Trace elements in atmospheric particular matter play a significant role in controlling aerosolbehavior, and can thereby endanger air quality. Here, the comprehensive investigation on the elemental characteristics and sources in fine and coarse particles at Chifeng was presented. The daily samples of particular matter (PM2.5 and PM10) were collected at six sites for a one-year period, and concentrations of 19 elements were analyzed. Results showed that Al, K, Ca, Fe were the most concentrated elements, in both PM2.5 and PM10. The crustal elements mainly in coarse particles (PM2.5–10) presented higher levels during March to May, due to the increased dust suspension in springtime. The highly enriched elements as Pb, Cd mainly in fine particles (PM2.5) presented elevated levels in cold seasons, related to the increased emissions of coal combustion for heating. Site Songshan had significantly higher Pb, As, Cd levels, ascribing to the influence of coal mining. The influences of metallurgy industries on Fe, Cu, Zn levels in both size fractions were also observed. Positive matrix factorization (PMF) identified four common sources for trace elements in both fine and coarse fractions, namely fugitive dust, coal mining, a mixed industrial factor with iron and zinc, and copper smelting. The factors of coal combustion, biomass burning, oil combustion, vehicle emission and fireworks were merely obtained for fine particles. The crustal elements were mainly related to the impact of fugitive dust, while the notable impacts of coal combustion and iron/steel production were also confirmed. Cu was attributed to copper smelting in both sizes, while the major sources of Zn varied from vehicle emission (44.3%), coal combustion (32.1%) in PM2.5 to mixed industrial factor (89.3%) in PM2.5–10. Although coal combustion, coal mining and copper smelting contributed <20% of the total elemental concentrations, they were responsible for >80% of the toxic elements Pb, As, Cd.

Air quality monitoring networks play a significant role in identifying the spatiotemporal patterns of air pollution, and they need to be deployed efficiently, with a minimum number of sites. The revision and optimal adjustment of existing monitoring networks is crucial for cities that have undergone rapid urban expansion and experience temporal variations in pollution patterns. The approach based on the Weather Research and Forecasting–California PUFF (WRF-CALPUFF) model and genetic algorithm (GA) was developed to design an optimal monitoring network. The maximization of coverage with minimum overlap and the ability to detect violations of standards were developed as the design objectives for redistributed networks. The non-dominated sorting genetic algorithm was applied to optimize the network size and site locations simultaneously for Shijiazhuang city, one of the most polluted cities in China. The assessment on the current network identified the insufficient spatial coverage of SO2 and NO2 monitoring for the expanding city. The optimization results showed that significant improvements were achieved in multiple objectives by redistributing the original network. Efficient coverage of the resulting designs improved to 60.99% and 76.06% of the urban area for SO2 and NO2, respectively. The redistributing design for multi-pollutant including 8 sites was also proposed, with the spatial representation covered 52.30% of the urban area and the overlapped areas decreased by 85.87% compared with the original network. The abilities to detect violations of standards were not improved as much as the other two objectives due to the conflicting nature between the multiple objectives. Additionally, the results demonstrated that the algorithm was slightly sensitive to the parameter settings, with the number of generations presented the most significant effect. Overall, our study presents an effective and feasible procedure for air quality network optimization at a city scale.

Fine particulate matter (PM2.5), largely composed of secondary organic aerosol (SOA), is currently one of the most intractable environmental problems in China. As crucial precursors for SOA, understanding the formation propensity of various volatile organic compound (VOC) species and sources is useful for pollution control. In this work, we estimated the SOA formation potential (SOAP) of anthropogenic VOC emissions based on an improved speciated VOC emission inventory and investigated its distribution in China. According to our estimates, toluene had the largest SOAP, followed by n-dodecane, m-/p-xylene, styrene, n-decane, and n-undecane, while passenger cars, chemical fiber manufacturing, asphalt paving, and building coating were the top five SOAP-contributing sources nationwide. The spatial distribution of SOAP in China shows a distinct pattern of high values in the southeast and low values in the northwest. Beijing–Tianjin–Hebei and surroundings, the Yangtze River Delta, Pearl River Delta, and Sichuan–Chongqing District were found to have the highest SOAP, particularly in urban areas. The major SOAP-contributing species and sources differed among these regions, which was attributed to local industrial and energy structures. Our results suggest that to mitigate PM2.5 pollution in China, more efficient SOAP-based control measures should be implemented instead of current emissions-based policies, and VOC control strategies should be adapted to local conditions.

