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Article

Preliminary Study Using Sensor Measurements in Selected Homes in Cornwall, England, over a One-Year Period Confirms Increased Indoor Exposure from Second-Hand Smoking but Not from Second-Hand Vaping

by
Gareth David Walsh
1,
Tamaryn Menneer
2,3 and
Richard Alan Sharpe
1,2,*
1
Public Health, Cornwall Council, Treyew Road, Truro TR1 3AY, Cornwall, UK
2
European Centre for Environment and Human Health, University of Exeter Medical School, Penryn TR10 8RD, Cornwall, UK
3
Environment and Sustainability Institute, Penryn Campus, University of Exeter, Penryn TR10 9FE, Cornwall, UK
*
Author to whom correspondence should be addressed.
Pollutants 2025, 5(4), 34; https://doi.org/10.3390/pollutants5040034
Submission received: 28 February 2025 / Revised: 21 September 2025 / Accepted: 27 September 2025 / Published: 6 October 2025
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Abstract

Introduction: Increased exposure to air pollution poses a burden to society and healthcare systems worldwide, with increased risk of morbidity and mortality. Indoor concentrations of air pollutants, such as particulate matter, are a public health concern because they can be present in higher concentrations than outside. Unlike the effects of indoor environmental tobacco smoke (ETS), there is a dearth of research that includes the impact of e-cigarettes on particulate matter concentrations in the home, which is the focus of this study. Method: Participant, household, and sensor information were obtained from 164 lower-income households located in Cornwall, South West of England. Daily sensor readings were obtained for PM2.5 for one year. Descriptive statistics were used to describe study participant characteristics and health status. Mean indoor averages, median PM2.5 measurements, and two-tailed tests were used to assess differences in concentrations of PM2.5. Results: The 164 surveyed households included 315 residents (67% female) with a mean adult age of 57 (22–92). Half of all homes were in the 10% most deprived neighbourhoods in England. Thirty-four per cent of participants were current smokers, and of these 36% have asthma and had seen a doctor in the last year (cf. never smokers 14%, ex-smokers 25%). Mean annual PM2.5 was highest in smoking households (14.07 µg/m3) and smoking and vaping households (9.18 µg/m3), and lower in exclusive vaping households (2.00 µg/m3) and smoke and vape-free households (1.28 µg/m3). Monthly levels of PM2.5 fluctuated seasonally for all groups, with the highest recordings in winter and the lowest in summer. Discussion and Conclusion: In this preliminary study, we conducted secondary data analyses using monitoring data from a large health and housing study to assess factors leading to elevated indoor concentrations of particulate matter. Indoor concentrations appeared to be highest in homes where residents smoked indoors. The use of e-cigarettes in the home also appeared to modify concentrations of particulate matter, but levels were lower than in homes with tobacco smoke. We were not able to determine the relationship between smoking and/or vaping indoors and particulate matter, which supports the need for studies of larger sample sizes and more complex longitudinal monitoring. This will help assess the timing and extent of exposures resulting from smoking and vaping indoors, along with a range of other chemical and biological exposures and their corresponding health effects.

1. Introduction

Elevated outdoor air pollution represents a significant burden to society and healthcare systems worldwide, with increased risk of cardiovascular disease and respiratory disease [1]. Indoor air pollution is a public health priority because people in North America and Europe spend 89% of their time indoors. A total of 69% of time is spent in indoor home environments, which increases to 90% for the very young, elderly, and infirm [2]. Outdoor air pollutants (e.g., particulate matter, carbon monoxide, and nitrogen dioxide) are effectively transferred into the home environment, where concentrations can be up to ten times higher than outdoor concentrations [3,4,5]. Smoking indoors increases concentrations of combustible by-products such as particulate matter (PM) [6]. Studies investigating indoor concentrations in natural settings are limited by small sample sizes, and to our knowledge, none have monitored the concentrations of combustible by-products resulting from real-time indoor levels of environmental tobacco smoking (ETS) and/or vaping in a large sample throughout a year [7], which is the focus of this study.
Smoking is a public health priority because it remains the greatest cause of preventable mortality and morbidity [8] and includes passive exposure to ETS due to increased risk of heart disease, lung cancer, and respiratory diseases [9]. The prevalence of smoking increases with levels of deprivation (for example, routine and manual workers and/or those living in social housing) and consequently widens health inequalities experienced by low-income populations [10,11]. Those living in social housing are more likely to have lower socio-economic status than the general population and more likely to be unemployed [12,13], which is exacerbated by levels of smoking in this population [14]. In comparison, levels of vaping or the use of e-cigarettes (an umbrella term) are increasing in use as an alternative to smoking and/or used in smoking cessation interventions [15,16]. Whilst this approach is currently supported in England [17], the USA and Australian governments and the World Health Organisation view both e-cigarettes and nicotine as being harmful [18,19,20]. Furthermore, a declining minority of current smokers believe e-cigarettes are less harmful than cigarettes, and an increasing proportion believes they are equally or more harmful [17].
Smoking, which increases levels of environmental tobacco smoke (ETS) and/or use of e-cigarettes, leads to elevated exposure to combustible by-products. This includes increased levels of particulate matter (PM), which refers to solids or liquid droplets of varying particle sizes and different chemical constituents found in the air [21]. Reducing exposure to PM2.5 is of interest because it is readily inhaled, where it can penetrate deep into the bronchioles and is associated with the greatest risk to health, as it can cross the blood-gas barrier [22] and has a synergistic effect with environmental PM and ETS [23]. Despite the health risk associated with increased exposure [1,24], there are no guidelines for indoor particulate matter, and there is no defined safe level for PM exposure.
Although PM exposure from tobacco smoke has been well documented [25,26,27], there is a paucity of studies that have investigated e-cigarette aerosols in the home environment in the long term [15,28,29,30] and focus on controlled studies [29], which have shown that emissions from e-cigarettes are lower than passive ETS [31]. There has been concern around exposure of e-cigarettes which can be used in everyday settings where vulnerable people, such as children and pregnant women, are exposed [28]. Other studies have not measured and/or appropriately reported PM2.5 from e-cigarettes [32,33,34,35,36,37,38,39]. The limitations of prior studies, including methodological problems such as short-term experiments not reflecting reality and inconsistent findings, have been previously highlighted [29,31,40,41]. This study aimed to better understand long-term indoor concentrations of PM2.5 resulting from both ETS and e-cigarettes, and which modifiable demographic and behavioural characteristics influenced indoor levels. This is of public health interest because our novel study represents the largest real-world study exploring how concentrations of PM2.5 over a 12-month period are modified by household occupants’ smoking and/or vaping in the home.

