both PM 2.5 and O 3 -1h. The study demonstrates that ensemble predictions from combining predictions from individual emission inventories can improve the accuracy of predicted temporal and spatial distributions of air pollutants. This study is the first ensemble model study in China using multiple emission inventories, and the results are publicly available for future health effect studies.

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“…Compared with numerous studies demonstrating the variability of PM concentrations (Eeftens et al, 2012;Hu et al, 2014;Akinlade et al, 2015;Ghim et al, 2015;Huang et al, 2015;Zhang and Cao, 2015;Zhou et al, 2015;Xu et al, 2016a), few studies have reported on the spatio-temporal variability of the PM 2.5 /PM 10 ratio (Blanco-Becerra et al, 2015;Zhang and Cao, 2015;Munir, 2016;Zhou et al, 2016). The goal of this study is to investigate the spatiotemporal variability of the PM 2.5 /PM 10 ratio at a city scale systematically and comprehensively.…”

Section: Introductionmentioning

confidence: 99%

Spatial and Temporal Variability of the PM2.5/PM10 Ratio in Wuhan, Central China

Jiao

Zhang

et al. 2017

Aerosol Air Qual. Res.

162

113

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Fine particles (PM 2.5 ) and coarse particles (PM 2.5-10 ) are generally produced by different sources, so the PM 2.5 /PM 10 ratio reveals characteristics of particle pollution. The ratio can be used to characterize the underlying atmospheric processes and evaluate historical PM 2.5 pollution in absence of direct measurements. However, application of the ratio needs its varying pattern because PM concentrations change significantly at time and space. Hourly PM 2.5 and PM 10 observations at nine monitoring sites in urban area (Urban-sites) and one remote Background-site in Wuhan in 2013-2015 were collected to investigate both long-term, short-term temporal variation and spatial distribution, spatial disparity of the ratio at a city scale. The results show that annual average PM 2.5 /PM 10 ratio is 0.62 at Urban-sites and 0.68 at Background-site with apparent seasonal, monthly and daily variations. The ratio reaches the maximum in winter because of stable atmospheric conditions. There are apparent night-day differences of daily variation of the ratio, which increases at night in all seasons in consequence of temperature inversion and declines in the daytime with a moderate rise in the afternoon. We find obvious spatial gradients of the ratio that gradually increases from urban core to urban fringe and to suburban. This study provides further insights to the spatio-temporal variability of PM 2.5 /PM 10 ratio. The evidence indicates that the variability of PM 2.5 /PM 10 should be noticed in its applications.

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Spatial and temporal variability of PM2.5 and PM10 over the North China Plain and the Yangtze River Delta, China

Cited by 386 publications

References 60 publications

High-resolution sampling and analysis of ambient particulate matter in the Pearl River Delta region of Southern China: source apportionment and health risk implications

High-resolution sampling and analysis of ambient particulate matter in the Pearl River Delta region of Southern China: source apportionment and health risk implications

Ensemble prediction of air quality using the WRF/CMAQ model system for health effect studies in China

Spatial and Temporal Variability of the PM2.5/PM10 Ratio in Wuhan, Central China

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