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1.
Environ Sci Technol ; 54(3): 1372-1384, 2020 02 04.
Artigo em Inglês | MEDLINE | ID: mdl-31851499

RESUMO

NO2 is a combustion byproduct that has been associated with multiple adverse health outcomes. To assess NO2 levels with high accuracy, we propose the use of an ensemble model to integrate multiple machine learning algorithms, including neural network, random forest, and gradient boosting, with a variety of predictor variables, including chemical transport models. This NO2 model covers the entire contiguous U.S. with daily predictions on 1-km-level grid cells from 2000 to 2016. The ensemble produced a cross-validated R2 of 0.788 overall, a spatial R2 of 0.844, and a temporal R2 of 0.729. The relationship between daily monitored and predicted NO2 is almost linear. We also estimated the associated monthly uncertainty level for the predictions and address-specific NO2 levels. This NO2 estimation has a very high spatiotemporal resolution and allows the examination of the health effects of NO2 in unmonitored areas. We found the highest NO2 levels along highways and in cities. We also observed that nationwide NO2 levels declined in early years and stagnated after 2007, in contrast to the trend at monitoring sites in urban areas, where the decline continued. Our research indicates that the integration of different predictor variables and fitting algorithms can achieve an improved air pollution modeling framework.


Assuntos
Poluentes Atmosféricos , Poluição do Ar , Algoritmos , Monitoramento Ambiental , Dióxido de Nitrogênio , Incerteza , Estados Unidos
2.
Environ Int ; 130: 104909, 2019 09.
Artigo em Inglês | MEDLINE | ID: mdl-31272018

RESUMO

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.

3.
Environ Int ; 126: 228-233, 2019 05.
Artigo em Inglês | MEDLINE | ID: mdl-30822651

RESUMO

Human-induced climate change has accelerated in recent decades, causing adverse health effects. However, the impact of the changing climate on neurological disorders in the older population is not well understood. We applied time-varying Cox proportional hazards models to estimate the associations between hospital admissions for dementia and the mean and variability of summer and winter temperatures in New England. We estimated seasonal temperatures for each New England zip code using a satellite-based prediction model. By characterizing spatial differences and temporal fluctuations in seasonal temperatures, we observed a lower risk of dementia-associated hospital admissions in years when local temperatures in either summer (hazard ration [HR] = 0.98; 95% confidence interval [CI]: 0.96, 1.00) or winter (HR = 0.97; 95% CI: 0.94, 0.99) were higher than average, and a greater risk of dementia-associated admissions for older adults living in zip codes with higher temperature variations. Effect modifications by sex, race, age, and dual eligibility were considered to examine vulnerability of population subgroups. Our results suggest that cooler-than-average temperatures and higher temperature variability increase the risk of dementia-associated hospital admissions. Thus, climate change may affect progression of dementia and associated hospitalization costs.


Assuntos
Mudança Climática , Demência/epidemiologia , Hospitalização/estatística & dados numéricos , Temperatura Ambiente , Idoso , Idoso de 80 Anos ou mais , Feminino , Humanos , Masculino , New England/epidemiologia , Estações do Ano
4.
Inhal Toxicol ; 30(3): 99-113, 2018 02.
Artigo em Inglês | MEDLINE | ID: mdl-29869579

RESUMO

Asthma, a chronic respiratory disorder with complex etiology and various phenotypes, is a considerable public health concern in the USA and worldwide. While there is evidence suggesting ambient ozone exposure may exacerbate asthma, information regarding the potential role of ozone in asthma development is more limited. Thus, we conducted a critical review of observational epidemiology studies to determine whether long-term ambient ozone exposure is a risk factor for asthma development. We identified 14 relevant studies; 11 evaluated asthma development in children, while three studies, based on a single cohort, assessed this outcome in adults. Studies of childhood asthma and long-term ozone exposure - including exposure in utero, during the first year of life and during early childhood - reported inconsistent findings, which were further weakened by critical methodological limitations in statistical analyses and in exposure and outcome assessments, such as exposure measurement error and a lack of adjustment for key confounders. For adult-onset asthma, long-term ozone exposure was associated with an increased risk in men but not women. In addition to considerable uncertainties due to potential exposure measurement error and a lack of adjustment for key confounders, this study has limited generalizability to the US general population. While experimental evidence indicates that it may be biologically plausible that long-term ozone exposure could contribute to asthma development, it does not provide insight regarding an established mode of action. Future research is needed to address the uncertainties regarding the role of long-term ambient ozone exposure in asthma development.


