The air quality indicator approximated by satellite measurements is known as an atmospheric particulate loading, which is evaluated in terms of the columnar optical thickness of aerosol scattering. The effect brought by particulate pollution has gained interest among researchers to study aerosol and particulate matter. In this study we presents the potentiality of retrieving concentrations of particulate matter with diameters less than ten micrometer (PM10) in the atmosphere using the Landsat 7 ETM+ slc-off satellite images over Makkah, Mina and Arafah. A multispectral algorithm is developed by assuming that surface condition of study area are lambertian and homogeneous. In situ PM10 measurements were collected using DustTrak aerosol monitor 8520 and their locations were determined by a handheld global positioning system (GPS). The multispectral algorithm model shows that PM10 high during Hajj season compared to other season. The retrieval dataset gives the accuracy > 0.8 of R coefficient value over Makkah, Mina and Arafah. These results provide confidence that the multispectral algorithm PM10 models can make accurate predictions of the concentrations of PM10.

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Penang Island is an important economic center in Malaysia and most of its population live in the coastal areas. Although previous studies have shown that it is vulnerable to rising sea levels, the combination of sea-level rise and local land subsidence would be devastating. Therefore, the objective of this study is to apply the local land subsidence model to estimate the inundated areas which relate to sea level rise by 2100. Land subsidence is quantified by the SBAS-InSAR technique on the basis of Sentinel-1 radar images for both ascending and descending tracks. For the first time, the geostatistical analyst method is used to merge the different track results and create the land subsidence models, the results show this method can maximize land deformation fields and minimize deformation errors. According to the land deformation results, all of the coastlines in the east of the island have differing medium levels of subsidence, especially in reclaimed lands and building areas. Lastly, the bathtub model is used to quantify the inundated areas by combing regional sea-level rise projection and local land subsidence models under CoastalDEM in 2100 projections. The results of this study indicate land subsidence that would increase 2.0% and 5.9% of the inundated area based on the different scenarios by 2100 projections.

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Spatial mapping of potential geothermal areas is an effective tool for a preliminary investigation and the development of a clean and renewable energy source around the globe. Specific locations within the Earth’s crust display some manifestations of sub-surface geothermal occurrences, such as hot springs, a volcanic plug, mud volcanoes, and hydrothermal alterations, that need to be investigated further. The present area of investigations also reveals some of these manifestations. However, no attempt was made to examine the prospectivity of this terrain using the efficient GIS-based multicriteria evaluation (MCE) within the scope of the Analytic hierarchy process (AHP). The integration of remote sensing, Geographic information system (GIS), and other geophysical methods (Magnetic and gravity) was performed to map the promising geothermal areas. Multiple input data sets such as aero-magnetic, aero-gravity, aero-radiometric, digital elevation model (DEM), geological map, and Landsat-8 Operational Land Imager (OLI) data were selected, processed, and use to generate five thematic layers, which include heat flow, temperature gradients, integrated lineaments, residual gravity, and lithology maps. The five thematic layers were standardized and synthesized into a geothermal prospectivity map. The respective ranks and weight of the thematic layers and their classes were assigned based on expert opinion and knowledge of the local geology. This research aims to apply an efficient method to evaluate the factors influencing the geothermal energy prospects, identify and map prospective geothermal regions, and, finally, create a geothermal prospectivity model.

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