Speciated OVOC and VOC emission inventories and their implications for reactivity-based ozone control strategy in the Pearl River Delta region, China

Ou, Jiamin; Zheng, Junyu; Li, Rongrong; Huang, Xiaobo; Zhong, Zhuangmin; Zhong, Lexuan; Lin, Hui

doi:10.1016/j.scitotenv.2015.05.062

Cited by 163 publications

(78 citation statements)

References 15 publications

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“…Ozone formation potential is used in the assessment of the roles of different VOC species in O 3 formation (Ou et al, 2015). VOC concentration influences the O 3 chemical production, and different VOC species make different contributions to O 3 formation (Geng et al, 2009).…”

Section: Estimation Of Ozone Formation Potentialmentioning

confidence: 99%

“…VOCs are also crucial precursors to secondary organic aerosols (SOAs) (Zheng et al, 2017). An individual VOC can react at different rates with different reaction mechanisms and has different O 3 formation potentials; therefore, the control of VOC emission sources needs to consider their emission amounts and chemical reactivities (e.g., maximum incremental reactivity) (Ou et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Source Apportionment of Air Pollution and Characteristics of Volatile Organic Compounds in a Municipal Wastewater Treatment Plant, North Taiwan

Widiana

Tsai

Wang

et al. 2017

Aerosol Air Qual. Res.

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Monitoring air quality in the municipal wastewater treatment plants is an initial stage in preventing several problems related to air emissions. This study measured 103 volatile organic compounds (VOCs), total VOC (TVOC), and some prominent air pollutants (CO, CO 2 , NH 3 , H 2 S, PM 1 , PM 2.5 , PM 7 , PM 10 , TSP) in the municipal wastewater treatment plant X located in the north of Taiwan. Thirty-three VOCs were identified, which categorized as alkane, aromatic, alkene, ester, ether, haloalkane and ketone. Five dominant factors were determined from principal component analysis (PCA). The first factor involves indoor activities (such as particulate matter resuspension), and outdoor activities (such as vehicles exhausts), which explained 32.42% of total variance. Factor 2 was paint applications and domestic wastewater decomposition, with an explained variance of 17.23%. Factor 3 was solvent use, with an explained variance of 14.26%. Factor 4 was solvent use and road dust, with an explained variance of 8.54%. Factor 5 was a byproduct of the chlorination treatment process, with an explained variance of 6.92%. The five factors explained 79.37% of total variance. By applying absolute principal component scores (APCS), source apportionments were obtained having 35.21%, 26.04%, 16.13%, 7.03%, and 15.59% for factors 1, 2, 3, 4, and 5, respectively. Toluene had the highest ozone formation potential (OFP).

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Section: Estimation Of Ozone Formation Potentialmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Source Apportionment of Air Pollution and Characteristics of Volatile Organic Compounds in a Municipal Wastewater Treatment Plant, North Taiwan

Widiana

Tsai

Wang

et al. 2017

Aerosol Air Qual. Res.

View full text Add to dashboard Cite

show abstract

“…A routine central monitoring network in China has been operating since 2013, but it is still limited in spatial coverage and lacks detailed information of the chemical composition, PM size fractions, and source origins of air pollutants. Chemical transport models (CTMs) have been widely used in health effect studies to overcome the limitations in central monitoring measurements for exposure estimates (Philip et al, 2014;Lelieveld et al, 2015;Liu et al, 2016;Laurent et al, 2016a, b;Ostro et al, 2015). However, the accuracy of the predictions from CTMs is largely affected by the accuracies of the emission inventories (Wang et al, 2010), meteorological fields (Hu et al, 2010), and numerical solutions to the equations that describe various atmospheric processes Yu et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

“…Several emission inventories have been created to cover China. Different emission inventories focus on specific geographical regions in the urban, regional (Zhao et al, 2012;Zhang et al, 2008), and national or continental Kurokawa et al, 2013) scales, and/or focus on specific pollutants (Su et al, 2011;Ou et al, 2015) and specific sectors (Zhao et al, 2008;Xu et al, 2017).…”

Section: Introductionmentioning

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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“…The monthly change of ozone did not fluctuate as sharp as VOCs, meaning VOCs was possibly not the only source, for instance, NO x could also influence the depletion of ozone (Wei et al, 2014a). Another explanation was that the chemical reactivity of VOCs affected the formation of ozone more than the mass of VOCs (Ou et al, 2015).…”

Section: Resultsmentioning

confidence: 99%

Characterization of Ambient Volatile Organic Compounds (VOCs) in the Area Adjacent to a Petroleum Refinery in Jinan, China

Zhang

Wang

Jiang

et al. 2017

Aerosol Air Qual. Res.

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ABSTRACT56 VOCs were monitored for a whole year in a refinery vicinity area in suburban of Jinan, as well as PM 2.5 , PM 10 , and ozone. The results of VOCs and particulate matters showed that January was the most polluted month during the sampling period, possibly resulting from coal burning in the district heating period of Jinan. According to the concentration profiles of monthly variety of the ambient pollutants, the change of VOCs showed a certain extent of positive correlation with PM 2.5 and PM 10 , while negative correlation with ozone. It was found that the average concentration of total VOCs was 50.58 µg m -3 while ethane and ethene were the most abundant VOC species with the concentrations of 24.58 and 3.94 µg m -3 , respectively. The high relative contribution of ethane was unusual, compared with the previous related VOC research in urban area of same city or other cities in China. Based on the analysis of the monitoring data, this area was not majorly affected by major VOCs pollutants from refinery emission. m & p-Xylene of 1.34 µg m -3 was the most abundant pollutant of BTEX. The BTEX ratio analysis presented that traffic exhaust was not the major VOCs source.

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Speciated OVOC and VOC emission inventories and their implications for reactivity-based ozone control strategy in the Pearl River Delta region, China

Cited by 163 publications

References 15 publications

Source Apportionment of Air Pollution and Characteristics of Volatile Organic Compounds in a Municipal Wastewater Treatment Plant, North Taiwan

Source Apportionment of Air Pollution and Characteristics of Volatile Organic Compounds in a Municipal Wastewater Treatment Plant, North Taiwan

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

Characterization of Ambient Volatile Organic Compounds (VOCs) in the Area Adjacent to a Petroleum Refinery in Jinan, China

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