Abstract. The cities of Chengdu, Deyang, and
Mianyang in the northwest Sichuan Basin are part of a rapidly developing
urban agglomeration adjoining the eastern slopes of the Tibetan Plateau.
Heavy air pollution events have frequently occurred over these cities in recent
decades, but the effects of meteorological conditions on these pollution
events are unclear. We explored the effects of weather systems on winter
heavy air pollution from 1 January 2006 to 31 December 2012 and from
1 January 2014 to 28 February 2017. A total of 10 heavy air pollution events occurred
during the research period and 8 of these took place while the region was
affected by a dry low-pressure system at 700 hPa. When the urban
agglomeration was in front of the low-pressure system and the weather
conditions were controlled by a warm southerly air flow, a strong
temperature inversion appeared above the atmospheric boundary layer acting as
a lid. Forced by this strong inversion layer, the local secondary circulation
was confined to the atmospheric boundary layer, and the horizontal wind
speed in the lower troposphere was low. As a result, vertical mixing and
horizontal dispersion in the atmosphere were poor, favoring the formation of
heavy air pollution events. After the low-pressure system had transited over
the region, the weather conditions in the urban agglomeration were controlled
by a dry and cold air flow from the northwest at 700 hPa. The strong
inversion layer gradually dissipated, the secondary circulation enhanced and
uplifted, and the horizontal wind speed in the lower troposphere also
increased, resulting in a sharp decrease in the concentration of air
pollutants. The strong inversion layer above the atmospheric boundary layer
induced by the low-pressure system at 700 hPa thus played a key role in the
formation of heavy air pollution during the winter months in this urban
agglomeration. This study provides scientific insights for forecasting heavy
air pollution in this region of China.
Heavy PM2.5 (particulate matter with aerodynamic diameter equal to or less than 2.5 μm) pollution and urban heat island (UHI) pose increasing threats to human health and living environment in populated cities. However, how PM2.5 pollution affects the UHI intensity (UHII) has not been fully understood. The impacts of PM2.5 on the wintertime UHII in the Beijing‐Tianjin‐Hebei megalopolis of China are explored during 2013–2017. The results show that the UHII at the time of daily maximum/minimum temperature (UHIImax/UHIImin) exhibits a decreasing/increasing tendency as PM2.5 concentration increases, causing a continuous decrease in the diurnal temperature range. These effects are mediated via aerosol‐radiation interaction (aerosol‐cloud interaction) under clear‐sky (cloudy) condition. The changes in PM2.5 concentration further cause different relative trends of UHIImax/UHIImin/diurnal temperature range across different cities in the Beijing‐Tianjin‐Hebei region, which are likely related to the differences in both the PM2.5 composition and city size. This study provides insights on how air pollution affects urban climate and would help to design effective mitigation strategies.
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.
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