Discovery of associative patterns between workplace sound level and physiological wellbeing using wearable devices and empirical Bayes modeling.

We conducted a field study using multiple wearable devices on 231 federal office workers to assess the impact of the indoor environment on individual wellbeing. Past research has established that the workplace environment is closely tied to an individual's wellbeing. Since sound is the most-reported environmental factor causing stress and discomfort, we focus on quantifying its association with physiological wellbeing. Physiological wellbeing is represented as a latent variable in an empirical Bayes model with heart rate variability measures-SDNN and normalized-HF as the observed outcomes and with exogenous factors including sound level as inputs. We find that an individual's physiological wellbeing is optimal when sound level in the workplace is at 50 dBA. At lower (<50dBA) and higher (>50dBA) amplitude ranges, a 10 dBA increase in sound level is related to a 5.4% increase and 1.9% decrease in physiological wellbeing respectively. Age, body-mass-index, high blood pressure, anxiety, and computer use intensive work are person-level factors contributing to heterogeneity in the sound-wellbeing association.

Local- and regional-scale racial and ethnic disparities in air pollution determined by long-term mobile monitoring.

Disparity in air pollution exposure arises from variation at multiple spatial scales: along urban-to-rural gradients, between individual cities within a metropolitan region, within individual neighborhoods, and between city blocks. Here, we improve on existing capabilities to systematically compare urban variation at several scales, from hyperlocal (<100 m) to regional (>10 km), and to assess consequences for outdoor air pollution experienced by residents of different races and ethnicities, by creating a set of uniquely extensive and high-resolution observations of spatially variable pollutants: NO, NO2, black carbon (BC), and ultrafine particles (UFP). We conducted full-coverage monitoring of a wide sample of urban and suburban neighborhoods (93 km2 and 450,000 residents) in four counties of the San Francisco Bay Area using Google Street View cars equipped with the Aclima mobile platform. Comparing scales of variation across the sampled population, greater differences arise from localized pollution gradients for BC and NO (pollutants dominated by primary sources) and from regional gradients for UFP and NO2 (pollutants dominated by secondary contributions). Median concentrations of UFP, NO, and NO2 are, for Hispanic and Black populations, 8 to 30% higher than the population average; for White populations, average exposures to these pollutants are 9 to 14% lower than the population average. Systematic racial/ethnic disparities are influenced by regional concentration gradients due to sharp contrasts in demographic composition among cities and urban districts, while within-group extremes arise from local peaks. Our results illustrate how detailed and extensive fine-scale pollution observations can add new insights about differences and disparities in air pollution exposures at the population scale.

Comparison of Mobile and Fixed-Site Black Carbon Measurements for High-Resolution Urban Pollution Mapping.

Urban concentrations of black carbon (BC) and other primary pollutants vary on small spatial scales (<100m). Mobile air pollution measurements can provide information on fine-scale spatial variation, thereby informing exposure assessment and mitigation efforts. However, the temporal sparsity of these measurements presents a challenge for estimating representative long-term concentrations. We evaluate the capabilities of mobile monitoring in the represention of time-stable spatial patterns by comparing against a large set of continuous fixed-site measurements from a sampling campaign in West Oakland, California. Custom-built, low-cost aerosol black carbon detectors (ABCDs) provided 100 days of continuous measurements at 97 near-road and 3 background fixed sites during summer 2017; two concurrently operated mobile laboratories collected over 300 h of in-motion measurements using a photoacoustic extinctiometer. The spatial coverage from mobile monitoring reveals patterns missed by the fixed-site network. Time-integrated measurements from mobile lab visits to fixed-site monitors reveal modest correlation (spatial R2 = 0.51) with medians of full daytime fixed-site measurements. Aggregation of mobile monitoring data in space and time can mitigate high levels of uncertainty associated with measurements at precise locations or points in time. However, concentrations estimated by mobile monitoring show a loss of spatial fidelity at spatial aggregations greater than 100 m.

Mobile-Platform Measurement of Air Pollutant Concentrations in California: Performance Assessment, Statistical Methods for Evaluating Spatial Variations, and Spatial Representativeness.

