Modeling Brake and Tire Wear Emissions in Regulatory Models in the United States
Darrell SonntagChad R. BaileyRichard BaldaufChristina ParsonsC. F. OwenAlison EythSonya CollierSam PournazeriSeungju Yoon
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MOVES and EMFAC (for California only) are the regulatory vehicle emission models used in the United States to estimate brake and tire wear emissions. The emissions estimates are then used in conjunction with air dispersion models (including AERMOD, US EPA's preferred dispersion model) to estimate PM2.5 and PM10 concentrations in the near-road environment.We will provide an overview of the data and methods that MOVES and EMFAC uses to estimate brake and tire emissions, including a comparison to exhaust particulate matter emissions. For example, MOVES does not account for the important differences in chemical speciation between brake, tire and exhaust particulate matter emissions. We will discuss current efforts to evaluate and improve the brake and tire emission rates in the models. We will also provide an overview of regulatory context in which the emission and air quality modeling tools are used, and the limitations of these tools for conducting exposure and epidemiology studies.Keywords:
AERMOD
Ambient air composition over the earth has undergone several changes. Anthropogenic pollutants generated locally often have regional air quality affected significantly. In addition to the short-term episodic effects, atmospheric pollutants are known to generate long-term adverse effects, which are difficult to forecast statistically. The air pollution dispersion modeling exercise forecasts fairly accurate air quality for a given emission scenario, incorporating geographical and meteorological conditions. Industrial Source Complex Dispersion Model is used to forecast ground level concentrations (GLC) of an industrial region as case study. The usefulness of mathematical modeling and computational simulation techniques used to predict the horizontal and vertical pollutant concentrations of pollutants are explained in this paper. Finally a case study of an industrial area in Gujarat region is presented to arrive at setting up of air quality monitoring station for monitoring/management of regional air quality.
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Abstract Air quality models are typically used to predict the fate and transport of air emissions from industrial sources to comply with federal and state regulatory requirements and environmental standards, as well as to determine pollution control requirements. For many years, the U.S. Environmental Protection Agency (EPA) widely used the Industrial Source Complex (ISC) model because of its broad applicability to multiple source types. Recently, EPA adopted a new rule that replaces ISC with AERMOD, a state-of-the-practice air dispersion model, in many air quality impact assessments. This study compared the two models as well as their enhanced versions that incorporate the Plume Rise Model Enhancements (PRIME) algorithm. PRIME takes into account the effects of building downwash on plume dispersion. The comparison used actual point, area, and volume sources located on two separate facilities in conjunction with site-specific terrain and meteorological data. The modeled maximum total period average ground-level air concentrations were used to calculate potential health effects for human receptors. The results show that the switch from ISC to AERMOD and the incorporation of the PRIME algorithm tend to generate lower concentration estimates at the point of maximum ground-level concentration. However, the magnitude of difference varies from insignificant to significant depending on the types of the sources and the site-specific conditions. The differences in human health effects, predicted using results from the two models, mirror the concentrations predicted by the models.
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This paper applies AERMOD model to the prediction of regional air quality,taking Rizhao as an example.This paper also analyzes the effects of different pollutants on regional air quality with this model and predicts the air quality of Rizhao in the next five years.Application results show that this model can well simulate the relationship between air quality and pollutants in small and medium sized cities,and can be applied to the prediction of regional air quality.
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AERMOD is an atmospheric dispersion model developed by U.S.Environmental Protection Agency (EPA)and American Meteorological Society (AMS).Up to date atmospheric boundary and dispersion theories were used in the model,which substituted for ISC(Industrial Source Complex)model to be U.S.regulatory model.AERMOD model was also listed as one of the recommended models in the national technical guidelines of environmental impact assessment(EIA)——atmospheric environment (HJ/T2.2-2008)in China.Lots of studies were done on the model,with focus on comparison studies between AERMOD model and other models and AERMOD model evaluation,meteorological data simulation for successful AERMOD model runs and the application of AERMOD model in certain fields.AERMOD model is superior to the last recommended model in national technical guidelines of environmental impact assessment——atmospheric environmen(tHJ/T2.2-93),but due to its ever developing and not too much application in China,it suggested that the model should pass the appraisal process before its application in a new area or a new city where the model has never been used before,in order to verify its reliability,feasibility and accuracy.
