Health Impact Assessment of the 2020 Washington State Wildfire Smoke Episode: Excess Health Burden Attributable to Increased PM2.5 Exposures and Potential Exposure Reductions.
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SUBMITTER: Liu Y
PROVIDER: S-EPMC8101535 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Ambient fine particulate matter <2.5 µm (PM2.5) air pollution increases premature mortality globally. Some PM2.5 is natural, but anthropogenic PM2.5 is comparatively avoidable. We determined the impact of long-term exposures to the anthropogenic PM component on mortality in Australia. PM2.5-attributable deaths were calculated for all Australian Statistical Area 2 (SA2; n = 2310) regions. All-cause death rates from Australian mortality and population databases were combined with annual anthropogenic PM2.5 exposures for the years 2006-2016. Relative risk estimates were derived from the literature. Population-weighted average PM2.5 concentrations were estimated in each SA2 using a satellite and land use regression model for Australia. PM2.5-attributable mortality was calculated using a health-impact assessment methodology with life tables and all-cause death rates. The changes in life expectancy (LE) from birth, years of life lost (YLL), and economic cost of lost life years were calculated using the 2019 value of a statistical life. Nationally, long-term population-weighted average total and anthropogenic PM2.5 concentrations were 6.5 µg/m3 (min 1.2-max 14.2) and 3.2 µg/m3 (min 0-max 9.5), respectively. Annually, anthropogenic PM2.5-pollution is associated with 2616 (95% confidence intervals 1712, 3455) deaths, corresponding to a 0.2-year (95% CI 0.14, 0.28) reduction in LE for children aged 0-4 years, 38,962 (95%CI 25,391, 51,669) YLL and an average annual economic burden of $6.2 billion (95%CI $4.0 billion, $8.1 billion). We conclude that the anthropogenic PM2.5-related costs of mortality in Australia are higher than community standards should allow, and reductions in emissions are recommended to achieve avoidable mortality.
| S-EPMC7795118 | biostudies-literature
Project description:BackgroundSubstantial increases in wildfire activity have been recorded in recent decades. Wildfires influence the chemical composition and concentration of particulate matter ≤2.5 μm in aerodynamic diameter (PM2.5). However, relatively few epidemiologic studies focus on the health impacts of wildfire smoke PM2.5 compared with the number of studies focusing on total PM2.5 exposure.ObjectivesWe estimated the associations between cardiorespiratory acute events and exposure to smoke PM2.5 in Colorado using a novel exposure model to separate smoke PM2.5 from background ambient PM2.5 levels.MethodsWe obtained emergency department visits and hospitalizations for acute cardiorespiratory outcomes from Colorado for May-August 2011-2014, geocoded to a 4 km geographic grid. Combining ground measurements, chemical transport models, and remote sensing data, we estimated smoke PM2.5 and non-smoke PM2.5 on a 1 km spatial grid and aggregated to match the resolution of the health data. Time-stratified, case-crossover models were fit using conditional logistic regression to estimate associations between fire smoke PM2.5 and non-smoke PM2.5 for overall and age-stratified outcomes using 2-day averaging windows for cardiovascular disease and 3-day windows for respiratory disease.ResultsPer 1 μg/m3 increase in fire smoke PM2.5, statistically significant associations were observed for asthma (OR = 1.081 (1.058, 1.105)) and combined respiratory disease (OR = 1.021 (1.012, 1.031)). No significant relationships were evident for cardiovascular diseases and smoke PM2.5. Associations with non-smoke PM2.5 were null for all outcomes. Positive age-specific associations related to smoke PM2.5 were observed for asthma and combined respiratory disease in children, and for asthma, bronchitis, COPD, and combined respiratory disease in adults. No significant associations were found in older adults.DiscussionThis is the first multi-year, high-resolution epidemiologic study to incorporate statistical and chemical transport modeling methods to estimate PM2.5 exposure due to wildfires. Our results allow for a more precise assessment of the population health impact of wildfire-related PM2.5 exposure in a changing climate.
