Monitoring and assessment of the physical and chemical constituents of airborne pollutants are now common features of the efforts of Queensland Environmental Protection Agency (EPA) to protect the public from the adverse effects of ambient air pollution. This paper describes the evaluation of the profiles of the 21 chemical constituents of 24-hourly integrated fine particle samples collected by EPA at Rocklea from 1995 to 2003. Because of the multivariate nature of the data, they were subjected to the multi-criteria decision making method, Preference Ranking Organisation Method for Enrichment Evaluation (PROMETHEE) and Principal Component Analysis. The PROMETHEE results indicated that the quality of the fine particulate constituents at the site decreased in the order 1995 >1997 >1996 >1998 >2002 >2000 > 2003 > 2001 > 1999. The most important constituents influencing the ranking included the elemental carbon, sulfur, lead, bromine, vanadium, and chromium contents of the samples. Such multi-criteria ranking provides information that can assist the formulation and prioritization of mitigating measures. For example, the phasing out of leaded fuels contributed significantly to the lowering of Pb and Br in the samples. On the other hand, Principal Component Analysis showed that the major constituents clustered into groups with compositional profiles which suggested that the main sources of the particles were airborne soil, combustion activities (vehicular emission and vegetative burning), industrial emissions, secondary aerosols and sea salt. Correlation with other pollutants and meteorological data concurrently measured at the site provided further insights into the sources of the fine particles

