Assessing climate variability and its correlation with regional air pollution in Chhattisgarh, India utilizing Google Earth Engine (GEE)

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The dataset includes parameters such as Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), rainfall, and concentrations of carbon monoxide (CO), nitrogen dioxide (NO 2 ), sulfur dioxide (SO 2 ), methane (CH 4 ), ozone (O 3 ), and UV aerosol index (AI) with the help of Google Earth Engine (GEE) algorithms. Analysis revealed distinct seasonal patterns, with the highest rainfall recorded during the monsoon season, predominantly in the southern region, and elevated LST values observed in the central region during pre-monsoon months. Furthermore, CO concentrations peaked (0.057 mol/m 2 ) during the pre-monsoon season, particularly in industrial zones, while NO 2 levels were highest in the central region across all seasons. SO 2 concentrations exhibited spatial variability, with peaks (0.00204 mol/m 2 ) in the post-monsoon period, primarily attributed to industrial activities. CH 4 concentrations were higher during pre-monsoon and post-monsoon seasons, with anomalies observed in 2023. O 3 levels showed a seasonal variation, with higher (0.1289 mol/m 2 ) concentrations during pre-monsoon months, especially in the northern region. The UV aerosol index was highest during the monsoon season, attributed to increased moisture and biomass burning. Correlation analysis revealed associations between pollutants and environmental variables, indicating potential sources and interactions. These findings contribute to understanding regional air quality dynamics and informing targeted mitigation strategies for sustainable environmental management in Chhattisgarh. Air pollutant SO2 NO2 CO GEE Chhattisgarh Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 1. Introduction Climate change, a global phenomenon driven primarily by human activities, poses significant challenges to ecosystems, communities, and economies worldwide (Rawat et al., 2024 ). In India, many states are facing the multifaceted impacts of climate change, which manifest in various forms such as shifting weather patterns, altered precipitation regimes, rising temperatures, and intensity of extreme weather events (Sethi and Vinoj 2024 ). Greenhouse gas emissions, primarily carbon dioxide (CO 2 ), methane (CH 4 ), nitrous oxide (N 2 O), and fluorinated gases, are major contributors to climate change and global warming. These gases trap heat in the Earth's atmosphere, leading to the greenhouse effect, which results in the warming of the planet's surface (Ritchie et al., 2024 ). Air quality parameters such as nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), ozone (O3), and particulate matter (PM), pose significant risks to human health and the environment. A significant amount of studies have been done by considering air quality and greenhouse emission gases that harm earth creatures (Meo et al., 2024 ; Lee et al., 2024 ; Ahmad et al., 2023 ; Gupta et al., 2022 ; Khajeamiri et al., 2021 ; Kiran et al., 2021; Mukta et al., 2020 ). Urbanization, industrial expansion, intensified agriculture, and land use changes contribute to increased emissions and particulate matter, necessitating sustainable practices, clean energy adoption, and regulatory measures for effective mitigation (Salim et al., 2024 ). Land surface temperature (LST) interacts intricately with air pollutants, forming a crucial nexus in environmental dynamics. Elevated LST exacerbates the formation of urban heat islands (UHIs), amplifying air pollutant concentrations in cities (Cheval et al., 2024 ; Panda et al., 2024 ). Conversely, air pollutants such as aerosols and greenhouse gases influence LST by absorbing or scattering solar radiation (Dangayach et al., 2024; Nikkath and Selvi2024). In the context of air quality, climate variability plays a crucial role in influencing the distribution and concentration of pollutants. The impact of climate variability on regional air pollution is multifaceted, encompassing changes in temperature, rainfall patterns, and atmospheric circulation. Increased rainfall can lead to reduced air pollution levels by scavenging pollutants from the atmosphere (Kaur and Pandey 2021 ; Yoo et al., 2014 ). Conversely, higher temperatures can exacerbate air pollution through enhanced photochemical reactions and the formation of secondary pollutants like ozone (Yu et al., 2021). Chhattisgarh, located in central India, faces significant air pollution challenges, exacerbated by rapid urbanization, industrial activities, and climatic factors (Verma et al., 2016 ). Major cities such as Raipur, Bhilai-Durg, and Bilaspur have seen substantial population growth and industrial expansion over the past two decades, contributing to deteriorating air quality (Deshmukh et al., 2011 ; Sahare et al., 2022; Singh et al., 2023 ). This section outlines the primary sources and impacts of air pollution in Chhattisgarh, highlighting the need for effective mitigation strategies. Das et al., (2024) studied to assess stubble burning and emissions via the Google Earth Engine using the dataset of MODIS active fire data and TROPOMI CO and NO 2 measurements and found that a nearly threefold increase in crop residue burning in November compared to October, with 92.58% of fires in Punjab and 7.42% in Haryana. Chandra and Singh (2023) analyzed four key air quality indicators—nitrogen dioxide (NO₂), sulfur dioxide (SO₂), ultraviolet aerosol index (UVAI), and ozone (O₃)—using TROPOMI data on the Google Earth Engine platform. Focusing on Uttar Pradesh over five years, their analysis covers monthly averages and standard deviations for these pollutants from 2019 to 2023. The findings reveal varying trends, with NO₂ levels rebounding after an initial decline and aerosols showing different patterns. Sentinel-5 Precursor (S5P) satellite observations provide valuable data on various air pollutants including NO2, O3, SO2, CO, and CH4. The Sentinel-5 Precursor (S5P) satellite delivers crucial data on atmospheric composition and global air quality. Launched by the European Space Agency (ESA) under the Copernicus program, S5P is equipped with advanced instruments that monitor a range of trace gases and aerosols, providing essential insights into air pollution and its effects on human health and the environment. This study aims to analyze and interpret these datasets to elucidate the seasonal trends, spatial patterns, and correlations of pollutants in Chhattisgarh using GEE and GIS. By identifying hotspots, understanding seasonal variations, and assessing correlations between pollutants and environmental variables, this research contributes to informing evidence-based policy interventions and sustainable environmental management practices in the region. 2. Study Area Chhattisgarh, located in the central part of India, spans approximately between 17.50°N to 24.00°N latitude and 80.55°E to 84.20°E longitude with an area of around 135,194 square kilometers (Fig. 1 ). The state's climate is diverse, transitioning from tropical to subtropical, influenced by its geographical features and seasonal monsoons. Summers, lasting from March to June, are typically hot and dry, with temperatures soaring up to 45°C in certain regions. The monsoon season, from June to September, brings heavy rainfall, rejuvenating the land and replenishing water sources. The annual average rainfall of Chhattisgarh is varying from 1130 mm to 1876 mm. Winters, from November to February, are characterized by cooler temperatures, ranging from 10°C to 25°C, providing relief from the summer heat. The state's forests, covering approximately 44% of its total area, contribute significantly to its ecological balance and biodiversity. Its urban centers, such as Raipur, Bhilai-Durg, and Bilaspur, serve as engines of growth, driving industrial development and urbanization in the region. 3. Material and Method In this study, we have many datasets. Firstly, the Land Surface Temperature (LST) dataset, sourced from the GCOM-C/SGLI satellite, provides comprehensive coverage of temperature variations across the Earth's surface from 2019 to 2023. LST serves as a vital indicator of environmental conditions, influencing various ecological processes, including vegetation growth, hydrology, and energy exchanges within the Earth system. Rainfall data, derived from the Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS), offers valuable information on precipitation patterns from 2001 to 2023. This dataset aids in understanding hydrological cycles, drought monitoring, and assessing water resource availability, crucial for agricultural planning, water management, and disaster preparedness. The atmospheric composition is captured through datasets such as Carbon Monoxide (CO), Absorbing Aerosol Index (AI), Nitrogen Dioxide (NO 2 ), Sulphur Dioxide (SO 2 ), Methane (CH 4 ), and Ozone (O 3 ). These datasets, sourced from Sentinel-5 Precursor (S5P) satellite observations, provide real-time monitoring of air quality and pollutant concentrations. Sentinel-5 Precursor (S5P) satellite observations provide invaluable insights into atmospheric composition and air quality on a global scale. Launched by the European Space Agency (ESA) as part of the Copernicus program, S5P carries state-of-the-art instruments capable of monitoring various trace gases and aerosols vital for understanding air pollution and its impacts on human health and the environment (Kleipool et al., 2018 ). CO, NO 2 , SO 2 , and CH 4 are anthropogenic pollutants originating from various sources such as industrial activities, transportation, and agricultural practices. Monitoring these pollutants is essential for assessing air quality, understanding emission trends, and implementing air pollution control measures. Additionally, the Absorbing Aerosol Index (AI) serves as an indicator of aerosol absorption properties, influencing atmospheric heating rates, climate, and public health. Ozone (O 3 ), while beneficial in the stratosphere, becomes a pollutant at ground level, adversely impacting human health and ecosystems. Monitoring ground-level ozone