The association of long-term exposure to PM2.5 on all-cause mortality in the Nurses' Health Study and the impact of measurement-error correction

A comparison of PM2.5 exposure estimates from different estimation methods and their associations with cognitive testing and brain MRI outcomes

BACKGROUND

Reported associations between particulate matter with aerodynamic diameter ≤2.5 μm (PM2.5) and cognitive outcomes remain mixed. Differences in exposure estimation method may contribute to this heterogeneity.

OBJECTIVES

To assess agreement between PM2.5 exposure concentrations across 11 exposure estimation methods and to compare resulting associations between PM2.5 and cognitive or MRI outcomes.

METHODS

We used Visit 5 (2011-2013) cognitive testing and brain MRI data from the Atherosclerosis Risk in Communities (ARIC) Study. We derived address-linked average 2000-2007 PM2.5 exposure concentrations in areas immediately surrounding the four ARIC recruitment sites (Forsyth County, NC; Jackson, MS; suburbs of Minneapolis, MN; Washington County, MD) using 11 estimation methods. We assessed agreement between method-specific PM2.5 concentrations using descriptive statistics and plots, overall and by site. We used adjusted linear regression to estimate associations of method-specific PM2.5 exposure estimates with cognitive scores (n = 4678) and MRI outcomes (n = 1518) stratified by study site and combined site-specific estimates using meta-analyses to derive overall estimates. We explored the potential impact of unmeasured confounding by spatially patterned factors.

RESULTS

Exposure estimates from most methods had high agreement across sites, but low agreement within sites. Within-site exposure variation was limited for some methods. Consistently null findings for the PM2.5-cognitive outcome associations regardless of method precluded empirical conclusions about the potential impact of method on study findings in contexts where positive associations are observed. Not accounting for study site led to consistent, adverse associations, regardless of exposure estimation method, suggesting the potential for substantial bias due to residual confounding by spatially patterned factors.

DISCUSSION

PM2.5 estimation methods agreed across sites but not within sites. Choice of estimation method may impact findings when participants are concentrated in small geographic areas. Understanding unmeasured confounding by factors that are spatially patterned may be particularly important in studies of air pollution and cognitive or brain health.

Assessing Adverse Health Effects of Long-Term Exposure to Low Levels of Ambient Air Pollution: The HEI Experience and What's Next?

Although concentrations of ambient air pollution continue to decline in high-income regions, epidemiological studies document adverse health effects at levels below current standards in many countries. The Health Effects Institute (HEI) recently completed a comprehensive research initiative to investigate the health effects of long-term exposure to low levels of air pollution in the United States (U.S.), Canada, and Europe. We provide an overview and synthesis of the results of this initiative along with other key research, the strengths and limitations of the research, and remaining research needs. The three studies funded through the HEI initiative estimated the effects of long-term ambient exposure to fine particulate matter (PM2.5), nitrogen dioxide, ozone, and other pollutants on a broad range of health outcomes, including cause-specific mortality and cardiovascular and respiratory morbidity. To ensure high quality research and comparability across studies, HEI worked actively with the study teams and engaged independent expert panels for project oversight and review. All three studies documented positive associations between mortality and exposure to PM2.5 below the U.S. National Ambient Air Quality Standards and current and proposed European Union limit values. Furthermore, the studies observed nonthreshold linear (U.S.), or supra-linear (Canada and Europe) exposure-response functions for PM2.5 and mortality. Heterogeneity was found in both the magnitude and shape of this association within and across studies. Strengths of the studies included the large populations (7-69 million), state-of-the-art exposure assessment methods, and thorough statistical analyses that applied novel methods. Future work is needed to better understand potential sources of heterogeneity in the findings across studies and regions. Other areas of future work include the changing and evolving nature of PM components and sources, including wildfires, and the role of indoor environments. This research initiative provided important new evidence of the adverse effects of long-term exposures to low levels of air pollution at and below current standards, suggesting that further reductions could yield larger benefits than previously anticipated.

Is model-estimated PM2.5 exposure equivalent to station-observed in mortality risk assessment? A literature review and meta-analysis

Model-estimated air pollution exposure assessments have been extensively employed in the evaluation of health risks associated with air pollution. However, few studies synthetically evaluate the reliability of model-estimated PM2.5 products in health risk assessment by comparing them with ground-based monitoring station air quality data. In response to this gap, we undertook a meticulously structured systematic review and meta-analysis. Our objective was to aggregate existing comparative studies to ascertain the disparity in mortality effect estimates derived from model-estimated ambient PM2.5 exposure versus those based on monitoring station-observed PM2.5 exposure. We conducted searches across multiple databases, namely PubMed, Scopus, and Web of Science, using predefined keywords. Ultimately, ten studies were included in the review. Of these, seven investigated long-term annual exposure, while the remaining three studies focused on short-term daily PM2.5 exposure. Despite variances in the estimated Exposure-Response (E-R) associations, most studies revealed positive associations between ambient PM2.5 exposure and all-cause and cardiovascular mortality, irrespective of the exposure being estimated through models or observed at monitoring stations. Our meta-analysis revealed that all-cause mortality risk associated with model-estimated PM2.5 exposure was in line with that derived from station-observed sources. The pooled Relative Risk (RR) was 1.083 (95% CI: 1.047, 1.119) for model-estimated exposure, and 1.089 (95% CI: 1.054, 1.125) for station-observed sources (p = 0.795). In conclusion, most model-estimated air pollution products have demonstrated consistency in estimating mortality risk compared to data from monitoring stations. However, only a limited number of studies have undertaken such comparative analyses, underscoring the necessity for more comprehensive investigations to validate the reliability of these model-estimated exposure in mortality risk assessment.

Understanding the Cardiovascular and Metabolic Health Effects of Air Pollution in the Context of Cumulative Exposomic Impacts

Poor air quality accounts for more than 9 million deaths a year globally according to recent estimates. A large portion of these deaths are attributable to cardiovascular causes, with evidence indicating that air pollution may also play an important role in the genesis of key cardiometabolic risk factors. Air pollution is not experienced in isolation but is part of a complex system, influenced by a host of other external environmental exposures, and interacting with intrinsic biologic factors and susceptibility to ultimately determine cardiovascular and metabolic outcomes. Given that the same fossil fuel emission sources that cause climate change also result in air pollution, there is a need for robust approaches that can not only limit climate change but also eliminate air pollution health effects, with an emphasis of protecting the most susceptible but also targeting interventions at the most vulnerable populations. In this review, we summarize the current state of epidemiologic and mechanistic evidence underpinning the association of air pollution with cardiometabolic disease and how complex interactions with other exposures and individual characteristics may modify these associations. We identify gaps in the current literature and suggest emerging approaches for policy makers to holistically approach cardiometabolic health risk and impact assessment.

Ambient fine particulate matter and daily mortality: a comparative analysis of observed and estimated exposure in 347 cities

BACKGROUND

Model-estimated air pollution exposure products have been widely used in epidemiological studies to assess the health risks of particulate matter with diameters of ≤2.5 µm (PM2.5). However, few studies have assessed the disparities in health effects between model-estimated and station-observed PM2.5 exposures.

METHODS

We collected daily all-cause, respiratory and cardiovascular mortality data in 347 cities across 15 countries and regions worldwide based on the Multi-City Multi-Country collaborative research network. The station-observed PM2.5 data were obtained from official monitoring stations. The model-estimated global PM2.5 product was developed using a machine-learning approach. The associations between daily exposure to PM2.5 and mortality were evaluated using a two-stage analytical approach.

RESULTS

We included 15.8 million all-cause, 1.5 million respiratory and 4.5 million cardiovascular deaths from 2000 to 2018. Short-term exposure to PM2.5 was associated with a relative risk increase (RRI) of mortality from both station-observed and model-estimated exposures. Every 10-μg/m3 increase in the 2-day moving average PM2.5 was associated with overall RRIs of 0.67% (95% CI: 0.49 to 0.85), 0.68% (95% CI: -0.03 to 1.39) and 0.45% (95% CI: 0.08 to 0.82) for all-cause, respiratory, and cardiovascular mortality based on station-observed PM2.5 and RRIs of 0.87% (95% CI: 0.68 to 1.06), 0.81% (95% CI: 0.08 to 1.55) and 0.71% (95% CI: 0.32 to 1.09) based on model-estimated exposure, respectively.

CONCLUSIONS

Mortality risks associated with daily PM2.5 exposure were consistent for both station-observed and model-estimated exposures, suggesting the reliability and potential applicability of the global PM2.5 product in epidemiological studies.

Geospatial Science for the Environmental Epidemiology of Cancer in the Exposome Era

Geospatial science is the science of location or place that harnesses geospatial tools, such as geographic information systems (GIS), to understand the features of the environment according to their locations. Geospatial science has been transformative for cancer epidemiologic studies through enabling large-scale environmental exposure assessments. As the research paradigm for the exposome, or the totality of environmental exposures across the life course, continues to evolve, geospatial science will serve a critical role in determining optimal practices for how to measure the environment as part of the external exposome. The objectives of this article are to provide a summary of key concepts, present a conceptual framework that illustrates how geospatial science is applied to environmental epidemiology in practice and through the lens of the exposome, and discuss the following opportunities for advancing geospatial science in cancer epidemiologic research: enhancing spatial and temporal resolutions and extents for geospatial data; geospatial methodologies to measure climate change factors; approaches facilitating the use of patient addresses in epidemiologic studies; combining internal exposome data and geospatial exposure models of the external exposome to provide insights into biological pathways for environment-disease relationships; and incorporation of geospatial data into personalized cancer screening policies and clinical decision making.

Comparison of PM2.5 Air Pollution Exposures and Health Effects Associations Using 11 Different Modeling Approaches in the Women's Health Initiative Memory Study (WHIMS)

BACKGROUND

Many approaches to quantifying air pollution exposures have been developed. However, the impact of choice of approach on air pollution estimates and health-effects associations remains unclear.

OBJECTIVES

Our objective is to compare particulate matter with aerodynamic diameter ≤ 2.5 μ m (PM 2.5 ) concentrations and resulting health effects associations using multiple estimation approaches previously used in epidemiologic analyses.

