Studies have shown that long-term exposure to air pollution increases mortality. However, evidence is limited for air-pollution levels below the most recent National Ambient Air Quality Standards. ...Previous studies involved predominantly urban populations and did not have the statistical power to estimate the health effects in underrepresented groups.
We constructed an open cohort of all Medicare beneficiaries (60,925,443 persons) in the continental United States from the years 2000 through 2012, with 460,310,521 person-years of follow-up. Annual averages of fine particulate matter (particles with a mass median aerodynamic diameter of less than 2.5 μm PM
) and ozone were estimated according to the ZIP Code of residence for each enrollee with the use of previously validated prediction models. We estimated the risk of death associated with exposure to increases of 10 μg per cubic meter for PM
and 10 parts per billion (ppb) for ozone using a two-pollutant Cox proportional-hazards model that controlled for demographic characteristics, Medicaid eligibility, and area-level covariates.
Increases of 10 μg per cubic meter in PM
and of 10 ppb in ozone were associated with increases in all-cause mortality of 7.3% (95% confidence interval CI, 7.1 to 7.5) and 1.1% (95% CI, 1.0 to 1.2), respectively. When the analysis was restricted to person-years with exposure to PM
of less than 12 μg per cubic meter and ozone of less than 50 ppb, the same increases in PM
and ozone were associated with increases in the risk of death of 13.6% (95% CI, 13.1 to 14.1) and 1.0% (95% CI, 0.9 to 1.1), respectively. For PM
, the risk of death among men, blacks, and people with Medicaid eligibility was higher than that in the rest of the population.
In the entire Medicare population, there was significant evidence of adverse effects related to exposure to PM
and ozone at concentrations below current national standards. This effect was most pronounced among self-identified racial minorities and people with low income. (Supported by the Health Effects Institute and others.).
GEOS-Chem, a chemical transport model, provides time-space continuous estimates of atmospheric pollutants including PM2.5 and its major components, but model predictions are not highly correlated ...with ground monitoring data. In addition, its spatial resolution is usually too coarse to characterize the spatial pattern in pollutant concentrations in urban environments. Our objective was to calibrate daily GEOS-Chem simulations using ground monitoring data and incorporating meteorological variables, land-use terms and spatial-temporal lagged terms. Major PM2.5 components of our interest include sulfate, nitrate, organic carbon, elemental carbon, ammonium, sea salt and dust. We used a backward propagation neural network to calibrate GEOS-Chem predictions with a spatial resolution of 0.500° × 0.667° using monitoring data collected during the period from 2001 to 2010 for the Northeastern United States. Subsequently, we made predictions at 1 km × 1 km grid cells. We determined the accuracy of the spatial-temporal predictions using ten-fold cross-validation and “leave-one-day-out” cross-validation techniques. We found a high total R2 for PM2.5 mass (all data R2 0.85, yearly values: 0.80–0.88) and PM2.5 components (R2 for individual components were around 0.70–0.80). Our model makes it possible to assess spatially- and temporally-resolved short- and long-term exposures to PM2.5 mass and components for epidemiological studies.
•A hybrid model is developed for total PM2.5 and PM2.5 chemical components.•Neural network is used to handle complexity in modeling.•Model results are available at 1 km × 1 km grid cell at daily basis.•Model outcome demonstrates high correlation with monitoring data.
Ground-level ozone is an important atmospheric oxidant, which exhibits considerable spatial and temporal variability in its concentration level. Existing modeling approaches for ground-level ozone ...include chemical transport models, land-use regression, Kriging, and data fusion of chemical transport models with monitoring data. Each of these methods has both strengths and weaknesses. Combining those complementary approaches could improve model performance. Meanwhile, satellite-based total column ozone, combined with ozone vertical profile, is another potential input. The authors propose a hybrid model that integrates the above variables to achieve spatially and temporally resolved exposure assessments for ground-level ozone. The authors used a neural network for its capacity to model interactions and nonlinearity. Convolutional layers, which use convolution kernels to aggregate nearby information, were added to the neural network to account for spatial and temporal autocorrelation. The authors trained the model with the Air Quality System (AQS) 8-hr daily maximum ozone in the continental United States from 2000 to 2012 and tested it with left out monitoring sites. Cross-validated R
2
on the left out monitoring sites ranged from 0.74 to 0.80 (mean 0.76) for predictions on 1 km × 1 km grid cells, which indicates good model performance. Model performance remains good even at low ozone concentrations. The prediction results facilitate epidemiological studies to assess the health effect of ozone in the long term and the short term.