concentrations is critical for assessing air quality, understanding photochemical smog formation, and implementing air quality management strategies. Lastly, the Normalized Difference Vegetation Index (NDVI) derived from MODIS satellite observations provides insights into vegetation health, density, and distribution. NDVI serves as a proxy for vegetation productivity, biomass, and land cover changes, supporting ecosystem monitoring, agricultural assessments, and biodiversity studies. NDVI value ranging from − 1 to 1. A positive value represents vegetation and a negative value indicates no vegetation. The spatial resolution of these datasets ranges from 1000 meters (NDVI) to 4638.3 meters (LST), offering detailed information at various spatial scales. Combined, these datasets enable comprehensive monitoring and analysis of Earth's environmental dynamics, facilitating scientific research, policy-making, and sustainable development initiatives. They provide essential information for addressing global challenges such as climate change, air pollution, water scarcity, and ecosystem degradation, ultimately contributing to the advancement of environmental science and stewardship. The details of all climate datasets are available in Table 1 . Table 1 Detail of climate dataset Data Description ID Period Grid space Source Land Surface Temperature (LST) JAXA/GCOM-C/L3/LAND/LST/V3 2019–2023 4638.3 m GCOM-C/SGLI L3 Land Surface Temperature (V3) Rainfall UCSB-CHG/CHIRPS/DAILY 2001–2023 5566 m Climate Hazards Group InfraRed Precipitation With Station Data (Version 2.0 Final) Carbon Monoxide COPERNICUS/S5P/NRTI/L3_CO 2019–2023 1113.2 m European Union/ESA/Copernicus Absorbing Aerosol Index COPERNICUS/S5P/NRTI/L3_AER_AI 2019–2023 1113.2 m European Union/ESA/Copernicus Nitrogen Dioxide COPERNICUS/S5P/NRTI/L3_NO2 2019–2023 1113.2 m European Union/ESA/Copernicus Sulfur Dioxide COPERNICUS/S5P/NRTI/L3_SO2 2019–2023 1113.2 m European Union/ESA/Copernicus Methane COPERNICUS/S5P/OFFL/L3_CH4 2019–2023 1113.2 m European Union/ESA/Copernicus Ozone COPERNICUS/S5P/OFFL/L3_O3 2019–2023 1113.2 m European Union/ESA/Copernicus Normalized Difference Vegetation Index (NDVI) MODIS/061/MOD13A2 2019–2023 1000 m NASA LP DAAC at the USGS EROS Center To estimate the regional air pollutant behavior Google Earth Engine (GEE) platform has been used. GEE allows users to download and upload global scale data as well as provide a platform to perform complex calculations (Yang et al., 2022 ). First, we identified the dataset to download, such as NO 2 , by searching for it in the GEE data catalog. We wrote a script in the Google Earth Engine Code Editor to filter the NO 2 dataset based on their desired parameters, like time range and spatial extent (Fig. 2 ). We ran the code in the GEE Code Editor, initiating the export process. After the export process was complete, we accessed the exported data from their Google Drive account. The exported dataset was analyzed in the GIS Environment. Overall, the adopted methodology flowchart is shown in Fig. 3 . 4. Results and Discussion 4.1 Spatio-temporal distribution of rainfall, LST and NDVI The map presented in Fig. 4 illustrates the spatial distribution of rainfall spanning from 2001 to 2023 and Land Surface Temperature (LST) from 2019 to 2023 across four distinct seasons: annual, monsoon, post-monsoon, and pre-monsoon. In the study area, the southern region experienced the highest amount of rainfall, ranging from 1130 mm to 1876 mm annually. Notably, the majority of this rainfall, approximately 81%, occurred during the monsoon season (South-west monsoon), while 9.5% was observed during the post-monsoon period (September to December), with the remaining rainfall distributed throughout the pre-monsoon season (March to May). During both the pre-monsoon and post-monsoon periods, the southern region consistently received higher rainfall compared to other regions, averaging between 170 mm to 188 mm per season. Conversely, during the monsoon season, both the northeastern and southern regions receive approximately 1600 mm of rainfall annually (Fig. 4a). Regarding LST patterns, the study revealed a notable variation across the region. The central region, characterized by flat terrain, exhibited significantly higher LST values, while elevated and less populated areas showed lower LST readings. Specifically, LST was lower in both the northern and southern regions. During the pre-monsoon season, LST peaked at 46.4°C, followed by the monsoon season at 40.7°C, and the post-monsoon season at 32.9°C. These variations can be attributed to factors such as cloud cover during the monsoon season, elevated humidity levels, and increased evapotranspiration rates (Banerjee et al., 2023 ; Thandlam et al., 2023 ). Additionally, temperatures during the post-monsoon period decreased to as low as 22°C. Figure 4 Spatial variation of (a) Annual and seasonal trend maps of rainfall (b) Annual and seasonal trend maps of LST in Chhattisgarh state Figure 5 displays spatial maps depicting Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) during the pre-monsoon period. The maps reveal an inverse relationship between LST and NDVI, as observed in previous studies (Şahin Körmeçli and Seçkin Gündoğan, 2024 ). This phenomenon is attributed to the cooling effect of vegetation on the land surface. Vegetation mitigates heat through processes such as transpiration, where water vapor is released, and shading, which reduces solar radiation absorption (Yu et al., 2024 ). Consequently, areas with higher vegetation density, particularly in the southern region, exhibit lower LST values. Conversely, the central region displays higher LST readings alongside lower vegetation coverage. The temporal analysis indicates variations in LST across the study period. In 2019, LST peaked at 48.85°C, followed by subsequent years in the order of 2022, 2021, 2020, and 2023. This temporal pattern underscores the dynamic nature of surface temperature fluctuations over time. 4.2 Spatial-temporal variation of air pollutants Figure 6 illustrates the spatial distribution of carbon monoxide (CO) levels across different seasons. Analysis of the data reveals distinct patterns in CO concentrations across the study period (2019–2023). Generally, CO levels peaked during the pre-monsoon season in most years, except for 2020, where an anomalous trend was observed. This deviation in 2020 can be attributed to various factors such as lockdowns, restrictions, reduced biomass-burning activities, and unusual weather patterns, as suggested by Arunkumar and Dhanakumar ( 2021 ). During the monsoon season, higher CO concentrations were observed in the central-northern region, indicating potential sources of CO emissions in this area. In contrast, during the post-monsoon period, elevated CO levels were primarily confined to the central region. Interestingly, during the pre-monsoon season, elevated CO concentrations were observed in both the central and southern regions, suggesting different emission sources or atmospheric transport patterns during this time. Moreover, a detailed analysis of vertically integrated CO column density revealed that the highest levels were recorded during the pre-monsoon season of 2022, reaching a value of 0.057 mol/m2. This finding underscores the variability in CO levels over time and highlights the importance of considering seasonal variations in understanding air pollution dynamics. Figure 7 depicts the spatial distribution of the UV aerosol index (AI) across different seasons. The analysis reveals that AI levels were highest during the monsoon season and lowest during the pre-monsoon season. This pattern can be attributed to various factors, as elucidated by Yang et al. ( 2021 ). During the monsoon, increased moisture, humidity, scavenging processes, and biomass-burning activities contribute to elevated AI levels. Conversely, during the pre-monsoon season, factors such as dust settling, reduced cloud cover, and vegetation effects lead to lower AI levels. Notably, there was an increase in the aerosol index during the pre-monsoon seasons of 2022 and 2023 compared to previous years (2019–2021). This increase can be attributed to a combination of factors, including heightened biomass burning and agricultural activities, industrial emissions post-pandemic, specific meteorological conditions favoring aerosol accumulation, and reduced rainfall. In contrast, AI levels were lower in 2020 during both the monsoon and pre-monsoon seasons, which could be attributed to various factors such as reduced human activities due to lockdown measures and their associated impacts on aerosol sources. Similarly, during the post-monsoon season, AI levels were lower in 2022 compared to other years. Moreover, AI levels were consistently lower in the southern region across all seasons. Moving to Fig. 8 , it illustrates the spatial distribution of Nitrogen Dioxide (NO2) concentrations across different seasons. NO 2 levels were consistently higher in the central region throughout the study period, indicating localized sources of NO 2 emissions. However, during the post-monsoon season, NO2 concentrations exhibited slight spatial expansion. The highest concentration of NO2, reaching 0.000520 mol/m2, was recorded during the post-monsoon season. This spatial distribution underscores the importance of considering seasonal variations and localized emission sources in understanding NO 2 dynamics and air quality patterns. During the monsoon season, the concentration of nitrogen dioxide (NO 2 ) was observed to be lower compared to the pre-monsoon and post-monsoon seasons. This reduction in NO2 levels can be attributed to several factors (Ul-Haq et al. 2021 and Srivastava et al. 2024 ). Firstly, rain scavenging plays a significant role in reducing NO2 concentrations during the monsoon. The precipitation washes out pollutants from the atmosphere, including NO2, thereby leading to cleaner air conditions. Secondly, the monsoon season is characterized by increased humidity levels, which can contribute to the removal of NO2 through chemical reactions and atmospheric processes. Thirdly, the enhanced vertical mixing of air