Using a new WALSPMF model to quantify the source ...

Atmospheric Environment 126 (2016) 66e75

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Using a new WALSPMF model to quantify the source contributions to PM2.5 at a harbour site in China Guo-Liang Shi a, d, Jiao Xu a, Xing Peng a, Ying-Ze Tian a, Wei Wang b, Bo Han c, Yu-Fen Zhang a, Yin-Chang Feng a, *, Armistead G. Russell d a

State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin, 300071, China College of Software, Nankai University, No. 94 Weijin Road, Tianjin, 300071, China c Tianjin Key Laboratory for Air Traffic Operation Planning and Safety Technology, Civil Aviation University of China, Tianjin, 300300, China d School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, 30332-0512, Georgia b

h i g h l i g h t s
WALSPMF model was proposed in this work.
Synthetic receptor dataset was apportioned by the new model.
PM2.5 samples were collected in a large harbour in China.
WALSPMF and EPAPMF model were used to apportion PM2.5 sources.

a r t i c l e i n f o

a b s t r a c t

Article history: Received 8 July 2015 Received in revised form 21 November 2015 Accepted 23 November 2015 Available online 1 December 2015

PM2.5 variances have adverse impacts on human beings and the environment; therefore, source apportionment is very important and is a hot global topic. In this work, a new model called WALSPMF is proposed and evaluated for its accuracy. First, a synthetic test was carried out to compare the estimated source profile and contributions with the synthetic ones. Average absolute error (AAE) values were also calculated between the estimated and synthetic source contributions; most of the values were low (10 in ambient air, Yokohama, Japan. Atmos. Res. 96, 159e172. Kim, E., Hopke, P.K., 2004. Source apportionment of fine particles in Washington, DC, utilizing temperature-resolved carbon fractions. J. Air Waste Manag. Assoc. 54, 773e785. Kwok, R.H.F., Napelenok, S.L., Baker, K.R., 2013. Implementation and evaluation of PM2.5 source contribution analysis in a photochemical model. Atmos. Environ. 80, 398e407. Lee, S., Russell, A., 2007. Estimating uncertainties and uncertainty contributors of CMB PM2.5 source apportionment results. Atmos. Environ. 41, 9616e9624. Li, L., 2014. Ambient particle characterization by single particle aerosol mass spectrometry in an urban area of Beijing. Atmos. Environ. 94, 323e331. Linares, C., Díaz, J., 2009. Impact of particulate matter with diameter of less than 2.5 microns [PM2. 5] on daily hospital admissions in 0-10-year-olds in Madrid. Spain [2003-2005]. Gac. Sanit. 23, 192e197. Liu, Y., Wang, S.Y., Rainer, L., Yu, N., Zhang, C.K., Gao, Y., Zhao, J.F., Ma, L.M., 2015. Source apportionment of gaseous and particulate PAHs from traffic emission using tunnel measurements in Shanghai, China. Atmos. Environ. 107, 129e136. Manousakas, M., Diapouli, E., Papaefthymiou, H., 2015. Source apportionment by PMF on elemental concentrations obtain3qed by PIXE analysis of PM10 samples collected at the vicinity of lignite power plants and mines in Megalopolis, Greece. Nucl. Instrum. Methods Phys. Res. Sect. B Beam Interact. Mater. Atoms 349, 114e124. Maykut, N.N., Lewtas, J., Kim, E., Larson, T.V., 2003. Source apportionment of PM2.5 at an urban improve site in Seattle, Washington. Environ. Sci. Technol. 37, 5135e5142. Mimura, T., Ichinose, T., Yamagami, S., Fujishima, H., Kamei, Y., Goto, M., 2014. Airborne particulate matter (PM2.5) and the prevalence of allergic conjunctivitis in Japan. Sci. Total Environ. 487, 493e499. Mooibroek, D., Schaap, M., Weijers, E.P., Hoogerbrugge, R., 2011. Source apportionment and spatial variability of PM2.5 using measurements at five sites in the Netherlands. Atmos. Environ. 45, 4180e4191. Paatero, P., Eberly, S., Brown, S.G., Norris, G.A., 2014. Methods for estimating uncertainty in factor analytic solutions. Atmos. Meas. Tech 7, 781e797. Paatero, P., Hopke, P.K., Begum, B.A., Biswas, S.K., 2005. A graphical diagnostic method for assessing the rotation in factor analytical models of atmospheric pollution. Atmos. Environ 39, 193e201. Paatero, P., Hopke, P.K., Song, X.H., Ramadan, Z., 2002. Understanding and controlling rotations in factor analytic models. Chemom. Intell. Lab. Syst. 60, 253e264. Paatero, P., 2007. User's Guide for Positive Matrix Factorization Programs PMF2 and PMF3, Part1e2: Tutorial. University of Helsinki, Helsinki, Finland. Paatero, P., Tapper, U., 1994. Positive matrix factorization: a non-negative factor model with optimal utilization of error estimates of data values. Environmetrics 5, 111e126. Pant, P., Harrison, R.M., 2012. Critical review of receptor modelling for particulate matter: a case study of India. Atmos. Environ. 49, 1e12. Park, S.S., Kim, Y.J., 2005. Source contributions to fine particulate matter in an urban atmosphere. Chemosphere 59, 217e226. Parworth, C., Fast, J., Mei, F., 2015. Long-term measurements of submicrometer aerosol chemistry at the Southern Great Plains (SGP) using an Aerosol chemical

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