Effects Of Pollution Exposure Research Articles

London Fog: A Century of Pollution and Mortality, 1866–1965

Abstract This study draws on London’s long experience with air pollution in order to improve our understanding of the overall effects of pollution exposure and how and why these effects evolve as locations develop. I compare uniquely detailed new mortality data covering 1866 to 1965 to the timing of London’s famous fog events, which trapped emissions in the city. I show that air pollution was a major contributor to mortality in London over this period and that it interacted strongly with specific infectious diseases. As a consequence of this interaction, reductions in the infectious disease burden substantially altered the health costs of pollution.

Calculating toxic pressure for mixtures of endocrine disruptors

Incidence of autoimmune disorders, birth defects, and neurological diseases rose over the past 50 years due to increasing variety and quantity of pollutants. To date, there appear few methods capable to evaluate and predict mixture effects by endocrine disruptors (EDs). For the first time, we have developed calculus to determine mixture effects by all kinds of EDs.Our method uses the golden ratio φ and draws from bifurcation and chaos theory. Using also the concept of molecular mimicry, we developed the equation: . We successfully tested the equation using a range of cohort studies and biomarkers, and for different pollutants like heavy metals, thyroid hormone mimickants, chromate/chlorate, etc.The equation is simple enough to use with only minor prior knowledge and understanding of basic algebra. The method is universal and calculation is data 'light', requiring only pollutant concentrations [C], potencies K and an integer n for endocrinal involvement. This study offers a comprehensive framework to assess the health effects of pollutant exposure across diverse populations, envisioning far-reaching impact, and presenting practical examples and insights.

Molecular mechanisms of environmental pollutant-induced cartilage damage: from developmental disorders to osteoarthritis.

The objective of the present study was to review the molecular mechanisms of the adverse effects of environmental pollutants on chondrocytes and extracellular matrix (ECM). Existing data demonstrate that both heavy metals, including cadmium (Cd), lead (Pb), and arsenic (As), as well as organic pollutants, including polychlorinated dioxins and furans (PCDD/Fs) and polychlorinated biphenyls (PCB), bisphenol A, phthalates, polycyclic aromatic hydrocarbons (PAH), pesticides, and certain other organic pollutants that target cartilage ontogeny and functioning. Overall, environmental pollutants reduce chondrocyte viability through the induction apoptosis, senescence, and inflammatory response, resulting in cell death and impaired ECM production. The effects of organic pollutants on chondrocyte development and viability were shown to be mediated by binding to the aryl hydrocarbon receptor (AhR) signaling and modulation of non-coding RNA expression. Adverse effects of pollutant exposures were observed in articular and growth plate chondrocytes. These mechanisms also damage chondrocyte precursors and subsequently hinder cartilage development. In addition, pollutant exposure was shown to impair chondrogenesis by inhibiting the expression of Sox9 and other regulators. Along with altered Runx2 signaling, these effects also contribute to impaired chondrocyte hypertrophy and chondrocyte-to-osteoblast trans-differentiation, resulting in altered endochondral ossification. Several organic pollutants including PCDD/Fs, PCBs and PAHs, were shown to induce transgenerational adverse effects on cartilage development and the resulting skeletal deformities. Despite of epidemiological evidence linking human environmental pollutant exposure to osteoarthritis or other cartilage pathologies, the data on the molecular mechanisms of adverse effects of environmental pollutant exposure on cartilage tissue were obtained from studies in laboratory rodents, fish, or cell cultures and should be carefully extrapolated to humans, although they clearly demonstrate that cartilage should be considered a putative target for environmental pollutant toxicity.

