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

The influence of spatial patterning on modeling PM2.5 constituents in Eastern Massachusetts

Geostatistical exposure methods for air pollution have inherent uncertainties, resulting in varying levels of exposure misclassification. In this study, we propose that areas representing clusters of PM_2.5 elements are potential predictor variables to be included in spatial models for particle composition. The inclusion of these clusters may minimize the exposure misclassification. We evaluated the influence of spatial patterning on modeling of 10 components of ambient PM_2.5, which included Al, Cu, Fe, K, Ni, Pb, S, Ti, V, and Zn. This study was performed in three stages. First, we applied a hybrid approach (combination of Empirical Bayesian Kriging and land use regression) to estimate spatial variability for each one of the 10 components of ambient PM_2.5. In this stage, we accounted for numerous predictors representing land use, transportation, demographic, and geographical characteristics. In the second stage, we applied the same hybrid approach adding clusters of each PM_2.5 component to the set of predictor variables. The clusters here were estimated by a multivariate clustering approach based on k means. Finally, in the last stage, we compared the estimates obtained from the model without clusters (first stage) and the model with clusters (second stage). Overall, our findings suggest significant influence of spatial clusters on modeling some PM_2.5 components. We observed that the clusters may affect the error of the prediction values and especially the proportion of explained variance for most of the PM_2.5 constituents evaluated in this study. The model with cluster presented a better performance for all PM_2.5 components, except for Pb, which the R² value decreased 8.51% when we included the clusters in the analysis; and for V, which the R² value did not change with the clusters. Models for Cu and Fe explained the highest concentration variance. The R² value for the model without cluster was 0.55 for both pollutants. When we accounted for clusters, R² value increased 13 and 7% for Cu (R² = 0.62) and Fe (R² = 0.59), respectively. The models for K and S presented the lowest performance for both models with and without cluster (although the model with cluster improved substantially the R² values). Better knowledge of the influence of spatial patterns on air pollution modeling should be of interest to policy makers to devise future strategies to improve human exposure assessment to air particulates while controlling for spatial patterns of ambient PM_2.5 elemental concentration.

Multivariate spatial patterns of ambient PM2.5 elemental concentrations in Eastern Massachusetts

Understanding the factors that affect spatial differences in PM_2.5 composition is crucial for implementing emissions control and health policies. Although previous studies have explored modeling of spatial patterns as a tool to improve human exposure assessment, little work has employed a multivariate clustering approach to identify spatial patterns in particle composition. In this study, we used this approach to assess the spatial patterns of ambient PM_2.5 elemental concentrations in Eastern Massachusetts in the United States. To distinguish one cluster of sites from another, we considered air pollution sources and geodemographic variables. We evaluated spatial patterns for 11 elemental components of ambient PM_2.5, which included S, K, Ca, Fe, Zn, Cu, Ti, Al, Pb, V, and Ni. The analyses for S, Ca, Cu, Ti, Al, and Pb resulted in: 2 clusters for Fe, Zn, V, and Ni; 3 clusters; and for 12 clusters for K. Overall, our findings suggest substantial variation of clusters among PM_2.5 components. In addition, land use, population density, and daily traffic were used as variables to more effectively characterize clusters of sites. We used R² values to estimate the effectiveness of each variable in characterizing clusters. Larger R² values indicate better the discrimination among the sites. For example, population density had the highest R² value when the analysis was performed for S, Ca, Zn, Ti, Al, Pb, and V; land use presented the highest R² value for Cu, V, and Ni; and, traffic showed the highest R² value for PM_2.5 mass concentration. This study improves the ability to model both the between- and within-area variability of source emissions and pollution regime, using concentrations of PM_2.5 components.

Long-term exposure to PM2.5 and ozone and hospital admissions of Medicare participants in the Southeast USA

We examined the association between average annual fine particulate matter (PM_2.5) and ozone and first hospital admissions of Medicare participants for stroke, chronic obstructive pulmonary disease (COPD), pneumonia, myocardial infarction (MI), lung cancer, and heart failure (HF). Annual average PM_2.5 and ozone levels were estimated using high-resolution spatio-temporal models. We fit a marginal structural Cox proportional hazards model, using stabilized inverse probability weights (IPWs) to account for the competing risk of death and confounding. Analyses were then repeated after restricting to exposure levels below the current U.S. standards. The results showed that PM_2.5 was significantly associated with an increased hazard of admissions for all studied outcomes; the highest observed being a 6.1% (95% CI: 5.9%-6.2%) increase in the hazard of admissions with pneumonia for each μg/m³ increase in particulate levels. Ozone was also significantly associated with an increase in the risk of first hospital admissions of all outcomes. The hazard of pneumonia increased by 3.0% (95% CI: 2.9%-3.1%) for each ppb increase in the ozone level. Our results reveal a need to regulate long-term ozone exposure, and that associations persist below current PM_2.5 standards.

Assessing the association between fine particulate matter (PM2.5) constituents and cardiovascular diseases in a mega-city of Pakistan

Concerning PM_2.5 concentrations, rapid industrialization, along with increase in cardiovascular disease (CVD) were recorded in Pakistan, especially in urban areas. The degree to which air pollution contributes to the increase in the burden of CVD in Pakistan has not been assessed due to lack of data. This study aims to describe the characteristics of PM_2.5 constituents and investigate the impact of individual PM_2.5 constituent on cardiovascular morbidity in Karachi, a mega city in Pakistan. Daily levels of twenty-one constituents of PM_2.5 were analyzed using samples collected at two sites from fall 2008 to summer 2009 in Karachi. Hospital admission and emergency room visits due to CVD were collected from two large hospitals. Negative Binominal Regression was used to estimate associations between pollutants and the risk of CVD. All PM_2.5 constituents were assessed in single-pollutant models and selected constituents were assessed in multi-pollutant models adjusting for PM_2.5 mass and gaseous pollutants. The most common CVD subtypes among our participants were ischemic heart disease, hypertension, heart failure, and cardiomyopathy. Extremely high levels of PM_2.5 constituents from fossil-fuels combustion and industrial emissions were observed, with notable peaks in winter. The most consistent associations were found between exposure to nickel (5-14% increase per interquartile range) and cardiovascular hospital admissions. Suggestive evidence was also observed for associations between cardiovascular hospital admissions and Al, Fe, Ti, and nitrate. Our findings suggested that PM_2.5 generated from fossil-fuels combustion and road dust resuspension were associated with the increased risk of CVD in Pakistan.

Long-term exposure to PM2.5 and ozone and hospital admissions of Medicare participants in the Southeast USA

We examined the association between average annual fine particulate matter (PM_2.5) and ozone and first hospital admissions of Medicare participants for stroke, chronic obstructive pulmonary disease (COPD), pneumonia, myocardial infarction (MI), lung cancer, and heart failure (HF). Annual average PM_2.5 and ozone levels were estimated using high-resolution spatio-temporal models. We fit a marginal structural Cox proportional hazards model, using stabilized inverse probability weights (IPWs) to account for the competing risk of death and confounding. Analyses were then repeated after restricting to exposure levels below the current U.S. standards. The results showed that PM_2.5 was significantly associated with an increased hazard of admissions for all studied outcomes; the highest observed being a 6.1% (95% CI: 5.9%-6.2%) increase in the hazard of admissions with pneumonia for each μg/m³ increase in particulate levels. Ozone was also significantly associated with an increase in the risk of first hospital admissions of all outcomes. The hazard of pneumonia increased by 3.0% (95% CI: 2.9%-3.1%) for each ppb increase in the ozone level. Our results reveal a need to regulate long-term ozone exposure, and that associations persist below current PM_2.5 standards.

Pediatric Psychiatric Emergency Department Utilization and Fine Particulate Matter: A Case-Crossover Study

BACKGROUND

Acute exposure to ambient particulate matter < 2.5 μ m in aerodynamic diameter (PM 2.5 ) has been associated with adult psychiatric exacerbations but has not been studied in children.

OBJECTIVES

Our objectives were to estimate the association between acute exposures to ambient PM 2.5 and psychiatric emergency department (ED) utilization and to determine if it is modified by community deprivation.

METHODS

We used a time-stratified case-crossover design to analyze all pediatric, psychiatric ED encounters at Cincinnati Children's Hospital Medical Center in Cincinnati, Ohio, from 2011 to 2015 (n = 13,176 ). Conditional logistic regression models adjusted for temperature, humidity, and holiday effects were used to estimate the odds ratio (OR) for a psychiatric ED visit 0-3 d after ambient PM 2.5 exposures, estimated at residential addresses using a spatiotemporal model.

