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. IMPLICATIONS Backward-trajectory analysis is one of the standard procedures for determining the spatial locations of possible emission sources affecting given receptors, and it is frequently used to enhance receptor modeling results. This analysis simplifies some of the relevant processes such as pollutant dispersion, and additional methods have been used to improve receptor-source relationships. A methodology of inverse Lagrangian stochastic particle dispersion modeling was used in this study to complement and improve standard backward-trajectory analysis. The results show that inverse dispersion modeling can identify regional sources of haze in national parks and other regions of interest.

[1]  Sven-Erik Gryning,et al.  Air pollution modeling and its application , 1981 .

[2]  M. Follows.  Computer techniques in environmental studies III: edited by P. Zannetti. Proceedings of the Third International Conference on Development and Application of Computer Techniques to Environmental Studies (Montreal, 11–13 September 1990). Computational Mechanics Publications (1990), Southampton, Boston , 1992 .

[3]  L. Chen,et al.  Origins of fine aerosol mass in the Baltimore–Washington corridor: implications from observation, factor analysis, and ensemble air parcel back trajectories , 2002 .

[4]  H. C. Rodean.  Stochastic Lagrangian Models of Turbulent Diffusion , 1996 .

[5]  Stephen B. Reid,et al.  Toward effective source apportionment using positive matrix factorization: experiments with simulated PM2.5 data. , 2010, Journal of the Air & Waste Management Association.

[6]  Frédéric Hourdin,et al.  Sub‐surface nuclear tests monitoring through the CTBT Xenon Network , 2000 .

[7]  A. Stohl.  Computation, accuracy and applications of trajectories—A review and bibliography , 1998 .

[8]  Marc Pitchford,et al.  Attribution of sulfate aerosols in Federal Class I areas of the western United States based on trajectory regression analysis , 2006 .

[9]  Characterization of Dust Storms to Hong Kong in April 1998 , 2003 .

[10]  N. Mahowald,et al.  Inverse methods in global biogeochemical cycles , 2000 .

[11]  R. Desjardins,et al.  Estimating Gas Emissions from Multiple Sources Using a Backward Lagrangian Stochastic Model , 2008, Journal of the Air & Waste Management Association.

[12]  Jenise L. Swall,et al.  A statistical approach for estimating uncertainty in dispersion modeling: An example of application in southwestern USA , 2007 .

[13]  D. Koracin,et al.  Local and transported pollution over San Diego, California , 2005 .

[14]  L. Ashbaugh.  A statistical trajectory technique for determining air pollution source regions , 1983 .

[15]  Judith C. Chow,et al.  Source Apportionment: Findings from the U.S. Supersites Program , 2008, Journal of the Air & Waste Management Association.

[16]  Ilias G. Kavouras,et al.  Development of a geospatial screening tool to identify source areas of windblown dust , 2009, Environ. Model. Softw..

[17]  John D. Wilson,et al.  Review of Lagrangian Stochastic Models for Trajectories in the Turbulent Atmosphere , 1996 .

[18]  W. Malm,et al.  Association of airmass transport patterns and particulate sulfur concentrations at Big Bend National Park, Texas , 2006 .

[19]  J. Angell,et al.  Air stagnation climatology for the United States (1948--1998) , 1999 .

[20]  R. Prinn,et al.  Estimation of atmospheric methane emissions between 1996 and 2001 using a three-dimensional global chemical transport model , 2006 .

[21]  B. Schichtel,et al.  Directional Biases in Back Trajectories Caused by Model and Input Data , 2005, Journal of the Air & Waste Management Association.

[22]  Judith C. Chow,et al.  Review of volatile organic compound source apportionment by chemical mass balance , 2001 .

[23]  D. Koracin,et al.  A hybrid model for ozone forecasting , 2008 .

[24]  Petra Seibert,et al.  Source-receptor matrix calculation with a Lagrangian particle dispersion model in backward mode , 2004 .

[25]  Stefan Reimann,et al.  An analytical inversion method for determining regional and global emissions of greenhouse gases: Sensitivity studies and application to halocarbons , 2008 .

[26]  Tan Zhu,et al.  Receptor modeling application framework for particle source apportionment. , 2002, Chemosphere.

[27]  Choong-Min Kang,et al.  Source Identification and Trends in Concentrations of Gaseous and Fine Particulate Principal Species in Seoul, South Korea , 2006, Journal of the Air & Waste Management Association.

