Harita Dergisi, Temmuz 2023; 170: 23-40 RESEARCH ARTICLE / ARAŞTIRMA MAKALESİ Spatio-temporal Analysis of Sentinel-5P Data of Konya City Between 20192021 (2019-2021 Yılları Arası Konya İli Sentinel-5P Verilerinin Mekâna-Zamana Dayalı Analizi) Hasan Bilgehan MAKİNECİ* , Duygu ARIKAN , Damlanur ALKAN , Lütfiye KARASAKA Konya Teknik Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Harita Mühendisliği, Konya *Sorumlu yazar: hbmakineci@ktun.edu.tr, darikan@ktun.edu.tr, dalkan@ktun.edu.tr, lkarasaka@ktun.edu.tr Received (Geliş Tarihi): 09.04.2023 Accepted (Kabul Tarihi): 20.07.2023 bozmaktadır. Bu nedenle kirlilik miktarının düzenli olarak izlenmesi ve gerekli önlemlerin alınması gerekmektedir. Bu amaçla gerçekleştirilen bu çalışmada, uzaktan algılama verileri kullanılarak Konya ilinde 2019-2021 yılları arasındaki kirletici gazların durumu incelenmiştir. Veriler, Avrupa Uzay Ajansı (ESA) tarafından yörüngeye fırlatılan Sentinel-5 Precursor (S5P) uydusuna bağlı TROPOMI algılayıcısından elde edilmiştir. Bu algılayıcıdaki sensörler tarafından çeşitli atmosferik gazlara (ozon, metan, karbon monoksit, nitrojen dioksit, kükürt dioksit ve formaldehit) ait zamansal değişim belirlenebilir. Bu amaçla Google Earth Engine (GEE) platformu ile S5P Düzey-2 verilerine ulaşılmış ve her bir veriyi elde etmek için farklı kodlar yazılmıştır. Daha sonra çalışma bölgesi için veri elde etme işlemi tamamlanmıştır. Çalışma alanı için belirlenen tarihler arasındaki aylık karbon monoksit, ozon, amonyak ve nitrojen dioksit gazları miktarları grafiklerde gösterilmiş ve her yıl için haritalar üretilmiştir. Sonuç olarak geniş sulak alanlarda ve ormanlık alanlarda metan gazının görülmediği, NO2, O3 ve CO gazlarının minimum değerlerinin yaz aylarında, CH4 gazının ise kış aylarında olduğu tespit edilmiştir. İl merkezinin batısı ile güneyi arasında kalan bölgenin havayı kirletici gazlar açısından en sağlıklı bölge olduğu da belirlendi. ABSTRACT Uncontrolled energy consumption by human beings, such as the increase in the demand for the gases used for heating purposes in houses, the rise in the number of industrial production facilities, and the uncontrollable levels of the gases emitted from the exhausts of motor vehicles, are among the causes that trigger chemical gas formation in the atmosphere. Increases in air pollution threaten people's health and disrupt ecological balances. For this reason, regularly monitoring the amount of pollution and taking the necessary precautions is crucial. In this study, which was conducted for this reason, the state of polluting gases between 2019-2021 in the province of Konya was examined using remote sensing data. The data were obtained from the TROPOMI instrument attached to the Sentinel-5 Precursor (S5P) satellite launched into orbit by the European Space Agency (ESA). Various atmospheric gases (ozone, methane, carbon monoxide, nitrogen dioxide, sulfur dioxide, and formaldehyde) can be detected by the sensors in this device. For this purpose, S5P Level-2 data was accessed via the Google Earth Engine (GEE) platform, and different codes were written to obtain each data. Then, the data collection for the study region was completed. The monthly amount of carbon monoxide, ozone, methane, and nitrogen dioxide gases for the study area between the specified dates are shown in the graphs, and maps are produced for each year. As a result, it has been determined that methane gas is not observed in extensive wetlands and forest areas, and the minimum values of NO2, O3, and CO gases are in the summer months and CH4 gas in the winter months. It has also been determined that the region between the west and the south of the city center is the healthiest region in terms of air pollutant gases. Anahtar Kelimeler: Google Earth Engine, Kirletici Gazlar, Konya Hava Kalitesi, Sentinel-5P, TROPOMI uydusu 1. INTRODUCTION In recent studies, polluting air emissions have been associated with meteorological conditions and human activities (Balmes, 2019; Cheung, et al., 2020; Kurata, et al., 2020; Lu, et al., 2019; Ghasempour, et al., 2021). The seasonal and temporal analysis of different meteorological