The Role of Particle Composition on the Association Between PM2.5 and Mortality

Association between ambient fine particulate matter constituents and mortality and morbidity of cardiovascular and respiratory diseases: A systematic review and meta-analysis

Existing studies have explored the associations between short-term or long-term exposures to PM2.5 constituents and mortality and morbidity outcomes related to all-causes, natural causes and the cardiopulmonary system, but the results are still inconclusive. To explore the associations between short-term and long-term exposures to ambient PM2.5 constituents with mortality and morbidity outcomes of all-cause, natural, cardiovascular and respiratory diseases. We comprehensively searched PubMed, Embase, Web of Science and Scopus electronic databases to identify studies until 28, August 2023. We estimated the pooled relative risk (RR) with 95 % confidence interval (CI) using random-effect models. Then we explored potential sources of heterogeneity across different studies by subgroup and meta-regression analysis. A total of 67 articles were included in the meta-analysis. We found that short-term exposures to PM2.5 carbon-containing constituents (black carbon and organic carbon) and metal iron showed the most consistent associations with the eight health outcomes among different PM2.5 constituents. Meanwhile, only long-term exposures to nitrate ions and silicon in PM2.5 showed significant positive associations with cardiovascular and natural mortality outcomes. Additionally, subgroup and meta-regression analyses highlight that geographical region and age group are primary sources of heterogeneity. The current meta-analysis reveals potential key PM2.5 constituents associated with elevated risk of mortality and morbidity of cardiovascular and respiratory diseases. Thus, greater attention should be directed toward establishing exposure limits for specific PM2.5 constituents to protect public health and alleviate the associated burden of diseases.

The effect of chronic inflammation on female fertility

In brief

Chronic inflammation causes serious medical conditions in many organs and tissues, including female fertility. Here we review the current literature, showing that chronic inflammation has a negative impact on oocyte quality, folliculogenesis, hormone production, immune signaling and other processes that affect fertility in females.

Abstract

Inflammation has key biological roles in the battle against pathogens and additional key processes in development and tissue homeostasis. However, when inflammation becomes chronic, it can become a serious medical concern. Chronic inflammation has been shown to contribute to the etiology and symptoms of serious medical conditions such as ulcerative colitis, cardiovascular diseases, endometriosis and various cancers. One of the less recognized symptoms associated with chronic inflammation is its effect on reproduction, specifically on female fertility. Here we review the current literature, showing that chronic inflammation has a negative impact on oocyte quality, folliculogenesis, hormone production, immune signaling and other processes that affect fertility in females. We discuss several factors involved in the etiology of chronic inflammation and its effect on female fertility. We also discuss possible mechanisms by which these effects may be mediated and how interventions may mitigate the effect of chronic inflammation. Finally, we discuss the notion that in many cases, the effect of chronic inflammation is tightly correlated with and resembles the effect of aging, drawing interesting parallels between these processes, possibly through the effect of aging-associated inflammaging.

Fine Particulate Matter Components and Risk of Rheumatoid Arthritis: A Large General Canadian Open Cohort Study

OBJECTIVE

Exposure to fine particulate matter (PM2.5) has been linked to many diseases. However, it remains unclear which PM2.5 chemical components for these diseases, including rheumatoid arthritis (RA), are more harmful. This study aimed to assess potential associations between PM2.5 components and RA and quantify the individual effects of each chemical component on RA risk.

METHODS

An open cohort of 11,696,930 Canadian adults was assembled using Ontario administrative health data from January 2007 onward. Individuals were followed until RA onset, death, emigration from Ontario, or the end of the study (December 2019). Incident RA cases were defined by physician billing and hospitalization discharge diagnostic codes. The average levels of PM2.5 components (ammonium, black carbon, mineral dust, nitrate, organic matter, sea salt, and sulfate) for 5 years before cohort entry were assigned to participants based on residential postal codes. A quantile g-computation and Cox proportional hazard models for time to RA onset were developed for the mixture of PM2.5 components and environmental overall PM2.5, respectively.

RESULTS

We identified 67,676 new RA cases across 130,934,256 person-years. The adjusted hazard ratios for the time to RA onset were 1.027 and 1.023 (95% confidence intervals 1.021-1.033 and 1.017-1.029) per every decile increase in exposures to all seven components and per 1 μg/m3 increase in the overall PM2.5, respectively. Ammonium contributed the most to RA onset in the seven components.

CONCLUSION

Exposure to PM2.5 components was modestly associated with RA risk. Public health efforts focusing on specific components (eg, ammonium) may be a more efficient way to reduce RA burden.

Response of South Asia PM2.5 pollution to ammonia emission changes and associated impacts on human health

Countries in South Asia are suffering severe PM2.5 pollution with rapid economic development, impacting human health and the environment. Whilst much attention has been given to understanding the contribution of primary emissions, the contribution of agriculture to PM2.5 concentrations, especially from agricultural ammonia (NH3) emissions, remains less explored. Using an advanced regional atmospheric chemistry and transport modelling system (WRF-EMEP) with a new estimate of anthropogenic NH3 emissions inputs, we estimate the influence of agricultural NH3 emissions on surface PM2.5 in South Asia and evaluate the health impacts and the economic losses attributable to PM2.5 in 2018. Results show that WRF-EMEP can reproduce magnitudes and variations of PM2.5 well, with a high annual mean PM2.5 concentration that exceeds 120 µg/m2 and mainly appeared in the Indo-Gangetic Plain. We estimate 2,228,000 (95 % Confidence Interval: 2,052,000-2,400,000) premature deaths and US$ 596,000 (95 % CI: 549,000-642,000) million in economic losses are attributable to total ambient PM2.5 under the current emissions. We calculate that NH3 emissions are associated with 11 % of the annual average PM2.5 concentrations across South Asia. Changes in PM2.5 concentrations follow a non-linear response to NH3 emissions reductions, highlighting increased efficiency with 70 %-100 % reductions in NH3 emissions reductions. We estimate that 247,000 (227,000-265,000) premature deaths and US$ 66,000 (61,000-71,000) million economic losses through this pathway can be attributed to NH3 emissions. These findings confirm that in the current NH3-rich chemical environment of South Asia, the efficiency of PM2.5 reduction is only moderately sensitive to the reduction in intensity of NH3 emissions until emissions are cut very severely. Thus, SO2, NOx and NH3 emissions controls need to be considered jointly for greater mitigation of ambient secondary PM2.5 in South Asia.

Ambient air pollution exposure and adult asthma incidence: a systematic review and meta-analysis

BACKGROUND

Ambient (outdoor) air pollutant exposures have emerged as a plausible risk factor for incident childhood asthma. However, the effect of ambient air pollutant exposures on risk of incident adult asthma is unclear. We aimed to investigate associations between specific ambient air pollutants and the risk of incident adult asthma.

METHODS

In this systematic review and meta-analysis, we searched MEDLINE, Embase, Cochrane Central Register of Controlled Trials, and Web of Science from inception to Nov 27, 2023. We included observational studies with the outcome of new-onset asthma during adulthood (onset at ≥18 years), and metric of exposure of ambient air pollutants (particulate matter [PM]2·5, nitrogen dioxide [NO2], ozone [O3], and sulphur dioxide [SO2]). Study data were extracted independently by two reviewers and study quality was assessed using the Newcastle-Ottawa scale. When four or more eligible studies were available for a given pollutant, we applied meta-analysis using inverse variance weighting in a random effects model to estimate pooled relative risk (RR), and used meta-regression to explore sources of heterogeneity. The protocol was registered with PROSPERO, CRD42023420139.

FINDINGS

Our search identified 1891 references. After excluding 651 (34%) duplicates and ineligible studies, we included 25 studies in the systematic review. After excluding studies with overlapping populations or reporting effect estimates that could not be pooled, we performed meta-analysis for PM2·5 (nine studies), NO2 (nine studies), and O3 (four studies). Pooled random effects RRs for incident adult asthma per 5 μg/m3 increase in PM2·5 were 1·07 (95% CI 1·01 to 1·13) and per 10 μg/m3 in NO2 were 1·11 (1·03 to 1·20). We found no significant association between increasing O3 concentration and incident adult asthma (per 60-μg/m3 increase in O3, pooled RR 1·04 [0·79 to 1·36]). We found substantial heterogeneity across studies (I2=88% for all analyses). In exploratory meta-regression, average exposure level was a significant source of heterogeneity for the pooled NO2 estimate (95% CI -0·0077 to -0·0025 per μg/m3).

INTERPRETATION

Exposure to increased ambient PM2·5 or NO2 might present an additional risk factor for incident adult asthma, although high heterogeneity among included studies warrants caution in interpretation. Evidence was inconsistent for O3 and insufficient for SO2. To increase confidence and population representation in pooled estimates, further primary investigations are necessary, ideally with aligned methodology and reporting.

FUNDING

None.

Air pollution mixture complexity and its effect on PM2.5-related mortality: A multicountry time-series study in 264 cities

Background

Fine particulate matter (PM2.5) occurs within a mixture of other pollutant gases that interact and impact its composition and toxicity. To characterize the local toxicity of PM2.5, it is useful to have an index that accounts for the whole pollutant mix, including gaseous pollutants. We consider a recently proposed pollutant mixture complexity index (PMCI) to evaluate to which extent it relates to PM2.5 toxicity.

