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Human-perceived thermal comfort (known as human-perceived temperature) measures the combined effects of multiple meteorological factors (e.g., temperature, humidity, and wind speed) and can be aggravated under the influences of global warming and local human activities. With the most rapid urbanization and the largest population, China is being severely threatened by aggravating human thermal stress. However, the variations of thermal stress in China at a fine scale have not been fully understood. This gap is mainly due to the lack of a high-resolution gridded dataset of human thermal indices. Here, we generated the first high spatial resolution (1 km) dataset of monthly human thermal index collection (HiTIC-Monthly) over China during 2003–2020. In this collection, 12 commonly used thermal indices were generated by the Light Gradient Boosting Machine (LightGBM) learning algorithm from multi-source data, including land surface temperature, topography, land cover, population density, and impervious surface fraction. Their accuracies were comprehensively assessed based on the observations at 2419 weather stations across the mainland of China. The results show that our dataset has desirable accuracies, with the mean R2, root mean square error, and mean absolute error of 0.996, 0.693 ∘C, and 0.512 ∘C, respectively, by averaging the 12 indices. Moreover, the data exhibit high agreements with the observations across spatial and temporal dimensions, demonstrating the broad applicability of our dataset. A comparison with two existing datasets also suggests that our high-resolution dataset can describe a more explicit spatial distribution of the thermal information, showing great potentials in fine-scale (e.g., intra-urban) studies. Further investigation reveals that nearly all thermal indices exhibit increasing trends in most parts of China during 2003–2020. The increase is especially significant in North China, Southwest China, the Tibetan Plateau, and parts of Northwest China, during spring and summer. The HiTIC-Monthly dataset is publicly available from Zenodo at https://doi.org/10.5281/zenodo.6895533 (Zhang et al., 2022a).

Long-term, ground-based daily global solar radiation (DGSR) at Zhongshan Station in Antarctica can quantitatively reveal the basic characteristics of Earth’s surface radiation balance and validate satellite data for the Antarctic region. The fixed station was established in 1989, and conventional radiation observations started much later in 2008. In this study, a random forest (RF) model for estimating DGSR is developed using ground meteorological observation data, and a high-precision, long-term DGSR dataset is constructed. Then, the trend of DGSR from 1990 to 2019 at Zhongshan Station, Antarctica is analyzed. The RF model, which performs better than other models, shows a desirable performance of DGSR hindcast estimation with an R2 of 0.984, root-mean-square error of 1.377 MJ m−2, and mean absolute error of 0.828 MJ m−2. The trend of DGSR annual anomalies increases during 1990–2004 and then begins to decrease after 2004. Note that the maximum value of annual anomalies occurs during approximately 2004/05 and is mainly related to the days with precipitation (especially those related to good weather during the polar day period) at this station. In addition to clouds and water vapor, bad weather conditions (such as snowfall, which can result in low visibility and then decreased sunshine duration and solar radiation) are the other major factors affecting solar radiation at this station. The high-precision, long-term estimated DGSR dataset enables further study and understanding of the role of Antarctica in global climate change and the interactions between snow, ice, and atmosphere.

Human thermal comfort measures the combined effects of temperature, humidity, and wind speed, etc., and can be aggravated under the influences of global warming and local human activities. With the most rapid urbanization and the largest population, China is being severely threatened by aggravating human thermal stress. However, the variations of thermal stress in China at a fine scale have not been fully understood. This gap is mainly due to the lack of a high-resolution gridded dataset of human thermal indices. Here, we generate the first high spatial resolution (1 km1 km) dataset of monthly human thermal index collection (HiTIC-Monthly) over China from 2003 to 2020. In this collection, 12 commonly used thermal indicators are generated by the LGBM machine learning algorithm from multi-source gridded data, including MODIS land surface temperature, topography, land cover and land use, population density, and impervious surface fraction. Their accuracies were comprehensively assessed based on observations at 2419 weather stations across the mainland of China. The results show that our dataset has desirable performance, with mean R2, root mean square error, mean absolute error, and bias of 0.996, 0.693 °C, 0.512 °C, and 0.003 °C, respectively, by averaging the 12 indicators. Moreover, the predictions exhibit high agreements with observations across spatial and temporal dimensions, demonstrating the broad applicability of our dataset. The comparison with two existing datasets also suggests that our high-resolution dataset can describe a more explicit spatial distribution of the thermal information, showing great potentials in fine-scale (e.g., intra-urban) study. Further investigation reveals that nearly all indicators exhibit increasing trends in most parts of China during the year 2003~2020. The increase is especially stronger in North China, Southwest China, the Tibetan Plateau, and parts of Northwest China, and in the spring and summer seasons. The HiTIC-Monthly dataset is publicly available via https://zenodo.org/record/6895533 (Zhang et al., 2022a).

