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. NO2 levels were consistently higher in the central region throughout the study period, indicating localized sources of NO2 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 NO2 dynamics and air quality patterns. During the monsoon season, the concentration of nitrogen dioxide (NO2) 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 NO2, across a larger vertical extent of the atmosphere. This dispersion process helps in diluting the concentration of NO2, 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 NO2 concentrations. The spatial distribution of NO2 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 NO2 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 (SO2) concentrations across different seasons. The analysis reveals notable variations in SO2 levels across the study period. During the post-monsoon season, SO2 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 SO2 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, SO2 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 SO2 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 SO2 across all seasons. This finding suggests that industrial and anthropogenic activities, rather than natural sources, are the primary contributors to SO2 emissions in the study area. Overall, the results indicate a consistent pattern of SO2 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 (CH4) concentrations across different seasons. The analysis reveals distinct patterns in CH4 levels, with varying concentrations observed during the monsoon, pre-monsoon, and post-monsoon periods. During the monsoon season, CH4 concentrations were notably lower compared to the pre-monsoon and post-monsoon seasons. This reduction in CH4 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 CH4 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 CH4 concentrations observed during this period. In contrast, both the pre-monsoon and post-monsoon seasons exhibit higher CH4 concentrations, particularly in the central region. The elevated CH4 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 CH4 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 (O3) concentrations across different seasons. The analysis reveals distinct patterns in O3 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 O3 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 O3 in the atmosphere, leading to higher concentrations during this period. In contrast, O3 concentrations were lower during the post-monsoon season. This decrease in O3 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 O3 formation, contributing to lower concentrations. Additionally, higher humidity levels during this period can lead to O3 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 O3 levels, with the northern region experiencing elevated concentrations during this season. Interestingly, the concentration of O3 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 O3 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 O3, CO and NO2 with values of 0.93, 0.38, and 0.26 and negatively correlated with CH4 and SO2. CO and NO2 (0.99) Indicate that high levels of CO are usually accompanied by high levels of NO2. O3 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 NO2 (-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. O3 and CH4 (-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).