Quantifying road dust resuspension in urban environment by Multilinear Engine: A comparison with PMF2

Abstract Atmospheric PM pollution from traffic comprises not only direct emissions but also non-exhaust emissions because resuspension of road dust that can produce high human exposure to heavy metals, metalloids, and mineral matter. A key task for establishing mitigation or preventive measures is estimating the contribution of road dust resuspension to the atmospheric PM mixture. Several source apportionment studies, applying receptor modeling at urban background sites, have shown the difficulty in identifying a road dust source separately from other mineral sources or vehicular exhausts. The Multilinear Engine (ME-2) is a computer program that can solve the Positive Matrix Factorization (PMF) problem. ME-2 uses a programming language permitting the solution to be guided toward some possible targets that can be derived from a priori knowledge of sources (chemical profile, ratios, etc.). This feature makes it especially suitable for source apportionment studies where partial knowledge of the sources is available. In the present study ME-2 was applied to data from an urban background site of Barcelona (Spain) to quantify the contribution of road dust resuspension to PM 10 and PM 2.5 concentrations. Given that recently the emission profile of local resuspended road dust was obtained (Amato, F., Pandolfi, M., Viana, M., Querol, X., Alastuey, A., Moreno, T., 2009. Spatial and chemical patterns of PM 10 in road dust deposited in urban environment. Atmospheric Environment 43 (9), 1650–1659), such a priori information was introduced in the model as auxiliary terms of the object function to be minimized by the implementation of the so-called “pulling equations”. ME-2 permitted to enhance the basic PMF solution (obtained by PMF2) identifying, beside the seven sources of PMF2, the road dust source which accounted for 6.9 μg m −3 (17%) in PM 10 , 2.2 μg m −3 (8%) of PM 2.5 and 0.3 μg m −3 (2%) of PM 1 . This reveals that resuspension was responsible of the 37%, 15% and 3% of total traffic emissions respectively in PM 10 , PM 2.5 and PM 1 . Therefore the overall traffic contribution resulted in 18 μg m −3 (46%) in PM 10 , 14 μg m −3 (51%) in PM 2.5 and 8 μg m −3 (48%) in PM 1 . In PMF2 this mass explained by road dust resuspension was redistributed among the rest of sources, increasing mostly the mineral, secondary nitrate and aged sea salt contributions.

[1]  Eliseo Monfort,et al.  Source origin of trace elements in PM from regional background, urban and industrial sites of Spain , 2007 .

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

[3]  Bert Brunekreef,et al.  Chemical composition and mass closure of particulate matter at six urban sites in Europe , 2006 .

[4]  M. Thompson Variation of precision with concentration in an analytical system , 1988 .

[5]  Roy M Harrison,et al.  Sources and properties of non-exhaust particulate matter from road traffic: a review. , 2008, The Science of the total environment.

[6]  Xavier Querol,et al.  PM10 and PM2.5 source apportionment in the Barcelona Metropolitan area, Catalonia, Spain , 2001 .

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

[8]  P. Hopke,et al.  Source characterization of ambient fine particles at multiple sites in the Seattle area , 2008 .

[9]  B. Huebert,et al.  Uncertainties in data on organic aerosols , 2000 .

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

[11]  P. Paatero,et al.  Source identification of bulk wet deposition in Finland by positive matrix factorization , 1995 .

[12]  Haidong Kan,et al.  Differentiating the effects of fine and coarse particles on daily mortality in Shanghai, China. , 2007, Environment international.

[13]  P. Zhao,et al.  Characterizations of resuspended dust in six cities of North China , 2006 .

[14]  G. M. Hidy,et al.  Multivariate analysis of particulate sulfate and other air quality variables by principal components-Part I: Annual data from Los Angeles and New York , 1979 .

[15]  Shuiyuan Cheng,et al.  Characteristics of re-suspended road dust and its impact on the atmospheric environment in Beijing , 2007 .

[16]  B. Brunekreef,et al.  Heterogeneities in Inflammatory and Cytotoxic Responses of RAW 264.7 Macrophage Cell Line to Urban Air Coarse, Fine, and Ultrafine Particles From Six European Sampling Campaigns , 2007, Inhalation toxicology.

