YouTube as a Source of Information on Air Pollution: Significance for Community Health

Air pollution is a pervasive global public health threat. The purpose of this study was to assess the content of 100 widely viewed English language YouTube videos on air pollution using the search term 'air pollution.' Content categories were created using comprehensive fact sheets and were coded as being present or not in each video. Cumulatively, the 100 videos sampled received 32,826,294 views and 303,692 likes, with averages of 328,263 and 3,068 respectively. The majority of videos (n = 72) were created by broadcast or internet-based news, 12 of the videos were created by professionals, 7 were created by a consumer and 9 were created by others. Three kinds of content were featured in a majority (> 50%) of the videos: "sources of pollution," "environmental impacts," and "solutions offered" and the videos covering each of these topics collectively garnered more than 26 million views and 249,000 likes. Independent one-tailed t-tests (α = 0.05) showed an association between covering sources of pollution and the number of views and likes. For both, videos featuring this content had a higher average number of views (467,391.08 vs. 80,924.03, p = .0383) and likes (4,450.78 vs. 647.03, p = .0383). Additionally, videos showing environmental impacts received more views than those that did not (547,901.49 vs. 80,585.43, p = .0373). This research can serve as a starting point to describe information being conveyed about an important global public health problem. Future research is needed to improve understanding about ways to utilize YouTube and other social media for public health education and advocacy to increase consumers' awareness and facilitate the informed decision-making process.

Ozone Exposure Limits Cardiorespiratory Function During Maximal Cycling Exercise in Endurance Athletes

Ground-level ozone (O3) is a potent air pollutant well recognized to acutely induce adverse respiratory symptoms and impairments in pulmonary function. However, it is unclear how the hyperpnea of exercise may modulate these effects, and the subsequent consequences on exercise performance. We tested the hypothesis that pulmonary function and exercise capability would be diminished, and symptom development would be increased during peak real-world levels of O3 exposure compared to room air. Twenty aerobically trained participants [13M, 7F; maximal O2 uptake (O2max), 64.1 ± 7.0 mL·kg-1·min-1)] completed a three-visit double-blinded, randomized crossover trial. Following a screening visit, participants were exposed to 170 ppb O3 or room air (<10 ppb O3) on separate visits during exercise trials, consisting of a 25-minute moderate intensity warmup, 30-minute heavy intensity bout, and a subsequent time-to-exhaustion (TTE) performance test. No differences in O2 uptake or ventilation were observed during submaximal exercise between conditions. During the TTE test, we observed significantly lower end-exercise O2 uptake (-3.2 ± 4.3%, p=0.004), minute ventilation (-3.2 ± 6.5%, p=0.043), tidal volume (-3.6 ± 5.1%, p=0.008), and a trend towards lower exercise duration in O3 compared to room air(-10.8 ± 26.5%, p=0.092). As decreases in O2 uptake and alterations in respiratory pattern were also present at matched time segments between conditions, a limitation of oxygen transport seems likely during maximal exercise. A more comprehensive understanding of the direct mechanisms that limit oxygen transport during exercise in high-pollutant concentrations is key for mitigating performance changes.

Evaluation of respiratory allergies burden and management in primary care and comparative analysis of health care data from Romania, Poland, Czech Republic and Bulgaria - preliminary study

BACKGROUND

Respiratory allergies mostly allergic rhinitis and asthma represent an important and increasing public health problem and one of the priorities for the European health systems. There is an increasing public concern regarding the persistence and severity of allergic diseases and many difficulties of health systems in providing prompt specialized medical assistance. Our study aims to highlight the main results of the Alliance 4Life project focused on the evaluation of the burden and management of respiratory allergies in primary care from Romania and comparative health-related data from four Central and Eastern European countries.

METHOD

We developed a questionnaire focused on patients with allergic rhinitis and asthma directly addressed to general practitioner (GP) specialists from Romania who attended the annual national conference in Bucharest.

RESULTS

The main results showed that patients with respiratory allergies are frequently encountered in primary care practice, only a few patients are evaluated by allergists and there is a clear need for education in this field.

CONCLUSIONS

This preliminary study confirms that respiratory allergies represent a considerable burden in primary care and the questionnaire may be a useful tool in further studies considering the experience of other healthcare systems. More advanced studies integrating epidemiology with data on air pollution and environmental conditions should be envisaged.

Association Between Individual Air Pollution (PM10, PM2.5) Exposure and Adverse Pregnancy Outcomes in Korea: A Multicenter Prospective Cohort, Air Pollution on Pregnancy Outcome (APPO) Study

BACKGROUND

Prenatal exposure to ambient air pollution is linked to a higher risk of unfavorable pregnancy outcomes. However, the association between pregnancy complications and exposure to indoor air pollution remains unclear. The Air Pollution on Pregnancy Outcomes research is a hospital-based prospective cohort research created to look into the effects of aerodynamically exposed particulate matter (PM)10 and PM2.5 on pregnancy outcomes.

METHODS

This prospective multicenter observational cohort study was conducted from January 2021 to June 2023. A total of 662 women with singleton pregnancies enrolled in this study. An AirguardK® air sensor was installed inside the homes of the participants to measure the individual PM10 and PM2.5 levels in the living environment. The time-activity patterns and PM10 and PM2.5, determined as concentrations from the time-weighted average model, were applied to determine the anticipated exposure levels to air pollution of each pregnant woman. The relationship between air pollution exposure and pregnancy outcomes was assessed using logistic and linear regression analyses.

RESULTS

Exposure to elevated levels of PM10 throughout the first, second, and third trimesters as well as throughout pregnancy was strongly correlated with the risk of pregnancy problems according to multiple logistic regression models adjusted for variables. Except for in the third trimester of pregnancy, women exposed to high levels of PM2.5 had a high risk of pregnancy complications. During the second trimester and entire pregnancy, the risk of preterm birth (PTB) increased by 24% and 27%, respectively, for each 10 μg/m3 increase in PM10. Exposure to high PM10 levels during the second trimester increased the risk of gestational diabetes mellitus (GDM) by 30%. The risk of GDM increased by 15% for each 5 μg/m3 increase in PM2.5 during the second trimester and overall pregnancy, respectively. Exposure to high PM10 and PM2.5 during the first trimester of pregnancy increased the risk of delivering small for gestational age (SGA) infants by 96% and 26%, respectively.

CONCLUSION

Exposure to high concentrations of PM10 and PM2.5 is strongly correlated with the risk of adverse pregnancy outcomes. Exposure to high levels of PM10 and PM2.5 during the second trimester and entire pregnancy, respectively, significantly increased the risk of PTB and GDM. Exposure to high levels of PM10 and PM2.5 during the first trimester of pregnancy considerably increased the risk of having SGA infants. Our findings highlight the need to measure individual particulate levels during pregnancy and the importance of managing air quality in residential environment.

Exploring the association between early-life air pollution exposure and autism spectrum disorders in children: A systematic review and meta-analysis

The objective of this meta-analysis is to investigate the association between air pollution and the vulnerability of children to autism spectrum disorders (ASD). A thorough examination and analysis of data obtained from a compilation of 14 studies was undertaken, with a particular emphasis on investigating the effects of nitrogen dioxide (NO2), oxide of nitrogen (NOx), ozone (O3), and particulate matter (PM10 and PM2.5) on individuals diagnosed with ASD. The findings demonstrate a moderate association between exposure to nitrogen dioxide (NO2) and ASD, as indicated by a combined odds ratio (OR) of 1.13 and a 95% confidence interval (CI) spanning from 0.77 to 1.549. O3 shows a combined odds ratio (OR) of 0.82, along with a 95% confidence interval (CI) ranging from 0.49 to 1.14. NOx shows a moderate level of heterogeneity (I² = 75.9%, p = 0.002), suggesting that the impact of NOx on the risk of ASD. There is a statistically significant relationship between exposure to O3 and ASD, although the strength of this relationship is diminished. The findings demonstrated a noteworthy correlation between exposure to PM10 and PM2.5 and the occurrence of ASD. The study found a significant correlation, in relation to PM2.5, with a combined odds ratio (OR) of 1.22 and a 95% confidence interval (CI) ranging from 1.11 to 1.34. The findings have significant implications for the formulation of programs aimed at reducing exposure to harmful chemicals, especially among vulnerable groups such as children.

Opportunities and Challenges Associated with the Uptake of Residential Clean Fuel Usage

PURPOSE OF REVIEW

Almost 3 billion people worldwide use solid fuel for cooking and heating. This review examines (i) household energy practices and infrastructures and their influence on fuel usage in different contexts; (ii) barriers in adoption of household clean energy technologies and uses in diverse settings and population groups and (iii) potential air pollution exposure reduction in homes through using processed fuel.

