Estimating particulate matter-mortality dose-response curves and threshold levels: an analysis of daily time-series for the 20 largest US cities

Airborne microplastics detected in the lungs of wild birds in Japan

Microplastics (MPs) have been found in a wide range of animal species including humans. The detection of MPs in human lungs suggests that humans inhale airborne microplastics (AMPs). Although birds respire more efficiently than mammals and are therefore more susceptible to air pollution, little is known about their inhalation exposure to MPs. In this study, we analyzed samples isolated from the lungs of several species of wild birds in Japan by attenuated total reflection (ATR) imaging method of micro-Fourier transform infrared (μFTIR) spectroscopy to clear whether AMPs can be inhaled and accumulate within the lungs of wild birds. To isolate MPs from lung samples of rock doves (Columba livia), black kites (Milvus migrans), and barn swallows (Hirundo rustica) euthanized for pest control, digestion and density separation were performed. After each sample collected on an alumina filter was measured by ATR imaging method using μFTIR spectroscopy, the physical and chemical characteristics of the detected MPs were evaluated. Six MPs were detected in 3 of 22 lung samples. Polypropylene and polyethylene were found in rock doves and ethylene vinyl acetate was found in a barn swallow. Most MPs were fragments of 28.0-70.5 μm. Our results demonstrated that in addition to dietary sources, some wild birds are exposed to MPs by inhalation, and these MPs reach the lungs.

On the supra-linearity of the relationship between air pollution, mortality and hospital admission in 18 French cities

PURPOSE

Understanding the relationship between an environmental determinant and a given health outcome is key to inform public health policies. The short-term mortality and morbidity responses to outdoor air pollutants are traditionally assessed as a log-linear relationship, but few studies suggest a possible deviation from linearity. This paper investigates the shape of the relationship between ozone, NO₂ and fine particulate matter (PM₁₀ and PM_2.5), mortality and hospital admissions in 18 French cities between 2000 and 2017.

METHOD

A multi-centric time series design, using quasi-Poisson generalized additive models, was used. Four approaches were compared to model concentration-response curves (log-linear, piecewise-linear with a priori defined breakpoints, piecewise-linear with no a priori breakpoint and cubic spline).

RESULTS

All the models indicated evidence of supra-linearity between PM₁₀, PM_2.5, NO₂, mortality and hospital admissions. For instance, with a log-linear model, a 10 µg/m³ increase in PM_2.5 was associated with a 0.4% [95% CI 0.2; 0.7] increase in non-accidental mortality. When using a piecewise model with a priori set breakpoint at 10 µg/m³, the mortality increase was 3.8% [4.4; 6.3] below 10 µg/m³, and 0.3% [0; 0.6] above. Non-significant impacts of ozone were found for concentrations below 90 µg/m³ to 120 µg/m³, with some variability in the identified threshold across the heath indicator studied.

CONCLUSION

The supra-linearity of the relationship between PM₁₀, PM_2.5, NO₂, mortality and hospital admissions supports the need to further reduce air pollution concentrations well below regulatory values.

Pulmonary microbiota intervention alleviates fine particulate matter-induced lung inflammation in broilers

Fine particulate matter (PM2.5) released during the livestock industry endangers the respiratory health of animals. Our previous findings suggested that broilers exposed to PM2.5 exhibited lung inflammation and changes in the pulmonary microbiome. Therefore, this study was to investigate whether the pulmonary microbiota plays a causal role in the pathogenesis of PM2.5-induced lung inflammation. We first used antibiotics to establish a pulmonary microbiota intervention broiler model, which showed a significantly reduced total bacterial load in the lungs without affecting the microbiota composition or structure. Based on it, 45 AA broilers of similar body weight were randomly assigned to three groups: control (CON), PM2.5 (PM), and pulmonary microbiota intervention (ABX-PM). From 21 d of age, broilers in the ABX-PM group were intratracheally instilled with antibiotics once a day for 3 d. Meanwhile, broilers in the other two groups were simultaneously instilled with sterile saline. On 24 and 26 d of age, broilers in the PM and ABX-PM groups were intratracheally instilled with PM2.5 suspension to induce lung inflammation, and broilers in the CON group were simultaneously instilled with sterile saline. The lung histomorphology, inflammatory cytokines' expression levels, lung microbiome, and microbial growth conditions were analyzed to determine the effect of the pulmonary microbiota on PM2.5-induced lung inflammation. Broilers in the PM group showed lung histological injury, while broilers in the ABX-PM group had normal lung histomorphology. Furthermore, microbiota intervention significantly reduced mRNA expression levels of interleukin-1β, tumor necrosis factor-α, interleukin-6, interleukin-8, toll-like receptor 4 and nuclear factor kappa-B. PM2.5 induced significant changes in the β diversity and structure of the pulmonary microbiota in the PM group. However, no significant changes in microbiota structure were observed in the ABX-PM group. Moreover, the relative abundance of Enterococcus cecorum in the PM group was significantly higher than that in the CON and ABX-PM groups. And sterile bronchoalveolar lavage fluid from the PM group significantly promoted the growth of E. cecorum, indicating that PM2.5 altered the microbiota's growth condition. In conclusion, pulmonary microbiota can affect PM2.5-induced lung inflammation in broilers. PM2.5 can alter the bacterial growth environment and promote dysbiosis, potentially exacerbating inflammation.

A simulation-based assessment of the ability to detect thresholds in chronic risk concentration-response functions in the presence of exposure measurement error

An important question when setting appropriate air quality standards for fine particulate matter (PM_2.5) is whether there exists a “threshold” in the concentration-response (C-R) function, such that PM_2.5 levels below this threshold are not expected to produce adverse health effects. We hypothesize that measurement error may affect the recognition of a threshold in long-term cohort epidemiological studies. This study conducts what is, to the best of our knowledge, the first simulation of the effects of measurement error on the statistical models commonly employed in long-term cohort studies. We test the degree to which classical-type measurement error, such as differences between the true population-weighted exposure level to a pollutant and the observed measures of that pollutant, affects the ability to statistically detect a C-R threshold. The results demonstrate that measurement error can obscure the existence of a threshold in a cohort study’s C-R function for health risks from chronic exposures. With increased measurement error the ability to statistically detect a C-R threshold decreases, and both the estimated location of the C-R threshold and the estimated hazard ratio associated with PM_2.5 are attenuated. This result has clear implications for determining appropriate air quality standards for pollutants.

Selecting Data Analytic and Modeling Methods to Support Air Pollution and Environmental Justice Investigations: A Critical Review and Guidance Framework

Given the serious adverse health effects associated with many pollutants, and the inequitable distribution of these effects between socioeconomic groups, air pollution is often a focus of environmental justice (EJ) research. However, EJ analyses that aim to illuminate whether and how air pollution hazards are inequitably distributed may present a unique set of requirements for estimating pollutant concentrations compared to other air quality applications. Here, we perform a scoping review of the range of data analytic and modeling methods applied in past studies of air pollution and environmental injustice and develop a guidance framework for selecting between them given the purpose of analysis, users, and resources available. We include proxy, monitor-based, statistical, and process-based methods. Upon critically synthesizing the literature, we identify four main dimensions to inform method selection: accuracy, interpretability, spatiotemporal features of the method, and usability of the method. We illustrate the guidance framework with case studies from the literature. Future research in this area includes an exploration of increasing data availability, advanced statistical methods, and the importance of science-based policy.

Modeling complex effects of exposure to particulate matter and extreme heat during pregnancy on congenital heart defects: A U.S. population-based case-control study in the National Birth Defects Prevention Study

BACKGROUND/OBJECTIVE

Research suggests gestational exposure to particulate matter ≤2.5 μm (PM2.5) and extreme heat may independently increase risk of birth defects. We investigated whether duration of gestational extreme heat exposure modifies associations between PM2.5 exposure and specific congenital heart defects (CHDs). We also explored nonlinear exposure-outcome relationships.

