Incorporating the impact of roadside barrier effects on dispersion into AERMOD.

This paper focuses on the impact of solid barriers located upwind of a highway in reducing vehicle related concentrations that occur downwind of the roadway, compared to a highway without barriers. Measurements made in the United States Environmental Protection Agency's meteorological wind tunnel show that the mitigating impact of an upwind barrier is comparable to that of a downwind barrier. Upwind barriers lead to reductions in pollution concentrations by drawing emissions in from the highway toward the barrier. The emissions are then entrained into the flow above the recirculation zone and dispersed vertically as they are advected downwind. This upwind transport of vehicle emissions leads to concentrations at the center of the roadways that are roughly 200-300% higher than those measured on roadways with downwind barriers. This difference between on-road concentrations indicates that although both types of barriers mitigate the impact of vehicle emissions downwind of a roadway, the upwind barrier may create adverse air quality impacts for the people on the road.We have formulated a semiempirical dispersion model that incorporates the physics revealed by the wind tunnel measurements. This model improves upon a model proposed by Ahangar et al. (2017) by adjusting the wind speed to get a more realistic plume dispersion just downwind of the upwind barrier and also by providing vertical profiles of concentrations in addition to ground-level concentrations. The upwind barrier model proposed in this paper and the downwind barrier model described in Francisco et al. (2022) have been incorporated into AERMOD (version 21112) as a nonregulatory option, including the new two-barrier option when modeling both barriers on the same roadway.Implications: Our paper presents an air dispersion model algorithm for modeling the effect of upwind noise barriers on dispersion of traffic-related emissions from roadways, which was incorporated into EPA's AERMOD and then evaluated using observations from a wind tunnel experiment. The results are compared and contrasted with results from both a no-barrier case and downwind barrier cases. This manuscript expands on previously published work analyzing the effect of barrier height and source-to-barrier distance on downwind dispersion (Atmos. Pollut. Res., 13:101385, 2022, https://doi.org/10.1016/j.apr.2022.101385). The current manuscript uses the same wind tunnel setup as reported there, but focuses on a different subset of cases, namely the upwind barrier cases, when developing dispersion model algorithms to simulate the observed effects. We believe the evaluations of the vertical profiles from the wind tunnel study, development, and incorporation of the upwind barrier algorithms into AERMOD, and model evaluation of these new algorithms are significant contributions to understanding the effects of these commonly used roadside barriers.

Accounting for Area Sources in Air Pollution Models.

Area sources are important components of comprehensive air pollution models. The literature describes several approaches to modeling dispersion from such sources, but there is little consensus on an approach that can be applied to arbitrarily shaped area sources and is numerically efficient at the same time. This paper brings together ideas from previous work to propose an approach that meets these requirements. It is based on representing an area source as a set of line sources perpendicular to the wind direction; the number of line sources is determined by the specified precision of the concentration computed at a receptor impacted by the area source. Although AERMOD and the OML model incorporate versions of this approach, the open literature lacks an adequate description. This paper fills this important gap and also provides examples of its application. We show that different shaped area sources with the same emissions and emission density yield significantly different downwind concentration patterns. We then demonstrate the utility of the method through inverse modeling to estimate methane emissions from manure lagoons located in a dairy.

Estimating inputs for dispersion modeling in mobile platform applications.

Mobile monitoring platforms (MMP) are popular in air quality studies. One application of MMP is in estimating pollutant emissions from area sources. The MMP is used to measure concentrations of the relevant species at several locations around the area source, while the associated meteorological information is measured at the same time. Emissions from the area source are inferred by fitting the measured concentrations to estimates from dispersion models. These models require meteorological inputs, such as the kinematic heat flux and the surface friction velocity, that are best computed with measurements of time resolved velocity and temperature made with 3-D sonic anemometers. Because the setting up and dismantling of a 3-D sonic anemometer is not compatible with the necessary mobility of the MMP, it is useful to use alternative instrumentation and methods that provide accurate estimates of these inputs. In this study, we demonstrate such a method based on measurements of horizontal wind speed and temperature fluctuations at a single height. The method was evaluated by comparing methane emissions from a dairy manure lagoon inferred from a dispersion model that uses modeled meteorological inputs to those inferred from measurements with 3-D sonic anemometers. The emission estimates from the modeled meteorological inputs were close to those based on measurements made with 3-D sonic anemometers. We then demonstrate how this approach can be adapted for mobile platform applications by showing that winds measured using a 2-D sonic anemometer and temperature fluctuations measured with a bead thermistor, which can all be carried or mounted on a MMP, yields results that are close to those from a 3-D sonic anemometer.