Mobile platform measurements provide new opportunities for characterizing spatial variations of air pollution within urban areas, identifying emission sources, and enhancing knowledge of atmospheric processes. The Aclima, Inc. mobile measurement and data acquisition platform was used to equip four Google Street View cars with research-grade instruments, two of which were available for the duration of this study. On-road measurements of air quality were made during a series of sampling campaigns between May 2016 and September 2017 at high (i.e., 1-second [s]) temporal and spatial resolution at several California locations: Los Angeles, San Francisco, and the northern San Joaquin Valley (including non-urban roads and the cities of Tracy, Stockton, Manteca, Merced, Modesto, and Turlock). The results demonstrate that the approach is effective for quantifying spatial variations of air pollutant concentrations over measurement periods as short as two weeks. Measurement accuracy and precision are evaluated using results of weekly performance checks and periodic audits conducted through the sampler inlets, which show that research instruments located within stationary vehicles are capable of reliably measuring nitric oxide (NO), nitrogen dioxide (NO2), ozone (O3), methane (CH4) black carbon (BC), and particle number (PN) concentration with bias and precision ranging from <10 % for gases to <25 % for BC and PN at 1-s time resolution. The quality of the mobile measurements in the ambient environment is examined by comparisons with data from an adjacent (< 9 m) stationary regulatory air quality monitoring site and by paired collocated vehicle comparisons, both stationary and driving. The mobile measurements indicate that U.S. EPA classifications of two Los Angeles stationary regulatory monitors' scales of representation are appropriate. Paired time-synchronous mobile measurements are used to characterize the spatial scales of concentration variations when vehicles were separated by <1 to 10 kilometers (km). A data analysis approach is developed to characterize spatial variations while limiting the confounding influence of diurnal variability. The approach is illustrated using data from San Francisco, revealing 1-km scale differences in mean NO2 and O3 concentrations up to 117 % and 46 %, respectively, of mean values during a two-week sampling period. In San Francisco and Los Angeles, spatial variations up to factors of 6 to 8 occur at sampling scales of 100 - 300m, corresponding to 1-minute averages.

Uncertainty in collocated mobile measurements of air quality.

Mobile mapping of air pollution has the potential to provide pollutant concentration data at unprecedented spatial scales. Characterizing instrument performance in the mobile context is challenging, but necessary to analyze and interpret the resulting data. We used robust statistical methods to assess mobile platform performance using data collected with the Aclima Inc. mobile air pollution measurement and data acquisition platform installed on three Google Street View cars. They were driven throughout the greater Denver metropolitan area between July 25, 2014 and August 14, 2014, measuring ozone (O3), nitrogen dioxide (NO2), nitric oxide (NO), black carbon (BC), and size-resolve particle number counts (PN) between 0.3 µm and 5.0 µm diameter. August 6, 2014 was dedicated to parked and moving collocations among the three cars, allowing an assessment of measurement precision and bias. We used the median absolute deviation (MAD) to estimate instrument precision from outdoor, parked collocations. Bias was assessed by measurements obtained from parked cars using the standard deviation of median values over a collocated measurement period, as well as by Passing-Bablok regression statistics while the cars were moving and collocated. For the moving collocation periods, we compared the distribution of 1-σ standard deviations among the 3 cars to the estimated distribution assuming only measurement uncertainty (precision and bias). The distribution of mobile measurements agreed well with the theoretical uncertainty distribution at the lower end of the distribution for O3, NO2, and PN. We assert that the difference between the actual and theoretical distributions is due to real spatial variability between pollutants. The agreement between the parked car estimates of uncertainty and that measured during the mobile collocations (at the lower quantiles) provides evidence that on-road collocation while parked could be sufficient for estimating measurement uncertainties of a mobile platform, even when extended to the moving environment.

Mapping Air Pollution with Google Street View Cars: Efficient Approaches with Mobile Monitoring and Land Use Regression.

Air pollution measurements collected through systematic mobile monitoring campaigns can provide outdoor concentration data at high spatial resolution. We explore approaches to minimize data requirements for mapping a city's air quality using mobile monitors with "data-only" versus predictive modeling approaches. We equipped two Google Street View cars with 1-Hz instruments to collect nitric oxide (NO) and black carbon (BC) measurements in Oakland, CA. We explore two strategies for efficiently mapping spatial air quality patterns through Monte Carlo analyses. First, we explore a "data-only" approach where we attempt to minimize the number of repeated visits needed to reliably estimate concentrations for all roads. Second, we combine our data with a land use regression-kriging (LUR-K) model to predict at unobserved locations; here, measurements from only a subset of roads or repeat visits are considered. Although LUR-K models did not capture the full variability of on-road concentrations, models trained with minimal data consistently captured important covariates and general spatial air pollution trends, with cross-validation R2 for log-transformed NO and BC of 0.65 and 0.43. Data-only mapping performed poorly with few (1-2) repeated drives but obtained better cross-validation R2 than the LUR-K approach within 4 to 8 repeated drive days per road segment.

