Biomonitoring potentially toxic elements in atmospheric particulate matter of greater Dhaka region using leaves of higher plants.

In this study, four different plant species, namely Artocarpus heterophyllus, Mangifera indica, Psidium guajava, and Swietenia mahagoni, were selected from seven different locations to assess the feasibility of using them as a cost-effective alternative for biomonitoring air quality. Atmospheric coarse particulate matter (PM10), soil samples, and leaf samples were collected from residential, industrial, and traffic-congested sites located in the greater Dhaka region. The heavy metal concentrations (Cd, Cr, Cu, Fe, Mn, Ni, Pb, and Zn) in the leaves of the different species, PM10, and soil samples were analyzed. The highest Pb (718 ng/m3) and Zn (15,956 ng/m3) concentrations were found in PM10 of Kodomtoli which is an industrial area. On the other hand, the highest Fe (6,152 ng/m3) and Ni (61.1 ng/m3) concentrations were recorded in the PM10 of Gabtoli, a heavy-traffic area. A significant positive correlation (r = 0.74; p < 0.01) between Pb content in plant leaves and PM fraction was found which indicated that atmospheric PM-bound Pb may contribute to the uptake of Pb by plant leaves. The analysis of the enrichment factor (EF) revealed that soils were contaminated with Cd, Ni, Pb, and Zn. The abaxial leaf surfaces of Psidium guajava growing at the polluted site exhibited up to a 40% decrease in stomatal pores compared to the control site. Saet's summary index (Zc) demonstrated that Mangifera indica had the highest bioaccumulation capacity. The metal accumulation index (MAI) was also evaluated to assess the overall metal accumulation capacity of the selected plants. Of the four species, Swietenia mahagoni (3.05) exhibited the highest MAI value followed by Mangifera indica (2.97). Mangifera indica and Swietenia mahagoni were also found to accumulate high concentrations of Pb and Cr in their leaves and are deemed to be good candidates to biomonitor Pb and Cr contents in ambient air.

Household air pollution and attributable burden of disease in rural China: A literature review and a modelling study.

Household air pollution prevails in rural residences across China, yet a comprehensive nationwide comprehending of pollution levels and the attributable disease burdens remains lacking. This study conducted a systematic review focusing on elucidating the indoor concentrations of prevalent household air pollutants-specifically, PM2.5, PAHs, CO, SO2, and formaldehyde-in rural Chinese households. Subsequently, the premature deaths and economic losses attributable to household air pollution among the rural population of China were quantified through dose-response relationships and the value of statistical life. The findings reveal that rural indoor air pollution levels frequently exceed China's national standards, exhibiting notable spatial disparities. The estimated annual premature mortality attributable to household air pollution in rural China amounts to 966 thousand (95% CI: 714-1226) deaths between 2000 and 2022, representing approximately 22.2% (95% CI: 16.4%-28.1%) of total mortality among rural Chinese residents. Furthermore, the economic toll associated with these premature deaths is estimated at 486 billion CNY (95% CI: 358-616) per annum, constituting 0.92% (95% CI: 0.68%-1.16%) of China's GDP. The findings quantitatively demonstrate the substantial disease burden attributable to household air pollution in rural China, which highlights the pressing imperative for targeted, region-specific interventions to ameliorate this pressing public health concern.

Microplastics suspended in dust from different indoor environments in Barranquilla, Colombia: predominant microparticles?

