Particulate air pollution as a predictor of mortality in a prospective study of U.S. adults

Long-term exposure to air pollution on cardio-respiratory, and lung cancer mortality: a systematic review and meta-analysis

Air pollution is a major cause of specific deaths worldwide. This review article aimed to investigate the results of cohort studies for air pollution connected with the all-cause, cardio-respiratory, and lung cancer mortality risk by performing a meta-analysis. Relevant cohort studies were searched in electronic databases (PubMed/Medline, Web of Science, and Scopus). We used a random effect model to estimate the pooled relative risks (RRs) and their 95% CIs (confidence intervals) of mortality. The risk of bias for each included study was also assessed by Office of Health Assessment and Translation (OHAT) checklists. We applied statistical tests for heterogeneity and sensitivity analyses. The registration code of this study in PROSPERO was CRD42023422945. A total of 88 cohort studies were eligible and included in the final analysis. The pooled relative risk (RR) per 10 μg/m3 increase of fine particulate matter (PM2.5) was 1.080 (95% CI 1.068-1.092) for all-cause mortality, 1.058 (95% CI 1.055-1.062) for cardiovascular mortality, 1.066 (95%CI 1.034-1.097) for respiratory mortality and 1.118 (95% CI 1.076-1.159) for lung cancer mortality. We observed positive increased associations between exposure to PM2.5, PM10, black carbon (BC), and nitrogen dioxide (NO2) with all-cause, cardiovascular and respiratory diseases, and lung cancer mortality, but the associations were not significant for nitrogen oxides (NOx), sulfur dioxide (SO2) and ozone (O3). The risk of mortality for males and the elderly was higher compared to females and younger age. The pooled effect estimates derived from cohort studies provide substantial evidence of adverse air pollution associations with all-cause, cardiovascular, respiratory, and lung cancer mortality.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40201-024-00900-6.

Indoor and outdoor air quality in street corner kiosks in a large metropolitan area

Poor air quality in workplaces constitutes a great concern on human health as a good fraction of our time is spent at work. In Greece, very unique workplaces are the street corner kiosks, which are freestanding boxes placed on sidewalks next to city streets and vehicular traffic, where one can find many consumer goods. As such, its employees are exposed to both outdoor and indoor air pollutants. Very few studies have examined the occupational exposure of kiosk workers to air pollutants, and thus the magnitude of this unique indoor and outdoor exposure remains unknown. The objective of this study is to investigate and compare the levels of indoor and outdoor particulate matter (PM10 and PM2.5), ultrafine particles (UFPs) and black carbon (BC) in different kiosks located in Athens, Greece, in urban-traffic and urban-background environments. Continuous measurements of the above-mentioned pollutants were carried out on a 24-h basis over 7 consecutive days at three kiosks from September to October 2019. Indoor PM10 concentrations in the urban kiosk ranged from 19.0 to 44.0 μg/m3, PM2.5 values ranged from 14.0 to 33.0 μg/m3, whereas BC concentrations ranged from 1.2 to 7.0 μg/m3 and UFPs from almost 9.5 to 47.0 × 103 pt/cm3. Outdoor PM10 and PM2.5 measurements ranged from 29.0 to 59.0 μg/m3 and from 22.0 to 39.0 μg/m3, respectively. BC outdoor concentrations ranged from 1.1 to 2.2 μg/m3. The mean hazard quotient (HQ) for PM10 (4.9) and PM2.5 (4.7) among all participants was >1. The health risk of exposure to PM10 and PM2.5 was found to be at moderate hazard levels, although in some cases we observed HQ values higher than 10 due to high PM10 and PM2.5 concentrations in the kiosks. Overall our study indicates that people working at kiosks can be exposed to very high concentrations on particulate pollution depending on a number of factors including the traffic that strongly depends on location and the time of the day.

Model misspecification, measurement error, and apparent supralinearity in the concentration-response relationship between PM2.5 and mortality

A growing number of studies have produced results that suggest the shape of the concentration-response (C-R) relationship between PM2.5 exposure and mortality is "supralinear" such that incremental risk is higher at the lowest exposure levels than at the highest exposure levels. If the C-R function is in fact supralinear, then there may be significant health benefits associated with reductions in PM2.5 below the current US National Ambient Air Quality Standards (NAAQS), as each incremental tightening of the PM2.5 NAAQS would be expected to produce ever-greater reductions in mortality risk. In this paper we undertake a series of tests with simulated cohort data to examine whether there are alternative explanations for apparent supralinearity in PM2.5 C-R functions. Our results show that a linear C-R function for PM2.5 can falsely appear to be supralinear in a statistical estimation process for a variety of reasons, such as spatial variation in the composition of total PM2.5 mass, the presence of confounders that are correlated with PM2.5 exposure, and some types of measurement error in estimates of PM2.5 exposure. To the best of our knowledge, this is the first simulation-based study to examine alternative explanations for apparent supralinearity in C-R functions.

The stress-responsive cytotoxic effect of diesel exhaust particles on lymphatic endothelial cells

Diesel exhaust particles (DEPs) are very small (typically < 0.2 μm) fragments that have become major air pollutants. DEPs are comprised of a carbonaceous core surrounded by organic compounds such as polycyclic aromatic hydrocarbons (PAHs) and nitro-PAHs. Inhaled DEPs reach the deepest sites in the respiratory system where they could induce respiratory/cardiovascular dysfunction. Additionally, a previous study has revealed that a portion of inhaled DEPs often activate immune cells and subsequently induce somatic inflammation. Moreover, DEPs are known to localize in lymph nodes. Therefore, in this study we explored the effect of DEPs on the lymphatic endothelial cells (LECs) that are a constituent of the walls of lymph nodes. DEP exposure induced cell death in a reactive oxygen species (ROS)-dependent manner. Following exposure to DEPs, next-generation sequence (NGS) analysis identified an upregulation of the integrated stress response (ISR) pathway and cell death cascades. Both the soluble and insoluble components of DEPs generated intracellular ROS. Three-dimensional Raman imaging revealed that DEPs are taken up by LECs, which suggests internalized DEP cores produce ROS, as well as soluble DEP components. However, significant cell death pathways such as apoptosis, necroptosis, ferroptosis, pyroptosis, and parthanatos seem unlikely to be involved in DEP-induced cell death in LECs. This study clarifies how DEPs invading the body might affect the lymphatic system through the induction of cell death in LECs.

Seasonal extreme temperatures and short-term fine particulate matter increases pediatric respiratory healthcare encounters in a sparsely populated region of the intermountain western United States

BACKGROUND

Western Montana, USA, experiences complex air pollution patterns with predominant exposure sources from summer wildfire smoke and winter wood smoke. In addition, climate change related temperatures events are becoming more extreme and expected to contribute to increases in hospital admissions for a range of health outcomes. Evaluating while accounting for these exposures (air pollution and temperature) that often occur simultaneously and may act synergistically on health is becoming more important.

METHODS

We explored short-term exposure to air pollution on children's respiratory health outcomes and how extreme temperature or seasonal period modify the risk of air pollution-associated healthcare events. The main outcome measure included individual-based address located respiratory-related healthcare visits for three categories: asthma, lower respiratory tract infections (LRTI), and upper respiratory tract infections (URTI) across western Montana for ages 0-17 from 2017-2020. We used a time-stratified, case-crossover analysis with distributed lag models to identify sensitive exposure windows of fine particulate matter (PM2.5) lagged from 0 (same-day) to 14 prior-days modified by temperature or season.

RESULTS

For asthma, increases of 1 µg/m3 in PM2.5 exposure 7-13 days prior a healthcare visit date was associated with increased odds that were magnified during median to colder temperatures and winter periods. For LRTIs, 1 µg/m3 increases during 12 days of cumulative PM2.5 with peak exposure periods between 6-12 days before healthcare visit date was associated with elevated LRTI events, also heightened in median to colder temperatures but no seasonal effect was observed. For URTIs, 1 unit increases during 13 days of cumulative PM2.5 with peak exposure periods between 4-10 days prior event date was associated with greater risk for URTIs visits that were intensified during median to hotter temperatures and spring to summer periods.

CONCLUSIONS

Delayed, short-term exposure increases of PM2.5 were associated with elevated odds of all three pediatric respiratory healthcare visit categories in a sparsely population area of the inter-Rocky Mountains, USA. PM2.5 in colder temperatures tended to increase instances of asthma and LRTIs, while PM2.5 during hotter periods increased URTIs.

