Dispersion Modeling Guidance for Airports Addressing Local Air Quality Health Concerns (2017)

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1 The motivation for this research was to develop guidance for analysts in applying disper- sion models to study local air quality health impacts from airport emissions. (Terms shown in italics appear with brief definitions in the glossary.) Airport analysts have used the Emissions and Dispersion Modeling System (EDMS), since replaced by the Aviation Environmental Design Tool (AEDT), for modeling local air quality. AEDT uses EPA’s AERMOD disper- sion model, a Gaussian plume model in which concentrations are most commonly 1-hour averages. Typically, AERMOD is used to model dispersion from point and area sources (e.g., power plants, industrial activities) and is used to assess local air quality impacts. A range of other models is available, however, and these models are time-varying and take into account additional chemical transformation mechanisms, which may expand analysts’ capabilities and provide added insights. To date, these models have had limited use in the United States in studies of airport-related local air quality and health impacts; therefore, proper guidance on their use for modeling airport-related emissions sources has been lacking. ACRP Project 02-58 conducted a direct intercomparison of four dispersion models using a common set of input data for a single airport. This guidance document begins with a primer on dispersion modeling and then presents the results of the model intercomparison. It also provides guidance on selecting an appropriate model for future studies of the health impacts of airport emissions. Based upon an extensive review of the literature, four models were chosen for the inter- comparison: AERMOD, SCICHEM, CALPUFF and ADMS-Airport. These models were chosen based upon several criteria and based upon an extensive literature review. AERMOD is the de facto model used for regulatory dispersion modeling in the United States. In addi- tion, AERMOD is coupled with EDMS/AEDT for airport-related modeling in the United States. The first three models are US-based dispersion models available at no cost, while the fourth is a UK-based proprietary model that has been used to study airport-related air quality, mostly in Europe. Comparing the four models, AERMOD and ADMS-Airport have a limited treatment of chemical transformation (converting NO to NO2, which are likely important for capturing localized effects), whereas SCICHEM and CALPUFF have addi- tional processes for gas-phase and aerosol chemistry, with SCICHEM more detailed than CALPUFF. Also, AERMOD and ADMS-Airport are Gaussian plume models, and SCICHEM and CALPUFF are puff models that were originally designed to model large elevated sources such as power plants but nevertheless provide a viable alternate choice given their detailed treatment of chemical processes. To support a model intercomparison study, a detailed suite of measurements at a large airport is ideal. The researchers reviewed the literature and chose the Los Angeles International Airport (LAX) in Southern California as the candidate airport. In 2012, LAX was one of the S u m m a r y Dispersion Modeling Guidance for Airports Addressing Local Air Quality Health Concerns