RESULTS

A 10 - μ g / m 3 increase in PM 2.5 was associated with a significant increase in any psychiatric ED utilization 1 [OR = 1.07 (95% CI: 1.02, 1.12)] and 2 [OR = 1.05 (95% CI: 1.00, 1.10)] d later. When stratified by visit reason, associations were significant for ED visits related to adjustment disorder {e.g., 1-d lag [OR = 1.24 (95% CI: 1.02, 1.52)] and suicidality 1-d lag [OR = 1.44 (95% CI: 1.03, 2.02)]}. There were significant differences according to community deprivation, with some lags showing stronger associations among children in high versus low deprivation areas for ED visits for anxiety {1-d lag [OR = 1.39 (95% CI: 0.96, 2.01) vs. 0.85 (95% CI: 0.62, 1.17)] and suicidality same day [OR = 1.98 (95% CI: 1.22, 3.23) vs. 0.93 (95% CI: 0.60, 1.45)]}. In contrast, for some lags, associations with ED visits for adjustment disorder were weaker for children in high-deprivation areas {1-d lag [OR = 1.00 (95% CI: 0.76, 1.33) vs. 1.50 (95% CI: 1.16, 1.93)]}.

DISCUSSION

These findings warrant additional research to confirm the associations in other populations. https://doi.org/10.1289/EHP4815.

The effect of exposure time and concentration of airborne PM2.5 on lung injury in mice: A transcriptome analysis

The association between airborne fine particulate matter (PM2.5) concentration and the risk of respiratory diseases has been well documented by epidemiological studies. However, the mechanism underlying the harmful effect of PM2.5 has not been fully understood. In this study, we exposed the C57BL/6J mice to airborne PM2.5 for 3 months (mean daily concentration ~50 or ~110 μg/m3, defined as PM2.5-3L or PM2.5-3H) or 6 months (mean daily concentration ~50 μg/m3, defined as PM2.5-6L) through a whole-body exposure system. Histological and biochemical analysis revealed that PM2.5-3H exposure caused more severe lung injury than did PM2.5-3L, and the difference was greater than that of PM2.5-6L vs PM2.5-3L exposure. With RNA-sequencing technique, we found that the lungs exposed with different concentration of PM2.5 have distinct transcriptional profiles. PM2.5-3H exposure caused more differentially expressed genes (DEGs) in lungs than did PM2.5-3L or PM2.5-6L. The DEGs induced by PM2.5-3L or PM2.5-6L exposure were mainly enriched in immune pathways, including Hematopoietic cell lineage and Cytokine-cytokine receptor interaction, while the DEGs induced by PM2.5-3H exposure were mainly enriched in cardiovascular disease pathways, including Hypertrophic cardiomyopathy and Dilated cardiomyopathy. In addition, we found that upregulation of Cd5l and reduction of Hspa1 and peroxiredoxin-4 was associated with PM2.5-induced pulmonary inflammation and oxidative stress. These results may provide new insight into the cytotoxicity mechanism of PM2.5 and help to development of new strategies to attenuate air pollution associated respiratory disease.

Evaluation of predictive capabilities of ordinary geostatistical interpolation, hybrid interpolation, and machine learning methods for estimating PM2.5 constituents over space

Numerous modeling approaches to estimate concentrations of PM_2.5 components have been developed to derive better exposures for health studies, including geostatistical interpolation approaches, land use regression models and, models based on remote sensing technology. Recently, there have been some efforts to develop models based on machine learning algorithms. Each one of these exposure assessment methods has inherent uncertainties resulting in varying levels of exposure misclassification. To date, only a few studies have attempted to systematically compare exposure estimates from different PM_2.5 constituent models. Our research addresses this gap, by comparing the predictive capabilities of ordinary geostatistical interpolation (Ordinary Kriging - OK), hybrid interpolation (combination of Empirical Bayesian Kriging and land use regression), and machine learning techniques (forest-based regression) for estimating PM_2.5 constituents in Eastern Massachusetts in the United States. We compared the estimates of 10 ambient PM_2.5 components, which included Al, Cu, Fe, K, Ni, Pb, S, Ti, V, and Zn. The OK model performed poorest for all PM_2.5 components, with an R² under 0.30. The hybrid model presented a slight improvement, especially for Cu and Fe, for which the R² value increased to 0.62 and 0.59, respectively. These elements presented the highest R² value from the hybrid model. The forest model presented the best performance, with R² values higher than 0.7 for most of the particle components, including Cu, Fe, Ni, Pb, Ti, and V. Same as observed with the hybrid model, the forest model for Cu and Fe explained the highest concentration variance, with a R² value equal to 0.88 and 0.92, respectively. The forest model for K, S, and Zn performed poorest with an R² value of 0.54, 0.37, and 0.44, respectively. The results presented here can be useful for the environmental health community to more accurately estimate PM_2.5 constituents over space.

Living Well with Pollution? The Impact of the Concentration of PM2.5 on the Quality of Life of Patients with Asthma

While the negative influence of environmental pollution on the respiratory system is well established, especially for people with bronchial hyper-reactivity, the impact of particulate matter on quality of life in asthma patients is not well understood. Three hundred adult asthma patients were recruited for a study; for each patient, the daily concentrations of particulate matter of 2.5 µm or less in diameter (PM_2.5) were recorded from air quality monitoring stations. The study was conducted over two weeks. After two weeks, the patients filled out the Asthma Quality of Life Questionnaire (AQLQ), evaluating the quality of their lives throughout the monitored period. Patients exposed to a higher concentration of PM_2.5 had significantly lower AQLQ scores. Every 10 µg/m³ of an increase in the concentration of PM_2.5 resulted in a decrease of the AQLQ score by 0.16. All domains of quality of life (symptoms, activity limitations, emotional functioning, and environmental stimuli) assessed in the questionnaire were negatively affected by PM_2.5. These findings provide an important argument in favor of educating physicians and patients and raising awareness about the detrimental health effects of air pollution. Improving the quality of life of people with asthma requires an immediate and substantial reduction of air pollution.

Bibliometric analysis of cardiometabolic disorders studies involving NO2, PM2.5 and noise exposure

BACKGROUND

This study uses bibliometric analysis to describe the state of research about the association of NO2, PM2.5 and noise exposures - three traffic-related pollutants - with cardiometabolic disorders.

METHODS

We retrieved references published 1994-2017 from Scopus and classified references with respect to exposure, health outcome and study design using index keywords. Temporal trend, top cited references, used index keywords and the number of hypothesis testing and non-hypothesis testing study design for each group were identified.

RESULTS

Results show PM2.5 is the most frequently studied exposure (47%), followed by both NO2 and PM2.5 exposure (29%). Only 3% of references considered multiple exposures between NO2 and/or PM2.5 and noise, and these were published after 2008. While we observed a growing trend in studies with NO2 and/or PM2.5 and noise and diabetes in the last decade, there is a diminishing trend in studies with noise and diabetes. Different patterns of study designs were found through H/NH ratio, the number of references classified as having a hypothesis (H)-testing design relative to the number of references classified as having a non-hypothesis (NH)-testing design. Studies with NO2 and/or PM2.5 exposure are more likely to have a H-testing design, while those with noise exposure are more likely to have a NH-testing design, such as cross-sectional study design.

CONCLUSIONS

We conclude with three themes about research trends. First, the study of simultaneous exposures to multiple pollutants is a current trend, and likely to continue. Second, the association between traffic-related pollutants and diabetes and metabolic symptoms is an area for growth in research. Third, the transition to the use of H-testing study designs to explore associations between noise and cardiometabolic outcomes may be supported by improved understanding of the mechanism of action, and/or improvements to the accuracy and precision of air pollution and noise exposure assessments for environmental health research.

Health benefits and control costs of tightening particulate matter emissions standards for coal power plants - The case of Northeast Brazil

Exposure to ambient particulate matter (PM) caused an estimated 4.2 million deaths worldwide in 2015. However, PM emission standards for power plants vary widely. To explore if the current levels of these standards are sufficiently stringent in a simple cost-benefit framework, we compared the health benefits (avoided monetized health costs) with the control costs of tightening PM emission standards for coal-fired power plants in Northeast (NE) Brazil, where ambient PM concentrations are below World Health Organization (WHO) guidelines. We considered three Brazilian PM₁₀ (PM_x refers to PM with a diameter under x micrometers) emission standards and a stricter U.S. EPA standard for recent power plants. Our integrated methodology simulates hourly electricity grid dispatch from utility-scale power plants, disperses the resulting PM_2.5, and estimates selected human health impacts from PM_2.5 exposure using the latest integrated exposure-response model. Since the emissions inventories required to model secondary PM are not available in our study area, we modeled only primary PM so our benefit estimates are conservative. We found that tightening existing PM₁₀ emission standards yields health benefits that are over 60 times greater than emissions control costs in all the scenarios we considered. The monetary value of avoided hospital admissions alone is at least four times as large as the corresponding control costs. These results provide strong arguments for considering tightening PM emission standards for coal-fired power plants worldwide, including in regions that meet WHO guidelines and in developing countries.

