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In 2013, China issued the “Action Plan for the Prevention and Control of Air Pollution” (“Ten Statements of Atmosphere”) and implemented a series of pollution reduction measures from 2013 to 2017. In key regions of China, the mass concentrations of particulate matter with aerodynamic equivalent diameters less than 2.5 µm (PM 2.5 ) have dropped significantly. However, the contributions of meteorological changes to PM 2.5 reduction are largely uncertain, which has attracted particular concern from the government and the public. Here, we investigated the impact of large-scale and boundary layer (BL) meteorological conditions on aerosol pollution and estimated the contributions of meteorological changes to PM 2.5 reduction based on in-depth analysis and diagnosis of various observed meteorological elements and an integrated pollution-linked meteorological index (PLAM, which is approximately and linearly related to PM mass concentration). In this study, we found that the meteorological conditions worsened in 2014 and 2015 and improved in 2016 and 2017 relative to those in 2013 in key regions in China. In 2017 relative to 2013, only ∼5% (approximately 13% of the total PM 2.5 decline) of the 39.6% reduction in PM 2.5 mass concentrations can be attributed to meteorological changes in the Beijing-Tianjin-Hebei (BTH) region, and only ∼7% (approximately 20% of the total PM 2.5 decline) of the 34.3% reduction can be attributable to meteorological changes in the Yangtze River Delta (YRD) region. Overall, the PM 2.5 reduction due to meteorological improvement is much lower than the observed PM 2.5 reduction in these areas, which indicates that emission reduction during the five-year implementation of the “Ten Statements of Atmosphere” is the dominant factor in the improvement in air quality. The changes in meteorology and climate are conducive to PM 2.5 reduction but do not dominate the substantial improvement in air quality. Similar to the above regions, in the Pearl River Delta (PRD) region, the impact of meteorological changes on the annual averaged PM 2.5 concentration from 2013 to 2017 was relatively weak, and the PM 2.5 reduction was mainly due to emission reductions. During winter 2017 (January, February, and December of this year), the meteorological conditions improved ∼20% in the BTH region (observed total PM 2.5 reduction: 40.2%) and ∼30% in the YRD region (observed total PM 2.5 reduction: 38.2%) relative to those in 2013, showing the meteorological factors played more important role in the decrease of PM 2.5 in winter of these years in the two regions, respectively. The meteorological conditions in winter 2016 were 14% better than those in winter 2017, but the PM 2.5 reduction in winter 2016 was still less than that in winter 2017, reinforcing the significant contributions of the increasing efforts to reduce PM 2.5 emissions in 2017. The substantial progress of strict emission measures was also confirmed by a comparison of several persistent heavy aerosol pollution episodes (HPEs) with similar meteorological conditions. It is found that the decrease of PM 2.5 mass caused by emission reduction increases year by year, especially the decrease of PM 2.5 concentration in 2016 and 2017. In China, HPEs mainly occur in winter, when meteorological conditions are approximately 40–100% worse than in other seasons. This worsening is partly due to the harbor effect of high topography, including downdrafts and the weak wind zone, and partly due to the increasingly stable regional BL structure caused by climate warming. For the formation of HPEs, it occurred under regional stagnant and stable conditions associated with upper-level circulation patterns, including the zonal westerly winds type and high-pressure ridges. After pollution formation, PM 2.5 with mass accumulated to a certain degree can further worsen the BL meteorological conditions. The feedback effect associated with worsening conditions dominates PM 2.5 mass explosive growth. In the context of high air pollutant emissions in China, unfavorable meteorological conditions are the necessary external conditions for the formation and accumulation of HPEs. Therefore, reducing aerosol pollution significantly during the earlier transport stage is critical in reducing persistent HPEs. Currently, even under favorable meteorological conditions, allowing emissions without restriction is also not advisable because aerosol pollution allowed to accumulate to a certain extent will significantly worsen the BL meteorological conditions and close the “meteorological channels” available for pollution dispersion.

Cold hazard is one of the major meteorological disasters in winter. However, the meteorological conditions for the cold hazard events vary significantly with both the feature of the event and the region of occurrence. This study divides winter cold hazard events in China into three categories based on the daily gridded dataset of cold hazards from November 1980 to March 2020: events without wintry precipitation (hazardous low temperature, abrupt temperature drop, and/or freezing), with wintry precipitation only (hazardous sleet and/or snowstorm), and with both. The region-dependent multivariate meteorological conditions for each category of cold hazards are investigated using ERA5 reanalysis data. Results show that the surface air temperature (T2m) and its anomaly (T2m_anom) are lower than climatology during cold hazards. But the difference in T2m among provinces exceeds 30 °C, and even for the same province, the difference among different categories of cold hazards exceeds 10 °C. The region- and category-dependent differences of T2m_anom and daily temperature drop (∆T24) are also large, about 5 °C and 2 °C d−1, respectively. The Multivariate Empirical Orthogonal Function analysis has further been applied to not only the abovementioned temperature-related variables but also the precipitation-related variables (i.e., daily accumulated total precipitation, daily accumulated snowfall, and daily mean snow depth) in the middle and lower Yangtze River region, which reveals the event-mean state and spatial–temporal coupling evolution during the progression of the event for the selected key meteorological variables. The meteorological conditions for cold hazards put forward by this study could provide region-dependent and category-dependent reference for the prediction and warning of cold hazards.