Considering that microplastics (MPs) are classified as ubiquitous pollutants, that air quality affects human health, and that people remain indoors most of the time, the need has arisen to evaluate the exposure to MPs within the suspended dust in indoor environments. With this objective, the present study carried out passive sampling to analyze the precipitation of microparticles in some indoor residential environments (2 apartments) and workplaces (an office, a pastry shop, a gift shop, and a paint shop) in Barranquilla, Colombia. The quantification and physical characterization of microparticles were carried out under a stereomicroscope, and the chemical characterization was carried out by infrared microspectroscopy (µFTIR). The highest average concentration of MPs in the apartments was found in the air-conditioned rooms (1.1×104 MPs/m2/day), and concerning the workplaces, the gift shop and the paint shop were the spaces with a higher proportion of MPs (6.0-6.1×103 MPs/m2/day), with polyesters being the main synthetic polymers, but being semi-synthetic particles the predominant among the samples. Regarding its morphology, fibers were the most abundant shape (>90%), grouping mainly in the 1000-5000 µm range, while the few fragments found were mostly grouped below 50 µm. Exposure by inhalation of MPs in adults was estimated between 1.7×102 - 1.6×103 MPs/kg/day, while by ingestion it ranged between 1.3×103 -1.2×104 MPs/kg/day. On the other hand, within our research, a significant presence of non-plastic microparticles was found, which reached up to 69% in analyzed samples, corresponding mainly to cotton and cellulose, so we suggest that these should also be included in future studies that aim to estimate potential health implications from exposure to suspended micropollutants.

Air pollution mixture associated with oxidative stress exacerbation and symptoms deterioration in allergic rhinitis patients: Evidence from a panel study.

With allergic rhinitis (AR) on the rise globally, there has been a growing focus on the role of environmental pollutants in the onset of AR. However, the potential mechanisms by how and which these pollutants exacerbate AR conditions remain unknown. This panel study of 49 patients diagnosed with AR over one year aimed to assess the individual and combined effects of short-term exposure to multiple ambient pollutants on oxidative stress, symptoms, and quality of life among patients with AR. All participants underwent four repeated assessments of health conditions and personal environmental exposures (PM2.5, O3, SO2, and NO2) over warm and cold seasons during 2017-2018. We evaluated two oxidative stress biomarkers (malondialdehyde [MDA], and superoxide dismutase [SOD]) via nasal lavage. We collected information on self-reported symptoms and quality of life using the Rhinitis Symptom Scale (SRS), the Visual Analog Scale (VAS), and the Rhinoconjunctivitis Quality of Life Questionnaire (RQLQ) through in-person interviews. Bayesian kernel machine regression (BKMR) was used to evaluate the joint effects of pollutant mixture and identify key contributors. The results revealed a significant association of the pollutant mixture when all four pollutants were at or above their median levels, with increased oxidative stress. This was evidenced by elevated MDA and reduced SOD. We found a joint detrimental effect of the pollutant mixture on AR symptoms with a strong association with increased SRS scores, but a non-significant positive association with VAS and RQLQ scores. PM2.5, O3, and SO2 presented as the potentially primary contributors to the adverse health effects associated with the pollutant mixture in Taiyuan city. Patients with AR exposed to short-term air pollutant mixture are more likely to have greater nasal symptoms and worse quality of life from increased oxidative stress and reduced antioxidant capacity. Further research is warranted to better elucidate the underlying mechanisms.

Air Pollution and Cardiac Arrest: A More Significant Intermediate Role of COPD than Cardiac Events.

No prior studies have linked long-term air pollution exposure to incident sudden cardiac arrest (SCA) or its possible development trajectories. We aimed to investigate the association between long-term exposure to air pollution and SCA, as well as possible intermediate diseases. Based on the UK Biobank cohort, Cox proportional hazard model was applied to explore associations between air pollutants and SCA. Chronic obstructive pulmonary disease (COPD) and major adverse cardiovascular events (MACE) were selected as intermediate conditions, and multistate model was fitted for trajectory analysis. During a median follow-up of 13.7 years, 2884 participants developed SCA among 458 237 individuals. The hazard ratios (HRs) for SCA were 1.04-1.12 per interquartile range increment in concentrations of fine particulate matter, inhalable particulate matter, nitrogen dioxide, and nitrogen oxides. Most prominently, air pollutants could induce SCA through promoting transitions from baseline health to COPD (HRs: 1.06-1.24) and then to SCA (HRs: 1.16-1.27). Less importantly, SCA could be developed through transitions from baseline health to MACE (HRs: 1.02-1.07) and further to SCA (HRs: 1.12-1.16). This study provides novel and compelling evidence that long-term exposure to air pollution could promote the development of SCA, with COPD serving as a more important intermediate condition than MACE.

