Characterizing peak exposure of secondhand smoke using a real-time PM2.5 monitor

Although short-duration elevated exposures (peak exposures) to pollutants may trigger adverse acute effects, epidemiological studies to understand their influence on different health effects are hampered by lack of methods for objectively identifying peaks. Secondhand smoke from cigarettes (SHS) in the residential environment can lead to peak exposures. The aim of this study was to explore whether peaks in continuous PM_2.5 data can indicate SHS exposure. A total of 41 children (21 with and 20 without SHS exposure based on self-report) from 28 families in New York City (NY, USA) were recruited. Both personal and residential continuous PM_2.5 monitoring were performed for five consecutive days using MicroPEM sensors (RTI International, USA). A threshold detection method based on cumulative distribution function was developed to identify peaks. When children were home, the mean accumulated peak area (APA) for peak exposures was 297 ± 325 hour*µg/m³ for children from smoking families and six times that of the APA from non-smoking families (~50 ± 54 hour*µg/m³). Average PM_2.5 mass concentrations for SHS exposed and unexposed children were 24 ± 15 µg/m³ and 15 ± 9 µg/m³, respectively. The average SHS exposure duration represents ~5% of total exposure time, but ~13% of children's total PM_2.5 exposure dose, equivalent to an additional 2.6 µg/m³ per day. This study demonstrated the feasibility of peak analysis for quantifying SHS exposure. The developed method can be adopted more widely to support epidemiology studies on impacts of short-term exposures.     

Concurrent assessment of personal,indoor, and outdoor PM2.5 and PM1 levels and source contributions using novel low-cost sensing devices

The intensity, frequency, duration, and contribution of distinct PM_2.5 sources in Asian households have seldom been assessed; these are evaluated in this work with concurrent personal, indoor, and outdoor PM_2.5 and PM₁ monitoring using novel low-cost sensing (LCS) devices, AS-LUNG. GRIMM-comparable observations were acquired by the corrected AS-LUNG readings, with R² up to 0.998. Twenty-six non-smoking healthy adults were recruited in Taiwan in 2018 for 7-day personal, home indoor, and home outdoor PM monitoring. The results showed 5-min PM_2.5 and PM₁ exposures of 11.2 ± 10.9 and 10.5 ± 9.8 µg/m³, respectively. Cooking occurred most frequently; cooking with and without solid fuel contributed to high PM_2.5 increments of 76.5 and 183.8 µg/m³ (1 min), respectively. Incense burning had the highest mean PM_2.5 indoor/outdoor (1.44 ± 1.44) ratios at home and on average the highest 5-min PM_2.5 increments (15.0 µg/m³) to indoor levels, among all single sources. Certain events accounted for 14.0%-39.6% of subjects’ daily exposures. With the high resolution of AS-LUNG data and detailed time-activity diaries, the impacts of sources and ventilations were assessed in detail.     

Biological and environmental exposure monitoring of volatile organic compounds among nail technicians in the Greater Boston area

Nail technicians are exposed to volatile organic compounds (VOCs) from nail products, but no studies have previously measured VOC biomarkers for these workers. This study of 10 nail technicians aimed to identify VOCs in nail salons and explore relationships between air concentrations and biomarkers. Personal and area air samples were collected using thermal desorption tubes during a work shift and analyzed using gas chromatography/mass spectrometry (GC/MS) for 71 VOCs. Whole blood samples were collected pre‐shift and post‐shift, and analyzed using GC/MS for 43 VOCs. Ventilation rates were determined using continuous CO₂ measurements. Predominant air VOC levels were ethyl methacrylate (median 240 µg/m³), methyl methacrylate (median 205 µg/m³), toluene (median 100 µg/m³), and ethyl acetate (median 639 µg/m³). Blood levels were significantly higher post‐shift than pre‐shift for toluene (median pre‐shift 0.158 µg/L and post‐shift 0.360 µg/L) and ethyl acetate (median pre‐shift <0.158 µg/L and post‐shift 0.510 µg/L); methacrylates were not measured in blood because of their instability. Based on VOCs measured in these seven nail salons, we estimated that emissions from Greater Boston area nail salons may contribute to ambient VOCs. Ventilation rates did not always meet the ASHRAE guideline for nail salons. There is a need for changes in nail product formulation and better ventilation to reduce VOC occupational exposures.     

