Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2024 Sep 1.
Published in final edited form as: Drug Alcohol Depend. 2023 Jul 7;250:110889. doi: 10.1016/j.drugalcdep.2023.110889

Secondhand electronic cigarette aerosol in vehicles impacts indoor air quality

Eric K Soule 1,2, Sinan Sousan 3,4, Jack Pender 5, Luke Thomas 1, Emily Gold 1, Sarah Fresquez 1, Ronald Mooring 1, Vivien Coombs 1, Anish Gogineni 1, Alex Tiet 1
PMCID: PMC10528711  NIHMSID: NIHMS1919909  PMID: 37478503

Abstract

Background:

Electronic cigarette (ECIG) use in vehicles represents a public health concern due to the potential for exposure to high concentrations of particulate matter (PM) concentrations and other toxicants. This study examined the impact of ECIG use on air quality in vehicles.

Methods:

People who reported current ECIG use (n=60; mean age=20.5, SD=2.3) completed a brief survey and a 30-minute ECIG use session in their own vehicle. Using a protocol similar to clinical laboratory studies involving tobacco use, participants took 10 directed puffs (i.e., directed bout with one puff every 30 seconds for 5 minutes) followed by a 25-minute ad libitum period in which participants took as many puffs as desired. PM 2.5 μm in diameter or smaller (PM2.5) were measured using aerosol monitors set up to sample air from the breathing zone of the passenger seat and total puffs were recorded. The association between peak PM2.5 concentrations and puff count was examined.

Results:

Participants took a median 18 total puffs during the sessions. Median PM2.5 concentrations increased from 4.78 μg/m3 at baseline to 107.40 μg/m3 after the directed bout. Median peak PM2.5 concentration was 464.48 μg/m3 and ranged from 9.56 μg/m3 to 143,503.91 μg/m3 (IQR=132.72-1,604.68). After removing two extreme outliers for puff count and PM2.5 concentrations, puff count was significantly correlated with peak PM2.5 concentration during the ad libitum bout (r=0.32, p=0.015).

Conclusions:

ECIG use in vehicles impacts air quality negatively and may pose health risks to those present in vehicles when ECIG use is occurring.

Keywords: electronic cigarettes, secondhand electronic cigarette aerosol exposure, electronic cigarette use in vehicles

1. Introduction

Electronic cigarettes (ECIGs) are battery powered devices that heat a liquid containing propylene glycol, vegetable glycerin, nicotine (typically derived from tobacco), and chemical flavorants to create an aerosol for user inhalation (Breland et al., 2017). Rates of ECIG use in the United States (U.S.) have increased since ECIGs were introduced to the market in 2006. Among youth, current ECIG use (i.e., vaping) peaked in 2019, with 27.5% of high school students (4.7 million) and 12.5% of middle school students (1.5 million) reporting current ECIG use (Wang et al., 2019). Youth ECIG use has decreased in recent years, but still represents a public health concern with 11.3% of high school students and 2.8% of middle school students reporting ECIG use in 2021 (Gentzke et al., 2022). Adults have reported lower ECIG use rates compared to youth, with 4.5% of adults reporting current ECIG use in 2019 (Cornelius et al., 2020), and 7.6% of young adults between the ages of 18-24 reporting current ECIG use in 2018 (Dai and Leventhal, 2019),

ECIG use generates particulate matter (PM) that can impact indoor air quality. Research examining air quality in small exposure chambers (Czogala et al., 2014; Protano et al., 2017; Schober et al., 2014) and in natural settings, such as the homes of those who use ECIGs (Ballbé et al., 2014; Fernández et al., 2015), have demonstrated that ECIG use generates measurable amounts of PM. In other real-world locations where ECIG use is common, such as vaping conventions where many people engage in ECIG use indoors, the effect of ECIG use on air quality can be considerable: indoor PM concentrations measured during vaping conventions have been reported to be more than 300 times higher than when no ECIG use was occurring (Chen et al., 2018; Soule et al., 2017b). However, even a single person who is using an ECIG in a small room can increase airborne PM concentrations to three times the concentrations prior to ECIG use (Volesky et al., 2018).

Indoor ECIG use can result in exposures to ECIG-generated emissions among bystanders (i.e., secondhand exposure). Secondhand ECIG exposures refer to the instances when a person who is not using an ECIG is exposed to ECIG aerosol that has been exhaled by someone who is using an ECIG. Limited data are available regarding the health effects of secondhand ECIG exposure. However, because ECIG aerosol contains toxicants including tobacco-specific nitrosamines (TSNAs) that are formed from nicotine and volatile organic compounds (El-Hage et al., 2019; Erythropel et al., 2019; Goniewicz et al., 2018; Keith et al., 2019), and these toxicants are present in secondhand ECIG aerosol (Kaufman et al., 2018), exposure to secondhand ECIG aerosol may lead to negative health effects (Hess et al., 2016). Research has demonstrated that passive exposure to ECIG aerosol results in increases in urine levels of toxicants found in ECIG aerosol among those who do not use ECIG (Johnson et al., 2019). Youth who are exposed to ECIG aerosol are more likely to report asthma symptoms (Bayly et al., 2019). Additionally, ECIG use produces large amounts of PM2.5 and toxicants (El-Hage et al., 2019; Erythropel et al., 2019; Keith et al., 2019; Soussy et al., 2016; Talih et al., 2017; Vreeke et al., 2018). Thus, any amount of exposure to secondhand ECIG aerosol may pose health risks.

