Air quality in greater Algiers, in Algeria was assessed analyzing aerosol particulate matter (PM10 and PM2.5) at a site influenced by heavy road traffic. Particulate matters were collected using a Gent sampler to characterize the atmospheric aerosol of Algiers. An Energy dispersive X ray spectrometer (EDXRF) was used to determine the heavy metal concentrations in the PM2.5 and PM10 size fractions. Principal Component analysis and Enrichment factor were used to identify the major sources of air pollutants for PM10 fraction in the studied area. Backward trajectories were calculated in order to identify potential distant sources that contribute to particulate pollution in our site. Significant concentrations of PM 2.5 and PM10 as well as associated heavy metals have been documented. The mean concentrations of heavy metals contained in PM10 and PM2.5 were, in descending order, Fe>Zn>Ni>Pb>Mn>Co>Cr; Pb>Mn>Co>Fe>Zn>Ni>Cr respectively. The contribution of road traffic to the levels of fine (PM2.5), and coarse (PM10) particles were studied.

Defined as any substance in the air that may harm humans, animals, vegetation, and materials, air pollution poses a great danger to human health. It has turned into a worldwide problem as well as a huge environmental risk. Recent years have witnessed the increase of air pollution in many cities around the world. Similarly, it has become a big problem in Iran. Although ground-level monitoring can provide accurate PM2.5 measurements, it has limited spatial coverage and resolution. As a result, Satellite Remote Sensing (RS) has emerged as an approach to estimate ground-level ambient air pollution, making it possible to monitor atmospheric particulate matters continuously and have a spatial coverage of them. Recent studies show a high correlation between ground level PM2.5, estimated by RS on the one hand, and measurements, collected at regulatory monitoring sites on the other. As such, the present study addresses the relation between air pollution and satellite images. For so doing, it derives RS estimates, using satellite measurements from Landsat satellite images. Monitoring data is the daily concentration of PM2.5 contaminants, obtained from air pollution stations. The relation between the concentration of pollutants and the values of various bands of Landsat satellite images is examined through 19 regression models. Among them, the Ensembles Bagged Trees has the lowest Root-Mean-Square Error (RMSE), equal to 21.88. Results show that this model can be used to estimate PM2.5 contaminants, based on Landsat satellite images.