Michael L. Deaton

Increasing frequencies of warm and humid air masses over the conterminous United States from 1948 to 2005

Time series of individual climate variables, such as air temperature and precipitation, have been thoroughly examined to evaluate climate change, but few studies have evaluated how air masses have varied over time. We use the Spatial Synoptic Classification air mass approach to classify multivariate meteorological surface variables into discrete groups and examine trends in air mass frequencies over the period 1948-2005 for the continental United States. We observe increases in warm, moist air masses at the expense of cold, dry air masses, consistent with expectations in an atmosphere with increasing greenhouse gas concentrations. Temporal variations in the North Atlantic Oscillation, Pacific/North American teleconnection pattern, Arctic Oscillation, and El Nino-Southern Oscillation partially explain some of these observed trends in winter.

Understanding U.S. Biodiesel Industry Growth using System Dynamics Modeling

The production capacity of the biodiesel industry is experiencing exponential growth. Demand is driven by environmental, social, and economic factors and helped along by government mandates and incentives. Suppliers are having difficulty keeping up with demand. The U.S. production capacity has grown by a factor of ten in the past two years, and between thirty and forty new plants are currently in or near construction phase. Continued strong growth of biodiesel production capacity depends on producer/supplier profitability, which will be influenced by several factors such as biomass oil feedstock prices, product/co-product prices, production technologies, and government regulations/incentives. How, why, and to what extent will the growth of the biodiesel industry be influenced by these factors? To explore possible answers to these questions, we describe the formulation of a system dynamics model of the U.S. biodiesel marketplace. The construction and use of this model will provide a framework for understanding the causal-loop/feedback structure and dynamics of this industry and how changes in key variables (e.g. feedstock price or change in government incentives) impact growth. Using system dynamics modeling, we envision and put into perspective the possible growth behavior scenarios for this industry over the next decade

HAZARDOUS AIR POLLUTION FROM MOBILE SOURCES: A COMPARISON OF ALTERNATIVE FUEL AND REFORMULATED GASOLINE VEHICLES

Although there have been several studies examining emissions of criteria pollutants from in-use alternative fuel vehicles (AFVs), little is known about emissions of hazardous air pollutants (HAPs) from these vehicles. This paper explores HAP tailpipe emissions from a variety of AFVs operating in the federal government fleet and compares these emissions to emissions from identical vehicles operating on reformulated gasoline. Emissions estimates are presented for a variety of fuel/model combinations and on four HAPs (acetaldehyde, 1,3-butadi-ene, benzene, and formaldehyde). The results indicate that all AFVs tested offer reduced emissions of HAPs, with the following exceptions: ethanol fueled vehicles emit more acetaldehyde than RFG vehicles, and ethanol- and methanol-fueled vehicles emit more formaldehyde than RFG vehicles. The results from this paper can lead to more accurate emissions factors for HAPs, thus improving HAP inventory and associated risk estimates for both AFVs and conventional vehicles.

A respiratory alert model for the Shenandoah Valley, Virginia, USA

Respiratory morbidity (particularly COPD and asthma) can be influenced by short-term weather fluctuations that affect air quality and lung function. We developed a model to evaluate meteorological conditions associated with respiratory hospital admissions in the Shenandoah Valley of Virginia, USA. We generated ensembles of classification trees based on six years of respiratory-related hospital admissions (64,620 cases) and a suite of 83 potential environmental predictor variables. As our goal was to identify short-term weather linkages to high admission periods, the dependent variable was formulated as a binary classification of five-day moving average respiratory admission departures from the seasonal mean value. Accounting for seasonality removed the long-term apparent inverse relationship between temperature and admissions. We generated eight total models specific to the northern and southern portions of the valley for each season. All eight models demonstrate predictive skill (mean odds ratio = 3.635) when evaluated using a randomization procedure. The predictor variables selected by the ensembling algorithm vary across models, and both meteorological and air quality variables are included. In general, the models indicate complex linkages between respiratory health and environmental conditions that may be difficult to identify using more traditional approaches.