parameters, and the relationship between the atmosphere and air temperature in urban areas, has been supported by several studies in the literature (Arıkan & Yıldız, 2022; Ghasempour, et al., 2021; Safarianzengir, et al., 2020; Sager, 2019; Sobhani & Zengir, 2019; Sobhani & Zengir, 2020). Researchers have determined that the primary purpose for this is to determine which factors influence the air temperature in the atmosphere during the day in urban areas. In Keywords: Google Earth Engine, Konya Air Quality, Pollutant Gases, Sentinel-5P, TROPOMI Satellite ÖZ Konutlarda ısınma amaçlı kullanılan gazlara olan talebin artması, endüstriyel üretim tesislerinin sayısının artması, motorlu taşıtların egzozlarından çıkan gazların kontrol edilemeyen seviyelere ulaşması gibi insanoğlunun kontrolsüz enerji tüketimi, atmosferde kimyasal gaz oluşumunu tetikleyen nedenler arasındadır. Hava kirliliğindeki artışlar insan sağlığını tehdit etmekte ve çevresel yaşam dengelerini To cite this article/Atıf: Makineci, H.B., Arıkan, D., Alkan, D., & Karasaka, L. (2021). Spatio-temporal Analysis of Sentinel-5P Data of Konya City Between 2019-2021. Harita Dergisi, 170, 23-40 23 Harita Dergisi, Temmuz 2023; 170: 23-40 Spatio-Temporal Analysis of Sentinel-5P Data of Konya City Between 2019-2021 addition, as a result of these studies, the factors that cause air pollution events in urban areas are identified. Ghasempour et al., 2021b). Makineci (2022) conducted a study on the region’s air pollution by using S5P data and terrestrial stations on the Anatolian and European parts of İstanbul, the metropolitan city of Türkiye. As a result of the study, in which ground stations and remote sensing data were used, consistent results were revealed for two different centers of air pollution. Stratoulias and Nuthammachot (2020) found a strong positive correlation between data from ground stations for an urban area and data from S5P. Air pollution refers to the fact that air pollutants in the atmosphere reach a level above average due to human or natural causes, and their presence in the atmosphere in density, amount, and time that may adversely affect human health, living life, and ecological balance. In other words, it is expressed as the deterioration of the natural composition of the air. Artificial sources of air pollution occur as a result of human activities. As a result of heating, transportation, and industry, there is an increase in the number of pollutants emitted into the atmosphere. Heating boilers and solid, liquid, and gas fuel stoves for heating, ships, motor vehicles, railways, and airplanes in the field of transportation, processes carried out in the industry and power plants that produce thermal and hydraulic energy have a direct negative effect on air pollution (Ibbetson, Symonds, & Hutchinson). In contrast, natural sources of air pollution are natural events (URL-2). Particularly prevalent examples of natural sources are earthquakes, forest fires, volcanic eruptions, dust storms, swamps, oceans, and seas. Air pollutants from natural events do not linger in the atmosphere for long. Investigation of changes in the concentration of carbon monoxide, nitrogen dioxide, ozone, and methane, known as air pollutants, is seen as a threat to human health, the ecosystem, biodiversity, and climate (Safarianzengir et al., 2020). For this reason, monitoring polluting gases and examining their effects on humans and the environment is necessary. In line with the findings obtained from the studies, the consistency of the terrestrial and remote sensing data in determining anthropogenic emissions is observed. As a result, it has been concluded that satellite images can be used to detect pollutants, present the spatial distribution of gases and particles that cause air pollution, cover large areas, and provide repeated measurements due to the cost of establishing ground-based stations in the studies. In addition, S5P satellite data have benefited from the amount of gas released into the atmosphere and find solutions to the problems such as in studies on forest fires (Arikan & Yildiz, 2023; Singh et al., 2021), in the