Fine particulate air pollution and its components in association with cause-specific emergency admissions

Association between fine particulate air pollution and hospital admissions for chest pain in a subtropical city: Taipei, Taiwan

This study determined the association between fine particles (PM_2.5) levels and hospital admissions for chest pain (CP) in Taipei, Taiwan. Hospital admissions for CP and ambient air pollution data for Taipei were obtained for the period 2009-2013. The relative risk of hospital admissions was estimated using a case-crossover approach, after controlling for weather variables, day of the week, seasonality, and long-term time trends. For single-pollutant models (without adjustment for other pollutants), increased frequency of CP admissions was significantly associated with PM_2.5 levels on warm days (>23°C), with an interquartile range rise correlated with a 15% (95% confidence interval = 11-31%) elevation in number of CP admissions. In two-pollutant models, PM_2.5 remained significant after inclusion of each of the other four pollutants: sulfur dioxide (SO₂), nitrogen dioxide (NO₂), carbon monoxide (CO), and ozone (O₃) on warm days. Generally, no marked associations were observed between PM_2.5 levels and risk of CP admissions on cool days in both single- and two-pollutant models. This study provides evidence that higher PM_2.5 concentrations enhance the risk of hospital admissions for CP on warm days.

Who Among the Elderly Is Most Vulnerable to Exposure to and Health Risks of Fine Particulate Matter From Wildfire Smoke?

Wildfires burn more than 7 million acres in the United States annually, according to the US Forest Service. Little is known about which subpopulations are more vulnerable to health risks from wildfire smoke, including those associated with fine particulate matter. We estimated exposure to fine particles specifically from wildfires, as well as the associations between the presence of wildfire-specific fine particles and the amount of hospital admissions for respiratory causes among subpopulations older than 65 years of age in the western United States (2004-2009). Compared with other populations, higher fractions of persons who were black, lived in urban counties, and lived in California were exposed to more than 1 smoke wave (high-pollution episodes from wildfire smoke). The risks of respiratory admissions on smoke-wave days compared with non-smoke-wave days increased 10.4% (95% confidence interval: 1.9, 19.6) for women and 21.7% (95% confidence interval: 0.4, 47.3) for blacks. Our findings suggest that increased risks of respiratory admissions from wildfire smoke was significantly higher for women than for men (10.4% vs. 3.7%), blacks than whites (21.7% vs. 6.9%), and, although associations were not statistically different, people in lower-education counties than higher-educated counties (12.7% vs. 6.1%). Our study raised important environmental justice issues that can inform public health programs and wildfire management. As climate change increases the frequency and intensity of wildfires, evidence on vulnerable subpopulations can inform disaster preparedness and the understanding of climate change consequences.

Estimating the Public Health Impact of Air Pollution for Informing Policy in the Twin Cities: A Minnesota Tracking Collaboration

OBJECTIVE

The Minnesota Department of Health and the Minnesota Pollution Control Agency used local air pollution and public health data to estimate the impacts of particulate matter and ozone on population health, to identify disparities, and to inform decisions that will improve health.

SETTING

While air quality in Minnesota currently meets federal standards, urban communities are concerned about the impact of air pollution on their health. The Twin Cities (Minneapolis-St Paul) metropolitan area includes 7 counties where fine particulate levels and rates of asthma exacerbations are elevated in some communities.

DESIGN

We used the Environmental Protection Agency's BenMAP (Environmental Benefits Mapping and Analysis Program) software, along with local PM2.5 (fine particulate) and ozone ambient concentrations, census and population health data, to calculate impacts for 2008 at the zip code level. The impacts were summed across all zip codes for area-wide estimates. American Community Survey data were used to stratify zip codes by poverty and race for assessment of disparities.

MAIN OUTCOME MEASURES

Attributable fraction, attributable rate and counts for all-cause mortality, asthma and chronic obstructive pulmonary disease hospitalizations, asthma emergency department (ED) visits, and cardiovascular disease hospitalizations.

RESULTS

In the Twin Cities (2008), air pollution was a contributing cause for an estimated 2% to 5% of respiratory and cardiovascular hospitalizations and ED visits and between 6% and 13% of premature deaths. The elderly (aged 65+ years) experienced the highest air pollution-attributable rates of death and respiratory hospitalizations; children experienced the highest asthma ED visit rates. Geographical and demographic differences in air pollution-attributable health impacts across the region reflected the differences in the underlying morbidity and mortality rates.

CONCLUSIONS

Method was effective in demonstrating that changes in air quality can have quantifiable health impacts across the Twin Cities. Key messages and implications from this work were shared with the media, community groups, legislators and the public. The results are being used to inform initiatives aimed at reducing sources of air pollution and to address health disparities in urban communities.

Ambient PM2.5 in the residential area near industrial complexes: Spatiotemporal variation, source apportionment, and health impact

This study systemically investigated the ambient PM_2.5 (n=108) with comprehensive analyses of the chemical composition, identification of the potential contributors, and estimation of the resultant respiratory physician visits in the residential regions near energy-consuming and high-polluting industries in central Taiwan. The positive matrix fraction (PMF) model with chemical profiles of trace metals, water-soluble ions, and organic/elemental carbons (OC/EC) was applied to quantify the potential sources of PM_2.5. The influences of local sources were also explored using the conditional probability function (CPF). Associations between the daily PM_2.5 concentration and the risk of respiratory physician visits for the elderly (≥65years of age) were estimated using time-series analysis. A seasonal variation, with higher concentrations of PM_2.5, metals (As, Cd, Sb, and Pb), OC/EC and ions (i.e., NO^3-, SO₄^2- and NH₄⁺) in the winter than in the spring and summer, was observed. Overall, an increase of 10μgm^-3 in the same-day PM_2.5 was associated with an ~2% (95% CI: 1.5%-2.5%) increase in respiratory physician visits. Considering the health benefits of an effective reduction, we suggest that the emission from coal combustion (23.5%), iron ore and steel industry (17.1%), and non-ferrous metallurgy (14.4%), accounting for ~70% of the primary PM_2.5 in the winter are prioritized to control.

A cost-benefit analysis of a pellet boiler with electrostatic precipitator versus conventional biomass technology: A case study of an institutional boiler in Syracuse, New York

BACKGROUND

Biomass facilities have received increasing attention as a strategy to increase the use of renewable fuels and decrease greenhouse gas emissions from the electric generation and heating sectors, but these facilities can potentially increase local air pollution and associated health effects. Comparing the economic costs and public health benefits of alternative biomass fuel, heating technology, and pollution control technology options provides decision-makers with the necessary information to make optimal choices in a given location.

METHODS

For a case study of a combined heat and power biomass facility in Syracuse, New York, we used stack testing to estimate emissions of fine particulate matter (PM_2.5) for both the deployed technology (staged combustion pellet boiler with an electrostatic precipitator) and a conventional alternative (wood chip stoker boiler with a multicyclone). We used the atmospheric dispersion model AERMOD to calculate the contribution of either fuel-technology configuration to ambient primary PM_2.5 in a 10km×10km region surrounding the facility, and we quantified the incremental contribution to population mortality and morbidity. We assigned economic values to health outcomes and compared the health benefits of the lower-emitting technology with the incremental costs.

RESULTS

In total, the incremental annualized cost of the lower-emitting pellet boiler was $190,000 greater, driven by a greater cost of the pellet fuel and pollution control technology, offset in part by reduced fuel storage costs. PM_2.5 emissions were a factor of 23 lower with the pellet boiler with electrostatic precipitator, with corresponding differences in contributions to ambient primary PM_2.5 concentrations. The monetary value of the public health benefits of selecting the pellet-fired boiler technology with electrostatic precipitator was $1.7 million annually, greatly exceeding the differential costs even when accounting for uncertainties. Our analyses also showed complex spatial patterns of health benefits given non-uniform age distributions and air pollution levels.

CONCLUSIONS

The incremental investment in a lower-emitting staged combustion pellet boiler with an electrostatic precipitator was well justified by the population health improvements over the conventional wood chip technology with a multicyclone, even given the focus on only primary PM_2.5 within a small spatial domain. Our analytical framework could be generalized to other settings to inform optimal strategies for proposed new facilities or populations.

Evaluation of Sources and Patterns of Elemental Composition of PM2.5 at Three Low-Income Neighborhood Schools and Residences in Quito, Ecuador

Elemental characterization of fine particulate matter was undertaken at schools and residences in three low income neighborhoods in Quito, Ecuador. The three zones were located in the northern (Cotocollao), south central (El Camal), and south east (Los Chillos) neighborhoods and were classified as zones 1–3, respectively. Forty elements were quantified via ICP-MS analysis. Amongst the geogenic elements, the concentration of Si was the most abundant followed by S, Al, and Ca. Elements with predominantly anthropogenic sources such as Zn, V, and Ni were higher in zone 3 school followed by zone 2 and zone 1 schools. Enrichment factors were calculated to study the role of crustal sources in the elemental concentrations. Geogenic elements, except K, all had values <10 and anthropogenic elements such as Ni, V, Zn, Pb, As, Cr had >10. Principal Component Analysis suggested that Ni and V concentrations were strongly attributable to pet coke and heavy oil combustion. Strong associations between As and Pb could be attributed to traffic and other industrial emissions. Resuspended dust, soil erosion, vehicular emissions (tailpipe, brake and tire wear, and engine abrasion), pet coke, heavy oil combustion, and heavy industrial operations were major contributors to air pollution.

