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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.

In this study, we present seasonal atmospheric measurements of δ13CCH4 from dairy farms in the San Joaquin Valley of California. We used δ13CCH4 to characterize emissions from enteric fermentation by measuring downwind of cattle housing (e.g., freestall barns, corrals) and from manure management areas (e.g., anaerobic manure lagoons) with a mobile platform equipped with cavity ring-down spectrometers. Across seasons, the δ13CCH4 from enteric fermentation source areas ranged from −69.7 ± 0.6 per mil (‰) to −51.6 ± 0.1‰ while the δ13CCH4 from manure lagoons ranged from −49.5 ± 0.1‰ to −40.5 ± 0.2‰. Measurements of δ13CCH4 of enteric CH4 suggest a greater than 10‰ difference between cattle production groups in accordance with diet. Isotopic signatures of CH4 were used to characterize enteric and manure CH4 from downwind plume sampling of dairies. Our findings show that δ13CCH4 measurements could improve the attribution of CH4 emissions from dairy sources at scales ranging from individual facilities to regions and help constrain the relative contributions from these different sources of emissions to the CH4 budget.

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.

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.

Smoke from human-induced fires such as prescribed fires can occasionally cause significant reduction in visibility on highways in the southern United States. Visibility reduction to less than 3 m has been termed “superfog” and environmental conditions that lead to its formation have been proposed previously. Accurate characterization and prediction of precursor conditions for superfog is needed to prevent dangerous low visibility situations when planning prescribed fires. It is hypothesized that extremely hygroscopic cloud condensation nuclei from the smoldering phase of a fire can produce a large number of droplets smaller in size than in naturally occurring fog. This large number of small droplets can produce superfog conditions with relatively low liquid water content. A thermodynamics-based model for fog formation was developed. Laboratory generated superfog measured by a Phase Doppler Particle Analyzer determined that mean droplet radius was 1.5 μm and the size distribution could be modeled with a lognormal distribution. Experiments in an environmentally-conditioned wind tunnel using longleaf pine (Pinus palustris Mill.) needle fuel beds provided visibility, heat flux, temperature, humidity, and particle data for model validation. Numerical modeling was used to approximate the growth of a superfog boundary-layer with liquid water content values of 2 g m−3 or greater. The model successfully predicted previous superfog events.

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.

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.

This paper evaluates the performance of a multirotor uncrewed aircraft and AirCore system (UAAS) for measuring vertical profiles of wind velocity (speed and direction) and the mole fractions of methane (CH 4) and carbon dioxide (CO 2) , and it presents a use case that combines UAAS measurements and dispersion modeling to quantify CH 4 emissions from a dairy farm. To evaluate the atmospheric sensing performance of the UAAS, four field deployments were performed at three locations in the San Joaquin Valley of California where CH 4 hotspots were observed downwind of dairy farms. A comparison of the observations collected on board the UAAS and an 11 m meteorological tower show that the UAAS can measure wind velocity trends with a root mean squared error varying between 0.4 and 1.1 m s -1 when the wind magnitude is less than 3.5 m s -1. Findings from UAAS flight deployments and a calibration experiment also show that the UAAS can reliably resolve temporal variations in the mole fractions of CH 4 and CO 2 occurring over periods of 10 s or longer. Results from the UAAS and dispersion modeling use case further demonstrate that UAASs have great potential as low-cost tools for detecting and quantifying CH 4 emissions in near real time. [ABSTRACT FROM AUTHOR]

S. Trivikrama Rao-retired director of the Atmospheric Modeling and Analysis Division of the B EPA and an AMS CCM, Fellow, and winner of the Award for Outstanding Contributions to Applied [...]

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.

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, ). 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 [ABSTRACT FROM AUTHOR]

This paper discusses the need for critically evaluating regional-scale (~200–2,000 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.