Ayoko, Godwin

Eltahir, Sohair

English

Queensland University of Technology ePrints Archive

  This is the author version of an article published as:   Ayoko, Godwin and El-tahir, Sohair (2007) Multivariate statistical evaluation of the chemical composition of fine particles: A case study. In Proceedings 14th International IUAPPA World Congress, Brisbane, Australia.  Copyright 2007 Please consult Author   Accessed from   http://eprints.qut.edu.au          MULTIVARIATE STATISTICAL EVALUATION OF THE CHEMICAL COMPOSITION OF FINE PARTICLES:  A CASE STUDY  Godwin A. Ayoko and Sohair E. El-tahirInternational Laboratory for Air Quality and Health, School of Physical and Chemical Sciences,  Queensland University of Technology, GPO 2434, Brisbane QLD 4001, Australia  Abstract Monitoring and assessment of the physical and chemical constituents of airborne pollutants are now common features of the efforts of Queensland Environmental Protection Agency (EPA) to protect the public from the adverse effects of ambient air pollution. This paper describes the evaluation of the profiles of the 21 chemical constituents of 24-hourly integrated fine particle samples collected by EPA at Rocklea from 1995 to 2003. Because of the multivariate nature of the data, they were subjected to the multi-criteria decision making method, Preference Ranking Organisation Method for Enrichment Evaluation (PROMETHEE) and Principal Component Analysis. The PROMETHEE results indicated that the quality of the fine particulate constituents at the site decreased in the order 1995 >1997 >1996 >1998 >2002 >2000 > 2003 > 2001 > 1999. The most important constituents influencing the ranking included the elemental carbon, sulfur, lead, bromine, vanadium, and chromium contents of the samples. Such multi-criteria ranking provides information that can assist the formulation and prioritization of mitigating measures. For example, the phasing out of leaded fuels contributed significantly to the lowering of Pb and Br in the samples. On the other hand, Principal Component Analysis showed that the major constituents clustered into groups with compositional profiles which suggested that the main sources of the particles were airborne soil, combustion activities (vehicular emission and vegetative burning), industrial emissions, secondary aerosols and sea salt. Correlation with other pollutants and meteorological data concurrently measured at the site provided further insights into the sources of the fine particles.  Keywords: Multi-Criteria Decision Making, PROMETHEE, PCA, Source identification.   1. Introduction  Public concerns about urban air quality have increased over the past decade due to recognized links between exposure to airborne particles and increased frequency and severity of respiratory diseases such as asthma (Oberdorster et al, 1995). Since particles with different sizes and chemical compositions are associated with different types of human health risks, understanding and preventing air pollution has become a major challenge for the world. The challenge is more in urban areas not only because of the ever-increasing levels of anthropogenic activities which generate considerable amounts of air pollutants but also because human exposure risks are higher in urban areas where an appreciable proportion of the population usually resides in most countries. Additionally, airborne particles have been shown to scatter light effectively and consequently affect visibility and atmospheric energy balance appreciably (Twomey, 1977 and Charlson et al, 1992). Therefore, monitoring and assessment of the physical and chemical constituents of airborne pollutants are now common features of the efforts of Queensland Environmental Protection Agency  (QEPA) to protect the public from the health and environmental effects of ambient air pollution (EPA, 2003). In the present investigation, an analysis of the air quality data gathered for the period of April 1995 to December 2003 by the QEPA’s Aerosol Sampling Program (ASP) at  Rocklea air monitoring station was undertaken. The aims of this study are: to determine the sources of the pollutants in PM 2.5 samples collected at Rocklea station by applying different methods of data analysis and to evaluate the effectiveness of these methods in maximizing the chemical information which were extracted from the data.  2. Data Collection The data used for this work was generated from the PM2.5 24-hourly samples collected twice weekly at the Rocklea air monitoring station from 1995 to 2003 by QEPA. The samples were analysed for the 21 chemical species listed in Table 1 using ion  1beams from the Australian Nuclear Science and Technology Organisation environment accelators in Sydney. 3. Data Analysis  The data matrix consisted of the days on which the PM2.5 samples were collected as objects (arranged in rows) and the chemical constituents, namely, H, Na, Al, Si, P, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Br, Pb and EC as the variables (arranged in the columns). A total of 883 objects (days) and 21 variables were used for the analysis. Wind speed was measured by QEPA by using an instrument called an anemometer while wind vanes were used to monitor wind direction.   Table (1) Summary statistics for the species used for the study (µg m3)  Species  Min Median Max Name       H 22.10 193.10 2603.00 Na  48.60 330.22 2143.90 Al 0.30 16.00 1723.50 Si 6.60 45.40 4598.10 P 0.10 4.16 25.00 S 79.60 320.20 2060.00 Cl 0.20 135.30 2118.60 K 7.40 47.05 7421.20 Ca 6.20 24.70 477.20 Ti 0.10 3.40 157.20 V 0.10 0.58 7.90 Cr 0.10 0.52 7.00 Mn 0.10 1.88 118.20 Fe 3.70 36.85 1237.20 Co 0.10 0.50 29.20 Ni 0.10 0.46 9.40 Cu 0.10 2.53 118.40 Zn 0.10 11.80 3836.60 Br 0.10 7.55 148.00 Pb 0.10 23.50 323.00 EC 276.45 1456.85 15004.70  3.1 Principal Component Analysis Principal Component Analysis (PCA) is a well-known data display methods used for multivariate analysis (Ayoko et al, 2007).It effects data reduction by transforming an original data matrix into a set of linear, orthogonal latent variables referred to as Principal Components (PCs) such that the first PC describes most of the data variance, the second PC the next largest amount and the so on until the number of PCs are the same as the number of the original variables. To facilitate visual appreciation of PCA results, they are usually displayed as “scores” plot, which describes the relationships among the objects or “loadings” plot, which describes the relationships of the original variables with one another. When both plots are displayed on the same visual representation, the resultant “biplot” provides additional information about the association between the objects and the variables.  PCA was carried out in this study with the aid SIMCA-P-10 software.  3.2 Multi-criteria Decision Making A Multi-criteria decision making (MCDM) method is a very powerful and useful tool. It provides a pathway for bringing together many different variables and stakeholders. It also provides an experimental platform for visually modeling competing scenarios by providing valuable guidance to the decision makers who ultimately makes the final decision (Kokot and Ayoko, 2005).  