Assuntos
Poluentes Atmosféricos/análise , Asma/epidemiologia , Exposição Ambiental/análise , Ozônio/análise , Humanos
5.
J Environ Manage ; 211: 296-305, 2018 Apr 01.
Artigo em Inglês | MEDLINE | ID: mdl-29408079

RESUMO

A phosphorus resource crisis threatens the security of global crop production, especially in developing countries like China and Brazil. Legacy phosphorus (legacy-P), which is left behind in agricultural soil by over-fertilization, can help address this issue as a new resource in the soil phosphorus pool. However, issues involved with calculating and defining the spatial distribution of legacy-P hinder its future utilization. To resolve these issues, this study applied remote sensing and ecohydrological modeling to precisely quantify legacy-P and define its spatial distribution in China's Sanjiang Plain from 2000 to 2014. The total legacy-P in the study area was calculated as 579,090 t with an annual average of 38,600 t; this comprises 51.83% of the phosphorus fertilizer applied annually. From 2000 to 2014, the annual amount of legacy-P increased by more than 3.42-fold, equivalent to a 2460-ton increase each year. The spatial distribution of legacy-P showed heterogeneity and agglomeration in this area, with peaks in cultivated land experiencing long-term agricultural development. This study supplies a new approach to finding legacy-P in soil as a precondition for future utilization. Once its spatial distribution is known, legacy-P can be better utilized in agriculture to help alleviate the phosphorus resource crisis.


Assuntos
Agricultura , Fósforo/análise , Brasil , China , Fertilizantes , Solo
6.
Environ Int ; 109: 181-192, 2017 12.
Artigo em Inglês | MEDLINE | ID: mdl-28967432

RESUMO

Human exposure to toluene diisocyanate (TDI) occurs mainly through inhalation of vapors in occupational settings where TDI is produced or used, but dermal exposure to TDI is also possible during some operations. Because of a recent epidemiology study reporting a possible association with lung cancer risk in workers with potential dermal exposure to TDI, we evaluated the evidence from epidemiological, toxicological, and toxicokinetic studies to assess whether it is likely that dermal exposure to TDI can cause human respiratory cancers. We found that the reported associations with respiratory cancers in the epidemiology studies do not support TDI as a causal factor, as there are other explanations that are more likely than causation, such as confounding by smoking and low socioeconomic status. Experimental animal and genotoxicity studies indicate that the carcinogenic potential of TDI depends on its conversion to toluene diamine (TDA), and there is no evidence of systemic availability of TDA after dermal or inhalation exposure to TDI. Also, systemic uptake of TDI is very low after dermal exposure, and any absorbed TDI is more likely to react with biomolecules on or below the skin surface than to form TDA. Even if some TDA formation occurred after dermal exposure to TDI, TDA does not induce respiratory tract tumors in experimental animals after either dermal or oral exposure. We conclude that the available evidence indicates that dermal TDI exposure does not cause respiratory cancers in humans.


Assuntos
Poluentes Atmosféricos/toxicidade , Neoplasias Pulmonares/epidemiologia , Doenças Profissionais/epidemiologia , Tolueno 2,4-Di-Isocianato/toxicidade , Administração Cutânea , Animais , Humanos , Exposição por Inalação , Exposição Ocupacional , Risco
8.
Artigo em Inglês | MEDLINE | ID: mdl-28895893

RESUMO

Short-term exposure to fine particulate matter (PM2.5) has been associated with increased risks of cardiovascular diseases (CVDs), but whether such associations are supportive of a causal relationship is unclear, and few studies have employed formal causal analysis methods to address this. We employed nonparametric methods to examine the associations between daily concentrations of PM2.5 and hospital admissions (HAs) for CVD among adults aged 75 years and older in Texas, USA. We first quantified the associations in partial dependence plots generated using the random forest approach. We next used a Bayesian network learning algorithm to identify conditional dependencies between CVD HAs of older men and women and several predictor variables. We found that geographic location (county), time (e.g., month and year), and temperature satisfied necessary information conditions for being causes of CVD HAs among older men and women, but daily PM2.5 concentrations did not. We also found that CVD HAs of disjoint subpopulations were strongly predictive of CVD HAs among older men and women, indicating the presence of unmeasured confounders. Our findings from nonparametric analyses do not support PM2.5 as a direct cause of CVD HAs among older adults.