Ambient air pollution and daily hospital admissions for cardiovascular diseases in Arak, Iran

BACKGROUND

Outdoor air pollution has been considered as one of the most serious health concerns over the last decade. This study aimed to investigate the association between ambient air pollution and cardiovascular hospital admissions.

METHODS

This investigation was carried out from January 1, 2010 to December 31, 2015, in the urban population of Arak, Iran. Daily records of concentrations of air pollutants including particulate matter less than 10 µm (PM10), nitrogen dioxide (NO2), particulate matter less than 2.5 µm (PM2.5), ozone (O3), carbon monoxide (CO), and sulfur dioxide (SO2) as well as the daily number of hospital admissions due to cardiovascular disease were inquired from the Arak Department of Environment and two major hospitals, respectively. Time-series regression analysis was used to evaluate the effect of the pollutants on cardiovascular hospital admissions with different lag structures, controlling for weather variables, seasonality and long-term time trends, and day of the week.

RESULTS

Each 10 µg/m3 increase in PM10 and NO2 and 1 mg/m3 increase in CO concentrations at lag 0 (day) were significantly associated with an increase of 0.7% (P = 0.004), 3.3% (P = 0.006), and 9.4% (P < 0.001), respectively in overall cardiovascular hospital admissions. The elderly were more susceptible than those under 60 years to exposure to the pollutants (especially NO2) with regard to cardiovascular hospital admission.

CONCLUSION

The results of this study showed that hospital admission for cardiovascular disease is partly related to the levels of ambient air pollutions in Arak. Susceptibility to air pollutants varies by age groups and sex.

Advanced solid-state NMR spectroscopy of natural organic matter

Solid-state NMR is essential for the characterization of natural organic matter (NOM) and is gaining importance in geosciences and environmental sciences. This review is intended to highlight advanced solid-state NMR techniques, especially a systematic approach to NOM characterization, and their applications to the study of NOM. We discuss some basics of how to acquire high-quality and quantitative solid-state ¹³C NMR spectra, and address some common technical mistakes that lead to unreliable spectra of NOM. The identification of specific functional groups in NOM, primarily based on ¹³C spectral-editing techniques, is described and the theoretical background of some recently-developed spectral-editing techniques is provided. Applications of solid-state NMR to investigating nitrogen (N) in NOM are described, focusing on limitations of the widely used ¹⁵N CP/MAS experiment and the potential of improved advanced NMR techniques for characterizing N forms in NOM. Then techniques used for identifying proximities, heterogeneities and domains are reviewed, and some examples provided. In addition, NMR techniques for studying segmental dynamics in NOM are reviewed. We also briefly discuss applications of solid-state NMR to NOM from various sources, including soil organic matter, aquatic organic matter, organic matter in atmospheric particulate matter, carbonaceous meteoritic organic matter, and fossil fuels. Finally, examples of NMR-based structural models and an outlook are provided.

Associations between long-term exposure to PM2.5 component species and blood DNA methylation age in the elderly: The VA normative aging study

BACKGROUND

Long-term PM_2.5 exposure and aging have been implicated in multiple shared diseases; studying their relationship is a promising strategy to further understand the adverse impact of PM_2.5 on human health.

OBJECTIVE

We assessed the relationship of major PM_2.5 component species (ammonium, elemental carbon, organic carbon, nitrate, and sulfate) with Horvath and Hannum DNA methylation (DNAm) age, two DNA methylation-based predictors of chronological age.

METHODS

This analysis included 552 participants from the Normative Aging Study with multiple visits between 2000 and 2011 (n=940 visits). We estimated 1-year PM_2.5 species levels at participants' addresses using the GEOS-chem transport model. Blood DNAm-age was calculated using CpG sites on the Illumina HumanMethylation450 BeadChip. We fit linear mixed-effects models, controlling for PM_2.5 mass and lifestyle/environmental factors as fixed effects, with the adaptive LASSO penalty to identify PM_2.5 species associated with DNAm-age.

RESULTS

Sulfate and ammonium were selected by the LASSO in the Horvath DNAm-age models. In a fully-adjusted multiple-species model, interquartile range increases in both 1-year sulfate (95%CI: 0.28, 0.74, P<0.0001) and ammonium (95%CI: 0.02, 0.70, P=0.04) levels were associated with at least a 0.36-year increase in Horvath DNAm-age. No PM_2.5 species were selected by the LASSO in the Hannum DNAm-age models. Our findings persisted in sensitivity analyses including only visits with 1-year PM_2.5 levels within US EPA national ambient air quality standards.

CONCLUSION

Our results demonstrate that sulfate and ammonium were most associated with Horvath DNAm-age and suggest that DNAm-age measures differ in their sensitivity to ambient particle exposures and potentially disease.

A hierarchical modeling approach to estimate regional acute health effects of particulate matter sources

Exposure to particulate matter (PM) air pollution has been associated with a range of adverse health outcomes, including cardiovascular disease hospitalizations and other clinical parameters. Determining which sources of PM, such as traffic or industry, are most associated with adverse health outcomes could help guide future recommendations aimed at reducing harmful pollution exposure for susceptible individuals. Information obtained from multisite studies, which is generally more precise than information from a single location, is critical to understanding how PM impacts health and to informing local strategies for reducing individual-level PM exposure. However, few methods exist to perform multisite studies of PM sources, which are not generally directly observed, and adverse health outcomes. We developed SHared Across a REgion (SHARE), a hierarchical modeling approach that facilitates reproducible, multisite epidemiologic studies of PM sources. SHARE is a two-stage approach that first summarizes information about PM sources across multiple sites. Then, this information is used to determine how community-level (i.e., county-level or city-level) health effects of PM sources should be pooled to estimate regional-level health effects. SHARE is a type of population value decomposition that aims to separate out regional-level features from site-level data. Unlike previous approaches for multisite epidemiologic studies of PM sources, the SHARE approach allows the specific PM sources identified to vary by site. Using data from 2000 to 2010 for 63 northeastern US counties, we estimated regional-level health effects associated with short-term exposure to major types of PM sources. We found that PM from secondary sulfate, traffic, and metals sources was most associated with cardiovascular disease hospitalizations. Copyright © 2017 John Wiley & Sons, Ltd.

Fine particulate matter on hospital admissions for acute exacerbation of chronic obstructive pulmonary disease in southwestern Taiwan during 2006-2012

This study explored the effects of PM_2.5 on hospital admissions (HAs) for acute exacerbation of chronic obstructive pulmonary disease (AECOPD) in southwestern Taiwan. Data on HAs for AECOPD, pollutants, and meteorological variables were obtained from the National Health Insurance Research Database and Environmental Protection Administration. The relative risks (RRs) of HAs for AECOPD was estimated using the Quasi-Poisson generalized additive model. A total of 38,715 HAs for AECOPD were recorded. The average daily HAs for AECOPD and mean 24-h average level of PM_2.5 were 15.2 and 38.8 µg/m³, respectively. For both single and multiple pollutant (adjusted for O₃ and NO₂) models, increased AECOPD admissions were significantly associated with PM_2.5 during cold season, with the RRs for every 10 µg/m³ increase in PM_2.5 being 1.02 (95% CI = 1.007-1.040) at lag 0-1 in single-pollutant, and 1.02 (95 % CI = 1.001-1.042) at lag 0 day in multiple pollutant model. People 65 years of age and older had higher risk of HAs for AECOPD after PM_2.5 exposure. The RRs of PM_2.5 on HAs for AECOPD were robust after adjusting for O₃ and NO₂. Findings reveal an association between PM_2.5 and HAs for AECOPD in southwestern Taiwan, particularly during cold season.

Estimation of the PM2.5 health effects in China during 2000-2011

Exposure to fine particulate matter (PM_2.5) has been associated with mortality, but the extent of the adverse impacts differs across various regions. A quantitative estimation of health effects attributed to PM_2.5 in China is urgently required, particularly because it has the largest population and high air pollution levels. Based on the remote sensing-derived PM_2.5 and grid population data, we estimated the acute health effects of PM_2.5 in China using an exposure-response function. The results suggest the following: (1) The proportion of the population exposed to high PM_2.5 concentrations (>35 μg/m³) increased consistently from 2000 to 2011, and the population exposed to concentrations above the threshold defined by World Health Organization (WHO) (>10 μg/m³) rose from 1,191,191,943 to 1,290,562,965. (2) The number of deaths associated with PM_2.5 exposure increased steadily from 107,608 in 2000 to 173,560 in 2010, with larger numbers in the eastern region. (3) PM_2.5 health effects decreased in three pollution control scenarios estimated for 2017, i.e., the Air Pollution Prevention and Control Action Plan (APPCAP) scenario, the APPCAP under WHO IT-1 scenario (35 μg/m³), and the APPCAP under WHO IT-3 scenario (15 μg/m³), which indicates that pollution control can effectively reduce PM_2.5 effects on mortality.

