Statistical Considerations in Determining the Health Significance of Constituents of Airborne Particulate Matter

ABSTRACT

Because of the U.S. Environmental Protection Agency’s (EPA) new ambient air quality standard for fine particles, the need is likely to continue for more detailed scientific investigation of various types of particles and their effects on human health. Epidemiology studies have become the method of choice for investigating health responses to such particles and to other air pollutants in community settings. Health effects have been associated with virtually all of the gaseous criteria pollutants and with the major constituents of airborne particulate matter (PM), including all size fractions less than about 20 gm, inorganic ions, carbonaceous particles, metals, crustal material, and biological aerosols. In many of the more recent studies, multiple pollutants or agents (including weather variables) have been significantly associated with health responses, and various methods have been used to suggest which ones might be the most important. In an ideal situation, classical least-squares regression methods are capable of performing this task. However, in the real world, where most of the pollutants are correlated with one another and have varying degrees of measurement precision and accuracy, such regression results can be misleading. This paper presents some guidelines for dealing with such collinearity and model comparison problems in both single- and multiple-pollutant regressions. These techniques rely on mean effect (attributable risk) rather than statistical significance per se as the preferred indicator of importance for the pollution variables.     

Daily Mortality in the Philadelphia Metropolitan Area and Size-Classified Particulate Matter

ABSTRACT

Time-series of daily mortality data from May 1992 to September 1995 for various portions of the seven-county Philadelphia, PA, metropolitan area were analyzed in relation to weather and a variety of ambient air quality parameters. The air quality data included measurements of size-classified PM, SO₄ ^2-, and H+ that had been collected by the Harvard School of Public Health, as well as routine air pollution monitoring data. Because the various pollutants of interest were measured at different locations within the metropolitan area, it was necessary to test for spatial sensitivity by comparing results for different combinations of locations. Estimates are presented for single pollutants and for multiple-pollutant models, including gaseous pollutants and mutually exclusive components of PM (PM_2.5 and coarse particles, SO₄ ^2- and non-SO₄ ^2- portions of total suspended particulate [TSP] and PM₁₀), measured on the day of death and the previous day.

We concluded that associations between air quality and mortality were not limited to data collected in the same part of the metropolitan area; that is, mortality for one part may be associated with air quality data from another, not necessarily neighboring, part. Significant associations were found for a wide variety of gaseous and particulate pollutants, especially for peak O₃. Using joint regressions on peak O₃ with various other pollutants, we found that the combined responses were insensitive to the specific other pollutant selected. We saw no systematic differences according to particle size or chemistry. In general, the associations between daily mortality and air pollution depended on the pollutant or the PM metric, the type of collection filter used, and the location of sampling. Although peak O₃ seemed to exhibit the most consistent mortality responses, this finding should be confirmed by analyzing separate seasons and other time periods.     

Seasonal Variability in CH4 Emissions from a Landfill in a Cool,Semiarid Climate

ABSTRACT

Methane exchange with the atmosphere was measured during three seasons at the Rooney Road landfill in Jefferson County, CO. Substantial spatial and temporal variability in exchange rates were observed. Mean fluxes to the atmosphere were 534, 1290, and 538 mg CH₄/m²/day, respectively, in the fall of 1994, winter of 1994–1995, and summer of 1995. Median fluxes were 12.42, 8.62, and 5.65 mg CH₄/m²/day, respectively, during those seasons. Forty-three of 177 measurements had small negative fluxes, suggesting methanotrophic activity in the landfill cover soils. Despite probable methanotrophic activity in cover soils, landfills without gas collection systems may emit substantial CH₄ to the atmosphere, with large spatial and seasonal variability.     