detection of oil refineries and fields (Farahat, 2022), and in explaining the relationship between air pollution and diseases (Arıkan & Yıldız, 2021; Balmes, 2019; Ibbetson et al., 2020; Kurata et al., 2020; Lu et al., 2019). Since air pollution is a global issue, the whole world must reduce their emissions of polluting gases to the minimum level locally. For this purpose, a comprehensive analysis of air pollution in Konya, Türkiye's largest city, was carried out to contribute to the literature. Temporal and spatial analyzes of methane (CH4), ozone (O3), carbon monoxide (CO), and nitrogen dioxide (NO2) gases, which are the primary air pollutants within the borders of Konya Province, were conducted between July 2019 and December 2021, using the Google Earth Engine (GEE) platform and the obtained S5P data. The air quality of Konya province was monitored monthly between 20192021 by dividing the data into separate equal grids (25 x 25 m). It has been attempted to determine the increase or decrease of the pollutants in the 30-month period and the responsible factor. The fact that there is no other research in the literature that maps the air pollutants of Konya for such a long period by using the GEE platform on the S5P satellite since the data began to be published to the users encouraged this study. The density of the data, the long-term spatio-temporal analysis, High-resolution Sentinel-5P (S5P) TROPOMI satellite data have recently gained popularity in air quality monitoring research (Singh et al., 2021; Wang, et al., 2022). The S5P, developed to use institutes in the Netherlands, the United Kingdom, Finland, and Germany for scientific studies, was launched into orbit in 2017 on a seven-year operation (Farahat, 2022). The satellite data have a spatial resolution of 3.5 × 5.5 km and a temporal resolution with daily periods (Theys et al., 2017). Thanks to this remote sensing platform, spatial and temporal analyzes, and monitoring of air quality variability are carried out more comprehensively. Griffin et al. (2019) found a high correlation between the vertical column density of NO2 gas and ground-based observations. In the study by Wang et al. (2022), NO2, CO, and CO2 gases were used to compare the data results produced by ground observations and remote sensing techniques (Ghasempour et al., 2021a; 24 Harita Dergisi, Temmuz 2023; 170: 23-40 Hasan Bilgehan MAKİNECİ, Duygu ARIKAN, Damlanur ALKAN, Lütfiye KARASAKA and the study's wide scope can be said to be the study's original aspect. 2. MATERIALS AND METHODS a. Study Area and Workflow Konya which is located in the Central Anatolia region of Türkiye and has the largest surface area (38 873 km²), was chosen as the study area. The city is located between 36°41' and 39°16' north latitudes and 31°14' and 34°26' east longitudes (Figure 1). In the city, which is influenced by the continental climate, the summer is dry and hot, while the winter is extremely cold and has heavy snowfall. Rainfall is seen throughout the city in the spring and winter seasons. The city is located at an altitude of 1016 m above sea level. Due to the mountains surrounding the city, the effect of the wind is limited and causes fog/smoke condensation in the city (Kunt & Dursun, 2016). This causes polluted air to accumulate in the city. According to 2022 population data, it is Türkiye's sixth most populous city, with 2 296 347 people. Although the primary economic income source of the region is known as agricultural production, it has also acquired the reputation of an industrial production city due to the increasing industrialization in recent years. The city trades quickly with other central provinces in the country thanks to its widespread transportation lines. It also contributes to the national economy by producing goods in a variety of sectors (agricultural machinery and equipment, defense and automotive industry, food, etc.). SEYDİŞEHİR Figure 1. (a) (b) Study area (Figure 1. (a) Produced by the General Directorate of Mapping)) b. S5P Data The data obtained from the TROPOMI device connected to the Sentinel-5 satellite by the European Space Agency was used in this