Development of TracMyAir Smartphone Application for Modeling Exposures to Ambient PM2.5 and Ozone.
ABSTRACT: Air pollution epidemiology studies of ambient fine particulate matter (PM2.5) and ozone (O3) often use outdoor concentrations as exposure surrogates. Failure to account for the variability of the indoor infiltration of ambient PM2.5 and O3, and time indoors, can induce exposure errors. We developed an exposure model called TracMyAir, which is an iPhone application ("app") that determines seven tiers of individual-level exposure metrics in real-time for ambient PM2.5 and O3 using outdoor concentrations, weather, home building characteristics, time-locations, and time-activities. We linked a mechanistic air exchange rate (AER) model, a mass-balance PM2.5 and O3 building infiltration model, and an inhaled ventilation model to determine outdoor concentrations (Tier 1), residential AER (Tier 2), infiltration factors (Tier 3), indoor concentrations (Tier 4), personal exposure factors (Tier 5), personal exposures (Tier 6), and inhaled doses (Tier 7). Using the application in central North Carolina, we demonstrated its ability to automatically obtain real-time input data from the nearest air monitors and weather stations, and predict the exposure metrics. A sensitivity analysis showed that the modeled exposure metrics can vary substantially with changes in seasonal indoor-outdoor temperature differences, daily home operating conditions (i.e., opening windows and operating air cleaners), and time spent outdoors. The capability of TracMyAir could help reduce uncertainty of ambient PM2.5 and O3 exposure metrics used in epidemiology studies.
Project description:Air pollution epidemiology studies of ambient fine particulate matter (PM2.5) often use outdoor concentrations as exposure surrogates, which can induce exposure error. The goal of this study was to improve ambient PM2.5 exposure assessments for a repeated measurements study with 22 diabetic individuals in central North Carolina called the Diabetes and Environment Panel Study (DEPS) by applying the Exposure Model for Individuals (EMI), which predicts five tiers of individual-level exposure metrics for ambient PM2.5 using outdoor concentrations, questionnaires, weather, and time-location information. Using EMI, we linked a mechanistic air exchange rate (AER) model to a mass-balance PM2.5 infiltration model to predict residential AER (Tier 1), infiltration factors (Finf_home, Tier 2), indoor concentrations (Cin, Tier 3), personal exposure factors (Fpex, Tier 4), and personal exposures (E, Tier 5) for ambient PM2.5. We applied EMI to predict daily PM2.5 exposure metrics (Tiers 1-5) for 174 participant-days across the 13?months of DEPS. Individual model predictions were compared to a subset of daily measurements of Fpex and E (Tiers 4-5) from the DEPS participants. Model-predicted Fpex and E corresponded well to daily measurements with a median difference of 14% and 23%; respectively. Daily model predictions for all 174?days showed considerable temporal and house-to-house variability of AER, Finf_home, and Cin (Tiers 1-3), and person-to-person variability of Fpex and E (Tiers 4-5). Our study demonstrates the capability of predicting individual-level ambient PM2.5 exposure metrics for an epidemiological study, in support of improving risk estimation.
Project description:Air pollution epidemiological studies often use outdoor concentrations from central-site monitors as exposure surrogates, which can induce measurement error. The goal of this study was to improve exposure assessments of ambient fine particulate matter (PM2.5), elemental carbon (EC), nitrogen oxides (NOx), and carbon monoxide (CO) for a repeated measurements study with 15 individuals with coronary artery disease in central North Carolina called the Coronary Artery Disease and Environmental Exposure (CADEE) Study. We developed a fine-scale exposure modeling approach to determine five tiers of individual-level exposure metrics for PM2.5, EC, NOx, CO using outdoor concentrations, on-road vehicle emissions, weather, home building characteristics, time-locations, and time-activities. We linked an urban-scale air quality model, residential air exchange rate model, building infiltration model, global positioning system (GPS)-based microenvironment model, and accelerometer-based inhaled ventilation model to determine residential outdoor concentrations (Cout_home, Tier 1), residential indoor concentrations (Cin_home, Tier 2), personal outdoor concentrations (Cout_personal, Tier 3), exposures (E, Tier 4), and inhaled doses (D, Tier 5). We applied the fine-scale exposure model to determine daily 24-h average PM2.5, EC, NOx, CO exposure metrics (Tiers 1-5) for 720 participant-days across the 25 months of CADEE. Daily modeled metrics showed considerable temporal and home-to-home variability of Cout_home and Cin_home (Tiers 1-2) and person-to-person variability of Cout_personal, E, and D (Tiers 3-5). Our study demonstrates the ability to apply an urban-scale air quality model with an individual-level exposure model to determine multiple tiers of exposure metrics for an epidemiological study, in support of improving health risk assessments.