Identification of Selected Hormonally Active Agents and Animal Mammary Carcinogens in Commercial and Residential Air and Dust Samples

ABSTRACT

In order to characterize typical indoor exposures to chemicals of interest for research on breast cancer and other hormonally mediated health outcomes, methods were developed to analyze air and dust for target compounds that have been identified as animal mammary carcinogens or hormonally active agents and that are used in commercial or consumer products or building materials. These methods were applied to a small number of residential and commercial environments to begin to characterize the extent of exposure to these classes of compounds. Phenolic compounds, including nonylphenol, octylphenol, bisphenol A, and the methoxychlor metabolite 2,2-bis (p-hydroxyphenyl)-1,1,1-trichloroethane (HPTE), were extracted, derivatized, and analyzed by gas chromatography/mass spectrometry (GC/MS)–selective ion monitoring (SIM). Selected phthalates, pesticides, polycyclic aromatic hydrocarbons (PAHs), and polychlorinated biphenyls (PCBs) were extracted and analyzed by GC/MS-SIM. Residential and workplace samples showed detectable levels of twelve pesticides in dust and seven in air samples. Phthalates were abundant in dust (0.3524 μg/g) and air (0.005-2.8 μg/m³). Nonylphenol and its mono- and di-ethoxylates were prevalent in dust (0.82-14 μg/g) along with estrogenic phenols such as bisphenol A and o-phenyl phenol. In this 7-sample pilot study, 33 of 86 target compounds were detected in dust, and 24 of 57 target compounds were detected in air. In a single sample from one home, 27 of the target compounds were detected in dust and 15 in air, providing an indication of chemical mixtures to which humans are typically exposed.     

The 1999 Fresno Particulate Matter Exposure Studies: Comparison of Community,Outdoor, and Residential PM Mass Measurements

ABSTRACT

Two collaborative studies have been conducted by the U.S. Environmental Protection Agency (EPA) National Exposure Research Laboratory (NERL) and National Health and Environmental Effects Research Laboratory to determine personal exposures and physiological responses to par-ticulate matter (PM) of elderly persons living in a retirement facility in Fresno, CA. Measurements of PM and other criteria air pollutants were made inside selected individual residences within the retirement facility and at a central outdoor site on the premises. In addition, personal PM exposure monitoring was conducted for a subset of the participants, and ambient PM monitoring data were available for comparison from the NERL PM research monitoring platform in central Fresno. Both a winter (February 1-28, 1999) and a spring (April 19-May 16, 1999) study were completed so that seasonal effects could be     

An Intelligent Emissions Controller for Fuel Lean Gas Reburn in Coal-Fired Power Plants

ABSTRACT

The application of artificial intelligence techniques for performance optimization of the fuel lean gas reburn (FLGR) system is investigated. A multilayer, feedforward artificial neural network is applied to model static nonlinear relationships between the distribution of injected natural gas into the upper region of the furnace of a coal-fired boiler and the corresponding oxides of nitrogen (NO_x) emissions exiting the furnace. Based on this model, optimal distributions of injected gas are determined such that the largest NO_x reduction is achieved for each value of total injected gas. This optimization is accomplished through the development of a new optimization method based on neural networks. This new optimal control algorithm, which can be used as an alternative generic tool for solving multidimensional nonlinear constrained optimization problems, is described and its results are successfully validated against an off-the-shelf tool for solving mathematical programming problems. Encouraging results obtained using plant data from one of Commonwealth Edison's coal-fired electric power plants demonstrate the feasibility of the overall approach.

Preliminary results show that the use of this intelligent controller will also enable the determination of the most cost-effective operating conditions of the FLGR system by considering, along with the optimal distribution of the injected gas, the cost differential between natural gas and coal and the open-market price of NO_x emission credits. Further study, however, is necessary, including the construction of a more comprehensive database, needed to develop high-fidelity process models and to add carbon monoxide (CO) emissions to the model of the gas reburn system.     

Electricity from Fossil Fuels without CO2 Emissions: Assessing the Costs of Carbon Dioxide Capture and Sequestration in U.S. Electricity Markets

ABSTRACT

The decoupling of fossil-fueled electricity production from atmospheric CO₂ emissions via CO₂ capture and sequestration (CCS) is increasingly regarded as an important means of mitigating climate change at a reasonable cost. Engineering analyses of CO₂ mitigation typically compare the cost of electricity for a base generation technology to that for a similar plant with CO₂ capture and then compute the carbon emissions mitigated per unit of cost. It can be hard to interpret mitigation cost estimates from this plant-level approach when a consistent base technology cannot be identified. In addition, neither engineering analyses nor general equilibrium models can capture the economics of plant dispatch. A realistic assessment of the costs of carbon sequestration as an emissions abatement strategy in the electric sector therefore requires a systems-level analysis. We discuss various frameworks for computing mitigation costs and introduce a simplified model of electric sector planning. Results from a “bottom-up” engineering-economic analysis for a representative U.S. North American Electric Reliability Council (NERC) region illustrate how the penetration of CCS technologies and the dispatch of generating units vary with the price of carbon emissions and thereby determine the relationship between mitigation cost and emissions reduction.     

