Numerous modeling approaches to estimate concentrations of PM2.5 components have been developed to derive better exposures for health studies, including geostatistical interpolation approaches, land ...use regression models and, models based on remote sensing technology. Recently, there have been some efforts to develop models based on machine learning algorithms. Each one of these exposure assessment methods has inherent uncertainties resulting in varying levels of exposure misclassification. To date, only a few studies have attempted to systematically compare exposure estimates from different PM2.5 constituent models. Our research addresses this gap, by comparing the predictive capabilities of ordinary geostatistical interpolation (Ordinary Kriging – OK), hybrid interpolation (combination of Empirical Bayesian Kriging and land use regression), and machine learning techniques (forest-based regression) for estimating PM2.5 constituents in Eastern Massachusetts in the United States. We compared the estimates of 10 ambient PM2.5 components, which included Al, Cu, Fe, K, Ni, Pb, S, Ti, V, and Zn. The OK model performed poorest for all PM2.5 components, with an R2 under 0.30. The hybrid model presented a slight improvement, especially for Cu and Fe, for which the R2 value increased to 0.62 and 0.59, respectively. These elements presented the highest R2 value from the hybrid model. The forest model presented the best performance, with R2 values higher than 0.7 for most of the particle components, including Cu, Fe, Ni, Pb, Ti, and V. Same as observed with the hybrid model, the forest model for Cu and Fe explained the highest concentration variance, with a R2 value equal to 0.88 and 0.92, respectively. The forest model for K, S, and Zn performed poorest with an R2 value of 0.54, 0.37, and 0.44, respectively. The results presented here can be useful for the environmental health community to more accurately estimate PM2.5 constituents over space.
•The OK model performed poorest for all PM2.5 components, with an R2 under 0.30.•The hybrid model presented a slight improvement, especially for Cu and Fe.•The forest model presented the best performance, with R2 values higher than 0.7 for most of the particle components.
There is increasing interest in evaluating the association between specific fine-particle (particles with aerodynamic diameters less than 2.5 µm; PM2.5) constituents and adverse health outcomes ...rather than focusing solely on the impact of total PM2.5. Because PM2.5 may be related to both constituent concentration and health outcomes, constituents that are more strongly correlated with PM2.5 may appear more closely related to adverse health outcomes than other constituents even if they are not inherently more toxic. Therefore, it is important to properly account for potential confounding by PM2.5 in these analyses. Usually, confounding is due to a factor that is distinct from the exposure and outcome. However, because constituents are a component of PM2.5, standard covariate adjustment is not appropriate. Similar considerations apply to source-apportioned concentrations and studies assessing either short-term or long-term impacts of constituents. Using data on 18 constituents and data from 1,060 patients admitted to a Boston medical center with ischemic stroke in 2003-2008, the authors illustrate several options for modeling the association between constituents and health outcomes that account for the impact of PM2.5. Although the different methods yield results with different interpretations, the relative rankings of the association between constituents and ischemic stroke were fairly consistent across models.
Epidemiologic studies on acute effects of air pollution have generally been limited to larger cities, leaving questions about rural populations behind. Recently, we had developed a spatiotemporal ...model to predict daily PM2.5 level at a 1 km(2) using satellite aerosol optical depth (AOD) data. Based on the results from the model, we applied a case-crossover study to evaluate the acute effect of PM2.5 on mortality in North Carolina, South Carolina, and Georgia between 2007 and 2011. Mortality data were acquired from the Departments of Public Health in the States and modeled PM2.5 exposures were assigned to the zip code of residence of each decedent. We performed various stratified analyses by age, sex, race, education, cause of death, residence, and environmental protection agency (EPA) standards. We also compared results of analyses using our modeled PM2.5 levels and those imputed daily from the nearest monitoring station. 848,270 non-accidental death records were analyzed and we found each 10 μg/m(3) increase in PM2.5 (mean lag 0 and lag 1) was associated with a 1.56% (1.19 and 1.94) increase in daily deaths. Cardiovascular disease (2.32%, 1.57-3.07) showed the highest effect estimate. Blacks (2.19%, 1.43-2.96) and persons with education ≤ 8 year (3.13%, 2.08-4.19) were the most vulnerable populations. The effect of PM2.5 on mortality still exists in zip code areas that meet the PM2.5 EPA annual standard (2.06%, 1.97-2.15). The effect of PM2.5 below both EPA daily and annual standards was 2.08% (95% confidence interval=1.99-2.17). Our results showed more power and suggested that the PM2.5 effects on rural populations have been underestimated due to selection bias and information bias. We have demonstrated that our AOD-based exposure models can be successfully applied to epidemiologic studies. This will add new study populations in rural areas, and will confer more generalizability to conclusions from such studies.
Long-term exposure to air pollution has been linked with an increase in risk of mortality. Whether existing US Environmental Protection Agency standards are sufficient to protect health is unclear. ...Our study aimed to examine the relationship between exposure to lower concentrations of air pollution and the risk of mortality.