RECENT FINDINGS

Population health burden from solid fuel combustion-derived particulate air pollution has been estimated in several low- and middle-income countries. However, such studies have not been carried out in high income countries (e.g., UK). Irrespective of the region, fuel prices are the most dominant factor influencing the choice of fuel. Laboratory studies suggest processed fuel - pellets and briquettes - reduce particulate matter emissions by 70-80% and can be a promising alternative. Adoption of clean fuels for domestic energy needs facilitates progress towards five of the UN Sustainable Development Goals (SDGs). There is evidence that a variety of factors, including cost savings, encourage and hinder such uptake. These factors include price fluctuations, expenses, and the usage of clean fuels. Due to their distinct development scenarios, more expansive policy frameworks, and political economies of energy, these determinants are localized in character and differ significantly amongst economies. Therefore, in order to create innovative plans for the adoption of clean fuel use, strategies centred on local settings must be developed while keeping broad socio-technical and socio-economic issues in mind. Solid fuel processing - pelletization and briquetting - have the potential to reach Liquefied Petroleum Gas (LPG)-like emissions, and could be a potential strategy to mitigate exposure to household air pollution.

Association between short-term air pollution exposure and perturbation in thyrotropin levels in 1.38 million Chinese women: A national longitudinal analysis, 2014-2019

Prevalence of subclinical hypothyroidism substantially increased during the last decade in China, which has been commonly/clinically diagnosed as elevation in thyrotropin (thyroid-stimulating hormone [TSH]). Tobacco smoke containing toxic substances has been linked to thyroid dysfunction; however, data on perturbation of TSH following air pollution exposure in human has not been assessed at nationwide population level. We investigated the longitudinal impact of daily ambient air pollution estimated at residential level on serum TSH in 1.38 million women from China's 29 mainland provinces between 2014 and 2019. We observed that particulate matter with aerodynamic diameter ≤ 10 and ≤ 2.5 µm (PM10, PM2.5) and nitrogen dioxide (NO2) at cumulative lag 0-7 days of exposure were associated with percent elevations in TSH (0.88% [95% CI: 0.71, 1.05] per [interquartile range, IQR: 54.8 μg/m3] of PM10; 0.89% [95% CI, 0.71, 1.07] per IQR [40.3 μg/m3] of PM2.5; 2.01% [95% CI: 1.81, 2.22] per IQR [27.4 μg/m3] of NO2). Greater associations were observed in participants living in areas with ≥adequate iodine intake and those with low BMI levels and high inflammation status. Our results suggest that increased concentrations of recent ambient air pollutants at exposure ranges commonly encountered in Asia were associated with increases in TSH, supporting disturbing role of short-term air pollution exposure on the regulation of thyroid hormone homeostasis.

Reduced atmospheric levels of PM2.5 and decreased admissions and surgery for Ischemic stroke in Italy

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Air pollution is associated with increased incidence-rate of head and neck cancers: A nationally representative ecological study

INTRODUCTION

Early studies show conflicting findings regarding particulate matter ≤ 2.5 μm in diameter (PM2.5) exposure and development of head and neck cancers (HNC). We analyzed the relationship between PM2.5 exposure and various types of HNC in a nationally representative ecological sample.

METHODS

We determined HNC incidence in 608 US counties from 2011 to 2019 using the Surveillance, Epidemiology and End Results (SEER) Program from the National Cancer Institute. We also collected information on sociodemographic factors from SEER and data on smoking and alcohol intake from CDC data frames (county level). PM2.5 exposure levels were estimated using satellite and meteorological data via previously validated general additive models. Flexible semi-nonparametric regression models were used to test the relationship between PM2.5 exposure levels and HNC incidence, adjusting for demographics, socioeconomic factors, and comorbidity.

RESULTS

Increased PM2.5 exposure levels were associated with higher incidence-rates of oral cavity and pharyngeal cancers controlling for confounders in our primary analyses (IRR = 1.04, 95 % CI 1.01, 1.07, p = 0.02 per 1 μg/m3 increase in PM2.5). This relationship was maintained after adjusting for multiple testing (Holm s method, p = 0.04) and in ordinary least squares (OLS) regression (β = 0.17, 95 % CI 0.01, 0.57, p = 0.01). Increased exposure was also associated with other HNC: esophagus (IRR = 1.06, 95 % CI 1.01, 1.11, p = 0.02), lip (IRR = 1.16, 95 % CI 1.03, 1.31, p = 0.01), tonsil (IRR = 1.10, 95 % CI 1.03, 1.16, p < 0.01). However, these relationships were not maintained in secondary analyses.

CONCLUSIONS

This nationally representative ecological study shows that increased levels of air pollution are associated with increased incidence of overall oral cavity and pharyngeal cancers in the US.

Characterization of Mild Delayed Gestational Hypertension in Rats Following Ozone Exposure

The contribution of air pollution induced cardio-pulmonary damage on the development of hypertensive disorders of pregnancy and other adverse outcomes of pregnancy has gained increased attention as epidemiological data continues to highlight spatiotemporal pregnancy trends related to air pollution exposure. However clinical mechanistic data surrounding gestational complications remains sparse, necessitating the need for the use of animal models to study these types of complications of pregnancy. The current study seeks to examine the real-time effects of mid-gestational ozone exposure on maternal blood pressure and body temperature through the use of radiotelemetry in a rat model. The exposure resulted in acute depression of heart rate and core body temperature as compared to control animals. Ozone exposed animals also presented with a slight but significant increase in arterial blood pressure which was perpetuated until term. The data presented here illustrates the feasibility of murine models to assess cardiovascular complications caused by inhaled toxicants during the window of pregnancy.

PM2.5 threshold exceedances during the prenatal period and risk of intellectual disability

BACKGROUND

Research demonstrates that chronic exposure to fine particulates (PM2.5) increases risks of neurodevelopmental conditions, such as intellectual disability (ID). Few studies have examined neurodevelopmental health impacts of pollution spikes exceeding 24-h (24-h) PM2.5 guidelines, despite relevance to the regulatory landscape. The current potential for regulatory changes to 24-h PM2.5 standards in the United States makes research on exceedances relevant.

OBJECTIVE

To examine associations between 24-h PM2.5 exceedances and the risk of ID.

METHODS

We conducted a retrospective case-control study of a sample of children in Utah, USA. We used generalized estimating equations to predict odds of ID based on the number of 24-h PM2.5 exceedance days during the preconception period and three trimesters of pregnancy. Exceedance days are defined as per current World Health Organization (WHO) [≥15 μg/m3] and current US Environmental Protection Agency (EPA) [≥35 μg/m3] 24-h guidelines.

RESULTS

PM2.5 exceedances are associated with ID risk during the preconception and first trimester periods and not the second and third trimesters. During the preconception period, each day exceeding 15 μg/m3 or 35 μg/m3 was associated with a 1.023 (CI: 1.011-1.040) or 1.042 (CI: 1.026-1.059, p < 0.001) increase in odds of ID, respectively. During the first trimester, each day exceeding 15 μg/m3 or 35 μg/m3 was associated with a 1.032 (CI: 1.017-1.047) or 1.059 (CI: 1.030-1.088) increase in odds of ID, respectively.

IMPACT STATEMENT

Potential regulatory movement on the US 24-h PM2.5 standard makes research that explicitly studies exceedances highly relevant. Yet few studies examine health effects of exceeding 24-h guidelines for any air pollutants. This study fills important gaps in the literature by examining associations between odds of intellectual disability and the count of days exceeding current 24-h PM2.5 guidelines, as established by the World Health Organization and US Environmental Protection Agency, during the prenatal period. We find that exceedances of both sets of guidelines, during the preconception and first trimester periods, are associated with ID risk.

Environmental impact assessment of the coal yard and ambient pollution

This study investigates the vertical distribution of pollutants emitted from coal yards using unmanned aerial vehicles (UAVs). Vertical concentration measurements of black carbon (BC) and particulate matter (PM) in a range of 1 m to 100 m above ground level (AGL) in the central coal yard showed clear spatial patterns and gradients of these pollutants. In addition, measurements were taken at specific heights (1 m, 30 m AGL, and 60 m AGL) at seven locations approximately 3 km from the yard. Thirteen measurements were carried out during the non-heating period under similar weather conditions. The measured BC concentrations decreased significantly with increasing altitude, with ground-level concentrations reaching 1.88 ± 0.61 µg/m3 and decreasing by over 46% at 80 m AGL. Similarly, PM10 concentrations at 60 m AGL decreased by 21.7%, with values of 25.99 ± 9.24 µg/m3 measured near the ground level and 16.52 ± 8.31 µg/m3 at 60 m AGL. The maximum coal particle pollution from the coal depot ranges from 500 to 1,000 m. The study showed a significant decrease in BC concentrations with height above the coal yard surface. Concentrations of PM10 and PM10-TSP showed a complex distribution influenced by local emissions and long-range particle transport. Meteorological factors, especially wind speed and direction, significantly influenced the pollutant dispersion. In addition, higher pollutant concentrations were measured during dry periods than after rainfall. The findings of this study contribute to a better understanding of the dispersion patterns and potential impacts of coal dust, enabling the implementation of targeted mitigation strategies and improved pollution control measures.