Methods

The PMCI is constructed as an index spanning seven different pollutants, relative to the PM2.5 levels. We consider a standard two-stage analysis using data from 264 cities in the Northern Hemisphere. The first stage estimates the city-specific relative risks between daily PM2.5 and all-cause mortality, which are then pooled into a second-stage meta-regression model with which we estimate the effect modification from the PMCI.

Results

We estimate a relative excess risk of 1.0042 (95% confidence interval: 1.0023, 1.0061) for an interquartile range increase (from 1.09 to 1.95) of the PMCI. The PMCI predicts a substantial part of within-country relative risk heterogeneity with much less between-country heterogeneity explained. The Akaike information criterion and Bayesian information criterion of the main model are lower than those of alternative meta-regression models considering the oxidative capacity of PM2.5 or its composition.

Conclusions

The PMCI represents an efficient and simple predictor of local PM2.5-related mortality, providing evidence that PM2.5 toxicity depends on the surrounding gaseous pollutant mix. With the advent of remote sensing for pollutants, the PMCI can provide a useful index to track air quality.

Fine particulate matter components associated with exacerbated depressive symptoms among middle-aged and older adults in China

Growing awareness acknowledges ambient fine particulate matter (PM2.5) as an environmental risk factor for mental disorders, especially among older people. However, there remains limited evidence regarding which specific chemical components of PM2.5 may be more detrimental. This nationwide prospective cohort study included 22,126 middle-aged and older adult participants of the China Health and Retirement Longitudinal Study (CHARLS, 2011-2016), to explore the individual and joint associations between long-term exposure to various PM2.5 components (sulfate, nitrate, ammonium, organic matter, and black carbon) and depressive symptoms. The depressive symptoms were assessed using the 10-item Center for Epidemiological Studies-Depression Scale (CES-D-10). Using the novel quantile-based g-computation for multi-pollutant mixture analysis, we found that exposure to the mixture of major PM2.5 components was significantly associated with aggravating depressive symptoms, with the exposure-response curve exhibiting consistent linear or supra-linear shape without a lower threshold. The estimated weight index indicated that, among major PM2.5 components, only nitrate, sulfate, and black carbon significantly contributed to the exacerbation of depressive symptoms. Given the expanding aging population, stricter regulation on the emissions of particularly toxic PM2.5 components may mitigate the escalating disease burden of depression.

Associations between Fine Particulate Matter Components, Their Sources, and Cognitive Outcomes in Children Ages 9-10 Years Old from the United States

BACKGROUND

Emerging literature suggests that fine particulate matter [with aerodynamic diameter ≤ 2.5 μ m (PM 2.5 )] air pollution and its components are linked to various neurodevelopmental outcomes. However, few studies have evaluated how PM 2.5 component mixtures from distinct sources relate to cognitive outcomes in children.

OBJECTIVES

This cross-sectional study investigated how ambient concentrations of PM 2.5 component mixtures relate to neurocognitive performance in 9- to 10-year-old children, as well as explored potential source-specific effects of these associations, across the US.

METHODS

Using spatiotemporal hybrid models, annual concentrations of 15 chemical components of PM 2.5 were estimated based on the residential address of child participants from the Adolescent Brain Cognitive Development (ABCD) Study. General cognitive ability, executive function, and learning/memory scores were derived from the NIH Toolbox. We applied positive matrix factorization to identify six major PM 2.5 sources based on the 15 components, which included crustal, ammonium sulfate, biomass burning, traffic, ammonium nitrate, and industrial/residual fuel burning. We then utilized weighted quantile sum (WQS) and linear regression models to investigate associations between PM 2.5 components' mixture, their potential sources, and children's cognitive scores.

RESULTS

Mixture modeling revealed associations between cumulative exposure and worse cognitive performance across all three outcome domains, including shared overlap in detrimental effects driven by ammonium nitrates, silicon, and calcium. Using the identified six sources of exposure, source-specific negative associations were identified between ammonium nitrates and learning & memory, traffic and executive function, and crustal and industrial mixtures and general cognitive ability. Unexpected positive associations were also seen between traffic and general ability as well as biomass burning and executive function.

DISCUSSION

This work suggests nuanced associations between outdoor PM 2.5 exposure and childhood cognitive performance, including important differences in cognition related both to individual chemicals as well as to specific sources of these exposures. https://doi.org/10.1289/EHP14418.

Accurate PM2.5 urban air pollution forecasting using multivariate ensemble learning Accounting for evolving target distributions

Over the past decades, air pollution has caused severe environmental and public health problems. According to the World Health Organization (WHO), fine particulate matter (PM2.5), a key component reflecting air quality, is the fourth leading cause of death worldwide after cardiovascular disease, smoking, and diet. Various research efforts have aimed to develop PM2.5 forecasting models that can be integrated into a solution to mitigate the adverse effects of air pollution. However, PM2.5 forecasting is challenging because air pollution data are non-stationary and influenced by multiple random effects. This paper proposes an effective multivariate multi-step ensemble machine learning model for predicting continuous 24-h PM2.5 concentrations, considering meteorological conditions, the rolling mean of PM2.5 time series, and temporal features. PM2.5 is strongly correlated with space and time. Therefore, forecasting results from one location are insufficient to represent the level of air pollution for an entire city. In this study, we established six real-time air quality monitoring sites in different regions, including traffic, residential, and industrial areas in Ho Chi Minh City (HCMC), and generated forecasting results for each station. Various statistical methods are incorporated to evaluate the performance of the model. The experimental results confirm that the model performs well, substantially improving its forecasting accuracy compared to existing PM2.5 forecasting models developed for HCMC. In addition, we analyze to determine the contribution of different feature groups to model performance. The model can serve as a reference for citizens scheduling local travel and for healthcare providers to provide early warnings.

Decomposing PM2.5 concentrations in urban environments into meaningful factors 2. Extracting the contribution of traffic-related exhaust emissions

Vehicle-emitted fine particulate matter (PM2.5) has been associated with significant health outcomes and environmental risks. This study estimates the contribution of traffic-related exhaust emissions (TREE) to observed PM2.5 using a novel factorization framework. Specifically, co-measured nitrogen oxides (NOx) concentrations served as a marker of vehicle-tailpipe emissions and were integrated into the optimization of a Non-negative Matrix Factorization (NMF) analysis to guide the factor extraction. The novel TREE-NMF approach was applied to long-term (2012-2019) PM2.5 observations from air quality monitoring (AQM) stations in two urban areas. The extracted TREE factor was evaluated against co-measured black carbon (BC) and PM2.5 species to which the TREE-NMF optimization was blind. The contribution of the TREE factor to the observed PM2.5 concentrations at an AQM station from the first location showed close agreement (R2=0.79) with monitored BC data. In the second location, a comparison of the extracted TREE factor with measurements at a nearby Surface PARTiculate mAtter Network (SPARTAN) station revealed moderate correlations with PM2.5 species commonly associated with fuel combustion, and a good linear regression fit with measured equivalent BC concentrations. The estimated concentrations of the TREE factor at the second location accounted for 7-11 % of the observed PM2.5 in the AQM stations. Moreover, analysis of specific days known to be characterized by little traffic emissions suggested that approximately 60-78 % of the traffic-related PM2.5 concentrations could be attributed to particulate traffic-exhaust emissions. The methodology applied in this study holds great potential in areas with limited monitoring of PM2.5 speciation, in particular BC, and its results could be valuable for both future environmental health research, regional radiative forcing estimates, and promulgation of tailored regulations for traffic-related air pollution abatement.

Analysis of PM2.5, black carbon, and trace metals measurements from the Kansas City Transportation and Local-Scale Air Quality Study (KC-TRAQS)

Communities near transportation sources can be impacted by higher concentrations of particulate matter (PM) and other air pollutants. Few studies have reported on air quality in complex urban environments with multiple transportation sources. To better understand these environments, the Kansas City Transportation and Local-Scale Air Quality Study (KC-TRAQS) was conducted in three neighborhoods in Southeast Kansas City, Kansas. This area has several emissions sources including transportation (railyards, vehicles, diesel trucks), light industry, commercial facilities, and residential areas. Stationary samples were collected for 1-year (October 24, 2017, to October 31, 2018) at six sites using traditional sampling methods and lower-cost air sensor packages. This work examines PM less than 2.5 μm in diameter (PM2.5), black carbon (BC), and trace metals data collected during KC-TRAQS. PM2.5 filter samples showed the highest 24-h mean concentrations (9.34 μg/m3) at the sites located within 20-50 m of the railyard. Mean 24-h PM2.5 concentrations, ranging from 7.96 to 9.34 μg/m3, at all sites were lower than that of the nearby regulatory site (9.83 μg/m3). Daily maximum PM2.5 concentrations were higher at the KC-TRAQS sites (ranging from 25.31 to 43.76 μg/m3) compared to the regulatory site (20.50 μg/m3), suggesting short-duration impacts of localized emissions sources. Across the KC-TRAQS sites, 24-h averaged PM2.5 concentrations from the sensor package (P-POD) ranged from 3.24 to 5.69 µg/m3 showing that, out-of-the-box, the PM sensor underestimated the reference concentrations. KC-TRAQS was supplemented by elemental and organic carbon (EC/OC) and trace metal analysis of filter samples. The EC/OC data suggested the presence of secondary organic aerosol formation, with the highest mean concentrations observed at the site within 20 m of the railyard. Trace metals data showed daily, monthly, and seasonal variations for iron, copper, zinc, chromium, and nickel, with elevated concentrations occurring during the summer at most of the sites.Implications: This work reports on findings from a year-long air quality study in Southeast Kansas City, Kansas to understand micro-scale air quality in neighborhoods impacted by multiple emissions sources such as transportation sources (including a large railyard operation), light industry, commercial facilities, and residential areas. While dozens of studies have reported on air quality near roadways, this work will provide more information on PM2.5, black carbon, and trace metals concentrations near other transportation sources in particular railyards. This work can also inform additional field studies near railyards.