Effects of office workstation type on physical activity and stress.

OBJECTIVE: Office environments have been causally linked to workplace-related illnesses and stress, yet little is known about how office workstation type is linked to objective metrics of physical activity and stress. We aimed to explore these associations among office workers in US federal office buildings. METHODS: We conducted a wearable, sensor-based, observational study of 231 workers in four office buildings. Outcome variables included workers' physiological stress response, physical activity and perceived stress. Relationships between office workstation type and these variables were assessed using structural equation modelling. RESULTS: Workers in open bench seating were more active at the office than those in private offices and cubicles (open bench seating vs private office=225.52 mG (31.83% higher on average) (95% CI 136.57 to 314.46); open bench seating vs cubicle=185.13 mG (20.16% higher on average) (95% CI 66.53 to 303.72)). Furthermore, workers in open bench seating experienced lower perceived stress at the office than those in cubicles (-0.27 (9.10% lower on average) (95% CI -0.54 to -0.02)). Finally, higher physical activity at the office was related to lower physiological stress (higher heart rate variability in the time domain) outside the office (-26.12 ms/mG (14.18% higher on average) (95% CI -40.48 to -4.16)). CONCLUSIONS: Office workstation type was related to enhanced physical activity and reduced physiological and perceived stress. This research highlights how office design, driven by office workstation type, could be a health-promoting factor.

High-Resolution Air Pollution Mapping with Google Street View Cars: Exploiting Big Data.

Air pollution affects billions of people worldwide, yet ambient pollution measurements are limited for much of the world. Urban air pollution concentrations vary sharply over short distances (≪1 km) owing to unevenly distributed emission sources, dilution, and physicochemical transformations. Accordingly, even where present, conventional fixed-site pollution monitoring methods lack the spatial resolution needed to characterize heterogeneous human exposures and localized pollution hotspots. Here, we demonstrate a measurement approach to reveal urban air pollution patterns at 4-5 orders of magnitude greater spatial precision than possible with current central-site ambient monitoring. We equipped Google Street View vehicles with a fast-response pollution measurement platform and repeatedly sampled every street in a 30-km2 area of Oakland, CA, developing the largest urban air quality data set of its type. Resulting maps of annual daytime NO, NO2, and black carbon at 30 m-scale reveal stable, persistent pollution patterns with surprisingly sharp small-scale variability attributable to local sources, up to 5-8× within individual city blocks. Since local variation in air quality profoundly impacts public health and environmental equity, our results have important implications for how air pollution is measured and managed. If validated elsewhere, this readily scalable measurement approach could address major air quality data gaps worldwide.

Particle Deposition onto People in a Transit Venue.

Following the release of an aerosolized biological agent in a transit venue, material deposited on waiting passengers and subsequently shed from their clothing may significantly magnify the scope and consequences of such an attack. Published estimates of the relevant particle deposition and resuspension parameters for complex indoor environments such as a transit facility are nonexistent. In this study, measurements of particle deposition velocity onto cotton fabric samples affixed to stationary and walking people in a large multimodal transit facility were obtained for tracer particle releases carried out as part of a larger study of subway airflows and particulate transport. Deposition velocities onto cotton and wool were also obtained using a novel automated sampling mechanism deployed at locations in the transit facility and throughout the subway. The data revealed higher deposition velocities than have been previously reported for people exposed in test chambers or office environments. The relatively high rates of deposition onto people in a transit venue obtained in this study suggest it is possible that fomite transport by subway and commuter/regional rail passengers could present a significant mechanism for rapidly dispersing a biological agent throughout a metropolitan area and beyond.

Toward refined estimates of ambient PM2.5 exposure: Evaluation of a physical outdoor-to-indoor transport model.