Air pollution related adverse respiratory health outcomes in California’s San Joaquin Valley: evidence from 2016 linked emergency department and hospital records

The San Joaquin Valley (SJV) of California has been consistently identified as having one of the highest levels of air pollution in the US. Despite federal and state standards, the SJV has been in non-attainment status for daily PM2.5 concentrations, extreme non-attainment for 8 hr O3 levels, and attainment for NO2. An epidemiological time-stratified case-crossover design was used to estimate the relationship between exposure to NO2, O3, PM2.5 and adverse health outcomes in asthma and upper respiratory infections (URIs). This study compared pollutant exposure effects for each case during limited time intervals and adjusted for seasonality. Elevated concentrations of three criteria outdoor air pollutants are associated with increased asthma and URI-related ED visits and hospitalizations in the SJV for all ages. NO2 exposure increased the odds of having an ED visit by 2.4% in lag 1 (95% CI: 1.017, 1.031). Lags 2, 3, 4, 5, 7, 9, and 14 were statistically significant. O3 modestly increased the odds of ED visits by 0.3% (95% CI:1.000, 1.006) after immediate exposure in the warm season. In the cold season, PM2.5 estimates were significant for all lags except for lags 4 and 12. The two-week lag increased the odds by 28% (95% CI:1.218, 1.345) for ED visits, and 16.5% (95% CI:1.009, 1.345) increased the odds of being hospitalized after cumulative exposure to PM2.5. Findings suggest that SJV residents experience adverse health effects due to elevated exposure to NO2 despite attainment of federal and state pollutant standards. This study provides new evidence about the effects of three criteria air pollutants and adverse health outcomes in the SJV region. The air quality regulatory and public health governing bodies should consider revisions to regional pollutant thresholds and local public health strategies to prevent adverse health outcomes during short and prolonged periods of air pollution exposure.

Ultrafine particulate matter exposure during second year of life, but not before, associated with increased risk of autism spectrum disorder in BKMR mixtures model of multiple air pollutants

Prenatal and early postnatal air pollution exposures have been shown to be associated with autism spectrum disorder (ASD) risk but results regarding specific air pollutants and exposure timing are mixed and no study has investigated the effects of combined exposure to multiple air pollutants using a mixtures approach. We aimed to evaluate prenatal and early life multipollutant mixtures for the drivers of associations of air pollution with ASD. This study examined 484 typically developing (TD) and 660 ASD children from the CHARGE case-control study. Daily air concentrations for NO2, O3, ultrafine (PM0.1), fine (PM0.1-2.5), and coarse (PM2.5-10) particles were predicted from chemical transport models with statistical bias adjustment based on ground-based monitors. Daily averages were calculated for each exposure period (pre-pregnancy, each trimester of pregnancy, first and second year of life) between 2000 and 2016. Air pollution variables were natural log-transformed and then standardized. Individual and joint effects of pollutant exposure with ASD, and potential interactions, were evaluated for each period using hierarchical Bayesian Kernel Machine Regression (BKMR) models, with three groups: PM size fractions (PM0.1, PM0.1-2.5, PM2.5-10), NO2, and O3. In BKMR models, the PM group was associated with ASD in year 2 (group posterior inclusion probability (gPIP) = 0.75), and marginally associated in year 1 (gPIP = 0.497). PM2.5-10 appeared to drive the association (conditional PIP (cPIP) = 0.64) in year 1, while PM0.1 appeared to drive the association in year 2 (cPIP = 0.76), with both showing a moderately strong increased risk. Pre-pregnancy O3 showed a slight J-shaped risk of ASD (gPIP = 0.55). No associations were observed for exposures during pregnancy. Pre-pregnancy O3 and year 2 p.m.0.1 exposures appear to be associated with an increased risk of ASD. Future research should examine ultrafine particulate matter in relation to ASD.

The influence of PM2.5 exposure duration and concentration on outpatient visits of urban hospital in a typical heavy industrial city.

