To elucidate the variance in the correlation between PM2.5 and O3 concentrations and their influencing factors in the southern Sichuan region, we conducted an analysis of PM2.5 concentrations in the cities of Yibin, Luzhou, Zigong, and Neijiang across different seasons in 2021. This analysis was juxtaposed with the daily maximum 8-hour average of O3 (MDA8 O3) and the atmospheric total oxidant concentration Ox (O3 + NO2). Augmenting this with reanalyzed data on the primary chemical constituents of PM2.5, we further explored the key chemical components predominantly influencing the correlations between PM2.5-O3 and PM2.5-Ox. Additionally, we elucidated the influence of meteorological conditions on these relationships using meteorological data. Our findings indicate that PM2.5 concentrations in the four cities exhibited a consistent seasonal trend, peaking in winter and reaching their lowest levels in summer, whereas O3 and Ox displayed an inverse trend compared to PM2.5. Organic matter (OM) and nitrate (NO3–) constituted the primary components of PM2.5. Except for autumn, positive correlations between PM2.5 and O3 were observed in each city across the seasons, with the strongest positive correlation manifesting during summer. Notably, OM and sulfate (SO42–) were dominant chemical components during summer, while NO3– and ammonium (NH4+) were influential in autumn, and OM in spring and winter, impacting the PM2.5-O3 correlations. Comparatively, PM2.5-Ox exhibited notably stronger positive correlations, particularly prominent in winter. Correlation coefficients between the main chemical components and Ox surpassed those with O3, indicating that atmospheric oxidation more effectively stimulated secondary components in PM2.5. The dominant chemical components across all seasons were similar to those observed in the PM2.5-O3 relationship. Meteorological conditions primarily weakened the positive synergistic relationships between PM2.5-O3 and PM2.5-Ox. Temperature (T) emerged as the dominant factor in spring, summer, and autumn, attenuating these correlations, while humidity (RH) dominated in winter.

Instead of passively receiving and transmitting air-sea interaction signals from the Pacific Ocean, the view that the Tropical Indian Ocean (TIO) significantly influences the climate of neighbouring continents is becoming increasingly accepted. Through the investigation of interannual variations in extreme wet spells (EWS) and their potential causes across three subregions of Southwest China (SWC), we have discerned the differing impacts of the Indian Ocean Basin mode (IOBM) and ENSO on the interannual variation of EWS in SWC. Specifically, 1) the positive phase of the IOBM favours anomalous diabatic cooling extending from northern India to the northeastern maritime continents, which induces anomalous anticyclonic circulation there and a negative phase of East Asia-Pacific teleconnection, resulting in more EWS in the west Sichuan Plateau; 2) the atmospheric heating anomaly over the maritime continents in the El Nino decaying summertime shows an anomalous diabatic cooling in the northeastern part and anomalous diabatic warming in the southwestern part, which corresponds to a more robust and westward extending western Pacific subtropical high and favours the occurrence of EWS in the Sichuan Basin; 3) to the interannual variation of EWS in the Yunnan-Guizhou Plateau, it is closely related to the anomalous cyclonic circulation covering South China, which is significantly associated with the anomalous diabatic cooling over the Arabian Sea-Bay of Bengal region. Our findings provide a comprehensive understanding of the interannual EWS variations in three subregions of SWC and their possible causes, thereby giving us more insights into the interannual prediction of regional climate.

The adverse health effects of air pollution have long been a major public health concern. However, significant regional disparities exist in epidemiological findings. This study utilizes ambient air quality monitoring data and mortality records from a Basin City near the Qinghai-Tibet Plateau in 2023, applying the Generalized Additive Model to quantitatively assess the association between air pollution and mortality. The measurement record revealed that ozone concentrations exceeded the National Ambient Air Quality Standard II during more than 40

The mapping of emission inventory to the grids of the air quality model is a crucial pre-processing step in both atmospheric chemistry research and air quality forecasting. While some classic methods have been widely used for this mapping process, there is a lack of optimized methods that take into account the characteristics of the emission inventory and model grids. Inspired by the fact that the sum of the sub-areas of a unit cell is equal to 1, this paper introduces a novel method called “area-weight”, which simultaneously transfers and zooms the coordinates of the emission inventory and model grid nodes, then interpolates the emissions to the model grid nodes by considering the sub-areas of each unit cell as interpolation weights. Compared to the two classic methods, the area-weight method demonstrates its advantages in terms of both spatial smoothness and temporal efficiency. To assess the suitability of the area-weight method in practical applications, an emission inventory was mapped to the WRF-Chem model grid nodes to investigate the regional contribution of anthropogenic emissions to a PM2.5 pollution episode that occurred in Sichuan Basin, China. Through validation by observations, the feasibility of the area-weight method and WRF-Chem model configurations was confirmed. The model results indicate that emission reduction should be implemented especially in Chengdu Plain Economic Zone, because this region exhibits high PM2.5 contribution to the entire Sichuan Basin, especially to the southern Basin.

