Asian Journal of Atmospheric Environment最新文献

{"title":"Update of the year 2019 modeling emission inventory in China","authors":"Seoyeon Kim,&nbsp;Jinseok Kim,&nbsp;Hyejung Hu,&nbsp;Meongdo Jang,&nbsp;Jae-Bum Lee,&nbsp;Sung Chul Hong,&nbsp;Okgil Kim,&nbsp;Jung-Hun Woo","doi":"10.1007/s44273-023-00012-x","DOIUrl":"10.1007/s44273-023-00012-x","url":null,"abstract":"<div><p>Using updated emission inventories can enhance the accuracy of air quality forecast models. Given China’s rapid economic growth and Korea’s geographical and meteorological position on the windward side of China, updating China’s emission inventory has become particularly crucial for Korea’s air quality modeling. This study aimed to develop an updated version of China’s Emission Inventory in Comprehensive Regional Emissions for Atmospheric Transport Experiments version 3 for the base year of 2019 (CREATEv3 (YR 2019)). To achieve this goal, we utilized the Chinese emission inventory of CREATEv3 for the base year of 2015 (CREATEv3 (YR 2015)) as a framework to incorporate the latest Chinese emission data from the Multi-resolution Emission Inventory Model for Climate and Air Pollution Research for the base year of 2019 (MEIC COVID-19 (YR 2019)) and update the inventory. The updated China’s annual emissions are now reflected in CREATEv3 (YR 2019), and the amounts are as follows: 132 Tg for CO, 21 Tg for NO_<i>x</i>, 8 Tg for SO₂, 7 Tg for PM_2.5, 9 Tg for NH₃, and 28 Tg for volatile organic compound (VOC). By comparing previous Chinese emission inventories with the updated inventory developed in this study, it was found that SO₂, NO_<i>x</i>, VOC, and NH₃ emissions were decreased. Therefore, using the updated inventory seemingly reduces the impact of China’s fine dust on Korea. By comparing emissions by pollutant and region in China using CREATEv3 (YR 2019), it was found that regions with high emissions of targeted pollutants strongly correlated with major industries operating in those areas. This study is expected to provide insights into China’s emission changes in 2019 and support air quality forecasting.</p></div>","PeriodicalId":45358,"journal":{"name":"Asian Journal of Atmospheric Environment","volume":"17 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s44273-023-00012-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138624816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physicochemical characteristics and seasonal variations of PM2.5 in urban, industrial, and suburban areas in South Korea","authors":"Kyucheol Hwang,&nbsp;Jeongho Kim,&nbsp;Jae Young Lee,&nbsp;Jong-Sung Park,&nbsp;Sechan Park,&nbsp;Gahye Lee,&nbsp;Chang Hyeok Kim,&nbsp;Pilho Kim,&nbsp;Su Hyun Shin,&nbsp;Kwang Yul Lee,&nbsp;Joon-Young An,&nbsp;Jungmin Park,&nbsp;Jong Bum Kim","doi":"10.1007/s44273-023-00018-5","DOIUrl":"10.1007/s44273-023-00018-5","url":null,"abstract":"<div><p>Among countries that are a part of the Organization for Economic Co-operation and Development, South Korea is the most exposed to PM_2.5. Despite the country having implemented various strategies to limit PM_2.5 emissions, its concentrations are still high enough to pose serious environmental and health concerns. Herein, we monitored various physiochemical properties of PM_2.5 across different regions in South Korea from January 1 to December 31, 2021. Specifically, the study area consisted of the city center, industrial complexes, and suburban areas. Before analyzing dynamics of emissions specific to each site, the Clean Air Policy Support System data for the three areas were compared to elucidate their respective primary emission sources. The particle concentrations for the three areas were 21.8–26.44 µg/m³, with the highest concentrations being observed in March. All the three areas exhibited high ratios of NO₃⁻ across all seasons. The particle number concentrations in the three sites were 1.3–1.5 × 10⁷, and the peak points of the concentrations were different in every site: city center (40 nm), industrial complexes (60 nm), and suburban areas (80 nm). We also conducted potential source contribution function and conditional bivariate probability function analyses. These analyses were conducted to determine the inflow direction of the pollution sources for high PM_2.5 episodes. For the episodes that occurred in spring and winter, there were no differences in the PM_2.5 concentrations between the three sites. Overall, the insights gained from this study offer a framework for developing air-quality management policies in South Korea, specifically in the context of PM_2.5 emissions.</p></div>","PeriodicalId":45358,"journal":{"name":"Asian Journal of Atmospheric Environment","volume":"17 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s44273-023-00018-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138608776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Combined effects of elevated air temperature and CO2 on growth, yield, and yield components of japonica rice (Oryza sativa L.)","authors":"Masahiro Yamaguchi,&nbsp;Nobuyuki Tazoe,&nbsp;Tomoki Nakayama,&nbsp;Tetsushi Yonekura,&nbsp;Takeshi Izuta,&nbsp;Yoshihisa Kohno","doi":"10.1007/s44273-023-00019-4","DOIUrl":"10.1007/s44273-023-00019-4","url":null,"abstract":"<div><p>In the region where heat stress has become evident, the elevation of air temperature could reduce yield of heat stress-susceptible crops, such as rice (<i>Oryza sativa</i> L.), which is a major food staple in Asia. In addition to air temperature, atmospheric CO₂ is projected to be elevated in the future. To project rice yield in the future, it is necessary to clarify the responses of rice to concurrent elevations of air temperature and atmospheric CO₂. In the present study, two japonica rice cultivars with different heat tolerance, Hinohikari (sensitive) and Nikomaru (tolerant), were grown in pots inside open-top chambers and exposed to elevated air temperature and/or CO₂. The degrees of increase in the air temperature and CO₂ concentration by the treatments were approximately 1 °C and 120 µmol mol⁻¹ (ppm). The study was conducted in Nagasaki, Japan, where heat stress on rice has become evident. Elevated air temperature significantly decreased both whole-plant growth and grain yield. Elevated CO₂ significantly increased the growth but significantly decreased the yield. The effects of elevated air temperature and elevated CO₂ on growth and yield did not significantly differ between two cultivars. In both cultivars, the main cause of yield reduction by both treatments was reduction in spikelet fertility, which is typical heat stress on rice. The elevated CO₂-induced reduction in spikelet fertility could be explained partially by high-temperature regime during flowering due to acceleration of heading and by increase in canopy temperature via stomatal closure in flag leaves. Because elevated air temperature and elevated CO₂ treatments additively reduced spikelet fertility in both cultivars, concurrent elevations of air temperature and CO₂ caused considerable reduction in grain yield.</p></div>","PeriodicalId":45358,"journal":{"name":"Asian Journal of Atmospheric Environment","volume":"17 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s44273-023-00019-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139200718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: Characteristics of STILT footprints driven by KIM model simulated meteorological fields: implication for developing near real-time footprints","authors":"Samuel Takele Kenea,&nbsp;Haeyoung Lee,&nbsp;Sangwon Joo,&nbsp;Miloslav Belorid,&nbsp;Shanlan Li,&nbsp;Lev D. Labzovskii,&nbsp;Sanghun Park","doi":"10.1007/s44273-023-00020-x","DOIUrl":"10.1007/s44273-023-00020-x","url":null,"abstract":"","PeriodicalId":45358,"journal":{"name":"Asian Journal of Atmospheric Environment","volume":"17 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s44273-023-00020-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142415172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simultaneous Sampling of NO, NO2, HONO and HNO3 in the Atmosphere by a Filter-Pack Method","authors":"Takumi Oda,&nbsp;Yusuke Fujii,&nbsp;Norimichi Takenaka","doi":"10.5572/ajae.2022.006","DOIUrl":"10.5572/ajae.2022.006","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":45358,"journal":{"name":"Asian Journal of Atmospheric Environment","volume":"16 2","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.5572/ajae.2022.006.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70710516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Changes in Inorganic Chemical Species in Fog Water over Delhi","authors":"Rahul Sheoran,&nbsp;Umesh Chandra Dumka,&nbsp;Hulivahana Nagaraju Sowmya,&nbsp;Deewan Singh Bisht,&nbsp;Atul Kumar Srivastava,&nbsp;Suresh Tiwari,&nbsp;Shiv Dev Attri,&nbsp;Philip Karl Hopke","doi":"10.5572/ajae.2021.092","DOIUrl":"10.5572/ajae.2021.092","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":45358,"journal":{"name":"Asian Journal of Atmospheric Environment","volume":"16 2","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.5572/ajae.2021.092.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70709220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Association of Air Pollutant Index (API) on SARS-CoV-2 of Coronavirus Disease 2019 (COVID-19) in Malaysia","authors":"Samsuri Abdullah,&nbsp;Muhammad Azhari Imran,&nbsp;Amalina Abu Mansor,&nbsp;Ku Mohd Kalkausar Ku Yusof,&nbsp;Nazri Che Dom,&nbsp;Siti Khamisah Saijan,&nbsp;Siti Rohana Mohd Yatim,&nbsp;Ali Najah Ahmed,&nbsp;Marzuki Ismail","doi":"10.5572/ajae.2021.094","DOIUrl":"10.5572/ajae.2021.094","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":45358,"journal":{"name":"Asian Journal of Atmospheric Environment","volume":"16 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.5572/ajae.2021.094.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70709368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characterization of PM2.5 Mass in Relation to PM1.0 and PM10 in Megacity Seoul","authors":"Jihyun Han,&nbsp;Seahee Lim,&nbsp;Meehye Lee,&nbsp;Young Jae Lee,&nbsp;Gangwoong Lee,&nbsp;Changsub Shim,&nbsp;Lim-Seok Chang","doi":"10.5572/ajae.2021.124","DOIUrl":"10.5572/ajae.2021.124","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":45358,"journal":{"name":"Asian Journal of Atmospheric Environment","volume":"16 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.5572/ajae.2021.124.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70709787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Experimental Characterization of PM2.5 Organic Carbon by Using Carbon-fraction Profiles of Organic Materials","authors":"Shuichi Hasegawa","doi":"10.5572/ajae.2021.128","DOIUrl":"10.5572/ajae.2021.128","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":45358,"journal":{"name":"Asian Journal of Atmospheric Environment","volume":"16 2","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.5572/ajae.2021.128.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70709825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling of the Calm Situations in the Atmosphere of Almaty","authors":"Edige Zakarin,&nbsp;Alexander Baklanov,&nbsp;Larissa Balakay,&nbsp;Tatyana Dedova,&nbsp;Kairat Bostanbekov","doi":"10.5572/ajae.2022.007","DOIUrl":"10.5572/ajae.2022.007","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":45358,"journal":{"name":"Asian Journal of Atmospheric Environment","volume":"16 2","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.5572/ajae.2022.007.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70710145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}