Association between ambient particulate matter exposure and mitochondrial DNA copy number: A systematic review and meta-analysis

BACKGROUND

Ambient particulate matter (PM) has been recognized as inducing oxidative stress, which could contribute to mitochondrial damage and dysfunction. However, studies investigating the association between ambient PM and mitochondria, particularly mitochondrial DNA copy number (mtDNA-CN), have yielded inconsistent results.

METHODS

We conducted comprehensive literature searches to identify observational studies published before July 17, 2023, examining the association between ambient PM exposure and mtDNA-CN. Meta-analysis using random effects model was employed to calculate the pooled effect estimates for general individual exposures, as well as for prenatal exposure with specific trimester. Additionally, the quality and level of evidence for each exposure-outcome pair was evaluated.

RESULTS

A total of 10 studies were included in the systematic review and meta-analysis. The results indicated that general individual exposure to PM2.5 (β = -0.084, 95 % CI: -0.521, 0.353; I2 = 93 %) and PM10 (β = 0.035, 95 % CI: -0.129, 0.199; I2 = 95 %) did not significantly affect mtDNA-CN. Prenatal exposure to PM2.5 (β = 0.023, 95 % CI: -0.087, 0.133; I2 = 0 %) and PM10 (β = 0.006, 95 % CI: -0.135; 0.147; I2 = 51 %) were also not significantly associated with mtDNA-CN in offspring. The level of evidence for each tested exposure-outcome pair was assessed as "inadequate."

CONCLUSIONS

The findings of this systematic review and meta-analysis indicate that there is an "inadequate" strength of evidence for the association between general individual or prenatal exposure to ambient PM and mtDNA-CN. Future research necessitates studies with more rigorous design, enhanced control of confounding factors, and improved measures of exposure to substantiate our findings.

Exposure to PM2.5 modulate the pro-inflammatory and interferon responses against influenza virus infection in a human 3D bronchial epithelium model

Epidemiological studies showed a positive association between exposure to PM2.5 and the severity of influenza virus infection. However, the mechanisms by which PM2.5 can disrupt antiviral defence are still unclear. From this perspective, the objective of this study was to evaluate the effects of PM2.5 on antiviral signalling in the respiratory epithelium using the bronchial Calu-3 cell line grown at the air-liquid interface. Pre-exposure to PM2.5 before infection with the influenza virus was investigated, as well as a co-exposure. Although a physical interaction between the virus and the particles seems possible, no effect of PM2.5 on viral replication was observed during co-exposure, although a downregulation of IFN-β release was associated to PM2.5 exposure. However, pre-exposure slightly increased the viral nucleoprotein production and the pro-inflammatory response. Conversely, the level of the myxovirus resistance protein A (MxA), an interferon-stimulated gene (ISG) induced by IFN-β, was reduced. Therefore, these results suggest that pre-exposure to PM2.5 could alter the antiviral response of bronchial epithelial cells, increasing their susceptibility to viral infection.

Variations of oxidative potential of PM2.5 in a medium-sized residential city in South Korea measured using three different chemical assays

Although it is evident that PM2.5 has serious adverse health effects, there is no consensus on what the biologically effective dose is. In this study, the intrinsic oxidative potential (OPm) and the extrinsic oxidative potential (OPv) of PM2.5 were measured using three chemical assays including dithiothreitol (DTT), ascorbic acid (AA), and reduced glutathione (GSH), along with chemical compositions of PM2.5 in South Korea. Among the three chemical assays, only OPmAA showed a statistically significant correlation with PM2.5 while OPmGSH and OPmDTT were not correlated with PM2.5 mass concentration. When the samples were categorized by PM2.5 mass concentrations, the variations in the proportion of Ni, As, Mn, Cd, Pb, and Se to PM2.5 mass closely coincided with changes in OPm across all three assays, suggesting a potential association between these elements and PM2.5 OP. Multiple linear regression analysis identified the significant PM components affecting the variability in extrinsic OPv. OPvAA was determined to be significantly influenced by EC, K+, and Ba while OC and Al were common significant factors for OPvGSH and OPvDTT. It was also found that primary OC was an important variable for OPvDTT while secondary OC significantly affected the variability of OPvGSH.

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.

Microfluidics in environmental analysis: advancements, challenges, and future prospects for rapid and efficient monitoring

Microfluidic devices have emerged as advantageous tools for detecting environmental contaminants due to their portability, ease of use, cost-effectiveness, and rapid response capabilities. These devices have wide-ranging applications in environmental monitoring of air, water, and soil matrices, and have also been applied to agricultural monitoring. Although several previous reviews have explored microfluidic devices' utility, this paper presents an up-to-date account of the latest advancements in this field for environmental monitoring, looking back at the past five years. In this review, we discuss devices for prominent contaminants such as heavy metals, pesticides, nutrients, microorganisms, per- and polyfluoroalkyl substances (PFAS), etc. We cover numerous detection methods (electrochemical, colorimetric, fluorescent, etc.) and critically assess the current state of microfluidic devices for environmental monitoring, highlighting both their successes and limitations. Moreover, we propose potential strategies to mitigate these limitations and offer valuable insights into future research and development directions.

Changes in industrial air pollution and the onset of childhood asthma in Quebec, Canada

Ambient air pollution has been associated with asthma onset and exacerbation in children. Whether improvement in air quality due to reduced industrial emissions has resulted in improved health outcomes such as asthma in some localities has usually been assessed indirectly with studies on between-subject comparisons of air pollution from all sources and health outcomes. In this study we directly assessed, within small areas in the province of Quebec (Canada), the influence of changes in local industrial fine particulate matter (PM2.5), nitrogen dioxide (NO2), and sulfur dioxide (SO2) concentrations, on changes in annual asthma onset rates in children (≤12 years old) with a longitudinal ecological design. We identified the yearly number of new cases of childhood asthma in 1282 small areas (census tracts or local community service centers) for the years 2002, 2004, 2005, 2006, and 2015. Annual average concentrations of industrial air pollutants for each of the geographic areas, and three sectors (i.e., pulp and paper mills, petroleum refineries, and metal smelters) were estimated by the Polair3D chemical transport model. Fixed-effects negative binomial models adjusted for household income were used to assess associations; additional adjustments for environmental tobacco smoke, background pollutant concentrations, vegetation coverage, and sociodemographic characteristics were conducted in sensitivity analyses. The incidence rate ratios (IRR) for childhood asthma onset for the interquartile increase in total industrial PM2.5, NO2, and SO2 were 1.016 (95% confidence interval, CI: 1.006-1.026), 1.063 (1.045-1.090), and 1.048 (1.031-1.080), respectively. Positive associations were also found with pollutant concentrations from most individual sectors. Results suggest that changes in industrial pollutant concentrations influence childhood asthma onset rates in small localities.

Air Pollutants and Risk of Parkinson's Disease among Women in the Sister Study

BACKGROUND

Air pollutants may contribute to the development of Parkinson's disease (PD), but empirical evidence is limited and inconsistent.

OBJECTIVES

This study aimed to prospectively investigate the associations of PD with ambient exposures to fine particulate matter with aerodynamic diameter ≤ 2.5 μ m (PM 2.5 ) and nitrogen dioxide (NO 2 ).

METHODS

We analyzed data from 47,108 US women from the Sister Study, enrolled from 2003-2009 (35-80 years of age) and followed through 2018. Exposures of interest included address-level ambient PM 2.5 and NO 2 in 2009 and their cumulative averages from 2009 to PD diagnosis with varying lag-years. The primary outcome was PD diagnosis between 2009 and 2018 (n = 163 ). We used multivariable Cox proportional hazards and time-varying Cox models to calculate hazard ratios (HRs) and 95% confidence intervals (CIs).

RESULTS

NO 2 exposure in 2009 was associated with PD risk in a dose-response manner. The HR and 95% CI were 1.22 (95% CI: 1.03, 1.46) for one interquartile [4.8 parts per billion (ppb)] increment in NO 2 , adjusting for age, race and ethnicity, education, smoking status, alcohol drinking, caffeine intake, body mass index, physical activity, census region, residential area type, area deprivation index (ADI), and self-reported health status. The association was confirmed in secondary analyses with time-varying averaged cumulative exposures. For example, the multivariable adjusted HR for PD per 4.8   ppb increment in NO 2 was 1.25 (95% CI: 1.05, 1.50) in the 2-year lag analysis using cumulative average exposure. Post hoc subgroup analyses overall confirmed the association. However, statistical interaction analyses found that the positive association of NO 2 with PD risk was limited to women in urban, rural, and small town areas and women with ≥ 50 th percentile ADI but not among women from suburban areas or areas with < 50 th percentile ADI. In contrast, PM 2.5 exposure was not associated with PD risk with the possible exception for women from the Midwest region of the US (HR interquartile -range = 2.49 , 95% CI: 1.20, 5.14) but not in other census regions.

