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

Abstract

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.

Inhalation exposure to respirable particulate matter among workers in relation to their e-waste open burning activities in Buriram Province, Thailand

“…The average PM 2.5-10 concentration in this study was compared with Ogundele et al [17], who identified the potential sources responsible for the particulate matter emission from a secondary iron and steel smelting factory environment. PM 2.5-10 particles were collected using the low-volume air samplers twice a week for a year.…”

An overview of optical and thermal methods for the characterization of carbonaceous aerosol

“…Carbonaceous compounds (OC, EC, BrC, single organic compounds, etc.) are usually present in (incomplete and sample list): various kinds of traffic exhausts [124], re-suspended dust [125], ships [126] and aircrafts [127] plumes [126], industrial processes [128], power plants [129] and biomass burning [130]. Most advanced receptor models, as the Multi-linear Engine, ME [131] (see Fig.…”

Using multi-site data to apportion PM-bound metal(loid)s: Impact of a manganese alloy plant in an urban area

“…The use of RMs has been extensively applied to the identification and apportionment of PM sources, using both, chemical composition (Agarwal et al, 2017;Alleman et al, 2010;Contini et al, 2012;Gregoris et al, 2016;Manousakas et al, 2017;Ramírez et A C C E P T E D M A N U S C R I P T al., Viana et al, 2007;Zhang et al, 2018) and number size distribution (Friend et al, 2013;Leoni et al, 2018) as input datasets. Model inter-comparison was also used in a significant number of studies (Arruti et al, 2011;Belis et al, 2015;Bove et al, 2018;Cesari et al, 2016;Deng et al, 2018;Ogundele et al, 2016;Viana et al, 2008b), reporting strengths and limitations of the different approaches. Most frequent RMs are Chemical Mass Balance (CMB), Principal Component Analysis (PCA) and Positive Matrix Factorization (PMF) (Belis et al, 2013;Viana et al, 2008a;Watson et al, 2012).…”