One potential location that could result in undesired secondhand ECIG aerosol exposures is in vehicles. Indeed, many people who use ECIGs report that a benefit is the ability to use ECIGs in vehicles without the smell of tobacco smoke and not harm bystanders, including children due to low perceived harm of secondhand ECIG aerosol exposure (Mello et al., 2016; Patel et al., 2016; Soule et al., 2017a, 2016). Some jurisdictions in the U.S. have established policies prohibiting smoking in vehicles when children are present (Public Health Law Center, 2017; “STATE System E-Cigarette Fact Sheet,” 2022), however, these laws do not extend to ECIG use in vehicles. Not surprisingly, research indicates that many children and bystanders are exposed to ECIG aerosol in vehicles (Agaku et al., 2019; Drehmer et al., 2019). Therefore, those who do not use ECIGs, including children who ride in vehicles when ECIG use occurs, may be exposed to unwanted levels of potentially harmful ECIG-generated toxicants.

There is a need to examine how ECIG use impacts air quality in vehicles. Previous research on cigarette smoking in vehicles shows that PM concentrations can elevate quickly due to the small air volume within vehicles (Ott et al., 2008; Rees and Connolly, 2006). However, ECIG aerosol differs from cigarette smoke and thus data on the impacts of ECIG use in vehicles are needed. Additionally, data collected from vaping conventions, homes, or exposure chambers indicate ECIG use may impact air quality differently compared to ECIG use in a vehicle by a single person due to the differences in space, number of people using ECIGs, and user behaviors that are common in these locations. Thus, data are needed regarding secondhand exposures that result from realistic ECIG use behaviors in vehicles. The purpose of this study was to examine the impacts of ECIG use on air quality in vehicles.

2. Methods

2.1. Participants

This study was approved by the University and Medical and Review Board at East Carolina University. People who used ECIG currently were recruited to participate in the study by posting flyers in the community including at local vape/tobacco shops, bars and restaurants, at the local community college and university, and by research staff distributing flyers to individuals in the community who were seen using ECIGs. Flyers directed participants to a study website with a link to an online screening questionnaire. To be eligible, individuals were required to be over the age of 18, report they had used an ECIG in the past 30 days, report they owned or leased a vehicle that they drove in regularly, and respond “yes” to the question, “Do you vape inside of your vehicle?” Individuals who reported they smoked other tobacco products, such as cigarettes or cigars, were eligible to participate, however, if participants reported that they or anyone else smoked other tobacco products in their vehicle, they were not eligible to participate. Women who reported being pregnant, intending to become pregnant, or were breastfeeding were ineligible to participate.

2.2. Procedure

Eligible individuals were scheduled for an in-person visit. Prior to the visit, participants were instructed to abstain from the use of ECIGs or any other tobacco products for 12 hours before their scheduled appointment, consistent with clinical laboratory studies involving administration of nicotine containing products (Breland et al., 2019; Hiler et al., 2017). This procedure ensures that all participants started their sessions in a similar level of nicotine withdrawal. Participants were instructed to drive their own vehicle to a parking lot located next to the research lab building and bring their preferred charged ECIG device and ECIG liquid. Research staff met participants at their vehicles and completed an informed consent process. After consenting, participants completed a paper-based survey that examined their ECIG use and other tobacco use behaviors as well as their demographic characteristics. While participants completed the survey, research staff placed air-quality monitoring equipment in the participants’ vehicles (described further below). After completion of the survey and installation of the air-quality monitoring equipment in the vehicle, participants sat in the driver’s seat of their vehicles, the air-quality monitoring equipment was turned on for data recording, and all doors and windows to the vehicle were shut. Participants were allowed to have their vehicles’ engine on and climate control system adjusted to their comfort. Participants were given a walkie-talkie to communicate with research staff from within their vehicle without having to open the windows or doors.

After a 1-minute baseline period, research staff instructed participants in a 5-minute 10-puff directed-bout followed by a 25-minute ad libitum bout. For the directed bout, participants took 10 puffs from their ECIG device with a 30-second interpuff interval. Research staff communicated to participants when to take each directed-bout puff using the walkie talkies. During the 25-minute ad libitum bout, participants were instructed that they could take as many puffs as they desired. This puffing protocol was similar to procedures used in clinical laboratory studies involving ECIG administration (Hiler et al., 2017; Lopez et al., 2016; Vansickel et al., 2010) and the total session time was similar to the average work commute time in the U.S. of 27.6 minutes (Bureau, 2017). Research staff recorded the number of puffs participants took during the directed and ad libitum bouts. The doors and windows to participants’ vehicles remained closed for the entirety of the session. At the end of the session, research staff opened all doors of the vehicle and turned off data recording for the air quality monitoring equipment. Participants were provided a $50 gift card for participating in the study.

2.3. PM2.5 Measurement

PM2.5 was chosen as an indicator of secondhand ECIG aerosol exposure because it can be measured easily in real-time and it is highly correlated with the harmful toxicants that are produced from ECIG use (Talih et al., 2017). Real-time PM2.5 concentrations (in μg/m3) in participants’ vehicles were measured using three devices: the GRIMM Mini Wide-Range Aerosol Spectrometer (MiniWRAS 1371, Grimm Aerosol Technik, Ainring, Germany), the personal DataRAM (pDR 1500 Real time monitor, Thermo Scientific, Franklin, MA, USA), and the SidePak personal aerosol monitor (AM520, TSI Inc., Shoreview, MN, USA). The SidePak was placed on the front seat and the MiniWRAS and pDR were placed on the floor in the rear of the vehicles. Conductive silicon tubing of approximately 4-6 feet in length was attached to each device. The tubing was attached to a PVC pipe assembly that was located in the passenger seat of the vehicles (see Supplemental Figure 1). This allowed all devices to sample air from the breathing area of the passenger side seat (i.e., next to the passenger headrest). The SidePak and pDR were calibrated (zero-air) before each session. The MiniWRAS, pDR, and SidePak operated at flow rates of 1.2 L/m, 1.52 L/m, and 1.8 L/m, respectively. The MiniWRAS was set to sample at 1-minute frequency and the pDR and SidePak were set to sample at 1-second frequency.

