Annual meeting & exhibition proceedings CD-ROM. Air & Waste Management Association. Meeting最新文献

The National Research Council recommended both a risk- and performance-based multipollutant approach to air quality management. Specifically, management decisions should be based on minimizing the exposure to, and risk of adverse effects from, multiple sources of air pollution and that the success of these decisions should be measured by how well they achieved this objective. We briefly describe risk analysis and its application within the current approach to air quality management. Recommendations are made as to how current practice could evolve to support a fully risk- and performance-based multipollutant air quality management system. The ability to implement a risk assessment framework in a credible and policy-relevant manner depends on the availability of component models and data which are scientifically sound and developed with an understanding of their application in integrated assessments. The same can be said about accountability assessments used to evaluate the outcomes of decisions made using such frameworks. The existing risk analysis framework, although typically applied to individual pollutants, is conceptually well suited for analyzing multipollutant management actions. Many elements of this framework, such as emissions and air quality modeling, already exist with multipollutant characteristics. However, the framework needs to be supported with information on exposure and concentration response relationships that result from multipollutant health studies. Because the causal chain that links management actions to emission reductions, air quality improvements, exposure reductions and health outcomes is parallel between prospective risk analyses and retrospective accountability assessments, both types of assessment should be placed within a single framework with common metrics and indicators where possible. Improvements in risk reductions can be obtained by adopting a multipollutant risk analysis framework within the current air quality management system, e.g. focused on standards for individual pollutants and with separate goals for air toxics and ambient pollutants. However, additional improvements may be possible if goals and actions are defined in terms of risk metrics that are comparable across criteria pollutants and air toxics (hazardous air pollutants), and that encompass both human health and ecological risks.

Factors that influence in-vehicle PM(2.5) exposure are indentified and assessed. The methodology used in the current version of Stochastic Exposure and Dose Simulation model for Particulate Matter (SHEDS-PM) for in-vehicle PM(2.5) concentration is reviewed, and alternative modeling approaches are identified and evaluated. SHEDS-PM uses a linear regression model to estimate in-vehicle PM(2.5) concentration based on ambient PM(2.5) concentration, such as from a fixed site monitor (FSM) or a grid cell average concentration estimate from an air quality model. The ratio of in-vehicle to FSM concentration varies substantially with respect to location, vehicle type and other factors. SHEDS-PM was used to estimate PM(2.5) exposure for 1% of people living in Wake County, NC in order to assess the importance of in-vehicle exposures. In-vehicle PM(2.5) exposure can be as much as half of the total exposure for some individuals, depending on employment status and the time spent in-vehicle during commuting. An alternative modeling approach is explored based on the use of a dispersion model to estimate near-road PM(2.5) concentration based on FSM data and a mass balance model for estimating in-vehicle concentration.Recommendations for updating the input data to the existing model, and implementation of the alternative modeling approach are made.

Environmental tobacco smoke (ETS) is estimated to be a major contributor to indoor PM concentration and human exposures to fine particulate matter of 2.5 microns or smaller (PM_2.5). The Stochastic Human Exposure and Dose Simulation for Particulate Matter (SHEDS-PM) model developed by the US Environmental Protection Agency estimates distributions of outdoor and indoor PM_2.5 exposure for a specified population based on ambient concentrations and indoor emissions sources. Because indoor exposures to ETS can be high, especially in indoor residential microenvironments, a critical assessment was conducted of the methodology and data used in SHEDS-PM for estimation of indoor exposure to ETS. For the residential microenvironment, SHEDS uses a mass-balance approach which is comparable to best practices. The default inputs in SHEDS-PM were reviewed and more recent and extensive data sources were identified. Sensitivity analysis was used to determine which inputs should be prioritized for updating. Data regarding the cigarette emission rate was found to be the most important. SHEDS-PM does not currently account for in-vehicle ETS exposure; however, in-vehicle ETS-related PM_2.5 levels can exceed those in residential microenvironments by a factor of 10 or more. Therefore, a mass-balance based methodology for estimating in-vehicle ETS PM_2.5 concentration is evaluated. Recommendations are made regarding updating of input data and algorithms related to ETS exposure in the SHEDS-PM model.