2. Materials and Methods

2.1. Context

This study involved secondary data obtained from a large multidisciplinary health and housing project, which was designed to study the indoor home environment and better understand people’s health and wellbeing. This provided an opportunity to further research into the importance of the indoor environment and health outcomes [42,43,44,45] and the use of indoor monitors to better understand indoor air pollution [46]. This study was located in Cornwall in the South West of England, which is a rural county and experiences a strong maritime climate that is dominated by mild temperatures, strong wind speeds and wet winters [47]. The target population resides in homes owned and managed by a medium-sized Social Housing Association, which is predominantly located in the 20% most deprived neighbourhoods in England [48]. Social Housing Associations are not-for-profit organisations responsible for the provision of homes for lower-income populations [49].
To monitor the indoor environment of social housing properties, this study installed digital sensors to measure particulate matter concentrations, humidity, temperature, water, and energy consumption in 329 homes. A cross-sectional survey was undertaken to determine participant characteristics and health and well-being outcomes [50]. This study complied with the guidelines of the 1964 Declaration of Helsinki. Ethical approval was obtained by the University of Exeter Research Ethics Committee, reference no. eUEBS002996 v2.0 (on 16 June 2017 and 5 December 2019). This secondary data analysis was approved by the Cornwall Council Research Governance Framework and the University of St Mark and St John on 7 October 2019.

2.2. Participant Questionnaire and Indoor Sensors

In the original research, trained volunteers conducted a face-to-face questionnaire with the 329 participants living in social housing from September 2017 to November 2018. Using previous research [49,51], the questions were designed to collect information about participants’ age, gender, employment status, education, whether the occupants smoke, smoke in the home or vape in the home. Between October 2017 and December 2018, digital sensors were installed inside the properties. SmartRF laser-based sensors (Invisible Systems, Milnthorpe, England) measured real-time PM2.5 mass concentrations (range 0 μg/m3 to 1000 μg/m3) through light scattering at 5-min intervals. In addition to the internal sensors, 30 sets of external sensors were sited across the study area. Sensor data were cleaned and extracted between 1 December 2018 and 30 November 2019, to provide mean monthly and annual PM2.5 readings. In accordance with previous research, any zero readings were treated as anomalous missing data, though outlier data whose sensors were checked as working were not removed but included in the mean value calculation [52,53]. The values reported are actual, unadjusted numbers. A total of 164 households were included in the study after excluding participants where:
  • They had withdrawn from the Smartline project, or sensors were not installed at the property.
  • The Smartline baseline survey questionnaire was missing information on indoor smoking and vaping.
  • PM2.5 sensor data for the property did not span the complete date range.
Property data was available from the social housing landlord and linked to the anonymous participant questionnaires and sensor data using a unique property identifier. This included housing characteristics shown to influence PM2.5 levels [2], which included heating and cooking systems [54,55] and levels of ventilation or the presence of air filtration systems [56,57,58]. Google Maps distance tool [59] was used to calculate the distance of all properties from a main road due to the potential impact on indoor air quality [60].

2.3. Data Analysis

Descriptive statistics were used to describe study participant characteristics [42,44], including demographics (e.g., age, gender), household data (e.g., build type, age of property, heating and ventilation) and health and behavioural data (e.g., smoking status, asthma, COPD). Median values of PM2.5 are reported alongside mean values, because the median is less sensitive to outliers and peaks where data is skewed [53]. Non-parametric tests were most appropriate for comparing the PM values across the 164 households, given a skewed distribution (skew = 6.03, kurtosis = 52.68, after log10 transformation, Shapiro-Wilk test for normality p < 0.05). A validity check [61] showed similar results with independent t-tests, not reported here. In accordance with prior studies [16,37,38,52,62], Kruskal-Wallis H and Mann-Whitney U tests were conducted between households to determine whether differences in PM2.5 are due to random variation or whether smoking and vaping lead to statistical differences in mean PM2.5. Due to low numbers within the study groups, sub-analysis of PM2.5 levels in relation to covariates (e.g., heating and ventilation, frequency of vacuuming) was only descriptive and multivariate analysis was not conducted. All statistical tests were two-tailed, with p < 0.05 considered significant.
Data analyses were performed using Microsoft Excel, version 2008 (Microsoft Corporation, Redmond, WA, USA) and STATA/IC 15.0 for Windows (Stata Corporation, College Station, TX, USA).