A Systematic Review of the Short-Term Health Effects of Air Pollution in Persons Living with Coronary Heart Disease

Persons living with chronic medical conditions (such as coronary artery disease (CAD)) are thought to be at increased risk when exposed to air pollution. This systematic review critically evaluated the short-term health effects of air pollution in persons living with CAD. Original research articles were retrieved systematically through searching electronic databases (e.g., Medical Literature Analysis and Retrieval System Online (MEDLINE)), cross-referencing, and the authors' knowledge. From 2884 individual citations, 26 eligible articles were identified. The majority of the investigations (18 of 22 (82%)) revealed a negative relationship between air pollutants and cardiac function or overall health. Heart rate variability (HRV) was the primary cardiovascular outcome measure, with 10 out of 13 studies reporting at least one index of HRV being significantly affected by air pollutants. However, there was some inconsistency in the relationship between HRV and air pollutants, mediated (at least in part) by the confounding effects of beta-blocker medications. In conclusion, there is strong evidence that air pollution can have adverse effects on cardiovascular function in persons living with CAD. All persons living with CAD should be educated on how to monitor air quality, should recognize the potential risks of excessive exposure to air pollution, and be aware of strategies to mitigate these risks. Persons living with CAD should minimize their exposure to air pollution by limiting outdoor physical activity participation when the forecast air quality health index indicates increased air pollution (i.e., an increased risk).

Air Pollution and Noncommunicable Diseases: A Review by the Forum of International Respiratory Societies' Environmental Committee, Part 2: Air Pollution and Organ Systems

Although air pollution is well known to be harmful to the lung and airways, it can also damage most other organ systems of the body. It is estimated that about 500,000 lung cancer deaths and 1.6 million COPD deaths can be attributed to air pollution, but air pollution may also account for 19% of all cardiovascular deaths and 21% of all stroke deaths. Air pollution has been linked to other malignancies, such as bladder cancer and childhood leukemia. Lung development in childhood is stymied with exposure to air pollutants, and poor lung development in children predicts lung impairment in adults. Air pollution is associated with reduced cognitive function and increased risk of dementia. Particulate matter in the air (particulate matter with an aerodynamic diameter < 2.5 μm) is associated with delayed psychomotor development and lower child intelligence. Studies link air pollution with diabetes mellitus prevalence, morbidity, and mortality. Pollution affects the immune system and is associated with allergic rhinitis, allergic sensitization, and autoimmunity. It is also associated with osteoporosis and bone fractures, conjunctivitis, dry eye disease, blepharitis, inflammatory bowel disease, increased intravascular coagulation, and decreased glomerular filtration rate. Atopic and urticarial skin disease, acne, and skin aging are linked to air pollution. Air pollution is controllable and, therefore, many of these adverse health effects can be prevented.

High-Level PM2.5/PM10 Exposure Is Associated With Alterations in the Human Pharyngeal Microbiota Composition

Previous studies showed that high concentration of particulate matter (PM) 2.5 and PM10 carried a large number of bacterial and archaeal species, including pathogens and opportunistic pathogens. In this study, pharyngeal swabs from 83 subjects working in an open air farmer's market were sampled before and after exposure to smog with PM2.5 and PM10 levels up to 200 and 300 μg/m3, respectively. Their microbiota were investigated using high-throughput sequencing targeting the V3-V4 regions of the 16S rRNA gene. The genus level phylotypes was increased from 649 to 767 in the post-smog pharyngeal microbiota, of which 142 were new and detected only in the post-smog microbiota. The 142 new genera were traced to sources such as soil, marine, feces, sewage sludge, freshwater, hot springs, and saline lakes. The abundance of the genera Streptococcus, Haemophilus, Moraxella, and Staphylococcus increased in the post-smog pharyngeal microbiota. All six alpha diversity indices and principal component analysis showed that the taxonomic composition of the post-smog pharyngeal microbiota was significantly different to that of the pre-smog pharyngeal microbiota. Redundancy analysis showed that the influences of PM2.5/PM10 exposure and smoking on the taxonomic composition of the pharyngeal microbiota were statistically significant (p < 0.001). Two days of exposure to high concentrations of PM2.5/PM10 changed the pharyngeal microbiota profiles, which may lead to an increase in respiratory diseases. Wearing masks could reduce the effect of high-level PM2.5/PM10 exposure on the pharyngeal microbiota.

Airway microbiome is associated with respiratory functions and responses to ambient particulate matter exposure

BACKGROUND

Ambient particulate matter (PM) exposure has been associated with respiratory function decline in epidemiological studies. We hypothesize that a possible underlying mechanism is the perturbation of airway microbiome by PM exposure.

METHODS

During October 2016-October 2017, on two human cohorts (n = 115 in total) in Shanghai China, we systematically collected three categories of data: (1) respiratory functions, (2) airway microbiome from sputum, and (3) PM2.5 (PM of ≤ 2.5 µm in diameter) level in ambient air. We investigated the impact of PM2.5 on airway microbiome as well as the link between airway microbiome and respiratory functions using linear mixed regression models.

RESULTS

The respiratory function of our primary interest includes forced vital capacity (FVC) and forced expiratory volume in 1st second (FEV1). FEV1/FVC, an important respiratory function trait and key diagnosis criterion of COPD, was significantly associated with airway bacteria load (p = 0.0038); and FEV1 was associated with airway microbiome profile (p = 0.013). Further, airway microbiome was significantly influenced by PM2.5 exposure (p = 4.48E-11).

CONCLUSIONS

To our knowledge, for the first time, we demonstrated the impact of PM2.5 on airway microbiome, and reported the link between airway microbiome and respiratory functions. The results expand our understanding on the scope of PM2.5 exposure's influence on human respiratory system, and point to novel etiological mechanism of PM2.5 exposure induced diseases.

Effects of Increasing Aridity on Ambient Dust and Public Health in the U.S. Southwest Under Climate Change

The U.S. Southwest is projected to experience increasing aridity due to climate change. We quantify the resulting impacts on ambient dust levels and public health using methods consistent with the Environmental Protection Agency's Climate Change Impacts and Risk Analysis framework. We first demonstrate that U.S. Southwest fine (PM2.5) and coarse (PM2.5-10) dust levels are strongly sensitive to variability in the 2-month Standardized Precipitation-Evapotranspiration Index across southwestern North America. We then estimate potential changes in dust levels through 2099 by applying the observed sensitivities to downscaled meteorological output projected by six climate models following an intermediate (Representative Concentration Pathway 4.5, RCP4.5) and a high (RCP8.5) greenhouse gas concentration scenario. By 2080-2099 under RCP8.5 relative to 1986-2005 in the U.S. Southwest: (1) Fine dust levels could increase by 57%, and fine dust-attributable all-cause mortality and hospitalizations could increase by 230% and 360%, respectively; (2) coarse dust levels could increase by 38%, and coarse dust-attributable cardiovascular mortality and asthma emergency department visits could increase by 210% and 88%, respectively; (3) climate-driven changes in dust concentrations can account for 34-47% of these health impacts, with the rest due to increases in population and baseline incidence rates; and (4) economic damages of the health impacts could total $47 billion per year additional to the 1986-2005 value of $13 billion per year. Compared to national-scale climate impacts projected for other U.S. sectors using the Climate Change Impacts and Risk Analysis framework, dust-related mortality ranks fourth behind extreme temperature-related mortality, labor productivity decline, and coastal property loss.

Association between air pollution and type 2 diabetes: an updated review of the literature

Air pollution and type 2 diabetes mellitus (T2DM) are critical public health issues worldwide. A large number of epidemiological studies have highlighted the adverse effects of air pollution on diabetes, and include risk profiles for different exposure durations, study design types, subgroup populations, and effects of air pollution components. We researched PubMed, Google Scholar, and Web of Science to identify studies on the association between air pollution and T2DM from January 2009 to January 2019. The aim of this review is to provide a brief overview of epidemiological and experimental studies on air pollution associated with T2DM from the latest research, which may provide practical information about this relationship and possible mechanisms. Current cumulative evidence appears to suggest that T2DM-related biomarkers increase with increasing exposure duration and concentration of air pollutants. The chemical constituents of the air pollutant mixture may affect T2DM to varying degrees. The suggested mechanisms whereby air pollutants induce T2DM include increased inflammation, oxidative stress, and endoplasmic reticulum stress.