Implications: Ozone monitors do not provide full data coverage over the United States, which is an obstacle to assess the health effect of ozone when monitoring data are not available. This paper used a hybrid approach to combine satellite-based ozone measurements, chemical transport model simulations, land-use terms, and other auxiliary variables to obtain spatially and temporally resolved ground-level ozone estimation.
Various approaches have been proposed to model PM2.5 in the recent decade, with satellite-derived aerosol optical depth, land-use variables, chemical transport model predictions, and several ...meteorological variables as major predictor variables. Our study used an ensemble model that integrated multiple machine learning algorithms and predictor variables to estimate daily PM2.5 at a resolution of 1 km × 1 km across the contiguous United States. We used a generalized additive model that accounted for geographic difference to combine PM2.5 estimates from neural network, random forest, and gradient boosting. The three machine learning algorithms were based on multiple predictor variables, including satellite data, meteorological variables, land-use variables, elevation, chemical transport model predictions, several reanalysis datasets, and others. The model training results from 2000 to 2015 indicated good model performance with a 10-fold cross-validated R2 of 0.86 for daily PM2.5 predictions. For annual PM2.5 estimates, the cross-validated R2 was 0.89. Our model demonstrated good performance up to 60 μg/m3. Using trained PM2.5 model and predictor variables, we predicted daily PM2.5 from 2000 to 2015 at every 1 km × 1 km grid cell in the contiguous United States. We also used localized land-use variables within 1 km × 1 km grids to downscale PM2.5 predictions to 100 m × 100 m grid cells. To characterize uncertainty, we used meteorological variables, land-use variables, and elevation to model the monthly standard deviation of the difference between daily monitored and predicted PM2.5 for every 1 km × 1 km grid cell. This PM2.5 prediction dataset, including the downscaled and uncertainty predictions, allows epidemiologists to accurately estimate the adverse health effect of PM2.5. Compared with model performance of individual base learners, an ensemble model would achieve a better overall estimation. It is worth exploring other ensemble model formats to synthesize estimations from different models or from different groups to improve overall performance.
Display omitted
•An ensemble model integrates three machine learning algorithms and estimates PM2.5.•Satellite measurements, land-use terms, and many variables were predictors.•Model predicts daily PM2.5 at 1 km × 1 km grid cells in the entire United States.•Model predictions were downscaled to 100 m × 100 m level.•Monthly uncertainty level of prediction was also estimated.
BACKGROUND:Although the association between exposure to particulate matter (PM) mass and mortality is well established, there remains uncertainty about which chemical components of PM are most ...harmful to human health.
METHODS:A hierarchical approach was used to determine how the association between daily PM2.5 mass and mortality was modified by PM2.5 composition in 25 US communities. First, the association between daily PM2.5 and mortality was determined for each community and season using Poisson regression. Second, we used meta-regression to examine how the pooled association was modified by community and season-specific particle composition.
RESULTS:There was a 0.74% (95% confidence interval = 0.41%–1.07%) increase in nonaccidental deaths associated with a 10 μg/m increase in 2-day averaged PM2.5 mass concentration. This association was smaller in the west (0.51% 0.10%–0.92%) than in the east (0.92% 0.23%–1.36%), and was highest in spring (1.88% 0.23%–1.36%). It was increased when PM2.5 mass contained a higher proportion of aluminum (interquartile range = 0.58%), arsenic (0.55%), sulfate (0.51%), silicon (0.41%), and nickel (0.37%). The combination of aluminum, sulfate, and nickel also modified the effect. These species proportions explained residual variability between the community-specific PM2.5 mass effect estimates.
CONCLUSIONS:This study shows that certain chemical species modify the association between PM2.5 and mortality and illustrates that mass alone is not a sufficient metric when evaluating health effects of PM exposure.
Although the association between exposure to particulate matter and health is well established, there remains uncertainty as to whether certain chemical components are more harmful than others. We ...explored whether the association between cause-specific hospital admissions and PM(2.5) was modified by PM(2.5) chemical composition.
We estimated the association between daily PM(2.5) and emergency hospital admissions for cardiac causes (CVD), myocardial infarction (MI), congestive heart failure (CHF), respiratory disease, and diabetes in 26 US communities, for the years 2000-2003. Using meta-regression, we examined how this association was modified by season- and community-specific PM(2.5) composition, controlling for seasonal temperature as a surrogate for ventilation.