The Hunga Tonga-Hunga Ha'apai eruption on January 15, 2022 was one of the most explosive volcanic eruptions of the 21st century and has attracted global attention. Here we show that large numbers of the volcanic aerosols from the eruption broke through the tropopause into the lower stratosphere, forming an ash plume with an overshooting top at 25-30 km altitude. In the four days following the eruption, the ash plume moved rapidly westward for nearly 10,000 km under stable stratospheric conditions characterized by strong tropical easterlies, weak meridional winds and weak vertical motion. The intrusion of the ash plume into the stratosphere resulted in a marked increase in atmospheric aerosol loading across northern Australia, with the aerosol optical depth (AOD) observed by satellites and sun-photometers peaking at 1.5 off the coast of northeastern Australia; these effects lasted for nearly three days. The ash plume was characterized by fine-mode particles clustered at a radius of about 0.26 µm, with an observed peak volume of 0.25 µm

Precipitable water vapor (PWV) plays a vital role in climate research, especially for Antarctica in which meteorological observations are insufficient due to the adverse climate and topography therein. Reanalysis data sets provide a great opportunity for Antarctic water vapor research. This study investigates the climatological PWV means, variability and trends over Antarctica from four reanalyses, including the fifth generation of European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA5), the Second Modern-Era Retrospective analysis for Research and Applications (MERRA-2), Japanese 55-year Reanalysis (JRA-55) and National Centers for Environmental Prediction/Department of Energy (NCEP/DOE), in the period of 2001–2018 based on radiosonde and GNSS observations. PWV data from the ERA5, MERRA-2, JRA-55 and NCEP/DOE have been evaluated by radiosonde and GNSS observations, showing that ERA5 and MERRA-2 perform better than JRA-55 and NCEP/DOE with mean root mean square (RMS) errors below 1.2 mm. The climatological PWV mean distribution over Antarctica roughly shows a decreasing trend from west to east, with the highest content in summer and the lowest content in winter. The PWV variability is generally small over Antarctica, showing a seasonal dependence that is larger in the cold season and smaller in the warm season. PWV trends for all reanalyses at most Antarctic regions are insignificant and most reanalyses present overall drying trends from 2001 to 2018, except for ERA5 exhibiting a moistening trend. PWV trends also show seasonal and regional dependence. All reanalyses are generally consistent with radiosonde and GNSS observations in reproducing the PWV means (mean differences within 1.1 mm), variability (mean differences within 3%) and trends (mean differences within 6.4% decade−1) over Antarctica, except for NCEP/DOE showing spurious variability and trends in East Antarctica. Results can help us further understand these four reanalysis PWV products and promote climate research in Antarctica.

As one of the aridest regions in the mid-latitudes, arid Northwest China (ANC) has scarce water resources year-round, its annual total precipitation is mainly contributed by summer extreme precipitation events, which may also cause disastrous flash floods. However, the physical mechanism and climatic features of extreme precipitation events in ANC are not fully understood and warrant in-depth investigations. Taking Xinjiang as an example, this study investigates the underlying mechanisms and long-term trend of extreme precipitation events in ANC during 1961‒2019 using observational and reanalysis datasets. Results suggest that summer extreme precipitation events in ANC are characterized by a zonal wave pattern with the deepening of the western Siberian trough, central Asian high, and Mongolian high. Under the effects of the zonal wave pattern, the westerly and the easterly airflows anomalously intensify and converge in ANC, transporting a large amount of water vapor from the Arctic Ocean, the Caspian Sea, the Aral Sea, the Arabian Sea, and eastern China to ANC. Meanwhile, a deep anomalous cyclone center induced by the deep wave pattern appears over ANC, thus providing favorable conditions for the occurrence of extreme precipitation events in ANC. Over the long-term period, total summer precipitation and the frequency of extreme precipitation events in ANC show significant increasing trends, indicating that summer climate in ANC is becoming more humid, and extreme precipitation events are becoming more frequent. These findings provide scientific insights for understanding the formation mechanism of summer extreme precipitation events and mitigating the corresponding disasters in arid regions.