| S-EPMC8163094 | biostudies-literature
Project description:BackgroundPhotochemical modeling can predict the level and distribution of pollutant concentrations over time, but is resource-intensive. Partly for this reason, there are few studies exploring the multi-year trajectory of the historical change in fine particle (PM2.5) levels and associated health impacts in the U.S.ObjectivesWe used a unique dataset of Community Multi-Scale Air Quality (CMAQ) model simulations performed for a subset of years over a decade-long period fused with observations to estimate the change in ambient levels of PM2.5 across the contiguous U.S. We also quantified the change in PM2.5-attributable health risks and characterized the level of risk inequality over this period.MethodsWe estimated annual mean PM2.5 concentrations in 2005, 2011 and 2014. Using log-linear and logistic concentration-response coefficients we estimated changes in the numbers of deaths, hospital admissions and other morbidity outcomes. Calculating the Gini coefficient and Atkinson Index, we characterized the extent to which PM2.5 attributable risks were shared equally across the population or instead concentrated among certain subgroups.ResultsIn 2005 the estimated fraction of deaths due to PM2.5 was 6.1%. This estimated value falls to 4.6% by 2014. Every portion of the contiguous U.S. experiences a decline in the risk of PM-related premature death over the 10-year period. As measured by the Gini coefficient and Atkinson index, the level of PM mortality risk is shared more equally in 2014 than in 2005 among all subgroups.ConclusionsBetween 2005 and 2014, the level of PM2.5 concentrations fall, and the risk of premature death, declined and became more equitably distributed across the U.S.Population
| S-EPMC6716061 | biostudies-literature
Project description:China's unprecedented lockdown to contain the spread of the novel coronavirus disease (COVID-19) in early 2020, provided a tragic natural experiment to investigate the responses of atmospheric pollution to emission reduction at regional scale. Primarily driven by primary emissions, particulate trace elements is vitally important due to their disproportionally adverse impacts on human health and ecosystem. Here 14 trace elements in PM2.5 were selected for continuous measurement hourly in urban representative site of Shanghai, for three different phases: pre-control period (1-23 January 2020), control period (24 January-10 February 2020; overlapped with Chinese Lunar New Year holiday) and post control period (11-26 February 2020) the city's lockdown measures. The results show that all meteorological parameters (including temperature, RH, mixing layer height et al.) were generally consistent among different periods. Throughout the study period, the concentrations of most species displayed a "V-shaped" trend, suggesting significant effects by the restriction measures imposed during the lockdown period. While this is not the case for species like K, Cu and Ba, indicating their unusual origins. As a case study, the geographical origins of Cu were explored. Seven major sources, i.e., Vehicle-related emission (including road dust; indicative of Ca, Fe, Ba, Mn, Zn, Cu; accounting for 30.1%), shipping (Ni; 5.0%), coal combustion (As, Pb; 4.2%), Se and Cr industry (24.9%), nonferrous metal smelting (Au, Hg; 7.5%) and fireworks burning (K, Cu, Ba; 28.3%) were successfully pinpointed based on positive matrix factorization (PMF) analysis. Our source apportionment results also highlight fireworks burning was one of the dominant source of trace elements during the Chinese Lunar New Year holiday. It is worth noting that 56% of the total mass vehicular emissions are affiliated with non-exhaust sources (tire wear, brake wear, and road surface abrasion).
| S-EPMC8718126 | biostudies-literature
Project description:Background: Exposure to 2.5-micron diameter air pollutants (PM 2.5) has been associated with an increased risk of illness and death worldwide; however, in Latin American health impacts assessment of this risk factor is scarce. Medellín is one of the most polluted cities in the region, with a population growth rate that is twice as high as that of other Colombian cities, which implies a growing population at risk. Methods: A descriptive study of the disease burden was carried out using the city as the unit of observation. Health events were selected based on epidemiologic evidence and the availability of the population attributable fraction associated with PM 2.5. The mortality records were taken from the module of deceased of the Single Registry of Affiliates of the Health System; the morbidity records were taken from the Individual Health Services Registries. For the estimation of the burden of disease, the current Global Burden of Disease guidelines were followed. Results: Attributable disability-adjusted life years to exposure to ambient PM 2.5 pollution (DALYs PM2.5) constituted 13.8% of total burden of the city. Males showed the greatest loss of DALYs PM2.5 due to acute events, while in women the greatest loss was due to chronic events. Ischemic heart disease, chronic diseases of the lower respiratory tract, and influenza and pneumonia were the events that contributed the most to DALYs PM2.5. 71.4% of the DALYs PM2.5 corresponded to mortality, mainly in the population over 65 years of age. Regarding attributable morbidity, acute events were more prevalent in both sexes, especially due to respiratory diseases Conclusion: Premature death among the elderly population has the greatest weight on burden of disease attributable to ambient PM 2.5 pollution, mainly due to respiratory and cardiovascular diseases, without significant differences according to gender.