Because people spend the majority of their time indoors, the variable efficiency with which ambient PM2.5 penetrates and persists indoors is a source of error in epidemiologic studies that use PM2.5 concentrations measured at central-site monitors as surrogates for ambient PM2.5 exposure. To reduce this error, practical methods to model indoor concentrations of ambient PM2.5 are needed. Toward this goal, we evaluated and refined an outdoor-to-indoor transport model using measured indoor and outdoor PM2.5 species concentrations and air exchange rates from the Relationships of Indoor, Outdoor, and Personal Air Study. Herein, we present model evaluation results, discuss what data are most critical to prediction of residential exposures at the individual-subject and populations levels, and make recommendations for the application of the model in epidemiologic studies. This paper demonstrates that not accounting for certain human activities (air conditioning and heating use, opening windows) leads to bias in predicted residential PM2.5 exposures at the individual-subject level, but not the population level. The analyses presented also provide quantitative evidence that shifts in the gas-particle partitioning of ambient organics with outdoor-to-indoor transport contribute significantly to variability in indoor ambient organic carbon concentrations and suggest that methods to account for these shifts will further improve the accuracy of outdoor-to-indoor transport models.

The triggering of myocardial infarction by fine particles is enhanced when particles are enriched in secondary species.

Previous studies have reported an increased risk of myocardial infarction (MI) associated with acute increases in PM concentration. Recently, we reported that MI/fine particle (PM2.5) associations may be limited to transmural infarctions. In this study, we retained data on hospital discharges with a primary diagnosis of acute myocardial infarction (using International Classification of Diseases ninth Revision [ICD-9] codes), for those admitted January 1, 2004 to December 31, 2006, who were ≥ 18 years of age, and were residents of New Jersey at the time of their MI. We excluded MI with a diagnosis of a previous MI and MI coded as a subendocardial infarction, leaving n = 1563 transmural infarctions available for analysis. We coupled these health data with PM2.5 species concentrations predicted by the Community Multiscale Air Quality chemical transport model, ambient PM2.5 concentrations, and used the same case-crossover methods to evaluate whether the relative odds of transmural MI associated with increased PM2.5 concentration is modified by the PM2.5 composition/mixture (i.e., mass fractions of sulfate, nitrate, elemental carbon, organic carbon, and ammonium). We found the largest relative odds estimates on the days with the highest tertile of sulfate mass fraction (OR = 1.13; 95% CI = 1.00, 1.27), nitrate mass fraction (OR = 1.18; 95% CI = 0.98, 1.35), and ammonium mass fraction (OR = 1.13; 95% CI = 1.00 1.28), and the lowest tertile of EC mass fraction (OR = 1.17; 95% CI = 1.03, 1.34). Air pollution mixtures on these days were enhanced in pollutants formed through atmospheric chemistry (i.e., secondary PM2.5) and depleted in primary pollutants (e.g., EC). When mixtures were laden with secondary PM species (sulfate, nitrate, and/or organics), we observed larger relative odds of myocardial infarction associated with increased PM2.5 concentrations. Further work is needed to confirm these findings and examine which secondary PM2.5 component(s) is/are responsible for an acute MI response.

Refined ambient PM2.5 exposure surrogates and the risk of myocardial infarction.

Using a case-crossover study design and conditional logistic regression, we compared the relative odds of transmural (full-wall) myocardial infarction (MI) calculated using exposure surrogates that account for human activity patterns and the indoor transport of ambient PM(2.5) with those calculated using central-site PM(2.5) concentrations to estimate exposure to PM(2.5) of outdoor origin (exposure to ambient PM(2.5)). Because variability in human activity and indoor PM(2.5) transport contributes exposure error in epidemiologic analyses when central-site concentrations are used as exposure surrogates, we refer to surrogates that account for this variability as "refined" surrogates. As an alternative analysis, we evaluated whether the relative odds of transmural MI associated with increases in ambient PM(2.5) is modified by residential air exchange rate (AER), a variable that influences the fraction of ambient PM(2.5) that penetrates and persists indoors. Use of refined exposure surrogates did not result in larger health effect estimates (ORs=1.10-1.11 with each interquartile range (IQR) increase), narrower confidence intervals, or better model fits compared with the analysis that used central-site PM(2.5). We did observe evidence for heterogeneity in the relative odds of transmural MI with residential AER (effect-modification), with residents of homes with higher AERs having larger ORs than homes in lower AER tertiles. For the level of exposure-estimate refinement considered here, our findings add support to the use of central-site PM(2.5) concentrations for epidemiological studies that use similar case-crossover study designs. In such designs, each subject serves as his or her own matched control. Thus, exposure error related to factors that vary spatially or across subjects should only minimally impact effect estimates. These findings also illustrate that variability in factors that influence the fraction of ambient PM(2.5) in indoor air (e.g., AER) could possibly bias health effect estimates in study designs for which a spatiotemporal comparison of exposure effects across subjects is conducted.