DISCUSSION

In this nationwide cohort of US women, higher level exposure to ambient NO 2 is associated with a greater risk of PD. This finding needs to be independently confirmed and the underlying mechanisms warrant further investigation. https://doi.org/10.1289/EHP13009.

In-vehicle exposure to NO2 and PM2.5: A comprehensive assessment of controlling parameters and reduction strategies to minimise personal exposure

Vehicles are the third most occupied microenvironment, other than home and workplace, in developed urban areas. Vehicle cabins are confined spaces where occupants can mitigate their exposure to on-road nitrogen dioxide (NO2) and fine particulate matter (PM2.5) concentrations. Understanding which parameters exert the greatest influence on in-vehicle exposure underpins advice to drivers and vehicle occupants in general. This study assessed the in-vehicle NO2 and PM2.5 levels and developed stepwise general additive mixed models (sGAMM) to investigate comprehensively the combined and individual influences of factors that influence the in-vehicle exposures. The mean in-vehicle levels were 19 ± 18 and 6.4 ± 2.7 μg/m3 for NO2 and PM2.5, respectively. sGAMM model identified significant factors explaining a large fraction of in-vehicle NO2 and PM2.5 variability, R2 = 0.645 and 0.723, respectively. From the model's explained variability on-road air pollution was the most important predictor accounting for 22.3 and 30 % of NO2 and PM2.5 variability, respectively. Vehicle-based predictors included manufacturing year, cabin size, odometer reading, type of cabin filter, ventilation fan speed power, window setting, and use of air recirculation, and together explained 48.7 % and 61.3 % of NO2 and PM2.5 variability, respectively, with 41.4 % and 51.9 %, related to ventilation preference and type of filtration media, respectively. Driving-based parameters included driving speed, traffic conditions, traffic lights, roundabouts, and following high emitters and accounted for 22 and 7.4 % of in-vehicle NO2 and PM2.5 exposure variability, respectively. Vehicle occupants can significantly reduce their in-vehicle exposure by moderating vehicle ventilation settings and by choosing an appropriate cabin air filter.

Characterizing metals in particulate pollution in communities at the fenceline of heavy industry: combining mobile monitoring and size-resolved filter measurements

Exposures to metals from industrial emissions can pose important health risks. The Chester-Trainer-Marcus Hook area of southeastern Pennsylvania is home to multiple petrochemical plants, a refinery, and a waste incinerator, most abutting socio-economically disadvantaged residential communities. Existing information on fenceline community exposures is based on monitoring data with low temporal and spatial resolution and EPA models that incorporate industry self-reporting. During a 3 week sampling campaign in September 2021, size-resolved particulate matter (PM) metals concentrations were obtained at a fixed site in Chester and on-line mobile aerosol measurements were conducted around Chester-Trainer-Marcus Hook. Fixed-site arsenic, lead, antimony, cobalt, and manganese concentrations in total PM were higher (p < 0.001) than EPA model estimates, and arsenic, lead, and cadmium were predominantly observed in fine PM (<2.5 μm), the PM fraction which can penetrate deeply into the lungs. Hazard index analysis suggests adverse effects are not expected from exposures at the observed levels; however, additional chemical exposures, PM size fraction, and non-chemical stressors should be considered in future studies for accurate assessment of risk. Fixed-site MOUDI and nearby mobile aerosol measurements were moderately correlated (r ≥ 0.5) for aluminum, potassium and selenium. Source apportionment analyses suggested the presence of four major emissions sources (sea salt, mineral dust, general combustion, and non-exhaust vehicle emissions) in the study area. Elevated levels of combustion-related elements of health concern (e.g., arsenic, cadmium, antimony, and vanadium) were observed near the waste incinerator and other industrial facilities by mobile monitoring, as well as in residential-zoned areas in Chester. These results suggest potential co-exposures to harmful atmospheric metal/metalloids in communities surrounding the Chester-Trainer-Marcus Hook industrial area at levels that may exceed previous estimates from EPA modeling.

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.

Proposal of a Methodology for Prediction of Indoor PM2.5 Concentration Using Sensor-Based Residential Environments Monitoring Data and Time-Divided Multiple Linear Regression Model

This study aims to propose an indoor air quality prediction method that can be easily utilized and reflects temporal characteristics using indoor and outdoor input data measured near the indoor target point as input to calculate indoor PM_2.5 concentration through a multiple linear regression model. The atmospheric conditions and air pollution detected in one-minute intervals using sensor-based monitoring equipment (Dust Mon, Sentry Co Ltd., Seoul, Korea) inside and outside houses from May 2019 to April 2021 were used to develop the prediction model. By dividing the multiple linear regression model into one-hour increments, we attempted to overcome the limitation of not representing the multiple linear regression model's characteristics over time and limited input variables. The multiple linear regression (MLR) model classified by time unit showed an improvement in explanatory power by up to 9% compared to the existing model, and some hourly models had an explanatory power of 0.30. These results indicated that the model needs to be subdivided by time period to more accurately predict indoor PM_2.5 concentrations.

Improving interventional causal predictions in regulatory risk assessment

In 2022, the US EPA published an important risk assessment concluding that "Compared to the current annual standard, meeting a revised annual standard with a lower level is estimated to reduce PM2.5-associated health risks in the 30 annually-controlled study areas by about 7-9% for a level of 11.0 µg/m3… and 30-37% for a level of 8.0 µg/m3." These are interventional causal predictions: they predict percentage reductions in mortality risks caused by different counterfactual reductions in fine particulate (PM2.5) levels. Valid causal predictions are possible if: (1) Study designs are used that can support valid causal inferences about the effects of interventions (e.g., quasi-experiments with appropriate control groups); (2) Appropriate causal models and methods are used to analyze the data; (3) Model assumptions are satisfied (at least approximately); and (4) Non-causal sources of exposure-response associations such as confounding, measurement error, and model misspecification are appropriately modeled and adjusted for. This paper examines two long-term mortality studies selected by the EPA to predict reductions in PM2.5-associated risk. Both papers use Cox proportional hazards (PH) models. For these models, none of these four conditions is satisfied, making it difficult to interpret or validate their causal predictions. Scientists, reviewers, regulators, and members of the public can benefit from more trustworthy and credible risk assessments and causal predictions by insisting that risk assessments supporting interventional causal conclusions be based on study designs, methods, and models that are appropriate for predicting effects caused by interventions.

Applying principal component pursuit to investigate the association between source-specific fine particulate matter and myocardial infarction hospitalizations in New York City

The association between fine particulate matter (PM_2.5) and cardiovascular outcomes is well established. To evaluate whether source-specific PM_2.5 is differentially associated with cardiovascular disease in New York City (NYC), we identified PM_2.5 sources and examined the association between source-specific PM_2.5 exposure and risk of hospitalization for myocardial infarction (MI).

Methods

We adapted principal component pursuit (PCP), a dimensionality-reduction technique previously used in computer vision, as a novel pattern recognition method for environmental mixtures to apportion speciated PM_2.5 to its sources. We used data from the NY Department of Health Statewide Planning and Research Cooperative System of daily city-wide counts of MI admissions (2007-2015). We examined associations between same-day, lag 1, and lag 2 source-specific PM_2.5 exposure and MI admissions in a time-series analysis, using a quasi-Poisson regression model adjusting for potential confounders.

Results

We identified four sources of PM_2.5 pollution: crustal, salt, traffic, and regional and detected three single-species factors: cadmium, chromium, and barium. In adjusted models, we observed a 0.40% (95% confidence interval [CI]: -0.21, 1.01%) increase in MI admission rates per 1 μg/m³ increase in traffic PM_2.5, a 0.44% (95% CI: -0.04, 0.93%) increase per 1 μg/m³ increase in crustal PM_2.5, and a 1.34% (95% CI: -0.46, 3.17%) increase per 1 μg/m³ increase in chromium-related PM_2.5, on average.

Conclusions

In our NYC study, we identified traffic, crustal dust, and chromium PM_2.5 as potentially relevant sources for cardiovascular disease. We also demonstrated the potential utility of PCP as a pattern recognition method for environmental mixtures.

Fine Particulate Air Pollution and the "No-Multiple-Versions-of-Treatment" Assumption: Does Particle Composition Matter for Causal Inference?

Here we discuss possible violations of the "no-multiple-versions-of-treatment" assumption in studies of outdoor fine particulate air pollution (particulate matter with an aerodynamic diameter less than or equal to 2.5 μm (PM2.5)) owing to differences in particle composition, which in turn influence health. This assumption is part of the potential outcomes framework for causal inference, and it is needed for well-defined potential outcomes, as multiple versions of the same treatment could lead to different health risks for the same level of treatment. Since 2 locations can have the same outdoor PM2.5 mass concentration (i.e., treatment) but different chemical compositions (i.e., versions of treatment), violations of the "no-multiple-versions-of-treatment" assumption seem likely. Importantly, violations of this assumption will not bias health risk estimates for PM2.5 mass concentrations if there are no unmeasured confounders of the "version of treatment"-outcome relationship. However, confounding can occur if these factors are not identified and controlled for in the analysis. We describe situations in which this may occur and provide simulations to estimate the magnitude and direction of this possible bias. In general, violations of the "no-multiple-versions-of-treatment" assumption could be an underappreciated source of bias in studies of outdoor PM2.5. Analysis of the health impacts of outdoor PM2.5 mass concentrations across spatial domains with similar composition could help to address this issue.

