This study investigates the relative contributions of anthropogenic and meteorological factors to ozone (O₃) variations in major cities on the northern slope of the Tianshan Mountains (Urumqi, Changji, Shihezi, Wujiaqu) from 2018 to 2023 using a stepwise multiple linear regression (MLR) model. Based on OMI satellite data (HCHO and NO₂ tropospheric column concentrations) and ground-based observation data (O₃ and NO₂), the fractional nucleation rate (FNR) threshold for the northern slope of the Tianshan Mountains was determined. The study highlights regional differences, seasonal patterns, and variations in the sensitivity of precursor substances. The results indicate that O₃ on the northern slope of the Tianshan Mountains generally exhibited a fluctuating upward trend, with an annual average concentration of 86.97μg/m³, and the O₃ concentration in the warm season (107.63μg/m³) was higher than that in the cold season (66.31μg/m³). The O₃ concentration decreased to its lowest level (86.71μg/m³) in 2020 attributed to the decreased emissions resulting from the pandemic. The annual growth rates varied significantly among the cities, with Changji at 3.87μg/(m³·a) and Shihezi at 1.51μg/(m³·a). Meteorological factors have a significant impact on O₃ concentration variations, with temperature and humidity as key factors. The contribution rates of meteorological factors in Urumqi and Wujiaqu were higher (50% and 60%, respectively), while those in Changji and Shihezi were lower (29% and 44%, respectively). The NO₂ column concentration showed a downward trend (annual decrease rate of 0.22 × 10¹⁵ molec/(cm²·a)), while the HCHO column concentration remained relatively stable, indicating that VOCs emissions may have increased. FNR analysis indicates that the ozone formation sensitivity (OFS) threshold was as follows: when FNR < 1.75, ozone formation was controlled by VOCs; when FNR > 3.67, ozone formation was controlled by NO_x; and when 1.75 < FNR < 3.67, it is in a transitional state. During the warm season (April to September), the sensitivity of O₃ photochemical production was mainly controlled by NO_x. This study reveals the spatiotemporal variation characteristics of O₃ formation and precursor sensitivities in the northern slope area of the Tianshan Mountains, providing scientific guidance for the development of more precise O₃ pollution control strategies.

This study investigated the characteristics of O₃ and its precursors in Dongguan during the autumns of 2018 and 2022. By integrating observational data, backward trajectory analysis, and box model simulations, we examined the changes in O₃ and precursor concentrations, O₃ formation pathways, and the evolution of control regimes during both polluted and clean periods, which provided insights into ozone pollution mitigation. The average O₃ concentration in Dongguan was higher in autumn 2022 than in 2018, whereas both the average concentrations of nitrogen oxides (NO_x) and volatile organic compounds (VOCs) decreased in 2022. Alkanes accounted for the largest proportion of total VOCs (TVOCs) in both autumn periods, while aromatics and alkenes contributed the most to ozone formation potential (OFP). Notably, the contribution of biogenic VOCs (specifically isoprene) to OFP increased from 6.0% in 2018 to 12.1% in 2022. Comparative studies between polluted and clean periods revealed that TVOC concentrations during polluted periods in 2018 (73.7×10^-9) and their growth rates (148.5%) were significantly higher than those in 2022 (26.1×10^-9 and 67.0%), driven primarily by alkanes and aromatics. In 2018, the OFP growth rate of alkanes was highest during the polluted periods, whereas aromatics dominated in 2022. Box model simulations revealed that the enhancement of O₃ formation rates (16.6×10^-9/h) was notably higher in the polluted periods of 2018 than in 2022 (1.8×10^-9/h), mainly due to increased production of RO₂ and HO₂ radicals from high concentrations of alkanes and aromatics. Additionally, the marked decline in TVOC concentrations during the polluted periods of 2022 suggests a shift in O₃ formation from a VOC-limited regime to a transitional regime, highlighting the need for joint control of TVOCs and NO_x. The results of VOCs emission reduction indicated that O₃ was most sensitive to reductions in the top 10 OFP-contributing species, with relative incremental reactivities (RIR) of 0.85%/% in 2018 and 0.81%/% in 2022, which was more effective than reducing alkenes (0.74%/% in 2018 and 0.37%/% in 2022), aromatics (0.44%/% in 2018and 0.49%/% in 2022), and the top 10species with the highest concentrations (0.20%/% in 2018and 0.37%/% in 2022).