METHODS

We assigned annual PM 2.5 exposure estimates from 1999 to 2004 derived from 11 different approaches to Women's Health Initiative Memory Study (WHIMS) participant addresses within the contiguous US. Approaches included geostatistical interpolation approaches, land-use regression or spatiotemporal models, satellite-derived approaches, air dispersion and chemical transport models, and hybrid models. We used descriptive statistics and plots to assess relative and absolute agreement among exposure estimates and examined the impact of approach on associations between PM 2.5 and death due to natural causes, cardiovascular disease (CVD) mortality, and incident CVD events, adjusting for individual-level covariates and climate-based region.

RESULTS

With a few exceptions, relative agreement of approach-specific PM 2.5 exposure estimates was high for PM 2.5 concentrations across the contiguous US. Agreement among approach-specific exposure estimates was stronger near PM 2.5 monitors, in certain regions of the country, and in 2004 vs. 1999. Collectively, our results suggest but do not quantify lower agreement at local spatial scales for PM 2.5 . There was no evidence of large differences in health effects associations with PM 2.5 among estimation approaches in analyses adjusted for climate region.

CONCLUSIONS

Different estimation approaches produced similar spatial patterns of PM 2.5 concentrations across the contiguous US and in areas with dense monitoring data, and PM 2.5 -health effects associations were similar among estimation approaches. PM 2.5 estimates and PM 2.5 -health effects associations may differ more in samples drawn from smaller areas or areas without substantial monitoring data, or in analyses with finer adjustment for participant location. Our results can inform decisions about PM 2.5 estimation approach in epidemiologic studies, as investigators balance concerns about bias, efficiency, and resource allocation. Future work is needed to understand whether these conclusions also apply in the context of other air pollutants of interest. https://doi.org/10.1289/EHP12995.

Correcting for bias due to mismeasured exposure history in longitudinal studies with continuous outcomes

Epidemiologists are often interested in estimating the effect of functions of time-varying exposure histories in relation to continuous outcomes, for example, cognitive function. However, the individual exposure measurements that constitute the history upon which an exposure history function is constructed are usually mismeasured. To obtain unbiased estimates of the effects for mismeasured functions in longitudinal studies, a method incorporating main and validation studies was developed. Simulation studies under several realistic assumptions were conducted to assess its performance compared to standard analysis, and we found that the proposed method has good performance in terms of finite sample bias reduction and nominal confidence interval coverage. We applied it to a study of long-term exposure toPM 2.5 $\text{PM}_{2.5}$ , in relation to cognitive decline in the Nurses' Health Study Previously, it was found that the 2-year decline in the standard measure of cognition was 0.018 (95% CI, -0.034 to -0.001) units worse per 10μ g/m 3 $\mu \text{g/m}^3$ increase inPM 2.5 $\text{PM}_{2.5}$ exposure. After correction, the estimated impact ofPM 2.5 $\text{PM}_{2.5}$ on cognitive decline increased to 0.027 (95% CI, -0.059 to 0.005) units lower per 10μ g/m 3 $\mu \text{g/m}^3$ increase. To put this into perspective, effects of this magnitude are about 2/3 of those found in our data associated with each additional year of aging: 0.044 (95% CI, -0.047 to -0.040) units per 1 year older after applying our correction method.

Red meat intake and risk of type 2 diabetes in a prospective cohort study of United States females and males

BACKGROUND

Studies with methodological advancements are warranted to confirm the relation of red meat consumption to the incidence of type 2 diabetes (T2D).

OBJECTIVE

We aimed to assess the relationships of intakes of total, processed, and unprocessed red meat to risk of T2D and to estimate the effects of substituting different protein sources for red meats on T2D risk.

METHODS

Our study included 216,695 participants (81% females) from the Nurses' Health Study (NHS), NHS II, and Health Professionals Follow-up Study (HPFS). Red meat intakes were assessed with semiquantitative food frequency questionnaires (FFQs) every 2 to 4 y since the study baselines. We used multivariable-adjusted proportional hazards models to estimate the associations between red meats and T2D.

RESULTS

Over 5,483,981 person-years of follow-up, we documented 22,761 T2D cases. Intakes of total, processed, and unprocessed red meat were positively and approximately linearly associated with higher risks of T2D. Comparing the highest to the lowest quintiles, hazard ratios (HR) were 1.62 (95% confidence interval [CI]: 1.53, 1.71) for total red meat, 1.51 (95% CI: 1.44, 1.58) for processed red meat, and 1.40 (95% CI: 1.33, 1.47) for unprocessed red meat. The percentage lower risk of T2D associated with substituting 1 serving/d of nuts and legumes for total red meat was 30% (HR = 0.70, 95% CI: 0.66, 0.74), for processed red meat was 41% (HR = 0.59, 95% CI: 0.55, 0.64), and for unprocessed red meat was 29% (HR = 0.71, 95% CI: 0.67, 0.75); Substituting 1 serving/d of dairy for total, processed, or unprocessed red meat was also associated with significantly lower risk of T2D. The observed associations became stronger after we calibrated dietary intakes to intakes assessed by weighed diet records.

CONCLUSIONS

Our study supports current dietary recommendations for limiting consumption of red meat intake and emphasizes the importance of different alternative sources of protein for T2D prevention.

Iron and Copper Alter the Oxidative Potential of Secondary Organic Aerosol: Insights from Online Measurements and Model Development

The oxidative potential (OP) of particulate matter has been widely suggested as a key metric for describing atmospheric particle toxicity. Secondary organic aerosol (SOA) and redox-active transition metals, such as iron and copper, are key drivers of particle OP. However, their relative contributions to OP, as well as the influence of metal-organic interactions and particulate chemistry on OP, remains uncertain. In this work, we simultaneously deploy two novel online instruments for the first time, providing robust quantification of particle OP. We utilize online AA (OPAA) and 2,7-dichlorofluoroscein (ROSDCFH) methods to investigate the influence of Fe(II) and Cu(II) on the OP of secondary organic aerosol (SOA). In addition, we quantify the OH production (OPOH) from these particle mixtures. We observe a range of synergistic and antagonistic interactions when Fe(II) and Cu(II) are mixed with representative biogenic (β-pinene) and anthropogenic (naphthalene) SOA. A newly developed kinetic model revealed key reactions among SOA components, transition metals, and ascorbate, influencing OPAA. Model predictions agree well with OPAA measurements, highlighting metal-ascorbate and -naphthoquinone-ascorbate reactions as important drivers of OPAA. The simultaneous application of multiple OP assays and a kinetic model provides new insights into the influence of metal and SOA interactions on particle OP.

Ecological study on household air pollution exposure and prevalent chronic disease in the elderly

Older people spend most of their time indoors. Limited evidence demonstrates that exposure to indoor air pollutants might be related to chronic complications. This study aimed to estimate the correlation between household air pollution (HAP)'s long-term exposure and the prevalence of elevated hypertension, diabetes mellitus (DM), obesity, and low-density lipoprotein (LDL) cholesterol. From the Global Burden disease dataset, we extracted HAP, hypertension, DM, body mass index, and LDL cholesterol data from Iran from 1990 to 2019 to males and females in people over 50 years. We present APC and AAPC and their confidence intervals using Joinpoint Software statistical software. R software examined the correlation between HAP and hypertension, DM2, Obesity, and high LDL cholesterol. Our finding showed a significant and positive correlation between HAP exposure and prevalence of high low-density lipoprotein cholesterol (p ≤ 0.001, r = 0.70), high systolic blood pressure (p ≤ 0.001, r = 0.63), and high body mass index (p ≤ 0.001, r = 0.57), and DM2 (p ≤ 0.001, r = 0.38). The analysis results also illustrated a positive correlation between indoor air pollution and smoking (p ≤ 0.001, r = 0.92). HAP exposure might be a risk factor for elevated blood pressure, DM, obesity, and LDL cholesterol and, consequently, more serious health problems. According to our results, smoking is one of the sources of HAP. However, ecological studies cannot fully support causal relationships, and this article deals only with Iran. Our findings should be corroborated in personal exposure and biomonitoring approach studies.

Impact assessment of COVID-19 global pandemic on water, environment, and humans

One of the most significant threats to global health since the Second World War is the COVID-19 pandemic. Due to COVID-19 widespread social, environmental, economic, and health concerns. Other unfavourable factors also emerged, including increased trash brought on by high consumption of packaged foods, takeout meals, packaging from online shopping, and the one-time use of plastic products. Due to labour shortages and residents staying at home during mandatory lockdowns, city municipal administrations' collection and recycling capacities have decreased, frequently damaging the environment (air, water, and soil) and ecological and human systems. The COVID-19 challenges are more pronounced in unofficial settlements of developing nations, particularly for developing nations of the world, as their fundamental necessities, such as air quality, water quality, trash collection, sanitation, and home security, are either non-existent or difficult to obtain. According to reports, during the pandemic's peak days (20 August 2021 (741 K cases), 8 million tonnes of plastic garbage were created globally, and 25 thousand tonnes of this waste found its way into the ocean. This thorough analysis attempts to assess the indirect effects of COVID-19 on the environment, human systems, and water quality that pose dangers to people and potential remedies. Strong national initiatives could facilitate international efforts to attain environmental sustainability goals. Significant policies should be formulated like good quality air, pollution reduction, waste management, better sanitation system, and personal hygiene. This review paper also elaborated that further investigations are needed to investigate the magnitude of impact and other related factors for enhancement of human understanding of ecosystem to manage the water, environment and human encounter problems during epidemics/pandemics in near future.

