Quantifying Fine Particle Emission Events from Time ‐Resolved Measurements: Method Description and Application to 18 California Low‐Income Apartments

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=0.10), bedrooms (ρ=0.08), house volume (ρ=0.29), and indoor‐outdoor CO2 difference (ρ=0.27). The event schedules can be used in probabilistic modeling of PM2.5 in low‐income apartments. This article is protected by copyright. All rights reserved.
Source: Indoor Air - Category: Occupational Health Authors: Tags: Original Article Source Type: research