study. Various atmospheric gases (ozone, methane, carbon monoxide, nitrogen dioxide, sulfur dioxide, and formaldehyde) are detected and monitored by the sensors in this device (Slagter, et al., 2020; Veefkind et al., 2012). The satellite's primary purpose, launched on October 13, 2017, is to collect atmospheric measurements with high spatio-temporal resolution for air quality, ozone, UV radiation, climate monitoring, and forecasting (Safarianzengir et al., 2020). It scans the Earth daily and records images with a coverage area of 7 km × 3.5 km. The satellite, expected to have a seven-year operational life, is in the low-orbiting satellite class with an orbital reference altitude of 824 km. It is in a sun-synchronous orbit with the equatorial transit at the nodal point rising at 13:30 local time, with an orbital inclination of approximately 98.7 degrees in polar orbit. The While these factors contribute to the development of the city, their effects on airpolluting gases should also be identified. For this reason, in this study, the air pollution of Konya province was investigated temporally and spatially by using S5P satellite data. Sentinel-5 data of NO2, CO, O3, and CH4 polluting gases were obtained from the GEE platform within the specified date range for analysis. Each gas with the same temporal and spatial resolution used in the study has been analyzed with charts, then visualized and represented on maps. The workflow diagram of the study is presented in Figure 2. 25 Harita Dergisi, Temmuz 2023; 170: 23-40 Spatio-Temporal Analysis of Sentinel-5P Data of Konya City Between 2019-2021 orbital cycle is 16 days. The satellite is equipped with the most advanced TROPOMI instrument for measuring the ultraviolet-visible (270–500 nm), near-infrared (675–775 nm), and short-wave infrared (2305–2385 nm) spectral bands (Rohi & Ofualagba, 2020; Safarianzengir et al., 2020). The TROPOMI spectrometer in Sentinel 5P measures high spectral UV luminance (https://sentinels.copernicus.eu/web/sentinel/miss ions/sentinel-5p). The download of Sentinel-5P Level-2 data was carried out through the GEE platform, an infrastructure of Google. A separate coding process was carried out for each pollutant gas. L2 level data received from the Sentinel satellite is defined at the L3 level through GEE. Therefore, Spatial Resolution corresponds to around 1 km. This platform is a web service developed for spatial analysis (Gorelick et al., 2017). It has gained popularity in remote sensing studies in recent years because it is free to use and provides easy access to data (Farahat, 2022; Singh et al., 2021). In addition, GEE (Li et al., 2022), which enables extensive data processing, offers its users the opportunity to create and save their catalogs. Query and evaluation processes are also carried out through the functional structures in the system (Gorelick et al., 2017). gases. From 2019 to 2021, 118 images were obtained for all of the used gases. Then, the downloaded images were opened in the GIS environment and visualized (Figure 11, Figure 12, Figure 13, Figure 14 and Figure 15). In order to eliminate the gaps seen in the methane gas daily data and to create a standard for all gases, a three-day data format was downloaded. Three-day data yield more meaningful results, so the gaps in the daily data are not filled with interpolation, based on the principle of producing maps entirely from S5P sensor data. In the GEE platform, there is a preacceptance assumption that the data will be downloaded for three days. The NO2 (nitrogen dioxide) vertical column, CH4 (methane) column average dry air mixing ratio, total O3 (ozone) atmospheric column between the surface and upper atmosphere, and CO (carbon monoxide) column data for 2019-2021 are downloaded as offline (OFFL). This is primarily because it should contain data from a single orbit. Additionally, only the OFFL feature is available for CH4 data. In this study, data on certain monthly days were downloaded for each gas using the median code in GEE. This path was followed as there was a lack of data on certain days for some Figure 2. Workflow of research Table 