The emission control in adverse meteorological conditions is one of the ways to reduce a negative impact of air pollution. A complex meteorological pollution dispersion index (MPDI) based on the numerical model data is proposed for early forecasting of such conditions. An algorithm for calculating the hourly and periodic MPDI is presented. The results of the method verification according to ground-based and high-altitude observations in the layer of 2–250 m and with the use of average urban pollution data are analyzed. A method for probabilistic forecasting of the MPDI is proposed to increase the reliability of the deterministic forecasting of adverse meteorological conditions. An automated technology for preparing and transmitting an information package of forecast products to the Roshydromet operational and production divisions is described. The package contains the forecasts of vertical temperature profiles, wind in the lower atmospheric layer, precipitation, and the height of the mixing layer with a time step of 3 hours, as well as the forecasts of two kinds of the MPDI with a lead time of two days.

Air contamination is identified with individuals’ wellbeing and furthermore affects the sustainable development of economy and society. This paper gathered the time series data of seven meteorological conditions variables of Beijing city from 1 November 2013 to 31 October 2017 and utilized the generalized regression neural network optimized by the particle swarm optimization algorithm (PSO-GRNN) to explore seasonal disparity in the impacts of mean atmospheric humidity, maximum wind velocity, insolation duration, mean wind velocity and rain precipitation on air quality index (AQI). The results showed that in general, the most significant impacting factor on air quality in Beijing is insolation duration, mean atmospheric humidity, and maximum wind velocity. In spring and autumn, the meteorological diffusion conditions represented by insolation duration and mean atmospheric humidity had a significant effect on air quality. In summer, temperature and wind are the most significant variables influencing air quality in Beijing; the most important reason for air contamination in Beijing in winter is the increase in air humidity and the deterioration of air diffusion condition. This study investigates the seasonal effects of meteorological conditions on air contamination and suggests a new research method for air quality research. In future studies, the impacts of different variables other than meteorological conditions on air quality should be assessed.

The local surface meteorological condition (SMC) is decisive for dust storm (DS) occurrence (DSO), and SMC’s impact properties on DSO, such as its impact style, intensity and significance on, its contribution to and decisiveness for DSO, and so on, are expected to help deepen knowledge of SMC’s impact mechanism on DSO so as to improve the DS prediction. This paper has selected 23 SMC factors and assumed they can wholly quantify SMC; so and SMC’s impact properties on DSO can be well reflected by each SMC factor or factor group’s, and the latter can be decided according to the DSO possibility’s (DSOP’s) variations with the factor or group. Based on all SMC factors’ daily datasets together with DS records during 1970–2007 at 139 weather stations within North and Northwest China (NNWC), who encounters the dust storm most frequently and has widely and densely covered weather stations, this paper has put forward a set of universal statistical techniques to respectively quantify DSOP under a certain SMC, a certain SMC factor’s impact style on DSO, and a certain SMC factor or factor group’s impact intensity and significance on and its contribution to and decisiveness for DSO. After quantifying SMC’s impact properties on DSO, their factor-to-factor, i.e., factorial, and station-to-station, i.e., spatial, variations within NNWC have been specially analyzed on in detail, resulting in some interesting conclusions: (1) generally, if one SMC factor rises (drops) from the NDS to DS day and it is positively (negatively) correlated to the DS frequentness (DSF), then it will usually impact on DSO positively (negatively), i.e., that factor’s increase can usually raise (reduce) DSOP; The wind speed and evaporation (relative humidity and vapor pressure) impact on DSO positively (negatively) at all or almost all stations within NNWC, however, other factors impact positively here but negatively there. (2) The SMC factor or factor group’s correlation degree to DSF and its sensitivity to DSO can somewhat decide its impact intensity and significance on and its contribution to and thus decisiveness for DSO: the more sensitive to DSO and the more highly correlated to DSF the factor or group, the more intensively, significantly, contributively, and thus decisively it impacts on DSO. (3) Of all factors, the wind speed has proved to impact on DSO much more intensively, significantly, contributively and thus decisively at all stations within NNWC, and SMC seems to impact on DSO almost wholly by modifying the wind speed. (4) Using the cluster technique onto SMC’s impact properties on DSO, all stations within NNWC have been classified into 7 clusters, who can well display the regionality of SMC’s impact mechanism on DSO.