Precipitable water vapor (PWV), despite being the key factor for assessing climate change over the Tibetan Plateau (TP), is undersampled in this region due to the complex terrain and climate conditions therein. The fifth‐generation European Center for Medium‐Range Weather Forecasts Reanalysis (ERA5) and Second Modern‐Era Retrospective Analysis for Research and Applications (MERRA‐2) data sets are the two newly released global reanalyzes that provide hourly PWV products with a high spatiotemporal resolution for water vapor research. The accuracies of these two PWV products on the TP require a comprehensive and cautious assessment. In this study, the hourly PWV values derived from ERA5 and MERRA‐2 are systematically assessed by a comparison with 33 Global Navigation Satellite System (GNSS) stations from 2017 to 2018 over the TP. To improve the reliability of this evaluation, an enhanced Tm model and a PWV vertical correction model, namely, TP‐Tm and TP‐PWVC, respectively, are developed, and both models show better performance when compared against other widely used models. The accuracies of the ERA5‐ and MERRA‐2‐derived PWV at the 33 GNSS stations are very high; ERA5 has the smallest mean bias and root mean square (RMS) error of −0.08 and 1.77 mm, respectively, compared to those of 0.53 and 2.12 mm for MERRA‐2. In addition, the biases and RMS errors show significant regional and seasonal differences. The performance of ERA5 is slightly better than MERRA‐2, mainly due to its higher spatial resolution. The spatial distributions of the PWV derived from the two reanalyzes are consistent with the GNSS‐derived PWV distribution. The diurnal PWV variations derived from ERA5 are in good agreement with the diurnal variation of the GNSS‐derived PWV, whereas significant differences are observed for MERRA‐2 at several stations. These results indicate that ERA5 can provide more reliable hourly PWV estimates over the TP, which can help users further understand the strengths and weaknesses of these two PWV products and promote climate change research.

Chinese University of Hong Kong ‐ University of Exeter Joint Centre for Environmental Sustainability and Resilience (ENSURE)

Accurate estimation of the spatiotemporal variations of solar radiation is crucial for assessing and utilizing solar energy, one of the fastest‐growing and most important clean and renewable resources. Based on observations from 2,379 meteorological stations along with scare solar radiation observations, the random forest (RF) model is employed to construct a high‐density network of daily global solar radiation (DGSR) and its spatiotemporal variations in China. The RF‐estimated DGSR is in good agreement with site observations across China, with an overall correlation coefficient (R) of 0.95, root‐mean‐square error of 2.34 MJ/m2, and mean bias of −0.04 MJ/m2. The geographical distributions of R values, root‐mean‐square error, and mean bias values indicate that the RF model has high predictive performance in estimating DGSR under different climatic and geographic conditions across China. The RF model further reveals that daily sunshine duration, daily maximum land surface temperature, and day of year play dominant roles in determining DGSR across China. In addition, compared with other models, the RF model exhibits a more accurate estimation performance for DGSR. Using the RF model framework at the national scale allows the establishment of a high‐resolution DGSR network, which can not only be used to effectively evaluate the long‐term change in solar radiation but also serve as a potential resource to rationally and continually utilize solar energy.