To obtain more accurate estimates of the true PM2.5 concentrations present in vehicles during the sessions, ECIG aerosol- and sensor-specific correction factors derived from gravimetric analyses conducted in a controlled exposure chamber (Sousan et al., 2023, 2022) were applied to real-time PM2.5 concentration measurements for each aerosol monitor. The participants in this study used 13 unique ECIG devices (e.g., JUUL, Hyde Edge, Kang Onee Stick, etc.). As a result, device-specific correction factors were able to be calculated for most sessions (57 out of 60). For the three participants’ devices that ECIG device-specific correction factors were not developed, correction factors that had been developed for similar ECIG device types were used. This was done for two participants who used box mod ECIG devices and one participant who used a disposable ECIG device. The pDR includes a filter for discrete measurements through gravimetric analysis. This study used a fiber glass filter to protect the pDR pump, but the filter was not used for gravimetric analysis, and filter correction factors were adopted from those developed previously (Sousan et al., 2023, 2022). Correction factors were not applied to the first data point (i.e., minute 1) because no ECIG aerosol was generated until the second minute reading, and thus the ECIG-specific correction factor did not apply.

2.4. Data Preparation and Analysis

For PM2.5 concentrations, the MiniWRAS was the most sensitive of the three sensors used and thus was the preferred PM2.5 concentration data source. However, device malfunction occurred for two participants’ sessions with the MiniWRAS. For these participants, data from the pDR were used. The filter correction factors determined for the MiniWRAS and pDR in previous laboratory studies were performed by collocating the MiniWRAS and pDR and using the same discrete filter measurements (Sousan et al., 2023, 2022). Therefore, both aerosol instruments were pushed to the same truth. Post hoc analyses comparing one-minute average PM2.5 concentration measurements between the MiniWRAS and the pDR found that PM2.5 concentration measures were highly-correlated between the two sensors at all time points (mean r=0.93, ps<0.005) and thus were appropriate to be included together in analyses. Baseline PM2.5 mean (SD) and median concentrations were calculated for the first recorded value (i.e., baseline or minute 1). Mean corrected PM2.5 (SD) and median PM2.5 concentrations were calculated for the remaining timepoints (i.e., minutes 2-31). Peak PM2.5 concentrations were calculated separately for the directed bout (minutes 2-7), the ad libitum bout (minutes 8-31), and the entire session. A Pearson’s correlation was used to assess the association between peak ad libitum PM2.5 concentration and total puff count during the session. Puff count and mean and median peak PM2.5 concentrations were compared between men and women, by ECIG device type used, and whether participants’ vehicles were running during the session using independent samples t-tests and Mann-Whitney U tests. Analyses were conducted with and without puff count outliers included. An alpha of 0.05 was used to determine statistical significance. Data were analyzed in SPSS v28.0.

3. Results

3.1. Participant Characteristics

Sixty eligible participants were invited for an in-person visit and completed the study protocol. Participant characteristics are displayed in Table 1. In summary, participants had a mean age of 20.5 (SD=2.3), 36.7% identified as women, and most identified as white (83.3%) and non-Hispanic (95.0%). Participants reported ECIG use every day (81.7%) or some days (18.3%), and most (81.7%) reported they had used ECIGs on a regular basis for 1 year or more and 70% reported they used their ECIG “fairly frequently throughout the day” (50.0%) or “almost always through the day” (20%). Participants reported using ECIGs on average 27.0 days (SD=6.27) out of the past 30 days. Disposable vapes, such as Puff Bars or similar devices, were the most used devices (61.7% of participants), followed by pod mods, such as Juul (35%), and then box mods (3.3%). Participants reported ECIG use “almost always” (46.7%) or “sometimes” (43.3%) when in their vehicles. Nearly three-quarters (73.3%) felt they were “addicted to vaping/using e-cigarettes.”

Table 1.

Sample demographics and ECIG/tobacco use characteristics.

Characteristic N %
Age (M, SD) 20.5 (2.3)
Gender
  Women 22 36.7
  Men 38 63.3
Ethnicity
  Hispanic/Latino(a) 3 5.0
Race
  American Indian/Alaskan Native 0 0
  Asian 4 6.7
  Native Hawaiian/Pacific Islander 0 0
  Black/African American 2 3.3
  White/European American 50 83.3
  More than one race 3 5.0
  Prefer not to answer 1 1.7
ECIG use days in past 30 days
  1-9 2 3.3
  10-19 4 6.7
  20-29 12 20.0
  30 42 70.0
Regular ECIG use history1
  0-3 months 8 13.3
  4-12 months 3 5.0
  1-2 years 22 36.7
  More than 2 years 27 45.0
ECIG frequency
  At least once per day 6 10.0
  Every once in a while throughout the day 12 20.0
  Fairly frequently throughout the day 30 50.0
  Almost always throughout most of the day 12 20.0
ECIG device used during session
  Disposable vape 36 60.0
  Pod mod 22 36.7
  Box mod 2 3.3
ECIG use inside of vehicle frequency
  Almost always 28 46.7
  Sometimes 26 43.3
  Rarely 4 6.7
  Never 2 3.3
Window position when vaping in vehicles
  Completely closed/all the way up 37 61.7
  Partially closed/part of the way up 20 33.3
  Completely open/all the way down 3 5.0
Lifetime use of 100+ cigarettes
  Yes 13 21.7
Current cigarette smoking a
  Every day 0 0
  Some days 13 21.7
  Not at all 46 76.7
Open in a new tab
a

One participant did not indicate if they currently smoked cigarettes.