Associations between satellite-derived estimates of PM2.5 species concentrations for organic carbon, elemental carbon, nitrate, and sulfate with birth weight and preterm birth in California during 2005-2014.

BACKGROUND: Characterizing the spatial distribution of PM2.5 species concentrations is challenging due to the geographic sparsity of the stationary monitoring network. Recent advances have enabled valid estimation of PM2.5 species concentrations using satellite remote sensing data for use in epidemiologic studies. OBJECTIVE: In this study, we used satellite-based estimates of ambient PM2.5 species concentrations to estimate associations with birth weight and preterm birth in California. METHODS: Daily 24 h averaged ground-level PM2.5 species concentrations of organic carbon, elemental carbon, nitrate, and sulfate were estimated during 2005-2014 in California at 1 km resolution. Birth records were linked to ambient pollutant exposures based on maternal residential zip code. Linear regression and Cox regression were conducted to estimate the effect of 1 µg/m3 increases in PM2.5 species concentrations on birth weight and preterm birth. RESULTS: Analyses included 4.7 million live singleton births having a median 28 days with exposure measurements per pregnancy. In single pollutant models, the observed changes in mean birth weight (per 1 µg/m3 increase in speciated PM2.5 concentrations) were: organic carbon -3.12 g (CI: -4.71, -1.52), elemental carbon -14.20 g (CI: -18.76, -9.63), nitrate -5.51 g (CI: -6.79, -4.23), and sulfate 9.26 g (CI: 7.03, 11.49). Results from multipollutant models were less precise due to high correlation between pollutants. Associations with preterm birth were null, save for a negative association between sulfate and preterm birth (Hazard Ratio per 1 µg/m3 increase: 0.973 CI: 0.958, 0.987).

Potential Strategy to Control the Organic Components of Condensable Particulate Matter: A Critical Review.

More and more attention has been paid to condensable particulate matter (CPM) since its emissions have surpassed that of filterable particulate matter (FPM) with the large-scale application of ultralow-emission reform. CPM is a gaseous material in the flue stack but instantly turns into particles after leaving the stack. It is composed of inorganic and organic components. Organic components are an important part of CPM, and they are an irritant, teratogenic, and carcinogenic, which triggers photochemical smog, urban haze, and acid deposition. CPM organic components can aggravate air pollution and climate change; therefore, consideration should be given to them. Based on existing methods for removing atmospheric organic pollutants and combined with the characteristics of CPM organic components, we provide a critical overview from the aspects of (i) fundamental cognition of CPM, (ii) common methods to control CPM organic components, and (iii) catalytic oxidation of CPM organic components. As one of the most encouraging methods, catalytic oxidation is discussed in detail, especially in combination with selective catalytic reduction (SCR) technology, to meet the growing demands for multipollutant control (MPC). We believe that this review is inspiring for a fuller understanding and deeper exploration of promising approaches to control CPM organic components.

A review of air pollution as a driver of cardiovascular disease risk across the diabetes spectrum.