Influence of human activity patterns, particle composition, and residential air exchange rates on modeled distributions of PM2.5 exposure compared with central-site monitoring data.

Central-site monitors do not account for factors such as outdoor-to-indoor transport and human activity patterns that influence personal exposures to ambient fine-particulate matter (PM(2.5)). We describe and compare different ambient PM(2.5) exposure estimation approaches that incorporate human activity patterns and time-resolved location-specific particle penetration and persistence indoors. Four approaches were used to estimate exposures to ambient PM(2.5) for application to the New Jersey Triggering of Myocardial Infarction Study. These include: Tier 1, central-site PM(2.5) mass; Tier 2A, the Stochastic Human Exposure and Dose Simulation (SHEDS) model using literature-based air exchange rates (AERs); Tier 2B, the Lawrence Berkeley National Laboratory (LBNL) Aerosol Penetration and Persistence (APP) and Infiltration models; and Tier 3, the SHEDS model where AERs were estimated using the LBNL Infiltration model. Mean exposure estimates from Tier 2A, 2B, and 3 exposure modeling approaches were lower than Tier 1 central-site PM(2.5) mass. Tier 2A estimates differed by season but not across the seven monitoring areas. Tier 2B and 3 geographical patterns appeared to be driven by AERs, while seasonal patterns appeared to be due to variations in PM composition and time activity patterns. These model results demonstrate heterogeneity in exposures that are not captured by the central-site monitor.

Variability in the fraction of ambient fine particulate matter found indoors and observed heterogeneity in health effect estimates.

Exposure to ambient (outdoor-generated) fine particulate matter (PM(2.5)) occurs predominantly indoors. The variable efficiency with which ambient PM(2.5) penetrates and persists indoors is a source of exposure error in air pollution epidemiology and could contribute to observed temporal and spatial heterogeneity in health effect estimates. We used a mass balance approach to model F for several scenarios across which heterogeneity in effect estimates has been observed: with geographic location of residence, residential roadway proximity, socioeconomic status, and central air-conditioning use. We found F is higher in close proximity to primary combustion sources (e.g. proximity to traffic) and for lower income homes. F is lower when PM(2.5) is enriched in nitrate and with central air-conditioning use. As a result, exposure error resulting from variability in F will be greatest when these factors have high temporal and/or spatial variability. The circumstances for which F is lower in our calculations correspond to circumstances for which lower effect estimates have been observed in epidemiological studies and higher F values correspond to higher effect estimates. Our results suggest that variability in exposure misclassification resulting from variability in F is a possible contributor to heterogeneity in PM-mediated health effect estimates.

Measurement of black carbon and particle number emission factors from individual heavy-duty trucks.

Emission factors for black carbon (BC) and particle number (PN) were measured from 226 individual heavy-duty (HD) diesel trucks driving through a 1-km-long California highway tunnel in August 2006. Emission factors were based on concurrent increases in BC, PN, and CO2 concentrations (measured at 1 Hz) that corresponded to the passage of individual HD trucks. The distributions of BC and PN emission factors from individual HD trucks are skewed, meaning that a large fraction of pollution comes from a small fraction of the in-use vehicle fleet. The highest-emitting 10% of trucks were responsible for approximately 40% of total BC and PN emissions from all HD trucks. BC emissions were log-normally distributed with a mean emission factor of 1.7 g kg(-1) and maximum values of approximately 10 g kg(-1). Corresponding values for PN emission factors were 4.7 x 10(15) and 4 x 10(16) # kg(-1). There was minimal overlap among high-emitters of these two pollutants: only 1 of the 226 HD trucks measured was found to be among the highest 10% for both BC and PN. Monte Carlo resampling of the distribution of BC emission factors observed in this study revealed that uncertainties (1sigma) in extrapolating from a random sample of n HD trucks to a population mean emission factor ranged from +/- 43% for n=10 to +/- 8% for n=300, illustrating the importance of vehicle sample sizes in emissions studies. When n=10, sample means are more likely to be biased due to misrepresentation of high-emitters. As vehicles become cleaner on average in the future, skewness of the emissions distributions will increase, and thus sample sizes needed to extrapolate reliably from a subset of vehicles to the entire in-use vehicle fleet will become more of a challenge.