3.2. PM2.5 Concentrations

Table 2 displays summary statistics, including mean, median, standard deviation, and range, for PM2.5 concentrations during participant sessions. At the baseline timepoint, median PM2.5 concentrations were 4.78 μg/m3 (mean=7.13 μg/m3, SD=11.85). The highest median PM2.5 concentration values were recorded at minute 7 (median=107.40 μg/m3), approximately 30 seconds after the last puff was taken in the directed bout. Mean PM2.5 concentration at minute 7 was 559.0 μg/m3. At the end of the ad libitum period (minute 31), the median PM2.5 concentration of 13.26 μg/m3 was 8.48 μg/m3 higher than baseline concentrations and the mean value of 4,386.50 μg/m3 (SD=23,087.37) was 4,379.37 μg/m3 higher than mean baseline PM2.5 concentration.

Table 2.

Summary statistics for 1-minute average PM2.5 concentration measurements (in μg/m3).

Observations
(N)		 Mean Median Std.
Dev.		 Min. 25th
Percentile		 75th
Percentile		 Max
Minute 1: Baseline 56 7.13 4.78 11.85 1.11 3.17 6.73 90.03
Minute 7: After directed bout 60 559.01 107.40 972.90 5.05 22.88 735.48 4,819.27
Minute 19: Halfway through ad lib bout 60 2,529.84 21.33 13,707.35 0.59 7.56 95.82 101,959.27
Minute 31: End of session 60 4,386.50 13.26 23,087.37 0 6.37 69.59 143,377.42
Peak directed bout concentration 60 563.63 164.36 943.96 9.46 53.55 538.25 4,470.32
Peak ad lib bout concentration 60 5,584.89 338.12 23,021.39 5.05 101.27 1,454.45 143,503.91
Peak overall concentration 60 5,625.20 464.48 23,013.89 9.56 132.72 1,604.68 143,503.91
Open in a new tab

Note. Values represent summary PM2.5 concentrations (in μg/m3) for 60 participants except for “Minute 1: Baseline” which includes data for 56 participants due to the devices not recording for the first minute value for 4 participants as noted by the Observations (N) column. Of the 60 participants, 49 had their vehicle running during the session, and whether the vehicle was running or not was not associated with Peak PM2.5 concentrations.

At minute 7, when median PM2.5 concentrations were highest, median PM2.5 concentration (107.40 μg/m3) increased by 102.62 μg/m3 from baseline and ranged from 5.05 μg/m3 to 4,819.28 μg/m3. At minute 19, approximately halfway through the ad libitum bout, PM2.5 concentrations ranged from 0.59-101,959.27 μg/m3 (median=21.32, mean=2,529.84, SD=13,707.35). Similarly, at the last minute of the session, PM2.5 concentrations ranged from 0.00-143,377.42 μg/m3 (median=13.26, mean=4,386.50, SD=23,087.37). Table 2 provides summary statistics for the PM2.5 concentrations measured during the sessions and Figure 1 displays 1-minute average median PM2.5 concentrations. Peak PM2.5 concentrations also varied greatly. While the median peak PM2.5 concentration was 464.48 μg/m3 (mean=5,625 μg/m3, SD=23,013,89), the lowest peak PM2.5 concentration was 9.56 μg/m3 and the highest peak PM2.5 concentration was 143,503.91 μg/m3. Supplemental Figure 2 shows representative 1-minute average PM2.5 concentrations for participants who had peak PM2.5 concentration values at the 25th percentile, 50th percentile, and 75th percentile.

Figure 1.

Figure 1.

Open in a new tab

Median real time PM2.5 concentration. The first arrow on the x-axis indicates when the directed bout began. The second arrow on the x-axis represents when the directed bout was completed and the ad libitum period began.

3.3. Puffing Behavior and PM2.5 Concentrations

Participants took a median of 18.00 puffs (mean=29.4 puffs, SD=42.76) during the entire session. That is, participants took a median of 8 puffs during the ad libitum period in addition to the required 10 puffs taken during the directed bout. For 28.3% of the participants, peak PM2.5 concentrations occurred during or in the first measurement after completion of the directed bout (i.e.,. minute-7). Participants whose peak PM2.5 concentration measurement occurred during or immediately after the directed bout took on average 17.12 puffs (SD=6.01). Visual inspection of the puffing data indicated that two participants represented extreme outliers, with these two participants taking more than 11 times the median puff count values (205 and 285 puffs). Figure 2 shows a scatter plot the correlation between peak PM2.5 concentration during the ad libitum period and puff count and indicated that increased puffs during the ad libitum period were associated with higher peak PM2.5 concentrations (Pearson r=0.92, p<0.001). After removing the two extreme outliers who took more than 200 puffs during the session, the correlation reduced, but remained significant (r=0.32, p=0.015).

Figure 2.

Figure 2.

Open in a new tab

Correlation between total puffs taken during session and peak PM2.5 concentration (r=0.32, p=0.015), excluding two extreme outliers of 205 puffs and 143,503.91 μg/m3 and 285 puffs and 109,668.96 μg/m3.

3.4. Subgroup comparisons

Because of the influence of extreme puff counts and PM2.5 concentrations for two participants, subgroup analyses were conducted with and without these two outliers included. Men and women differed in their puffing behaviors during the session. Specifically, men took significantly more total puffs (mean=36.87, SD=52.35) compared to women (mean=16.55, SD=5.80; t(38.55)=2.37, p=0.023). This difference remained when the two outlier participants, who both identified as men, were removed (men’s mean=25.31 puffs, SD=13.89; women’s mean=16.55, SD=5.80; t(50.90)=3.34, p=0.002). While men had higher mean peak PM2.5 concentrations (mean=8561.40 μg/m3, SD=28,632.55) than women (mean=553.56 μg/m3, SD=1,018.05), this difference was not statistically significant (t(37.16)=1.72, p=0.93). However, after removing the two outlier participants who were both men, peak PM2.5 concentrations for men (mean=2,004.46 μg/m3, SD=3,177.91) were significantly higher than peak PM2.5 concentrations for women (mean=553.56 μg/m3, SD=1,018.04; t(45.60, p=0.015). With the outliers included, an independent-samples Mann-Whitney U test indicated men also had a higher median peak PM2.5 concentration value (756.12 μg/m3) compared to women (151.72 μg/m3, p=0.001).

