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This study aimed to biomonitor air pollution by measuring heavy metals (HMs) accumulation levels in the leaves of common urban trees, Cupressus arizonica Greene, Melia azedarach L., Morus alba L. and Buxus colchica in different regions of Isfahan. Their association with the levels of PM2.5 and PM2.5-bond HMs was also investigated. PM2.5 were collected on a glass-fiber filter and measured by gravimetric method. The HM contents of the PM2.5 and tree leaves were extracted and analyzed by ICP-OES. The average PM2.5 concentrations in ambient air of all areas varied from 52.34 to 103.96 μg/m3. The mean HMs levels in the leaves were in the following orderZn(31.2) > Cu(11.04) > Pb(4.38) > Ni(4.01) > Cr(3.03) > Co(0.61) > Cd(0.04) (μg/g). The highest level of HMs was detected in the leaves of Morus alba L., followed by Buxus colchica, Melia azedarach L. and Cupressus arizonica Greene. There was a significant correlation between the amounts of Pb and Cu in tree leaves and those in ambient PM2.5 (p value ≤ 0.05). In conclusion, tree leaves can be used as a suitable bioindicator in the evaluation of air pollution. Morus alba L. compared to the other species can be confidently used for green space development. NOVELTY STATEMENT: Isfahan is one of the most populated, industrialized cities in Iran experiencing serious air pollution. The currently operated air pollution monitoring stations in the city do not measure atmospheric heavy metals, a hazardous component of PM2.5. To assess air pollution and level of exposure, biomonitor species such as tree leaves are the best tool due to their availability and low cost. Tree leaves can absorb and retain air pollutants. In this study, we found a good correlation between the concentration of heavy metals especially Pb and Cu in the leaves of commonly used tree species and their concentration in the airborne PM2.5. Also, the results revealed that among the tree species, Morus alba L. retains more heavy metals followed by Buxus colchica, Melia azedarach L., and Cupressus arizonica Greene, which can be used for green space development. [ABSTRACT FROM AUTHOR]

Previous observational studies have reported inconsistent associations between air pollution and autoimmune eye diseases (AEDs). The primary objective of this Mendelian randomization (MR) study was to investigate the causal link of air pollution with AEDs risk. The instrumental variables were selected based on genome-wide association study data. Univariable and multivariable MR analyses were conducted to disentangle the causality of air pollutants with AEDs. The estimates of univariable MR analysis revealed a suggestively causal link between NO2 or NOx exposure and diabetic retinopathy (OR=1.29, 95% CI=1.05-1.58, P=0.015; OR=1.33, 95% CI=1.05-1.69, P=0.019, respectively). A suggestive association was observed between PM2.5 exposure and age-related macular degeneration (OR=1.46, 95% CI=1.09-1.97, P=0.013). In addition, multivariable MR indicated that the observed association was remained consistent and robust. Rigorous sensitivity analyses confirmed the robustness and consistency of these findings. Our study firstly provided the genetic evidence linking air pollution, specially NO2, NOx and PM2.5, to AEDs susceptibility. [ABSTRACT FROM AUTHOR]

Achieving synergistic effects in pollution reduction and carbon mitigation is a major national strategy for China. Given the common origins and processes of air pollutants and greenhouse gases, this study constructs a theoretical framework for the study of the synergistic effects of air pollution and carbon emissions. Based on the coupling coordination degree model and the geographically and temporally weighted regression model, it identifies significant factors influencing the synergistic effects of air pollution and carbon emissions and their varying mechanisms of action. Results are as follows: 1) The spatial and temporal trends of PM2.5 pollution and carbon emissions in the Wuhan metropolitan area exhibit homogeneity. The coupling coordination degree between air pollution and carbon emissions shows an initial increase followed by a decrease over time and a spatial pattern of "local clustering of areas with medium–high-level coupling coordination". 2) Twelve factors significantly impact the synergistic effects of air pollution and carbon emissions at the county level in the Wuhan metropolitan area: number of inversion days, precipitation, temperature, vegetation coverage, number of green patents, total population, regional GDP, per capita regional GDP, proportion of secondary industry, total nighttime light, energy consumption efficiency and built-up area. 3) The impact intensity of these factors on the synergistic effects of air pollution and carbon emissions varies not only over time but also across different regions within the same year. Regions with strong impact forces shift over time. This manuscript provides a solid foundation for theoretical research on and practical strategies for advancing differentiated pollution reduction and carbon mitigation coordination. [ABSTRACT FROM AUTHOR]