Size distribution and lung-deposited doses of particulate matter from household exposure to biomass smoke

Exposure to high concentrations of particulate matter (PM) is associated with a number of adverse health effects. However, it is unclear which aspects of PM are most hazardous, and a better understanding of particle sizes and personal exposure is needed. We characterized particle size distribution (PSD) from biomass-related pollution and assessed total and regional lung-deposited doses using multiple-path deposition modeling. Gravimetric measurements of kitchen and personal PM_2.5 (<2.5 µm in size) exposures were collected in 180 households in rural Puno, Peru. Direct-reading measurements of number concentrations were collected in a subset of 20 kitchens for particles 0.3-25 µm, and the continuous PSD was derived using a nonlinear least-squares method. Mean daily PM_2.5 kitchen concentration and personal exposure was 1205 ± 942 µg/m³ and 115 ± 167 µg/m³, respectively, and the mean mass concentration consisted of a primary accumulation mode at 0.21 µm and a secondary coarse mode at 3.17 µm. Mean daily lung-deposited surface area (LDSA) and LDSA during cooking were 1009.6 ± 1469.8 µm²/cm³ and 10,552.5 ± 8261.6 µm²/cm³, respectively. This study presents unique data regarding lung deposition of biomass smoke that could serve as a reference for future studies and provides a novel, more biologically relevant metric for exposure-response analysis compared to traditional size-based metrics.     

Real-time measurements of PM2.5 and ozone to assess the effectiveness of residential indoor air filtration in Shanghai homes

Portable air cleaners are increasingly used in polluted areas in an attempt to reduce human exposure; however, there has been limited work characterizing their effectiveness at reducing exposure. With this in mind, we recruited forty-three children with asthma from suburban Shanghai and deployed air cleaners (with HEPA and activated carbon filters) in their bedrooms. During both 2-week filtration and non-filtration periods, low-cost PM_2.5 and O₃ air monitors were used to measure pollutants indoors, outdoors, and for personal exposure. Indoor PM_2.5 concentrations were reduced substantially with the use of air cleaners, from 34 ± 17 to 10 ± 8 µg/m³, with roughly 80% of indoor PM_2.5 estimated to come from outdoor sources. Personal exposure to PM_2.5 was reduced from 40 ± 17 to 25 ± 14 µg/m³. The more modest reductions in personal exposure and high contribution of outdoor PM_2.5 to indoor concentrations highlight the need to reduce outdoor PM_2.5 and/or to clean indoor air in multiple locations. Indoor O₃ concentrations were generally low (mean = 8±4 ppb), and no significant difference was seen by filtration status. The concentrations of pollutants and the air cleaner effectiveness were highly variable over time and across homes, highlighting the usefulness of real-time air monitors for understanding individual exposure reduction strategies.     

Controlled experiments measuring personal exposure to PM2.5 in close proximity to cigarette smoking

Few measurements of exposure to secondhand smoke (SHS) in close proximity to a smoker are available. Recent health studies have demonstrated an association between acute (<2 h) exposures to high concentrations of SHS and increased risk of cardiovascular and respiratory disease. We performed 15 experiments inside naturally ventilated homes and 16 in outdoor locations, each with 2–4 non‐smokers sitting near a cigarette smoker. The smoker's and non‐smokers' real‐time exposures to PM_2.5 from SHS were measured by using TSI SidePak monitors to sample their breathing zones. In 87% of the residential indoor experiments, the smoker received the highest average exposure to SHS, with PM_2.5 concentrations ranging from 50–630 μg/m³. During the active smoking period, individual non‐smokers sitting within approximately 1 m of a smoker had average SHS exposures ranging from negligible up to >160 μg/m³ of PM_2.5. The average incremental exposure of the non‐smokers was higher indoors (42 μg/m³, n = 35) than outdoors (29 μg/m³, n = 47), but the overall indoor and outdoor frequency distributions were similar. The 10‐s PM_2.5 averages during the smoking periods showed great variability, with multiple high concentrations of short duration (microplumes) both indoors and outdoors.     