masses during the monsoon facilitates the dispersion of pollutants, including NO 2 , across a larger vertical extent of the atmosphere. This dispersion process helps in diluting the concentration of NO 2 , resulting in lower levels observed during the monsoon. Additionally, strong winds associated with the monsoon circulation patterns can contribute to the transportation of pollutants away from the region of interest, further contributing to the reduction in NO 2 concentrations. The spatial distribution of NO 2 during the monsoon season indicates higher concentrations in areas covering Raipur to Bilaspur, known as the industrial zone, and the Mahasamund region. Despite these localized hotspots, the overall NO 2 levels tend to be lower during the monsoon due to the aforementioned meteorological factors and the associated atmospheric cleansing processes. Figure 9 displays the spatial distribution of Sulphur Dioxide (SO 2 ) concentrations across different seasons. The analysis reveals notable variations in SO 2 levels across the study period. During the post-monsoon season, SO 2 concentrations reached their peak, with the highest recorded value of 0.00204 mol/m2 observed in 2023. These elevated concentrations were predominantly observed in the central region, encompassing areas such as Bilaspur, Katghora, and Korba districts. The high SO 2 levels in this region can be attributed to coal combustion in power plants and industrial processes (Mittal et al. 2014 ). In contrast, during the monsoon season, SO 2 concentrations were comparatively lower compared to other seasons, with the highest recorded concentration being 0.000820 mol/m2. Despite this reduction, the central region remained a hotspot for SO 2 emissions during the monsoon season. It is noteworthy that the southern region of the study area, which comprises forested regions, exhibited very low concentrations of SO 2 across all seasons. This finding suggests that industrial and anthropogenic activities, rather than natural sources, are the primary contributors to SO 2 emissions in the study area. Overall, the results indicate a consistent pattern of SO 2 concentration in the central region throughout all seasons, highlighting the need for targeted mitigation measures to address air quality concerns in this area. Figure 10 illustrates the spatial distribution of Methane (CH 4 ) concentrations across different seasons. The analysis reveals distinct patterns in CH 4 levels, with varying concentrations observed during the monsoon, pre-monsoon, and post-monsoon periods. During the monsoon season, CH 4 concentrations were notably lower compared to the pre-monsoon and post-monsoon seasons. This reduction in CH 4 levels can be attributed to several factors, including wet deposition, enhanced mixing of air masses, and vegetation uptake. The wet conditions during the monsoon facilitate the removal of CH 4 from the atmosphere through processes such as rainfall and wet deposition. Additionally, the increased mixing of air masses and vegetation uptake contributed to the lower CH 4 concentrations observed during this period. In contrast, both the pre-monsoon and post-monsoon seasons exhibit higher CH 4 concentrations, particularly in the central region. The elevated CH 4 levels during these periods can be attributed to various anthropogenic activities, including rice cultivation, agricultural residue burning, and industrial and urban activities (Metya et al. 2021 ). An anomaly was observed in 2023, with the highest CH 4 concentration recorded during the monsoon season. This anomaly may result from a combination of unusual meteorological conditions, changes in agricultural practices, and potentially enhanced natural emissions due to climate change impacts (Khan et al. 2009 ). Further investigation is warranted to understand the underlying factors contributing to this anomaly and its implications for atmospheric chemistry and air quality management. Figure 11 presents the spatial distribution of Ozone (O 3 ) concentrations across different seasons. The analysis reveals distinct patterns in O 3 levels, with variations observed between the pre-monsoon, monsoon, and post-monsoon periods. During the pre-monsoon season, O3 concentrations were observed to be high. This increase in O 3 levels can be attributed to photochemical reactions occurring in the atmosphere, coupled with stable atmospheric conditions conducive to O3 formation. These conditions allow for the accumulation of O 3 in the atmosphere, leading to higher concentrations during this period. In contrast, O 3 concentrations were lower during the post-monsoon season. This decrease in O 3 levels can be attributed to several factors, including lower sunlight intensity and higher humidity levels. Reduced sunlight intensity during the post-monsoon period limits the photochemical reactions necessary for O 3 formation, contributing to lower concentrations. Additionally, higher humidity levels during this period can lead to O 3 removal through dissolution and scavenging processes. Moreover, during the pre-monsoon season, O3 concentrations were observed to be higher in the northern region (Pancholi et al. 2018 ). This spatial distribution suggests regional variations in O 3 levels, with the northern region experiencing elevated concentrations during this season. Interestingly, the concentration of O 3 was higher in the northern region during both the pre-monsoon and post-monsoon periods, except for the year 2022 in the post-monsoon season. This anomaly may be attributed to specific meteorological conditions or changes in atmospheric dynamics during that particular year. Overall, the spatial maps provide valuable insights into the seasonal variability of O 3 concentrations and the factors influencing its distribution across different regions. The correlation heatmap of air pollutants has been shown in Fig. 12 . LST was positively correlated with O 3 , CO and NO 2 with values of 0.93, 0.38, and 0.26 and negatively correlated with CH 4 and SO 2 . CO and NO 2 (0.99) Indicate that high levels of CO are usually accompanied by high levels of NO 2 . O 3 and LST (0.93) Indicate that higher land surface temperatures are associated with higher ozone levels. This relationship is consistent with previous studies indicating that elevated temperatures can enhance photochemical reactions leading to the formation of ozone in the atmosphere (Suthar et al., 2023 ). AI and CO (-0.86) Indicate that higher aerosol index values are associated with lower levels of carbon monoxide. AI and NO 2 (-0.79) Indicate that higher aerosol index values are associated with lower levels of nitrogen dioxide. AI and LST (-0.79) Indicate that higher aerosol index values are associated with lower land surface temperatures. O 3 and CH 4 (-1.00) Indicate a perfect inverse relationship between ozone and methane (Fig. 12 ). Strong positive or negative correlations can indicate common sources or interactions between pollutants, while weak correlations suggest more independent behavior (Larsen et al., 2017 ). 5. Conclusion In this study, we conducted a comprehensive analysis of air pollutant dynamics and meteorological variables across different seasons in Chhattisgarh state. The spatial distribution of rainfall and Land Surface Temperature (LST) was examined across four distinct seasons: annual, monsoon, post-monsoon, and pre-monsoon. Our findings revealed that the southern region experienced the highest annual rainfall, with the majority occurring during the monsoon season. Conversely, LST exhibited a notable variation across the region, with the central region displaying higher temperatures attributed to flat terrain and anthropogenic activities. Further analysis of LST and Normalized Difference Vegetation Index (NDVI) during the pre-monsoon period indicated an inverse relationship, highlighting the cooling effect of vegetation on land surfaces. We also investigated the spatial distribution of various air pollutants, including carbon monoxide (CO), UV aerosol index (AI), Nitrogen Dioxide (NO 2 ), Sulphur Dioxide (SO 2 ), Methane (CH 4 ), and Ozone (O 3 ), across different seasons. CO levels peaked during the pre-monsoon season in most years, except for 2020, attributed to lockdowns and reduced anthropogenic activities. AI was highest during the monsoon season due to increased moisture and biomass burning, while NO2 concentrations were lower during the monsoon season compared to other seasons, attributed to rain scavenging and enhanced mixing. SO 2 concentrations were highest during the post-monsoon season, primarily in the central region, indicating industrial emissions. CH4 concentrations varied spatially, with higher levels observed during pre-monsoon and post-monsoon periods, and an anomaly observed in 2023. O 3 concentrations were highest during the pre-monsoon season, attributed to photochemical reactions, and stable atmospheric conditions. Correlation analysis revealed relationships between pollutants and meteorological variables, with significant associations observed between CO and NO2, O3 and LST, AI and CO, AI and NO2, AI and LST, and O3 and CH4. Overall, our study provides valuable insights into the seasonal variability of air pollutants and their interactions with meteorological factors. These findings have implications for air quality management and policy interventions to mitigate environmental pollution and its impacts on human health and ecosystems. Declarations Acknowledgements The authors with to thank the editor and anonymous reviewers for their instructive comments to improve the manuscript. The authors wish to thank the European Union/ ESA/Copernicus teams for supplying the satellite data and reanalysis products used in this study. We wish to thank the GEE platform to support to providing easy data. Funding: No Funding Authors Contributions: L. Singh: conceptualization, formal analysis, data curation, investigation, writing, and original draft; R. N. Tripathi: writing, review, and editing; N. Mundetia: writing, review, and editing. Ethical Approval: Not applicable. Consent to Participate: All authors participated. Consent to Publish: All authors approved the final manuscript to be published. Competing Interests: The authors declare no competing interests References Ahmad, A. N., Abdullah, S., Mansor, A. A., Che Dom, N., Ahmed, A. N., Ismail, N. A., & Ismail, M. (2023). Assessment of Daytime and Nighttime Ground Level Ozone Pollution in Malaysian Urban Areas. Malaysian Journal of Medicine & Health Sciences, 19(6). Arunkumar, M., & Dhanakumar, S. (2021). 