Representativeness of the US EPA PM monitoring site locations to the US population: implications for air pollution prediction modeling

Air pollution prediction modeling establishes relationships between measurements and geographical and meteorological characteristics to infer concentrations at locations without measurements. Since air pollution monitors are limited in number, predictions may be generated for locations different than those used to train the model. The epidemiologic impacts of this potential mismatch hinge on whether the population resides in areas well-represented by monitoring sites. Here we quantify the fraction of the population with geographical characteristics not reflected by the 2000, 2010, and 2020 EPA PM2.5 and PM10 regulatory sites. We evaluated this measure nationwide, regionally, and by race. Nationally, the networks were very representative of the population experience; however, there was less overlap regionally and in regions stratified by race. This suggests that sub-national exposure modeling should carefully consider the representativeness of monitors for their populations. It also highlights that exposure models often borrow information from distal places to predict full population exposure.

Nitrous oxide splurge in a tertiary health care center and its environmental impact: No more laughing stock

Background

Nitrous oxide has been an integral part of surgical anesthesia for many years in the developed world and is still used in developing countries such as India. The other main concerns in low-resource countries are the lack of an advanced anesthesia gas-scavenging system and modular surgical theatres. As a greenhouse gas that has been present in the atmosphere for more than 100 years and damages the ozone layer, nitrous oxide is three times worse than sevoflurane. Here, we conducted an observational study to quantify the annual nitrous oxide consumption and its environmental impact in terms of carbon dioxide equivalence in one of busiest tertiary health care and research centers in Northern India.

Methods

Data related to nitrous oxide expenditure' from the operation theatre and manifold complex of our tertiary care hospital and research center from 2018 to 2021 were collected monthly and analyzed. The outcomes were extracted from our observational study, which was approved by our institutional ethics board (INT/IEC/2017/1372 Dated 25.11.2017) and registered prospectively under the Central Registry (CTRI/2018/07/014745 Dated 05.07.2018).

Results

The annual nitrous oxide consumption in our tertiary care hospital was 22,081.00, 22,904.00, 17,456.00, and 18,392.00 m3 (cubic meters) in 2018, 2019, 2020, and 2021, respectively. This indicates that the environmental impact of nitrous oxide (in terms of CO2 equivalents) from our hospital in 2018, 2019, 2020, and 2021 was 13,016.64, 13,287.82, 10,289.94, and 10,841.24 tons, respectively.

Conclusion

This huge amount of nitrous oxide splurge is no longer a matter of laughter, and serious efforts should be made at every central and peripheral health center level to reduce it.

Spatio-temporal correlation between admissions for ruptured abdominal aortic aneurysms and levels of atmospheric pollution in Italy

AIMS

The aim of our study was to determine a correlation between rates and number of patients admitted with ruptured abdominal aortic aneurysms (rAAA) in Italian regions with different levels of atmospheric pollution.

METHODS

We analyzed a possible correlation between the number and rate (ruptured versus not ruptured) of patients with rAAA admitted in eight Italian regions with different levels of atmospheric pollution.

RESULTS

Number and rates of patients with rAAA were statistically correlated with levels of air pollution and low air temperature (RR = 1.90, 95% CI: 1.42, 2.1.0) (p<0.01). Even if low temperatures amplified the correlation between admissions for rAAA and PMs exposure, also during Summer and Spring there were sudden increases of the number of admissions for rAAA patients in periods with higher air pollution. The regions with high levels of atmospheric pollution had higher rates of admissions of patients with rAAA in comparison with regions with low level of air pollution. However, there was no difference between regions with low and very low level of atmospheric pollution. Mean age, sex distribution, exposure to established risk factors were similar for the population of the eight analyzed Italian regions.

CONCLUSIONS

The findings of this study highlight the potential to reduce AAA related mortality and burden by addressing the negative effects of exposure to high levels of atmospheric pollution. The possibility of a dose-dependent effect of atmospheric pollution on the cardiovascular system opens research initiatives and discussions about when and how to modulate interventions to reduce atmospheric pollutants.

Association of long-term exposure to various ambient air pollutants, lifestyle, and genetic predisposition with incident cognitive impairment and dementia

BACKGROUND

Long-term exposure to air pollution has been found to contribute to the development of cognitive decline. Our study aimed to assess the association between various air pollutants and cognitive impairment and dementia. Additionally, explore the modification effects of lifestyle and genetic predisposition.

METHODS

The exposure levels to various air pollutants, including particulate matter (PM) with diameters ≤ 2.5 (PM2.5), ≤ 10 (PM10), and between 2.5 and 10 μm (PM2.5-10) and nitrogen oxides (NO and NO2) were identified. An air pollution score (APS) was calculated to evaluate the combined exposure to these five air pollutants. A genetic risk estimate and healthy lifestyle score (HLS) were also generated. The Cox regression model adjusted by potential confounders was adopted to access the association between pollution exposure and cognitive decline, and several sensitivity analyses were additionally conducted to test the robustness.

RESULTS

The combined exposure to air pollutants was associated with an increased risk of incident cognitive decline. Compared with the low exposure group, the hazard ratio (HR) and 95% confidence interval (CI) for all-cause dementia, Alzheimer's dementia, vascular dementia, and mild cognitive impairment (MCI) in those exposed to the highest levels of air pollutants were respectively 1.07 (95% CI: 1.04 to 1.09), 1.08 (95% CI: 1.04 to 1.12), 1.07 (95% CI: 1.02 to 1.13), and 1.19 (95% CI: 1.12 to 1.27). However, the modification effects from genetic predisposition were not widely observed, while on the contrary for the healthy lifestyle. Our findings were proven to be reliable and robust based on the results of sensitivity analyses.

CONCLUSIONS

Exposure to air pollution was found to be a significant contributing factor to cognitive impairment and dementia, and this association was not easily modified by an individual's genetic predisposition. However, adopting a healthy lifestyle may help to manage the risk of cognitive decline related to air pollution.

The Open Burning of Plastic Wastes is an Urgent Global Health Issue

The open burning of mixed wastes that contain plastics is a widespread practice across the globe, resulting in the release of gas emissions and ash residues that have toxic effects on human and environmental health. Although plastic pollution is under scrutiny as a pressing environmental concern, it is often conflated with plastic litter, and the contribution of the open burning of plastics to air, soil, and water pollution gets overlooked. Therefore, campaigns to raise awareness about plastic pollution often end up leading to increased open burning. Many countries or regions where open burning is prevalent have laws in place against the practice, but these are seldom effective. In this viewpoint, we direct attention to this critical but largely overlooked dimension of plastic pollution as an urgent global health issue. We also advocate interventions to raise awareness about the risks of open burning and emphasize the necessity of phasing out some particularly pernicious plastics in high-churn, single-use consumer applications.

Corridor-Level Impacts of Battery-Electric Heavy-Duty Trucks and the Effects of Policy in the United States

Electrifying freight trucks will be key to alleviating air pollution burdens on disadvantaged communities and mitigating climate change. The United States plans to pursue this aim by adding vehicle charging infrastructure along specific freight corridors. This study explores the coevolution of the electricity grid and freight trucking landscape using an integrated assessment framework to identify when each interstate and drayage corridor becomes advantageous to electrify from a climate and human health standpoint. Nearly all corridors achieve greenhouse gas emission reductions if electrified now. Most can reduce health impacts from air pollution if electrified by 2040 although some corridors in the Midwest, South, and Mid-Atlantic regions remain unfavorable to electrify from a human health standpoint, absent policy support. Recent policy, namely, the Inflation Reduction Act, accelerates this timeline to 2030 for most corridors and results in net human health benefits on all corridors by 2050, suggesting that near-term investments in truck electrification, particularly drayage corridors, can meaningfully reduce climate and health burdens.

Systematic review of microplastics and nanoplastics in indoor and outdoor air: identifying a framework and data needs for quantifying human inhalation exposures

BACKGROUND

Humans are likely exposed to microplastics (MPs) in a variety of places including indoor and outdoor air. Research to better understand how exposure to MPs correlates to health is growing. To fully understand the possible impacts of MPs on human health, it is necessary to quantify MP exposure and identify what critical data gaps exist.