There were no differences in total puffs taken (t(37.09)=1.62, p=0.114) or mean peak PM2.5 concentration (t(35.13)=1.66, p=0.105) between participants who used pod mods or disposable ECIGs during their sessions. Participants who used box mods (n=2) were not included in the analysis due to small sample sizes. However, when the two outlier participants were removed, disposable users had significantly higher peak PM2.5 concentrations (mean=1,896.63 μg/m3, SD=3,298.79) compared to pod mod users (mean=662.72 μg/m3, SD=989.13; t(41.55)=2.044, p=0.047), but still did not differ significantly in total puff count (disposables mean=23.21 puffs, SD=14.72; pod mods mean=20.64, SD=7.37).

There was no difference in peak PM2.5 concentration between participants who had their vehicle running during the session (mean=3,538.85 μg/m3, SD=15,885.2) compared to those whose vehicle was not running (mean=15,177.02 μg/m3, SD=42,605.1; t(10.65)=0.89, p=0.392). This was also true when the two outlier participants were removed from the analysis: mean PM2.5 concentration for those with their vehicle on was 1,280.77 μg/m3 (SD=2,784.65) and 2,344.33 μg/m3 (SD=2,037.91) for those without their vehicles off (t(55)=1.14, p=0.259).

4. Discussion

In this study, participants engaged in ECIG use in their own vehicles using a 5-minute 10-puff standardized ECIG use puffing protocol and 25-minute ad libitum period generated measurable amounts of PM2.5 within their vehicles. Concentrations of PM2.5 varied greatly between participants, however, median peak PM2.5 concentrations during the session were approximately 22 times higher than baseline levels before any ECIG use had occurred. Vehicle PM2.5 concentration was directly related to puffing behavior, which was also highly variable between participants. This study demonstrated that a single person using an ECIG in a vehicle with the windows closed can cause a measurable increase in PM2.5 concentration. Additionally, use of disposable ECIG devices that have become the most popular device type on the market (Delnevo et al., 2020) resulted in the highest PM2.5 concentrations compared to pod mods. Together, these data suggest that bystanders are likely exposed to secondhand ECIG aerosol when in a vehicle with someone who is actively using an ECIG.

This study is the first to measure PM2.5 concentrations in vehicles during active ECIG use using a standardized puffing protocol and an ad libitum period. This protocol that allowed participants to take as few as 10 puffs and as many as 285 puffs resulted in wide range of peak PM2.5 concentrations in participants’ vehicles. More than half of the participants’ vehicles reached peak PM2.5 concentrations that were similar to PM2.5 concentrations measured at vaping conventions that had more than 50 people using ECIGs actively (Soule et al., 2017b). Two participants who took more than 200 puffs in a 30-minute ECIG use session generated PM2.5 concentrations that were more than 200 times average PM2.5 concentrations found in vaping conventions. While the concentrations achieved by these two participants may not be typical in most vehicles where ECIG use occurs, they are cause for concern. Because PM2.5 generated from ECIG use is correlated with harmful toxicants produced from ECIG use (Talih et al., 2017), those who ride in vehicles when active ECIG use is occurring are likely exposed to high concentrations of ECIG-generated toxic chemicals. Research on cigarette smoking in vehicles (Ott et al., 2008) suggests that opening windows in vehicles could decrease PM2.5 concentrations considerably. However, in the current study, nearly two thirds of participants reported that they typically kept their windows completely closed when they used ECIGs in their vehicles. Thus, the high PM2.5 concentrations observed in the current study are feasible and may be representative of exposures that occur in real world settings.

As ECIG use in indoor settings has increased in recent years, particularly when youth are present (Dai, 2020; Gentzke et al., 2019; Wang et al., 2017), there is a need to understand the potential impact of secondhand ECIG aerosol exposure. Data from the current study demonstrate that bystanders in vehicles are exposed to ECIG chemicals. A recent meta-analysis reported that ECIG use may cause asthma and chronic obstructive pulmonary disorder (Wills et al., 2021) due to exposure to harmful chemicals. Secondhand exposure to ECIG aerosol may also increase risk of negative respiratory health effects, such as asthma (Alnajem et al., 2020). Longitudinal survey research that examines exposure to secondhand ECIG aerosol, including in vehicles, and the associated negative health effects among those who do not use ECIGs will be important for understanding the impacts of increased ECIG use in indoor settings, including around youth.

This study had several limitations. While PM2.5 concentrations were correlated with puff count, detailed puff topography, including puff duration, puff flowrate, and total puff volume were not measured due to the inability to use cumbersome puff topography equipment that also requires a compatible ECIG device. Future studies that are better able to assess detailed ECIG puff topography may find a stronger relationship between ECIG puffing behavior and PM2.5 concentration in vehicles. The current study examined PM2.5 concentrations, but did not examine the chemical content generated from ECIG use in vehicles, similar to other research examining indoor air quality in locations such as indoor hookah bars or bars that allow cigarette smoking (Cobb et al., 2013). Future studies may examine the chemicals present in vehicles in which ECIG use is occurring. In the current study, indoor air quality was examined in vehicles under controlled conditions, including a 10-puff directed bout and a limited time period, which may differ from typical ECIG use in vehicles. For example, participants were required to keep their windows shut during the session and PM2.5 concentrations would likely have been lower if participants were able to open their windows while engaging in ECIG use. However, ECIG use with the windows completely closed was reported by nearly two-thirds or participants, representing the most common window position when participants engaged in ECIG use in their vehicles. Additionally, participants were allowed to adjust the climate control in their vehicles as they desired which may have impacted PM2.5 concentrations in vehicles. Future studies should expand on the current work to examine ECIG use behaviors and indoor air quality in more naturalistic conditions.

