The international journal of Aerosol and Air Quality Research (AAQR) covers all aspects of aerosol science and technology, atmospheric science and air quality related issues.

In order to identify the pollution characteristics and sources of PM_2.5 in Urumqi, fine particulate matter samples were collected from September 2017 to August 2018, and the water-soluble ions (WSIs), organic carbon (OC), elemental carbon (EC), polycyclic aromatic hydrocarbons (PAHs), and metal elements were analyzed. The results indicate that the annual mass concentration of PM_2.5 in Urumqi was 158.85 ± 15.11 μg/m³, with the highest seasonal average in autumn (180.49 ± 87.22 μg/m³) and the lowest in summer (148.41 ± 52.60 μg/m³). SO₄²⁻ (13.58 ± 16.4 μg/m³), NO₃⁻ (13.46 ± 17.5 μg/m³), and NH₄⁺ (10.88 ± 12.2 μg/m³) were the most abundant WSIs, and the secondary inorganic ions (SNA = SO₄²⁻ + NO₃⁻ + NH₄⁺) accounted for 87.23% of the WSIs. The NO₃⁻/SO₄²⁻ ratio indicates that the contribution of stationary sources was dominant. The annual concentrations of OC and EC were 12.00 ± 4.4 µg/m³ and 5.00 ± 3.5 µg/m³, respectively, the OC/EC ratios in winter (2.55 ± 0.7), spring (3.43 ± 1.3), and summer (3.22 ± 1.1) were greater than 2, and there was the formation of secondary organic carbon (SOC). The correlation between OC and EC in spring in Urumqi (R² = 0.53) was low. In the PM_2.5, Al and Fe were the most abundant elements. The highest mass concentration season occurred in autumn, with mass concentrations of 15.11 ± 10.1 µg/m³ and 8.33 ± 6.9 µg/m³, respectively. The enrichment factor (EF) shows that most of the metal elements come from natural sources, and the Cd element mainly comes from anthropogenic sources. PAHs with a middle molecular weight were the main ones, and the annual average annual mass concentration of the PAHs was 451.35 ng/m³. The positive matrix factor model (PMF) source analysis shows that there are five main sources of PM_2.5 in Urumqi, including crustal minerals, biomass combustion, coal combustion, vehicular transport, and secondary aerosols. The distribution percentages of these different sources were 19.2%, 10.2%, 12.1%, 8.2%, and 50.3%, respectively.

PM_2.5 and PM_2.5–10 samples were simultaneously collected in Urumqi from January to December 2011, and 14 priority polycyclic aromatic hydrocarbons (PAHs) were determined. The mean concentrations of total PAHs in PM_2.5 and PM_2.5–10 were 20.90~844.22 ng m⁻³ and 19.65~176.5 ng m⁻³ respectively, with the highest in winter and the lowest in summer. Above 80% of PAHs were enriched in PM_2.5, which showed remarkable seasonal variations compared to coarse particles. High molecular weight (HMW) PAHs were predominant in PM_2.5 (46.61~85.13%), whereas the proportions of lower molecular weight (LMW) and HMW PAHs in PM_2.5–10 showed a decreasing and an increasing trend, respectively, from spring to winter. The estimated concentrations of benzo[a]pyrene equivalent carcinogenic potency (BaP_eq) in PM_2.5 (10.49~84.52 ng m⁻³) were higher than that of in PM_2.5–10 (1.15~13.33 ng m⁻³) except in summer. The estimated value of inhalation cancer risk in PM_2.5 and PM_2.5–10 were 1.63 × 10⁻⁴~7.35 × 10⁻³ and 9.94 × 10⁻⁵~1.16 × 10⁻³, respectively, far exceeding the health-based guideline level of 10⁻⁴. Diagnostic ratios and positive matrix factorization results demonstrated that PAHs in PM_2.5 and PM_2.5–10 were from similar sources, such as coal combustion, biomass burning, coking, and petroleum combustion, respectively. Coal combustion was the most important source for PAHs both in PM_2.5 and PM_2.5–10, accounting for 54.20% and 50.29%, respectively.

Accurate ozone concentration simulation can provide a health reference for people’s daily lives. Simulating ozone concentrations is a complex task because near-surface ozone production is determined by a combination of volatile organic compounds (VOCs) and NOx emissions, atmospheric photochemical reactions, and meteorological factors. In this study, we applied a genetic algorithm-optimized back propagation (GA-BP) neural network, multiple linear regression (MLR), BP neural network, random forest (RF) algorithm, and long short-term memory network (LSTM) to model ozone concentrations in three regions of Xinjiang, China (Urumqi, Hotan, and Dushanzi districts) for the first time by inputting wind speed, humidity, visibility, temperature, and wind direction. The results showed that the average relative errors of the model simulations in the Urumqi, Hotan, and Dushanzi districts were BP (61%, 14%, and 16%), MLR (97%, 14%, and 23%), RF (39%, 11%, and 14%), LSTM (50%, 12%, and 16%), and GA-BP (16%, 4%, and 6%) and that the significance coefficients R² were BP (0.73, 0.65, and 0.83), MLR (0.68, 0.62, and 0.74), RF (0.85, 0.80, and 0.88), LSTM (0.78, 0.74, and 0.85), and GA-BP (0.92, 0.93, and 0.94), respectively, with the simulated values of GA-BP being the closest to the true values. The GA-BP model results showed that among the 100 samples with the same wind speed, humidity, visibility, temperature, and wind direction data, the highest simulated ozone concentrations in the Urumqi, Hotan, and Dushanzi districts were 173.5 μg/m³, 114.3 μg/m³, and 228.4 μg/m³, respectively. The results provide a theoretical basis for the effective control of regional ozone pollution in urban areas (Urumqi), dusty areas (Hotan), and industrial areas (Dushanzi) in Xinjiang.