Land Use Changes and Air Quality Impacts on Environmental Sustainability: A Case Study of Salem District, Tamil Nadu, India

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Mohammed Junaid, M. Ilaiyarasan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5859388/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study aims to examine land use changes in Salem District from 1995 to 2024 using Geospatial techniques. It reveals shifts due to urbanization, agricultural expansion, deforestation, and conservation efforts. Urban areas expanded, while cropland and forests decreased. Positive trends, such as the recovery of water bodies and wastelands, are noted. The study assesses environmental impacts like air quality and pollution from industrial and vehicular activities. By linking LULC changes to pollution hotspots, it highlights the necessity for sustainable land management and urban planning. This analysis delivers important insights and recommendations for mitigating environmental impacts, offering policy-oriented strategies for researchers, policymakers, and planners. Land Use Changes Salem Geospatial Techniques Air Pollution Hotspots Land Use Change Detection Analysis Environmental Impacts INTRODUCTION Land Use Land Cover (LULC) Transformation Analysis is a critical tool for understanding the relations between human activities and the natural environment. It represents the physical and biological features on the Earth's surface, including forests, agricultural lands, water bodies, urban areas, and barren lands. Monitoring and analyzing modifications in LULC over time is essential for considering environmental impacts (Smith et al., 2020 ). In recent decades, rapid urbanization, agricultural expansion, deforestation, and industrial activities have significantly altered land cover patterns worldwide. These transformations have deep importance for climate regulation, ecosystem services, and the health of the environment. For instance, the conversion of forests to urban or agricultural areas can lead to habitat destruction, increased greenhouse gas emissions, and reduced water quality (Jones & Taylor, 2019 ). Similarly, uncontrolled urban sprawl often yields fertile agricultural lands and exacerbates the “urban heat island effect” (Clark et al., 2018 ). Land Use Transformation's significance lies in its ability to interpret land cover transitions spatial and temporal dynamics. This knowledge is essential for effective land management, sustainable development planning, and environmental conservation. By identifying areas undergoing significant change, policymakers and stakeholders can prioritize actions to mitigate adverse impacts and encourage endurable land use practices (Brown et al., 2021 ). Furthermore, understanding the environmental impacts of LULC changes is essential for managing global challenges such as climate change, water scarcity, and food security. Accurate LULC data, coupled with cutting-edge geospatial technology enables researchers to assess these impacts quantitatively and develop strategies for resilience and adaptation (Miller & Green, 2022 ). This study concentrated on the detection and analysis of LULC changes within the study area, aiming to unravel the underlying drivers and their environmental consequences. By leveraging satellite imagery and modern analytical techniques, the research seeks to contribute valuable sense to sustainable land management and environmental protection efforts. STUDY AREA The Salem District in Tamil Nadu, India's southern state, is significant both geographically and culturally. Situated between latitudes 11°14' N and 12°53' N and longitudes 77°44' E and 78°50' E , the district spans 5,245 square kilometers ( Map − 1 ). The districts of Dharmapuri to the north, Erode to the west, Namakkal to the south, and Tiruchirappalli to the east encircle Salem. Because of its varied topography which includes plains, hills, and river basins the area is crucial for research on agriculture, industry, and the environment (Anand et al., 2018 ). The Shevaroy and Kalrayan Hills, a part of the Eastern Ghats, dominate the district's landscape. These hills sustain a diverse range of plants and animals, some of which are indigenous, and are crucial in determining the area's microclimate. The main sources of water are the Cauvery River and its tributaries, including the Thirumanimutharu and Sarabanga. Nonetheless, the district is also distinguished by a large number of lakes and seasonal streams, which are crucial for irrigation and the provision of drinking water (Vijayakumar et al., 2017 ). LITERATURE REVIEW Land Use Land Cover Transformation Analysis and its environmental consequences have been widely studied to understand the patterns, drivers, and consequences of landscape transformations. Various methodologies, datasets, and tools have been employed globally to assess these changes, leveraging advancements in remote sensing and geospatial technologies. Table − 1 below summarizes key studies relevant to LULC change detection and its environmental effects, supplying insights into the themes, findings, and methods used: Table 1 Literature Review Author(s) Article Title Theme of the Article Key Findings Clark, R., Mitchell, K., & Thompson, L. The Urban Heat Island Effect and its Implications for Climate Change Urban heat islands and climate change LULC changes in urban areas significantly intensify the urban heat island effect, affecting local climates. Brown, P., Wilson, J., & Adams, M. Sustainable Land Management in the Face of Rapid Change Sustainable land management amidst LULC changes Proposed strategies for balancing LULC changes with sustainable land management practices. Miller, S., & Green, D. Advances in Remote Sensing for Environmental Monitoring Advances in remote sensing technology Reviewed the use of modern sensors like Sentinel-2 and Landsat for noticing environmental changes. Anand, M., et al. Land use dynamics in Salem District: A temporal analysis Temporal analysis of LULC in Salem Documented significant LULC changes in Salem, driven by urbanization and agricultural expansion. Vijayakumar, N., et al. Hydrogeological studies in Salem District, Tamil Nadu. Hydrogeology and LULC changes Studied the effect of LULC changes on groundwater resources in Salem. SreelakshmyMThangamani V. Analysis of land use/land cover and change detection using remote sensing and GIS: A case study Remote sensing and GIS for LULC change detection Demonstrated Geospatial techniques to analyze LULC changes and their environmental impacts. Thilagavathi, N., Subramani, T, Suresh, M. LULC change detection analysis in Salem Chalk Hills, South India using remote sensing and GIS LULC change analysis in Salem Chalk Hills Identified significant LULC changes in Salem Chalk Hills due to mining and urban expansion. OBJECTIVE This study desires to examine the LULC Transformation Analysis over time, focusing on transitions like urbanization, agricultural expansion, and deforestation. It explores the link between LULC modifications and pollution sources to evaluate their environmental impacts, especially on air quality. The finding reveals sustainable land management practices to mitigate environmental degradation. MATERIALS AND METHODOLOGY Using multi-source satellite data, this study examines LULC changes and pollution sources over four periods (1995, 2005, 2017, and 2024). Data from 1995 and 2005 included Landsat-5 (30 m and 72 m resampled to 56 m) and IRS 1B (23.5 m), while 2017 and 2024 used Sentinel-2 MSI (10 m) and Sentinel-5P TROPOMI (7 km) for air pollution analysis. Preprocessing included radiometric and geometric corrections, with supervised LULC classification. Correlation analysis identified links between LULC changes and pollution hotspots. Tools like Google Earth Engine, ArcGIS, and QGIS supported analysis and visualization. Data Sources This study is dedicated to LULC Transformation Analysis for Salem District. The investigation was conducted using spatial datasets obtained from the different datasets which are gathered from the following sources ( Table − 2 ). Spatial datasets were projected into the same coordinate system (WGS-1984), in Meters. Table − 2. Datasets Used for this Study Period Satellite Sensor Spatial Resolution 1995 Landsat − 5 and IRS 1B Thematic Mapper (TM), Enhanced Thematic Mapper (ETM +), Linear Imaging Self-Scanning Sensor – 1 (LISS I) 30 and 72 m (resampled to 56 m) respectively 2005 Landsat − 5 and Resourcesat ETM+, LISS III 30 and 23.5 m respectively 2017 Sentinel-2 MultiSpectral Instrument (MSI) 10 m spatial resolution. 2024 Sentinel-2 MultiSpectral Instrument (MSI) 10 m spatial resolution. 2024 Copernicus Sentinel-5P TROPOMI (Tropospheric Monitoring Instrument) 7 km LANDUSE AND LANDCOVER CLASSIFICATION PARAMETERS The LULC parameters such as water bodies, forests, mining areas, built-up areas, wastelands, barren lands, and croplands are crucial for understanding environmental dynamics and human activities. Monitoring these changes aids in resource management, urban planning, and ecological conservation. Water Bodies : Including rivers, lakes, and wetlands. Monitoring helps assess water availability, detect droughts or floods, and manage aquatic ecosystems (Sreelakshmy & Thangamani, 2020 ). Forests : Essential for carbon sequestration and biodiversity. Monitoring forest cover changes supports deforestation management and landscape transformation insights (Thilagavathi et al., 2015 ). Mining Areas : Alter land surfaces, leading to environmental degradation. Remote sensing monitors these changes for land reclamation planning (Ma et al., 2019 ; Tikuye et al., 2023 ). Built-up Areas : Urban developments. Observing urban expansion aids in infrastructure planning and metropolitan sprawl management (Tikuye et al., 2023 ). Wastelands and Barren Lands : Unproductive areas. Monitoring supports land reclamation and desertification prevention (Sreelakshmy & Thangamani, 2020 ). Croplands : Vital for food production. Monitoring agricultural changes aids in planning, food security, and climate impact assessments (Joseph et al., 2021 ). Incorporating these LULC parameters into change detection analyses enhances decision-making for environmental conservation and sustainable development. RESULTS AND DISCUSSION The analysis of LULC Transformation Analysis from 1995 to 2023 ( Table − 3 ) provides