There are many MCDM methods. These include methods such as: Elimination Et Choix Traduisant la Realite’ (ELECTRE), Simple Multi Attribute Ranking Technique and Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE). Of these, PROMETHEE was used in this work because it is becoming an increasingly popular method for evaluating environmental data (see e.g. Ayoko et al, 2004 and Ayoko et al, 2007). PROMETHEE is a nonparametric method applied in Euclidian space to rank objects. Each variable in the raw data matrix is set to maximize or minimize, indicating that higher or lower variables values are preferred. All variables were minimized in this work to reflect the preference for lower pollutant concentrations. To refine the preference selection process, positive (φ+) and negative (φ-) outranking flows were computed; followed by the calculation of the net out ranking flow, φ = φ+ - φ- (where φ+ denotes how an object outranks all other objects and φ- denotes how it is ranked by all other objects). Objects with more positive net outranking flow values are preferred to those with less positive out ranking flows. The coupling of this method with the principal components analysis approach of GAIA provides a powerful tool for multi-criteria decision making (Keller et. al ., 1991).  GAIA, other hand, is a special PCA biplot obtained the decomposition of the PROMETHEE net outranking flows (Keller et. al , 1991).  It reduces a large number of variables into two  2principal components (PC1and PC2) that are orthogonal to each other and incorporates a decision axis, which facilitates the interpretation of the PROMETHEE results. A typical GAIA biplot shows visually how objects relate to one another and to the variables, as well as how variables relate to each other. Thus, it facilitates the interpretation of the significance of the different variables.    The GAIA analysis reported in this work was performed with the aid of Decision Lab 2000 Executive Edition Software and the results obtained were interpreted according to the guidelines described by Espinasse et. al. (1997).                                                                                  4. Results 4.1 Principal Component Analysis  Six clusters of variables are discernible from the loading plot displayed as Fig.1.   Cluster 1:  In this cluster, WD (wind direction) is closely correlated with Na and Cl in keeping with the fact that the marine aerosols were transported to the receptor site from the sea, which (relative to the position of the site) .is located at the north eastern part of the city. This cluster was identified as the sea salt cluster (see e.g. Kim, et al, 2003; Xie et al, 1999; Thurston and Spengler, 1985).   Cluster 2. The second cluster shows crustal elements such as Ca, Ti, Fe, Al and Si to be  correlated with one another. Thus, this   cluster is regarded as the airborne soil cluster in line with similar assignments in the literature (see e.g. Kim, et al, 2003; Xie et al, 1999; Thurston and Spengler, 1985)  Cluster 3. Pb, Br, H and EC are strongly correlated in this cluster, indicating that these elements originated from the same source (emissions from diesel and petrol powered vehicles).  Cluster 4. Cu, Cr and Mn are   strongly correlated in this cluster. This suggests that they may have a common origin such as emissions from local industrial activities (Kim et al, 2003).  Cluster 5. This cluster has the two elements (Ni and V) that are usually associated with oil combustion (Thurston and Spengler, 1985). Therefore, the cluster is assigned oil combustion.  Cluster 6. P, Zn and S are strongly correlated in this cluster. S is a key marker of secondary aerosols (Kim et al, 2003; Xie et al, 1999). Hence the cluster is identified as a secondary aerosols cluster.   Fig. 1 Loading plot for the site showing correlations between parameters PC1=33.85%variance; PC2=15% variance) 4.2. Multi Criteria Decision Making   The results of the PROMETHEE II ranking of the objects, based on simultaneous assessment of the 21 chemical species, are presented in Table 2. It showed that the highest ranking year (with least pollution) on the basis of the concentrations of the chemical species was 1999, and the lowest ranking with year was 1995.  Table 2 Ranking of objects (Years)  Rank Year Outranking flow,ø 1 1999 0.55 2 2001 0.31 3 2003 0.28 4 2000 0.00 5 2002 -0.03 6 1996 -0.13 6 1998 -0.13 7 1997 -0.55 8 1995 -0.56  Two clusters of objects were evident from the plot displayed in Fig 2.  The first cluster consisted of objects that generally had positive PC1 (1999-2003).These objects are located in the direction of the decision axis (π), indicating that they have the lowest levels of the pollutants. In the second cluster, the objects (1995-1998) had negative PC1 and the decision axis (π) pointed away from them indicating that these years had the highest levels of the pollutants. A close look at the PROMETEE  3results presented in Table 2 and the GAIA plot in Fig 2 shows that the results from both procedures reinforce each other and that objects that are associated with the period when leaded fuel was phased out or being phased out generally had lower concentrations of the pollutants.   Fig. 2 GAIA analysis for the site showing correlations between parameters (■), years (▲) and pi (π) decision- making axis ( ●)  It can be seen from Fig 3 and Fig 4 that the concentrations of airborne Pb and Br, which are markers for leaded fuel, decreased from 1995 to 2003. The similarity of these trends to that for the ranking of the overall pollutant levels obtained in each year probably reflect the importance  of vehicular emission in urban air pollution.      Fig 3.  Annual variation of Pb concentrations during the study period.   Fig 4.  Annual variation of Br concentrations during the study period.   5. Conclusions  Multivariate data analysis methods have been applied to the PM2.5 samples collected at Rocklea, Brisbane, Australia by QEPA from 1995 to 2003. The results suggested that airborne soil, vehicular emission, emissions from local industries, secondary aerosols, sea salts and oil combustion are the most important sources of the aerosols found in the PM2.5 samples collected at this air monitoring station. Thus, any attempts to reduce the level of aerosols reaching the station and indeed Brisbane, must include the formulation and implementation of mitigating measures that address the reduction of the strengths of these sources. It was evident from the study that anthropogenic sources such as vehicle emissions and local industry are important sources of air pollution around this station. The fact that the levels of Pb and Br, which are markers for emissions from vehicles using leaded fuel, were lower after the phasing out of leaded fuel, highlight the impact that source control can have on ambient air quality.  Further analysis of the results from this station is being carried out in order to gain insights into the quantitative contributions of each of the most important sources.  6. Acknowledgments  We thank Queensland EPA for the data used for this analysis.       4References   Ayoko G.A, Morawska. L . Kokot, S., S., Gilbert, D., (2004). Application of multi-criteria decision making methods to air quality in the microenvironments of residential houses in Brisbane, Australia .  Environmental Science and Technology 38(9), 2609-2616. Ayoko, G. A., Singh, K., Balerea, S., Kokot, S. (2007) Exploratory multivariate modelling and prediction of the physico-chemical properties of surface water and groundwater. Journal of Hydrology,336, 115-124.  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Multivariate Statistical Evaluation of the Chemical Composition of Fine Particles : A Case Study

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