For a 10 microg/m3 increase in 2-day averaged PM(2.5) concentration we found an increase of 1.89% (95% CI: 1.34- 2.45) in CVD, 2.25% (95% CI: 1.10- 3.42) in MI, 1.85% (95% CI: 1.19- 2.51) in CHF, 2.74% (95% CI: 1.30- 4.2) in diabetes, and 2.07% (95% CI: 1.20- 2.95) in respiratory admissions. The association between PM2.5 and CVD admissions was significantly modified when the mass was high in Br, Cr, Ni, and Na(+), while mass high in As, Cr, Mn, OC, Ni, and Na(+) modified MI, and mass high in As, OC, and SO(4)(2-) modified diabetes admissions. For these species, an interquartile range increase in their relative proportion was associated with a 1-2% additional increase in daily admissions per 10 microg/m(3) increase in mass.
We found that PM(2.5) mass higher in Ni, As, and Cr, as well as Br and OC significantly increased its effect on hospital admissions. This result suggests that particles from industrial combustion sources and traffic may, on average, have greater toxicity.
Land use regression (LUR) models provide good estimates of spatially resolved long-term exposures, but are poor at capturing short term exposures. Satellite-derived Aerosol Optical Depth (AOD) ...measurements have the potential to provide spatio-temporally resolved predictions of both long and short term exposures, but previous studies have generally showed relatively low predictive power. Our objective was to extend our previous work on day-specific calibrations of AOD data using ground PM₂.₅ measurements by incorporating commonly used LUR variables and meteorological variables, thus benefiting from both the spatial resolution from the LUR models and the spatio-temporal resolution from the satellite models. Later we use spatial smoothing to predict PM₂.₅ concentrations for day/locations with missing AOD measures. We used mixed models with random slopes for day to calibrate AOD data for 2000–2008 across New-England with monitored PM₂.₅ measurements. We then used a generalized additive mixed model with spatial smoothing to estimate PM₂.₅ in location–day pairs with missing AOD, using regional measured PM₂.₅, AOD values in neighboring cells, and land use. Finally, local (100 m) land use terms were used to model the difference between grid cell prediction and monitored value to capture very local traffic particles. Out-of-sample ten-fold cross-validation was used to quantify the accuracy of our predictions. For days with available AOD data we found high out-of-sample R² (mean out-of-sample R² = 0.830, year to year variation 0.725–0.904). For days without AOD values, our model performance was also excellent (mean out-of-sample R² = 0.810, year to year variation 0.692–0.887). Importantly, these R² are for daily, rather than monthly or yearly, values. Our model allows one to assess short term and long-term human exposures in order to investigate both the acute and chronic effects of ambient particles, respectively.
Little is known about the health risks of air pollution and cardiorespiratory diseases, globally, across regions and populations, which may differ because of external factors.
We systematically ...reviewed the evidence on the association between air pollution and cardiorespiratory diseases (hospital admissions and mortality), including variability by energy, transportation, socioeconomic status, and air quality.
We conducted a literature search (PubMed and Web of Science) for studies published between 2006 and May 11, 2016.
We included studies if they met all of the following criteria: (1) considered at least 1 of these air pollutants: carbon monoxide, sulfur dioxide, nitrogen dioxide, ozone, or particulate matter (PM
or PM
); (2) reported risk for hospital admissions, mortality, or both; (3) presented individual results for respiratory diseases, cardiovascular diseases, or both; (4) considered the age groups younger than 5 years, older than 65 years, or all ages; and (5) did not segregate the analysis by gender.
We extracted data from each study, including location, health outcome, and risk estimates. We performed a meta-analysis to estimate the overall effect and to account for both within- and between-study heterogeneity. Then, we applied a model selection (least absolute shrinkage and selection operator) to assess the modifier variables, and, lastly, we performed meta-regression analyses to evaluate the modifier variables contributing to heterogeneity among studies.
We assessed 2183 studies, of which we selected 529 for in-depth review, and 70 articles fulfilled our study inclusion criteria. The 70 studies selected for meta-analysis encompass more than 30 million events across 28 countries. We found positive associations between cardiorespiratory diseases and different air pollutants. For example, when we considered only the association between PM
and respiratory diseases ( Figure 1 , we observed a risk equal to 2.7% (95% confidence interval = 0.9%, 7.7%). Our results showed statistical significance in the test of moderators for all pollutants, suggesting that the modifier variables influence the average cardiorespiratory disease risk and may explain the varying effects of air pollution.
Variables related to aspects of energy, transportation, and socioeconomic status may explain the varying effect size of the association between air pollution and cardiorespiratory diseases. Public Health Implications. Our study provides a transferable model to estimate the health effects of air pollutants to support the creation of environmental health public policies for national and international intervention.