Long-term exposure to PM2.5 species and all-cause mortality among Medicare patients using mixtures analyses

BACKGROUND

The relationship between long-term exposure to PM2.5 and mortality is well-established; however, the role of individual species is less understood.

OBJECTIVES

In this study, we assess the overall effect of long-term exposure to PM2.5 as a mixture of species and identify the most harmful of those species while controlling for the others.

METHODS

We looked at changes in mortality among Medicare participants 65 years of age or older from 2000 to 2018 in response to changes in annual levels of 15 PM2.5 components, namely: organic carbon, elemental carbon, nickel, lead, zinc, sulfate, potassium, vanadium, nitrate, silicon, copper, iron, ammonium, calcium, and bromine. Data on exposure were derived from high-resolution, spatio-temporal models which were then aggregated to ZIP code. We used the rate of deaths in each ZIP code per year as the outcome of interest. Covariates included demographic, temperature, socioeconomic, and access-to-care variables. We used a mixtures approach, a weighted quantile sum, to analyze the joint effects of PM2.5 species on mortality. We further looked at the effects of the components when PM2.5 mass levels were at concentrations below 8 μg/m3, and effect modification by sex, race, Medicaid status, and Census division.

RESULTS

We found that for each decile increase in the levels of the PM2.5 mixture, the rate of all-cause mortality increased by 1.4% (95% CI: 1.3%-1.4%), the rate of cardiovascular mortality increased by 2.1% (95% CI: 2.0%-2.2%), and the rate of respiratory mortality increased by 1.7% (95% CI: 1.5%-1.9%). These effects estimates remained significant and slightly higher when we restricted to lower concentrations. The highest weights for harmful effects were due to organic carbon, nickel, zinc, sulfate, and vanadium.

CONCLUSIONS

Long-term exposure to PM2.5 species, as a mixture, increased the risk of all-cause, cardiovascular, and respiratory mortality.

New health index derived from oxidative potential and cell toxicity of fine particulate matter to assess its potential health effect

Toxicological data and exposure levels of fine particulate matters (PM2.5) are necessary to better understand their health effects. Simultaneous measurements of PM2.5 oxidative potential (OP) and cell toxicity in urban areas (Beijing, China and Gwangju, Korea) reveal their dependence on chemical composition. Notably, acids (Polar), benzocarboxylic acids, and Pb were the chemical components that affected both OP and cell toxicity. OP varied more significantly among different locations and seasons (winter and summer) than cell toxicity. Using the measured OP, cell toxicity, and PM2.5 concentration, a health index was developed to better assess the potential health effects of PM2.5. The health index was related to the sources of PM2.5 derived from the measured chemical components. The contributions of secondary organic aerosols and dust to the proposed health index were more significant than their contributions to PM2.5 mass. The developed regression equation was used to predict the health effect of PM2.5 without further toxicity measurements. This new index could be a valuable health metric that provides information beyond just the PM2.5 concentration level.

Reconstructing individual-level exposures in cohort analyses of environmental risks: an example with the UK Biobank

Recent developments in linkage procedures and exposure modelling offer great prospects for cohort analyses on the health risks of environmental factors. However, assigning individual-level exposures to large population-based cohorts poses methodological and practical problems. In this contribution, we illustrate a linkage framework to reconstruct environmental exposures for individual-level epidemiological analyses, discussing methodological and practical issues such as residential mobility and privacy concerns. The framework outlined here requires the availability of individual residential histories with related time periods, as well as high-resolution spatio-temporal maps of environmental exposures. The linkage process is carried out in three steps: (1) spatial alignment of the exposure maps and residential locations to extract address-specific exposure series; (2) reconstruction of individual-level exposure histories accounting for residential changes during the follow-up; (3) flexible definition of exposure summaries consistent with alternative research questions and epidemiological designs. The procedure is exemplified by the linkage and processing of daily averages of air pollution for the UK Biobank cohort using gridded spatio-temporal maps across Great Britain. This results in the extraction of exposure summaries suitable for epidemiological analyses of both short and long-term risk associations and, in general, for the investigation of temporal dependencies. The linkage framework presented here is generally applicable to multiple environmental stressors and can be extended beyond the reconstruction of residential exposures. IMPACT: This contribution describes a linkage framework to assign individual-level environmental exposures to population-based cohorts using high-resolution spatio-temporal exposure. The framework can be used to address current limitations of exposure assessment for the analysis of health risks associated with environmental stressors. The linkage of detailed exposure information at the individual level offers the opportunity to define flexible exposure summaries tailored to specific study designs and research questions. The application of the framework is exemplified by the linkage of fine particulate matter (PM2.5) exposures to the UK Biobank cohort.

Meteorology-driven PM2.5 interannual variability over East Asia

Atmospheric fine particulate matter (PM2.5) is a human health risk factor, but its ambient concentration depends on both precursor emissions and meteorology. While emission reductions are used to set PM2.5-related health policies, the effect of meteorology is often overlooked. To explore this aspect, we examined PM2.5 interannual variability (IAV) associated with meteorological parameters using the long-term simulation from the Community Earth System Model (CESM1), a global climate-chemistry model, with fixed emissions. The results are subsequently contrasted with the MERRA-2 reanalysis dataset, which inherently considers emission and meteorology effects. Over continental East Asia, the CESM1 domain-average PM2.5 IAV is 6.7 %, mainly attributed to humidity, precipitation, and ventilation variation. The grid-cell PM2.5 IAVs over southern East China are larger, up to 12 % due to the more substantial influence of El Niño-induced meteorological anomalies. Under such climate extreme, sub-regional PM2.5 concentration may occasionally exceed WHO air quality guideline levels despite the compliance of the long-term mean. The simulated PM2.5 IAV over continental East Asia is ~25 % of that derived from the MERRA-2 data, which highlights the influence of both emission and meteorology-driven variations and trends inherent in the latter. Although emission-driven variability is significant to PM2.5 IAV, in remote areas downwind of major source regions in East Asia, North America, and Western Europe, the MERRA-2 data revealed that meteorological variations contributed more to PM2.5 IAV than emission variations. Thus, when setting policies for complying with the WHO PM2.5-related air quality guideline levels, the highest annual PM2.5 associated with climate extremes should be considered instead of that based on average climate conditions.

Redox and inflammatory mechanisms linking air pollution particulate matter with cardiometabolic derangements

Air pollution is the largest environmental risk factor for disease and premature death. Among the different components that are present in polluted air, fine particulate matter below 2.5 μm in diameter (PM2.5) has been identified as the main hazardous constituent. PM2.5 mainly arises from fossil fuel combustion during power generation, industrial processes, and transportation. Exposure to PM2.5 correlates with enhanced mortality risk from cardiovascular diseases (CVD), such as myocardial infarction and stroke. Over the last decade, it has been increasingly suggested that PM2.5 affects CVD already at the stage of risk factor development. Among the multiple biological mechanisms that have been described, the interplay between oxidative stress and inflammation has been consistently highlighted as one of the main drivers of pulmonary, systemic, and cardiovascular effects of PM2.5 exposure. In this context, PM2.5 uptake by tissue-resident immune cells in the lung promotes oxidative and inflammatory mediators release that alter tissue homeostasis at remote locations. This pathway is central for PM2.5 pathogenesis and might account for the accelerated development of risk factors for CVD, including obesity and diabetes. However, transmission and end-organ mechanisms that explain PM2.5-induced impaired function in metabolic active organs are not completely understood. In this review, the main features of PM2.5 physicochemical characteristics related to PM2.5 ability to induce oxidative stress and inflammation will be presented. Hallmark and recent epidemiological and interventional studies will be summarized and discussed in the context of current air quality guidelines and legislation, knowledge gaps, and inequities. Lastly, mechanistic studies at the intersection between redox metabolism, inflammation, and function will be discussed, with focus on heart and adipose tissue alterations. By offering an integrated analysis of PM2.5-induced effects on cardiometabolic derangements, this review aims to contribute to a better understanding of the pathogenesis and potential interventions of air pollution-related CVD.

Environmental pollutants increase the risks of acute exacerbation in patients with chronic airway disease

Objective

Respiratory infections are a common cause of acute exacerbations in patients with chronic airway disease, however, environmental factors such as air pollution can also contribute to these exacerbations. The study aimed to determine the correlation between pollutant levels and exacerbation risks in areas exposed to environmental pollution sources.

Methods

From 2015 to 2016, a total of 788 patients with chronic airway diseases were enrolled in a study. Their medical records, including hospital visits due to acute exacerbations of varying severity were analyzed. Additionally, data on daily pollutant levels from the Air Quality Monitoring Network from 2014 to 2016 was also collected and analyzed.

