Source apportionment of surface ozone in the Yangtze River Delta, China in the summer of 2013

Liu, Jinchu; An, Jingyu; Shi, Y.Y.; Zhou, Min; Yan, Rusha; Huang, Chong; Wang, H.L.; Lou, Shengrong; Wang, Q.; Lu, Qihong; Wu, Jianbin

doi:10.1016/j.atmosenv.2016.08.076

Cited by 91 publications

(38 citation statements)

References 30 publications

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“…5w). Li et al (2016) For India, O3 formation is most sensitive to the transport vehicle sector (~8 ppb) in all seasons, slightly higher than it is to the biogenic source ( Fig. 5m-5p).…”

Section: Seasonality Of Surface O3 In Different Regionsmentioning

confidence: 89%

“…Using a similar approach, found that biogenic emissions contributed 8.2 ppb in urban Xi'an. Other source apportionment studies indicate that the contribution of biogenic emissions to O3 formation is about 20% in China (Li et al, 2016;Wang et al, 2019). The enhancements due to biogenic emissions are larger over south China during winter, and the significantly impacted regions extend northwards in spring and autumn ( Fig.…”

Section: Seasonality Of Surface O3 In Different Regionsmentioning

confidence: 91%

“…J. concluded that YRD local emissions contribute 13.6%-20.6% to daytime O3 under different wind conditions, and the contribution of super regional sources (Outside) ranges from 32 to 34% in May. In Hangzhou (a megacity within YRD), source apportionment results reveal that longrange transport contributes 36.5% to daily maximum O3 with the overall contribution dominated by local sources (Li et al, 2016).…”

Section: Seasonality Of Surface O3 In Different Regionsmentioning

confidence: 99%

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Ozone Pollution over China and India: Seasonality and Sources

Gao¹,

Gao²,

Zhu³

et al. 2019

Preprint

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Abstract. A regional fully coupled meteorology-chemistry Weather Research and Forecasting model with Chemistry (WRF-Chem) was employed to study the seasonality of ozone (O3) pollution and its sources in both China and India. Observations and model results suggest that O3 in the North China Plain (NCP), Yangtze River Delta (YRD), Pearl River Delta (PRD) and India exhibit distinctive seasonal features, which are linked to the influence of summer monsoons. Through a factor separation approach, we examined the sensitivity of O3 to individual anthropogenic, biogenic, and biomass burning emissions. We found that summer O3 formation is more sensitive to industrial sources than to other source sectors for China, while the transport vehicle sector is more important in all seasons for India. For India, in addition to transport, the residential sector also plays an important role in winter when O3 concentrations peak. Tagged simulations suggest that sources in east China play an important role in the formation of the summer O3 peak in the NCP, but sources from Northwest China should not be neglected to control summer O3 in the NCP. For the YRD region, prevailing winds and cleaner air from the ocean in summer lead to reduced transport from polluted regions, and the major source region in addition to local sources is Southeast China. For the PRD region, the upwind region is replaced by contributions from polluted east China as autumn approaches, leading to an autumn peak. The major upwind regions in autumn for the PRD are YRD (11&#8201;%) and Southeast China (10&#8201;%). For India, sources in North India are more important than sources in the south. These analyses emphasize the relative importance of source sectors and regions as they change with seasons, providing important implications for O3 control strategies.

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“…5w). Li et al (2016) For India, O3 formation is most sensitive to the transport vehicle sector (~8 ppb) in all seasons, slightly higher than it is to the biogenic source ( Fig. 5m-5p).…”

Section: Seasonality Of Surface O3 In Different Regionsmentioning

confidence: 89%

Section: Seasonality Of Surface O3 In Different Regionsmentioning

confidence: 91%

Section: Seasonality Of Surface O3 In Different Regionsmentioning

confidence: 99%

See 1 more Smart Citation

Ozone Pollution over China and India: Seasonality and Sources

Gao¹,

Gao²,

Zhu³

et al. 2019

Preprint

View full text Add to dashboard Cite

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“…Shanghai domain included 10 × 10 horizontal grids with grid resolution of 12-km spacing ( Figure S2). The CMAQ model has been detailed elsewhere (L. Li et al, 2016;L. Li et al, 2012).…”