OBJECTIVES

The current paper provides a human exposure assessment of microplastics in the air using systematically reviewed literature that provided concentration of MPs in air as well as doses used in toxicology studies to calculate inhalation exposure dose.

METHODS

All published peer-reviewed journal articles, non-published papers, and grey literature that focused on micro- or nano-plastics in indoor and outdoor air were systematically searched using PRISMA guidelines. Literature that defined specific concentrations and size of MPs in air or exposed to human lung cells, animals, or humans with measurable health impacts were included in data extraction. Inhalational exposures were calculated for different age groups using published MP concentrations from the included literature using exposure dose equations and values from U.S. ATSDR and EPA.

RESULTS

Calculated mean indoor inhalational exposures from passive sampling methods were higher than those calculated from active sampling methods. When comparing indoor and outdoor sampling, calculated inhalation exposures from indoor samples were greater than those from outdoor samples. Inhalation exposures of MPs differed between age groups with infants having the highest calculated dose values for all locations followed by preschool age children, middle-school aged children, pregnant women, adolescents, and non-pregnant adults. MP doses used in toxicology studies produced higher calculated mean inhalational exposures than those from environmental samples.

IMPACT

This study is the first known systematic review of inhalational MP exposure from indoor and outdoor air. It also provides inhalational exposures calculated from previously published environmental samples of MPs as well as from toxicology studies.

Hyper-local to regional exposure contrast of source-resolved PM2.5 components across the contiguous United States: implications for health assessment

BACKGROUND

Improved understanding of sources and processes that drive exposure contrast of fine particulate matter (PM2.5) is essential for designing and interpreting epidemiological study outcomes.

OBJECTIVE

We investigate the contribution of various sources and processes to PM2.5 exposure contrasts at different spatial scales across the continental United States.

METHODS

We consider three cases: exposure contrast within a metro area, nationwide exposure contrast with high spatial resolution, and nationwide exposure contrast with low spatial resolution. Using national empirical model estimates of source- and chemically specific PM2.5 concentration predictions, we quantified the contribution of various sources and processes to PM2.5 exposure contrasts in these three cases.

RESULTS

At the metro level (i.e., metropolitan statistical area; MSA), exposure contrasts of PM2.5 vary between -1.8 to 1.4 µg m-3 relative to the MSA-mean with about 50% of within-MSA exposure contrast of PM2.5 caused by cooking and mobile source primary PM2.5. For the national exposure contrast at low-resolution (i.e., using MSA-average mean concentrations), exposure contrasts (relative to the national mean: -3.9 to 3.2 µg m-3) are larger than within an MSA with ~80% of the variation due to secondary PM2.5. National exposure contrast at high resolution (census block) has the largest absolute range (relative to the national mean: -4.7 to 3.7 µg m-3) due to both regional and intra-urban contributions; on average, 65% of the national exposure contrast is due to secondary PM2.5 with the remaining from the primary PM2.5 (cooking and mobile source 26%, other 9%).

IMPACT

Our study provides a comprehensive analysis of the sources and processes that contribute to exposure contrasts of PM2.5 across different geographic areas in the US. For the first time on a national scale, we used high spatial resolution source-specific exposure estimates to identify the primary contributors to PM2.5 exposure contrasts. The study also highlights the advantages of different study designs for investigating the health impacts of specific PM2.5 components. The findings provide novel insights that can inform public health policies aimed at reducing PM2.5 exposure and advance the understanding of the epidemiological study outcomes.

Climate change as a threat multiplier to environmental reproductive justice

Legacies of racial capitalism and colonialism drive present day racial disparities in perinatal health outcomes. Climate change amplifies existing social inequalities associated with environmental exposures and reproductive health, of which BIPOC (Black, Indigenous, and people of color) communities bear a disproportionate burden. Through case studies, this article summarizes three examples of climate justice issues with reproductive healthcare outcomes: traffic related air pollution exposure, chemical exposures in personal care products and plastics, and natural disaster frequency. We advocate for incorporation of climate justice and environmental health impact into medical school curriculum, increased prenatal screening for environmental toxins, and physician engagement with local environmental issues.

The association of asthma and air pollution: Evidence from India

In the last two decades, air pollution has increased throughout India resulting in the deterioration of air quality. This paper estimates the prevalence of self-reported asthma in women aged 15-49 years and examines the link between outdoor air pollution and disease prevalence in India by combining satellite data on particulate matter (PM2.5) and the National Family Health Survey (NFHS-4), 2015-16. The results indicate that both indoor pollution as well as outdoor air pollution are important risk factors for asthma in women as both independently increase the probability of asthma among this group. Strategies around the prevention of asthma need to recognize the role of both indoor as well as outdoor air pollution. The other significant risk factors for asthma are smoking, second-hand smoking, type of diet and obesity.

The impact of air pollution on asthma: clinical outcomes, current epidemiology, and health disparities

INTRODUCTION

Air pollution has been shown to have a significant impact on morbidity and mortality of respiratory illnesses including asthma.

AREAS COVERED

Outdoor air pollution consists of a mixture of individual pollutants including vehicle traffic and industrial pollution. Studies have implicated an array of individual components of air pollution, with PM2.5, NO2, SO2, and ozone being the most classically described, and newer literature implicating other pollutants such as black carbon and volatile organic compounds. Epidemiological and cohort studies have described incidence and prevalence of pollution-related asthma and investigated both acute and chronic air pollution exposure as they relate to asthma outcomes. There is an increasing body of literature tying disparities in pollution exposure to clinical outcomes. In this narrative review, we assessed the published research investigating the association of pollution with asthma outcomes, focusing on the adult population and health care disparities.

EXPERT OPINION

Pollution has multiple deleterious effects on respiratory health but there is a lack of data on individualized pollution monitoring, making it difficult to establish a temporal relationship between exposure and symptoms, thereby limiting our understanding of safe exposure levels. Future research should focus on more personalized monitoring and treatment plans for mitigating exposure.

Air quality low-cost sensors and monitoring stations NO2 raw dataset in Rouen (France)

This article presents a dataset comprising measurements made by co-located devices, with the aim of calibrating sensors for an upcoming in-situ use. The dataset includes hourly averaged data from 9 low-cost sensors and 2 traffic monitoring stations (thereafter named QDP and SUD3) in Rouen spanning from October 20, 2021 to March 25, 2022. In addition, the dataset is enriched by covariates measured by the sensors: temperature, relative humidity, atmospheric pressure, plus Ox and CO measures. The experiment was conducted as part of TIGA's call for project, and designed to have a better understanding of sensors' drawbacks, particularly when they are moved or shut down. Knowledge about the effect of air pollution on health has gained significant attention from both the scientific community and citizens, making air quality a growing issue for urban area. As a result, the city of Rouen in Normandy, France, has prioritized air quality monitoring as a key initiative. Concurrently, several means to measure air pollutants have been made more accessible, such as the use of low-cost sensors. Those sensors offer affordability, but are known to be less accurate than monitoring stations. Thus, they need to be cautiously studied so as to be used properly.

Ambient particulate matter (PM2.5) exposure contributes to neurodegeneration through the microbiome-gut-brain axis: Therapeutic role of melatonin

Exposure to ambient particulate matter (PM2.5) has been shown to disturb the gut microbiome homeostasis and cause initiation of neuroinflammation and neurodegeneration via gut-brain bi-directional axis. Polyaromatic hydrocarbons (PAHs), which are carcinogenic and mutagenic, are important organic constituents of PM2.5 that could be involved in the microbiome-gut-brain axis-mediated neurodegeneration. Melatonin (ML) has been shown to modulate the microbiome and curb inflammation in the gut and brain. However, no studies have been reported for its effect on PM2.5-induced neuroinflammation. In the current study, it was observed that treatment with ML at 100 µM significantly inhibits microglial activation (HMC-3 cells) and colonic inflammation (CCD-841 cells) by the conditioned media from PM2.5 exposed BEAS2B cells. Further, melatonin treatment at a dose of 50 mg/kg to C57BL/6 mice exposed to PM2.5 (at a dose of 60 µg/animal) for 90 days significantly alleviated the neuroinflammation and neurodegeneration caused by PAHs in PM2.5 by modulating olfactory-brain and microbiome-gut-brain axis.

Effect measure modification of the association between short-term exposures to PM2.5 and hospitalizations by longs-term PM2.5 exposure among a cohort of people with Chronic Obstructive Pulmonary Disease (COPD) in North Carolina, 2002-2015

BACKGROUND

Approximately nine million adults in the United States are living with chronic obstructive pulmonary disease (COPD), and positive associations between short-term air pollution exposure and increased risk of COPD hospitalizations in older adults are consistently reported. We examined the association between short-term PM2.5 exposure and hospitalizations and assessed if there is modification by long-term exposure in a cohort of individuals with COPD.