5. Conclusion

In a short period of time, ECIG use in vehicles can generate large amounts of PM2.5 which could result in unwanted secondhand ECIG aerosol exposures among those who do not use ECIGs, including children, who ride in vehicles when ECIG use occurs. ECIG devices have evolved over time and data from the current study indicate that the most commonly used ECIG devices are capable of producing large amounts of PM2.5 resulting in increased risk for secondhand exposures. The long-term health effects of secondhand ECIG aerosol exposure are unknown, but burgeoning data suggest harm potential due to the presence of known and unknown toxicants (e.g., Tehrani et al., 2021) and the harms associated with inhaling ECIG aerosol. Policy makers should consider extending policies that restrict smoking in public and private vehicles to also include ECIG use to prevent unwanted and potentially harmful exposures to non-users and youth. Future research should examine patterns of ECIG use in vehicles in more natural settings as well as measuring PM and chemicals present when ECIG users vape in their vehicles.

Supplementary Material

1
2

Highlights.

  • ECIG use generates large amounts of particulate matter (PM).

  • ECIG use in a vehicle can increase PM concentrations to high levels.

  • ECIG-generated PM concentrations are directly correlated with puffing behaviors.

  • PM concentrations in vehicles with ECIG use are similar to those reported in vaping conventions.

  • Secondhand ECIG exposures in vehicles may pose health risks to bystanders.

Funding

This research is supported by grant numbers R15ES032138 from National Institute of Environmental Health of the National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the views of the NIH.

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Conflict of Interest

ES is named on a patent application for a smartphone app that determines electronic cigarette device and liquid characteristics. All other authors have no conflicts to report.

Author Disclosures

ES is named on a patent application for a smartphone app that determines device and liquid characteristics. All other authors have no conflicts to report.

