Fine particulate matter (PM_2.5) concentrations are highly variable indoors, with evidence for exposure disparities. Real-time monitoring coupled with novel statistical approaches can better characterize drivers of elevated PM_2.5 indoors. We collected real-time PM_2.5 data in 71 homes in an urban community of Greater Boston, Massachusetts using Alphasense OPC-N2 monitors. We estimated indoor PM_2.5 concentrations of non-ambient origin using mass balance principles, and investigated their associations with indoor source activities at the 0.50 to 0.95 exposure quantiles using mixed effects quantile regressions, overall and by homeownership. On average, the majority of indoor PM_2.5 concentrations were of non-ambient origin (≥77%), with a higher proportion at increasing quantiles of the exposure distribution. Major source predictors of non-ambient PM_2.5 concentrations at the upper quantile (0.95) were cooking (1.4–23 μg/m³) and smoking (15 μg/m³, only among renters), with concentrations also increasing with range hood use (3.6 μg/m³) and during the heating season (5.6 μg/m³). Across quantiles, renters in multifamily housing experienced a higher proportion of PM_2.5 concentrations from non-ambient sources than homeowners in single- and multifamily housing. Renters also more frequently reported cooking, smoking, spray air freshener use, and second-hand smoke exposure, and lived in units with higher air exchange rate and building density. Accounting for these factors explained observed PM_2.5 exposure disparities by homeownership, particularly in the upper exposure quantiles. Our results suggest that renters in multifamily housing may experience higher PM_2.5 exposures due to a combination of behavioral and building factors that are amenable to intervention.

室内细颗粒物 (PM _2.5 ) 浓度变化很大，有证据表明存在暴露差异。实时监测与新颖的统计方法相结合可以更好地描述室内PM _{2.5升高的驱动因素。}我们使用 Alphasense OPC-N2 监视器收集了马萨诸塞州大波士顿城市社区 71 个家庭的实时 PM _2.5数据。我们使用质量平衡原理估算了非环境来源的室内 PM 2.5 浓度，并使用混合效应分位数回归（整体和按住房拥有情况）研究了它们与 0.50 至 0.95 暴露分位数的室内源活动的关联_。平均而言，大多数室内 PM _2.5浓度均来自非环境来源（≥77%），随着暴露分布分位数的增加，比例较高。非环境 PM _2.5浓度上分位数 (0.95) 的主要预测因素是烹饪 (1.4–23 μg/m ³ ) 和吸烟 (15 μg/m ³，仅在租房者中)，浓度也随着抽油烟机的使用而增加（3.6 μg/m ³）和采暖季节（5.6 μg/m ³）。在各个分位数中，多户住宅的租户经历的非环境来源PM _2.5浓度比例高于单户和多户住宅的房主。租房者还更频繁地报告做饭、吸烟、使用喷雾空气清新剂和接触二手烟，并且居住在空气交换率和建筑密度较高的单位。考虑到这些因素，解释了观察到的按住房拥有率划分的PM _{2.5暴露差异，特别是在较高的暴露分位数中。}我们的结果表明，由于易于干预的行为和建筑因素的结合，多户住宅的租户可能会经历更高的 PM _2.5暴露。