METHODS

In a time-referent case-crossover design, we used a cohort of randomly selected individuals with electronic health records from the University of North Carolina Healthcare System, restricted to patients with a medical encounter coded with a COPD diagnosis from 2004-2016 (n = 520), and estimated ambient PM2.5 concentrations from an ensemble model. Odds ratios and 95% confidence intervals (OR (95%CI)) were estimated with conditional logistic regression for respiratory-related, cardiovascular (CVD), and all-cause hospitalizations. Exposures examined were 0-2 and 0-3 day lags of PM2.5 concentration, adjusting for daily census-tract temperature and humidity, and models were stratified by long-term (annual average) PM2.5 concentration at the median value.

RESULTS

We observed generally null or low-magnitude negative associations with short-term PM2.5 exposure and respiratory-related (OR per 5 µg/m3 increase in 3-day lag PM2.5: 0.971 (0.885, 1.066)), CVD (2-day lag: 0.976 (0.900, 1.058) and all-cause (3 day lag: 1.003 (0.927, 1.086)) hospitalizations. Associations between short-term PM2.5 exposure and hospitalizations were higher among patients residing in areas with higher levels of annual PM2.5 concentrations (OR per 5 µg/m3 in 3-day lag PM2.5 for all-cause hospitalizations: 1.066 (0.958, 1.185)) than those in areas with lower annual PM2.5 concentrations (OR per 5 µg/m3 in 3-day lag PM2.5 for all-cause hospitalizations: 0.914 (0.804, 1.039)).

CONCLUISONS

Differences in associations demonstrate that people in areas with higher annual PM2.5 exposure may be associated with higher risk of hospitalization during short-term increases in PM2.5 exposure.

Investigating Patterns of Air Pollution in Metropolises Using Remote Sensing and Neural Networks During the COVID-19 Pandemic

The purpose of this study is to determine the amount of air pollution in Tehran, Isfahan, Semnan, Mashhad, Golestan, and Shiraz during the Corona era and before. For this purpose, Sentinel satellite images were used to investigate the concentration of Methane (CH₄), Carbon Monoxide (CO), Carbon Dioxide (CO₂), Nitrogen Dioxide (NO₂), Ozone (O₃), Sulfur Dioxide (SO₂), aerosol pollutants in In the era before and during Corona. Furthermore, greenhouse effect-prone areas were determined in this study. In the following, the state of air inversion in the studied area was determined by taking the temperature on the surface of the earth and in the upper atmosphere, as well as the wind speed into account. In this research, the prediction of air temperature for the year 2040 was conducted using the Markov and Cellular Automaton (CA)-Markov methods, considering the impact of air pollution on the air temperature of metropolises. Additionally, the Radial Basis Function (RBF) and Multilayer Perceptron (MLP) methods have been developed to determine the relationship between pollutants, areas prone to air inversions, and temperature values. According to the results, pollution caused by pollutants has decreased in the Corona era. According to the results, there is more pollution in Tehran and Isfahan metropolises. In addition, the results showed that air inversions in Tehran is the highest. Additionally, the results showed a high correlation between temperature and pollution levels (R²=0.87). Thermal indices in the studied area indicate that Isfahan and Tehran, with high values of Surface Urban Heat Island (SUHI) and being in the 6th class of thermal comfort (Urban Thermal Field Variance Index (UTFVI)), are affected by thermal pollution. The results showed that parts of southern Tehran province, southern Semnan and northeastern Isfahan will have higher temperatures in 2040 (class 5 and 6). Finally, the results of the neural network method showed that the MLP method with R²=0.90 is more accurate than the RBF method in predicting pollution amounts. This study significantly contributes by introducing innovative advancements through the application of RBF and MLP methods to assess air pollution levels during the COVID-19 and pre-pandemic periods, while also investigating the intricate relationships among greenhouse gases, air inversion, air temperature, and pollutant indices within the atmosphere. The utilization of these methods notably enhances the accuracy and reliability of pollution predictions, amplifying the originality and significance of this research.

Influence of Air Pollutants on the Disease Activity and Quality of Life in Rheumatoid Arthritis, an Iranian Observational Longitudinal Study

Background

Environmental exposures and genetic predisposition interactions may result in autoimmune rheumatic diseases. This study aimed to determine the effect of outdoor air pollutants on the activity of rheumatoid arthritis (RA) in a longitudinal follow-up.

Methods

We longitudinally studied 50 patients with RA bimonthly over 6 months in Mashhad, one of the most polluted cities in Iran. Disease activity and health-related quality of life (HRQoL) were examined according to the disease activity score (DAS28ESR), health assessment questionnaires (HAQ), physical health component summary (PCS), and visual analogue scale (VAS) criteria. The outdoor air pollutant was measured by monitoring the average concentration of nitrogen oxide (NO), carbon monoxide (CO), O2 level, Sulfur dioxide (SO2), and some particles less than 10 and 2.5 micrometers in diameter (PM <10 µm, PM <2.5 µm). The temperature and humidity levels were also measured. The univariate and multivariate statistical analyses were used for data analysis and the role of confounding factors was determined using the generalized estimation equation method.

Results

Statistical analysis indicated a significant increase of the DAS28ESR (B = 0.04 [0.08]; P = 0.01) and VAS (B = 4.48 [1.73]; P = 0.01) by CO concentration. Moreover, a number of polluted days increased the VAS in patients. In addition, other air pollutants, temperature, and humidity were not affected significantly by the DAS28ESR and quality of life indexes by considering confounders such as medications, age, and job.

Conclusion

Based on our findings, CO concentration was the only effective outdoor air pollutant that could increase RA disease activity. In addition, CO concentration and the number of polluted days make patients feel more ill. As the role of indoor air pollutants is highly important, further research on this critical topic is required to establish the role of air pollution on RA disease activity.

Effects of In-Utero Personal Exposure to PM2.5 Sources and Components on Birthweight

Background

In-utero exposure to fine particulate matter (PM2.5) and specific sources and components of PM2.5 have been linked with lower birthweight. However, previous results have been mixed, likely due to heterogeneity in sources impacting PM2.5 and due to measurement error from using ambient data. Therefore, we investigated the effect of PM2.5 sources and their high-loading components on birthweight using data from 198 women in the 3rd trimester from the MADRES cohort 48-hour personal PM2.5 exposure monitoring sub-study.

Methods

The mass contributions of six major sources of personal PM2.5 exposure were estimated for 198 pregnant women in the 3rd trimester using the EPA Positive Matrix Factorization v5.0 model, along with their 17 high-loading chemical components using optical carbon and X-ray fluorescence approaches. Single- and multi-pollutant linear regressions were used to evaluate the association between personal PM2.5 sources and birthweight. Additionally, high-loading components were evaluated with birthweight individually and in models further adjusted for PM2.5 mass.

Results

Participants were predominately Hispanic (81%), with a mean (SD) gestational age of 39.1 (1.5) weeks and age of 28.2 (6.0) years. Mean birthweight was 3,295.8g (484.1) and mean PM2.5 exposure was 21.3 (14.4) μg/m3. A 1 SD increase in the mass contribution of the fresh sea salt source was associated with a 99.2g decrease in birthweight (95% CI: -197.7, -0.6), while aged sea salt was associated with lower birthweight (β =-70.1; 95% CI: -141.7, 1.4). Magnesium sodium, and chlorine were associated with lower birthweight, which remained after adjusting for PM2.5 mass.

Conclusions

This study found evidence that major sources of personal PM2.5 including fresh and aged sea salt were negatively associated with birthweight, with the strongest effect on birthweight from Na and Mg. The effect of crustal and fuel oil sources differed by infant sex with negative associations seen in boys compared to positive associations in girls.

Particulate matter concentration and composition in the New York City subway system

This study investigated the concentration and composition of particulate matter (PM_2.5) in the New York City subway system. Realtime measurements, at a one-second cadence, and gravimetric measurements were performed inside train cars along 300 kilometers of nine subway lines, as well as on 333 platforms from 287 subway stations. The mean (±SD) PM_2.5 concentration on the underground platforms was 142 ± 69 μg/m³ versus 29 ± 20 μg/m³ for aboveground stations. The average Concentrations inside train cars were 88 ± 14 μg/m³ when traveling through underground tunnels and platforms and 29 ± 31 μg/m³ while on aboveground tracks. The particle composition analysis of filtered samples was done using X-ray fluorescence (XRF), revealing that iron made up approximately 43% of the total PM_2.5 mass on station platforms, around 126 times higher than the outdoor ambient iron concentration. Other trace elements include silicon, sulfur, copper, nickel, aluminum, calcium, barium, and manganese. Considering the very high iron content, the comparative analysis of the measured concentration versus the standards set by the Environmental Protection Agency (US EPA) is questionable since those limits are largely based on particulate matter from fossil fuel combustion. Health impact analysis of iron-based particles will complement the study results presented here.