We examine the performance of several methods to estimate meteorological inputs for modelling dispersion in urban areas during convective conditions. Sensible heat flux, surface friction velocity and turbulent velocities are estimated from measurements of mean wind speed and the standard deviation of temperature fluctuations at a single level on a tower at two suburban sites and at one urban site in Riverside, California. These estimates are compared with observations made at these sites during a field study conducted in 2007. The sensible heat flux is overestimated in the urban area, while it is underestimated at a suburban site when temperature fluctuations are used in the free convection formulation to estimate heat flux. The bias in heat flux estimates can be reduced through a correction that depends on stability. It turns out that the bias in heat flux estimates has a minor effect on the prediction of surface friction velocity and turbulent velocities. Estimates of sensible heat flux, surface friction velocity and turbulent velocities are sensitive to estimates of aerodynamic roughness length, and we suggest estimating the aerodynamic roughness length through detailed micrometeorological measurements made during a limited field study. An examination of the impact of the uncertainty in estimating surface micrometeorology on concentrations indicates that, at small distances from a surface release, ground-level concentrations computed using estimates of heat flux and surface friction compare well with the those based on observed values: the bias is small and the 95% confidence interval of the ratio of the two concentrations is 1.7. However, at distances much larger than the Obukhov length, this confidence interval is close to 2.3 because errors in both friction velocity and heat flux affect plume spread. Finally, we show that using measurements of temperature fluctuations in estimating heat flux is an improvement on that based on the surface energy balance, even when net radiation measurements are available.

Distributed generation (DG) has been adopted in California because of its potential to supply a significant fraction of increased power demand in the future. DG offers several advantages over conventional centralized power plants. The relatively small size of a DG unit (usually less than 5 MW) allows location of the unit in the immediate vicinity of the area that requires the power. This independence from a large power supplier results in 1) reliability of power supply, 2) potential cost control because the DG unit is tied to the community that it services, 3) reduction of transmission losses, and 4) increased efficiency when waste heat is utilized for local heating and cooling needs. However, the emissions from a DG unit can impact the air quality in the populated area where it is located. This report summarizes the results from a project designed to estimate the local air quality impact of DG growth in the South Coast Air Basin. This project takes advantage of emission inventories developed by UCI (Samuelsen et al., 2005), for several DG growth scenarios in the South Coast Air Basin (SoCAB). We first developed and applied a systematic method to allocate the 5 km by 5 km gridded emission inventory from UCI to individual DG units within each grid. Then, a state of the art dispersion model, AERMOD (Cimorelli et al., 2005) was used to examine the air quality impact of the DG units in these emission inventories. The meteorological inputs for AERMOD were constructed from observations made at 26 meteorological stations maintained by the South Coast Air Quality Management District (AQMD). Finally, we examines the air quality impact of using distributed generation (DG) to satisfy future growth in power demand in the South Coast Air Basin, relative to the impact when the same power is supplied by expanding current central generation (CG) units. In these two scenarios, the emission factors for both the DGs and the CGS are assumed to meet the California standard for new sources. The impact of decreasing boiler emissions by capturing the waste heat from DGs is not examined. The impact from some micro turbines and fuel cells, which are located on the ground but have relative low emissions, is not examined either. The results from this study indicate that 1) Meteorology plays a major role in determining both the maximum hourly as well as annually averaged concentrations associated with DG units, 2) Because of the interaction between buoyant plume rise and meteorology, ground-level impact does not always increase with size of the DG: a 10 MW unit can have a smaller air quality impact than a 1 MW unit. These results suggest that siting of DG units has to pay attention to meteorology to reduce air quality impact. Simulations with AERMOD suggest that DG growth in the SoCAB is likely to have its greatest impact on NO2 and PM levels, which are already close to or exceed either NAAQS or CAAQS in several areas in the SoCAB in 2007. Areas near Central LA meteorological station will exceed the California NO2 annual standard if any future generating capacity is located in the area. The air quality impacts of the two alternate scenarios indicate that the shift to DGs has the potential for decreasing maximum hourly impacts of power generation in the vicinity of the power plants. The maximum impact on hourly concentrations is reduced from 24.5 ppb to 6 ppb if DGs rather than CGs are used to generate power. However, the annually averaged concentrations are likely to be higher than the scenario in which existing CGs are used to satisfy power demand growth. Future DG penetration will add an annual average of 0.1 ppb to the current basin average of 20 ppb, while expanding existing CGs will add 0.05 ppb to the existing level.