Source Apportionment of PM2.5 in the Metropolitan Area of Costa Rica Using Receptor Models

Herrera-Murillo, Jorge; Román, Susana Rodríguez; Marín, José Félix Rojas

doi:10.4236/acs.2013.34059

Cited by 7 publications

(1 citation statement)

References 26 publications

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“…To improve the quality of air, control measures have to be taken at the source itself. For that prioritization of sources has to be carried out which in turn requires source and risk apportionment studies (Ghosh et al 2018;Murillo et al 2013;Liu et al 2008;Yatkin and Bayram 2008). These studies predict critical sources whose emissions when controlled can bring a drastic improvement in the air quality.…”

Section: Introductionmentioning

confidence: 99%

Risk assessment and apportionment studies of particulate pollution at Ankamaly, South India

Selvaraju¹,

Keerthi²,

Varghese³

Global NEST International Conference on Environmental Science &Amp; Technology

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In the present world which is engulfed by the ill-effects of air pollution, stringent permissible emission standards to curb pollution are mandatory. Source and risk apportionment studies help to understand the sources and their contributions. Four parameters, lifetime average daily dose, hazard quotient, hazard index, excess cancer risk helped in assessing the risk at sampling site selected at Ankamaly, Kerala. Lifetime average daily dose was found be in the descending order as Fe> K> Na> Ca. Hazard quotient values were found to be greater than one for Mn and Cr, which conveys the non-carcinogenic effects due to pollution. Cu, Ni, and Zn were within safe limits. Chromium was found to be the main risk causing pollutant. Excess cancer risk (ECR) values shoot up to 6.67×10-3 for child and 2.86×10-3 for adults, which shows it is alarming. Risk apportionment was done and the highest contributors to risk were found to be smelting activities (92.4%) followed by wood residue burning (4.2%) and paved road dust (3.2%).

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Section: Introductionmentioning

confidence: 99%

Risk assessment and apportionment studies of particulate pollution at Ankamaly, South India

Selvaraju¹,

Keerthi²,

Varghese³

Global NEST International Conference on Environmental Science &Amp; Technology

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

Matawle

Pervez

Deb

et al. 2016

Environ Geochem Health

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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 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 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 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.

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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

Ogundele

Owoade

Olise

et al. 2016

Environ Monit Assess

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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.

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Source Apportionment of PM2.5 in the Metropolitan Area of Costa Rica Using Receptor Models

Cited by 7 publications

References 26 publications

Risk assessment and apportionment studies of particulate pollution at Ankamaly, South India

Risk assessment and apportionment studies of particulate pollution at Ankamaly, South India

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

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

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