Understanding how thermal environments influence ultrafine particle (UFP) dynamics during cooking is essential for assessing indoor exposure. While emissions from gas stoves have been widely studied, UFP emissions from electric heating systems remain poorly understood. This study investigated time-resolved UFP number concentrations and size distributions under six experimental configurations with varying cookware materials and surface conditions using electric hot plate settings of 250 °C and 540 °C, including a heat-off decay condition. The heating period was divided into two regimes: Mode 1, characterized by active particle emission, and Mode 2, representing subsequent particle decay. Under the no-pan condition, total UFP concentrations reached 2.53 × 106 cm-3 at 250 °C and 5.08 × 106 cm-3 at 540 °C, showing an approximately twofold increase at the higher setting. Repeated heating cycles reduced emissions by up to 91%, depending on cookware configuration and cycle number, suggesting that particles were primarily generated from surface-bound residues or semi-volatile organic compounds accumulated between heating events. Direct stovetop heating without cookware produced the highest emissions, exceeding all cookware configurations by more than twofold. During Mode 2, coagulation accounted for more than 60% of the early particle number reduction, while continued heating changed the decay behavior from logarithmic to approximately linear. These findings demonstrate that surface temperature and cookware material strongly influence both UFP generation and post-emission dynamics, emphasizing the importance of thermal conditions in exposure assessment. Effective mitigation in increasingly airtight, all-electric buildings should integrate smart appliance operation, informed material selection, and targeted post-cooking ventilation strategies.

Monitoring airborne nanoparticles has a vital role in indoor air quality control due to their hazardous effects on human health. Detecting particles becomes more challenging as their sizes decrease. While research-grade instruments like the scanning mobility particle sizer (SMPS) can provide detailed and useful information, they are not practical for personal use due to their size and cost. This study aims to provide a comparable prediction of the temporal size distribution of ultrafine particles (UFPs, <100nm) using mid-cost measurements from a handheld particle sizer, which is more economical but has a narrower detectable size range. To achieve this, the study builds upon a computational modeling approach based on a mass-balance equation to estimate the time-varying particle size distribution, while accounting for particle evolution processes such as coagulation, deposition, and ventilation. The analytical model for indoor UFPs requires prior information regarding particle dynamic behavior, such as the size-resolved deposition rate and source emission rate. This study estimates, rather than pre-determines, the model parameters required for the temporal prediction of indoor UFP size distribution by applying Bayesian parameter inference with the analytical model of indoor aerosol. The results indicate that the present model reasonably predicts the temporal evolution of particle distributions, comparable to that of the SMPS. Furthermore, this study demonstrates the identifiability of model parameters, considering both the entire and detectable size ranges, through variance-based global sensitivity analysis.

Cooking has been recognized as one of the most important sources of indoor air pollutants. Several studies evaluated black carbon emissions from cooking fumes. Black carbon (BC) could affect human health as a carbonaceous part of the cooking particles. This study develops a statistical model to estimate the emission fluxes of black carbon from heating 17 different oils (avocado, canola, coconut, corn, olive, peanut, vegetable (soybean), grapeseed, hazelnut, macadamia, almond, sunflower, safflower, flax, walnut, pumpkin seed, and sesame). The oils were heated in a beaker for 20 min at 195-200 degrees C. Macadamia oil showed the highest BC emission rate of 11.08 (SD = 4.94) mu g/min, while peanut oil resulted in the lowest BC emission rate of 0.68 (SD = 0.63) mu g/min. Oils including macadamia (11.08 [SD = 4.94] mu g/min), coconut (9.85 [SD = 2.20] mu g/min), flax (7.93 [SD = 2.46] mu g/min), pumpkin (5.65 [SD = 2.22] mu g/min), grapeseed (5.43 [SD = 7.85] mu g/min), hazelnut (4.65 [SD = 2.48] mu g/min) and sesame (4.33 [SD = 2.53] mu g/min) were among the high BC-emitting oils (>4 mu g/min) at 195 degrees C, while avocado (3.74 [SD = 3.20] mu g/min), olive (3.73 [SD = 1.59] mu g/min), corn (2.71 [SD = 2.09] mu g/min), almond (2.44 [SD = 1.55] mu g/min), walnut (1.76 [SD = 0.56] mu g/min), canola (1.58 [SD = 0.90] mu g/min), vegetable (1.30 [SD = 1.10] mu g/min), safflower (0.92 [SD = 0.56] mu g/min), sunflower (0.88 [SD = 0.44] mu g/min), and peanut (0.68 [SD = 0.63] mu g/min) were among the low BC-emitting oils (<4 g/min). We present correlations between the BC emission flux from heating these cooking oils and two cooking factors, oil temperature and oil smoke temperature. Despite some exceptions, most of the oils showed that oil temperatures above the smoke point of the oil is an insignificant factor in BC emissions. Copyright (c) 2024 American Association for Aerosol Research [GRAPHICS] .

