Vehicular emissions in China in 2006 and 2010

Tang, Guiqian; Chao, Nengfang; Wang, Yuesi; Chen, Jiashan

doi:10.1016/j.jes.2016.01.031

Cited by 45 publications

(15 citation statements)

References 35 publications

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“…This suggests the important role of motor vehicle exhaust in urban PM 2.5 pollution, especially in Beijing where the number of motor vehicles increased to 5.7 million by 2016. Notably, secondary aerosols are mainly produced by the gas-to-particle transformations of SO 2 , NO x , NH 3 , and VOCs, and motor vehicle exhaust is an important source of emissions of NO x and VOCs in urban areas (Huang et al, 2011;Tang et al, 2016b). Therefore, the actual contribution of motor vehicle exhaust to aerosols should be higher when considering the secondary formation of gaseous exhaust in the atmosphere.…”

Section: Source Apportionment Using Pmfmentioning

confidence: 99%

Chemical characterization and source identification of PM2.5 at multiple sites in the Beijing–Tianjin–Hebei region, China

et al. 2017

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Abstract. The simultaneous observation and analysis of atmospheric fine particles (PM 2.5 ) on a regional scale is an important approach to develop control strategies for haze pollution. In this study, samples of filtered PM 2.5 were collected simultaneously at three urban sites (Beijing, Tianjin, and Shijiazhuang) and at a regional background site (Xinglong) in the Beijing-Tianjin-Hebei (BTH) region from June 2014 to April 2015. The PM 2.5 at the four sites was mainly comprised of organic matter, secondary inorganic ions, and mineral dust. Positive matrix factorization (PMF) demonstrated that, on an annual basis, secondary inorganic aerosol was the largest PM 2.5 source in this region, accounting for 29.2-40.5 % of the PM 2.5 mass at the urban sites; the secondlargest PM 2.5 source was motor vehicle exhaust, particularly in Beijing (24.9 %), whereas coal combustion was also a large source in Tianjin (12.4 %) and Shijiazhuang (15.5 %), with particular dominance in winter. Secondary inorganic aerosol plays a vital role in the haze process, with the exception of the spring haze in Shijiazhuang and Tianjin, for which the dust source was crucial. In addition to secondary transformations, local direct emissions (coal combustion and motor vehicle exhaust) significantly contribute to the winter haze at the urban sites. Moreover, with the aggravation of haze pollution, the OC / EC mass ratio of PM 2.5 decreased considerably and the nitrate-rich secondary aerosol increased during all four seasons in Beijing, both of which indicate that local motor vehicle emissions significantly contribute to the severe haze episodes in Beijing. To assess the impacts of regional transport on haze pollution, the PMF results were further processed with backward-trajectory cluster analysis, revealing that haze pollution usually occurred when air masses originating from polluted industrial regions in the south prevailed and is characterized by high PM 2.5 loadings with considerable contributions from secondary aerosols. This study suggests that control strategies to mitigate haze pollution in the BTH region should focus on the reduction of gaseous precursor emissions from fossil fuel combustion (motor vehicle emissions in Beijing and coal combustion in Tianjin, Hebei, and nearby provinces).

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Section: Source Apportionment Using Pmfmentioning

confidence: 99%

Chemical characterization and source identification of PM2.5 at multiple sites in the Beijing–Tianjin–Hebei region, China

et al. 2017

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“…For example, coalfired power plants have been forced to install flue gas desulfurization and denitration (Zhang et al, 2012;Chen et al, 2014), coal has been replaced with natural gas and electricity in megacities (Wang et al, 2009;Duan et al, 2012;Zhao et al, 2013a;, stricter emission standards have been implemented for vehicles and industrial boilers (Zhang et al, 2012;Tang et al, 2016) and so on, resulting in a decreasing trend of primary pollutants including PM 2.5 in recent years Wen et al, 2016;Zhang et al, 2016). However, the PM 2.5 levels were still larger than 1000 µg m −3 in some areas of the Beijing-Tianjin-Hebei (BTH) region during the period of the red alert for haze in December 2016 (http://english.mep.gov.cn/News_service/ media_news/201612/t20161220_369317.shtml).…”

Section: Introductionmentioning

confidence: 99%

The contribution of residential coal combustion to atmospheric PM2. 5 in northern China during winter

et al. 2017

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Abstract. A vast area in northern China, especially during wintertime, is currently suffering from severe haze events due to the high levels of atmospheric PM 2.5 . To recognize the reasons for the high levels of PM 2.5 , daily samples of PM 2.5 were simultaneously collected at the four sampling sites of Beijing city (BJ), Baoding city (BD), Wangdu county (WD) and Dongbaituo (DBT) during the winter and spring of [2014][2015] at DBT than at WD, BD and BJ during the winter of 2015 indicated that the pollutants in the rural area were not due to transportation from neighbouring cities but dominated by local emissions. As the distinct source of atmospheric OC and EC in the rural area, the residential coal combustion also made a contribution to secondary inorganic ions through the emissions of their precursors (NO x , SO 2 , NH 3 and HCl) as well as heterogeneous or multiphase reactions on the surface of OC and EC. The average mass proportions of OC, EC, NO − 3 and SO 2− 4 at BD and WD were found to be very close to those at DBT, but were evidently different from those at BJ, implying that the pollutants in the cities of WD and BD, which are fully surrounded by the countryside, were strongly affected by the residential coal combustion. The OC / EC ratios at the four sampling sites were almost the same value (4.8) when the concentrations of PM 2.5 were greater than 150 µg m −3 , suggesting that the residential coal combustion could also make a dominant contribution to atmospheric PM 2.5 at BJ during the severe pollution period when the air parcels were usually from southwest-south regions, where a high density of farmers reside. The evident increase in the number of the species involved in significant correlations (p < 0.05) from the countryside to the cities further confirmed that residential coal combustion was the dominant source of key species in the rural area. However, the complex sources including local emissions and regional transportation were responsible for the atmospheric species in the cities. Strong correlations among OC, EC, Cl − , NO − . Based on the chemical mass closure (CMC) method, the contributions of the primary particle emission from residential coal combustion to atmospheric PM 2.5 at BJ, BD, WD and DBT were estimated to be 32, 49, 43 and 58 %, respectively.