The COVID-19 lockdown measures have resulted in significant changes in human activities and thereby providing a natural experiment for understanding the underlying mechanism of air pollutants’ response to emission changes. Here, we use surface observations, MODIS AOD, and Tropospheric Monitoring Instrument (TROPOMI) NO2 columns to investigate the spatial and temporal changes of criteria air pollutants during various stages of lockdown across the Sichuan Basin (SCB) situated in southwestern China. Basin-wide declines of CO (-13.8

Nitrate and organic matters (OM) have become dominant components in fine particles (PM2.5) during winter haze in recent years. Based on continuous observations of gaseous pollutants, the chemical composition of PM2.5, and other relevant data collected over a one-month period (December 1-31, 2021), we investigated the main controlling factors contributing to the formation of wintertime haze in Yibin, located in the southern Sichuan Basin. Our observations reveal that two major haze episodes occurred during the campaign. Nitrate and OM were the dominant components in PM2.5, with an overall contribution of more than 50%. Nitrate and OM concentrations nearly quadrupled and more than tripled, respectively, from the non-pollution phase to the pollution phase. Furthermore, the mixing ratios of high-activity VOCs also noticeably increased during the pollution period, particularly OVOCs mixing ratios increased by 123.83%. PM2.5 concentrations were positively correlated with O-x concentrations, with a stronger relationship observed when O-x concentrations exceeded 80 mu g m(-3). There were also significant positive correlations between nitrate and O-x concentrations, as well as between OVOCs and OM concentrations. Furthermore, the pollution period showed a much higher degree of photochemical aging compared to the non-pollution period. Potential Source Contribution Function (PSCF) analysis revealed that, in addition to local emissions, regional transport, particularly air pollutants from Chengdu and Chongqing, significantly contributed to winter haze in Yibin. Our findings suggest that intense atmospheric photochemical oxidation and pronounced photochemistry contributed greatly to the occurrence of severe winter haze events.

基于IASI、OMI与TROPOMI卫星数据识别了2008～2019年四川盆地氨与氮氧化物柱浓度的变化趋势,并进一步采用空气质量模型CMAQ对2019年冬季四川盆地氨排放的大气环境影响进行了研究,评估了氨与氮氧化物单独减排及氨与氮氧化物协同减排情景下对四川盆地颗粒物污染的影响.结果表明:2008～2012年四川盆地氮氧化物排放逐年升高随后在2013～2019年迅速下降,而氨柱浓度在2008～2013年期间较为稳定,自2014年起迅速增长.四川盆地氨排放的高值区主要集中在人为活动强烈的成都及周边地区和川南城市群以及农业源氨排放主导的川西北地区.铵根离子在川南城市群的PM2.5当中占比高达11.4％,而对川西地区城市的PM2.5贡献较低.敏感性实验结果表明,氨与氮氧化物协同减排50％能有效降低大气中硝酸铵与硫酸铵的浓度,从而减少细颗粒物污染,改善四川盆地区域环境空气质量.

It's urgent to clarify the quantitative relationship between declining fine−particle (PM2.5) concentrations and increasing ozone (O3) levels as ozone pollution is increasingly severe in China. Here, we analyze the ground–based data of aerosol optical properties, particulates, and gaseous pollutants collected in a simultaneous observation in Kunming, locate in the Yunnan–Guizhou Plateau, from June 2014 to May 2017. The data exhibit slightly rising levels of PM2.5, O3, and carbon monoxid (CO), declining AOD (aerosol optical depth) but increasing AE (Angstrom exponent). Both the seasonal PM2.5 and CO concentrations were low in the wet season and high in the dry season. The seasonal ozone levels and AE almost increased monotonically while the seasonal AOD decreased wavelike. Overall, the maximum daily 8 h average (MDA8) O3 concentrations positively related to PM2.5 concentrations with temperature>22 °C, RH<45%, and AOD<1.0 in the wet season. Generally, MDA8 O3 concentrations didn't fluctuate with PM2.5 concentrations in the dry season. High SSA (single scattering albedo) corresponded to high MDA8 O3 concentrations and low SSA corresponded to low MDA8 O3 concentrations in the wet season. However, lower SSA values in the dry season were observable. Most high MDA8 O3 concentrations (>100 μg m−3) in the wet season occurred with AE at 1.0–1.5. High MDA8 O3 concentrations (>100 μg m−3) in dry season appeared with an AE range of ~0.75–1.75. Low MDA8 O3 concentrations occurred with AOD>2.0 and RH>75%, despite T>20 °C. The biomass−burning/urban aerosols were dominant (71.11% in the wet season and 74.40% in the dry season). The PSCF (Potential Source Contribution Function) results revealed the air pollutants from inside and outside China will have great impacts on the pollutant levels in Kunming besides the local emissions. Particularly, the severe pollution in India may aggravate Kunming's air pollution.