To explain the duration and dose effects of pollutant exposure on public health and provide scientific data for air pollution prevention and control and disease prevention by examining the influence of PM2.5 concentration and exposure duration on daily outpatient visits among patients with cardiovascular, cerebrovascular, and respiratory diseases in a typical heavy industrial city in China. Daily outpatient data on cardiovascular, cerebrovascular, and respiratory diseases and regional PM2.5 exposure duration and concentration were collected from a provincial hospital in Taiyuan, China, from 2016 to 2021. The correlations of numeric variables were analyzed using the Pearson correlation method. A generalized additive model (GAMs) was also established to investigate the effects of PM2.5 concentration and exposure duration on outpatient visits. Correlation analysis showed that the outpatient visits in Taiyuan was significantly correlated with the PM2.5 concentration and exposure duration. The longer the exposure time of PM2.5 pollution, the stronger the correlation of PM2.5 with outpatient visits showed. Cardiovascular outpatient visits were extremely significant related with medium to long-term exposure of PM2.5 (exposure with more than 30days) (p < 0.001). In addition, outpatient visits of cerebrovascular and respiratory disease were extremely significant correlated with PM2.5 (exposures within 0-360days) (p < 0.001). The results of GAMs showed the linear or the nonlinear relationships between outpatient visits and exposure of PM2.5. Among the linear relationships, when average concentration of PM2.5 (exposure within less than 15days) increased by 1mg/m3, the cardiovascular outpatient visits increased most dramatically (by about 440 people). For nonlinear relationships, when the average PM2.5 concentration (exposure with over 30days or more) increased by 1mg/m3, the most dramatic increase occurred in cardiovascular outpatient visits (with a maximum increase of 7000), followed by cerebrovascular outpatient visits (with a maximum increase of 1200), and respiratory outpatient visits (with a maximum increase of 250). The GAMs also revealed a dose effect in the relationship between outpatient visits and PM2.5 exposure. In moderately polluted air (based on air quality standards of China, GB3095-2012), when the average concentration of PM2.5 increased by 1mg/m3, the cardiovascular outpatient visits increased the most (by 1200 people), followed by cerebrovascular outpatient visits (by 200 people) and respiratory outpatient visits (by 20 people). We concluded that outpatient visits in cardiovascular, cerebrovascular, and respiratory disease are closely correlated with the concentration and exposure duration of air pollution. There is a linear relationship between short-term air pollution exposure (exposure within less than 15days) and outpatient visits. As PM2.5 concentration increases, cardiovascular outpatient visits increase gradually, with its growth trend exceeding that of cerebrovascular and respiratory disease. There is a nonlinear relationship between medium and long-term air pollution exposure (exposure with more than 30days) and outpatient visits, with cardiovascular and cerebrovascular outpatient visits showed a nonlinear but overall upward trend when the atmosphere is moderately polluted.

Relationship Between Outdoor Air Pollutant Exposure and Premature Delivery in China- Systematic Review and Meta-Analysis.

Objective: Preterm birth (PTB) is considered as a public health problem and one of the main risk factors related to the global disease burden. The purpose of this study aims to explore the influence of exposure to major air pollutants at different pregnancies on PTB. Methods: The relationship between air pollutants and PTB in China was collected from cohort studies and case-control studies published before 30 April 2022. Meta-analysis was carried out with STATA 15.0 software. Results: A total of 2,115 papers were retrieved, of which 18 papers met the inclusion criteria. The comprehensive effect of pollutant exposure and PTB were calculated. PM2.5 during entire pregnancy and O3 exposure during third trimester were positively associated with preterm birth. Every 10μg/m3 increase in the average concentration of PM2.5 during the whole pregnancy will increase the risk of premature delivery by 4%, and every 10μg/m3 increase in the average concentration of O3 in the third trimester will increase the risk of premature delivery by 1%. Conclusion: Exposure to PM2.5 entire prenatal pregnancy and O3 in third trimester is associated with an increased risk of preterm birth occurrence.

Traffic-related air pollution is associated with spontaneous extremely preterm birth and other adverse perinatal outcomes