The reliability of an environmental epidemiology meta-analysis, a case study

Claims made in science papers are coming under increased scrutiny with many claims failing to replicate. Meta-analyses are questionable when based on data from observational studies which are often unreliable. We examine the reliability of the base studies used in an air quality/heart attack meta-analysis and the resulting meta-analysis. A meta-analysis study that includes 14 observational air quality/heart attack studies is examined for its statistical reliability. We use simple counting to evaluate the reliability of the base papers and a p-value plot of the p-values from the base studies to examine study heterogeneity. We find that the base papers have the potential for massive multiple testing and multiple modeling with no statistical adjustments. Statistics coming from the base papers are not guaranteed to be unbiased, a requirement for a valid meta-analysis. There is study heterogeneity for the base papers with strong evidence for so called p-hacking in some, possibly many, of the studies. We make two observations: there are many claims at issue in each of the 14 base studies so uncorrected multiple testing is a serious issue. We find that some of the base papers exhibit the characteristics of p-hacking and are therefore not reliable; the resulting meta-analysis is not reliable.

PM2.5 Particle Detection in a Microfluidic Device by Using Ionic Current Sensing

We have demonstrated a PM_2.5 analysis method that adds information on the number concentration and size by using microfluidic-based ionic current sensing with a bridge circuit. The bridge circuit allows for suppression of the background current and the detection of small PM_2.5 particles, even if a relatively large micropore is used. This is the first demonstration of the detection of PM_2.5 particles via ionic current sensing; our method enables analyses of both the number concentration and size.

[The Effectiveness of Counseling Using Preventative Informational Sheets with Climate and Environmental Data from Insurance Pharmacies in Preventing Worsened Asthma Symptoms]

We investigated the possibility that having pharmacists give asthma patients informational sheets on climate and environmental changes at insurance pharmacies during patient counseling might prevent the worsening of asthma symptoms. Patients with hyperlipidemia were comparative subjects. We created informational sheets about climate and environmental changes and their influence on asthma. During patient counseling, pharmacists gave them to all asthma patients who visited insurance pharmacies over a period of 2 months, between November and December 2017. Based on previous studies, we called days which showed certain climate or environmental changes as compared to the previous day "change days". We compared the number of visiting patients on change days after preventative information was provided (between January and March 2018) with the number before information was provided (between January and March 2017). In addition, we compared those numbers with the number of patients who visited the target pharmacies between January and March 2016 in order to examine the influence of yearly climate change. The same procedure was used with hyperlipidemic patients. The number of visiting asthma patients after information was provided significantly decreased (5.1±2.1, p=0.03) compared with the number before information was provided, between January and March 2017 (6.1±2.8). The number of aforementioned visits compared to those between January and March 2016 also significantly decreased (p=0.01). Our results suggest that preventative information about climate and environmental changes provided by pharmacists during patient counseling might influence the number of asthma patient visits and prevent the exacerbation of their symptoms.

Adaptive predictive principal components for modeling multivariate air pollution

Air pollution monitoring locations are typically spatially misaligned with locations of participants in a cohort study, so to analyze pollution-health associations, exposures must be predicted at subject locations. For a pollution measure like PM_2.5 (fine particulate matter) comprised of multiple chemical components, the predictive principal component analysis (PCA) algorithm derives a low-dimensional representation of component profiles for use in health analyses. Geographic covariates and spatial splines help determine the principal component loadings of the pollution data to give improved prediction accuracy of the principal component scores. While predictive PCA can accommodate pollution data of arbitrary dimension, it is currently limited to a small number of pre-selected geographic covariates. We propose an adaptive predictive PCA algorithm, which automatically identifies a combination of covariates that is most informative in choosing the principal component directions in the pollutant space. We show that adaptive predictive PCA improves the accuracy of multi-pollutant exposure predictions at subject locations.

Particulate matter air pollution and respiratory impact on humans and animals

Air pollution is now fully acknowledged to be a public health problem and a social issue. Particulate matter (PM) concentration has been linked with several clinical manifestations of pulmonary and cardiovascular diseases and is associated with morbidity and mortality induced by respiratory diseases both in human and animals. Current research on airborne particle-induced health effects investigates the critical characteristics of particulate matter that determine their biological effects. Scientific evidence assessed that the size of the airborne particles and their surface area determine the potential to elicit inflammatory injury, oxidative damage, and other biological effects. Thus, the present review paper aims to summarize the current evidences and findings on the effect of air pollution on lung function in both humans and animals.

A multicity study of air pollution and cardiorespiratory emergency department visits: Comparing approaches for combining estimates across cities

Determining how associations between ambient air pollution and health vary by specific outcome is important for developing public health interventions. We estimated associations between twelve ambient air pollutants of both primary (e.g. nitrogen oxides) and secondary (e.g. ozone and sulfate) origin and cardiorespiratory emergency department (ED) visits for 8 specific outcomes in five U.S. cities including Atlanta, GA; Birmingham, AL; Dallas, TX; Pittsburgh, PA; St. Louis, MO. For each city, we fitted overdispersed Poisson time-series models to estimate associations between each pollutant and specific outcome. To estimate multicity and posterior city-specific associations, we developed a Bayesian multicity multi-outcome (MCM) model that pools information across cities using data from all specific outcomes. We fitted single pollutant models as well as models with multipollutant components using a two-stage chemical mixtures approach. Posterior city-specific associations from the MCM models were somewhat attenuated, with smaller standard errors, compared to associations from time-series regression models. We found positive associations of both primary and secondary pollutants with respiratory disease ED visits. There was some indication that primary pollutants, particularly nitrogen oxides, were also associated with cardiovascular disease ED visits. Bayesian models can help to synthesize findings across multiple outcomes and cities by providing posterior city-specific associations building on variation and similarities across the multiple sources of available information.

The association between short and long-term exposure to PM2.5 and temperature and hospital admissions in New England and the synergistic effect of the short-term exposures

BACKGROUND

Particulate matter < 2.5 μm in diameter (PM2.5) and heat are strong predictors of morbidity, yet few studies have examined the effects of long-term exposures on non-fatal events, or assessed the short and long-term effect on health simultaneously.

OBJECTIVE

We jointly investigated the association of short and long-term exposures to PM2.5 and temperature with hospital admissions, and explored the modification of the associations with the short-term exposures by one another and by temperature variability.

METHODS

Daily ZIP code counts of respiratory, cardiac and stroke admissions of adults ≥65 (N = 2,015,660) were constructed across New-England (2001-2011). Daily PM2.5 and temperature exposure estimates were obtained from satellite-based spatio-temporally resolved models. For each admission cause, a Poisson regression was fit on short and long-term exposures, with a random intercept for ZIP code. Modifications of the short-term effects were tested by adding interaction terms with temperature, PM2.5 and temperature variability.

RESULTS

Associations between short and long-term exposures were observed for all of the outcomes, with stronger effects of long-term exposures to PM2.5. For respiratory admissions, the short-term PM2.5 effect (percent increase per IQR) was larger on warmer days (1.12% versus -0.53%) and in months of higher temperature variability (1.63% versus -0.45%). The short-term temperature effect was higher in months of higher temperature variability as well. For cardiac admissions, the PM2.5 effect was larger on colder days (0.56% versus -0.30%) and in months of higher temperature variability (0.99% versus -0.56%).

CONCLUSIONS

We observed synergistic effects of short-term exposures to PM2.5, temperature and temperature variability. Long-term exposures to PM2.5 were associated with larger effects compared to short-term exposures.

Impacts of prescribed fires and benefits from their reduction for air quality, health and visibility in the Pacific Northwest of the United States

Using a WRF-SMOKE-CMAQ modeling framework, we investigate the impacts of smoke from prescribed fires on model performance, regional and local air quality, health impacts, and visibility in protected natural environments using three different prescribed fire emission scenarios - 100% fire, no fire, and 30% fire. The 30% fire case reflects a 70% reduction in fire activities due to harvesting of logging residues for use as a feedstock for a potential aviation biofuel supply chain. Overall model performance improves for several performance metrics when fire emissions are included, especially for organic carbon, irrespective of the model goals and criteria used. This effect on model performance is more pronounced for the rural and remote IMPROVE sites for organic carbon and total PM2.5. A reduction in prescribed fire emissions (30% fire case) results in significant improvement in air quality in areas in western Oregon, northern Idaho and western Montana where most prescribed fires occur. Prescribed burning contributes to visibility impairment and a relatively large portion of protected class I areas will benefit from a reduced emission scenario. For the haziest 20% days, prescribed burning is an important source of visibility impairment and approximately 50% of IMPROVE sites in the model domain show a significant improvement in visibility for the reduced fire case. Using BenMAP, a health impact assessment tool, we show that several hundred additional deaths, several thousand upper and lower respiratory symptom cases, several hundred bronchitis cases, and more than 35,000 work day losses can be attributed to prescribed fires and these health impacts decrease by 25-30% when a 30% fire emission scenario is considered. Implications This study assesses the potential regional and local air quality, public health and visibility impacts from prescribed burning activities as well as benefits that can be achieved by a potential reduction in emissions for a scenario where biomass is harvested for conversion to biofuel. As prescribed burning activities become more frequent, they can be more detrimental for air quality and health. Forest residue based biofuel industry can be source of cleaner fuel with co-benefits of improved air quality, reduction in health impacts and improved visibility.

