The Juniper forest reserve of Ziarat is one of the biggest Juniperus forests in the world. This study assessed the land-use changes and carbon stock of Ziarat. Different types of carbon pools were quantified in terms of storage in the study area in tons/ha i.e., above ground, soil, shrubs and litter. The Juniper species of this forest is putatively called Juniperus excelsa Beiberstein. To estimate above-ground biomass, different allometric equations were applied. Average above ground carbon stock of the forest was estimated as 8.34 ton/ha, 7.79 ton/ha and 8.4 ton/ha using each equation. Average carbon stock in soil, shrubs and litter was calculated as 24.35 ton/ha, 0.05 ton/ha and 1.52 ton/ha, respectively. Based on our results, soil carbon stock in the Juniper forest of Ziarat came out to be higher than the living biomass. Furthermore, the spatio-temporal classified maps for Ziarat showed that forest area has significantly decreased, while agricultural and barren lands increased from 1988 to 2018. This was supported by the fact that estimated carbon stock also showed a decreasing pattern between the evaluation periods of 1988 to 2018. Furthermore, the trend for land use and carbon stock was estimated post 2018 using a linear prediction model. The results corroborate the assumption that under a business as usual scenario, it is highly likely that the Juniperus forest will severely decline.

Deforestation and forest degradation is a global concern, especially in developing countries. The Margalla Hills of Pakistan—Himalayan foothills—also face the threat of deforestation and forest degradation. These Margalla Hills, considering the need for forest protection activities in Pakistan, were declared a reserved national forest and named “the Margalla Hills National Park (MHNP)”. This study quantitively evaluates whether deforestation and forest degradation have occurred at MHNP and identifies their possible drivers. Satellite (Landsat) data 1988–2020 was employed for the land use change analysis, whereas a socio-economic survey of the local population and structured interviews with government officials were conducted to identify the drivers of deforestation. Supervised classification was performed for imagery classification and the Vegetation Condition Index (VCI) was also calculated to measure degradation. Supervised classification showed that the forest cover increased from 65% of the total area in 1988 to 69% in 2020. The VCI results show that the moderate level of degradation has increased from 3.5% of MHNP area in 1988 to 8.8% in 2020. The cumulative measure of degradation from 1988 to 2020 is 1.09% of the total forest (using p < 0.05). Major drivers identified are fuel wood and timber collection. The results reveal a decline in both deforestation and forest degradation. There is a need for further quantitative analysis of the drivers, strict implementation of legislative and control measures, and continuous invigilation of the deforestation trends in MHNP.

Aerosol properties and effects vary by region because of emission sources, meteorological conditions, topography, elevation, and land–sea interaction. These factors can produce substantial aerosol changes within a day, rendering it important to study the aerosol diurnal behavior. Using available 2015–2025 observations from Quetta and Karachi, this study demonstrates an integrated approach for characterizing diurnal aerosol variability where ground monitoring is limited. The approach combines DSCOVR EPIC aerosol optical depth (AOD) and ultraviolet aerosol index (UVAI), MODIS Ångström exponent (AE), ERA5 meteorology, and Karachi AERONET measurements. Paired representative-scene analysis showed a larger afternoon enhancement over Quetta (ΔAOD = 0.159) than over Karachi (ΔAOD = 0.066). In the GeoDetector analysis, wind speed showed the strongest morning spatial association over Quetta, and multiyear mean annual precipitation the strongest afternoon association. Surface pressure ranked highest in both Karachi periods, closely followed by relative humidity in the Karachi morning. City-specific AE–UVAI models identified four aerosol regimes per city. The models showed a small redistribution toward strongly absorbing regimes in Quetta and a larger coarse-to-fine regime shift in Karachi. Across the nine clustering years, fine–absorbing aerosols increased significantly. These case studies demonstrate how complementary observations and explicit temporal sampling controls can jointly characterize daytime aerosol loading, aerosol regimes, and meteorological spatial associations in sparsely monitored regions.