Airborne Fine Particles and Risk of Hospital Admissions for Understudied Populations: Effects by Urbanicity and Short-Term Cumulative Exposures in 708 U.S. Counties

BACKGROUND

Evidence of health risks associated with ambient airborne fine particles in nonurban populations is extremely limited.

OBJECTIVE

We estimated the risk of hospitalization associated with short-term exposures to particulate matter with an aerodynamic diameter < 2.5 μm (PM_2.5) in urban and nonurban counties with population ≥ 50,000.

METHODS

We utilized a database of daily cardiovascular- and respiratory-related hospitalization rates constructed from Medicare National Claims History files (2002-2006), including 28 million Medicare beneficiaries in 708 counties. Daily PM_2.5 exposures were estimated using the Community Multiscale Air Quality (CMAQ) downscaler. We used time-series analysis of hospitalization rates and PM_2.5 to evaluate associations between PM_2.5 levels and hospitalization risk in single-pollutant models.

RESULTS

We observed an association between cardiovascular hospitalizations and same-day PM_2.5 with higher risk in urban counties: 0.35% [95% posterior interval (PI): -0.71%, 1.41%] and 0.98% (95% PI: 0.73%, 1.23%) increases in hospitalization risk per 10-μg/m³ increment in PM_2.5 were observed in the least-urban and most-urban counties, respectively. The largest association for respiratory hospitalizations, a 2.57% (95% PI: 0.87%, 4.30%) increase per 10-μg/m³ increase in PM_2.5, was observed in the least-urban counties; in the most-urban counties, a 1.13% (0.73%, 1.54%) increase was observed. Effect estimates for cardiovascular hospitalizations were highest for smaller lag times, whereas effect estimates for respiratory hospitalizations increased as more days of exposure were included.

CONCLUSION

In nonurban counties with population ≥ 50,000, exposure to PM_2.5 is associated with increased risk for respiratory hospitalizations; in urban counties, exposure is associated with increased risk of cardiovascular hospitalizations. Effect estimates based on a single day of exposure may underestimate true effects for respiratory hospitalizations.

Effect of exposure to ambient PM2.5 pollution on the risk of respiratory tract diseases: a meta-analysis of cohort studies

The International Agency for Research on Cancer and the World Health Organization have designated airborne particulates, including particulates of median aerodynamic diameter ≤ 2.5 μm (PM_2.5), as Group 1 carcinogens. It has not been determined, however, whether exposure to ambient PM_2.5 is associated with an increase in respiratory related diseases. This meta-analysis assessed the association between exposure to ambient fine particulate matter (PM_2.5) and the risk of respiratory tract diseases, using relevant articles extracted from PubMed, Web of Science, and Embase. In results, of the 1,126 articles originally identified, 35 (3.1%) were included in this meta-analysis. PM_2.5 was found to be associated with respiratory tract diseases. After subdivision by age group, respiratory tract disease, and continent, PM_2.5 was strongly associated with respiratory tract diseases in children, in persons with cough, lower respiratory illness, and wheezing, and in individuals from North America, Europe, and Asia. The risk of respiratory tract diseases was greater for exposure to traffic-related than non-traffic-related air pollution. In children, the pooled relative risk (RR) represented significant increases in wheezing (8.2%), cough (7.5%), and lower respiratory illness (15.3%). The pooled RRs in children were 1.091 (95%CI: 1.049, 1.135) for exposure to <25 μg/m³ PM_2.5, and 1.126 (95%CI: 1.067, 1.190) for exposure to ≥ 25 μg/m³ PM_2.5. In conclusion, exposure to ambient PM_2.5 was significantly associated with the development of respiratory tract diseases, especially in children exposed to high concentrations of PM_2.5.

Health risk of inhalation exposure to sub-10 µm particulate matter and gaseous pollutants in an urban-industrial area in South Africa: an ecological study

OBJECTIVE

To assess the health risks associated with exposure to particulate matter (PM10), sulphur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO) and ozone (O3).

DESIGN

The study is an ecological study that used the year 2014 hourly ambient pollution data.

SETTING

The study was conducted in an industrial area located in Pretoria West, South Africa. The area accommodates a coal-fired power station, metallurgical industries such as a coke plant and a manganese smelter.

DATA AND METHOD

Estimate of possible health risks from exposure to airborne PM10, SO2, NO2, CO and O3 was performed using the US Environmental Protection Agency human health risk assessment framework. A scenario-assessment approach where normal (average exposure) and worst-case (continuous exposure) scenarios were developed for intermediate (24-hour) and chronic (annual) exposure periods for different exposure groups (infants, children, adults). The normal acute (1-hour) exposure to these pollutants was also determined.

OUTCOME MEASURES

Presence or absence of adverse health effects from exposure to airborne pollutants.

RESULTS

Average annual ambient concentration of PM10, NO2 and SO2 recorded was 48.3±43.4, 11.50±11.6 and 18.68±25.4 µg/m3, respectively, whereas the South African National Ambient Air Quality recommended 40, 40 and 50 µg/m3 for PM10, NO2 and SO2, respectively. Exposure to an hour's concentration of NO2, SO2, CO and O3, an 8-hour concentration of CO and O3, and a 24-hour concentration of PM10, NO2 and SO2 will not likely produce adverse effects to sensitive exposed groups. However, infants and children, rather than adults, are more likely to be affected. Moreover, for chronic annual exposure, PM10, NO2 and SO2 posed a health risk to sensitive individuals, with the severity of risk varying across exposed groups.

CONCLUSIONS

Long-term chronic exposure to airborne PM10, NO2 and SO2 pollutants may result in health risks among the study population.

COVARIATE-ADAPTIVE CLUSTERING OF EXPOSURES FOR AIR POLLUTION EPIDEMIOLOGY COHORTS

Cohort studies in air pollution epidemiology aim to establish associations between health outcomes and air pollution exposures. Statistical analysis of such associations is complicated by the multivariate nature of the pollutant exposure data as well as the spatial misalignment that arises from the fact that exposure data are collected at regulatory monitoring network locations distinct from cohort locations. We present a novel clustering approach for addressing this challenge. Specifically, we present a method that uses geographic covariate information to cluster multi-pollutant observations and predict cluster membership at cohort locations. Our predictive k-means procedure identifies centers using a mixture model and is followed by multi-class spatial prediction. In simulations, we demonstrate that predictive k-means can reduce misclassification error by over 50% compared to ordinary k-means, with minimal loss in cluster representativeness. The improved prediction accuracy results in large gains of 30% or more in power for detecting effect modification by cluster in a simulated health analysis. In an analysis of the NIEHS Sister Study cohort using predictive k-means, we find that the association between systolic blood pressure (SBP) and long-term fine particulate matter (PM2.5) exposure varies significantly between different clusters of PM2.5 component profiles. Our cluster-based analysis shows that for subjects assigned to a cluster located in the Midwestern U.S., a 10 μg/m3 difference in exposure is associated with 4.37 mmHg (95% CI, 2.38, 6.35) higher SBP.

Estimating State-Specific Contributions to PM2.5- and O3-Related Health Burden from Residential Combustion and Electricity Generating Unit Emissions in the United States

BACKGROUND

Residential combustion (RC) and electricity generating unit (EGU) emissions adversely impact air quality and human health by increasing ambient concentrations of fine particulate matter (PM_2.5) and ozone (O₃). Studies to date have not isolated contributing emissions by state of origin (source-state), which is necessary for policy makers to determine efficient strategies to decrease health impacts.

OBJECTIVES

In this study, we aimed to estimate health impacts (premature mortalities) attributable to PM_2.5 and O₃ from RC and EGU emissions by precursor species, source sector, and source-state in the continental United States for 2005.

METHODS

We used the Community Multiscale Air Quality model employing the decoupled direct method to quantify changes in air quality and epidemiological evidence to determine concentration-response functions to calculate associated health impacts.

RESULTS

We estimated 21,000 premature mortalities per year from EGU emissions, driven by sulfur dioxide emissions forming PM_2.5. More than half of EGU health impacts are attributable to emissions from eight states with significant coal combustion and large downwind populations. We estimate 10,000 premature mortalities per year from RC emissions, driven by primary PM_2.5 emissions. States with large populations and significant residential wood combustion dominate RC health impacts. Annual mortality risk per thousand tons of precursor emissions (health damage functions) varied significantly across source-states for both source sectors and all precursor pollutants.