Assuntos
Poluentes Atmosféricos/análise , Doenças Cardiovasculares/epidemiologia , Material Particulado/análise , Idoso , Poluição do Ar/análise , Teorema de Bayes , Feminino , Hospitalização , Humanos , Masculino , Estatísticas não Paramétricas , Temperatura Ambiente , Texas/epidemiologia
9.
Epidemiology ; 28(6): 771-779, 2017 11.
Artigo em Inglês | MEDLINE | ID: mdl-28832358

RESUMO

BACKGROUND: The effect of an exposure on survival can be biased when the regression model is misspecified. Hazard difference is easier to use in risk assessment than hazard ratio and has a clearer interpretation in the assessment of effect modifications. METHODS: We proposed two doubly robust additive hazards models to estimate the causal hazard difference of a continuous exposure on survival. The first model is an inverse probability-weighted additive hazards regression. The second model is an extension of the doubly robust estimator for binary exposures by categorizing the continuous exposure. We compared these with the marginal structural model and outcome regression with correct and incorrect model specifications using simulations. We applied doubly robust additive hazard models to the estimation of hazard difference of long-term exposure to PM2.5 (particulate matter with an aerodynamic diameter less than or equal to 2.5 microns) on survival using a large cohort of 13 million older adults residing in seven states of the Southeastern United States. RESULTS: We showed that the proposed approaches are doubly robust. We found that each 1 µg m increase in annual PM2.5 exposure was associated with a causal hazard difference in mortality of 8.0 × 10 (95% confidence interval 7.4 × 10, 8.7 × 10), which was modified by age, medical history, socioeconomic status, and urbanicity. The overall hazard difference translates to approximately 5.5 (5.1, 6.0) thousand deaths per year in the study population. CONCLUSIONS: The proposed approaches improve the robustness of the additive hazards model and produce a novel additive causal estimate of PM2.5 on survival and several additive effect modifications, including social inequality.


Assuntos
Exposição Ambiental/estatística & dados numéricos , Material Particulado , Taxa de Sobrevida , Idoso , Idoso de 80 Anos ou mais , Causalidade , Simulação por Computador , Feminino , Humanos , Modelos Lineares , Masculino , Modelos Estatísticos , Probabilidade , Pontuação de Propensão , Modelos de Riscos Proporcionais , Sudeste dos Estados Unidos
10.
Environ Int ; 104: 139-145, 2017 07.
Artigo em Inglês | MEDLINE | ID: mdl-28434561

RESUMO

BACKGROUND: Short-term exposure to ozone has been associated with asthma hospital admissions (HA) and emergency department (ED) visits, but the shape of the concentration-response (C-R) curve is unclear. METHODS: We conducted a time series analysis of asthma HAs and ambient ozone concentrations in six metropolitan areas in Texas from 2001 to 2013. Using generalized linear regression models, we estimated the effect of daily 8-hour maximum ozone concentrations on asthma HAs for all ages combined, and for those aged 5-14, 15-64, and 65+years. We fit penalized regression splines to evaluate the shape of the C-R curves. RESULTS: Using a log-linear model, estimated risk per 10ppb increase in average daily 8-hour maximum ozone concentrations was highest for children (relative risk [RR]=1.047, 95% confidence interval [CI]: 1.025-1.069), lower for younger adults (RR=1.018, 95% CI: 1.005-1.032), and null for older adults (RR=1.002, 95% CI: 0.981-1.023). However, penalized spline models demonstrated significant nonlinear C-R relationships for all ages combined, children, and younger adults, indicating the existence of thresholds. We did not observe an increased risk of asthma HAs until average daily 8-hour maximum ozone concentrations exceeded approximately 40ppb. CONCLUSION: Ozone and asthma HAs are significantly associated with each other; susceptibility to ozone is age-dependent, with children at highest risk. C-R relationships between average daily 8-hour maximum ozone concentrations and asthma HAs are significantly curvilinear for all ages combined, children, and younger adults. These nonlinear relationships, as well as the lack of relationship between average daily 8-hour maximum and peak ozone concentrations, have important implications for assessing risks to human health in regulatory settings.


