To better understand the impact of land-sea air masses interaction processes on ozone (O₃) pollution in Shenzhen, two typical land-sea interaction processes (long-range air masses transport and sea-land breeze (SLB) circulation) were analyzed based on ten years of observational ozone concentrations, meteorological dataset, meteorological reanalysis data and the HYSPLIT backward trajectory model. Long-range air masses were identified and further categorized into oceanic, coastal, and inland types according to trajectory pathways. The corresponding mean O₃ concentrations were (45.5±26.1), (71.3±33.7) and (68.4±30.6)μg/m³, respectively. Among them, coastal air masses exhibited the highest O₃ pollution levels, with a frequency of 39%, and showed particularly strong transport potential in autumn, making it a major source of O₃ pollution in Shenzhen. In addition, the SLB circulation further influenced O₃ concentration in Shenzhen. The annual frequency of SLB days in Shenzhen over the past 10 years was 18.8%, with the highest occurrence in spring and summer. O₃ concentrations on SLB days were significantly higher than that on non-SLB days, with an average annual difference of 9.6%, primarily due to low wind speeds, inversions, and recirculation conditions. The difference was most pronounced in autumn (12.28μg/m³) which could be attributed to favorable conditions, including clear and dry weather, lower background wind speeds, and a higher likelihood of SLB development. Overall, O₃ pollution in Shenzhen was strongly influenced by land-sea air masses interactions, particularly in autumn, which should be prioritized for mitigation.

Given the ozone (O₃) pollution events in the Pearl River Delta (PRD) region frequently occur in autumn and the increasing application of objective weather classification methods in urban agglomeration pollution studies, this study employed the Self-Organizing Map (SOM) method to classify the sea level pressure fields in the PRD and to investigate the impacts of different weather patterns on severe autumn O₃ pollution. The results indicated that 15weather patterns identified from sea level pressure fields could be categorized into four groups based on O₃ pollution levels: heavy pollution, moderate pollution, light pollution, and clean. Among them, heavy and moderate pollution weather patterns exhibited distinct seasonal distributions, with the weak cold high-pressure combined with typhoon and subtropical high pattern (T12) (The average O₃ concentration was 118.2 μg/m³, with an exceedance rate of 22.4%) was associated with the most severe O₃ pollution. Autumn O₃ exceedance days under heavy pollution patterns were generally accompanied by high solar radiation (554.1kJ/m² above the mean), high temperature (3.1℃ above the mean), and low relative humidity (9.2% below the mean), which were highly favorable for O₃ formation. Using the circulation field of heavy pollution weather patterns with autumn O₃ exceedance day as a reference, the study found that the number of days favorable for O₃ exceedance during 2015~2024 was significantly correlated with autumn O₃ concentration. Furthermore, the number of favorable days under similar circulation conditions showed a significant increasing trend during 1995~2060 (with an average growth rate of 0.14d/a).

Based on tropical cyclone (TC) data, reanalysis data, and ozone monitoring data from 2014 to 2024, the synoptic mechanism by which approaching tropical cyclones (ATCs) trigger ozone pollution in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) was analyzed. A prediction model was developed using Spearman's rank correlation coefficient and the random forest regression method. The results indicated that: ATCs inducing ozone pollution in the GBA were primarily distributed within a range of 270~1758km, and a clear disparity in intensity was observed on the eastern and western sides of the line connecting Taiwan Island and Luzon Island. The ATCs east of the line all reach typhoon intensity or above, while those west range from tropical depression to severe typhoon intensity. Ozone pollution was induced by ATCs through the generation of subsiding airflows in upper levels, which reduces cloud cover over the GBA, increases downward shortwave radiation, elevates surface temperatures, and reduces relative humidity, precipitation and wind speed. Through SOM clustering analysis, four ozone pollution distribution patterns triggered by ATCs in the GBA were identified: the southwestern pollution pattern, the widespread pollution pattern, the western pollution pattern, and the northeastern pollution pattern. These patterns are associated with the transport and convergence effects of different surface wind fields. Ozone concentrations in the 11cities of the GBA were found to correlate well with tropical cyclone characteristics (radius of 7-level wind circle and proximity rate), as well as significant correlations with surface meteorological factors (such as land-sea wind direction difference and land-sea meridional wind speed difference) and upper-air meteorological factors (such as 200 hPa vertical velocity, 700 hPa meridional wind, 700 hPa specific humidity, and 925 hPa zonal wind). The forecasting model constructed based on 18factors, including tropical cyclone characteristics and surface and upper-air meteorological elements, demonstrates high generalizability and accuracy. The average correlation coefficient between the model's forecast values and the actual monitoring values reached 0.811, the average root mean square error was 30.2μg/m³, and the average TS score for ozone level prediction was 0.668.