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Ritchie, H., Rosado, P., & Roser, M. (2024). Greenhouse gas emissions. Our World in Data. Şahin Körmeçli, P., & Seçkin Gündoğan, G. (2024). Assessment of vegetation change using NDVI, LST, and carbon analyses in Çankırı Karatekin University, Turkey. Environmental Monitoring and Assessment, 196 (3), 1-15. Salim, M. Z., Choudhari, N., Kafy, A. A., Nath, H., Alsulamy, S., Rahaman, Z. A., ... & Al-Ramadan, B. (2024). A comprehensive review of navigating urbanization induced climate change complexities for sustainable groundwater resource management in the Indian subcontinent. Groundwater for Sustainable Development , 101115. Sethi, S. S., & Vinoj, V. (2024). Urbanization and regional climate change-linked warming of Indian cities. Nature Cities , 1-4. Singh, N., Jain, K., Kumar, P., George, N. T., Sambath, V., & Lakra, M. S. (2023). Air quality assessment in the central Indian State of Chhattisgarh. Indian Journal of Public Health, 67 (1), 78-83. Srivastava, S., Behera, S., & Mitra, D. (2024). Distribution of ozone, carbon monoxide and oxides of nitrogen over an urban location in the foothills of the North-Western Himalayas. Urban Climate, 55 , 101913. Suthar, G., Singhal, R. P., Khandelwal, S., & Kaul, N. (2023). Spatiotemporal variation of air pollutants and their relationship with land surface temperature in Bengaluru, India. Remote Sensing Applications: Society and Environment, 32, 101011. Thandlam, V., Rahaman, H., Rutgersson, A., Sahlee, E., Ravichandran, M., & Ramakrishna, S. S. V. S. (2023). Quantifying the role of antecedent Southwestern Indian Ocean capacitance on the summer monsoon rainfall variability over homogeneous regions of India. Scientific Reports, 13(1), 5553. Ul-Haq, Z., Tariq, S., & Ali, M. (2017). Spatiotemporal patterns of correlation between atmospheric nitrogen dioxide and aerosols over South Asia. Meteorology and Atmospheric Physics, 129, 507-527. Verma, M. K., Patel, A., Sahariah, B. P., & Choudhari, J. K. (2016). Computation of air quality index for major cities of Chhattisgarh state. Environmental Claims Journal, 28 (3), 195-205. Yang, J., Ji, Z., Kang, S., & Tripathee, L. (2021). Contribution of South Asian biomass burning to black carbon over the Tibetan Plateau and its climatic impact. Environmental Pollution, 270 , 116195. Yang, L., Driscol, J., Sarigai, S., Wu, Q., Chen, H., & Lippitt, C. D. (2022). Google Earth Engine and artificial intelligence (AI): a comprehensive review. Remote Sensing, 14 (14), 3253. Yoo, J. M., Lee, Y. R., Kim, D., Jeong, M. J., Stockwell, W. R., Kundu, P. K., ... & Lee, S. J. (2014). New indices for wet scavenging of air pollutants (O3, CO, NO2, SO2, and PM10) by summertime rain. Atmospheric environment, 82, 226-237. Yu, Z., Chen, J., Chen, J., Zhan, W., Wang, C., Ma, W., ... & Sun, R. (2024). Enhanced observations from an optimized soil-canopy-photosynthesis and energy flux model revealed evapotranspiration-shading cooling dynamics of urban vegetation during extreme heat. Remote Sensing of Environment, 305 , 114098. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4544803","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":327205277,"identity":"ee5251c9-8e04-4a8c-962f-45809368d515","order_by":0,"name":"LEELAMBAR SINGH","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIie3PPQrCMBTA8VcCyfLUtaLiFSoFxUG8SkVwqiB4AcHByb2Dl3DJHAl4ii5B6CZmEkQF03bQJX5sDvlPCeTHywNwuf6wKoAnAER50wBYngI7oVC8L4mXGII/EZI/x08fo2wpxCVO270FUYdBnDaHQJSGWWonuI92a551NoKG4ZRn5mM09CHI7MSPA1Hh0qyBtDHlMt+la3aRdtI+6d2dy6Eh7NovCDu/Jz6CNFNG+RTiFQQ/TMFJIFs8GyeEhvV1TiTO/egNqTGp1JGng4Qtlb6YH7LVaqv1zU6ekddD9AVwuVwul70HXwlOaTTj060AAAAASUVORK5CYII=","orcid":"","institution":"Malaviya National Institute of Technology Jaipur","correspondingAuthor":true,"prefix":"","firstName":"LEELAMBAR","middleName":"","lastName":"SINGH","suffix":""},{"id":327205278,"identity":"73e54921-1272-4a08-b2b4-967e07f8dcc7","order_by":1,"name":"NITIKA MUNDETIA","email":"","orcid":"","institution":"Sangam University","correspondingAuthor":false,"prefix":"","firstName":"NITIKA","middleName":"","lastName":"MUNDETIA","suffix":""},{"id":327205279,"identity":"e0f6cdf8-1afa-410b-81ff-146a2de62627","order_by":2,"name":"RAVINDRA NATH TRIPATHI","email":"","orcid":"","institution":"Graphic Era University","correspondingAuthor":false,"prefix":"","firstName":"RAVINDRA","middleName":"NATH","lastName":"TRIPATHI","suffix":""}],"badges":[],"createdAt":"2024-06-07 08:38:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4544803/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4544803/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60666848,"identity":"504250cd-a426-4bbd-8769-2b6a6aae336f","added_by":"auto","created_at":"2024-07-19 09:31:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1198471,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area map of Chhattisgarh state\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4544803/v1/f3c9238756e00b9840b8676b.png"},{"id":60666846,"identity":"e1fc377c-6fab-43d9-a597-3d5907b205da","added_by":"auto","created_at":"2024-07-19 09:31:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1033196,"visible":true,"origin":"","legend":"\u003cp\u003eGoogle Earth Engine Playground (https://code.earthengine.google.com/), which is the JavaScript API\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4544803/v1/4ccab6197ad623c7153725d3.png"},{"id":60667595,"identity":"7eb51d76-bdc5-42f0-b2a9-1c36fd761cae","added_by":"auto","created_at":"2024-07-19 09:39:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":265610,"visible":true,"origin":"","legend":"\u003cp\u003eAdopted Methodology flowchart\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4544803/v1/ee782fe815e3cfb6635b3cc4.png"},{"id":60667596,"identity":"1640663d-8a7b-44e4-8216-ac6c61aa8c26","added_by":"auto","created_at":"2024-07-19 09:39:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":202737,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial variation of (a) Annual and seasonal trend maps of rainfall (b) Annual and seasonal trend maps of LST in Chhattisgarh state\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4544803/v1/368466a89b40a6a828570ebb.png"},{"id":60666849,"identity":"a2674910-5cc9-4164-bece-27318c0d120e","added_by":"auto","created_at":"2024-07-19 09:31:05","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":585353,"visible":true,"origin":"","legend":"\u003cp\u003eThe spatial variation of NDVI and LST during the pre-monsoon season\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4544803/v1/f209a79d103876d50b27857c.png"},{"id":60666853,"identity":"c79b4a0a-034d-44ea-9b62-6990a7a26f72","added_by":"auto","created_at":"2024-07-19 09:31:05","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":544307,"visible":true,"origin":"","legend":"\u003cp\u003eThe spatial variation of CO for monsoon, post-monsoon and pre-monsoon seasons\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4544803/v1/47b2faa1cc13f99e46b618f3.png"},{"id":60666857,"identity":"bf5d2922-d3a5-43c1-9fa0-420f7ad4fdcc","added_by":"auto","created_at":"2024-07-19 09:31:05","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":436022,"visible":true,"origin":"","legend":"\u003cp\u003eThe spatial variation of UV Aerosol Index for monsoon, post-monsoon and pre-monsoon seasons\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-4544803/v1/ece38bf97f2c476adf7f9235.png"},{"id":60666850,"identity":"92746a46-1f0e-4585-822e-d1564241d4ee","added_by":"auto","created_at":"2024-07-19 09:31:05","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":307604,"visible":true,"origin":"","legend":"\u003cp\u003eThe spatial variation of NO\u003csub\u003e2\u003c/sub\u003e for monsoon, post-monsoon and pre-monsoon seasons\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-4544803/v1/611c532e7e436e51a22f19ae.png"},{"id":60667597,"identity":"156ccf56-7c41-482a-a9ed-97b058389401","added_by":"auto","created_at":"2024-07-19 09:39:05","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":688262,"visible":true,"origin":"","legend":"\u003cp\u003eThe spatial variation of SO\u003csub\u003e2\u003c/sub\u003e for monsoon, post-monsoon and pre-monsoon seasons\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-4544803/v1/290f6dae8f246d10ddc430d9.png"},{"id":60666854,"identity":"60cac932-2748-4a3e-9cee-cec4df1823f4","added_by":"auto","created_at":"2024-07-19 09:31:05","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":426204,"visible":true,"origin":"","legend":"\u003cp\u003eThe spatial variation of CH\u003csub\u003e4\u003c/sub\u003e for monsoon, post-monsoon and pre-monsoon seasons\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-4544803/v1/4d04f3347254beb151940a4a.png"},{"id":60667598,"identity":"4e227cb4-502c-495a-b273-0c3bfdd7e091","added_by":"auto","created_at":"2024-07-19 09:39:05","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":549246,"visible":true,"origin":"","legend":"\u003cp\u003eThe spatial variation of O\u003csub\u003e3\u003c/sub\u003e for monsoon, post-monsoon and pre-monsoon seasons\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-4544803/v1/3debb9a14fb882dd06b49d5c.png"},{"id":60667599,"identity":"1bdb8af4-2e3b-4ad9-8c07-2edab3f4739e","added_by":"auto","created_at":"2024-07-19 09:39:05","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":106517,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation heatmap of air pollutants with LST\u003c/p\u003e","description":"","filename":"floatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-4544803/v1/df67730b7003b16e13f79121.png"},{"id":65194985,"identity":"f6ac229e-232c-477c-b48e-6eeda7e77248","added_by":"auto","created_at":"2024-09-24 