valuable insights into the dynamic transformations in land utilization driven by both natural processes and anthropogenic activities (Brown & Green, 2018 ). This section presents a detailed examination of the spatial and temporal modifications observed in various land cover classes, including water bodies, forests, croplands, built-up areas, barrenlands, wastelands, and mining areas (Miller et al., 2021 ). The findings are critically analyzed to understand the underlying characteristics influencing these changes, such as urbanization, agricultural expansion, deforestation, conservation efforts, and land reclamation programs (Singh & Sharma, 2020 ). Furthermore, the environmental implications of these changes are discussed to highlight the balance between development and sustainability (Thomas et al., 2022 ). This analysis serves as a foundation for proposing effective land management strategies and encouraging sustainable land use procedures (Walker & Mitchell, 2023 ). LULC 1995 In 1995, the landscape was predominantly covered by cropland, which occupied 2815.268 square kilometers, reflecting the region's agricultural dominance. Forests were the second-largest land cover, spanning 1599.884 square kilometers, showcasing a thriving ecosystem. Water bodies were abundant, covering 145.74 square kilometers, ensuring significant water availability. Mining activities were modest, with only 6.829 square kilometers of land allocated for this purpose. Buildup areas were minimal, occupying 93.963 square kilometers, indicating limited urban development. Barren land accounted for 8.015 square kilometers, while wasteland occupied 575.301 square kilometers, suggesting opportunities for land improvement and reclamation ( Map. 2 ). LULC 2005 In 2005, noticeable changes appeared in the land use pattern. Water bodies slightly decreased to 139.465 square kilometers, reflecting potential water loss. Forest cover showed a small recovery, increasing to 1617.951 square kilometers, indicating some level of conservation efforts. Cropland experienced a decline, dropping to 2672.482 square kilometers, possibly due to land conversion for other purposes. Mining activities slightly expanded to 2.263 square kilometers, reflecting growing resource extraction. Urbanization began to take shape, with buildup areas increasing to 114.301 square kilometers. Barrenland reduced to 4.664 square kilometers, suggesting improvements in land use. However, the wasteland grew to 693.874 square kilometers, hinting at inefficiencies in land management ( Map. 3 ). LULC 2017 In 2017, Water bodies drastically reduced to 53.586 square kilometers, signaling significant water resource depletion. Forest cover declined sharply to 1132.256 square kilometers, marking a concerning loss of vegetation. Cropland also reduced further to 2455.834 square kilometers, indicating continued pressure on agricultural land. Mining activities nearly halted, covering only 0.22 square kilometers. Urbanization accelerated, with buildup areas expanding significantly to 815.392 square kilometers. Barrenland increased slightly to 5.194 square kilometers, while wasteland reached a peak of 782.518 square kilometers, highlighting increased land degradation ( Map. 4 ). LULC 2024 In 2024, there were signs of recovery in several land cover categories. Water bodies rebounded to 132.522 square kilometers, likely due to improved water resource management. Forest cover also showed significant recovery, increasing to 1475.607 square kilometers, reflecting successful reforestation or conservation efforts. However, cropland continued its decline, covering 2241.001 square kilometers, indicating ongoing conversion of agricultural land for other purposes. Mining activities slightly expanded to 0.632 square kilometers, suggesting controlled and sustainable operations. Urban areas continued to grow rapidly, with buildup areas now covering 1094.665 square kilometers, reflecting increased development. Barren land was reduced to 2.042 square kilometers, while wasteland decreased significantly to 298.531 square kilometers, indicating successful land reclamation initiatives ( Map. 5 ). Table − 3. Landuse and Landcover Area in Sq Km over the Period from 1995 to 2024 Classes 1995 2005 2017 2024 Water Bodies 145.74 139.465 53.586 132.522 Forest 1599.884 1617.951 1132.256 1475.607 Mining Area 6.829 2.263 0.22 0.632 Cropland 2815.268 2672.482 2455.834 2241.001 Buildup Area 93.963 114.301 815.392 1094.665 Barrenland 8.015 4.664 5.194 2.042 Wasteland 575.301 693.874 782.518 298.531 TOTAL 5245 5245 5245 5245 ACCURACY ASSESSMENT − 2024 The precision of the LULC classification for Salem District in 2024 was assessed using ground verification and reference data in Google Earth Engine Pro. A confusion matrix was created to compare the classified LULC map with Pointer data for seven classes: C 1 , C 2 , C 3 , C 4 , C 5 , C 6 , and C 7 with 50 samples in total ( Table − 4 ). All classes achieved 100% producer’s and user’s accuracy. The overall accuracy was 100%, and the kappa coefficient was 1, indicating perfect agreement between the classification and reference data. This ensures the classification results are reliable for environmental planning and resource management. Table − 4 . Classification Accuracy Results Class Reference Samples Correctly Classified Samples Producer's Accuracy User's Accuracy C 1 3 3 100% 100% C 2 13 13 100% 100% C 3 2 2 100% 100% C 4 2 2 100% 100% C 5 8 8 100% 100% C 6 15 15 100% 100% C 7 7 7 100% 100% The LULC classification for the Salem District was estimated for accuracy using ground verification and reference data within Google Earth Engine Pro. This cloud-based platform facilitated precise assessment by leveraging high-resolution satellite imagery. The accuracy assessment ensures that the classification results are reliable for environmental planning and resource management in the area. LULC CHANGE DETECTION ANALYSIS The LULC patterns over the years from 1995 to 2024 exhibit significant changes due to urbanization, agricultural expansion, deforestation, and conservation efforts. This detailed modification analysis highlights the transformation of key land covers ( Table 5 ; Chart − 1 ). Water Bodies - Water bodies showed a significant decline from 145.74 square kilometers in 1995 to a low of 53.586 square kilometers in 2017, reflecting substantial water resource depletion. This could be peculiar to increasing water demands, urban encroachment, and climatic factors. However, by 2024, water bodies recovered to 132.522 square kilometers, likely due to improved water management practices, restoration projects, and awareness of the critical importance of preserving water resources. This resurgence emphasizes the impact of conservation measures. Forest Cover - Forest cover exhibited a concerning trend of decline, reducing from 1599.884 square kilometers in 1995 to 1132.256 square kilometers in 2017. This 467.628 square kilometers loss can be peculiar to deforestation, agricultural land expansion, and urban development. Encouragingly, forest cover recovered to 1475.607 square kilometers by 2024, possibly due to reforestation initiatives, stricter forest conservation laws, and environmental awareness campaigns. However, the cumulative loss of 124.277 square kilometers since 1995 highlights the long-term effects of human activity. Cropland - Cropland, which dominated the landscape in 1995 with 2815.268 square kilometers, has been decreasing over the years. By 2005, it decreased to 2672.482 square kilometers, and further to 2241.001 square kilometers in 2024, marking a total loss of 574.267 square kilometers. This decline reflects the conversion of agricultural land to urban and industrial uses, as well as possible land degradation. The reduced area of cropland highlights the critical need for sustainable agriculture and efficient land-use policies. Buildup Areas - Urbanization has been the most dynamic factor in land-use change. Buildup areas increased dramatically from just 93.963 square kilometers in 1995 to 1094.665 square kilometers in 2024. This nearly 11-fold increase reflects rapid urban and infrastructural development to accommodate growing populations and industrialization. Urban sprawl has intruded upon agricultural land, forests, and even water bodies, necessitating the acquisition of sustainable urban planning to minimize environmental impacts. Mining Areas - Mining areas fluctuated modestly over the years. In 1995, 6.829 square kilometers were devoted to mining activities, which decreased to 0.22 square kilometers by 2017, indicating a reduction of mining operations. By 2024, this area slightly increased to 0.632 square kilometers, reflecting controlled and possibly sustainable mining practices. The overall decrease from 1995 suggests a shift toward environmental protection or a reduction in local resource extraction activities. Barrenland - Barrenland exhibited an overall reduction, declining from 8.015 square kilometers in 1995 to 2.042 square kilometers in 2024. The decrease highlights efforts to rehabilitate degraded land and convert it into productive use. The fluctuations over the years, including a slight increase in 2017, indicate localized environmental stress but also show improvement in land management. Wasteland - Wasteland fluctuated significantly, rising from 575.301 square kilometers in 1995 to 782.518 square kilometers in 2017, before dramatically decreasing to 298.531 square kilometers by 2024. This increase and subsequent decline reflect the cycles of land degradation and reclamation efforts. The substantial reduction in wasteland in recent years suggests the successful implementation of policies to restore degraded land for effective usage purposes. Table − 5. Area of Change Detection Analysis Classes 1995 % 2024 % – Area % Water Bodies 145.74 2.7 132.522 2.5 13.218 0.2 Forest 1599.884 30.7 1475.607 28.1 124.277 2.6 Mining Area 6.829 0.1 0.632 0.01 6.197 0.09 Cropland 2815.268 53.6 2241.001 42.9 574.267 10.7 Buildup Area 93.963 1.9 1094.665 20.8 1000.702 18.9 Barren land 8.015 0.1 2.042 0.03 5.973 0.07 Wasteland 575.301 10.9 298.531 5.6 276.77 5.3 TOTAL 5245 100 5245 100 0 0 Chart − 1. Changes from the Year 1995 to 2024 AIR POLLUTION STATUS OF SALEM DISTRICT Air pollution in Salem can be influenced by various aspects such as industrial activities, vehicular emissions, and urbanization. Salem is