Quantifying fine particle emission events from time‐resolved measurements: Method description and application to 18 California low‐income apartments. (4th October 2017)

• Record Type: Journal Article
• Authors: Chan, W. R.; Logue, J. M.; Wu, X.; Klepeis, N. E.; Fisk, W. J.; Noris, F.; Singer, B. C.
• Is Part Of: Indoor air. Volume 28:Number 1(2018)
• Page Start: 89
• Page End: 101
• Publication Date: 2017-10-04
• Subjects: cooking -- exposure -- multifamily -- PM2.5 -- residential; Indoor air pollution -- Periodicals; Sick building syndrome -- Periodicals; Ventilation -- Periodicals; 613.5
• DOI: 10.1111/ina.12425
• Languages: English
• ISSNs: 0905-6947

Abstract

Abstract: 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, ρ=.10), bedrooms (ρ=.08), house volume (ρ=.29), and indoor‐outdoor CO2 difference (ρ=.27). The event schedules can be used in probabilistic modeling of PM2.5 in low‐income apartments.