Estimating the economic value of ultrafine particle information: a contingent valuation method

Global concern regarding ultrafine particles (UFPs), which are particulate matter (PM) with a diameter of less than 100 nm, is increasing. These particles are difficult to measure using the current methods because their characteristics are different from those of other air pollutants. Therefore, a new monitoring system is required to obtain accurate UFP information, which will raise the financial burden of the government and people. In this study, we estimated the economic value of UFP information by evaluating the willingness-to-pay (WTP) for the UFP monitoring and reporting system. We used the contingent valuation method (CVM) and the one-and-one-half-bounded dichotomous choice (OOHBDC) spike model. We analyzed how the respondents' socio-economic variables, as well as their cognition level of PM, affected their WTP. Therefore, we collected WTP data of 1040 Korean respondents through an online survey. The estimated mean WTP for building a UFP monitoring and reporting system is KRW 6958.55-7222.55 (USD 6.22-6.45) per household per year. We found that people satisfied with the current air pollutant information, and generally possessing relatively greater knowledge of UFPs, have higher WTP for a UFP monitoring and reporting system. We found that people are willing to pay more than the actual installation and operating costs of current air pollution monitoring systems. If the collected UFP data is disclosed in an easily accessible manner, as is current air pollutant data, it will be possible to secure more public acceptance for expanding the UFP monitoring and reporting system nationwide.

Measurement error correction for ambient PM2.5 exposure using stratified regression calibration: Effects on all-cause mortality

BACKGROUND

Previous studies on the impact of measurement error for PM2.5 were mostly simulation studies, did not control for other pollutants, or used a single regression calibration model to correct for measurement error. However, the relationship between actual and error-prone PM2.5 concentration may vary by time and region. We aim to correct the measurement error of PM2.5 predictions using stratified regression calibration and investigate how the measurement error biases the association between PM2.5 and mortality in the Medicare Cohort.

METHODS

The "gold-standard" measurements of PM2.5 were defined as daily monitoring data. We regressed daily monitoring PM2.5 on modeled PM2.5 using the simple linear regression by strata of season, elevation, census division and time period. Calibrated PM2.5 was calculated with stratum-specific calibration parameters β0 (intercept) and β1 (slope) for each strata and aggregated to annual level. Associations between calibrated and error-prone annual PM2.5 and all-cause mortality among Medicare beneficiaries were estimated with Quasi-Poisson regression models.

RESULTS

Across 208 strata, the median of β0 and β1 were 0.62 (25% 0.0.20, 75% 1.06) and 0.93 (25% 0.87, 75% 0.99). From calibrated and error-prone PM2.5 data, we estimated that each 10 μg/m3 increase in PM2.5 was respectively associated with 4.9% (95%CI 4.6-5.2) and 4.6% (95%CI 4.4-4.9) increases in the mortality rate among Medicare beneficiaries, conditional on confounders.

CONCLUSIONS

Regression calibration parameters of PM2.5 varied by time and region. Using error-prone measures of PM2.5 underestimated the association between PM2.5 and all-cause mortality. Modern exposure models produce relatively small bias.

Synergy of Biostatistics and Epidemiology in Air Pollution Health Effects Studies

The extraordinary advances in quantifying the health effects of ambient air pollution over the last five decades have led to dramatic improvement in air quality in the United States. This work has been possible through innovative epidemiologic study designs coupled with advanced statistical analytic methods. This paper presents a historical perspective on the coordinated developments of epidemiologic designs and statistical methods for air pollution health effects studies at the Harvard School of Public Health.

Associations between long-term exposures to airborne PM2.5 components and mortality in Massachusetts: mixture analysis exploration

BACKGROUND

Numerous studies have documented PM_2.5's links with adverse health outcomes. Comparatively fewer studies have evaluated specific PM_2.5 components. The lack of exposure measurements and high correlation among different PM_2.5 components are two limitations.

METHODS

We applied a novel exposure prediction model to obtain annual Census tract-level concentrations of 15 PM_2.5 components (Zn, V, Si, Pb, Ni, K, Fe, Cu, Ca, Br, SO₄^2-, NO₃^-, NH₄⁺, OC, EC) in Massachusetts from 2000 to 2015, to which we matched geocoded deaths. All non-accidental mortality, cardiovascular mortality, and respiratory mortality were examined for the population aged 18 or over. Weighted quantile sum (WQS) regression models were used to examine the cumulative associations between PM_2.5 components mixture and outcomes and each component's contributions to the cumulative associations. We have fit WQS models on 15 PM_2.5 components and a priori identified source groups (heavy fuel oil combustion, biomass burning, crustal matter, non-tailpipe traffic source, tailpipe traffic source, secondary particles from power plants, secondary particles from agriculture, unclear source) for the 15 PM_2.5 components. Total PM_2.5 mass analysis and single component associations were also conducted through quasi-Poisson regression models.

RESULTS

Positive cumulative associations between the components mixture and all three outcomes were observed from the WQS models. Components with large contribution to the cumulative associations included K, OC, and Fe. Biomass burning, traffic emissions, and secondary particles from power plants were identified as important source contributing to the cumulative associations. Mortality rate ratios for cardiovascular mortality were of greater magnitude than all non-accidental mortality and respiratory mortality, which is also observed in cumulative associations estimated from WQS, total PM_2.5 mass analysis, and single component associations.

CONCLUSION

We have found positive associations between the mixture of 15 PM_2.5 components and all non-accidental mortality, cardiovascular mortality, and respiratory mortality. Among these components, Fe, K, and OC have been identified as having important contribution to the cumulative associations. The WQS results also suggests potential source effects from biomass burning, traffic emissions, and secondary particles from power plants.

Systematic review of the association between long-term exposure to fine particulate matter and mortality

We used a transparent systematic review framework based on best practices for evaluating study quality and integrating evidence to conduct a review of the available epidemiology studies evaluating associations between long-term exposure to ambient concentrations of PM_2.5 and mortality (all-cause and non-accidental) conducted in North America. We found that while there is some consistency across studies for reporting positive associations, these associations are weak and several important methodological issues have led to uncertainties with regard to the evidence from these studies, including potential confounding by measured and unmeasured factors, exposue measurement error, and model misspecification. These uncertainties provide a plausible, alternative explanation to causality for the weakly positive findings across studies. Using a causality framework that incorporates best practices for making causal determinations, we concluded that the evidence for a causal relationship between long-term exposure to ambient PM_2.5 concentrations and mortality from these studies is inadequate.

The Impact of Exposure Measurement Error on the Estimated Concentration-Response Relationship between Long-Term Exposure to PM2.5 and Mortality

BACKGROUND

Exposure measurement error is a central concern in air pollution epidemiology. Given that studies have been using ambient air pollution predictions as proxy exposure measures, the potential impact of exposure error on health effect estimates needs to be comprehensively assessed.

OBJECTIVES

We aimed to generate wide-ranging scenarios to assess direction and magnitude of bias caused by exposure errors under plausible concentration-response relationships between annual exposure to fine particulate matter [PM ≤2.5μm in aerodynamic diameter (PM2.5)] and all-cause mortality.

METHODS

In this simulation study, we use daily PM2.5 predictions at 1-km2 spatial resolution to estimate annual PM2.5 exposures and their uncertainties for ZIP Codes of residence across the contiguous United States between 2000 and 2016. We consider scenarios in which we vary the error type (classical or Berkson) and the true concentration-response relationship between PM2.5 exposure and mortality (linear, quadratic, or soft-threshold-i.e., a smooth approximation to the hard-threshold model). In each scenario, we generate numbers of deaths using error-free exposures and confounders of concurrent air pollutants and neighborhood-level covariates and perform epidemiological analyses using error-prone exposures under correct specification or misspecification of the concentration-response relationship between PM2.5 exposure and mortality, adjusting for the confounders.

RESULTS

We simulate 1,000 replicates of each of 162 scenarios investigated. In general, both classical and Berkson errors can bias the concentration-response curve toward the null. The biases remain small even when using three times the predicted uncertainty to generate errors and are relatively larger at higher exposure levels.

DISCUSSION

Our findings suggest that the causal determination for long-term PM2.5 exposure and mortality is unlikely to be undermined when using high-resolution ambient predictions given that the estimated effect is generally smaller than the truth. The small magnitude of bias suggests that epidemiological findings are relatively robust against the exposure error. In practice, the use of ambient predictions with a finer spatial resolution will result in smaller bias. https://doi.org/10.1289/EHP10389.

Long-term nitrogen dioxide exposure and cause-specific mortality in the U.S. Medicare population

BACKGROUND

Since 1971, the annual National Ambient Air Quality Standard (NAAQS) for nitrogen dioxide (NO₂) has remained at 53 ppb, the impact of long-term NO₂ exposure on mortality is poorly understood.

OBJECTIVES

We examined associations between long-term NO₂ exposure (12-month moving average of NO₂) below the annual NAAQS and cause-specific mortality among the older adults in the U.S.

METHODS

Cox proportional-hazard models were used to estimate Hazard Ratio (HR) for cause-specific mortality associated with long-term NO₂ exposures among about 50 million Medicare beneficiaries living within the conterminous U.S. from 2001 to 2008.

RESULTS

A 10 ppb increase in NO₂ was associated with increased mortality from all-cause (HR: 1.06; 95% CI: 1.05-1.06), cardiovascular (HR: 1.10; 95% CI: 1.10-1.11), respiratory disease (HR: 1.09; 95% CI: 1.08-1.11), and cancer (HR: 1.01; 95% CI: 1.00-1.02) adjusting for age, sex, race, ZIP code as strata ZIP code- and state-level socio-economic status (SES) as covariates, and PM_2.5 exposure using a 2-stage approach. NO₂ was also associated with elevated mortality from ischemic heart disease, cerebrovascular disease, congestive heart failure, chronic obstructive pulmonary disease, pneumonia, and lung cancer. We found no evidence of a threshold, with positive and significant HRs across the range of NO₂ exposures for all causes of death examined. Exposure-response curves were linear for all-cause, supra-linear for cardiovascular-, and sub-linear for respiratory-related mortality. HRs were highest consistently among Black beneficiaries.

CONCLUSIONS

Long-term NO₂ exposure is associated with elevated risks of death by multiple causes, without evidence of a threshold response. Our findings raise concerns about the sufficiency of the annual NAAQS for NO₂.

Long-term Exposure to Fine Particulate Matter and Mortality A Longitudinal Cohort Study of 400,459 Adults

BACKGROUND

Cohort studies on the association between long-term exposure to fine particulate matter (PM2.5) and mortality have been well established for America and Europe, but limited and inconsistent in Asia with much higher air pollution. This study aims to investigate the associations between ambient PM2.5 and all-cause and cause-specific mortality over a period of rising and then declining PM2.5.