1. Sentinel-5P data used in research. Data Type Temporal Resolution Spatial Resolution O3 First 3 days of every month 1113.2 meters CH4 First 4 days of every month 1113.2 meters CO First 10 days of every month 1113.2 meters NO2 First 10 days of every month 1113.2 meters 26 Calibration Type Radiometric and Geometric Calibrated Radiometric and Geometric Calibrated Radiometric and Geometric Calibrated Radiometric and Geometric Calibrated Harita Dergisi, Temmuz 2023; 170: 23-40 Hasan Bilgehan MAKİNECİ, Duygu ARIKAN, Damlanur ALKAN, Lütfiye KARASAKA c. Statistical analysis can be expressed as oil and natural gas systems, agricultural activities, coal mining, stationary and mobile incineration, wastewater treatment, and specific industrial processes. Swamps can be an example of natural causes (Lorente et al., 2021). Thanks to the near-infrared (NIR) and short-wave infrared (SWIR) spectral bands found in satellites, CH4 gas is observed through its spread on the Earth's surface and atmosphere. Data from TROPOMI measurements are recorded with the RemoTeC algorithm (Lorente et al., 2021). The residence time of this gas in the atmosphere has been determined as approximately ten years (Hu et al., 2018). After obtaining the data, all visual base maps were gridded after trimming, and classification processes were applied to the leading data visualized in the GIS environment. The value corresponding to the midpoint of each grid was then calculated. Following the calculation, the thirty-month data of the values corresponding to the grid midpoints according to the months were tabulated. In Table 1, there are blank values for different data types in some months of the data consisting of 332 points. Gaps without data were not included in the statistical analysis of the study. No interpolation was applied to the non-data regions, and their values were left as naught. When the July 2019 - December 2021 data for the whole of Konya were examined (Figure 3), it was determined that the highest amount of CH4 was in September 2021, with 1900 mg/m3. When the maximum and minimum values of methane gas are examined within each month, it is understood that the mean is 40 mg/m3. Since methane gas is seen primarily as a result of heating, there is a standard distribution across urban areas. In addition, it has been determined that CH4 release is not significantly affected by season because a balanced graphic curve was obtained in the summer and winter seasons. Among the values obtained in Table 1, the maximum, minimum, mean, and median values were determined according to each data type. Additionally, standard deviation (StD.) values were calculated for the analysis. 3. RESULTS a. Findings Obtained for CH4 Gas Methane gas (CH4) is the greenhouse gas with the most critical polluting effect after carbon dioxide (Hu et al., 2018). Therefore, it is a type of gas that causes global warming at a higher rate than other pollutants (Stocker, 2014). CH4 gas is emitted into the atmosphere, either by natural or anthropogenic resources. Anthropogenically, it In the time period of the study, 6,925 mg/m3 minimum StD. values in July 2021 and 12,658 mg/m3 maximum StD. values in April 2020 were determined (Figure 4). Figure 3. CH4 Data Analysis (Minimum, Maximum, Mean and Median Values) 27 Harita Dergisi, Temmuz 2023; 170: 23-40 Spatio-Temporal Analysis of Sentinel-5P Data of Konya City Between 2019-2021 b. Findings Obtained for CO Gas values are presented in Figure 6. The highest amount of CO in the monthly period was determined in April 2020, and the maximum was reached with a value of 0.1818 mol/m2 in that month. The lowest amount of CO in the study period was recorded in November 2020, with a value of 0.1255 mol/m2. It is worth mentioning that the change in temperature because, as can be seen from the graph, while there is a decrease in the amount of CO in the winter, it increases in the summer. CO gas remains in the atmosphere for less time (by approximately two months) than methane gas. It is a product of incomplete combustion encountered in building heating, coal power generation, wood and grass burning, vehicles, and biomass