Low-cost monitors have made possible for the first time measurements of long-term (months to years) potential indoor exposures to fine particles. Indoor and outdoor measurements made over nearly 5 years (2017-2021) by the largest network of low-cost monitors in the United States (PurpleAir) are compared to the prevalence of adult smokers in 1650 Zip codes within the three West Coast states of California, Oregon, and Washington. The results show that mean potential indoor exposures above the 75th percentile of adult smoking prevalence are more than 50% higher than those below the 25th percentile. Mean outdoor concentrations are also elevated, but by a smaller amount (~20%). Both comparisons are significant at the p<0.001 level. The elevation of PM2.5 concentrations with increasing smoking prevalence is evidence of environmental disparities in income, education, and other socioeconomic indices. The relatively stronger effect on indoor rather than outdoor PM2.5 exposures highlights the importance of including indoor measurements when possible in environmental justice studies.

Low-cost monitors have made possible for the first time measurements of long-term (months to years) potential indoor exposures to fine particles. Indoor and outdoor measurements made over nearly 5 years (2017-2021) by the largest network of low-cost monitors in the United States (PurpleAir) are compared to the prevalence of adult smokers in 1650 Zip codes within the three West Coast states of California, Oregon, and Washington. The results show that mean indoor exposures above the 75th percentile of adult smoking prevalence are more than 50% higher than those below the 25th percentile. Mean outdoor concentrations are also elevated, but by a smaller amount (20%). Both comparisons are significant at the p<0.001 level. The elevation of the PM2.5 concentrations with increasing smoking prevalence is evidence of environmental disparities in income, education, and other socioeconomic indices..

Multiple studies have considered socioeconomic or ethnic group inequities in outdoor fine particle (PM2.5) concentrations. Due to the lack of indoor measurements, these studies are forced to assume that indoor exposures are directly related to outdoor concentrations. In general, this assumption may be reasonable, but it is violated when indoor-generated fine particles form a substantial contribution to total potential indoor exposure. We now have for the first time access to long-term (months or years) indoor potential exposures, made possible by the development of low-cost optical particle counters. A large database of 4.86 million hourly PM2.5 indoor and outdoor concentrations measured by 10,000 outdoor and >4,000 indoor PurpleAir monitors over a 5-year period (2017-2021) in three West Coast states (Washington, Oregon, California) has been used to compare with US Census 2021 estimates of median household income, educational attainment, housing characteristics, and ethnic groups. Clear evidence of inequities is found using indoor as well as outdoor PM2.5 concentrations.

Recently, a hypothesis providing a detailed equation for the Plantower CF_1 algorithm for PM2.5 has been published. The hypothesis was originally validated using eight independent Plantower sensors in four PurpleAir PA-II monitors providing PM2.5 estimates from a single site in 2020. If true, the hypothesis makes important predictions regarding PM2.5 measurements using CF_1. Therefore, we test the hypothesis using 18 Plantower sensors from four datasets from two sites in later years (2021–2023). The four general models from these datasets agreed to within 10% with the original model. A competing algorithm known as “pm2.5 alt” has been published and is freely available on the PurpleAir API site. The accuracy, precision, and limit of detection for the two algorithms are compared. The CF_1 algorithm overestimates PM2.5 by about 60–70% compared to two calibrated PurpleAir monitors using the pm2.5 alt algorithm. A requirement that the two sensors in a single monitor agree to within 20% was met by 85–99% of the data using the pm2.5 alt algorithm, but by only 22–74% of the data using the CF_1 algorithm. The limit of detection (LOD) of the CF_1 algorithm was about 10 times the LOD of the pm2.5 alt algorithm, resulting in 71% of the CF_1 data falling below the LOD, compared to 1 % for the pm2.5 alt algorithm.

