An extensive assessment on the distribution pattern of organic contaminants in the aerosols samples in the Middle East

Nowadays, in spite of a significant progress in indoor air quality (IAQ), an assessable and predictive understanding of atmospheric aerosol sources, chemical composition, transformation processes, and environmental effects are still rather incomplete and therefore signifies a key research challenge in the atmospheric science. Thus, the current comprehensive review is concerned with the dominant sources, organic compositions, and potential health impacts of the organic contaminants in the atmospheric particle matters (PMs) in the Middle East (ME). The ME contributes a major impact of organic contaminants on the atmosphere along with other Asian and African countries. In the Gulf Cooperation Council (GCC) countries, the communities are noted for being the center of the great majority of the world’s oil reserves and infrastructure for producing crude oil. The review starts with a historical outlook on the scientific queries regarding major source of organic contaminants to the atmospheric aerosols over the past centuries, followed by an explanation of the distribution, sources, transformation processes, and chemical and physical properties as they are formerly assumed. Natural product chemicals from biota, manufactured organic compounds including pesticides, chlorinated hydrocarbons, and lubricants, as well as organic compounds from the use and combustion of fossil fuels make up the aerosol contamination. Thus, in the recent years, IAQ may be seen as a significant health issue because of the increase in industrial activity. Fugitive emissions from industrial processes, as well as natural and anthropogenic emissions from other sources such as forest fires, volcanic eruptions, incomplete combustion of fossil fuels, wood, agricultural waste, or leaves, are typical sources of organic pollutants to the aerosol. In the spring and early summer in the GCC countries, aerosol concentration increases because of dust storms; however, in winter, there are fewer dust storms and higher precipitation rates, and aerosol concentrations are lower. Significances of future research and major suggestions are also outlined to narrow the gap between the present understanding of the contribution of both anthropogenic and biogenic aerosols to radiative forcing, resulting from the spatial nonuniformity, intermittency of sources, unresolved composition, and reactivity.

PM2.5 pollution from household solid fuel burning practices in Central India: 2. Application of receptor models for source apportionment

USEPA's UNMIX, positive matrix factorization (PMF) and effective variance-chemical mass balance (EV-CMB) receptor models were applied to chemically speciated profiles of 125 indoor PM_2.5 measurements, sampled longitudinally during 2012-2013 in low-income group households of Central India which uses solid fuels for cooking practices. Three step source apportionment studies were carried out to generate more confident source characterization. Firstly, UNMIX6.0 extracted initial number of source factors, which were used to execute PMF5.0 to extract source-factor profiles in second step. Finally, factor analog locally derived source profiles were supplemented to EV-CMB8.2 with indoor receptor PM_2.5 chemical profile to evaluate source contribution estimates (SCEs). The results of combined use of three receptor models clearly describe that UNMIX and PMF are useful tool to extract types of source categories within small receptor dataset and EV-CMB can pick those locally derived source profiles for source apportionment which are analog to PMF-extracted source categories. The source apportionment results have also shown three fold higher relative contribution of solid fuel burning emissions to indoor PM_2.5 compared to those measurements reported for normal households with LPG stoves. The previously reported influential source marker species were found to be comparatively similar to those extracted from PMF fingerprint plots. The comparison between PMF and CMB SCEs results were also found to be qualitatively similar. The performance fit measures of all three receptor models were cross-verified and validated and support each other to gain confidence in source apportionment results.

Source identification and apportionment of PM2.5 and PM2.5-10 in iron and steel scrap smelting factory environment using PMF, PCFA and UNMIX receptor models

To identify the potential sources responsible for the particulate matter emission from secondary iron and steel smelting factory environment, PM2.5 and PM2.5-10 particles were collected using the low-volume air samplers twice a week for a year. The samples were analyzed for the elemental and black carbon content using x-ray fluorescence spectrometer and optical transmissometer, respectively. The average mass concentrations were 216.26, 151.68, and 138. 62 μg/m(3) for PM2.5 and 331.36, 190.01, and 184.60 μg/m(3) for PM2.5-10 for the production, outside M1 and outside M2 sites, respectively. The same size resolved data set were used as input for the positive matrix factorization (PMF), principal component factor analysis (PCFA), and Unmix (UNMIX) receptor modeling in order to identify the possible sources of particulate matter and their contribution. The PMF resolved four sources with their respective contributions were metal processing (33 %), e-waste (33 %), diesel emission (22 %) and soil (12 %) for PM2.5, and coking (50 %), soil (29 %), metal processing (16 %) and diesel combustion (5 %) for PM2.5-10. PCFA identified soil, metal processing, Pb source, and diesel combustion contributing 45, 41, 9, and 5 %, respectively to PM2.5 while metal processing, soil, coal combustion and open burning contributed 43, 38, 12, and 7 %, respectively to the PM2.5-10. Also, UNMIX identified metal processing, soil, and diesel emission with 43, 42 and 15 % contributions, respectively for the fine fraction, and metal processing (71 %), soil (21 %) and unidentified source (1 %) for the coarse fraction. The study concluded that metal processing and e-waste are the major sources contributing to the fine fraction while coking and soil contributed to the coarse fraction within the factory environment. The application of PMF, PCFA and UNMIX receptor models improved the source identification and apportionment of particulate matter drive in the study area.