| S-EPMC8564742 | biostudies-literature
Project description:IntroductionEstimating PM2.5 concentrations and their prediction uncertainties at a high spatiotemporal resolution is important for air pollution health effect studies. This is particularly challenging for California, which has high variability in natural (e.g, wildfires, dust) and anthropogenic emissions, meteorology, topography (e.g. desert surfaces, mountains, snow cover) and land use.MethodsUsing ensemble-based deep learning with big data fused from multiple sources we developed a PM2.5 prediction model with uncertainty estimates at a high spatial (1 km × 1 km) and temporal (weekly) resolution for a 10-year time span (2008-2017). We leveraged autoencoder-based full residual deep networks to model complex nonlinear interrelationships among PM2.5 emission, transport and dispersion factors and other influential features. These included remote sensing data (MAIAC aerosol optical depth (AOD), normalized difference vegetation index, impervious surface), MERRA-2 GMI Replay Simulation (M2GMI) output, wildfire smoke plume dispersion, meteorology, land cover, traffic, elevation, and spatiotemporal trends (geo-coordinates, temporal basis functions, time index). As one of the primary predictors of interest with substantial missing data in California related to bright surfaces, cloud cover and other known interferences, missing MAIAC AOD observations were imputed and adjusted for relative humidity and vertical distribution. Wildfire smoke contribution to PM2.5 was also calculated through HYSPLIT dispersion modeling of smoke emissions derived from MODIS fire radiative power using the Fire Energetics and Emissions Research version 1.0 model.ResultsEnsemble deep learning to predict PM2.5 achieved an overall mean training RMSE of 1.54 μg/m3 (R2: 0.94) and test RMSE of 2.29 μg/m3 (R2: 0.87). The top predictors included M2GMI carbon monoxide mixing ratio in the bottom layer, temporal basis functions, spatial location, air temperature, MAIAC AOD, and PM2.5 sea salt mass concentration. In an independent test using three long-term AQS sites and one short-term non-AQS site, our model achieved a high correlation (>0.8) and a low RMSE (<3 μg/m3). Statewide predictions indicated that our model can capture the spatial distribution and temporal peaks in wildfire-related PM2.5. The coefficient of variation indicated highest uncertainty over deciduous and mixed forests and open water land covers.ConclusionOur method can be generalized to other regions, including those having a mix of major urban areas, deserts, intensive smoke events, snow cover and complex terrains, where PM2.5 has previously been challenging to predict. Prediction uncertainty estimates can also inform further model development and measurement error evaluations in exposure and health studies.
| S-EPMC7643812 | biostudies-literature
Project description:Wildfire smoke is a growing public health concern in the United States. Numerous studies have documented associations between ambient smoke exposure and severe patient outcomes for single-fire seasons or limited geographic regions. However, there are few national-scale health studies of wildfire smoke in the United States, few studies investigating Intensive Care Unit (ICU) admissions as an outcome, and few specifically framed around hospital operations. This study retrospectively examined the associations between ambient wildfire-related PM2.5 at a hospital ZIP code with total hospital ICU admissions using a national-scale hospitalization data set. Wildfire smoke was characterized using a combination of kriged PM2.5 monitor observations and satellite-derived plume polygons from National Oceanic and Atmospheric Administration's Hazard Mapping System. ICU admissions data were acquired from Premier, Inc. and encompass 15%-20% of all U.S. ICU admissions during the study period. Associations were estimated using a distributed-lag conditional Poisson model under a time-stratified case-crossover design. We found that a 10 μg/m3 increase in daily wildfire PM2.5 was associated with a 2.7% (95% CI: 1.3, 4.1; p = 0.00018) increase in ICU admissions 5 days later. Under stratification, positive associations were found among patients aged 0-20 and 60+, patients living in the Midwest Census Region, patients admitted in the years 2013-2015, and non-Black patients, though other results were mixed. Following a simulated severe 7-day 120 μg/m3 smoke event, our results predict ICU bed utilization peaking at 131% (95% CI: 43, 239; p < 10-5) over baseline. Our work suggests that hospitals may need to preposition vital critical care resources when severe smoke events are forecast.
