Asian Journal of Atmospheric Environment最新文献

In a situation where various policy measures can be used to reduce atmospheric particulates, effectiveness and efficiency may vary depending on how the policy is designed. Therefore, this study evaluated the effectiveness and efficiency of atmospheric particulates reduction policy in order to contribute to effective and efficient policy design. To this end, this study demonstrated the effectiveness of 1^st Basic Plan on Metropolitan Area Air Quality Improvement and explored the cause of the effectiveness. As a result of the study, this study did not confirm that the effect of reducing PM₁₀ caused by the plan in the metropolitan area was significantly different from that of the non-metropolitan area where the policy was not implemented. In particular, distinct effect was not confirmed on the installation of DPF, which required a large number of costs. Based on the results, more effective and efficient policy measures will be used based on the causal relationship of atmospheric particulates generation.

Air pollution monitoring devices are widely used to quantify at-site air pollution. However, such monitoring sites represent pollution of a limited area, and installing multiple devices for a vast area is costly. This limitation of unavailability of data at non-monitoring sites has necessitated the Spatio-temporal analysis of air pollution and its prediction. Few commonly used methods for Spatio-temporal prediction of pollutants include - ‘Averaging’; ‘Best correlation coefficient method’; ‘Inverse distance weighting method’ and ‘Grid interpolation method.’ Apart from these conventional methods, a new methodology, ‘Weighted average method,’ is proposed and compared for air pollution prediction at non-monitoring sites. The weights in this method are calculated based on both on the distance and directional basis. To compare the proposed method with the existing ones, the air pollution levels of NO₂ (Nitrogen dioxide), O₃ (Ozone), PM₁₀ (Particulate matter of 10 microns or smaller), PM_2.5 (Particulate matter of 2.5 microns or smaller), and SO₂ (Sulphur dioxide) were predicted at the non-monitoring site (test stations) by utilizing the available data at monitoring sites in Delhi, India. Preliminary correlation analysis showed that NO₂, PM_2.5, and SO₂ have a directional dependency between different stations. The ‘average’ method performed best with the mode RMSE of 18.85 µg/m³ and R² value 0.7454 when compared with all the methods. The RMSE value of the new proposed method ‘weighted average method’ was 21.25 µg/m³, resulting in the second-best prediction for the study area. The inverse distance weighting method and the Grid interpolation method were third and fourth, respectively, while the ‘best correlation coefficient’ was the worst with an RMSE value of 41.60 µg/m³. Results also showed that the methods that used dependent stations had performed better when compared to methods that used all station data.

Concentrations of 19 elements (Al, Fe, Ca, K, Mg, Na, S, Ti, Ba, Sr, Zn, V, Cu, Mn, Cr, Pb, Ni, Co, and Cd) in foliar dust samples were determined from 6 different roadside locations of Bilaspur city (Chhattisgarh), India. Principal component analysis (PCA) indicated the significance of vehicular activities followed by sources such as firework events and other industrial/regional/transboundary sources in foliar dust in the area of study. Risk assessment of metal levels in foliar dust was performed using several indices based on the data collected from different sites. The geo-accumulation index (Igeo) analysis indicated foliar dust was moderately and extremely polluted with S and Cd, respectively, while practically unpolluted with most other elements (Al, Fe, Ca, K, Mg, Na, Ti, Ba, Sr, Zn, V, Cu, Mn, Cr, Pb, Ni, and Co). The values of pollution (I_POLL) index and contamination factor (CF) of Cd indicated a high pollution level. Comparable results were found for the ecological risk (Erⁱ) of Cd (above 320) with a very high Erⁱ at all sites. In addition, the overall Erⁱ index (RI) of foliar dust at all sites was very high due to a greater Cd contribution.

Surface ozone (O₃) data at Pune (1998–2014) and Delhi (1998–2013) are studied to examine their temporal characteristics. Study also examines role of meteorology and atmospheric boundary layer height (ABLH) in modulating surface O₃ at these sites. Using diurnal variability of surface O₃, rate of change of surface O₃, [d(O₃)/dt] is estimated to infer the nature of surface O₃ formation/destruction mechanisms. Analysis of data reveals that at both locations, surface O₃ concentrations during daytime are significantly high as compared to those during nighttime. Seasonally, at Pune averaged daytime surface O₃ concentrations are high during pre-monsoon and low in monsoon while those during winter and post-monsoon are found to be significantly higher than those in monsoon but half as compared to those in pre-monsoon. At Delhi, averaged daytime surface O₃ concentration is minimum in winter and maximum in pre-monsoon with monsoon and post-monsoon values being about 0.79–0.82 times with respect to pre-monsoon O₃ concentrations. High natural/anthropogenic pollutant concentration, abundance of ozone precursor gases, high temperature and high rate of photo-oxidation of precursor gases due to solar flux are the causal factors for increased surface O₃ concentrations in pre-monsoon season. Reduced solar flux decreases photo-dissociation of ozone precursor gases resulting in low O₃ concentration during winter season. Occurrence of low surface O₃ during early morning hours in monsoon, post-monsoon and winter seasons is because of low ABLH and low stratosphere-troposphere exchange (STE). [d(O₃)/dt] values during morning/evening at Pune and Delhi are indicative of asymmetric and symmetric nature of ozone formation/destruction mechanisms.