METHODS

We identified CHD case children (n = 2824) and non-malformed live-birth control children (n = 4033) from pregnancies ending between 1999 and 2007 in the National Birth Defects Prevention Study, a U.S. population-based multicenter case-control study. We assigned mothers 6-week averages of PM2.5 exposure during the cardiac critical period (postconceptional weeks 3-8) using the closest monitor within 50 km of maternal residence. We assigned a count of extreme heat days (EHDs, days above the 90th percentile of daily maximum temperature for year, season, and weather station) during this period using the closest weather station. Using generalized additive models, we explored logit-nonlinear exposure-outcome relationships, concluding logistic models were reasonable. We estimated joint effects of PM2.5 and EHDs on six CHDs using logistic regression models adjusted for mean dewpoint and maternal age, education, and race/ethnicity. We assessed multiplicative and additive effect modification.

RESULTS

Conditional on the highest observed EHD count (15) and at least one critical period day during spring/summer, each 5 μg/m3 increase in average PM2.5 exposure was significantly associated with perimembranous ventricular septal defects (VSDpm; OR: 1.54 [95% CI: 1.01, 2.41]). High EHD counts (8+) in the same population were positively, but non-significantly, associated with both overall septal defects and VSDpm. Null or inverse associations were observed for lower EHD counts. Multiplicative and additive effect modification estimates were consistently positive in all septal models.

CONCLUSIONS

Results provide limited evidence that duration of extreme heat exposure modifies the PM2.5-septal defects relationship. Future research with enhanced exposure assessment and modeling techniques could clarify these relationships.

Assessing Health Impacts of Winter Smog in Lahore for Exposed Occupational Groups

The goal of this research was to investigate the health effects of winter pollution on various occupations in Lahore and its neighboring peri-urban areas. A questionnaire survey, key informants, and focused group discussions were employed to collect data, which included demographic, socioeconomic, and health-related information. Descriptive statistics and the multivariate logistic regression model (MLRM) were used to examine the effects of pollution on exposed occupational groups who experienced symptoms such as coughing, shortness of breath, and eye discomfort. According to data from interviews, MLRM revealed that individuals working in various occupations with outdoor and indoor environments are equally affected by winter smog, but being middle-aged (odds ratio OR = 5.73), having a history of a respiratory ailment (OR = 4.06), and location (OR = 2.26) all play important roles in determining health. However, less educated people, elders, and people who already live in polluted areas are more likely to develop respiratory health symptoms. During the smog incident, it was determined that diverse health and socioeconomic factors exacerbate an individual’s negative health impact more than others.

The effects of an air quality alert program on premature mortality: A difference-in-differences evaluation in the region of Paris

BACKGROUND

Daily exposure to air pollution has been shown to increase cardiovascular and respiratory mortality. While increases in short-term exposure to air pollutants at any daily concentrations has been shown to be associated to adverse health outcomes, days with extreme levels, also known as air pollution peaks based on specific thresholds, have been used to implement air quality alerts in various cities across the globe.

OBJECTIVES

We aimed at evaluating the potential effects of the Air Quality Alerts (AQA) system on different causes of premature mortality in Paris, France.

METHODS

Air quality alerts (AQA) based on particulate matter (PM₁₀) levels and related interventions were implemented in the region of Paris in 2008 and were revised to be more stringent in 2011. In this study, we applied a difference-in-differences (DID) approach coupled with propensity-score matching (PSM) to daily mortality data for the period 2000 to 2015 to evaluate the effects of the Paris AQA program on different causes of premature mortality for the entire population and for adults > 75 years old.

RESULTS

Overall, results did not show evidence of a reduction in mortality of the PM₁₀ AQA program when first implemented in 2008 with initial thresholds (80 µg/m³); DID estimates were slightly above 1 for cardiovascular and respiratory mortality. However, when evaluating the drastic reduction in revised thresholds in 2011 (50 µg/m³) to trigger interventions, we identified a reduction in cardiovascular (DID = 0.84, 95% CI: 0.755 to 0.930) mortality, but no change in respiratory mortality was detected (DID = 0.97, 95% CI: 0.796, 1.191).

DISCUSSION

Our study suggests that AQA may not have health benefits for the population when thresholds are set at high daily PM₁₀ levels. Given that such policies are implemented in many other metropolitan areas across the globe, evaluating the effectiveness of AQA is important to provide public authorities and researchers a rationale for defining specific thresholds and extending the scope of these policies to lower air pollution levels.

Prenatal Particulate Matter Exposure Is Associated with Saliva DNA Methylation at Age 15: Applying Cumulative DNA Methylation Scores as an Exposure Biomarker

Exposure in utero to particulate matter (PM2.5 and PM10) is associated with maladaptive health outcomes. Although exposure to prenatal PM2.5 and PM10 has cord blood DNA methylation signatures at birth, signature persistence into childhood and saliva cross-tissue applicability has not been tested. In the Fragile Families and Child Wellbeing Study, a United States 20-city birth cohort, average residential PM2.5 and PM10 during the three months prior to birth was estimated using air quality monitors with inverse distance weighting. Saliva DNA methylation at ages 9 (n = 749) and 15 (n = 793) was measured using the Illumina HumanMethylation 450 k BeadArray. Cumulative DNA methylation scores for particulate matter were estimated by weighting participant DNA methylation at each site by independent meta-analysis effect estimates and standardizing the sums. Using a mixed-effects regression analysis, we tested the associations between cumulative DNA methylation scores at ages 9 and 15 and PM exposure during pregnancy, adjusted for child sex, age, race/ethnicity, maternal income-to-needs ratio, nonmartial birth status, and saliva cell-type proportions. Our study sample was 50.5% male, 56.3% non-Hispanic Black, and 19.8% Hispanic, with a median income-to-needs ratio of 1.4. Mean exposure levels for PM2.5 were 27.9 μg/m³/day (standard deviation: 7.0; 23.7% of observations exceeded safety standards) and for PM10 were 15.0 μg/m³/day (standard deviation: 3.1). An interquartile range increase in PM2.5 exposure (10.73 μg/m³/day) was associated with a −0.0287 standard deviation lower cumulative DNA methylation score for PM2.5 (95% CI: −0.0732, 0.0158, p = 0.20) across all participants. An interquartile range increase in PM10 exposure (3.20 μg/m³/day) was associated with a −0.1472 standard deviation lower cumulative DNA methylation score for PM10 (95% CI: −0.3038, 0.0095, p = 0.06) across all participants. The PM10 findings were driven by the age 15 subset where an interquartile range increase in PM10 exposure was associated with a −0.024 standard deviation lower cumulative DNA methylation score for PM10 (95% CI: −0.043, −0.005, p = 0.012). Findings were robust to adjustment for PM exposure at ages 1 and 3. In utero PM10-associated DNA methylation differences were identified at age 15 in saliva. Benchmarking the timing and cell-type generalizability is critical for epigenetic exposure biomarker assessment.

Using Crowd-Sourced Data to Assess the Temporal and Spatial Relationship between Indoor and Outdoor Particulate Matter

Using hourly measures across a full year of crowd-sourced data from over 1000 indoor and outdoor pollution monitors in the state of California, we explore the temporal and spatial relationship between outdoor and indoor particulate matter (PM) concentrations for different particle sizes. The scale of this study offers new insight into both average penetration rates and drivers of heterogeneity in the outdoor-indoor relationship. We find that an increase in the daily outdoor PM concentration of 10% leads to an average increase of 4.2-6.1% in indoor concentrations. The penetration of outdoor particles to the indoor environment occurs rapidly and almost entirely within 5 h. We also provide evidence showing that penetration rates are associated with building age and climatic conditions in the vicinity of the monitor. Since people spend a substantial amount of each day indoors, our findings fill a critical knowledge gap and have significant implications for government policies to improve public health through reductions in exposure to ambient air pollution.

