Journal of the Air & Waste Management Association (1995)
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J Air Waste Manag Assoc · May 2015
Impact of smoke from prescribed burning: Is it a public health concern?
Given the increase in wildfire intensity and frequency worldwide, prescribed burning is becoming a more common and widespread practice. Prescribed burning is a fire management tool used to reduce fuel loads for wildfire suppression purposes and occurs on an annual basis in many parts of the world. Smoke from prescribed burning can have a substantial impact on air quality and the environment. Prescribed burning is a significant source of fine particulate matter (PM2.5 aerodynamic diameter<2.5µm) and these particulates are found to be consistently elevated during smoke events. Due to their fine nature PM2.5 are particularly harmful to human health. Here we discuss the impact of prescribed burning on air quality particularly focussing on PM2.5. We have summarised available case studies from Australia including a recent study we conducted in regional Victoria, Australia during the prescribed burning season in 2013. The studies reported very high short-term (hourly) concentrations of PM2.5 during prescribed burning. Given the increase in PM2.5 concentrations during smoke events, there is a need to understand the influence of prescribed burning smoke exposure on human health. This is important especially since adverse health impacts have been observed during wildfire events when PM2.5 concentrations were similar to those observed during prescribed burning events. Robust research is required to quantify and determine health impacts from prescribed burning smoke exposure and derive evidence based interventions for managing the risk. ⋯ Given the increase in PM2.5 concentrations during PB smoke events and its impact on the local air quality, the need to understand the influence of PB smoke exposure on human health is important. This knowledge will be important to inform policy and practice of the integrated, consistent, and adaptive approach to the appropriate planning and implementation of public health strategies during PB events. This will also have important implications for land management and public health organizations in developing evidence based objectives to minimize the risk of PB smoke exposure.
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J Air Waste Manag Assoc · May 2015
Respiratory hospitalizations in association with fine PM and its components in New York State.
Despite observed geographic and temporal variation in particulate matter (PM)-related health morbidities, only a small number of epidemiologic studies have evaluated the relation between PM2.5 chemical constituents and respiratory disease. Most assessments are limited by inadequate spatial and temporal resolution of ambient PM measurements and/or by their approaches to examine the role of specific PM components on health outcomes. In a case-crossover analysis using daily average ambient PM2.5 total mass and species estimates derived from the Community Multiscale Air Quality (CMAQ) model and available observations, we examined the association between the chemical components of PM (including elemental and organic carbon, sulfate, nitrate, ammonium, and other remaining) and respiratory hospitalizations in New York State. We evaluated relationships between levels (low, medium, high) of PM constituent mass fractions, and assessed modification of the PM2.5-hospitalization association via models stratified by mass fractions of both primary and secondary PM components. In our results, average daily PM2.5 concentrations in New York State were generally lower than the 24-hr average National Ambient Air Quality Standard (NAAQS). Year-round analyses showed statistically significant positive associations between respiratory hospitalizations and PM2.5 total mass, sulfate, nitrate, and ammonium concentrations at multiple exposure lags (0.5-2.0% per interquartile range [IQR] increase). Primarily in the summer months, the greatest associations with respiratory hospitalizations were observed per IQR increase in the secondary species sulfate and ammonium concentrations at lags of 1-4 days (1.0-2.0%). Although there were subtle differences in associations observed between mass fraction tertiles, there was no strong evidence to support modification of the PM2.5-respiratory disease association by a particular constituent. We conclude that ambient concentrations of PM2.5 and secondary aerosols including sulfate, ammonium, and nitrate were positively associated with respiratory hospitalizations, although patterns varied by season. Exposure to specific fine PM constituents is a plausible risk factor for respiratory hospitalization in New York State. ⋯ The association between ambient concentrations of PM2.5 components has been evaluated in only a small number of epidemiologic studies with refined spatial and temporal scale data. In New York State, fine PM and several of its constituents, including sulfate, ammonium, and nitrate, were positively associated with respiratory hospitalizations. Results suggest that PM species relationships and their influence on respiratory endpoints are complex and season dependent. Additional work is needed to better understand the relative toxicity of PM species, and to further explore the role of co-pollutant relationships and exposure prediction error on observed PM-respiratory disease associations.