Results

Patients with chronic airway disease and poor lung function (FEV1 < 50% or obstructive ventilatory defect) have a higher risk of severe acute exacerbations and are more likely to experience more than two severe acute exacerbations within a year. The study found that in areas exposed to environmental pollution sources, there is a significant correlation between NO2, O3, and humidity with the main causes of severe acute exacerbation. When the levels of NO2 were higher than 16.65 ppb, O3 higher than 35.65 ppb, or humidity higher than 76.95%, the risk of severe acute exacerbation in patients with chronic airway disease increased.

Conclusion

Acute exacerbations of chronic airway disease can be triggered by both the underlying disease state and the presence of air pollution. Computer simulations and early warning systems should be developed to predict acute exacerbations of chronic airway disease based on dynamic changes in air pollution.

Effectiveness of Tap Water in Reducing the Generation of Ultrafine Wear Particles from the Wheel-Rail Contact by Eliminating the Water Vapor Effect

This study aimed to assess the impact of tap water application on reducing the generation of ultrafine particles from the wheel-rail contact using a twin-disk rig under dry and wet conditions, with train velocities of 45 and 80 km/h. A small amount of 0.3 L/min tap water was applied at the wheel-rail contact, and a diffusion dryer was used to eliminate water vapor. The Fast Mobility Particle Sizer measured the number concentration (NC) of nano-sized wear particles in the range of 6 to 560 nm. The tap water application method effectively reduced the NC of ultrafine and fine particles by 67-72% and 86-88%, respectively. Positive reduction rates were observed for all diameters at 45 km/h and for most diameters, except for approximately 70 nm and 80 nm, at 80 km/h. Even with a small amount of water, this approach successfully decreased nano-sized wear particle generation. However, the potential influence of mineral crystals in tap water on NC requires further investigation. Overall, this method shows promise for enhancing air quality and public health by mitigating nano-sized wear particle generation in subway systems.

Seasonal extreme temperatures and short-term fine particulate matter increases child respiratory hospitalizations in a sparsely populated region of the intermountain western United States

Background

Western Montana, USA, experiences complex air pollution patterns with predominant exposure sources from summer wildfire smoke and winter wood smoke. In addition, climate change related temperatures events are becoming more extreme and expected to contribute to increases in hospital admissions for a range of health outcomes. Few studies have evaluated these exposures (air pollution and temperature) that often occur simultaneously and may act synergistically on health.

Methods

We explored short-term exposure to air pollution on childhood respiratory health outcomes and how extreme temperature or seasonal period modify the risk of air pollution-associated hospitalizations. The main outcome measure included all respiratory-related hospital admissions for three categories: asthma, lower respiratory tract infections (LRTI), and upper respiratory tract infections (URTI) across western Montana for all individuals aged 0-17 from 2017-2020. We used a time-stratified, case-crossover analysis and distributed lag models to identify sensitive exposure windows of fine particulate matter (PM2.5) lagged from 0 (same-day) to 15 prior-days modified by temperature or season.

Results

Short-term exposure increases of 1 μg/m3 in PM2.5 were associated with elevated odds of all three respiratory hospital admission categories. PM2.5 was associated with the largest increased odds of hospitalizations for asthma at lag 7-13 days [1.87(1.17-2.97)], for LRTI at lag 6-12 days [2.18(1.20-3.97)], and for URTI at a cumulative lag of 13 days [1.29(1.07-1.57)]. The impact of PM2.5 varied by temperature and season for each respiratory outcome scenario. For asthma, PM2.5 was associated most strongly during colder temperatures [3.11(1.40-6.89)] and the winter season [3.26(1.07-9.95)]. Also in colder temperatures, PM2.5 was associated with increased odds of LRTI hospitalization [2.61(1.15-5.94)], but no seasonal effect was observed. Finally, 13 days of cumulative PM2.5 prior to admissions date was associated with the greatest increased odds of URTI hospitalization during summer days [3.35(1.85-6.04)] and hotter temperatures [1.71(1.31-2.22)].

Conclusions

Children's respiratory-related hospital admissions were associated with short-term exposure to PM2.5. PM2.5 associations with asthma and LRTI hospitalizations were strongest during cold periods, whereas associations with URTI were largest during hot periods.

Classification

environmental public health, fine particulate matter air pollution, respiratory infections.

Multiphase Kinetic Modeling of Air Pollutant Effects on Protein Modification and Nitrotyrosine Formation in Epithelial Lining Fluid

Exposure to ambient air pollution is a major risk factor for human health. Inhalation of air pollutants can enhance the formation of reactive species in the epithelial lining fluid (ELF) of the respiratory tract and can lead to oxidative stress and oxidative damage. Here, we investigate the chemical modification of proteins by reactive species from air pollution and endogenous biological sources using an extended version of the multiphase chemical kinetic model KM-SUB-ELF 2.0 with a detailed mechanism of protein modification. Fine particulate matter (PM2.5) and nitrogen dioxide (•NO2) act synergistically and increase the formation of nitrotyrosine (Ntyr), a common biomarker of oxidative stress. Ozone (O3) is found to be a burden on the antioxidant defense system but without substantial influence on the Ntyr concentration. In simulations with low levels of air pollution, the Ntyr concentration in the ELF is consistent with the range of literature values for bronchoalveolar lavage fluid from healthy individuals. With high levels of air pollution, however, we obtain strongly elevated Ntyr concentrations. Our model analysis shows how chemical reactions of air pollutants can modify proteins and thus their functionality in the human body, elucidating a molecular pathway that may explain air pollutant effects on human health.

Long-term exposure to ambient fine particulate components and leukocyte epigenome-wide DNA Methylation in older men: the Normative Aging Study

BACKGROUND

Epigenome-wide association studies of ambient fine particulate matter (PM2.5) have been reported. However, few have examined PM2.5 components (PMCs) and sources or included repeated measures. The lack of high-resolution exposure measurements is the key limitation. We hypothesized that significant changes in DNA methylation might vary by PMCs and the sources.

METHODS

We predicted the annual average of 14 PMCs using novel high-resolution exposure models across the contiguous U.S., between 2000-2018. The resolution was 50 m × 50 m in the Greater Boston Area. We also identified PM2.5 sources using positive matrix factorization. We repeatedly collected blood samples and measured leukocyte DNAm with the Illumina HumanMethylation450K BeadChip in the Normative Aging Study. We then used median regression with subject-specific intercepts to estimate the associations between long-term (one-year) exposure to PMCs / PM2.5 sources and DNA methylation at individual cytosine-phosphate-guanine CpG sites. Significant probes were identified by the number of independent degrees of freedom approach, using the number of principal components explaining > 95% of the variation of the DNA methylation data. We also performed regional and pathway analyses to identify significant regions and pathways.

RESULTS

We included 669 men with 1,178 visits between 2000-2013. The subjects had a mean age of 75 years. The identified probes, regions, and pathways varied by PMCs and their sources. For example, iron was associated with 6 probes and 6 regions, whereas nitrate was associated with 15 probes and 3 regions. The identified pathways from biomass burning, coal burning, and heavy fuel oil combustion sources were associated with cancer, inflammation, and cardiovascular diseases, whereas there were no pathways associated with all traffic.

CONCLUSIONS

Our findings showed that the effects of PM2.5 on DNAm varied by its PMCs and sources.

Long-term exposure to low-concentration PM2.5 and heart disease in older men in Perth, Australia: The Health in Men Study

Exposure to particulate matter with an aerodynamic diameter less than or equal to 2.5 μm (PM2.5) is associated with increased risk of heart disease, but less is known about the relationship at low concentrations. This study aimed to determine the dose-response relationship between long-term PM2.5 exposure and risk of incident ischemic heart disease (IHD), incident heart failure (HF), and incident atrial fibrillation (AF) in older men living in a region with relatively low ambient air pollution.

Methods

PM2.5 exposure was estimated for 11,249 older adult males who resided in Perth, Western Australia and were recruited from 1996 to 1999. Participants were followed until 2018 for the HF and AF outcomes, and until 2017 for IHD. Cox-proportional hazards models, using age as the analysis time, and adjusting for demographic and lifestyle factors were used. PM2.5 was entered as a restricted cubic spline to model nonlinearity.

Results

We observed a mean PM2.5 concentration of 4.95 μg/m3 (SD 1.68 μg/m3) in the first year of recruitment. After excluding participants with preexisting disease and adjusting for demographic and lifestyle factors, PM2.5 exposure was associated with a trend toward increased incidence of IHD, HF, and AF, but none were statistically significant. At a PM2.5 concentration of 7 μg/m3 the hazard ratio for incident IHD was 1.04 (95% confidence interval [CI] = 0.86, 1.25) compared with the reference category of 1 μg/m3.

