Estimating ambient-origin PM2.5 exposure for epidemiology: observations, prediction, and validation using personal sampling in the Multi-Ethnic Study of Atherosclerosis.
ABSTRACT: 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: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 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:Exposure measurement error is a concern in long-term PM2.5 health studies using ambient concentrations as exposures. We assessed error magnitude by estimating calibration coefficients as the association between personal PM2.5 exposures from validation studies and typically available surrogate exposures.Daily personal and ambient PM2.5, and when available sulfate, measurements were compiled from nine cities, over 2 to 12 days. True exposure was defined as personal exposure to PM2.5 of ambient origin. Since PM2.5 of ambient origin could only be determined for five cities, personal exposure to total PM2.5 was also considered. Surrogate exposures were estimated as ambient PM2.5 at the nearest monitor or predicted outside subjects' homes. We estimated calibration coefficients by regressing true on surrogate exposures in random effects models.When monthly-averaged personal PM2.5 of ambient origin was used as the true exposure, calibration coefficients equaled 0.31 (95% CI:0.14, 0.47) for nearest monitor and 0.54 (95% CI:0.42, 0.65) for outdoor home predictions. Between-city heterogeneity was not found for outdoor home PM2.5 for either true exposure. Heterogeneity was significant for nearest monitor PM2.5, for both true exposures, but not after adjusting for city-average motor vehicle number for total personal PM2.5.Calibration coefficients were <1, consistent with previously reported chronic health risks using nearest monitor exposures being under-estimated when ambient concentrations are the exposure of interest. Calibration coefficients were closer to 1 for outdoor home predictions, likely reflecting less spatial error. Further research is needed to determine how our findings can be incorporated in future health studies.
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: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:Fine particulate matter (PM2.5) air pollution and environmental temperatures influence cardiovascular morbidity and mortality. Recent evidence suggests that several air pollutants can promote dyslipidemia; however, the impact of ambient PM2.5 and temperature on high-density lipoprotein (HDL) function remains unclear. We hypothesized that daily exposures to higher levels of ambient PM2.5 and colder outdoor temperatures would impair HDL functionality. Lipoproteins, serum cholesterol efflux capacity (CEC), and HDL oxidation markers were measured twice in 50 healthy adults (age 32.1 ± 9.6 years) living in southeast Michigan and associated with ambient and personal-level exposures using mixed models. Although previous 7-day mean outdoor temperature (4.4 ± 9.8°C) and PM2.5 levels (9.1 ± 1.8 µg/m3) were low, higher ambient PM2.5 exposures (per 10 µg/m3) were associated with significant increases in the total cholesterol-to-HDL-C ratio (rolling average lag days 1 and 2) as well as reductions in CEC by -1.93% (lag day 5, p?=?0.022) and -1.62% (lag day 6, p?=?0.032). Colder outdoor temperatures (per 10°C) were also associated with decreases in CEC from -0.62 to -0.63% (rolling average lag days 5 and 7, p?=?0.027 and 0.028). Previous 24-hour personal-level PM2.5 and temperature exposures did not impact outcomes, nor were any exposures associated with changes in HDL-oxidation metrics. In conclusion, we provide the first evidence that ambient PM2.5 (even at low levels) and outdoor temperatures may influence serum CEC, a critical antiatherosclerotic HDL function.
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:This paper considers the evidence on whether outdoor secondhand smoke (SHS) is present in hospitality venues at high levels enough to potentially pose health risks, particularly among employees.Searches in PubMed and Web of Science included combinations of environmental tobacco smoke, secondhand smoke, or passive smoke AND outdoor, yielding 217 and 5,199 results, respectively through June, 2012.Sixteen studies were selected that reported measuring any outdoor SHS exposures (particulate matter (PM) or other SHS indicators).The SHS measurement methods were assessed for inclusion of extraneous variables that may affect levels or the corroboration of measurements with known standards.The magnitude of SHS exposure (PM2.5) depends on the number of smokers present, measurement proximity, outdoor enclosures, and wind. Annual excess PM2.5 exposure of full-time waitstaff at outdoor smoking environments could average 4.0 to 12.2 ?g/m3 under variable smoking conditions.Although highly transitory, outdoor SHS exposures could occasionally exceed annual ambient air quality exposure guidelines. Personal monitoring studies of waitstaff are warranted to corroborate these modeled estimates.
Project description:Studies of air pollution effects during pregnancy generally only consider exposure in the outdoor air at the home address. We aimed to compare exposure models differing in their ability to account for the spatial resolution of pollutants, space-time activity and indoor air pollution levels. We recruited 40 pregnant women in the Grenoble urban area, France, who carried a Global Positioning System (GPS) during up to 3 weeks; in a subgroup, indoor measurements of fine particles (PM2.5) were conducted at home (n=9) and personal exposure to nitrogen dioxide (NO2) was assessed using passive air samplers (n=10). Outdoor concentrations of NO2, and PM2.5 were estimated from a dispersion model with a fine spatial resolution. Women spent on average 16 h per day at home. Considering only outdoor levels, for estimates at the home address, the correlation between the estimate using the nearest background air monitoring station and the estimate from the dispersion model was high (r=0.93) for PM2.5 and moderate (r=0.67) for NO2. The model incorporating clean GPS data was less correlated with the estimate relying on raw GPS data (r=0.77) than the model ignoring space-time activity (r=0.93). PM2.5 outdoor levels were not to moderately correlated with estimates from the model incorporating indoor measurements and space-time activity (r=-0.10 to 0.47), while NO2 personal levels were not correlated with outdoor levels (r=-0.42 to 0.03). In this urban area, accounting for space-time activity little influenced exposure estimates; in a subgroup of subjects (n=9), incorporating indoor pollution levels seemed to strongly modify them.
Project description:Fine particulate matter (PM2.5), levels of which are about 6 times the 2014 WHO air quality guidelines for 190 cities in China, has been found to be associated with various adverse health outcomes. In this study, personal PM2.5 exposures were monitored along a fixed routine that included 19 types of non-residential micro-environments (MEs) on 4 hazy days (ambient PM2.5 292 ± 70 ?g m-3) and 2 non-hazy days (55 ± 16 ?g m-3) in Nanjing, China using miniaturized real-time portable particulate sensors that also collect integrated filters of PM2.5 (MicroPEMs, Research Triangle Institute (RTI), NC). Gravimetric correction is necessary for nephelometer devices in calculating real-time PM levels. During both hazy and non-hazy days, personal PM2.5 levels were generally higher in MEs with noticeable PM2.5 sources than MEs serving as receptor sites, higher in open MEs than indoor MEs, and higher in densely populated MEs than MEs with few people. Personal PM2.5 levels measured during hazy and non-hazy days were 242 ± 91 ?g m-3 and 103 ± 147 ?g m-3, respectively. The ratio of personal exposure to ambient PM2.5 levels (rp/a ) was less than 1.0 and less variable on hazy days (0.85 ± 0.31); while it was larger than 1.0 and more variable on non-hazy days (1.71 ± 1.93), confirming the importance of local sources other than ambient during non-hazy days. Air handling methods (e.g., ventilation/filtration) impacted personal exposures in enclosed locations on both types of days. Street food vendors with cooking emissions were MEs with the highest personal PM2.5 levels while subway cars in Nanjing were relatively clean due to good air filtration on both hazy and non-hazy days. In summary, on hazy days, personal exposure was mainly affected by the regional ambient levels, while on non-hazy days, local sources together with ambient levels determined personal exposure levels.