Quantifying fine particle emission events from time‐resolved measurements: Method description and application to 18 California low‐income apartments

Abstract

PM 2.5 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 PM 2.5 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 CO 2 difference (ρ=.27).The event schedules can be used in probabilistic modeling of PM 2.5 in low-income apartments.

Investigating measurements of fine particle ( PM 2.5 ) emissions from the cooking of meals and mitigating exposure using a cooker hood

“…Our values of G and can also be compared with those derived from in situ measurements of multiple household activities that include cooking to give a broader understanding of their significance. Chan et al calculated emission rates for 836 cooking and non‐cooking events in 18 dwellings in California, finding a mean source strength and emission rate of 30 mg and 1.72 mg/min, respectively, which are broadly similar to ours. Dacunto et al identified source strengths and emission rates of 1.4 mg and 0.1 mg/min for oven cooked frozen pizza, 72.5 mg and 9.5 mg/min for toasting of bread (90%‐95% charred), 18.3 mg and 1.6 mg/min for fried salmon, 24.3 mg and 2.1 mg/min for fried pork chop, 19.9 mg and 3.8 mg/min for cigarette smoking, 16.9 mg and 1.3 mg/min for the burning of stick incense, and 215.4 mg and 16.4 mg/min for an open fire.…”

“…Our values of G and g(T) can also be compared with those derived from in situ measurements of multiple household activities that include cooking to give a broader understanding of their significance. Chan et al 11 Table 3 shows that the particle counts exceeded 2 × 10 6 particles/L in 10 of the 24 tests. Here, the Grimm may experience coincidence errors where multiple particle may be seen as one larger particle.…”

“…Volume bias is rarely considered; for example, the emission rates reported in Section 1 8,11,18,21 electric hot plate and in an aluminum frying pan. First, they measured an emission rate of 0.4 mg/min and G = 5.7 mg for chicken, vegetables, and soy sauce stir fried in olive oil (2 tests), which are less than those for all meals cooked here.…”

“…[6][7][8][9][10] Frying and grilling were found to increase indoor PM 2.5 concentrations by up to 30 and 90 times the ambient concentration, respectively. 8 Furthermore, Chan et al 11 found emission events exceeding 30 minutes were more frequent around meal times, with the highest occurrence between 17:00 and 21:00, suggesting that the cooking of an evening meal is an important PM 2.5 emission source. Elevated risks of lung cancer, particularly in women, are associated with emissions from cooking and with poor kitchen ventilation.…”

Evaluating the Sensitivity of the Mass-Based Particle Removal Calculations for HVAC Filters in ISO 16890 to Assumptions for Aerosol Distributions

“…The work followed a similar procedure as that described in ISO 16890 and in Section 2 herein, albeit with the following exceptions: (1) 194 long-term average trimodal ambient aerosol distributions from around the world were used, rather than a single static urban or rural bimodal distribution; (2) fractional filter removal efficiency data from laboratory tests that measured down to as small as 30 nm were used (and the remaining data were fit with a model down to just a few nanometers) [35]; and (3) in addition to estimating PM2.5 and UFP removal efficiencies assuming 100% outdoor air delivery, PM2.5 and UFP removal efficiencies for indoor particles of outdoor origin were also estimated assuming ambient aerosols penetrated through the building envelope of a typical residential building with a recirculating HVAC system. This approach allowed for estimates of the PM2.5 and UFP removal efficiency for various filters for indoor particles of outdoor origin, which is a condition that frequently occurs in many buildings when indoor sources are not present [36][37][38][39][40]. The number and volume distributions from the 194 outdoor particle size distributions reviewed in Azimi et al (2014) [29] are shown in Figure 2, with the number and volume distributions from Jaenicke (1993) [31] overlaid.…”