Conclusions

We did not observe a significant association between long-term exposure to low-concentration PM2.5 air pollution and IHD, HF, or AF.

An integrated watershed-scale framework to model nitrogen transport and transformations

Excess nitrogen in water bodies is associated with a number of environmental problems, including hypoxia and eutrophication. Originating from anthropogenic activities such as fertilizer application, and influenced by watershed characteristics such as the structure of the drainage network, stream discharge, temperature, and soil moisture, factors influencing nitrogen transport and transformation are many and interconnected. This paper describes the development and application of a process-oriented nitrogen model based on the modeling framework of PAWS (Process-based Adaptive Watershed Simulator) that can describe coupled hydrologic, thermal and nutrient processes. The integrated model was tested for an agricultural watershed with complex land use, namely the Kalamazoo River watershed in Michigan, USA. Nitrogen transport and transformations on the landscape were modeled by representing multiple sources and processes (fertilizer/manure application, point sources, atmospheric deposition, nitrogen retention and removal in wetlands and other lowland storage, etc.) across multiple hydrologic domains (streams, groundwater, soil water). The coupled model provides a tool to examine nitrogen budgets and to quantify the impacts of human activities and agricultural practices on the riverine export of nitrogen species. Model results indicate that the river network removed approximately 5.96 % of the total anthropogenic nitrogen input to the watershed, and that the riverine export of nitrogen accounted for 29.22 % of the total anthropogenic inputs during 2004-2009 while the groundwater contribution of nitrogen to the rivers during the same period was found to be 18.53 % highlighting the important role of groundwater within the watershed.

Long-term exposure to traffic-related air pollution and non-accidental mortality: A systematic review and meta-analysis

BACKGROUND

The health effects of traffic-related air pollution (TRAP) continue to be of important public health interest across the globe. Following its 2010 review, the Health Effects Institute appointed a new expert Panel to systematically evaluate the epidemiological evidence regarding the associations between long-term exposure to TRAP and selected health outcomes. This paper describes the main findings of the systematic review on non-accidental mortality.

METHODS

The Panel used a systematic approach to conduct the review. An extensive search was conducted of literature published between 1980 and 2019. A new exposure framework was developed to determine whether a study was sufficiently specific to TRAP, which included studies beyond the near-roadway environment. We performed random-effects meta-analysis when at least three estimates were available of an association between a specific exposure and outcome. We evaluated confidence in the evidence using a modified Office of Health Assessment and Translation (OHAT) approach, supplemented with a broader narrative synthesis.

RESULTS

Thirty-six cohort studies were included. Virtually all studies adjusted for a large number of individual and area-level covariates-including smoking, body mass index, and individual and area-level socioeconomic status-and were judged at a low or moderate risk for bias. Most studies were conducted in North America and Europe, and a few were based in Asia and Australia. The meta-analytic summary estimates for nitrogen dioxide, elemental carbon and fine particulate matter-pollutants with more than 10 studies-were 1.04 (95% CI 1.01, 1.06), 1.02 (1.00, 1.04) and 1.03 (1.01, 1.05) per 10, 1 and 5 µg/m³, respectively. Effect estimates are interpreted as the relative risk of mortality when the exposure differs with the selected increment. The confidence in the evidence for these pollutants was judged as high, because of upgrades for monotonic exposure-response and consistency across populations. The consistent findings across geographical regions, exposure assessment methods and confounder adjustment resulted in a high confidence rating using a narrative approach as well.

CONCLUSIONS

The overall confidence in the evidence for a positive association between long-term exposure to TRAP and non-accidental mortality was high.

Does the Level of Air Pollution Affect the Incidence of Lung Adenocarcinoma in South-Eastern Poland?

The aim of this study was to assess the association of long-term exposure to particulate matter with aerodynamic diameter, PM2.5, PM10, NO2 and SO2 as well as CO, with lung adenocarcinoma (AD) in south-east Poland for the years from 2004 to 2014. The study group consisted of 4296 patients with lung adenocarcinoma and the level of selected pollutants. To analyse the data, a standard statistics for cohort data, that is a risk ratio (RR), was used. The dependencies occurring in the distribution of pollutants and cancer incidence were examined using Moran's I correlation coefficient. The current study suggests that PM10, NO2 and SO2 exposure as an air pollutant may increase female lung adenocarcinoma incidence. In men, the increased risk of adenocarcinoma lung cancer is affected by SO2 and PM10. A high morbidity rate in urban areas and suburbs may be connected with commuting from moderately polluted living areas to highly polluted working areas.

Ecological Study on Global Health Effects due to Source-Specific Ambient Fine Particulate Matter Exposure

Ambient air pollution of fine particulate matter with diameters less than 2.5 μm (PM2.5) is associated with millions of premature deaths per year, recognized as a leading global health concern. The dose-response relation between ambient PM2.5 exposure and mortality risk is the most fundamental information for assessments of the health effects of PM2.5. The existing dose-response relations were generally developed based on the assumption of equal contribution to toxicity from various sources. However, the sources of PM2.5 may significantly influence health effects. In this study, we conducted an ecological study to investigate the global long-term correlation between source-specific PM2.5 exposure and cause-specific mortality risk (SPECM) based on the regional aggregate data of the publically available official health databases from 528 regions worldwide with a total registered population of 3.2 billion. The results provided preliminary epidemiological evidence for differing chronic health effects across various sources. The relative mortality risks of lung cancer and circulatory diseases were closely correlated with the primary emissions from industrial and residential combustion sources. Chronic lower respiratory diseases were mostly associated with the mass concentration of particulate matter.

A satellite-driven model to estimate long-term particulate sulfate levels and attributable mortality burden in China

Ambient fine particulate matter (PM2.5) pollution is a major environmental and public health challenge in China. In the recent decade, the PM2.5 level has decreased mainly driven by reductions in particulate sulfate as a result of large-scale desulfurization efforts in coal-fired power plants and industrial facilities. Emerging evidence also points to the differential toxicity of particulate sulfate affecting human health. However, estimating the long-term spatiotemporal trend of sulfate is difficult because a ground monitoring network of PM2.5 constituents has not been established in China. Spaceborne sensors such as the Multi-angle Imaging SpectroRadiometer (MISR) instrument can provide complementary information on aerosol size and type. With the help of state-of-the-art machine learning techniques, we developed a sulfate prediction model under support from available ground measurements, MISR-retrieved aerosol microphysical properties, and atmospheric reanalysis data at a spatial resolution of 0.1°. Our sulfate model performed well with an out-of-bag cross-validationR2 of 0.68 at the daily level and 0.93 at the monthly level. We found that the national mean population-weighted sulfate concentration was relatively stable before the Air Pollution Prevention and Control Action Plan was enforced in 2013, ranging from 10.4 to 11.5 µg m-3. But the sulfate level dramatically decreased to 7.7 µg m-3 in 2018, with a change rate of -28.7 % from 2013 to 2018. Correspondingly, the annual mean total non-accidental and cardiopulmonary deaths attributed to sulfate decreased by 40.7 % and 42.3 %, respectively. The long-term, full-coverage sulfate level estimates will support future studies on evaluating air quality policies and understanding the adverse health effect of particulate sulfate.

Spatial and seasonal variations in the carbon and lead isotopes of PM2.5 in air of residential buildings and their applications for source identification

To understand isotope distributions of PM_2.5 in residential buildings and apply them for source identification, carbon (δ¹³C) and lead (Pb) isotope ratios in indoor and outdoor air of residential buildings were analyzed. Moreover, factor analysis (FA) was employed to investigate sources, which were compared through isotopic analyses. The average δ¹³C values of indoor air are -26.94 ± 1.22‰ and -27.04 ± 0.44‰ in warm (August to October) and cold (February to March) seasons, respectively, and the corresponding values for outdoor air are -26.77 ± 0.54‰ and -26.57 ± 0.39‰. The average ²⁰⁶Pb/²⁰⁷Pb (²⁰⁸Pb/²⁰⁷Pb) ratios of indoor air are 1.1584 ± 0.0091 (2.4309 ± 0.0125) and 1.1529 ± 0.0032 (2.4227 ± 0.0081) in warm and cold seasons, respectively, and the corresponding values for outdoor air are 1.1594 ± 0.0069 (2.4374 ± 0.0103) and 1.1538 ± 0.0077 (2.4222 ± 0.0085). Seasonal variation in δ¹³C values or Pb isotope ratios of indoor air was not significant, and similar results were obtained for outdoor air. Significant differences were not observed between δ¹³C values or Pb isotope ratios of indoor and outdoor air. Traffic emission is the major contributor to indoor and outdoor PM_2.5 based on isotopic analyses; this result was consistent with the results of FA. The δ¹³C values of indoor air in buildings with poor ventilation conditions were significantly lighter than those of outdoor air. In summary, the spatial and seasonal variations of isotopes were similar in residential buildings, which can be used to identify sources of indoor PM_2.5, and ventilation condition is an influencing factor.