Long-Term Exposure to PM10 and in vivo Alzheimer’s Disease Pathologies

Background: Previous studies indicated an association between Alzheimer’s disease (AD) dementia and air particulate matter (PM) with aerodynamic diameter <10μm (PM10), as well as smaller PM. Limited information, however, is available for the neuropathological links underlying such association. Objective: This study aimed to investigate the relationship between long-term PM10 exposure and in vivo pathologies of AD using multimodal neuroimaging. Methods: The study population consisted of 309 older adults without dementia (191 cognitively normal and 118 mild cognitive impairment individuals), who lived in Republic of Korea. Participants underwent comprehensive clinical assessments, 11C-Pittsburg compound B (PiB) positron emission tomography (PET), and magnetic resonance imaging scans. A subset of 78 participants also underwent 18F-AV-1451 tau PET evaluation. The mean concentration of PM with aerodynamic diameter <10μm over the past 5 years (PM10mean) collected from air pollution surveillance stations were matched to each participant’s residence. Results: In this non-demented study population, of which 62% were cognitively normal and 38% were in mild cognitive impairment state, exposure to the highest tertile of PM10mean was associated with increased risk of amyloid-β (Aβ) positivity (odds ratio 2.19, 95% confidence interval 1.13 to 4.26) even after controlling all potential confounders. In contrast, there was no significant associations between PM10mean exposure and tau accumulation. AD signature cortical thickness and white matter hyperintensity volume were also not associated with PM10mean exposure. Conclusion: The findings suggest that long-term exposure to PM10 may contribute to pathological Aβ deposition.

Elasticity as a measure for online determination of remission points in ongoing epidemics

The correct identification of change-points during ongoing outbreak investigations of infectious diseases is a matter of paramount importance in epidemiology, with major implications for the management of health care resources, public health and, as the COVID-19 pandemic has shown, social live. Onsets, peaks, and inflexion points are some of them. An onset is the moment when the epidemic starts. A "peak" indicates a moment at which the incorporated values, both before and after, are lower: a maximum. The inflexion points identify moments in which the rate of growth of the incorporation of new cases changes intensity. In this study, after interpreting the concept of elasticity of a random variable in an innovative way, we propose using it as a new simpler tool for anticipating epidemic remission change-points. In particular, we propose that the "remission point of change" will occur just at the instant when the speed in the accumulation of new cases is lower than the average speed of accumulation of cases up to that moment. This gives stability and robustness to the estimation in the event of possible remission variations. This descriptive measure, which is very easy to calculate and interpret, is revealed as informative and adequate, has the advantage of being distribution-free and can be estimated in real time, while the data is being collected. We use the 2014-2016 Western Africa Ebola virus epidemic to demonstrate this new approach. A couple of examples analyzing COVID-19 data are also included.

Adverse Birth Outcomes Related to NO2 and PM Exposure: European Systematic Review and Meta-Analysis

There is a growing number of international studies on the association between ambient air pollution and adverse pregnancy outcomes, and this systematic review and meta-analysis has been conducted focusing on European countries, to assess the crucial public health issue of this suspected association on this geographical area. A systematic literature search (based on Preferred Reporting Items for Systematic reviews and Meta-Analyses, PRISMA, guidelines) has been performed on all European epidemiological studies published up until 1 April 2020, on the association between maternal exposure during pregnancy to nitrogen dioxide (NO₂) or particular matter (PM) and the risk of adverse birth outcomes, including: low birth weight (LBW) and preterm birth (PTB). Fourteen articles were included in the systematic review and nine of them were included in the meta-analysis. Our meta-analysis was conducted for 2 combinations of NO₂ exposure related to birth weight and PTB. Our systematic review revealed that risk of LBW increases with the increase of air pollution exposure (including PM₁₀, PM_2.5 and NO₂) during the whole pregnancy. Our meta-analysis found that birth weight decreases with NO₂ increase (pooled beta = −13.63, 95% confidence interval (CI) (−28.03, 0.77)) and the risk of PTB increase for 10 µg/m3 increase in NO₂ (pooled odds ratio (OR) = 1.07, 95% CI (0.90, 1.28)). However, the results were not statistically significant. Our finding support the main international results, suggesting that increased air pollution exposure during pregnancy might contribute to adverse birth outcomes, especially LBW. This body of evidence has limitations that impede the formulation of firm conclusions. Further studies, well-focused on European countries, are called to resolve the limitations which could affect the strength of association such as: the exposure assessment, the critical windows of exposure during pregnancy, and the definition of adverse birth outcomes. This analysis of limitations of the current body of research could be used as a baseline for further studies and may serve as basis for reflection for research agenda improvements.

A causal exposure response function with local adjustment for confounding: Estimating health effects of exposure to low levels of ambient fine particulate matter

In the last two decades, ambient levels of air pollution have declined substantially. At the same time, the Clean Air Act mandates that the National Ambient Air Quality Standards (NAAQS) must be routinely assessed to protect populations based on the latest science. Therefore, researchers should continue to address the following question: is exposure to levels of air pollution below the NAAQS harmful to human health? Furthermore, the contentious nature surrounding environmental regulations urges us to cast this question within a causal inference framework. Several parametric and semi-parametric regression approaches have been used to estimate the exposure-response (ER) curve between long-term exposure to ambient air pollution concentrations and health outcomes. However, most of the existing approaches are not formulated within a formal framework for causal inference, adjust for the same set of potential confounders across all levels of exposure, and do not account for model uncertainty regarding covariate selection and the shape of the ER. In this paper, we introduce a Bayesian framework for the estimation of a causal ER curve called LERCA (Local Exposure Response Confounding Adjustment), which a) allows for different confounders and different strength of confounding at the different exposure levels; and b) propagates model uncertainty regarding confounders' selection and the shape of the ER. Importantly, LERCA provides a principled way of assessing the observed covariates' confounding importance at different exposure levels, providing researchers with important information regarding the set of variables to measure and adjust for in regression models. Using simulation studies, we show that state of the art approaches perform poorly in estimating the ER curve in the presence of local confounding. LERCA is used to evaluate the relationship between long-term exposure to ambient PM2.5, a key regulated pollutant, and cardiovascular hospitalizations for 5,362 zip codes in the continental U.S. and located near a pollution monitoring site, while adjusting for a potentially varying set of confounders across the exposure range. Our data set includes rich health, weather, demographic, and pollution information for the years of 2011-2013. The estimated exposure-response curve is increasing indicating that higher ambient concentrations lead to higher cardiovascular hospitalization rates, and ambient PM2.5 was estimated to lead to an increase in cardiovascular hospitalization rates when focusing at the low exposure range. Our results indicate that there is no threshold for the effect of PM2.5 on cardiovascular hospitalizations.

Assessing short-term impact of PM10 on mortality using a semiparametric generalized propensity score approach

BACKGROUND

The shape of the exposure-response curve describing the effects of air pollution on population health has crucial regulatory implications, and it is important in assessing causal impacts of hypothetical policies of air pollution reduction.

METHODS

After having reformulated the problem of assessing the short-term impact of air pollution on health within the potential outcome approach to causal inference, we developed a method based on the generalized propensity score (GPS) to estimate the average dose-response function (aDRF) and quantify attributable deaths under different counterfactual scenarios of air pollution reduction. We applied the proposed approach to assess the impact of airborne particles with a diameter less than or equal to 10 μm (PM₁₀) on deaths from natural, cardiovascular and respiratory causes in the city of Milan, Italy (2003-2006).

RESULTS

As opposed to what is commonly assumed, the estimated aDRFs were not linear, being steeper for low-moderate values of exposure. In the case of natural mortality, the curve became flatter for higher levels; this behavior was less pronounced for cause-specific mortality. The effect was larger in days characterized by higher temperature. According to the curves, we estimated that a hypothetical intervention able to set the daily exposure levels exceeding 40 μg/m³ to exactly 40 would have avoided 1157 deaths (90%CI: 689, 1645) in the whole study period, 312 of which for respiratory causes and 771 for cardiovascular causes. These impacts were higher than those obtained previously from regression-based methods.

CONCLUSION

This novel method based on the GPS allowed estimating the average dose-response function and calculating attributable deaths, without requiring strong assumptions about the shape of the relationship. Its potential as a tool for investigating effect modification by temperature and its use in other environmental epidemiology contexts deserve further investigation.