Although there is growing awareness of the relationship between air pollution and preterm birth, limited data exist regarding the relationship with spontaneous preterm birth and severe neonatal outcomes. This study aimed to examine the association between traffic-associated air pollution exposure in pregnancy and adverse perinatal outcomes including extremes of preterm birth, neonatal intensive care unit admissions, low birthweight, neonatal respiratory diagnosis, neonatal respiratory support, and neonatal sepsis evaluation. This was a retrospective cohort study of singleton pregnancies of patients residing in a metropolitan area in the southern United States. Using monitors strategically located across the region, average nitrogen dioxide concentrations were obtained from the Environmental Protection Agency Air Quality System database. For patients living within 10 miles of a monitoring station, average exposure to nitrogen dioxide was estimated for individual patients' pregnancy by trimester. Logistic regression models were used to assess the effect of pollutant exposure on gestational age at birth, indicated vs spontaneous delivery, and neonatal outcomes while adjusting for maternal age, self-reported race, parity, season of conception, diabetes mellitus, body mass index, registered Health Equity Index, and nitrogen dioxide monitor region. Adjusted odds ratios and 95% confidence intervals were calculated for an interquartile increase in average nitrogen dioxide exposure. Between January 1, 2013 and December 31, 2021, 93,164 patients delivered a singleton infant. Of these, 62,189 had measured nitrogen dioxide exposure during the pregnancy from a nearby monitoring station. Higher average nitrogen dioxide exposure throughout pregnancy was significantly associated with preterm birth (adjusted odds ratio, 1.94; 95% confidence interval, 1.77-2.12) and an increase in neonatal intensive care unit admissions, low birthweight infants, neonatal respiratory diagnosis, neonatal respiratory support, and neonatal sepsis evaluation. This relationship persisted for nulliparous patients and spontaneous preterm birth, and had a greater association with earlier preterm birth. In a metropolitan area, increased exposure to the air pollutant nitrogen dioxide in pregnancy was associated with spontaneous preterm birth and had a greater association with extremely preterm birth. A greater association with neonatal intensive care unit admissions, low-birthweight infants, neonatal respiratory diagnosis, neonatal respiratory support, and neonatal sepsis evaluation was found even in term infants.

Effect of air pollution on adult chronic diseases: Evidence from a quasi-natural experiment in China.

We utilize a quasi-experiment derived from China's Huai River policy to investigate the effect of air pollution on adult chronic diseases. The policy led to higher pollution exposure in cities north of the river boundary because they received centralized coal-based heating supply from the government during winter, whereas cities in the south did not. By applying a geographic regression discontinuity design based on distance from the Huai River, we determine that a 10 μg/m3 increase in fine particulate matter (PM2.5) raises chronic diseases rates by 3.2% in adults, particularly cardiorespiratory system diseases. Furthermore, the same effects are observed on multiple chronic disease rates, but the rates are reduced to 1.3%. The effect of pollution exposure varies depending on age, gender, and urban/rural status. Our findings imply that reducing 10 μg/m3 of the average nationwide level of PM2.5 concentration will save 27.46 billion CNY (4.16 billion USD) in chronic disease costs.

Detecting Elevated Air Pollution Levels by Monitoring Web Search Queries: Algorithm Development and Validation

Real-time air pollution monitoring is a valuable tool for public health and environmental surveillance. In recent years, there has been a dramatic increase in air pollution forecasting and monitoring research using artificial neural networks. Most prior work relied on modeling pollutant concentrations collected from ground-based monitors and meteorological data for long-term forecasting of outdoor ozone (O3), oxides of nitrogen, and fine particulate matter (PM2.5). Given that traditional, highly sophisticated air quality monitors are expensive and not universally available, these models cannot adequately serve those not living near pollutant monitoring sites. Furthermore, because prior models were built based on physical measurement data collected from sensors, they may not be suitable for predicting the public health effects of pollution exposure. This study aimed to develop and validate models to nowcast the observed pollution levels using web search data, which are publicly available in near real time from major search engines. We developed novel machine learning-based models using both traditional supervised classification methods and state-of-the-art deep learning methods to detect elevated air pollution levels at the US city level by using generally available meteorological data and aggregate web-based search volume data derived from Google Trends. We validated the performance of these methods by predicting 3 critical air pollutants (O3, nitrogen dioxide, and PM2.5) across 10 major US metropolitan statistical areas in 2017 and 2018. We also explore different variations of the long short-term memory model and propose a novel search term dictionary learner-long short-term memory model to learn sequential patterns across multiple search terms for prediction. The top-performing model was a deep neural sequence model long short-term memory, using meteorological and web search data, and reached an accuracy of 0.82 (F1-score 0.51) for O3, 0.74 (F1-score 0.41) for nitrogen dioxide, and 0.85 (F1-score 0.27) for PM2.5, when used for detecting elevated pollution levels. Compared with using only meteorological data, the proposed method achieved superior accuracy by incorporating web search data. The results show that incorporating web search data with meteorological data improves the nowcasting performance for all 3 pollutants and suggest promising novel applications for tracking global physical phenomena using web search data.

