Academic journal article Environmental Health Perspectives

Evaluation of a Wildfire Smoke Forecasting System as a Tool for Public Health Protection

Academic journal article Environmental Health Perspectives

Evaluation of a Wildfire Smoke Forecasting System as a Tool for Public Health Protection

Article excerpt

Introduction

As the global climate continues to change, more frequent and intense wildfire events and longer wildfire seasons are expected (Flannigan and Van Wagner 1991; Wotton and Flannigan 1993; Wotton et al. 2010). Wildfire smoke can degrade local, regional, and global air quality (Dirksen et al. 2009; Dutkiewicz et al. 2011; Viswanathan et al. 2006). Exposure to wildfire smoke has been associated with adverse cardiopulmonary health effects, with the most consistent associations being found for respiratory outcomes (Dennekamp and Abramson 2011), including dispensations of respiratory reliever medications (Caamano-Isorna 2011; Elliott et al. 2013), physician and emergency department visits (Henderson et al. 2011; Lee et al. 2009; Rappold et al. 2011), and hospital admissions (Delfino et al. 2009; Henderson et al. 2011; Johnston et al. 2007; Morgan et al. 2010; Tham et al. 2009).

Among the different constituents of the complex smoke mixture, [PM.sub.2.5] (particulate matter [less than or equal to] 2.5 [micro]m in aerodynamic diameter) has been the most consistently elevated and widely measured exposure metric (Naeher et al. 2007; Sapkota et al. 2005). Tools conventionally used for estimating wildfire smoke exposures include surface [PM.sub.2.5] monitoring and remote sensing products such as the National Oceanic and Atmospheric Administration's (NOAA) Hazard Mapping System (HMS; U.S. Department of Commerce 2013), which produces hand-drawn smoke plumes by integrating images from multiple satellites (U.S. Department of Commerce 2013). These tools, however, have important limitations. For example, although monitoring networks may accurately reflect ground-level [PM.sub.2.5] concentrations with adequate temporal resolution, they typically do not cover all populated areas affected by fire smoke, and monitors can fail when affected by heavy smoke or actual fire. On the other hand, data from remote sensing products may cover vast geographic areas, but they cannot measure ground-level concentrations, they have different sampling frequencies, and observations can be obscured by clouds. Furthermore, both of these tools provide only retrospective or near-real-time observations. From the perspective of supporting public health responses during wildfire smoke episodes, prospective information is more desirable.

Forecasts have been implemented for many health hazards, including extreme heat (Hajat et al. 2010), pollen (Pasken and Pietrowicz 2005), and ultraviolet radiation (Burrows et al. 1994). An important motivation for using forecasting tools is to provide prospective information for public health actions in order to mitigate the adverse impacts before the hazards actually occur. To support the utility of forecasts for health protection, it is important to know a) whether forecasts are accurate and precise compared with reference measurements, and b) whether forecasts are associated with population health responses. Most evaluations of forecasting models address only the first question, but for an exposure without a "gold standard" reference measurement, like wildfire smoke, answering the second question is also important. Here we address both questions in an integrated evaluation of the operational BlueSky Western Canada Wildfire Smoke Forecasting Framework (BlueSky; http://www.bcairquality.ca/bluesky/).

BlueSky has produced publicly available forecasts of [PM.sub.2.5] concentrations from wildfires up to 60 hr in advance since 2010. Detailed information about the system is described elsewhere (Sakiyama 2013). Briefly, meteorological forecasts, fire locations, fuel consumption estimates, and smoke emissions estimates are combined in a dispersion model to estimate the resulting ground-level [PM.sub.2.5] concentrations in the modeling domain (Figure 1). To date there has been no systematic, quantitative evaluation of general BlueSky performance or of the associations between BlueSky output and population health indicators. …

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