The prevalence of diabetes is estimated to reach almost 630 million cases worldwide by the year 2045; of current and projected cases, over 90% are type 2 diabetes. Air pollution exposure has been implicated in the onset and progression of diabetes. Increased exposure to fine particulate matter air pollution (PM2.5) is associated with increases in blood glucose and glycated hemoglobin (HbA1c) across the glycemic spectrum, including normoglycemia, prediabetes, and all forms of diabetes. Air pollution exposure is a driver of cardiovascular disease onset and exacerbation and can increase cardiovascular risk among those with diabetes. In this review, we summarize the literature describing the relationships between air pollution exposure, diabetes and cardiovascular disease, highlighting how airborne pollutants can disrupt glucose homeostasis. We discuss how air pollution and diabetes, via shared mechanisms leading to endothelial dysfunction, drive increased cardiovascular disease risk. We identify portable air cleaners as potentially useful tools to prevent adverse cardiovascular outcomes due to air pollution exposure across the diabetes spectrum, while emphasizing the need for further study in this particular population. Given the enormity of the health and financial impacts of air pollution exposure on patients with diabetes, a greater understanding of the interventions to reduce cardiovascular risk in this population is needed.

Effect of air pollution exposure on risk of acute coronary syndromes in Poland: a nationwide population-based study (EP-PARTICLES study).

Background: Air pollution (AP) is linked up to 20% of cardiovascular deaths. The aim of this nationwide study was to investigate subpopulations vulnerable to AP for non-ST- (NSTEMI) and ST-elevation myocardial infarction (STEMI) incidence. Methods: We analysed short- (lags up to seven days) and mid-term (0-30 days moving average) influence of particulate matter (PM2.5), sulphur dioxide (SO2), nitrogen dioxide (NO2) and benzo(a)pyrene (BaP) on hospitalizations due NSTEMI and STEMI in 2011-2020. Data on AP concentrations were derived using GEM-AQ model. Study included residents of five voivodeships in eastern Poland, inhabited by over 8,000,000 individuals. Findings: Higher NO2 and PM2.5 concentrations increased mid-term risk of NSTEMI in patients aged < 65 years by 1.3-5.7%. Increased SO2 and PM2.5 concentration triggered STEMI in the short- (SO2, PM2.5) and mid-term (PM2.5) amongst those aged ≥ 65 years. In the short- and mid-term, women were more susceptible to PM2.5 and BaP influence resulting in increased STEMI incidence. In rural regions, STEMI risk was triggered by SO2, PM2.5 and BaP. Income-based stratification showed disproportions regarding influence of BaP concentrations on NSTEMI incidence based on gross domestic product (up to 1.4%). Interpretation: There are significant disparities in the influence of air pollution depending on the demographic and socio-economic factors. AP exposure is associated with the threat of a higher risks of NSTEMI and STEMI, especially to younger people, women, residents of rural areas and those with lower income. Funding: National Science Center and Medical University of Bialystok, Poland.

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

BACKGROUND: Western Montana, USA, experiences complex air pollution patterns with predominant exposure sources from summer wildfire smoke and winter wood smoke. In addition, climate change related temperatures events are becoming more extreme and expected to contribute to increases in hospital admissions for a range of health outcomes. Evaluating while accounting for these exposures (air pollution and temperature) that often occur simultaneously and may act synergistically on health is becoming more important. METHODS: We explored short-term exposure to air pollution on children's respiratory health outcomes and how extreme temperature or seasonal period modify the risk of air pollution-associated healthcare events. The main outcome measure included individual-based address located respiratory-related healthcare visits for three categories: asthma, lower respiratory tract infections (LRTI), and upper respiratory tract infections (URTI) across western Montana for ages 0-17 from 2017-2020. We used a time-stratified, case-crossover analysis with distributed lag models to identify sensitive exposure windows of fine particulate matter (PM2.5) lagged from 0 (same-day) to 14 prior-days modified by temperature or season. RESULTS: For asthma, increases of 1 µg/m3 in PM2.5 exposure 7-13 days prior a healthcare visit date was associated with increased odds that were magnified during median to colder temperatures and winter periods. For LRTIs, 1 µg/m3 increases during 12 days of cumulative PM2.5 with peak exposure periods between 6-12 days before healthcare visit date was associated with elevated LRTI events, also heightened in median to colder temperatures but no seasonal effect was observed. For URTIs, 1 unit increases during 13 days of cumulative PM2.5 with peak exposure periods between 4-10 days prior event date was associated with greater risk for URTIs visits that were intensified during median to hotter temperatures and spring to summer periods. CONCLUSIONS: Delayed, short-term exposure increases of PM2.5 were associated with elevated odds of all three pediatric respiratory healthcare visit categories in a sparsely population area of the inter-Rocky Mountains, USA. PM2.5 in colder temperatures tended to increase instances of asthma and LRTIs, while PM2.5 during hotter periods increased URTIs.