Assuntos
Poluentes Atmosféricos/análise , Asma/epidemiologia , Hospitalização/estatística & dados numéricos , Ozônio/análise , Adolescente , Adulto , Idoso , Poluentes Atmosféricos/efeitos adversos , Criança , Pré-Escolar , Exposição Ambiental/efeitos adversos , Exposição Ambiental/análise , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Ozônio/efeitos adversos , Medição de Risco , Texas/epidemiologia , Adulto Jovem
11.
Epidemiology ; 28(2): 207-214, 2017 03.
Artigo em Inglês | MEDLINE | ID: mdl-28005571

RESUMO

BACKGROUND: Little is known about what factors modify the effect of long-term exposure to PM2.5 on mortality, in part because in most previous studies certain groups such as rural residents and individuals with lower socioeconomic status (SES) are under-represented. METHODS: We studied 13.1 million Medicare beneficiaries (age ≥65) residing in seven southeastern US states during 2000-2013 with 95 million person-years of follow-up. We predicted annual average of PM2.5 in each zip code tabulation area (ZCTA) using a hybrid spatiotemporal model. We fit Cox proportional hazards models to estimate the association between long-term PM2.5 and mortality. We tested effect modification by individual-level covariates (race, sex, eligibility for both Medicare and Medicaid, and medical history), neighborhood-level covariates (urbanicity, percentage below poverty level, lower education, median income, and median home value), mean summer temperature, and mass fraction of 11 PM2.5 components. RESULTS: The hazard ratio (HR) for death was 1.021 (95% confidence interval: 1.019, 1.022) per 1 µg m increase in annual PM2.5. The HR decreased with age. It was higher among males, non-whites, dual-eligible individuals, and beneficiaries with previous hospital admissions. It was higher in neighborhoods with lower SES or higher urbanicity. The HR increased with mean summer temperature. The risk associated with PM2.5 increased with relative concentration of elemental carbon, vanadium, copper, calcium, and iron and decreased with nitrate, organic carbon, and sulfate. CONCLUSIONS: Associations between long-term PM2.5 exposure and death were modified by individual-level, neighborhood-level variables, temperature, and chemical compositions.


Assuntos
Poluição do Ar/estatística & dados numéricos , Exposição Ambiental/estatística & dados numéricos , Grupos Étnicos/estatística & dados numéricos , Mortalidade , Material Particulado , Afro-Americanos/estatística & dados numéricos , Idoso , Idoso de 80 Anos ou mais , Grupo com Ancestrais do Continente Europeu/estatística & dados numéricos , Feminino , Humanos , Masculino , Medicaid , Medicare , Modelos de Riscos Proporcionais , Características de Residência/estatística & dados numéricos , Sudeste dos Estados Unidos , Temperatura Ambiente , Estados Unidos
12.
Environ Res ; 151: 610-617, 2016 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-27611992

RESUMO

There are many studies that have posited an association between extreme temperature and increased mortality. However, most studies use temperature at a single station per city as the reference point to analyze deaths. This leads to exposure misclassification and usually the exclusion of exurban, small town, and rural populations. In addition, few studies control for confounding by PM2.5, which is expected to induce upward bias. The high-resolution temperature and PM2.5 data at a resolution of 1km2 were derived from satellite images and other land use sources. To capture the nonlinear association of temperature with mortality we fit a piecewise linear spline function for temperature, with a change in slope at -1°C and 28°C, the temperature threshold at which mortality in Georgia, North Carolina, and South Carolina increases due to cold and heat, respectively. We conducted stratified analyses by age group, sex, race, education, and urban vs nonurban, as well as sensitivity analyses of different temperature threshold and covariate sets. We found a 0.19% (95% CI=-0.98, 1.34%) increase in mortality for each 1°C decrease in temperature below -1°C and a 2.05% (95% CI=0.87, 3.24%) increase in mortality for each 1°C increase in temperature above 28°C, a 79.8% larger effect size for heat compared to the station-based metric. The effect estimates relying on the monitoring stations were 0.09% (95% CI=-0.79, 0.95%) and 1.14% (95% CI=0.08, 1.57%) for the equivalent temperature changes. The estimates were not confounded by PM2.5. Children under 15 years of age had the largest percentage increase per 1°C increase in temperature (8.19%, 95% CI=-0.38 to 17.49%) followed by Blacks (4.35%, 95% CI=2.22 to 6.53%). Higher education was a protective factor for the effect of extreme temperature on mortality. There was a suggestion that people in less urban areas were more susceptible to extreme temperature. The relationship between temperature and mortality was stronger when using exposure data with more spatial variability than using exposure data based on existing monitors alone.