CONCLUSIONS

Our findings reinforce the importance of pollutant-specific, location-specific, and source-specific models of health impacts in design of health-risk minimizing emissions control policies. Citation: Penn SL, Arunachalam S, Woody M, Heiger-Bernays W, Tripodis Y, Levy JI. 2017. Estimating state-specific contributions to PM_2.5- and O₃-related health burden from residential combustion and electricity generating unit emissions in the United States. Environ Health Perspect 125:324-332; http://dx.doi.org/10.1289/EHP550.

Long-term Exposure to PM2.5 and Mortality Among Older Adults in the Southeastern US

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.

Exposure assessment models for elemental components of particulate matter in an urban environment: A comparison of regression and random forest approaches

Exposure assessment for elemental components of particulate matter (PM) using land use modeling is a complex problem due to the high spatial and temporal variations in pollutant concentrations at the local scale. Land use regression (LUR) models may fail to capture complex interactions and non-linear relationships between pollutant concentrations and land use variables. The increasing availability of big spatial data and machine learning methods present an opportunity for improvement in PM exposure assessment models. In this manuscript, our objective was to develop a novel land use random forest (LURF) model and compare its accuracy and precision to a LUR model for elemental components of PM in the urban city of Cincinnati, Ohio. PM smaller than 2.5 μm (PM2.5) and eleven elemental components were measured at 24 sampling stations from the Cincinnati Childhood Allergy and Air Pollution Study (CCAAPS). Over 50 different predictors associated with transportation, physical features, community socioeconomic characteristics, greenspace, land cover, and emission point sources were used to construct LUR and LURF models. Cross validation was used to quantify and compare model performance. LURF and LUR models were created for aluminum (Al), copper (Cu), iron (Fe), potassium (K), manganese (Mn), nickel (Ni), lead (Pb), sulfur (S), silicon (Si), vanadium (V), zinc (Zn), and total PM2.5 in the CCAAPS study area. LURF utilized a more diverse and greater number of predictors than LUR and LURF models for Al, K, Mn, Pb, Si, Zn, TRAP, and PM2.5 all showed a decrease in fractional predictive error of at least 5% compared to their LUR models. LURF models for Al, Cu, Fe, K, Mn, Pb, Si, Zn, TRAP, and PM2.5 all had a cross validated fractional predictive error less than 30%. Furthermore, LUR models showed a differential exposure assessment bias and had a higher prediction error variance. Random forest and other machine learning methods may provide more accurate exposure assessment.

PM2.5 as a marker of exposure to tobacco smoke and other sources of particulate matter in Cairo, Egypt

SETTING

Cairo and Giza governorates of Egypt.

BACKGROUND

Particulate matter under 2.5 μm in diameter (PM2.5) arises from diverse sources, including tobacco smoke from cigarettes and waterpipes, and is recognized as a cause of acute and chronic morbidity and mortality.

OBJECTIVE

To measure PM2.5 in workplaces with different intensities of smoking and varying levels of smoking restrictions.

DESIGN

We conducted an air sampling study to measure PM2.5 levels in a convenience sample of indoor and outdoor venues in 2005-2006.

RESULTS

Using a calibrated SidePak instrument, 3295 individual measurements were collected at 96 venues. Compared to indoor venues where tobacco smoking was banned (PM2.5 levels 72-81 μg/m(3)), places offering waterpipes to patrons of cafes (478 μg/m(3)) and Ramadan tents (612 μg/m(3)) had much higher concentrations, as did venues such as public buildings with poor enforcement of smoking restrictions (range 171-704 μg/m(3)). Both the number of waterpipe smokers and the number of cigarette smokers observed at each venue contributed significantly to the overall burden of PM2.5.

CONCLUSION

Such data will support smoke-free policies and programs aimed specifically at reducing environmental tobacco exposure and improving air quality in general, and will provide a baseline for monitoring the impact of tobacco control policies.

Accuracy of quantification of risk using a single-pollutant Air Quality Index

Health risks associated with short-term exposure to ambient air pollution are communicated to the public by the US EPA through the Air Quality Index (AQI), but it remains unclear whether the current regulatory-based, single-pollutant AQI fully represents the actual risks of air pollution-related illness. The objective of this study is to quantify cardiovascular hospital admissions attributable to PM_2.5 at each AQI category. Based on National Ambient Air Quality Standards (NAAQS), the highest AQI value among criteria pollutants (driver pollutant) is reported daily. We investigated excess cardiovascular hospital admissions attributable to fine particulate matter (PM_2.5) exposure from 2000 to 2010 in Bronx, Erie, Queens, and Suffolk counties of New York. Daily total, unscheduled cardiovascular hospital admissions (principal diagnosis) for individuals aged 20-99 years, concentration-response functions for PM_2.5, and estimated quarterly effective daily concentrations were used to calculate excess cardiovascular hospital admissions when PM_2.5 was reported as the driver pollutant and when PM_2.5 was not reported as the driver pollutant at each AQI category. A higher proportion of excess hospital admissions attributable to PM_2.5 occurred when PM_2.5 was the driver pollutant (i.e., ~70% in Bronx County). The majority of excess hospital admissions (i.e., >90% in Bronx County) occurred when the AQI was <100 ("good" or "moderate" level of health concern) regardless of whether PM_2.5 was the driver pollutant. During the warm season (April-September), greater excess admissions in Suffolk County occurred when PM_2.5 was not the AQI driver pollutant. These results indicate that a single-pollutant index may inadequately communicate the adverse health risks associated with air pollution.

Associations of short-term exposure to air pollution with respiratory hospital admissions in Arak, Iran

BACKGROUND

Ambient air pollution, is one of the most frequently stated environmental problems. Many epidemiological studies have documented adverse health effects for ambient air pollution. This study aimed to investigate the association between ambient air pollution and respiratory hospital admissions.

METHODS

In this ecological time series study data about air pollutant concentrations including CO, NO₂, O₃, PM_2.5, PM₁₀ and SO₂ and, respiratory hospital admissions in the urban population of Arak, from January 1st 2010 to December 31st 2015; were inquired, from the Arak Department of Environment, and two major hospitals, respectively. Meteorological data were inquired for the same period as well. Time-series regression analysis with a distributed lag model, controlled for seasonality long-time trends, weather and day of the week, was used for data analysis.

RESULTS

Every 10 μg/m³ increase in NO₂, and PM₁₀ and every 1 mg/m³ increase in CO at lag 0 corresponded to a RR = 1.032 (95%CI, 1.003-1.06), RR = 1.01 (95%CI, 1.004-1.017) and RR = 1.09 (95%CI, 1.04-1.14), increase in respiratory disease hospitalizations, respectively. Males and the elderly were found to be more susceptible than females and other age groups to air pollutants in regard to respiratory disease admissions.

CONCLUSIONS

The results of this study showed that outdoor air pollutants significantly increase respiratory hospital admissions; especially among the men and elders in Arak.

Linking Air Quality and Human Health Effects Models: An Application to the Los Angeles Air Basin

Proposed emission control strategies for reducing ozone and particulate matter are evaluated better when air quality and health effects models are used together. The Community Multiscale Air Quality (CMAQ) model is the US Environmental Protection Agency's model for determining public policy and forecasting air quality. CMAQ was used to forecast air quality changes due to several emission control strategies that could be implemented between 2008 and 2030 for the South Coast Air Basin that includes Los Angeles. The Environmental Benefits Mapping and Analysis Program-Community Edition (BenMAP-CE) was used to estimate health and economic impacts of the different emission control strategies based on CMAQ simulations. BenMAP-CE is a computer program based on epidemiologic studies that link human health and air quality. This modeling approach is better for determining optimum public policy than approaches that only examine concentration changes.

Short-term effects of fine particulate air pollution on hospital admissions for hypertension: A time-stratified case-crossover study in Taipei

This study was undertaken to determine whether there was a correlation between fine particle (PM_2.5) levels and hospital admissions for hypertension in Taipei, Taiwan. Hospital admissions for hypertension and ambient air pollution data for Taipei were obtained for the period from 2009 to 2013. The relative risk of hospital admissions was estimated using a case-crossover approach, controlling for weather variables, day of the week, seasonality, and long-term time trends. For the single pollutant model (without adjustment for other pollutants), the risk of hospital admissions for hypertension was estimated to increase by 12% on warm days (>23°C) and 2% on cool days (<23°C), respectively. There was no indication of an association between levels of PM_2.5 and risk of hospital admissions for hypertension. In two-pollutant model, PM_2.5 remained nonsignificant after inclusion of any of the other air pollutants (SO₂, NO₂, CO, or O₃) both on warm and cool days, but a numerically greater response was seen on warm days. Data thus indicate that in Taipei, hospital admissions for hypertension occur as a consequence of factors not related to ambient air exposure.

The movement and deposition of PM2.5 in the upper respiratory tract for the patients with heart failure: an elementary CFD study

BACKGROUND

PM2.5 is an important factor to affect the patients with respiratory and cardiovascular diseases. Clinical studies have found that the morbidity and mortality of patients with heart failure (HF) have a close relationship with the movement and deposition state of PM2.5. One reason is that the breathing pattern of patients with HF has obvious difference with healthy people, however the effect caused by these differences on the distribution regularity of PM2.5 in the respiratory tract is still unclear. Hence, a computational fluid dynamics simulation was conducted to clarify the aerodynamic effect of breathing pattern of patients with HF on respiratory system.