Display omitted
•Temperature and stagnation increased and precipitation less frequent, mainly in warmer months.•Strong variability of weather effects for different regions, seasons and ...pollutants.•Regional weather penalties up to 0.883 μg⋅m−3⋅yr−1 (95% CI: 0.834, 1.000) for PM10.•3220 deaths (CI: 626, 5813) due to weather penalties on PM10 in 25 years.•10% greater air quality-related health benefits if weather had remained constant.
Climate change is a major public health concern. In addition to its direct impacts on temperature patterns and extreme weather events, climate change affects public health indirectly through its influence on air quality. Pollution trends are not only affected by emissions changes but also by weather changes. In this paper we analyze air quality trends in Spain of important air pollutants (C6H6, CO, NO2, NOx, O3, PM10, PM2.5, and SO2) recorded during the last 25 years, from 1993 to 2017. We found substantial reductions in ambient concentration levels for all the pollutants studied except for O3. To assess the influence of recent weather changes on air quality trends we applied generalized additive models (GAMs) using nonparametric smoothing; with and without adjusting for weather parameters including temperature, wind speed, humidity and precipitation frequency. The difference of annual slopes estimated by the models without and with adjusting for these meteorological variables represents the impact of weather changes on pollutant trends, i.e. the ‘weather penalty’. The analyses were seasonally and geographically stratified to account for temporal and regional differences across Spain. The results were meta-analyzed to estimate weather penalties on ambient concentration trends at a national level as well as the impact on mortality for the most relevant pollutants. We found significant penalties for most pollutants, implying that air quality would have improved even more during our study period if weather conditions had remained constant. The largest weather influences were found for PM10, with seasonal penalties up to 22 μg⋅m−3 accumulated over the 25-year period in some regions. The national meta-analysis shows penalties of 0.060 μg⋅m−3 per year (95% Confidence Interval, CI: 0.004, 0.116) in cold months and 0.127 μg⋅m−3 per year (95% CI: 0.089, 0.164) in warm months. Penalties of this magnitude would correspond to 129 annual deaths (95% CI: 25, 233), i.e. approximately 3200 deaths over the 25-year period in Spain. According to our results, the health benefits of recent emission abatements for this pollutant in Spain would have been up to 10% greater if weather conditions had remained constant during the last 25 years.
The link between PM2.5 exposure and adverse health outcomes is well documented from studies across the world. However, the reported effect estimates vary across studies, locations and constituents. ...We aimed to conduct a meta-analysis on associations between short-term exposure to PM2.5 constituents and mortality using city-specific estimates, and explore factors that may explain some of the observed heterogeneity.
We systematically reviewed epidemiological studies on particle constituents and mortality using PubMed and Web of Science databases up to July 2015.We included studies that examined the association between short-term exposure to PM2.5 constituents and all-cause, cardiovascular, and respiratory mortality, in the general adult population. Each study was summarized based on pre-specified study key parameters (e.g., location, time period, population, diagnostic classification standard), and we evaluated the risk of bias using the Office of Health Assessment and Translation (OHAT) Method for each included study. We extracted city-specific mortality risk estimates for each constituent and cause of mortality. For multi-city studies, we requested the city-specific risk estimates from the authors unless reported in the article. We performed random effects meta-analyses using city-specific estimates, and examined whether the effects vary across regions and city characteristics (PM2.5 concentration levels, air temperature, elevation, vegetation, size of elderly population, population density, and baseline mortality).
We found a 0.89% (95% CI: 0.68, 1.10%) increase in all-cause, a 0.80% (95% CI: 0.41, 1.20%) increase in cardiovascular, and a 1.10% (95% CI: 0.59, 1.62%) increase in respiratory mortality per 10μg/m3 increase in PM2.5. Accounting for the downward bias induced by studies of single days, the all-cause mortality estimate increased to 1.01% (95% CI: 0.81, 1.20%). We found significant associations between mortality and several PM2.5 constituents. The most consistent and stronger associations were observed for elemental carbon (EC) and potassium (K). For most of the constituents, we observed high variability of effect estimates across cities.
Our meta-analysis suggests that (a) combustion elements such as EC and K have a stronger association with mortality, (b) single lag studies underestimate effects, and (c) estimates of PM2.5 and constituents differ across regions. Accounting for PM mass in constituent's health models may lead to more stable and comparable effect estimates across different studies.
PROSPERO: CRD42017055765.
•A meta-analysis of acute effects of PM2.5 constituents on mortality was conducted.•EC and K had the strongest and most consistent association with mortality.•Single lag studies underestimate effects.•Mortality effects of PM2.5 and constituents differ across regions.
PDF