The contribution of improved air quality to reduced cardiovascular mortality: Declines in socioeconomic differences over time

Major improvements in air quality since 1990, observed through reductions in fine particulate matter (PM2.5), have been associated with reduced cardiovascular mortality rates (CMR). However, it is not well understood whether the health benefit attributed to PM2.5 reductions has been similar across strata of socioeconomic deprivation (SED). Using mixed effect regression models, we estimated the PM2.5-related change in the CMR across 2,132 US counties in five SED strata between 1990 and 2010. The analysis included annual county CMR (deaths/100,000 person-year), annual county PM2.5 (μg/m3), and an index of county SED based on socioeconomic factors from the 1990 US Census. The contribution of PM2.5 reductions to decreased CMR varied by SED strata and over time. Yearly differences resulted from varying rates of PM2.5 reduction and because of the non-linear relationship between CMR and PM2.5 concentration. In early years, PM2.5-related CMR reductions were smallest in the most deprived counties compared to all other counties (range: 0.4-0.6 vs 0.7-1.6 fewer deaths/100,000 person-year), due to slower rates of PM2.5 reduction in these counties. However, in later years, PM2.5-related CMR reductions were highest counties with moderate to high deprivation, compared to counties with the least deprivation (range: 1.0-2.2 vs 0.5-0.9 fewer deaths/100,000 person-year) due to larger CMR reductions per decrease in PM2.5. We identified that CMR reductions related to air quality improvements have become more similar over time between socioeconomic strata.

An Air Quality Health Index (AQHI) with Different Health Outcomes Based on the Air Pollution Concentrations in Stockholm during the Period of 2015–2017

The Air Quality Health Index (AQHI) is a tool that has been developed in order to address the health effects caused by simultaneous exposure to several different air pollutants. Short-term health effects in terms of mortality or morbidity are used in order to construct an index. In this study, different indexes for different health outcomes, based on the concentrations of NO2, O3, and PM10 at an urban background measuring station in Stockholm during the period of 2015–2017, are calculated by using different risk-coefficients obtained from a meta-analysis. An AQHI based on local risk-coefficients for asthma emergency department visits (AEDV) in Stockholm is also included in the analysis. Correlation coefficients between different pairs of AQHIs, where the additive effects associated with exposure to NO2, O3, and PM10 during 2015–2017 are used, exhibit R-values as in 12 out of 15 cases exceed 0.80. However, the average risk increase for different AQHIs are very different, where indexes based on hospital admissions for asthma are larger than those based on mortality outcomes. An overall conclusion is that different AQHIs for different population groups are not needed, but the index may need to be weighted differently for different population groups.

Air Pollution (Particulate Matter) Exposure and Associations with Depression, Anxiety, Bipolar, Psychosis and Suicide Risk: A Systematic Review and Meta-Analysis

BACKGROUND

Particulate air pollution's physical health effects are well known, but associations between particulate matter (PM) exposure and mental illness have not yet been established. However, there is increasing interest in emerging evidence supporting a possible etiological link.

OBJECTIVES

This systematic review aims to provide a comprehensive overview and synthesis of the epidemiological literature to date by investigating quantitative associations between PM and multiple adverse mental health outcomes (depression, anxiety, bipolar disorder, psychosis, or suicide).

METHODS

We undertook a systematic review and meta-analysis. We searched Medline, PsycINFO, and EMBASE from January 1974 to September 2017 for English-language human observational studies reporting quantitative associations between exposure to PM < 1.0 μ m in aerodynamic diameter (ultrafine particles) and PM < 2.5 and < 10 μ m in aerodynamic diameter (PM 2.5 and PM 10 , respectively) and the above psychiatric outcomes. We extracted data, appraised study quality using a published quality assessment tool, summarized methodological approaches, and conducted meta-analyses where appropriate.

RESULTS

Of 1,826 citations identified, 22 met our overall inclusion criteria, and we included 9 in our primary meta-analyses. In our meta-analysis of associations between long-term (> 6   months ) PM 2.5 exposure and depression (n = 5 studies), the pooled odds ratio was 1.102 per 10 - μ g / m 3 PM 2.5 increase (95% CI: 1.023, 1.189; I 2 = 0.00 % ). Two of the included studies investigating associations between long-term PM 2.5 exposure and anxiety also reported statistically significant positive associations, and we found a statistically significant association between short-term PM 10 exposure and suicide in meta-analysis at a 0-2 d cumulative exposure lag.

DISCUSSION

Our findings support the hypothesis of an association between long-term PM 2.5 exposure and depression, as well as supporting hypotheses of possible associations between long-term PM 2.5 exposure and anxiety and between short-term PM 10 exposure and suicide. The limited literature and methodological challenges in this field, including heterogeneous outcome definitions, exposure assessment, and residual confounding, suggest further high-quality studies are warranted to investigate potentially causal associations between air pollution and poor mental health. https://doi.org/10.1289/EHP4595.

The difference in effect of ambient particles on mortality between days with and without yellow dust events: Using a larger dataset in Seoul, Korea from 1998 to 2015

BACKGROUND

Yellow Dust (YD) is a natural source of particulate matter (PM) in Korea. It remarkably increases the concentration of PM. However, characteristics of PM in YD period are different from those of PM in non-YD period.

OBJECTIVES

To investigate whether the association of PM with mortality is different between all days and non-YD days in Seoul, Korea, 1998-2015.

METHODS

We applied time-stratified case-crossover design to estimate effects of PM₁₀ and PM_2.5 on non-accidental cardiovascular and respiratory mortality. Effect estimates of PM were compared for all days in the study period and days without YD events. To identify whether different effect estimates between all days and non-YD days were not merely caused by the exclusion of high PM concentrations but rather by YD itself, we estimated effects of PM by randomly excluding the same number of days as days of YD.

RESULTS

A total of 4,509,392 deaths were observed during the study period. A 10 μg/m³ increase in PM₁₀ or PM_2.5 was associated with a 0.15% (95% CI: 0.06% to 0.24%) or 0.27% (95% CI: 0.07% to 0.47%) increase in risk of non-accidental mortality for all days, respectively. These associations were changed to 0.30% (95% CI: 0.18% to 0.42%) and 0.33% (95% CI: 0.10% to 0.55%) when YD days were excluded from analyses. We also found that effect estimates of PM were larger when YD days were excluded than those when high PM concentrations were randomly excluded.

CONCLUSIONS

The effect estimates of PM differed between all days and non-YD days. Our study suggests that including YD days in the analyses is likely to attenuate the effect of PM in a usual urban environment.

Effects of pollution, low temperature and influenza syndrome on the excess mortality risk in winter 2016-2017

BACKGROUND

In the winter of 2016-2017, the number of deaths recorded in the north-west Europe was significantly higher than that in previous years. This spike in mortality was attributed principally to an influenza epidemic, but the contribution of air pollution and cold temperature has not been investigated. Information on the combined effect of low temperatures, influenza epidemic, and air pollution on mortality is inadequate. The objective of this study was to estimate the excess mortality in the winter of 2016-2017 in the metropolitan area of Milan, and to evaluate the independent short-term effect of 3 risk factors: low temperatures, the influenza epidemic, and air pollution.

METHODS

We used a case-crossover, time-stratified study design. Mortality data were collected on all people aged > 65 years who died of natural causes, due to respiratory diseases or cardiovascular diseases, between December 1, 2016 and February 15, 2017. Environmental data were extracted from the Regional Environmental Protection Agency. The National Surveillance Network provided data on influenza epidemic.

RESULTS

Among the 7590 natural deaths in people aged > 65 years, 965 (13%) were caused by respiratory conditions, and 2688 (35%) were caused by cardiovascular conditions. There were statistically significant associations between the minimum recorded temperature and deaths due to natural causes (OR = 0.966, 95% CI: 0.944-0.989), and cardiovascular conditions (OR = 0.961, 95% CI: 0.925-0.999). There were also statistically significant association between the influenza epidemic and deaths due to natural causes (OR = 1.198, 95% CI: 1.156-1.241), cardiovascular conditions (OR = 1.153, 95% CI: 1.088-1.223), and respiratory conditions (OR = 1.303, 95% CI: 1.166-1.456). High levels of PM10 (60 and 70 μg/m3) were associated with a statistically significant increase in natural and cause-specific mortality. There were statistically significant interactions between PM10 and influenza for cardiovascular-related mortality, and between influenza and temperature for deaths due to natural causes.

