Coke production is a significant source of ambient volatile organic compound emissions; thus, stringent control measures must be applied. We fully characterized the trends in volatile organic compound emissions by the coking industry in China between 1949 and 2016 based on a factory-based database and process-specific emission factors. We then projected the reduction potentials in these emissions if different control policies were implemented in 2020 based on three emission scenarios. The results indicate that: (1) the emission factor of volatile organic compounds for coke plants under uncontrolled conditions was 3.065 g/kg coke, and benzene, toluene, and acetone were the most abundant emission species. (2) The annual volatile organic compound emissions from the coking industry increased by an order of magnitude from 3.38 Gg in 1949 to 1376.54 Gg in 2016. The emissions show distinct spatial characteristics, with significantly higher emissions in northern China than in other areas. (3) Compared to the uncontrolled scenario, if basic or more stringent control measures were fully implemented in China in 2020, then volatile organic compound emissions would be reduced by 59% or 82%, respectively. (4) Controlling coke oven flue gases through efficient combustion, sealing and cleaning the openings of coke ovens, and using gas blanketing or carbon absorbers in by-product facilities were the most effective technologies for controlling volatile organic compound emissions from coke production.

Biomass burning (BB) seriously affect air pollution, human health and global climate. A severe pollution episode (PE) caused by BB was investigated in the southern Sichuan Basin (SSB), one of the most polluted areas in China. Hourly variations in criteria air pollutants (PM2.5, PM10, SO2, NO2, CO, and O3), chemical components, and sources of PM2.5 before, during, and after the severe regional air PE were characterized at three sites, namely Neijiang (NJ), Zigong (ZG), and Yibin (YB). The results showed that combination of intensive pollution from BB, stable meteorological conditions, and the basin topography caused this severe regional PE in the SSB. The average daily concentrations of PM2.5 during the PE were 1.8–6 times those measured during the periods before and after the PE, and 4.0–7.4 times that of World Health Organization air quality guidelines in the SSB. The highest PM levels occurred in ZG, where the peak values of PM2.5 and PM10 reached 536 μg m−3 and 578 μg m−3 at night, respectively. PM10, NO2, and CO also increased dramatically at night in the SSB. O3 formation was affected by BB, showing lower levels at night but higher levels in the day during the PE than before and after the PE, whereas SO2 levels were not affected. Sulfate–nitrate–ammonium in PM2.5 was the main chemical compositions before the PE, whereas organic matter (OM) and K+ became characteristics compositions during and after the PE. Higher OC/EC and Kexcess/EC ratios were observed during the PE and Kexcess/EC ratio was a better indicator of BB in the SSB than OC/EC ratio. The results of a positive matrix factorization model indicated that BB was the most significant contributor to PM2.5 during the PE, accounting for 58% in NJ, 65% in ZG, and 56% in YB. Backward trajectory analysis confirmed that the SSB is susceptible to pollutants from Chongqing and other surrounding cities, especially in ZG and NJ, due to the unique topography of the basin. Our findings suggest that BB in the basin topography can cause severe regional air pollution events at night, thus supporting the critical need for BB control in the basin to improve regional air quality.

Carbonaceous aerosols are linked to severe haze and health effects, while its origins remain still unclear over China. PM2.5 samples covering four seasons from Jan. 2016 to Jan. 2017 were collected at six sites in Chifeng, a representative agro-pastoral transitional zone of North China focusing on the characteristics and sources of organic carbon (OC) and elemental carbon (EC). The annual averages of OC, EC were 9.00 ± 7.24 μg m−3, 1.06 ± 0.79 μg m−3 with site Songshan in coal mining region exhibited significantly enhanced levels. The residential heating emissions, air stagnation, and secondary organic formation all contributed the higher OC, EC levels in winter. Meanwhile, the impacts from open biomass burning were most intensive in spring. The retroplumes via Lagrangian model highlighted a strong seasonality of regional sources which had more impacts on EC increases. The Positive Matrix Factorization (PMF) model resolved six primary sources, namely, coal combustion, biomass burning, industrial processes, oil combustion, fugitive dust, and fireworks. Coal combustion and biomass burning comprised large fractions of OC (30.57%, 30.40%) and EC (23.26%, 38.47%) across the sites, while contributions of industrial processes and oil combustion clearly increased in the sites near industrial sources as smelters. PMF and EC tracer method gave well correlated (r=0.65) estimates of Secondary OC (SOC). The proportion of coal combustion and SOC were more enhanced along with PM2.5 elevation compared to other sources, suggesting their importances during the pollution events.