Water vapor is one of the major greenhouse gases in the atmosphere and also the key parameter affecting the hydrological cycle, aerosol properties, aerosol-cloud interactions, the energy budget, and the climate. This study analyzed the temporal and spatial distribution of precipitable water vapor (PWV) in China using MODerate resolution Imaging Spectroradiometer near-infrared (MODIS-NIR)-Clear PWV products from 2011 to 2013. We then compared the four PWV products (Global Positioning System PWV (GPS-PWV), radiosonde PWV (RS-PWV), MODIS-NIR-Clear PWV, and Aerosol Robotic Network sunphotometer PWV (AERONET-PWV)) at six typical sites in China from 2011 to 2013. The analysis of the temporal and spatial distribution showed that the PWV distribution in China has clear geographical differences, and its basic distribution characteristics gradually change from the coast in the southeast to inland in the northwest. Affected by the East Asian monsoon, the PWV over China showed clear seasonal distribution features, with highest values in the summer, followed by autumn and spring, and the lowest values in winter. Intercomparison results showed that GPS-PWV and RS-PWV had a slightly higher correlation (R2 = 0.975) at 0000 UTC than that at 1200 UTC (R2 = 0.967). The mean values of Bias, SD, and RMSE between GPS-PWV and RS-PWV (GPS-RS) were − 0.03 mm, 2.36 mm, and 2.60 mm at 0000 UTC, and − 0.23 mm, 2.76 mm, and 2.95 mm at 1200 UTC, respectively. This showed that GPS-PWV was slightly lower than RS-PWV, and this difference was more obvious during the nighttime. The MODIS-NIR-Clear PWV product showed a similar correlation coefficient (R2 = 0.88) with GPS-PWV compared with RS-PWV. In addition, MODIS-NIR-Clear PWV was greater than GPS-PWV and RS-PWV. The MODIS-NIR-Clear PWV showed a larger deviation from GPS-PWV (MODIS-GPS Bias = 1.50 mm, RMSE = 5.76 mm) compared with RS PWV (MODIS-RS Bias = 0.75 mm, RMSE = 5.31 mm). The correlation coefficients between AERONET-PWV and the PWV from GPS, RS, and MODIS-NIR-Clear were 0.970, 0.963, and 0.923 (with RMSE of 2.53 mm, 3.67 mm, and 4.39 mm), respectively. In the Beijing area, the overall mean bias of the AERONET-PWV product with GPS-PWV, RS-PWV and MODIS-NIR-Clear PWV was − 0.09 mm, − 1.82 mm, and − 1.54 mm, respectively, which shows that the AERONET-PWV product was lower than the other three PWV products.

This study analyzes the annual and seasonal trends in precipitable water vapor (PWV) and surface temperature (Ts) over China from 1979 to 2015, and the relationships between PWV and Ts and between PWV and aerosol absorption optical depth (AAOD), using data from radiosonde stations, weather stations and multiple satellite observations. The results revealed a positive PWV trend between 1979 and 1999, and a negative PWV trend between 2000 and 2015. Analysis of the differences in the PWV trend among different stations types showed that the magnitude of the trends were in the order main urban stations > provincial capital stations > suburb stations, suggesting that anthropogenic activities have a strong influence on the PWV trend. The AAOD exhibited a significant positive trend in most regions of China from 2005 to 2015 (confidence level 95%). Using spatial correlation analysis, we showed that PWV trend derived from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations is correlated with Ts, with an annual correlation coefficient of 0.596. In addition, the spatial correlation between PWV and AAOD showed a negative relationship, with the highest correlation coefficients of −0.76 and −0.71 observed in mid-eastern China and central northwest China, respectively, suggesting that the increase in AAOD in recent years may be one of the reasons for the decrease in PWV since the 2000s in China.

The verification and correction of CALIPSO aerosol products is key to understanding the atmospheric environment and climate change. However, CALIPSO often cannot detect the full profile of aerosol for the low instrument sensitivity near the surface. Thus, a correction scheme for the aerosol extinction coefficient (AECs) in the planetary boundary layer (PBL) is proposed to improve the quality of the CALIPSO-based aerosol optical depth (AOD) at 532 nm. This scheme assumed that the aerosol is vertically and uniformly distributed below the PBL, and that the AECs in the whole PBL are equal to those at the top of the PBL; then, the CALIPSO AOD was obtained by vertically integrating AECs throughout the whole atmosphere. Additionally, the CALIPSO AOD and corrected CALIPSO AOD were validated against seven ground-based sites across eastern China during 2007–2015. Our results show that the initial CALIPSO AOD obtained by cloud filtering was generally lower than that of the ground-based observations. After accounting for the AECs in the PBL, the adjustment method tended to improve the CALIPSO AOD data quality. The average R (slope) value from all sites was improved by 7% (46%). Further, the relative distance between the ground track of CALIPSO and the ground station exhibited an influence on the validation result of CALIPSO AOD. The retrieval precision of CALIPSO AOD worsened with the increase in water vapor in the atmosphere. Our findings indicate that our scheme significantly improves the accuracy of CALIPSO AOD, which will help to provide alternative AOD products in the presence of severe atmospheric pollution.