Assuntos
Poluentes Atmosféricos/análise , Temperatura Baixa/efeitos adversos , Exposição Ambiental/análise , Temperatura Alta/efeitos adversos , Mortalidade/tendências , Material Particulado/análise , Adolescente , Adulto , Fatores Etários , Idoso , Poluentes Atmosféricos/efeitos adversos , Relação Dose-Resposta a Droga , Exposição Ambiental/efeitos adversos , Exposição Ambiental/estatística & dados numéricos , Feminino , Georgia/epidemiologia , Humanos , Masculino , Pessoa de Meia-Idade , Modelos Estatísticos , North Carolina/epidemiologia , Material Particulado/efeitos adversos , Saúde da População Rural , Estações do Ano , South Carolina/epidemiologia , Saúde da População Urbana , Adulto Jovem
13.
Sci Total Environ ; 573: 397-408, 2016 Dec 15.
Artigo em Inglês | MEDLINE | ID: mdl-27572533

RESUMO

The detection of critical source areas (CSAs) is a key step in managing soil phosphorus (P) loss and preventing the long-term eutrophication of water bodies at regional scale. Most related studies, however, focus on a local scale, which prevents a clear understanding of the spatial distribution of CSAs for soil P loss at regional scale. Moreover, the continual, long-term variation in CSAs was scarcely reported. It is impossible to identify the factors driving the variation in CSAs, or to collect land surface information essential for CSAs detection, by merely using the conventional methodologies at regional scale. This study proposes a new regional-scale approach, based on three satellite sensors (ASTER, TM/ETM and MODIS), that were implemented successfully to detect CSAs at regional scale over 15years (2000-2014). The approach incorporated five factors (precipitation, slope, soil erosion, land use, soil total phosphorus) that drive soil P loss from CSAs. Results show that the average area of critical phosphorus source areas (CPSAs) was 15,056km2 over the 15-year period, and it occupied 13.8% of the total area, with a range varying from 1.2% to 23.0%, in a representative, intensive agricultural area of China. In contrast to previous studies, we found that the locations of CSAs with P loss are spatially variable, and are more dispersed in their distribution over the long term. We also found that precipitation acts as a key driving factor in the variation of CSAs at regional scale. The regional-scale method can provide scientific guidance for managing soil phosphorus loss and preventing the long-term eutrophication of water bodies at regional scale, and shows great potential for exploring factors that drive the variation in CSAs at global scale.

14.
Sci Rep ; 6: 30161, 2016 07 20.
Artigo em Inglês | MEDLINE | ID: mdl-27436237

RESUMO

Climate change may affect human health, particularly for elderly individuals who are vulnerable to temperature changes. While many studies have investigated the acute effects of heat, only a few have dealt with the chronic ones. We have examined the effects of seasonal temperatures on survival of the elderly in the Southeastern USA, where a large fraction of subpopulation resides. We found that both seasonal mean temperature and its standard deviation (SD) affected long-term survival among the 13 million Medicare beneficiaries (aged 65+) in this region during 2000-2013. A 1 °C increase in summer mean temperature corresponded to an increase of 2.5% in death rate. Whereas, 1 °C increase in winter mean temperature was associated with a decrease of 1.5%. Increases in seasonal temperature SD also influence mortality. We decomposed seasonal mean temperature and its temperature SD into long-term geographic contrasts between ZIP codes and annual anomalies within ZIP code. Effect modifications by different subgroups were also examined to find out whether certain individuals are more vulnerable. Our findings will be critical to future efforts assessing health risks related to the future climate change.


Assuntos
Mudança Climática/mortalidade , Mortalidade/tendências , Idoso , Temperatura Baixa/efeitos adversos , Feminino , Temperatura Alta/efeitos adversos , Humanos , Masculino , Estações do Ano , Temperatura Ambiente , Estados Unidos
15.
Environ Int ; 94: 141-149, 2016 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-27248660