METHODS

Ideal upper respiratory tract geometric model was established based on standardized aerosol research laboratory of Alberta and Weibel A dimension. The discrete phase method is used to calculate the movement of the airflow and particles. The flow rate were chosen as the inlet boundary conditions, and the outlets are set at a constant pressure. The rate of particle deposition, distribution location, wall pressure, flow velocity and wall shear stress are obtained, and compared to the normal control.

RESULTS

The results demonstrated that the rate of escaped particles in every bronchial outlet of the patients with HF was more than the normal controls, meanwhile the trapped was less (1024 < 1160). There was higher by 12.9% possibility that the PM2.5 entered the lungs than the normal control.

CONCLUSION

The aerodynamic performances of HF patients are different from normal control. Compared to the normal control, under similar environment, there is higher possibility of PM2.5 moving into lungs, and these particles could affect the function of the respiratory system, resulting in the deterioration of the state of cardiovascular system. In short, it's necessary to pay more attention to the living environment of HF patients, to reduce the content of PM2.5 particles in the air, and reduce the damage of PM2.5 particles caused by breathing patterns.

Differential DNA methylation and PM2.5 species in a 450K epigenome-wide association study

Although there is growing evidence that exposure to ambient particulate matter is associated with global DNA methylation and gene-specific methylation, little is known regarding epigenome-wide changes in DNA methylation in relation to particles and, especially, particle components. Using the Illumina Infinium HumanMethylation450 BeadChip, we examined the relationship between one-year moving averages of PM_2.5 species (Al, Ca, Cu, Fe, K, Na, Ni, S, Si, V, and Zn) and DNA methylation at 484,613 CpG probes in a longitudinal cohort that included 646 subjects. Bonferroni correction was applied to adjust for multiple comparisons. Bioinformatics analysis of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment was also performed. We observed 20 Bonferroni significant (P-value < 9.4× 10^-9) CpGs for Fe, 8 for Ni, and 1 for V. Particularly, methylation at Schlafen Family Member 11 (SLFN11) cg10911913 was positively associated with measured levels of all 3 species. The SLFN11 gene codes for an interferon-induced protein that inhibits retroviruses and sensitizes cancer cells to DNA-damaging agents. Bioinformatics analysis suggests that gene targets may be relevant to pathways including cancers, signal transduction, and cell growth and death. Ours is the first study to examine the epigenome-wide association between ambient particles species and DNA methylation. We found that long-term exposures to specific components of ambient particle pollution, especially particles emitted during oil combustion, were associated with methylation changes in genes relevant to immune responses. Our findings provide insight into potential biologic mechanisms on an epigenetic level.

Chemical components of respirable particulate matter associated with emergency hospital admissions for type 2 diabetes mellitus in Hong Kong

BACKGROUND

Epidemiological studies have shown that short-term exposure to particulate matter (PM) mass is associated with diabetes morbidity and mortality, although inconsistencies still exist. Variation of chemical components in PM may have contributed to these inconsistencies. We hypothesize that certain components of respirable particulate matter (PM₁₀), not simply PM₁₀ mass, can exacerbate symptoms or cause acute complications for type 2 diabetes mellitus (T2DM).

METHODS

We used a Poisson time-series model to examine the association between 17 chemical components of PM₁₀ and daily emergency hospital admissions for T2DM among residents aged 65years or above from January 1998 to December 2007 in Hong Kong. We estimated excess risk (ER%) for T2DM hospitalizations per interquartile range (IQR) increment in chemical component concentrations of days at lag₀ through lag₃, and the moving average of the same-day and previous-day (lag_0-1) in single-pollutant models. To further evaluate the independent effects of chemical components on T2DM, we controlled for PM₁₀ mass, major PM₁₀ chemical components, and gaseous pollutants in two-pollutant models.

RESULTS

In the single-pollutant models, PM₁₀ components associated with T2DM admissions include: elemental carbon, organic carbon, nitrate, and nickel. The ER% estimates per IQR increment at lag_0-1 for these four components were 3.79% (1.63, 5.95), 3.74% (0.83, 6.64), 4.58% (2.17, 6.99), and 1.91% (0.43, 3.38), respectively. Risk estimates for nitrate and elemental carbon were robust to adjustment for co-pollutant concentrations.

CONCLUSIONS

Short-term exposure to some PM₁₀ chemical components such as nitrate and elemental carbon increases the risk of acute complications or exacerbation of symptoms for the T2DM patients. These findings may have potential biological and policy implications.

Contrasting biological potency of particulate matter collected at sites impacted by distinct industrial sources

BACKGROUND

Industrial sources contribute a significant proportion of anthropogenic particulate matter (PM) emissions, producing particles of varying composition that may differentially impact health. This study investigated the in vitro toxicity of ambient PM collected near industrial sites in relation to particle size and composition.

METHODS

Size-fractionated particles (ultrafine, PM_0.1-2.5, PM_2.5-10, PM_>10) were collected in the vicinity of steel, copper, aluminium, and petrochemical industrial sites. Human lung epithelial-like A549 and murine macrophage-like J774A.1 cells were exposed for 24 h to particle suspensions (0, 30, 100, 300 μg/cm²). Particle potency was assessed using cytotoxic (resazurin reduction, lactate dehydrogenase (LDH) release) and inflammatory (cytokine release) assays, and regressed against composition (metals, polycyclic aromatic hydrocarbons (PAHs), endotoxin).

RESULTS

Coarse (PM_2.5-10, PM_>10) particle fractions were composed primarily of iron and aluminium; in contrast, ultrafine and fine (PM_0.1-2.5) fractions displayed considerable variability in metal composition (especially water-soluble metals) across collection sites consistent with source contributions. Semi-volatile and PM-associated PAHs were enriched in the fine and coarse fractions collected near metal industry. Cell responses to exposure at equivalent mass concentrations displayed striking differences among sites (SITE x SIZE and SITE x DOSE interactions, p < 0.05), suggesting that particle composition, in addition to size, impacted particle toxicity. While both J774A.1 and A549 cells exhibited clear particle size-dependent effects, site-dependent differences were more pronounced in J774A.1 cells, suggesting greater sensitivity to particle composition. Plotting particle potency according to cytotoxic and inflammatory response grouped particles by size and site, and showed that particles of similar composition tended to cluster together. Cytotoxic effects in J774A.1 cells correlated with metal and PAH content, while inflammatory responses were associated primarily with endotoxin content in coarse particles.

CONCLUSIONS

Industrial sources produce particulate emissions with varying chemical composition that differ in their in vitro potency in relation to particle size and the levels of specific constituents.

Mortality and emergency room visits associated with ambient particulate matter constituents in metropolitan Taipei

OBJECTIVE

To evaluate the elderly mortality and all population emergency room visits (ERV) associated with exposing to particulate matter (PM) ≤10μm in aerodynamic diameter (PM10), PM2.5, and PM constituents in Taipei, Taiwan.

METHODS

This study used a distributed lag nonlinear model with Poisson distribution to estimate cumulative 6-day (lags 0-5) relative risks (RRs) and confidence intervals (CIs) of mortality and ERV of all causes, circulatory diseases, and respiratory diseases associated with daily concentrations of PM10, PM2.5 and PM constituents (organic carbon (OC), elemental carbon (EC), nitrate and sulfate).

RESULTS

Mortality and ERVs were associated with PM concentrations and its constituents. The elderly all-cause mortality was significantly associated with nitrate concentrations (RR=1.12 (95% CI: 1.02, 1.23) at 1μg/m(3)) and OC higher than 15μg/m(3); and mortality from circulatory diseases was significantly associated with PM10 (RR=1.13 (95% CI: 1.02, 1.25) at 50μg/m(3)), PM2.5 (RR=1.16 (95% CI: 1.01, 1.34) at 30μg/m(3)), nitrate higher than 8μg/m(3), and sulfate (RR=1.31 (95% CI: 1.12, 1.54) at 7μg/m(3)). An increase in PM2.5 from 5μg/m(3) to 30μg/m(3) was associated with increased ERV risk of all causes and respiratory diseases with cumulative 6-day RRs of 1.12 (95% CI: 1.06, 1.18) and 1.27 (95% CI: 1.10, 1.46), respectively. All-cause ERV was significantly associated with concentrations of OC (RR=1.03 (95% CI: 1.00, 1.06) at 4μg/m(3)), EC (RR=1.08 (95% CI: 1.05, 1.12) at 2μg/m(3)), nitrate higher than 6μg/m(3), and sulfate higher than 8μg/m(3).

CONCLUSIONS

Ambient air quality management should regularly evaluate PM constituents and related health effects.

