ReviewCritical review and meta-analysis of ambient particulate matter source apportionment using receptor models in Europe
Highlights
► Receptor models evolve towards tools with refined uncertainty treatment. ► Positive Matrix Factorization and Chemical Mass Balance are the most used models. ► Gas-to-particle conversion is the main PM mass and particulate organic carbon source. ► To abate exceedances, secondary inorganic and traffic are the main sources to target. ► More long term speciated PM datasets would foster source identification studies.
Introduction
The apportionment of sources for air pollution in areas where the legislation thresholds are exceeded is of utmost relevance for the development of remediation measures. Different approaches have been used in Europe: a) chemical transport models based upon pollutant emission rates and meteorological data, and b) receptor-oriented models (RMs), especially for airborne particulate matter (PM), based on statistical analysis of pollutant concentrations measured at a sampling site (receptor site) to infer the source-types and estimate their contributions to the measured site concentrations. The pollutants used for RMs are referred to here as receptor species.
How to classify receptor models? Receptor oriented source apportionment (SA) encompasses many tools ranging from simple techniques based on elementary mathematical calculations and basic physical assumptions (e.g. enrichment factor analysis) to complex models with pre- and post-data processing and user-friendly interfaces. Although all such tools deal with measured pollutant concentrations at the receptor site, the nature of the input data and the formats vary considerable. In general, there are three kinds of data input: ambient pollutant mass concentrations, source profiles, and meteorological data (i.e. wind speed and direction or backward trajectories). In addition, there are extended models, which can process other kind of information like season, week-day, precipitation, etc.
The present study presents a review of the most common methods and a meta-analysis of receptor models' source contribution estimation (SCE) for PM in Europe yielding a quantitative estimation of the most relevant source types and a mapping of their spatial distributions.
In this work, the approach used by Viana et al. (2008b) and Karagulian and Belis (2012) was extended and updated. SA methods are critically described and classified, including those dealing with particulate organic carbon. The information on pollution sources is sorted, summarized, and quantitatively evaluated, taking into account the sources of uncertainty. In this research the causes of exceedances are quantified on the basis of source contributions providing inputs for policy planning.
Section snippets
Receptor model techniques used in Europe for source apportionment of PM
For the present study, a total of 108 peer-reviewed, European studies and air quality research centres reports were scrutinized. These publications contain 332 records (every site is a record and more datasets on the same site are considered as different records). In the majority of the records (272), the complete PM mass is apportioned while the remaining 60 records deal with sources for the carbonaceous fraction. Included in these, are studies reporting aerosol mass spectrometer (AMS)
Results of European source apportionment of PM by receptor modelling
The majority of the studies considered in the present review focus on the PM10 (56%) and PM2.5 mass fractions (37%) and a few studies address PM1 (6%) and total suspended particles (TSP, 1%). About 67% of the studies were conducted at urban background sites, out of which 7% located in suburban and residential areas. The remaining part was carried out at source-oriented sites (18%), rural background sites (13%), and remote sites (4%). The source-oriented sites were focused on traffic,
Conclusions and recommendations
In Europe, source apportionment of PM and its organic fractions has been conducted over the past two decades with a variety of receptor models shifting from principal component analysis techniques, enrichment factors and classical factor analysis towards models able to handle uncertainties on the input and output such as e.g. Positive Matrix Factorization. A wider use of advanced factor analysis techniques able to deal with heterogeneous and complex data and to provide improved uncertainty
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