Impact of ambient air pollution on colorectal cancer risk and survival: insights from a prospective cohort and epigenetic Mendelian randomization study.

BACKGROUND: This study investigates the associations between air pollution and colorectal cancer (CRC) risk and survival from an epigenomic perspective. METHODS: Using a newly developed Air Pollutants Exposure Score (APES), we utilized a prospective cohort study (UK Biobank) to investigate the associations of individual and combined air pollution exposures with CRC incidence and survival, followed by an up-to-date systematic review with meta-analysis to verify the associations. In epigenetic two-sample Mendelian randomization analyses, we examine the associations between genetically predicted DNA methylation related to air pollution and CRC risk. Further genetic colocalization and gene-environment interaction analyses provided different insights to disentangle pathogenic effects of air pollution via epigenetic modification. FINDINGS: During a median 12.97-year follow-up, 5767 incident CRC cases among 428,632 participants free of baseline CRC and 533 deaths in 2401 patients with CRC were documented in the UK Biobank. A higher APES score was associated with an increased CRC risk (HR, 1.03, 95% CI = 1.01-1.06; P = 0.016) and poorer survival (HR, 1.13, 95% CI = 1.03-1.23; P = 0.010), particularly among participants with insufficient physical activity and ever smokers (Pinteraction > 0.05). A subsequent meta-analysis of seven observational studies, including UK Biobank data, corroborated the association between PM2.5 exposure (per 10 µg/m3 increment) and elevated CRC risk (RR,1.42, 95% CI = 1.12-1.79; P = 0.004; I2 = 90.8%). Genetically predicted methylation at PM2.5-related CpG site cg13835894 near TMBIM1/PNKD and cg16235962 near CXCR5, and NO2-related cg16947394 near TMEM110 were associated with an increased CRC risk. Gene-environment interaction analysis confirmed the epigenetic modification of aforementioned CpG sites with CRC risk and survival. INTERPRETATION: Our study suggests the association between air pollution and CRC incidence and survival, underscoring the possible modifying roles of epigenomic factors. Methylation may partly mediate pathogenic effects of air pollution on CRC, with annotation to epigenetic alterations in protein-coding genes TMBIM1/PNKD, CXCR5 and TMEM110. FUNDING: Xue Li is supported by the Natural Science Fund for Distinguished Young Scholars of Zhejiang Province (LR22H260001), the National Nature Science Foundation of China (No. 82204019) and Healthy Zhejiang One Million People Cohort (K-20230085). ET is supported by a Cancer Research UK Career Development Fellowship (C31250/A22804). MGD is supported by the MRC Human Genetics Unit Centre Grant (U127527198).

Emerging concern on air pollution and health: Trade-off between air pollution exposure and physical activity.

Air pollution is a major contributor to the global disease burden, especially affecting respiratory and cardiovascular health. However, physical activity is associated with improved lung function, a slower decline in lung function, and lower mortality. The public is more likely to be exposed to air pollution during outdoor physical activity. However, studies on how long-term and short-term exposure to air pollution interacts with physical activity yield inconsistent results, and the thresholds for air pollution and physical activity remain unclear. Thus, more studies are needed to provide sufficient evidence to guide the public to safely engage in outdoor physical activity when exposed to air pollution.

A pilot study on AI-driven approaches for classification of mental health disorders.