3. Results

3.1. Descriptive Statistics

The mean age of the participants was 57 years, with 67% being female, 25% having a respiratory condition, 39% having retired, and 25% either on long-term sick or disabled (Table 1). Of the 164 included households, there were more flats (60%) than houses or bungalows, and 46% of all participants lived on their own (Table 2). Property ranged from those built before 1960 (32%), those built between 1960–1979 (31%), and those built after 1980 (36%). All except two households (99%) were in the 40% most deprived neighbourhoods in England, with half (51%) of all households being in the 10% most deprived.
Thirty-four per cent of participants were current smokers, although smoking and/or vaping status varied by participant and household characteristics (Table S1 in the Supplementary Materials). Slightly more women smoked (35%) than men (31%), and only one participant stated their household exclusively vaped. Sixty-six per cent of those who exclusively smoke in the home live alone, compared to 45% living in smoke and vape-free households. More smokers and dual users open their front room window at least weekly (smokers 84% vs. smoke and vape free 78%). Out of the 43 participants who have never smoked, 14% have asthma and have seen a doctor in the last year, compared to 25% of the ex-smokers and 36% of the current smokers. Of those participants who reported wheezing or dry coughing in the last year, 21% had never smoked, 36% were ex-smokers, and 43% were current smokers. Irrespective of whether participants smoke indoors, 11% of ex-smokers and 11% of current smokers have chronic bronchitis, emphysema or COPD, whilst only 5% of those who have never smoked have the same conditions.
The mean PM2.5 concentrations indoors varied seasonally by those smoking and/or vaping in the home (Table 3), as well as by a range of participant behaviours, housing characteristics and time of year (Table 4). The highest annual mean PM2.5 measurements are in exclusive smoking households (14.07 µg/m3), followed by smoking and vaping households (9.18 µg/m3), exclusive vaping households (2.00 µg/m3) and smoke and vape-free households (1.28 µg/m3). The exclusive smoking households had annual readings seven times greater than smokefree households and exceeded all but one of the external sensor readings for PM2.5 (mean 10.52 µg/m3) and the national monitoring in the study area, which ranged from 6.27 to 8.10 µg/m3. With the exception of one sensor, all external sensors with complete values for the study period had similar readings to Department for Environment, Food and Rural Affairs (DEFRA) sensors across the same geography (Table S2). This relationship was consistent over the time period of the study, with concentrations of PM2.5 being greater in the exclusive smoking and smoking and vaping households. However, concentrations of PM2.5 varied considerably across the study period, with the highest PM2.5 measurements occurring during the winter, followed by spring and autumn. The summer had the lowest recordings (Figure S1).
Mean concentrations of PM2.5 were modified by the presence of a range of building and behavioural characteristics (Table 4), which need to be considered alongside levels of smoking and/or vaping indoors. For example, these include occupant behaviours. There is an increase in PM2.5 with the number of hours spent indoors (except for non-smokers). There is a rise in PM2.5 for those who do not open the front room window at least weekly (except for exclusive vapers). Homes using mechanical ventilation (e.g., fans, dehumidifiers) have higher PM2.5 measurements compared to those that do not. In single-occupancy homes, there were increased levels of PM2.5 (except for households that smoke and vape).
Households that exclusively smoke, or smoke and vape, have greater PM2.5 readings if they have no pets. Exclusive vapers and smoke and vape-free households, however, have elevated PM2.5 measurements if they have pets. These two smoke-free cohorts also have higher PM2.5 levels with increasing distance from a main road, though the opposite is true for those who smoke and vape indoors. For exclusive smokers, the annual mean PM2.5 was largest at 100–500 m from a main road (17.24 µg/m3), followed by those within 100 m (11.91 µg/m3). Those over 500 m away had the lowest PM2.5 readings (9.17 µg/m3). Due to the sample size, we were unable to fully account for a range of covariates and differences between building and behavioural characteristics, which could modify mean concentrations. There were also household characteristics that could modify readings and need to be considered (Table S3).
Indoor annual mean concentrations of PM2.5 were highest in households that exclusively smoked, which was followed by households that smoked and vaped in the home. Four respondents from the exclusive smoking households were noted as not having a smoker resident. This highlights the need to consider other occupant behaviours in future studies (Table S4). Three current smokers stated their partners also smoked over 15 times a day (mean 16.06 µg/m3), whilst all four respondents who stated they smoked less than 5 times a day did not have a partner recorded as smoking and had a household annual PM2.5 of 11.55 µg/m3 or higher (median 18.70 µg/m3). Without the presence of the outlier participant house (mean 96.38 µg/m3), this group would still have the highest average of 16.32 µg/m

3.2. Comparison of Mean PM2.5 Concentrations Between Households

There was a significant difference in PM2.5 concentrations between the four groups, χ2(3) = 78.021, p = 0.0001 (<0.05), which included exclusively smoking, smoking and vaping households, vaping only, smoking and vaping free homes (Figure 1), with smokers having the highest levels.
To explore further, we compared annual mean PM2.5 values between households with different smoking and vaping status (outliers excluded) (Table 5). The distributions of mean PM2.5 are not statistically different between the smoke and vape-free group and the exclusive vaping group (z = 1.926, p = 0.0542) at a significance level of 0.05. Comparing exclusive vapers and the exclusive smoking households indicated that the mean PM2.5 is different between the two groups (z = 3.450, p = 0.0006). There is an 86% probability that a random PM2.5 measurement drawn from an exclusive smoking household is larger than one drawn from an exclusive vaping household. Conducting the same test between the smoke and vape-free group and the exclusive smoking group also revealed a statistical difference between the two (z = 7.190, p <0.0001), with a 92% probability that a random PM2.5 measurement drawn from an exclusive smoking household is larger than one drawn from a smoke and vape-free household.
The smoking and vaping group were compared against the exclusive vapers and then the smoke and vape-free households. Both tests indicated a difference between the mean PM2.5 of both groups, with a 94% probability that a random PM2.5 measurement drawn from a smoking and vaping household is larger than one drawn from an exclusive vaping household (z = 3.742, p = 0.0002), and a 97% probability that a random PM2.5 measurement drawn from a smoking and vaping household is larger than one drawn from a smoke and vape free household (z = 6.052, p <0.0001). Exclusive smokers were compared with the smoking and vaping group, and the distributions of mean PM2.5 are not statistically different between the two groups (z = 0.682, p = 0.4951).

