Source apportionment. Existing Garfield County speciated VOC data will be statistically analyzed using a technique known as Positive Matrix Factorization (PMF). PMF analyzes correlations and temporal variability in individual VOC concentrations to determine groups of VOCs that derive from specific source “factors”. By examining the composition of VOCs in individual factors, those factors are interpreted as being associated with particular source types (e.g., vegetation, motor vehicles, natural gas production, etc…). The contribution of each factor to observed VOC concentrations is then specified for each ambient VOC sample, allowing quantification of relative contributions of different factors (sources) to measured concentrations of individual VOCs as a function of location and time. A second source apportionment tool will be explored in the analysis. Known as Chemical Mass Balance (CMB), this tool takes measured source profiles and attempts to fit combinations of emissions from those sources to the observed VOC data set. This approach, which is complementary to PMF, makes direct use of measured source profiles and provides an independent estimate of the contributions of different source types to measured VOC concentrations.
Source apportionment. Source Apportionment (SA) is the practice of deriving information about pollution sources and the amount they contribute to Source apportionment (SA) is the practice of deriving information about pollution sources and the amount they contribute to ambient air pollution levels. ambient air pollution levels. Different approaches are used to determine and quantify the impacts of air pollution sources on air quality, namely: exploratory methods; emission inventories; inverse modeling; artificial neural networks; source oriented models (such as lagrangean, xxxxxxxx, and eulerian models); and receptor models. The study by Xxxxx et al. (2008) focused on receptor model source apportionment studies in Europe from 1987 to 2007. PM10 was the preferred target metric (46%) followed by PM2.5 (33%). The majority of the studies were carried out in urban background locations. Overall, a generally good spatial coverage of SA studies over Europe was seen and four main source categories were identified: traffic source; mineral/crustal matter source; sea salt, sea-spray and marine source; regional scale pollution and long-range transboundary anthropogenic pollution sources. A dramatic increase in the number of scientific publication on SA receptor approaches during the last decade and an increasing number of ready-to- use tools was identified. Xxxxxxxxxx and Xxxxx (2012) updated this review on receptor models for PM source apportionment in Europe between 2001 and 2010. A dramatic increase in the number of scientific publications on this topic during the last decade and an increasing number of ready-to-use tools was identified. The highest rate increase in the number of studies coincides with the adoption of the limit value for PM10 (1999/30/EC) and the target value for PM2.5. About 60% of the studies were carried out in urban background sites, 16% in source oriented sites, and 15% in rural sites. The prime reasons of MS for using SA within the framework of integrated assessment studies are associated to obligations linked to the AQD (2008/50, Article 22, Annex XV - 2011/850 (Article 13, Xxxxx XX): design air The prime reason for member states for using SA within the framework of integrated assessment studies are associated to obligations linked to the Air Quality Directive. quality plans or action plans, identify the causes of exceedances, and identify the contribution from other countries (transboundary pollution). Other motivations for SA studies are the evaluation of geogra...
Source apportionment. This topic has been specifically added to the database structure because nowadays source apportionment methodologies are used more frequently and it is important to understand how these are applied and what their added-value to integrated assessment is.