The alarming increase of ambient ozone (O3) levels across China raises an urgent need in understanding underlying mechanisms of regional O3 events for highly urbanized city clusters. Sichuan Basin (SCB) situated in southwestern China has experienced severe O3 pollution at times in summer from 2013 to 2020. Here, we use the WRF-CMAQ model with the Integrated Source Apportionment Method (ISAM) to investigate the evolution mechanism and conduct source attribution of an extreme O3 episode in the SCB from June 1 to 8, 2019. This typical summer O3 episode is associated with the synoptic-driven meteorological phenomenon and transboundary flow of O3 and precursors across the SCB. Weak ventilation in combination with stagnant conditions triggered the basin-wide high O3 concentrations and enhanced BVOC emissions substantially contribute up to 57.9 μg/m3 MDA8 O3. CMAQ-ISAM indicates that precursor emissions from industrial and transportation have the largest impacts on elevating ambient O3 concentrations, while power plant emissions exhibit insignificant contributions to basin-wide O3 episodes. These results improve the understanding of the summertime O3 episode in the SCB and contribute insights into designing O3 mitigation policy.

In order to study the impact of different leaders on the U.S. economy, this paper uses a gray correlation model to obtain the more sensitive and prominent factors among all the factors. After that, these factors are used to calculate the corresponding multiple linear regression equations to predict the U.S. economy.

基于四川雅安城市空气质量预报和大气污染防控的需求以及冬季以颗粒物(PM2.5和PM10)污染为主、夏季以臭氧(O3)污染为主的特点,本文利用雅安市2015-2018年空气污染监测数据以及同期气象观测资料,重点分析雅安市空气污染物PM2.5、PM10和O3浓度变化特征的基础上,利用灰色关联度方法对上述污染物浓度与气象要素的相关关系进行了细致分析;通过BP神经网络进行两者的数学建模,构建了雅安市空气质量短期预报模型,并进行了试预报检验.研究表明:雅安市2015-2017年期间污染物O3、PM2.5、PM10浓度呈上升的趋势,空气质量达标率自2015年的92.7％降低到2017年的82.2％,2018年达标率略有上升为88％,但仍出现了9天中度污染和1天重污染.污染物浓度与气象要素变化相关密切,其中,降雨量和气压与PM2.5和PM10污染关联最大,表明雅安作为四川盆地的“雨城”,其降水对颗粒物的湿清除效应是很显著的;而气温和风速与O3污染关联最大,恰好反映了高温和由高温所隐含的强辐射对O3生成的促进作用.由BP神经网络所建立的雅安O3预报模型,其准确度较稳定,各季7天平均相对误差都＜19％,并且预报效果排序为夏季＞冬季＞秋季＞春季;由BP神经网络所建立的雅安PM2.5预报模型,其在春季和夏季预测准确度较好,两季7日平均相对误差都＜16％,秋季相对误差略高一点,其四季预报准确度排序为夏季＞春季＞秋季＞冬季.此研究结果可为当地空气质量预报业务的开展提供技术支持.

Biogenic volatile organic compounds (BVOC) play an important role in global environmental chemistry and climate. In the present work, biogenic emissions from China in 2017 were estimated based on the Model of Emissions of Gases and Aerosols from Nature (MEGAN). The effects of BVOC emissions on ozone and secondary organic aerosol (SOA) formation were investigated using the WRF-CMAQ modeling system. Three parallel scenarios were developed to assess the impact of BVOC emissions on China's ozone and SOA formation in July 2017. Biogenic emissions were estimated at 23.54 Tg/yr, with a peak in the summer and decreasing from southern to northern China. The high BVOC emissions across eastern and southwestern China increased the surface ozone levels, particularly in the BTH (Beijing-Tianjin-Hebei), SCB (Sichuan Basin), YRD (Yangtze River Delta) and central PRD (Pearl River Delta) regions, with increases of up to 47 μg m−3 due to the sensitivity of VOC-limited urban areas. In summer, most SOA concentrations formed over China are from biogenic sources (national average of 70%). And SOA concentrations in YRD and SCB regions are generally higher than other regions. Excluding anthropogenic emissions while keeping biogenic emissions unchanged results that SOA concentrations reduce by 60% over China, which indicates that anthropogenic emissions can interact with biogenic emissions then facilitate biogenic SOA formation. It is suggested that controlling anthropogenic emissions would result in reduction of both anthropogenic and biogenic SOA.

