Abstract
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 PM2.5 were consistent between the PMF2 and EPA PMF (17.94±0.30 ㎍/㎥ and 17.94±0.30 ㎍/㎥, 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.
Highlights
To manage ambient air quality and establish effective emission reduction strategies, it is necessary to identify sources and to apportion the ambient particulate matter (PM) mass
Among the multivariate statistical receptor models used for PM source identification and apportionment, the positive matrix factorization (PMF) model was developed to provide a multivariate receptor modeling approach based on explicit least-squares techniques (Paatero, 1997)
If specific species in the source profiles do not seem to be realistic compared with the measured source profiles, and if the values obtained in the previous analysis are similar, it is possible to adjust the values toward zero in order to obtain a reasonable source profile using the Fkey matrix
Summary
To manage ambient air quality and establish effective emission reduction strategies, it is necessary to identify sources and to apportion the ambient particulate matter (PM) mass. Quantitative and qualitative source analyses are needed to facilitate control policies to reduce ambient air pollutants. To this end, receptor models have been developed to analyze various characteristics of the pollutants at the receptor site and to estimate the source contributions. Receptor modeling is based on a mathematical model that analyzes the physicochemical properties of gaseous and/or particulate pollutants at various atmospheric receptors. Among the multivariate statistical receptor models used for PM source identification and apportionment, the positive matrix factorization (PMF) model was developed to provide a multivariate receptor modeling approach based on explicit least-squares techniques (Paatero, 1997). A more flexible multivariate analysis tool, the multilinear engine (ME), was developed to solve a variety of multilinear problems (Paatero, 1999). ME has already been applied in several studies because of its flexibility (Buset et al, 2006; Ogulei et al, 2005; Zhou et al, 2004; YliTuomi et al, 2003)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
