Quantifying fine particle emission events from time-resolved measurements: Method description and application to 18 California low-income apartments
Quantifying fine particle emission events from time-resolved measurements: Method description and application to 18 California low-income apartments
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DOI：
10.1111/ina.12425
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发表时间：
2018-01-01
期刊：
影响因子：
5.8
通讯作者：
Singer, B. C.
中科院分区：
文献类型：
--
作者：
Chan, W. R.;Logue, J. M.;Singer, B. C.
PM2.5暴露与重大健康风险相关。家庭中的暴露来自室外和室内来源，排放主要发生在离散事件中。需要关于排放事件量级和时间表的数据，以支持基于模拟的暴露和缓解研究。本研究应用了一种识别和表征算法，从224天的监测期间收集的数据，在18个加州公寓与低收入居民的时间分辨PM2.5排放事件进行量化。我们确定并表征了836个不同的事件，其中位数和平均值分别为12和30 mg发射质量，16和23分钟发射持续时间，37和103 mg/h发射速率，以及1.3和2.0/h的伪一级衰变速率。使用确定的事件特征计算的平均事件平均浓度在14个公寓的测量值的6%以内。不同家庭的活动时间表和排放质量各不相同，夜间活动很少，大多数排放发生在下午晚些时候和晚上。事件特征在工作日和周末相似。排放质量与居民人数（斯皮尔曼系数，rho= 0.10）、卧室（rho= 0.08）、房屋体积（rho= 0.29）和室内外CO2差异（rho= 0.27）呈正相关。事件时间表可用于低收入公寓PM2.5的概率建模。
PM2.5 exposure is associated with significant health risk. Exposures in homes derive from both outdoor and indoor sources, with emissions occurring primarily in discrete events. Data on emission event magnitudes and schedules are needed to support simulation-based studies of exposures and mitigations. This study applied an identification and characterization algorithm to quantify time-resolved PM2.5 emission events from data collected during 224 days of monitoring in 18 California apartments with low-income residents. We identified and characterized 836 distinct events with median and mean values of 12 and 30 mg emitted mass, 16 and 23 minutes emission duration, 37 and 103 mg/h emission rates, and pseudo-first-order decay rates of 1.3 and 2.0/h. Mean event-averaged concentrations calculated using the determined event characteristics agreed to within 6% of measured values for 14 of the apartments. There were variations in event schedules and emitted mass across homes, with few events overnight and most emissions occurring during late afternoons and evenings. Event characteristics were similar during weekdays and weekends. Emitted mass was positively correlated with number of residents (Spearman coefficient, rho=.10), bedrooms (rho=.08), house volume (rho=.29), and indoor-outdoor CO2 difference (rho=.27). The event schedules can be used in probabilistic modeling of PM2.5 in low-income apartments.