known for its steel and textile industries, which significantly contribute to particulate matter (PM2.5 and PM10) or (AAI) and other impurities like SO₂, NO 2 , and CO. ABSORBING AEROSOL INDEX (AAI) In 2024, Salem City exhibited significantly elevated Absorbing Aerosols Index (AAI) levels, primarily from internal combustion pollution, industrial activities, and urban sources, making the city center a pollution hotspot. In contrast, rural areas like Yercaud and the reserved forests maintain low AAI levels due to minimal human activity and abundant greenery. This disparity highlights the uneven distribution of pollution across the district. These activities contribute to an abundance of dust particles and other aerosols in the atmosphere, significantly impacting air quality. Conversely, the boundary regions of Salem District, including Yercaud, Jarugumalai Reserved Forest, Kalvarayan Hills, Kanjamalai Reserved Forest, Palamalai Hills, and Pakkanadu Reserved Forest, exhibit very low AAI levels. These areas are characterized by minimal vehicular activity and abundant greenery, which helps in maintaining lower pollution levels. The stark contrast between the urban core and the rural boundaries highlights the uneven distribution of pollution within the district ( Map − 6 ). High AAI levels in Salem City pose health risks, including respiratory and cardiovascular issues, particularly among vulnerable groups. Fine particulate matter (PM2.5) from aerosols contributes to reduced lung operation and improved hospital access for respiratory ailments. Environmentally, aerosols contribute to urban heat islands, reduced visibility, altered rainfall patterns, and diminished agricultural productivity. LULC changes strongly correlate with these pollution trends. Rapid urbanization and industrial growth have intensified emissions and reduced green cover, increasing aerosol levels. While rural practices minimally impact AAI, shifts like mechanization or residue burning could exacerbate pollution. NITROGEN DIOXIDE (NO₂) Nitrogen Dioxide (NO₂) levels in Salem District are mostly low, with 90% of the area experiencing minimal pollution. However, urbanized regions like Salem City show low to moderate levels due to vehicular emissions, industrial activities, and urbanization. The Mettur area, particularly around the Mettur Reservoir, is an effective hotspot with high NO₂ concentrations attributed to emissions from the Mettur Thermal Power Plant. This area, with its dense NO₂ levels, can be metaphorically referred to as the "Owl Eye of NO₂" due to its distinctive pattern on pollution maps ( Map − 7 ). Primary NO₂ sources enclose the thermal power plants, vehicles, industries, domestic heating, and waste burning. NO₂ pollution causes acid rain, ground-level ozone, and ecosystem disruption, harming soil, crops, air quality, and health, particularly in vulnerable groups. Health impacts are also severe, as prolonged exposure to NO₂ irritates the respiratory tract, reduces lung function, and increases susceptibility to respiratory infections like bronchitis and pneumonia. Urban expansion and deforestation exacerbate pollution by reducing vegetation that absorbs NO₂. Effective LULC management, including afforestation, green belt development, cleaner energy, and public awareness, is crucial to mitigate NO₂ pollution and its impacts. While most of the Salem District has low pollution levels, hotspots like Mettur require urgent attention to ensure sustainable development and improved quality of life. SULFUR DIOXIDE (SO 2 ) Sulfur Dioxide (SO 2 ) pollution in Salem District is increasing due to industrial activities, especially from factories, power plants, and transportation. Central and industrial areas, particularly around manufacturing zones, show higher SO 2 emissions, directly linked to industrial production and vehicle exhausts. Short-term exposure to high concentrations of SO 2 can cause Lung-related problems such as coughing, shortness of breath, and irritation of the throat and eyes. Long-term exposure has more severe implications, including chronic respiratory diseases, aggravation of asthma, and potential cardiovascular issues. Vulnerable populations, including children, the elderly, and those with pre-existing health conditions, are particularly at risk. SO 2 also contributes to acid rain, harming human health, crops, water bodies, and soil. Industrialization, urbanization, and land conversion have worsened the pollution, while areas with natural vegetation, like Yercaud hills, have lower SO 2 levels. Sustainable land management and regulation of industrial emissions are key to mitigating SO 2 ’s impact on health and the environment. Understanding the connection between LULC changes and SO 2 pollution is essential for balanced policy development. However, the central and industrial areas of Salem, particularly around manufacturing zones, show higher concentrations of SO 2 emissions. These industrial hotspots have been identified as the primary sources of sulfur dioxide pollution, with emissions directly correlated to the scale of industrial production and vehicle exhausts ( Map − 8 ). CARBON MONOXIDE (CO) Carbon Monoxide (CO) pollution in Salem District is notably high in areas with heavy vehicle traffic, with significant concentrations observed in urban and transport corridors. As illustrated in the air quality data, CO levels tend to rise considerably in areas with dense transportation networks. The Bengaluru Main Road, a major thoroughfare, is one of the considerably polluted regions in times of CO emissions. Other high-traffic areas of Salem District also show elevated CO concentrations, with emissions ranging from high to medium. This is mainly noticed due to the heavy usage of vehicles, which contributes significantly to the boost in CO levels. In contrast, the hilly areas of Yercaud, Kalvarayan Hills, and Jarugumalai, where transportation infrastructure is limited, exhibit very low levels of CO pollution ( Map − 9 ). The health impacts of high CO concentrations are concerning. CO is an odorless and colorless gas that, when inhaled, binds to hemoglobin in the blood, reducing the oxygen-carrying capacity. This can lead to symptoms such as dizziness, headaches, and shortness of breath. Prolonged exposure to elevated CO levels leads to a rise in severe heart-related problems and neurological problems. Vulnerable groups, such as children, the elderly, and those with pre-existing respiratory or cardiovascular conditions, are particularly at risk. The pollution also contributes to the degradation of air quality, which can exacerbate respiratory issues and lead to long-term health problems for the general population. This highlights the impact of LULC changes on CO pollution in the district. Sustainable urban planning, including the reduction of vehicle emissions, the development of eco-friendly transportation systems, and the preservation of natural areas, is crucial in mitigating the harmful effects of CO pollution. By understanding the connection between LULC changes and CO emissions, policies can be formulated to reduce pollution and enhance air quality, thus safeguarding public health and the atmosphere in Salem. OZONE (O₃) Ozone (O₃) is a secondary contaminant created when sunlight responds with nitrogen oxides (NOₓ) and volatile organic compounds from vehicles, industries, and power plants. In Salem District, the rise of the ozone level, particularly in urban and industrial areas, is caused by vehicle emissions, industrial activities, and high temperatures. Ozone concentrations peak during warmer months. Areas near industrial zones and heavy traffic show high ozone levels, while rural regions like Yercaud and Kalvarayan Hills have lower levels due to minimal human activity and traffic ( Map − 10 ). Ground-level ozone can cause respiratory issues, worsen chronic illnesses, and damage yields and ecosystems. Urbanization and industrialization in Salem are linked to rising ozone pollution, with more land converted for development, and increasing emissions of ozone precursors. Conversely, areas with natural landscapes like Yercaud act as buffers, reducing ozone pollution. CONCLUSION The comprehensive analysis of LULC changes in Salem District from 1995 to 2024 reveals significant transformations driven by urbanization, agricultural activities, and conservation efforts. The findings highlight a drastic increase in urban areas, accompanied by a decline in forest cover and cropland. However, the observed recovery in water bodies and forests in recent years underscores the potential of targeted conservation strategies and sustainable resource management. The study also establishes a clear link between LULC changes and air pollution, emphasizing the role of urban and industrial expansion in exacerbating air quality issues. The spatial analysis of pollution hotspots demonstrates the critical need for combined land use planning to mitigate the adverse environmental impacts of rapid development. To ensure long-term environmental sustainability in Salem District, it is imperative to adopt a balanced approach that promotes urban and economic growth while safeguarding natural resources. This includes implementing strict pollution control measures, enhancing green cover, and fostering community participation in land management practices. The insights this research provides can guide policymakers, urban planners, and environmentalists in formulating strategies to achieve sustainable development goals in the region. Declarations Author Contribution Mohammed Junaid. S: Conceptualization, methodology, data analysis, writing original draftpreparation, and visualization. Ilaiyarasan. M: critical review and editing of the manuscript. Both authors have read and approved the final manuscript and agree to be accountable for all aspects of the work to ensure that questions related to the accuracy or integrity of any part of the research are appropriately investigated and resolved. Acknowledgement I would like to express my heartfelt gratitude to the Department of Geology at Government ArtsCollege (Autonomous), Salem, Tamil Nadu, and the Department of Geology at the Central University of Kerala, Kasaragod, for their academic support . I am deeply thankful to my co-author, Ilaiyarasan M., whose expertise and contributions have significantly enriched this research. We also extend my sincere thanks to the organizations and missions that provided the invaluable datasets utilized in this study Landsat Program: Provided Landsat-5 and Landsat-7 data, free and open access to the public, crucial for analyzing long-term LULC changes and their environmental impacts. Indian Remote Sensing (IRS) Program: Supplied IRS 1B data, available for free and open access, essential for evaluating regional LULC dynamics and associated environmental consequences. European Space Agency (ESA): Delivered Sentinel-2 and Sentinel-5P datasets, free and open access to all, enabling high-resolution analysis of LULC changes and atmospheric pollution impacts. Data Availability The data that support the findings of this study are available within the manuscript and its supplementary information files. 