Spatial variation of indoor and outdoor PM2.5 within three states for a five-year period is studied using regulatory and low-cost PurpleAir monitors. Most of these data were collected in an earlier study (Wallace et al., 2022 Indoor Air 32:13105) investigating the relative contribution of indoor-generated and outdoor-infiltrated particles to indoor exposures. About 260 regulatory monitors and ~10,000 outdoor and ~4000 indoor PurpleAir monitors are included. Daily mean PM2.5 concentrations, correlations, and coefficients of divergence (COD) are calculated for pairs of monitors at distances ranging from 0 (collocated) to 200 km. We use a transparent and reproducible open algorithm that avoids the use of the proprietary algorithms provided by the manufacturer of the sensors in PurpleAir PA-I and PA-II monitors. The algorithm is available on the PurpleAir API website under the name “PM2.5_alt”. This algorithm is validated using several hundred pairs of regulatory and PurpleAir monitors separated by up to 0.5 km. The PM2.5 spatial variation outdoors is homogeneous with high correlations to at least 10 km, as shown by the COD index under 0.2. There is also a steady improvement in outdoor PM2.5 concentrations with increasing distance from the regulatory monitors. The spatial variation of indoor PM2.5 is not homogeneous even at distances < 100 m. There is good agreement between PurpleAir outdoor monitors located <100 m apart and collocated Federal Equivalent Methods (FEM).

Indoor airborne ultrafine particles (UFPs) are mainly originated from occupant activities, such as candle burning and cooking. Elevated exposure to UFPs has been found to increase oxidative stress and cause DNA damage. UFPs originating from indoor sources undergo dynamic aerosol transformation mechanisms. This study investigates the dynamics of UFPs following episodic indoor releases of the six distinct emission sources: 1) candle, 2) gas stove, 3) clothes dryer, 4) tea & toast, 5) broiled fish, and 6) incense. Based on the analytical model of aerosol dynamic processes, this study reports size-resolved source emission rates along with relative contributions of coagulation, deposition, and ventilation to the particle size distribution dynamics. The study findings indicate a significant variation in the geometric mean diameter (GMD) and size-resolved number concentration over time for the sources that emit a substantial amount of UFPs smaller than 10 nm. As the emission progresses, the UFP number concentrations increase in a log-normal distribution, while the GMD shows a tendency to increase over time. The observed result suggests that coagulation can have a considerable impact on UFP number concentration and size, even during the indoor UFP emission. The estimated emission rates of the six indoor sources appear to follow a log-normal distribution while the emission rate ranges from 107 min-1 to 1012 min-1. The indoor UFP concentration and size distribution dynamics are substantially affected by the interplay of the three aerosol loss mechanisms that compete with each other, and this impact varies according to the source type and the indoor environmental conditions. Ultimately, using the aerosol transformation mechanisms examined in this study, researchers can refine exposure assessment for epidemiological studies on indoor ultrafine particles.

Some manufacturers of low-cost particle sensors use proprietary algorithms to estimate particle mass concentrations such as PM2.5. Often little or no information is given regarding the calibration aerosol, how the algorithm was created or tested, or how the mass was estimated from the particle number counts. If the algorithm is faulty in some way, researchers have little ability to correct it in a fundamental way, although they can multiply the output by some calibration factor to match the particular aerosol combination they are studying. However, the adjustment still requires the use of the proprietary algorithm, which may have quirks that make it impossible to fix completely using a single calibration factor. It might be possible in some cases to avoid using the proprietary algorithm at all. That is the approach of this study. The low-cost sensor studied is the Plantower PMS 5003, and the algorithm is the CF_1 algorithm offered by the manufacturer. Data from a six-month study of four collocated PurpleAir PA-II monitors, each containing two independent Plantower PMS 5003 sensors, were collected. Two of these monitors had previously been calibrated against research-grade monitors. The best-fitting model for PM1 was found to be of the form PM1 = a*(N1 + N2) + d, where N1 and N2 are the particle numbers in the size categories 0.3-0.5 μm and 0.5-1 μm, and d is an additive constant. The best-fitting model for PM2.5 was of the form a*(N1 + N2) + b*N3 + d, where N3 is the number of particles in the third size fraction (1-2.5 μm). The individual models for all 8 sensors matched the reported CF_1 values for both PM1 and PM2.5 with R2 values exceeding 0.99, intercepts near zero, and slopes in the 0.99-1.01 range. The proposed models may also explain why the CF_1 algorithm reports values of zero for a substantial portion of PM1 and PM2.5 estimates. General models capable of being applied to other datasets were developed and estimated to have mean absolute errors (MAEs) <1 μg/m3.