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.

The impacts of reduction in ambient fine particulate (PM2.5) air pollution on life expectancy in Taiwan

Fine particulate matter, particles less than 2.5 um in diameter (PM_2.5), is an important environmental human health factor to consider. The long- and short-term influence of PM_2.5 on health has been extensively studied in relation to many health outcomes, although few investigations examined the consequences of chronic ambient PM_2.5 on life expectancy, which constitutes an important gauge of public human health status. Therefore, the aim of this study was to investigate the effects of reducing ambient PM_2.5 levels in Taiwan on life expectancy there from 2000 to 2020. Officially reported island-wide annually average concentrations of ambient PM_2.5, county-level life expectancies, and demographic and socioeconomic and proxy variable were collected for the prevalence of smoking from various national public agencies and organizations, since variables these might potentially confound life expectancy results. The relationship between changes in ambient PM_2.5 levels and life expectancy were determined using linear regression. Data demonstrated that counties with greater reductions in ambient PM_2.5 concentrations were associated with higher life expectancies. Adjusting for alterations in demographic and socioeconomic variables and proxy parameter, the prevalence of smoking data from a multiple regression model, it was found that a 0.3-year rise in life expectancy was noted for each 10 ug/m³ decrease in PM_2.5 in those counties. Our findings show that reducing ambient PM_2.5 levels play an important role for prolongation of life expectancy in Taiwan.

Lung cancer as adverse health effect by indoor radon exposure in China from 2000 to 2020: A systematic review and meta-analysis

Indoor radon exposure is thought to be associated with adverse health effect as lung cancer. Lung cancer incidences in China have been the highest worldwide during the past two decades. It is important to quantitively address indoor radon exposure and its health effect, especially in countries like China. In this paper, we have conducted a meta-analysis based on indoor radon and its health effect studies from a systematic review between 2000 and 2020. A total of 8 studies were included for lung cancer. We found that the relative risk (RR) was 1.01 (95% CI: 1.01-1.02) per 10 Bq/m³ increase of indoor radon for lung cancer in China. The subgroup analysis found no significant difference between the conclusions from the studies from China and other regions. The health effect of indoor radon exposure is relatively consistent for the low-exposure and high-exposure groups in the subgroup analysis. With a better understanding of exposure level of indoor radon, the outcomes and conclusions of this study will provide supports for next phase of researches on estimation of environmental burden of disease by indoor radon exposures in countries like China.

Aerosol mass and size-resolved metal content in urban Bangkok, Thailand

Inhalable particulate matter (PM) is a health concern, and people living in large cities such as Bangkok are exposed to high concentrations. This exposure has been linked to respiratory and cardiac diseases and cancers of the lung and brain. Throughout 2018, PM was measured in northern Bangkok near a toll road (13.87°N, 100.58°E) covering all three seasons (cool, hot and rainy). PM₁₀ was measured in 24- and 72-h samples. On selected dates aerodynamic size and mass distribution were measured as 3-day samples from a fixed 5th floor inlet. Particle number concentration was measured from the 5th floor inlet and in roadside survey measurements. There was a large fraction of particle number concentration in the sub-micron range, which showed the greatest variability compared with larger fractions. Metals associated with combustion sources were most found on the smaller size fraction of particles, which may have implications for associated adverse health outcomes because of the likely location of aerosol deposition in the distal airways of the lung. PM₁₀ samples varied between 30 and 100 μg m^-3, with highest concentrations in the cool season. The largest metal fractions present in the PM₁₀ measurements were calcium, iron and magnesium during the hot season with average airborne concentrations of 13.2, 3.6 and 2.0 μg m^-3, respectively. Copper, zinc, arsenic, selenium, molybdenum, cadmium, antimony and lead had large non-crustal sources. Principal component analysis (PCA) identified likely sources of the metals as crustal minerals, tailpipe exhaust and non-combustion traffic. A health risk analysis showed a higher risk of both carcinogenic and non-carcinogenic health effects in the drier seasons than the wet season due to ingestion of nickel, arsenic, cadmium and lead.

Lagrangian particle dispersion (HYSPLIT) model analysis of the sea breeze case with extreme mean daily PM10 concentration in Split, Croatia

The case of a sea breeze where the mean daily PM10 concentration reached the recommended limit value for human health for the period from 2007 to 2009 at the air quality station AMS3 Split-1 in Split, Croatia, is analysed. The Pennsylvania State University/National Center for Atmospheric Research (PSU/NCAR) atmospheric model MM5 and the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model are used to simulate the lower atmospheric flow and dispersion within 100 km from the coast. The simulation is made with four point sources in the hinterland of Split: Kaštel-Sućurac, Solin and Klis, where the local cement plants are located, and Vranjic, where an asbestos-cement plant is located. Statistical analysis and rotational statistics showed good agreement of the measurement data with the modelled wind speed and direction at 10 m and temperature at 2 m height. The backward trajectories show that the pollutants are caught in the sea breeze circulation from all sources and in the early afternoon the plume is placed over parts of Split from all sources. From the peak concentrations during the selected day, it can be concluded that pollution from Kaštel-Sućurac had the greatest impact on the high PM10 concentrations measured at the Split-1 station.

Evaluation of ·OH Production Potential of Particulate Matter (PM2.5) Collected on TiO2-Supporting Quartz Filters

Oxidative stress induced by fine particulate matter 2.5 (PM2.5) is a potential cause of adverse health effects owing to the production of reactive oxygen species (ROS). Air filtration is a key technology for preventing exposure to particulate contaminations; however, particulate matter trapped by filters has the potential risk of human contact with condensed PM2.5. Thus, this study aims to reduce the hydroxyl radical (·OH) production potential of PM2.5 collected on such TiO2-supporting quartz filters. The ·OH production potential was evaluated for PM2.5, which was collected in Kanagawa, Japan, using a terephthalate assay coupled with flow injection analysis. Although the PM2.5 levels at the sampling site were not severe, the PM2.5 samples exhibited ·OH production potential, which was mostly attributed to organic aerosols. The effect was verified using a TiO2-supporting quartz filter for the collection and subsequent degradation of PM2.5. The ·OH production potential was significantly reduced from 0.58 ± 0.40 pmol/(min m3) to 0.22 ± 0.13 pmol/(min m3) through ultraviolet irradiation for 24 h. This suggests that the photocatalytic reaction of the TiO2 filter is effective in reducing the ·OH production potential of PM2.5.

Characteristics of Resuspended Road Dust with Traffic and Atmospheric Environment in South Korea

Characterizing the influencing factors of resuspended dust on paved roads according to the atmospheric environment and traffic conditions is important to provide a basis for road atmospheric pollution control measures suitable for various road environments in the future. This study attempts to identify factors in the concentration of resuspended dust according to the level of road dust loading and PM10 emission characteristics according to atmospheric weather environment and traffic conditions using real-time vehicle-based resuspended PM10 concentration measuring equipment. This study mainly focuses on the following main topics: (1) the increased level of resuspended dust according to vehicle speed and silt loading (sL) level; (2) difference between atmospheric pollution at adjacent monitoring station concentration and background concentration levels on roads due to atmospheric weather changes; (3) the correlation between traffic and weather factors with resuspended dust levels; (4) the evaluation of resuspended dust levels by road section. Based on the results, the necessity of research to more appropriately set the focus of analysis in order to characterize the resuspended dust according to changes in the traffic and weather environment in urban areas is presented.

Impact of limited residential address on health effect analysis of predicted air pollution in a simulation study

BACKGROUND

Recent epidemiological studies of air pollution have adopted spatially-resolved prediction models to estimate air pollution concentrations at people's homes. However, the benefit of these models was limited in many studies that used existing health data relying on incomplete addresses resulting from confidentiality concerns or lack of interest when designed.

OBJECTIVE

This simulation study aimed to understand the impact of incomplete addresses on health effect estimation based on the association between particulate matter with diameter ≤10 µm (PM₁₀) and low birth weight (LBW).