Severe Air Pollution Exposure and Long-Term Health Outcomes.

There is a large literature that documents the negative health implications of exposure to air pollution, particularly PM2.5. Much of this literature, however, relies on short-term cross-sectional data, which cannot establish a true causal link between pollution and health. There are also very few studies that document long- and very long-term effects. This study intends to estimate a causal relationship between exposure to severe air pollution and negative health outcomes that persist over long periods of time. We use a large longitudinal dataset that spans almost 2 decades and that allows us to not only document the persistence of negative health effects, but also a pattern of recovery from a severe pollution episode. We use multivariate regression methods to estimate a causal link between air pollution and health over time. A large pollution shock that occurred in 1997 in Indonesia is used as a natural experiment to pinpoint the true causal effects of pollution exposure and not mere correlations. Exposure to an additional unit of pollution in 1997 leads to a loss of roughly six units of lung capacity and to an increase of 4.3% in the probability of being in poor general health, as measured ten years after the pollution exposure. These effects somewhat diminish over time, to a loss of roughly three units of lung capacity and to an increase of only about 3% in the probability of being in poor general health, as measured 17 years after exposure. Our study finds significant health consequences of exposure to air pollution, which persist over long periods of time, with some patterns of recovery. Policymakers should pay special attention to such massive sources of pollution and try to mitigate these negative health consequences.

The short-term impact of air pollution on medical expenditures: Evidence from Beijing

We identify the short-term effects of PM2.5 concentrations on medical costs in Beijing by analyzing two datasets: one detailing daily air quality indexes over a four-year period and the other containing individual-level records of all health care visits and medical transactions that occurred under a government insurance program that covers most city residents. We find that both higher levels of air pollution and longer-lasting pollution episodes significantly increase health care visits and medical expenditures. An analysis of multiple-day pollution episodes shows that marginal health care visits and marginal health costs start to increase as the pollution event lasts for consecutive days. Omitting the variation in the magnitude of the marginal effects of pollution exposure over the course of a pollution episode would lead to the underestimation of the total health costs of air pollution. Our findings provide empirical evidence that both the intensity and the duration of pollution episodes are critical considerations when designing policies to reduce the health costs of air pollution.

Ambient air pollution exposure and radiographic pulmonary vascular volumes.

Among 2428 Framingham Heart Study participants who underwent chest computed tomography (CT) between 2008 and 2011, pulmonary vascular volumes were calculated by image analysis, including the total vascular volume and small vessel volume (cross-sectional area <5 mm2; BV5 defined as small vessel volume). Using spatiotemporal models and participant home address, we assigned 1-year (2008) and 5-year (2004-2008) average concentrations of fine particulate matter (PM2.5), elemental carbon (EC), and ground-level ozone (O3), and distance to major roadway. We examined associations of 1- and 5-year exposures, and distance to road, with CT vascular volumes using multivariable linear regression models. There was a consistent negative association of higher O3 with lower small vessel volumes, which persisted after adjustment for distance to road. Per interquartile range (IQR) of 2008 O3, BV5 was 0.34 mL lower (95% confidence intervals [CI], -0.61 to -0.06; P = 0.02), with similar results for 5-year exposure. One-year EC exposure and closer proximity to road were weakly associated with small vessel volumes; BV5 was 0.18 mL higher per IQR of 2008 EC (95% CI, -0.05 to 0.42; P = 0.13) and 0.40 mL higher per IQR closer proximity to road (95% CI: -0.10 to 0.89; P = 0.12). PM2.5 was not associated with small vascular volumes; BV5 was 0.26 mL lower per IQR of 2008 PM2.5 (95% CI: -0.68 to 0.16; P = 0.22). Among community-dwelling adults living in the northeastern United States, higher exposure to O3 was associated with lower small pulmonary vessel volumes on CT.

