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Rapid urbanization attributed to population growth is affecting the built environment's thermal and landscape dynamics. Using Landsat satellite datasets, this study investigated the complex interplay between urban Land Cover (LC) modification, fluctuation in Land Surface Temperature (LST) and severity of Urban Heat Island (UHI) from 1990 to 2020 in Peshawar City, Pakistan. Thermal bands were used to calculate LST and severity of UHI using the Urban Thermal Field Variance Index (UTFVI). Furthermore, through Cellular Automata (CA), Logistic Regression (LR), and Artificial Neural Network (ANN), future predictions on thermal characteristics associated with land use changes were made. The results showed that the urban areas expanded by ∼25 % from 1990 to 2020, while a ∼10 % decrease occurred in urban vegetation. The city is projected to expand by ∼45 % and ∼56 % in 2035 and 2050, respectively. Notably, the results also demonstrated that urban hotspots were found the warmest with the strongest UHI severity (∼34 °C), followed by the barren land (∼32 °C), and vegetation. The results further predicted an increase of LST (∼55 % and ∼82 %) and UTFVI (∼62 % and ∼83 %) in 2035 and 2050, respectively. These findings provide useful insights for policymakers and city planners to mitigate heat stress and create a sustainable urban environment through the development of effective urban land use policies and urban greening.

Rapid urban sprawl adversely impacts the local climate and the ecosystem components. Islamabad, one of South Asia's green and environment-friendly capitals, has experienced major Land Use Land Cover (LULC) changes over the past three decades consequently, elevating the seasonal and annual Land Surface Temperature (LST) in planned and unplanned urban areas. The focus of this study was to quantify the fluctuations in LULC and LST in planned and unplanned urban areas using Landsat data and Machine Learning algorithms involving the Support Vector Machine (SVM) over the 1990–2020 data period. Moreover, hybrid Cellular Automata-Markov (CA-Markov) and Artificial Neural Network (ANN) models were employed to project the future changes in LULC and annual LST, respectively, for the years 2035 and 2050. The findings of the study reveal a distinct difference in seasonal and annual LST in planned and unplanned areas. Results showed an increase of ∼22 % in the built-up area but vegetation and bare soil decreased by ∼10 % and ∼12 %, respectively. Built-up land showed a maximum annual mean LST followed by bare-soil and vegetative surfaces. Seasonal analysis showed that summer months experience the highest LST, followed by spring, autumn and winter. Future projections revealed that the built-up areas (∼27 % in 2020) are likely to increase to ∼37 % and ∼50 %, and the areas under the highest annual mean LST class i.e., ≥28 °C are likely to increase to ∼19 % and ∼21 % in planned, and ∼38 % and ∼42 % in unplanned urban areas for the years 2035 and 2050, respectively. Planned areas have better temperature control with urban green spaces, and controlled infrastructure. The Capital Development Authority of Islamabad may be advised to control the expansion of built-up areas, grow urban forests, and thus mitigate the possible Urban Heat Island (UHI) effect.

Background: Groundwater Arsenic (As) contamination is a global public health concern responsible for various health implications and a neglected area of environmental health research in Pakistan. Because of interindividual differences in genetic predisposition, As-related health issues may not be equally distributed among the As-exposed population. However, till date, no studies have been conducted including multiple SNPs involved in As metabolism and disease risk using a linear mixed effect model approach to analyze peripheral blood transcriptomics results. Objectives: In order to detect early responses on the gene expression level and to evaluate the impact of selected SNPs inferring disease risks associated with As exposure, we designed a systematic study to investigate blood transcriptomics profiles of 57 differentially exposed rural subjects living in drinking water As-contaminated settings of Lahore and Kasur districts in Punjab Province in southeast Pakistan. Exposure among the subjects was correlated with individual transcriptome responses applying urinary As profiles as the main biomarker for risk stratification. Methods: We performed whole genome gene expression analysis in blood of subjects using microarrays. Linear effect mixed models were applied for evaluating the combined impact of SNPs hypothetically increasing the risk for As exposure-induced health effects (GSTM1, GSTT1, As3MT, DNMT1, MTHFR, ERCC2 and EGFR). Results: Our findings confirmed important signaling, growth factor, cancer and other disease related pathways known to be associated with increased As exposure levels. In addition, upon implementing our integrative SNPs-based genetic risk factor, pathways associated with an increased risk of NAFLD and diabetes appeared significantly enhanced by down-regulation of genes NDUFV3, IKBKB, IL6R, ADIPOR1, PPARA, OGT and FOXO1. Conclusion: We report the first comprehensive study applying state-of-the-art bioinformatics approaches to address multiple SNP-based inter-individual variability in adverse molecular responses among subjects exposed to drinking water As contamination in Pakistan thereby providing strong evidence of various gene expression targets associated with development of known As-related diseases. Keywords: Arsenic, Drinking water, Health, Pathways, Transcriptomics, Pakistan