METHODS

We generated true annual average concentrations of PM₁₀ at 46,007 mothers' homes and their LBW status, using the parameters obtained from our data analysis and a previous study in Seoul, Korea. Then, we hypothesized that mothers' address information is limited to the district and compared the properties of their health effect estimates of PM₁₀ with those using complete addresses. We performed this comparison across eight environmental scenarios that represent various spatial distributions of PM₁₀ and nine exposure prediction methods that provide different sets of predicted PM₁₀ concentrations of mothers.

RESULTS

We observed increased bias and root mean square error consistently across all environmental scenarios and prediction methods using incomplete addresses compared to complete addresses. However, the bias related to incomplete addresses decreased when we used population-representative exposures averaged to the district from predicted PM₁₀ at census tract centroids.

SIGNIFICANCE

Our simulation study suggested that individual exposure estimated by prediction approaches and averaged across population-representative points can provide improved accuracy in health effect estimates when complete address data are unavailable.

IMPACT STATEMENT

Our simulation study focused on a common and practical challenge of limited address information in air pollution epidemiology, and investigated its impact on health effect analysis. Cohort studies of air pollution have developed advanced exposure prediction model to allow the estimation of individual-level long-term air pollution concentrations at people's addresses. However, it is common that address information of existing health data is available at the coarse spatial scale such as city, district, and zip code area. Our findings can help understand the possible consequences of limited address information and provide practical guidance in achieving the accuracy in health effect analysis.

Mortality Attributable to Long-Term Exposure to Ambient Fine Particulate Matter: Insights from the Epidemiologic Evidence for Understudied Locations

Epidemiologic cohort studies have consistently demonstrated that long-term exposure to ambient fine particles (PM_2.5) is associated with mortality. Nevertheless, extrapolating results to understudied locations may involve considerable uncertainty. To explore this issue, this review discusses the evidence for (i) the associated risk of mortality, (ii) the shape of the concentration-response function, (iii) a causal interpretation, and (iv) how the source mix/composition of PM_2.5 and population characteristics may alter the effect. The accumulated evidence suggests the following: (i) In the United States, the change in all-cause mortality risk per μg/m³ is about 0.8%. (ii) The concentration-response function appears nonlinear. (iii) Causation is overwhelmingly supported. (iv) Fossil fuel combustion-related sources are likely more toxic than others, and age, race, and income may modify the effect. To illustrate the use of our findings in support of a risk assessment in an understudied setting, we consider Kuwait. However, given the complexity of this relationship and the heterogeneity in reported effects, it is unreasonable to think that, in such circumstances, point estimates can be meaningful. Consequently, quantitative probabilistic estimates, which cannot be derived objectively, become essential. Formally elicited expert judgment can provide such estimates, and this review provides the evidence to support an elicitation.

Cardiopulmonary Mortality and Fine Particulate Air Pollution by Species and Source in a National U.S. Cohort

The purpose of this study was to estimate cardiopulmonary mortality associations for long-term exposure to PM_2.5 species and sources (i.e., components) within the U.S. National Health Interview Survey cohort. Exposures were estimated through a chemical transport model for six species (i.e., elemental carbon (EC), primary organic aerosols (POA), secondary organic aerosols (SOA), sulfate (SO₄), ammonium (NH₄), nitrate (NO₃)) and five sources of PM_2.5 (i.e., vehicles, electricity-generating units (EGU), non-EGU industrial sources, biogenic sources (bio), "other" sources). In single-pollutant models, we found positive, significant (p < 0.05) mortality associations for all components, except POA. After adjusting for remaining PM_2.5 (total PM_2.5 minus component), we found significant mortality associations for EC (hazard ratio (HR) = 1.36; 95% CI [1.12, 1.64]), SOA (HR = 1.11; 95% CI [1.05, 1.17]), and vehicle sources (HR = 1.06; 95% CI [1.03, 1.10]). HRs for EC, SOA, and vehicle sources were significantly larger in comparison to those for remaining PM_2.5 (per unit μg/m³). Our findings suggest that cardiopulmonary mortality associations vary by species and source, with evidence that EC, SOA, and vehicle sources are important contributors to the PM_2.5 mortality relationship. With further validation, these findings could facilitate targeted pollution regulations that more efficiently reduce air pollution mortality.

Comparison of metropolitan cities for mortality rates attributed to ambient air pollution using the AirQ model

In the present study, the air pollution dynamics of the metropolitan cities of Balıkesir, Bursa, Istanbul, Kocaeli, Sakarya and Tekirdağ in the Marmara Region, which is the geographical region with the highest urban and industrial activity in Turkey, were examined for the time period between 2016 and 2019. Annual changes in the cities in terms of air pollution, which was examined with a focus on the PM_2.5 parameter as indicated by United Nations (UN) Sustainable Development Goals (SDGs); differences in the cities by years; and the seasonal changes in air pollution in the cities were investigated. Additionally, mortality rates attributed to air pollution were calculated with the AirQ + software based on integrated exposure-response function recommended by the World Health Organization (WHO) and the UN using city-scale statistics of fatal disease cases that can be attributed to air pollution. It was determined that all cities in the Marmara Region study area exceeded the limit PM_2.5 values specified by the European Union (EU) in the years 2016, 2017 and 2018 while only Kocaeli and Tekirdağ were below the limit values in 2019. The limit values specified by the WHO were exceeded in all cities in each year. A total of 46,920 premature deaths attributed to the exceedance of WHO limit values were calculated for the years 2016, 2017, 2018 and 2019 with 11,895, 13,853, 11,748 and 9,429, respectively. Determining national limit values for the PM_2.5 parameter, which is among the most important factors of air pollution, and monitoring it in a sustainable manner using a sufficient number of well-equipped stations is of great importance. This way, national, regional and urban action plans regarding the impact of air pollution on human health, as indicated by UN SDGs, can be prepared.

Applied Machine Learning in Industry 4.0: Case-Study Research in Predictive Models for Black Carbon Emissions

Industry 4.0 constitutes a major application domain for sensor data analytics. Industrial furnaces (IFs) are complex machines made with special thermodynamic materials and technologies used in industrial production applications that require special heat treatment cycles. One of the most critical issues while operating IFs is the emission of black carbon (EoBC), which is due to a large number of factors such as the quality and amount of fuel, furnace efficiency, technology used for the process, operation practices, type of loads and other aspects related to the process conditions or mechanical properties of fluids at furnace operation. This paper presents a methodological approach to predict EoBC during the operation of IFs with the use of predictive models of machine learning (ML). We make use of a real data set with historical operation to train ML models, and through evaluation with real data we identify the most suitable approach that best fits the characteristics of the data set and implementation constraints in real production environments. The evaluation results confirm that it is possible to predict the undesirable EoBC well in advance, by means of a predictive model. To the best of our knowledge, this paper is the first approach to detail machine-learning concepts for predicting EoBC in the IF industry.

Monitoring in vivo behavior of size-dependent fluorescent particles as a model fine dust

Background

There has been growing concern regarding the impact of air pollution, especially fine dust, on human health. However, it is difficult to estimate the toxicity of fine dust on the human body because of its diverse effects depending on the composition and environmental factors.

Results

In this study, we focused on the difference in the biodistribution of fine dust according to the size distribution of particulate matter after inhalation into the body to predict its impact on human health. We synthesized Cy7-doped silica particulate matters (CSPMs) having different particle sizes and employed them as model fine dust, and studied their whole-body in vivo biodistribution in BALB/c nude mice. Image-tracking and quantitative and qualitative analyses were performed on the ex vivo organs and tissues. Additionally, flow cytometric analysis of single cells isolated from the lungs was performed. Smaller particles with a diameter of less than 100 nm (CSPM0.1) were observed to be removed relatively rapidly from the lungs upon initial inhalation. However, they were confirmed to accumulate continuously over 4 weeks of observation. In particular, smaller particles were found to spread rapidly to other organs during the early stages of inhalation.

Conclusions

The results show in vivo behavioral differences that arisen from particle size through mouse experimental model. Although these are far from the human inhalation studies, it provides information that can help predict the effect of fine dust on human health. This study might provide with insights on association between CSPM0.1 accumulation in several organs including the lungs and adverse effect to underlying diseases in the organs.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s12951-022-01419-4.