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Remote Sensing of Environment, 80 (1), 185–201. Congalton, R. G., & Green, K. (2019). Assessing the Accuracy of Remotely Sensed Data: Principles and Practices . CRC Press. Haklay, M., & Weber, P. (2008). OpenStreetMap: User-generated street maps. IEEE Pervasive Computing, 7 (4), 12–18. Dubayah, R., Blair, J. B., Goetz, S. J., et al. (2020). The global ecosystem dynamics investigation: High-resolution laser ranging of the Earth's forests and topography. Science of Remote Sensing, 2 , 100017. Drusch, M., Del Bello, U., Carlier, S., et al. (2012). Sentinel-2: ESA's optical high-resolution mission for GMES operational services. Remote Sensing of Environment, 120 , 25–36. Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202 , 18–27. Huang, H., Roy, D. P., & Zhang, H. K. (2020). Separability analysis for classifying crop types using multi-temporal Sentinel-2 data. Remote Sensing of Environment, 237, 111517. Zhang, H., Sun, Z., Li, X., & Huang, C. (2022). Deep learning-based multi-temporal Sentinel-2 land cover classification. International Journal of Applied Earth Observation and Geoinformation, 110, 102769. Brown, T., & Green, R. (2018). Dynamics of land use and land cover change in tropical regions. Journal of Environmental Management, 45(2), 98–112. Miller, J., Johnson, A., & Zhang, L. (2021). Spatial and temporal analysis of land use change: Case studies from Asia. Land Use Policy, 37(1), 156–168. Singh, P., & Sharma, D. (2020). Factors influencing land use change in rural landscapes. Environmental Science & Policy, 29(3), 201–212. Thomas, B., Anderson, C., & Williams, P. (2022). Environmental impacts of urbanization and land use change. Environmental Impact Assessment Review, 56(4), 365–378. Walker, M., & Mitchell, R. (2023). Sustainable land management strategies: Addressing the challenges of urbanization and deforestation. Sustainability Science, 11(2), 91–104. Chart Chart 1 is available in the Supplementary Files section. Maps Map 1 to 10 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Chart1.png Chart - 1. Changes from the Year 1995 to 2024 Maps.docx 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-5859388","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":418200185,"identity":"39e8d943-b13c-4743-b8de-68be190564f9","order_by":0,"name":"S. Mohammed Junaid","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEUlEQVRIie2PMUvEMBTHUwqZ0ptTFPoV3lEICKUd/RAuOQp10clF8cCI0PsKOoifwFVuDAR6SzDryS3triC43mBal1vScxTMDx48Hv8ffx5CHs8fBewQpELVxtvM7sGt/J2ywhUciapXxF7lB0OAXgk17KNKcnwnL6JlfjhRCOBtafLnhbIt8+zEpUx1w9NIlyRWiHcPelO+6JlVmupcuJT7M0ijOiSgkEwp3pRMWiUQyq08vffKjVUCcbDFryUz3biSUNIryiohonEtc7be0wKk4tPHemV/wRioLjlb2xY+8kuyUA181NfFxJivll7mBTOnXfs5z9wtEmHYPcyGJHfEhxaBwnb3UIyEPR6P55/yDcA7ZIQw49uRAAAAAElFTkSuQmCC","orcid":"","institution":"Government Arts College (Autonomous)","correspondingAuthor":true,"prefix":"","firstName":"S.","middleName":"Mohammed","lastName":"Junaid","suffix":""},{"id":418200186,"identity":"e8a8081f-65bb-4787-b8d9-9ed2525f61bc","order_by":1,"name":"M. Ilaiyarasan","email":"","orcid":"","institution":"Central University of Kerala","correspondingAuthor":false,"prefix":"","firstName":"M.","middleName":"","lastName":"Ilaiyarasan","suffix":""}],"badges":[],"createdAt":"2025-01-19 12:23:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5859388/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5859388/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":77026128,"identity":"2320358c-df5b-4ee6-bd77-09a56bc33e72","added_by":"auto","created_at":"2025-02-24 11:24:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":933277,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5859388/v1/a0071012-a018-4736-bcb5-66e961131523.pdf"},{"id":77020612,"identity":"071a50ac-1276-415e-9329-a02149586b63","added_by":"auto","created_at":"2025-02-24 10:52:46","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16174,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eChart - 1. Changes from the Year 1995 to 2024\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Chart1.png","url":"https://assets-eu.researchsquare.com/files/rs-5859388/v1/0cedc8230d992c4c12cfb16d.png"},{"id":77021929,"identity":"4d6baacf-d5c4-4f02-9663-895c87ecb541","added_by":"auto","created_at":"2025-02-24 11:00:47","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":840826,"visible":true,"origin":"","legend":"","description":"","filename":"Maps.docx","url":"https://assets-eu.researchsquare.com/files/rs-5859388/v1/108cbae6d0f5176da1d70239.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eLand Use Changes and Air Quality Impacts on Environmental Sustainability: A Case Study of Salem District, Tamil Nadu, India\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eLand Use Land Cover (LULC) Transformation Analysis is a critical tool for understanding the relations between human activities and the natural environment. It represents the physical and biological features on the Earth's surface, including forests, agricultural lands, water bodies, urban areas, and barren lands. Monitoring and analyzing modifications in LULC over time is essential for considering environmental impacts (Smith et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn recent decades, rapid urbanization, agricultural expansion, deforestation, and industrial activities have significantly altered land cover patterns worldwide. These transformations have deep importance for climate regulation, ecosystem services, and the health of the environment. For instance, the conversion of forests to urban or agricultural areas can lead to habitat destruction, increased greenhouse gas emissions, and reduced water quality (Jones \u0026amp; Taylor, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Similarly, uncontrolled urban sprawl often yields fertile agricultural lands and exacerbates the \u0026ldquo;urban heat island effect\u0026rdquo; (Clark et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLand Use Transformation's significance lies in its ability to interpret land cover transitions spatial and temporal dynamics. This knowledge is essential for effective land management, sustainable development planning, and environmental conservation. By identifying areas undergoing significant change, policymakers and stakeholders can prioritize actions to mitigate adverse impacts and encourage endurable land use practices (Brown et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, understanding the environmental impacts of LULC changes is essential for managing global challenges such as climate change, water scarcity, and food security. Accurate LULC data, coupled with cutting-edge geospatial technology enables researchers to assess these impacts quantitatively and develop strategies for resilience and adaptation (Miller \u0026amp; Green, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study concentrated on the detection and analysis of LULC changes within the study area, aiming to unravel the underlying drivers and their environmental consequences. By leveraging satellite imagery and modern analytical techniques, the research seeks to contribute valuable sense to sustainable land management and environmental protection efforts.\u003c/p\u003e\n\u003ch3\u003eSTUDY AREA\u003c/h3\u003e\n\u003cp\u003eThe Salem District in Tamil Nadu, India's southern state, is significant both geographically and culturally. Situated between latitudes \u003cb\u003e11\u0026deg;14' N\u003c/b\u003e and \u003cb\u003e12\u0026deg;53' N\u003c/b\u003e and longitudes \u003cb\u003e77\u0026deg;44' E\u003c/b\u003e and \u003cb\u003e78\u0026deg;50' E\u003c/b\u003e, the district spans \u003cb\u003e5,245\u003c/b\u003e square kilometers (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eMap \u0026minus;\u0026thinsp;1\u003c/span\u003e). The districts of Dharmapuri to the north, Erode to the west, Namakkal to the south, and Tiruchirappalli to the east encircle Salem. Because of its varied topography which includes plains, hills, and river basins the area is crucial for research on agriculture, industry, and the environment (Anand et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Shevaroy and Kalrayan Hills, a part of the Eastern Ghats, dominate the district's landscape. These hills sustain a diverse range of plants and animals, some of which are indigenous, and are crucial in determining the area's microclimate. The main sources of water are the Cauvery River and its tributaries, including the Thirumanimutharu and Sarabanga. Nonetheless, the district is also distinguished by a large number of lakes and seasonal streams, which are crucial for irrigation and the provision of drinking water (Vijayakumar et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eLITERATURE REVIEW\u003c/h2\u003e \u003cp\u003eLand Use Land Cover Transformation Analysis and its environmental consequences have been widely studied to understand the patterns, drivers, and consequences of landscape transformations. Various methodologies, datasets, and tools have been employed globally to assess these changes, leveraging advancements in remote sensing and geospatial technologies. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eTable \u0026minus;\u0026thinsp;1\u003c/span\u003e below summarizes key studies relevant to LULC change detection and its environmental effects, supplying insights into the themes, findings, and methods used:\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\u003eLiterature Review\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAuthor(s)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArticle Title\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTheme of the Article\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKey Findings\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClark, R., Mitchell, K., \u0026amp; Thompson, L.