Quantitative filter forensics: Size distribution and particulate matter concentrations in residential buildings

We applied filter forensics, the analysis of dust from the heating, ventilation, and air-conditioning (HVAC) filters, to measure particle size distribution in 21 residences in Toronto, Canada over a year. Four filters with different nominal efficiencies (Minimum Efficiency Reporting Value (MERV) 8–14 from ASHRAE Standard 52.2) were deployed in each residence each for three months, while the effective filtration volumes (the product of flow rate, runtime, and in-situ filter efficiency) were characterized over each filter lifetime. Using extraction and laser diffraction, we found that approximately 90% of the volumetric distributions were >10 µm and the volume median diameter (VMD) ranged from 23.4 to 75.1 µm. Using quantitative filter forensics (QFF), total suspended particle (TSP) concentrations ranged from 2.9 to 823.7 µg/m³ (median = 89.8 µg/m³) with a moderate correlation with the content of TSP on the filters (in terms of g) and with the TSP effective filtration volume (m³) indicating the importance of both filter forensics and HVAC metadata parameters to QFF concentration estimates. There was no strong correlation between PM₁₀ or PM_2.5 concentrations and hourly airborne particle number concentrations measured by low-cost sensors suggesting an evaluation of QFF is warranted, particularly for the exploration of smaller particles.     

Household and behavioral determinants of indoor PM2.5 in a rural solid fuel burning Native American community

Indoor and outdoor concentrations of PM_2.5 were measured for 24 h during heating and non-heating seasons in a rural solid fuel burning Native American community. Household building characteristics were collected during the initial home sampling visit using technician walkthrough questionnaires, and behavioral factors were collected through questionnaires by interviewers. To identify seasonal behavioral factors and household characteristics associated with indoor PM_2.5, data were analyzed separately by heating and non-heating seasons using multivariable regression. Concentrations of PM_2.5 were significantly higher during the heating season (indoor: 36.2 μg/m³; outdoor: 22.1 μg/m³) compared with the non-heating season (indoor: 14.6 μg/m³; outdoor: 9.3 μg/m³). Heating season indoor PM_2.5 was strongly associated with heating fuel type, housing type, indoor pests, use of a climate control unit, number of interior doors, and indoor relative humidity. During the non-heating season, different behavioral and household characteristics were associated with indoor PM_2.5 concentrations (indoor smoking and/or burning incense, opening doors and windows, area of surrounding environment, building size and height, and outdoor PM_2.5). Homes heated with coal and/or wood, or a combination of coal and/or wood with electricity and/or natural gas had elevated indoor PM_2.5 concentrations that exceeded both the EPA ambient standard (35 μg/m³) and the WHO guideline (25 μg/m³).     

Response of consumer and research grade indoor air quality monitors to residential sources of fine particles

