Faraz Enayati Ahangar

Bio: Faraz Enayati Ahangar is an academic researcher. The author has contributed to research in topics: Air quality index & Area source. The author has an hindex of 1, co-authored 1 publications receiving 6 citations.

The Development and Application of Semi-Empirical Dispersion Models: Impact of Roadside Structures, Identifying Emission Sources, and Producing Concentration Maps at Different Temporal and Spatial Scales

Abstract: Author(s): Enayati Ahangar, Faraz | Advisor(s): Venkatram, Akula | Abstract: Dispersion models play an essential role in understanding the impact of pollutant emissions on air quality. Once their results have been evaluated with observations, they are used by regulatory agencies and planning bodies to permit new sources and develop policies to mitigate the impact of emissions on air quality. In my research, I developed and applied a class of dispersion models referred to as semi-empirical models whose formulation depends on representing some of the governing processes with parameters whose values are obtained by fitting model estimates to corresponding observations. In recent years, roadway design is suggested as a potential strategy to mitigate the impact of vehicular emissions on near-road air quality. In my research, I developed a dispersion model to estimate the impact of a solid noise barrier upwind of a highway on concentrations downwind of the road. The results showed that an upwind barrier reduces the downwind concentration by enhancing turbulence and shifting the emissions upwind through the action of the recirculating zone formed behind the upwind barrier. I also propose a tentative model to estimate on-road concentrations within the recirculation zone.. The applicability of the downwind barrier dispersion models to real-world measurements was also explored in my research. First, a field study was conducted to measure ultra-fine particles (UFP) concentration and micrometeorology data near a roadside barrier in Riverside, California. Two models for downwind barriers were evaluated with data collected and emission factors were estimated for the fleet. The primary effect of a downwind barrier was equivalent to shifting the line sources on the road upwind by a distance of about HU(H/2)/u*.Next, UFP concentrations were measured downwind of a solid barrier and a solid barrier with vegetation simultaneously to estimate the incremental effect of tall vegetation on the mitigation caused by a solid barrier. The vegetation above the solid barrier reduced turbulence levels of the air passing through it and added to the concentration reduction induced by the solid barrier most of the time; however, this was not the case for all of the observed data. I then apply dispersion models at regional scales by interpreting PM_2.5 concentrations measured by a network of 40 low-cost monitors located in the Imperial Valley of southern California. This valley is bordered by deserts on the east and the west, the Salton Sea on the North, and Mexico to the South. Particulate matter can be transport into the valley from across these borders, and be generated from within the valley itself because of agricultural activity. These borders are represented by line sources and the valley by an area source. I estimate the emissions from these sources by fitting model estimates to daily and annually averaged measurements made at 40 monitors. Once these emissions are determined, I use them as inputs in the dispersion model to construct PM2.5 maps at a much finer resolution than that provided by the monitors.

Supporting Information for "Roadside Vegetation Barrier Designs to Mitigate Near-Road Air Pollution Impacts"

Abstract: With increasing evidence that exposures to air pollution near large roadways increases risks of a number of adverse human health effects, identifying methods to reduce these exposures has become a public health priority. Roadside vegetation barriers have shown the potential to reduce near-road air pollution concentrations; however, the characteristics of these barriers needed to ensure pollution reductions are not well understood. Designing vegetation barriers to mitigate near-road air pollution requires a mechanistic understanding of how barrier configurations affect the transport of traffic-related air pollutants. We first evaluated the performance of the Comprehensive Turbulent Aerosol Dynamics and Gas Chemistry (CTAG) model with Large Eddy Simulation (LES) to capture the effects of vegetation barriers on near-road air quality, compared against field data. Next, CTAG with LES was employed to explore the effects of six conceptual roadside vegetation/solid barrier configurations on near-road size-resolved particle concentrations, governed by dispersion and deposition. Two potentially viable design options are revealed: a) a wide vegetation barrier with high Leaf Area Density (LAD), and b) vegetation-solid barrier combinations, i.e., planting trees next to a solid barrier. Both designs reduce downwind particle concentrations significantly. The findings presented in the study will assist urban planning and forestry organizations with evaluating different green infrastructure design options.