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“…Recently studies had shown that traffic forecasting models are effective within cities (Min et al, 2009;Cortez et al, 2012;Vlahogianni et al, 2014). These models allow one to obtain predicted traffic-based on-road emissions.…”

Section: Discussionmentioning

confidence: 99%

“…Many speed-flow models exist for this purpose and each of them has certain advantages and disadvantages. In this study, the Underwood volume calculation model (Underwood, 1961) was used to retrieve the information on traffic volume because of its history of successful application in China . The model is described by Eq.…”

Section: Traffic Data From Floating Car Datamentioning

confidence: 99%

Development of the Real-time On-road Emission (ROE v1.0) model for street-scale air quality modeling based on dynamic traffic big data

Chang

Wang

et al. 2020

Geosci. Model Dev.

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Abstract. Rapid urbanization in China has led to heavy traffic flows in street networks within cities, especially in eastern China, the economically developed region. This has increased the risk of exposure to vehicle-related pollutants. To evaluate the impact of vehicle emissions and provide an on-road emission inventory with higher spatiotemporal resolution for street-network air quality models, in this study, we developed the Real-time On-road Emission (ROE v1.0) model to calculate street-scale on-road hot emissions by using real-time big data for traffic provided by the Gaode Map navigation application. This Python-based model obtains street-scale traffic data from the map application programming interface (API), which are open-access and updated every minute for each road segment. The results of application of the model to Guangzhou, one of the three major cities in China, showed on-road vehicle emissions of carbon monoxide (CO), nitrogen oxide (NOx), hydrocarbons (HCs), PM2.5, and PM10 to be 35.22×104, 12.05×104, 4.10×104, 0.49×104, and 0.55×104 Mg yr−1, respectively. The spatial distribution reveals that the emission hotspots are located in some highway-intensive areas and suburban town centers. Emission contribution shows that the dominant contributors are light-duty vehicles (LDVs) and heavy-duty vehicles (HDVs) in urban areas and LDVs and heavy-duty trucks (HDTs) in suburban areas, indicating that the traffic control policies regarding trucks in urban areas are effective. In this study, the Model of Urban Network of Intersecting Canyons and Highways (MUNICH) was applied to investigate the impact of traffic volume change on street-scale photochemistry in the urban areas by using the on-road emission results from the ROE model. The modeling results indicate that the daytime NOx concentrations on national holidays are 26.5 % and 9.1 % lower than those on normal weekdays and normal weekends, respectively. Conversely, the national holiday O3 concentrations exceed normal weekday and normal weekend amounts by 13.9 % and 10.6 %, respectively, owing to changes in the ratio of emission of volatile organic compounds (VOCs) and NOx. Thus, not only the on-road emissions but also other emissions should be controlled in order to improve the air quality in Guangzhou. More significantly, the newly developed ROE model may provide promising and effective methodologies for analyzing real-time street-level traffic emissions and high-resolution air quality assessment for more typical cities or urban districts.

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Vehicular emissions in China in 2006 and 2010

Cited by 45 publications

References 35 publications

Chemical characterization and source identification of PM<sub>2.5</sub> at multiple sites in the Beijing–Tianjin–Hebei region, China

Chemical characterization and source identification of PM<sub>2.5</sub> at multiple sites in the Beijing–Tianjin–Hebei region, China

The contribution of residential coal combustion to atmospheric PM<sub>2. 5</sub> in northern China during winter

Development of the Real-time On-road Emission (ROE v1.0) model for street-scale air quality modeling based on dynamic traffic big data

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Vehicular emissions in China in 2006 and 2010

Cited by 45 publications

References 35 publications

Chemical characterization and source identification of PM&lt;sub&gt;2.5&lt;/sub&gt; at multiple sites in the Beijing–Tianjin–Hebei region, China

Chemical characterization and source identification of PM&lt;sub&gt;2.5&lt;/sub&gt; at multiple sites in the Beijing–Tianjin–Hebei region, China

The contribution of residential coal combustion to atmospheric PM&lt;sub&gt;2. 5&lt;/sub&gt; in northern China during winter

Development of the Real-time On-road Emission (ROE v1.0) model for street-scale air quality modeling based on dynamic traffic big data

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Chemical characterization and source identification of PM<sub>2.5</sub> at multiple sites in the Beijing–Tianjin–Hebei region, China

Chemical characterization and source identification of PM<sub>2.5</sub> at multiple sites in the Beijing–Tianjin–Hebei region, China

The contribution of residential coal combustion to atmospheric PM<sub>2. 5</sub> in northern China during winter