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Regional source identification using Lagrangian stochastic particle dispersion and HYSPLIT backward-trajectory models
Darko Koracin1, Ramesh Vellore, Douglas H Lowenthal
1Division of Atmospheric Sciences, Desert Research Institute, Reno, NV 89512, USA. Darko.Koracin@DRI.edu
Journal of the Air & Waste Management Association (1995)
|July 15, 2011
Summary
The Lagrangian Stochastic Particle Dispersion Model (LSPDM) significantly improved air pollution source identification compared to HYSPLIT, accurately pinpointing emission regions impacting receptors. This research highlights LSPDM
Area of Science:
- Atmospheric Science
- Environmental Modeling
- Air Quality Assessment
Background:
- Accurate source identification is crucial for effective air quality management and policy development.
- Traditional trajectory models like HYSPLIT have limitations in accurately simulating dispersion and identifying emission sources.
- Mesoscale meteorological models provide essential wind field data for dispersion modeling.
Purpose of the Study:
- To evaluate the effectiveness of the receptor-oriented inverse mode Lagrangian Stochastic Particle Dispersion Model (LSPDM) for source identification.
- To compare LSPDM's performance against the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model using MM5 and EDAS wind inputs.
- To assess the impact of different vertical levels on backward trajectory analysis for source apportionment.
Main Methods:
- Utilized the Lagrangian Stochastic Particle Dispersion Model (LSPDM) in inverse mode with 12-km Mesoscale Model 5 (MM5) wind fields.
- Compared LSPDM results with Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) using MM5 (12-km) and Eta Data Assimilation System (EDAS, 80-km) wind data.
- Analyzed four 7-day summertime events in 2002, calculating residence times and comparing them with Community Multiscale Air Quality (CMAQ) model-predicted sulfate concentrations.
Main Results:
- LSPDM successfully identified the first- and second-ranked emission regions in 37 out of 40 cases, significantly outperforming HYSPLIT (21-16 cases).
- LSPDM achieved a higher correlation coefficient (0.89) and a lower normalized root mean square error (0.17) compared to HYSPLIT (0.55-0.62 and 0.30-0.32, respectively).
- HYSPLIT underestimated near-receptor sources and overestimated distant ones due to a lack of stochastic dispersion treatment.
Conclusions:
- The receptor-oriented inverse mode LSPDM demonstrates superior capabilities for air pollution source identification compared to standard HYSPLIT backward-trajectory analysis.
- LSPDM's inclusion of stochastic dispersion provides more accurate residence time distributions and source apportionment.
- Averaging HYSPLIT results from multiple vertical levels improved its consistency but did not match LSPDM's overall performance.
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