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Brick kilns add enormous quantities of organic pollutants to the air that can cause serious health issues, especially in developing countries; poor air quality is associated with community health problems, yet receives no attention in Northern Pakistan. The present study, therefore, assessed the chemical composition and investigated the impacts of air pollution from brick kilns on public health. A field-based investigation of air pollutants, i.e., PM1, PM2.5 and PM10, CO2, CO, NO, NO2, H2S, and NH3 using mobile scientific instruments was conducted in selected study area locations. Social surveys were conducted to investigate the impacts of air pollution on community health. The results reveal the highest concentrations of PM1, PM2.5, and PM10, i.e., 3377, 2305, and 3567.67 µg/m3, respectively, in specific locations. Particulate matter concentrations in sampling points exceeded the permissible limits of the Pakistan National Environmental Quality Standard and, therefore, may risk the local population’s health. The highest mean value of CO2 was 529 mg/L, and other parameters, such as CO, NO, NO2, H2S, and NH3 were within the normal range. The social survey’s findings reveal that particulate matter was directly associated with respiratory diseases such as asthma, which was reported in all age groups selected for sampling. The study concluded by implementing air pollution reduction measures in brick kiln industries to protect the environment and community health. In addition, the region’s environmental protection agency needs to play an active role in proper checking and integrated management to improve air quality and protect the community from air hazards.

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Drinking water quality is a major problem in Pakistan, especially in the Abbottabad region of Pakistan. The main objective of this study was to use a Principal Component Analysis (PCA) and integrated Geographic Information System (GIS)-based statistical model to estimate the spatial distribution of exceedance levels of groundwater quality parameters and related health risks for two union councils (Mirpur and Jhangi) located in Abbottabad, Pakistan. A field survey was conducted, and samples were collected from 41 sites to analyze the groundwater quality parameters. The data collection includes the data for 15 water quality parameters. The Global Positioning System (GPS) Essentials application was used to obtain the geographical coordinates of sampling locations in the study area. The GPS Essentials is an android-based GPS application commonly used for collection of geographic coordinates. After sampling, the laboratory analyses were performed to evaluate groundwater quality parameters. PCA was applied to the results, and the exceedance values were calculated by subtracting them from the World Health Organization (WHO) standard parameter values. The nine groundwater quality parameters such as Arsenic (As), Lead (Pb), Mercury (Hg), Cadmium (Cd), Iron (Fe), Dissolved Oxygen (DO), Electrical Conductivity (EC), Total Dissolved Solids (TDS), and Colony Forming Unit (CFU) exceeded the WHO threshold. The highly exceeded parameters, i.e., As, Pb, Hg, Cd, and CFU, were selected for GIS-based modeling. The Inverse Distance Weighting (IDW) technique was used to model the exceedance values. The PCA produced five Principal Components (PCs) with a cumulative variance of 76%. PC-1 might be the indicator of health risks related to CFU, Hg, and Cd. PC-2 could be the sign of natural pollution. PC-3 might be the indicator of health risks due to As. PC-4 and PC-5 might be indicators of natural processes. GIS modeling revealed that As, Pb, Cd, CFU, and Hg exceeded levels 3, 4, and 5 in both union councils. Therefore, there could be greater risk for exposure to diseases such as cholera, typhoid, dysentery, hepatitis, giardiasis, cryptosporidiosis, and guinea worm infection. The combination of laboratory analysis with GIS and statistical techniques provided new dimensions of modeling research for analyzing groundwater and health risks.

Pheasants play a distinctive and significant role in high altitudinal ecosystems. These are good indicators of environmental changes, and their presence determines the health and balance of the bio-network. Recent human pressure continues to degrade their populations, and some pheasant species are already extinct. Therefore, the current study focuses on pheasant abundance and emerging conservation issues. The pheasant population was assessed using “Call count methods” and analyzed by DISTANCE software. The results revealed that the valleys where human interference is minimum had significantly higher encounter rates and densities of pheasants. At the same time, the pheasant population was severely affected, whether found at lower or higher altitudes, showing seasonal migration toward human settlements. The habitat suitability modeling was performed using the MaxEnt model and showed that human activities overlap with the suitable natural habitats of pheasants. The threats were identified using a systematic questionnaire survey from the nearest villages of the potential habitat, and particular attention was paid to valleys where human pressures were found to be high. Major infrastructure development projects, illegal hunting, and deforestation were identified as the major threats to the pheasant population. The study concluded that proper conservation measures are required to protect pheasants in their potential habitats.

The main objective of this study was to assess the quality and quantity of roof-harvested rainwater to overcome the water shortage problem in the study area. We also aimed to find health hazards associated with rainwater in the study area. For this purpose, rainwater samples were collected from five sites in the study area. The samples were analyzed using standard methods of the World Health Organization and the American Public Health Association in a laboratory. The analysis showed that all the physicochemical parameters were within the permissible limits of the WHO’s guidelines except pH, turbidity, and some trace metals such as iron (Fe) and lead (Pb). The mean values of pH range from 5.18 to 6.26, indicating slight acidity, while the highest mean turbidity was found at 5.77 NTU. Similarly, the highest mean concentrations of Fe and Pb were 0.95 mg/L and 0.056 mg/L, respectively, which was above the permissible limit of the WHO’s guidelines for drinking water. The annual rainwater-harvesting potential was assessed using the formula annual rainfall × roof area× runoff coefficient. The annual rainwater-harvesting potential of the study area was 56.803 L per household. At the same time, the average monthly rainwater-harvesting potential was 4733 L in the study area. This shows the potential for roof-harvested rainwater in the study area. A risk assessment of heavy metals showed that the rainwater of the study area is safe and does not pose any risk. This study concludes that rainwater is suitable for drinking and other domestic consumption if proper care is taken to clean the roof area and storage system and divert the first flush from the storage system.

Anthropogenic activities are changing the current Land use Land Cover (LULC) and Land Surface Temperature (LST) patterns worldwide. The current study uses Landsat satellite images (Landsat 5 TM and Landsat 8 OLI) during the years 1988, 2002, and 2016 in an alpine environment of Islamabad Capital Territory, Pakistan, to assess the past patterns of LULC variation using Maximum Likelihood Classification (MLC) method. The LST was derived from thermal bands (6, 10 and 11) of Landsat series data. The Human Modification Index (HMI) relationship with LULC and LST was also assessed using Google Earth Engine (GEE) data. The built-up area expanded by + 9.94%, while agricultural and bare soil dropped by -3.81% and − 3.94%, respectively. The results showed a considerable shift in the LULC and LST with a -1.99% loss in vegetation. The built-up region has the greatest temperature, followed by barren, agricultural, and vegetation classes, according to the LST study for various land cover classes. Similarly, the results of the HMI in different LST classes indicated that high LST classes have high human modification compared to lower LST classes. The statistical analysis between HMI and LST showed a significant association (R-value = 0.61). The results could be used for sustainable urban management and biodiversity conservation.