An urban heat island (UHI) is an urban area that is significantly warmer than its surrounding rural areas due to antropogenic activities. The urban area of the city of Skopje has been rising rapidly in the past decade. In this study, the effect of UHI is analyzed using Landsat 8 data in the summer period of 2013-2017 as a case study in Skopje, Macedonia. An algorithm was applied to retrieve the land surface temperature (LST) distribution from the Landsat 8 data. In addition, the correlation between land surface temperature and the normalized difference vegetation index (NDVI) and the normalized difference build-up index (NDBI) were analyzed to explore the impacts of the green areas and the build-up land on the urban heat island. The results indicate that the effect of the urban heat island in Skopje is located in many suburban areas. The negative correlation between LST and NDVI indicates that the green area can weaken the effect on the urban heat island, while the positive correlation between LST and NDBI means that the built-up land can strengthen the effect of the urban heat island in the study area.

This study presents an analysis of the mean atmospheric column nitrogen dioxide (NO2) and carbon monoxide (CO) over the Republic of North Macedonia during a six-month period. Measurements of NO2 and CO obtained from the recently launched Sentinel-5 Precursor spacecraft with TROPOspheric Monitoring Instrument (Sentinel-5P TROPOMI) have been used. The aim of this study was to use relatively high-resolution satellite data for local air quality/air pollution monitoring and to investigate the relation of the pollutants with geographical and demographical data of the study area. For that purpose, along with CO and NO2 data from TROPOMI, population statistics, digital elevation model and vegetation cover have been used for geo-spatial and statistical analyses. The findings show significantly high CO and NO2 values in several parts of the study area, especially high CO values in the Vardar and Polog Valleys, and high NO2 values in the densely populated cities. According to the analyses, there is high positive correlation between the NO2 and the population statistics (r = 0.78; R 2 = 0.61) and high negative correlation (r = -0.9; R 2 = 0.80) between the altitude and the CO values of the study area. The overall results of this study confirmed the capability of Sentinel-5P TROPOMI data to be used in monitoring the air quality and air pollution over local areas.

With rapid population growth, both urbanization and transportation affect air pollution, population health, and global warming. A number of air pollutants are released from industrial facilities and other activities and may cause adverse effects on human health and the environment. One of the biggest air pollutants, nitrogen dioxide (NO2), is mainly caused by the combustion of fossil fuels, especially from traffic exhaust gases. Over the years, air pollution has been monitored using satellite remote sensing data. In this study, we investigate the relationship of the tropospheric NO2 retrieved from the recently launched Sentinel-5 Precursor, a low-earth-orbit atmosphere mission dedicated to monitoring air pollution equipped with the spectrometer Tropomoi (Tropospheric Monitoring Instrument), and the population density over Turkey. For this purpose, we use the mean value of the NO2 collected from July 2018 to January 2019 and the statistic population data from 2017. The results showed a significant correlation of higher than 0.72 between the population density and the maximum NO2 values. For future studies, we recommend investigating the correlation of different air pollutants with population and other factors contributing to air and environmental pollution.

Remotely sensed data can reinforce the abilities of water resources researchers and decision-makers to monitor water quality more effectively. In the past few decades, remote sensing techniques have been widely used to measure qualitative water quality parameters. However, the use of moderate resolution sensors may not meet the requirements for monitoring small water bodies. Water quality in a small dam was assessed using high-resolution satellite data from RapidEye and in situ measurements collected a few days apart. The satellite carries a five-band multispectral optical imager with a ground sampling distance of 5 m at its nadir and a swath width of 80 km. Several different algorithms were evaluated using Pearson correlation coefficients for electrical conductivity (EC), total dissolved soils (TDS), water transparency, water turbidity, depth, suspended particular matter (SPM), and chlorophyll-a. The results indicate strong correlation between the investigated parameters and RapidEye reflectance, especially in the red and red-edge portion with highest correlation between red-edge band and water turbidity (r2 = 0.92). Two of the investigated indices showed good correlation in almost all of the water quality parameters with correlation higher than 0.80. The findings of this study emphasize the use of both high-resolution remote sensing imagery and red-edge portion of the electromagnetic spectrum for monitoring several water quality parameters in small water areas.