Effects of NO x and VOCs from five emission sources on summer surface O3 over the Beijing-Tianjin-Hebei region

Yu, Qian; An, Junling; Li, Jian; Chen, Yong; Liu, Ying; Liu, Xingang; Hu, Min

doi:10.1007/s00376-013-3132-x

Cited by 32 publications

(16 citation statements)

References 57 publications

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“…Although biogenic emissions were high in Yan Mountains that located in southern BTH and Taihang Mountains that located in western BTH, the impacts on surface O 3 were low due to the lack of NO x emissions there (Qu et al, 2014). It was noticeable that northern part of Anhui province was the major influential source areas for the overall O 3 in both SC and NC.…”

Section: The High Positive ϕ O 3 − E No X and ϕ O 3 − E Vocmentioning

confidence: 94%

“…The Huai River-Qin Mountains, a geographical dividing line acted as a barrier which leaded to differences in climate, air pollution and agricultural diversity, was used to geographically divide SC and NC (Li et al, 2017a;Lin, 2017;Wu et al, 2013;Xue et al, 2008). It was known that mega cities and city clusters in China had severe O 3 pollution problems due to larger amounts of anthropogenic emission sources especially in BTH (Qu et al, 2014;Tang et al, 2012), YRD (Li et al, 2012a;Wang et al, 2006), and PRD Shao et al, 2009). Therefore, the regions of SC, NC, PRD, YRD and BTH were designed as the receptor regions of O 3 concentration.…”

Section: Methodsmentioning

confidence: 99%

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Source contributions of surface ozone in China using an adjoint sensitivity analysis

Wang

Yim

Wong

et al. 2019

Science of The Total Environment

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Air pollution has become an adverse environmental problem in China, resulting in serious public health impacts. This study advanced and applied the CMAQ adjoint model to quantitatively assess the source-receptor relationships between surface ozone (O 3) changes over different receptor regions and precursor emissions across all locations in China. Five receptor regions were defined based on the administrative division, including northern China (NC), southern China (SC), Pearl River Delta region (PRD), Yangtz River Delta region (YRD), and Beijing-Tianjin-Hebei region (BTH). Our results identified the different influential pathways of atmospheric processes and emissions to O 3 pollution. We found that the atmospheric processes such as horizontal and vertical advection could offset the O 3 removal through chemical reactions in VOC-limited areas inside the receptor regions. In addition, O 3 pollution can be induced by transport of O 3 directly or its precursors. Our results of relative source contributions to O 3 show that transboundary O 3 pollution was significant in SC, NC and YRD, while the O 3 pollution in PRD and BTH were more contributed by local sources. Anhui, Hubei and Jiangsu provinces were the three largest source areas of NO x and VOC emissions to O 3 in SC (>52%) and YRD (>69%). NO x and VOC emissions from Tianjin and Beijing were the largest contributors to O 3 in NC (>34%) and BTH (>51%). PRD was the dominant source areas of NO x (>89%) and VOC emissions (~98%) to its own regional O 3 .

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Section: The High Positive ϕ O 3 − E No X and ϕ O 3 − E Vocmentioning

confidence: 94%

Section: Methodsmentioning

confidence: 99%

Source contributions of surface ozone in China using an adjoint sensitivity analysis

Wang

Yim

Wong

et al. 2019

Science of The Total Environment

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“…O 3 in the troposphere is an important greenhouse gas that has the third-highest radiative forcing after CO 2 and CH 4 (Stevenson et al, 2013;IPCC, 2013). In addition, O 3 is the primary source of the hydroxyl radical (OH), which has a major influence on the oxidizing capacity of the atmosphere and thus impacts the oxidation chemistry of secondary pollutants (e.g., sulfate and secondary organic aerosol) (Ehhalt et al, 2000;Rohrer et al, 2006). In recent years, the surface O 3 level has been increasing in most Chinese cities.…”

Section: Introductionmentioning

confidence: 99%

Impacts of biogenic and anthropogenic emissions on summertime ozone formation in the Guanzhong Basin, China

Greenberg

et al. 2018

Atmos. Chem. Phys.