CONCLUSIONS

Excess of mortality in Milan during winter 2016-2017 was associated with influenza epidemic and concomitant environmental exposures, specifically, the combined effect of air pollution and low temperatures. Policies mitigating the effects of environmental risk factors should be implemented to prevent future excess mortality.

Threshold Effects of PM2.5 Exposure on Particle-Related Mortality in China

Ambient air pollution from energy use and other sources is a major environmental risk factor in the incidence and progression of serious diseases, such as cardiovascular and respiratory diseases. This study elucidates the health effects of energy consumption from air pollution in China based on multiple threshold effects of the population-weighted exposure to PM2.5 (fine particles less than 2.5 microns in diameter) on particle-related mortality rate. We firstly estimate the causal relationship between coal consumption and PM2.5 in China for 2004-2010 using a panel regression model. Panel threshold models are applied to access the non-linear relationships between PM2.5 and cause-specific mortality rates that indicate the health effects are dependent on the PM2.5 ranges. By combining these steps, we calculate the health impacts of coal consumption based on threshold effects of PM2.5. We find that a 1% coal consumption increase induces a 0.23% increase in PM2.5. A triple threshold effect is found between PM2.5 and cardiovascular mortality; for example, increasing PM2.5 exposure causes cardiovascular mortality rate to increase when PM2.5 lies in 17.7-21.6 μg/m3 and 21.6-34.3 μg/m3, with the estimated increments being 0.81% and 0.26%, respectively, corresponding to 1% PM2.5 increase. A single threshold effect of SO2 on respiratory mortality rate is identified and allows the estimation of the mortality effects of PM2.5 regarding the two regimes of SO2. Finally, we access the health impacts of coal consumption under specific estimated thresholds. This study provides a better understanding of sources contributing to related-air pollution mortality. The multi-threshold effect of PM2.5 could be considered for further applications in harmonizing emission standards in China and other developing countries.

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.

The Shape of the Concentration-Response Association between Fine Particulate Matter Pollution and Human Mortality in Beijing, China, and Its Implications for Health Impact Assessment

BACKGROUND

Studies found approximately linear short-term associations between particulate matter (PM) and mortality in Western communities. However, in China, where the urban PM levels are typically considerably higher than in Western communities, some studies suggest nonlinearity in this association. Health impact assessments (HIA) of PM in China have generally not incorporated nonlinearity in the concentration-response (C-R) association, which could result in large discrepancies in estimates of excess deaths if the true association is nonlinear.

OBJECTIVES

We investigated nonlinearity in the C-R associations between with PM with aerodynamic diameter [Formula: see text] ([Formula: see text]) and mortality in Beijing, China, and the sensitivity of HIA to linearity assumptions.

METHODS

We modeled the C-R association between [Formula: see text] and cause-specific mortality in Beijing, China (2009-2012), using generalized linear models (GLM). [Formula: see text] was included through either linear, piecewise-linear, or spline functions to investigate evidence of nonlinearity. To determine the sensitivity of HIA to linearity assumptions, we estimated [Formula: see text]-attributable deaths using both linear- and nonlinear-based C-R associations between [Formula: see text] and mortality.

RESULTS

We found some evidence that, for nonaccidental and circulatory mortality, the shape of the C-R association was relatively flat at lower concentrations of [Formula: see text], but then had a positive slope at higher concentrations, indicating nonlinearity. Conversely, the shape for respiratory mortality was positive and linear at lower concentrations of [Formula: see text], but then leveled off at the higher concentrations. Estimates of excess deaths attributable to short-term [Formula: see text] exposure were, in some cases, very sensitive to the linearity assumption in the association, but in other cases robust to this assumption.

CONCLUSIONS

Our results demonstrate some evidence of nonlinearity in [Formula: see text]-mortality associations and that an assumption of linearity in this association can influence HIAs, highlighting the importance of understanding potential nonlinearity in the [Formula: see text]-mortality association at the high concentrations of [Formula: see text] in developing megacities like Beijing. https://doi.org/10.1289/EHP4464.

Association between Exposure to Ambient Air Pollution and Rheumatoid Arthritis in Adults

Environmental factors may play roles in the development of rheumatoid arthritis (RA), and some studies have shown that air pollution was associated with the development of autoimmune disease. This study was designed to investigate the effect of air pollutants on the development of adult RA. A nested case-control cohort study was performed using the National Health Insurance Service-National Sample Cohort during 2002⁻2014 in Korea. Air pollution data were collected from the National Ambient Air Monitoring System (NAMIS), and exposure levels were extrapolated using geographic information systems. The group with RA (n = 444) was compared with a propensity score-matched control group (n = 1776), and one-year average concentrations of air pollution were predicted at each patient's residence. The adjusted binary logistic regression analysis showed a positive association between O₃ exposure and the incidence risk of RA for the third (odds ratios (OR) = 1.45, 95% confidence intervals (CI): 1.08⁻1.96) and fourth (OR = 1.35, 95% CI: 1.00⁻1.83) quartiles in adults over 20 years of age. The third quartile CO exposure was also associated with an increased risk of RA (OR = 1.57, 95% CI: 1.16⁻2.12). The results of this nationwide population-based study showed that a one-year exposure to CO and O₃ in adults was associated with an increased risk of RA.

Estimating mortality burden attributable to short-term PM2.5 exposure: A national observational study in China

Studies worldwide have estimated the number of deaths attributable to long-term exposure to fine airborne particles (PM2.5), but limited information is available on short-term exposure, particularly in China. In addition, most existing studies have assumed that short-term PM2.5-mortality associations were linear. For this reason, the use of linear exposure-response functions for calculating disease burden of short-term exposure to PM2.5 in China may not be appropriate. There is an urgent need for a comprehensive, evidence-based assessment of the disease burden related to short-term PM2.5 exposure in China. Here, we explored the non-linear association between short-term PM2.5 exposure and all-cause mortality in 104 counties in China; estimated county-specific mortality burdens attributable to short-term PM2.5 exposure for all counties in the country and analyzed spatial characteristics of the mortality burden due to short-term PM2.5 exposure in China. The pooled PM2.5-mortality association was non-linear, with a reversed J-shape. We found an approximately linear increased risk of mortality from 0 to 62 μg/m3 and decreased risk from 62 to 250 μg/m3. We estimated a total of 169,862 additional deaths from short-term PM2.5 exposure throughout China in 2015. Models using linear exposure-response functions for the PM2.5-mortality association estimated 32,186 deaths attributable to PM2.5 exposure, which is 5.3 times lower than estimates from the non-linear effect model. Short-term PM2.5 exposure contributed greatly to the death burden in China, approximately one seventh of the estimates from the chronic effect. It is essential and crucial to incorporate short-term PM2.5-related mortality estimations when considering the disease burden attributable to PM2.5 in developing countries such as China. Traditional linear effect models likely underestimated the mortality burden due to short-term exposure to PM2.5.

Economic Evaluation of Environmental Interventions: Reflections on Methodological Challenges and Developments

Evaluation of the costs and outcomes associated with environmental policies and interventions is often required to inform public policy and allocate scarce resources. Methods to conduct assessments of cost-effectiveness have been developed in the context of pharmaceuticals, but have more recently been applied in public health, diagnostics, and other more complex interventions. The suitability of existing economic evaluation methodology has been explored in many contexts, however, this is yet to be undertaken for interventions and policies pertaining to the natural environment, such as urban green spaces and strategies to reduce indoor and outdoor air pollution. To make significant inroads into the evaluation of interventions and policies relating to the natural environment requires an understanding of the challenges faced in this context. Many of these challenges may be practical (data-related), however, a number are also methodological, and thus have implications for the appropriate framework for economic evaluation. This paper considers some of the challenges faced when conducting cost-effectiveness analyses in this context and explores what solutions have been proposed thus far. The intention is to help pave the way for consideration of which existing framework is most appropriate for the evaluation of natural environment (NE) interventions, or if a distinct framework is required. Environmental policies and interventions relating to the built environment, for example, housing, are not explicitly included here.