Air Pollution, Socioeconomic Status, and Age-Specific Mortality Risk in the United States

IMPORTANCE

Prior studies on the association between fine particulate matter with diameters 2.5 μm or smaller (PM2.5) and probability of death have not applied multilevel analysis disaggregating data for US census tract, states, and counties, nor tested its interaction by socioeconomic status (SES). Such an approach could provide a more refined identification and targeting of populations exposed to increased risk from PM2.5.

OBJECTIVE

To assess the association between PM2.5 and age-specific mortality risk (ASMR) using disaggregated data at the census tract level and evaluate such association according to census tract SES.

DESIGN, SETTING, AND PARTICIPANTS

This nationwide cross-sectional study used a linkage of 3 different data sets. ASMR for the period of 2010 to 2015 was obtained from the National Center for Health Statistic, SES data covering a period from 2006 to 2016 came from the American Community Survey, and mean PM2.5 exposure levels from 2010 to 2015 were derived from well-validated atmospheric chemistry and machine learning models. Data were analyzed in April 2021.

EXPOSURES

The main exploratory variable was mean census tract-level long-term exposure to PM2.5 from 2010 to 2015.

MAIN OUTCOMES AND MEASURES

The primary outcome was census tract-level ASMR. Multilevel models were used to quantify the geographic variation in ASMR at levels of census tract, county, and state. Additional analysis explored the interaction of SES in the association of ASMR with PM2.5 exposure.

RESULTS

Data from 67 148 census tracts nested in 3087 counties and 50 states were analyzed. The association between exposure to PM2.5 and ASMR varied substantially across census tracts. The magnitude of such association also varied across age groups, being higher among adults and older adults. Census tracts accounted for most of the total geographic variation in mortality risk (range, 77.0%-94.2%). ASMR was higher in deciles with greater PM2.5 concentration. For example, ASMR for age 75 to 84 years was 54.6 per 1000 population higher in the decile with the second-highest PM2.5 concentration than in the decile with the lowest PM2.5 concentration. The ASMR, PM2.5 concentrations, and magnitude of the association between both were higher in the census tracts with the lowest SES.

CONCLUSIONS AND RELEVANCE

This cross-sectional study found that census tracts with lower SES presented higher PM2.5 concentrations. ASMR and air pollution varied substantially across census tracts. There was an association between air pollution and ASMR across all age groups in the United States. These findings suggest that equitable public policies aimed at improving air quality are needed and important to increase life expectancy.

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.

Study on the effect of vehicular pollution on the ambient concentrations of particulate matter and carbon dioxide in Srinagar City

The present study was carried out to monitor the ambient concentrations of particulate matter and carbon dioxide caused by vehicular pollution in Srinagar City of Jammu and Kashmir, India, for a period of 12 months from June 2019 to May 2020 as the major contributions in these areas are due to vehicular movement. Out of five, four locations (viz. Dalgate, Jehangir Chowk, Parimpora and Pantha Chowk) had highest traffic density in the city and the fifth location (Shalimar) had low traffic volume. The sampling was done on every fortnight using AEROCET 831-aerosol mass monitor and CDM 901-CO₂ monitor with each sampling being carried out three times a day, i.e. morning (9:00 am-10:30 am), afternoon (1:00 pm-2:30 pm) and evening (4:30 pm-6:00 pm) with three replications at each site based on the peak traffic hours. The results show that during the whole period, average PM₁ concentrations ranged from 15.10 to 108.9 µg/m³, PM_2.5 (28.70-577.50 µg/m³), PM₄ (44.50-780.87 µg/m³), PM₁₀ (57.13-1225.53 µg/m³), total suspended particulates (77.77-1410.27 µg/m³) and CO₂ (332.4-655.0 ppm). The average concentrations of these parameters showed that the maximum PM₁ concentration was found at Dalgate (53.77 µg/m³) and PM_2.5 had its maximum average concentration at Jehangir Chowk (140.13 µg/m³). Other parameters like PM₄, PM₁₀, TSP and CO₂ had a maximum average values at Jehangir Chowk (240.23 µg/m³, 633.40 µg/m³, 853.50 µg/m³ and 533.20 ppm, respectively). The pollution load was observed to be maximum during winter season followed by autumn, summer and spring. The lowest concentration of all pollutants except CO₂ was observed in April 2020 and this might be due to COVID-19 lockdown observed in the country during the same period.

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

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

Estimating Associations Between Annual Concentrations of Particulate Matter and Mortality in the United States, Using Data Linkage and Bayesian Maximum Entropy

BACKGROUND

Exposure to fine particulate matter (PM2.5) is an established risk factor for human mortality. However, previous US studies have been limited to select cities or regions or to population subsets (e.g., older adults).

METHODS

Here, we demonstrate how to use the novel geostatistical method Bayesian maximum entropy to obtain estimates of PM2.5 concentrations in all contiguous US counties, 2000-2016. We then demonstrate how one could use these estimates in a traditional epidemiologic analysis examining the association between PM2.5 and rates of all-cause, cardiovascular, respiratory, and (as a negative control outcome) accidental mortality.

RESULTS

We estimated that, for a 1 log(μg/m3) increase in PM2.5 concentration, the conditional all-cause mortality incidence rate ratio (IRR) was 1.029 (95% confidence interval [CI]: 1.006, 1.053). This implies that the rate of all-cause mortality at 10 µg/m3 would be 1.020 times the rate at 5 µg/m3. IRRs were larger for cardiovascular mortality than for all-cause mortality in all gender and race-ethnicity groups. We observed larger IRRs for all-cause, nonaccidental, and respiratory mortality in Black non-Hispanic Americans than White non-Hispanic Americans. However, our negative control analysis indicated the possibility for unmeasured confounding.

CONCLUSION

We used a novel method that allowed us to estimate PM2.5 concentrations in all contiguous US counties and obtained estimates of the association between PM2.5 and mortality comparable to previous studies. Our analysis provides one example of how Bayesian maximum entropy could be used in epidemiologic analyses; future work could explore other ways to use this approach to inform important public health questions.

Assessment of children's exposure to carbonaceous matter and to PM major and trace elements

Particulate matter (PM) pollution is one of the major environmental concerns due to its harmful effects on human health. As children are particularly vulnerable to particle exposure, this study integrates the concentration of PM chemical compounds measured in the micro-environments (MEs) where children spend most of their time to assess the daily exposure and inhaled dose. PM samples were analysed for organic and elemental carbon and for major and trace elements. Results showed that the MEs that contribute most to the children's daily exposure (80%) and inhaled dose (65%) were homes and schools. Results indicated that the high contribution of particulate organic matter (POM) indoors indicate high contributions of indoor sources to the organic fraction of the particles. The highest concentrations of PM chemical compounds and the highest Indoor/Outdoor ratios were measured in schools, where the contribution of mineral elements stands out due to the resuspension of dust caused by the students and to the chalk used in blackboards. The contribution of the outdoor particles to inhaled dose (24%) was higher than to the exposure (12%), due to the highest inhalation rates associated with the activities performed outdoor. This study indicates the importance of indoor air quality for the children's exposure and health.

The Potential Health Costs of PM10 Impacts on a Gold Mine Village, during Company Liquidation: An Analysis of 2013–2017

Windblown dust from tailings storage facilities (TSFs), particularly in towns with liquidated mining companies, exacerbate air pollution. Companies of suddenly closed mine operations evade the responsibility of environmental and socio-economic care required by law. It is common for suddenly closed mines to have poorly rehabilitated TSFs which become a significant source of pollution by dust for the surrounding communities. There is strong evidence that acute exposure to high levels of air pollutants causes significant mortality and morbidity. However, very few studies have estimated the externalities of PM10 emanating from gold TSFs especially when a mine closes suddenly owing to company liquidation. By exploring the externalities of PM10 arising from wind erosion of suspended particulate matter from TSFs, this study fills an existing gap in the literature. A ‘bottom-up’ approach was implemented in this study following the External Energy (ExternE) project, and a gold mine operation that was liquidated between 2013 and 2017, was used as the case study. In this study, the externality of PM10 estimated was the cost of illness focusing specifically on respiratory-related illnesses. The results showed that the estimated cost of illness associated with PM10 inhalation was a total of R 5,560,022 including assessments of both neighboring Wedela and the Gold mine village based on the threshold concentration set by the South African National Ambient Air Quality Standards (NAAQS) guideline and R 66,092,760 when considering the Fund for Research into Industrial Development Growth Equity (FRIDGE), Airshed and Infotox. Thus, it was concluded that air pollution by windblown dust from partially rehabilitated TSFs has the potential to significantly affect surrounding mining communities’ socio-economic status through poor health and the costs thereof.