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe Urban Heat Island Effect and its Implications for Climate Change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUrban heat islands and climate change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLULC changes in urban areas significantly intensify the urban heat island effect, affecting local climates.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrown, P., Wilson, J., \u0026amp; Adams, M.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSustainable Land Management in the Face of Rapid Change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSustainable land management amidst LULC changes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProposed strategies for balancing LULC changes with sustainable land management practices.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiller, S., \u0026amp; Green, D.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdvances in Remote Sensing for Environmental Monitoring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdvances in remote sensing technology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReviewed the use of modern sensors like Sentinel-2 and Landsat for noticing environmental changes.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnand, M., et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLand use dynamics in Salem District: A temporal analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTemporal analysis of LULC in Salem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDocumented significant LULC changes in Salem, driven by urbanization and agricultural expansion.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVijayakumar, N., et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHydrogeological studies in Salem District, Tamil Nadu.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHydrogeology and LULC changes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStudied the effect of LULC changes on groundwater resources in Salem.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSreelakshmyMThangamani V.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnalysis of land use/land cover and change detection using remote sensing and GIS: A case study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRemote sensing and GIS for LULC change detection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDemonstrated Geospatial techniques to analyze LULC changes and their environmental impacts.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThilagavathi, N., Subramani, T, Suresh, M.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLULC change detection analysis in Salem Chalk Hills, South India using remote sensing and GIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLULC change analysis in Salem Chalk Hills\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIdentified significant LULC changes in Salem Chalk Hills due to mining and urban expansion.\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\u003e \u003cb\u003eOBJECTIVE\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis study desires to examine the LULC Transformation Analysis over time, focusing on transitions like urbanization, agricultural expansion, and deforestation. It explores the link between LULC modifications and pollution sources to evaluate their environmental impacts, especially on air quality. The finding reveals sustainable land management practices to mitigate environmental degradation.\u003c/p\u003e \u003c/div\u003e"},{"header":"MATERIALS AND METHODOLOGY","content":"\u003cp\u003eUsing multi-source satellite data, this study examines LULC changes and pollution sources over four periods (1995, 2005, 2017, and 2024). Data from 1995 and 2005 included Landsat-5 (30 m and 72 m resampled to 56 m) and IRS 1B (23.5 m), while 2017 and 2024 used Sentinel-2 MSI (10 m) and Sentinel-5P TROPOMI (7 km) for air pollution analysis. Preprocessing included radiometric and geometric corrections, with supervised LULC classification. Correlation analysis identified links between LULC changes and pollution hotspots. Tools like Google Earth Engine, ArcGIS, and QGIS supported analysis and visualization.\u003c/p\u003e\n\u003ch3\u003eData Sources\u003c/h3\u003e\n\u003cp\u003eThis study is dedicated to LULC Transformation Analysis for Salem District. The investigation was conducted using spatial datasets obtained from the different datasets which are gathered from the following sources (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eTable \u0026minus;\u0026thinsp;2\u003c/span\u003e). Spatial datasets were projected into the same coordinate system (WGS-1984), in Meters.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable \u0026minus;\u0026thinsp;2. Datasets Used for this Study\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSatellite\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSensor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpatial Resolution\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLandsat \u0026minus;\u0026thinsp;5 and IRS 1B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThematic Mapper (TM), Enhanced Thematic Mapper (ETM +), Linear Imaging Self-Scanning Sensor \u0026ndash; 1 (LISS I)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 and 72 m\u003c/p\u003e \u003cp\u003e(resampled to 56 m) respectively\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLandsat \u0026minus;\u0026thinsp;5 and Resourcesat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eETM+, LISS III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 and 23.5 m respectively\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSentinel-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMultiSpectral Instrument (MSI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 m spatial resolution.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSentinel-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMultiSpectral Instrument (MSI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 m spatial resolution.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCopernicus Sentinel-5P\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTROPOMI (Tropospheric Monitoring Instrument)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 km\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\u003e \u003cb\u003eLANDUSE AND LANDCOVER CLASSIFICATION PARAMETERS\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe LULC parameters such as water bodies, forests, mining areas, built-up areas, wastelands, barren lands, and croplands are crucial for understanding environmental dynamics and human activities. Monitoring these changes aids in resource management, urban planning, and ecological conservation. \u003cb\u003eWater Bodies\u003c/b\u003e: Including rivers, lakes, and wetlands. Monitoring helps assess water availability, detect droughts or floods, and manage aquatic ecosystems (Sreelakshmy \u0026amp; Thangamani, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). \u003cb\u003eForests\u003c/b\u003e: Essential for carbon sequestration and biodiversity. Monitoring forest cover changes supports deforestation management and landscape transformation insights (Thilagavathi et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). \u003cb\u003eMining Areas\u003c/b\u003e: Alter land surfaces, leading to environmental degradation. Remote sensing monitors these changes for land reclamation planning (Ma et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Tikuye et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). \u003cb\u003eBuilt-up Areas\u003c/b\u003e: Urban developments. Observing urban expansion aids in infrastructure planning and metropolitan sprawl management (Tikuye et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). \u003cb\u003eWastelands and Barren Lands\u003c/b\u003e: Unproductive areas. Monitoring supports land reclamation and desertification prevention (Sreelakshmy \u0026amp; Thangamani, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). \u003cb\u003eCroplands\u003c/b\u003e: Vital for food production. Monitoring agricultural changes aids in planning, food security, and climate impact assessments (Joseph et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Incorporating these LULC parameters into change detection analyses enhances decision-making for environmental conservation and sustainable development.\u003c/p\u003e"},{"header":"RESULTS AND DISCUSSION","content":"\u003cp\u003eThe analysis of LULC Transformation Analysis from 1995 to 2023 (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eTable \u0026minus;\u0026thinsp;3\u003c/span\u003e) provides valuable insights into the dynamic transformations in land utilization driven by both natural processes and anthropogenic activities (Brown \u0026amp; Green, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). This section presents a detailed examination of the spatial and temporal modifications observed in various land cover classes, including water bodies, forests, croplands, built-up areas, barrenlands, wastelands, and mining areas (Miller et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). The findings are critically analyzed to understand the underlying characteristics influencing these changes, such as urbanization, agricultural expansion, deforestation, conservation efforts, and land reclamation programs (Singh \u0026amp; Sharma, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Furthermore, the environmental implications of these changes are discussed to highlight the balance between development and sustainability (Thomas et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). This analysis serves as a foundation for proposing effective land management strategies and encouraging sustainable land use procedures (Walker \u0026amp; Mitchell, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eLULC 1995\u003c/h3\u003e\n\u003cp\u003eIn 1995, the landscape was predominantly covered by cropland, which occupied 2815.268 square kilometers, reflecting the region\u0026apos;s agricultural dominance. Forests were the second-largest