Maureen Cribb

Aerosol effects on the lightning intensity and polarity of a continental supercell storm were investigated using a three‐dimensional lightning scheme within the Weather Research and Forecasting model. We find that both intra‐cloud (IC) and cloud‐to‐ground (CG) flashes are enhanced by the increasing number of cloud condensation nuclei (CCN), especially the percentage of positive CG (+CG) strokes peaking at 42%. Electrical characteristics of the storm varied in different aerosol scenarios through microphysical processes. Added aerosols increase the number of cloud droplets and ice‐phase hydrometeors. The greater ice‐crystal concentration and larger graupel size ensure sufficient charge separation, leading to higher charge density and more lightning discharges. In addition, an inverted polarity charge structure with a strong positive‐charge region in the mid‐levels was formed mainly due to the positively charged graupel in the presence of higher supercooled cloud water content. Positive lightning channels originating from this positive‐charge region propagated to the ground, producing more +CG strokes. When the aerosol concentration was low, the charge density in the upper positive‐charge region was much lower due to smaller ice‐particle content. Consequently, there were barely any +CG strokes. Most of the negative CG flashes deposited positive charge in the lower negative‐charge region.

The contrasting trends of surface particulate matter (PM_2.5), ozone (O₃), and nitrogen dioxide (NO₂) and their relationships with meteorological parameters from 2015 to 2019 were investigated in the coastal city of Shanghai (SH) and the inland city of Hefei (HF), located in the Yangtze River Delta (YRD). In both cities, PM_2.5 declined substantially, while O₃ and NO₂ showed peak values during 2017 when the most frequent extreme high-temperature events occurred. Wind speed was correlated most negatively with PM_2.5 and NO₂ concentrations, while surface temperature and relative humidity were most closely related to O₃. All of the studied pollutants were reduced by rainfall scavenging, with the greatest reduction seen in PM_2.5, followed by NO₂ and O₃. By contrast, air pollutants in the two cities were moderately strongly correlated, although PM_2.5 concentrations were much lower and O_x (O₃ + NO₂) concentrations were higher in SH. Additionally, complex air pollution hours occurred more frequently in SH. Air pollutant concentrations changed more with wind direction in SH. A more effective washout effect was observed in HF, likely due to the more frequent strong convection and thunderstorms in inland areas. This research suggests pertinent air quality control measures should be designed accordingly for specific geographical locations.

PM _2.5 is hazardous to human health, and high-quality data are thus needed on a routine basis. An attempt is made here to improve the accuracy of near-surface PM _2.5 estimates using the newly released aerosol product derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) satellite with the Deep Blue retrieval algorithm. A high-quality PM _2.5 data set is generated at a spatial resolution of 6 km from 2013 to 2018 by applying the space-time extremely randomized trees (STET) model, which also aims to extend the Earth Observing System (EOS) long-term PM _2.5 data records in China. The PM _2.5 estimates are highly consistent with ground-based measurements, with an out-of-sample cross-validation coefficient of determination (CV-R ² ) of 0.88, a root-mean-square error (RMSE) of $16.52~\mu \text{g}/\text{m}^{3}$ , and a mean absolute error of $10~\mu \text{g}/\text{m}^{3}$ at the national scale. Spatiotemporal PM _2.5 variations at monthly scales are also well captured (e.g., $R^{2} =0.91$ –0.94, RMSE = 5.8– $11.6~\mu \text{g}/\text{m}^{3})$ . PM _2.5 varied greatly at regional and seasonal scales across China. Benefiting from emission reduction and air pollution controls, PM _2.5 pollution has reduced dramatically in China with an average of $- 5.6~\mu \text{g}/\text{m}^{3}$ /yr ⁻¹ during 2013–2018. Significant regional reductions are also seen, in particular, in the Beijing–Tianjin–Hebei region ( $- 6.6~\mu \text{g}/\text{m}^{3}$ /yr ⁻¹ , $p < 0.001$ ), and the Deltas of Yangtze River ( $- 6.3~\mu \text{g}/\text{m}^{3}$ /yr ⁻¹ , $p < 0.001$ ) and Pearl River Delta ( $- 4.5~\mu \text{g}/\text{m}^{3}$ /yr ⁻¹ , $p < 0.001$ ). Our study improved the accuracy of near-surface PM _2.5 estimates in terms of their spatiotemporal variations at a relatively long-term record, which is important for future air pollution and health studies in China.

Urbanization and air pollution are major anthropogenic impacts on Earth’s environment, weather, and climate. Each has been studied extensively, but their interactions have not. Urbanization leads to a dramatic variation in the spatial distribution of air pollution (fine particles) by altering surface properties and boundary-layer micrometeorology, but it remains unclear, especially between the centers and suburbs of metropolitan regions. Here, we investigated the spatial variation, or inhomogeneity, of air quality in urban and rural areas of 35 major metropolitan regions across China using four different long-term observational datasets from both ground-based and space-borne observations during the period 2001–2015. In general, air pollution in summer in urban areas is more serious than in rural areas. However, it is more homogeneously polluted, and also more severely polluted in winter than that in summer. Four factors are found to play roles in the spatial inhomogeneity of air pollution between urban and rural areas and their seasonal differences: (1) the urban–rural difference in emissions in summer is slightly larger than in winter; (2) urban structures have a more obvious association with the spatial distribution of aerosols in summer; (3) the wind speed, topography, and different reductions in the planetary boundary layer height from clean to polluted conditions have different effects on the density of pollutants in different seasons; and (4) relative humidity can play an important role in affecting the spatial inhomogeneity of air pollution despite the large uncertainties.

Fine particulate matter with aerodynamic diameters ≤2.5 µm (PM_2.5) has adverse effects on human health and the atmospheric environment. The estimation of surface PM_2.5 concentrations has made intensive use of satellite-derived aerosol products. However, it has been a great challenge to obtain high-quality and high-resolution PM_2.5 data from both ground and satellite observations, which is essential to monitor air pollution over small-scale areas such as metropolitan regions. Here, the space–time extremely randomized trees (STET) model was enhanced by integrating updated spatiotemporal information and additional auxiliary data to improve the spatial resolution and overall accuracy of PM_2.5 estimates across China. To this end, the newly released Moderate Resolution Imaging Spectroradiometer Multi-Angle Implementation of Atmospheric Correction AOD product, along with meteorological, topographical and land-use data and pollution emissions, was input to the STET model, and daily 1 km PM_2.5 maps for 2018 covering mainland China were produced. The STET model performed well, with a high out-of-sample (out-of-station) cross-validation coefficient of determination (R²) of 0.89 (0.88), a low root-mean-square error of 10.33 (10.93) µg m⁻³, a small mean absolute error of 6.69 (7.15) µg m⁻³ and a small mean relative error of 21.28 % (23.69 %). In particular, the model captured well the PM_2.5 concentrations at both regional and individual site scales. The North China Plain, the Sichuan Basin and Xinjiang Province always featured high PM_2.5 pollution levels, especially in winter. The STET model outperformed most models presented in previous related studies, with a strong predictive power (e.g., monthly R²=0.80), which can be used to estimate historical PM_2.5 records. More importantly, this study provides a new approach for obtaining high-resolution and high-quality PM_2.5 dataset across mainland China (i.e., ChinaHighPM_2.5), important for air pollution studies focused on urban areas.