METHODS

We enrolled a total of 400,459 adults from an open cohort between 2001 and 2016, and followed them up until 31 May 2019. We obtained mortality data from the National Death Registry maintained by the Ministry of Health and Welfare in Taiwan. We estimated ambient PM2.5 exposures using a satellite-based spatiotemporal model. We performed a Cox regression model with time-dependent covariates to investigate the associations of PM2.5 with deaths from all causes and specific causes.

RESULTS

This study identified 14,627 deaths and had a total of 5 million person-years of follow-up. Each 10 µg/m3 increase in PM2.5 was associated with an increased hazard risk of 29% (95% confidence interval: 24%-35%) in all-cause mortality. Risk of death increased by 30% for natural causes, 20% for cancer, 42% for cardiovascular disease (CVD) causes, and 53% for influenza and pneumonia causes, for each 10 µg/m3 increase in PM2.5. Sensitivity analyses generally yielded similar results.

CONCLUSION

Long-term exposure to ambient PM2.5 was associated with increased risks of all-cause mortality and deaths from cancers, natural causes, CVD, and influenza and pneumonia. Longitudinal study design should be encouraged for air pollution epidemiologic investigation.

Meteorological factors contributing to organic and elemental carbon concentrations in PM10 near an open-pit coal mine

Variations in the carbonaceous aerosol contents, organic carbon (OC) and elemental carbon (EC), in particulate matter less than 10 μm in size (PM₁₀), were analyzed at sites influenced by coal mining in an open-pit mine located in northern Colombia. Samples were collected during different seasonal periods throughout 2015. Meteorological variables for each site were examined during the different seasons. Aerosols were detected using a thermal-optical reflectance protocol method. The highest PM₁₀ concentrations, between the ranges of 28.2 ± 8.2 μg m^-3 and 75.0 ± 36.5 μg m^-3, were recorded during the dry season. However, the highest concentrations of OC (4.8-14.2 μg m^-3) and EC (2.9-13.9 μg m^-3) in PM₁₀ were observed during the transition period between the dry and wet seasons. The strong correlation between OC and EC in PM₁₀ (r = 0.6-1.0) during the transition season indicates a common primary combustion source. High OC (> 8.3 μg m^-3) and EC (> 6.9 μg m^-3) concentrations were associated with low wind speeds (< 2.1 m s^-1) moving in different directions. Analyses of the sources of atmospheric aerosol pollutants in the mining area in northern Colombia showed that the daily maximum total carbon concentrations were mainly associated with regional atmospheric transport of particulate matter from industrial areas and biomass burning sites located in the territory of Venezuela.

Different Mortality Risks of Long-Term Exposure to Particulate Matter across Different Cancer Sites

Particulate matter (PM) air pollution has challenged the global community and the International Agency for Research on Cancer (IARC) classified airborne particulate matter as carcinogenic to humans. However, while most studies of cancer examined a single cancer type using different cohorts, few studies compared the associations of PM between different cancer types. We aimed to compare the association of long-term exposure to PM (PM₁₀ and PM_2.5) and cancer mortality across 17 different types of cancer using a population-based cohort in the Seoul Metropolitan Area (SMA), South Korea; Our study population includes 87,608 subjects (mean age: 46.58 years) residing in the SMA from the National Health Insurance Services-National Sample cohort (NHIS-NSC) and followed up for 2007-2015. We used the time-dependent Cox proportional hazards model to estimate hazard ratios (HRs) and 95% confidence intervals (95% CIs) of each cancer mortality per 10 μg/m³ increase in PM concentrations, after adjusting for individual and areal characteristics. During eight years of follow-up, 1487 people died with any of 17 cancer types. Lung cancer death was the highest, followed by liver and stomach cancer. Although we did not find the association for all cancer types, possibly because of limited cancer cases, HRs of PM_2.5 were relatively high for lung, stomach, pancreas, non-Hodgkin's lymphoma, prostate, esophagus, oral and pharynx, and brain cancer mortality (HRs = 1.44-7.14). High HRs for pancreas, non-Hodgkin's lymphoma, esophagus, and oral and pharynx cancer were also seen for PM₁₀; our findings suggest PM air pollution as a potential risk factor of cancer mortality for upper digestive tracts, mouth, pancreas, and non-Hodgkin's lymphoma in a highly urbanized population with high exposure to PM for a long time.

Designing health impact functions to assess marginal changes in outdoor fine particulate matter

Estimating health benefits from improvements in ambient air quality requires the characterization of the magnitude and shape of the association between marginal changes in exposure and marginal changes in risk, and its uncertainty. Several attempts have been made to do this, each requiring different assumptions. These include the Log-Linear(LL), IntegratedExposure-Response(IER), and GlobalExposureMortalityModel(GEMM). In this paper we develop an improved relative risk model suitable for use in health benefits analysis that incorporates features of existing models while addressing limitations in each model. We model the derivative of the relative risk function within a meta-analytic framework; a quantity directly applicable to benefits analysis, incorporating a Fusion of algebraic functions used in previous models. We assume a constant derivative in concentration over low exposures, like the LL model, a declining derivative over moderate exposures observed in cohort studies, and a derivative declining as the inverse of concentration over high global exposures in a similar manner to the GEMM. The model properties are illustrated with examples of fitting it to data for the six specific causes of death previously examined by the GlobalBurdenofDisease program with ambient fine particulate matter (PM_2.5). In a test case analysis assuming a 1% (benefits analysis) or 100% (burden analysis), reduction in country-specific fine particulate matter concentrations, corresponding estimated global attributable deaths using the Fusion model were found to lie between those of the IER and LL models, with the GEMM estimates similar to those based on the LL model.

Large uncertainty in individual polygenic risk score estimation impacts PRS-based risk stratification

Although the cohort-level accuracy of polygenic risk scores (PRSs)-estimates of genetic value at the individual level-has been widely assessed, uncertainty in PRSs remains underexplored. In the present study, we show that Bayesian PRS methods can estimate the variance of an individual's PRS and can yield well-calibrated credible intervals via posterior sampling. For 13 real traits in the UK Biobank (n = 291,273 unrelated 'white British'), we observe large variances in individual PRS estimates which impact interpretation of PRS-based stratification; averaging across traits, only 0.8% (s.d. = 1.6%) of individuals with PRS point estimates in the top decile have corresponding 95% credible intervals fully contained in the top decile. We provide an analytical estimator for the expectation of individual PRS variance as a function of SNP heritability, number of causal SNPs and sample size. Our results showcase the importance of incorporating uncertainty in individual PRS estimates into subsequent analyses.

Parkinson's disease aggravation in association with fine particle components in New York State

BACKGROUND

Long-term exposure to fine particulate matter (PM2.5) has been associated with neurodegenerative diseases, including disease aggravation in Parkinson's disease (PD), but associations with specific PM2.5 components have not been evaluated.

OBJECTIVE

To characterize the association between specific PM2.5 components and PD first hospitalization, a surrogate for disease aggravation.

METHODS

We obtained data on hospitalizations from the New York Department of Health Statewide Planning and Research Cooperative System (2000-2014) to calculate annual first PD hospitalization counts in New York State per county. We used well-validated prediction models at 1 km2 resolution to estimate county level population-weighted annual black carbon (BC), organic matter (OM), nitrate, sulfate, sea salt (SS), and soil particle concentrations. We then used a multi-pollutant mixed quasi-Poisson model with county-specific random intercepts to estimate rate ratios (RR) of one-year exposure to each PM2.5 component and PD disease aggravation. We evaluated potential nonlinear exposure-outcome relationships using penalized splines and accounted for potential confounders.

RESULTS

We observed a total of 197,545 PD first hospitalizations in NYS from 2000 to 2014. The annual average count per county was 212 first hospitalizations. The RR (95% confidence interval) for PD aggravation was 1.06 (1.03, 1.10) per one standard deviation (SD) increase in nitrate concentrations and 1.06 (1.04, 1.09) for the corresponding increase in OM concentrations. We also found a nonlinear inverse association between PD aggravation and BC at concentrations above the 96th percentile. We found a marginal association with SS and no association with sulfate or soil exposure.

CONCLUSION

In this study, we detected associations between the PM2.5 components OM and nitrate with PD disease aggravation. Our findings support that PM2.5 adverse effects on PD may vary by particle composition.

Modeling and forecasting of monthly PM2.5 emission of Paris by periodogram-based time series methodology

In this study, monthly particulate matter (PM_2.5) of Paris for the period between January 2000 and December 2019 is investigated by utilizing a periodogram-based time series methodology. The main contribution of the study is modeling the PM_2.5 of Paris by extracting the information purely from the examined time series data, where proposed model implicitly captures the effects of other factors, as all their periodic and seasonal effects reside in the air pollution data. Periodicity can be defined as the patterns embedded in the data other than seasonality, and it is crucial to understand the underlying periodic dynamics of air pollutants to better fight pollution. The method we use successfully captures and accounts for the periodicities, which could otherwise be mixed with seasonality under an alternative methodology. Upon the unit root test based on periodograms, it is revealed that the investigated data has periodicities of 1 year and 20 years, so harmonic regression is utilized as an alternative to Box-Jenkins methodology. As the harmonic regression displayed a better performance both in and out-of-sample forecasts, it can be considered as a powerful alternative to model and forecast time series with a periodic structure.

Long-term Weight Changes and Risk of Rheumatoid Arthritis Among Women in a Prospective Cohort: A Marginal Structural Model Approach

OBJECTIVE

To examine the association of long-term weight change with rheumatoid arthritis (RA) risk in a large prospective cohort study.

METHODS

The Nurses' Health Study (NHS) II started in 1989 (baseline); after exclusions, we studied 108,505 women 25-42 years old without RA. Incident RA was reported by participant and confirmed by medical record review. Body weight was reported biennially through 2015. We investigated two time-varying exposures: weight changes from baseline and from age 18; change was divided into 5 categories. We used a marginal structural model (MSM) approach to account for time-varying weight change and covariates.

