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This paper presents a quantitative comparison of the four most commonly usedreceptor models, namely absolute principal component scores (APCS),pragmatic mass closure (PMC), chemical mass balance (CMB) and positivematrix factorization (PMF). The models were used to predict thecontributions of a wide variety of sources to PM2.5 mass in Halifax,Nova Scotia during the experiment to quantify the impact of BOReal forest fireson Tropospheric oxidants over the Atlantic using Aircraft and Satellites(BORTAS). However, particular emphasis was placed on the capacityof the models to predict the boreal wildfire smoke contributions during theBORTAS experiment. The performance of the four receptor models was assessedon their ability to predict the observed PM2.5 with an R2 closeto 1, an intercept close to zero, a low bias and low RSME. Using PMF, anew woodsmoke enrichment factor of 52 was estimated for use in the PMCreceptor model. The results indicate that the APCS and PMC receptor modelswere not able to accurately resolve total PM2.5 mass concentrationsbelow 2 μg m−3. CMB was better able to resolve these low PM2.5 concentrations, but it could not be run on 9 of the 45 days of PM2.5 samples. PMF was found to be the most robust of the four modelssince it was able to resolve PM2.5 mass below 2 μg m−3,predict PM2.5 mass on all 45 days and utilise an unambiguouswoodsmoke chemical tracer. The median woodsmoke relative contributions to PM2.5 estimated using PMC, APCS, CMB and PMF were found to be 0.08,0.09, 3.59 and 0.14 μg m−3 respectively. The contributionpredicted by the CMB model seemed to be clearly too high based on otherobservations. The use of levoglucosan as a tracer for woodsmoke was found tobe vital for identifying this source

atmospheric chemistry and physics

A comparison of four receptor models used to quantify the boreal wildfire smoke contribution to surface PM&amp;lt;sub&amp;gt;2.5&amp;lt;/sub&amp;gt; in Halifax, Nova Scotia during the BORTAS-B experiment