Fine particulate matter components and emergency department visits among a privately insured population in Greater Houston

Although adverse health effects of PM2.5 (particulate matter with aerodynamic diameter less than 2.5μm) mass have been extensively studied, it remains unclear regarding which PM2.5 components are most harmful. No studies have reported the associations between PM2.5 components and adverse health effects among a privately insured population. In our study, we estimated the short-term associations between exposure to PM2.5 components and emergency department (ED) visits for all-cause and cause-specific diseases in Greater Houston, Texas, during 2008-2013 using ED visit data extracted from a private insurance company (Blue Cross Blue Shield Texas [BCBSTX]). A total of 526,453 ED visits were included in our assessment, with an average of 236 (±63) visits per day. We selected 20 PM2.5 components from the U.S. Environmental Protection Agency's Chemical Speciation Network site located in Houston, and then applied Poisson regression models to assess the previously mentioned associations. Interquartile range increases in bromine (0.003μg/m(3)), potassium (0.048μg/m(3)), sodium ion (0.306μg/m(3)), and sulfate (1.648μg/m(3)) were statistically significantly associated with the increased risks in total ED of 0.71% (95% confidence interval (CI): 0.06, 1.37%), 0.71% (95% CI: 0.21, 1.22%), 1.28% (95% CI: 0.34, 2.24%), and 1.22% (95% CI: 0.23, 2.23%), respectively. Seasonal analysis suggested strongest associations occurred during the warm season. Our findings suggest that a privately insured population, presumably healthier than the general population, may be still at risk of adverse health effects due to exposure to ambient PM2.5 components.

Air quality co-benefits of subnational carbon policies

To mitigate climate change, governments ranging from city to multi-national have adopted greenhouse gas (GHG) emissions reduction targets. While the location of GHG reductions does not affect their climate benefits, it can impact human health benefits associated with co-emitted pollutants. Here, an advanced modeling framework is used to explore how subnational level GHG targets influence air pollutant co-benefits from ground level ozone and fine particulate matter. Two carbon policy scenarios are analyzed, each reducing the same total amount of GHG emissions in the Northeast US: an economy-wide Cap and Trade (CAT) program reducing emissions from all sectors of the economy, and a Clean Energy Standard (CES) reducing emissions from the electricity sector only. Results suggest that a regional CES policy will cost about 10 times more than a CAT policy. Despite having the same regional targets in the Northeast, carbon leakage to non-capped regions varies between policies. Consequently, a regional CAT policy will result in national carbon reductions that are over six times greater than the carbon reduced by the CES in 2030. Monetized regional human health benefits of the CAT and CES policies are 844% and 185% of the costs of each policy, respectively. Benefits for both policies are thus estimated to exceed their costs in the Northeast US. The estimated value of human health co-benefits associated with air pollution reductions for the CES scenario is two times that of the CAT scenario.

IMPLICATIONS

In this research, an advanced modeling framework is used to determine the potential impacts of regional carbon policies on air pollution co-benefits associated with ground level ozone and fine particulate matter. Study results show that spatially heterogeneous GHG policies have the potential to create areas of air pollution dis-benefit. It is also shown that monetized human health benefits within the area covered by policy may be larger than the model estimated cost of the policy. These findings are of particular interest both as U.S. states work to develop plans to meet state-level carbon emissions reduction targets set by the EPA through the Clean Power Plan, and in the absence of comprehensive national carbon policy.

Effects of Particulate Matter and Its Chemical Constituents on Elderly Hospital Admissions Due to Circulatory and Respiratory Diseases

Various fractions of particulate matter have been associated with increased mortality and morbidity. The purpose of our study is to analyze the associations between concentrations of PM2.5, PM2.5-10, PM10 and their chemical constituents (soluble ions) with hospital admissions due to circulatory and respiratory diseases among the elderly in a medium-sized city in Brazil. A time series study was conducted using Poisson regression with generalized additive models adjusted for confounders. Statistically significant associations were identified between PM10 and PM2.5-10 and respiratory diseases. Risks of hospitalization increased by 23.5% (95% CI: 13.5; 34.3) and 12.8% (95% CI: 6.0; 20.0) per 10 μg/m³ of PM2.5-10 and PM10, respectively. PM2.5 exhibited a significant association with circulatory system diseases, with the risk of hospitalization increasing by 19.6% (95% CI: 6.4; 34.6) per 10 μg/m³. Regarding the chemical species; SO₄(2-), NO₃(-), NH₄⁺ and K⁺ exhibited specific patterns of risk, relative to the investigated outcomes. Overall, SO₄(2-) in PM2.5-10 and K⁺ in PM2.5 were associated with increased risk of hospital admissions due to both types of diseases. The results agree with evidence indicating that the risks for different health outcomes vary in relation to the fractions and chemical composition of PM10. Thus, PM10 speciation studies may contribute to the establishment of more selective pollution control policies.

The effects of air pollution on asthma hospital admissions in Adelaide, South Australia, 2003-2013: time-series and case-crossover analyses

BACKGROUND

Air pollution can have adverse health effects on asthma sufferers, but the effects vary with geographic, environmental and population characteristics. There has been no long time-series study in Australia to quantify the effects of environmental factors including pollen on asthma hospitalizations.

OBJECTIVES

This study aimed to assess the seasonal impact of air pollutants and aeroallergens on the risk of asthma hospital admissions for adults and children in Adelaide, South Australia.

METHODS

Data on hospital admissions, meteorological conditions, air quality and pollen counts for the period 2003-2013 were sourced. Time-series analysis and case-crossover analysis were used to assess the short-term effects of air pollution on asthma hospitalizations. For the time-series analysis, generalized log-linear quasi-Poisson and negative binomial regressions were used to assess the relationships, controlling for seasonality and long-term trends using flexible spline functions. For the case-crossover analysis, conditional logistic regression was used to compute the effect estimates with time-stratified referent selection strategies.

RESULTS

A total of 36,024 asthma admissions were considered. Findings indicated that the largest effects on asthma admissions related to PM_2.5 , NO₂ , PM₁₀ and pollen were found in the cool season for children (0-17 years), with the 5-day cumulative effects of 30.2% (95% CI: 13.4-49.6%), 12.5% (95% CI: 6.6-18.7%), 8.3% (95% CI: 2.5-14.4%) and 4.2% (95% CI: 2.2-6.1%) increases in risk of asthma hospital admissions per 10 unit increments, respectively. The largest effect for ozone was found in the warm season for children with the 5-day cumulative effect of an 11.7% (95% CI: 5.8-17.9%) increase in risk of asthma hospital admissions per 10 ppb increment in ozone level.

CONCLUSION

Findings suggest that children are more vulnerable and the associations between exposure to air pollutants and asthma hospitalizations tended to be stronger in the cool season compared to the warm season, with the exception of ozone. This study has important public health implications and provides valuable evidence for the development of policies for asthma management.

The contribution of motor vehicle emissions to ambient fine particulate matter public health impacts in New York City: a health burden assessment

BACKGROUND

On-road vehicles are an important source of fine particulate matter (PM2.5) in cities, but spatially varying traffic emissions and vulnerable populations make it difficult to assess impacts to inform policy and the public.

METHODS

We estimated PM2.5-attributable mortality and morbidity from on-road vehicle generated air pollution in the New York City (NYC) region using high-spatial-resolution emissions estimates, air quality modeling, and local health incidence data to evaluate variations in impacts by vehicle class, neighborhood, and area socioeconomic status. We developed multiple 'zero-out' emission scenarios focused on regional and local cars, trucks, and buses in the NYC region. We simulated PM2.5 concentrations using the Community Multi-scale Air Quality Model at a 1-km spatial resolution over NYC and combined modeled estimates with monitored data from 2010 to 2012. We applied health impact functions and local health data to quantify the PM2.5-attributable health burden on NYC residents within 42 city neighborhoods.

RESULTS

We estimate that all on-road mobile sources in the NYC region contribute to 320 (95 % Confidence Interval (CI): 220-420) deaths and 870 (95 % CI: 440-1280) hospitalizations and emergency department visits annually within NYC due to PM2.5 exposures, accounting for 5850 (95 % CI: 4020-7620) years of life lost. Trucks and buses within NYC accounted for the largest share of on-road mobile-attributable ambient PM2.5, contributing up to 14.9 % of annual average levels across 1-km grid cells, and were associated with 170 (95 % CI: 110-220) PM2.5-attributable deaths each year. These contributions were not evenly distributed, with high poverty neighborhoods experiencing a larger share of the exposure and health burden than low poverty neighborhoods.

CONCLUSION

Reducing motor vehicle emissions, especially from trucks and buses, could produce significant health benefits and reduce disparities in impacts. Our high-spatial-resolution modeling approach could improve assessment of on-road vehicle health impacts in other cities.