In the United States (US), neonatal intensive care units (NICUs) monitor and treat newborns for a variety of adverse health concerns including preterm status, respiratory distress and restricted growth. As such, NICU admission is an integrated measure of neonatal risk. We linked 2018 US national birth registry NICU admission data among singleton births with satellite and modelled air pollution levels for the month prior to birth to examine whether late-pregnancy exposure to ambient air pollutants is associated with adverse neonatal health outcomes. Regardless of season, higher ambient levels of nitrogen dioxide (NO2) and fine particulate matter < 2.5 microns (PM2.5) increased the likelihood of NICU admission 30–35% for NO2 and 11–22% for PM2.5 even after adjustment for parental characteristics. Results for ozone exposure were inconsistent with largely null or reduced risk except for summer months. Despite the relatively low-moderate US exposure levels, traffic-related pollutants near the end of pregnancy appear to increase overall adverse health risks for newborns, underscoring the need to reduce prenatal exposure to ambient pollutants. [ABSTRACT FROM AUTHOR]

As a significant global concern, air pollution triggers enormous challenges in public health and ecological sustainability, necessitating the development of precise algorithms to forecast and mitigate its impacts, which has led to the development of many machine learning (ML)-based models for predicting air quality. Meanwhile, overfitting is a prevalent issue with ML algorithms that decreases their efficacy and generalizability. The present investigation, using an extensive collection of data from 16 sensors in Tehran, Iran, from 2013 to 2023, focuses on applying the Least Absolute Shrinkage and Selection Operator (Lasso) regularisation technique to enhance the forecasting precision of ambient air pollutants concentration models, including particulate matter (PM2.5 and PM10), CO, NO2, SO2, and O3 while decreasing overfitting. The outputs were compared using the R-squared (R2), mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and normalised mean square error (NMSE) indices. Despite the preliminary findings revealing that Lasso dramatically enhances model reliability by decreasing overfitting and determining key attributes, the model's performance in predicting gaseous pollutants against PM remained unsatisfactory (R2PM2.5 = 0.80, R2PM10 = 0.75, R2CO = 0.45, R2NO2 = 0.55, R2SO2 = 0.65, and R2O3 = 0.35). The minimal degree of missing data presumably explained the strong performance of the PM model, while the high dynamism of gases and their chemical interactions, in conjunction with the inherent characteristics of the model, were the primary factors contributing to the poor performance of the model. Simultaneously, the successful implementation of the Lasso regularisation approach in mitigating overfitting and selecting more important features makes it highly suggested for application in air quality forecasting models. [ABSTRACT FROM AUTHOR]

The growing fetus is very sensitive to environmental conditions. There is limited and conflicting evidence about the short-term effects of exposure to air pollutants on the pregnancy outcome. In this time-stratified case-crossover study, the effect of several air pollutants (i.e. O3, CO, NO2, SO2, and PM2.5) on the occurrence of stillbirth was evaluated in Tehran (the capital of Iran) between December 2018 and March 2023. Using a quasi-Poisson regression model and distributed lag nonlinear models (DLNM), we estimated the effect of exposure to air pollutants measured as lags (0 to 7 days) and cumulative average days (0–2, 0–6, and 0–14-day lag) before delivery on stillbirth. The association was adjusted for potential confounding factors including meteorological factors. During the study period in Tehran, 5311 stillbirths were reported. In single-pollutant models, during the entire year, SO2 (lag 1 day) and NO2 (lag 2 days) were found to have a direct and significant relationship with stillbirth. In the warm seasons, we found direct and inverse relationships between NO2 (lag 2 days) and PM2.5 (lag 4 days), respectively. In cold seasons, PM 2.5 (lag 1 day) and cumulative lag (0–2 days), SO2 (lag 1 day), and cumulative lag (0–2 and 0–6 days) were found to have direct and significant relationships with stillbirth. In two-pollutant models, SO2 & CO, and SO2 & O3, direct and significant associations were observed between SO2 exposures and stillbirth for the entire year. A similar pattern was observed for PM2.5 in combinations with NO2, and O3 and for NO2 in the two-pollutant model (O3 & NO2). However, in warm seasons, inverse associations were observed between PM2.5 and stillbirth in combinations with O3, NO2, and SO2, while NO2 showed a direct association in combinations with PM2.5, CO, and O3. In the cold seasons, direct and significant associations were observed between SO2 and stillbirth in model combinations with CO, O3, and NO2. This relationship was observed for PM2.5 in combination with CO, and NO2. Also, for CO in the two-pollutant model of CO & NO2. As a result, this study showed evidence of a relationship between short-term exposure to ambient air pollution before birth, especially SO2, NO2, PM2.5, and CO with increased risk of stillbirth. [ABSTRACT FROM AUTHOR]