Study on PM2.5 pollution and the mortality due to lung cancer in China based on geographic weighted regression model

BACKGROUND

PM2.5 has become a major component of air pollution in China and has led to a series of health problems. The mortality rate caused by lung cancer has reached the point where it cannot be ignored in China. Air pollution is becoming more and more serious in China, which is increasingly affecting people's lives and health.

METHODS

Considering the variations in the geographical environment in China, this paper studied the relationship between PM2.5 concentration and lung cancer mortality based on the geographical weighted regression model in 31 provinces in 2004 and 2008, autonomous regions and municipalities of China.

RESULTS

The results indicated there was a significant positive correlation between PM2.5 concentration and lung cancer mortality (r = 0.0052, P = 0.036). Additionally, the longer the time of exposure to PM2.5 is, the higher morbidity is.

CONCLUSION

It is suggested that the Chinese government should launch some environmental policy, especially in those areas with severe PM2.5 pollutions, and keep the citizens away from exposure to PM2.5 pollution in the long term.

Editor's Highlight: Modifying Role of Endothelial Function Gene Variants on the Association of Long-Term PM2.5 Exposure With Blood DNA Methylation Age: The VA Normative Aging Study

Recent studies have reported robust associations of long-term PM2.5 exposure with DNA methylation-based measures of aging; yet, the molecular implications of these relationships remain poorly understood. We evaluated if genetic variation in 3 biological pathways implicated in PM2.5-related disease-oxidative stress, endothelial function, and metal processing-could modify the effect of PM2.5 on DNAm-age, one prominent DNA methylation-based measure of biological age. This analysis was based on 552 individuals from the Normative Aging Study with at least one visit between 2000 and 2011 (n = 940 visits). A genetic-score approach was used to calculate aging-risk variant scores for endothelial function, oxidative stress, and metal processing pathways. One-year PM2.5 and PM2.5 component (sulfate and ammonium) levels at participants' addresses were estimated using the GEOS-chem transport model. Blood DNAm-age was calculated using CpG sites on the Illumina HumanMethylation450 BeadChip. In fully-adjusted linear mixed-effects models, the effects of sulfate on DNAm-age (in years) were greater in individuals with high aging-risk endothelial function variant scores when compared with individuals with low aging-risk endothelial function variant scores (Pinteraction = 0.0007; βHigh = 1.09, 95% CIHigh: 0.70, 1.48; βLow = 0.40, 95% CILow: 0.14, 0.67). Similar trends were observed in fully adjusted models of ammonium and total PM2.5 alone. No effect modification was observed by oxidative stress and metal processing variant scores. Secondary analyses revealed significant associations of serum endothelial markers, intercellular adhesion molecule-1 (β = 0.01, 95% CI: 0.002, 0.012) and vascular cell adhesion molecule-1 (β = 0.002, 95% CI: 0.0005, 0.0026), with DNAm-age. Our results add novel evidence that endothelial physiology may be important to DNAm-age relationships, but further research is required to establish their generalizability.

A Functional Data Analysis of Spatiotemporal Trends and Variation in Fine Particulate Matter

In this paper we illustrate the application of modern functional data analysis methods to study the spatiotemporal variability of particulate matter components across the United States. The approach models the pollutant annual profiles in a way that describes the dynamic behavior over time and space. This new technique allows us to predict yearly profiles for locations and years at which data are not available and also offers dimension reduction for easier visualization of the data. Additionally it allows us to study changes of pollutant levels annually or for a particular season. We apply our method to daily concentrations of two particular components of PM_2.5 measured by two networks of monitoring sites across the United States from 2003 to 2015. Our analysis confirms existing findings and additionally reveals new trends in the change of the pollutants across seasons and years that may not be as easily determined from other common approaches such as Kriging.

The pro-inflammatory effects of particulate matter on epithelial cells are associated with elemental composition

BACKGROUND

Adverse health effects of particulate matter (PM) vary with chemical composition; however, evidence regarding which elements are the most detrimental is limited. The roof space area provides an open and stable environment for outdoor PM to settle and deposit. Therefore, this study used roof space PM samples as a proxy of residential cumulative exposure to outdoor air pollution to investigate their pro-inflammatory effects on human lung cells and the contribution of the endotoxin and chemical content.

METHODS

Roof space PM samples of 36 different homes were collected and analysed using standardised techniques. We evaluated cytotoxicity and cytokine production of BEAS-2B cells after PM exposure using MTS and ELISA, respectively. Principle component analysis (PCA) and linear regression analyses were employed to assess the associations between cytokine production and the PM components.

RESULTS

PM caused significant time- and dose-dependent increases in cellular cytokine production (p < 0.05). PCA identified four factors that explained 68.33% of the variance in the chemical composition. An increase in Factor 1 (+Fe, +Al, +Mn) score and a decrease in Factor 2 (-Ca, +Pb, +PAH) score were associated with increased interleukin (IL)-6 (Factor 1; p = 0.010; Factor 2; p = 0.006) and IL-8 (Factor 1; p = 0.003; Factor 2; p = 0.020) production, however, only the association with Factor 1 was evident after correcting for endotoxin and particle size.

CONCLUSIONS

Our study provides novel insight into the positive associations between pro-inflammatory effects of roof space PM samples with Fe, Al and Mn levels.

Incorporating long-term satellite-based aerosol optical depth, localized land use data, and meteorological variables to estimate ground-level PM2.5 concentrations in Taiwan from 2005 to 2015

Satellite-based aerosol optical depth (AOD) is now comprehensively applied to estimate ground-level concentrations of fine particulate matter (PM_2.5). This study aimed to construct the AOD-PM_2.5 estimation models over Taiwan. The AOD-PM_2.5 modeling in Taiwan island is challenging owing to heterogeneous land use, complex topography, and humid tropical to subtropical climate conditions with frequent cloud cover and prolonged rainy season. The AOD retrievals from the MODerate resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites were combined with the meteorological variables from reanalysis data and high resolution localized land use variables to estimate PM_2.5 over Taiwan island from 2005 to 2015. Ten-fold cross validation was carried out and the residuals of the estimation model at various locations and seasons are assessed. The cross validation (CV) R² based on monitoring stations were 0.66 and 0.66, with CV root mean square errors of 14.0 μg/m³ (34%) and 12.9 μg/m³ (33%), respectively, for models based on Terra and Aqua AOD. The results provided PM_2.5 estimations at locations without surface stations. The estimation revealed PM_2.5 concentration hotspots in the central and southern part of the western plain areas, particularly in winter and spring. The annual average of estimated PM_2.5 concentrations over Taiwan consistently declined during 2005-2015. The AOD-PM_2.5 model is a reliable and validated method for estimating PM_2.5 concentrations at locations without monitoring stations in Taiwan, which is crucial for epidemiological study and for the assessment of air quality control policy.

Public Value of Enforcing the PM2.5 Concentration Reduction Policy in South Korean Urban Areas

As the number of cars and the electricity produced from coal-fired generation has been increasing, PM2.5, particles smaller than 2.5 μm in diameter, has become a serious problem in South Korean urban areas. This is especially notable, given that the PM2.5 warning was issued 89 times during 2016. Because of this, the South Korean government is seeking to enforce a policy of reducing the number of PM2.5 warnings by half using various policy instruments from now until 2022. This article tries to obtain information about the public value of the enforcement. For this purpose, household willingness to pay (WTP) for the enforcement is investigated, applying the contingent valuation (CV) approach. A survey of 1000 households was carried out in South Korean urban areas. The data on the WTP were gathered using a dichotomous choice question and analyzed employing the spike model. The mean WTP estimate is obtained as KRW 5591 (USD 4.97) per household per year, which is statistically significant. The total public value expanded to the population amounts to KRW 98.9 billion (USD 87.8 million) per year. The information can be utilized in policy-making and decision-making about the reduction of the PM2.5 concentration.

Associations between ambient air pollution and daily mortality in a cohort of congestive heart failure: Case-crossover and nested case-control analyses using a distributed lag nonlinear model

BACKGROUND

Persons with congestive heart failure may be at higher risk of the acute effects related to daily fluctuations in ambient air pollution. To meet some of the limitations of previous studies using grouped-analysis, we developed a cohort study of persons with congestive heart failure to estimate whether daily non-accidental mortality were associated with spatially-resolved, daily exposures to ambient nitrogen dioxide (NO₂) and ozone (O₃), and whether these associations were modified according to a series of indicators potentially reflecting complications or worsening of health.

