Rabia Shabbir

As the global market for renewable energy solutions expands, geospatial analysis is becoming crucial for optimizing solar potential. The current study assesses the suitability of installing PV solar system in the Mardan, Peshawar, and Nowshera districts in Pakistan using a multi-criteria decision-making (MCDM) approach. Analysis of different parameters, such as topography, land use and land cover (LULC), solar radiation and land surface temperature (LST), were performed to find the appropriate locations for solar in their respective regions. The study employed binary classification and weighted overlay methods to detect patterns of spatial suitability. Peshawar showed maximum ability with 859.8 km² categorized as favorable with a projected annual power output capacity of 67.77 trillion kWh and a decrease in CO₂ emission of 2.78 billion metric tons. Mardan closely followed the suitable area with 828.4 km² with energy generation of 39.74 trillion kWh/year and reduction of CO₂ emissions by 1.63 billion metric tons. Nowshera has an appropriate area of 503.0 km², for energy output of 670.06 billion kWh, and CO₂ reduction of 27.46 million metric tons. These results underline the importance of combining geospatial and meteorological data for accurate planning of solar energy systems. By highlighting location-specific features, including topography and solar irradiance illustrates the importance of tailoring energy outputs and environmental impacts to local contexts. These insights help guide policymakers in driving renewable energy projects, and support Pakistan’s sustainable development and climate targets.

Air pollution is a serious public health issue in Pakistan’s metropolitan cities, including Lahore, Karachi, Faisalabad, Islamabad, and Rawalpindi. Pakistan’s urban areas are vulnerable due to air pollution drivers such as industrial activities, vehicular emissions, burning processes, emissions from brick kilns, urbanization, and other human activities that have resulted in significant human health issues. The purpose of this study was to examine the impact of air pollutants and smog, as well as their causes and effects on human health. The PRISMA technique was used to assess the impact of environmental contaminants on human health. This study looked at air pollution sources and pollutants such as PM_2.5, PM₁₀, CO₂, CO, SO_X, and NO_x from waste combustion and agriculture. The population included people of all ages and sexes from both urban and rural areas of Pakistan. Data were retrieved and analyzed using SRDR⁺ software and Microsoft Excel spreadsheets. The data suggested that Karachi and Lahore had the highest levels of air pollution and disease prevalence, which were attributed to heavy industrial activity and traffic emissions. Smog was a serious concern in Lahore during winter, contributing to the spread of several diseases. Other cities, including Islamabad, Rawalpindi, Jhang, Sialkot, Faisalabad, and Kallar Kahar, were impacted by agricultural operations, industrial pollutants, brick kilns, and urbanization. Due to these drivers of air pollution, some diseases such as respiratory and cardiovascular diseases had notably higher incidences in these cities. Other diseases were connected with air pollution exposure, asthma, eye and throat problems, allergies, lung cancer, morbidities, and mortalities. To reduce air pollution’s health effects, policies should focus on reducing emissions, supporting cleaner technologies, and increasing air quality monitoring.

The vulnerability evaluation is always an initial step toward sustainable development. Therefore, the regionalization of the assessment to rationally utilize and develop water resources and planning for the amelioration of the vulnerability status is very important and has practical significance. The present study was aimed to analyze the vulnerability status of the water resource system in Rawalpindi and Islamabad with the help of Analytic Hierarchy Process (AHP) keeping in view the complex, integrated, comprehensive and hierarchical nature of vulnerability evaluation of water resources. The vulnerability index developed as a combination of climatic and socio-economic factors selected on the basis of their significance, relevance and scientific credibility. The water resources in both areas were found relatively vulnerable with the socio-economic factors enhancing the effect of climatic factors. The vulnerability of water resources is of special significance and needs a lot of attention of researchers and policy makers

One of the detailed and useful ways to develop land use classification maps is use of geospatial techniques such as remote sensing and Geographic Information System (GIS). It vastly improves the selection of areas designated as agricultural, industrial and/or urban sector of a region. In Islamabad city and its surroundings, change in land use has been observed and new developments (agriculture, commercial, industrial and urban) are emerging every day. Thus, the rationale of this study was to evaluate land use/cover changes in Islamabad from 1992 to 2012. Quantification of spatial and temporal dynamics of land use/cover changes was accomplished by using two satellite images, and classifying them via supervised classification algorithm and finally applying post-classification change detection technique in GIS. The increase was observed in agricultural area, built-up area and water body from 1992 to 2012. On the other hand forest and barren area followed a declining trend. The driving force behind this change was economic development, climate change and population growth. Rapid urbanization and deforestation resulted in a wide range of environmental impacts, including degraded habitat quality.

One of the features of medical geography that has made it so useful in health research is statistical spatial analysis, which enables the quantification and qualification of health events. The main objective of this research was to study the spatial distribution patterns of malaria in Rawalpindi district using spatial statistical techniques to identify the hot spots and the possible risk factor. Spatial statistical analyses were done in ArcGIS, and satellite images for land use classification were processed in ERDAS Imagine. Four hundred and fifty water samples were also collected from the study area to identify the presence or absence of any microbial contamination. The results of this study indicated that malaria incidence varied according to geographical location, with eco-climatic condition and showing significant positive spatial autocorrelation. Hotspots or location of clusters were identified using Getis-Ord Gi* statistic. Significant clustering of malaria incidence occurred in rural central part of the study area including Gujar Khan, Kaller Syedan, and some part of Kahuta and Rawalpindi Tehsil. Ordinary least square (OLS) regression analysis was conducted to analyze the relationship of risk factors with the disease cases. Relationship of different land cover with the disease cases indicated that malaria was more related with agriculture, low vegetation, and water class. Temporal variation of malaria cases showed significant positive association with the meteorological variables including average monthly rainfall and temperature. The results of the study further suggested that water supply and sewage system and solid waste collection system needs a serious attention to prevent any outbreak in the study area.