Exposure to air pollutants is linked to an increased risk of obesity, and socioeconomic status (SES) could modulate this risk. We employed the "Jinchang Cohort" as a platform to investigate the influence of SES (education level, monthly income per household, and marital status) on the obesity risk associated with PM2.5 and its constituents. Study has demonstrated that air pollutant exposure enhances the likelihood of overweight/obesity, with a risk ratio (HR) of 1.229 for each quartile increase in PM2.5 concentration (95% CI: 1.137–1.328, P < 0.05). The risk of overweight/obesity rises with pollutant levels across various SES strata, with the effect being most marked among those with higher SES. For instance, the HRs and 95% CIs for overweight/obesity with each quartile increase in SO42− concentration were 1.338 (1.207–1.484), 1.311 (1.121–1.533), and 2.224 (1.823, 2.714) at low, medium, and high SES levels, respectively (all P < 0.05). An interaction between air pollutants and SES was observed in the context of obesity risk, with RERIs of 0.723 (0.473–0.973) and 0.562 (0.268–0.856) for medium-high SES levels and high NO3− exposure, respectively (both P < 0.05). These findings have practical implications. Public health campaigns could be launched to raise awareness among higher SES individuals about the obesity risk associated with air pollutants and encourage them to adopt preventive measures such as using air purifiers and increasing physical activity. [ABSTRACT FROM AUTHOR]

Air pollution is strongly associated with autoimmune diseases (ADs), however, the genetic causality between them remains poorly understood. Therefore, the aim of this study is to determine the relationship between common air pollutants and ADs through Mendelian randomization (MR) analysis. We conducted a MR study using aggregated data from publicly available genome-wide association studies (GWAS). Single nucleotide polymorphisms (SNPs) associated with 5 common air pollutants are used as instrumental variables. Random-effects inverse variance weighted (IVW) is used as the primary method to assess causal relationships, with results reported in terms of odds ratios (OR). In addition, we used a two-step MR to assess the mediating role of common risk factors for ADs in the effects of air pollution on ADs. Our analysis revealed causal associations between selected air pollutants and specific ADs. Exposure to nitrogen oxides was positively associated with the risk of rheumatoid arthritis (RA) (OR = 1.47,95% CI 1.01–2.14, P = 0.043), Sjogren's syndrome (SS) (OR = 2.29,95% CI 1.08–4.89, P = 0.032), and systemic lupus erythematosus (OR = 7.26,95% CI 2.25–23.40, P = 9.10E−04). Genetically predicted PM2.5 and PM10 were risk factors for ulcerative colitis (OR = 1.68,95% CI 1.05–2.68, P = 0.032) and psoriasis (OR = 1.34,95% CI 1.02–1.76, P = 0.037), respectively. Our results also suggest a negative causal relationship between PM2.5–10 and SS (OR = 0.29, 95% CI 0.10–0.90, P = 0.032). In risk factor-related mediation analyses, BMI and smoking mediated 6% (95% CI 1–10%) and 9% (95% CI 2–17%) of the effect of nitrogen oxides on RA, respectively. This study provides evidence of a causal relationship between air pollutants and specific ADs risks, suggesting that improving air pollution may be important in preventing ADs. [ABSTRACT FROM AUTHOR]

Fluorescent foams with interconnected pores are attractive for the detection and quantification of various products. However, many fluorescent probes are suffering from aggregation‐caused fluorescence quenching in their solid/aggregated state, are costly, and/or not straightforward to incorporate in foams, limiting their utility for this application. Herein, non‐isocyanate polyurethane foams, prepared by the simple water‐induced self‐blowing process, present a nonconventional fluorescence behaviour, i.e. they are intrinsically fluorescent with a multicolor emission without requiring ex situ traditional fluorescent probes. These foams demonstrate utility for capturing‐sensing gaseous formaldehyde (an emblematic indoor air pollutant), as well as for detecting and quantifying various metal ions (Fe2+, Cu2+, Fe3+, Hg2+). They are also able to selectively sense tetracycline antibiotic in a ratiometric way with a high sensitivity. By exploiting the unique multicolor photoluminescent foam properties, a smartphone‐compatible device is used for the facile antibiotic quantification. This nonconventional fluorescence behaviour is discussed experimentally and theoretically, and is mainly based on clusteroluminescence originating from multiple hydrogen bonding and hetero‐atomic sub‐luminophores, thus from aggregation‐induced emission luminogens that are naturally present in the foams. This work illustrates that easily accessible non‐conventional fluorescent NIPU foams characterized by a modular emission wavelength have an enormous potential for multiple substrates detection and quantification. [ABSTRACT FROM AUTHOR]