Health Outcomes of Exposure to Biological and Chemical Components of Inhalable and Respirable Particulate Matter

Particulate matter (PM) is a key indicator of air pollution and a significant risk factor for adverse health outcomes in humans. PM is not a self-contained pollutant but a mixture of different compounds including chemical and biological fractions. While several reviews have focused on the chemical components of PM and associated health effects, there is a dearth of review studies that holistically examine the role of biological and chemical components of inhalable and respirable PM in disease causation. A literature search using various search engines and (or) keywords was done. Articles selected for review were chosen following predefined criteria, to extract and analyze data. The results show that the biological and chemical components of inhalable and respirable PM play a significant role in the burden of health effects attributed to PM. These health outcomes include low birth weight, emergency room visit, hospital admission, respiratory and pulmonary diseases, cardiovascular disease, cancer, non-communicable diseases, and premature death, among others. This review justifies the importance of each or synergistic effects of the biological and chemical constituents of PM on health. It also provides information that informs policy on the establishment of exposure limits for PM composition metrics rather than the existing exposure limits of the total mass of PM. This will allow for more effective management strategies for improving outdoor air quality.

An Analysis of Costs and Health Co-Benefits for a U.S. Power Plant Carbon Standard

Reducing carbon dioxide (CO2) emissions from power plants can have important "co-benefits" for public health by reducing emissions of air pollutants. Here, we examine the costs and health co-benefits, in monetary terms, for a policy that resembles the U.S. Environmental Protection Agency's Clean Power Plan. We then examine the spatial distribution of the co-benefits and costs, and the implications of a range of cost assumptions in the implementation year of 2020. Nationwide, the total health co-benefits were $29 billion 2010 USD (95% CI: $2.3 to $68 billion), and net co-benefits under our central cost case were $12 billion (95% CI: -$15 billion to $51 billion). Net co-benefits for this case in the implementation year were positive in 10 of the 14 regions studied. The results for our central case suggest that all but one region should experience positive net benefits within 5 years after implementation.

Applications of a Novel Clustering Approach Using Non-Negative Matrix Factorization to Environmental Research in Public Health

Often data can be represented as a matrix, e.g., observations as rows and variables as columns, or as a doubly classified contingency table. Researchers may be interested in clustering the observations, the variables, or both. If the data is non-negative, then Non-negative Matrix Factorization (NMF) can be used to perform the clustering. By its nature, NMF-based clustering is focused on the large values. If the data is normalized by subtracting the row/column means, it becomes of mixed signs and the original NMF cannot be used. Our idea is to split and then concatenate the positive and negative parts of the matrix, after taking the absolute value of the negative elements. NMF applied to the concatenated data, which we call PosNegNMF, offers the advantages of the original NMF approach, while giving equal weight to large and small values. We use two public health datasets to illustrate the new method and compare it with alternative clustering methods, such as K-means and clustering methods based on the Singular Value Decomposition (SVD) or Principal Component Analysis (PCA). With the exception of situations where a reasonably accurate factorization can be achieved using the first SVD component, we recommend that the epidemiologists and environmental scientists use the new method to obtain clusters with improved quality and interpretability.

In vitro short-term exposure to air pollution PM2.5-0.3 induced cell cycle alterations and genetic instability in a human lung cell coculture model

Although its adverse health effects of air pollution particulate matter (PM2.5) are well-documented and often related to oxidative stress and pro-inflammatory response, recent evidence support the role of the remodeling of the airway epithelium involving the regulation of cell death processes. Hence, the overarching goals of the present study were to use an in vitro coculture model, based on human AM and L132 cells to study the possible alteration of TP53-RB gene signaling pathways (i.e. cell cycle phases, gene expression of TP53, BCL2, BAX, P21, CCND1, and RB, and protein concentrations of their active forms), and genetic instability (i.e. LOH and/or MSI) in the PM2.5-0.3-exposed coculture model. PM2.5-0.3 exposure of human AM from the coculture model induced marked cell cycle alterations after 24h, as shown by increased numbers of L132 cells in subG1 and S+G2 cell cycle phases, indicating apoptosis and proliferation. Accordingly, activation of the TP53-RB gene signaling pathways after the coculture model exposure to PM2.5-0.3 was reported in the L132 cells. Exposure of human AM from the coculture model to PM2.5-0.3 resulted in MS alterations in 3p chromosome multiple critical regions in L132 cell population. Hence, in vitro short-term exposure of the coculture model to PM2.5-0.3 induced cell cycle alterations relying on the sequential occurrence of molecular abnormalities from TP53-RB gene signaling pathway activation and genetic instability.

MicroRNAs are associated with blood-pressure effects of exposure to particulate matter: Results from a mediated moderation analysis

AIMS

Exposure to particulate air pollution is associated with increased blood pressure (BP), a well-established risk factor for cardiovascular disease. To elucidate the mechanisms underlying this relationship, we investigated whether the effects of particulate matter of less than 10μm in aerodynamic diameter (PM10) on BP are mediated by microRNAs.

METHODS AND RESULTS

We recruited 90 obese individuals and we assessed their PM10 exposure 24 and 48h before the recruitment day. We performed multivariate linear regression models to investigate the effects of PM10 on BP. Using the TaqMan® Low-Density Array, we experimentally evaluated and technically validated the expression levels of 377 human miRNAs in peripheral blood. We developed a mediated moderation analysis to estimate the proportion of PM10 effects on BP that was mediated by miRNA expression. PM10 exposure 24 and 48h before the recruitment day was associated with increased systolic BP (β=1.22mmHg, P=0.019; β=1.24mmHg, P=0.019, respectively) and diastolic BP (β=0.67mmHg, P=0.044; β=0.91mmHg, P=0.007, respectively). We identified nine miRNAs associated with PM10 levels 48h after exposure. A conditional indirect effect (CIE=-0.1431) of PM10 on diastolic BP, which was mediated by microRNA-101, was found in individuals with lower values of mean body mass index.

CONCLUSIONS

Our data provide evidence that miRNAs are a molecular mechanism underlying the BP-related effects of air pollution exposure, and indicate miR-101 as epigenetic mechanism to be further investigated.

Short-term effects of atmospheric particulate matter on myocardial infarction: a cumulative meta-analysis

Atmospheric particulate matter (PM) is hypothesized to increase the risk of myocardial infarction (MI). However, the epidemiological evidence is inconsistent. We identified 33 studies with more than 4 million MI patients and applied meta-analysis and meta-regression to assess the available evidence. Twenty-five studies presented the effects of the PM level on hospitalization for MI patients, while eight studies showed the effects on mortality. An increase in PM10 was associated with hospitalization and mortality in myocardial infarction patients (RR per 10 μg/m(3) = 1.011, 95% CI 1.006-1.016; RR per 10 μg/m(3) = 1.008, 95 % CI 1.004-1.012, respectively); PM2.5 also increased the risk of hospitalization and mortality in MI patients (RR per 10 μg/m(3) = 1.024, 95% CI 1.007-1.041 for hospitalization and RR per 10 μg/m(3) = 1.012, 95% CI 1.010-1.015 for mortality). The results of the cumulative meta-analysis indicated that PM10 and PM2.5 were associated with myocardial infarction with the addition of new studies each year. In conclusion, short-term exposure to high PM10 and PM2.5 levels revealed to increase risk of hospitalization and mortality for myocardial infarction. Policy support of pollution control and individual protection was strongly recommended.

Source apportionment of indoor PM10 in Elderly Care Centre

Source contribution to atmospheric particulate matter (PM) has been exhaustively modelled. However, people spend most of their time indoors where this approach is less explored. This evidence worsens considering elders living in Elderly Care Centres, since they are more susceptible. The present study aims to investigate the PM composition and sources influencing elderly exposure. Two 2-week sampling campaigns were conducted-one during early fall (warm phase) and another throughout the winter (cold phase). PM10 were collected with two TCR-Tecora(®) samplers that were located in an Elderly Care Centre living room and in the correspondent outdoor. Chemical analysis of the particles was performed by neutron activation analysis for element characterization, by ion chromatography for the determination of water soluble ions and by a thermal optical technique for the measurement of organic and elemental carbon. Statistical analysis showed that there were no statistical differences between seasons and environments. The sum of the indoor PM10 components measured in this work explained 57 and 53 % of the total PM10 mass measured by gravimetry in warm and cold campaigns, respectively. Outdoor PM10 concentrations were significantly higher during the day than night (p value < 0.05), as well as Ca(2+), Fe, Sb and Zn. The contribution of indoor and outdoor sources was assessed by principal component analysis and showed the importance of the highways and the airport located less than 500 m from the Elderly Care Centre for both indoor and outdoor air quality.

Acute Effects of Particulate Air Pollution on the Incidence of Coronary Heart Disease in Shanghai, China

INTRODUCTION

Evidence based on ecological studies in China suggests that short-term exposure to particulate matter (PM) is associated with cardiovascular mortality. However, there is less evidence of PM-related morbidity for coronary heart disease (CHD) in China. This study aims to investigate the relationship between acute PM exposure and CHD incidence in people aged above 40 in Shanghai.

