Extreme air pollution meteorological events (EAPMEs), such as atmospheric stagnation (AS), heat wave (HW), and temperature inversion, significantly affect regional air quality. In the present study, considerably increased EAPME frequency with clear spatial variations is identified in China from 1960 to 2019. Strong air quality sensitivities to EAPMEs are identified by combing air quality and meteorological observations. We found that HW greatly affects ozone concentration and may enhance the generation of fine particles. AS showed a noticeable but opposite impact on fine particle pollution between northern and southern China owing to the wintertime heating policy. EAPMEs may increase the probability of air pollution for pollution levels greater than 2. For example, the synthetic effects of multiple EAPMEs could increase the probability of summer ozone and winter fine particle pollution in China by factors of up to 5 and 2, respectively. Based on such information, more attention may be needed on the collaborative prediction of extreme meteorological events and air pollution episodes.

Extreme weather events are on the rise against the backdrop of climate change. Impact of tropical cyclone (TC) system, one of the extreme weather events, on air pollution remain poorly understood. A total of 103 recorded TCs (tropical storms and stronger) together with the surface air quality data collected from over 2000 observational sites in mainland China in 2014–2020 were utilized to examine the co‐occurrence of TC with continental ozone (O₃) and fine particulate matters (PM_2.5) pollution. TCs exhibit cleaning effects on PM_2.5 within its coverage (typically ∼650 km), but enhance the co‐occurrence of O₃ and PM_2.5 in an extended region. The co‐occurrence frequencies are greater than 30% for any two co‐occurring events, and ∼8% for TC + PM_2.5 + O₃. We observed superregional trans‐boundary transportation of air pollutants driven by TC, led to ubiquitous spatial offsets and temporal lags of O₃, PM_2.5 pollution and TC, which are often inconsistent with traditional mechanisms such as accelerated chemical reactions for O₃ formation. O₃ pollution tends to occur 1∼2 days after PM_2.5 pollution moved west. In contrast, PM_2.5 pollution usually occur 4–12 hr following O₃ pollution within a day over most regions except the southeast of China. Such spatial and temporal offsets vary with the paths of TCs. Composite analysis shows that temperature, wind field and large pressure systems are the key factors for air pollution formation and transportation over TC periphery. Our results have implications for understanding the interactions between TCs and air pollution, particularly for coastal areas where there are recurring TCs.

In this study, deep convolutional neural network (CNN) models of stimulated tropical cyclone intensity (TCI), minimum central pressure (MCP), and maximum 2 min mean wind speed at near center (MWS) were constructed based on ocean and atmospheric reanalysis, as well Best Track of tropical hurricane data over 2014–2018. In order to explore the interpretability of the model structure, sensitivity experiments were designed with various combinations of predictors. The model test results show that simplified VGG-16 (VGG-16 s) outperforms the other two general models (LeNet-5 and AlexNet). The results of the sensitivity experiments display good consistency with the hypothesis and perceptions, which verifies the validity and reliability of the model. Furthermore, the results also suggest that the importance of predictors varies in different targets. The top three factors that are highly related to TCI are sea surface temperature (SST), temperature at 500 hPa (TEM_500), and the differences in wind speed between 850 hPa and 500 hPa (vertical wind shear speed, VWSS). VWSS, relative humidity (RH), and SST are more significant than MCP. For MWS and SST, TEM_500, and temperature at 850 hPa (TEM_850) outweigh the other variables. This conclusion also implies that deep learning could be an alternative way to conduct intensive and quantitative research.

Complex topography has nonnegligible effects on local meteorological conditions as well as the transportation of atmospheric pollutants, which deserves more extensive study. In this study, the impacts of complex terrain features (mountains and river valleys) on local wind field and PM_2.5 concentration in a typically developed mega city along the Yangtze River were studied numerically using the WRFCALMET-CALPUFF system. The impacts of different model grid and terrain horizontal resolutions were firstly investigated against observations. Then, the impacts of terrain features, specifically the impacts of Mt. LS and the Yangtze River, on wind field and PM_2.5 transportation were analyzed by “removing” Mt. LS and the Yangtze River from the meteorological diagnostic model and simulating the dispersion of PM_2.5 from three virtual point sources in the chemical model. Results showed that: (i) higher terrain elevation and model horizontal resolutions, and updated land cover types, can effectively improve the prediction of wind direction where terrain features are complex; (ii) Mt. LS mainly acts as a barrier, and ridge wind is weakened after “removing” Mt. LS; (iii) after “removing” the Yangtze River, the transport of PM_2.5 along the Yangtze River is weakened; (iv) the simulation of PM_2.5 from virtual point sources showed that Mt. LS could have an effect of up to 55% on the PM_2.5 concentration in Nanjing. This study showed that the local complex topographies have an obvious effect on the local wind field and the concentration of PM_2.5. Therefore, it is important to consider the influence of local topographies and land cover types when predicting local wind field and air quality.

The international journal of Aerosol and Air Quality Research (AAQR) covers all aspects of aerosol science and technology, atmospheric science and air quality related issues.