The use of bootstrapping to estimate conditional probability fields for source locations of airborne pollutants

A receptor model has been developed in which meteorological information in the form of air parcel back trajectories are combined with on the atmospheric constituent concentration data to produce conditional probability fields pointing to areas that are likely to have made significant contributions to samples with higher than average concentrations. This approach, potential source contribution function (PSCF) analysis, has proven quite successful in producing maps that have a good correspondence with areas of known high emissions on a variety of spatial scales from large urban scale problems in the air basin that includes Los Angeles, CA to regional transport of pollutants to southern Ontario to semi-global scale transport to several sites in the high Arctic. However, there are cells having a limited numbers of endpoints because trajectories to that region have low probabilities and there is estimate of the uncertainties in the PSCF values. Thus, we have examined the use of bootstrapping to provide better estimates of the probability values and their uncertainties. This approach has been tested on data from several locations at differing levels of geographical scale for varying numbers of trajectories selected and trials made. The results of the studies for data from the high Arctic at Ny Alesund on Spitsbergen (78 degrees 55' N, 11 degrees 57' E, 5 m above mean sea level) are presented. The results of these studies for the transport of pollutants to the Arctic basin suggest that in many cases the bootstrapped PSCF maps are clearer and more easily interpreted in terms of known sources.