To evaluate the impact of air pollution prevention and control policies on the spatiotemporal evolution of Ozone (O₃) and fine particulate matter (PM_2.5), this study analyzed the spatiotemporal stratified heterogeneity of O₃ and PM_2.5 pollution across 331 Chinese cities from 2014 to 2024 using a Bayesian spatiotemporal hierarchical model. The GeoDetector model was further employed to quantify the determinant power and interactive effects of meteorological, socioeconomic, and other pollutant factors on summer O₃ and winter PM_2.5 pollution during the implementation of three key policies: the Air Pollution Prevention and Control Action Plan (2014~2017), the Blue Sky Defense Action Plan (2018~2020), and a circular on further Promoting the Nationwide Battle to Prevent and Control Pollution (2021~2024). The results indicate that O₃ concentration exhibited a trend of initial rapid growth followed by a slower rate, with hotspots mainly distributed in North China and the Yangtze River Delta (YRD). Conversely, PM_2.5 concentration continuously decreased, and high-value areas shrank and concentrated in North China, the YRD, and the Chengdu-Chongqing region. A positive correlation between O₃ and PM_2.5 was frequently observed in the southern and northeastern humid regions, whereas a negative correlation prevailed in the northwestern semi-arid areas. Although the dominant factors for O₃ and PM_2.5 evolved across policy stages, the determinant power of the interactions among different factors were consistently stronger than that of individual factors. Specifically, from 2014 to 2017, O₃ was mainly affected by maximum temperature (Geo_q=0.24), industrial output (Geo_q=0.22), and PM₁₀ (Geo_q = 0.60); PM_2.5 was primarily affected by maximum temperature (Geo_q = 0.41), population density (Geo_q= 0.40), and PM₁₀ (Geo_q = 0.88). From 2018 to 2020, the dominant factors for O₃ shifted to precipitation, population density, and PM₁₀, while those for PM_2.5 shifted to average temperature, population density, and PM₁₀. From 2021 to 2024, the dominant factors for both shifted toward average temperature, population density, and PM₁₀, albeit with significant differences in their determinant power. This research reveals the dynamic evolution of the spatiotemporal patterns of O₃ and PM_2.5 pollution driven by policies implementation, emphasizing the dominant role of multi-factor interactions in the composite pollution formation process. The findings provide a crucial scientific basis for formulating regionalized and phased synergistic control strategies for O₃ and PM_2.5.

The concentration and carbon isotope compositions (δ¹³C-CH₄, δ¹³C-CO₂) of methane (CH₄) and carbon dioxide (CO₂) along the coastal of eastern China were studied using vehicle mounted mobile measurement methods. The results indicated that the CH₄ concentration was observed to range from (1.84~2.94) ×10^-6, with an average concentration of 2.09×10^-6. The highest value was recorded in Jiangmen, Guangdong (2.32×10^-6), while the lowest value was recorded in Zhangzhou, Fujian (1.94×10^-6). The CO₂ concentration was found to range from (420.51~857.33) ×10^-6, with an average concentration of 525.70×10^-6. The highest value was measured in Shenzhen, Guangdong (599.08×10^-6), and the lowest value was measured in Nantong, Jiangsu (477.42×10^-6). Both CH₄ and CO₂ levels in the study area were found to exceed the atmospheric background values. The δ¹³C-CH₄ values ranged from-62.62‰ to -29.70‰, with an average of -43.65‰. The highest value was observed in Qingdao, Shandong (-40.24‰), and the lowest value was observed in Zhangzhou, Fujian (-48.25‰). The δ¹³C-CO₂ values ranged from -28.64‰ to -9.19‰, with an average of -17.53‰. The highest value was recorded in Nantong, Jiangsu (-14.45‰), and the lowest value was recorded in Shenzhen, Guangdong (-19.13‰). In the study area, CH₄ concentration was found to be positively correlated with its carbon isotope signature, while CO₂ concentration was negatively correlated with its carbon isotope signature. Per capita GDP, the number of civilian vehicles, livestock inventory, and rice cultivation area were all positively correlated with CH₄ concentration and its carbon isotope signature. In contrast, the forest coverage rate was negatively correlated with CH₄ concentration and its carbon isotope signature. δ¹³C (CO₂) was utilized to trace the sources and sinks of atmospheric CO₂ along the coastal regions of Shandong, Jiangsu, Shanghai, Zhejiang, Fujian, and Guangdong. Their respective δ¹³C (CO₂) source signatures (δ¹³Cs) were determined to be: -37.90‰, -37.67‰, -41.24‰, -34.12‰, -33.99‰, and -32.39‰. These values indicated that the combustion of petroleum and natural gas was identified as the primary contributing source to CO₂ emissions in the study area.

To evaluate the applicability of the MERRA-2 and TAP datasets over the Plateau, ground-based observations from the Qomolangma, Nam Co, and Waliguan stations were utilized to validate the accuracy of atmospheric black carbon (BC) in both datasets. Results showed that Nam Co had the lowest annual mean BC concentration (0.04μg/m³), while Qomolangma (0.26μg/m³) and Waliguan (0.28μg/m³) recorded higher levels. All stations exhibited a clear seasonal cycle, with higher concentrations in winter and spring and lower values in summer. MERRA-2 effectively reproduced the seasonal and temporal variations of BC, whereas TAP displayed a systematic overestimation across the Plateau, with annual deviations reaching up to four times in some years, indicating lower accuracy. During the March 2021 transboundary BC transport event from South Asia, MERRA-2 showed good capability in capturing the process, providing direct scientific evidence for cross-border pollutant transmission to the Plateau.