4. Discussion

4.1. Principal Findings

To our knowledge, this is the first study to explore how concentrations of PM2.5 over a 12-month period are modified by household occupants smoking and/or vaping in the home in a large natural setting using sensors, building and behavioural characteristics. Our findings provide novel insights into the impact of exclusively smoking indoors (mean 14.07 µg/m3) when compared to smoking and vaping households (9.18 µg/m3), exclusive vaping households (2.00 µg/m3), smoke and vape-free households (1.28 µg/m3). We need to consider that there is a diverse range of other sources, biological, chemical and physical agents, that could influence levels of PM2.5 in the home. While we could not account for these factors, the differences in PM2.5 between smoking households and those who are smoke-free or exclusively vape were statistically significant, with a 92% probability that a random PM2.5 measurement drawn from an exclusive smoking household is larger than one drawn from a smoke and vape-free household.
The findings highlight the need to reduce exposure to environmental tobacco smoke and/or vaping. While vaping may provide an effective smoking cessation intervention in the UK [63], dual use may actually hinder complete cessation and increase toxicant exposure [64], with the current study showing no statistical difference in levels of PM2.5 between the households that allowed smoking. There needs to be a whole population approach to smoking cessation policies and further evidence to support the potential health effects resulting from vaping indoors and contributions to overall exposure to PM2.5 and other combustion by-products, particularly to children [65]. Policies need to consider the potential role of vaping across the life course (from younger to older age) and on its long-term effects.

4.2. Strengths and Limitations

A strength of this study is its duration, with monitoring data from the same 164 households for a full 12-month period, which presents actual annual mean PM2.5. The study in a natural indoor setting also provides an opportunity to explore concentrations of PM2.5 in a real-life comparison between smoking, vaping households and those that are smoke and vape-free. The study also used household questionnaires that were delivered face-to-face by trained volunteers and were based on questions that had been delivered from previous research, including large international studies [49]. This study also looked at the impact of external concentrations of PM2.5, distance from roads and different seasons.
Some limitations exist. This is a potential bias resulting from the household questionnaire, such as selection bias, self-reporting or those with a pre-existing respiratory condition being more aware of the health risks associated with indoor air quality [51]. Due to our secondary data analyses, we are also unable to conduct a follow-up questionnaire and account for how occupant behaviours could change over time. We also relied on self-reported smoking and/or vaping frequency, which were not validated by carbon monoxide or cotinine tests [66,67].
Whilst partner smoking was asked, several participants did not answer this question, which is important to consider because PM2.5 concentrations increase with the number of smokers and frequency of smoking [68,69]. While we included building characteristics and behaviours, we could not compare PM2.5 concentrations in different room sizes or ventilation rates and dilution of the aerosol due to dispersion [56]. Frequency of window opening and natural ventilation can be either detrimental or beneficial to air quality, depending on the location of the house, the emission source and the season [70]. Due to the secondary data analysis sample size, we were unable to account for other sources of PM2.5. Due to the sample size, we were unable to fully account for a range of covariates and differences between building and behavioural characteristics, which could modify mean concentrations. There were also household characteristics that could modify indoor concentrations (Table S3) and should be included in future research where a larger sample is possible. For example, this could include the use of incense, new furniture, household cleaning products, cleaning behaviours, heating and cooking. Whilst this is a limitation, the impact of these sources may not impact our study due to the length of the monitoring period. We were also unable to account for the use of an open fire/stove during colder months, which would impact the level of PM2.5 during winter months. Whilst we did not have access to this data, this housing sector has actively targeted homes with energy efficiency measures, which included draft proofing and sealing up open fires/stoves. This means that less than 4% of social housing homes have an open fire/stove [49], which would have a limited impact on our study findings. In summary, whilst there is noise from other potential sources, there is little reason to believe that variation in other sources would be systematic across the samples (non/smoking/vaping).
Whilst our secondary data analyses were limited by our sample size and ability to account for a range of covariates, we believe that our study over a 12-month period represents the largest monitoring of PM2.5 in a real-world and natural setting in the home environment. For example, Fernández et al. [31] observational study in the homes of one conventional cigarette smoker, one e-cigarette user, and two non-smokers (smoke-free homes) over 1 h period. Additionally, due to our study design and size of the study, it was not feasible to account for other factors such as the size of the houses, the rooms where smoking or vaping occurred, air exchange rate, the number of smokers, variations in smoking habits and topography and smoking duration for example. This requires a much larger sample size and represents a clear need for further research that considers the wider factors influencing PM (and other biological, chemical and physical agents) and health outcomes.
Due to the nature of the original study, we were limited to the fact that monitors were positioned within the main living space within each home and were not able to adjustment PM2.5 measurements with the property data (e.g., age and type) due to our sample size. We are also unable to conduct a follow-up questionnaire and account for how occupant behaviours could change over time. We relied on self-reported smoking and/or vaping frequency, which could change over time and were not validated by carbon monoxide or cotinine tests. Due to the scale of the resident questionnaire of the original study and the time impact on participants, we did not have data on e-cigarette type, power output, and frequency of use to include in our analyses. Whilst partner smoking was asked, several participants did not answer this question, which is important to consider because PM2.5 concentrations increase with the number of smokers and frequency of smoking. However, in our study, 66% of those who exclusively smoked in the home lived alone.