burning (Ghahremanloo, Lops, Choi, & Mousavinezhad, 2021). About 40% of CO is naturally generated (volcanic eruptions, natural gas emissions, degradation of plants and animals, forest fires, and 60% results from the consumption of fossil fuels, garbage disposal systems, tobacco smoke, and coal fires (Kaplan, Avdan, & Avdan, 2019). Graphs were created each month from the CO findings obtained to cover the entire study area. Monthly maximum, minimum, mean and median values are given in Figure 5, and StD. According to the StD. values of CO gas, it reached maximum levels of 0.0042 mol/m2 in the same period (April 2020) as methane gas. It has been detected at a minimum in December 2020 with 0.0005 mol/m2. Figure 4. CH4 Data Analysis (StD. Values) Figure 5. CO Data Analysis (Minimum, Maximum, Mean and Median Values) 28 Harita Dergisi, Temmuz 2023; 170: 23-40 Hasan Bilgehan MAKİNECİ, Duygu ARIKAN, Damlanur ALKAN, Lütfiye KARASAKA Figure 6. CO Data Analysis (StD. Values) c. Findings Obtained for NO2 Gas to the StD. values in 30 months, A minimum of 0.0005 mol/m2 was obtained in December 2020, and a maximum of 0.0046 mol/m2 was obtained in April 2020. Nitrogen oxides (NOx) are generally the type of gas that occurs during combustion (Ialongo, Virta, Eskes, Hovila, & Douros, 2020). The leading sources of this type of gas are natural (lightning strikes) and anthropogenic pollution (burning of agricultural fields, burning fossil fuels, industrial production, and emissions from vehicle exhaust) sources (Hashim, et al., 2021; Ialongo et al., 2020). ç. Findings Obtained for O3 Gas The O3 concentration commonly found in the air consists of the photochemical interaction of the precursor gases (nitrogen oxides, volatile organic compounds) (Hashim et al., 2021). In cases of exposure to O3 gas, airway inflammation, and respiratory disorders can be observed in living things due to oxidative stress (Adhikari & Yin, 2020). The magnitude and rate of O3 emissions depend on atmospheric conditions and seasonal factors (Hashim et al., 2021). NO2 gas increases the symptoms of respiratory diseases (such as coughing, wheezing, or difficulty breathing) by affecting human health (Stratoulias & Nuthammachot, 2020). In addition, it causes acid rain by interacting with water, oxygen, and other chemicals in the atmosphere (Hashim et al., 2021). This occurrence weakens the soil and reduces productivity in agricultural activities. In areas where nitrogen dioxide increases, the air becomes hazy, and there is even a decrease in visibility. O3 emission appears to be directly proportional to NOx. If O3 is high in a region, NOx is also found to be high, or vice versa (Hashim et al., 2021). On the other hand, while the amount of NOx is high in urban areas with high traffic density, reductions in O3 emissions are observed. This is because the gas density in urban areas is transported toward rural areas (Dentener et al., 2020). Due to the increase in temperatures and the contribution of sun rays to the formation of O3, seasonal changes cause a decrease in O3 gas (Stratoulias & Nuthammachot, 2020). As determined in Figure 9, while O3 was at its maximum levels in April 2020 and 2021, it appears to have been at minimum levels in October, November, and December. When the StD. values for O3 are examined (Figure 10), the minimum value of 0.0004 mol/m2 and maximum value of 0.0040 mol/m2 were obtained in December 2020 and April 2020, respectively. When the results of the Std. values of all gases are examined, it can be determined that the Figures 7 and 8 display the time series of NO 2 measurements used in the analysis, covering the period from July 2019 to December 2021. In Figure 7, the maximum, minimum, median, and mean values of the daily monthly data are given, while in Figure 8, the StD. the graph is shown. While the maximum level of NO2 in the studied period was observed in April 2020 with a value of 0.18176 mol/m2, the minimum level was observed in November 2020 with a value of 0.11895 mol/m2. It can be said that there is a seasonal effect in NO2 gas. As shown in Figure 7, while there is an increase in the amount of this gas