METHODS

We constructed the cohort from the linkage of administrative health databases. Daily exposure was assigned from different methods we developed previously to predict spatially-resolved, time-dependent concentrations of ambient NO₂ (all year) and O₃ (warm season) at participants' residences. We performed two distinct types of analyses: a case-crossover that contrasts the same person at different times, and a nested case-control that contrasts different persons at similar times. We modelled the effects of air pollution and weather (case-crossover only) on mortality using distributed lag nonlinear models over lags 0 to 3 days. We developed from administrative health data a series of indicators that may reflect the underlying construct of "declining health", and used interactions between these indicators and the cross-basis function for air pollutant to assess potential effect modification.

RESULTS

The magnitude of the cumulative as well as the lag-specific estimates of association differed in many instances according to the metric of exposure. Using the back-extrapolation method, which is our preferred exposure model, we found for the case-crossover design a cumulative mean percentage changes (MPC) in daily mortality per interquartile increment in NO₂ (8.8 ppb) of 3.0% (95% CI: -0.4, 6.6%) and for O₃ (16.5 ppb) 3.5% (95% CI: -4.5, 12.1). For O₃ there was strong confounding by weather (unadjusted MPC = 7.1%; 95% CI: 1.7, 12.7%). For the nested case-control approach the cumulative MPC for NO₂ in daily mortality was 2.9% (95% CI: -0.9, 6.9%) and for O₃ 7.3% (95% CI: 3.0, 11.9%). We found evidence of effect modification between daily mortality and cumulative NO₂ and O₃ according to the prescribed dose of furosemide in the nested case-control analysis, but not in the case-crossover analysis.

CONCLUSIONS

Mortality in congestive heart failure was associated with exposure to daily ambient NO₂ and O₃ predicted from a back-extrapolation method using a land use regression model from dense sampling surveys. The methods used to assess exposure can have considerable influence on the estimated acute health effects of the two air pollutants.

Effects of exposure to air pollutants on children's health in Cuiabá, Mato Grosso State, Brazil

Exposure to air pollutants, usually measured by environmental agencies that are not present in all states, may be associated with respiratory admissions in children. An ecological time series study was conducted with data on hospitalizations due to selected respiratory diseases in children under 10 years of age in 2012 in the city of Cuiabá, Mato Grosso State, Brazil. Mean levels of fine particulate matter (PM2.5) were estimated with a mathematical model, data on low temperatures and relative humidity were obtained from the Brazilian National Institute of Meteorology, and the numbers of brush burnings were obtained from the Environmental Information System. The statistical approach used the Poisson regression generalized additive model with lags of 0 to 7 days. The financial costs and increases in hospitalizations due to increments in PM2.5 were estimated. There were 565 hospitalizations (mean 1.54 admissions/day; SD = 1.52), and mean PM2.5 concentration was 15.7µg/m3 (SD = 3.2). Associations were observed between exposure and hospitalizations in the second semester at lags 2 and 3, and at lag 2 when the entire year was analyzed. An increment of 5µg/m3 in PM2.5 was associated with an increase of 89 hospitalizations and costs exceeding BRL 95,000 (≈ USD 38,000) for the Brazilian Unified National Health System. Data estimated by mathematical models can be used in locations where pollutants are not monitored.

Short-term effects of ambient fine particulate matter pollution on hospital visits for chronic obstructive pulmonary disease in Beijing, China

BACKGROUND

Little is known about the effect of ambient fine particulate matter (PM_2.5) on chronic obstructive pulmonary disease (COPD) in China. The objective of this study was to explore the short-term effects of PM_2.5 on outpatient and inpatient visits for COPD in Beijing, China.

METHODS

A total of 3,503,313 outpatient visits and 126,982 inpatient visits for COPD between January 1, 2010, and June 30, 2012, were identified from the Beijing Medical Claim Data for Employees. A generalized additive Poisson model was applied to estimate the percentage change with 95% confidence interval (CI) in hospital visits for COPD in relation to an interquartile range (IQR) (90.8 μg/m³) increase in PM_2.5 concentrations.

RESULTS

Short-term exposure to PM_2.5 was significantly associated with increased use of COPD-related health services. There were clear exposure-response associations of PM_2.5 with COPD outpatient and inpatient visits. An IQR increase in the concurrent day PM_2.5 concentrations was significantly associated with a 2.38% (95% CI, 2.22%-2.53%) and 6.03% (95% CI, 5.19%-6.87%) increase in daily outpatient visits and inpatient visits, respectively. Elderly people were more sensitive to the adverse effects. The estimated risk was higher during the warm season compared to the cool season.

CONCLUSIONS

Short-term exposure to PM_2.5 was associated with increased risk of hospital visits for COPD. Our findings contributed to the limited evidence concerning the effects of ambient PM_2.5 on COPD morbidity in developing countries.

Impact of Particulate Air Pollution on Cardiovascular Health

PURPOSE OF REVIEW

Air pollution is established as an independent risk factor for cardiovascular diseases (CVDs). Ambient particulate matter (PM), a principal component of air pollutant, has been considered as a main culprit of the adverse effects of air pollution on human health.

RECENT FINDINGS

Extensive epidemiological and toxicological studies have demonstrated particulate air pollution is positively associated with the development of CVDs. Short-term PM exposure can trigger acute cardiovascular events while long-term exposure over years augments cardiovascular risk to an even greater extent and can reduce life expectancy by a few years. Inhalation of PM affects heart rate variability, blood pressure, vascular tone, blood coagulability, and the progression of atherosclerosis. The potential molecular mechanisms of PM-caused CVDs include direct toxicity to the cardiovascular system or indirect injury by inducing systemic inflammation and oxidative stress in circulation. This review mainly focuses on the acute and chronic effects of ambient PM exposure on the development of cardiovascular diseases and the possible mechanisms for PM-induced increases in cardiovascular morbidity and mortality. Additionally, we summarized some appropriate interventions to attenuate PM air pollution-induced cardiovascular adverse effects, which may promote great benefits to public health.

Wildfire-specific Fine Particulate Matter and Risk of Hospital Admissions in Urban and Rural Counties

BACKGROUND

The health impacts of wildfire smoke, including fine particles (PM2.5), are not well understood and may differ from those of PM2.5 from other sources due to differences in concentrations and chemical composition.

METHODS

First, for the entire Western United States (561 counties) for 2004-2009, we estimated daily PM2.5 concentrations directly attributable to wildfires (wildfires-specific PM2.5), using a global chemical transport model. Second, we defined smoke wave as ≥2 consecutive days with daily wildfire-specific PM2.5 > 20 μg/m, with sensitivity analysis considering 23, 28, and 37 μg/m. Third, we estimated the risk of cardiovascular and respiratory hospital admissions associated with smoke waves for Medicare enrollees. We used a generalized linear mixed model to estimate the relative risk of hospital admissions on smoke wave days compared with matched comparison days without wildfire smoke.

RESULTS

We estimated that about 46 million people of all ages were exposed to at least one smoke wave during 2004 to 2009 in the Western United States. Of these, 5 million are Medicare enrollees (≥65 years). We found a 7.2% (95% confidence interval: 0.25%, 15%) increase in risk of respiratory admissions during smoke wave days with high wildfire-specific PM2.5 (>37 μg/m) compared with matched non smoke wave days. We did not observe an association between smoke wave days with wildfire-specific PM2.5 ≤ 37 μg/mand respiratory or cardiovascular admissions. Respiratory effects of wildfire-specific PM2.5 may be stronger than that of PM2.5 from other sources.

CONCLUSION

Short-term exposure to wildfire-specific PM2.5was associated with risk of respiratory diseases in the elderly population in the Western United States during severe smoke days. See video abstract at, http://links.lww.com/EDE/B137.

Estimating Acute Cardiorespiratory Effects of Ambient Volatile Organic Compounds

BACKGROUND

The health effects of ambient volatile organic compounds (VOCs) have received less attention in epidemiologic studies than other commonly measured ambient pollutants. In this study, we estimated acute cardiorespiratory effects of ambient VOCs in an urban population.

METHODS

Daily concentrations of 89 VOCs were measured at a centrally-located ambient monitoring site in Atlanta and daily counts of emergency department visits for cardiovascular diseases and asthma in the five-county Atlanta area were obtained for the 1998-2008 period. To understand the health effects of the large number of species, we grouped these VOCs a priori by chemical structure and estimated the associations between VOC groups and daily counts of emergency department visits in a time-series framework using Poisson regression. We applied three analytic approaches to estimate the VOC group effects: an indicator pollutant approach, a joint effect analysis, and a random effect meta-analysis, each with different assumptions. We performed sensitivity analyses to evaluate copollutant confounding.

