Receptor models application to multi-year ambient PM10 measurements in an industrialized ceramic area: comparison of source apportionment results

Ambient PM10 data collected in one of the largest industrialized ceramic areas of Europe were used to study similarities and differences in the source apportionment results from three widespread receptor models: chemical mass balance (CMB), positive matrix factorization (PMF) and principal component analysis (PCA). Particulate emissions were collected from a variety of sources including soil dust and different mixed raw materials used for the manufacture of ceramic tiles in the area. The chemical profiles of these emission sources are presented in this work. The analysis of the PMF scaled residuals was used as a diagnostic tool for adjusting species uncertainties and to assess the PMF model fit by comparison with the robust CMB results. The Q robust value, the degree of correlation between the predicted and measured species concentrations, the sample-by-sample correlation of the PMF source contributions compared with the CMB improved after the new error structure was used within the PMF model. The robustness of the CMB analysis used for the comparison with the PMF analysis was inspected by means of the CMB performances parameters as well as by comparing the results with a previous CMB analysis performed on the same database but with different speciated source profiles. Moreover, the results showed that PMF and PCA models were not able to distinguish between the two most important sources of crustal material in the selected area (one natural and one anthropogenic). With the CMB model a contribution from both sources was calculated without observing collinearity between the profiles. However, high correlation was found by adding the two crustal contributions from CMB and comparing the results with the single crustal factor from PCA and PMF. Low correlation was observed between the contribution values of the vehicular source for each model pairs. The lack of a local vehicular experimental profile for the CMB analysis and the non-specific chemical speciation performed for the ambient organic matter explained the low observed correlation.

[1]  Philip K. Hopke,et al.  A graphical diagnostic method for assessing the rotation in factor analytical models of atmospheric pollution , 2005 .

[2]  M. Sanz,et al.  PM10 speciation and determination of air quality target levels. A case study in a highly industrialized area of Spain. , 2007, The Science of the total environment.

[3]  X. Querol,et al.  PM sources in a highly industrialised area in the process of implementing PM abatement technology. Quantification and evolution. , 2007, Journal of environmental monitoring : JEM.

[4]  P. Hopke,et al.  Estimation of Organic Carbon Blank Values and Error Structures of the Speciation Trends Network Data for Source Apportionment , 2005, Journal of the Air & Waste Management Association.

[5]  P. Paatero,et al.  PM source apportionment and health effects: 1. Intercomparison of source apportionment results , 2006, Journal of Exposure Science and Environmental Epidemiology.

[6]  P. Paatero,et al.  Understanding and controlling rotations in factor analytic models , 2002 .

[7]  P. Paatero,et al.  Atmospheric aerosol over Alaska: 2. Elemental composition and sources , 1998 .

[8]  P. Paatero,et al.  Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values† , 1994 .

[9]  Impact of the implementation of PM abatement technology on the ambient air levels of metals in a highly industrialised area , 2007 .

[10]  A. Gupta,et al.  Source apportionment of PM10 at residential and industrial sites of an urban region of Kolkata, India , 2007 .

[11]  Judith C. Chow,et al.  Chemical Mass Balance Source Apportionment of PM10 during the Southern California Air Quality Study , 1994 .

[12]  P. Paatero.  Least squares formulation of robust non-negative factor analysis , 1997 .

[13]  Judith C. Chow,et al.  PM10 source apportionment in California's San Joaquin valley , 1992 .

[14]  Vasil Simeonov,et al.  Chemical mass balance source apportionment of PM10 in an industrialized urban area of Northern Greece , 2003 .

[15]  Ronald C. Henry,et al.  Current factor analysis receptor models are ill-posed , 1987 .

[16]  D. Norbäck,et al.  Source apportionment of ambient PM2.5 at five spanish centres of the european community respiratory health survey (ECRHS II) , 2007 .

[17]  J. Schauer,et al.  Characterization of metals emitted from motor vehicles. , 2006, Research report.

[18]  C. Borrego,et al.  Assessment of air pollution sources in an industrial atmosphere using principal component and multilinear regression analysis. , 1989, The Science of the total environment.

[19]  Xavier Querol,et al.  Monitoring of PM10 and PM2.5 around primary particulate anthropogenic emission sources , 2001 .

[20]  P. Paatero,et al.  Analysis of different modes of factor analysis as least squares fit problems , 1993 .

[21]  Philip K. Hopke,et al.  Discarding or downweighting high-noise variables in factor analytic models , 2003 .

[22]  Philip K. Hopke,et al.  Improving source identification of Atlanta aerosol using temperature resolved carbon fractions in positive matrix factorization , 2004 .

[23]  Tan Zhu,et al.  Receptor modeling application framework for particle source apportionment. , 2002, Chemosphere.

[24]  M. Viana,et al.  Inter-comparison of receptor models for PM source apportionment: Case study in an industrial area , 2008 .

[25]  J. Chow,et al.  Sources of PM10 and sulfate aerosol at McMurdo Station, Antarctica. , 2001, Chemosphere.

[26]  Hampden Kuhns,et al.  Source profiles for industrial, mobile, and area sources in the Big Bend Regional Aerosol Visibility and Observational study. , 2004, Chemosphere.

[27]  C. P. Thomas,et al.  Receptor Modelling for Air Quality Management , 1994 .

[28]  Prakash V. Bhave,et al.  Receptor Modeling of Ambient Particulate Matter Data Using Positive Matrix Factorization: Review of Existing Methods , 2007, Journal of the Air & Waste Management Association.