Project description:Individual housing characteristics can modify outdoor ambient air pollution infiltration through air exchange rate (AER). Time and labor-intensive methods needed to measure AER has hindered characterization of AER distributions across large geographic areas. Using publicly-available data and regression models associating AER with housing characteristics, we estimated AER for all Massachusetts residential parcels. We conducted an exposure disparities analysis, considering ambient PM2.5 concentrations and residential AERs. Median AERs (h-1) with closed windows for winter and summer were 0.74 (IQR: 0.47-1.09) and 0.36 (IQR: 0.23-0.57), respectively, with lower AERs for single family homes. Across residential parcels, variability of indoor PM2.5 concentrations of ambient origin was twice that of ambient PM2.5 concentrations. Housing parcels above the 90th percentile of both AER and ambient PM2.5 (i.e., the leakiest homes in areas of highest ambient PM2.5)-vs. below the 10 percentile-were located in neighborhoods with higher proportions of Hispanics (20.0% vs. 2.0%), households with an annual income of less than $20,000 (26.0% vs. 7.5%), and individuals with less than a high school degree (23.2% vs. 5.8%). Our approach can be applied in epidemiological studies to estimate exposure modifiers or to characterize exposure disparities that are not solely based on ambient concentrations.
Project description: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.
Project description:OBJECTIVES:We aim to characterize the qualities of estimation approaches for individual exposure to ambient-origin fine particulate matter (PM2.5), for use in epidemiological studies. METHODS:The analysis incorporates personal, home indoor, and home outdoor air monitoring data and spatio-temporal model predictions for 60 participants from the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air). We compared measurement-based personal PM2.5 exposure with several measured or predicted estimates of outdoor, indoor, and personal exposures. RESULTS:The mean personal 2-week exposure was 7.6 (standard deviation 3.7) µg/m3. Outdoor model predictions performed far better than outdoor concentrations estimated using a nearest-monitor approach (R?=?0.63 versus R?=?0.43). Incorporating infiltration indoors of ambient-derived PM2.5 provided better estimates of the measurement-based personal exposures than outdoor concentration predictions (R?=?0.81 versus R?=?0.63) and better scaling of estimated exposure (mean difference 0.4 versus 5.4?µg/m3 higher than measurements), suggesting there is value to collecting data regarding home infiltration. Incorporating individual-level time-location information into exposure predictions did not increase correlations with measurement-based personal exposures (R?=?0.80) in our sample consisting primarily of retired persons. CONCLUSIONS:This analysis demonstrates the importance of incorporating infiltration when estimating individual exposure to ambient air pollution. Spatio-temporal models provide substantial improvement in exposure estimation over a nearest monitor approach.
Project description:Epidemiological studies routinely use central-site particulate matter (PM) as a surrogate for exposure to PM of ambient (outdoor) origin. Below we quantify exposure errors that arise from variations in particle infiltration to aid evaluation of the use of this surrogate, rather than actual exposure, in PM epidemiology. Measurements from 114 homes in three cities from the Relationship of Indoor, Outdoor and Personal Air (RIOPA) study were used. Indoor PM2.5 of outdoor origin was calculated as follows: (1) assuming a constant infiltration factor, as would be the case if central-site PM were a "perfect surrogate" for exposure to outdoor particles; (2) including variations in measured air exchange rates across homes; (3) also incorporating home-to-home variations in particle composition, and (4) calculating sample-specific infiltration factors. The final estimates of PM2.5 of outdoor origin take into account variations in building construction, ventilation practices, and particle properties that result in home-to-home and day-to-day variations in particle infiltration. As assumptions became more realistic (from the first, most constrained model to the fourth, least constrained model), the mean concentration of PM2.5 of outdoor origin increased. Perhaps more importantly, the bandwidth of the distribution increased. These results quantify several ways in which the use of central site PM results in underestimates of the ambient PM2.5 exposure distribution bandwidth. The result is larger uncertainties in relative risk factors for PM2.5 than would occur if epidemiological studies used more accurate exposure measures. In certain situations this can lead to bias.