Design and Characterization of Atorvastatin Dry Powder Formulation as a potential Lung Cancer Treatment

Lung cancer is the leading cause of cancer death. Many studies have shown the beneficial effects of Atorvastatin in decreasing the mortality risk and improving survival among patients with lung cancer. This research paper focuses on improving AVT cytotoxic activity and cellular uptake by developing mannitol microcarriers as a promising drug delivery system for lung cancer treatment and, studying the impact of improving inhalation deposition on the delivery and Dry Powder formulations efficiency. The AVT loaded mannitol (AM) microparticles (AVT-AM) formulation was prepared by spray drying and characterized for its physicochemical properties and aerodynamic deposition. The results revealed that the AVT-AM formulation has good flow properties and aerosol deposition with a particle size of 3418 nm ± 26.86. The formulation was also assessed in vitro for cytotoxicity effects (proliferation, apoptosis, and cell cycle progression) on A549 human lung adenocarcinoma. Compared with free AVT, the AVT-AM formulation has significantly higher cellular uptake and anti-cancer properties by disrupting cell cycle progression via either apoptosis or cell cycle arrest in the G2/M phase. This study shows that AVT loaded mannitol microcarriers may provide a potentially effective and sustained pulmonary drug delivery for lung cancer treatment.

Ambient Air Pollution and Cardiorespiratory Outcomes amongst Adults Residing in Four Informal Settlements in the Western Province of South Africa

Few studies have investigated the relationship between ambient air pollution and cardiorespiratory outcomes in Africa. A cross-sectional study comprising of 572 adults from four informal settlements in the Western Cape, South Africa was conducted. Participants completed a questionnaire adapted from the European Community Respiratory Health Survey, and the National Health and Nutrition Examination Survey questionnaire. Exposure estimates were previously modelled using Land-Use Regression for Particulate Matter (PM_2.5) and Nitrogen Dioxide (NO₂) at participants' homes. The median age of the participants was 40.7 years, and 88.5% were female. The median annual NO₂ level was 19.7 µg/m³ (interquartile range [IQR: 9.6-23.7]) and the median annual PM_2.5 level was 9.7 µg/m³ (IQR: 7.3-12.4). Logistic regression analysis was used to assess associations between outcome variables and air pollutants. An interquartile range increase of 5.12 µg/m³ in PM_2.5 was significantly associated with an increased prevalence of self-reported chest-pain, [Odds ratio: 1.38 (95% CI: 1.06-1.80)], adjusting for NO₂, and other covariates. The study found preliminary circumstantial evidence of an association between annual ambient PM_2.5 exposure and self-reported chest-pain (a crude proxy of angina-related pain), even at levels below the South African National Ambient Air Quality Standards.

Reducing the nanoparticles generated at the wheel-rail contact by applying tap water lubricant at subway train operational velocities

The formation characteristics and the reduction of nanoparticles emitted from wheel–rail contacts at subway-train velocities of 73, 90, and 113 km/h under dry and water-lubricated conditions (using tap water) were studied using a twin-disk rig. The resulting number concentration (NC) of ultrafine and fine particles increased with train velocity under both conditions. Particle generation varied with slip rate under both conditions in both the particle categories. Furthermore, the formation characteristics at 113 km/h under dry conditions showed a notable deviation from those under water-lubricated conditions in three aspects: (i) The maximum NC of ultrafine particles was higher than that of fine particles, (ii) the predominant peak diameter was in the ultrafine particles category, and (iii) the proportion of ultrafine particles was much higher than those of the fine particles. Applying water decreased the NC of ultrafine and fine particles significantly at all tested velocities (by 54–69% and 87–91%, respectively). Adding water increased the NC of particles ≤ 35 nm in diameter, possibly owing to the increase in water vapor and mineral crystals from tap water. Overall, this study provides a reference for researchers aiming to minimize nanoparticle formation at the wheel–rail contacts by applying a lubricant.

Mortality Risk from PM2.5: A Comparison of Modeling Approaches to Identify Disparities across Racial/Ethnic Groups in Policy Outcomes

BACKGROUND

Regulatory analyses of air pollution policies require the use of concentration-response functions and underlying health data to estimate the mortality and morbidity effects, as well as the resulting benefits, associated with policy-related changes in fine particulate matter ≤2.5μm (PM2.5)]. Common practice by U.S. federal agencies involves using underlying health data and concentration-response functions that are not differentiated by racial/ethnic group.

OBJECTIVES

We aim to explore the policy implications of using race/ethnicity-specific concentration-response functions and mortality data in comparison to standard approaches when estimating the impact of air pollution on non-White racial/ethnic subgroups.

METHODS

Using new estimates from the epidemiological literature on race/ethnicity-specific concentration-response functions paired with race/ethnicity-specific mortality rates, we estimated the mortality impacts of air pollution from all sources from a uniform increase in concentrations and from the regulations imposed by the Mercury Air Toxics Standards.

RESULTS

Use of race/ethnicity-specific information increased PM2.5-related premature mortality estimates in older populations by 9% and among older Black Americans by 150% for all-source pollution exposure. Under a uniform degradation of air quality and race/ethnicity-specific information, older Black Americans were found to have approximately 3 times higher mortality relative to White Americans, which is obscured under a non-race/ethnicity-specific modeling approach. Standard approaches of using non-racial/ethnic specific information underestimate the benefits of the Mercury Air Toxics Standards to older Black Americans by almost 60% and overestimate the benefits to older White Americans by 14% relative to using a race/ethnicity-specific modeling approach.

DISCUSSION

Policy analyses incorporating race/ethnicity-specific concentration-response functions and mortality data relative to nondifferentiated inputs underestimate the overall magnitude of PM2.5 mortality burden and the disparity in impacts on older Black American populations. Based on our results, we recommend that the best available race/ethnicity-specific inputs are used in regulatory assessments to understand and reduce environmental injustices. https://doi.org/10.1289/EHP9001.

The Process and Platform for Predicting PM2.5 Inhalation and Retention during Exercise

In recent years, people have been increasingly concerned about air quality and pollution since a number of studies have proved that air pollution, especially PM2.5 (particulate matter), can affect human health drastically. Though the research on air quality prediction has become a mainstream research field, most of the studies focused only on the prediction of urban air quality and pollution. These studies did not predict the actual impact of these pollutants on people. According to the researchers’ best knowledge, the amount of polluted air inhaled by people and the amount of polluted air that remains inside their body are two important factors that affect their health. In order to predict the quantity of PM2.5 inhaled by people and what they have retained in their body, a process and a platform have been proposed in the current research work. In this research, the experimental process is as follows: (1) First, a personalized PM2.5 sensor is designed and developed to sense the quantity of PM2.5 around people. (2) Then, the Bruce protocol is applied to collect the information and calculate the relationship between heart rate and air intake under different activities. (3) The amount of PM2.5 retained in the body is calculated in this step using the International Commission on Radiological Protection (ICRP) air particle retention formula. (4) Then, a cloud platform is designed to collect people’s heart rate under different activities and PM2.5 values at respective times. (5) Finally, an APP is developed to show the daily intake of PM2.5. The result reveals that the developed app can show a person’s daily PM2.5 intake and retention in a specific population.

Assessing the value of complex refractive index and particle density for calibration of low-cost particle matter sensor for size-resolved particle count and PM2.5 measurements

Low-cost optical scattering particulate matter (PM) sensors report total or size-specific particle counts and mass concentrations. The PM concentration and size are estimated by the original equipment manufacturer (OEM) proprietary algorithms, which have inherent limitations since particle scattering depends on particles' properties such as size, shape, and complex index of refraction (CRI) as well as environmental parameters such as temperature and relative humidity (RH). As low-cost PM sensors are not able to resolve individual particles, there is a need to characterize and calibrate sensors' performance under a controlled environment. Here, we present improved calibration algorithms for Plantower PMS A003 sensor for mass indices and size-resolved number concentration. An aerosol chamber experimental protocol was used to evaluate sensor-to-sensor data reproducibility. The calibration was performed using four polydisperse test aerosols. The particle size distribution OEM calibration for PMS A003 sensor did not agree with the reference single particle sizer measurements. For the number concentration calibration, the linear model without adjusting for the aerosol properties and environmental conditions yields an absolute error (NMAE) of ~ 4.0% compared to the reference instrument. The calibration models adjusted for particle CRI and density account for non-linearity in the OEM's mass concentrations estimates with NMAE within 5.0%. The calibration algorithms developed in this study can be used in indoor air quality monitoring, occupational/industrial exposure assessments, or near-source monitoring scenarios where field calibration might be challenging.