\textlessp\textgreater\textlessstrong\textgreaterAbstract.\textless/strong\textgreater Improving the accuracy of the anthropogenic volatile organic compound (VOC) emission inventory is essential for reducing air pollution. In this study, we established an emission inventory of anthropogenic VOCs in the Beijing–Tianjin–Hebei (BTH) region of China for 2015 based on the emission factor (EF) method. Online ambient VOC observations were conducted in one urban area of Beijing in January, April, July, and October, which, respectively, represented winter, spring, summer, and autumn in 2015. Furthermore, the developed emission inventory was evaluated by a comprehensive verification system based on the measurements and satellite retrieval results. Firstly, emissions of the individual species of the emission inventory were evaluated according to the ambient measurements and emission ratios versus carbon monoxide (CO). Secondly, the source structure of the emission inventory was evaluated using source appointment with the Positive Matrix Factorization (PMF) model. Thirdly, the spatial and temporal distribution of the developed emission inventory was evaluated by a satellite-derived emission inventory. According to the results of the emission inventory, the total anthropogenic VOC emissions in the BTH region were 3277.66 Gg in 2015. Online measurements showed that the average mixing ratio of VOCs in Beijing was approximately 49.94 ppbv in 2015, ranging from 10.67 to 245.54 ppbv. The annual emissions for 51 of 56 kinds of non-methane hydrocarbon species derived from the measurements agreed within \textlessspan class="inline-formula"\textgreater±100\textless/span\textgreater % with the results of the emission inventory. Based on the PMF results and the emission inventory, it is evident that vehicle-related emissions dominate the composition of anthropogenic VOCs in Beijing. The spatial correlation between the emission inventory and satellite inversion result was significant (\textlessspan class="inline-formula"\textgreater\textitp<0.01\textless/span\textgreater) with a correlation coefficient of 0.75. However, there were discrepancies between the relative contributions of fuel combustion, emissions of oxygenated VOCs (OVOCs), and halocarbons from the measurements and inventory. To obtain a more accurate emission inventory, we propose the investigation of the household coal consumption, the adjustment of EFs based on the latest pollution control policies, and the verification of the source profiles of OVOCs and halocarbons.\textless/p\textgreater

Volatile organic compounds (VOCs) pollution, which is closely linked to photochemical smog and secondary organic aerosols, has become a severe concern in China. Therefore, we compiled a new high-resolution emission inventory for the industrial non-methane Volatile organic compounds (NMVOCs) using “bottom-up” approaches throughout 2010 and 2016. In this work, the industrial sources were divided into five major categories, and 108 specific sources, as well as an emission factor database, was developed for industrial NMVOCs. Results indicated that the total NMVOCs emissions from industrial sources increased from 16.88 Tg in 2010 to 21.04 Tg in 2016 at an annual average rate of 3.7%. The five major source categories including “production of VOCs”, “storage and transportation”, “industrial processes using VOCs as raw material”, “processes using VOCs-containing products”, and “fossil fuel combustion” generated 1.92 Tg, 0.94 Tg, 6.54 Tg, 10.04 Tg, and 1.60 Tg NMVOCs, respectively, in 2016. Coke production, plastic manufacturing, raw medicine industry, and architectural decoration were the primary sources of industrial NMVOCs and emissions of these sources increased by 140 Gg, 190 Gg, 640 Gg, and 700 Gg between 2010 and 2016. The emissions displayed distinct spatial characteristics, with significantly higher emissions in the Beijing-Tianjin-Hebei region, the Pearl River Delta, the Yangtze River Delta, and the Cheng-Yu region than in other areas. Shandong, Guangdong, Jiangsu, Zhejiang, and Henan were the top five provinces with the highest NMVOCs emissions, while the emission hotspots in the county-level were mainly distributed in Guangzhou urban area, Shanghai Pudong New Area, Hangzhou urban area, and Shenzhen urban area. The emissions in Henan province, Hubei province, and Cheng-Yu region increased significantly during the study period. Instead, emissions in some counties of Zhejiang province and Hebei province decreased than in 2010.