The spatial-temporal variations of the ground-based and satellite-derived PM2.5 are crucial for studying air quality, human health, and climate change. However, the existing ground-based PM2.5 monitoring network has sparsely-distributed sites and satellite cannot give 24-h PM2.5, which make it difficult to grasp the spatial and sub-daily variation characteristics of PM2.5. This study aims to fill that gap by establishing a virtual network of hourly PM2.5 concentration using the LightGBM model, based on the high-density ground meteorological observations at ~2400 sites across China. The virtual network shows a desirable performance of hourly PM2.5 estimation across China, with R2 of 0.86, root-mean-square error values of 14.99 μg/m3, and mean absolute error of 9.48 μg/m3 (the results of Cross-Validation). It also exhibits high spatial-temporal consistencies with the observed PM2.5. Spatially, the heaviest PM2.5 pollution is mainly distributed in eastern China (especially the Beijing-Tianjin-Hebei, the Yangtze and Pearl river deltas, and the Sichuan-Chongqing areas). Temporarily, PM2.5 exhibits remarkable seasonal and diurnal changes characterized by higher concentration in winter and nighttime and lower in summer and daytime. Meanwhile, we found that visibility can be used as the primary predictor in the machine learning model to enhance the accuracy of estimated PM2.5. The established virtual hourly PM2.5 network (~2400 stations) provides a more intuitive and detailed PM2.5 data for us to understand the diurnal variation of PM2.5 and monitor inter-regional transport of haze over China. It thus is of benefit to the study of air pollution control and related diseases.

Air pollution with high concentrations of fine particulate matter (PM2.5) poses severe threats to human health. Accurate estimation of PM2.5 concentrations can timely assist relevant agencies to conduct air pollution treatment and provide essential data sources for epidemiological research related to PM2.5 exposure. Although China has established a network for monitoring ground-level PM2.5 concentrations over the past decades, the limited available records from the sparsely-located PM2.5 monitoring sites hinder the fine-resolution research of air pollution. Many studies have been conducted to fill the data gap caused by sparsely distributed monitoring sites, but the accuracy of different models varies greatly. In recent years, machine learning models have become the preferred choices due to their high estimation accuracy. However, the estimation accuracy may differ significantly in different study areas with different models, and there are few studies on model performance evaluation regarding the Pearl River Delta (PRD) region of China. This study evaluated the performance of six machine learning models for estimating PM2.5 concentrations in PRD from August 2014 to December 2019. Moreover, multi-source data were adopted for reliable daily PM2.5 concentration estimation, including meteorology, vegetation, topography, and point of interest (POI). The results show that the tree-structured models (i.e., Random Forest (RF) and Gradient Boosting Regression Tree (GBRT)) generally produce better estimations than other models. Two neural network models (i.e., Back Propagation Neural Network (BPNN) and Elman Neural Network (ENN)) show a similar estimation accuracy. Additionally, the Generalized Additive Model (GAM) generally gives the worst performance, followed by the Support Vector Machines (SVM) model. RF is thus highly recommended based on the estimation accuracy, while GBRT is also a promising model for daily PM2.5 estimation in PRD. Our study provides a reference for selecting an appropriate model for daily PM2.5 concentration estimation in PRD and other regions with climate background.