Heterogeneous air pollutant exposure effects on episodic memory decline: Evidence from WHIMS‐ECHO study

AbstractBackgroundEpisodic memory (EM) declines vary by age and underlying neuropathology. Whether exposure to ambient air pollutants contributes to the observed heterogeneity of EM decline in older people is unclear.MethodWe examined the associations between exposures and EM trajectories in the US‐based Women’s Health Initiative Memory Study‐Epidemiology of Cognitive Health Outcomes (WHIMS‐ECHO) cohort (N=2042, aged 74‐92 in 2008). EM was assessed annually (2008‐18) using the immediate recall total correct (3 trials) in the telephone‐based California Verbal Learning Test. Recent 3‐year average exposures to regional PM2.5 (particulate matter with aerodynamic diameter &lt; 2.5μm) and NO2 (a proxy of traffic‐related air pollutants) and the corresponding remote 3‐year averages approximately at 10 years prior to inception were derived from yearly exposures estimated by regionalized national universal kriging. Latent‐class mixed models were used to classify EM trajectories and examine the exposure effect in each identified latent class. All models adjusted for social‐demographic factors, lifestyle, cardiovascular risk and other clinical covariates.ResultThere were two latent EM trajectories that differed by WHIMS‐ECHO baseline age (≤80 vs. oldest‐old 80+; Figure 1) and exposure characteristics (Table 1). In women aged ≤80 (N=836), greater EM decline was associated with higher PM2.5 in the subgroup with slow‐decline (76%; −0.18/year), but not in those with fast‐decline (24%; −1.75/year). The observed association, equivalent to 1‐2 years of aging, was stronger for recent 3‐year exposure (−0.151/year, per inter‐quartile‐range [IQR]=2.86µg/m3, p=0.02), as compared with remote exposure (−0.096/year, per IQR=3.28µg/m3, p=0.09). In the oldest‐old (aged&gt;80; N=1206), EM decline (70% steady decline by −1.37/year; 30% no apparent decline) was not associated with recent or remote PM2.5 exposures. In both age groups, no appreciable NO2 effects on EM decline were observed, and neither PM2.5 nor NO2 exposures during recent or remote years affected the levels of EM trajectories. Adjusted models further accounting for propensity scores of exposures revealed very similar results.ConclusionThe accelerated EM declines associated with late‐life exposures to ambient air pollutants vary by age‐related cognitive trajectories, exposure time‐windows and pollution sources. The observed association was most consistent with PM2.5 exposure during recent years in older women aged ≤80 with slow EM decline.

Heterogenous air pollution exposure effects on the dynamic association between depressive symptoms and episodic memory in women aged 80 and older

Background/aim: Elucidating how exposure influences interrelationships between episodic memory (EM) and depressive symptoms (DS) helps understand air pollution neurotoxicity on aging brain. Previously, in women primarily aged 70-years old (PMID:31881430), we showed PM2.5 (particulate matter &lt;2.5 μm) may indirectly contribute to increased DS mediated by EM declines, but no indirect associations between exposure and decreased EM mediated by increased DS were found. How exposure was associated with such longitudinal interrelationship has not been examined in women aged ≥ 80, despite observed increased incidence with possibly different etiologies of these syndromes.Methods: Women aged ≥80 years at study baseline (N=1,430;age=83.80± 2.74 years), enrolled in the Women’s Health Initiative Memory Study-Epidemiology of Cognitive Health Outcomes, completed up to 5-annual assessments of EM (latent composite of East Boston Memory Test and Telephone Interview for Cognitive Status) and DS (15-item Geriatric Depression Scale). We estimated 3-year average exposures to regional PM2.5 and NO2 (proxy of traffic pollutants) both at baseline and during the remote period 10 years earlier, using regionalized national universal kriging. Latent-change structural equation models examined how exposures were associated with the interrelationship of annual changes in T-score standardized EM and DS while adjusting for covariates.Results: A statistically significant indirect effect of remote (p&lt;0.05), but not recent (p&gt;0.05), NO2 exposure on EM decline (β=-0.042, per interquartile=9.32ppb) mediated by increased DS was present. Remote NO2 was associated with increased DS (β=.270) which was associated with EM decline (β=-.155). PM2.5 exposure, remote or recent, was not indirectly associated with declines in EM. Regardless of exposure time period, no significant indirect effects of PM2.5 or NO2 on increases in DS mediated by EM were found.Conclusions: The adverse effect air pollution on the interplay between DS and EM of older women is heterogenous, likely varying by pollutants, exposure time period, and age.