Observations and Parameterization of the Effects of Barrier Height and Source-to-Barrier Distance on Concentrations Downwind of a Roadway.

New results are presented from wind tunnel studies performed at the United States Environmental Protection Agency (U.S. EPA), which include cases with solid roadside barriers of varying heights and cases with varying distances between the line source (roadway) and a 6-m-tall barrier. The Source-to-Barrier Distance cases include seven lanes of traffic with each lane acting as an independent source of continuous emissions along a line (i.e., line source). A mixed-wake algorithm that accounts for barrier effects within a steady-state air dispersion model was updated based on the recent wind tunnel studies. To study the effects of a solid roadside barrier, varying barrier heights and varying distances between the line source and barrier were modeled with the U.S. EPA regulatory air dispersion model AERMOD (v. 21112) using the line-source option that includes an experimental barrier option (RLINEXT). The mixed-wake algorithm reproduced the shape of the vertical concentration profiles observed in the wind tunnel data, including the uniform concentration profile from the ground vertically to a height somewhat greater than the height of the barrier. The algorithm responded appropriately to changes in barrier height and source-to-barrier distance, producing greater reductions in ground-level concentrations for taller barriers and for shorter source-to-barrier distances. Additionally, a rule of thumb that approximates the effect of a downwind barrier was formulated by converting an estimated vertical dispersion into an additional travel distance. The wind tunnel results, the update to the mixed-wake algorithm, and a comparison of the two data sets are described in this paper.

Modeling turbulent transport of aerosols inside rooms using eddy diffusivity.

One major approach to modeling dispersion of pollutants inside confined spaces describes the turbulent transport of material as the product of an eddy diffusivity and the local concentration gradient. This paper examines the applicability of this eddy diffusivity/gradient model by (1) describing the conditions under which this approach is an appropriate representation of turbulent transport, and (2) re-analysis of data provided in studies that have successfully applied gradient transport to describe tracer concentrations. We find that the solutions of the mass conservation equation based on gradient transport provide adequate descriptions of concentration measurements from two studies representative of two types of sources: instantaneous and continuous release of aerosols. We then provide the rationale for the empirical success of the gradient transport model. The solutions of the gradient transport model allow us to examine the relationship between the ventilation rate and the spatial and temporal behavior of the dose of material associated with aerosol releases in a room. We conclude with the associated implications on mitigation of exposure to aerosols such as airborne virus or bacteria.

Dispersion at the edges of near road noise barriers.

This paper presents an analysis of data from a wind tunnel study conducted to examine the dispersion of emissions at the edges of near-road noise barriers. The study is motivated by the concern that a barrier positioned downwind of a roadway may guide highly polluted plumes along the barrier leading to heightened concentrations as the plume spills around and downwind of the barrier end. The wind tunnel database consists of measurements of dispersion around a simulated roadway segment with various noise barrier configurations. Each roadway segment simulated in the wind tunnel had full-scale equivalent dimensions of 135 m long. Barrier segments, 135 m long with a height (H) of 6 m, were located on the downwind side of the source at a distance of 18 m from it (measured perpendicularly from the line source). Examination of the concentration patterns associated with the cases indicates that 1) vertical mixing induced by barriers persists at crosswind distances up to the edge (lateral end) of the barrier and downwind distances of x/H = 10, 2) concentration levels at all heights below z/H = 1 increase towards the edge of the barrier at downwind distances less than x/H = 7, and 3) concentration is well mixed in the vertical at the edge of the barrier, and the levels can be higher than in the middle of the barrier even when the source ends at the edge of the barrier. We have formulated a parameterization that captures the major features of these observations and can be incorporated in models such as RLINE.

