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Comparison of Source Apportionment of PM2.5 Using PMF2 and EPA PMF Version 2
황인조,Philip K. Hopke 한국대기환경학회 2011 Asian Journal of Atmospheric Environment (AJAE) Vol.5 No.2
The positive matrix factorization (PMF2) and multilinear engine (ME2) models have been shown to be powerful environmental analysis techniques and have been successfully applied to the assessment of ambient particulate matter (PM) source contributions. Because these models are difficult to apply practically,the US EPA developed a more user-friendly version of the PMF. The initial version of the EPA PMF model does not provide any rotational capabilities; for this reason, the model was upgraded to include rotational functions in the EPA PMF ver. 2.0. In this study,PMF and EPA PMF modeling identified ten particulate matter sources including secondary sulfate I, vehicle gasoline, secondary sulfate II, secondary nitrate,secondary sulfate III, incinerators, aged sea salt,airborne soil particles, oil combustion, and diesel emissions. All of the source profiles determined by the two models showed excellent agreement. The calculated average concentrations of PM_2.5 were consistent between the PMF2 and EPA PMF (17.94±0.30μg/m^3 and 17.94±0.30 μg/m^3, respectively). Also,each set of estimated source contributions of the PMF2 and EPA PMF showed good agreement. The results from the new EPA PMF version applying rotational functions were consistent with those of PMF2. Therefore, the updated version of EPA PMF with rotational capabilities will provide more reasonable solutions compared with those of PMF2 and can be more widely applied to air quality management.
Sources of Carbonaceous Materials in the Airborne Particulate Matter of Dhaka
Bilkis A. Begum,Philip K. Hopke,Anwar Hossain,Golam Saroar,Swapan K. Biswas,Md. Nasiruddin,Nurun Nahar,Zohir Chowdury 한국대기환경학회 2011 Asian Journal of Atmospheric Environment (AJAE) Vol.5 No.4
To explore the sources of carbonaceous material in the airborne particulate matter (PM), comprehensive PM sampling was performed (3 to 14 January 2010)at a traffic hot spot site (HS), Farm Gate, Dhaka using several samplers: AirMetrics MiniVol (for PM_10and PM_2.5) and MOUDI (for size fractionated submicron PM). Long-term PM data (April 2000 to March 2006 and April 2000 to March 2010 in two size fractions (PM2.2 and PM_2.2-10) obtained from two air quality-monitoring stations, one at Farm Gate (HS) and another at a semi-residential (SR) area (Atomic Energy Centre, Dhaka Campus, (AECD)), respectively were also analyzed. The long-term PM trend shows that fine particulate matter concentrations have decreased over time as a result of government policy interventions even with increasing vehicles on the road. The ratio of PM_2.5/PM_10 showed that the average PM_2.5 mass was about 78% of the PM10 mass. It was also found that about 63% of PM_2.5 mass is PM_1. The total contribution of BC to PM_2.5 is about 16%and showed a decreasing trend over the years. It was observed that PM_1 fractions contained the major amount of carbonaceous materials, which mainly originated from high temperature combustion process in the PM_2.5. From the IMPROVE TOR protocol carbon fraction analysis, it was observed that emissions from gasoline vehicles contributed to PM_1 given the high abundance of EC1 and OC2 and the contribution of diesel to PM_1 is minimal as indicated by the low abundance of OC1 and EC2. Source apportionment results also show that vehicular exhaust is the largest contributors to PM in Dhaka. There is also transported PM_2.2 from regional sources. With the increasing economic activities and recent GDP growth,the number of vehicles and brick kilns has significantly increased in and around Dhaka. Further action will be required to further reduce PM-related air pollution in Dhaka.
Exploring the Variation between EC and BC in a Variety of Locations
Salako, Gbenga Oladoyin,Hopke, Philip K.,Cohen, David D.,Begum, Bilkis A.,Biswas, Swapan K.,Pandit, Gauri Girish,Lodoysamba, Sereeter,Wimolwattanapun, Wanna,Bunprapob, Supamatthree,Chung, Yong-Sam,Rah Taiwan Association for Aerosol Research 2012 Aerosol and air quality research Vol.12 No.1
Park, Eun Sug,Hopke, Philip K.,Oh, Man-Suk,Symanski, Elaine,Han, Daikwon,Spiegelman, Clifford H. Oxford University Press 2014 Biostatistics Vol.15 No.3
<P>There has been increasing interest in assessing health effects associated with multiple air pollutants emitted by specific sources. A major difficulty with achieving this goal is that the pollution source profiles are unknown and source-specific exposures cannot be measured directly; rather, they need to be estimated by decomposing ambient measurements of multiple air pollutants. This estimation process, called multivariate receptor modeling, is challenging because of the unknown number of sources and unknown identifiability conditions (model uncertainty). The uncertainty in source-specific exposures (source contributions) as well as uncertainty in the number of major pollution sources and identifiability conditions have been largely ignored in previous studies. A multipollutant approach that can deal with model uncertainty in multivariate receptor models while simultaneously accounting for parameter uncertainty in estimated source-specific exposures in assessment of source-specific health effects is presented in this paper. The methods are applied to daily ambient air measurements of the chemical composition of fine particulate matter ([Formula]), weather data, and counts of cardiovascular deaths from 1995 to 1997 for Phoenix, AZ, USA. Our approach for evaluating source-specific health effects yields not only estimates of source contributions along with their uncertainties and associated health effects estimates but also estimates of model uncertainty (posterior model probabilities) that have been ignored in previous studies. The results from our methods agreed in general with those from the previously conducted workshop/studies on the source apportionment of PM health effects in terms of number of major contributing sources, estimated source profiles, and contributions. However, some of the adverse source-specific health effects identified in the previous studies were not statistically significant in our analysis, which probably resulted because we incorporated parameter uncertainty in estimated source contributions that has been ignored in the previous studies into the estimation of health effects parameters.</P>