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This chapter presents the most common approaches of the regulatory framework for air quality and air dispersion modeling. The first major federal initiative in the United States to regulate air quality was the Clean Air Act of 1963. The Clean Air Act required the Environmental Protection Agency (EPA) to develop air quality standards. Air emissions are regulated by the states. They are responsible to ensure that the air quality standards are met. Each state (U.S.) and province (Canada) has its own air quality standards, based on the local conditions, industry, and the like. A screening technique in air dispersion modeling is the use of a simple model such as SCREEN3 to calculate the worst-case scenario resulting from a proposed air pollution source. When screening techniques predict the ambient air quality objectives to be exceeded, a refined modeling technique must be used to evaluate the proposed pollution source.
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Abstract. Degradation of air quality level can affect human’s health especially respiratory and circulatory system. This is because the harmful particles will penetrate into human’s body through exposure to surrounding. The existence of air pollution event is one of the causes for air quality to be low in affected urban area. To monitor this event, a proper management of urban air quality is required to solve and reduce the impact on human and environment. One of the ways to manage urban air quality is by modelling ambient air pollutants. So, this paper reviews three modelling tools which are AERMOD, CALPUFF and CFD in order to visualise the air pollutants in urban area. These three tools have its own capability in modelling the air quality. AERMOD is better to be used in short range dispersion model while CALPUFF is for wide range of dispersion model. Somehow, it is different for CFD model as this model can be used in wide range of application such as air ventilation in clothing and not specifically for air quality modelling only. Because of this, AERMOD and CALPUFF model can be classified in air quality modelling tools group whereas CFD modelling tool is classified into different group namely a non-specific modelling tool group which can be implemented in many fields of study. Earlier air quality researches produced results in two-dimensional (2D) visualization. But there are several of disadvantages for this technique. It cannot provide height information and exact location of pollutants in three-dimensional (3D) as perceived in real world. Moreover, it cannot show a good representation of wind movement throughout the study area. To overcome this problem, the 3D visualization needs to be implemented in the urban air quality study. Thus, this paper intended to give a better understanding on modeling tools with the visualization technique used for the result of performed research.
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This study developed new procedures to loosely integrate an air dispersion model, AERMOD, and a geographic information system (GIS) package, ArcGIS, to simulate air dispersion from stationary sources in the Bronx, New York City, for five pollutants: PM(10), PM(2.5), NO(x), CO, and SO(2). Plume buffers created from the model results were used as proxies of human exposure to the pollution from the sources and they modified the commonly used fixed-distance proximity buffers by considering the realities of air dispersion. The application of the plume buffers confirmed that the higher asthma hospitalization rates were associated with the higher potential exposure to local air pollution. The air dispersion modeling exhibited advantages over proximity analysis and geostatistical methods for environmental health research. The loose integration provides a relatively simple and feasible method for health scientists to take advantage of both air dispersion modeling and GIS by avoiding the need for intensive programming and substantial GIS expertise.
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One of the primary modes for pollutants to reach the human body is through their release into the atmosphere and dispersion in the nearby areas. This makes air emissions one of the important components of an environmental assessment. They are frequently of interest to the people living in the vicinity of the site. The analysis carried out in this research provides a framework for atmospheric dispersion modeling of air pollutants using AERMOD, the United States Environmental Protection Agency (US EPA) approved regulatory model, for modeling port related emissions. There are various sources of emissions from the ports, however for this study, emissions from roadways, small equipment, and marine related sources are considered. AERMOD was used to estimate ambient pollutant concentrations of selective criteria pollutants such as NOx, SO2, CO and PM10 from a hypothetical port.
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