A new bottom-up emissions estimation approach for aircraft sources in support of air quality modelling for community-scale assessments around airports.

Transportation infrastructure (including roadway traffic, ports, and airports) is critical to the nation's economy. With a growing economy, aircraft activity is expected to grow across the world. In the US, airport-related emissions, while generally small, are not an insignificant source of air pollution and related adverse health effects. However, currently there is a lack of tools that can easily be applied to study near-source pollution and explore the benefits of improvements to air quality and exposures. Screening-level air quality modelling is a useful tool for examining urban-scale air quality impacts of airport operations. Spatially-resolved aircraft emissions are needed for the screening-level modelling. In order to create spatially-resolved aircraft emissions, we developed a bottom-up emissions estimation methodology that includes data from a global chorded inventory dataset from the aviation environmental design tool (AEDT). The initial implementation of this method was performed for Los Angeles International Airport (LAX). This paper describes a new emissions estimation methodology for aircraft emissions in support of community-scale assessments of air quality around airports and presents an illustration of its application at the Los Angeles International Airport during the LAX 2011/2012 Air Quality Source Apportionment Study.

Using Low-Cost Air Quality Sensor Networks to Improve the Spatial and Temporal Resolution of Concentration Maps.

We present an approach to analyzing fine particulate matter (PM2.5) data from a network of "low cost air quality monitors" (LCAQM) to obtain a finely resolved concentration map. In the approach, based on a dispersion model, we first identify the probable locations of the sources, and then estimate the magnitudes of the emissions from these sources by fitting model estimates of concentrations to corresponding measurements. The emissions are then used to estimate concentrations on a grid covering the domain of interest. The residuals between model estimates at the monitor locations and the measured concentrations are then interpolated to the grid points using Kriging. We illustrate this approach by applying it to a network of 20 LCAQMs located in the Imperial Valley of Southern California. Estimating the underlying mean concentration field with a dispersion model provides a more realistic estimate of the spatial distribution of PM2.5 concentrations than that from the Kriging observations directly.

The effects of roadside vegetation characteristics on local, near-road air quality.

Roadside vegetation has been shown to impact downwind, near-road air quality, with some studies identifying reductions in air pollution concentrations and others indicating increases in pollutant levels when vegetation is present. These widely contradictory results have resulted in confusion regarding the capability of vegetative barriers to mitigate near-road air pollution, which numerous studies have associated with significant adverse human health effects. Roadside vegetation studies have investigated the impact of many different types and conditions of vegetation barriers and urban forests, including preserved, existing vegetation stands usually consisting of mixtures of trees and shrubs or plantings of individual trees. A study was conducted along a highway with differing vegetation characteristics to identify if and how the changing characteristics affected downwind air quality. The results indicated that roadside vegetation needed to be of sufficient height, thickness, and coverage to achieve downwind air pollutant reductions. A vegetation stand which was highly porous and contained large gaps within the stand structure had increased downwind pollutant concentrations. These field study results were consistent with other studies that the roadside vegetation could lead to reductions in average, downwind pollutant concentrations by as much as 50% when this vegetation was thick with no gaps or openings. However, the presence of highly porous vegetation with gaps resulted in similar or sometimes higher concentrations than measured in a clearing with no vegetation. The combination of air quality and meteorological measurements indicated that the vegetation affects downwind pollutant concentrations through attenuation of meteorological and vehicle-induced turbulence as air passes through the vegetation, enhanced mixing as portions of the traffic pollution plume are blocked and forced over the vegetation, and through particulate deposition onto leaf and branch surfaces. Computational fluid dynamic modeling highlighted that density of the vegetation barrier affects pollutant levels, with a leaf area density of 3.0 m2 m-3 or higher needed to ensure downwind pollutant reductions for airborne particulate matter. These results show that roadside bushes and trees can be preserved or planted along highways and other localized pollution sources to mitigate air quality and human health impacts near the source if the planting adheres to important characteristics of height, thickness, and density with full coverage from the ground to the top of the canopy. The results also highlight the importance of planting denser vegetation and maintaining the integrity and structure of these vegetation barriers to achieve pollution reductions and not contribute to unintended increases in downwind air pollutant concentrations.