With increasing public concerns on air pollution in China, there is a demand for long-term continuous PM2.5 datasets. However, it was not until the end of 2012 that China established a national PM2.5 observation network. Before that, satellite-retrieved aerosol optical depth (AOD) was frequently used as a primary predictor to estimate surface PM2.5. Nevertheless, satellite-retrieved AOD often encounter incomplete daily coverage due to its sampling frequency and interferences from cloud, which greatly affect the representation of these AOD-based PM2.5. Here, we constructed a virtual ground-based PM2.5 observation network at 1180 meteorological sites across China using the Extreme Gradient Boosting (XGBoost) model with high-density meteorological observations as major predictors. Cross-validation of the XGBoost model showed strong robustness and high accuracy in its estimation of the daily (monthly) PM2.5 across China in 2018, with R2, root-mean-square error (RMSE) and mean absolute error values of 0.79 (0.92), 15.75 μg/m3 (6.75 μg/m3) and 9.89 μg/m3 (4.53 μg/m3), respectively. Meanwhile, we find that surface visibility plays the dominant role in terms of the relative importance of variables in the XGBoost model, accounting for 39.3% of the overall importance. We then use meteorological and PM2.5 data in the year 2017 to assess the predictive capability of the model. Results showed that the XGBoost model is capable to accurately hindcast historical PM2.5 at monthly (R2 = 0.80, RMSE = 14.75 μg/m3), seasonal (R2 = 0.86, RMSE = 12.28 μg/m3), and annual (R2 = 0.81, RMSE = 10.10 μg/m3) mean levels. In general, the newly constructed virtual PM2.5 observation network based on high-density surface meteorological observations using the Extreme Gradient Boosting model shows great potential in reconstructing historical PM2.5 at ~1000 meteorological sites across China. It will be of benefit to filling gaps in AOD-based PM2.5 data, as well as to other environmental studies including epidemiology.

It is known that rapid expansion in urban area exerts prominent effects on surface or near-surface temperature and the urban heat island effect. However, whether and how urban land expansion influences atmospheric humidity, a key indicator closely relating to human comfort and urban environment, warrant further investigations. By taking Guangdong Province as an example, in this study we examined the change in atmospheric moisture over South China during 1961–2014 and evaluated the urban expansion effects. We found a prominent drying tendency over Guangdong, as characterized by decreasing relative and specific humidities and increasing vapor pressure deficit. The drying trend has a positive correlation with urban expansion speed and exhibited regional differences, with particularly stronger trends in more urbanized and populated areas such as the Pearl River Delta. Using a dynamic classification of urban / rural stations based on time-varying high-resolution land use / land cover maps, we estimated that urbanization contributed to approximately 50% of the total drying trend in the urban areas of Guangdong. Possible mechanisms and processes underlying the urban expansion effect were also discussed. We suggested that the changes in atmospheric moisture and the urban expansion effect should be considered in future climate change mitigation, urban planning, and landscape design to support sustainable development in urban areas.

To assess the health risk of PM2.5, it is necessary to accurately estimate the actual exposure level of the population to PM2.5. However, the spatial distribution of PM2.5 may be inconsistent with that of the population, making it necessary for a high-spatial-resolution and refined assessment of the population exposure to air pollution. This study takes the Yangtze River Delta (YRD) Region as an example since it has a high-density population and a high pollution level. The brightness reflectance of night-time light, and MODIS-based (Moderate Resolution Imaging Spectroradiometer) vegetation index, elevation, and slope information are used as independent variables to construct a random-forest (RF) model for the estimation of the population spatial distribution, before any combination with the PM2.5 data retrieved from MODIS. This enables assessment of the population exposure to PM2.5 (i.e., intensity of population exposure to PM2.5 and population-weighted PM2.5 concentration) at a 3-km resolution, using the year 2013 as an example. Results show that the variance explained for the RF-model-estimated population density reaches over 80%, while the estimated errors in half of counties are < 20%, indicating the high accuracy of the estimated population. The spatial distribution of population exposure to PM2.5 exhibits an obvious urban−suburban−rural difference consistent with the population distribution but inconsistent with the PM2.5 concentration. High and low PM2.5 concentrations are mainly distributed in the northern and southern YRD Region, respectively, with the mean proportions of the population exposed to PM2.5 concentrations > 35μg/m3 close to 100% in all four seasons. A high-level population exposure to PM2.5 is mainly found in Shanghai, most of the Jiangsu Province, the central Anhui Province, and some coastal cities of the Zhejiang Province. The highest risk of population exposure to PM2.5 occurs in winter, followed by spring and autumn, and the lowest in summer, consistent with the PM2.5 seasonal variation. Seasonal-averaged population-weighted PM2.5 concentrations are different from PM2.5 concentrations in the region, which are closely related to the urban-exposed population density and pollution levels. This work provides a novel assessment of the proposed population-density exposure to PM2.5 by using multi-satellite retrievals to determine the high-spatial-resolution risk of air pollution and detailed regional differences in the population exposure to PM2.5.