Investigation of the Potential Antimicrobial Efficacy of Sealants Used in HVAC Systems

ABSTRACT

Recent experiments confirm field experience that duct cleaning alone may not provide adequate protection from regrowth of fungal contamination on fiberglass duct liner (FGDL). Current recommendations for remediation of fungally contaminated fiberglass duct materials specify complete removal of the materials. But removal of contaminated materials can be extremely expensive. Therefore, a common practice in the duct-cleaning industry is the postcleaning use of antimicrobial surface coatings with the implication that they may contain or limit regrowth.

Little information is available on the efficacy of these treatments. This paper describes a study to evaluate whether three commercially available antimicrobial coatings, placed on a cleaned surface that 1 year previously had been actively growing microorganisms, would be able to prevent regrowth. The three coatings contained different active antimicrobial compounds. All three of the coatings were designed for use on heating, ventilation, and air conditioning (HVAC) system components or interior surfaces of lined and unlined duct systems. Coating I was a polyacrylate copolymer containing zinc oxide and borates. Coating II was an acrylic coating containing decabromodiphenyl oxide and antimony trioxide. Coating III was an acrylic primer containing a phosphated quaternary amine complex.

The study included field and laboratory assessments. The three treatments were evaluated in an uncontrolled field setting in an actual duct system. The laboratory study broadened the field study to include a range of humidities under controlled conditions. Both static and dynamic chamber laboratory experiments were performed. The results showed that two of the three antimicrobial coatings limited the regrowth of fungal contamination, at least in the short term (the 3-month time span of the study); the third did not. Before use in the field, testing of the efficacy of antimicrobial coatings under realistic use conditions is recommended because antimicrobials have different baseline activities and interact differently with the substrate that contains them and their local environment.     

Methodology for Siting Ambient Air Monitors at the Neighborhood Scale

Abstract

In siting a monitor to measure compliance with U.S. National Ambient Air Quality Standards (NAAQS) for par-ticulate matter (PM), there is a need to characterize variations in PM concentration within a neighborhood-scale region to achieve monitor siting objectives. A simple methodology is provided here for the selection of a neighborhood-scale site for meeting either of the two objectives identified for PM monitoring. This methodology is based on analyzing middle-scale (from 100 to 500 m) data from within the area of interest. The required data can be obtained from widely available dispersion models and emissions databases.

The performance of the siting methodology was evaluated in a neighborhood-scale field study conducted in Hudson County, NJ, to characterize the area’s inhalable particulate (PM₁₀) concentrations. Air monitors were located within a 2- by 2-km area in the vicinity of the Lincoln Tunnel entrance in Hudson County. Results indicate the siting methodology performed well, providing a positive relationship between the predicted concentration rank at each site and the actual rank experienced during the field study. Also discussed are factors that adversely affected the predictive capabilities of the model.     

Source Apportionment of Phoenix PM2.5 Aerosol with the Unmix Receptor Model

Abstract

The multivariate receptor model Unmix has been used to analyze a 3-yr PM_2.5 ambient aerosol data set collected in Phoenix, AZ, beginning in 1995. The analysis generated source profiles and overall average percentage source contribution estimates (SCEs) for five source categories: gasoline engines (33 ± 4%), diesel engines (16 ± 2%), secondary SO₄ ^2? (19 ± 2%), crustal/soil (22 ± 2%), and vegetative burning (10 ± 2%). The Unmix analysis was supplemented with scanning electron microscopy (SEM) of a limited number of filter samples for information on possible additional low-strength sources. Except for the diesel engine source category, the Unmix SCEs were generally consistent with an earlier multivariate receptor analysis of essentially the same data using the Positive Matrix Factorization (PMF) model. This article provides the first demonstration for an urban area of the capability of the Unmix receptor model.