The increasing prevalence of mental disorders among youth worldwide is one of society's most pressing issues. The proposed methodology introduces an artificial intelligence-based approach for comprehending and analyzing the prevalence of neurological disorders. This work draws upon the analysis of the Cities Health Initiative dataset. It employs advanced machine learning and deep learning techniques, integrated with data science, statistics, optimization, and mathematical modeling, to correlate various lifestyle and environmental factors with the incidence of these mental disorders. In this work, a variety of machine learning and deep learning models with hyper-parameter tuning are utilized to forecast trends in the occurrence of mental disorders about lifestyle choices such as smoking and alcohol consumption, as well as environmental factors like air and noise pollution. Among these models, the convolutional neural network (CNN) architecture, termed as DNN1 in this paper, accurately predicts mental health occurrences relative to the population mean with a maximum accuracy of 99.79%. Among the machine learning models, the XGBoost technique yields an accuracy of 95.30%, with an area under the ROC curve of 0.9985, indicating robust training. The research also involves extracting feature importance scores for the XGBoost classifier, with Stroop test performance results attaining the highest importance score of 0.135. Attributes related to addiction, namely smoking and alcohol consumption, hold importance scores of 0.0273 and 0.0212, respectively. Statistical tests on the training models reveal that XGBoost performs best on the mean squared error and R-squared tests, achieving scores of 0.013356 and 0.946481, respectively. These statistical evaluations bolster the models' credibility and affirm the best-fit models' accuracy. The proposed research in the domains of mental health, addiction, and pollution stands to aid healthcare professionals in diagnosing and treating neurological disorders in both youth and adults promptly through the use of predictive models. Furthermore, it aims to provide valuable insights for policymakers in formulating new regulations on pollution and addiction.

Particulate matter 1µm (PM1) dataset collected by low-cost sensors in residential and industrial areas at the neighborhood level.

The incursion of low-cost sensors (LCS) for monitoring particulate matter in different fractions of particles (PM10, PM2.5, and PM1) allows the characterization of the concentration levels of specific sources or events, including the analysis of ultrafine fractions (PM1). Several studies have documented adverse effects on human health due to exposure to PM1, such as morbidity and mortality from respiratory, cardiovascular, and, in some cases, carcinogenic diseases. Hence, studying the concentration levels and the sources that cause PM1 is imperative. LCS is an alternative to understanding contaminant concentration levels by considering spatial and temporal community dynamics by monitoring critical zones. Furthermore, collecting and managing large amounts of data through automatic processing and analysis generates information to support decision-making to reduce exposure and risks to people's health. The dataset presents the concentration level of PM1 (µg/m3) calculated from the particles of size 0.03 µm, 0.05 µm, and 1.0 µm recorded and counted by the sensor in a sample per minute for 24 h for seven continuous days. The values of the meteorological factors of relative humidity, temperature, and heat index complement these attributes. The dataset comprises records collected (in the same period) at four particulate matter monitoring stations, which compose an LCS network supported by Internet of Things (IoT) technologies. The data collection points were located in different areas of Reynosa, Mexico, considering strategic places for monitoring environmental pollution, such as industrial parks, residential areas, avenues with high vehicular traffic and transportation of heavy cargo, and an airport.

Outdoor artificial light at night exposure and gestational diabetes mellitus: a case-control study.