RESUMO

The frequency, duration, and intensity of cold waves are expected to decrease in the near future under the changing climate. However, there is a lack of understanding on future mortality related to cold waves. The present study conducted a large-scale national projection to estimate future mortality attributable to cold waves during 1960-2050 in 209 US cities. Cold waves were defined as two, three, or at least four consecutive days with daily temperature lower than the 5th percentile of temperatures in each city. The lingering period of a cold wave was defined as the non-cold wave days within seven days following that cold wave period. First, with 168million residents in 209 US cities during 1962-2006, we fitted over-dispersed Poisson regressions to estimate the immediate and lingering effects of cold waves on mortality and tested if the associations were modified by the duration of cold waves, the intensity of cold waves, and mean winter temperature (MWT). Then we projected future mortality related to cold waves using 20 downscaled climate models. Here we show that the cold waves (both immediate and lingering) were associated with an increased but small risk of mortality. The associations varied substantially across climate regions. The risk increased with the duration and intensity of cold waves but decreased with MWT. The projected mortality related to cold waves would decrease from 1960 to 2050. Such a decrease, however, is small and may not be able to offset the potential increase in heat-related deaths if the adaptation to heat is not adequate.


Assuntos
Temperatura Baixa/efeitos adversos , Mortalidade , Cidades , Mudança Climática , Previsões , Humanos , Estações do Ano , Estados Unidos
16.
Environ Res ; 146: 51-8, 2016 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-26717080

RESUMO

Accurate estimates of spatio-temporal resolved near-surface air temperature (Ta) are crucial for environmental epidemiological studies. However, values of Ta are conventionally obtained from weather stations, which have limited spatial coverage. Satellite surface temperature (Ts) measurements offer the possibility of local exposure estimates across large domains. The Southeastern United States has different climatic conditions, more small water bodies and wetlands, and greater humidity in contrast to other regions, which add to the challenge of modeling air temperature. In this study, we incorporated satellite Ts to estimate high resolution (1km×1km) daily Ta across the southeastern USA for 2000-2014. We calibrated Ts-Ta measurements using mixed linear models, land use, and separate slopes for each day. A high out-of-sample cross-validated R(2) of 0.952 indicated excellent model performance. When satellite Ts were unavailable, linear regression on nearby monitors and spatio-temporal smoothing was used to estimate Ta. The daily Ta estimations were compared to the NASA's Modern-Era Retrospective Analysis for Research and Applications (MERRA) model. A good agreement with an R(2) of 0.969 and a mean squared prediction error (RMSPE) of 1.376°C was achieved. Our results demonstrate that Ta can be reliably predicted using this Ts-based prediction model, even in a large geographical area with topography and weather patterns varying considerably.


Assuntos
Meteorologia/métodos , Modelos Estatísticos , Astronave , Temperatura Ambiente , Calibragem , Geografia , Modelos Lineares , Tecnologia de Sensoriamento Remoto , Estudos Retrospectivos , Estações do Ano , Sudeste dos Estados Unidos , Tennessee
17.
Environ Health Perspect ; 124(1): 46-52, 2016 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-26038801

RESUMO

BACKGROUND: Both short- and long-term exposures to fine particulate matter (≤ 2.5 µm; PM2.5) are associated with mortality. However, whether the associations exist at levels below the new U.S. Environmental Protection Agency (EPA) standards (12 µg/m3 of annual average PM2.5, 35 µg/m3 daily) is unclear. In addition, it is not clear whether results from previous time series studies (fit in larger cities) and cohort studies (fit in convenience samples) are generalizable. OBJECTIVES: We estimated the effects of low-concentration PM2.5 on mortality. METHODS: High resolution (1 km × 1 km) daily PM2.5 predictions, derived from satellite aerosol optical depth retrievals, were used. Poisson regressions were applied to a Medicare population (≥ 65 years of age) in New England to simultaneously estimate the acute and chronic effects of exposure to PM2.5, with mutual adjustment for short- and long-term exposure, as well as for area-based confounders. Models were also restricted to annual concentrations < 10 µg/m3 or daily concentrations < 30 µg/m3. RESULTS: PM2.5 was associated with increased mortality. In the study cohort, 2.14% (95% CI: 1.38, 2.89%) and 7.52% (95% CI: 1.95, 13.40%) increases were estimated for each 10-µg/m3 increase in short- (2 day) and long-term (1 year) exposure, respectively. The associations held for analyses restricted to low-concentration PM2.5 exposure, and the corresponding estimates were 2.14% (95% CI: 1.34, 2.95%) and 9.28% (95% CI: 0.76, 18.52%). Penalized spline models of long-term exposure indicated a larger effect for mortality in association with exposures ≥ 6 µg/m3 versus those < 6 µg/m3. In contrast, the association between short-term exposure and mortality appeared to be linear across the entire exposure distribution. CONCLUSIONS: Using a mutually adjusted model, we estimated significant acute and chronic effects of PM2.5 exposure below the current U.S. EPA standards. These findings suggest that improving air quality with even lower PM2.5 than currently allowed by U.S. EPA standards may benefit public health. CITATION: Shi L, Zanobetti A, Kloog I, Coull BA, Koutrakis P, Melly SJ, Schwartz JD. 2016. Low-concentration PM2.5 and mortality: estimating acute and chronic effects in a population-based study. Environ Health Perspect 124:46-52; http://dx.doi.org/10.1289/ehp.1409111.