Spatio-temporal health benefits attributable to PM2.5 reduction in an Indian city

Fine particulate matter (PM_2.5) is linked with a wide spectrum of human health effects and has the highest contribution to total air pollution mortality. This study aims to quantify health benefits of reducing PM_2.5 concentration to World Health Organization standard (annual mean = 10 µg m^-3) for various health endpoints during 2011-2019 period using AirQ+ and BenMAP-CE software packages. Intraurban assessment in Vellore city, India was done by estimating health benefits at ward level. Both software packages estimated annual average all-cause, ischemic heart disease, stroke, and chronic obstructive pulmonary disease health benefits in the range of 919-945, 175-234, 70-152, and 99-175 cases at city level and 15-16, 3-4, 1-3, and 2-3 cases at ward level, respectively. Sensitivity analysis showed that relative risk had a large influence on health benefit estimates. Present study results will play a crucial role in the future air quality and public health policies of Vellore city.

Design and evaluation of short-term monitoring campaigns for long-term air pollution exposure assessment

BACKGROUND

Short-term mobile monitoring campaigns to estimate long-term air pollution levels are becoming increasingly common. Still, many campaigns have not conducted temporally-balanced sampling, and few have looked at the implications of such study designs for epidemiologic exposure assessment.

OBJECTIVE

We carried out a simulation study using fixed-site air quality monitors to better understand how different short-term monitoring designs impact the resulting exposure surfaces.

METHODS

We used Monte Carlo resampling to simulate three archetypal short-term monitoring sampling designs using oxides of nitrogen (NOx) monitoring data from 69 regulatory sites in California: a year-around Balanced Design that sampled during all seasons of the year, days of the week, and all or various hours of the day; a temporally reduced Rush Hours Design; and a temporally reduced Business Hours Design. We evaluated the performance of each design's land use regression prediction model.

RESULTS

The Balanced Design consistently yielded the most accurate annual averages; while the reduced Rush Hours and Business Hours Designs generally produced more biased results.

SIGNIFICANCE

A temporally-balanced sampling design is crucial for short-term campaigns such as mobile monitoring aiming to assess long-term exposure in epidemiologic cohorts.

IMPACT STATEMENT

Short-term monitoring campaigns to assess long-term air pollution trends are increasingly common, though they rarely conduct temporally balanced sampling. We show that this approach produces biased annual average exposure estimates that can be improved by collecting temporally-balanced samples.

Environment impact assessment of agricultural diesel engines utilizing biodiesel derived from phoenix sylvestris oil

Uncontrolled emissions, massive price increases, and other factors encourage searching for a suitable diesel engine fuel alternative. In its processed form, vegetable oil biodiesel is an appealing green alternative fuel for compression ignition engines. Vegetable oil esters have qualities comparable to those of standard diesel fuel. As a result, biodiesel may be utilized to run a diesel engine without any further alterations. This article analyses the potential of Phoenix sylvestris oil, which may be found in forest belts across the globe, as a viable feedstock for biodiesel extraction. Phoenix sylvestris oil is found to be abundant in different forest belts worldwide. The free fatty acid must first be transformed into esters using catalytic acid esterification before proceeding to alkaline catalytic esterification. The molar ratio (6:1), catalyst concentration (1 wt%), reaction temperature (60 °C), and reaction time (2 h) have all been optimized for biodiesel extraction. Biodiesel produced had characteristics that were similar to standard biodiesel specifications. The biodiesel yield from Phoenix sylvestris oil was 92.3% under optimum conditions. The experimental results revealed that the Phoenix sylvestris oil biodiesel performed better than neat Phoenix sylvestris oil and its blends. Phoenix sylvestris oil blend produced better brake thermal efficiency with lower smoke, hydrocarbon, and CO emissions. The biodiesel produced from non-edible Phoenix sylvestris oil has the potential to be employed as a viable alternative to diesel fuel.

Clean fleets, different streets: evaluating the effect of New York City's clean bus program on changes to estimated ambient air pollution

BACKGROUND

Motor vehicles, including public transit buses, are a major source of air pollution in New York City (NYC) and worldwide. To address this problem, governments and transit agencies have implemented policies to introduce cleaner vehicles into transit fleets. Beginning in 2000, the Metropolitan Transit Agency began deploying compressed natural gas, hybrid electric, and low-sulfur diesel buses to reduce urban air pollution.

OBJECTIVE

We hypothesized that bus fleet changes incorporating cleaner vehicles would have detectable effects on air pollution concentrations between 2009 and 2014, as measured by the New York City Community Air Survey (NYCCAS).

METHODS

Depot- and route-specific information allowed identification of areas with larger or smaller changes in the proportion of distance traveled by clean buses. Data were assembled for 9670 300 m × 300 m grid cell areas with annual concentration estimates for nitrogen oxide (NO), nitrogen dioxide (NO2), and black carbon (BC) from NYCCAS. Spatial error models adjusted for truck route presence and total traffic volume.

RESULTS

While concentrations of all three pollutants declined between 2009 and 2014 even in the 39.7% of cells without bus service, the decline in concentrations of NO and NO2 was greater in areas with more bus service and with higher proportional shifts toward clean buses. Conversely, the decline in BC concentration was slower in areas with more bus service and higher proportional clean bus shifts.

SIGNIFICANCE

These results provide evidence that the NYC clean bus program impacted concentrations of air pollution, particularly in reductions of NO2. Further work can investigate the potential impact of these changes on health outcomes in NYC residents.

IMPACT STATEMENT

Urban air pollution from diesel-burning buses is an important health exposure. The New York Metropolitan Transit Agency has worked to deploy cleaner buses into their fleet, but the impact of this policy has not been evaluated. Successful reductions in air pollution are critical for public health.

Long-term exposure to ambient particulate matter and stroke etiology: Results from the Women's Health Initiative

BACKGROUND

Ambient particulate matter (PM) air pollution is a leading cause of global disability and accounts for an annual 2.9 million deaths globally. PM is established as an important risk factor for cardiovascular disease, however the evidence supporting a link specifically between long-term exposure to ambient PM and incident stroke is less clear. We sought to evaluate the association of long-term exposure to different size fractions of ambient PM with incident stroke (overall and by etiologic subtypes) and cerebrovascular deaths within the Women's Health Initiative, a large prospective study of older women in the US.

METHODS

We studied 155,410 postmenopausal women without previous cerebrovascular disease enrolled into the study between 1993 and 1998, with follow-up through 2010. We assessed geocoded participant address-specific concentrations of ambient PM (fine [PM2.5], respirable [PM10] and coarse [PM10-2.5]), as well as nitrogen dioxide [NO2] using spatiotemporal models. We classified hospitalization events into ischemic, hemorrhagic, or other/unclassified stroke. Cerebrovascular mortality was defined as death from any stroke etiology. We used Cox proportional hazard models to calculate hazard ratios (HR) and 95% confidence intervals (CI), adjusting for individual and neighborhood-level characteristics.

RESULTS

During a median follow-up time of 15 years, participants experienced 4,556 cerebrovascular events. The hazard ratio for all cerebrovascular events was 2.14 (95% CI: 1.87, 2.44) comparing the top versus bottom quartiles of PM2.5. Similarly, there was a statistically significant increase in events comparing the top versus bottom quartiles of PM10 and NO2 (HR: 1.17; 95% CI: 1.03, 1.33 and HR:1.26; 95% CI: 1.12, 1.42). The strength of association did not vary substantially by stroke etiology. There was little evidence of an association between PMcoarse and incident cerebrovascular events.

CONCLUSIONS

Long-term exposure to fine (PM2.5) and respirable (PM10) particulate matter as well as NO2 was associated with a significant increase of cerebrovascular events among postmenopausal women. Strength of the associations were consistent by stroke etiology.

Associations of Particulate Matter Exposures With Brain Gray Matter Thickness and White Matter Hyperintensities: Effect Modification by Low-Grade Chronic Inflammation

BACKGROUND

Numerous studies have shown the effect of particulate matter exposure on brain imaging markers. However, little evidence exists about whether the effect differs by the level of low-grade chronic systemic inflammation. We investigated whether the level of c-reactive protein (CRP, a marker of systemic inflammation) modifies the associations of particulate matter exposures with brain cortical gray matter thickness and white matter hyperintensities (WMH).

METHODS

We conducted a cross-sectional study of baseline data from a prospective cohort study including adults with no dementia or stroke. Long-term concentrations of particulate matter ≤ 10 µm in diameter (PM10) and ≤ 2.5 µm (PM2.5) at each participant's home address were estimated. Global cortical thickness (n = 874) and WMH volumes (n = 397) were estimated from brain magnetic resonance images. We built linear and logistic regression models for cortical thickness and WMH volumes (higher versus lower than median), respectively. Significance of difference in the association between the CRP group (higher versus lower than median) was expressed as P for interaction.