Gene-environment interactions and their impact on human health

The molecular processes underlying human health and disease are highly complex. Often, genetic and environmental factors contribute to a given disease or phenotype in a non-additive manner, yielding a gene-environment (G × E) interaction. In this work, we broadly review current knowledge on the impact of gene-environment interactions on human health. We first explain the independent impact of genetic variation and the environment. We next detail well-established G × E interactions that impact human health involving environmental toxicants, pollution, viruses, and sex chromosome composition. We conclude with possibilities and challenges for studying G × E interactions.

Effects of Light-absorbing Carbons in Intraoperative Molecular Imaging-Guided Lung Cancer Resections

BACKGROUND

One of the novel advancements to enhance the visual aspects of lung cancer identification is intraoperative molecular imaging (IMI), which can reliably detect tumors that would otherwise be missed by standard techniques such as tactile and visual feedback, particularly for sub-centimeter or ground-glass nodules. However, there remains a subset of patients who do not benefit from IMI due to excessive background fluorescence secondary to parenchymal light-absorbing carbon deposition. Our goal was to identify the effects of these carbonaceous materials on the quality of IMI-guided lung cancer resections.

STUDY DESIGN AND METHODS

Between July 2014 and May 2021, a total of 311 patients were included in the study. Patients underwent infusion of the study drug OTL38 or ICG up to 24 h prior to VATS for lung cancer. Several factors such as age, tumor subtype, PET SUV, smoking, demographics, chronic lung conditions, patient domicile, and anthracosis were analyzed with respect to lung fluorescence during IMI. P values < 0.05 were considered statistically significant.

RESULTS

Variables such as age, sex, and race had no statistical correlation to IMI success. However, smoking status and pack year had a statistically significant correlation with background parenchymal fluorescence and lung inflammation (p < 0.05). MFI of background (lung parenchyma) correlated with smoking history (p < 0.05) which led to decreased tumor-to-background ratio (TBR) measurements for all patients with proven malignancy (p < 0.05). Patients with chronic lung disease appear to have increased background parenchymal fluorescence regardless of smoking history (287 vs. 154, p < 0.01). City dwellers compared to other groups appear to be exposed to higher pollutant load and have higher rates of anthracosis, but living location's impact on fluorescence quantification appears to be not statistically significant.

CONCLUSION

Smokers with greater than 10 PPY and those with chronic lung disease appear to have decreased lesion-to-background discrimination, significant anthracosis, and reduced IMI efficacy secondary to light-absorbing carbon deposition.

Drought and ozone air quality in California: Identifying susceptible regions in the preparedness of future drought

California experienced extreme and prolonged drought conditions during the early 2010s. To date, little is known regarding the influence of drought on air quality. Our study quantified site-specific associations between drought (defined by the Standardized Precipitation-Evapotranspiration Index; SPEI) and daily maximum 8-h ozone (O₃) concentrations for California, USA, and then pooled these associations for the years 2009-2015. Overall, ambient O₃ concentration was higher during droughts by 1.18 ppb (95% confidence interval (CI) = 1.00-1.36). The sensitivity of O₃ to drought was greater during the warm season than during the cool season (1.73 ppb versus 0.79 ppb higher O₃ during droughts) with substantial regional variation. In a pooled analysis with meteorological parameters as potential effect modifiers, the spatial heterogeneity of drought-O₃ associations was explained strongly by average relative humidity for each season (71.9% (warm season) and 73.4% (cool season) of the drought-O₃ associations explained), followed by the drought-related changes in relative humidity (47.6% (warm season)) and temperature (53.6% (cool season)). The pooled regression further identified regions susceptible for drought-related O₃ increases as those with relatively low average relative humidity (10-25^th percentiles or 44.3-47.3%) and larger drought-related decrease in relative humidity and increase in temperature. As the drought events are projected to occur with increased frequency and intensity in the era of climate change, the excess health burdens from O₃ exposures attributed to the projected drought events need to be taken into account when allocating air quality and health resources. The impacts of O₃ on health during droughts would confound the health burdens from the drought itself.

Acute health effects of bushfire smoke on mortality in Sydney, Australia

BACKGROUND

Bushfire smoke is a major ongoing environmental hazard in Australia. In the summer of 2019-2020 smoke from an extreme bushfire event exposed large populations to high concentrations of particulate matter (PM) pollution. In this study we aimed to estimate the effect of bushfire-related PM of less than 2.5 μm in diameter (PM_2.5) on the risk of mortality in Sydney, Australia from 2010 to 2020.

METHODS

We estimated concentrations of PM_2.5 for three subregions of Sydney from measurements at monitoring stations using inverse-distance weighting and cross-referenced extreme days (95th percentile or above) with satellite imagery to determine if bushfire smoke was present. We then used a seasonal and trend decomposition method to estimate the Non-bushfire PM_2.5 concentrations on those days. Daily PM_2.5 concentrations above the Non-bushfire concentrations on bushfire smoke days were deemed to be Bushfire PM_2.5. We used distributed-lag non-linear models to estimate the effect of Bushfire and Non-bushfire PM_2.5 on daily counts of mortality with sub-analyses by age. These models controlled for seasonal trends in mortality as well as daily temperature, day of week and public holidays.

RESULTS

Within the three subregions, between 110 and 134 days were identified as extreme bushfire smoke days within the subregions of Sydney. Bushfire-related PM_2.5 ranged from 6.3 to 115.4 µg/m³. A 0 to 10 µg/m³ increase in Bushfire PM_2.5 was associated with a 3.2% (95% CI 0.3, 6.2%) increase in risk of all-cause death, cumulatively, in the 3 days following exposure. These effects were present in those aged 65 years and over, while no effect was observed in people under 65 years.

CONCLUSION

Bushfire PM_2.5 exposure is associated with an increased risk of mortality, particularly in those over 65 years of age. This increase in risk was clearest at Bushfire PM_2.5 concentrations up to 30 µg/m³ above background (Non-bushfire), with possible plateauing at higher concentrations of Bushfire PM_2.5.

Modern Concepts of the Role of Fine Particles (PM 2.5) in the Genesis of Atherosclerosis and Myocardial Damage: Clinical and Epidemiological Data, the Main Pathophysiological Mechanisms

Due to the fact that atherosclerotic cardiovascular diseases (CVDs) dominate in the structure of morbidity, disability and mortality of the population, the study of the risk factors for the development of atherosclerotic CVDs, as well as the study of the underlying pathogenetic mechanisms thereof, is the most important area of scientific research in modern medicine. Understanding these aspects will allow improving the set of treatment and preventive measures and activities. One of the important risk factors for the development of atherosclerosis, which has been actively studied recently, is air pollution with fine particulate matter (PM 2.5). According to clinical and epidemiological data, the level of air pollution with PM 2.5 exceeds the normative indicators in most regions of the world and is associated with subclinical markers of atherosclerosis and mortality from atherosclerotic CVDs. The aim of this article is to systematize and discuss in detail the role of PM 2.5 in the development of atherosclerosis and myocardial damage with the consideration of epidemiological and pathogenetic aspects. Materials and Methods: This narrative review is based on the analysis of publications in the Medline, PubMed, and Embase databases. The terms "fine particles" and "PM 2.5" in combination with "pathophysiological mechanisms," "cardiovascular diseases", "atherosclerosis", "cardiac troponins", "myocardial damage" and "myocardial injury" were used to search publications. Conclusion: According to the conducted narrative review, PM 2.5 should be regarded as the significant risk factor for the development of atherosclerotic CVDs. The pro-atherogenic effect of fine particulate matter is based on several fundamental and closely interrelated pathophysiological mechanisms: endothelial dysfunction, impaired lipid metabolism, increased oxidative stress and inflammatory reactions, impaired functioning of the vegetative nervous system and increased activity of the hemostatic system. In addition, PM 2.5 causes subclinical damage to cardiac muscle cells by several mechanisms: apoptosis, oxidative stress, decreased oxygen delivery due to coronary atherosclerosis and ischemic damage of cardiomyocytes. Highly sensitive cardiac troponins are promising markers for detecting subclinical myocardial damage in people living in polluted regions.

Associations between short-term exposure of ambient particulate matter and hemodialysis patients death: A nationwide, longitudinal case-control study in China

BACKGROUND

Long-term exposure to particulate air pollutants can lead to an increase in mortality of hemodialysis patients, but evidence of mortality risk with short-term exposure to ambient particulate matter is lacking. This study aimed to estimate the association of short-term exposure to ambient particulate matter across a wide range of concentrations with hemodialysis patients mortality.

METHODS

We performed a time-stratified case-crossover study to estimate the association between short-term exposures to PM_2.5 and PM₁₀ and mortality of hemodialysis patients. The study included 18,114 hemodialysis death case from 279 hospitals in 41 cities since 2013. Daily particulate matter exposures were calculated by the inverse distance-weighted model based on each case's dialysis center address. Conditional logistic regression were implemented to quantify exposure-response associations. The sensitivity analysis mainly explored the lag effect of particulate matter.

