title: A method to quantitatively apportion pollutants at high spatial and temporal resolution: the Stochastic Lagrangian Apportionment Method (SLAM).
abstract: We introduce a method to quantify upwind contributions to concentrations of atmospheric pollutants. The Stochastic Lagrangian Apportionment Method (SLAM) carries out the following: (1) account for chemical transformations and depositional losses; (2) incorporate the effects of turbulent dispersion; (3) simulate the locations of the sources with high spatial and temporal resolution; and (4) minimize the impact from numerical diffusion. SLAM accomplishes these four features by using a time-reversed Lagrangian particle dispersion model and then simulating chemical changes forward in time, while tagging and keeping track of different sources. As an example of SLAM's application, we show its use in apportioning sources contributing to ammonia (NH3) and ammonium particulates (p-NH4(+)) at a site in southern Ontario, Canada. Agricultural emissions are seen to dominate contributions to NH3 and p-NH4(+) at the site. The source region of NH3 was significantly smaller than that of p-NH4(+), which covered numerous states of the American Midwest. The source apportionment results from SLAM were compared against those from zeroing-out individual sources ("brute force method"; BFM). The comparisons show SLAM to produce almost identical results as BFM for NH3, but higher concentrations of p-NH4(+), likely due to indirect effects that affect BFM. Finally, uncertainties in the SLAM approach and ways to address such shortcomings by combining SLAM with inverse methods are discussed.
authors.affiliation: Department of Atmospheric Sciences, University of Utah , Salt Lake City, Utah 84112-0102, United States.
authors.forename: John C
authors.initials: JC
authors.lastname: Lin
authors.forename: Deyong
authors.initials: D
authors.lastname: Wen
mesh_terms: D000383:Agriculture; D000393:Air Pollutants / Q000737:chemistry*; D000641:Ammonia / Q000032:analysis; D015386:Hazardous Substances / Q000737:chemistry*; D015146:Midwestern United States; D008962:Models, Theoretical; D009864:Ontario
chemical_list: D000393:Air Pollutants; D015386:Hazardous Substances; D000641:Ammonia