The Sichuan and Chongqing regions suffer from severe haze weather in winter due to the unfavourable atmospheric diffusion conditions. Reanalysis and precipitation datasets were applied in this study to calculate and distinguish air stagnation events using a developed criterion, and the impacts of the occurrence of air stagnation events on air quality were analysed in combination with the PM2.5 concentration data for the winters of 2013-2016. The highest occurrence frequency of air stagnation events was observed in 2013, and the lowest, 2015. The meteorological conditions during winter in the Sichuan Basin were inclined to form unfavourable atmospheric diffusion conditions, and the occurrence frequency of air stagnation days was up to 76.6% on average during the four winters. The effects of air stagnation events on air quality were most obvious in the western and southern Sichuan Basin. The mean concentrations of PM2.5 during air stagnation days were higher by 41.9% than those during non-air stagnation days. The PM2.5 concentrations were adjusted using the favourable atmospheric diffusion conditions in 2015 as a baseline to quantify the PM2.5 contribution to the improvement of air quality in the other years, which revealed that the level of PM2.5 in the Sichuan and Chongqing regions was declining at a rate of approximately 10.7% overall during the winters of 2013-2016, implying that the air pollutant reduction measures have been highly effective. Furthermore, the occurrence frequency of air stagnation days and events were increased in recent ten years of 2007-2016, with linear slopes of 0.61yr-1 and 0.26yr-1, respectively. The study revealed that the government might face a greater challenge in improving the air quality over winter and should pay more attention to reduction of pollutant emission in areas of Chengdu, Chongqing and cities in the south of the Sichuan Basin.

East Asia is one of the world's largest sources of dust and anthropogenic pollution. Dust particles originating from East Asia have been recognized to travel across the Pacific to North America and beyond, thereby affecting the radiation incident on the surface as well as clouds aloft in the atmosphere. In this study, integrated analyses are performed focusing on one trans-Pacific dust episode during 12–22 March 2015, based on space-borne, ground-based observations, reanalysis data combined with Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT), and the Weather Research and Forecasting Model coupled with Chemistry (WRF-Chem). From the perspective of synoptic patterns, the location and strength of Aleutian low pressure system largely determined the eastward transport of dust plumes towards western North America. Multi-sensor satellite observations reveal that dust aerosols in this episode originated from the Taklimakan and Gobi Deserts. Moreover, the satellite observations suggest that the dust particles can be transformed to polluted particles over the East Asian regions after encountering high concentration of anthropogenic pollutants. In terms of the vertical distribution of polluted dust particles, at the very beginning, they were mainly located in the altitudes ranging from 1 km to 7 km over the source region, then ascended to 2 km–9 km over the Pacific Ocean. The simulations confirm that these elevated dust particles in the lower free troposphere were largely transported along the prevailing westerly jet stream. Overall, observations and modeling demonstrate how a typical springtime dust episode develops and how the dust particles travel over the North Pacific Ocean all the way to North America.

An extreme haze event occurred in the Beijing area from 17 to 23 December 2015. Ground-based measurements and satellite observations during this event were used to further our understanding of the formation process of haze pollution and aerosol optical properties. The results suggest that high relative humidity, poor diffusion conditions (low wind speed and stable stratification) and favorable secondary transformation conditions under the hygroscopic growth of aerosol and high emissions led to this serious haze episode. During the haze period, the daily average value was 1.15 and 0.42 for aerosol optical depth (AOD500nm) and columnar water-vapor (CWV, in cm), respectively. On 19 December, the correlation coefficient between CWV and AOD500nm was 0.91, indicating the effect of hygroscopic growth of fine-mode articles. The daily average values for Ångström exponent, fine-mode fraction, aerosol absorption optical depth, and Ångström absorption exponent were 1.19, 0.81, 0.11 and 1.47, respectively, which suggests that fine aerosol particles were dominant in the atmosphere and fine-mode particles were the dominant contributor to atmospheric extinction during the haze period. Moreover, it also reflects that there were more absorbing aerosol particles during the haze period. Compared with other polluted periods with a bimodal distribution, there was an obvious trimodal distribution on 19 December. There were three peaks at radii of about 0.1 μm, 0.5–0.8 μm and 4 μm. Satellite observations show that there was an obvious aerosol layer in the Beijing area during the haze period, concentrated at ground level to within 2 km in the upper layers. The types of aerosol were mainly composed of mixed pollution aerosols.