Meteorological Variables and Synoptic Patterns Associated with Air Pollutions in Eastern China during 2013-2018

The deep blue day is decreasing in China

The deep blue sky is an indicator of a lower concentration of aerosols and a cloudless sky. With increasing human emissions, a trend towards days with fewer deep blue skies might indicate a decline in a good living environment for humans. This study investigates the long-term changes of the deep blue sky in China from 1980 to 2018. Due to a lack of direct measurements, we use atmospheric visibility and low cloud cover to classify blue sky days into three grades: light blue day, medium blue day, and deep blue day. Climatologically, annual deep blue days increase from southeast China to northwest China, with the maximum number in Xinjiang and eastern Inner Mongolia and the minimum number in western Qinghai and southern Hebei. From 1980 to 2018, annual deep blue days show a prominent decreasing trend in most of China, with area-mean annual deep blue days decreasing by -0.48 days per year (d/y) in China, and the variation becomes more obvious after 2013. The maximum decreasing trend is observed in eastern China. The most prominent decreases of deep blue days are seen in winter. Both air pollution and the change in meteorological conditions contribute to the decrease of wintertime deep blue days in China. Specifically, the decrease in surface wind speed hinders the cleaning of air by winds, the increase in surface air temperature, and decrease in relative humidity is favorable for low cloud increase, and the increasing emission of pollution reduces atmospheric visibility.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s00704-021-03898-1.

A Simple New Method for Calculating Precipitation Scavenging Effect on Particulate Matter: Based on Five-Year Data in Eastern China

A “rain-only” method is proposed to find out the precipitation effect on particle aerosol removal from the atmosphere, and this method is not only unique and novel but also very simple and can be easily adapted to predict aerosol particle scavenging over any region across the world irrespective of the topographical, orographical, and climatic features. By using this simple method, the influences of the rain intensity and particle mass concentration on the aerosol scavenging efficiency are discussed. The results show that a higher concentration, a higher rain intensity, and a larger particle size lead to a higher scavenging efficiency and a higher scavenging rate. The greater the rain intensity, the higher the scavenging efficiency. The scavenging efficiency of PM10 by precipitation is better than that of PM2.5. When the rain intensity is 10 mm h−1, the scavenging efficiency of PM2.5 reaches 5.1 μg m−3 h−1, and the scavenging efficiency of PM10 reaches 15.8 μg m−3 h−1. The scavenging rate increases faster when accumulative precipitation is below 15 mm. The scavenging rate has obvious monthly variation, and the scavenging rate of coastal areas is less than that of inland Jiangsu. The growth of the particle mass concentration after precipitation is divided into two stages: the rapid growth stage after precipitation ends, and the slow growth stage about 24 h after precipitation ends.

Evolution of Urban Haze in Greater Bangkok and Association with Local Meteorological and Synoptic Characteristics during Two Recent Haze Episodes

This present work investigates several local and synoptic meteorological aspects associated with two wintertime haze episodes in Greater Bangkok using observational data, covering synoptic patterns evolution, day-to-day and diurnal variation, dynamic stability, temperature inversion, and back-trajectories. The episodes include an elevated haze event of 16 days (14–29 January 2015) for the first episode and 8 days (19–26 December 2017) for the second episode, together with some days before and after the haze event. Daily PM_2.5 was found to be 50 µg m⁻³ or higher over most of the days during both haze events. These haze events commonly have cold surges as the background synoptic feature to initiate or trigger haze evolution. A cold surge reached the study area before the start of each haze event, causing temperature and relative humidity to drop abruptly initially but then gradually increased as the cold surge weakened or dissipated. Wind speed was relatively high when the cold surge was active. Global radiation was generally modulated by cloud cover, which turns relatively high during each haze event because cold surge induces less cloud. Daytime dynamic stability was generally unstable along the course of each haze event, except being stable at the ending of the second haze event due to a tropical depression. In each haze event, low-level temperature inversion existed, with multiple layers seen in the beginning, effectively suppressing atmospheric dilution. Large-scale subsidence inversion aloft was also persistently present. In both episodes, PM_2.5 showed stronger diurnality during the time of elevated haze, as compared to the pre- and post-haze periods. During the first episode, an apparent contrast of PM_2.5 diurnality was seen between the first and second parts of the haze event with relatively low afternoon PM_2.5 over its first part, but relatively high afternoon PM_2.5 over its second part, possibly due to the role of secondary aerosols. PM_2.5/PM₁₀ ratio was relatively lower in the first episode because of more impact of biomass burning, which was in general agreement with back-trajectories and active fire hotspots. The second haze event, with little biomass burning in the region, was likely to be caused mainly by local anthropogenic emissions. These findings suggest a need for haze-related policymaking with an integrated approach that accounts for all important emission sectors for both particulate and gaseous precursors of secondary aerosols. Given that cold surges induce an abrupt change in local meteorology, the time window to apply control measures for haze is limited, emphasizing the need for readiness in mitigation responses and early public warning.

Key Points in Air Pollution Meteorology

Although emissions have a direct impact on air pollution, meteorological processes may influence inmission concentration, with the only way to control air pollution being through the rates emitted. This paper presents the close relationship between air pollution and meteorology following the scales of atmospheric motion. In macroscale, this review focuses on the synoptic pattern, since certain weather types are related to pollution episodes, with the determination of these weather types being the key point of these studies. The contrasting contribution of cold fronts is also presented, whilst mathematical models are seen to increase the analysis possibilities of pollution transport. In mesoscale, land-sea and mountain-valley breezes may reinforce certain pollution episodes, and recirculation processes are sometimes favoured by orographic features. The urban heat island is also considered, since the formation of mesovortices determines the entry of pollutants into the city. At the microscale, the influence of the boundary layer height and its evolution are evaluated; in particular, the contribution of the low-level jet to pollutant transport and dispersion. Local meteorological variables have a major influence on calculations with the Gaussian plume model, whilst some eddies are features exclusive to urban environments. Finally, the impact of air pollution on meteorology is briefly commented on.

WRF-Chem Simulation of Winter Visibility in Jiangsu, China, and the Application of a Neural Network Algorithm

In this paper, the winter visibility in Jiangsu Province is simulated by WRF-Chem (Weather Research and Forecasting (WRF) model coupled with Chemistry) with high spatiotemporal resolutions. Simulation results show that WRF-Chem has good capability to simulate the visibility and related local meteorological elements and air pollutants in Jiangsu in the winters of 2013–2017. For visibility inversion, this study adopts the neural network algorithm. Meteorological elements, including wind speed, humidity and temperature, are introduced to improve the performance of WRF-Chem relative to the visibility inversion scheme, which is based on the Interagency Monitoring of Protected Visual Environments (IMPROVE) extinction coefficient algorithm. The neural network offers a noticeable improvement relative to the inversion scheme of the IMPROVE visibility extinction coefficient, substantially improving the underestimation of winter visibility in Jiangsu Province. For instance, the correlation coefficient increased from 0.17 to 0.42, and root mean square error decreased from 2.62 to 1.76. The visibility inversion results under different humidity and wind speed levels show that the underestimation of the visibility using the IMPROVE scheme is especially remarkable. However, the underestimation issue is essentially solved using the neural network algorithm. This study serves as a basis for further predicting winter haze events in Jiangsu Province using WRF-Chem and deep-learning methods.