Objective: This study aims to explore the association between outdoor artificial light at night (ALAN) exposure and gestational diabetes mellitus (GDM). Methods: This study is a retrospective case-control study. According with quantiles, ALAN has been classified into three categories (Q1-Q3). GDM was diagnosed through oral glucose tolerance tests. Conditional logistic regression models were used to evaluate the association between ALAN exposure and GDM risk. The odds ratio (OR) with 95% confidence interval (CI) was used to assess the association. Restricted cubic spline analysis (RCS) was utilized to investigate the no liner association between ALAN and GDM. Results: A total of 5,720 participants were included, comprising 1,430 individuals with GDM and 4,290 matched controls. Pregnant women exposed to higher levels of ALAN during the first trimester exhibited an elevated risk of GDM compared to those with lower exposure levels (Q2 OR = 1.39, 95% CI 1.20-1.63, p < 0.001); (Q3 OR = 1.70, 95% CI 1.44-2.00, p < 0.001). Similarly, elevated ALAN exposure during the second trimester also conferred an increased risk of GDM (second trimester: Q2 OR = 1.70, 95% CI 1.45-1.98, p < 0.001; Q3 OR = 2.08, 95% CI 1.77-2.44, p < 0.001). RCS showed a nonlinear association between ALAN exposure and GDM risk in second trimester pregnancy, with a threshold value of 4.235. Conclusion: Outdoor ALAN exposure during pregnancy is associated with an increased risk of GDM.

Statistical data pre-processing and time series incorporation for high-efficacy calibration of low-cost NO2 sensor using machine learning.

Air pollution stands as a significant modern-day challenge impacting life quality, the environment, and the economy. It comprises various pollutants like gases, particulate matter, biological molecules, and more, stemming from sources such as vehicle emissions, industrial operations, agriculture, and natural events. Nitrogen dioxide (NO2), among these harmful gases, is notably prevalent in densely populated urban regions. Given its adverse effects on health and the environment, accurate monitoring of NO2 levels becomes imperative for devising effective risk mitigation strategies. However, the precise measurement of NO2 poses challenges as it traditionally relies on costly and bulky equipment. This has prompted the development of more affordable alternatives, although their reliability is often questionable. The aim of this article is to introduce a groundbreaking method for precisely calibrating cost-effective NO2 sensors. This technique involves statistical preprocessing of low-cost sensor readings, aligning their distribution with reference data. Central to this calibration is an artificial neural network (ANN) surrogate designed to predict sensor correction coefficients. It utilizes environmental variables (temperature, humidity, atmospheric pressure), cross-references auxiliary NO2 sensors, and incorporates short time series of previous readings from the primary sensor. These methods are complemented by global data scaling. Demonstrated using a custom-designed cost-effective monitoring platform and high-precision public reference station data collected over 5 months, every component of our calibration framework proves crucial, contributing to its exceptional accuracy (with a correlation coefficient near 0.95 concerning the reference data and an RMSE below 2.4 µg/m3). This level of performance positions the calibrated sensor as a viable, cost-effective alternative to traditional monitoring approaches.

Costs of Air Pollution in California's San Joaquin Valley: A Societal Perspective of the Burden of Asthma on Emergency Departments and Inpatient Care.

Introduction: The San Joaquin Valley (SJV) is often recognized as one of the most polluted regions in the US. Periods of pollution exposure are associated with increased health burden related to respiratory inflammation and undermined lung function, which aggravates respiratory diseases such as asthma and leads to symptoms such as coughing, wheezing, or difficulty breathing. Asthma costs US$ 82 billion annually in healthcare costs, missed work and school in the US. Methods: Employing a societal perspective, a cost of illness design was combined with environmental epidemiological methods to analyze the economic impact of O3, NO2, and PM2.5-related adverse respiratory health outcomes amongst SJV residents who attended the emergency department (ED) or were hospitalized in 2016. Results: Asthma exacerbations monetized value ranged from US$ 3353 to US$ 5003 per ED visit and for hospital admissions US$ 2584 per inpatient day for adults 65 years and older to US$ 3023 per child. The estimated value to society in healthcare costs, productivity losses, school absences, and opportunity costs from air pollution adverse health outcomes totaled US$ 498,014,124 in ED visits and US$ 223,552,720 in hospital admissions for the SJV population in 2016. The marginal reduction in the background concentrations of pollutants would avert 21,786 ED adverse events and 19,328 hospitalizations from the health burden on the SJV population or US$ 8,024,505 cost savings due to O3, US$ 82,482,683 from NO2 reductions, and US$ 46,214,702 from decreased concentration of PM2.5. Conclusion: This study provides evidence that air pollution is a negative externality that imposes substantial social, environmental, and healthcare costs on the SJV. Furthermore, the region would avert significant adverse health outcomes realizing economic savings by reducing air pollution and exposures.