The ability to inexpensively monitor PM_2.5 to identify sources and enable controls would advance residential indoor air quality (IAQ) management. Consumer IAQ monitors incorporating low‐cost optical particle sensors and connections with smart home platforms could provide this service if they reliably detect PM_2.5 in homes. In this study, particles from typical residential sources were generated in a 120 m³ laboratory and time‐concentration profiles were measured with 7 consumer monitors (2‐3 units each), 2 research monitors (Thermo pDR‐1500, MetOne BT‐645), a Grimm Mini Wide‐Range Aerosol Spectrometer (GRM), and a Tapered Element Oscillating Microbalance with Filter Dynamic Measurement System (FDMS), a Federal Equivalent Method for PM_2.5. Sources included recreational combustion (candles, cigarettes, incense), cooking activities, an unfiltered ultrasonic humidifier, and dust. FDMS measurements, filter samples, and known densities were used to adjust the GRM to obtain time‐resolved mass concentrations. Data from the research monitors and 4 of the consumer monitors—AirBeam, AirVisual, Foobot, Purple Air—were time correlated and within a factor of 2 of the estimated mass concentrations for most sources. All 7 of the consumer and both research monitors substantially under‐reported or missed events for which the emitted mass was comprised of particles smaller than 0.3 μm diameter.     

Comparison of next-generation portable pollution monitors to measure exposure to PM2.5 from household air pollution in Puno,Peru

Assessment of personal exposure to PM_2.5 is critical for understanding intervention effectiveness and exposure-response relationships in household air pollution studies. In this pilot study, we compared PM_2.5 concentrations obtained from two next-generation personal exposure monitors (the Enhanced Children MicroPEM or ECM; and the Ultrasonic Personal Air Sampler or UPAS) to those obtained with a traditional Triplex Cyclone and SKC Air Pump (a gravimetric cyclone/pump sampler). We co-located cyclone/pumps with an ECM and UPAS to obtain 24-hour kitchen concentrations and personal exposure measurements. We measured Spearmen correlations and evaluated agreement using the Bland-Altman method. We obtained 215 filters from 72 ECM and 71 UPAS co-locations. Overall, the ECM and the UPAS had similar correlation (ECM ρ = 0.91 vs UPAS ρ = 0.88) and agreement (ECM mean difference of 121.7 µg/m³ vs UPAS mean difference of 93.9 µg/m³) with overlapping confidence intervals when compared against the cyclone/pump. When adjusted for the limit of detection, agreement between the devices and the cyclone/pump was also similar for all samples (ECM mean difference of 68.8 µg/m³ vs UPAS mean difference of 65.4 µg/m³) and personal exposure samples (ECM mean difference of −3.8 µg/m³ vs UPAS mean difference of −12.9 µg/m³). Both the ECM and UPAS produced comparable measurements when compared against a cyclone/pump setup.     

Modeling approaches and performance for estimating personal exposure to household air pollution: A case study in Kenya

This study assessed the performance of modeling approaches to estimate personal exposure in Kenyan homes where cooking fuel combustion contributes substantially to household air pollution (HAP). We measured emissions (PM_2.5, black carbon, CO); household air pollution (PM_2.5, CO); personal exposure (PM_2.5, CO); stove use; and behavioral, socioeconomic, and household environmental characteristics (eg, ventilation and kitchen volume). We then applied various modeling approaches: a single-zone model; indirect exposure models, which combine person-location and area-level measurements; and predictive statistical models, including standard linear regression and ensemble machine learning approaches based on a set of predictors such as fuel type, room volume, and others. The single-zone model was reasonably well-correlated with measured kitchen concentrations of PM_2.5 (R² = 0.45) and CO (R² = 0.45), but lacked precision. The best performing regression model used a combination of survey-based data and physical measurements (R² = 0.76) and a root mean-squared error of 85 µg/m³, and the survey-only-based regression model was able to predict PM2.5 exposures with an R² of 0.51. Of the machine learning algorithms evaluated, extreme gradient boosting performed best, with an R² of 0.57 and RMSE of 98 µg/m³.     