in the spring season (April, May), there is a decrease in the autumn season (October, November). According 29 Harita Dergisi, Temmuz 2023; 170: 23-40 Spatio-Temporal Analysis of Sentinel-5P Data of Konya City Between 2019-2021 maximum and minimum values of all gases are observed in the same months. In all gases, the maximum level was reached in April 2020. While the minimum CO, NO2, and O3 gas are December 2020, the time when CH4 gas is minimum is July 2021. Figure 7. NO2 Data Analysis (Minimum, Maximum, Mean and Median Values) Figure 8. NO2 Data Analysis (StD. Values) 30 Harita Dergisi, Temmuz 2023; 170: 23-40 Hasan Bilgehan MAKİNECİ, Duygu ARIKAN, Damlanur ALKAN, Lütfiye KARASAKA Figure 9. O3 Data Analysis (Minimum, Maximum, Mean and Median Values) Figure 10. O3 Data Analysis Results (StD. Values) d. Produced Map Outcomes When the CH4 gas map is examined between the years 2019-2021, it is seen that no data is recorded in the wetlands. The main reason for this is that the effect of methane gas is mainly seen in swamps, reeds, or garbage areas. When the maps of NO2 and O3 gases are examined, it has been determined that the amount of NO 2 is high and the amount of O3 is low in the parts where the transportation lines are located. It has been determined that O3 emissions are primarily concentrated in rural areas. As seen in Figure 11, while the amount of O3 gas increases relatively in rural areas in the northern parts of the city, it is found in lower amounts in the southern parts of the city where the altitude is high. In this study, S5P satellite images taken from the GEE platform were converted to raster and vector maps in order to interpret the air pollutants in the Konya Province in spatio-temporal time. In order to determine the seasonal effect of all polluting gases used in the study, map outputs were produced by choosing the beginning of spring and the beginning of autumn. The most striking point is that the amount of polluting gas is high in the parts of the transportation networks on all maps. It can be said that pollutants vary according to the seasons and are also related to latitude, longitude, and altitude values. 31 Harita Dergisi, Temmuz 2023; 170: 23-40 Figure 11. September 2019 Air Quality Map of Konya (A-CH4, B-O3, C-CO, D-NO2) Spatio-Temporal Analysis of Sentinel-5P Data of Konya City Between 2019-2021 32 Harita Dergisi, Temmuz 2023; 170: 23-40 Figure 12. May 2020 Air Quality Map of Konya (A-CH4, B-O3, C-CO, D-NO2) Hasan Bilgehan MAKİNECİ, Duygu ARIKAN, Damlanur ALKAN, Lütfiye KARASAKA 33 Harita Dergisi, Temmuz 2023; 170: 23-40 Figure 13. September 2020 Air Quality Map of Konya (A-CH4, B-O3, C-CO, D-NO2) Spatio-Temporal Analysis of Sentinel-5P Data of Konya City Between 2019-2021 34 Harita Dergisi, Temmuz 2023; 170: 23-40 Figure 14. May 2021 Air Quality Map of Konya (A-CH4, B-O3, C-CO, D-NO2) Hasan Bilgehan MAKİNECİ, Duygu ARIKAN, Damlanur ALKAN, Lütfiye KARASAKA 35 Harita Dergisi, Temmuz 2023; 170: 23-40 Figure 15. September 2021 Air Quality Map of Konya (A-CH4, B-O3, C-CO, D-NO2) Spatio-Temporal Analysis of Sentinel-5P Data of Konya City Between 2019-2021 36 Harita Dergisi, Temmuz 2023; 170: 23-40 Hasan Bilgehan MAKİNECİ, Duygu ARIKAN, Damlanur ALKAN, Lütfiye KARASAKA The amount of all pollutants in 2020 is less than in other years. This is due to the epidemic disease (COVID-19), which spread worldwide during this period, showing its impact on Türkiye. As a result of the restrictions put in place to prevent the spread of the epidemic, it was determined that the impact of the pollutant gases decreased. It has also been determined that the number of pollutants in 2021 were higher than in other years (Figure 12 and Figure 13). This rise has also become meaningful due to the gradual removal of the measures taken for the epidemic. In 2020, methane gas was standard in the northern part of the city. The white parts in the methane gas map are the part where the data is not recorded. Kadınhanı, Sarayönü, and Yunak are the districts where methane gas is seen at high levels. When the methane gas values in March 2021 are examined, it is seen that there are increases in