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Evaluation of fire weather forecasts using PM2.5 sensitivity analysis

机译：使用PM2.5敏感性分析评估火灾天气预报

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Fire weather forecasts are used by land and wildlife managers to determine when meteorological and fuel conditions are suitable to conduct prescribed burning. In this work, we investigate the sensitivity of ambient PM2.5 to various fire and meteorological variables in a spatial setting that is typical for the southeastern US, where prescribed fires are the single largest source of fine particulate matter. We use the method of principle components regression to estimate sensitivity of PM2.5, measured at a monitoring site in Jacksonville, NC (JVL), to fire data and observed and forecast meteorological variables. Fire data were gathered from prescribed fire activity used for ecological management at Marine Corps Base Camp Lejeune, extending 10-50 km south from the PM2.5 monitor. Principal components analysis (PCA) was run on 10 data sets that included acres of prescribed burning activity (PB) along with meteorological forecast data alone or in combination with observations. For each data set, observed PM2.5 (unitless) was regressed against PCA scores from the first seven principal components (explaining at least 80% of total variance). PM2.5 showed significant sensitivity to PB: 3.6 +/- 2.2 mu g m(-3) per 1000 acres burned at the investigated distance scale of 10-50 km. Applying this sensitivity to the available activity data revealed a prescribed burning source contribution to measured PM2.5 of up to 25% on a given day. PM2.5 showed a positive sensitivity to relative humidity and temperature, and was also sensitive to wind direction, indicating the capture of more regional aerosol processing and transport effects. As expected, PM2.5 had a negative sensitivity to dispersive variables but only showed a statistically significant negative sensitivity to ventilation rate, highlighting the importance of this parameter to fire managers. A positive sensitivity to forecast precipitation was found, consistent with the practice of conducting prescribed burning on days when rain can naturally extinguish fires. Perhaps most importantly for land managers, our analysis suggests that instead of relying on the forecasts from a day before, prescribed burning decisions should be based on the forecasts released the morning of the burn when possible, since these data were more stable and yielded more statistically robust results. (C) 2016 Elsevier Ltd. All rights reserved.
机译：土地和野生动植物管理者使用火灾天气预报来确定气象和燃料条件何时适合进行规定的燃烧。在这项工作中，我们研究了在美国东南部典型的空间环境中，环境PM2.5对各种火灾和气象变量的敏感性，在美国东南部，规定的火灾是细颗粒物的最大单一来源。我们使用主成分回归方法来估计在北卡罗来纳州杰克逊维尔（JVL）的监测站点测得的PM2.5对火数据以及观测和预报气象变量的敏感性。火灾数据是从Lejeune海军陆战队营地的规定的用于生态管理的火灾活动收集的，该火灾活动从PM2.5监测仪向南延伸10-50公里。主成分分析（PCA）对10个数据集进行了分析，这些数据集包括英亩规定的燃烧活动（PB）以及单独的气象预报数据或与观测值相结合的数据。对于每个数据集，将观察到的PM2.5（无单位）与前七个主要成分的PCA分数进行回归（说明至少80％的总方差）。 PM2.5对PB表现出显着的敏感性：在10-50 km的调查距离范围内，每1000英亩土地燃烧3.6 +/- 2.2μg m（-3）。将这种敏感性应用于可用的活动数据后，在指定的一天中，规定的燃烧源对测得的PM2.5的贡献高达25％。 PM2.5对相对湿度和温度显示正敏感性，并且对风向也敏感，表明捕获了更多区域性气溶胶加工和运输效应。不出所料，PM2.5对分散变量具有负敏感性，但对通风率仅表现出统计学上显着的负敏感性，突出了该参数对消防人员的重要性。发现对预报的降水具有积极的敏感性，这与在雨水可以自然扑灭大火的日子进行规定的燃烧的做法一致。也许对土地管理人员而言，最重要的是，我们的分析表明，规定的燃烧决策应基于燃烧早晨的发布，而不是依赖于前一天的预测，因为这些数据更加稳定并且产生的统计数据也更多。可靠的结果。 （C）2016 Elsevier Ltd.保留所有权利。

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