Development and Evaluation of the R-LINE Model Algorithms to Account for Chemical Transformation in the Near-road Environment.

With increased urbanization, there is increased mobility leading to higher amount of traffic-related activity on a global scale. Most NOx from combustion sources (about 90-95%) are emitted as NO, which is then readily converted to NO2 in the ambient air, while the remainder is emitted largely as NO2. Thus, the bulk of ambient NO2 is formed due to secondary production in the atmosphere, and which R-LINE cannot predict given that it can only model the dispersion of primary air pollutants. NO2 concentrations near major roads are appreciably higher than those measured at monitors in existing networks in urban areas, motivating a need to incorporate a mechanism in R-LINE to account for NO2 formation. To address this, we implemented three different approaches in order of increasing degrees of complexity and barrier to implementation from simplest to more complex. The first is an empirical approach based upon fitting a 4th order polynomial to existing near-road observations across the continental U.S., the second involves a simplified two-reaction chemical scheme, and the third involves a more detailed set of chemical reactions based upon the Generic Reaction Set (GRS) mechanism. All models were able to estimate more than 75% of concentrations within a factor of two of the near-road monitoring data and produced comparable performance statistics. These results indicate that the performance of the new R-LINE chemistry algorithms for predicting NO2 is comparable to other models (i.e. ADMS-Roads with GRS), both showing less than ±15% fractional bias and less than 45% normalized mean square error.

Modeling Dispersion of Emissions from Depressed Roadways.

This paper presents an analysis of data from a wind tunnel (Heist et al., 2009) conducted to study dispersion of emissions from three depressed roadway configurations; a 6 m deep depressed roadway with vertical sidewalls, a 6 m deep depressed roadway with 30° sloping sidewalls, and a 9 m deep depressed roadway with vertical sidewalls. The width of the road at the bottom of the depression is 36 m for all cases. All these configurations induce complex flow fields, increase turbulence levels, and decrease surface concentrations downwind of the depressed road compared to those of the at-grade configuration. The parameters of flat terrain dispersion models are modified to describe concentrations measured downwind of the depressed roadways. In the first part of the paper, a flat terrain model proposed by van Ulden (1978) is adapted. It turns out that this model with increased initial vertical dispersion and friction velocity is able to explain the observed concentration field. The results also suggest that the vertical concentration profiles of all cases under neutral conditions are best explained by a vertical distribution function with an exponent of 1.3. In the second part of the paper, these modifications are incorporated into a model based on the RLINE (Snyder et al., 2013) line-source dispersion model. While this model can be adapted to yield acceptable estimates of near-surface concentrations (z< 6m) measured in the wind tunnel, the Gaussian vertical distribution in RLINE, with an exponent of 2, cannot describe the concentration at higher elevations. Our findings suggest a simple method to account for depressed highways in models such as RLINE and AERMOD through two parameters that modify vertical plume spread.

A web-based screening tool for near-port air quality assessments.

The Community model for near-PORT applications (C-PORT) is a screening tool with an intended purpose of calculating differences in annual averaged concentration patterns and relative contributions of various source categories over the spatial domain within about 10 km of the port. C-PORT can inform decision-makers and concerned citizens about local air quality due to mobile source emissions related to commercial port activities. It allows users to visualize and evaluate different planning scenarios, helping them identify the best alternatives for making long-term decisions that protect community health and sustainability. The web-based, easy-to-use interface currently includes data from 21 seaports primarily in the Southeastern U.S., and has a map-based interface based on Google Maps. The tool was developed to visualize and assess changes in air quality due to changes in emissions and/or meteorology in order to analyze development scenarios, and is not intended to support or replace any regulatory models or programs.