Volatile organic compounds (VOCs) play key roles in the formation of particulate matter (PM) and oxidants and hence enhance the summertime air pollution. With data analysis from various field observations (urban station: Haidian, HD; suburb station: Changping, CP; regional background station: Shangdianzi, SDZ) in June 2011, we found that the mean O3 concentrations throughout the observation period can be arranged in order: SDZ＞CP＞HD. The mean PM concentrations can be arranged in order: HD＞CP＞SDZ. The mean concentration of total VOCs at HD was close to that at CP, but much higher than that at SDZ. Oxygenated volatile organic compounds (OVOCs) made the largest portion of VOCs (~40%) at the three stations, followed by alkanes and alkenes. Good positive correlations between the concentrations of PM and Ox (=O3＋NO2) at high air temperature (≥30 °C at HD and CP; ≥28 °C at SDZ) and low RH (≤60%) were found in the daytime (07:00–18:00 LT). However, the PM concentrations were partly negatively correlated with Ox at lower temperatures (≤26 °C at HD and CP; ≤22 °C at SDZ) and higher RH (≥80%). The PM concentrations were found either positively (HD and CP) or negatively (SDZ) correlated with aromatics at high temperature and low RH. The aging degree of air masses at the three stations can be arranged in order: SDZ＞CP＞HD. This can qualitatively explain the different PM−aromatics correlations. Our data suggested the other groups of VOCs may promote the increase of PM concentrations unless the PM levels become higher than 140 μg m−3 under humid conditions. This study confirms the importance of VOCs in the formation of summertime PM and oxidants over Beijing.

It is known that air pollution is harmful to creatures, though until now most of the human thermal comfort indices that existed were calculated only with meteorological conditions. Therefore, a new index - meteorology and environment comfort (MEC) - was given out in this paper that considers both meteorology and air pollution conditions and presents the comprehensive and synergistic effects of meteorological and air pollution. The meteorology and air pollution data were used to establish the influence function of the five air pollutants (PM2.5, PM10, O3, NO2, and SO2) according to Fechner's law; then, we calculated the somatosensory temperature (ST, a class of human thermal comfort indices) and MEC values of five typical cities (Beijing, Xining, Nanjing, Kunming, and Guangzhou). The results showed average improvements of five cities on MEC as a new comprehensive human comfort index to new ST. In spring, the MEC comfort proportion fell by 29.25%. Besides, the extreme heat discomfort ratio in Nanjing and Kunming has increased over 20%. In summer, the comfort proportion fell 12.54%; the extreme heat discomfort proportion of Beijing increased 37.86% and Kunming increased 24.09%. Air pollution significantly raised discomfort stress in Beijing. In fall, the comfort proportion fell by 20.87%; and the extreme heat discomfort of Nanjing increased 23.67% caused by poor air quality. About winter, the comfort ratio decreased 12.72%, and the cold discomfort proportion of Nanjing increased 30.30%, signifying awful air quality in winter. Air pollution levels significantly affect the comfort levels in all seasons, which is more evident with good weather patterns. MEC can offer early warnings of extreme weather events and provide a basis for the better prevention and control of air pollution to protect human health basing on the predictions of meteorological and environmental impact factors.

The data of ozone (O3) and its precursors (NOx, CO, VOCs) observed at northern suburb of Nanjing from December 01, 2013 to November 30, 2014 were used to analyze the difference of pollutant concentrations on weekends and weekdays, and its causes. The results showed that there was an obvious "Weekend Effect" in northern suburb of Nanjing. The mass concentrations of O3 on weekdays were higher than those on weekends, whereas mass concentrations of its precursors were higher on the weekends; The average mass concentrations of O3 were 19.84 μg·m-3, 53.45 μg·m-3, 57.17 μg·m-3, and 40.43 μg·m-3 in winter, spring, summer, and autumn respectively. Compared with other seasons, "Weekend Effect " was more distinct in spring. The value of NO2/NO was 4.81% higher on weekdays (3.63) than on weekends (3.46). The longer cumulative time and higher accumulation rate of O3, and the stronger atmospheric oxidation capacity on weekdays were responsible for the higher O3 mass concentrations on weekdays. The correlation coefficients of the mass concentrations of O3 with VOCs, NOx, NO, and NO2 were higher on weekdays than on weekends.

