The paper analyzes air quality changes in Ukraine during a wildfire event in April 2020 and a dust storm episode during the 16th of April 2020. The wildfire event contained two episodes of active fires and huge pollutants' emission: 4-14 April and 16-21 April, respectively. Using the Sentinel-5P data of CO and NO 2 column number density and ground-based measurements, there was estimated air quality deterioration. Advection of polluted air masses and analysis of affected territories were made in combination with a Web-based HYSPLIT model. Satellite data described air quality changes better than in-situ measurements. Data intercomparison showed better coincidence in regions that were not affected by wildfire emissions. The paper described the dust storm event based on absorbing aerosol index (AAI) data that occurred between two wildfire episodes.
The study discusses atmospheric air quality changes in Ukraine due to forest fires influence in the north of Ukraine in April 2020. Using Sentinel-5P satellite, data of carbon monoxide, nitrogen dioxide and aerosol index, in combination with HYSPLIT model, the study analyzes spatio-temporal variability of burning products and its distribution trajectories. There were two main wildfires episodes for the period of 4-21 April, 2020, which affected air quality in Ukraine. Depending on the wind direction, the most affected territories were located at less than 50 km from active fires. Elevated carbon monoxide content was detected at the distance up to 300 km from the main emission sources. Atmospheric air quality deterioration was observed also during dust storm between two main fires. The study presents an analysis of wind speed and wind direction along the air masses movement, which caused the dust storm, and water content changes along main trajectories.
The study describes methods for operative monitoring of atmospheric air quality over the territory of Ukraine using the Sentinel-5P satellite data. The methods provide possibility for data specification over the cities. The data processing is fully automatic and deals with the column data of nitrogen dioxide (NO2), carbon monoxide (CO), formaldehyde (HCHO), sulfur dioxide (SO2) and total ozone (O3). The system works every day and starts processing approximately 3 hours after the scanning of Ukrainian territory. The paper describes the procedure of files creation which represents the third level of data archiving. There are implemented the procedures of the adjusting to regular grids and the filtering of statistically unreliable data. The methods for data specification are developed which allow to analyze the content of chemical compounds over the cities. The paper discusses the main features for the interpretation of chemicals’ spatio-temporal distribution. It is emphasized the typical reasons for false interpretation and mistaken conclusions about atmospheric air quality while analyzing the satellite observations.
вирішення поточних задач і управління розвитком території. Ці задачі включають в себе ряд менших: збір актуальної інформації про стан довіреної території, виявлення проблем і пріоритетних потреб, прийняття рішень, планування діяльності, складання проектів, залучення інвесторів, координація діяльності жителів території та багато інших. Одним із інструментів для спрощення такої Вступ. Комфортне проживання населення на певній території є одною із важливих задач урбаністики. Для реалізації цієї потреби населення певної території (муніципалітету) може делегувати відповідні повноваження органам влади. Функція останніх в цій частині, так чи інакше, зводиться до FUNCTIONS OF MUNICIPAL GEOGRAPHIC INFORMATION SYSTEMS THROUGH THE PRISM OF THEIR USAGE ANALYSIS Andrii ORESHCHENKOTaras Shevchenko National University of Kyiv logograd@ukr.net Abstract: Today, the ways of using the municipal geoinformation systems and their functions have not been sufficiently described in the literature. In given paper, functions of GIS are screened and discussed based on scientific publications and learning materials. Additionally they studied the appropriation of GIS, solved problems, target group or production process in which indicated operation of these programs. As far as analogic functions often have different names in different sources, ther were unified by merging in categories and reducing to generalized and laconic representation. We screened 45 scientific publications and 10 textbooks, and identified 62 functions. They were divided in 3 categories depending on their final usage result. Systemic functions (13) are those typical for software: queries, visualization and data collecting, import-export. Applied functions (17) allow getting more sensible result, e.g., map ready for use (cartographic function), model (modeling function), new regular occurrence or characteristic of an object (spatial analysis function), or data set (data management function). More attention is paid to high-level functions (32) because they are related to the final aim of designing the municipal GIS: production automatization, economic, research, reference, consolidation etc. The explanation is iven for the most complicated function definitions. The discussion section deals with the issues related to defining and classification of functions and ambiguous interpretation of them. In the conclusion some reflections on high GIS popularity are presented.Key words: GIS, municipality, function, usage, management, instrument. DOI ВИЯВЛЕННЯ ФУНКЦІЙ МУНІЦИПАЛЬНИХ ГЕОГРАФІЧНИХ ІНФОРМАЦІЙНИХ СИСТЕМ ШЛЯХОМ АНАЛІЗУ РЕЗУЛЬТАТІВ ЇХ ВИКОРИСТАННЯ Андрій ОРЕЩЕНКО Київський національний університет імені Тараса Шевченка logograd@ukr.netАнотація: Способи використання муніципальних геоінформаційних систем та їх функції розкриті на сьогодні не повністю. Функції цих програм виявляли шляхом аналізу способів їх використання, описаних у наукових публікаціях та навчальних матеріалах. Додатково вивчали призначення ГІС, вирішену проблему, цільову аудиторію або виробничі процесі, у яких ...
Literature overview. Precipitation measurements include random and systematic errors. Systematic errors increase in the following order: evaporation loss, wetting loss, and wind-induced undercatch (World Meteorological Organization, 2008). The last one occurs because of the aerodynamic blockage under the precipitation gauge collector (Baghapour et al. 2017; Sevruk & Nespor, 1994). Field experiments have shown that wind-induced undercatch reaches 14% for rain and 40% for snow for the Tretyakov wind-shielded gauge (Goodison et al., 1998). In Ukraine, precipitation records omit wind-induced undercatch correction. This study aims to calculate true precipitation values at Ukrainian weather stations, evaluate existing methodologies for precipitation measurements correction, and create the digital archive of corrected precipitation values based on sub-daily observations. Material and methods. We used four methods to quantify wind-related errors for the Tretyakov gauge with wind shield proposed by Golubev (Konovalov et al., 2000), Bryazgin (Aleksandrov et al., 2005), Norway meteorological institute (Forland et al., 1996), and Yang (Yang et al., 1995). Sub-daily records were requested from Central Geophysical Observatory named after Boris Sreznevsky covering 207 stations between 1976 and 2019; 187 stations had more than 20 years’ period. Results. For the Tretyakov gauge, annual wind-induced undercatch ranges from 5 to 9.5%, depending on correction methodology. The highest bias is observed for the solid precipitation – from 17.7 to 27.4%. The precipitation loss increases along with annual wind speed at the weather station (correlation coefficient r = 0.89). Conclusions. We suggest that Golubev’s and Yang’s methodologies estimate precipitation wind-induced undercatch more accurately at stations where blizzards are often observed, we recommended using the Golubev’s methodology because it takes into account “false” precipitations. The precipitation loss equals 0.2–4% according to the Golubev’s method at covered weather stations and reaches 13–19% at the bare mountain regions or seashore. Solid precipitation is more sensitive to the influence of wind – snow loss averages 17.3% according to the Golubev methodology or 21% according to the Yang methodology, while rain loss – 2.6% or 6.7%, respectively. The obtained database with corrected precipitation comprises sub-daily and daily records from 207 Ukrainian stations between 1976 and 2019. It could be used for hydrological and climatological research.
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