References

  1. Agaku IT, Odani S, King BA, Armour BS, 2019. Prevalence and correlates of secondhand smoke exposure in the home and in a vehicle among youth in the United States. Prev Med 126, 105745. 10.1016/j.ypmed.2019.06.003 [DOI] [PubMed] [Google Scholar]
  2. Alnajem A, Redha A, Alroumi D, Alshammasi A, Ali M, Alhussaini M, Almutairi W, Esmaeil A, Ziyab AH, 2020. Use of electronic cigarettes and secondhand exposure to their aerosols are associated with asthma symptoms among adolescents: a cross-sectional study. Respir Res 21, 300. 10.1186/s12931-020-01569-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Ballbè M, Martínez-Sánchez JM, Sureda X, Fu M, Pérez-Ortuño R, Pascual JA, Saltó E, Fernández E, 2014. Cigarettes vs. e-cigarettes: Passive exposure at home measured by means of airborne marker and biomarkers. Environ. Res 135, 76–80. 10.1016/j.envres.2014.09.005 [DOI] [PubMed] [Google Scholar]
  4. Bayly JE, Bernat D, Porter L, Choi K, 2019. Secondhand exposure to aerosols from electronic nicotine delivery systems and asthma exacerbations among youth with asthma. Chest 155, 88–93. 10.1016/j.chest.2018.10.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Breland A, Maloney SF, Soule EK, Ramôa C, Barnes A, Lipato T, Eissenberg T, 2019. Abuse liability of electronic cigarettes in men who are experienced electronic cigarette users. Exp Clin Psychopharmacol. 10.1037/pha0000305 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Breland A, Soule E, Lopez A, Ramôa C, El-Hellani A, Eissenberg T, 2017. Electronic cigarettes: what are they and what do they do? Ann. N. Y. Acad. Sci 1394, 5–30. 10.1111/nyas.12977 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Bureau, U.C., n.d. Average One-Way Commuting Time by Metropolitan Areas [WWW Document]. URL https://www.census.gov/library/visualizations/interactive/travel-time.html (accessed 10.14.19).
  8. Chen R, Aherrera A, Isichei C, Olmedo P, Jarmul S, Cohen JE, Navas-Acien A, Rule AM, 2018. Assessment of indoor air quality at an electronic cigarette (Vaping) convention. J Expo Sci Environ Epidemiol 28, 522–529. 10.1038/s41370-017-0005-x [DOI] [PubMed] [Google Scholar]
  9. Cobb CO, Vansickel AR, Blank MD, Jentink K, Travers MJ, Eissenberg T, 2013. Indoor air quality in Virginia waterpipe cafes. Tob Control 22, 338–343. 10.1136/tobaccocontrol-2011-050350 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Cornelius ME, Wang TW, Jamal A, Loretan CG, Neff LJ, 2020. Tobacco product use among adults - United States, 2019. MMWR Morb Mortal Wkly Rep 69, 1736–1742. 10.15585/mmwr.mm6946a4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Czogala J, Goniewicz ML, Fidelus B, Zielinska-Danch W, Travers MJ, Sobczak A, 2014. Secondhand exposure to vapors from electronic cigarettes. Nicotine Tob. Res 16, 655–662. 10.1093/ntr/ntt203 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Dai H, 2020. Exposure to secondhand aerosol from electronic cigarettes among US youth from 2015 to 2018. JAMA Pediatr 174, 298–300. 10.1001/jamapediatrics.2019.5665 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Dai H, Leventhal AM, 2019. Prevalence of e-cigarette use among adults in the United States, 2014-2018. JAMA, 10.1001/jama.2019.15331 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Delnevo C, Giovenco DP, Hrywna M, 2020. Rapid proliferation of illegal pod-mod disposable e-cigarettes. Tob Control 29, e150–e151. 10.1136/tobaccocontrol-2019-055485 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Drehmer JE, Nabi-Burza E, Hipple Walters B, Ossip DJ, Levy DE, Rigotti NA, Klein JD, Winickoff JP, 2019. Parental smoking and e-cigarette use in homes and cars. Pediatrics 143, e20183249. 10.1542/peds.2018-3249 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. El-Hage R, El-Hellani A, Haddad C, Salman R, Talih S, Shihadeh A, Eissenberg T, Saliba NA, 2019. Toxic emissions resulting from sucralose added to electronic cigarette liquids. Aerosol Science and Technology 53, 1197–1203. 10.1080/02786826.2019.1645294 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Erythropel HC, Jabba SV, DeWinter TM, Mendizabal M, Anastas PT, Jordt SE, Zimmerman JB, 2019. Formation of flavorant-propylene glycol adducts with novel toxicological properties in chemically unstable e-cigarette liquids. Nicotine Tob. Res 21, 1248–1258. 10.1093/ntr/nty192 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Fernández E, Ballbè M, Sureda X, Fu M, Saltó E, Martínez-Sánchez JM, 2015. Particulate matter from electronic cigarettes and conventional cigarettes: a systematic review and observational study. Curr Environ Health Rep 2, 423–429. 10.1007/s40572-015-0072-x [DOI] [PubMed] [Google Scholar]
  19. Gentzke AS, Wang TW, Cornelius M, Park-Lee E, Ren C, Sawdey MD, Cullen KA, Loretan C, Jamal A, Homa DM, 2022. Tobacco product use and associated factors among middle and high school students - National Youth Tobacco Survey, United States, 2021. MMWR Surveill Summ 71, 1–29. 10.15585/mmwr.ss7105a1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Gentzke AS, Wang TW, Marynak KL, Trivers KF, King BA, 2019. Exposure to secondhand smoke and secondhand e-cigarette aerosol among middle and high school students. Prev Chronic Dis 16, E42. 10.5888/pcd16.180531 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Goniewicz ML, Smith DM, Edwards KC, Blount BC, Caldwell KL, Feng J, Wang L, Christensen C, Ambrose B, Borek N, van Bemmel D, Konkel K, Erives G, Stanton CA, Lambert E, Kimmel HL, Hatsukami D, Hecht SS, Niaura RS, Travers M, Lawrence C, Hyland AJ, 2018. Comparison of nicotine and toxicant exposure in users of electronic cigarettes and combustible cigarettes. JAMA Netw Open 1, e185937. 10.1001/jamanetworkopen.2018.5937 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Hess IM, Lachireddy K, Capon A, 2016. A systematic review of the health risks from passive exposure to electronic cigarette vapour. Public Health Res Pract 26, 2621617. 10.17061/phrp2621617 [DOI] [PubMed] [Google Scholar]
  23. Hiler MM, Breland AB, Spindle TR, Maloney SF, Lipato T, Karaoghlanian N, Shihadeh AL, Lopez AA, Ramôa CP, Eissenberg T, n.d. Electronic cigarette user plasma nicotine concentration, puff topography, heart rate, and subjective effects: influence of liquid nicotine concentration and user experience. Experimental and Clinical Psychopharmacology. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Johnson JM, Naeher LP, Yu X, Sosnoff C, Wang L, Rathbun SL, De Jesús VR, Xia B, Holder C, Muilenburg JL, Wang J-S, 2019. A biomonitoring assessment of secondhand exposures to electronic cigarette emissions. Int J Hyg Environ Health 222, 816–823. 10.1016/j.ijheh.2019.04.013 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Kaufman P, Dubray J, Soule EK, Cobb CO, Zarins S, Schwartz R, 2018. Analysis of secondhand e-cigarette aerosol compounds in an indoor setting. Tob Reg Sci 4, 29–37. 10.18001/TRS.4.3.3 [DOI] [Google Scholar]