Review of modelling air pollution from traffic at street-level - The state of the science

Traffic emissions are a complex and variable cocktail of toxic chemicals. They are the major source of atmospheric pollution in the parts of cities where people live, commute and work. Reducing exposure requires information about the distribution and nature of emissions. Spatially and temporally detailed data are required, because both the rate of production and the composition of emissions vary significantly with time of day and with local changes in wind, traffic composition and flow. Increasing computer processing power means that models can accept highly detailed inputs of fleet, fuels and road networks. The state of the science models can simulate the behaviour and emissions of all the individual vehicles on a road network, with resolution of a second and tens of metres. The chemistry of the simulated emissions is also highly resolved, due to consideration of multiple engine processes, fuel evaporation and tyre wear. Good results can be achieved with both commercially available and open source models. The extent of a simulation is usually limited by processing capacity; the accuracy by the quality of traffic data. Recent studies have generated real time, detailed emissions data by using inputs from novel traffic sensing technologies and data from intelligent traffic systems (ITS). Increasingly, detailed pollution data is being combined with spatially resolved demographic or epidemiological data for targeted risk analyses.

Possible Mediation by Methylation in Acute Inflammation Following Personal Exposure to Fine Particulate Air Pollution

Air pollution may increase cardiovascular and respiratory risk through inflammatory pathways, but evidence for acute effects has been weak and indirect. Between December 2014 and July 2015, we enrolled 36 healthy, nonsmoking college students for a panel study in Shanghai, China, a city with highly variable levels of air pollution. We measured personal exposure to particulate matter with an aerodynamic diameter less than or equal to 2.5 μm (PM2.5) continuously for 72 hours preceding each of 4 clinical visits that included phlebotomy. We measured 4 inflammation proteins and DNA methylation at nearby regulatory cytosine-phosphate-guanine (CpG) loci. We applied linear mixed-effect models to examine associations over various lag times. When results suggested mediation, we evaluated methylation as mediator. Increased PM2.5 concentration was positively associated with all 4 inflammation proteins and negatively associated with DNA methylation at regulatory loci for tumor necrosis factor alpha (TNF-α) and soluble intercellular adhesion molecule-1. A 10-μg/m3 increase in average PM2.5 during the 24 hours preceding blood draw corresponded to a 4.4% increase in TNF-α and a statistically significant decrease in methylation at one of the two studied candidate CpG loci for TNF-α. Epigenetics may play an important role in mediating effects of PM2.5 on inflammatory pathways.

Associations between mortality and prolonged exposure to elevated particulate matter concentrations in East Asia

Previous epidemiological studies regarding mortality and particulate matter with an aerodynamic diameter of <10μm (PM₁₀) have considered only absolute concentrations of PM₁₀ as a risk factor. However, none have evaluated the durational effect of multi-day periods with high PM₁₀ concentrations. To evaluate the durational effect (i.e., number of days) of high PM₁₀ concentrations on mortality, we collected data regarding 3,662,749 deaths from 28 cities in Japan, South Korea, and China (1993-2009). Exposure was defined as consecutive days with daily PM₁₀ concentrations ≥75μg/m³. A Poisson model was used with duration as the variable of interest, while controlling for daily PM₁₀ concentrations, meteorological variables, seasonal trends, and day of the week. The increase in mortality risk for each additional consecutive day with PM₁₀ concentrations ≥75μg/m³ was 0.68% in Japan (95% confidence interval [CI]: 0.35-1.01%), 0.48% in South Korea (95% CI: 0.30-0.66%), and 0.24% in China (95% CI: 0.14-0.33%). The annual average maximum number of consecutive days with high PM₁₀ in Japan (2.40days), South Korea (6.96days), and China (42.26days) was associated with non-accidental death increases of 1.64% (95% CI: 1.31-1.98%), 3.37% (95% CI: 3.19-3.56%), and 10.43% (95% CI: 10.33-10.54%), respectively. These findings may facilitate the planning of public health interventions to minimize the health burden of air pollution.

Disease and Health Inequalities Attributable to Air Pollutant Exposure in Detroit, Michigan

The environmental burden of disease is the mortality and morbidity attributable to exposures of air pollution and other stressors. The inequality metrics used in cumulative impact and environmental justice studies can be incorporated into environmental burden studies to better understand the health disparities of ambient air pollutant exposures. This study examines the diseases and health disparities attributable to air pollutants for the Detroit urban area. We apportion this burden to various groups of emission sources and pollutants, and show how the burden is distributed among demographic and socioeconomic subgroups. The analysis uses spatially-resolved estimates of exposures, baseline health rates, age-stratified populations, and demographic characteristics that serve as proxies for increased vulnerability, e.g., race/ethnicity and income. Based on current levels, exposures to fine particulate matter (PM_2.5), ozone (O₃), sulfur dioxide (SO₂), and nitrogen dioxide (NO₂) are responsible for more than 10,000 disability-adjusted life years (DALYs) per year, causing an annual monetized health impact of $6.5 billion. This burden is mainly driven by PM_2.5 and O₃ exposures, which cause 660 premature deaths each year among the 945,000 individuals in the study area. NO₂ exposures, largely from traffic, are important for respiratory outcomes among older adults and children with asthma, e.g., 46% of air-pollution related asthma hospitalizations are due to NO₂ exposures. Based on quantitative inequality metrics, the greatest inequality of health burdens results from industrial and traffic emissions. These metrics also show disproportionate burdens among Hispanic/Latino populations due to industrial emissions, and among low income populations due to traffic emissions. Attributable health burdens are a function of exposures, susceptibility and vulnerability (e.g., baseline incidence rates), and population density. Because of these dependencies, inequality metrics should be calculated using the attributable health burden when feasible to avoid potentially underestimating inequality. Quantitative health impact and inequality analyses can inform health and environmental justice evaluations, providing important information to decision makers for prioritizing strategies to address exposures at the local level.

The Imperial County Community Air Monitoring Network: A Model for Community-based Environmental Monitoring for Public Health Action

SUMMARY

The Imperial County Community Air Monitoring Network (the Network) is a collaborative group of community, academic, nongovernmental, and government partners designed to fill the need for more detailed data on particulate matter in an area that often exceeds air quality standards. The Network employs a community-based environmental monitoring process in which the community and researchers have specific, well-defined roles as part of an equitable partnership that also includes shared decision-making to determine study direction, plan research protocols, and conduct project activities. The Network is currently producing real-time particulate matter data from 40 low-cost sensors throughout Imperial County, one of the largest community-based air networks in the United States. Establishment of a community-led air network involves engaging community members to be citizen-scientists in the monitoring, siting, and data collection process. Attention to technical issues regarding instrument calibration and validation and electronic transfer and storage of data is also essential. Finally, continued community health improvements will be predicated on facilitating community ownership and sustainability of the network after research funds have been expended. https://doi.org/10.1289/EHP1772

Quantitative cancer risk assessment and local mortality burden for ambient air pollution in an eastern Mediterranean City

Health risks posed by ambient air pollutants to the urban Lebanese population have not been well characterized. The aim of this study is to assess cancer risk and mortality burden of non-methane hydrocarbons (NMHCs) and particulates (PM) based on two field-sampling campaigns conducted during summer and winter seasons in Beirut. Seventy NMHCs were analyzed by TD-GC-FID. PM_2.5 elemental carbon (EC) components were examined using a Lab OC-EC aerosol Analyzer, and polycyclic aromatic hydrocarbons were analyzed by GC-MS. The US EPA fraction-based approach was used to assess non-cancer hazard and cancer risk for the hydrocarbon mixture, and the UK Committee on Medical Effects of Air Pollutants (COMEAP) guidelines were followed to determine the PM_2.5 attributable mortality burden. The average cumulative cancer risk exceeded the US EPA acceptable level (10^-6) by 40-fold in the summer and 30-fold in the winter. Benzene was found to be the highest contributor to cancer risk (39-43%), followed by 1,3-butadiene (25-29%), both originating from traffic gasoline evaporation and combustion. The EC attributable average mortality fraction was 7.8-10%, while the average attributable number of deaths (AD) and years of life lost (YLL) were found to be 257-327 and 3086-3923, respectively. Our findings provide a baseline for future air monitoring programs, and for interventions aiming at reducing cancer risk in this population.