Reduction of air pollution levels downwind of a road with an upwind noise barrier.

We propose a dispersion model to estimate the impact of a solid noise barrier upwind of a highway on air pollution concentrations downwind of the road. The model, based on data from wind tunnel experiments conducted by Heist et al. (2009), assumes that the upwind barrier has two main effects: 1) it creates a recirculation zone behind the barrier that sweeps the emissions from the highway back towards the wall, and 2) it enhances vertical dispersion and initial mixing. By combining the upwind barrier model with the mixed wake model for a downwind barrier described in Schulte et al. (2014), we are able to model dispersion of emissions from a highway with noise barriers on both sides. The model provides a good description of measurements made in the wind tunnel. The presence of an upwind barrier causes reductions in concentrations relative to those measured downwind of a road with no barriers. The reduction can be as large as that caused by a downwind barrier if the recirculation zone covers the width of the highway. Barriers on both sides of the highway result in larger reductions downwind of the barriers than those caused by a single barrier either upwind or downwind. As expected, barrier effects are small beyond 10 barrier heights downwind of the highway. We also propose a tentative model to estimate on-road concentrations within the recirculation zone induced by the upwind barrier.

Evaluation and development of tools to quantify the impacts of roadside vegetation barriers on near-road air quality.

Traffic emissions are associated with the elevation of health risks of people living close to highways. Roadside vegetation barriers have the potential of reducing these risks by decreasing near-road air pollution concentrations. However, while we understand the mechanisms that determine the mitigation caused by solid barriers, we still have questions about how vegetative barriers affect dispersion. The US EPA conducted several field experiments to understand the effects of vegetation barriers on dispersion of pollutants near roadways (e.g., 2008 North Carolina study and 2014 California study) that indicate the reduction of near-road pollutant concentrations can be up to 30% due to the barrier effects. The results of these field studies are being used to develop and evaluate dispersion models that account for the effects of near-road vegetative barriers. These models can be used for evaluating the effectiveness of vegetation barriers as a potential mitigation strategy to reduce exposure to traffic-related pollutants and their associated adverse health effects. This paper presents the results of the analysis of the field studies and discusses dispersion models being used to describe the data in order to simulate the effects of near-road barriers and to develop recommendations for model improvements.

A FRAMEWORK FOR EVALUATING REGIONAL-SCALE NUMERICAL PHOTOCHEMICAL MODELING SYSTEMS.

This paper discusses the need for critically evaluating regional-scale (~200-2000 km) three-dimensional numerical photochemical air quality modeling systems to establish a model's credibility in simulating the spatio-temporal features embedded in the observations. Because of limitations of currently used approaches for evaluating regional air quality models, a framework for model evaluation is introduced here for determining the suitability of a modeling system for a given application, distinguishing the performance between different models through confidence-testing of model results, guiding model development, and analyzing the impacts of regulatory policy options. The framework identifies operational, diagnostic, dynamic, and probabilistic types of model evaluation. Operational evaluation techniques include statistical and graphical analyses aimed at determining whether model estimates are in agreement with the observations in an overall sense. Diagnostic evaluation focuses on process-oriented analyses to determine whether the individual processes and components of the model system are working correctly, both independently and in combination. Dynamic evaluation assesses the ability of the air quality model to simulate changes in air quality stemming from changes in source emissions and/or meteorology, the principal forces that drive the air quality model. Probabilistic evaluation attempts to assess the confidence that can be placed in model predictions using techniques such as ensemble modeling and Bayesian model averaging. The advantages of these types of model evaluation approaches are discussed in this paper.

Resolving neighborhood scale in air toxics modeling: a case study in Wilmington, CA.