METHODS

Daily CHD events during 2005-2012 were identified from outpatient and emergency department visits. Daily average concentrations for particulate matter with aerodynamic diameter less than 10 microns (PM10) were collected over the 8-year period. Particulate matter with aerodynamic diameter less than 2.5 microns (PM2.5) were measured from 2009 to 2012. Analyses were performed using quasi-poisson regression models adjusting for confounders, including long-term trend, seasonality, day of the week, public holiday and meteorological factors. The effects were also examined by gender and age group (41-65 years, and >65 years).

RESULTS

There were 619928 CHD outpatient and emergency department visits. The average concentrations of PM10 and PM2.5 were 81.7 μg/m3 and 38.6 μg/m3, respectively. Elevated exposure to PM10 and PM2.5 was related with increased risk of CHD outpatients and emergency department visits in a short time course. A 10 μg/m3 increase in the 2-day PM10 and PM2.5 was associated with increase of 0.23% (95% CI: 0.12%, 0.34%) and 0.74% (95% CI: 0.44%, 1.04%) in CHD morbidity, respectively. The associations appeared to be more evident in the male and the elderly.

CONCLUSION

Short-term exposure to high levels of PM10 and PM2.5 was associated with increased risk of CHD outpatient and emergency department visits. Season, gender and age were effect modifiers of their association.

Short-term population-based non-linear concentration-response associations between fine particulate matter and respiratory diseases in Taipei (Taiwan): a spatiotemporal analysis

Fine particulate matter <2.5 μm (PM2.5) has been associated with human health issues; however, findings regarding the influence of PM2.5 on respiratory disease remain inconsistent. The short-term, population-based association between the respiratory clinic visits of children and PM2.5 exposure levels were investigated by considering both the spatiotemporal distributions of ambient pollution and clinic visit data. We applied a spatiotemporal structured additive regression model to examine the concentration-response (C-R) association between children's respiratory clinic visits and PM2.5 concentrations. This analysis was separately performed on three respiratory disease categories that were selected from the Taiwanese National Health Insurance database, which includes 41 districts in the Taipei area of Taiwan from 2005 to 2007. The findings reveal a non-linear C-R pattern of PM2.5, particularly in acute respiratory infections. However, a PM2.5 increase at relatively lower levels can elevate the same-day respiratory health risks of both preschool children (<6 years old) and schoolchildren (6-14 years old). In preschool children, same-day health risks rise when concentrations increase from 0.76 to 7.44 μg/m(3), and in schoolchildren, same-day health risks rise when concentrations increase from 0.76 to 7.52 μg/m(3). Changes in PM2.5 levels generally exhibited no significant association with same-day respiratory risks, except in instances where PM2.5 levels are extremely high, and these occurrences do exhibit a significant positive influence on respiratory health that is especially notable in schoolchildren. A significant high relative rate of respiratory clinic visits are concentrated in highly populated areas. We highlight the non-linearity of the respiratory health effects of PM2.5 on children to investigate this population-based association. The C-R relationship in this study can provide a highly valuable alternative for assessing the effects of ambient air pollution on human health.

Emergency room visits associated with particulate concentration and Asian dust storms in metropolitan Taipei

This study evaluated risks of emergency room visits (ERV) for all causes, circulatory diseases, and respiratory diseases associated with concentrations of particulate matter (PM10 and PM2.5) and Asian dust storms (ADS) from 2000 to 2008 in metropolitan Taipei. Cumulative 4-day (lag 0-3) relative risks (RR) and confidence intervals (CI) of cause-specific ERV associated with daily concentrations of PM10 or PM2.5 and ADS based on study period (ADS frequently inflicted period: 2000-2004 and less-inflicted period: 2005-2008) were estimated using a distributed lag non-linear model with Poisson distribution. Risks associated with ADS-inflicted season (winter and spring), strength (ratio of stations with Pollutant Standard Index above 100 is < 0.5 or ≥ 0.5), and duration (ADS lasting for 1-3 days or ≥ 4 days) were especially evaluated. In non-linear models, an increase in PM10 from 10 μg/m(3) to 50 μg/m(3) was associated with increased risk of ERV for all causes and respiratory disease with cumulative 4- day RR of 1.18 (95% CI: 1.13, 1.24) and 1.37 (95% CI: 1.23, 1.54), respectively. From 2005 to 2008, the cumulative 4-day RR for an ERV related to an increase in PM2.5 from 5 μg/m(3) to 30 μg/m(3) is 1.21 (95% CI: 1.03, 1.41) for respiratory diseases, and 1.15 (95% CI: 1.08, 1.22) for all causes. In comparison with normal days, elevated ERV of all causes and respiratory diseases was also associated with winter ADS (with corresponding RRs of 1.10 (95% CI: 1.07, 1.13) and 1.14 (95% CI: 1.08, 1.21)) and shorter and less area-affected ADS (with corresponding RRs of 1.07 (95% CI: 1.01, 1.10) and 1.09 (95% CI: 1.03, 1.14)) from 2000 to 2004. Results of this study demonstrate that population health risk varies not only with PM concentration, but also with the ADS characteristics.

The impact of PM2.5 on the human respiratory system

Recently, many researchers paid more attentions to the association between air pollution and respiratory system disease. In the past few years, levels of smog have increased throughout China resulting in the deterioration of air quality, raising worldwide concerns. PM2.5 (particles less than 2.5 micrometers in diameter) can penetrate deeply into the lung, irritate and corrode the alveolar wall, and consequently impair lung function. Hence it is important to investigate the impact of PM2.5 on the respiratory system and then to help China combat the current air pollution problems. In this review, we will discuss PM2.5 damage on human respiratory system from epidemiological, experimental and mechanism studies. At last, we recommend to the population to limit exposure to air pollution and call to the authorities to create an index of pollution related to health.

Associations of short-term exposure to traffic-related air pollution with cardiovascular and respiratory hospital admissions in London, UK

OBJECTIVES

There is evidence of adverse associations between short-term exposure to traffic-related pollution and health, but little is known about the relative contribution of the various sources and particulate constituents.

METHODS

For each day for 2011-2012 in London, UK over 100 air pollutant metrics were assembled using monitors, modelling and chemical analyses. We selected a priori metrics indicative of traffic sources: general traffic, petrol exhaust, diesel exhaust and non-exhaust (mineral dust, brake and tyre wear). Using Poisson regression models, controlling for time-varying confounders, we derived effect estimates for cardiovascular and respiratory hospital admissions at prespecified lags and evaluated the sensitivity of estimates to multipollutant modelling and effect modification by season.

RESULTS

For single day exposure, we found consistent associations between adult (15-64 years) cardiovascular and paediatric (0-14 years) respiratory admissions with elemental and black carbon (EC/BC), ranging from 0.56% to 1.65% increase per IQR change, and to a lesser degree with carbon monoxide (CO) and aluminium (Al). The average of past 7 days EC/BC exposure was associated with elderly (65+ years) cardiovascular admissions. Indicated associations were higher during the warm period of the year. Although effect estimates were sensitive to the adjustment for other pollutants they remained consistent in direction, indicating independence of associations from different sources, especially between diesel and petrol engines, as well as mineral dust.

CONCLUSIONS

Our results suggest that exhaust related pollutants are associated with increased numbers of adult cardiovascular and paediatric respiratory hospitalisations. More extensive monitoring in urban centres is required to further elucidate the associations.

PM2.5 and survival among older adults: effect modification by particulate composition

BACKGROUND

Fine particulate (PM2.5) air pollution has been consistently linked to survival, but reported effect estimates are geographically heterogeneous. Exposure to different types of particle mixtures may explain some of this variation.

METHODS

We used k-means cluster analyses to identify cities with similar pollution profiles, (ie, PM2.5 composition) across the United States. We examined the impact of PM2.5 on survival, and its variation across clusters of cities with similar PM2.5 composition, among Medicare enrollees in 81 US cities (2000-2010). We used time-varying annual PM2.5 averages, measured at ambient central monitoring sites, as the exposure of interest. We ran by-city Cox models, adjusting for individual data on previous cardiopulmonary-related hospitalizations and stratifying by follow-up time, age, gender, and race. This eliminates confounding by factors varying across cities and long-term trends, focusing on year-to-year variations of air pollution around its city-specific mean and trend. We then pooled the city-specific effects using a random effects meta-regression. In this second stage, we also assessed effect modification by cluster membership and estimated cluster-specific PM2.5 effects.

RESULTS

We followed more than 19 million subjects and observed more than 6 million deaths. We found a harmful impact of annual PM2.5 concentrations on survival (hazard ratio = 1.11 [95% confidence interval = 1.01, 1.23] per 10 μg/m). This effect was modified by particulate composition, with higher effects observed in clusters containing high concentrations of nickel, vanadium, and sulfate. For instance, our highest effect estimate was observed in cities with harbors in the Northwest, characterized by high nickel, vanadium, and elemental carbon concentrations (1.9 [1.1, 3.3]). We observed null or negative associations in clusters with high oceanic and crustal particles.

CONCLUSIONS

To the best of our knowledge, this is the first study to examine the association between PM2.5 composition and survival. Our findings indicate that long-term exposure to fuel oil combustion and power plant emissions have the highest impact on survival.