Personal exposure to PM2.5 in Chinese rural households in the Yangtze River Delta

High levels of PM_2.5 exposure and associated health risks are of great concern in rural China. For this study, we used portable PM_2.5 monitors for monitoring concentrations online, recorded personal time‐activity patterns, and analyzed the contribution from different microenvironments in rural areas of the Yangtze River Delta, China. The daily exposure levels of rural participants were 66 μg/m³ (SD 40) in winter and 65 μg/m³ (SD 16) in summer. Indoor exposure levels were usually higher than outdoor levels. The exposure levels during cooking in rural kitchens were 140 μg/m³ (SD 116) in winter and 121 μg/m³ (SD 70) in summer, the highest in all microenvironments. Winter and summer values were 252 μg/m³ (SD 103) and 204 μg/m³ (SD 105), respectively, for rural people using biomass for fuel, much higher than those for rural people using LPG and electricity. By combining PM_2.5concentrations and time spent in different microenvironments, we found that 92% (winter) and 85% (summer) of personal exposure to PM_2.5in rural areas was attributable to indoor microenvironments, of which kitchens accounted for 24% and 27%, respectively. Consequently, more effective policies and measures are needed to replace biomass fuel with LPG or electricity, which would benefit the health of the rural population in China.     

A recurrent neural network using historical data to predict time series indoor PM2.5 concentrations for residential buildings

Due to the severe outdoor PM2.5 pollution in China, many people have installed air-cleaning systems in homes. To make the systems run automatically and intelligently, we developed a recurrent neural network (RNN) that uses historical data to predict the future indoor PM2.5 concentration. The RNN architecture includes an autoencoder and a recurrent part. We used data measured in an apartment over the course of an entire year to train and test the RNN. The data include indoor/outdoor PM2.5 concentration, environmental parameters and time of day. By comparing three different input strategies, we found that a strategy employing historical PM2.5 and time of day as inputs performed best. With this strategy, the model can be applied to predict the relatively stable trend of indoor PM2.5 concentration in advance. When the input length is 2 h and the prediction horizon is 30 min, the median prediction error is 8.3 µg/m³ for the whole test set. For times with indoor PM2.5 concentrations between (20,50] µg/m³ and (50,100] µg/m³, the median prediction error is 8.3 and 9.2 µg/m³, respectively. The low prediction error between the ground-truth and predicted values shows that the RNN can predict indoor PM2.5 concentrations with satisfactory performance.     

A new exposure route to trace elements in indoor particulate matter

Concentrations and emission rates of sixteen trace elements in emitted PM during heating soybean oil using three types of pans, including Teflon, granitium, and cast-iron, were investigated. Statistically significant decreases in Mn and Co emission rates were observed when the oil was heated in the cast-iron pan compared to Teflon and granitium pans. Among the released trace elements, Ni, Ba, Zn, and Cr had more contribution to the emission rate. The concentrations of Fe in the emitted PM₁ were found to be higher when cast-iron pan (8.49 ± 3.35 µg/m³) was utilized compared to Teflon (8.05 ± 2.27 µg/m³) and granitium (7.45 ± 1.38 µg/m³). However, these increases were statistically insignificant. The results of our study support the hypothesis that the trace elements translocate from cooking pans into the heated oil and subsequently to the particulate phase. This translocation creates a new inhalation exposure route to trace elements in indoor environments.     

Effectiveness of portable HEPA air cleaners on reducing indoor endotoxin,PM10, and coarse particulate matter in an agricultural cohort of children with asthma: A randomized intervention trial

We conducted a randomized trial of portable HEPA air cleaners in the homes of children age 6–12 years with asthma in the Yakima Valley, Washington. All families received asthma education while intervention families also received two HEPA cleaners (child's bedroom, living room). We collected 14-day integrated samples of endotoxin in settled dust and PM₁₀ and PM_10-2.5 in the air of the children's bedrooms at baseline and one-year follow-up, and used linear regression to compare follow-up levels, adjusting for baseline. Seventy-one families (36 HEPA, 35 control) completed the study. Baseline geometric mean (GSD) endotoxin loadings were 1565 (6.3) EU/m² and 2110 (4.9) EU/m², respectively, in HEPA vs. control homes while PM₁₀ and PM_10-2.5 were 22.5 (1.9) μg/m³ and 9.5 (2.9) μg/m³, respectively, in HEPA homes, and 19.8 (1.8) μg/m³ and 7.7 (2.0) μg/m³, respectively, in control homes. At follow-up, HEPA families had 46% lower (95% CI, 31%–57%) PM₁₀ on average than control families, consistent with prior studies. In the best-fit heterogeneous slopes model, HEPA families had 49% (95% CI, 6%–110%) and 89% lower (95% CI, 28%–177%) PM_10-2.5 at follow-up, respectively, at 50th and 75th percentile baseline concentrations. Endotoxin loadings did not differ significantly at follow-up (4% lower, HEPA homes; 95% CI, −87% to 50%).     