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Abstract. This study is the first attempt to understand the synergistic impact of anthropogenic and biogenic emissions on summertime ozone (O3) formation in the Guanzhong (GZ) Basin where Xi'an, the oldest and the most populous city (with a population of 9 million) in northwestern China, is located. Month-long (August 2011) WRF-Chem simulations with different sensitivity experiments were conducted and compared with near-surface measurements. Biogenic volatile organic compounds (VOCs) concentrations was characterized from six surface sites among the Qinling Mountains, and urban air composition was measured in Xi'an city at a tower 100 ma.s. The WRF-Chem control experiment reasonably reproduced the magnitudes and variations of observed O3, VOCs, NOx, PM2.5, and meteorological parameters, with normalized mean biases for each parameter within ±21 %. Subsequent analysis employed the factor separation approach (FSA) to quantitatively disentangle the pure and synergistic impacts of anthropogenic and/or biogenic sources on summertime O3 formation. The impact of anthropogenic sources alone was found to be dominant for O3 formation. Although anthropogenic particles reduced NO2 photolysis by up to 60 %, the anthropogenic sources contributed 19.1 ppb O3 formation on average for urban Xi'an. The abundant biogenic VOCs from the nearby forests promoted O3 formation in urban areas by interacting with the anthropogenic NOx. The calculated synergistic contribution (from both biogenic and anthropogenic sources) was up to 14.4 ppb in urban Xi'an, peaking in the afternoon. Our study reveals that the synergistic impact of individual source contributions to O3 formation should be considered in the formation of air pollution control strategies, especially for big cities in the vicinity of forests.

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“…CAMx is an Eulerian (gridded) regional photochemical dispersion model, which could simulate the emission, dispersion, chemical reaction, and removal of pollutants by marching the Eulerian continuity equation forward in time for each chemical species on a system of nested three-dimensional grids. In CAMx, the Particulate Source Appointment Technology (PSAT), an efficient source tagging method (Li et al, 2015b), is employed as the source apportionment method to accurately reflect the pollutant transmission and appointment of sources at a regional scale, the effectiveness of emission control measures can also be investigated (Shen et al, 2011;Wang et al, 2014a;Qu et al, 2014;Koo et al, 2015;Li et al, 2015b;Pirovano et al, 2015). For example, Megaritis et al (2014) applied CAMx model to Europe to study the influence of emissions changes on fine PM levels, with the result was that reduction of NH 3 emissions seems to be the most effective control strategy for reducing PM 2.5 over Europe.…”

Section: Introductionmentioning

confidence: 99%

An Integrated WRF-CAMx Modeling Approach for Impact Analysis of Implementing the Emergency PM2.5 Control Measures during Red Alerts in Beijing in December 2015

Jia

Cheng

Liu

et al. 2017

Aerosol Air Qual. Res.

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In December 2015, the Beijing-Tianjin-Hebei (BTH) region experienced several episodes of heavy air pollution. Beijing municipal government therefore issued 2 red alerts on December 7 and 19, respectively, and also implemented emergency control measures to alleviate the negative effects of pollution. It is estimated that the heavy pollutions in 2 red alert periods in Beijing were due mainly to the accumulation of air pollutants from local emission sources and the transboundary transport of pollutants from surrounding areas. The collected meteorological and PM 2.5 data indicate that the severity of air pollutions were enlarged by the poor meteorological conditions along with lower mixing layer height. In this study, the WRF-CAMx modeling system was utilized not only for analyzing the contributions of PM 2.5 from different sources, but also for quantitatively assessing the effects of implementing various emergency control measures on PM 2.5 pollution control during the red alert periods. The modeling results show that local emissions were the most dominant contributors (64.8%-83.5%) among all emission sources, while the main external contributions came from the city of Baoding (3.4%-9.3%). In addition, among 5 different emission source categories, coal and traffic were the two dominant contributors to PM 2.5 concentration in urban area of Beijing. Then four pollution control scenarios were designed particularly to investigate the effectiveness of the emergency control measures, and the results show that, generally these emergency control measures have positive effects on air pollution reduction. In particular, restrictive measures of traffic volume control and industrial activity shutdown/suspension have been found as the most effective measures in comparison to other emergency control measures. It is recommended that such effective measures should be considered to implement when next time similar heavy air pollutions occur in the city of Beijing.

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Effects of NO x and VOCs from five emission sources on summer surface O3 over the Beijing-Tianjin-Hebei region

Cited by 32 publications

References 57 publications

Source contributions of surface ozone in China using an adjoint sensitivity analysis

Source contributions of surface ozone in China using an adjoint sensitivity analysis

Impacts of biogenic and anthropogenic emissions on summertime ozone formation in the Guanzhong Basin, China

An Integrated WRF-CAMx Modeling Approach for Impact Analysis of Implementing the Emergency PM2.5 Control Measures during Red Alerts in Beijing in December 2015

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