Asian Journal of Atmospheric Environment最新文献

A simultaneous sampling method for gaseous nitric oxide (NO), nitrogen dioxide (NO₂), nitrous acid (HONO) and nitric acid (HNO₃) was developed by a filter-pack sampling method to measure these concentrations at low cost in areas where monitoring stations are not available or at multiple locations. HONO and HNO₃ gases were collected with a conventional filter-pack method. NO₂ was collected with a guaiacol-impregnated filter at a flow rate of 0.3 dm³ min⁻¹. NO was collected using guaiacol by oxidizing it to NO₂ with potassium permanganate at a 0.3 dm³ min⁻¹ flow rate. The optimum concentration of KMnO₄ in the immersion solution for the impregnated filter was 0.16 mol dm⁻³ (in 0.51 mol dm⁻³ H₂SO₄). The concentrations of NO and NO₂ measured by the filter-pack method were in good agreement with those measured by the chemiluminescence method. It was calculated that 60 ppb NO could be oxidized to NO₂ with the KMnO₄-impregnated filter for 183 hours at a 0.3 dm³ min⁻¹ flow rate. This is enough time for sampling in a real environment. This method was applied to measure NO, NO₂, HONO and HNO₃ in the atmosphere at three points around Osaka, Japan.

Heavy fogs occur during the winter period over the part of northern India and impact aviation, public transport, the economy, public life, etc. During winter, fog water (FW) and non-monsoonal rainwater (NMRW) samples were collected in Delhi, which is a highly polluted and populated megacity in northern India. The collected FW and NMRW samples were analyzed for their inorganic chemical constituents (F⁻, Cl⁻, SO₄²⁻, NO₃⁻, NH₄⁺, Na⁺, K⁺, Ca²⁺, and Mg²⁺). The volume-weighted mean (VWM) pH, conductivity, and total dissolved solids (TDS) of FW were 6.89, 206 μS cm⁻¹, and 107 mg L⁻¹, respectively, indicating the dominance of alkaline species. The total measured ionic constituents (TMIC) in FW and NMRW were 5,738 and 814 μeq L⁻¹, respectively, indicating highly concentrated FW in Delhi. The TMIC in FW were factors of 16 and 7 times more concentrated than MRW and NMRW samples, respectively. The concentrations of inorganic acidic species (SO₄²⁻ and NO₃⁻) in FW were much higher than in monsoon rainwater (MRW: 3 and 5 times) and NMRW (8 and 12 times), respectively. Also, the concentrations of SO₄²⁻ and NO₃ in NMRW were approximately double compared to MRW indicating higher acidic species concentrations during the winter season over Delhi region. Significant decadal growth in the mean concentrations of ionic species in FW (SO₄²⁻ - ~9 times; NH₄⁺ - double) were observed between 1985 and 2010. However, the nitrate decreased by ~28%. The higher SO₄²⁻ is likely from heavy-duty vehicles that burn sulfur-containing fuel. The anions in FW, MRW, and NMRW contributed 20, 42, and 43%. However, the cation contributions were 80, 58, and 57%, respectively. The anion contributions were lower in FW than MRW and NMRW indicating the weak formation of acidic species in fog water. The observed alkalinity suggests that it is unlikely for acid precipitation to be present in this region.

Malaysia reported its first COVID-19 case on January 25, 2020, and the cases have continued to grow, necessitating the implementation of additional measures. Hence, determining the factors responsible for the significant increase in COVID-19 cases is the top priority issue for the government to take necessary action and ultimately restrain this virus before the vaccine availability. Researchers had predicted that air pollution had an indirect relationship with COVID-19 in terms of virus infections. As a result, this study focuses on the link between the Air Pollutant Index (API) and COVID-19 infections. The initial data set consists of daily confirmed COVID-19 cases in Malaysia and API readings obtained from the Ministry of Health (MOH) and the Department of the Environment (DOE). The results show that Klang (S22) recorded the highest mean of API which at 62.70 while the lowest is at Limbang (S37) (25.37). Next, due to the implementation of Movement Control Order (MCO) in Malaysia and reducing social movement, 27 stations recorded a good level of API compare to the stations that recorded moderate and unhealthy levels. There is positive relationship between API and COVID-19 at each of the region which are North 0.4% (R²=0.004), Central 2.1% (R²=0.021), South 0.04% (R²=0.0004), East 1.6% (R²=0.016), Sarawak 0.2% (R²=0.002), meanwhile Sabah recorded negative correlation at 4.3% (R²=0.043). To conclude, the API value did not have a strong relationship with the rising number of COVID-19 daily cases.