Different responses of influenza epidemic to weather factors among Shanghai, Hong Kong, and British Columbia

Weather factors have long been considered as key sources for regional heterogeneity of influenza seasonal patterns. As influenza peaks coincide with both high and low temperature in subtropical cities, weather factors may nonlinearly or interactively affect influenza activity. This study aims to assess the nonlinear and interactive effects of weather factors with influenza activity and compare the responses of influenza epidemic to weather factors in two subtropical regions of southern China (Shanghai and Hong Kong) and one temperate province of Canada (British Columbia). Weekly data on influenza activity and weather factors (i.e., mean temperature and relative humidity (RH)) were obtained from pertinent government departments for the three regions. Absolute humidity (AH) was measured by vapor pressure (VP), which could be converted from temperature and RH. Generalized additive models were used to assess the exposure-response relationship between weather factors and influenza virus activity. Interactions of weather factors were further assessed by bivariate response models and stratification analyses. The exposure-response curves of temperature and VP, but not RH, were consistent among three regions/cities. Bivariate response model revealed a significant interactive effect between temperature (or VP) and RH (P < 0.05). Influenza peaked at low temperature or high temperature with high RH. Temperature and VP are important weather factors in developing a universal model to explain seasonal outbreaks of influenza. However, further research is needed to assess the association between weather factors and influenza activity in a wider context of social and environmental conditions.

Comparison of wildfire smoke estimation methods and associations with cardiopulmonary-related hospital admissions

Climate forecasts predict an increase in frequency and intensity of wildfires. Associations between health outcomes and population exposure to smoke from Washington 2012 wildfires were compared using surface monitors, chemical-weather models, and a novel method blending three exposure information sources. The association between smoke particulate matter ≤2.5 μm in diameter (PM2.5) and cardiopulmonary hospital admissions occurring in Washington from 1 July to 31 October 2012 was evaluated using a time-stratified case-crossover design. Hospital admissions aggregated by ZIP code were linked with population-weighted daily average concentrations of smoke PM2.5 estimated using three distinct methods: a simulation with the Weather Research and Forecasting with Chemistry (WRF-Chem) model, a kriged interpolation of PM2.5 measurements from surface monitors, and a geographically weighted ridge regression (GWR) that blended inputs from WRF-Chem, satellite observations of aerosol optical depth, and kriged PM2.5. A 10 μg/m3 increase in GWR smoke PM2.5 was associated with an 8% increased risk in asthma-related hospital admissions (odds ratio (OR): 1.076, 95% confidence interval (CI): 1.019-1.136); other smoke estimation methods yielded similar results. However, point estimates for chronic obstructive pulmonary disease (COPD) differed by smoke PM2.5 exposure method: a 10 μg/m3 increase using GWR was significantly associated with increased risk of COPD (OR: 1.084, 95%CI: 1.026-1.145) and not significant using WRF-Chem (OR: 0.986, 95%CI: 0.931-1.045). The magnitude (OR) and uncertainty (95%CI) of associations between smoke PM2.5 and hospital admissions were dependent on estimation method used and outcome evaluated. Choice of smoke exposure estimation method used can impact the overall conclusion of the study.

Evaluation of Indoor Air Quality Screening Strategies: A Step-Wise Approach for IAQ Screening

Conducting a full indoor air quality (IAQ) assessment in air-conditioned offices requires large-scale material and manpower resources. However, an IAQ index can be adopted as a handy screening tool to identify any premises (with poor IAQ) that need more comprehensive IAQ assessments to prioritize IAQ improvements. This study proposes a step-wise IAQ screening protocol to facilitate its cost-effective management among building owners and managers. The effectiveness of three IAQ indices, namely θ₁ (with one parameter: CO₂), θ₂ (with two parameters: CO₂ and respirable suspended particulates, RSP) and θ₃ (with three parameters: CO₂, RSP, and total volatile organic compounds, TVOC) are evaluated. Compared in a pairwise manner with respect to the minimum satisfaction levels as stated in the IAQ Certification Scheme by the Hong Kong Environmental Protection Department, the results show that a screening test with more surrogate IAQ parameters is good at identifying both lower and higher risk groups for unsatisfactory IAQ, and thus offers higher resolution. Through the sensitivity and specificity for identifying IAQ problems, the effectiveness of alternative IAQ screening methods with different monitoring parameters is also reported.

The short-term association between meteorological factors and mumps in Jining, China

BACKGROUND

An increasing trend of the incidence of mumps has been observed in a few developing countries in recent years, presenting a major threat to children's health. A few studies have examined the relationship between meteorological factors and mumps with inconsistent findings.

METHODS

The daily data of meteorological variables and mumps from 2009 to 2013 were obtained from Jining, a temperate inland city of China. A generalized additive model was used to quantify the association between meteorological factors and mumps based on the exposure-response relationship.

RESULTS

A total of 8520 mumps cases were included in this study. We found a nonlinear relationship of daily mean temperature, sunshine duration and relative humidity with mumps, with an approximately linear association for mean temperature above 4°C (excess risk (ER) for 1°C increase was 2.72%, 95% confidence interval (CI): 2.38%, 3.05% on the current day), for relative humidity above 54%, the ER for 1% increase was -1.86% (95% CI: -2.06%, -1.65%) at lag day 14; and for sunshine duration higher than 5h/d, the ER for per 1h/d increase was12.91% (95% CI: 11.38%, 14.47%) at lag day 1. While we found linear effects for daily wind speed (ER: 2.98%, 95% CI: 2.71%, 3.26% at lag day 13).

CONCLUSIONS

This study suggests that meteorological factors might be important predictors of incidence of mumps, and should be considered in its control and prevention.

Potential health benefits of controlling dust emissions in Beijing

Although the adverse impact of fine particulate matter (i.e., PM2.5) on human health has been well acknowledged, little is known of the health effects of its specific constituents. Over the past decade, the annual average dust concentrations in Beijing were approximately ∼14 μg m(-3), a value that poses a great threat to the city's 20 million residents. In this study, we quantify the potential long-term health damages in Beijing resulting from the dust exposure that occurred from 2000 to 2011. Each year in Beijing, nearly 4000 (95% CI: 1000-7000) premature deaths may be associated with long-term dust exposure, and ∼20% of these deaths are attributed to lung cancer. A decomposition analysis of the inter-annual variability of premature deaths in Beijing indicates that dust concentrations determine the year-to-year tendency, whereas population growth and lung cancer mortality rates drive the increasing tendency of premature death. We suggest that if Beijing takes effective measures towards reducing dust concentrations (e.g., controlling the resuspension of road dust and the fugitive dust from construction sites) to a level comparable to that of New York City's, the associated premature deaths will be significantly reduced. This recommendation offers "low-hanging fruit" suggestions for pollution control that would greatly benefit the public health in Beijing.

Associations of air pollution exposure with blood pressure and heart rate variability are modified by oxidative stress genes: A repeated-measures panel among elderly urban residents

BACKGROUND

Oxidative stress has been suggested as a major cause of elevated blood pressure (BP) and reduced heart rate variability (HRV) due to air pollution. We hypothesized that the associations of air pollution exposure with BP and HRV are modified by oxidative stress gene polymorphisms.

METHODS

Between 2008 and 2010, we conducted up to 5 surveys of 547 elderly participants, measured their BP and HRV, and genotyped 47 single nucleotide polymorphisms (SNPs) in 18 oxidative stress genes. Linear mixed models were constructed to evaluate the associations of particulate matter ≤10 μm, nitrogen dioxide, and sulfur dioxide with BP and HRV, as well as the modifications of these associations by the genotyped SNPs.