The rise of low-cost sensing for managing air pollution in cities

Abstract: Ever growing populations in cities are associated with a major increase in road vehicles and air pollution. The overall high levels of urban air pollution have been shown to be of a significant risk to city dwellers. However, the impacts of very high but temporally and spatially restricted pollution, and thus exposure, are still poorly understood. Conventional approaches to air quality monitoring are based on networks of static and sparse measurement stations. However, these are prohibitively expensive to capture tempo-spatial heterogeneity and identify pollution hotspots, which is required for the development of robust real-time strategies for exposure control. Current progress in developing low-cost micro-scale sensing technology is radically changing the conventional approach to allow real-time information in a capillary form. But the question remains whether there is value in the less accurate data they generate. This article illustrates the drivers behind current rises in the use of low-cost sensors for air pollution management in cities, whilst addressing the major challenges for their effective implementation.

Comparing land use regression and dispersion modelling to assess residential exposure to ambient air pollution for epidemiological studies

Abstract: BACKGROUND Land-use regression (LUR) and dispersion models (DM) are commonly used for estimating individual air pollution exposure in population studies. Few comparisons have however been made of the performance of these methods. OBJECTIVES Within the European Study of Cohorts for Air Pollution Effects (ESCAPE) we explored the differences between LUR and DM estimates for NO2, PM10 and PM2.5. METHODS The ESCAPE study developed LUR models for outdoor air pollution levels based on a harmonised monitoring campaign. In thirteen ESCAPE study areas we further applied dispersion models. We compared LUR and DM estimates at the residential addresses of participants in 13 cohorts for NO2; 7 for PM10 and 4 for PM2.5. Additionally, we compared the DM estimates with measured concentrations at the 20-40 ESCAPE monitoring sites in each area. RESULTS The median Pearson R (range) correlation coefficients between LUR and DM estimates for the annual average concentrations of NO2, PM10 and PM2.5 were 0.75 (0.19-0.89), 0.39 (0.23-0.66) and 0.29 (0.22-0.81) for 112,971 (13 study areas), 69,591 (7) and 28,519 (4) addresses respectively. The median Pearson R correlation coefficients (range) between DM estimates and ESCAPE measurements were of 0.74 (0.09-0.86) for NO2; 0.58 (0.36-0.88) for PM10 and 0.58 (0.39-0.66) for PM2.5. CONCLUSIONS LUR and dispersion model estimates correlated on average well for NO2 but only moderately for PM10 and PM2.5, with large variability across areas. DM predicted a moderate to large proportion of the measured variation for NO2 but less for PM10 and PM2.5.

The Development and Application of Models for Dispersion of Roadway Emissions: The Effects of Roadway Configurations on Near Road Concentrations of Vehicle Emissions and Increasing the Spatial Resolution of Satellite-Derived PM2.5 Maps

Abstract: Author(s): Amini, Seyedmorteza | Advisor(s): Venkatram, Akula | Abstract: Near road air quality is a public concern because exposure to elevated concentrations of vehicular pollution is associated with adverse health effects. Roadway design is suggested as a potential strategy to mitigate near-road exposure. The first part of my dissertation describes the development and application of roadway dispersion models to examine the effectiveness of roadway configurations as pollutant mitigation strategies. These configurations include depressed roadways and at-grade roadways with the presence of solid/vegetative barriers.Roadside solid barriers increase dispersion of pollutants by lofting emissions and inducing a recirculation zone on their leeward edge. I adapt a model, developed using data from a wind tunnel, to describe ultrafine particle measurements made in a field study. This requires modifying the model to account for uncertainties in emissions and meteorological parameters of real-world studies. Results suggest that 1) a model developed under controlled conditions is useful in the complex environment of urban areas, 2) the surface can be taken neutral in modeling dispersion in urban areas., and 3) the primary impact of the barrier is equivalent to shifting the road upwind by a distance of H(U/u*)cosθ. I next analyze data from a wind tunnel that examined dispersion of emissions from depressed roadways using roadway dispersion models. I show that dispersion governed by the complex flow induced by depressed roads can be described using modified flat-terrain models. The modifications include 1) an initial vertical spread dependent on the geometry of the depressed roadway, and 2) increasing the friction velocity above its upwind value. Also, the vertical concentration profiles under neutral stability conditions are best explained with a vertical distribution function with an exponent of 1.3 rather than the 2 used in most currently used dispersion models.Health risk assessment of PM2.5 on the community scale requires PM2.5 concentration estimations at the scale of tens of meters. The PM2.5 measurement from air quality stations and satellite-derived PM2.5 estimates cannot provide concentrations at this spatial resolution. In the last part of my dissertation, I describe a “downscaling” system that adapts roadway dispersion models to yield the concentration gradients that are not captured by satellite maps.