The current study focuses on tracking urban sprawl in one of the rapidly growing cities of Pakistan i.e., Sargodha. The secondary cities have the capacity to persuade people from rural areas to relocate. In this regard, the current study is unique in that it will give a comprehensive analysis of urban sprawl of Sargodha City. The remotely sensed data is used for this purpose. The study is primarily based on the collecting of both primary and secondary data. For the last 30 years, from 1987 to 2017, primary data was gathered from the United States Geological Survey (USGS) and the Global Land Cover Facility (GLCF). ERDAS Imagine 2013 is used to classify land use using remotely sensed data. The Kappa Coefficient was used to compute the accuracy assessment of classified maps. Maps are used to depict a comparative analysis of urban sprawl in the city. In addition, regression analysis and simple statistical computations are also utilised to assess dynamic changes in urban sprawl. From 1987 to 2017, a 10-year interval was used to measure change in built-up land using an equation. According to the findings of the study, Sargodha City has experienced considerable changes in land use patterns. This current study is beneficial for policymakers to design the city in a well-planned manner. The appropriate design of cities can pave the way for higher living standards in rapidly growing cities of Pakistan.

Rapid urbanization, industrial expansion, and reliance on fossil fuels cause serious air pollution. Air pollution affects public health, especially in developing countries like Pakistan. Pakistan is among the most affected nations, particularly its major urban centers, which experience recurrent seasonal smog episodes. This study assesses the effectiveness of Sentinel-5P satellite products integrated with Google Earth Engine (GEE) for monitoring spatiotemporal variations of key air pollutants: nitrogen dioxide (NO₂), carbon monoxide (CO), and ozone (O₃) in Lahore, Faisalabad, Multan, and Gujranwala from 2018 to 2023. Monthly, seasonal, and annual composites of cloud-filtered Sentinel-5P TROPOMI data were analyzed using GEE, and statistical regression and correlation analyses were applied to examine relationships between pollutant concentrations and land surface temperature (LST). The results reveal pronounced seasonal variability in air pollutant levels, with elevated NO₂ and CO concentrations during winter due to intensified anthropogenic emissions and atmospheric stability, while O₃ concentrations peaked during spring and summer due to enhanced photochemical reactions driven by high temperatures and solar radiation. Quantitatively, wintertime NO₂ concentrations reached approximately 0.009 mol m⁻², whereas summer O₃ concentrations peaked at about 0.30 mol m⁻². Strong temperature–pollutant relationships were observed in highly urbanized cities such as Lahore and Faisalabad, reflecting the combined effects of dense infrastructure, industrial activity, and urban heat island intensity. Additionally, the COVID-19 lockdown period provided evidence of temporary reductions in pollutant concentrations and LST, highlighting the dominant role of anthropogenic activities in air quality degradation. Overall, the findings demonstrate that Sentinel-5P data, combined with automated cloud-based platforms such as GEE, provides a robust, cost-effective framework for large-scale air quality monitoring. This approach can support informed policymaking, targeted mitigation strategies, and sustainable urban planning to reduce smog intensity and protect public health in Pakistan.