Low-cost monitors make it possible now for the first time to collect long-term (months to years) measurements of potential indoor exposure to fine particles. Indoor exposure is due to two sources: particles infiltrating from outdoors and those generated by indoor activities. Calculating the relative contribution of each source requires identifying an infiltration factor. We develop a method of identifying periods when the infiltration factor is not constant and searching for periods when it is relatively constant. From an initial regression of indoor on outdoor particle concentrations, a Forbidden Zone can be defined with an upper boundary below which no observations should appear. If many observations appear in the Forbidden Zone, they falsify the assumption of a single constant infiltration factor. This is a useful quality assurance feature, since investigators may then search for subsets of the data in which few observations appear in the Forbidden Zone. The usefulness of this approach is illustrated using examples drawn from the PurpleAir network of optical particle monitors. An improved algorithm is applied with reduced bias, improved precision, and a lower limit of detection than either of the two proprietary algorithms offered by the manufacturer of the sensors used in PurpleAir monitors.

The widespread legalization of recreational marijuana raises growing concerns about exposure to secondhand marijuana smoke. An important location for marijuana smoking is the home, but few measurements of air pollutant concentrations in the home are available for a marijuana joint fully smoked in one of its rooms. We used research grade calibrated real-time continuous PM2.5 air monitors in controlled 5-hour experiments to measure fine particle concentrations in the 9 rooms of a detached, two-story, 4-bedroom home with either a tobacco cigarette or a marijuana joint fully smoked in the home's living room. The master bedroom's door was closed, and the other bedroom doors were open, as was the custom of occupants of this residence. In two experiments with a Marlboro tobacco cigarette smoked by a machine in the living room, the 5-hour mean PM2.5 concentrations in 9 rooms of the home were 15.2 μg/m3 (SD 5.6 μg/m3) and 15.0 μg/m3 (SD 3.7 μg/m3). In contrast, three experiments with pre-rolled marijuana joints smoked in the same manner in the living room produced 5-hour mean PM2.5 concentrations of 38.9 μg/m3 (SD 10.6 μg/m3), 79.8 μg/m3 (SD 25.7 μg/m3) and 80.7 μg/m3 (SD 28.8 μg/m3). In summary, the average secondhand PM2.5 concentrations from smoking a marijuana joint in the home were found to be 4.4 times as great as the secondhand PM2.5 concentrations from smoking a tobacco cigarette. Opening 3 windows by 12.7 cm reduced the high PM2.5 concentrations from marijuana smoking by 67 %, but the PM2.5 levels still exceeded those produced by tobacco smoking with the windows closed.

Large quantities of real-time particle data are becoming available from low-cost particle monitors. However, it is crucial to determine the quality of these measurements. The largest network of monitors in the United States is maintained by the PurpleAir company, which offers two monitors: PA-I and PA-II. PA-I monitors have a single sensor (PMS1003) and PA-II monitors employ two independent PMS5003 sensors. We determine a new calibration factor for the PA-I monitor and revise a previously published calibration algorithm for PA-II monitors (ALT-CF3). From the PurpleAir API site, we downloaded 83 million hourly average PM2.5 values in the PurpleAir database from Washington, Oregon, and California between 1 January 2017 and 8 September 2021. Daily outdoor PM2.5 means from 194 PA-II monitors were compared to daily means from 47 nearby Federal regulatory sites using gravimetric Federal Reference Methods (FRM). We find a revised calibration factor of 3.4 for the PA-II monitors. For the PA-I monitors, we determined a new calibration factor (also 3.4) by comparing 26 outdoor PA-I sites to 117 nearby outdoor PA-II sites. These results show that PurpleAir PM2.5 measurements can agree well with regulatory monitors when an optimum calibration factor is found.