Our nationwide cohort study investigated the effect of annual average exposure to air pollutants on all-cause mortality among Medicare enrolees from the beginning of 2000 to the end of 2016. Patients entered the cohort in the month of January following enrolment and were followed up until the end of the study period in 2016 or death. We restricted our analyses to participants who had only been exposed to lower concentrations of pollutants over the study period, specifically particulate matter less than 2·5 μg/m3 in diameter (PM2·5) at a concentration of up to 12 μg/m3, nitrogen dioxide (NO2) at a concentration of up to 53 parts per billion (ppb), and summer ozone (O3) at concentrations of up to 50 ppb. We adjusted for two types of covariates, which were individual level and postal code-level variables. We used a doubly-robust additive model to estimate the change in risk. We further looked at effect-measure modification by stratification on the basis of demographic and socioeconomic characteristics.
We found an increased risk of mortality with all three pollutants. Each 1 μg/m3 increase in annual PM2·5 concentrations increased the absolute annual risk of death by 0·073% (95% CI 0·071–0·076). Each 1 ppb increase in annual NO2 concentrations increased the annual risk of death by 0·003% (0·003–0·004), and each 1 ppb increase in summer O3 concentrations increased the annual risk of death by 0·081% (0·080–0·083). This increase translated to approximately 11 540 attributable deaths (95% CI 11 087–11 992) for PM2·5, 1176 attributable deaths (998–1353) for NO2, and 15 115 attributable deaths (14 896–15 333) for O3 per year for each unit increase in pollution concentrations. The effects were higher in certain subgroups, including individuals living in areas of low socioeconomic status. Long-term exposure to permissible concentrations of air pollutants increases the risk of mortality.
The US Environmental Protection Agency, National Institute of Environmental Health Services, and Health Effects Institute.
Many studies report significant associations between PM(2.5) (particulate matter <2.5 micrometers) and hospital admissions. These studies mostly rely on a limited number of monitors which introduces ...exposure error, and excludes rural and suburban populations from locations where monitors are not available, reducing generalizability and potentially creating selection bias.
Using prediction models developed by our group, daily PM(2.5) exposure was estimated across the Mid-Atlantic (Washington D.C., and the states of Delaware, Maryland, New Jersey, Pennsylvania, Virginia, New York and West Virginia). We then investigated the short-term effects of PM(2.5)exposures on emergency hospital admissions of the elderly in the Mid-Atlantic region.We performed case-crossover analysis for each admission type, matching on day of the week, month and year and defined the hazard period as lag01 (a moving average of day of admission exposure and previous day exposure).
We observed associations between short-term exposure to PM(2.5) and hospitalization for all outcomes examined. For example, for every 10-µg/m(3) increase in short-term PM(2.5) there was a 2.2% increase in respiratory diseases admissions (95% CI = 1.9 to 2.6), and a 0.78% increase in cardiovascular disease (CVD) admission rate (95% CI = 0.5 to 1.0). We found differences in risk for CVD admissions between people living in rural and urban areas. For every10-µg/m(3) increase in PM(2.5) exposure in the 'rural' group there was a 1.0% increase (95% CI = 0.6 to 1.5), while for the 'urban' group the increase was 0.7% (95% CI = 0.4 to 1.0).
Our findings showed that PM(2.5) exposure was associated with hospital admissions for all respiratory, cardio vascular disease, stroke, ischemic heart disease and chronic obstructive pulmonary disease admissions. In addition, we demonstrate that our AOD (Aerosol Optical Depth) based exposure models can be successfully applied to epidemiological studies investigating the health effects of short-term exposures to PM(2.5).
We aimed to assess the content of electronic cigarette (EC) emissions for five groups of potentially toxic compounds that are known to be present in tobacco smoke: nicotine, particles, carbonyls, ...volatile organic compounds (VOCs), and trace elements by flavor and puffing time.
We used ECs containing a common nicotine strength (1.8%) and the most popular flavors, tobacco and menthol. An automatic multiple smoking machine was used to generate EC aerosols under controlled conditions. Using a dilution chamber, we targeted nicotine concentrations similar to that of exposure in a general indoor environment. The selected toxic compounds were extracted from EC aerosols into a solid or liquid phase and analyzed with chromatographic and spectroscopic methods.
We found that EC aerosols contained toxic compounds including nicotine, fine and nanoparticles, carbonyls, and some toxic VOCs such as benzene and toluene. Higher mass and number concentrations of aerosol particles were generated from tobacco-flavored ECs than from menthol-flavored ECs.
We found that diluted machine-generated EC aerosols contain some pollutants. These findings are limited by the small number of ECs tested and the conditions of testing. More comprehensive research on EC exposure extending to more brands and flavor compounds is warranted.
