Feasibility of assessing public health impacts of air pollution reduction programs on a local scale: New Haven case study.

BACKGROUND: New approaches to link health surveillance data with environmental and population exposure information are needed to examine the health benefits of risk management decisions. OBJECTIVE: We examined the feasibility of conducting a local assessment of the public health impacts of cumulative air pollution reduction activities from federal, state, local, and voluntary actions in the City of New Haven, Connecticut (USA). METHODS: Using a hybrid modeling approach that combines regional and local-scale air quality data, we estimated ambient concentrations for multiple air pollutants [e.g., PM2.5 (particulate matter ≤ 2.5 µm in aerodynamic diameter), NOx (nitrogen oxides)] for baseline year 2001 and projected emissions for 2010, 2020, and 2030. We assessed the feasibility of detecting health improvements in relation to reductions in air pollution for 26 different pollutant-health outcome linkages using both sample size and exploratory epidemiological simulations to further inform decision-making needs. RESULTS: Model projections suggested decreases (~10-60%) in pollutant concentrations, mainly attributable to decreases in pollutants from local sources between 2001 and 2010. Models indicated considerable spatial variability in the concentrations of most pollutants. Sample size analyses supported the feasibility of identifying linkages between reductions in NOx and improvements in all-cause mortality, prevalence of asthma in children and adults, and cardiovascular and respiratory hospitalizations. CONCLUSION: Substantial reductions in air pollution (e.g., ~60% for NOx) are needed to detect health impacts of environmental actions using traditional epidemiological study designs in small communities like New Haven. In contrast, exploratory epidemiological simulations suggest that it may be possible to demonstrate the health impacts of PM reductions by predicting intraurban pollution gradients within New Haven using coupled models.

Combining regional- and local-scale air quality models with exposure models for use in environmental health studies.

Population-based human exposure models predict the distribution of personal exposures to pollutants of outdoor origin using a variety of inputs, including air pollution concentrations; human activity patterns, such as the amount of time spent outdoors versus indoors, commuting, walking, and indoors at home; microenvironmental infiltration rates; and pollutant removal rates in indoor environments. Typically, exposure models rely upon ambient air concentration inputs from a sparse network of monitoring stations. Here we present a unique methodology for combining multiple types of air quality models (the Community Multi-Scale Air Quality [CMAQ] chemical transport model added to the AERMOD dispersion model) and linking the resulting hourly concentrations to population exposure models (the Hazardous Air Pollutant Exposure Model [HAPEM] or the Stochastic Human Exposure and Dose Simulation [SHEDS] model) to enhance estimates of air pollution exposures that vary temporally (annual and seasonal) and spatially (at census-block-group resolution) in an urban area. The results indicate that there is a strong spatial gradient in the predicted mean exposure concentrations near roadways and industrial facilities that can vary by almost a factor of 2 across the urban area studied. At the high end of the exposure distribution (95th percentile), exposures are higher in the central district than in the suburbs. This is mostly due to the importance of personal mobility factors whereby individuals living in the central area often move between microenvironments with high concentrations, as opposed to individuals residing at the outskirts of the city. Also, our results indicate 20-30% differences due to commuting patterns and almost a factor of 2 difference because of near-roadway effects. These differences are smaller for the median exposures, indicating the highly variable nature of the reflected ambient concentrations. In conjunction with local data on emission sources, microenvironmental factors, and behavioral and socioeconomic characteristics, the combined source-to-exposure modeling methodology presented in this paper can improve the assessment of exposures in future community air pollution health studies.

Resolving local-scale emissions for modeling air quality near roadways.

A large body of literature published in recent years suggests increased health risk due to exposure of people to air pollution in close proximity to roadways. As a result, there is a need to more accurately represent the spatial concentration gradients near roadways to develop mitigation strategies. In this paper, we present a practical, readily adaptable methodology, using a "bottom-up" approach to develop a detailed highway vehicle emission inventory that includes emissions for individual road links. This methodology also takes advantage of geographic information system (GIS) software to improve the spatial accuracy of the activity information obtained from a Travel Demand Model. In addition, we present an air quality modeling application of this methodology in New Haven, CT. This application uses a hybrid modeling approach, in which a regional grid-based model is used to characterize average local ambient concentrations, and a Gaussian dispersion model is used to provide texture within the modeling domain because of spatial gradients associated with highway vehicle emissions and other local sources. Modeling results show substantial heterogeneity of pollutant concentrations within the modeling domain and strong spatial gradients associated with roadways, particularly for pollutants dominated by direct emissions.

Modeling population exposures to outdoor sources of hazardous air pollutants.

Accurate assessment of human exposures is an important part of environmental health effects research. However, most air pollution epidemiology studies rely upon imperfect surrogates of personal exposures, such as information based on available central-site outdoor concentration monitoring or modeling data. In this paper, we examine the limitations of using outdoor concentration predictions instead of modeled personal exposures for over 30 gaseous and particulate hazardous air pollutants (HAPs) in the US. The analysis uses the results from an air quality dispersion model (the ASPEN or Assessment System for Population Exposure Nationwide model) and an inhalation exposure model (the HAPEM or Hazardous Air Pollutant Exposure Model, Version 5), applied by the US. Environmental protection Agency during the 1999 National Air Toxic Assessment (NATA) in the US. Our results show that the total predicted chronic exposure concentrations of outdoor HAPs from all sources are lower than the modeled ambient concentrations by about 20% on average for most gaseous HAPs and by about 60% on average for most particulate HAPs (mainly, due to the exclusion of indoor sources from our modeling analysis and lower infiltration of particles indoors). On the other hand, the HAPEM/ASPEN concentration ratio averages for onroad mobile source exposures were found to be greater than 1 (around 1.20) for most mobile-source related HAPs (e.g. 1, 3-butadiene, acetaldehyde, benzene, formaldehyde) reflecting the importance of near-roadway and commuting environments on personal exposures to HAPs. The distribution of the ratios of personal to ambient concentrations was found to be skewed for a number of the VOCs and reactive HAPs associated with major source emissions, indicating the importance of personal mobility factors. We conclude that the increase in personal exposures from the corresponding predicted ambient levels tends to occur near locations where there are either major emission sources of HAPs or when individuals are exposed to either on- or nonroad sources of HAPs during their daily activities. These findings underscore the importance of applying exposure-modeling methods, which incorporate information on time-activity, commuting, and exposure factors data, for the purposes of assigning exposures in air pollution health studies.