PM_2.5 samples were collected from six types of industrial parks in the Guanzhong region of Shaanxi Province, including cement/concrete, brick/construction materials, forging/metallurgy, thermal power, chemical production, and glass/ceramics. The carbon fractions, water-soluble ions, elements, as well as oxidative potential (OP) of PM_2.5samples were analyzed to investigate the emission characteristics of different industrial sources and the relationships between PM_2.5 chemical components and OP. The results showed significant differences in PM_2.5 concentrations among the industrial parks, with brick/construction materials presenting the highest level (95.2μg/m³). The OC/EC (organic carbon/elemental carbon) ratio in brick/construction materials (5.5) was higher than those in other industries. Cement and concrete industry exhibited the highest Ca²⁺ concentrations. Elemental analysis showed elevated proportions of V in metal forging. Correlation analysis demonstrated significant positive relationships between OP and inorganic ions (SO₄^2-、NO₃^-、NH₄⁺) as well as metallic elements (Ca, Al, Cd, Cu). This study provides a scientific basis for identifying the source markers across common industries and for assessing their impacts on atmospheric oxidation capacity and human health.

To investigate the correlation between emissions and environmental quality in industrial parks, thereby achieving refined management and control constrained by the environmental quality baseline, this paper proposes a comprehensive method for determining the maximum allowable emission increments in industrial parks based on the dynamic equilibrium of PM_2.5 concentration impacts. The approach balances emission reductions in surrounding buffer zones against increased emissions within the parks, creating development space through emission mitigation in peripheral areas. Case studies were conducted in industrial parks of Tianfu, Shuangliu, and Longquan district, Chengdu. Evaluations demonstrate that buffer zone emission reductions lowered PM_2.5 concentrations in park stations by 2.6, 1.7, and 2.1μg/m³, respectively. Based on current and target concentrations at park stations, the maximum allowable concentration increases were calculated as 1.9, 1.5, and 2.3μg/m³. Derived from simulated PM_2.5 concentrations and sequential emission increments, the composite response functions quantified the maximum allowable emission increases at 21.5, 73.7, and 200.1t, respectively. Emission reduction and increasing balance verifications showed that implementing these maximum increments resulted in concentration changes of -0.18, -0.44, and -2.50μg/m³ at control stations, confirming no deterioration of air quality. This methodology provides a novel approach for refined park management, enabling industrial development through peripheral emission reductions without compromising environmental quality, thereby creating sustainable growth space for industrial parks.

This study investigated the spatiotemporal characteristics of open-field crop straw burning (hereinafter referred to as crop straw burning) in the North China Plain, based on thermal anomaly data from VIIRS/NPP and agricultural statistics from 2013 to 2023. Furthermore, an emission inventory for major atmospheric pollutants from 2013 to 2022 was developed, and the impact on PM_2.5 concentrations during summer harvest season was assessed. The results showed that: significant spatial clustering was observed in the distribution of crop straw burning fire points, with two primary high-density zones identified in the southern Henan-northern Anhui region and the Bohai Rim region. The number of fire points decreased significantly during the study period, with the southern experiencing greater reductions than the northern. Intra-annual variations were highly synchronous with agricultural activities, showing distinct peaks during the summer and autumn harvest periods. The pollutant emissions declined by over 50% from 2013 to 2022, with a rapid decrease from 2013 to 2016 followed by a stabilization. The seasonal peak of emissions shifted from summer-autumn to winter-spring over time, while the spatial distribution of high-emission areas became increasingly fragmented. In terms of crop-specific contributions, emissions from corn straw burning consistently exceeded those from wheat straw burning. During the June harvest period, a significant positive correlation (r = 0.70~0.91) was found between province-level PM_2.5 emissions from straw burning and ground-level PM_2.5 concentrations across Henan, Shandong, Jiangsu, and Anhui provinces on an interannual scale. Daily-scale analysis indicated that a strong contribution from straw burning to PM_2.5 levels in the southern Henan-northern Anhui region from 2013 to 2016 (r = 0.68), with its influence weakening after 2017. The impact in the Bohai Rim region was generally weaker throughout the study period. However, case studies demonstrated that concentrated burning events could still trigger significant short-term PM_2.5 pollution episodes. Therefore, synchronous large-scale burning across multiple regions should be avoided to prevent regional pollution episodes.

A network model based on mass conservation was established to simulate PM_2.5 dispersion in subway systems, integrating a source model for train-induced dust in tunnels. The model was applied to a representative line in southern China and validated with field measurements, showing errors within 10%. Results indicate that station PM_2.5 concentration increases nonlinearly with train frequency (approximately +86% for a +78% frequency increase) and exhibits a near-constant differential relative to outdoor concentration, suggesting a linear additive effect. Notably, for lines operating at over 24 trains per hour during peak periods, station PM_2.5 levels consistently exceeded the indoor air quality standard (50μg/m³), even when outdoor concentrations met the WHO guideline (15μg/m³).