RESULTS

Single-SNP analyses revealed interactions between air pollution and 15 SNPs (for BP) and 33 SNPs (for HRV) (all, P for interaction < 0.05). When we generated genetic risk scores for BP and HRV, using the SNPs with interactions in the single-SNP models, we found that associations of air pollution exposure with BP and HRV were modified by the genetic risk scores (P for interaction < 0.05).

CONCLUSIONS

These results strongly suggest that the associations of air pollution with BP and HRV are mediated by oxidative stress pathways.

Air pollution and its impacts on health in Vitoria, Espirito Santo, Brazil

OBJECTIVE

To analyze the impact of air pollution on respiratory and cardiovascular morbidity of children and adults in the city of Vitoria, state of Espirito Santo.

METHODS

A study was carried out using time-series models via Poisson regression from hospitalization and pollutant data in Vitoria, ES, Southeastern Brazil, from 2001 to 2006. Fine particulate matter (PM₁₀), sulfur dioxide (SO₂), and ozone (O₃) were tested as independent variables in simple and cumulative lags of up to five days. Temperature, humidity and variables indicating weekdays and city holidays were added as control variables in the models.

RESULTS

For each increment of 10 µg/m³ of the pollutants PM₁₀, SO₂, and O₃, the percentage of relative risk (%RR) for hospitalizations due to total respiratory diseases increased 9.67 (95%CI 11.84-7.54), 6.98 (95%CI 9.98-4.17) and 1.93 (95%CI 2.95-0.93), respectively. We found %RR = 6.60 (95%CI 9.53-3.75), %RR = 5.19 (95%CI 9.01-1.5), and %RR = 3.68 (95%CI 5.07-2.31) for respiratory diseases in children under the age of five years for PM₁₀, SO₂, and O₃, respectively. Cardiovascular diseases showed a significant relationship with O₃, with %RR = 2.11 (95%CI 3.18-1.06).

CONCLUSIONS

Respiratory diseases presented a stronger and more consistent relationship with the pollutants researched in Vitoria. A better dose-response relationship was observed when using cumulative lags in polynomial distributed lag models.

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.

The effect of pollutional haze on pulmonary function

Detrimental health effects of atmospheric exposure to ambient particulate matter (PM) have been investigated in numerous studies. Exposure to pollutional haze, the carrier of air pollutants such as PM and nitrogen dioxide (NO2) has been linked to lung and cardiovascular disease, resulting increases in both hospital admissions and mortality. This review focuses on the constituents of pollutional haze and its effects on pulmonary function. The article presents the available information and seeks to correlate pollutional haze and pulmonary function.

The influence of air quality model resolution on health impact assessment for fine particulate matter and its components

Health impact assessments for fine particulate matter (PM2.5) often rely on simulated concentrations generated from air quality models. However, at the global level, these models often run at coarse resolutions, resulting in underestimates of peak concentrations in populated areas. This study aims to quantitatively examine the influence of model resolution on the estimates of mortality attributable to PM2.5 and its species in the USA. We use GEOS-Chem, a global 3-D model of atmospheric composition, to simulate the 2008 annual average concentrations of PM2.5 and its six species over North America. The model was run at a fine resolution of 0.5 × 0.66° and a coarse resolution of 2 × 2.5°, and mortality was calculated using output at the two resolutions. Using the fine-modeled concentrations, we estimate that 142,000 PM2.5-related deaths occurred in the USA in 2008, and the coarse resolution produces a national mortality estimate that is 8 % lower than the fine-model estimate. Our spatial analysis of mortality shows that coarse resolutions tend to substantially underestimate mortality in large urban centers. We also re-grid the fine-modeled concentrations to several coarser resolutions and repeat mortality calculation at these resolutions. We found that model resolution tends to have the greatest influence on mortality estimates associated with primary species and the least impact on dust-related mortality. Our findings provide evidence of possible biases in quantitative PM2.5 health impact assessments in applications of global atmospheric models at coarse spatial resolutions.

Projecting future temperature-related mortality in three largest Australian cities

We estimated net annual temperature-related mortality in Brisbane, Sydney and Melbourne in Australia using 62 global climate model projections under three IPPC SRES CO2 emission scenarios (A2, A1B and B1). In all cities, all scenarios resulted in increases in summer temperature-related deaths for future decades, and decreases in winter temperature-related deaths. However, Brisbane and Sydney will increase the net annual temperature-related deaths in the future, while a slight decrease will happen in Melbourne. Additionally, temperature-related mortality will largely increase beyond the summer (including January, February, March, November and December) in Brisbane and Sydney, while temperature-related mortality will largely decrease beyond the winter in Melbourne. In conclusion, temperature increases for Australia are expected to result in a decreased burden of cold-related mortality and an increased burden of heat-related mortality, but the balance of these differences varied by city. In particular, the seasonal patterns in temperature-related deaths will be shifted.

Ambient temperature enhanced acute cardiovascular-respiratory mortality effects of PM2.5 in Beijing, China

Studies have shown that temperature could modify the effect of ambient fine particles on mortality risk. In assessing air pollution effects, temperature is usually considered as a confounder. However, ambient temperature can alter people's physiological response to air pollution and might "modify" the impact of air pollution on health outcomes. This study investigated the interaction between daily PM2.5 and daily mean temperature in Beijing, China, using data for the period 2005-2009. Bivariate PM2.5-temperature response surfaces and temperature-stratified generalized additive model (GAM) were applied to study the effect of PM2.5 on cardiovascular, respiratory mortality, and total non-accidental mortality across different temperature levels. We found that low temperature could significantly enhance the effect of PM2.5 on cardiovascular mortality. For an increase of 10 μg/m(3) in PM2.5 concentration in the lowest temperature range (-9.7∼2.6 °C), the relative risk (RR) of cardiovascular mortality increased 1.27 % (95 % CI 0.38∼2.17 %), which was higher than that of the whole temperature range (0.59 %, 95 % CI 0.22-1.16 %). The largest effect of PM2.5 on respiratory mortality appeared in the high temperature range. For an increase of 10 μg/m(3) in PM2.5 concentration, RR of respiratory mortality increased 1.70 % (95 % CI 0.92∼3.33 %) in the highest level (23.50∼31.80 °C). For the total non-accidental mortality, significant associations appeared only in low temperature levels (-9.7∼2.6 °C): for an increase of 10 μg/m(3) in current day PM2.5 concentration, RR increased 1.27 % (95 % CI 0.46∼2.00 %) in the lowest temperature level. No lag effect was observed. The results suggest that in air pollution mortality time series studies, the possibility of an interaction between air pollution and temperature should be considered.

Short-term effects of air pollution on daily mortality and years of life lost in Nanjing, China

The deteriorating air quality in Chinese cities is attracting growing public concern. We conducted analyses to quantify the associations between daily changes in ambient air pollution and mortality in Nanjing, China. Daily mortality, air pollution, and meteorological data from 1 January 2009 to 31 December 2013 were collected. Over-dispersed Poisson regression models were used to evaluate the risk of daily non-accidental mortality and years of life lost (YLL) from exposure to respirable particulate matter (PM10) and gaseous pollutants (NO2, SO2). Stratified analysis was conducted to indentify the modifying effect of individual-level factors on the association between air pollutants and mortality. We found that interquartile range (IQR) increases in the two-day average of PM10, NO2 and SO2 were significantly associated with 1.6% [95% confidence interval (CI):0.7%-2.6%], 2.9% (95% CI: 1.7%-4.2%) and 2.4% (95% CI: 1.2%-3.6%) higher rates of non-accidental mortality; and related to YLL increases of 20.5 (95% CI: 6.3-34.8), 34.9 (95% CI: 16.9-52.9) and 30.3 (95% CI: 12.2-48.4) years, respectively; Associations between air pollution and mortality were more pronounced in the warm season than in the cool season. We conclude that the risks of mortality and YLL were elevated corresponding to an increase in current ambient concentrations of the air pollutants, and season may modify the effects of outdoor air pollution in Nanjing.