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J Air Waste Manag Assoc · May 2015
Health benefits of air pollution abatement policy: Role of the shape of the concentration-response function.
There is strong evidence that fine particulate matter (aerodynamic diameter<2.5 μm; PM2.5) air pollution contributes to increased risk of disease and death. Estimates of the burden of disease attributable to PM2.5 pollution and benefits of reducing pollution are dependent upon the shape of the concentration response (C-R) functions. Recent evidence suggests that the C-R function between PM2.5 air pollution and mortality risk may be supralinear across wide ranges of exposure. Such results imply that incremental pollution abatement efforts may yield greater benefits in relatively clean areas than in highly polluted areas. The role of the shape of the C-R function in evaluating and understanding the costs and health benefits of air pollution abatement policy is explored. There remain uncertainties regarding the shape of the C-R function, and additional efforts to more fully understand the C-R relationships between PM2.5 and adverse health effects are needed to allow for more informed and effective air pollution abatement policies. Current evidence, however, suggests that there are benefits both from reducing air pollution in the more polluted areas and from continuing to reduce air pollution in cleaner areas. ⋯ Estimates of the benefits of reducing PM2.5 air pollution are highly dependent upon the shape of the PM2.5-mortality concentration-response (C-R) function. Recent evidence indicates that this C-R function may be supralinear across wide ranges of exposure, suggesting that incremental pollution abatement efforts may yield greater benefits in relatively clean areas than in highly polluted areas. This paper explores the role of the shape of the C-R function in evaluating and understanding the costs and health benefits of PM2.5 air pollution abatement.
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J Air Waste Manag Assoc · May 2015
Exploring the modeling of spatiotemporal variations in ambient air pollution within the land use regression framework: Estimation of PM10 concentrations on a daily basis.
Estimation of daily average exposure to PM10 (particulate matter with an aerodynamic diameter<10 μm) using the available fixed-site monitoring stations (FSMs) in a city poses a great challenge. This is because typically FSMs are limited in number when considering the spatial representativeness of their measurements and also because statistical models of citywide exposure have yet to be explored in this context. This paper deals with the later aspect of this challenge and extends the widely used land use regression (LUR) approach to deal with temporal changes in air pollution and the influence of transboundary air pollution on short-term variations in PM10. Using the concept of multiple linear regression (MLR) modeling, the average daily concentrations of PM10 in two European cities, Vienna and Dublin, were modeled. Models were initially developed using the standard MLR approach in Vienna using the most recently available data. Efforts were subsequently made to (i) assess the stability of model predictions over time; (ii) explores the applicability of nonparametric regression (NPR) and artificial neural networks (ANNs) to deal with the nonlinearity of input variables. The predictive performance of the MLR models of the both cities was demonstrated to be stable over time and to produce similar results. However, NPR and ANN were found to have more improvement in the predictive performance in both cities. Using ANN produced the highest result, with daily PM10 exposure predicted at R2=66% for Vienna and 51% for Dublin. In addition, two new predictor variables were also assessed for the Dublin model. The variables representing transboundary air pollution and peak traffic count were found to account for 6.5% and 12.7% of the variation in average daily PM10 concentration. The variable representing transboundary air pollution that was derived from air mass history (from back-trajectory analysis) and population density has demonstrated a positive impact on model performance. ⋯ The implications of this research would suggest that it is possible to produce a model of ambient air quality on a citywide scale using the readily available data. Most European cities typically have a limited FSM network with average daily concentrations of air pollutants as well as available meteorological, traffic, and land-use data. This research highlights that using these data in combination with advanced statistical techniques such as NPR or ANNs will produce reasonably accurate predictions of ambient air quality across a city, including temporal variations. Therefore, this approach reduces the need for additional measurement data to supplement existing historical records and enables a lower-cost method of air pollution model development for practitioners and policy makers.