the Karapınar district. Since this region is a reed and swamp area, methane gas increased due to the decrease in water and the resulting drought (Figure 14 and Figure 15). significant correlation between the COVID-19 outbreak and NO2 gas. In addition, in their analysis on a national basis, they found that NO2 gas showed a considerable decrease in the lockdown period, except for regular periods (Ghasempour et al., 2021a). The situation of four polluting gases over a 30month period has been examined using satellite data. In the study, national air quality ground station data were also downloaded. However, due to the lack of data on ground stations, it was not used in the analysis. In this study, analyses and discussions were carried out using only S5P satellite data due to factors such as missing terrestrial station data (disorder in stations) or stations representing a spatially narrow region. In recent years, air quality research has become one of the critical research topics due to the increase in air pollution, the adverse effects of air pollution on living health, and the availability of rapid and reliable evaluation of air quality with remote sensing satellites. The findings are of great importance to everyone because of the issue’s impact on human health and ecological order. Clean air is a vital element for the survival of all living things. For this reason, in this study, the pollutant gases in the Konya province were analyzed temporally and spatially using S5P satellite data from July 2019 to March 2021. As a result, over a 30-month period, the regions in which the pollutants are found and the months when the pollutants are at maximum and minimum levels have been determined. It has been documented that it is possible to collect information about pollutants not only temporally but also spatially, thanks to satellite data. In their study, Kaplan et al. (2019) and Ghasempour et al. (2021) examined countrybased polluting gases. Thus, using the Sentinel5P satellite, they showed that the pollution of a large-scale region could be detected. This study discussed the temporal analysis of the states of polluting gases in small-scale cities, and their detectability was revealed. In addition, more gases were analyzed together, and their relations with each other were established. As in this study, the GEE platform was used to obtain the data. It has been seen that GEE is easy to use and access data, and it can be preferred for accessing S5P data in future studies. 5. CONCLUSIONS 4. DISCUSSIONS The air quality in the atmosphere decreases because the smoke content that results from the combustion of fuels such as diesel and gas pollute the environment from factories and houses in winter, including the pollutants that are the subject of the research. In addition, pollutants are observed more intensely in the atmosphere in winter due to the effect of wind (air reversal phenomenon) compared to warmer seasons. When the gases released in the winter and those released in the summer are compared, it has been determined that the pollutants in hot weather are much more dangerous in terms of air quality. This is because gases such as NO2 from motor vehicles accumulate in the lower parts of the atmosphere during the summer months, and this is a factor that directly affects human health. ORCID Hasan Bilgehan MAKİNECİ https://orcid.org/0000-0003-3627-5826 Duygu ARIKAN https://orcid.org/0000-00019976-7479 Damlanur ALKAN https://orcid.org/0000-00022013-0262 Lütfiye KARASAKA https://orcid.org/00000002-2804-3219 In the study (Ghasempour et al., 2021b) conducted to detect and analyze NO2 gas from S5P data, researchers were able to notice a 37 Harita Dergisi, Temmuz 2023; 170: 23-40 Spatio-Temporal Analysis of Sentinel-5P Data of Konya City Between 2019-2021 REFERENCES Ghasempour, F., Sekertekin, A., & Kutoglu, S. H. (2021). Google Earth Engine based spatiotemporal analysis of air pollutants before and during the first wave COVID-19 outbreak over Turkey via remote sensing. Journal of Cleaner Production, 319, 128599. Adhikari, A., & Yin, J. (2020). 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