RESULTS

Hydrocarbon groups, particularly alkenes and alkynes, were associated with emergency department visits for cardiovascular diseases, while the ketone group was associated with emergency department visits for asthma.

CONCLUSIONS

The associations observed between emergency department visits for cardiovascular diseases and alkenes and alkynes may reflect the role of traffic exhaust, while the association between asthma visits and ketones may reflect the role of secondary organic compounds. The different patterns of associations we observed for cardiovascular diseases and asthma suggest different modes of action of these pollutants or the mixtures they represent.

Recent Approaches to Estimate Associations Between Source-Specific Air Pollution and Health

PURPOSE OF REVIEW

Estimating health effects associated with source-specific exposure is important for better understanding how pollution impacts health and for developing policies to better protect public health. Although epidemiologic studies of sources can be informative, these studies are challenging to conduct because source-specific exposures (e.g., particulate matter from vehicles) often are not directly observed and must be estimated. We reviewed recent studies that estimated associations between pollution sources and health to identify methodological developments designed to address important challenges.

RECENT FINDINGS

Notable advances in epidemiologic studies of sources include approaches for (1) propagating uncertainty in source estimation into health effect estimates, (2) assessing regional and seasonal variability in emissions sources and source-specific health effects, and (3) addressing potential confounding in estimated health effects. Novel methodological approaches to address challenges in studies of pollution sources, particularly evaluation of source-specific health effects, are important for determining how source-specific exposure impacts health.

Estimating Acute Cardiovascular Effects of Ambient PM2.5 Metals

BACKGROUND

Few epidemiologic studies have investigated health effects of water-soluble fractions of PM_2.5 metals, the more biologically accessible fractions of metals, in their attempt to identify health-relevant components of ambient PM_2.5.

OBJECTIVES

In this study, we estimated acute cardiovascular effects of PM_2.5 components in an urban population, including a suite of water-soluble metals that are not routinely measured at the ambient level.

METHODS

Ambient concentrations of criteria gases, PM_2.5, and PM_2.5 components were measured at a central monitor in Atlanta, Georgia, during 1998-2013, with some PM_2.5 components only measured during 2008-2013. In a time-series framework using Poisson regression, we estimated associations between these pollutants and daily counts of emergency department (ED) visits for cardiovascular diseases in the five-county Atlanta area.

RESULTS

Among the PM_2.5 components we examined during 1998-2013, water-soluble iron had the strongest estimated effect on cardiovascular outcomes [R͡R=1.012 (95% CI: 1.005, 1.019), per interquartile range increase (20.46ng/m³)]. The associations for PM_2.5 and other PM_2.5 components were consistent with the null when controlling for water-soluble iron. Among PM_2.5 components that were only measured during 2008-2013, water-soluble vanadium was associated with cardiovascular ED visits [R͡R=1.012 (95% CI: 1.000, 1.025), per interquartile range increase (0.19ng/m³)].

CONCLUSIONS

Our study suggests cardiovascular effects of certain water-soluble metals, particularly water-soluble iron. The observed associations with water-soluble iron may also point to certain aspects of traffic pollution, when processed by acidifying sulfate, as a mixture harmful for cardiovascular health. https://doi.org/10.1289/EHP2182.

Acute Effects of Ambient PM2.5 on All-Cause and Cause-Specific Emergency Ambulance Dispatches in Japan

Short-term health effects of ambient PM_2.5 have been established with numerous studies, but evidence in Asian countries is limited. This study aimed to investigate the short-term effects of PM_2.5 on acute health outcomes, particularly all-cause, cardiovascular, respiratory, cerebrovascular and neuropsychological outcomes. We utilized daily emergency ambulance dispatches (EAD) data from eight Japanese cities (2007–2011). Statistical analyses included two stages: (1) City-level generalized linear model with Poisson distribution; (2) Random-effects meta-analysis in pooling city-specific effect estimates. Lag patterns were explored using (1) unconstrained-distributed lags (lag 0 to lag 7) and (2) average lags (lag: 0–1, 0–3, 0–5, 0–7). In all-cause EAD, significant increases were observed in both shorter lag (lag 0: 1.24% (95% CI: 0.92, 1.56)) and average lag 0–1 (0.64% (95% CI: 0.23, 1.06)). Increases of 1.88% and 1.48% in respiratory and neuropsychological EAD outcomes, respectively, were observed at lag 0 per 10 µg/m³ increase in PM_2.5. While respiratory outcomes demonstrated significant average effects, no significant effect was observed for cardiovascular outcomes. Meanwhile, an inverse association was observed in cerebrovascular outcomes. In this study, we observed that effects of PM_2.5 on all-cause, respiratory and neuropsychological EAD were acute, with average effects not exceeding 3 days prior to EAD onset.

Fine particulate air pollution and hospital admissions and readmissions for acute myocardial infarction in 26 Chinese cities

Monitoring data on fine particulate matter (PM_2.5) level in China's major cities were available since 2013. We conducted a time-stratified case-crossover study to evaluate the association between short-term exposure to PM_2.5 and hospital admissions for acute myocardial infarction (AMI), as well as subsequent cardiac and AMI readmissions among AMI survivors. Hospital admissions for ST-elevation myocardial infarction (STEMI) and non ST-elevation myocardial infarction (NSTEMI) from 1 January 2014 through 31 December 2015 were identified from electronic Hospitalization Summary Reports. Conditional logistic regression was used to explore the relation between PM_2.5 and hospital admissions for AMI. Individuals discharged alive following STEMI in 2014 were followed up for subsequent readmissions through 31 December 2015. We used the Cox proportional hazards model to evaluate the effect of PM_2.5 pollution on subsequent cardiac and STEMI readmissions. Hospital admissions for STEMI (n = 106,467) and NSTEMI (n = 12,719) were examined separately. Exposure to an interquartile range (IQR) increase in PM_2.5 concentration (47.5 μg/m³) at lags 2, 3, 4 and 0-5 days corresponded with 0.6% (95% CI, 0.1%-1.1%), 0.8 (95% CI, 0.3%-1.3%), 0.6% (95% CI, 0.1%-1.1%) and 0.9% (95% CI, 0-1.8%) increases in STEMI admissions, respectively. For NSTEMI, no significant association was observed with PM_2.5. We also observed significant associations of PM_2.5 concentration with both subsequent cardiac and STEMI readmissions among STEMI survivors. In conclusion, short-term elevations in PM_2.5 concentration may increase the risk of STEMI but not NSTEMI, and the association appeared to be more evident among STEMI survivors.

The health impacts and economic value of wildland fire episodes in the U.S.: 2008-2012

INTRODUCTION

Wildland fires degrade air quality and adversely affect human health. A growing body of epidemiology literature reports increased rates of emergency departments, hospital admissions and premature deaths from wildfire smoke exposure.

OBJECTIVE

Our research aimed to characterize excess mortality and morbidity events, and the economic value of these impacts, from wildland fire smoke exposure in the U.S. over a multi-year period; to date no other burden assessment has done this.

METHODS

We first completed a systematic review of the epidemiologic literature and then performed photochemical air quality modeling for the years 2008 to 2012 in the continental U.S. Finally, we estimated the morbidity, mortality, and economic burden of wildland fires.

RESULTS

Our models suggest that areas including northern California, Oregon and Idaho in the West, and Florida, Louisiana and Georgia in the East were most affected by wildland fire events in the form of additional premature deaths and respiratory hospital admissions. We estimated the economic value of these cases due to short term exposures as being between $11 and $20B (2010$) per year, with a net present value of $63B (95% confidence intervals $6-$170); we estimate the value of long-term exposures as being between $76 and $130B (2010$) per year, with a net present value of $450B (95% confidence intervals $42-$1200).

CONCLUSION

The public health burden of wildland fires-in terms of the number and economic value of deaths and illnesses-is considerable.

Fine particulate air pollution associated with increased risk of hospital admissions for hypertension in a tropical city, Kaohsiung, Taiwan

The aim of this study was to assess whether a correlation exists between fine particles (PM_2.5) levels and number of hospital admissions for hypertension in Kaohsiung, Taiwan. Hospital admission frequency and ambient air pollution data were obtained for Kaohsiung for 2009-2013. A time-stratified case-crossover method was used to estimate relative risk for hospital admissions, controlling for weather, day of the week, seasonality, and long-term time trends. Odds ratios and 95% confidence intervals were calculated for a 10 µg/m³ increment of PM_2.5 for lags from days 0 to 6. Data showed no significant associations between PM_2.5 levels and number of hypertension-related hospital admissions on warm days (>25°C). However, on cool days (<25°C), a significant positive association was found with frequency of hypertension admissions in the single-pollutant model (without adjusting for other pollutants) with a 10 µg/m³ rise in PM_2.5 on day of admission (lag 0) associated with a 12% increase in number of admissions for hypertension. In the two-pollutant model, the association of PM_2.5 with rate of hypertension hospitalizations remained significant after including SO₂ or O₃ on lag day 0. Data demonstrate that an association between short-term exposure to PM_2.5 and elevated risk of hypertension-related hospital admissions may exist in Kaohsiung, Taiwan, a tropical city.