Low-cost particle sensors are now used worldwide to monitor outdoor air quality. However, they have only been in wide use for a few years. Are they reliable? Does their performance deteriorate over time? Are the algorithms for calculating PM2.5 concentrations provided by the sensor manufacturers accurate? We investigate these questions using continuous measurements of four PurpleAir monitors (8 sensors) under normal conditions inside and outside a home for 1.5–3 years. A recently developed algorithm (called ALT-CF3) is compared to the two existing algorithms (CF1 and CF_ATM) provided by the Plantower manufacturer of the PMS 5003 sensors used in PurpleAir PA-II monitors. Results. The Plantower CF1 algorithm lost 25–50% of all indoor data due in part to the practice of assigning zero to all concentrations below a threshold. None of these data were lost using the ALT-CF3 algorithm. Approximately 92% of all data showed precision better than 20% using the ALT-CF3 algorithm, but only approximately 45–75% of data achieved that level using the Plantower CF1 algorithm. The limits of detection (LODs) using the ALT-CF3 algorithm were mostly under 1 µg/m3, compared to approximately 3–10 µg/m3 using the Plantower CF1 algorithm. The percentage of observations exceeding the LOD was 53–92% for the ALT-CF3 algorithm, but only 16–44% for the Plantower CF1 algorithm. At the low indoor PM2.5 concentrations found in many homes, the Plantower algorithms appear poorly suited.

Low-cost monitors have made it possible for the first time to measure indoor PM2.5 concentrations over extended periods of time (months to years). Coupled with concurrent outdoor measurements, these indoor measurements can be divided into particles entering the building from outdoors and particles generated from indoor activities. Indoor-generated particles are not normally considered in epidemiological studies, but they can have health effects (e.g., passive smoking and high-temperature cooking). We employed The Random Component Superposition (RCS) regression model to estimate infiltration factors for up to 790 000 matched indoor and outdoor sites. The median infiltration factors for subgroups in the 3-state region ranged between 0.22 and 0.24, with an interquartile range (IQR) of 0.13-0.40. These infiltration factors allowed calculation of both the indoor-generated and outdoor-infiltrated PM2.5 . Indoor-generated particles contributed, on average, 46%-52% of total indoor PM2.5 concentrations. However, the site-specific fractional contribution of these indoor sources to total indoor PM2.5 ranged from near-zero to nearly 100%. The influence of indoor-generated particles on potential exposures varied widely relative to outdoor concentrations. The greatest influence of indoor-generated particles occurred at low-to-moderate daily mean outdoor PM2.5 levels around 6 μg/m3 and was negligible at outdoor concentrations >20 μg/m3 . Epidemiological studies incorporating only estimated exposures due to the particles of ambient origin may benefit from the newly available knowledge of long-term indoor-generated particle concentrations.

Airborne nanoparticles are frequently released in occupied spaces due to episodic indoor source activities. Once generated, nanoparticles undergo aerosol transformation processes such as coagulation and deposition. These aerosol processes lead to changes in particle concentration and size distribution over time and accordingly affect human exposure to nanoparticles. The present study establishes a framework for an indoor particle dynamic model that can predict time- and size-dependent particle concentrations after episodic indoor emission events. The model was evaluated with six experimental data sets obtained from previous measurement studies in the literature. The indoor particle dynamic model quantified the relative contributions of three particle loss mechanisms (i.e., coagulation, deposition, and ventilation) to the total reduction in number concentration. The results show that particle coagulation and indoor surface deposition are two dominant processes responsible for temporal changes in particle size and concentration following indoor emission events. The first-order equivalent coagulation loss rate notably varies with indoor emission source and accounts for up to 59% of the total particle loss for burning a candle, 42% for broiling a fish, and 10% for burning incense. The results reveal that while the coagulation loss rate changes markedly with the particle concentration and source type, the deposition loss rate is more dependent on particle size. Compared to coagulation and deposition, the effect of ventilation is marginal for most of the nanoparticle emission events indoors; however, ventilation loss becomes pronounced with the decrease of particle concentration below 5 × 104 cm-3, especially for particles larger than 100 nm in aerodynamic diameter.

