ReviewSource apportionment of particulate matter in Europe: A review of methods and results
Introduction
In the field of atmospheric sciences, source apportionment (SA) models aim to re-construct the impacts of emissions from different sources of atmospheric pollutants, e.g., particulate matter (PM), based on ambient data registered at monitoring sites (Bruinen de Bruin, Koistinen, Yli-Tuomi, Kephalopoulos, & Jantunen, 2006, Hopke and Song, 1997, Watson et al., 2002). There are three main groups of SA techniques:
- (a)
Methods based on the evaluation of monitoring data. Basic numerical data treatment is used to identify sources. Examples are: (1) correlation of wind direction with levels of measured components to identify source locations (Henry, Chang, & Spiegelman, 2002); (2) the correlation of gaseous pollutants with PM components to identify source associations; (3) subtraction of levels measured at regional background from those obtained at urban background and/or roadside levels to identify the contributions from the regional background, the city background and the monitored street (Lenschow et al., 2001), or (4) quantification of natural PM contributions (e.g., African dust) by subtracting PM levels at regional background sites from those at urban background locations for specific days (Escudero et al., 2007). The main advantage is the simplicity of the methods and the consequent low impact of mathematical artefacts due to data treatment.
- (b)
Methods based on emission inventories and/or dispersion models to simulate aerosol emission, formation, transport and deposition (Eldering and Cass, 1996, Visser et al., 2001). These models require detailed emission inventories that are not always available, and they are limited by the accuracy of emission inventories, especially when natural emissions are important. A significant advantage of these methods is that they may be used in scenario studies to evaluate the impact of emission abatement strategies on the anthropogenic contribution to ambient PM concentrations.
- (c)
Methods based on the statistical evaluation of PM chemical data acquired at receptor sites (receptor models). The fundamental principle of receptor modelling is that mass and species conservation can be assumed and a mass balance analysis can be used to identify and apportion sources of airborne PM in the atmosphere (Hopke et al., 2006). An overview of the wide range of statistical models and modelling approaches which are currently available in the literature is shown in Fig. 1. As shown in the graph, one of the main differences between models is the degree of knowledge required about the pollution sources prior to the application of receptor models. The two main extremes of receptor models are chemical mass balance (CMB) and multivariate models.
Principal component/factor analysis (e.g., principal component analysis or PCA, positive matrix factorisation or PMF, UNMIX) attempts to apportion the sources on the basis of observations (internal correlations) at the receptor site alone. These are commonly used tools, because software to perform this type of analysis is widely available and detailed prior knowledge of the sources and source profiles is not required. The choice of the model dimension and the search for non-negative solutions by axis rotations can be based entirely on mathematical criteria. Nevertheless, it has been suggested that factor analysis attempts to get more information out of atmospheric data than is really there (Henry, 1987). Furthermore, it is a common problem that the resulting components or factors may represent mixtures of emission sources, as opposed to clearly independent source profiles. Source signatures that change with time are a limitation for this and other types of receptor models.
To combine the advantages and reduce the disadvantages of CMB and factor analysis hybrid models have been developed. Examples are confirmatory or target transformation factor analysis, which offer some control of the solutions by “fixing” or “freeing” specific parameters, set according to the theoretical expectation of the researcher (Gleser, 1997, Hopke, 1988). With a constrained physical receptor model (COPREM, Wåhlin, 2003), an initial profile matrix with the main characteristics of known sources as columns is used, and a priori knowledge about the character of the sources can be used to achieve a solution with a sufficient number of sources. The multilinear engine (ME, Paatero, 1999) can solve multilinear problems with the possibility of implementing many kinds of constraints using a script language. Like COPREM, the program allows choosing hybrid versions in the full range between PMF and CMB-type models, with the difference that individual data points can be properly weighed (not possible in any eigenvector analysis).
The main objective of receptor models is, therefore, to identify the possible sources of PM (if not assumed already from the source profiles) and to obtain data on their contributions to the bulk PM mass. Even human exposure to these pollution components has been evaluated to assess their health effects and risks (Hopke et al., 2006, Ilacqua et al., 2007, Watson et al., 2002). Furthermore, policy-makers require sound scientific knowledge of the PM sources and their contributions to atmospheric PM levels and associated health risks for the development and implementation of policies to protect human health and the environment. Thus the information provided by receptor models is key to the design of effective mitigation strategies on the local- and meso-scale.
Despite the widespread need for these data, there is little information available on receptor modelling results from different European countries, the type of models applied or the input data utilised. Furthermore, parameters such as time resolution, type of monitoring site and PM sampler, and analytical methods vary widely from one study to another. Consequently, general European-wide conclusions cannot yet be extracted. In addition to this lack of harmonisation in the published studies, a large amount of valuable material is currently available in the grey literature only.
The need to tackle this issue was detected by the COST Action 633 (“Particulate matter: Properties related to health effects”) and subsequently addressed by compiling a database of SA studies available in Europe, more specifically in COST633 member countries (http://www2.dmu.dk/atmosphericenvironment/COST633/). Both meta-data on publications and data on PM sources and source contributions were collected, with the aim to obtain an overview of the results and analyse their comparability. The issue of the comparability between receptor modelling results is considered essential if SA data are to be used for the design of multinational mitigation strategies, the assessment of human exposure to PM from specific sources, and investigations of long-term changes in exposure situations over Europe.
Section snippets
Methodology
The compilation of meta-data on SA publications was carried out by means of a questionnaire, distributed via e-mail among researchers from COST633 member countries and based on already existing publications in international scientific journals or public reports. The authors were asked to report on meta-data such as the receptor modelling technique used, the location of the study area, the type of sampler or the analytical methodology used, among others.
Despite the fact that replies to the
Receptor models in use in Europe
When evaluating the European publications reported in the questionnaires, PCA was the most frequently used model up to 2005 (30% of the studies), followed by the Lenschow approach (11%) and back-trajectory analysis (11%). Other models commonly used were PMF (8%), CMB (7%) and mass balance analysis (addition of chemically analysed PM components, 7%). Methods such as ME, COPREM or UNMIX were only reported in one study each until 2005. Data from 2006–2007 show a continued use of PCA (50% of the
Gap analysis and possible research directions
The review of existing data and their meta-analysis, presented in this work, evidenced the following gaps, (a)–(f), and upcoming research directions, (g)–(l), which should be taken into account for the design of future SA studies
- (a)
Calculation of uncertainty estimates for SA results: by means of bootstrapping or other methods. Uncertainty estimates are currently unavailable from European SA studies.
- (b)
Quantification of natural source contributions (e.g., African or windblown dust, secondary organic
Acknowledgements
The authors would like to thank all the researchers who kindly provided data for the questionnaires. This work was carried out under COST Action 633, and it was partially funded by the Spanish Ministries of Education and Science (Secretaría de Estado de Universidades e Investigación) and of the Environment (B026/2007/3-10.1).
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