title: Regional source identification using Lagrangian stochastic particle dispersion and HYSPLIT backward-trajectory models.
abstract: The main objective of this study was to investigate the capabilities of the receptor-oriented inverse mode Lagrangian Stochastic Particle Dispersion Model (LSPDM) with the 12-km resolution Mesoscale Model 5 (MM5) wind field input for the assessment of source identification from seven regions impacting two receptors located in the eastern United States. The LSPDM analysis was compared with a standard version of the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) single-particle backward-trajectory analysis using inputs from MM5 and the Eta Data Assimilation System (EDAS) with horizontal grid resolutions of 12 and 80 km, respectively. The analysis included four 7-day summertime events in 2002; residence times in the modeling domain were computed from the inverse LSPDM runs and HYPSLIT-simulated backward trajectories started from receptor-source heights of 100, 500, 1000, 1500, and 3000 m. Statistics were derived using normalized values of LSPDM- and HYSPLIT-predicted residence times versus Community Multiscale Air Quality model-predicted sulfate concentrations used as baseline information. From 40 cases considered, the LSPDM identified first- and second-ranked emission region influences in 37 cases, whereas HYSPLIT-MM5 (HYSPLIT-EDAS) identified the sources in 21 (16) cases. The LSPDM produced a higher overall correlation coefficient (0.89) compared with HYSPLIT (0.55-0.62). The improvement of using the LSPDM is also seen in the overall normalized root mean square error values of 0.17 for LSPDM compared with 0.30-0.32 for HYSPLIT. The HYSPLIT backward trajectories generally tend to underestimate near-receptor sources because of a lack of stochastic dispersion of the backward trajectories and to overestimate distant sources because of a lack of treatment of dispersion. Additionally, the HYSPLIT backward trajectories showed a lack of consistency in the results obtained from different single vertical levels for starting the backward trajectories. To alleviate problems due to selection of a backward-trajectory starting level within a large complex set of 3-dimensional winds, turbulence, and dispersion, results were averaged from all heights, which yielded uniform improvement against all individual cases.
authors.affiliation: Division of Atmospheric Sciences, Desert Research Institute, Reno, NV 89512, USA. Darko.Koracin@DRI.edu
authors.forename: Darko
authors.initials: D
authors.lastname: Koracin
authors.forename: Ramesh
authors.initials: R
authors.lastname: Vellore
authors.forename: Douglas H
authors.initials: DH
authors.lastname: Lowenthal
authors.forename: John G
authors.initials: JG
authors.lastname: Watson
authors.forename: Julide
authors.initials: J
authors.lastname: Koracin
authors.forename: Travis
authors.initials: T
authors.lastname: McCord
authors.forename: David W
authors.initials: DW
authors.lastname: DuBois
authors.forename: L W Antony
authors.initials: LW
authors.lastname: Chen
authors.forename: Naresh
authors.initials: N
authors.lastname: Kumar
authors.forename: Eladio M
authors.initials: EM
authors.lastname: Knipping
authors.forename: Neil J M
authors.initials: NJ
authors.lastname: Wheeler
authors.forename: Kenneth
authors.initials: K
authors.lastname: Craig
authors.forename: Stephen
authors.initials: S
authors.lastname: Reid
mesh_terms: D000392:Air Movements; D000393:Air Pollutants / Q000737:chemistry*; D000397:Air Pollution; D004784:Environmental Monitoring / Q000379:methods*; D008962:Models, Theoretical; D013269:Stochastic Processes; D014481:United States
chemical_list: D000393:Air Pollutants