4.3. Strengths and Limitations in Relation to Existing Literature

Our study overcomes the limitations of other studies that have relied on smaller sample sizes, modelled or monitored daily or weekly sensor readings [71,72], which could miss PM2.5 spikes caused by smoking and/or vaping. This is important to consider because monitoring over a longer period of time in a natural indoor setting provides a truer reflection of indoor concentrations and potential personal exposure. Although there are exceptions [7,31], previous research has focused on laboratory studies using smoking chambers, simulated real-life scenarios, or extreme exposure situations, such as in vaping conventions. Additionally, our findings are consistent with those of Fernández et al. [31], whose short-term observational study found that indoor PM2.5 following vaping was similar to vape-free households, and 58 times less than those in smoking households.
The strength of our study was the ability to allow for monthly comparisons and monthly levels of PM2.5 fluctuate seasonally for all groups, with the highest recordings in winter and the lowest in summer. These seasonal changes in PM2.5 are consistent with others [57,73,74] and could be due to increased indoor space heating and reduced pollutant dispersion [70], particularly when burning fossil fuels [75]. Seasonal differences could also be influenced by transboundary air pollution originating outside the country [76], which can interact with and affect indoor levels. These findings indicate that ventilation could be increased in the winter to reduce the impact of environmental tobacco smoke on other household members, which needs to account for the ventilation requirements of different build types and resident behaviours [51]. Whilst PM2.5 concentrations were lower in exclusive vaping households compared to households that allowed smoking, there was no record of the e-cigarette type, or the frequency and number of e-cigarettes vaped, which is important to consider because prior studies have demonstrated that PM2.5 emissions vary between different e-cigarette generations [52,62], and different power outputs [22,77], and that exhaled PM2.5 may be dependent on the e-cigarette’s nicotine content [78,79,80]. Furthermore, the sensors used did not measure concentrations of ultrafine particulate matter, which could be higher in households where occupants use e-cigarettes [81,82].
Analyses of other occupant behaviours are consistent with other findings. We found that smokers living alone have elevated indoor concentrations of PM2.5, which may be due to increased consumption as a result of habit, boredom or stress [83], as well as interaction with lower socio-economic status. The presence of pets can increase indoor levels of PM [84], which we found in smoke-free households with pets, but not in houses that allowed smoking. This could be due to the existence of pet dander in the smoke-free homes and the resuspension of PM following cleaning, or that smokers with pets are outdoors more and do the majority of their smoking then. Additionally, smokers opened the front room window more frequently than all other groups, but those who did not have the highest PM2.5 concentrations. This is consistent with a study by Kaunelienė et al. [85] indicating that particles from smoking can take longer to disperse and be dependent on the ventilation in the room. Additionally, PM2.5 from environmental tobacco smoke can transfer to adjacent non-smoking apartments in multiunit residential buildings [86].
Consistent with our findings, prior studies have used mechanical vaping machines and shown that PM concentrations of environmental tobacco smoke are higher than e-cigarettes [87]. The PM2.5 emissions of the conventional cigarette were 281 µg/m3 after 1.5 min and 901 µg/m3 after 3 min, compared to the e-cigarette’s PM2.5 emissions of 3 µg/m3 and 43 µg/m3, respectively. Where it was not possible to understand how concentrations reduced over time using vaping machines, our study showed how concentrations vary over time and across different housing and behavioural characteristics. Concentration levels depend on the rate of use and the type and flavour of e-cigarette [88], and some were found to exceed the WHO PM2.5 annual guideline [89], which is in contrast to the findings of Pellegrino et al. [87]. Additionally, PM resulting from vaping is also shown to dissipate more quickly than environmental tobacco smoke, where resulting PM2.5 following smoking can be 7 times higher than from vaping [90].
There have been studies which have mimicked real-life settings in ventilated cafés [34,80]. PM concentrations varied between e-cigarettes with and without nicotine liquid, which we could not account for. However, the use of different e-cigarettes has been found to emit significantly less particulates than conventional smoking [79]. E-cigarette without nicotine emitted more particulates than those containing nicotine [80], which could be due to changes in optical properties or to coagulation /condensation or the semiliquid aerosols [79]. Similar findings have also been observed in other settings, such as an office room [52] and in a university and other office building models [22,62]. Differences in vaping and PM concentrations could result from the vaping of glycerine and/or propylene glycol [77]. In contrast to this study, we could demonstrate how PM concentrations varied over time.
Other observational studies in natural settings have shown significantly higher levels of PM2.5 in the smoker’s house compared to both the e-cigarette user’s and non-smokers’ houses [31] or in a hotel setting, which have been high when compared to cafes and bars where smoking is allowed [91]. Our findings differ because of the increased ventilation in public settings, which lowers PM2.5 concentrations (though depending on frequency of use) [16]. Other factors influencing PM concentrations include room size, ventilation [16], the power and type of e-cigarette used, and the presence of nicotine determines the particle size in the aerosol [22,52,62,77,79,80]. From a public health perspective, this is important to consider because some studies showed that the level of PM emitted from e-cigarettes was greater than that from conventional smoking [38]. There are other modifiable factors, including the time of measurement, frequency of use [16,77,88], extent of exposure in a variety of settings [37] and the fact that e-cigarette emissions result solely from the exhaled emissions [92].