Chengdu is a megacity in the southwest of China with high ozone (O3) mixing ratio. Observation of volatile organic compounds (VOCs), NO2 and O3 with high temporal resolution was conducted in Chengdu to investigate the chemical processes and causes of high O3 levels. The hourly mixing ratios of VOCs, NO2, and O3 were monitored by an online system from 28 August to 7 October, 2016. According to meteorological conditions, Chengdu, with relative warm weather and low wind speed, is favorable to O3 formation. Part of the O3 in Chengdu may be transported from the downtown area. In O3 episodes, the average mixing ratios of NO2 and O3 were 20.20 ppbv and 47.95 ppbv, respectively. In non-O3 episodes, the average mixing ratios of NO2 and O3 were 16.38 ppbv and 35.15 ppbv, respectively. The average mixing ratio of total VOCs (TVOCs) was 40.29 ppbv in non-O3episodes, which was lower than that in O3 episodes (53.19 ppbv). Alkenes comprised 51.7% of the total O3 formation potential (OFP) in Chengdu, followed by aromatics which accounted for 24.2%. Ethylene, trans-pentene, propene, and BTEX (benzene, ethylbenzene, toluene, m/p-xylene, o-xylene) were also major contributors to the OFP in Chengdu. In O3 episodes, intensive secondary formations were observed during the campaign. Oxygenated VOCs (OVOCs), such as acetone, Methylethylketone (MEK), and Methylvinylketone (MVK) were abundant. Isoprene rapidly converted to MVK and Methacrolein (MACR) during O3 episodes. Acetone was mainly the oxidant of C3-C5 hydrocarbons.

Chongqing is the largest megacity in southwest China and has a mountainous and humid climate. Online measurements of 96 volatile organic compound (VOC) species were performed at the three sites JYS, CJZ, and NQ, which are located in the northern, central, and southern sections of the Chongqing urban district, respectively. The measurements were performed from August to September 2015, at a time interval of 1 h. The spatiotemporal variation of VOC sources in Chongqing was characterized by combining the positive matrix factorization (PMF) model with the online measurement data. The average total VOC mixing ratios of the CJZ, NQ, and JYS sites were 41.2, 34.1, and 23.0 ppbv, respectively. The mixing ratios of tracers of incomplete combustion, exhibited obvious bimodal profiles at the CJZ and NQ sites, whereas those at the JYS site exhibited little change throughout the day. Isoprene at the three sites followed a similar pattern of average diurnal variations in mixing ratios, with minimums before sunrise and maximums at noon. The dominant sources of acetaldehyde and acetone were secondary anthropogenic sourceand aged air mass transport, respectively, in the city of Chongqing. Seven sources were apportioned to the results of PMF calculation using spatiotemporal VOCs composition data. The Vehicle-related sources were the largest contributor at CJZ and NQ, contributing 44% and 37% of the total VOC mixing ratios, respectively, and exhibited clear diurnal variations. Aged background air, with 68% of total VOC emissions, dominated the VOC emissions at JYS. Solvent utilization was a very important contributor at NQ and coincided with the higher levels of aromatics. O3 formation was generally VOC-limited at NQ and CJZ, and was NOx-limited and transition region alternatively at JYS. Alkenes were important for the O3formation at CJZ, and both alkenes and aromatics were important for the O3 formation at NQ.

Seasonal pattern of transport pathways and potential sources of PM2.5 in Chengdu during 2012–2013 were investigated based on hourly PM2.5 data, backward trajectories, clustering analysis, potential source contribution function (PSCF), and concentration-weighted trajectory (CWT) method. The annual hourly mean PM2.5 concentration in Chengdu was 97.4 mg·m–3. 5, 5, 5 and 3 mean clusters were generated in four seasons, respectively. Short-distance air masses, which travelled within the Sichuan Basin with no specific source direction and relatively high PM2.5 loadings (>80 mg·m–3) appeared as important pathways in all seasons. These short pathways indicated that emissions from both local and surrounding regions of Chengdu contributed significantly to PM2.5 pollution. The cities in southern Chengdu were major potential sources with PSCF>0.6 and CWT>90 mg·m–3. The northeastern pathway prevailed throughout the year with higher frequency in autumn and winter and lower frequency in spring and summer. In spring, long-range transport from southern Xinjiang was a representative dust invasion path to Chengdu, and the CWT values along the path were 30-60 mg·m–3. Long-range transport was also observed in autumn from southeastern Xinjiang along a northwesterly pathway, and in winter from the Tibetan Plateau along a westerly pathway. In summer, the potential source regions of Chengdu were smaller than those in other seasons, and no long-range transport pathway was observed. Results of PSCF and CWT indicated that regions in Qinghai and Tibet contributed to PM2.5 pollution in Chengdu as well, and their CWT values increased to above 30 mg·m–3 in winter.