  26. Keith RJ, Fetterman JL, Orimoloye OA, Dardari Z, Lorkiewicz P, Hamburg NM, DeFilippis AP, Blaha MJ, Bhatnagar A, 2019. Characterization of volatile organic compound (VOC) metabolites in cigarette smokers, electronic nicotine device users, dual users and non-users of tobacco. Nicotine Tob. Res. 10.1093/ntr/ntz021 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Lopez AA, Hiler MM, Soule EK, Ramôa CP, Karaoghlanian NV, Lipato T, Breland AB, Shihadeh AL, Eissenberg T, 2016. Effects of electronic cigarette liquid nicotine concentration on plasma nicotine and puff topography in tobacco cigarette smokers: a preliminary report. Nicotine Tob. Res. 18, 720–723. 10.1093/ntr/ntv182 [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Mello S, Bigman CA, Sanders-Jackson A, Tan ASL, 2016. Perceived harm of secondhand electronic cigarette vapors and policy support to restrict public vaping: results from a national survey of US adults. Nicotine Tob. Res. 18, 686–693. 10.1093/ntr/ntv232 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Ott W, Klepeis N, Switzer P, 2008. Air change rates of motor vehicles and in-vehicle pollutant concentrations from secondhand smoke. J Expo Sci Environ Epidemiol 18, 312–325. 10.1038/sj.jes.7500601 [DOI] [PubMed] [Google Scholar]
  30. Patel D, Davis KC, Cox S, Bradfield B, King BA, Shafer P, Caraballo R, Bunnell R, 2016. Reasons for current e-cigarette use among U.S. adults. Preventive Medicine 93, 14–20. 10.1016/j.ypmed.2016.09.011 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Protano C, Manigrasso M, Avino P, Vitali M, 2017. Second-hand smoke generated by combustion and electronic smoking devices used in real scenarios: ultrafine particle pollution and age-related dose assessment. Environ Int 107, 190–195. 10.1016/j.envint.2017.07.014 [DOI] [PubMed] [Google Scholar]
  32. Public Health Law Center, 2017. U.S. Prohibitions on smoking in cars with children.
  33. Rees VW, Connolly GN, 2006. Measuring air quality to protect children from secondhand smoke in cars. Am J Prev Med 31, 363–368. 10.1016/j.amepre.2006.07.021 [DOI] [PubMed] [Google Scholar]
  34. Schober W, Szendrei K, Matzen W, Osiander-Fuchs H, Heitmann D, Schettgen T, Jörres RA, Fromme H, 2014. Use of electronic cigarettes (e-cigarettes) impairs indoor air quality and increases FeNO levels of e-cigarette consumers. Int J Hyg Environ Health 217, 628–637. 10.1016/j.ijheh.2013.11.003 [DOI] [PubMed] [Google Scholar]
  35. Soule EK, Maloney SF, Guy MC, Eissenberg T, Fagan P, 2017a. User identified positive outcome expectancies of electronic cigarette use: A concept mapping study. Psychol Addict Behav 31, 343–353. 10.1037/adb0000263 [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Soule EK, Maloney SF, Spindle TR, Rudy AK, Hiler MM, Cobb CO, 2017b. Electronic cigarette use and indoor air quality in a natural setting. Tob Control 26, 109–112. 10.1136/tobaccocontrol-2015-052772 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Soule EK, Rosas SR, Nasim A, 2016. Reasons for electronic cigarette use beyond cigarette smoking cessation: A concept mapping approach. Addict Behav 56, 41–50. 10.1016/j.addbeh.2016.01.008 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Sousan S, Pender J, Streuber D, Haley M, Shingleton W, Soule E, 2022. Laboratory determination of gravimetric correction factors for real-time area measurements of electronic cigarette aerosols. Aerosol Sci Technol 56, 517–529. 10.1080/02786826.2022.2047152 [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Sousan S, Wu Q, Park YM, Fresquez S, Batts N, Bertges N, Johnston CA, Tunnell H, Pender J, Soule E, 2023. Laboratory determination of gravimetric correction factors for real-time area measurements of electronic cigarette aerosols: Part 2. Aerosol Science and Technology 1–16. 10.1080/02786826.2023.2193234 [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Soussy S, EL-Hellani A, Baalbaki R, Salman R, Shihadeh A, Saliba NA, 2016. Detection of 5-hydroxymethylfurfural and furfural in the aerosol of electronic cigarettes. Tobacco Control 25, ii88–ii93. 10.1136/tobaccocontrol-2016-053220 [DOI] [PubMed] [Google Scholar]
  41. STATE System E-Cigarette Fact Sheet [WWW Document], 2022. URL https://www.cdc.gov/statesystem/factsheets/ecigarette/ECigarette.html (accessed 12.8.22).
  42. Talih S, Salman R, Karaoghlanian N, El-Hellani A, Saliba N, Eissenberg T, Shihadeh A, 2017. “Juice monsters”: sub-ohm vaping and toxic volatile aldehyde emissions. Chemical Research in Toxicology 30, 1791–1793. 10.1021/acs.chemrestox.7b00212 [DOI] [PubMed] [Google Scholar]
  43. Tehrani MW, Newmeyer MN, Rule AM, Prasse C, 2021. Characterizing the chemical landscape in commercial e-cigarette liquids and aerosols by liquid chromatography-high-resolution mass spectrometry. Chem Res Toxicol 34, 2216–2226. 10.1021/acs.chemrestox.1c00253 [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Vansickel AR, Cobb CO, Weaver MF, Eissenberg TE, 2010. A clinical laboratory model for evaluating the acute effects of electronic “cigarettes”: nicotine delivery profile and cardiovascular and subjective effects. Cancer Epidemiol. Biomarkers Prev 19, 1945–1953. 10.1158/1055-9965.EPI-10-0288 [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Volesky KD, Maki A, Scherf C, Watson L, Van Ryswyk K, Fraser B, Weichenthal SA, Cassol E, Villeneuve PJ, 2018. The influence of three e-cigarette models on indoor fine and ultrafine particulate matter concentrations under real-world conditions. Environ. Pollut. 243, 882–889. 10.1016/j.envpol.2018.08.069 [DOI] [PubMed] [Google Scholar]
  46. Vreeke S, Peyton DH, Strongin RM, 2018. Triacetin enhances levels of acrolein, formaldehyde hemiacetals, and acetaldehyde in electronic cigarette aerosols. ACS Omega 3, 7165–7170. 10.1021/acsomega.8b00842 [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Wang TW, Gentzke AS, Creamer MR, Cullen KA, Holder-Hayes E, Sawdey MD, Anic GM, Portnoy DB, Hu S, Homa DM, Jamal A, Neff LJ, 2019. Tobacco product use and associated factors among middle and high school students - United States, 2019. MMWR Surveill Summ 68, 1–22. 10.15585/mmwr.ss6812a1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Wang TW, Marynak KL, Agaku IT, King BA, 2017. Secondhand exposure to electronic cigarette aerosol among US youths. JAMA Pediatr 171, 490–492. 10.1001/jamapediatrics.2016.4973 [DOI] [PubMed] [Google Scholar]
  49. Wills TA, Soneji SS, Choi K, Jaspers I, Tam EK, 2021. E-cigarette use and respiratory disorders: an integrative review of converging evidence from epidemiological and laboratory studies. Eur Respir J 57, 1901815. 10.1183/13993003.01815-2019 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

1
NIHMS1919909-supplement-1.docx (255.6KB, docx)
2
NIHMS1919909-supplement-2.docx (48.6KB, docx)

RESOURCES