4.4. Implications for Clinicians, Policy and Practice

This novel study explored the interaction between smoking and/or vaping indoors at home and the variation in PM. Public health policy and practice need to consider the findings and implications of this study in the context of current smoking cessation and use of e-cigarettes, and how they are perceived. Considering the limitations of this study (e.g., unable to quantify concentrations of ultrafine particles), the findings demonstrate that PM concentrations are lower in homes where occupants use e-cigarettes. The evidence reviewed here and findings suggest that use of e-cigarettes provides an opportunity for smoking cessation interventions, but do not eliminate exposure to PM or potential interaction with other biological and chemical agents not assessed in this study. In the UK, e-cigarettes are the most popular cessation aid [93] and recent evidence suggests they can be more effective than traditional nicotine replacement therapy [63,94], though there are concerns of an increase in youth uptake [17]. Further evidence is needed to better understand the health risks, as well as their safety and efficacy for public health smoking cessation interventions [20]. This is important to consider because whilst e-cigarettes may be perceived as less harmful than cigarettes, they may emit toxicants and PM2.5 that may have a range of health implications. Further studies could be improved by characterising chemicals emitted by different types of e-cigarettes, as well as the use of biological markers [31].
This needs to be considered in light of other international policy and practice, such as in the United States, where the use of e-cigarettes is viewed as equally harmful to environmental tobacco smoke due to a number of lung injuries amongst young people. This was a result of illicit e-liquid containing tetrahydrocannabinol, cannabinoid oils, and vitamin E acetate [95]. Our findings further contribute to the body of evidence and different views of public safety associated with vaping internationally [28]. For example, Australia opposed the use of e-cigarettes due to increasing evidence of the potential health risks, especially in young people [96]. Future policies need to consider the potential health risks of both smoking and/or vaping, which can lead to increased nicotine addiction [20]. Increasing knowledge about health risks, such as lung, heart, and brain damage, results from e-cigarettes containing cancer-causing agents, toxins, heavy metals, and very fine particles [20,97].
Policy and practice need to consider the interaction with outdoor concentrations of PM2.5, as it is readily transferred into the indoor environment [2]. This is important because of the interaction with combustible by-products and ambient PM2.5, which can negatively impact health [98], including mortality [99]. In this social housing population, there was evidence that interaction between indoor and outdoor PM concentrations exceeded the World Health Organisation guideline exposure limit and could pose a health risk [100,101]. For example, chronic low concentration exposure could have the same toxic effects as higher exposures over a shorter period [22]. These findings support the existing UK smoking cessation policies, as there were sustained levels of high PM2.5 exposure in households that allow smoking indoors. However, these need to support Smoke Free Homes initiatives, in conjunction with improved longitudinal data on vaping health and PM1 levels.

4.5. Unanswered Research Questions

Further research is needed in a natural setting with the use of different e-cigarettes, including concentrations of PM1 particles [81,82] and their health effects. This is significant as PM1 is able to penetrate directly through the lungs into the bloodstream, and be a contributor to arterial stiffness, endothelial dysfunction, and blood clotting, among other diseases [102]. This needs to be considered alongside the interaction between PM and other biological agents (e.g., house dust mites, mould and bacteria [51], and chemical agents such as volatile organic compounds, which increase from both environmental tobacco smoke [70] and the presence of indoor dampness [51,103]. Additionally, there is a reliance on chamber studies that may overestimate exposure [41], and consequently, there is a need for more natural experiments and studies of larger sample sizes and more complex longitudinal modelling to understand the impact of environmental tobacco smoke and/or vaping in indoor home environments. This needs to consider the timing and extent of personal exposures, as well as the use of other sources of PM, ventilation rates and more validated exposure and outcomes definitions. This requires more complex modelling to assess the relationship between smoking and/or vaping (including the chemical profiles/characteristics of both and their half-lives) in the home and resultant concentrations of combustion by-products [104,105], the interaction with other biological, chemical and physical exposures and health outcomes.

5. Conclusions

In this preliminary study, we conducted secondary data analyses on the particulate matter monitoring data collected from a large health and housing study on 164 homes over a period of 12 months. Despite some limitations that we were not able to address, we found that regardless of the time of year, smoking or the combination of smoking and vaping led to a seven-fold increase in annual indoor PM2.5, but there was no difference between PM2.5 levels of smoking households and households that smoke and vape. We were not able to determine the relationship between smoking and/or vaping indoors and particulate matter, which supports the need for studies of larger sample sizes and more complex longitudinal monitoring. This will help better understand the extent and timing of exposures resulting from both smoking and/or vaping indoors. This must consider factors modifying exposures to particulate matter, along with an interaction between other chemical and biological agents found indoors and a range of corresponding health effects. To be effective, this must be combined with objective exposure and outcome measures, along with the ability to account for a range of covariates such as variable heating and ventilation practices. This study will help inform work with social housing associations around developing smoke-free building policies, as well as smoking cessation messaging with families and peripartum smokers.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/pollutants5040034/s1, Table S1: Participant and household characteristics by indoor smoking and vaping status. Table S2: External sensors with complete values over study period (Dec 2018-Nov 2019). Table S3: Highest mean annual PM2.5 readings per household type. Figure S1: Indoor air monitoring overtime.

Author Contributions

G.D.W. completed the original study as part of a Master’s in Public Health thesis with support from R.A.S. and T.M., who helped with the conceptualisation and implementation of the project. All three authors significantly contributed to the conceptualisation and development of this manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

Menneer was funded (1) by the SenseWell project (“Using high temporal resolution sensor data to support independent living”), supported by the Engineering and Physical Sciences Research Council (EPSRC) and the National Institute for Health and Care Research (NIHR) [grant number EP/W031868/1], under the “Transforming care and health at home” programme, and (2) by the NIHR HealthTech Research Centre (HRC) in Brain Health (NIHR100523). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. The data were collected as part of the Smartline Project (05R16P00305), which received £3,740,920 and the Smartline Extension Project (05R18P02819), which received up to £3,307,703 of funding from the England European Regional Development Fund as part of the European Structural and Investment Funds Growth Programme 2014–2020, with additional funding of £25k from the Southwest Academic Health Science Network and £200k from Cornwall Council.