A method of assessing air toxics concentrations in urban areas using mobile platform measurements.

The objective of this paper is to demonstrate an approach to characterize the spatial variability in ambient air concentrations using mobile platform measurements. This approach may be useful for air toxics assessments in Environmental Justice applications, epidemiological studies, and environmental health risk assessments. In this study, we developed and applied a method to characterize air toxics concentrations in urban areas using results of the recently conducted field study in Wilmington, DE. Mobile measurements were collected over a 4- x 4-km area of downtown Wilmington for three components: formaldehyde (representative of volatile organic compounds and also photochemically reactive pollutants), aerosol size distribution (representing fine particulate matter), and water-soluble hexavalent chromium (representative of toxic metals). These measurements were,used to construct spatial and temporal distributions of air toxics in the area that show a very strong temporal variability, both diurnally and seasonally. An analysis of spatial variability indicates that all pollutants varied significantly by location, which suggests potential impact of local sources. From the comparison with measurements at the central monitoring site, we conclude that formaldehyde and fine particulates show a positive correlation with temperature, which could also be the reason that photochemically generated formaldehyde and fine particulates over the study area correlate well with the fine particulate matter measured at the central site.

Using prognostic model-generated meteorological output in the AERMOD dispersion model: an illustrative application in Philadelphia, PA.

In this study, we introduce the prospect of using prognostic model-generated meteorological output as input to steady-state dispersion models by identifying possible advantages and disadvantages and by presenting a comparative analysis. Because output from prognostic meteorological models is now routinely available and is used for Eulerian and Lagrangian air quality modeling applications, we explore the possibility of using such data in lieu of traditional National Weather Service (NWS) data for dispersion models. We apply these data in an urban application where comparisons can be made between the two meteorological input data types. Using the U.S. Environment Protection Agency's American Meteorological Society/U.S. Environmental Protection Agency Regulatory Model (AERMOD) air quality dispersion model, hourly and annual average concentrations of benzene are estimated for the Philadelphia, PA, area using both hourly MM5 model-generated meteorological output and meteorological data taken from the NWS site at the Philadelphia International Airport. Our intent is to stimulate a discussion of the relevant issues and inspire future work that examines many of the questions raised in this paper.

Inhalation exposure and risk from mobile source air toxics in future years.

Modeling of inhalation exposure and risks resulting from exposure to mobile source air toxics can be used to evaluate impacts of reductions from control programs on overall risk, as well as changes in relative contributions of different source sectors to risk, changes in contributions of different pollutants to overall risk, and changes in geographic distributions of risk. Such analysis is useful in setting regulatory priorities, and informing the decision-making process. In this paper, we have conducted national-scale air quality, exposure, and risk modeling for the US in the years 2015, 2020, and 2030, using similar tools and methods as the 1999 National-Scale Air Toxics Assessment. Our results suggest that US Environmental Protection Agency emission control programs will substantially reduce average inhalation cancer risks and potential noncancer health risks from exposure to mobile source air toxics. However, cancer risk and noncancer hazard due to inhalation of air toxics will continue to be a public health concern.

Impact of underestimating the effects of cold temperature on motor vehicle start emissions of air toxics in the United States.

Analyses of U.S. Environmental Protection Agency (EPA) certification data, California Air Resources Board surveillance testing data, and EPA research testing data indicated that EPA's MOBILE6.2 emission factor model substantially underestimates emissions of gaseous air toxics occurring during vehicle starts at cold temperatures for light-duty vehicles and trucks meeting EPA Tier 1 and later standards. An unofficial version of the MOBILE6.2 model was created to account for these underestimates. When this unofficial version of the model was used to project emissions into the future, emissions increased by almost 100% by calendar year 2030, and estimated modeled ambient air toxics concentrations increased by 6-84%, depending on the pollutant. To address these elevated emissions, EPA recently finalized standards requiring reductions of emissions when engines start at cold temperatures.

Air quality modeling of hazardous pollutants: current status and future directions.

The current requirements and status of air quality modeling of hazardous pollutants are reviewed. Many applications require the ability to predict the local impacts from industrial sources or large roadways as needed for community health characterization and evaluating environmental justice concerns. Such local-scale modeling assessments can be performed by using Gaussian dispersion models. However, these models have a limited ability to handle chemical transformations. A new generation of Eulerian grid-based models is now capable of comprehensively treating transport and chemical transformations of air toxics. However, they typically have coarse spatial resolution, and their computational requirements increase dramatically with finer spatial resolution. The authors present and discuss possible advanced approaches that can combine the grid-based models with local-scale information.

Spatial and temporal variability of ambient air toxics data.

This paper summarizes information on the spatial and temporal variability of selected air toxics pollutants collected on a national basis primarily for a period encompassing 1990-2003. Spatial information on pollutant concentrations is characterized in terms of within-city and between-city variability. Temporal information is summarized as diurnal and seasonal variability and in multiyear trends. The information on variability is presented in the framework of a larger need for systematic documentation of information on air toxics pollutants to assess progress in air pollution control programs.