RESULTS

During the study period, there were 18,114 case days and 61,726 control days. Of all case and control days, average PM_2.5 and PM₁₀ levels were 43.98 μg/m³ and 70.86 μg/m³, respectively. Each short-term increase of 10 μg/m³ in PM_2.5 and PM₁₀ were statistically significantly associated with a relative increase of 1.07 % (95 % confidence interval [CI]: 0.99 % - 1.15 %) and 0.89 % (95 % CI: 0.84 % - 0.94 %) in daily mortality rate of hemodialysis patients, respectively. There was no evidence of a threshold in the exposure-response relationship. The mean of daily exposure on the same day of death and one-day prior (Lag 01 Day) was the most plausible exposure time window.

CONCLUSIONS

This study confirms that short-term exposure to particulate matter leads to increased mortality in hemodialysis patients. Policy makers and public health practices have a clear and urgent opportunity to pass air quality control policies that care for hemodialysis populations and incorporate air quality into the daily medical management of hemodialysis patients.

PM2.5 exceedances and source appointment as inputs for an early warning system

Between June 2018 and April 2019, a sampling campaign was carried out to collect PM2.5, monitoring meteorological parameters and anthropogenic events in the Sartenejas Valley, Venezuela. We develop a logistic model for PM2.5 exceedances (≥ 12.5 µg m-3). Source appointment was done using elemental composition and morphology of PM by scanning electron microscopy coupled with energy dispersive spectroscopy (SEM-EDS). A proposal of an early warning system (EWS) for PM pollution episodes is presented. The logistic model has a holistic success rate of 94%, with forest fires and motor vehicle flows as significant variables. Source appointment analysis by occurrence of events showed that samples with higher concentrations of PM had carbon-rich particles and traces of K associated with biomass burning, as well as aluminosilicates and metallic elements associated with resuspension of soil dust by motor-vehicles. Quantitative source appointment analysis showed that soil dust, garbage burning/marine aerosols and wildfires are three majority sources of PM. An EWS for PM pollution episodes around the Sartenejas Valley is proposed considering the variables and elements mentioned.

PM2.5 exposure during pregnancy is associated with altered placental expression of lipid metabolic genes in a US birth cohort

Inhalation of ambient PM2.5, shown to be able to cross the placenta, has been linked to adverse obstetric and postnatal metabolic health outcomes. The placenta regulates fetal growth and influences postnatal development via fetal programming. Placental gene expression may be influenced by intrauterine exposures to PM2.5. Herein, we explore whether maternal PM2.5 exposure during pregnancy alters placental gene expression related to lipid and glucose metabolism in a U.S. birth cohort, the Rhode Island Child Health Study (RICHS). Average PM2.5 exposure level was estimated linking residential addresses and satellite data across the three trimesters using spatio-temporal models. Based on Gene Ontology annotations, we curated a list of 657 lipid and glucose metabolism genes. We conducted a two-staged analysis by leveraging placental RNA-Seq data from 148 subjects to identify top dysregulated metabolic genes associated with PM2.5 (Phase I) and then validated the results in placental samples from 415 participants of the cohort using RT-qPCR (Phase II). Associations between PM2.5 and placental gene expression were explored using multivariable linear regression models in the overall population and in sex-stratified analyses. The average level of PM2.5 exposure across pregnancy was 8.0μg/m³, which is below the national standard of 12μg/m³. Phase I revealed that expression levels of 32 out of the curated list of 657 genes were significantly associated with PM2.5 exposure (FDR P<0.01), 28 genes showed differential expression modified by sex of the infant. Five of these genes (ABHD3, ATP11A, CLTCL1, ST6GALNAC4 and PSCA) were validated using RT-qPCR. Associations were stronger in placentas from male births compared to females, indicating a sex-dependent effect. These genes are involved in inflammation, lipid transport, cell-cell communication or cell invasion. Our results suggest that gestational PM2.5 exposure may alter placental metabolic function. However, whether it confers long-term programming effects postnatally, especially in a sex-specific matter, warrants further studies.

Exposure models for particulate matter elemental concentrations in Southern California

Due to a scarcity of routine monitoring of speciated particulate matter (PM), there has been limited capability to develop exposure models that robustly estimate component-specific concentrations. This paper presents the largest such study conducted in a single urban area. Using samples that were collected at 220 locations over two seasons, quasi-ultrafine (PM_0.2), accumulation mode fine (PM_0.2-2.5), and coarse (PM_2.5-10) particulate matter concentrations were used to develop spatiotemporal regression, machine learning models that enabled predictions of 24 elemental components in eight Southern California communities. We used supervised variable selection of over 150 variables, largely from publicly available sources, including meteorological, roadway and traffic characteristics, land use, and dispersion model estimates of traffic emissions. PM components that have high oxidative potential (and potentially large health effects) or are otherwise important markers for major PM sources were the primary focus. We present results for copper, iron, and zinc (as non-tailpipe vehicle emissions); elemental carbon (diesel emissions); vanadium (ship emissions); calcium (soil dust); and sodium (sea salt). Spatiotemporal linear regression models with 17 to 36 predictor variables including meteorology; distance to different classifications of roads; intersections and off ramps within a given buffer distance; truck and vehicle traffic volumes; and near-roadway dispersion model estimates produced superior predictions over the machine learning approaches (cross validation R-squares ranged from 0.76 to 0.92). Our models are easily interpretable and appear to have more effectively captured spatial gradients in the metallic portion of PM than other comparably large studies, particularly near roadways for the non-tailpipe emissions. Furthermore, we demonstrated the importance of including spatiotemporally resolved meteorology in our models as it helped to provide key insights into spatial patterns and allowed us to make temporal predictions.

Aryl hydrocarbon receptor activation-mediated vascular toxicity of ambient fine particulate matter: contribution of polycyclic aromatic hydrocarbons and osteopontin as a biomarker

BACKGROUND

Exposure to ambient fine particulate matter (PM_2.5) is associated with vascular diseases. Polycyclic aromatic hydrocarbons (PAHs) in PM_2.5 are highly hazardous; however, the contribution of PM_2.5-bound PAHs to PM_2.5-associated vascular diseases remains unclear. The ToxCast high-throughput in vitro screening database indicates that some PM_2.5-bound PAHs activate the aryl hydrocarbon receptor (AhR). The present study investigated whether the AhR pathway is involved in the mechanism of PM_2.5-induced vascular toxicity, identified the PAH in PM_2.5 that was the major contributor of AhR activation, and identified a biomarker for vascular toxicity of PM_2.5-bound PAHs.

RESULTS

Treatment of vascular smooth muscle cells (VMSCs) with an AhR antagonist inhibited the PM_2.5-induced increase in the cell migration ability; NF-κB activity; and expression of cytochrome P450 1A1 (CYP1A1), 1B1 (CYP1B1), interleukin-6 (IL-6), and osteopontin (OPN). Most PM_2.5-bound PAHs were extracted into the organic fraction, which drastically enhanced VSMC migration and increased mRNA levels of CYP1A1, CYP1B1, IL-6, and OPN. However, the inorganic fraction of PM_2.5 moderately enhanced VSMC migration and only increased IL-6 mRNA levels. PM_2.5 increased IL-6 secretion through NF-κB activation; however, PM_2.5 and its organic extract increased OPN secretion in a CYP1B1-dependent manner. Inhibiting CYP1B1 activity and silencing OPN expression prevented the increase in VSMC migration ability caused by PM_2.5 and its organic extract. The AhR activation potencies of seven PM_2.5-bound PAHs, reported in the ToxCast database, were strongly correlated with their capabilities of enhancing the migration ability of VSMCs. Benzo(k)fluoranthene (BkF) contributed the most to the AhR agonistic activity of ambient PM_2.5-bound PAHs. The association between PM_2.5-induced vascular toxicity, AhR activity, and OPN secretion was further verified in mice; PM_2.5-induced intimal hyperplasia in pulmonary small arteries and OPN secretion were alleviated in mice with low AhR affinity. Finally, urinary concentrations of 1-hydroxypyrene, a major PAH metabolite, were positively correlated with plasma OPN levels in healthy humans.

CONCLUSIONS

The present study offers in vitro, animal, and human evidences supporting the importance of AhR activation for PM_2.5-induced vascular toxicities and that BkF was the major contributor of AhR activation. OPN is an AhR-dependent biomarker of PM_2.5-induced vascular toxicity. The AhR activation potency may be applied in the risk assessment of vascular toxicity in PAH mixtures.

