Li Zongrun, Maji Kamal J, Hu Yongtao, Vaidyanathan Ambarish, O'Neill Susan M, Odman M Talat, Russell Armistead G
School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.
National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, GA 30341, USA.
Remote Sens (Basel). 2023 May 24;15(11). doi: 10.3390/rs15112725.
Prescribed burning is a major source of a fine particular matter, especially in the southeastern United States, and quantifying emissions from burning operations accurately is an integral part of ascertaining air quality impacts. For instance, a critical factor in calculating fire emissions is identifying fire activity information (e.g., location, date/time, fire type, and area burned) and prior estimations of prescribed fire activity used for calculating emissions have either used burn permit records or satellite-based remote sensing products. While burn permit records kept by state agencies are a reliable source, they are not always available or readily accessible. Satellite-based remote sensing products are currently used to fill the data gaps, especially in regional studies; however, they cannot differentiate prescribed burns from the other types of fires. In this study, we developed novel algorithms to distinguish prescribed burns from wildfires and agricultural burns in a satellite-derived product, Fire INventory from NCAR (FINN). We matched and compared the burned areas from permit records and FINN at various spatial scales: individual fire level, 4 km grid level, and state level. The methods developed in this study are readily usable for differentiating burn type, matching and comparing the burned area between two datasets at various resolutions, and estimating prescribed burn emissions. The results showed that burned areas from permits and FINN have a weak correlation at the individual fire level, while the correlation is much higher for the 4 km grid and state levels. Since matching at the 4 km grid level showed a relatively higher correlation and chemical transport models typically use grid-based emissions, we used the linear regression relationship between FINN and permit burned areas at the grid level to adjust FINN burned areas. This adjustment resulted in a reduction in FINN-burned areas by 34%. The adjusted burned area was then used as input to the BlueSky Smoke Modeling Framework to provide long-term, three-dimensional prescribed burning emissions for the southeastern United States. In this study, we also compared emissions from different methods (FINN or BlueSky) and different data sources (adjusted FINN or permits) to evaluate uncertainties of our emission estimation. The comparison results showed the impacts of the burned area, method, and data source on prescribed burning emission estimations.
规定火烧是细颗粒物的一个主要来源，尤其是在美国东南部，准确量化火烧作业的排放是确定空气质量影响的一个重要组成部分。例如，计算火灾排放的一个关键因素是识别火灾活动信息（如位置、日期/时间、火灾类型和燃烧面积），而之前用于计算排放的规定火烧活动估计要么使用燃烧许可记录，要么使用基于卫星的遥感产品。虽然州机构保存的燃烧许可记录是一个可靠的来源，但它们并不总是可用或易于获取。目前基于卫星的遥感产品被用于填补数据空白，特别是在区域研究中；然而，它们无法区分规定火烧与其他类型的火灾。在本研究中，我们开发了新的算法，以在源自卫星的产品——美国国家大气研究中心火灾清单（FINN）中区分规定火烧与野火和农业火烧。我们在不同空间尺度上匹配并比较了许可记录和FINN的燃烧面积：单个火灾级别、4公里网格级别和州级别。本研究中开发的方法可很容易地用于区分燃烧类型、在不同分辨率下匹配和比较两个数据集之间的燃烧面积，以及估计规定火烧排放。结果表明，许可记录和FINN的燃烧面积在单个火灾级别上相关性较弱，而在4公里网格和州级别上相关性要高得多。由于在4公里网格级别上的匹配显示出相对较高的相关性，且化学传输模型通常使用基于网格的排放，我们使用网格级别上FINN与许可燃烧面积之间的线性回归关系来调整FINN燃烧面积。这一调整使FINN燃烧面积减少了34%。然后，将调整后的燃烧面积用作BlueSky烟雾建模框架的输入，以提供美国东南部长期的三维规定火烧排放。在本研究中，我们还比较了不同方法（FINN或BlueSky）和不同数据源（调整后的FINN或许可）的排放，以评估我们排放估计的不确定性。比较结果显示了燃烧面积、方法和数据源对规定火烧排放估计的影响。