| S-EPMC8095362 | biostudies-literature
Project description:Long-term air pollution exposure, notably fine particulate matter, is a global contributor to morbidity and mortality and a known risk factor for coronary artery disease (CAD) and myocardial infarctions (MI). Knowledge of impacts related to source-apportioned PM2.5 is limited. New modeling methods allow researchers to estimate source-specific long-term impacts on the prevalence of CAD and MI. The Catheterization Genetics (CATHGEN) cohort consists of patients who underwent a cardiac catheterization at Duke University Medical Center between 2002 and 2010. Severity of coronary blockage was determined by coronary angiography and converted into a binary indicator of clinical CAD. History of MI was extracted from medical records. Annual averages of source specific PM2.5 were estimated using an improved gas-constrained source apportionment model for North Carolina from 2002 to 2010. We tested six sources of PM2.5 mass for associations with CAD and MI using mixed effects multivariable logistic regression with a random intercept for county and multiple adjustments. PM2.5 fractions of ammonium bisulfate and ammonium nitrate were associated with increased prevalence of CAD (odds ratio [OR] 1.20; 95% CI = 1.11, 1.22 and OR 1.18; 95% CI = 1.05, 1.32, respectively). PM2.5 from ammonium bisulfate and ammonium nitrate were also associated with increased prevalence of MI (OR 1.20; 95% CI = 1.10, 1.29 and OR 1.35; 95% CI = 1.20, 1.53, respectively). Greater PM2.5 concentrations of ammonium bisulfate and ammonium nitrate are associated with greater MI and CAD prevalence. The association with bisulfate suggests aerosol acidity may play a role. Our findings suggest analyses of source specific PM2.5 mass can reveal novel associations.
| S-EPMC8196100 | biostudies-literature
Project description:ObjectivePM2.5, which is a major contributor to air pollution, has large effects on lung cancer mortality. We want to analyse the long-term trends in lung cancer burden attributable to PM2.5 exposure and provide evidence that can be used for preventive measures and health resource planning.MethodsMortality data related to lung cancer were obtained from the Global Burden of Disease (GBD) 2019 project. A joinpoint regression analysis was used to assess the magnitude and direction of the trends in mortality from 1990 to 2019, and the age-period-cohort method was used to analyse the temporal trends in the mortality rate of lung cancer attributable to PM2.5 exposure by age, period, and cohort.ResultsFrom 1990 to 2019, the age-standardized mortality rate (ASMR) attributable to PM2.5 exposure trended slowly upwards, and the ASMR due to ambient PM2.5 exposure (APE) increased significantly, that due to household PM2.5 exposure (HPE) decreased. The longitudinal age curves show that the mortality rates due to PM2.5 exposure among younger individuals were low, and they significantly increased from their levels among those in the 45-49 age group to their levels among those in the over-85 age group. From 1990 to 2019, the period RRs due to APE increased, but those due to HPE decreased. Similar trends were observed in the cohort RRs. The overall net drift per year attributable to PM2.5 exposure was below 0. The local drift values increased with age and were above 0 for the over-80 age groups. The overall net drifts per year were above zero for APE and below zero for HPE. The corresponding results among males were higher than those among females.ConclusionsIn China, the type of air pollution responsible for lung cancer has changed from household air pollution to ambient air pollution. PM2.5 exposure is more harmful among males and older people. Ambient air pollution should be emphasized, and China should strengthen its implementation of effective public policies and other interventions.
| S-EPMC8051098 | biostudies-literature
Project description:Studies worldwide have estimated the number of deaths attributable to long-term exposure to fine airborne particles (PM2.5), but limited information is available on short-term exposure, particularly in China. In addition, most existing studies have assumed that short-term PM2.5-mortality associations were linear. For this reason, the use of linear exposure-response functions for calculating disease burden of short-term exposure to PM2.5 in China may not be appropriate. There is an urgent need for a comprehensive, evidence-based assessment of the disease burden related to short-term PM2.5 exposure in China. Here, we explored the non-linear association between short-term PM2.5 exposure and all-cause mortality in 104 counties in China; estimated county-specific mortality burdens attributable to short-term PM2.5 exposure for all counties in the country and analyzed spatial characteristics of the mortality burden due to short-term PM2.5 exposure in China. The pooled PM2.5-mortality association was non-linear, with a reversed J-shape. We found an approximately linear increased risk of mortality from 0 to 62 μg/m3 and decreased risk from 62 to 250 μg/m3. We estimated a total of 169,862 additional deaths from short-term PM2.5 exposure throughout China in 2015. Models using linear exposure-response functions for the PM2.5-mortality association estimated 32,186 deaths attributable to PM2.5 exposure, which is 5.3 times lower than estimates from the non-linear effect model. Short-term PM2.5 exposure contributed greatly to the death burden in China, approximately one seventh of the estimates from the chronic effect. It is essential and crucial to incorporate short-term PM2.5-related mortality estimations when considering the disease burden attributable to PM2.5 in developing countries such as China. Traditional linear effect models likely underestimated the mortality burden due to short-term exposure to PM2.5.
| S-EPMC6548716 | biostudies-literature