RESULTS

Over 2,583,266 person-years, with a median follow-up time of 25.3 years, 541 women developed RA. Compared to women with stable weight from baseline, weight change was significantly associated with increased RA risk [weight gain 2-<10 kg: RR = 1.98 (95% CI 1.38, 2.85); 10-<20 kg: RR = 3.28 (95% CI 2.20, 4.89); ≥20 kg: RR = 3.81 (95% CI 2.39, 6.07); and weight loss >2 kg: RR = 2.05 (95% CI 1.28, 3.28)]. Weight gain of 10 kg or more from age 18 compared with stable weight was also associated with increased RA risk [10-< 20 kg: RR = 2.12 (95% CI 1.37, 3.27), ≥20 kg: RR = 2.31 (95% CI 1.50, 3.56)]. Consistent findings were observed for seropositive and seronegative RA.

CONCLUSION

Long-term weight gain was strongly associated with increased RA risk in women, with weight gain of ≥ 20 kg associated with more than a three-fold increased RA risk. Maintenance of healthy weight may be a strategy to prevent or delay RA.

Atmospheric conditions and composition that influence PM2.5 oxidative potential in Beijing, China

Epidemiological studies have consistently linked exposure to PM_2.5 with adverse health effects. The oxidative potential (OP) of aerosol particles has been widely suggested as a measure of their potential toxicity. Several acellular chemical assays are now readily employed to measure OP; however, uncertainty remains regarding the atmospheric conditions and specific chemical components of PM_2.5 that drive OP. A limited number of studies have simultaneously utilised multiple OP assays with a wide range of concurrent measurements and investigated the seasonality of PM_2.5 OP. In this work, filter samples were collected in winter 2016 and summer 2017 during the atmospheric pollution and human health in a Chinese megacity campaign (APHH-Beijing), and PM_2.5 OP was analysed using four acellular methods: ascorbic acid (AA), dithiothreitol (DTT), 2,7-dichlorofluorescin/hydrogen peroxidase (DCFH) and electron paramagnetic resonance spectroscopy (EPR). Each assay reflects different oxidising properties of PM_2.5, including particle-bound reactive oxygen species (DCFH), superoxide radical production (EPR) and catalytic redox chemistry (DTT/AA), and a combination of these four assays provided a detailed overall picture of the oxidising properties of PM_2.5 at a central site in Beijing. Positive correlations of OP (normalised per volume of air) of all four assays with overall PM_2.5 mass were observed, with stronger correlations in winter compared to summer. In contrast, when OP assay values were normalised for particle mass, days with higher PM_2.5 mass concentrations (μgm^-3) were found to have lower mass-normalised OP values as measured by AA and DTT. This finding supports that total PM_2.5 mass concentrations alone may not always be the best indicator for particle toxicity. Univariate analysis of OP values and an extensive range of additional measurements, 107 in total, including PM_2.5 composition, gas-phase composition and meteorological data, provided detailed insight into the chemical components and atmospheric processes that determine PM_2.5 OP variability. Multivariate statistical analyses highlighted associations of OP assay responses with varying chemical components in PM_2.5 for both mass- and volume-normalised data. AA and DTT assays were well predicted by a small set of measurements in multiple linear regression (MLR) models and indicated fossil fuel combustion, vehicle emissions and biogenic secondary organic aerosol (SOA) as influential particle sources in the assay response. Mass MLR models of OP associated with compositional source profiles predicted OP almost as well as volume MLR models, illustrating the influence of mass composition on both particle-level OP and total volume OP. Univariate and multivariate analysis showed that different assays cover different chemical spaces, and through comparison of mass- and volume-normalised data we demonstrate that mass-normalised OP provides a more nuanced picture of compositional drivers and sources of OP compared to volume-normalised analysis. This study constitutes one of the most extensive and comprehensive composition datasets currently available and provides a unique opportunity to explore chemical variations in PM_2.5 and how they affect both PM_2.5 OP and the concentrations of particle-bound reactive oxygen species.

Fine Particle Exposure and Clinical Aggravation in Neurodegenerative Diseases in New York State

BACKGROUND

Adult-onset neurodegenerative diseases affect millions and negatively impact health care systems worldwide. Evidence suggests that air pollution may contribute to aggravation of neurodegeneration, but studies have been limited.

OBJECTIVE

We examined the potential association between long-term exposure to particulate matter ≤ 2.5 μ m in aerodynamic diameter [fine particulate matter (PM 2.5 )] and disease aggravation in Alzheimer's (AD) and Parkinson's (PD) diseases and amyotrophic lateral sclerosis (ALS), using first hospitalization as a surrogate of clinical aggravation.

METHODS

We used data from the New York Department of Health Statewide Planning and Research Cooperative System (SPARCS 2000-2014) to construct annual county counts of first hospitalizations with a diagnosis of AD, PD, or ALS (total, urbanicity-, sex-, and age-stratified). We used annual PM 2.5 concentrations estimated by a prediction model at a 1 -km 2 resolution, which we aggregated to population-weighted county averages to assign exposure to cases based on county of residence. We used outcome-specific mixed quasi-Poisson models with county-specific random intercepts to estimate rate ratios (RRs) for a 1-y PM 2.5 exposure. We allowed for nonlinear exposure-outcome relationships using penalized splines and accounted for potential confounders.

RESULTS

We found a positive nonlinear PM 2.5 - PD association that plateaued above 11   μ g / m 3 (RR = 1.09 , 95% CI: 1.04, 1.14 for a PM 2.5 increase from 8.1 to 10.4   μ g / m 3 ). We also found a linear PM 2.5 - ALS positive association (RR = 1.05 , 95% CI: 1.01, 1.09 per 1 - μ g / m 3 PM 2.5 increase), and suggestive evidence of an association with AD. We found effect modification by age for PD and ALS with a stronger positive association in patients < 70  years of age but found insufficient evidence of effect modification by sex or urbanization level for any of the outcomes.

CONCLUSION

Our findings suggest that annual increase in county-level PM 2.5 concentrations may contribute to clinical aggravation of PD and ALS. Importantly, the average annual PM 2.5 concentration in our study was 8.1   μ g / m 3 , below the current American national standards, suggesting the standards may not adequately protect the aging population. https://doi.org/10.1289/EHP7425.

Interaction between Long-Term Exposure to Fine Particulate Matter and Physical Activity, and Risk of Cardiovascular Disease and Overall Mortality in U.S. Women

BACKGROUND

Increased respiration during physical activity may increase air pollution dose, which may attenuate the benefits of physical activity on cardiovascular disease (CVD) risk and overall mortality.

OBJECTIVES

We aimed to examine the multiplicative interaction between long-term ambient residential exposure to fine particulate matter < 2.5  microns (PM 2.5 ) and physical activity in the association with CVD risk and overall mortality.

METHODS

We followed 104,990 female participants of the U.S.-based prospective Nurses' Health Study from 1988 to 2008. We used Cox proportional hazards models to assess the independent associations of 24-months moving average residential PM 2.5 exposure and physical activity updated every 4 y and the multiplicative interaction of the two on CVD (myocardial infarction and stroke) risk and overall mortality, after adjusting for demographics and CVD risk factors.

RESULTS

During 20 years of follow-up, we documented 6,074 incident CVD cases and 9,827 deaths. In fully adjusted models, PM 2.5 exposure was associated with modest increased risks of CVD [hazard ratio (HR) for fifth quintile ≥ 16.5   μ g / m 3 compared to first quintile < 10.7   μ g / m 3 : 1.09, 95% confidence interval (CI): 0.99, 1.20; p trend = 0.05 ] and overall mortality (HR fifth compared to first quintile: 1.10, 95% CI: 1.02, 1.19; p trend = 0.07 ). Higher overall physical activity was associated with substantially lower risk of CVD [HR fourth quartile, which was ≥ 24.4  metabolic equivalent of task (MET)-h/wk, compared to first quartile (< 3.7 MET-h / wk ): 0.61, 95% CI: 0.57, 0.66; p t r e n d < 0.0001 ] and overall mortality (HR fourth compared to first quartile: 0.40, 95% CI: 0.37, 0.42; p t r e n d < 0.0001 ). We observed no statistically significant interactions between PM 2.5 exposure and physical activity (overall, walking, vigorous activity) in association with CVD risk and overall mortality.

DISCUSSION

In this study of U.S. women, we observed no multiplicative interaction between long-term PM 2.5 exposure and physical activity; higher physical activity was strongly associated with lower CVD risk and overall mortality at all levels of PM 2.5 exposure. https://doi.org/10.1289/EHP7402.

Incident cardiovascular disease and particulate matter air pollution in South Korea using a population-based and nationwide cohort of 0.2 million adults

BACKGROUND

While many studies reported the association between long-term exposure to particulate matter air pollution (PM) and cardiovascular disease (CVD), few studies focused on incidence with relatively high-dose exposure using a nationwide cohort. This study aimed to investigate the association between long-term exposure to PM₁₀ and PM_2.5 and incidence of CVD in a nationwide and population-based cohort in South Korea where the annual average concentration of PM_2.5 is above 20 μg/m³.

METHODS

We selected 196,167 adults in the National Health Insurance Service-National Sample Cohort (NHIS-NSC) constructed based on the entire South Korean population. Incidence of four CVD subtypes including ischemic heart disease (IHD), myocardial infarction, heart failure, and stroke, and total CVD including all four was identified as the first diagnosis for 2007-2015. To assess individual exposures, we used annually-updated district-level residential addresses and district-specific PM concentrations predicted by a previously developed universal kriging prediction model. We computed individual-level long-term PM concentrations for four exposure windows: previous 1, 3, and 5 year(s) and 5 years before baseline. We applied time-dependent Cox proportional hazards models to estimate hazard ratios (HRs) of incident CVDs per 10 μg/m³ increase in PM₁₀ and PM_2.5 after adjusting for individual- and area-level characteristics.