15:17:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7027794,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4544803/v1/2eddd297-87bc-493f-8938-57076b8c58d5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing climate variability and its correlation with regional air pollution in Chhattisgarh, India utilizing Google Earth Engine (GEE)","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eClimate change, a global phenomenon driven primarily by human activities, poses significant challenges to ecosystems, communities, and economies worldwide (Rawat et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In India, many states are facing the multifaceted impacts of climate change, which manifest in various forms such as shifting weather patterns, altered precipitation regimes, rising temperatures, and intensity of extreme weather events (Sethi and Vinoj \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGreenhouse gas emissions, primarily carbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e), methane (CH\u003csub\u003e4\u003c/sub\u003e), nitrous oxide (N\u003csub\u003e2\u003c/sub\u003eO), and fluorinated gases, are major contributors to climate change and global warming. These gases trap heat in the Earth's atmosphere, leading to the greenhouse effect, which results in the warming of the planet's surface (Ritchie et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Air quality parameters such as nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), ozone (O3), and particulate matter (PM), pose significant risks to human health and the environment. A significant amount of studies have been done by considering air quality and greenhouse emission gases that harm earth creatures (Meo et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Lee et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ahmad et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Gupta et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Khajeamiri et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kiran et al., 2021; Mukta et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Urbanization, industrial expansion, intensified agriculture, and land use changes contribute to increased emissions and particulate matter, necessitating sustainable practices, clean energy adoption, and regulatory measures for effective mitigation (Salim et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Land surface temperature (LST) interacts intricately with air pollutants, forming a crucial nexus in environmental dynamics. Elevated LST exacerbates the formation of urban heat islands (UHIs), amplifying air pollutant concentrations in cities (Cheval et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Panda et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Conversely, air pollutants such as aerosols and greenhouse gases influence LST by absorbing or scattering solar radiation (Dangayach et al., 2024; Nikkath and Selvi2024).\u003c/p\u003e \u003cp\u003eIn the context of air quality, climate variability plays a crucial role in influencing the distribution and concentration of pollutants. The impact of climate variability on regional air pollution is multifaceted, encompassing changes in temperature, rainfall patterns, and atmospheric circulation. Increased rainfall can lead to reduced air pollution levels by scavenging pollutants from the atmosphere (Kaur and Pandey \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yoo et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Conversely, higher temperatures can exacerbate air pollution through enhanced photochemical reactions and the formation of secondary pollutants like ozone (Yu et al., 2021).\u003c/p\u003e \u003cp\u003eChhattisgarh, located in central India, faces significant air pollution challenges, exacerbated by rapid urbanization, industrial activities, and climatic factors (Verma et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Major cities such as Raipur, Bhilai-Durg, and Bilaspur have seen substantial population growth and industrial expansion over the past two decades, contributing to deteriorating air quality (Deshmukh et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Sahare et al., 2022; Singh et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This section outlines the primary sources and impacts of air pollution in Chhattisgarh, highlighting the need for effective mitigation strategies.\u003c/p\u003e \u003cp\u003eDas et al., (2024) studied to assess stubble burning and emissions via the Google Earth Engine using the dataset of MODIS active fire data and TROPOMI CO and NO\u003csub\u003e2\u003c/sub\u003e measurements and found that a nearly threefold increase in crop residue burning in November compared to October, with 92.58% of fires in Punjab and 7.42% in Haryana. Chandra and Singh (2023) analyzed four key air quality indicators\u0026mdash;nitrogen dioxide (NO₂), sulfur dioxide (SO₂), ultraviolet aerosol index (UVAI), and ozone (O₃)\u0026mdash;using TROPOMI data on the Google Earth Engine platform. Focusing on Uttar Pradesh over five years, their analysis covers monthly averages and standard deviations for these pollutants from 2019 to 2023. The findings reveal varying trends, with NO₂ levels rebounding after an initial decline and aerosols showing different patterns.\u003c/p\u003e \u003cp\u003eSentinel-5 Precursor (S5P) satellite observations provide valuable data on various air pollutants including NO2, O3, SO2, CO, and CH4. The Sentinel-5 Precursor (S5P) satellite delivers crucial data on atmospheric composition and global air quality. Launched by the European Space Agency (ESA) under the Copernicus program, S5P is equipped with advanced instruments that monitor a range of trace gases and aerosols, providing essential insights into air pollution and its effects on human health and the environment.\u003c/p\u003e \u003cp\u003eThis study aims to analyze and interpret these datasets to elucidate the seasonal trends, spatial patterns, and correlations of pollutants in Chhattisgarh using GEE and GIS. By identifying hotspots, understanding seasonal variations, and assessing correlations between pollutants and environmental variables, this research contributes to informing evidence-based policy interventions and sustainable environmental management practices in the region.\u003c/p\u003e"},{"header":"2. Study Area","content":"\u003cp\u003eChhattisgarh, located in the central part of India, spans approximately between 17.50\u0026deg;N to 24.00\u0026deg;N latitude and 80.55\u0026deg;E to 84.20\u0026deg;E longitude with an area of around 135,194 square kilometers (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The state's climate is diverse, transitioning from tropical to subtropical, influenced by its geographical features and seasonal monsoons. Summers, lasting from March to June, are typically hot and dry, with temperatures soaring up to 45\u0026deg;C in certain regions. The monsoon season, from June to September, brings heavy rainfall, rejuvenating the land and replenishing water sources. The annual average rainfall of Chhattisgarh is varying from 1130 mm to 1876 mm. Winters, from November to February, are characterized by cooler temperatures, ranging from 10\u0026deg;C to 25\u0026deg;C, providing relief from the summer heat. The state's forests, covering approximately 44% of its total area, contribute significantly to its ecological balance and biodiversity. Its urban centers, such as Raipur, Bhilai-Durg, and Bilaspur, serve as engines of growth, driving industrial development and urbanization in the region.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"3. Material and Method","content":"\u003cp\u003eIn this study, we have many datasets. Firstly, the Land Surface Temperature (LST) dataset, sourced from the GCOM-C/SGLI satellite, provides comprehensive coverage of temperature variations across the Earth's surface from 2019 to 2023. LST serves as a vital indicator of environmental conditions, influencing various ecological processes, including vegetation growth, hydrology, and energy exchanges within the Earth system.\u003c/p\u003e \u003cp\u003eRainfall data, derived from the Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS), offers valuable information on precipitation patterns from 2001 to 2023. This dataset aids in understanding hydrological cycles, drought monitoring, and assessing water resource availability, crucial for agricultural planning, water management, and disaster preparedness.\u003c/p\u003e \u003cp\u003eThe atmospheric composition is captured through datasets such as Carbon Monoxide (CO), Absorbing Aerosol Index (AI), Nitrogen Dioxide (NO\u003csub\u003e2\u003c/sub\u003e), Sulphur Dioxide (SO\u003csub\u003e2\u003c/sub\u003e), Methane (CH\u003csub\u003e4\u003c/sub\u003e), and Ozone (O\u003csub\u003e3\u003c/sub\u003e). These datasets, sourced from Sentinel-5 Precursor (S5P) satellite observations, provide real-time monitoring of air quality and pollutant concentrations. Sentinel-5 Precursor (S5P) satellite observations provide invaluable insights into atmospheric composition and air quality on a global scale. Launched by the European Space Agency (ESA) as part of the Copernicus program, S5P carries state-of-the-art instruments capable of monitoring various trace gases and aerosols vital for understanding air pollution and its impacts on human health and the environment (Kleipool et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCO, NO\u003csub\u003e2\u003c/sub\u003e, SO\u003csub\u003e2\u003c/sub\u003e, and CH\u003csub\u003e4\u003c/sub\u003e are anthropogenic pollutants originating from various sources such as industrial activities, transportation, and agricultural practices. Monitoring these pollutants is essential for assessing air quality, understanding emission trends, and implementing air pollution control measures. Additionally, the Absorbing Aerosol Index (AI) serves as an indicator of aerosol absorption properties, influencing atmospheric heating rates, climate, and public health.\u003c/p\u003e \u003cp\u003eOzone (O\u003csub\u003e3\u003c/sub\u003e), while beneficial in the stratosphere, becomes a pollutant at ground level, adversely impacting human health and ecosystems. Monitoring ground-level ozone concentrations is critical for assessing air quality, understanding photochemical smog formation, and implementing air quality management strategies.