Project description:More than 80% of people living in urban areas who monitor air pollution are exposed to air quality levels that exceed limits defined by the World Health Organization (WHO). Although all regions of the world are affected, populations in low-income cities are the most impacted. According to average annual levels of fine particulate matter (PM2.5, ambient particles with aerodynamic diameter of 2.5 ?m or less) presented in the urban air quality database issued by WHO in 2016, as many as 33 Polish cities are among the 50 most polluted cities in the European Union (EU), with Silesian cities topping the list. The aim of this study was to characterize the indoor air quality in Silesian kindergartens based on the concentrations of gaseous compounds (SO2, NO2), PM2.5, and the sum of 15 PM2.5-bound polycyclic aromatic hydrocarbons (PAHs), including PM2.5-bound benzo(a)pyrene (BaP), as well as the mutagenic activity of PM2.5 organic extracts in Salmonella assay (strains: TA98, YG1024). The assessment of the indoor air quality was performed taking into consideration the pollution of the atmospheric air (outdoor). I/O ratios (indoor/outdoor concentration) for each investigated parameter were also calculated. Twenty-four-hour samples of PM2.5, SO2, and NO2 were collected during spring in two sites in southern Poland (Silesia), representing urban and rural areas. Indoor samples were taken in naturally ventilated kindergartens. At the same time, in the vicinity of the kindergarten buildings, the collection of outdoor samples of PM2.5, SO2, and NO2 was carried out. The content of BaP and the sum of 15 studied PAHs was determined in each 24-h sample of PM2.5 (indoor and outdoor). In the urban site, statistically lower concentrations of SO2 and NO2 were detected indoors compared to outdoors, whereas in the rural site, such a relationship was observed only for NO2. No statistically significant differences in the concentrations of PM2.5, PM2.5-bound BaP, and ?15 PAHs in kindergartens (indoor) versus atmospheric (outdoor) air in the two studied areas were identified. Mutagenic effect of indoor PM2.5 samples was twice as low as in outdoor samples. The I/O ratios indicated that all studied air pollutants in the urban kindergarten originated from the ambient air. In the rural site concentrations of SO2, PM2.5 and BaP in the kindergarten were influenced by internal sources (gas and coal stoves).
Project description: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.
Project description:Vapor intrusion (IV) exposure risks are difficult to characterize due to the role of atmospheric, building and subsurface processes. This study presents a three-dimensional VI model that extends the common subsurface fate and transport equations to incorporate wind and stack effects on indoor air pressure, building air exchange rate (AER) and indoor contaminant concentration to improve VI exposure risk estimates. The model incorporates three modeling programs: (1) COMSOL Multiphysics to model subsurface fate and transport processes, (2) CFD0 to model atmospheric air flow around the building, and (3) CONTAM to model indoor air quality. The combined VI model predicts AER values, zonal indoor air pressures and zonal indoor air contaminant concentrations as a function of wind speed, wind direction and outdoor and indoor temperature. Steady state modeling results for a single-story building with a basement demonstrate that wind speed, wind direction and opening locations in a building play important roles in changing the AER, indoor air pressure, and indoor air contaminant concentration. Calculated indoor air pressures ranged from approximately -10 Pa to +4 Pa depending on weather conditions and building characteristics. AER values, mass entry rates and indoor air concentrations vary depending on weather conditions and building characteristics. The presented modeling approach can be used to investigate the relationship between building features, AER, building pressures, soil gas concentrations, indoor air concentrations and VI exposure risks.
Project description:BACKGROUND:It is not known whether environmental gamma radiation measured in US cities has detectable adverse health effects. We assessed whether short-term exposure to gamma radiation emitted from ambient air particles [gamma particle activity (PR?)] is associated with reduced pulmonary function in chronic obstructive pulmonary disease (COPD) patients. OBJECTIVE:We hypothesize that the inhalation of gamma radiation emitted from ambient air particles may be associated with reduced pulmonary function in individuals with COPD. METHODS:In 125 patients with COPD from Eastern Massachusetts who had up to 4 seasonal one-week assessments of particulate matter ?2.5??m (PM2.5), black carbon (BC), and sulfur followed by spirometry. The US EPA continuously monitors ambient gamma (?) radiation including ? released from radionuclides attached to particulate matter that is recorded as 9 ?-energy spectra classes (i?=?3-9) in counts per minute (CPM?) in the Boston area (USA). We analyzed the associations between ambient and indoor PR?i (up to one week) and pre and post-bronchodilator (BD) forced expiratory volume in 1?s (FEV1) and with forced vital capacity (FVC) using mixed-effects regression models. We estimated indoor PR?i using the ratio of the indoor-to-outdoor sulfur in PM2.5 as a proxy for infiltration of ambient radionuclide-associated particles. RESULTS:Overall, exposures to ambient and indoor PR?i were associated with a similar decrease in pre- and post-BD FEV1 and FVC. For example, ambient PR?3 exposure averaged from the day of pulmonary function testing through the previous 3 days [IQR of 55.1?counts per minute (CPM?)] was associated with a decrease in pre-BD FEV1 of 21.0?ml (95%CI: -38.5 to -3.0?ml; p?<?0.01) and pre-BD FVC of 27.5?ml [95% confidence interval (CI): -50.7 to -5.0?ml; p?<?0.01] with similar effects adjusting for indoor and outdoor BC and PM2.5. CONCLUSION:Our results show that short-term ambient and indoor exposures to environmental gamma radiation associated with particulate matter are associated with reduced pre- and post-BD pulmonary function in patients with COPD.