RESULTS

During 1,578,846 person-year, there were 33,580 cases of total incident CVD. Average PM₁₀ and PM_2.5 concentrations for the previous 5 years were 52.3 and 28.1 μg/m³, respectively. A 10 μg/m³ increase in PM_2.5 exposed for the previous 5 years was associated with 4 and 10% increases in the incidence of total CVD (95% confidence interval: 0-9%) and IHD (4-16%), respectively. HRs tended to be higher with earlier exposure for IHD and more recent exposure for stroke. The estimated shape of the concentration-response relationship showed non-linear patterns. We did not find evidence of the association for PM₁₀.

CONCLUSIONS

Using a population-based nationwide cohort exposed to relatively high PM concentration, this study confirmed the association between PM_2.5 and CVD incidence that was reported in previous studies mostly with low-dose environments. The magnitude and the shape of the association were generally consistent with previous findings.

Long-term exposure to PM and all-cause and cause-specific mortality: A systematic review and meta-analysis

As new scientific evidence on health effects of air pollution is generated, air quality guidelines need to be periodically updated. The objective of this review is to support the derivation of updated guidelines by the World Health Organization (WHO) by performing a systematic review of evidence of associations between long-term exposure to particulate matter with diameter under 2.5 µm (PM_2.5) and particulate matter with diameter under 10 µm (PM₁₀), in relation to all-cause and cause-specific mortality. As there is especially uncertainty about the relationship at the low and high end of the exposure range, the review needed to provide an indication of the shape of the concentration-response function (CRF). We systematically searched MEDLINE and EMBASE from database inception to 9 October 2018. Articles were checked for eligibility by two reviewers. We included cohort and case-control studies on outdoor air pollution in human populations using individual level data. In addition to natural-cause mortality, we evaluated mortality from circulatory diseases (ischemic heart disease (IHD) and cerebrovascular disease (stroke) also specifically), respiratory diseases (Chronic Obstructive Pulmonary Disease (COPD) and acute lower respiratory infection (ALRI) also specifically) and lung cancer. A random-effect meta-analysis was performed when at least three studies were available for a specific exposure-outcome pair. Risk of bias was assessed for all included articles using a specifically developed tool coordinated by WHO. Additional analyses were performed to assess consistency across geographic region, explain heterogeneity and explore the shape of the CRF. An adapted GRADE (Grading of Recommendations Assessment, Development and Evaluation) assessment of the body of evidence was made using a specifically developed tool coordinated by WHO. A large number (N = 107) of predominantly cohort studies (N = 104) were included after screening more than 3000 abstracts. Studies were conducted globally with the majority of studies from North America (N = 62) and Europe (N = 25). More studies used PM_2.5 (N = 71) as the exposure metric than PM₁₀ (N = 42). PM_2.5 was significantly associated with all causes of death evaluated. The combined Risk Ratio (RR) for PM_2.5 and natural-cause mortality was 1.08 (95%CI 1.06, 1.09) per 10 µg/m³. Meta analyses of studies conducted at the low mean PM_2.5 levels (<25, 20, 15, 12, 10 µg/m³) yielded RRs that were similar or higher compared to the overall RR, consistent with the finding of generally linear or supra-linear CRFs in individual studies. Pooled RRs were almost identical for studies conducted in North America, Europe and Western Pacific region. PM₁₀ was significantly associated with natural-cause and most but not all causes of death. Application of the risk of bias tool showed that few studies were at a high risk of bias in any domain. Application of the adapted GRADE tool resulted in an assessment of "high certainty of evidence" for PM_2.5 with all assessed endpoints except for respiratory mortality (moderate). The evidence was rated as less certain for PM₁₀ and cause-specific mortality ("moderate" for circulatory, IHD, COPD and "low" for stroke mortality. Compared to the previous global WHO evaluation, the evidence base has increased substantially. However, studies conducted in low- and middle- income countries (LMICs) are still limited. There is clear evidence that both PM_2.5 and PM₁₀ were associated with increased mortality from all causes, cardiovascular disease, respiratory disease and lung cancer. Associations remained below the current WHO guideline exposure level of 10 µg/m³ for PM_2.5. Systematic review registration number (PROSPERO ID): CRD42018082577.

Quantitative Characterization of Uncertainty in the Concentration-Response Relationship between Long-Term PM2.5 Exposure and Mortality at Low Concentrations

Extensive epidemiologic evidence supports a linear, no-threshold concentration-response (C-R) relationship between long-term exposure to fine particles (PM_2.5) and mortality in the United States. While examinations of the C-R relationship are designed to assess the shape of the C-R curve, they do not provide the information needed to quantitatively characterize uncertainty at specific PM_2.5 concentrations, which is often needed in the context of risk assessments and benefits analyses. We developed a novel approach, using information that is typically available in published epidemiologic studies, to quantitatively characterize uncertainty at different concentrations along the PM_2.5 concentration distribution. Our approach utilizes the annual mean PM_2.5 concentration and corresponding standard deviation from a published epidemiologic study to estimate the standard deviation of hypothetical PM_2.5 concentration distributions defined at 0.1 μg/m³ increments. The hypothetical distributions are then used to derive adjusted uncertainty estimates in the reported effect estimate at low concentrations (i.e., concentrations lower than the annual mean observed in the study). We demonstrate the application of this method in six individual epidemiologic studies that examined the relationship between long-term PM_2.5 exposure and mortality and were conducted in different geographic locations worldwide and at different PM_2.5 concentrations. This new method allows for a more comprehensive quantitative evaluation of uncertainty in the shape of the C-R relationship between long-term PM_2.5 exposure and mortality at concentrations below the mean annual concentrations observed in current studies.

Involvement of fine particulate matter exposure with gene expression pathways in breast tumor and adjacent-normal breast tissue

BACKGROUND

Fine particulate matter (PM_2.5) has been associated with breast cancer specific mortality, particularly for women with Stage I cancer. We examined the biological pathways that are perturbed by PM_2.5 exposures by analyzing gene expression measurements from breast tissue specimens.

METHODS

The Nurses' Health Studies (NHS and NHSII) are prospective cohorts with archival breast tissue specimens from breast cancer cases. Global gene expression data were ascertained with the Affymetrix Glue Human Transcriptome Array 3.0. PM_2.5 was estimated using spatio-temporal models linked to participants' home addresses. All analyses were performed separately in tumor (n = 591) and adjacent-normal (n = 497) samples, and stratified by estrogen receptor (ER) status and stage. We used multivariable linear regression, gene-set enrichment analyses (GSEA), and the least squares kernel machine (LSKM) to assess whether 3-year cumulative average pre-diagnosis PM_2.5 exposure was associated with breast-tissue gene expression pathways among predominately Stage I and II women (90.7%) and postmenopausal (81.2%) women. Replication samples (tumor, n = 245; adjacent-normal, n = 165) were measured on Affymetrix Human Transcriptome Array (HTA 2.0).

RESULTS

Overall, no pathways in the tumor area were significantly associated with PM_2.5 exposure. Among 272 adjacent-normal samples from Stage I ER-positive women, PM_2.5 was associated with perturbations in the oxidative phosphorylation, protein secretion, and mTORC1 signaling pathways (GSEA and LSKM p-values <0.05); however, results were not replicated in a small set of replication samples (n = 80).

CONCLUSIONS

PM_2.5 was generally not associated with breast tissue gene expression though was suggested to perturb oxidative phosphorylation and regulation of proteins and cellular signaling in adjacent-normal breast tissue. More research is needed on the biological role of PM_2.5 that influences breast tumor progression.

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.

Fine particulate air pollution and human mortality: 25+ years of cohort studies

Much of the key epidemiological evidence that long-term exposure to fine particulate matter air pollution (PM_2.5) contributes to increased risk of mortality comes from survival studies of cohorts of individuals. Although the first two of these studies, published in the mid-1990s, were highly controversial, much has changed in the last 25 + years. The objectives of this paper are to succinctly compile and summarize the findings of these cohort studies using meta-analytic tools and to address several of the key controversies. Independent reanalysis and substantial extended analysis of the original cohort studies have been conducted and many additional studies using a wide variety of cohorts, including cohorts constructed from public data and leveraging natural experiments have been published. Meta-analytic estimates of the mean of the distribution of effects from cohort studies that are currently available, provide substantial evidence of adverse air pollution associations with all-cause, cardiopulmonary, and lung cancer mortality.

Estimating long-term pollution exposure effects through inverse probability weighting methods with Cox proportional hazards models

BACKGROUND

Fine particulate matter (PM2.5) is associated with negative health outcomes in both the short and long term. However, the cohort studies that have produced many of the estimates of long-term exposure associations may fail to account for selection bias in pollution exposure as well as covariate imbalance in the study population; therefore, causal modeling techniques may be beneficial.

METHODS

Twenty-nine years of data from the National Health Interview Survey (NHIS) was compiled and linked to modeled annual average outdoor PM2.5 concentration and restricted-use mortality data. A series of Cox proportional hazards models, adjusted using inverse probability weights, yielded causal risk estimates of long-term exposure to ambient PM2.5 on all-cause and cardiopulmonary mortality.

RESULTS

Covariate-adjusted estimated relative risks per 10 μg/m3 increase in PM2.5 exposure were estimated to be 1.117 (1.083, 1.152) for all-cause mortality and 1.232 (1.174, 1.292) for cardiopulmonary mortality. Inverse probability weighted Cox models provide relatively consistent and robust estimates similar to those in the unweighted baseline multivariate Cox model, though they have marginally lower point estimates and higher standard errors.

CONCLUSIONS

These results provide evidence that long-term exposure to PM2.5 contributes to increased mortality risk in US adults and that the estimated effects are generally robust to modeling choices. The size and robustness of estimated associations highlight the importance of clean air as a matter of public health. Estimated confounding due to measured covariates appears minimal in the NHIS cohort, and various distributional assumptions have little bearing on the magnitude or standard errors of estimated causal associations.

Particulate air pollution from different sources and mortality in 7.5 million adults - The Dutch Environmental Longitudinal Study (DUELS)

BACKGROUND

Long-term exposure to particulate air pollution has been associated with mortality in urban cohort studies. Few studies have investigated the association between emission contributions from different particle sources and mortality in large-scale population registries, including non-urban populations.