The FALCON decision support system: Preparing communities for weapons of opportunity

Abstract Since September 11, 2001, awareness of potential terrorist targets has increased greatly. Industrial chemicals, either in storage or transport, are now considered dangerously accessible materials that could be used to cause substantial harm. In response to this new threat, emergency organizations are beginning to plan for such possible chemical releases. Currently there is no tool that allows a community to track, analyze, query, and display data about these chemical “weapons of opportunity” and the readiness of the communities around them. Decision support systems are computer environments designed to assist decision makers within a particular problem-solving context. A particular type of DSS, environmental decision support systems (EDSS), assists environmental scientists and planners in making environmental management decisions. A hazardous materials decision support system called “FALCON” will assist emergency organizations by integrating information describing chemical inventories, security, health readiness, geography, and population into one information system. Emergency organizations will be able to assess response readiness of a community for chemical releases and prioritize antidote stockpiling, training, and security. Emergency organizations and first responders will use the FALCON DSS to simulate and prepare for real-time events, assess possible casualties, and receive emergency contact information. And with the help of FALCON, law enforcement and security personnel will be able to evaluate and augment protection of the most dangerous facilities.

COMPARING LIFETIME EMISSIONS OF NATURAL GAS AND CONVENTIONAL FUEL VEHICLES: AN APPLICATION OF THE GENERALIZED ANCOVA MODEL

ABSTRACT New regulations and incentives are encouraging the use of clean, alternative fuel vehicles (AFVs) in urban areas. These vehicles are seen as one option for reducing air pollution from mobile sources. However, because of the limited number of AFVs on the road, little is known about actual lifetime emissions characteristics of in-use AFVs. This study describes the use of a generalized analysis of covariance model to evaluate and compare the emissions from natural gas vehicles with emissions from reformulated gasoline vehicles. The model describes fleet-wide emissions deterioration, while also accounting for individual vehicle variability within the fleet. This ability to measure individual vehicle variability can then be used to provide realistic bounds for the emissions deterioration in individual vehicles and the fleet as a whole. In order to illustrate the use of the model, the carbon monoxide, oxides of nitrogen (NOx), non-methane hydrocarbon (NMHC), and carbon dioxide emissions characteristics of a fleet of dedicated natural gas Dodge Ram vans and a fleet of dedicated reformulated gasoline Dodge Ram vans operating in the U.S. government fleet are explored. The analysis demonstrates the utility of the statistical method and suggests a potential for natural gas Dodge Ram vans to be generally cleaner than their conventional fuel counterparts. However, in the case of NOx and NHMCs, the analysis also suggests that these emissions benefits might be reduced over the vehicle lifetime due to higher emissions deterioration rates for natural gas vehicles. As this paper is aimed at illustrating the analysis of the covari-ance model, the results reported herein should be considered within the context of a more comprehensive study of these data before general conclusions are possible. Generalization of these findings to other vehicle models and alternative fuel technologies is not justified without further study.

A Comparative Analysis of Emissions Deterioration for In-Use Alternative Fuel Vehicles

Abstract Although there have been several studies examining emissions from in–use alternative fuel vehicles (AFVs), little is known about the deterioration of these emissions over vehicle lifetimes and how this deterioration compares with deterioration from conventional vehicles (CVs). This paper analyzes emissions data from 70 AFVs and 70 CVs operating in the federal government fleet to determine whether AFV emissions deterioration differs significantly from CV emissions deterioration. An analysis is conducted on three alternative fuel types (natural gas, methanol, and ethanol) and on four pollutants (carbon monoxide, total hydrocarbons, non-methane hydrocarbons, and nitrogen oxides). The results indicate that for most cases studied, deterioration differences are not statistically significant; however, several exceptions (most notably with natural gas vehicles) suggest that air quality planners and regulators must further analyze AFV emissions deterioration to properly include these technologies in broad...

Overview of Environmental Systems

Chapter Objectives– After you finish this chapter, you should be able to: 1. Recognize many environmental phenomena as coming from dynamic systems. 2. Name the four components of a system and use those components to construct a simple model of a system. 3. Describe how difference equations are used to calculate the behavior of a dynamic system over time. 4. Distinguish between systems thinking and other kinds of thinking. 5. Explain how dynamic systems models can be used to understand environmental problems. 6. Define feedback and steady-state behavior and explain why these features are important to environmental systems.