To investigate the causes of ozone (O₃) pollution in the Pearl River Delta (PRD) region under non-high-temperature conditions, this study analyzed differences in O₃ pollution characteristics between high-temperature days (daily maximum temperature ≥28℃) and non-high-temperature days (daily maximum temperature <28℃) using air quality and meteorological data from 2015 to 2023. The key findings are as follows: O₃ pollution in the PRD primarily occurred on high-temperature days, yet 17.2% of total polluted cities were observed during non-high-temperature days. High-temperature pollution was concentrated in central and southwestern PRD, while non-high-temperature pollution predominantly occurred in the southwest. Non-high-temperature O₃ pollution typically occurred under conditions of daily maximum temperature (25.0~27.2℃), relative humidity (44.9%~56.8%), and sunshine duration (7.7~9.8h). Compared to high-temperature pollution days, non-high-temperature pollution days exhibited 6.0% lower relative humidity and a more concentrated distribution of sunshine duration in higher intervals. The SHAP method demonstrated that relative humidity is the most influential meteorological factor affecting O₃ concentrations. On high-temperature days and non-high-temperature days, the contribution of relative humidity to the O₃_8h concentration can reach up to 30 and 60μg/m³, respectively. Low relative humidity (<65%) paired with prolonged sunshine duration synergistically promoted O₃ formation, whereas high relative humidity (>65%) combined with reduced sunshine suppressed it. Additionally, the pronounced impact of sunshine duration under non-high-temperature conditions highlights sufficient solar radiation as a prerequisite for O₃ generation. Under non-high-temperature conditions, NO₂ exerts a weaker influence on O₃ concentrations compared to high-temperature days, with its contribution to O₃ concentration variations quantified at approximately 15 μg/m³.

Taking a region in the Hangzhou Bay area of China as an example, this study refined the local anthropogenic emission inventory and incorporated it into the regional air quality model WRF-CMAQ. The source contributions to ozone pollution during a typical month were quantitatively analyzed using the Integrated Source Apportionment Method (ISAM) and the Integrated Reaction Rate (IRR) method within CMAQ. The results showed that ozone pollution in this area was mainly attributed to contributions from outside the region, accounting for 73.3%, while local contributions accounted for 26.7%. However, on pollution days, the local contribution was found to increase to 35.6%. Furthermore, the contributions of different local emission sectors were analyzed, and industrial and transportation emissions were identified as the major anthropogenic sources of local ozone formation, contributing 38.3% and 35.4%, respectively. Based on the source apportionment results and considering the region's actual control policies and reduction potential, a refined emission reduction strategy was designed for 40subcategories of anthropogenic sources. This strategy was implemented as an emission control scenario in the CMAQ model to assess its effectiveness. The results indicated that the refined reduction plan significantly reduced locally formed ozone on pollution days, with a maximum reduction of up to 26.44μg/m³ and an average reduction of 3.5μg/m³, representing 7.1% of the locally produced ozone concentration during the pollution period.

This study utilized a rotor UAV equipped with multi-pollutant sensors to conduct vertical observations of multiple parameters in the Daya Bay District of Huizhou City, Guangdong Province, from October 12 to 18, 2023, during the morning (9:00), afternoon (15:00), and evening (21:00) each day. The results showed that temperature generally decreased with increasing altitude, with temperature inversion observed at 350 meters during certain periods. Relative humidity exhibited an overall increasing trend with height. NO₂ concentrations typically increased with altitude, with significant peaks around 350 meters at specific times. SO₂ concentrations exhibited a general decreasing trend, but significant elevations were also observed around 350meters at certain times, suggesting potential NO₂ and SO₂ pollution transport processes at this altitude. CO concentrations were relatively high at ground level and decreased rapidly with altitude, showing minimal variation above 200meters, indicating that high-altitude CO concentrations were primarily influenced by near-surface emissions and diffusion, with no significant pollution transport processes. PM₁₀ and PM_2.5 concentrations were generally low, with slight variations across altitudes. They tended to be higher in the morning and lower in the afternoon and evening, likely due to residual layer accumulation or overnight particle emissions. Backward trajectory analysis revealed that air masses at 0, 300, and 500meters all passed over the Daya Bay Petrochemical Industrial Park. Air masses at 300meters and 500meters originated from northeastern counties and cities of Guangdong Province from October 12 to 14, and from coastal areas of Guangdong and Fujian Provinces from October 16 to 18, indicating that emissions from the Daya Bay Petrochemical Industrial Park and upwind regions contributed to high-altitude pollutant levels in the Daya Bay area. WRF-CAMx model simulations further identified high-concentration zones of NO₂ and SO₂ upwind of Daya Bay, confirming the existence of high-altitude pollutant transport pathways. The vertical profile characteristics obtained from UAV observations provide valuable insights into the transport patterns of atmospheric pollutants at high altitudes, offering support for regional air quality impact studies.

To investigate the pollution characteristics and driving mechanisms of atmospheric gaseous nitric acid (HNO₃) in Shenzhen, this study conducted HNO₃ measurements during summer 2023 and winter 2024 using iodide ion chemical ionization time-of-flight mass spectrometry (I-CIMS), combined with machine learning for mechanism analysis. The results revealed significantly higher HNO₃ concentrations in winter (0.72±0.19×10^-9) compared to summer (0.09±0.05×10^-9), with distinct diurnal patterns showing daytime peaks. The model demonstrated good performance (R²=0.91), with SHAP analysis identifying temperature as the primary driver governing HNO₃ variation through gas-particle partitioning. Seasonal differences emerged: temperature dominated summer dynamics through thermodynamic control, while chemical processes prevailed in winter. Notably, chloride salts suppressed HNO₃ formation through nitrate displacement, revealing the complex role of chloride depletion in nitrogen cycling. Our findings highlight the priority of NH₃ emission control, which concurrently inhibits total nitrate formation and reduces HNO₃ particle-phase conversion. Special attention should be given to coastal urban nitrogen cycling complexities induced by chloride depletion, providing scientific basis for nitrate pollution mitigation in the Pearl River Delta region.