This study examines the PM_2.5 characteristics in Seoul in relation to those of PM_1.0 and PM₁₀. Samples were typically collected daily on filters and a few hours sampling were conducted during a few haze events (March 2007 to June 2008). Mean mass concentrations of PM_1.0, PM_2.5, and PM₁₀ were 19.7 μg/m³, 26.0 μg/m³, and 48.2 μg/m³, respectively, and PM_2.5 was reasonably correlated with PM_1.0 (γ=0.79) and PM₁₀ (γ=0.52). Three mass group types were mainly distinguished. Group 1 (31%): linear increase of PM_1.0 with PM₁₀ and high OC and NO₃⁻; Group 2 (17%): PM₁₀ considerably higher than PM_1.0 and high Ca²⁺ and SO₄²⁻; Group 3 (52%): PM_1.0 relatively more enhanced than PM₁₀ and highest carbonaceous fraction against mass. The fine mode fraction was lowest (highest) in Group 2 (Group 3). Haze and dust episodes relating to Chinese outflows were mostly evident in Groups 1 and 2, respectively; average PM_2.5 concentrations were visibly higher than in Group 3. Non-Negative Matrix Factorization analysis demonstrated that traffic-related urban primary (28%) and coal-fired industry (27%) emissions equally contributed to the PM_2.5 mass, followed by aged urban secondary (19%), soil mineral (16%), and biomass combustion (10%) sources. Seasonal variations were apparent in air mass trajectories. Urban primary and coal-fired industry factors were predominant in Group 3 under stagnant conditions in the warm season and under a strong northerly wind in the cold season, respectively. However, contributions of the other three factors were higher in Groups 1 and 2. This study shows that the PM_2.5 mass in Seoul is largely dependent on high concentration episodes occurring mostly in cold seasons. It also shows that local emissions contribute considerably during warm months, while the influence of Chinese outflow predominates during cold months.

Organic aerosols (OA) in the atmosphere have complex emission sources and formation processes that must be determined to understand the OA composition and behavior. The thermal optical method is generally used to analyze organic carbon (OC) in OAs, and the resulting thermally fractionated OC profiles can be considered to be a synthesis of the organic materials contained in OAs. In this study, carbon-fraction profiles of 43 organic materials were determined and categorized into five types on the basis of their profile patterns. Then a chemical mass balance (CMB) analysis using the five types and the measured carbon-fraction profiles of particulate OC from various emission sources was conducted. The major sources thus determined were generally reasonable considering the known chemical properties of emission source particles. In addition, the seasonal organic matter composition in ambient particulate OC measured at a suburban site of Tokyo was experimentally estimated by a CMB analysis using the five types, and the potential of making good use of thermally fractionated OC data to understand the characteristics of OAs was discussed.

This article addresses modeling of the atmospheric boundary layer of the city of Almaty (Kazakhstan) in stagnant, environmentally unfavorable conditions using WRF Model. The city is located on the northern slope of Trans-Ili Alatau, where the rate of recurrence of calm and low-wind (1–2 m/sec) days reaches about 80%. All simulations were made for a period from 28.11.2016 to 05.12.2016, covering main synoptic situations of the stagnant atmosphere: the extent of Asian anticyclone, higher and lower pressure gradient fields. The model integrated three nested domains with grid sizes 9, 3 and 1 km, respectively. The initial boundary conditions were formed based on ERA5-reanalysis. Subject to the WRF model requirements, the land-use map with a standard USGS set (24 categories) was developed, to which 3 categories of the urban areas were added. The most relevant configuration of parameterization methods was selected: short-wave and long-wave radiation (Mlawer), surface layer (Monin-Obukhov similarity theory), urban area (BEP), boundary layer (Bougeault-Lacarrere), turbulence (Smagorinsky). The article demonstrates that the WRF model adequately reflects fundamental urban atmosphere patterns in the most unfavorable anticyclone periods of the autumn-winter season. It was established that the accuracy of estimates decreases with the transition to weak cyclonic activity. Based on the simulation results and remote sensing data, the territory in question is divided into four climatic zones to which a comparative method was applied; however for a detailed correlative analysis a denser network of meteorological stations is required. Calculations showed that the wind along the Ili river valley prevails in the northern part, regularly changing its western direction to eastern. Near the mountain area mountain-valley wind circulation prevails. The blocking inversion layer has a strong impact. The urban heat islands strongly depend on wind conditions. For example, a nocturnal heat island is cooled by the cold wind flow from the mountains.

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.