Based on the data collected from May 15th to August 31st 2013 in an industrial area of Nanjing (a representative industrial area in the Yangtze River delta),characteristics of ozone (O3),PM2.5 and aerosol optical depth (AOD),and the relationships between O3 and PM2.5 and between O3 and AOD were analyzed.The effect of AOD on ozone formation was evaluated by the application of a detailed chemical mechanism model (NCAR MM).The average concentration of PM2.5 was 56.2±20.1 μg m-3,and the average AOD (500 nm) and Angstrom exponent α (440-870 nm) were 1.4± 0.9 and 1.0± 0.3,respectively.PM2.5 and O3 exceeded NAAQS-Ⅱ (the National Ambient Air Quality Standard Ⅱ) by 20.2％ and 10.1％,respectively.When PM2.5 exceeded the NAAQS-Ⅱ,the average AOD (500 nm) and α (440-870 nm) increased by 14.7％ and 23.91％,respectively,and O3 fell by 12.3％.When O3 exceeded the NAAQS-Ⅱ,the average AOD (500 nm) increased by 34.9％,and the average α (440-870 nm) did not vary significantly.There existed a significant linear correlation between daily ozone maximum concentration (y) and PM2.5 concentration (x) under the condition of high temperature and low relative humidity.When the relative humidity was less than 60％,the linear regression function was y=0.97x+43.96 [R2=0.60 (R2denotes the degree of fitting)].When the temperature was over 32℃,the linear regression function was y=1.24x+30.61 (R2=0.64).There existed a negative correlation between daily ozone maximum concentration (y) and ground-observed AOD (x) in general.There existed a good negative correlation between simulated daily ozone maximum concentration (y) and ground-observed AOD (x),and the regression functions could be written as v=-34.28x+ 181.62 (R2=0.93) and/or y=220.62.exp(-x/3.17)-19.50 (R2=0.99).

The Beijing–Tianjin–Hebei urban agglomeration is currently facing severe complex air pollution. In this paper, simultaneous observations conducted in 2014 show that the annual mean concentration of fine particulate matter (PM2.5) was 84 ± 70, 86 ± 60, and 118 ± 95 μg m−3 in Beijing, Tianjin, and Shijiazhuang, respectively. The mean O3_8 h max in the summer was 171 ± 43, 147 ± 45, and 146 ± 44 μg m−3, respectively. This research indicates that PM2.5 and O3 are positively correlated when the temperature exceeds 20 °C, and the urban agglomeration shows characteristics of complex air pollution consisting of superimposed O3 and PM2.5. In summer, when the humidity was less than 55%, secondary particles and O3 also increased in a coordinated manner (y = 1.35x + 29.85; R2 = 0.61), which demonstrates severe complex pollution. However, the mean PM2.5 (y) and mean O3_8 h max (x) in summer showed a negative correlation (y = −1.3x + 245; R2 = 0.61) in the three regions, indicating high concentrations of PM2.5 pollution partially inhibit O3 generation.

Volatile organic compounds (VOCs) were continuously observated in a northern suburb of Nanjing, a typical industrial area in the Yangtze River Delta, in a summer observation period from 15th May to 31st August 2013. The average concentration of total VOCs was (34.40±25.20)ppbv, including alkanes (14.98±12.72)ppbv, alkenes (7.35±5.93)ppbv, aromatics (9.06±6.64)ppbv and alkynes (3.02±2.01)ppbv, respectively. Source apportionment via Positive Matrix Factorization was conducted, and six major sources of VOCs were identified. The industry-related sources, including industrial emissions and industrial solvent usage, occupied the highest proportion, accounting for about 51.26% of the VOCs. Vehicular emissions occupied the second highest proportion, accounting for about 34.08%. The rest accounted for about 14.66%, including vegetation emission and liquefied petroleum gas/natural gas usage. Contributions of VOCs to photochemical O3 formation were evaluated by the application of a detailed chemical mechanism model (NCAR MM). Alkenes were the dominant contributors to the O3 photochemical production, followed by aromatics and alkanes. Alkynes had a very small impact on photochemical O3 formation. Based on the outcomes of the source apportionment, a sensitivity analysis of relative O3 reduction efficiency (RORE), under different source removal regimes such as using the reduction of VOCs from 10% to 100% as input, was conducted. The RORE was the highest (~20%–40%) when the VOCs from solvent-related sources decreased by 40%. The highest RORE values for vegetation emissions, industrial emissions, vehicle exhaust, and LPG/NG usage were presented in the scenarios of 50%, 80%, 40% and 40%, respectively.