Occurrence of respiratory symptoms in elderly persons heard by the family health strategy in Águas Lindas, Ananindeua, Pará State

Respiratory diseases are among the five leading causes of death in Brazil, particularly among the elderly. This study identified the occurrence of respiratory symptoms in older persons. Specifically, a descriptive, observational, and quantitative study was conducted using a sample of elderly people enrolled in the Hypertension and Diabetes sessions of the Family Health Strategy program in a Brazilian city. MRC-ATS-DLD78 questionnaires were used to analyze respiratory symptoms with a sample comprised of 50 volunteers (mean age = 69.96 years). The median time living in a dwelling within a polluted sample area was 30 years, with 82% reporting spending more time per day at home or in the neighborhood. The presence of coughing was reported by 18% persons, expectoration (17%), productive cough (14%), wheezing (34%), dyspnea (12%), and respiratory diseases (26%). Physiological changes in aging associated with the effects of pollution exposure leave elderly people more vulnerable to respiratory diseases because they are predisposed to diseases, such as decreased pulmonary elasticity, elevated lung compliance, reduced oxygen diffusion capacity, reduced expiratory flow, and premature closure of airways. The occurrence of respiratory symptoms in the elderly was 30% with one symptom, two (22%), three (10%), four (6%), and five (2%).

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

Fine particulate matter (PM2.5) is associated with negative health outcomes in both the short and long term. However, the cohort studies that have produced many of the estimates of long-term exposure associations may fail to account for selection bias in pollution exposure as well as covariate imbalance in the study population; therefore, causal modeling techniques may be beneficial. Twenty-nine years of data from the National Health Interview Survey (NHIS) was compiled and linked to modeled annual average outdoor PM2.5 concentration and restricted-use mortality data. A series of Cox proportional hazards models, adjusted using inverse probability weights, yielded causal risk estimates of long-term exposure to ambient PM2.5 on all-cause and cardiopulmonary mortality. Covariate-adjusted estimated relative risks per 10 μg/m3 increase in PM2.5 exposure were estimated to be 1.117 (1.083, 1.152) for all-cause mortality and 1.232 (1.174, 1.292) for cardiopulmonary mortality. Inverse probability weighted Cox models provide relatively consistent and robust estimates similar to those in the unweighted baseline multivariate Cox model, though they have marginally lower point estimates and higher standard errors. These results provide evidence that long-term exposure to PM2.5 contributes to increased mortality risk in US adults and that the estimated effects are generally robust to modeling choices. The size and robustness of estimated associations highlight the importance of clean air as a matter of public health. Estimated confounding due to measured covariates appears minimal in the NHIS cohort, and various distributional assumptions have little bearing on the magnitude or standard errors of estimated causal associations.

Cumulative exposure to organic pollutants of French children assessed by hair analysis