[17]  Michael J. Thompson,et al.  Duplicate analysis in geochemical practice. Part I. Theoretical approach and estimation of analytical reproducibility , 1976 .

[18]  M. Lag,et al.  Importance of Size and Composition of Particles for Effects on Cells In Vitro , 2007, Inhalation toxicology.

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

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

[21]  Ž. Bogdan,et al.  Modelling of SO3 Formation in the Flame of a Heavy-oil Fired Furnace , 2003 .

[22]  G. M. Hidy,et al.  The Health Relevance of Ambient Particulate Matter Characteristics: Coherence of Toxicological and Epidemiological Inferences , 2006, Inhalation toxicology.

[23]  P. Paatero The Multilinear Engine—A Table-Driven, Least Squares Program for Solving Multilinear Problems, Including the n-Way Parallel Factor Analysis Model , 1999 .

[24]  Hirokazu Kimura,et al.  Particle size and composition distribution analysis of automotive brake abrasion dusts for the evaluation of antimony sources of airborne particulate matter , 2007 .

[25]  Robert Tibshirani,et al.  Bootstrap Methods for Standard Errors, Confidence Intervals, and Other Measures of Statistical Accuracy , 1986 .

[26]  M. Viana,et al.  Partitioning of major and trace components in PM10–PM2.5–PM1 at an urban site in Southern Europe , 2008 .

[27]  Mar Viana,et al.  Spatial and chemical patterns of PM10 in road dust deposited in urban environment , 2009 .

[28]  B. Brunekreef,et al.  Epidemiological evidence of effects of coarse airborne particles on health , 2005, European Respiratory Journal.

[29]  Christopher A. Laroo,et al.  Brake Wear Particulate Matter Emissions , 2000 .

[30]  B. Brunekreef,et al.  Dose and Time Dependency of Inflammatory Responses in the Mouse Lung to Urban Air Coarse, Fine, and Ultrafine Particles From Six European Cities , 2007, Inhalation toxicology.

[31]  G. Kallos,et al.  Saharan dust contributions to PM10 and TSP levels in Southern and Eastern Spain , 2001 .

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

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

[34]  G. Dongarrà,et al.  Metal distribution in road dust samples collected in an urban area close to a petrochemical plant at Gela, Sicily , 2006 .

[35]  M. Viana,et al.  Comparative chemical mass closure of fine and coarse aerosols at two sites in south and west Europe : Implications for EU air pollution policies , 2007 .

[36]  Francesca Dominici,et al.  Coarse particulate matter air pollution and hospital admissions for cardiovascular and respiratory diseases among Medicare patients. , 2008, JAMA.

[37]  R. Hillamo,et al.  Effects of solubility of urban air fine and coarse particles on cytotoxic and inflammatory responses in RAW 264.7 macrophage cell line. , 2008, Toxicology and applied pharmacology.

[38]  John G. Watson,et al.  The effective variance weighting for least squares calculations applied to the mass balance receptor model , 1984 .

[39]  John D. Spengler,et al.  A QUANTITATIVE ASSESSMENT OF SOURCE CONTRIBUTIONS TO INHALABLE PARTICULATE MATTER POLLUTION IN METROPOLITAN BOSTON , 1985 .

[40]  M. Hestenes,et al.  Methods of conjugate gradients for solving linear systems , 1952 .

[41]  Vicki Stone,et al.  Inflammatory effects of coarse and fine particulate matter in relation to chemical and biological constituents. , 2004, Toxicology and applied pharmacology.

[42]  H. Hansson,et al.  Speciation and origin of PM10 and PM2.5 in selected European cities , 2004 .

[43]  Xavier Querol,et al.  Comparative PM10-PM2.5 source contribution study at rural, urban and industrial sites during PM episodes in Eastern Spain. , 2004, The Science of the total environment.

[44]  Philip K. Hopke,et al.  Rotational tools for factor analytic models , 2009 .

[45]  Christer Johansson,et al.  Studies of some measures to reduce road dust emissions from paved roads in Scandinavia , 2006 .