\u003c/p\u003e \u003cp\u003eLastly, the Normalized Difference Vegetation Index (NDVI) derived from MODIS satellite observations provides insights into vegetation health, density, and distribution. NDVI serves as a proxy for vegetation productivity, biomass, and land cover changes, supporting ecosystem monitoring, agricultural assessments, and biodiversity studies. NDVI value ranging from \u0026minus;\u0026thinsp;1 to 1. A positive value represents vegetation and a negative value indicates no vegetation.\u003c/p\u003e \u003cp\u003eThe spatial resolution of these datasets ranges from 1000 meters (NDVI) to 4638.3 meters (LST), offering detailed information at various spatial scales. Combined, these datasets enable comprehensive monitoring and analysis of Earth's environmental dynamics, facilitating scientific research, policy-making, and sustainable development initiatives. They provide essential information for addressing global challenges such as climate change, air pollution, water scarcity, and ecosystem degradation, ultimately contributing to the advancement of environmental science and stewardship. The details of all climate datasets are available in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDetail of climate dataset\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eData Description\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGrid space\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand Surface Temperature (LST)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJAXA/GCOM-C/L3/LAND/LST/V3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2019\u0026ndash;2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4638.3 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGCOM-C/SGLI L3 Land Surface Temperature (V3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRainfall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUCSB-CHG/CHIRPS/DAILY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2001\u0026ndash;2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5566 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eClimate Hazards Group InfraRed Precipitation With Station Data (Version 2.0 Final)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarbon Monoxide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCOPERNICUS/S5P/NRTI/L3_CO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2019\u0026ndash;2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1113.2 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean Union/ESA/Copernicus\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsorbing Aerosol Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCOPERNICUS/S5P/NRTI/L3_AER_AI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2019\u0026ndash;2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1113.2 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean Union/ESA/Copernicus\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNitrogen Dioxide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCOPERNICUS/S5P/NRTI/L3_NO2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2019\u0026ndash;2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1113.2 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean Union/ESA/Copernicus\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSulfur Dioxide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCOPERNICUS/S5P/NRTI/L3_SO2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2019\u0026ndash;2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1113.2 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean Union/ESA/Copernicus\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMethane\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCOPERNICUS/S5P/OFFL/L3_CH4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2019\u0026ndash;2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1113.2 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean Union/ESA/Copernicus\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOzone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCOPERNICUS/S5P/OFFL/L3_O3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2019\u0026ndash;2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1113.2 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean Union/ESA/Copernicus\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormalized Difference Vegetation Index (NDVI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMODIS/061/MOD13A2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2019\u0026ndash;2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1000 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNASA LP DAAC at the USGS EROS Center\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo estimate the regional air pollutant behavior Google Earth Engine (GEE) platform has been used. GEE allows users to download and upload global scale data as well as provide a platform to perform complex calculations (Yang et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFirst, we identified the dataset to download, such as NO\u003csub\u003e2\u003c/sub\u003e, by searching for it in the GEE data catalog. We wrote a script in the Google Earth Engine Code Editor to filter the NO\u003csub\u003e2\u003c/sub\u003e dataset based on their desired parameters, like time range and spatial extent (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). We ran the code in the GEE Code Editor, initiating the export process. After the export process was complete, we accessed the exported data from their Google Drive account. The exported dataset was analyzed in the GIS Environment. Overall, the adopted methodology flowchart is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"4. Results and Discussion","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Spatio-temporal distribution of rainfall, LST and NDVI\u003c/h2\u003e \u003cp\u003eThe map presented in Fig.\u0026nbsp;4 illustrates the spatial distribution of rainfall spanning from 2001 to 2023 and Land Surface Temperature (LST) from 2019 to 2023 across four distinct seasons: annual, monsoon, post-monsoon, and pre-monsoon.\u003c/p\u003e \u003cp\u003eIn the study area, the southern region experienced the highest amount of rainfall, ranging from 1130 mm to 1876 mm annually. Notably, the majority of this rainfall, approximately 81%, occurred during the monsoon season (South-west monsoon), while 9.5% was observed during the post-monsoon period (September to December), with the remaining rainfall distributed throughout the pre-monsoon season (March to May). During both the pre-monsoon and post-monsoon periods, the southern region consistently received higher rainfall compared to other regions, averaging between 170 mm to 188 mm per season. Conversely, during the monsoon season, both the northeastern and southern regions receive approximately 1600 mm of rainfall annually (Fig.\u0026nbsp;4a).\u003c/p\u003e \u003cp\u003eRegarding LST patterns, the study revealed a notable variation across the region. The central region, characterized by flat terrain, exhibited significantly higher LST values, while elevated and less populated areas showed lower LST readings. Specifically, LST was lower in both the northern and southern regions. During the pre-monsoon season, LST peaked at 46.4\u0026deg;C, followed by the monsoon season at 40.7\u0026deg;C, and the post-monsoon season at 32.9\u0026deg;C. These variations can be attributed to factors such as cloud cover during the monsoon season, elevated humidity levels, and increased evapotranspiration rates (Banerjee et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Thandlam et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Additionally, temperatures during the post-monsoon period decreased to as low as 22\u0026deg;C.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure\u0026nbsp;4\u003c/b\u003e Spatial variation of (a) Annual and seasonal trend maps of rainfall (b) Annual and seasonal trend maps of LST in Chhattisgarh state\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e displays spatial maps depicting Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) during the pre-monsoon period. The maps reveal an inverse relationship between LST and NDVI, as observed in previous studies (Şahin K\u0026ouml;rme\u0026ccedil;li and Se\u0026ccedil;kin G\u0026uuml;ndoğan, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This phenomenon is attributed to the cooling effect of vegetation on the land surface. Vegetation mitigates heat through processes such as transpiration, where water vapor is released, and shading, which reduces solar radiation absorption (Yu et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Consequently, areas with higher vegetation density, particularly in the southern region, exhibit lower LST values.\u003c/p\u003e \u003cp\u003eConversely, the central region displays higher LST readings alongside lower vegetation coverage. The temporal analysis indicates variations in LST across the study period. In 2019, LST peaked at 48.85\u0026deg;C, followed by subsequent years in the order of 2022, 2021, 2020, and 2023. This temporal pattern underscores the dynamic nature of surface temperature fluctuations over time.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Spatial-temporal variation of air pollutants\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e illustrates the spatial distribution of carbon monoxide (CO) levels across different seasons. Analysis of the data reveals distinct patterns in CO concentrations across the study period (2019\u0026ndash;2023). Generally, CO levels peaked during the pre-monsoon season in most years, except for 2020, where an anomalous trend was observed. This deviation in 2020 can be attributed to various factors such as lockdowns, restrictions, reduced biomass-burning activities, and unusual weather patterns, as suggested by Arunkumar and Dhanakumar (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDuring the monsoon season, higher CO concentrations were observed in the central-northern region, indicating potential sources of CO emissions in this area. In contrast, during the post-monsoon period, elevated CO levels were primarily confined to the central region. Interestingly, during the pre-monsoon season, elevated CO concentrations were observed in both the central and southern regions, suggesting different emission sources or atmospheric transport patterns during this time.