Children represent one of the most vulnerable parts of the population regarding the effects of pollutants exposure on health. In this study, hair samples were collected between October 2013 and August 2015 from 142 French children originating from different geographical areas (urban and rural) and analysed with a GC/MS-MS method, allowing for the detection of 55 biomarkers for pesticides and metabolites both persistent and non-persistent from different families, including: organochlorines, organophosphates, pyrethroids, azoles, dinitroanilines, oxadiazines, phenylpyrazoles and carboxamidas; 4 polychlorobiphenyls (PCBs) and 5 polybromodiphenylethers (PBDEs). The number of compounds detected in each sample ranged from 9 up to 37 (21 on average), which clearly highlighted the cumulative exposure of the children. The results also showed a wide range of concentration of the pollutants in hair (often more than 100 times higher in the most exposed child compared to the less exposed), suggesting significant disparities in the exposure level, even in children living in the same area. In addition to the detection of currently used chemicals, the presence of persistent organic pollutants (POPs) in children also suggests that the French population is still exposed to POPs nowadays. PCP, DEP, PNP, 3Me4NP, trans-Cl2CA, 3PBA, fipronil and fipronil sulfone, presented statistically significant higher concentration in the hair of boys compared to girls. PCP, PNP and 3Me4NP presented statistically significant higher concentration in younger children. Finally, this study also suggests that local environmental contamination would not be the main source of exposure, and that individual specificities (habits, diet…) would be the main contributors to the exposure to the pollutants analysed here. The present study strongly supports the relevance of hair for the biomonitoring of exposure and provides the first values of organic pollutant concentration in the hair of French children.

Stress hormones as potential mediators of air pollutant effects on the brain: Rapid induction of glucocorticoid-responsive genes

Air pollution is associated with adverse effects on brain health including cognitive decline, dementia, anxiety, depression, and suicide. While toxicological studies have demonstrated the potential for repeated or chronic pollutant exposure to lead to disease states, characterisation of initial biological responses to exposure is needed to better understand underlying mechanisms. The brain is highly sensitive to glucocorticoids (primarily cortisol in humans, corticosterone in rodents), stress hormones that play important roles in cognition and mental health. We tested whether glucocorticoids could be implicated in central nervous system (CNS) effects of pollutant exposure by examining glucocorticoid-dependent signaling across brain regions after exposure to the common pollutant ozone. Male Fischer-344 rats were exposed for 4 h to air or 0.8 ppm ozone ± metyrapone (50 mg/kg), a drug that blocks corticosterone synthesis (n = 5/group). Key glucocorticoid-responsive genes (serum- and glucocorticoid-inducible kinase, SGK; glucocorticoid-inducible leucine zipper, GILZ), and a gene responsive to both glucocorticoids and oxidative stress (metallothionein (MT)-1), were increased by ozone in all brain regions (olfactory bulb, frontal lobe, cortex, midbrain, hippocampus, cerebellum, brainstem), correlating with plasma corticosterone levels. Metyrapone prevented the increase in SGK and GILZ, and reduced but did not eliminate the effect on MT-1, suggesting glucocorticoid-dependent and –independent regulation. Administering exogenous corticosterone (10 mg/kg) to air-exposed rats reproduced the ozone effects, confirming specificity. The results demonstrate that early pollutant effects include stress hormone-dependent signaling. As both ozone and particulate matter activate the hypothalamic-pituitary-adrenal axis, and elevated glucocorticoids are implicated in brain pathologies, stress hormones could contribute to CNS impacts of air pollutants.

Long-term simulation for predicting indoor air pollutant concentration considering pollutant distribution based on concept of CRPS index

Modern people spend most of their time indoors and so are chronically exposed to indoor air pollutants. To identify the health effects of pollutant exposure, it is necessary to understand the changes over time in indoor pollutant concentrations. There are two approaches for simulating pollutant concentration changes: mass balance model, computational fluid dynamics (CFD). Although the mass balance model is suitable for long-term simulation because it is simple, there is a limit to the detailed analysis considering concentration distribution. CFD can simulate the distribution of indoor air pollutants, but long-term analyses require too many computational resources. This study proposed a novel simulation method that couples the mass balance model with the contribution ratio of pollutant sources (CRPS) index, which indicates the individual impact of all pollutant sources and is extracted from CFD result. By introducing the CRPS index, long-term pollutant concentrations can be calculated as fast as the mass balance model while considering the pollutant distribution like CFD. The method was validated using previous experimental data. The case study was conducted and simulated changes in pollutant concentrations in a new residential unit for one week. The results showed that the CRPS-coupled method was different from conventional methods in that it more realistically calculates pollutant concentrations using relatively little computational resources.