Urbanized hot spots incorporate a great diversity of microclimates dependent, among other factors, on local meteorological conditions. Until today, detailed analysis of the combination of climatic variables at local scale are very scarce in urban areas. Thus, there is an urgent need to produce a Local Weather Type (LWT) classification that allows to exhaustively distinguish different urban thermal patterns. In this study, hourly data from air temperature, wind speed and direction, accumulated precipitation, cloud cover and specific humidity (2009–2018) were integrated in a cluster analysis (K-means) in order to produce a LWT classification for Lisbon’s urban area. This dataset was divided by daytime and nighttime and thermal periods, which were generated considering the annual cycle of air temperatures. Therefore, eight LWT sets were generated. Results show that N and NW LWT are quite frequent throughout the year, with a moderate speed (daily average of 4–6 m/s). In contrast, the frequency of rainy LWT is considerably lower, especially in summer (below 10%). Moreover, during this season the moisture content of the air masses is higher, particularly at night. This methodology will allow deepening the knowledge about the multiple Urban Heat Island (UHI) patterns in Lisbon.

This study investigated the seasonal variation and chemical characteristics of atmospheric PM2.5 at an urban site in Hanoi City of Vietnam in summer (July 2020) and winter (January 2021) periods. The study results showed that the average value of daily PM2.5 concentrations observed for the winter period was about 3 times higher than the counterpart for the summer period. The concentrations of major species in atmospheric PM2.5 (SO42−, NH4+, K+, OC and EC) measured during the winter period were also significantly higher than those during the summer period. The contribution of secondary sources to the measured OC (the largest contributor to PM2.5) was larger than that of primary sources during the winter period, compared to those in the summer period. The correlation analysis among anions and cations in PM2.5 suggested that different sources and atmospheric processes could influence the seasonal variations of PM2.5 species. The unfavorable meteorological conditions (lower wind speed and lower boundary layer height) in the winter period were identified as one of the key factors contributing to the high PM2.5 pollution in this period. With the predominance of north and northeast winds during the winter period, the long-range transport of air pollutants which emitted from the highly industrialized areas and the intensive fire regions in the southern part of China and Southeast Asia region were likely other important sources for the highly elevated concentrations of PM2.5 and its chemical species in the study area.

The seasonal and interannual variations in ground-level ozone across eastern China from 2014 to 2022 were strongly influenced by meteorological conditions and large-scale atmospheric circulations. We applied empirical orthogonal function (EOF) and singular value decomposition (SVD) analyses to explore these relationships. The EOF analysis identified three primary patterns of ozone variability: a dominant seasonal cycle over most of mainland China, an anti-correlation between northern and southern China during transitional seasons, and elevated springtime ozone concentrations in coastal regions. The SVD results further demonstrated that seasonal ozone variability was primarily driven by the annual radiation cycle across much of China. In contrast, the East Asian summer monsoon (EASM) was linked to the relatively low summer ozone levels observed in southern China. The anti-correlation between northern and southern China was associated with western Pacific subtropical high (WPSH) movement, which promoted sunny weather conditions and was conducive to ozone formation. Additionally, high springtime ozone levels in northern coastal regions were influenced by pollutant transport from continental cold high (CCH) events, while the cloud-free conditions and intense solar radiation in southern China contributed to elevated ozone concentrations.

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 PM2.5 and O3 was also studied from the aspects of secondary formation and meteorological conditions to find the rules of cooperative management of PM2.5 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 PM2.5 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 PM2.5 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 PM2.5 were not all the same.

Surface ozone (O3) pollution has become one of the top environmental issues in recent years around the world and can be influenced by meteorological processes on multiple scales. Understanding the meteorological mechanism and contributions of O3 pollution is of great importance for O3 mitigation. In this study, we explored the impacts of meteorological conditions on O3 concentrations in a coastal city in Southeast China, with a particular focus on O3 pollution episodes inspringtime. A significant increase in the O3 pollution ratefrom 2015 to 2020 was observed (41.7% year−1) and the seasonal characteristics of O3 concentrations showed a two-peak pattern. We selected 12 pollution episodes during the springtime of 2015 to 2020 and identified four dominant synoptic weather patterns (SWPs) that could cause O3 pollution. The local meteorological conditions and vertical dynamic structures under different SWPs were analyzed. The results showed that high O3 levels tend to be associated with high temperature, weak wind, low relative humidity, and deep vertical sinking motion. We also established a quantitative linkage between the O3 values and meteorological factors. Based on meteorological conditions, 60.8~80.8% of the variation in O3 can be explained.