OBJECTIVES

The aim of the study was to evaluate the associations between long-term exposure to particulate air pollution from different source categories and non-accidental mortality in the Netherlands based on existing national databases.

METHODS

We used existing Dutch national databases on mortality, individual characteristics, residence history, neighbourhood characteristics and modelled air pollution concentrations from different sources and air pollution components: particulate matter PM10, primary particulate matter PM10 (PPM10), particulate matter PM2.5, primary particulate matter PM2.5 (PPM2.5), elemental carbon (EC), nitrogen dioxide (NO₂) and secondary inorganic aerosol (SIA) in PM10 (SIA10) or in PM2.5 (SIA2.5). We established a cohort of 7.5 million individuals 30 years or older. We followed the cohort for eight years (2008-2015). We applied Cox proportional hazard regression models adjusting for potential individual and area-specific confounders.

RESULTS

We found statistically significant associations between total and primary particulate matter (PM10 and PM2.5), elemental carbon and mortality. Adjustment for nitrogen dioxide did not change the associations. Secondary inorganic aerosol showed less consistent associations. All primary PM sources were associated with mortality, except agricultural emissions and, depending on the statistical model, industrial PM emissions.

CONCLUSIONS

We could not identify one or more specific source categories of particulate air pollution as main determinants of the mortality effects found in this and in a previous study. This suggests that present policy measures should be focussed on the wider spectrum of air pollution sources instead of on specific sources.

Air pollution and mortality in a large, representative U.S. cohort: multiple-pollutant analyses, and spatial and temporal decompositions

BACKGROUND

Cohort studies have documented associations between fine particulate matter air pollution (PM2.5) and mortality risk. However, there remains uncertainty regarding the contribution of co-pollutants and the stability of pollution-mortality associations in models that include multiple air pollutants. Furthermore, it is unclear whether the PM2.5-mortality relationship varies spatially, when exposures are decomposed according to scale of spatial variability, or temporally, when effect estimates are allowed to change between years.

METHODS

A cohort of 635,539 individuals was compiled using public National Health Interview Survey (NHIS) data from 1987 to 2014 and linked with mortality follow-up through 2015. Modelled air pollution exposure estimates for PM2.5, other criteria air pollutants, and spatial decompositions (< 1 km, 1-10 km, 10-100 km, > 100 km) of PM2.5 were assigned at the census-tract level. The NHIS samples were also divided into yearly cohorts for temporally-decomposed analyses. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) in regression models that included up to six criteria pollutants; four spatial decompositions of PM2.5; and two- and five-year lagged mean PM2.5 exposures in the temporally-decomposed cohorts. Meta-analytic fixed-effect estimates were calculated using results from temporally-decomposed analyses and compared with time-independent results using 17- and 28-year exposure windows.

RESULTS

In multiple-pollutant analyses, PM2.5 demonstrated the most robust pollutant-mortality association. Coarse fraction particulate matter (PM2.5-10) and sulfur dioxide (SO2) were also associated with excess mortality risk. The PM2.5-mortality association was observed across all four spatial scales of PM2.5, with higher but less precisely estimated HRs observed for local (< 1 km) and neighborhood (1-10 km) variations. In temporally-decomposed analyses, the PM2.5-mortality HRs were stable across yearly cohorts. The meta-analytic HR using two-year lagged PM2.5 equaled 1.10 (95% CI 1.07, 1.13) per 10 μg/m3. Comparable results were observed in time-independent analyses using a 17-year (HR 1.13, CI 1.09, 1.16) or 28-year (HR 1.09, CI 1.07, 1.12) exposure window.

CONCLUSIONS

Long-term exposures to PM2.5, PM2.5-10, and SO2 were associated with increased risk of all-cause and cardiopulmonary mortality. Each spatial decomposition of PM2.5 was associated with mortality risk, and PM2.5-mortality associations were consistent over time.

A Bayesian ensemble approach to combine PM2.5 estimates from statistical models using satellite imagery and numerical model simulation

Ambient fine particulate matter less than 2.5 μm in aerodynamic diameter (PM_2.5) has been linked to various adverse health outcomes. PM_2.5 arises from both natural and anthropogenic sources, and PM_2.5 concentrations can vary over space and time. However, the sparsity of existing air quality monitors greatly restricts the spatial-temporal coverage of PM_2.5 measurements, potentially limiting the accuracy of PM_2.5-related health studies. Various methods exist to address these limitations by supplementing air quality monitoring measurements with additional data. We develop a method to combine PM_2.5 estimated from satellite-retrieved aerosol optical depth (AOD) and chemical transport model (CTM) simulations using statistical models. While most previous methods utilize AOD or CTM separately, we aim to leverage advantages offered by both data sources in terms of resolution and coverage using Bayesian ensemble averaging. Our approach differs from previous ensemble approaches in its ability to not only incorporate uncertainties in PM_2.5 estimates from individual models but also to provide uncertainties for the resulting ensemble estimates. In an application of estimating daily PM_2.5 in the Southeastern US, the ensemble approach outperforms previously developed spatial-temporal statistical models that use either AOD or bias-corrected CTM simulations in cross-validation (CV) analyses. More specifically, in spatially clustered CV experiments, the ensemble approach reduced the AOD-only and CTM-only model's root mean squared error (RMSE) by at least 13%. Similar improvements were seen in R². The enhanced prediction performance that the ensemble technique provides at fine-scale spatial resolution, as well as the availability of prediction uncertainty, can be further used in health effect analyses of air pollution exposure.

Mortality-Air Pollution Associations in Low-Exposure Environments (MAPLE): Phase 1

INTRODUCTION

Fine particulate matter (particulate matter ≤2.5 μm in aerodynamic diameter, or PM2.5) is associated with mortality, but the lower range of relevant concentrations is unknown. Novel satellite-derived estimates of outdoor PM2.5 concentrations were applied to several large population-based cohorts, and the shape of the relationship with nonaccidental mortality was characterized, with emphasis on the low concentrations (<12 μg/m3) observed throughout Canada.

METHODS

Annual satellite-derived estimates of outdoor PM2.5 concentrations were developed at 1-km2 spatial resolution across Canada for 2000-2016 and backcasted to 1981 using remote sensing, chemical transport models, and ground monitoring data. Targeted ground-based measurements were conducted to measure the relationship between columnar aerosol optical depth (AOD) and ground-level PM2.5. Both existing and targeted ground-based measurements were analyzed to develop improved exposure data sets for subsequent epidemiological analyses.

Residential histories derived from annual tax records were used to estimate PM2.5 exposures for subjects whose ages ranged from 25 to 90 years. About 8.5 million were from three Canadian Census Health and Environment Cohort (CanCHEC) analytic files and another 540,900 were Canadian Community Health Survey (CCHS) participants. Mortality was linked through the year 2016. Hazard ratios (HR) were estimated with Cox Proportional Hazard models using a 3-year moving average exposure with a 1-year lag, with the year of follow-up as the time axis. All models were stratified by 5-year age groups, sex, and immigrant status. Covariates were based on directed acyclical graphs (DAG), and included contextual variables (airshed, community size, neighborhood dependence, neighborhood deprivation, ethnic concentration, neighborhood instability, and urban form). A second model was examined including the DAG-based covariates as well as all subject-level risk factors (income, education, marital status, indigenous identity, employment status, occupational class, and visible minority status) available in each cohort. Additional subject-level behavioral covariates (fruit and vegetable consumption, leisure exercise frequency, alcohol consumption, smoking, and body mass index [BMI]) were included in the CCHS analysis.

Sensitivity analyses evaluated adjustment for covariates and gaseous copollutants (nitrogen dioxide [NO2] and ozone [O3]), as well as exposure time windows and spatial scales. Estimates were evaluated across strata of age, sex, and immigrant status. The shape of the PM2.5-mortality association was examined by first fitting restricted cubic splines (RCS) with a large number of knots and then fitting the shape-constrained health impact function (SCHIF) to the RCS predictions and their standard errors (SE). This method provides graphical results indicating the RCS predictions, as a nonparametric means of characterizing the concentration-response relationship in detail and the resulting mean SCHIF and accompanying uncertainty as a parametric summary.

Sensitivity analyses were conducted in the CCHS cohort to evaluate the potential influence of unmeasured covariates on air pollution risk estimates. Specifically, survival models with all available risk factors were fit and compared with models that omitted covariates not available in the CanCHEC cohorts. In addition, the PM2.5 risk estimate in the CanCHEC cohort was indirectly adjusted for multiple individual-level risk factors by estimating the association between PM2.5 and these covariates within the CCHS.

RESULTS

Satellite-derived PM2.5 estimates were low and highly correlated with ground monitors. HR estimates (per 10-μg/m3 increase in PM2.5) were similar for the 1991 (1.041, 95% confidence interval [CI]: 1.016-1.066) and 1996 (1.041, 1.024-1.059) CanCHEC cohorts with a larger estimate observed for the 2001 cohort (1.084, 1.060-1.108). The pooled cohort HR estimate was 1.053 (1.041-1.065). In the CCHS an analogous model indicated a HR of 1.13 (95% CI: 1.06-1.21), which was reduced slightly with the addition of behavioral covariates (1.11, 1.04-1.18). In each of the CanCHEC cohorts, the RCS increased rapidly over lower concentrations, slightly declining between the 25th and 75th percentiles and then increasing beyond the 75th percentile. The steepness of the increase in the RCS over lower concentrations diminished as the cohort start date increased. The SCHIFs displayed a supralinear association in each of the three CanCHEC cohorts and in the CCHS cohort.

In sensitivity analyses conducted with the 2001 CanCHEC, longer moving averages (1, 3, and 8 years) and smaller spatial scales (1 km2 vs. 10 km2) of exposure assignment resulted in larger associations between PM2.5 and mortality. In both the CCHS and CanCHEC analyses, the relationship between nonaccidental mortality and PM2.5 was attenuated when O3 or a weighted measure of oxidant gases was included in models. In the CCHS analysis, but not in CanCHEC, PM2.5 HRs were also attenuated by the inclusion of NO2. Application of the indirect adjustment and comparisons within the CCHS analysis suggests that missing data on behavioral risk factors for mortality had little impact on the magnitude of PM2.5-mortality associations. While immigrants displayed improved overall survival compared with those born in Canada, their sensitivity to PM2.5 was similar to or larger than that for nonimmigrants, with differences between immigrants and nonimmigrants decreasing in the more recent cohorts.