Data Availability Statement

The original contributions presented in this study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.

Acknowledgments

Sharpe and Menneer for their support from the conceptualisation through to the completion of the study, and support with this manuscript. Thank all of the Smartline team at the European Centre for Environment and Human Health and Caroline De Brún from the UK Health Security Agency for their help and support during this study. This study formed part of a Master’s in Public Health, and would also like to thank Ben Jane for supportive supervision.

Conflicts of Interest

The authors have no relevant financial or non-financial interests to disclose.

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Pollutants 05 00034 g001
Figure 1. Annual mean PM2.5 concentrations across households stratified by indoor smoking and vaping status, excluding outliers.
Figure 1. Annual mean PM2.5 concentrations across households stratified by indoor smoking and vaping status, excluding outliers.
Pollutants 05 00034 g001
Table 1. Participant characteristics (N = 164 households).
Table 1. Participant characteristics (N = 164 households).
VariablesStudy Participants
N%MeanRangeSD
Proportion Males5433
Proportion Females11067
Mean adult age (≥18 years)162 5722–9215
Smoking status
Never-smoker43/16426
Ex-smoker65/16440
Ex < 5 a day14/6521
Ex 5–15 a day18/6528
Ex > 15 a day31/6548
Ex (quantity not reported)2/653
Current smoker56/16434
No smoking or vaping indoors105/16464
Exclusive smoking indoors33/16420
Smoking and vaping indoors16/16410
Exclusive vaping indoors10/1646
Respiratory Health
Emphysema or chronic bronchitis15/1646
Emphysema or chronic bronchitis in never-smoker2/435
Emphysema or chronic bronchitis in ex-smoker7/6511
Emphysema or chronic bronchitis in current smoker6/5611
Adults with wheeze or dry cough in ≤12 months56/16434
Seen a doctor in ≤ 12 months for:
Asthma43/16426
Allergy32/16420
Education
Secondary education (11–16 years of age)112/16269
Secondary/further education (16–18 years of age)41/16225
Undergraduate university education8/1625
Postgraduate university education1/1621
Employment status
Employed31/16319
Actively looking for work5/1633
Retired64/16339
Long-term sick or disabled41/16325
Looking after home or family14/1639
Student/Training4/1632
Other4/1632
Table 2. Household characteristics (N = 164 households).
Table 2. Household characteristics (N = 164 households).
VariablesStudy Participants
N%MeanRangeSD
Index of Multiple Deprivation (IMD)
10% most deprived8351
10% to 20%1912
20% to 30%1811
30% to 40%4226
40% to 50%00
50% to 60%21
Presence of pets in the house
Any96/16459
Cat47/16429
Dog50/16430
Mean household occupancy and household occupancy164 1.921–61.1
1-single occupancy 7546
2-double occupancy 5131
3-triple occupancy 2012
4+ multiple occupancy1811
Property age
Pre 19307/1624
1930–195945/16228
1960–197951/16231
1980–199931/16219
2000+28/16217
Property type
Flats (own entrance or communal entrance)93/15560
Non-standard constructed house5/1553
Mid-terrace17/15511
End terrace 20/15513
Semi-detached20/15513
Table 3. Annual and seasonal PM2.5 measurements by indoor smoking and vaping status (N = 164 households).
Table 3. Annual and seasonal PM2.5 measurements by indoor smoking and vaping status (N = 164 households).
Monitoring Period *Study Participants
Neither Smoking nor Vaping Indoors
n = 105		Exclusively Smoke Indoors
n = 33		Exclusively Vape Indoors
n = 10		Smoke and Vape Indoors
n = 16
Mean/
Median		RangeSD Mean/
Median		RangeSD Mean/
Median		RangeSD Mean/
Median		RangeSD
Annual 1.28/1.040.07–
7.28		1.14 14.07/
9.67		0.48–
96.58		17.3 2.00/
1.35		0.70–
5.65		1.65 9.18/
6.17		1.79–
21.01		6.21
Winter (December 2018–February 2019) 1.42/1.070.07–9.571.37 17.49/12.390.39–
99.73		19.30 2.28/
1.70		0.72–
7.51		2.00 11.42/
8.16		1.43–
28.77		8.59
Spring (March 2019–May 2019) 1.37/1.050.06–8.041.17 14.87/
10.14		0.51–
94.71		18.04 2.07/
1.31		0.71–
7.20		1.97 10.18/
7.02		1.20–
27.63		8.30
Summer (June 2019–August 2019) 1.09/0.900.05–9.721.04 10.73/
5.78		0.65–
94.40		17.03 1.63/
1.05		0.63–
6.08		1.65 6.20/
5.09		1.27–
15.93		4.00
Autumn (September 2019–November 2019) 1.28/0.890.09–14.81.73 12.84/
8.06		0.36–
96.67		17.23 2.05/
1.27		0.49–
5.98		1.97 8.92/
7.67		1.99–
20.45		6.06
* Indoor PM measurements as average PM2.5 (µg/m3) from monitor in the front room.
Table 4. Indoor heating, time spent indoors, natural ventilation, vacuuming, present of pets, occupancy rates and distance from main road modifying PM2.5 measurements by indoor smoking and vaping status (N = 164 households) over a 12-month period.