Air quality modeling is useful for characterizing exposures to air pollutants. Whereas models typically provide results on regional scales, new concerns regarding the potential for differential exposures among racial/ethnic populations and income strata within communities are driving the need for increasingly refined modeling approaches. These approaches need to be capable of resolving concentrations on the scale of tens of meters, across modeling domains 10-100 km2 in size. One approach for refined air quality modeling is to combine Gaussian and regional photochemical grid models. In this paper, the authors demonstrate this approach on a case study of Wilmington, CA, focused on diesel exhaust particulate matter. Modeling results suggest that pollutant concentrations in the vicinity of emission sources are elevated, and, therefore, an understanding of local emission sources is necessary to generate credible modeling results. A probabilistic evaluation of the Gaussian model application indicated that spatial allocation, emission rates, and meteorological data are important contributors to input and parameter uncertainty in the model results. This uncertainty can be substantially reduced through the collection and integration of site-specific information about the location of emission sources and the activity and emission rates of key sources affecting model concentrations.

Near-field dispersion modeling for regulatory applications.

This paper evaluates the application of dispersion models to estimate near-field pollutant concentrations in two case studies. The Industrial Source Complex Short-Term Model (ISCST3) was evaluated with hexavalent chromium measurements collected within 100 m of two facilities in Barrio Logan, San Diego, CA. ISCST3 provided reasonable estimates for higher pollutant concentrations but underestimated lower concentrations. To understand the observed distribution of concentrations in Barrio Logan, a recently conducted tracer experiment was analyzed. The tracer, sulfur hexafluoride, was released at ambient temperature from an urban facility at the University of California at Riverside, and concentrations were measured within 20 m of the source. Modeling results indicated that Industrial Source Complex-Plume Rise Model Enhancement and American Meteorological Society/U.S. Environmental Protection Agency Regulatory Model-Plume Rise Model Enhancement overestimated high concentrations and underestimated low concentrations. A diagnostic study with a simple Gaussian dispersion model that incorporated site-specific meteorology was used to evaluate model results. This study found that incorporating lateral meandering for nonbuoyant urban plumes in Gaussian dispersion models could improve concentration estimates even when downwash is not considered. Incorporating a meandering component in ISCST3 resulted in improvements in estimating hexavalent chromium concentrations in Barrio Logan. Credible near-source concentration estimates depend on accurate characterization of emissions, onsite micrometeorology, and a method to account for lateral meandering in the near field.

The Development of a Model to Examine Source-Receptor Relationships for Visibility on the Colorado Plateau.

This paper describes the development and application of the Visibility and Haze in the Western Atmosphere (VISHWA) model to understand the source-receptor relationships that govern chemical species relevant to visibility degradation in the western United States. The model was developed as part of a project referred to as Visibility Assessment for Regional Emission Distributions (VARED), the objective of which is to estimate the contributions of various geographical regions, compounds, and emission sources to light scattering and absorption by particles on the Colorado Plateau. The VISHWA model is a modified version of a comprehensive Eulerian model, known as the Acid Deposition and Oxidant Model.1 The modifications were designed to obtain the computational efficiency required to simulate a one-year period at about 1/25th of real time, and at the same time incorporate mechanistic features relevant to realistic modeling of the fate and transport of visibility degrading species. The modifications included use of a condensed chemical mechanism; incorporation of reactions to simulate the formation of secondary organic particles; and use of a semi-Lagrangian advection scheme to preserve concentration peaks during advection. The model was evaluated with 1992 air quality data from Project MOHAVE (Measurements of Haze and Visual Effects) intensive experiments. An important conclusion of this evaluation is that aqueous-phase oxidation of SO2 to sulfate in nonprecipitating clouds makes a significant contribution to observed sulfate levels during winter as well as summer. Model estimates of ambient sulfate for the winter intensive were within a factor of 2 of the observations for 75% of the values. The corresponding statistic for the summer intensive was 90%. Model estimates of carbon were within a factor of 2 of the limited set of observations.