Analysis of chemical composition characteristics of PM_2.5 under different pollution degrees can reveal the changes of pollution sources. In order to make clear the evolution process of PM_2.5 compositions in autumn and winter, PM_2.5 samples were continuously collected and analyzed at Liaocheng city, China. The collected samples were classified as clean days (CLD), mild-moderate pollution days (MMD) and severe-serious pollution days (SSD). It was concluded that with the increase of pollution degrees, the concentrations of water-soluble ions and carbon components increased significantly, while elements only increased slightly. In addition, as the pollution degrees increased, the percentage of NO₃⁻, SO₄²⁻ and NH₄⁺ increased significantly, from 23.0% in CLD to 49.0% in SSD, while the percentage of other components decreased, especially crust material. The PMF analyzed results showed that secondary transformation (36.7%), combustion sources (20.4%), secondary organic aerosols (SOA) (11.7%), vehicle sources (11%), dust (10.5%) and industrial processes (9.7%) were the main sources of PM_2.5 during autumn and winter in Liaocheng. The contribution of secondary transformation reached 57% at the SSD level, which indicated that it was the main reason for the increase of PM_2.5 concentrations. The air mass mainly came from five paths to Liaocheng. The secondary transformation contribution of the air mass with short transmission distance was higher, while the contribution of the dust was higher from the long distance.

Pollutants emission, meteorological conditions, secondary formation, and pollutants transport are the main reasons for air pollution. A comprehensive air pollution analysis was conducted from the above four aspects in the autumn–winter seasons of 2017–2018 and 2018–2019 at Xingtai, China. In addition, the relationship between PM_2.5 and O₃ was also studied from the aspects of secondary formation and meteorological conditions to find the rules of cooperative management of PM_2.5 and O₃ combined pollution. Taking measures of concentrated and clean heating and controlling biomass burning could make the concentrations of EC, K⁺ and SO₄²⁻ decrease. The variation trends of PM_2.5 and O₃ concentration in the autumn–winter season of Xingtai were different, and with the increase in secondary formation effects, the concentration of O₃ decreased. Furthermore, the key meteorological conditions that affected O₃ and PM_2.5 formation were temperature and relative humidity, respectively. The relationships of NOR (nitrate oxidation rate) and SOR (sulfate oxidation rate) against temperature presented a “U” shape, suggesting that gas-phase oxidation and gas–solid-phase oxidation were all suppressed at a temperature of around 4 °C. The cities located in the east had more pollutant transporting effects during the pollution processes of Xingtai, and the main transport routes of O₃ and PM_2.5 were not all the same.

Beijing-Tianjin-Hebei (BTH) and its surrounding areas are one of the most polluted regions in China. Xingtai, as a heavy industrial city of BTH and its surrounding areas, has been experiencing a severe PM_2.5 pollution in recent years, characterized by extremely high concentrations of PM_2.5. In 2014, PM_2.5 mass concentrations monitored by online instruments in urban areas of Xingtai were 116, 77, 128, and 200 µg m⁻³ in spring, summer, autumn and winter, respectively, with annually average concentrations of 130 µg m⁻³ exhibiting 3.7 times higher than National Ambient Air Quality Standard (NAAQS) value for PM_2.5 (35 µg m⁻³). To identify PM_2.5 emission sources, ambient PM_2.5 samples were collected during both cold and warm periods in 2014 in urban areas of Xingtai. Organic carbon (OC), sulfate, nitrate, ammonium and elemental carbon (EC) were the dominant components of PM_2.5, accounting for 13%, 11%, 12%, 11% and 8% in the cold period, respectively, and 11%, 12%, 9%, 6%, and 5% in the warm period, respectively. Source apportionment results indicated that coal combustion (24.4%) was the largest PM_2.5 emission source, followed by secondary sulfate (22.2%), secondary nitrate (18.4%), vehicle exhaust dust (12.4%), fugitive dust (9.7%), construction dust (5.5%), soil dust (3.4%) and metallurgy dust (1.6%). Based on PM_2.5 source apportionment results, some emission control measures, such as replacing bulk coal with clean energy sources, controlling coal consumption by coal-fired boiler upgrades, halting operations of unlicensed small polluters, and controlling fugitive and VOCs emission, were proposed to be implemented in order to improve Xingtai’s ambient air quality.

Non-methane hydrocarbons (NMHCs) from four sampling sites in Lanzhou, a petrochemical industrialized city in northwest China, was sampled by stainless steel canisters and measured by gas chromatography–mass selective detection/flame ionization detection (GC–MSD/FID) in May and June of 2017. Based on these results, the contributions of NMHCs to the ozone (O₃) and secondary organic aerosols (SOA), differences in tracer ratios, and source apportionment by principal component analysis (PCA) were analyzed. The results showed that the total NMHCs concentration in Lanzhou was 48.4 ± 48.3 ppbv (parts per billion by volume) during the observation and it was higher in May (78.6 ppbv) than in June (37.8 ppbv); the highest NMHCs concentration was observed in industrial areas. Alkanes were the dominant group at all sites in Lanzhou and account for more than 60% of the NMHCs, while isopentane, n-butane n-pentane, propane and ethane were the major compounds. Additionally, the NMHCs in Lanzhou have made great contributions to O₃ and SOA generation and the S1 site of the industrial area contributed the most to both of them. Propene, toluene, ethylbenzene and n-pentane were found to be more reactive with relatively high contributions to ozone formation. Aromatics and high carbon alkanes were major contributors to SOA formation potential (SOAp) (i.e., toluene, m,p-xylene, dodecane, undecane, n-tanane, benzene and ethylbenzene) in Lanzhou. Based on the specific volatile organic compounds (VOCs) ratio method and the PCA modem, the observation sites in Lanzhou were greatly affected by the surrounding industrial areas. The sources consisted of petrochemical industry, vehicle emissions, solvent usage and combustion sources, which contributed to 33.9%, 31.6%, 19.2% and 7.9% of the total monitored NMHCs, respectively. From different sites, though the influence of regional transport was not very significant on the whole, it also affected the NMHCs of nonindustrial areas based on the ratio of xylene to ethyl-benzene (X/E), especially the S4 site; vehicle emission was less important compared to sources from petrochemical industries in S1, as characterized by relatively higher toluene to benzene (T/B) ratios. However, vehicle emission has significant influence on NMHCs in S4. Overall, local emissions are the main source of NMHCs in Lanzhou and the petrochemical industry has a great influence on the distribution of NMHCs in the whole region.

The linear programming (LP) model has been used to identify a cost-effective strategy for reducing CO₂ emissions in power plants considering coal washing, pollutant removal, and carbon capture processes, thus CO₂ emissions in different production processes can be obtained. The direct emissions (combustion emissions and desulfurization emissions) and indirect emissions (pollutant removal, coal washing, and carbon capture) of CO₂ were all considered in the LP model. Three planning periods were set with different CO₂ emission control desirability to simulate CO₂ emissions of the different reduction requirements. The results can reflect the CO₂ emissions across the whole production process of a coal-fired power plant overall. The simulation results showed that for a coal-fired power plant containing two 1000 MW ultra super-critical sets, when the desirability was 0.9, the CO₂ total emissions were 2.15, 1.84, and 1.59 million tons for the three planning periods. The research results suggest that the methodology of LP combined with fuzzy desirability function is applicable to represent the whole production process of industry sectors such as coal-fired power plants. The government policy makers could predict CO₂ emissions by this method and use the results as a reference to conduct effective industrial and energy structure adjustment.