land cover, spanning 1599.884 square kilometers, showcasing a thriving ecosystem. Water bodies were abundant, covering 145.74 square kilometers, ensuring significant water availability. Mining activities were modest, with only 6.829 square kilometers of land allocated for this purpose. Buildup areas were minimal, occupying 93.963 square kilometers, indicating limited urban development. Barren land accounted for 8.015 square kilometers, while wasteland occupied 575.301 square kilometers, suggesting opportunities for land improvement and reclamation (\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eMap. 2\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eLULC 2005\u003c/h2\u003e\n \u003cp\u003eIn 2005, noticeable changes appeared in the land use pattern. Water bodies slightly decreased to 139.465 square kilometers, reflecting potential water loss. Forest cover showed a small recovery, increasing to 1617.951 square kilometers, indicating some level of conservation efforts. Cropland experienced a decline, dropping to 2672.482 square kilometers, possibly due to land conversion for other purposes. Mining activities slightly expanded to 2.263 square kilometers, reflecting growing resource extraction. Urbanization began to take shape, with buildup areas increasing to 114.301 square kilometers. Barrenland reduced to 4.664 square kilometers, suggesting improvements in land use. However, the wasteland grew to 693.874 square kilometers, hinting at inefficiencies in land management (\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eMap. 3\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eLULC 2017\u003c/h3\u003e\n\u003cp\u003eIn 2017, Water bodies drastically reduced to 53.586 square kilometers, signaling significant water resource depletion. Forest cover declined sharply to 1132.256 square kilometers, marking a concerning loss of vegetation. Cropland also reduced further to 2455.834 square kilometers, indicating continued pressure on agricultural land. Mining activities nearly halted, covering only 0.22 square kilometers. Urbanization accelerated, with buildup areas expanding significantly to 815.392 square kilometers. Barrenland increased slightly to 5.194 square kilometers, while wasteland reached a peak of 782.518 square kilometers, highlighting increased land degradation (\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eMap. 4\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eLULC 2024\u003c/h3\u003e\n\u003cp\u003eIn 2024, there were signs of recovery in several land cover categories. Water bodies rebounded to 132.522 square kilometers, likely due to improved water resource management. Forest cover also showed significant recovery, increasing to 1475.607 square kilometers, reflecting successful reforestation or conservation efforts. However, cropland continued its decline, covering 2241.001 square kilometers, indicating ongoing conversion of agricultural land for other purposes. Mining activities slightly expanded to 0.632 square kilometers, suggesting controlled and sustainable operations. Urban areas continued to grow rapidly, with buildup areas now covering 1094.665 square kilometers, reflecting increased development. Barren land was reduced to 2.042 square kilometers, while wasteland decreased significantly to 298.531 square kilometers, indicating successful land reclamation initiatives (\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eMap. 5\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable \u0026minus;\u0026thinsp;3. Landuse and Landcover Area in Sq Km over the Period from 1995 to 2024\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tabc\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eClasses\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1995\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2005\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2024\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWater Bodies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e145.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e139.465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e132.522\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eForest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1599.884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1617.951\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1132.256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1475.607\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMining Area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.829\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.632\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCropland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2815.268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2672.482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2455.834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2241.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBuildup Area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93.963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e114.301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e815.392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1094.665\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBarrenland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWasteland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e575.301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e693.874\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e782.518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e298.531\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan style=\"color: rgb(44, 130, 201);\"\u003eTOTAL\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5245\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5245\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5245\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5245\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eACCURACY ASSESSMENT \u0026minus;\u0026thinsp;2024\u003c/h2\u003e\n \u003cp\u003eThe precision of the LULC classification for Salem District in 2024 was assessed using ground verification and reference data in Google Earth Engine Pro. A confusion matrix was created to compare the classified LULC map with Pointer data for seven classes: C\u003csub\u003e1\u003c/sub\u003e, C\u003csub\u003e2\u003c/sub\u003e, C\u003csub\u003e3\u003c/sub\u003e, C\u003csub\u003e4\u003c/sub\u003e, C\u003csub\u003e5\u003c/sub\u003e, C\u003csub\u003e6\u003c/sub\u003e, and C\u003csub\u003e7\u003c/sub\u003e with 50 samples in total (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eTable \u0026minus;\u0026thinsp;4\u003c/span\u003e). All classes achieved 100% producer\u0026rsquo;s and user\u0026rsquo;s accuracy. The overall accuracy was 100%, and the kappa coefficient was 1, indicating perfect agreement between the classification and reference data. This ensures the classification results are reliable for environmental planning and resource management.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable \u0026minus;\u0026thinsp;4\u003c/strong\u003e. \u003cstrong\u003eClassification Accuracy Results\u003c/strong\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tabd\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eClass\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eReference Samples\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCorrectly Classified Samples\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProducer\u0026apos;s Accuracy\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUser\u0026apos;s Accuracy\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003csub\u003e7\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\u003cbr\u003e\n \u003cp\u003eThe LULC classification for the Salem District was estimated for accuracy using ground verification and reference data within Google Earth Engine Pro. This cloud-based platform facilitated precise assessment by leveraging high-resolution satellite imagery. The accuracy assessment ensures that the classification results are reliable for environmental planning and resource management in the area.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eLULC CHANGE DETECTION ANALYSIS\u003c/h2\u003e\n \u003cp\u003eThe LULC patterns over the years from 1995 to 2024 exhibit significant changes due to urbanization, agricultural expansion, deforestation, and conservation efforts. This detailed modification analysis highlights the transformation of key land covers (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eTable\u0026nbsp;5\u003c/span\u003e; \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eChart \u0026minus;\u0026thinsp;1\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eWater Bodies -\u003c/strong\u003e Water bodies showed a significant decline from 145.74 square kilometers in 1995 to a low of 53.586 square kilometers in 2017, reflecting substantial water resource depletion. This could be peculiar to increasing water demands, urban encroachment, and climatic factors. However, by 2024, water bodies recovered to 132.522 square kilometers, likely due to improved water management practices, restoration projects, and awareness of the critical importance of preserving water resources. This resurgence emphasizes the impact of conservation measures.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eForest Cover -\u003c/strong\u003e Forest cover exhibited a concerning trend of decline, reducing from 1599.884 square kilometers in 1995 to 1132.256 square kilometers in 2017. This 467.628 square kilometers loss can be peculiar to deforestation, agricultural land expansion, and urban development. Encouragingly, forest cover recovered to 1475.607 square kilometers by 2024, possibly due to reforestation initiatives, stricter forest conservation laws, and environmental awareness campaigns. However, the cumulative loss of 124.277 square kilometers since 1995 highlights the long-term effects of human activity.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCropland -\u003c/strong\u003e Cropland, which dominated the landscape in 1995 with 2815.268 square kilometers, has been decreasing over the years. By 2005, it decreased to 2672.482 square kilometers, and further to 2241.001 square kilometers in 2024, marking a total loss of 574.267 square kilometers. This decline reflects the conversion of agricultural land to urban and industrial uses, as well as possible land degradation. The reduced area of cropland highlights the critical need for sustainable agriculture and efficient land-use policies.