RESULTS

Particulate matter exposures were significantly associated with a reduced global cortical thickness only in the higher CRP group among men (P for interaction = 0.015 for PM10 and 0.006 for PM2.5). A 10 μg/m³ increase in PM10 was associated with the higher volumes of total WMH (odds ratio, 1.78; 95% confidence interval, 1.07-2.97) and periventricular WMH (2.00; 1.20-3.33). A 1 μg/m³ increase in PM2.5 was associated with the higher volume of periventricular WMH (odds ratio, 1.66; 95% confidence interval, 1.08-2.56). These associations did not significantly differ by the level of high sensitivity CRP.

CONCLUSION

Particulate matter exposures were associated with a reduced global cortical thickness in men with a high level of chronic inflammation. Men with a high level of chronic inflammation may be susceptible to cortical atrophy attributable to particulate matter exposures.

Evaluation of Model-Based PM2.5 Estimates for Exposure Assessment during Wildfire Smoke Episodes in the Western U.S

Investigating the health impacts of wildfire smoke requires data on people's exposure to fine particulate matter (PM2.5) across space and time. In recent years, it has become common to use machine learning models to fill gaps in monitoring data. However, it remains unclear how well these models are able to capture spikes in PM2.5 during and across wildfire events. Here, we evaluate the accuracy of two sets of high-coverage and high-resolution machine learning-derived PM2.5 data sets created by Di et al. and Reid et al. In general, the Reid estimates are more accurate than the Di estimates when compared to independent validation data from mobile smoke monitors deployed by the US Forest Service. However, both models tend to severely under-predict PM2.5 on high-pollution days. Our findings complement other recent studies calling for increased air pollution monitoring in the western US and support the inclusion of wildfire-specific monitoring observations and predictor variables in model-based estimates of PM2.5. Lastly, we call for more rigorous error quantification of machine-learning derived exposure data sets, with special attention to extreme events.

Prenatal exposure to ambient air pollution is associated with neurodevelopmental outcomes at 2 years of age

BACKGROUND

Higher prenatal ambient air pollution exposure has been associated with impaired neurodevelopment in preschoolers and school-aged children. The purpose of this study was to explore the relationships between prenatal ambient air pollution exposure and neurodevelopment during infancy.

METHODS

This study examined 161 Latino mother-infant pairs from the Southern California Mother's Milk Study. Exposure assessments included prenatal nitrogen dioxide (NO₂) and particulate matter smaller than 2.5 and 10 microns in diameter (PM_2.5 and PM₁₀, respectively). The pregnancy period was also examined as three windows, early, mid, and late, which describe the first, middle, and last three months of pregnancy. Infant neurodevelopmental outcomes at 2 years of age were measured using the Bayley-III Scales of Infant and Toddler Development. Multivariable linear models and distributed lag linear models (DLM) were used to examine relationships between prenatal exposures and neurodevelopmental scores, adjusting for socioeconomic status, breastfeeding frequency, time of delivery, pre-pregnancy body mass index, and infant birthweight and sex.

RESULTS

Higher prenatal exposure to PM₁₀ and PM_2.5 was negatively associated with composite cognitive score (β = -2.01 [-3.89, -0.13] and β = -1.97 [-3.83, -0.10], respectively). In addition, higher average prenatal exposure to PM₁₀ was negatively associated with composite motor (β = -2.35 [-3.95, -0.74]), scaled motor (β = -0.77 [-1.30, -0.24]), gross motor (β = -0.37 [-0.70, -0.04]), fine motor (β = -0.40 [-0.71, -0.09]), composite language (β = -1.87 [-3.52, -0.22]), scaled language (β = -0.61 [-1.18, -0.05]) and expressive communication scaled scores (β = -0.36 [-0.66, -0.05]). DLMs showed that higher prenatal air pollution exposure during mid and late pregnancy was inversely associated with motor, cognitive, and communication language scores.

CONCLUSIONS

Higher exposure to air pollutants during pregnancy, particularly in the mid and late prenatal periods, was inversely associated with scaled and composite motor, cognitive, and language scores at 2 years. These results indicate that prenatal ambient air pollution may negatively impact neurodevelopment in early life.

Examining the influence of built environment on sleep disruption

Modifying aspects of the built environment may be an effective strategy for population-level improvements to sleep. However, few comprehensive evaluations of built environment and sleep have been completed.

Methods

We conducted a cross-sectional study among participants of the British Columbia Generations Project (BCGP) who self-reported sleep duration (n = 28,385). Geospatial measures of light-at-night (LAN), greenness, air pollution (PM_2.5, NO₂, SO₂), and road proximity were linked to participant baseline residential postal codes. Logistic regression models, adjusted for age and sex, were used to estimate the association between these factors and self-reported sleep duration (<7 vs. ≥7 hours).

Results

Interquartile range (IQR) increases in LAN intensity, greenness, and SO₂ were associated with 1.04-fold increased (95% CI = 1.02, 1.07), 0.95-fold decreased (95% CI = 0.91, 0.98), and 1.07-fold increased (95% CI = 1.03, 1.11) odds, respectively, of reporting insufficient sleep (i.e., <7 hours per night). Living <100 m from a main roadway was associated with a 1.09-fold greater odds of insufficient sleep (95% CI = 1.02, 1.17). Results were unchanged when examining all factors together within a single regression model. In stratified analyses, associations with SO₂ were stronger among those with lower reported annual household incomes and those living in more urban areas.

Conclusions

BCGP's rich data enabled a comprehensive evaluation of the built environment, revealing multiple factors as potentially modifiable determinants of sleep disruption. In addition to longitudinal evaluations, future studies should pay careful attention to the role of social disparities in sleep health.

[Promoting Better Indoor Air Quality in Portugal for Disease Prevention and Control]

N/a.

Review of Internet of Things-Based Artificial Intelligence Analysis Method through Real-Time Indoor Air Quality and Health Effect Monitoring: Focusing on Indoor Air Pollution That Are Harmful to the Respiratory Organ

Everyone is aware that air and environmental pollutants are harmful to health. Among them, indoor air quality directly affects physical health, such as respiratory rather than outdoor air. However, studies that have examined the correlation between environmental and health information have been conducted with public data targeting large cohorts, and studies with real-time data analysis are insufficient. Therefore, this research explores the research with an indoor air quality monitoring (AQM) system based on developing environmental detection sensors and the internet of things to collect, monitor, and analyze environmental and health data from various data sources in real-time. It explores the usage of wearable devices for health monitoring systems. In addition, the availability of big data and artificial intelligence analysis and prediction has increased, investigating algorithmic studies for accurate prediction of hazardous environments and health impacts. Regarding health effects, techniques to prevent respiratory and related diseases were reviewed.

Particulate air pollution in the Copenhagen metro part 1: Mass concentrations and ventilation

The Copenhagen Metro comprises four lines, the M1, M2, M3 and M4, with 25 subterranean stations and an additional 14 stations above ground, serving ca. 80 million passengers annually. In this study we measure fine particulate matter (PM_2.5) and carbon dioxide (CO₂) concentrations in stations and in trains across the entire system. In partially underground lines, high PM_2.5 concentrations with an average of 109 μg m^-3 are found in below-ground stations. The observed correlation between PM_2.5 concentration and distance between a station and a tunnel exit is attributed to ventilation via the piston effect. The piston effect via tunnel draught relief shafts was therefore found to be relatively limited. Filter samples of particulate matter are analysed using particle-induced X-ray emission and show an iron content of 88.6 % by mass which is quite different from above-ground particulate matter and consistent with particle production by train wheels, rails and brakes. The average concentration measured at the stations of a recently opened (2019) fully underground M3 closed loop line is 168 μg m^-3, further demonstrating that while piston effect-driven ventilation is effective in close proximity to tunnel openings, it is relatively limited via tunnel draught relief shafts. Measurements onboard trains show even higher PM_2.5 concentrations and the patterns in CO₂ concentrations suggest carriage ventilation by tunnel air. Ventilation via doors during platform stops caused a drop in observed PM (and CO₂) at stations, but the system is surprisingly polluted despite its recent construction. CO₂ mixing ratios ranged from ambient to around 600 ppm. Measures should be taken to control PM levels using a combination of source control and increased clean air supply of the Copenhagen and other similar metro systems.