Exposure to fine,ultrafine particles and black carbon in two preschools in nur-sultan city of kazakhstan

Children in preschools were studied as an exceptionally vulnerable group to lung diseases due to their immature immune system. Few data are available in the literature addressing the exposure of children in preschools to ultrafine (>10 nm) particles. Exposure of children to fine, ultrafine (10 nm–1 µm) particles and black carbon particles present inside and near two preschools in Nur-Sultan, Kazakhstan, during Fall 2019 was investigated. For Preschool I, the average daily (6 h) indoor (outdoor) PM₁, PM_2.5, and PM₁₀ concentrations over three-week measurements were 15.0 (SD 12.5) µg/m³, 34.6 (SD 35.1) µg/m³, and 47.2 (SD 45.2) µg/m³, respectively. Average indoor UFP concentrations (>10.0 nm) including candle burning events were 5.20 × 10³ (SD 8.80 × 10³) particles/cm³, with the background UFP concentration to be 3.30 × 10³ (SD 1.80 × 10³) particles/cm³. In Preschool II, the average UFP concentration (>30.0 nm) in the morning and afternoon was 3.94 × 10³ (SD 5.34 × 10²) and 3.36 × 10³ (SD 1.90 × 10³) particles/cm³, respectively. Indoor black carbon (BC) concentrations were correlated with the outdoor smoking activity. The major sources of the indoor particles in the preschools were dust resuspension, candle burning, and infiltrated outdoor particles.     

Exposure to fine particulate matter in ten night clubs in Athens Greece: Studying the effect of ventilation, cigarette smoking and resuspension

The aim of the present work is to study the occupants' exposure to fine particulate concentrations in ten nightclubs (NCs) in Athens, Greece. Measurements of PM₁ and PM_2.5 were made in the outdoor and indoor environment of each NC. The average indoor PM₁ and PM_2.5 concentrations were found to be 181.77 μg m^− 3 and 454.08 μg m^− 3 respectively, while the corresponding outdoor values were 11.04 μg m^− 3 and 32.19 μg m^− 3. Ventilation and resuspension rates were estimated through consecutive numerical experiments with an indoor air quality model and were found to be remarkably lower than the minimum values recommended by national standards. The relative effects of the ventilation and smoking on the occupants' exposures were examined using multiple regression techniques. It was found that given the low ventilation rates, the effect of smoking as well as the occupancy is of the highest importance. Numerical evaluations showed that if the ventilation rates were at the minimum values set by national standards, then the indoor exposures would be reduced at the 70% of the present exposure values.     

Impact of fugitive emissions in ambient PM levels and composition: A case study in Southeast Spain