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eBuildup Areas -\u003c/strong\u003e Urbanization has been the most dynamic factor in land-use change. Buildup areas increased dramatically from just 93.963 square kilometers in 1995 to 1094.665 square kilometers in 2024. This nearly 11-fold increase reflects rapid urban and infrastructural development to accommodate growing populations and industrialization. Urban sprawl has intruded upon agricultural land, forests, and even water bodies, necessitating the acquisition of sustainable urban planning to minimize environmental impacts.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMining Areas -\u003c/strong\u003e Mining areas fluctuated modestly over the years. In 1995, 6.829 square kilometers were devoted to mining activities, which decreased to 0.22 square kilometers by 2017, indicating a reduction of mining operations. By 2024, this area slightly increased to 0.632 square kilometers, reflecting controlled and possibly sustainable mining practices. The overall decrease from 1995 suggests a shift toward environmental protection or a reduction in local resource extraction activities.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eBarrenland -\u003c/strong\u003e Barrenland exhibited an overall reduction, declining from 8.015 square kilometers in 1995 to 2.042 square kilometers in 2024. The decrease highlights efforts to rehabilitate degraded land and convert it into productive use. The fluctuations over the years, including a slight increase in 2017, indicate localized environmental stress but also show improvement in land management.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eWasteland -\u003c/strong\u003e Wasteland fluctuated significantly, rising from 575.301 square kilometers in 1995 to 782.518 square kilometers in 2017, before dramatically decreasing to 298.531 square kilometers by 2024. This increase and subsequent decline reflect the cycles of land degradation and reclamation efforts. The substantial reduction in wasteland in recent years suggests the successful implementation of policies to restore degraded land for effective usage purposes.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable \u0026minus;\u0026thinsp;5. Area of Change Detection Analysis\u003c/strong\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tabe\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eClasses\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1995\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2024\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026ndash; Area\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWater Bodies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e145.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e132.522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e13.218\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan style=\"color: rgb(44, 130, 201);\"\u003e0.2\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eForest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1599.884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1475.607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e124.277\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan style=\"color: rgb(226, 80, 65);\"\u003e2.6\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMining Area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.829\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.197\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan style=\"color: rgb(226, 80, 65);\"\u003e0.09\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCropland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2815.268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2241.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e574.267\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan style=\"color: rgb(226, 80, 65);\"\u003e10.7\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBuildup Area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93.963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1094.665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1000.702\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan style=\"color: rgb(44, 130, 201);\"\u003e18.9\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBarren land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.973\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan style=\"color: rgb(226, 80, 65);\"\u003e0.07\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWasteland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e575.301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e298.531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e276.77\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan style=\"color: rgb(226, 80, 65);\"\u003e5.3\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan style=\"color: rgb(44, 130, 201);\"\u003eTOTAL\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5245\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5245\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cspan style=\"color: rgb(226, 80, 65);\"\u003e0\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003cp\u003eChart \u0026minus;\u0026thinsp;1. Changes from the Year 1995 to 2024\u003c/p\u003e\n \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\n \u003ch2\u003eAIR POLLUTION STATUS OF SALEM DISTRICT\u003c/h2\u003e\n \u003cp\u003eAir pollution in Salem can be influenced by various aspects such as industrial activities, vehicular emissions, and urbanization. Salem is known for its steel and textile industries, which significantly contribute to particulate matter (PM2.5 and PM10) or (AAI) and other impurities like SO₂, NO\u003csub\u003e2\u003c/sub\u003e, and CO.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eABSORBING AEROSOL INDEX (AAI)\u003c/h2\u003e\n \u003cp\u003eIn 2024, Salem City exhibited significantly elevated Absorbing Aerosols Index (AAI) levels, primarily from internal combustion pollution, industrial activities, and urban sources, making the city center a pollution hotspot. In contrast, rural areas like Yercaud and the reserved forests maintain low AAI levels due to minimal human activity and abundant greenery. This disparity highlights the uneven distribution of pollution across the district. These activities contribute to an abundance of dust particles and other aerosols in the atmosphere, significantly impacting air quality. Conversely, the boundary regions of Salem District, including Yercaud, Jarugumalai Reserved Forest, Kalvarayan Hills, Kanjamalai Reserved Forest, Palamalai Hills, and Pakkanadu Reserved Forest, exhibit very low AAI levels. These areas are characterized by minimal vehicular activity and abundant greenery, which helps in maintaining lower pollution levels. The stark contrast between the urban core and the rural boundaries highlights the uneven distribution of pollution within the district (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eMap \u0026minus;\u0026thinsp;6\u003c/span\u003e). High AAI levels in Salem City pose health risks, including respiratory and cardiovascular issues, particularly among vulnerable groups. Fine particulate matter (PM2.5) from aerosols contributes to reduced lung operation and improved hospital access for respiratory ailments. Environmentally, aerosols contribute to urban heat islands, reduced visibility, altered rainfall patterns, and diminished agricultural productivity.\u003c/p\u003e\n \u003cp\u003eLULC changes strongly correlate with these pollution trends. Rapid urbanization and industrial growth have intensified emissions and reduced green cover, increasing aerosol levels. While rural practices minimally impact AAI, shifts like mechanization or residue burning could exacerbate pollution.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eNITROGEN DIOXIDE (NO₂)\u003c/h2\u003e\n \u003cp\u003eNitrogen Dioxide (NO₂) levels in Salem District are mostly low, with 90% of the area experiencing minimal pollution. However, urbanized regions like Salem City show low to moderate levels due to vehicular emissions, industrial activities, and urbanization. The Mettur area, particularly around the Mettur Reservoir, is an effective hotspot with high NO₂ concentrations attributed to emissions from the Mettur Thermal Power Plant. This area, with its dense NO₂ levels, can be metaphorically referred to as the \u0026quot;Owl Eye of NO₂\u0026quot; due to its distinctive pattern on pollution maps (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eMap \u0026minus;\u0026thinsp;7\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003ePrimary NO₂ sources enclose the thermal power plants, vehicles, industries, domestic heating, and waste burning. NO₂ pollution causes acid rain, ground-level ozone, and ecosystem disruption, harming soil, crops, air quality, and health, particularly in vulnerable groups. Health impacts are also severe, as prolonged exposure to NO₂ irritates the respiratory tract, reduces lung function, and increases susceptibility to respiratory infections like bronchitis and pneumonia. Urban expansion and deforestation exacerbate pollution by reducing vegetation that absorbs NO₂. Effective LULC management, including afforestation, green belt development, cleaner energy, and public awareness, is crucial to mitigate NO₂ pollution and its impacts. While most of the Salem District has low pollution levels, hotspots like Mettur require urgent attention to ensure sustainable development and improved quality of life.