The Role of Climate Change in Asthma

Human activity and increased use of fossil fuels have led to climate change. These changes are adversely affecting human health, including increasing the risk of developing asthma. Global temperatures are predicted to increase in the future. In 2019, asthma affected an estimated 262 million people and caused 455,000 deaths. These rates are expected to increase. Climate change by intensifying climate events such as drought, flooding, wildfires, sand storms, and thunderstorms has led to increases in air pollution, pollen season length, pollen and mold concentration, and allergenicity of pollen. These effects bear implications for the onset, exacerbation, and management of childhood asthma and are increasing health inequities. Global efforts to mitigate the effects of climate change are urgently needed with the goal of limiting global warming to between 1.5 and 2.0 °C of preindustrial times as per the 2015 Paris Agreement. Clinicians need to take an active role in these efforts in order to prevent further increases in asthma prevalence. There is a role for clinician advocacy in both the clinical setting as well as in local, regional, and national settings to install measures to control and curb the escalating disease burden of childhood asthma in the setting of climate change.

Identifying pre-conception and pre-natal periods in which ambient air pollution exposure affects fetal growth in the predominately Hispanic MADRES cohort

BACKGROUND

It is well documented that persons of color experience disproportionate exposure to environmental contaminants, including air pollution, and have poorer pregnancy outcomes. This study assessed the critical windows of exposure to ambient air pollution on in utero fetal growth among structurally marginalized populations in urban Los Angeles.

METHODS

Participants (N = 281) from the larger ongoing MADRES pregnancy cohort study were included in this analysis. Fetal growth outcomes were measured on average at 32 [Formula: see text] 2 weeks of gestation by a certified sonographer and included estimated fetal weight, abdominal circumference, head circumference, biparietal diameter and femur length. Daily ambient air pollutant concentrations were estimated for four pollutants (particulate matter less than 2.5 µm (PM_2.5) and less than 10 µm (PM₁₀) in aerodynamic diameter, nitrogen dioxide (NO₂), and 8-h maximum ozone (O₃)) at participant residences using inverse-distance squared spatial interpolation from ambient monitoring data. Weekly gestational averages were calculated from 12 weeks prior to conception to 32 weeks of gestation (44 total weeks), and their associations with growth outcomes were modeled using adjusted distributed lag models (DLMs).

RESULTS

Participants were on average 29 years [Formula: see text] 6 old and predominately Hispanic (82%). We identified a significant sensitive window of PM_2.5 exposure (per IQR increase of 6 [Formula: see text]³) between gestational weeks 4-16 for lower estimated fetal weight [Formula: see text] _averaged4-16 = -8.7 g; 95% CI -16.7, -0.8). Exposure to PM_2.5 during gestational weeks 1-23 was also significantly associated with smaller fetal abdominal circumference ([Formula: see text] _averaged1-23 = -0.6 mm; 95% CI -1.1, -0.2). Additionally, prenatal exposure to PM₁₀ (per IQR increase of 13 [Formula: see text]³) between weeks 6-15 of pregnancy was significantly associated with smaller fetal abdominal circumference ([Formula: see text] _{averaged6-15 =} -0.4 mm; 95% CI -0.8, -0.1).

DISCUSSION

These results suggest that exposure to particulate matter in early to mid-pregnancy, but not preconception or late pregnancy, may have critical implications on fetal growth.

Estimation of fine particulate matter in an arid area from visibility based on machine learning

BACKGROUND

The absence of air pollution monitoring networks makes it difficult to assess historical fine particulate matter (PM_2.5) exposures for countries in the areas, such as Kuwait, which are severe impacted by desert dust and anthropogenic pollution.

OBJECTIVE

We constructed an ensemble machine learning model to predict daily PM_2.5 concentrations for regions lack of PM_2.5 observations.

METHODS

The model was constructed based on daily PM_2.5, visibility, and other meteorological data collected at two sites in Kuwait. Then, our model was applied to predict the daily level of PM_2.5 concentrations for eight airports located in Kuwait and Iraq from 2013 to 2020.

RESULTS

As compared to traditional statistic models, the proposed machine learning methods improved the accuracy in using visibility to predict daily PM_2.5 concentrations with a cross-validation R² of 0.68. The predicted level of daily PM_2.5 concentrations were consistent with previous measurements. The predicted average yearly PM_2.5 concentration for the eight stations is 50.65 µg/m³. For all stations, the monthly average PM_2.5 concentrations reached their maximum in July and their minimum in November.

SIGNIFICANCE

These findings make it possible to retrospectively estimate daily PM_2.5 exposures using the large-scale databases of historical visibility in regions with few particulate matter monitoring stations.

IMPACT STATEMENT

The scarcity of air pollution ground monitoring networks makes it difficult to assess historical fine particulate matter exposures for countries in arid areas such as Kuwait. Visibility is closely related to atmospheric particulate matter concentrations and historical airport visibility records are commonly available in most countries. Our model make it possible to retrospectively estimate daily PM_2.5 exposures using the large-scale databases of historical visibility in arid regions with few particulate matter ground monitoring stations. The product of such models can be critical for environmental risk assessments and population health studies.

NO2 and PM2.5 air pollution co-exposure and temperature effect modification on pre-mature mortality in advanced age: a longitudinal cohort study in China

BACKGROUND

There is a discourse on whether air pollution mixture or air pollutant components are causally linked to increased mortality. In particular, there is uncertainty on whether the association of NO₂ with mortality is independent of fine particulate matter (PM_2.5). Furthermore, effect modification by temperature on air pollution-related mortality also needs more evidence.

METHODS

We used the Chinese Longitudinal Healthy Longevity Study (CLHLS), a prospective cohort with geographical and socio-economic diversity in China. The participants were enrolled in 2008 or 2009 and followed up in 2011-2012, 2014, and 2017-2018. We used remote sensing and ground monitors to measure nitrogen dioxide (NO₂), fine particulate matter (PM_2.5) , and temperature. We used the Cox-proportional hazards model to examine the association between component and composite air pollution and all-cause mortality, adjusted for demographic characteristics, lifestyle, geographical attributes, and temperature. We used the restricted cubic spline to visualize the concentration-response curve.

RESULTS

Our study included 11 835 individuals with an average age of 86.9 (SD: 11.4) at baseline. Over 55 606 person-years of follow-up, we observed 8 216 mortality events. The average NO₂ exposure was 19.1 μg/m³ (SD: 14.1); the average PM_2.5 exposure was 52.8 μg/m³ (SD: 15.9). In the single pollutant models, the mortality HRs (95% CI) for 10 μg/m³ increase in annual average NO₂ or PM_2.5 was 1.114 (1.085, 1.143) and 1.244 (1.221, 1.268), respectively. In the multi-pollutant model co-adjusting for NO₂ and PM_2.5, the HR for NO₂ turned insignificant: 0.978 (0.950, 1.008), but HR for PM_2.5 was not altered: 1.252 (1.227, 1.279). PM_2.5 and higher mortality association was robust, regardless of NO₂. When acccounting for particulate matter, NO₂ exposure appeared to be harmful in places of colder climates and higher seasonal temperature variation.

CONCLUSIONS

We see a robust relationship of PM_2.5 exposure and premature mortality in advance aged individuals, however, NO₂ exposure and mortality was only harmful in places of colder climate such as northeast China, indicating evidence of effect modification by temperature. Analysis of NO₂ without accounting for its collinearity with PM_2.5, may lead to overestimation.

An analysis of particulate pollution using urban aerosol pollution island intensity over Delhi, India

The accent of the present study is determination of Urban Aerosol Pollution Island (UAPI) intensity and spatial variability in particulate matter concentration (PM10 and PM2.5) over Delhi. For analysis, the hourly concentration dataset of PM2.5 and PM10 from January 2019 to December 2020 was obtained from ten air quality monitoring stations of Delhi. Additionally, UAPI Index has been calculated to assess the intensity of particulate pollution. The daily, monthly, and annual variations in the trends of PM10, PM2.5, and UAPI index along with related meteorological parameters have been analyzed. Particulate pollution peaked majorly during two seasons, i.e., summer and winter. The highest concentration of PM10 was observed to be 426.77 µg/m3 while that of PM2.5 was observed to be 301.91 µg/m3 in January 2019 for traffic-affected regions. During winters, higher PM2.5 concentration was observed which can be ascribed to increased local emissions and enhanced secondary particle formations. While the increase in PM10 concentrations led to an increment in pollution episodes during summers over most of the sites in Delhi. The UAPI index was found to be declining in 2020 over traffic affected regions (77.92 and 27.22 for 2019 and 2020, respectively) as well as in the background regions (64.91 and 19.80 for 2019 and 2020, respectively) of Delhi. Low traffic intensity and reduced pollutant emission could have been responsible for the reduction of UAPI intensity in the year 2020. The result indicates that lockdown implemented to control the COVID-19 outbreak led to an unexpected decrease in the PM10 pollution over Delhi.