Risk of asthma hospitalization and its disparities associated with air pollutant exposures are less clear within socioeconomically disadvantaged populations, particularly at low degrees of exposure.
...To assess effects of short-term exposures to fine particulate matter (particulate matter with an aerodynamic diameter of ⩽2.5 μm PM
), warm-season ozone (O
), and nitrogen dioxide (NO
) on risk of asthma hospitalization among national Medicaid beneficiaries, the most disadvantaged population in the United States, and to test whether any subpopulations were at higher risk.
We constructed a time-stratified case-crossover dataset among 1,627,002 hospitalizations during 2000-2012 and estimated risk of asthma hospitalization associated with short-term PM
, O
, and NO
exposures. We then restricted the analysis to hospitalizations with degrees of exposure below increasingly stringent thresholds. Furthermore, we tested effect modifications by individual- and community-level characteristics.
Each 1-μg/m
increase in PM
, 1-ppb increase in O
, and 1-ppb increase in NO
was associated with 0.31% (95% confidence interval CI, 0.24-0.37%), 0.10% (95% CI, 0.05 - 0.15%), and 0.28% (95% CI, 0.24 - 0.32%) increase in risk of asthma hospitalization, respectively. Low-level PM
and NO
exposures were associated with higher risk. Furthermore, beneficiaries with only one asthma hospitalization during the study period or in communities with lower population density, higher average body mass index, longer distance to the nearest hospital, or greater neighborhood deprivation experienced higher risk.
Short-term air pollutant exposures increased risk of asthma hospitalization among Medicaid beneficiaries, even at concentrations well below national standards. The subgroup differences suggested individual and contextual factors contributed to asthma disparities under effects of air pollutant exposures.
Ambient particulate matter (PM) has been shown to have short- and long-term effects on cardiorespiratory mortality and morbidity. Most of the risk is associated with fine PM (PM(2.5)); however, ...recent evidence suggests that desert dust outbreaks are major contributors to coarse PM (PM(10-2.5)) and may be associated with adverse health effects. The objective of this study was to investigate the risk of total, cardiovascular and respiratory mortality associated with PM concentrations during desert dust outbreaks. We used a time-series design to investigate the effects of PM(10) on total non-trauma, cardiovascular and respiratory daily mortality in Cyprus, between 1 January 2004 and 31 December 2007. Separate PM(10) effects for non-dust and dust days were fit in generalized additive Poisson models. We found a 2.43% (95% CI: 0.53, 4.37) increase in daily cardiovascular mortality associated with each 10-μg/m(3) increase in PM(10) concentrations on dust days. Associations for total (0.13% increase, 95% CI: -1.03, 1.30) and respiratory mortality (0.79% decrease, 95% CI: -4.69, 3.28) on dust days and all PM(10) and mortality associations on non-dust days were not significant. Although further study of the exact nature of effects across different affected regions during these events is needed, this study suggests adverse cardiovascular effects associated with desert dust events.
Few studies have been performed on air pollution effects on lung function in the elderly, a vulnerable population with low reserve capacity, and even fewer have looked at changes in the rate of lung ...function decline.
We evaluated the effect of long-term exposure to black carbon on levels and rates of decline in lung function in the elderly.
FVC and FEV1 were measured one to six times during the period 1995-2011 in 858 men participating in the Normative Aging Study. Exposure to black carbon, a tracer of traffic emissions, was estimated by a spatiotemporal land use regression model. We investigated the effects of moving averages of black carbon of 1-5 years before the lung function measurement using linear mixed models.
A 0.5 μg/m(3) increase in long-term exposure to black carbon was associated with an additional rate of decline in FVC and FEV1 of between 0.5% and 0.9% per year, respectively, depending on the averaging time. In addition, black carbon exposure before the baseline visit was associated with lower levels of both FVC and FEV1, with effect estimates increasing up to 6-7% with a 5-year average exposure.
Our results support adverse effects of long-term exposure to traffic particles on lung function level and rate of decline in the elderly and suggest that functionally significant differences in health and risk of disability occur below the annual Environmental Protection Agency National Air Quality Standards.
Unconventional oil and natural gas development (UOGD) expanded extensively in the United States from the early 2000s. However, the influence of UOGD on the radioactivity of ambient particulate is not ...well understood. We collected the ambient particle radioactivity (PR) measurements of RadNet, a nationwide environmental radiation monitoring network. We obtained the information of over 1.5 million wells from the Enverus database. We investigated the association between the upwind UOGD well count and the downwind gross-beta radiation with adjustment for environmental factors governing the natural emission and transport of radioactivity. Our statistical analysis found that an additional 100 upwind UOGD wells within 20 km is associated with an increase of 0.024 mBq/m
(95% confidence interval CI, 0.020, 0.028 mBq/m
) in the gross-beta particle radiation downwind. Based on the published health analysis of PR, the widespread UOGD could induce adverse health effects to residents living close to UOGD by elevating PR.
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