Table 4. Indoor heating, time spent indoors, natural ventilation, vacuuming, present of pets, occupancy rates and distance from main road modifying PM2.5 measurements by indoor smoking and vaping status (N = 164 households) over a 12-month period.
VariablesStudy Participants
Neither Smoking nor Vaping Indoors
n = 105		Exclusively Smoke Indoors
n = 33		Exclusively Vape Indoors
n = 10		Smoke and Vape Indoors
n = 16
NMean/
Median		RangeSDNMean/
Median		RangeSDNMean/
Median		RangeSDNMean/
Median		RangeSD
Gas heating991.260.07–7.281.153114.490.48–96.5818.05101.960.70–
5.65		1.65169.181.79–
21.01		6.21
Electric heating61.560.59–3.581.0527.555.26–9.853.240N/A000N/A00
0–18 h spent indoors271.330.06–7.281.31712.010.48–
35.36		14.3330.990.69–
1.33		0.3215.14N/AN/A
>18 h spent indoors781.260.50–6.921.012614.620.81–96.5818.5672.421.38–4.811.95159.451.79–21.016.54
Opened front window weekly811.250.07–7.281.102713.051.03–35.369.5982.230.89–5.651.88158.731.79–21.016.37
Not opened window weekly241.380.51–6.921.31618.660.48–96.5838.3021.640.90–
2.38		1.05115.94N/AN/A
Vacuumed 0–10 times a month551.190.07–6.911.2119/3214.800.48–96.5822.1942.060.69–4.811.86610.372.73–19.196.92
Vacuumed 11–20 times a month181.250.60–2.890.583/
32		16.4110.64–35.2311.4321.100.99–1.220.1616.16N/AN/A
Vacuumed >21 times a month321.440.46–7.281.2710/3212.895.71–27.136.3142.370.89–5.652.2198.711.80–21.016.70
Any pet indoors591.320.46–
7.28		1.01199.710.81–
28.1		6.9272.320.69–
5.65		2.02
118.211.79–
21.01		6.39
No pets present461.230.06–
6.91		1.311419.980.48–
96.58		25.0731.230.99–
1.38		0.21511.302.73–
18.44		6.63
Single occupancy471.310.45–
7.28		1.362217.120.48–
96.58		20.2723.071.33–
4.82		2.4748.801.79–
19.19		7.60
Multiple occupancy581.250.07–
6.92		0.95117.970.81–
29.6		7.9781.730.69–
5.65		1.61129.301.96–
21.01		6.35
House is ventilated411.290.53–
6.92		1.05915.001.16–
29.58		10.7932.571.33–
4.82		1.95910.471.79–
21.01		7.26
No, house is not ventilated641.270.07–
7.28		1.212413.720.48–
96.58		19.7171.750.69–
5.65		1.7477.514.82–
19.19		5.18
<100 m from main road91.030.51–
3.58		0.98611.910.48–
35.36		12.4311.42N/AN/A119.19N/AN/A
>100–500 m from main road641.270.45–
6.92		1.111817.240.81–
96.58		21.761.800.70–
1.57		1.5168.954.82–
18.44		5.10
>500–1800 m from main road321.360.07–
7.28		1.2899.171.03–
31.50		9.2332.590.89–
5.65		2.6698.211.79–
21.01		6.87
N/A—Not applicable
Table 5. Mann-Whitney U significance tests of PM2.5 levels comparing households with different smoking and vaping status.
Table 5. Mann-Whitney U significance tests of PM2.5 levels comparing households with different smoking and vaping status.
HypothesisZpp Order **
HO: Exclusive vaping (10) = Smoke and vape free (105)1.9260.05420.685
HO: Exclusive smoking (33) = Exclusive vaping (10) 3.4500.0006 *0.864
HO: Smoking and vaping (16) = Exclusive vaping (10) 3.7420.0002 *0.944
HO: Exclusive smoking (33) = Smoke and vape free (105) 7.190<0.0001 *0.916
HO: Smoking and vaping (16) = Smoke and vape free (33)6.052<0.0001 *0.971
HO: Exclusive smoking (105) = Smoking and vaping (16)0.6820.49510.561
* Denotes significant difference, p < 0.05; ** Displays an estimate of the probability that a random measurement drawn from the first population is larger than one drawn from the second population.
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Walsh, G.D.; Menneer, T.; Sharpe, R.A. Preliminary Study Using Sensor Measurements in Selected Homes in Cornwall, England, over a One-Year Period Confirms Increased Indoor Exposure from Second-Hand Smoking but Not from Second-Hand Vaping. Pollutants 2025, 5, 34. https://doi.org/10.3390/pollutants5040034

AMA Style

Walsh GD, Menneer T, Sharpe RA. Preliminary Study Using Sensor Measurements in Selected Homes in Cornwall, England, over a One-Year Period Confirms Increased Indoor Exposure from Second-Hand Smoking but Not from Second-Hand Vaping. Pollutants. 2025; 5(4):34. https://doi.org/10.3390/pollutants5040034

Chicago/Turabian Style

Walsh, Gareth David, Tamaryn Menneer, and Richard Alan Sharpe. 2025. "Preliminary Study Using Sensor Measurements in Selected Homes in Cornwall, England, over a One-Year Period Confirms Increased Indoor Exposure from Second-Hand Smoking but Not from Second-Hand Vaping" Pollutants 5, no. 4: 34. https://doi.org/10.3390/pollutants5040034

APA Style

Walsh, G. D., Menneer, T., & Sharpe, R. A. (2025). Preliminary Study Using Sensor Measurements in Selected Homes in Cornwall, England, over a One-Year Period Confirms Increased Indoor Exposure from Second-Hand Smoking but Not from Second-Hand Vaping. Pollutants, 5(4), 34. https://doi.org/10.3390/pollutants5040034

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