Indoor secondary pollutants from household product emissions in the presence of ozone: A bench-scale chamber study.

Ozone-driven chemistry is a source of indoor secondary pollutants of potential health concern. This study investigates secondary air pollutants formed from reactions between constituents of household products and ozone. Gas-phase product emissions were introduced along with ozone at constant rates into a 198-L Teflon-lined reaction chamber. Gas-phase concentrations of reactive terpenoids and oxidation products were measured. Formaldehyde was a predominant oxidation byproduct for the three studied products, with yields for most conditions of 20-30% with respect to ozone consumed. Acetaldehyde, acetone, glycolaldehyde, formic acid, and acetic acid were each also detected for two or three of the products. Immediately upon mixing of reactants, a scanning mobility particle sizer detected particle nucleation events that were followed by a significant degree of secondary particle growth. The production of secondary gaseous pollutants and particles depended primarily on the ozone level and was influenced by other parameters such as the air-exchange rate. Hydroxyl radical concentrations in the range 0.04-200 x 10(5) molecules cm(-3) were determined by an indirect method. OH concentrations were observed to vary strongly with residual ozone level in the chamber, which was in the range 1-25 ppb, as is consistent with expectations from a simplified kinetic model. In a separate chamber study, we exposed the dry residue of two products to ozone and observed the formation of gas-phase and particle-phase secondary oxidation products.

Use of time- and chemically resolved particulate data to characterize the infiltration of outdoor PM2.5 into a residence in the San Joaquin Valley.

Recent studies associate particulate air pollution with adverse health effects. The indoor exposure to particles of outdoor origin is not well-characterized, particularly for individual chemical species. In response to this, a field study in an unoccupied, single-story residence in Clovis, CA, was conducted. Real-time particle monitors were used both outdoors and indoors to quantity PM2.5 nitrate, sulfate, and carbon. The aggregate of the highly time-resolved sulfate data, as well as averages of these data, was fit using a time-averaged form of the infiltration equation, resulting in reasonable values for the penetration coefficient and deposition loss rate. In contrast, individual values of the indoor/outdoor ratio can vary significantly from that predicted by the model for time scales ranging from a few minutes to several hours. Measured indoor ammonium nitrate levels were typically significantly lower than expected solely on the basis of penetration and deposition losses. The additional reduction is due to the transformation of ammonium nitrate into ammonia and nitric acid gases indoors, which are subsequently lost by deposition and sorption to indoor surfaces. This result illustrates that exposure assessments based on total outdoor particle mass can obscure the actual causal relationships for indoor exposures to particles of outdoor origin.

Automated measurements of ammonia and nitric acid in indoor and outdoor air.

Simultaneous temporally resolved indoor and outdoor measurements of ammonia and nitric acid are valuable for determining the gas-particle equilibrium conditions governing concentrations of ammonium nitrate aerosol. We report the results of simultaneous automated indoor and outdoor measurements of ammonia and nitric acid concentrations made at an unoccupied, single-story residence in Clovis, CA during three periods from October 2000 to January 2001. The measurements were conducted as part of a controlled study to explore mechanisms governing indoor concentrations of fine aerosols of outdoor origin. The gas-phase measurements were performed using diffusion denuders and ion chromatography with 30 min temporal resolution and detection limits below 1 ppb. The conditions of the field experiment span a wide range of outdoor climate as well as natural and forced indoor conditions. During all periods ammonia concentrations were generally slightly higher indoors than out, with both outdoor and indoor concentrations varying in a range from approximately 5 to 30 ppb. Nitric acid was only detected in outdoor air in October 2000, at concentrations up to 3 ppb. During the October period, the product of outdoor nitric acid and ammonia concentrations sometimes deviated from that expected for equilibrium between gas and ammonium nitrate particulate phases and the degree and direction of disequilibrium were correlated with trends in air temperature. The consistently low indoor concentrations of nitric acid were not consistent with equilibrium between gas and particle phases and suggest that a combination of low penetration into the building and a high loss rate for nitric acid reduce indoor concentrations significantly below those outdoors.