Based on the observation of particulate matter concentration in Shenzhen from October to December 2022, the characteristics and influencing factors of New Particle Formation (NPF) events in autumn and winter in Shenzhen were explored. During the observation period, the average particulate matter concentration on non-NPF days was measured as 1.09×10⁴ #/cm³, and on NPF days, it was measured as 1.57×10⁴ #/cm³. The overall particulate matter concentration on non-NPF days was found to be lower than that on NPF days. The Shenzhen NPF event was characterized by low particle formation rate, low growth rate, and low condensation sink, which were consistent with clean and coastal areas both domestically and internationally. By using the standard of whether the mean condensation concentration during the time before dawn to early morning period was greater than 0.02s^-1, NPF days were further classified into clean and polluted backgrounds. It was observed that the significant increase in boundary layer uplift and wind speed during the daytime created favorable conditions for NPF under polluted backgrounds. Under polluted backgrounds, NPF days were associated with stronger solar radiation compared to clean backgrounds. It was concluded that active photochemical reactions were the main driving force of the autumn and winter NPF events in Shenzhen. High temperatures, strong solar radiation, and low humidity were identified as factors controlling the formation of new particles, while high condensation and low wind speeds were found to control the loss of new particles.

Proposing a novel algorithm for identifying atmospheric pollutant transport pathways by coupling optical flow vectors with movement distance constraints—Farneback-Distance Transmission Path Recognition Algorithm (FBD-TPRA). Based on high spatiotemporal-resolution (2km spatial resolution, 1h temporal resolution) PM_2.5 concentration satellite remote sensing inversion data generated via a one-dimensional deep convolutional neural network (1D-CNN), this algorithm extracts inter-frame motion features of polluted air masses using optical flow and incorporates movement distance constraints to achieve continuous and refined reconstruction of pollutant transport pathways. Validation experiments conducted during three typical air pollution events in Changsha, Hunan Province (January 14, October 12, and October 27, 2024) demonstrate that the transport paths identified by the FBD-TPRA algorithm can reasonably explain the temporal relationships of urban concentration variations (Xiantao-Jingzhou-Yueyang-Changsha, Yueyang-Changde-Yiyang-Changsha, and Yueyang-Yiyang-Changsha). Compared with the widely used HYSPLIT model, the FBD-TPRA achieves strong consistency in net displacement direction(cosine similarity up to 0.99), but differ in path morphology, as reflected by dynamic time warping (DTW) values of 2.99°, 9.58°, and 3.69°, respectively. The study indicates that the FBD-TPRA algorithm enhances the characterization of pollutant transport processes by integrating high-resolution spatiotemporal pollutant concentration data and movement distance constraints, providing a new technical approach for identifying atmospheric pollution transport pathways.

This study systematically analyzed the evolution of winter air quality and pollutant emissions in Lanzhou through integrated multi-source data, including ground-based ambient monitoring, nighttime light observations, and emission inventories. Utilizing radiosonde data from Yuzhong Station, surface meteorological measurements, and ERA5reanalysis, we quantified changes in upper-atmospheric and surface wind fields alongside atmospheric stability via linear trend analysis and Mann-Kendall tests to identify drivers of the post-pandemic pollution rebound. Significant reductions were observed in SO₂, PM_2.5, and PM₁0 concentrations over the past 15 winters, with post-pandemic (2022~2023) levels rebounding by 6.59% (PM_2.5) and 21.14% (PM₁₀) relative to 2019 baselines. Notable declines in primary pollutant emissions since 2000 were documented, demonstrating effective mitigation measures. Deteriorating meteorological dispersion conditions were characterized by a 20-year trend of declining surface wind speed and increasing inversion frequency, with winter valley cold pool intensity in 2022~2023 reaching 1.2 times the historical average. The synergistic effect of weakened near-surface ventilation and enhanced atmospheric stability was identified as the dominant mechanism driving the post-pandemic particulate matter rebound.

This study examined the feasibility of using dispersion model back-calculation to estimate VOCs emissions from storage tanks by monitoring concentrations at breathing vents and downwind locations in an oilfield storage area. Area source and volume source dispersion models were used to back-calculate tank VOCs emissions and compared with flow rate method and petrochemical industry formula calculations. Results showed VOCs concentrations at breathing vents fluctuated significantly (10³~10⁵mg/m³) with high variability. The volume source dispersion model better accounted for microscale flow field characteristics influenced by tank structures, yielding results closer to those from flow rate and petrochemical formula methods. This demonstrates the volume source dispersion model back-calculation method has high applicability and validity for estimating storage tank VOCs emissions.