Spatio-temporal analysis of particulate matter intake fractions for vehicular emissions: Hourly variation by micro-environments in the Greater Toronto and Hamilton Area, Canada

Previous investigations have reported intake fraction (iF) for different environments, which include ambient concentrations (outdoor exposure) and microenvironments (indoor exposure). However, little is known about iF variations due to space-time factors, especially in microenvironments. In this paper, we performed a spatio-temporal analysis of particulate matter (PM_2.5) intake fractions for vehicular emissions. Specifically, we investigated hourly variation (12:00am-11:00pm) by micro-environments (residences and workplaces) in the Greater Toronto and Hamilton Area (GTHA), Canada. We used GIS modeling to estimate air pollution data (ambient concentration, and traffic emission) and population data in each microenvironment. Our estimates showed that the total iF at residences and workplaces accounts for 85% and 15%, respectively. Workplaces presented the highest 24h average iF (1.06ppm), which accounted for 25% higher than residences. Observing the iF by hour at residences, our estimates showed the highest average iF at 2:00am (iF=3.72ppm). These estimates indicate that approximately 4g of PM_2.5 emitted from motor vehicles are inhaled for every million grams of PM_2.5 emitted. For the workplaces, the highest exposure was observed at 10:00am, with average iF equal to 2.04ppm. The period of the day with the lower average iF for residences was at 8:00am (average iF=0.11ppm), while for the workplaces was at 4:00am (average iF=0.47ppm). Our approach provides a new perspective on human exposure to air pollution. Our results showed significant hourly variation in iF across the GTHA. Our findings can be incorporated in future investigations to advance environmental health effects research and human health risk assessment.

Acute effects of fine particulate matter constituents on mortality: A systematic review and meta-regression analysis

BACKGROUND

The link between PM_2.5 exposure and adverse health outcomes is well documented from studies across the world. However, the reported effect estimates vary across studies, locations and constituents. We aimed to conduct a meta-analysis on associations between short-term exposure to PM_2.5 constituents and mortality using city-specific estimates, and explore factors that may explain some of the observed heterogeneity.

METHODS

We systematically reviewed epidemiological studies on particle constituents and mortality using PubMed and Web of Science databases up to July 2015.We included studies that examined the association between short-term exposure to PM_2.5 constituents and all-cause, cardiovascular, and respiratory mortality, in the general adult population. Each study was summarized based on pre-specified study key parameters (e.g., location, time period, population, diagnostic classification standard), and we evaluated the risk of bias using the Office of Health Assessment and Translation (OHAT) Method for each included study. We extracted city-specific mortality risk estimates for each constituent and cause of mortality. For multi-city studies, we requested the city-specific risk estimates from the authors unless reported in the article. We performed random effects meta-analyses using city-specific estimates, and examined whether the effects vary across regions and city characteristics (PM_2.5 concentration levels, air temperature, elevation, vegetation, size of elderly population, population density, and baseline mortality).

RESULTS

We found a 0.89% (95% CI: 0.68, 1.10%) increase in all-cause, a 0.80% (95% CI: 0.41, 1.20%) increase in cardiovascular, and a 1.10% (95% CI: 0.59, 1.62%) increase in respiratory mortality per 10μg/m³ increase in PM_2.5. Accounting for the downward bias induced by studies of single days, the all-cause mortality estimate increased to 1.01% (95% CI: 0.81, 1.20%). We found significant associations between mortality and several PM_2.5 constituents. The most consistent and stronger associations were observed for elemental carbon (EC) and potassium (K). For most of the constituents, we observed high variability of effect estimates across cities.

CONCLUSIONS

Our meta-analysis suggests that (a) combustion elements such as EC and K have a stronger association with mortality, (b) single lag studies underestimate effects, and (c) estimates of PM_2.5 and constituents differ across regions. Accounting for PM mass in constituent's health models may lead to more stable and comparable effect estimates across different studies.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO: CRD42017055765.

Associations between Ambient Fine Particulate Oxidative Potential and Cardiorespiratory Emergency Department Visits

BACKGROUND

Oxidative potential (OP) has been proposed as a measure of toxicity of ambient particulate matter (PM).

OBJECTIVES

Our goal was to address an important research gap by using daily OP measurements to conduct population-level analysis of the health effects of measured ambient OP.

METHODS

A semi-automated dithiothreitol (DTT) analytical system was used to measure daily average OP (OP^DTT) in water-soluble fine PM at a central monitor site in Atlanta, Georgia, over eight sampling periods (a total of 196 d) during June 2012-April 2013. Data on emergency department (ED) visits for selected cardiorespiratory outcomes were obtained for the five-county Atlanta metropolitan area. Poisson log-linear regression models controlling for temporal confounders were used to conduct time-series analyses of the relationship between daily counts of ED visits and either the 3-d moving average (lag 0-2) of OP^DTT or same-day OP^DTT. Bipollutant regression models were run to estimate the health associations of OP^DTT while controlling for other pollutants.

RESULTS

OP^DTT was measured for 196 d (mean=0.32 nmol/min/m³, interquartile range=0.21). Lag 0-2 OP^DTT was associated with ED visits for respiratory disease (RR=1.03, 95% confidence interval (CI): 1.00, 1.05 per interquartile range increase in OP^DTT), asthma (RR=1.12, 95% CI: 1.03, 1.22), and ischemic heart disease (RR=1.19, 95% CI: 1.03, 1.38). Same-day OP^DTT was not associated with ED visits for any outcome. Lag 0-2 OP^DTT remained a significant predictor of asthma and ischemic heart disease in most bipollutant models.

CONCLUSIONS

Lag 0-2 OP^DTT was associated with ED visits for multiple cardiorespiratory outcomes, providing support for the utility of OP^DTT as a measure of fine particle toxicity. https://doi.org/10.1289/EHP1545.

In vitro toxicoproteomic analysis of A549 human lung epithelial cells exposed to urban air particulate matter and its water-soluble and insoluble fractions

Background

Toxicity of airborne particulate matter (PM) is difficult to assess because PM composition is complex and variable due to source contribution and atmospheric transformation. In this study, we used an in vitro toxicoproteomic approach to identify the toxicity mechanisms associated with different subfractions of Ottawa urban dust (EHC-93).

Methods

A549 human lung epithelial cells were exposed to 0, 60, 140 and 200 μg/cm² doses of EHC-93 (total), its insoluble and soluble fractions for 24 h. Multiple cytotoxicity assays and proteomic analyses were used to assess particle toxicity in the exposed cells.

Results

The cytotoxicity data based on cellular ATP, BrdU incorporation and LDH leakage indicated that the insoluble, but not the soluble, fraction is responsible for the toxicity of EHC-93 in A549 cells. Two-dimensional gel electrophoresis results revealed that the expressions of 206 protein spots were significantly altered after particle exposures, where 154 were identified by MALDI-TOF-TOF-MS/MS. The results from cytotoxicity assays and proteomic analyses converged to a similar finding that the effects of the total and insoluble fraction may be alike, but their effects were distinguishable, and their effects were significantly different from the soluble fraction. Furthermore, the toxic potency of EHC-93 total is not equal to the sum of its insoluble and soluble fractions, implying inter-component interactions between insoluble and soluble materials resulting in synergistic or antagonistic cytotoxic effects. Pathway analysis based on the low toxicity dose (60 μg/cm²) indicated that the two subfractions can alter the expression of those proteins involved in pathways including cell death, cell proliferation and inflammatory response in a distinguishable manner. For example, the insoluble and soluble fractions differentially affected the secretion of pro-inflammatory cytokines such as MCP-1 and IL-8 and distinctly altered the expression of those proteins (e.g., TREM1, PDIA3 and ENO1) involved in an inflammatory response pathway in A549 cells.

Conclusions

This study demonstrated the impact of different fractions of urban air particles constituted of various chemical species on different mechanistic pathways and thus on cytotoxicity effects. In vitro toxicoproteomics can be a valuable tool in mapping these differences in air pollutant exposure-related toxicity mechanisms.

Electronic supplementary material

The online version of this article (10.1186/s12989-017-0220-6) contains supplementary material, which is available to authorized users.