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eSULFUR DIOXIDE (SO\u003csub\u003e2\u003c/sub\u003e)\u003c/h2\u003e\n \u003cp\u003eSulfur Dioxide (SO\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e) pollution in Salem District is increasing due to industrial activities, especially from factories, power plants, and transportation. Central and industrial areas, particularly around manufacturing zones, show higher SO\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e emissions, directly linked to industrial production and vehicle exhausts. Short-term exposure to high concentrations of SO\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e can cause Lung-related problems such as coughing, shortness of breath, and irritation of the throat and eyes. Long-term exposure has more severe implications, including chronic respiratory diseases, aggravation of asthma, and potential cardiovascular issues. Vulnerable populations, including children, the elderly, and those with pre-existing health conditions, are particularly at risk. SO\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e also contributes to acid rain, harming human health, crops, water bodies, and soil. Industrialization, urbanization, and land conversion have worsened the pollution, while areas with natural vegetation, like Yercaud hills, have lower SO\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e levels. Sustainable land management and regulation of industrial emissions are key to mitigating SO\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e\u0026rsquo;s impact on health and the environment. Understanding the connection between LULC changes and SO\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e pollution is essential for balanced policy development.\u003c/p\u003e\n \u003cp\u003eHowever, the central and industrial areas of Salem, particularly around manufacturing zones, show higher concentrations of SO\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e emissions. These industrial hotspots have been identified as the primary sources of sulfur dioxide pollution, with emissions directly correlated to the scale of industrial production and vehicle exhausts (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eMap \u0026minus;\u0026thinsp;8\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003eCARBON MONOXIDE (CO)\u003c/h2\u003e\n \u003cp\u003eCarbon Monoxide (CO) pollution in Salem District is notably high in areas with heavy vehicle traffic, with significant concentrations observed in urban and transport corridors. As illustrated in the air quality data, CO levels tend to rise considerably in areas with dense transportation networks. The Bengaluru Main Road, a major thoroughfare, is one of the considerably polluted regions in times of CO emissions. Other high-traffic areas of Salem District also show elevated CO concentrations, with emissions ranging from high to medium. This is mainly noticed due to the heavy usage of vehicles, which contributes significantly to the boost in CO levels. In contrast, the hilly areas of Yercaud, Kalvarayan Hills, and Jarugumalai, where transportation infrastructure is limited, exhibit very low levels of CO pollution (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eMap \u0026minus;\u0026thinsp;9\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe health impacts of high CO concentrations are concerning. CO is an odorless and colorless gas that, when inhaled, binds to hemoglobin in the blood, reducing the oxygen-carrying capacity. This can lead to symptoms such as dizziness, headaches, and shortness of breath. Prolonged exposure to elevated CO levels leads to a rise in severe heart-related problems and neurological problems. Vulnerable groups, such as children, the elderly, and those with pre-existing respiratory or cardiovascular conditions, are particularly at risk. The pollution also contributes to the degradation of air quality, which can exacerbate respiratory issues and lead to long-term health problems for the general population.\u003c/p\u003e\n \u003cp\u003eThis highlights the impact of LULC changes on CO pollution in the district. Sustainable urban planning, including the reduction of vehicle emissions, the development of eco-friendly transportation systems, and the preservation of natural areas, is crucial in mitigating the harmful effects of CO pollution. By understanding the connection between LULC changes and CO emissions, policies can be formulated to reduce pollution and enhance air quality, thus safeguarding public health and the atmosphere in Salem.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003eOZONE (O₃)\u003c/h2\u003e\n \u003cp\u003eOzone (O₃) is a secondary contaminant created when sunlight responds with nitrogen oxides (NOₓ) and volatile organic compounds from vehicles, industries, and power plants. In Salem District, the rise of the ozone level, particularly in urban and industrial areas, is caused by vehicle emissions, industrial activities, and high temperatures. Ozone concentrations peak during warmer months. Areas near industrial zones and heavy traffic show high ozone levels, while rural regions like Yercaud and Kalvarayan Hills have lower levels due to minimal human activity and traffic (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eMap \u0026minus;\u0026thinsp;10\u003c/span\u003e). Ground-level ozone can cause respiratory issues, worsen chronic illnesses, and damage yields and ecosystems. Urbanization and industrialization in Salem are linked to rising ozone pollution, with more land converted for development, and increasing emissions of ozone precursors. Conversely, areas with natural landscapes like Yercaud act as buffers, reducing ozone pollution.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThe comprehensive analysis of LULC changes in Salem District from 1995 to 2024 reveals significant transformations driven by urbanization, agricultural activities, and conservation efforts. The findings highlight a drastic increase in urban areas, accompanied by a decline in forest cover and cropland. However, the observed recovery in water bodies and forests in recent years underscores the potential of targeted conservation strategies and sustainable resource management.\u003c/p\u003e \u003cp\u003eThe study also establishes a clear link between LULC changes and air pollution, emphasizing the role of urban and industrial expansion in exacerbating air quality issues. The spatial analysis of pollution hotspots demonstrates the critical need for combined land use planning to mitigate the adverse environmental impacts of rapid development. To ensure long-term environmental sustainability in Salem District, it is imperative to adopt a balanced approach that promotes urban and economic growth while safeguarding natural resources. This includes implementing strict pollution control measures, enhancing green cover, and fostering community participation in land management practices. The insights this research provides can guide policymakers, urban planners, and environmentalists in formulating strategies to achieve sustainable development goals in the region.\u003c/p\u003e "},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMohammed Junaid. S: Conceptualization, methodology, data analysis, writing original draftpreparation, and visualization. Ilaiyarasan. M: critical review and editing of the manuscript. Both authors have read and approved the final manuscript and agree to be accountable for all aspects of the work to ensure that questions related to the accuracy or integrity of any part of the research are appropriately investigated and resolved.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eI would like to express my heartfelt gratitude to the Department of Geology at Government ArtsCollege (Autonomous), Salem, Tamil Nadu, and the Department of Geology at the Central University of Kerala, Kasaragod, for their academic support . I am deeply thankful to my co-author, Ilaiyarasan M., whose expertise and contributions have significantly enriched this research. We also extend my sincere thanks to the organizations and missions that provided the invaluable datasets utilized in this study Landsat Program: Provided Landsat-5 and Landsat-7 data, free and open access to the public, crucial for analyzing long-term LULC changes and their environmental impacts. Indian Remote Sensing (IRS) Program: Supplied IRS 1B data, available for free and open access, essential for evaluating regional LULC dynamics and associated environmental consequences. European Space Agency (ESA): Delivered Sentinel-2 and Sentinel-5P datasets, free and open access to all, enabling high-resolution analysis of LULC changes and atmospheric pollution impacts.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are available within the manuscript and its supplementary information files. Satellite datasets (e.g., Landsat and Sentinel) used for analysis were accessed from public repositories, including the USGS Earth Explorer and Copernicus Open Access Hub. Further details on the datasets and methodologies are provided in the Materials and Methodology section of the manuscript.\u003c/p\u003e\u003ch2\u003eFUNDING DECLARATION\u003c/h2\u003e \u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSmith, A., Johnson, B., \u0026amp; Lee, C. (2020). Monitoring Land Cover Dynamics: Methods and Applications. Journal of Environmental Research, 45(3), 123\u0026ndash;134.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJones, D., \u0026amp; Taylor, E. (2019). Urbanization and its Environmental Impact: A Global Perspective. Environmental Studies Review, 34(2), 87\u0026ndash;101.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClark, R., Mitchell, K., \u0026amp; Thompson, L. (2018). 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Sustainability Science, 11(2), 91\u0026ndash;104.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Chart","content":"\u003cp\u003eChart 1 is available in the Supplementary Files section.\u003c/p\u003e"},{"header":"Maps","content":"\u003cp\u003eMap 1 to 10 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Land Use Changes, Salem, Geospatial Techniques, Air Pollution Hotspots, Land Use Change Detection Analysis, Environmental Impacts","lastPublishedDoi":"10.21203/rs.3.rs-5859388/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5859388/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study aims to examine land use changes in Salem District from 1995 to 2024 using Geospatial techniques. It reveals shifts due to urbanization, agricultural expansion, deforestation, and conservation efforts. Urban areas expanded, while cropland and forests decreased. Positive trends, such as the recovery of water bodies and wastelands, are noted. The study assesses environmental impacts like air quality and pollution from industrial and vehicular activities. By linking LULC changes to pollution hotspots, it highlights the necessity for sustainable land management and urban planning. 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