PM2.5 hourly average measurements from 33 outdoor PurpleAir particle monitors were compared with hourly measurements from 27 nearby US EPA Air Quality System (AQS) stations employing Federal Equivalent Method (FEM) monitors in California over an 18-month (77-week) period. A transparent and reproducible alternative method (ALT) of calculating PM2.5 from the particle numbers in three size categories was used in place of the estimates provided by Plantower, the manufacturer of the sensors used in PurpleAir monitors. The ALT method was superior in several ways (better precision, lower limit of detection, improved size distribution) compared to Plantower's CF1 or ATM data series. PurpleAir monitors were strongly correlated with the nearby US EPA Air Quality System AQS stations. A calibration factor (CF) ranging between 2.9 and 3.1 was empirically derived for the PurpleAir estimates using the ALT method. This value was based on comparing the average value of 177,329 PurpleAir measurements to the value calculated from the FEM stations. The monitoring period included about 13 weeks showing very high outdoor values due to several major fires covering several hundred thousand acres. The CF during these 13 weeks averaged 2.39, whereas the CF for the remaining 64 weeks averaged about 3.21, suggesting a different response to the smoke from wildfires compared with normal ambient fine particulate matter (PM2.5). The standard Plantower CF1 data series overestimated the FEM values by about 40%, in agreement with several other studies.

Fifteen states have legalized the sales of recreational marijuana, and California has the largest sales of any state. Cannabis is most often smoked indoors, but few measurements have been made of fine particle mass concentrations produced by secondhand cannabis smoke in indoor settings. We conducted 60 controlled experiments in a 43 m3 room of a residence, measuring PM2.5 concentrations, emission rates, and decay rates using real-time monitors designed to measure PM2.5 mass concentrations. We also measured the room's air exchange rate. During each experiment, an experienced smoker followed an identical puffing protocol on one of four different methods of consuming marijuana: the pre-rolled marijuana joint (24 experiments), the bong with its bowl containing marijuana buds (9 experiments), the glass pipe containing marijuana buds (9 experiments), and the commercially available electronic vaping pen with a cartridge attached containing cannabis vape liquid (9 experiments). For comparison, we used the same puffing protocol to measure the PM2.5 emissions from Marlboro cigarettes (9 experiments). The results indicated that cannabis joints produced the highest indoor PM2.5 concentrations and had the largest emission rates, compared with the other cannabis sources. The average PM2.5 emission rate of the 24 cannabis joints (7.8 mg/min) was 3.5 times the average emission rate of the Marlboro cigarettes (2.2 mg/min). The average emission rate of the cannabis bong was 67% that of the joint; the glass pipe's emission rate was 54% that of the joint, and the vaping pen's emission rate was 44% that of the joint. The differences compared to the joint were statistically significant.

We present a new method to estimate the fraction of an aerosol mixture that is volatile, as well as the time required for evaporation from a collecting surface. The method depends on an instrument (the Piezobalance) designed to measure the accumulated mass on a quartz crystal that can also measure the subsequent loss of mass due to evaporation. Commercially available e-liquids or marijuana liquids were heated using an e-cigarette device or a vapor pen, inhaled, and exhaled into a closed unventilated room (volume = 43 and 33 m3) in each of two residences. From a set of 88 measurements on an e-liquid containing 99.7% ?vegetable glycerin? (VG), we estimate the fraction of the e-cigarette aerosol that is volatile to be 88% (95% confidence interval (CI) 77?99%). We also estimate the time to reach 95% of the total loss of the volatile material from the crystal to be 47 min (CI 33?60 min). For pure propylene glycol (PG) liquid, we measured extremely high rates of evaporation, finding that 8?16 rapid-fire puffs were required to reach a high concentration at just 0.65 m distance. From 124 experiments on three types of marijuana cartridges, the corresponding estimates of the volatile fraction of exhaled marijuana aerosol were normally 5?7% for liquids heated to moderate temperatures (N = 106), but 25?34% for liquids heated to high temperatures (N = 18). In the latter case, the time to reach 95% of the total loss of volatile material was on the order of 5?10 h. This indicates the importance of volatility considerations in affecting exposure to indoor aerosols from these two common sources. Secondhand exposures to PM2.5 from e-cigarette aerosols are likely to be short-lived for most scenarios, whereas we show that secondhand PM2.5 exposures from marijuana vaping aerosols can be substantial and long-lived after a single puff.& nbsp; Practical implications: The method presented here is general and can be used on almost any aerosol mixtures. It has the advantage of requiring a single instrument that can measure both the source strength and decay rates of & nbsp;the aerosol created by the source and also the fraction of collected material that is volatile. The method identified a major difference in the expected exposure to e-cigarette aerosols vs. marijuana aerosols from vaping. The method should be of interest to investigators who study particulate air pollution and to companies that manufacture air monitoring systems. A number of important sources of indoor aerosol mixtures (e.g., cooking, candle use, incense, etc.) remain to be investigated for volatility effects using this method.