2 Dispersion modeling Guidance for airports addressing Local air Quality Health Concerns top five airports for commercial air traffic in the world, with more than 700,000 landing and take-off operations per year. LAX also is situated close to a very large metropolitan area. During 2011–2012, Los Angeles World Airports (LAWA) conducted a detailed Air Quality Source Apportionment Study (LAX AQSAS) in which detailed measurements of more than 400 chemical compounds were made for two 6-week periods during both summer and winter at 17 locations in and around the airport. Multiple source-based and receptor-based models were applied to perform source apportionment. Of the 17 data-collection sites, four sites had detailed measurements of various pollutants on an hourly resolution, and the other sites had 7-day average measurements. The research team used the detailed emissions inventories that were created in support of the LAX AQSAS using EDMS as input data to AERMOD; however, these emissions inven- tories could not be directly used in the other three models. Therefore, the researchers devel- oped converters to adapt EDMS-based inputs to the other three models. This guidance document highlights key issues faced by the project team during this conversion process and provides suggestions for addressing them in future development of the AEDT model. A key aspect of the LAX EDMS inventories is that there are more than 5,000 aircraft sources at LAX, and when combined with the other airport-related and background sources within the study domain, there are nearly 6,000 sources to be modeled. Although the con- version from EDMS-AERMOD to SCICHEM and CALPUFF was relatively straightforward, the conversion of EDMS-based outputs for use in ADMS-Airport was challenging. For future applications of ADMS-Airport, the project team recommends that airport practitio- ners start directly from EDMS inputs. Minor issues also arose because of differences between the version of EDMS used by LAWA in 2011–2012 and the more recent version used for this project. Improvements and bug fixes to the more recent EDMS caused differences in the modeled emissions for some sources at LAX, compared to the modeled emissions from the LAX AQSAS. Another key challenge faced during the model intercomparison was missing meteorologi- cal data for a few specific time periods. While this is usually a non-issue with steady-state models such as AERMOD, non-steady-state models such as CALPUFF and SCICHEM need continuous (hourly) valid meteorological data. Thus, airport practitioners should ensure there are no gaps in input meteorological fields, should they choose one of these alternate models. To fill the gaps for the few hours of missing data, the researchers used data from one of the four nearby meteorological stations near LAX. The general objective of ACRP Project 02-58 was to use equivalent input data in all four models to keep the inputs consistent so that any differences in output would be the result of differences in the dispersion models. In some cases, the researchers performed additional sensitivity simulations in a given model to take advantage of any specific enhancements that the model offered to provide improved characterization of local-scale air quality at the air- port. To support the model evaluation and intercomparison, the research team programmed all models to predict concentrations at the LAWA AQSAS measurement locations. The team also set the models to predict pollutant concentrations for a uniform Cartesian grid centered on the airport for a 5 × 5 km region (with receptors every 500 m), a Polar grid centered on the airport for a 50 × 50 km region (with receptors every 5 km), and flagpole receptors aloft to capture vertical gradients. Based on computational demands of some models, however, this expanded set of receptors was used only in AERMOD and CALPUFF, and not in the other two models. All models were configured to predict seven pollutants—CO, NOx, SO2, VOCs, TOG, PM2.5 and PM10—on an hourly basis for each of the two 6-week periods.

Summary 3 The researchers were able to assess the computational demands of each model and con- cluded that, in its current form, the SCICHEM model has computational demands that may not be practical. Improvements to source characterization (e.g., reducing the number of sources or reducing the number of elongated sources) or to simplify the second-order clo- sure treatment (which provides a direct relationship between the predicted dispersion rates and the measurable turbulent velocity statistics of the wind field at a high computational cost) could make SCICHEM more practical for use by airports. All models but one (ADMS- Airport) can be run on Linux Operating Systems, and thus parallel processing on multiple CPUs is possible. ADMS-Airport runs only through a Windows-based GUI. However, note that the “Run Manager” system available to ADMS-Airport users allows parallel processing on multiple Windows CPUs and/or PCs. With the exception of AERMOD, the models could not directly support the nearly 5,000 area sources to represent aircraft activity and needed to be customized in order to use the models with this many sources. The model outputs were compared against observations, and against each other, using an extensive set of graphical and statistical measures of model performance. To provide a summary assessment of model performance, the research team developed a scoring scheme using select performance criteria. Based on this scoring scheme, AERMOD and ADMS- Airport results seemed to match more closely with observations. All models underestimated the observed PM2.5, which was due to the lack of data on background concentrations in the local-scale modeling. A need exists for providing information on background concentra- tions using a regional-scale model like the Community Multi-scale Air Quality (CMAQ) model, or using geostatistical techniques such as Space-Time Ordinary Kriging (STOK) of observed concentrations from remote background locations. The researchers concluded the study by identifying several areas of potential future research: 1. Incorporation of background concentrations, 2. Representation of aircraft sources at the airport, 3. Inventory of ultra-fine particles, 4. Plume rise from aircraft emissions, 5. Aircraft downwash effects on plume rise and dispersion, 6. Aircraft dispersion based on instantaneous “line puffs,” 7. Effects of light winds and atmospheric stability on dispersion, and 8. Other limitations such as lack of chemical treatment in some models. With regard to plume rise from aircraft emissions, in this guide the researchers propose and discuss three general plume rise models: 1. The existing model with a zero plume rise, 2. An empirical model for plume rise and initial spread based on the LIDAR measurements, and 3. A fluid-mechanical entrainment model (FEM).