Section: Cmaq Modelmentioning

confidence: 99%

Observation Constrained Aromatic Emissions in Shanghai, China

Wang

Yan

et al. 2020

JGR Atmospheres

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Aromatics in the atmosphere are a large contributor to ozone and secondary organic aerosol formation and have an adverse impact on human health. Observation‐based emissions are urgently needed for air quality management and decision making due to the large uncertainties in the emissions inventories. Atmospheric aromatics were continuously observed at one urban site in Shanghai of China in 2015, which were compared with the simulations of the Community Multiscale Air Quality (CMAQ) model. Large differences were found in the temporal variations between the model and measurements, corroborating with previous studies that the current bottom‐up emission inventories are highly biased. We corrected the aromatic emissions biases of the emission inventories using the observations with the CMAQ simulations, with the assumption that the response relationship of the emissions input and aromatic concentrations was reasonable on a monthly basis in CMAQ simulations, suggesting that the results are dependent on the model performance. The resulting aromatic emissions exhibited clear monthly variations, increasing from 5.5 ± 2.1 Gg month−1 in February to 17.4 ± 7.7 Gg month−1 in July, being different from the flat monthly pattern in the current bottom‐up emissions inventory. Toluene, xylenes, and ethylbenzene dominated aromatic emissions, accounting for ~74% on average but with considerable monthly variations, which was different from the nearly uniform value of 77% throughout the whole year in the emissions inventory. These highly temporally resolved and speciated in situ aromatics measurements provide observation‐based constraints on aromatic emissions, which cannot be obtained from satellite measurements. This study proposes a top‐down emission estimation method constrained by ambient measurements.

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“…Previous studies in China mainly focused on the measurements of VOCs in urban agglomerations such as the Pearl River Delta (PRD) region (Tang et al, 2007;Liu et al, 2008;Cheng et al, 2010;Ling et al, 2011), Yangtze River Delta (YRD) region Li et al, 2016;Shao et al, 2016), and Beijing-Tianjin-Hebei (BTH) region and key megacities including Beijing (Song et al, 2007;Yuan et al, 2010), Shanghai (Cai et al, 2010;, Guangzhou (Zou et al, 2015) and Wuhan (Lyu et al, 2016). These studies found that vehicle emissions and solvent usage contributed most to the ambient VOCs in urban areas.…”

mentioning

confidence: 99%

Monitoring of volatile organic compounds (VOCs) from an oil and gas station in northwest China for 1 year

et al. 2018

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Abstract. Oil and natural gas are important for energy supply around the world. The exploring, drilling, transportation and processing in oil and gas regions can release a lot of volatile organic compounds (VOCs). To understand the VOC levels, compositions and sources in such regions, an oil and gas station in northwest China was chosen as the research site and 57 VOCs designated as the photochemical precursors were continuously measured for an entire year (September 2014-August 2015) using an online monitoring system. The average concentration of total VOCs was 297 ± 372 ppbv and the main contributor was alkanes, accounting for 87.5 % of the total VOCs. According to the propylene-equivalent concentration and maximum incremental reactivity methods, alkanes were identified as the most important VOC groups for the ozone formation potential. Positive matrix factorization (PMF) analysis showed that the annual average contributions from natural gas, fuel evaporation, combustion sources, oil refining processes and asphalt (anthropogenic and natural sources) to the total VOCs were 62.6 ± 3.04, 21.5 ± .99, 10.9 ± 1.57, 3.8 ± 0.50 and 1.3 ± 0.69 %, respectively. The five identified VOC sources exhibited various diurnal patterns due to their different emission patterns and the impact of meteorological parameters. Potential source contribution function (PSCF) and concentration-weighted trajectory (CWT) models based on backward trajectory analysis indicated that the five identified sources had similar geographic origins. Raster analysis based on CWT analysis indicated that the local emissions contributed 48.4-74.6 % to the total VOCs. Based on the high-resolution observation data, this study clearly described and analyzed the temporal variation in VOC emission characteristics at a typical oil and gas field, which exhibited different VOC levels, compositions and origins compared with those in urban and industrial areas.

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Source apportionment of surface ozone in the Yangtze River Delta, China in the summer of 2013

Cited by 91 publications

References 30 publications

Ozone Pollution over China and India: Seasonality and Sources

Ozone Pollution over China and India: Seasonality and Sources

Observation Constrained Aromatic Emissions in Shanghai, China

Monitoring of volatile organic compounds (VOCs) from an oil and gas station in northwest China for 1 year

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