Impact of Meteorological Changes on Particulate Matter and Aerosol Optical Depth in Seoul during the Months of June over Recent Decades

Spring 2020 Atmospheric Aerosol Contamination over Kyiv City

Extraordinarily high aerosol contamination was observed in the atmosphere over the city of Kyiv, Ukraine, during the March–April 2020 period. The source of contamination was the large grass and forest fires in the northern part of Ukraine and the Kyiv region. The level of PM2.5 load was investigated using newly established AirVisual sensor mini-networks in five areas of the city. The aerosol data from the Kyiv AERONET sun-photometer site were analyzed for that period. Aerosol optical depth, Ångström exponent, and the aerosol particles properties (particle size distribution, single-scattering albedo, and complex refractive index) were analyzed using AERONET sun-photometer observations. The smoke particles observed at Kyiv site during the fires in general correspond to aerosol with optical properties of biomass burning aerosol. The variability of the optical properties and chemical composition indicates that the aerosol particles in the smoke plumes over Kyiv city were produced by different burning materials and phases of vegetation fires at different times. The case of enormous PM2.5 aerosol contamination in the Kyiv city reveals the need to implement strong measures for forest fire control and prevention in the Kyiv region, especially in its northwest part, where radioactive contamination from the Chernobyl disaster is still significant.

Transboundary aerosol transport process and its impact on aerosol-radiation-cloud feedbacks in springtime over Northeast Asia

Northeast Asia has been suffering from dramatic increases of particulate matter (PM) since the late 1990s, and it still continues to undergo haze despite various abating regulations. In this study, we investigated aerosol-cloud-precipitation (ACP) interactions with the varied PM, and the impact of long-range transport (LRT) process on ACP in springtime was assessed in Northeast Asia. Our long-term (1995–2019) analysis of PM₁₀ exhibited the correlation with decreases of both sunshine duration and drizzle occurrences that can be interpreted as direct and indirect aerosol effects, while cloud cover induced by the varied PM₁₀ was found only in more than 90% cloud cover (9/10–10/10 category). The online WRF-Chem with wind-blown dust simulation indicated that cloud water was affected by secondary inorganic aerosol (SIA) formation near the surface in upwind areas dominantly, whereas, along the LRT pathway, cloud water perturbation altitudes were increased quasi-linearly toward downward between 1 and 3 km. The gas-to-particle conversion ratios of sulfur ([SO₄²⁻]/[SO₂ + SO₄²⁻]) and nitrogen ([NO₃⁻]/[NO₂ + NO₃⁻]) both remain aloft long at the same vertical levels of most perturbed cloud altitude enough to be transported over long distance in springtime. Formations of sulfate and nitrate showed different ACP interaction timing; distinctive shifts in the ratios observed at the exit (Shanghai-Yellow Sea) by nitrate, and entrance areas (Seoul-Tokyo) by sulfate along the LRT pathway, respectively, with higher ratios of 0.8 or more in springtime. Our results indicate that ACP processes have been enhanced at a LRT-related altitude with different SIA production timings that can be considered in species-specific springtime PM abatements over Northeast Asia.

Estimation of the PM2.5 and PM10 Mass Concentration over Land from FY-4A Aerosol Optical Depth Data

The purpose of this study is to estimate the particulate matter (PM2.5 and PM10) in China using the improved geographically and temporally weighted regression (IGTWR) model and Fengyun (FY-4A) aerosol optical depth (AOD) data. Based on the IGTWR model, the boundary layer height (BLH), relative humidity (RH), AOD, time, space, and normalized difference vegetation index (NDVI) data are employed to estimate the PM2.5 and PM10. The main processes of this study are as follows: firstly, the feasibility of the AOD data from FY-4A in estimating PM2.5 and PM10 mass concentrations were analysed and confirmed by randomly selecting 5–6 and 9–10 June 2020 as an example. Secondly, hourly concentrations of PM2.5 and PM10 are estimated between 00:00 and 09:00 (UTC) each day. Specifically, the model estimates that the correlation coefficient R2 of PM2.5 is 0.909 and the root mean squared error (RMSE) is 5.802 μg/m3, while the estimated R2 of PM10 is 0.915, and the RMSE is 12.939 μg/m3. Our high temporal resolution results reveal the spatial and temporal characteristics of hourly PM2.5 and PM10 concentrations on the day. The results indicate that the use of data from the FY-4A satellite and an improved time–geographically weighted regression model for estimating PM2.5 and PM10 is feasible, and replacing land use classification data with NDVI facilitates model improvement.

Analysis of Aerosol Optical Depth from Sun Photometer at Shouxian, China

We use two cloud screening methods—the clustering method and the multiplet method—to process the measurements of a sun photometer from March 2020 to April 2021 in Shouxian. The aerosol optical depth (AOD) and Angström parameters α and β are retrieved; variation characteristics and single scattering albedo are studied. The results show that: (1) The fitting coefficient of AOD retrieved by the two methods is 0.921, and the changing trend is consistent. The clustering method has fewer effective data points and days, reducing the overall average of AOD by 0.0542 (500 nm). (2) Diurnal variation of AOD can be divided into flat type, convex type, and concave type. Concave type and convex type occurred the most frequently, whereas flat type the least. (3) During observation, the overall average of AOD is 0.48, which is relatively high. Among them, AOD had a winter maximum (0.70), autumn and spring next (0.54 and 0.40), and a summer minimum (0.26). The variation trend of AOD and β is highly consistent, and the monthly mean of α is between 0.69 and 1.61, concerning mainly continental and urban aerosols. (4) Compared with others, the single scattering albedo in Shouxian is higher, reflecting strong scattering and weak aerosol absorption.