Association between Acute Exposure to PM2.5 Chemical Species and Mortality in Megacity Delhi, India

The association between daily all-cause mortality and short-term fine particulate matter (PM_2.5) exposure is well established in the literature. However, association between acute exposure to PM_2.5 chemical species and mortality is not well known, especially in developing countries like India. Here we examined associations between mortality and acute exposure to PM_2.5 mass concentration and their 15 chemical components using data from 2013 to 2016 in megacity Delhi using a semiparametric quasi-Poisson regression model, adjusting for mean temperature, relative humidity, and long-term time trend as the major potential confounders. Mortality estimates were further checked for effect modification by sex, age group, and season. The subspecies of NO₃^-, NH₄NO₃, Cr, NH₄⁺, EC, and OC showed a higher mortality impact than the total PM_2.5 mass. Males were at higher risk from NO₃^-, SO₄^2-, and their NH₄⁺ compounds along with carcinogen Cr, whereas female group was at higher risk from EC and OC. Among all age groups, the elderly above 65 years were the most vulnerable group prone to mortality effects from maximum species. The major mortality risk from all hazardous species arose from their winter exposures. Our study provides the first evidence of association between acute exposure to PM_2.5 chemical species and mortality anywhere in India and recommends similar studies in other regions so that sectoral mitigation emitting the most toxic species can be prioritized to maximize the health benefits.

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.

Estimating High-Resolution PM2.5 Concentrations by Fusing Satellite AOD and Smartphone Photographs Using a Convolutional Neural Network and Ensemble Learning

Aerosol optical depth (AOD) data derived from satellite products have been widely used to estimate fine particulate matter (PM2.5) concentrations. However, existing approaches to estimate PM2.5 concentrations are invariably limited by the availability of AOD data, which can be missing over large areas due to satellite measurements being obstructed by, for example, clouds, snow cover or high concentrations of air pollution. In this study, we addressed this shortcoming by developing a novel method for determining PM2.5 concentrations with high spatial coverage by integrating AOD-based estimations and smartphone photograph-based estimations. We first developed a multiple-input fuzzy neural network (MIFNN) model to measure PM2.5 concentrations from smartphone photographs. We then designed an ensemble learning model (AutoELM) to determine PM2.5 concentrations based on the Collection-6 Multi-Angle Implementation of Atmospheric Correction AOD product. The R2 values of the MIFNN model and AutoELM model are 0.85 and 0.80, respectively, which are superior to those of other state-of-the-art models. Subsequently, we used crowdsourced smartphone photographs obtained from social media to validate the transferability of the MIFNN model, which we then applied to generate smartphone photograph-based estimates of PM2.5 concentrations. These estimates were fused with AOD-based estimates to generate a new PM2.5 distribution product with broader coverage than existing products, equating to an average increase of 12% in map coverage of PM2.5 concentrations, which grows to an impressive 25% increase in map coverage in densely populated areas. Our findings indicate that the robust estimation accuracy of the ensemble learning model is due to its detection of nonlinear correlations and high-order interactions. Furthermore, our findings demonstrate that the synergy of smartphone photograph-based estimations and AOD-based estimations generates significantly greater spatial coverage of PM2.5 distribution than AOD-based estimations alone, especially in densely populated areas where more smartphone photographs are available.

Differential Mortality Risks Associated With PM2.5 Components: A Multi-Country, Multi-City Study

BACKGROUND

The association between fine particulate matter (PM2.5) and mortality widely differs between as well as within countries. Differences in PM2.5 composition can play a role in modifying the effect estimates, but there is little evidence about which components have higher impacts on mortality.

METHODS

We applied a 2-stage analysis on data collected from 210 locations in 16 countries. In the first stage, we estimated location-specific relative risks (RR) for mortality associated with daily total PM2.5 through time series regression analysis. We then pooled these estimates in a meta-regression model that included city-specific logratio-transformed proportions of seven PM2.5 components as well as meta-predictors derived from city-specific socio-economic and environmental indicators.

RESULTS

We found associations between RR and several PM2.5 components. Increasing the ammonium (NH4+) proportion from 1% to 22%, while keeping a relative average proportion of other components, increased the RR from 1.0063 (95% confidence interval [95% CI] = 1.0030, 1.0097) to 1.0102 (95% CI = 1.0070, 1.0135). Conversely, an increase in nitrate (NO3-) from 1% to 71% resulted in a reduced RR, from 1.0100 (95% CI = 1.0067, 1.0133) to 1.0037 (95% CI = 0.9998, 1.0077). Differences in composition explained a substantial part of the heterogeneity in PM2.5 risk.

CONCLUSIONS

These findings contribute to the identification of more hazardous emission sources. Further work is needed to understand the health impacts of PM2.5 components and sources given the overlapping sources and correlations among many components.

Elemental composition of fine and coarse particles across the greater Los Angeles area: Spatial variation and contributing sources

The inorganic components of particulate matter (PM), especially transition metals, have been shown to contribute to PM toxicity. In this study, the spatial distribution of PM elements and their potential sources in the Greater Los Angeles area were studied. The mass concentration and detailed elemental composition of fine (PM_2.5) and coarse (PM_2.5-10) particles were assessed at 46 locations, including urban traffic, urban community, urban background, and desert locations. Crustal enrichment factors (EFs), roadside enrichments (REs), and bivariate correlation analysis revealed that Ba, Cr, Cu, Mo, Pd, Sb, Zn, and Zr were associated with traffic emissions in both PM_2.5 and PM_2.5-10, while Fe, Li, Mn, and Ti were affected by traffic emissions mostly in PM_2.5. The concentrations of Ba, Cu, Mo, Sb, Zr (brake wear tracers), Pd (tailpipe tracer), and Zn (associated with tire wear) were higher at urban traffic sites than urban background locations by factors of 2.6-4.6. Both PM_2.5 and PM_2.5-10 elements showed large spatial variations, indicating the presence of diverse emission sources across sampling locations. Principal component analysis extracted four source factors that explained 88% of the variance in the PM_2.5 elemental concentrations, and three sources that explained 86% of the variance in the PM_2.5-10 elemental concentrations. Based on multiple linear regression analysis, the contribution of traffic emissions (27%) to PM_2.5 was found to be higher than mineral dust (23%), marine aerosol (18%), and industrial emissions (8%). On the other hand, mineral dust was the dominant source of PM_2.5-10 with 45% contribution, followed by marine aerosol (22%), and traffic emissions (19%). This study provides novel insight into the spatial variation of traffic-related elements in a large metropolitan area.

Short- and intermediate-term exposure to ambient fine particulate elements and leukocyte epigenome-wide DNA methylation in older men: the Normative Aging Study

BACKGROUND

Several epigenome-wide association studies (EWAS) of ambient particulate matter with aerodynamic diameter ≤ 2.5 µm (PM_2.5) have been reported. However, EWAS of PM_2.5 elements (PEs), reflecting different emission sources, are very limited.

OBJECTIVES

We performed EWAS of short- and intermediate-term exposure to PM_2.5 and 13 PEs. We hypothesized that significant changes in DNAm may vary by PM_2.5 mass and its elements.

METHODS

We repeatedly collected blood samples in the Normative Aging Study and measured leukocyte DNA methylation (DNAm) with the Illumina HumanMethylation450K BeadChip. We collected daily PM_2.5 and 13 PEs at a fixed central site. To estimate the associations between each PE and DNAm at individual cytosine-phosphate-guanine (CpG) sites, we incorporated a distributed-lag (0-27 d) term in the setting of median regression with subject-specific intercept and examined cumulative lag associations. We also accounted for selection bias due to loss to follow-up and mortality prior to enrollment. Significantly differentially methylated probes (DMPs) were identified using Bonferroni correction for multiple testing. We further conducted regional and pathway analyses to identify significantly differentially methylated regions (DMRs) and pathways.

RESULTS

We included 695 men with 1,266 visits between 1999 and 2013. The subjects had a mean age of 75 years. The significant DMPs, DMRs, and pathways varied by to PM_2.5 total mass and PEs. For example, PM_2.5 total mass was associated with 2,717 DMPs and 10,470 DMRs whereas Pb was associated with 3,173 DMPs and 637 DMRs. The identified pathways by PM_2.5 mass were mostly involved in mood disorders, neuroplasticity, immunity, and inflammation, whereas the pathways associated with motor vehicles (BC, Cu, Pb, and Zn) were related with cardiovascular disease and cancer (e.g., "PPARs signaling").

CONCLUSIONS

PM_2.5 and PE were associated with methylation changes at multiple probes and along multiple pathways, in ways that varied by particle components.

Fine particles matter components and interstitial lung disease in rheumatoid arthritis

Exposure to ambient fine particulate matter (PM_2.5) is a risk factor for pulmonary and systemic autoimmune diseases, however evidence on which PM_2.5 chemical components are more harmful is still scant. Our goal is to investigate potential associations between PM_2.5 components and interstitial lung disease (ILD) onset in rheumatoid arthritis (RA).New-onset RA subjects identified from a United States health care insurance database (MarketScan) were followed for new onset of RA associated ILD (RA-ILD) from 2011 to 2018. Annual ambient PM_2.5 concentrations of its chemical components (i.e. sulfate, nitrate, ammonium, organic matter, black carbon, mineral dust, and sea salt) were estimated by combining satellite retrievals with chemical transport modelling and refined by geographically weighted regression. Exposures from 2006 up to one year before ILD onset or end of study were assigned to subjects based on their metropolitan division or core-based statistical area codes. A novel time-to-event quantile-based g(generalised)-computation approach was used to estimate potential associations between RA-ILD onset and the exposure mixture of all seven PM_2.5 chemical components adjusting for age, sex, and prior chronic obstructive pulmonary disease (as a proxy for smoking).We followed 280 516 new-onset RA patients and detected 2194 RA-ILD cases across 1 394 385 person-years. The adjusted hazard ratio for RA-ILD onset was 1.54 (95% confidence interval 1.47-1.63) per every decile increase in all seven exposures. Ammonium, mineral dust, and black carbon contributed more to ILD risk than the other PM_2.5 components.In conclusion, exposure to elements of PM_2.5, particularly ammonium, increases ILD risk in RA.