A toxicological perspective on climate change and the exposome.

Climate change is accompanied by changes in the exposome, including increased heat, ground-level ozone, and other air pollutants, infectious agents, pollens, and psychosocial stress. These exposures alter the internal component of the exposome and account for some of the health effects of climate change. The adverse outcome pathways describe biological events leading to an unfavorable health outcome. In this perspective study, I propose to use this toxicological framework to better describe the biological steps linking a stressor associated with climate change to an adverse outcome. Such a framework also allows for better identification of possible interactions between stressors related to climate change and others, such as chemical pollution. More generally, I call for the incorporation of climate change as part of the exposome and for improved identification of the biological pathways involved in its health effects.

Short-term association of particulate matter and cardiovascular disease mortality in Shanghai, China between 2003 and 2020.

Objective: Evidence regarding the effects of particulate matter (PM) pollutants on cardiovascular disease (CVD) mortality remains limited in Shanghai, China. Our objective was to thoroughly evaluate associations between PM pollutants and CVD mortality. Methods: Daily data on CVD mortality, PM (PM10 and PM2.5) pollutants, and meteorological variables in Shanghai, China were gathered from 2003 to 2020. We utilized a time-series design with the generalized additive model to assess associations between PM pollutants and CVD mortality. Additionally, we conducted stratified analyses based on sex, age, education, and seasons using the same model. Results: We found that PM pollutants had a significant association with CVD mortality during the study period. Specifically, there was a 0.29% (95%CI: 0.14, 0.44) increase in CVD mortality for every 10 µg/m3 rise in a 2-day average (lag01) concentration of PM10. A 0.28% (95% CI: 0.07, 0.49) increase in CVD mortality was associated with every 10 µg/m3 rise in PM2.5 concentration at lag01. Overall, the estimated effects of PM10 and PM2.5 were larger in the warm period compared with the cold period. Furthermore, males and the older adult exhibited greater susceptibility to PM10 and PM2.5 exposure, and individuals with lower education levels experienced more significant effects from PM10 and PM2.5 than those with higher education levels. Conclusion: Our findings suggested that PM pollutants have a substantial impact on increasing CVD mortality in Shanghai, China. Moreover, the impacts of air pollution on health may be altered by factors such as season, sex, age, and educational levels.

Exercise-Mediated Protection against Air Pollution-Induced Immune Damage: Mechanisms, Challenges, and Future Directions.

Air pollution, a serious risk factor for human health, can lead to immune damage and various diseases. Long-term exposure to air pollutants can trigger oxidative stress and inflammatory responses (the main sources of immune impairment) in the body. Exercise has been shown to modulate anti-inflammatory and antioxidant statuses, enhance immune cell activity, as well as protect against immune damage caused by air pollution. However, the underlying mechanisms involved in the protective effects of exercise on pollutant-induced damage and the safe threshold for exercise in polluted environments remain elusive. In contrast to the extensive research on the pathogenesis of air pollution and the preventive role of exercise in enhancing fitness, investigations into exercise resistance to injury caused by air pollution are still in their infancy. In this review, we analyze evidence from humans, animals, and cell experiments on the combined effects of exercise and air pollution on immune health outcomes, with an emphasis on oxidative stress, inflammatory responses, and immune cells. We also propose possible mechanisms and directions for future research on exercise resistance to pollutant-induced damage in the body. Furthermore, we suggest strengthening epidemiological studies at different population levels and investigations on immune cells to guide how to determine the safety thresholds for exercise in polluted environments.