Pacific Northwest Research Station, USDA Forest Service , Seattle , WA , USA.
J Air Waste Manag Assoc. 2019 Oct;69(10):1215-1229. doi: 10.1080/10962247.2019.1640808. Epub 2019 Sep 4.
A new statistical model for predicting daily ground level fine scale particulate matter (PM) concentrations at monitoring sites in the western United States was developed and tested operationally during the 2016 and 2017 wildfire seasons. The model is site-specific, using a multiple linear regression schema that relies on the previous day's PM value, along with fire and smoke related variables from satellite observations. Fire variables include fire radiative power (FRP) and the National Fire Danger Rating System Energy Release Component index. Smoke variables, in addition to ground monitored PM, include aerosol optical depth (AOD) and smoke plume perimeters from the National Oceanic and Atmospheric Administration's Hazard Mapping System. The overall statistical model was inspired by a similar system developed for British Columbia (BC) by the BC Center for Disease Control, but it has been heavily modified and adapted to work in the United States. On average, our statistical model was able to explain 78% of the variance in daily ground level PM. A novel method for implementation of this model as an operational forecast system was also developed and was tested and used during the 2016 and 2017 wildfire seasons. This method focused on producing a continuously-updating prediction that incorporated the latest information available throughout the day, including both updated remote sensing data and real-time PM observations. The diurnal pattern of performance of this model shows that even a few hours of data early in the morning can substantially improve model performance. : Wildfire smoke events produce significant air quality impacts across the western United States each year impacting millions. We present and evaluate a statistical model for making updating predictions of fine particulate (PM) levels during smoke events. These predictions run hourly and are being used by smoke incident specialists assigned to wildfire operations, and may be of interest to public health officials, air quality regulators, and the public. Predictions based on this model will be available on the web for the 2019 western U.S. wildfire season this summer.
开发并测试了一种新的统计模型，用于预测美国西部监测站点的日常地面细颗粒物 (PM) 浓度，该模型在 2016 年和 2017 年野火季节进行了操作。该模型是特定于站点的，使用多元线性回归方案，该方案依赖于前一天的 PM 值以及来自卫星观测的火灾和烟雾相关变量。火灾变量包括火灾辐射功率 (FRP) 和国家火灾危险评级系统能量释放成分指数。除了地面监测的 PM 之外，烟雾变量还包括气溶胶光学深度 (AOD) 和美国国家海洋和大气管理局的危害测绘系统中的烟雾羽流周长。该综合统计模型的灵感来自不列颠哥伦比亚省 (BC) 的疾病控制中心为不列颠哥伦比亚省开发的类似系统，但它已被大量修改和改编，以适用于美国。平均而言，我们的统计模型能够解释每日地面 PM 变化的 78%。还开发了一种新颖的方法，将此模型作为操作预测系统实施，并在 2016 年和 2017 年野火季节进行了测试和使用。该方法侧重于生成持续更新的预测，该预测全天整合了最新可用信息，包括最新的遥感数据和实时 PM 观测。该模型的日变化模式表明，即使在清晨有几个小时的数据，也可以大大提高模型的性能。野火烟雾事件每年都会在美国西部造成重大空气质量影响，影响数百万人。我们提出并评估了一种统计模型，用于在烟雾事件期间进行细颗粒物 (PM) 水平的更新预测。这些预测每小时运行一次，并且正在由分配到野火行动的烟雾事件专家使用，这可能对公共卫生官员、空气质量监管机构和公众感兴趣。基于此模型的预测将在今年夏天为 2019 年美国西部野火季节在网上提供。
