Source Identification of the elements in PM10 Aerosols, Case Study: Kermanshah City

Particulate matters are particles that have placed as dispersed particles, in solid or liquid, in gaseous intermediators. Recently, aerosols as one of the air pollutants, from natural or anthropogenic sources, have received the attention of many researchers due to the roles they play in global climate change and the environmental and health problems. They can scatter or absorb solar radiation and thermal radiation emitted from the earth surface. The aerosols as condensation nuclei of cloud droplets can also affect cloud formation and precipitation. Numerical studies have performed particulate matter (PM10) and their source identification.
Source identification of trace elements in PM2.5 in Mira Loma, in southern California, was performed using factor analysis, the backward air mass trajectory analysis, enrichment factor calculation and the Al/Zn ratio. Al is a major constituent in the earth crust but it is not in vehicle emissions. This study suggests that the Al/Zn ratio can be used to understand the dominance of soil-related sources. The low Al/Zn ratio shows that dominant sources are vehicle emissions.The purpose of this research is source identification of the main and trace elements of PM10 aerosols and also, investigation of effective areas in production of the aerosols (PM10) using satellite images in Kermanshah. In this research, the exploratory factor analysis has been used to identify natural and anthropogenic sources of the elements in PM10 aerosols.
In this research, sampling of aerosols has been performed by aerosol sampler. Then, the concentrations of the eleven elements (K,Al, Na, Ca, Cu, Ni, Pb, Mn, Fe, Mg, V) have been detected by ICP-OES.
In this study, the exploratory factor analysis has been used to identify potential sources of major and trace elements in aerosols. In this method, variables are placed in the factors so that the percentage of variance decreases from first factor to next factors. Hence, the variables in the first factor are most effective.
In this study, the days that particulate matter concentration was higher than permissible level, we have determined daily information of particulate matter concentration at Air Quality Monitoring Station in Kermanshah. Then, effective areas in production of aerosols (PM10) have been investigated using satellite images during February to July 2010.
In the factor analysis, the variables (elements) with the factor-loading less than 0.5 are not listed in the table. The total number of factors has been selected so that the cumulative percentage of the variance explained by all the selected factors was more than 77%. Only the factors are selected that had an eigenvalue greater than one. The last column shows the values of the communalities which explains the amount of common variance for each of the variables (elements) with the four factors.