Dataset of filtration efficiency associated with "Quantifying the health benefits of face masks and respirators to mitigate exposure to severe air pollution"

Familiarity with the use of face coverings to reduce the risk of respiratory disease has increased during the coronavirus pandemic; however, recommendations for their use outside of the pandemic remains limited. Here, we develop a modeling framework to quantify the potential health benefits of wearing a face covering or respirator to mitigate exposure to severe air pollution. This framework accounts for the wide range of available face coverings and respirators, fit factors and efficacy, air pollution characteristics, and exposure-response data. Our modeling shows that N95 respirators offer robust protection against different sources of air pollution, reducing exposure by more than a factor of 14 when worn with a leak rate of 5%. Synthetic-fiber masks offer less protection with a strong dependence on aerosol size distribution (protection factors ranging from 4.4 to 2.2.), while natural-fiber and surgical masks offer reductions in exposure of 1.9 and 1.7, respectively. To assess the ability of face coverings to provide population-level health benefits to wildfire smoke, we perform a case study for the 2012 Washington state fire season. Our models suggest that although natural-fiber masks offer minor reductions in respiratory hospitalizations attributable to smoke (2-11%) due to limited filtration efficiency, N95 respirators and to a lesser extent surgical and synthetic-fiber masks may lead to notable reductions in smoke-attributable hospitalizations (22-39%, 9-24%, and 7-18%, respectively). The filtration efficiency, bypass rate, compliance rate (fraction of time and population wearing the device) are the key factors governing exposure reduction potential and health benefits during severe air pollution events.新冠疫情（coronavirus pandemic）大流行期间，公众对使用面部遮盖物（face coverings）降低呼吸道疾病风险的认知有所提升；然而针对疫情之外场景下使用此类物品的相关推荐仍较为有限。本研究构建了一套建模框架，用于量化佩戴面部遮盖物或呼吸器（respirator）以减轻严重空气污染暴露的潜在健康收益。该框架覆盖了当前可获取的各类面部遮盖物与呼吸器、适配系数（fit factors）、防护效能、空气污染特征以及暴露-反应数据（exposure-response data）。建模结果表明，N95呼吸器（N95 respirators）对各类污染源的空气污染物均具备优异的防护效果，当泄漏率为5%时，可将暴露量降低14倍以上。合成纤维口罩（synthetic-fiber masks）的防护效果较弱，且其防护能力高度依赖气溶胶粒径分布，防护系数（protection factors）介于4.4至2.2之间；而天然纤维口罩（natural-fiber masks）与外科口罩（surgical masks）的暴露量降低幅度分别为1.9倍与1.7倍。为评估面部遮盖物在野火烟雾（wildfire smoke）场景下为人群带来的健康收益，本研究针对2012年华盛顿州（Washington state）野火季开展了案例分析。模型结果显示，尽管天然纤维口罩因过滤效率（filtration efficiency）有限，仅能小幅降低烟雾相关的呼吸住院率（respiratory hospitalizations），降幅为2%~11%；但N95呼吸器、以及防护效果次之的外科口罩与合成纤维口罩，可显著降低烟雾相关的呼吸住院率，降幅分别为22%~39%、9%~24%与7%~18%。过滤效率、泄漏率（bypass rate）与依从率（compliance rate，即佩戴设备的时间占比与人群佩戴比例）是决定严重空气污染事件中暴露量降低潜力与健康收益的核心因素。