University of Michigan, School of Public Health, Environmental Health Sciences, 1415 Washington Heights, Ann Arbor, MI 48109, USA.
Michigan Technological University, Michigan Tech Research Institute, 3600 Green Court, Suite 100, Ann Arbor, MI 48105, USA.
Sci Total Environ. 2022 Sep 10;838(Pt 3):156403. doi: 10.1016/j.scitotenv.2022.156403. Epub 2022 May 31.
Widespread population exposure to wildland fire smoke underscores the urgent need for new techniques to characterize fire-derived pollution for epidemiologic studies and to build climate-resilient communities especially for aging populations. Using atmospheric chemical transport modeling, we examined air quality with and without wildland fire smoke PM. In 12-km gridded output, the 24-hour average concentration of all-source PM in California (2007-2018) was 5.16 μg/m (S.D. 4.66 μg/m). The average concentration of fire-PM in California by year was 1.61 μg/m (~30% of total PM). The contribution of fire-source PM ranged from 6.8% to 49%. We define a "smokewave" as two or more consecutive days with modeled levels above 35 μg/m. Based on model-derived fire-PM, 99.5% of California's population lived in a county that experienced at least one smokewave from 2007 to 2018, yet understanding of the impact of smoke on the health of aging populations is limited. Approximately 2.7 million (56%) of California residents aged 65+ years lived in counties representing the top 3 quartiles of fire-PM concentrations (2007-2018). For each year (2007-2018), grid cells containing skilled nursing facilities had significantly higher mean concentrations of all-source PM than cells without those facilities, but they also had generally lower mean concentrations of wildland fire-specific PM. Compared to rural monitors in California, model predictions of wildland fire impacts on daily average PM carbon (organic and elemental) performed well most years but tended to overestimate wildland fire impacts for high-fire years. The modeling system isolated wildland fire PM from other sources at monitored and unmonitored locations, which is important for understanding exposures for aging population in health studies.
广泛的人群接触到野火烟雾，这突显出迫切需要新的技术来描述流行病学研究中的火灾衍生污染，并为老龄化人口建立有气候适应能力的社区。我们使用大气化学输送模型，研究了有和没有野火烟雾 PM 的空气质量。在 12 公里的网格化输出中，加利福尼亚州（2007-2018 年）所有来源 PM 的 24 小时平均浓度为 5.16μg/m（标准偏差 4.66μg/m）。加利福尼亚州每年野火 PM 的平均浓度为 1.61μg/m（约占总 PM 的 30%）。火源 PM 的贡献范围从 6.8%到 49%。我们将“烟雾波”定义为连续两天以上的模型水平高于 35μg/m。根据模型得出的火灾 PM，从 2007 年到 2018 年，加利福尼亚 99.5%的人口生活在经历过至少一次烟雾波的县，然而，对烟雾对老龄化人口健康的影响的理解是有限的。大约 270 万（56%）的加利福尼亚州 65 岁以上的居民居住在代表火灾 PM 浓度前三个四分位数的县（2007-2018 年）。对于每一年（2007-2018 年），包含熟练护理设施的网格单元的所有来源 PM 的平均浓度明显高于没有这些设施的单元，但它们的野火特定 PM 的平均浓度通常较低。与加利福尼亚州的农村监测站相比，模型对每日平均 PM 碳（有机和元素）的野火影响的预测在大多数年份表现良好，但在高火灾年份往往高估了野火的影响。该模型系统在监测和非监测地点将野火 PM 与其他来源分离，这对于在健康研究中了解老龄化人口的暴露情况非常重要。