The changes in air quality were investigated in six megacities during the shutdown phases in 2020 and were compared to the same time periods in the previous 10 years (2010–2019) using the data of Modern-Era Retrospective Analysis and Research and Application, version 2 (MERRA-2). The concentrations of PM₁₀ and PM_2.5 were greatly reduced in all megacities during the lockdown in 2020 when compared to the same period in 2019 and in the previous ten years. The highest reduction in PM₁₀ was recorded in Delhi, and São Paulo (21%, and 15% and by 27%, and 9%), when compared with the concentrations in 2019 and in the period 2010–2019, respectively. Similarly, levels of PM_2.5 in Delhi, São Paulo, Beijing, and Mumbai decreased by 20%, 14%, 12%, and 10%, respectively in 2020 when compared to the last ten years. Results indicated that the lockdown is an effective mitigation measure to improve air quality. The MERRA-2 reanalysis dataset could be a vital tool in air quality studies in places with a lack of In-situ observations.

A thick foggy weather and worst visibility in Fukuoka, Japan and Busan, South Korea occurred from the late July to early August 2020 due to the Nishinoshima volcanic eruption. In this study, an intensive measurement was made to clarify the chemical nature of the ambient particulate matter (PM) and rain water collected in Fukuoka and Busan during the Nishinoshima volcanic eruption (episode period) and non-eruption (non-episode period). In this study, one week after volcanic eruption, which recorded the usual PM concentration, was defined as the non-episode period. Compared to non-episode period, the PM_2.5 concentration during the episode period increased 4.32 times in Busan and 6.03 times in Fukuoka. The sulfur and chlorine concentrations in the total suspended particles (TSP) and rainwater of episode period were particularly higher than those of non-episode period. The sulfate concentration in PM_2.5 was 1.81 and 27.98 µg/m³ in non-episode and episode periods, respectively. The sulfate concentration during the episode period accounted for 55.4% of PM_2.5 (50.45 µg/m³). Strong correlation between trace elements in TSP and those in rainwater during the episode period indicates that the volcanic ashes could be incorporated into raindrops.

The valence state and concentration of metallic pollutants are important factors contributing to the health effects of respirable particulate matter (PM); however, they have not been well studied. In this study, coarse and fine powder samples of atmospheric PM were collected using a cyclone system at Kanagawa (KO), Saitama (SA), and Fukuoka (FU) in Japan in 2017. Energy dispersive X-ray fluorescence spectroscopy (EDXRF) was used to measure the concentrations of nine metallic elements (Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, and Pb), and X-ray absorption fine structure (XAFS) spectroscopy was used to analyze the valence states of target elements (Cr, Mn, Fe, Cu, and Zn). The EDXRF results indicated that the average contents of Fe, Ti, and Zn were much higher than those of the other six elements in all samples. The XAFS results showed that the major valence states of the elements were Cr(III), Mn(II), Fe(III), Cu(II), and Zn(II). The percentages of Mn(IV), Fe(II), and Cu(0) were higher in KO and SA samples than in FU samples. Mn(0) and Zn(0) were detected in some samples only, and Cu(I) was not detected in any samples. Correlation analysis, principal component analysis, and cluster analysis were performed on the EDXRF and XAFS data of the target elements. The source identification results showed that the sources of metal contaminants in the samples varied considerably between sampling sites and depended on the industrial structure and geographical location of the sampling area. Our findings on the different valence states of the elements may be important for determining the toxicity of PM at different locations.

This study analyzed the BC associated with PM₁ and the contribution of biomass burning to the BC using a portable seven-channel Dual spot Aethalometer in and around Gangtok, the capital city of Sikkim, India, during April 2021. Additionally, CO₂ and meteorological parameters (Temperature, Pressure, and Relative Humidity) was measured. The minimum concentration of BC was found in rural areas where the contribution of biomass burning to the BC is highest. The observed spatial variability of BC over Gangtok Municipal Corporation (GMC) area is minimal. Five days back-trajectory analysis was done using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model to understand the regional influences of air masses at Gangtok. The air mass of the studied region is under influence of trans-regional transport from Indo-Gangetic Plains affecting the BC concentration over the studied region. The black carbon presence in the ambient air near the glacier heights in the Eastern Himalayan region may significantly cause localized warming, thereby enhancing glacier melts. The results have significant bearing for the policy-makers to take corrective steps in addressing the issue of rising BC concentration in high altitude regions. A further detailed study is needed to examine the effect of BC on radiative forcing and its large-scale effect on the East Asian summer monsoon using regional climate models.

Satellite data is a collection of various atmospheric environmental information through continuous earth observations. Those data observed for a long time-series provide detailed information on environmental changes which has been processed as two-dimensional information representing the atmospheric columnar integrated properties or multi-dimensional data combining space and time. In this review, we investigate the characteristics of various earth observing satellites that have been deriving the global atmospheric information up to date. In terms of applications, the patterns of global atmospheric environmental changes based on statistical and comparative analysis with the long-term observations are also addressed. The spatio-temporal changes in the atmospheric environmental parameters are discussed, in order to provide a quantitative grasp of the statistical relationship. Finally, future developments are put forward. This information will help to understand the atmospheric environment and climate-related interactions.

WRF wind forecasts from four operative schemes used by OHMC (Observatorio HidroMeteorológico de Córdoba), a test scheme (WRF-E) and two daily runs with 4 km horizontal resolution were analyzed. Wind simulations were compared with measurements from eight ground stations with anemometers at 10 m high during the period from June, 2019 to June, 2020. WRF-E incorporates more vertical levels, and an activated topo_wind option. The wind speed results show that WRF overestimates wind speed at most stations and the WRF-E model reduces the BIAS and the RMSE when compared with the operational models. The wind direction analysis shows that the higher the wind speed is, the more accurate the models are. In addition, a wind gust forecasting has been implemented and evaluated in this work. Wind gust correlation coefficient values are between 0.3 and 0.6, RMSE is between 3 and 5 m/s, and a positive BIAS(<2 m/s) at most stations.