CONCLUSIONS

In several large population-based cohorts exposed to low levels of air pollution, consistent associations were observed between PM2.5 and nonaccidental mortality for concentrations as low as 5 μg/m3. This relationship was supralinear with no apparent threshold or sublinear association.

Integration of Remote Sensing and Social Sensing Data in a Deep Learning Framework for Hourly Urban PM2.5 Mapping

Fine spatiotemporal mapping of PM2.5 concentration in urban areas is of great significance in epidemiologic research. However, both the diversity and the complex nonlinear relationships of PM2.5 influencing factors pose challenges for accurate mapping. To address these issues, we innovatively combined social sensing data with remote sensing data and other auxiliary variables, which can bring both natural and social factors into the modeling; meanwhile, we used a deep learning method to learn the nonlinear relationships. The geospatial analysis methods were applied to realize effective feature extraction of the social sensing data and a grid matching process was carried out to integrate the spatiotemporal multi-source heterogeneous data. Based on this research strategy, we finally generated hourly PM2.5 concentration data at a spatial resolution of 0.01°. This method was successfully applied to the central urban area of Wuhan in China, which the optimal result of the 10-fold cross-validation R2 was 0.832. Our work indicated that the real-time check-in and traffic index variables can improve both quantitative and mapping results. The mapping results could be potentially applied for urban environmental monitoring, pollution exposure assessment, and health risk research.

Low concentrations of fine particle air pollution and mortality in the Canadian Community Health Survey cohort

BACKGROUND

Approximately 2.9 million deaths are attributed to ambient fine particle air pollution around the world each year (PM2.5). In general, cohort studies of mortality and outdoor PM2.5 concentrations have limited information on individuals exposed to low levels of PM2.5 as well as covariates such as smoking behaviours, alcohol consumption, and diet which may confound relationships with mortality. This study provides an updated and extended analysis of the Canadian Community Health Survey-Mortality cohort: a population-based cohort with detailed PM2.5 exposure data and information on a number of important individual-level behavioural risk factors. We also used this rich dataset to provide insight into the shape of the concentration-response curve for mortality at low levels of PM2.5.

METHODS

Respondents to the Canadian Community Health Survey from 2000 to 2012 were linked by postal code history from 1981 to 2016 to high resolution PM2.5 exposure estimates, and mortality incidence to 2016. Cox proportional hazard models were used to estimate the relationship between non-accidental mortality and ambient PM2.5 concentrations (measured as a three-year average with a one-year lag) adjusted for socio-economic, behavioural, and time-varying contextual covariates.

RESULTS

In total, 50,700 deaths from non-accidental causes occurred in the cohort over the follow-up period. Annual average ambient PM2.5 concentrations were low (i.e. 5.9 μg/m3, s.d. 2.0) and each 10 μg/m3 increase in exposure was associated with an increase in non-accidental mortality (HR = 1.11; 95% CI 1.04-1.18). Adjustment for behavioural covariates did not materially change this relationship. We estimated a supra-linear concentration-response curve extending to concentrations below 2 μg/m3 using a shape constrained health impact function. Mortality risks associated with exposure to PM2.5 were increased for males, those under age 65, and non-immigrants. Hazard ratios for PM2.5 and mortality were attenuated when gaseous pollutants were included in models.

CONCLUSIONS

Outdoor PM2.5 concentrations were associated with non-accidental mortality and adjusting for individual-level behavioural covariates did not materially change this relationship. The concentration-response curve was supra-linear with increased mortality risks extending to low outdoor PM2.5 concentrations.

Low Levels of Air Pollution and Health: Effect Estimates, Methodological Challenges, and Future Directions

PURPOSE OF REVIEW

Fine particle (PM2.5) levels have been decreasing in the USA over the past decades. Our goal was to assess the current literature to characterize the association between PM2.5 and adverse health at low exposure levels.

RECENT FINDINGS

We reviewed 26 papers that examined the association between short- and long-term exposure to PM2.5 and cardio-respiratory morbidity and mortality. There is evidence suggesting that these associations are stronger at lower levels. However, there are certain methodological and interpretational limitations specific to studies of low PM2.5 levels, and further methodological development is warranted. There is strong agreement across studies that air pollution effects on adverse health are still observable at low concentrations, even well below current US standards. These findings suggest that US standards need to be reevaluated, given that further improving air quality has the potential of benefiting public health.

Combustion and friction-derived nanoparticles and industrial-sourced nanoparticles: The culprit of Alzheimer and Parkinson's diseases

Redox-active, strongly magnetic, combustion and friction-derived nanoparticles (CFDNPs) are abundant in particulate matter air pollution. Urban children and young adults with Alzheimer disease Continuum have higher numbers of brain CFDNPs versus clean air controls. CFDNPs surface charge, dynamic magnetic susceptibility, iron content and redox activity contribute to ROS generation, neurovascular unit (NVU), mitochondria, and endoplasmic reticulum (ER) damage, and are catalysts for protein misfolding, aggregation and fibrillation. CFDNPs respond to external magnetic fields and are involved in cell damage by agglomeration/clustering, magnetic rotation and/or hyperthermia. This review focus in the interaction of CFDNPs, nanomedicine and industrial NPs with biological systems and the impact of portals of entry, particle sizes, surface charge, biomolecular corona, biodistribution, mitochondrial dysfunction, cellular toxicity, anterograde and retrograde axonal transport, brain dysfunction and pathology. NPs toxicity information come from researchers synthetizing particles and improving their performance for drug delivery, drug targeting, magnetic resonance imaging and heat mediators for cancer therapy. Critical information includes how these NPs overcome all barriers, the NPs protein corona changes as they cross the NVU and the complexity of NPs interaction with soluble proteins and key organelles. Oxidative, ER and mitochondrial stress, and a faulty complex protein quality control are at the core of Alzheimer and Parkinson's diseases and NPs mechanisms of action and toxicity are strong candidates for early development and progression of both fatal diseases. Nanoparticle exposure regardless of sources carries a high risk for the developing brain homeostasis and ought to be included in the AD and PD research framework.

Evaluation of the complexity of indoor air in hospital wards based on PM2.5, real-time PCR, adenosine triphosphate bioluminescence assay, microbial culture and mass spectrometry

Background

The aim of this study was to establish a set of assessment methods suitable for evaluating the complex indoor environment of hospital wards and to ascertain the composition of bacteria and microbial ecology of hospital wards.

Methods

Colony-forming units (CFUs), PM2.5 detection, real-time PCR, and adenosine triphosphate (ATP) bioluminescence assay were employed to evaluate the complexity of indoor air in 18 wards of nine departments in a hospital and two student dormitories in a university. Subsequently, the microbial samples were quantified and identified using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS).

Results

Although the studied indices were relatively independent, the PM2.5 content was correlated with bacterial CFUs determined by passive sedimentation method, bacterial and fungal counts measured by real-time PCR, and ATP bioluminescence assay. The composition of microorganisms in the air of hospital wards differed from that in the air of student dormitories. The dominant genera in hospital wards were Staphylococcus (39.4%), Micrococcus (21.9%), Corynebacterium (11.7%), Kocuria (4.4%), Bacillus (2.9%), Streptococcus (1.6%), Moraxella (1.6%), and Enterococcus (1.3%), and the microbial ecology differed between Respiration Dept. III and other hospital departments. Additionally, 11.1 and 27.3% of bacteria in hospital wards and student dormitories were not identified, respectively.

Conclusions

Assessment of environmental quality of hospital wards should be based on comprehensive analysis with multiple indicators. There may be imbalances in the microbial diversity in the hospital wards, therefore, monitoring of the environmental quality of hospitals is important in the prevention of nosocomial infections.

Electronic supplementary material

The online version of this article (10.1186/s12879-019-4249-z) contains supplementary material, which is available to authorized users.

Long-Term Particulate Matter Exposure and Onset of Depression in Middle-Aged Men and Women

BACKGROUND

Long-term exposure to particulate matter (PM) air pollution is associated with all-cause mortality and adverse cognitive outcomes, but the association with developing depression remains inconsistent.

OBJECTIVE

Our goal was to evaluate the prospective association between PM air pollution and developing depression assessed using the Center for Epidemiological Studies Depression (CES-D) scale.

METHODS

Subjects were drawn from a prospective cohort study of 123,045 men and women free of depressive symptoms at baseline who attended regular screening exams in Seoul and Suwon, South Korea, from 2011 to 2015. Exposure to PM with an aerodynamic diameter of [Formula: see text] ([Formula: see text] and [Formula: see text], respectively) was estimated using a land-use regression model based on each subject's residential postal code. Incident depression was defined as a CES-D score [Formula: see text] during follow-up. As a sensitivity analyses, we defined incident depression using self-reports of doctor's diagnoses or use of antidepressant medications during follow-up.

RESULTS

The mean baseline 12-month concentrations of [Formula: see text] and [Formula: see text] were 50.6 (4.5) and [Formula: see text], respectively. The hazard ratios (HRs) and 95% confidence intervals (CIs) for developing depression associated with a [Formula: see text] increase in 12- and 60-month [Formula: see text] exposure were 1.11 (95% CI: 1.06, 1.16) and 1.06 (95% CI: 1.01, 1.11), respectively. The corresponding HRs for 12-month [Formula: see text] exposure was 0.96 (95% CI: 0.64, 1.43). Similar results were obtained when incident depression was identified using self-reports of doctor's diagnoses or the use of antidepressant medications.

CONCLUSION

In this large cohort study, we found a positive association between long-term exposure to outdoor [Formula: see text] air pollution and the developing depression. We did not find an association for outdoor [Formula: see text] air pollution; however, we had a much shorter follow-up for subjects' exposure to [Formula: see text]. https://doi.org/10.1289/EHP4094.