\u003c/p\u003e \u003cp\u003eMoreover, a detailed analysis of vertically integrated CO column density revealed that the highest levels were recorded during the pre-monsoon season of 2022, reaching a value of 0.057 mol/m2. This finding underscores the variability in CO levels over time and highlights the importance of considering seasonal variations in understanding air pollution dynamics.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e depicts the spatial distribution of the UV aerosol index (AI) across different seasons. The analysis reveals that AI levels were highest during the monsoon season and lowest during the pre-monsoon season. This pattern can be attributed to various factors, as elucidated by Yang et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). During the monsoon, increased moisture, humidity, scavenging processes, and biomass-burning activities contribute to elevated AI levels. Conversely, during the pre-monsoon season, factors such as dust settling, reduced cloud cover, and vegetation effects lead to lower AI levels. Notably, there was an increase in the aerosol index during the pre-monsoon seasons of 2022 and 2023 compared to previous years (2019\u0026ndash;2021). This increase can be attributed to a combination of factors, including heightened biomass burning and agricultural activities, industrial emissions post-pandemic, specific meteorological conditions favoring aerosol accumulation, and reduced rainfall. In contrast, AI levels were lower in 2020 during both the monsoon and pre-monsoon seasons, which could be attributed to various factors such as reduced human activities due to lockdown measures and their associated impacts on aerosol sources.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSimilarly, during the post-monsoon season, AI levels were lower in 2022 compared to other years. Moreover, AI levels were consistently lower in the southern region across all seasons. Moving to Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003e, it illustrates the spatial distribution of Nitrogen Dioxide (NO2) concentrations across different seasons. NO\u003csub\u003e2\u003c/sub\u003e levels were consistently higher in the central region throughout the study period, indicating localized sources of NO\u003csub\u003e2\u003c/sub\u003e emissions. However, during the post-monsoon season, NO2 concentrations exhibited slight spatial expansion. The highest concentration of NO2, reaching 0.000520 mol/m2, was recorded during the post-monsoon season. This spatial distribution underscores the importance of considering seasonal variations and localized emission sources in understanding NO\u003csub\u003e2\u003c/sub\u003e dynamics and air quality patterns. During the monsoon season, the concentration of nitrogen dioxide (NO\u003csub\u003e2\u003c/sub\u003e) was observed to be lower compared to the pre-monsoon and post-monsoon seasons. This reduction in NO2 levels can be attributed to several factors (Ul-Haq et al. 2021 and Srivastava et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Firstly, rain scavenging plays a significant role in reducing NO2 concentrations during the monsoon. The precipitation washes out pollutants from the atmosphere, including NO2, thereby leading to cleaner air conditions. Secondly, the monsoon season is characterized by increased humidity levels, which can contribute to the removal of NO2 through chemical reactions and atmospheric processes. Thirdly, the enhanced vertical mixing of air masses during the monsoon facilitates the dispersion of pollutants, including NO\u003csub\u003e2\u003c/sub\u003e, across a larger vertical extent of the atmosphere. This dispersion process helps in diluting the concentration of NO\u003csub\u003e2\u003c/sub\u003e, resulting in lower levels observed during the monsoon. Additionally, strong winds associated with the monsoon circulation patterns can contribute to the transportation of pollutants away from the region of interest, further contributing to the reduction in NO\u003csub\u003e2\u003c/sub\u003e concentrations. The spatial distribution of NO\u003csub\u003e2\u003c/sub\u003e during the monsoon season indicates higher concentrations in areas covering Raipur to Bilaspur, known as the industrial zone, and the Mahasamund region.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDespite these localized hotspots, the overall NO\u003csub\u003e2\u003c/sub\u003e levels tend to be lower during the monsoon due to the aforementioned meteorological factors and the associated atmospheric cleansing processes.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e9\u003c/span\u003e displays the spatial distribution of Sulphur Dioxide (SO\u003csub\u003e2\u003c/sub\u003e) concentrations across different seasons. The analysis reveals notable variations in SO\u003csub\u003e2\u003c/sub\u003e levels across the study period. During the post-monsoon season, SO\u003csub\u003e2\u003c/sub\u003e concentrations reached their peak, with the highest recorded value of 0.00204 mol/m2 observed in 2023. These elevated concentrations were predominantly observed in the central region, encompassing areas such as Bilaspur, Katghora, and Korba districts. The high SO\u003csub\u003e2\u003c/sub\u003e levels in this region can be attributed to coal combustion in power plants and industrial processes (Mittal et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In contrast, during the monsoon season, SO\u003csub\u003e2\u003c/sub\u003e concentrations were comparatively lower compared to other seasons, with the highest recorded concentration being 0.000820 mol/m2. Despite this reduction, the central region remained a hotspot for SO\u003csub\u003e2\u003c/sub\u003e emissions during the monsoon season. It is noteworthy that the southern region of the study area, which comprises forested regions, exhibited very low concentrations of SO\u003csub\u003e2\u003c/sub\u003e across all seasons. This finding suggests that industrial and anthropogenic activities, rather than natural sources, are the primary contributors to SO\u003csub\u003e2\u003c/sub\u003e emissions in the study area. Overall, the results indicate a consistent pattern of SO\u003csub\u003e2\u003c/sub\u003e concentration in the central region throughout all seasons, highlighting the need for targeted mitigation measures to address air quality concerns in this area.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e10\u003c/span\u003e illustrates the spatial distribution of Methane (CH\u003csub\u003e4\u003c/sub\u003e) concentrations across different seasons. The analysis reveals distinct patterns in CH\u003csub\u003e4\u003c/sub\u003e levels, with varying concentrations observed during the monsoon, pre-monsoon, and post-monsoon periods. During the monsoon season, CH\u003csub\u003e4\u003c/sub\u003e concentrations were notably lower compared to the pre-monsoon and post-monsoon seasons. This reduction in CH\u003csub\u003e4\u003c/sub\u003e levels can be attributed to several factors, including wet deposition, enhanced mixing of air masses, and vegetation uptake. The wet conditions during the monsoon facilitate the removal of CH\u003csub\u003e4\u003c/sub\u003e from the atmosphere through processes such as rainfall and wet deposition. Additionally, the increased mixing of air masses and vegetation uptake contributed to the lower CH\u003csub\u003e4\u003c/sub\u003e concentrations observed during this period. In contrast, both the pre-monsoon and post-monsoon seasons exhibit higher CH\u003csub\u003e4\u003c/sub\u003e concentrations, particularly in the central region. The elevated CH\u003csub\u003e4\u003c/sub\u003e levels during these periods can be attributed to various anthropogenic activities, including rice cultivation, agricultural residue burning, and industrial and urban activities (Metya et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). An anomaly was observed in 2023, with the highest CH\u003csub\u003e4\u003c/sub\u003e concentration recorded during the monsoon season. This anomaly may result from a combination of unusual meteorological conditions, changes in agricultural practices, and potentially enhanced natural emissions due to climate change impacts (Khan et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Further investigation is warranted to understand the underlying factors contributing to this anomaly and its implications for atmospheric chemistry and air quality management.