Assuntos
Mortalidade , Material Particulado/toxicidade , Poluição do Ar/efeitos adversos , Feminino , Humanos , Masculino , Saúde Pública , Estados Unidos , United States Environmental Protection Agency
18.
Eur J Prev Cardiol ; 23(6): 602-12, 2016 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-26025448

RESUMO

BACKGROUND: Epidemiological studies in humans that have evaluated the association between fine particulate matter (PM2.5) and atherosclerosis have yielded mixed results. DESIGN: In order to further investigate this relationship, we conducted a comprehensive search for studies published through May 2014 and performed a meta-analysis of all available observational studies that investigated the association between PM2.5 and three noninvasive measures of clinical and subclinical atherosclerosis: carotid intima media thickness, arterial calcification, and ankle-brachial index. METHODS AND RESULTS: Five reviewers selected studies based on predefined inclusion criteria. Pooled mean change estimates and 95% confidence intervals were calculated using random-effects models. Assessment of between-study heterogeneity was performed where the number of studies was adequate. Our pooled sample included 11,947 subjects for carotid intima media thickness estimates, 10,750 for arterial calcification estimates, and 6497 for ankle-brachial index estimates. Per 10 µg/m(3) increase in PM2.5 exposure, carotid intima media thickness increased by 22.52 µm but this did not reach statistical significance (p = 0.06). We did not find similar associations for arterial calcification (p = 0.44) or ankle-brachial index (p = 0.85). CONCLUSION: Our meta-analysis supports a relationship between PM2.5 and subclinical atherosclerosis measured by carotid intima media thickness. We did not find a similar relationship between PM2.5 and arterial calcification or ankle-brachial index, although the number of studies was small.


Assuntos
Poluentes Atmosféricos/efeitos adversos , Aterosclerose/induzido quimicamente , Doenças das Artérias Carótidas/induzido quimicamente , Material Particulado/efeitos adversos , Calcificação Vascular/induzido quimicamente , Índice Tornozelo-Braço , Doenças Assintomáticas , Aterosclerose/diagnóstico , Aterosclerose/epidemiologia , Doenças das Artérias Carótidas/diagnóstico , Doenças das Artérias Carótidas/epidemiologia , Espessura Intima-Media Carotídea , Humanos , Exposição por Inalação/efeitos adversos , Estudos Observacionais como Assunto , Tamanho da Partícula , Valor Preditivo dos Testes , Medição de Risco , Fatores de Risco , Calcificação Vascular/diagnóstico , Calcificação Vascular/epidemiologia
19.
Nat Clim Chang ; 5: 988-991, 2015 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-26640524

RESUMO

Rapid buildup of greenhouse gases is expected to increase the Earth surface mean temperature, with unclear effects on temperature variability1-3. This adds urgency to better understand the direct effects of the changing climate on human health. However, the effects of prolonged exposures to temperatures, which are important for understanding the public health burden, are unclear. Here we demonstrate that long-term survival was significantly associated with both seasonal mean values and standard deviations (SDs) of temperature among the Medicare population (aged 65+) in New England, and break that down into long-term contrasts between ZIP codes and annual anomalies. A rise in summer mean temperature of 1 °C was associated with 1.0% higher death rate whereas an increase in winter mean temperature corresponded to 0.6% lower mortality. Increases in temperature SDs for both summer and winter were harmful. The increased mortality in warmer summers was entirely due to anomalies, while it was long term average differences in summer SD across ZIP codes that drove the increased risk. For future climate scenarios, seasonal mean temperatures may in part account for the public health burden, but excess public health risk of climate change may also stem from changes of within season temperature variability.

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