[28]  M. Raupach,et al.  Markov-chain simulation of particle dispersion in inhomogeneous flows: The mean drift velocity induced by a gradient in Eulerian velocity variance , 1982 .

[29]  G. Grell,et al.  A description of the fifth-generation Penn State/NCAR Mesoscale Model (MM5) , 1994 .

[30]  B. Fisher.  A review of the processes and models of long-range transport of air pollutants , 1983 .

[31]  John D. Wilson,et al.  Review of Lagrangian stochastic models for trajectories in the turbulent atmosphere , 1996 .

[32]  F. Gifford.  Some Recent Long-Range Diffusion Observations , 1995 .

[33]  Stephen B. Reid,et al.  Evaluation of Regional-Scale Receptor Modeling , 2010, Journal of the Air & Waste Management Association.

[34]  T. Black.  The new NMC mesoscale Eta Model: description and forecast examples , 1994 .

[35]  N. Pirrone,et al.  Particle Size Distributions and Elemental Composition of Atmospheric Particulate Matter in Southern Italy , 2008, Journal of the Air & Waste Management Association.

[36]  J. Watson.  Visibility: Science and Regulation , 2002, Journal of the Air & Waste Management Association.

[37]  D. Thomson.  Criteria for the selection of stochastic models of particle trajectories in turbulent flows , 1987, Journal of Fluid Mechanics.

[38]  A. Andren,et al.  Evaluation of a Turbulence Closure Scheme Suitable for Air-Pollution Applications , 1990 .

[39]  Alexis K.H. Lau,et al.  Seasonal characteristics and regional transport of PM2.5 in Hong Kong , 2005 .

[40]  Paolo Zannetti.  Air quality modeling : theories, methodologies, computational techniques, and available databases and software , 2003 .

[41]  Eugene Yee,et al.  Backward-Time Lagrangian Stochastic Dispersion Models and Their Application to Estimate Gaseous Emissions , 1995 .

[42]  P. R. Rao,et al.  Atmospheric Turbulence and Air Pollution Modelling , 1984 .

[43]  D. Niyogi,et al.  Back-Trajectory Analysis and Source-Receptor Relationships: Particulate Matter and Nitrogen Isotopic Composition in Rainwater , 2008, Journal of the Air & Waste Management Association.

[44]  Melanie L. Sattler,et al.  Which Meteorological Conditions Produce Worst-Case Odors from Area Sources? , 2007, Journal of the Air & Waste Management Association.

[45]  R. Pielke.  Mesoscale Meteorological Modeling , 1984 .

[46]  R. Desjardins,et al.  Multi-Source Emission Determination Using an Inverse-Dispersion Technique , 2009 .

[47]  John C. Lin,et al.  A near-field tool for simulating the upstream influence of atmospheric observations: The Stochastic Time-Inverted Lagrangian Transport (STILT) model , 2003 .

[48]  A. Eliassen.  A Review of Long-Range Transport Modeling , 1980 .

[49]  G. Hidy.  Atmospheric Sulfur and Nitrogen Oxides: Eastern North American Source-Receptor Relationships , 1994 .

[50]  L. Morawska,et al.  A review of dispersion modelling and its application to the dispersion of particles : An overview of different dispersion models available , 2006 .

[51]  Receptor-Oriented Dispersion Modeling: Extension to Nonlinear Pollution Chemistry , 1992 .

[52]  Jerry L. Hatfield,et al.  Micrometeorology in Agricultural Systems , 2005 .

[53]  Joyce E. Penner,et al.  Inverse modeling of biomass burning emissions using Total Ozone Mapping Spectrometer aerosol index for 1997 , 2005 .

[54]  Tan Zhu,et al.  Designing monitoring networks to represent outdoor human exposure. , 2002, Chemosphere.

[55]  G. Mellor,et al.  A Hierarchy of Turbulence Closure Models for Planetary Boundary Layers. , 1974 .

[56]  Heini Wernli,et al.  An intercomparison of results from three trajectory models , 2001 .

[57]  D. Byun,et al.  Review of the Governing Equations, Computational Algorithms, and Other Components of the Models-3 Community Multiscale Air Quality (CMAQ) Modeling System , 2006 .

[58]  G. Mellor,et al.  Development of a turbulence closure model for geophysical fluid problems , 1982 .

[59]  Weixiang Zhao,et al.  Source Apportionment of Airborne Particulate Matter for the Speciation Trends Network Site in Cleveland, OH , 2009, Journal of the Air & Waste Management Association.