Mapping Total Exceedance PM2.5 Exposure Risk by Coupling Social Media Data and Population Modeling Data

The PM2.5 exposure risk assessment is the foundation to reduce its adverse effects. Population survey-related data have been deficient in high spatiotemporal detailed descriptions. Social media data can quantify the PM2.5 exposure risk at high spatiotemporal resolutions. However, due to the no-sample characteristics of social media data, PM2.5 exposure risk for older adults is absent. We proposed combining social media data and population survey-derived data to map the total PM2.5 exposure risk. Hourly exceedance PM2.5 exposure risk indicators based on population modeling (HEPEpmd) and social media data (HEPEsm) were developed. Daily accumulative HEPEsm and HEPEpsd ranged from 0 to 0.009 and 0 to 0.026, respectively. Three peaks of HEPEsm and HEPEpsd were observed at 13:00, 18:00, and 22:00. The peak value of HEPEsm increased with time, which exhibited a reverse trend to HEPEpsd. The spatial center of HEPEsm moved from the northwest of the study area to the center. The spatial center of HEPEpsd moved from the northwest of the study area to the southwest of the study area. The expansion area of HEPEsm was nearly 1.5 times larger than that of HEPEpsd. The expansion areas of HEPEpsd aggregated in the old downtown, in which the contribution of HEPEpsd was greater than 90%. Thus, this study introduced various source data to build an easier and reliable method to map total exceedance PM2.5 exposure risk. Consequently, exposure risk results provided foundations to develop PM2.5 pollution mitigation strategies as well as scientific supports for sustainability and eco-health achievement.

Exploration of PM mass, source, and component-related factors that might explain heterogeneity in daily PM2.5-mortality associations across the United States

Multi-city epidemiologic studies examining short-term (daily) differences in fine particulate matter (PM2.5) provide evidence of substantial spatial heterogeneity in city-specific mortality risk estimates across the United States. Because PM2.5 is a mixture of particles, both directly emitted from sources or formed through atmospheric reactions, some of this heterogeneity may be due to regional variations in PM2.5 toxicity. Using inverse variance weighted linear regression, we examined change in percent change in mortality in association with 24 "exposure" determinants representing three basic groupings based on potential explanations for differences in PM toxicity - size, source, and composition. Percent changes in mortality for the PM2.5-mortality association for 313 core-based statistical areas and their metropolitan divisions over 1999-2005 were used as the outcome. Several determinants were identified as potential contributors to heterogeneity: all mass fraction determinants, vehicle miles traveled (VMT) for diesel total, VMT gas per capita, PM2.5 ammonium, PM2.5 nitrate, and PM2.5 sulfate. In multivariable models, only daily correlation of PM2.5 with PM10 and long-term average PM2.5 mass concentration were retained, explaining approximately 10% of total variability. The results of this analysis contribute to the growing body of literature specifically focusing on assessing the underlying basis of the observed spatial heterogeneity in PM2.5-mortality effect estimates, continuing to demonstrate that this heterogeneity is multifactorial and not attributable to a single aspect of PM.

Exposure to nanoparticles derived from diesel particulate filter equipped engine increases vulnerability to arrhythmia in rat hearts

Air pollution is well recognized as a central player in cardiovascular disease. Exhaust particulate from diesel engines (DEP) is rich in nanoparticles and may contribute to the health effects of particulate matter in the environment. Moreover, diesel soot emitted by modern engines denotes defective surfaces alongside chemically-reactive sites increasing soot cytotoxicity. We recently demonstrated that engineered nanoparticles can cross the air/blood barrier and are capable to reach the heart. We hypothesize that DEP nanoparticles are pro-arrhythmogenic by direct interaction with cardiac cells. We evaluated the internalization kinetics and the effects of DEP, collected from Euro III (DEPe3, in the absence of Diesel Particulate Filter, DPF) and Euro IV (DEPe4, in the presence of DPF) engines, on alveolar and cardiac cell lines and on in situ rat hearts following DEP tracheal instillation. We observed significant differences in DEP size, metal and organic compositions derived from both engines. DEPe4 comprised ultrafine particles (<100 nm) and denoted a more pronounced toxicological outcome compared to DEPe3. In cardiomyocytes, particle internalization is fastened for DEPe4 compared to DEPe3. The in-vivo epicardial recording shows significant alteration of EGs parameters in both groups. However, the DEPe4-instilled group showed, compared to DEPe3, a significant increment of the effective refractory period, cardiac conduction velocity, and likelihood of arrhythmic events, with a significant increment of membrane lipid peroxidation but no increment in inflammation biomarkers. Our data suggest that DEPe4, possibly due to ultrafine nanoparticles, is rapidly internalized by cardiomyocytes resulting in an acute susceptibility to cardiac electrical disorder and arrhythmias that could accrue from cellular toxicity. Since the postulated transfer of nanoparticles from the lung to myocardial cells has not been investigated it remains open whether the effects on the cardiovascular function are the result of lung inflammatory reactions or due to particles that have reached the heart.

Coal beneficiation technology to reduce hazardous heavy metals in fly ash

Fly ash emitted from the coal-fired power plant is the major contributor of the outdoor airborne particulate matters (PMs). Coal beneficiation, an industrial process to improve the quality of raw coal by removing ash-bearing components, can be a cost-effective sustainable and clean technology to reduce the emission of hazardous trace metals. As the removal efficiency of mineral matters and heavy metals within the coal is depend highly on the raw coal and the employed beneficiation process, a wide range of case studies at laboratory- and industrial-scale, published in the last 20 years, are reviewed in this study. Our review indicates that the coal beneficiation processes can effectively reduce content of heavy metals of fly ash, encouraging the use of clean coal to reduce pollutants emission.

CC16-TNF-α negative feedback loop formed between Clara cells and normal airway epithelial cells protects against diesel exhaust particles exposure-induced inflammation

CC16 is almost exclusively expressed in non-ciliated epithelial Clara cells, and widely used as a Clara cell marker. Diesel exhaust particles (DEPs), the fine particulate matters produced by diesel engines, cause or exacerbate airway-related diseases. Our previous study documented that DEP inhibits the CC16 expression in the immortalized mouse Clara cell line through methylation of C/EBPα promoter. However, the molecular mechanism by which DEP regulates CC16 secretion is unclear. Here, we isolated CC16 containing Clara cells (CC16⁺) from human distal lung, and found that DEP inhibited CC16 secretion from CC16⁺ cells via methylation of C/EBPα and inhibition of Munc18b transcription. CC16⁺ cell conditioned media containing different concentrations of CC16 was prepared and used for culture of airway epithelial cells BEAS-2B with no expression of CC16. A positive correlation was observed between CC16 level and DEP-induced autophagy activity, and a negative correlation between CC16 level and DEP-induced pro-inflammatory cytokine TNF-α, IL-6, and IL-8 level, suggesting that CC16 might mitigate DEP-induced inflammation via promoting autophagy in BEAS-2B cells. This result was further confirmed by adding recombinant CC16 to BEAS-2B cells exposed to DEP. Moreover, CC16 level was significantly increased when CC16⁺ cells were cultured in BEAS-2B cell conditioned medium containing TNF-α or the normal medium supplemented with recombinant TNF-α, suggesting that TNF-α induced CC16 production and secretion from CC16⁺ cells. Collectively, these data point that CC16 and TNF-α form a negative feedback loop, and this negative feedback loop between Clara cells and normal airway epithelial cells protects against DEP exposure-induced inflammation.

lncRNA SOX2-OT ceRNA network enhances the malignancy of long-term PM2.5-exposed human bronchial epithelia

Exposure to fine particulate matter (PM_2.5) in outdoor air is carcinogenic and associated with the development of lung cancer; however, the underlying mechanism remains unclear. In the present study, the profiles of lncRNA, microRNA and mRNA expression profiles in human bronchial epithelia (HBE) following exposure to PM_2.5, diesel exhaust particles (DEPs), or aluminum oxide nanoparticles (Al₂O₃ NPs) were explored by microarray to reveal the lncRNA-microRNA-mRNA network participating in the malignant transformation of HBE cells following long-term PM_2.5 exposure. The results showed that lncRNA SOX2 overlapping transcript (SOX2-OT) was significantly induced in HBE cells exposed to PM_2.5, DEPs, or Al₂O₃ NPs, acting as a sponge to microRNA-345-5p, which subsequently increased the expression levels of epidermal growth factor receptor (EGFR). EGFR is a therapeutic target in non-small cell lung cancer. Here, we found that SOX2-OT is an upstream trigger of EGFR in HBE cells during long-term PM_2.5 exposure. Importantly, SOX2-OT knockdown effectively reduced the colony formation and migration capacities of HBE cells, compared to the wild type control. Collectively, SOX2-OT/microRNA-345-5p/EGFR is a ceRNA mechanism underlying the malignant transformation of bronchial epithelia exposed to PM_2.5, which improves our understanding of the association between ambient PM_2.5 exposure and the development of lung cancer.