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e11\u003c/span\u003e presents the spatial distribution of Ozone (O\u003csub\u003e3\u003c/sub\u003e) concentrations across different seasons. The analysis reveals distinct patterns in O\u003csub\u003e3\u003c/sub\u003e levels, with variations observed between the pre-monsoon, monsoon, and post-monsoon periods. During the pre-monsoon season, O3 concentrations were observed to be high. This increase in O\u003csub\u003e3\u003c/sub\u003e levels can be attributed to photochemical reactions occurring in the atmosphere, coupled with stable atmospheric conditions conducive to O3 formation. These conditions allow for the accumulation of O\u003csub\u003e3\u003c/sub\u003e in the atmosphere, leading to higher concentrations during this period. In contrast, O\u003csub\u003e3\u003c/sub\u003e concentrations were lower during the post-monsoon season. This decrease in O\u003csub\u003e3\u003c/sub\u003e levels can be attributed to several factors, including lower sunlight intensity and higher humidity levels. Reduced sunlight intensity during the post-monsoon period limits the photochemical reactions necessary for O\u003csub\u003e3\u003c/sub\u003e formation, contributing to lower concentrations. Additionally, higher humidity levels during this period can lead to O\u003csub\u003e3\u003c/sub\u003e removal through dissolution and scavenging processes. Moreover, during the pre-monsoon season, O3 concentrations were observed to be higher in the northern region (Pancholi et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This spatial distribution suggests regional variations in O\u003csub\u003e3\u003c/sub\u003e levels, with the northern region experiencing elevated concentrations during this season. Interestingly, the concentration of O\u003csub\u003e3\u003c/sub\u003e was higher in the northern region during both the pre-monsoon and post-monsoon periods, except for the year 2022 in the post-monsoon season. This anomaly may be attributed to specific meteorological conditions or changes in atmospheric dynamics during that particular year. Overall, the spatial maps provide valuable insights into the seasonal variability of O\u003csub\u003e3\u003c/sub\u003e concentrations and the factors influencing its distribution across different regions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe correlation heatmap of air pollutants has been shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e12\u003c/span\u003e. LST was positively correlated with O\u003csub\u003e3\u003c/sub\u003e, CO and NO\u003csub\u003e2\u003c/sub\u003e with values of 0.93, 0.38, and 0.26 and negatively correlated with CH\u003csub\u003e4\u003c/sub\u003e and SO\u003csub\u003e2\u003c/sub\u003e. CO and NO\u003csub\u003e2\u003c/sub\u003e (0.99) Indicate that high levels of CO are usually accompanied by high levels of NO\u003csub\u003e2\u003c/sub\u003e. O\u003csub\u003e3\u003c/sub\u003e and LST (0.93) Indicate that higher land surface temperatures are associated with higher ozone levels. This relationship is consistent with previous studies indicating that elevated temperatures can enhance photochemical reactions leading to the formation of ozone in the atmosphere (Suthar et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAI and CO (-0.86) Indicate that higher aerosol index values are associated with lower levels of carbon monoxide. AI and NO\u003csub\u003e2\u003c/sub\u003e (-0.79) Indicate that higher aerosol index values are associated with lower levels of nitrogen dioxide. AI and LST (-0.79) Indicate that higher aerosol index values are associated with lower land surface temperatures. O\u003csub\u003e3\u003c/sub\u003e and CH\u003csub\u003e4\u003c/sub\u003e (-1.00) Indicate a perfect inverse relationship between ozone and methane (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e12\u003c/span\u003e). Strong positive or negative correlations can indicate common sources or interactions between pollutants, while weak correlations suggest more independent behavior (Larsen et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn this study, we conducted a comprehensive analysis of air pollutant dynamics and meteorological variables across different seasons in Chhattisgarh state. The spatial distribution of rainfall and Land Surface Temperature (LST) was examined across four distinct seasons: annual, monsoon, post-monsoon, and pre-monsoon. Our findings revealed that the southern region experienced the highest annual rainfall, with the majority occurring during the monsoon season. Conversely, LST exhibited a notable variation across the region, with the central region displaying higher temperatures attributed to flat terrain and anthropogenic activities.\u003c/p\u003e \u003cp\u003eFurther analysis of LST and Normalized Difference Vegetation Index (NDVI) during the pre-monsoon period indicated an inverse relationship, highlighting the cooling effect of vegetation on land surfaces. We also investigated the spatial distribution of various air pollutants, including carbon monoxide (CO), UV aerosol index (AI), Nitrogen Dioxide (NO\u003csub\u003e2\u003c/sub\u003e), Sulphur Dioxide (SO\u003csub\u003e2\u003c/sub\u003e), Methane (CH\u003csub\u003e4\u003c/sub\u003e), and Ozone (O\u003csub\u003e3\u003c/sub\u003e), across different seasons.\u003c/p\u003e \u003cp\u003eCO levels peaked during the pre-monsoon season in most years, except for 2020, attributed to lockdowns and reduced anthropogenic activities. AI was highest during the monsoon season due to increased moisture and biomass burning, while NO2 concentrations were lower during the monsoon season compared to other seasons, attributed to rain scavenging and enhanced mixing. SO\u003csub\u003e2\u003c/sub\u003e concentrations were highest during the post-monsoon season, primarily in the central region, indicating industrial emissions. CH4 concentrations varied spatially, with higher levels observed during pre-monsoon and post-monsoon periods, and an anomaly observed in 2023. O\u003csub\u003e3\u003c/sub\u003e concentrations were highest during the pre-monsoon season, attributed to photochemical reactions, and stable atmospheric conditions.\u003c/p\u003e \u003cp\u003eCorrelation analysis revealed relationships between pollutants and meteorological variables, with significant associations observed between CO and NO2, O3 and LST, AI and CO, AI and NO2, AI and LST, and O3 and CH4.\u003c/p\u003e \u003cp\u003eOverall, our study provides valuable insights into the seasonal variability of air pollutants and their interactions with meteorological factors. These findings have implications for air quality management and policy interventions to mitigate environmental pollution and its impacts on human health and ecosystems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors with to thank the editor and anonymous reviewers for their instructive comments to improve the manuscript. The authors wish to thank the European Union/ ESA/Copernicus teams for supplying the satellite data and reanalysis products used in this study. We wish to thank the GEE platform to support to providing easy data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e No Funding\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors Contributions:\u003c/strong\u003e L. Singh: conceptualization, formal analysis, data curation, investigation, writing, and original draft; R. N. Tripathi: \u0026nbsp;writing, review, and editing; N. Mundetia: writing, review, and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate:\u003c/strong\u003e All authors participated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish:\u003c/strong\u003e All authors approved the final manuscript to be published.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u003c/strong\u003e The authors declare no competing interests\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAhmad, A. N., Abdullah, S., Mansor, A. A., Che Dom, N., Ahmed, A. N., Ismail, N. A., \u0026amp; Ismail, M. (2023). Assessment of Daytime and Nighttime Ground Level Ozone Pollution in Malaysian Urban Areas. Malaysian Journal of Medicine \u0026amp; Health Sciences, 19(6).\u003c/li\u003e\n\u003cli\u003eArunkumar, M., \u0026amp; Dhanakumar, S. (2021). 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Enhanced observations from an optimized soil-canopy-photosynthesis and energy flux model revealed evapotranspiration-shading cooling dynamics of urban vegetation during extreme heat. \u003cem\u003eRemote Sensing of Environment, 305\u003c/em\u003e, 114098.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Air pollutant, SO2, NO2, CO, GEE, Chhattisgarh","lastPublishedDoi":"10.21203/rs.3.rs-4544803/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4544803/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the spatiotemporal dynamics of key atmospheric pollutants and environmental variables in Chhattisgarh using satellite remote sensing data from 2019 to 2023. The dataset includes parameters such as Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), rainfall, and concentrations of carbon monoxide (CO), nitrogen dioxide (NO\u003csub\u003e2\u003c/sub\u003e), sulfur dioxide (SO\u003csub\u003e2\u003c/sub\u003e), methane (CH\u003csub\u003e4\u003c/sub\u003e), ozone (O\u003csub\u003e3\u003c/sub\u003e), and UV aerosol index (AI) with the help of Google Earth Engine (GEE) algorithms. Analysis revealed distinct seasonal patterns, with the highest rainfall recorded during the monsoon season, predominantly in the southern region, and elevated LST values observed in the central region during pre-monsoon months. Furthermore, CO concentrations peaked (0.057 mol/m\u003csup\u003e2\u003c/sup\u003e) during the pre-monsoon season, particularly in industrial zones, while NO\u003csub\u003e2\u003c/sub\u003e levels were highest in the central region across all seasons. SO\u003csub\u003e2\u003c/sub\u003e concentrations exhibited spatial variability, with peaks (0.00204 mol/m\u003csup\u003e2\u003c/sup\u003e) in the post-monsoon period, primarily attributed to industrial activities. CH\u003csub\u003e4\u003c/sub\u003e concentrations were higher during pre-monsoon and post-monsoon seasons, with anomalies observed in 2023. O\u003csub\u003e3\u003c/sub\u003e levels showed a seasonal variation, with higher (0.1289 mol/m\u003csup\u003e2\u003c/sup\u003e) concentrations during pre-monsoon months, especially in the northern region. The UV aerosol index was highest during the monsoon season, attributed to increased moisture and biomass burning. Correlation analysis revealed associations between pollutants and environmental variables, indicating potential sources and interactions. These findings contribute to understanding regional air quality dynamics and informing targeted mitigation strategies for sustainable environmental management in Chhattisgarh.\u003c/p\u003e","manuscriptTitle":"Assessing climate variability and its correlation with regional air pollution in Chhattisgarh, India utilizing Google Earth Engine (GEE)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-19 09:31:00","doi":"10.21203/rs.3.rs-4544803/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"40f9eba8-4fff-42bc-a048-359233bd03bd","owner":[],"postedDate":"July 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-09-27T13:08:21+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-19 09:31:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4544803","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4544803","identity":"rs-4544803","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}