The results of this study show the high impact that anthropogenic fugitive emissions of mineral dust have on air quality (levels of PM₁₀, PM_2.5 and some metals) in a region in SE Spain named L'Alacantí. This could be extensive to other areas of Europe with similar characteristics. Fugitive emissions, such as those arising from large public construction works, cement and ceramic manufacturing, mining, heavy industries, handling and transport of powdered raw materials and road dust, are very often left out of emission monitoring and inspections in Europe. The comparative study of daily PM₁₀ series in the area shows how the increase of annual average PM₁₀ concentrations over 40 μg/m³ is due to extreme episodes occurring in 2006 and 2007, at a regional scale, given the simultaneous recording of PM episodes at distant monitoring sites. The annual average values of the PM₁₀ concentrations were close to or slightly higher than 40 μg/m³ (limit value of Directive 2008/50/CE) during 2006–2007 (Alicante-University 39–41, Agost 40–42, Sant Vicent 42–46, Alicante-El Plà 40–42 μg/m³). The main PM₁₀ sources in the zone were identified with the assistance of the PMF receptor model. Six common factors were determined, mineral as a main source (37% at Agost and 32% at Sant Vicent), road traffic, secondary sulfate, petroleum coke, sea spray and industry. Mineralogical studies, with XRD and SEM-EDX techniques, support the hypothesis that the highest PM episodes are associated to fugitive emissions of mineral matter. Despite the fact that L'Alacantí region is a heavily industrialized area with two cement plants and a significant number of ceramic manufacturing plants, the fugitive emissions may have accounted for the exceedances of the PM limit values during these two years, part of them caused by the construction of a highway. These results may contribute to the interpretation of prior studies on source apportionment carried out in Southern Europe, with very high loads of anthropogenic dust in PM₁₀ and PM_2.5.     

Fine and ultrafine particle exposures on 73 trips by car to 65 non‐smoking restaurants in the San Francisco Bay Area

A number of studies indicate cooking is a major source of exposure to particulate matter, but few studies have measured indoor air pollution in restaurants, where cooking predominates. We made 73 visits by car to 65 different non‐smoking restaurants in 10 Northern California towns while carrying portable continuous monitors that unobtrusively measured ultrafine (down to 10 nm) and fine (PM_2.5) particles to characterize indoor restaurant exposures, comparing them with exposures in the car. The mean ultrafine number concentrations in the restaurants on dinner visits averaging 1.4 h was 71 600 particles/cm³, or 4.3 times the mean concentration on car trips, and 12.3 times the mean background concentration in the residence. Restaurants that cooked dinner in the same room as the patrons had higher ultrafine concentrations than restaurants with separate kitchens. Restaurant PM_2.5 mass concentrations averaged 36.3 μg/m³, ranging from 1.5 to 454 μg/m³, but were relatively low on most visits: 43% of the indoor means were below 10 μg/m³ and 66% were below 20 μg/m³, with 5.5% above 100 μg/m³. Exposure to fine and ultrafine particles when visiting a restaurant exceeded the exposure a person received while traveling by car to and from the restaurant.     

Seasonal variability in environmental tobacco smoke exposure in public housing developments

The risk of tobacco smoking and second‐hand smoke (SHS) exposure combined are the leading contributors to disease burden in high‐income countries. Recent studies and policies are focusing on reducing exposure to SHS in multiunit housing (MUH), especially public housing. We examined seasonal patterns of SHS levels within indoor common areas located on Boston Housing Authority (BHA) properties. We measured weekly integrated and continuous fine particulate matter (PM_2.5) and passive airborne nicotine in six buildings of varying building and occupant characteristics in summer 2012 and winter 2013. The average weekly indoor PM_2.5 concentration across all six developments was 9.2 μg/m³, higher during winter monitoring period (10.3 μg/m³) compared with summer (8.0 μg/m³). Airborne nicotine concentrations ranged from no detection to about 5000 ng/m³ (mean 311 ng/m³). Nicotine levels were significantly higher in the winter compared with summer (620 vs. 85 ng/m³; 95% CI: 72–998). Smoking‐related exposures within Boston public housing vary by season, building types, and resident smoking policy. Our results represent exposure disparities that may contribute to health disparities in low‐income communities and highlight the potential importance of efforts to mitigate SHS exposures during winter when outdoor–indoor exchange rates are low and smokers may tend to stay indoors. Our findings support the use of smoke‐free policy as an effective tool to eliminate SHS exposure and protect non‐smokers, especially residents of MUH.