This study examines the dynamics of indoor air quality in an office environment within a metropolis, with a specific focus on particulate matter (PM), formaldehyde, and total volatile organic compounds (TVOCs). The levels of PM concentrations stay constant at a value of 13.9±2.9 µg/m3 for PM2.5 throughout working hours, with a significant impact on human activities. The formaldehyde concentration inside increases thrice during 8 hours, from 9±5 µg/m3 to 27±14 µg/m3, primarily from furniture and electronics. The total volatile organic compounds (TVOCs) levels significantly increase from 0.050±0.044 µg/m3 at 8.00 to 0.14±0.11 µg/m3 at 15.00, which can be attributed to indoor contaminants such as plastics and consumer items. PM concentrations exhibit seasonal fluctuations, with higher levels observed during colder months (37±5 µg/m3 for PM2.5 in December and 8±1 µg/m3 for PM2.5 in August in the office, mainly due to outdoor contribution. Analysis of settled dust indicates a varied composition, suggesting the presence of both building materials and human activity. Employees exhibit symptoms consistent with Sick Building Syndrome, with a higher prevalence among females. The results emphasize the significance of dealing with variations in indoor air quality and identifying the causes that affect the health of occupants and the well-being of the workplace. [ABSTRACT FROM AUTHOR]

Since air pollution affects both public health and economic growth, the issue has received more attention recently. Model-based early warning systems or pollution management tactics can be used to assist in combating dangerous air pollutants if accurate prediction models are available. This paper presents an approach to forecasting air contaminants using a piecewise affine model, which has a high prediction power. To identify the piecewise affine model, this study adopts effective clustering to identify the model. The proposed hierarchical clustering method improves the widely used BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) by adding a refining step to handle clusters with arbitrary geometries. Additionally, an optimization strategy like GA (Genetic Algorithm) is used to jointly estimate the model order and parameters. Measurements of Shenyang's air quality are used to illustrate the proposed approach, and the outcomes reflect the method's good prediction ability. [ABSTRACT FROM AUTHOR]

Volatile organic compounds (VOCs) produced by the lung in response to exposure to environmental pollutants can be utilized to study their impact on lung health and function. Previously, we developed a method to measure VOCs emitted from well-differentiated tracheobronchial epithelial cells in vitro. Using this method, we exposed well-differentiated proximal (PECs) and distal airway epithelial cells (DECs) to varying doses of traffic-related air pollutants (TRAP) and wildfire particulates to determine specific VOC signatures after exposure. We utilized PM2.5 TRAP collected from the Caldecott tunnel in Oakland, CA and the 2018 Camp Fire to model "real-life" exposures. The VOCs were collected and extracted from Twisters and analyzed using gas chromatography-mass spectrometry. Exposure to both types of particulate matter (PM) resulted in specific VOC responses grouped by individual subjects with little overlap. Interestingly the VOCs produced by the PECs and DECs were also differentiated from each other. Our studies suggest that PM exposure induces a specific compartmentalized cellular response that can be exploited for future studies. This response is cell-type specific and potentially related to a phenotype we have yet to uncover. [ABSTRACT FROM AUTHOR]

Primary graft dysfunction (PGD) contributes substantially to both short- and long-term mortality after lung transplantation, but the mechanisms that lead to PGD are not well understood. Exposure to ambient air pollutants is associated with adverse events during waitlisting for lung transplantation and chronic lung allograft dysfunction, but its association with PGD has not been studied. We hypothesized that long-term exposure of the lung donor and recipient to high levels of ambient air pollutants would increase the risk of PGD in lung transplant recipients. Using data from 1428 lung transplant recipients and their donors enrolled in the Lung Transplant Outcomes Group observational cohort study, we evaluated the association between the development of PGD and zip-code-based estimates of long-term exposure to 6 major air pollutants (ozone, nitrogen dioxide, sulfur dioxide, carbon monoxide, particulate matter 2.5, and particulate matter 10) in both the lung donor and the lung recipient. Exposure estimates used daily EPA air pollutant monitoring data and were based on the geographic centroid of each subject's residential zip code. Associations were tested in both univariable and multivariable models controlling for known PGD risk factors. We did not find strong associations between air pollutant exposures in either the donor or the recipient and PGD. Exposure to ambient air pollutants, at the levels observed in this study, may not be sufficiently harmful to prime the donor lung or the recipient to develop PGD, particularly when considering the robust associations with other established PGD risk factors. [ABSTRACT FROM AUTHOR]

Cooking has been recognized as one of the most important sources of indoor air pollutants. Several studies evaluated black carbon emissions from cooking fumes. Black carbon (BC) could affect human health as a carbonaceous part of the cooking particles. This study develops a statistical model to estimate the emission fluxes of black carbon from heating 17 different oils (avocado, canola, coconut, corn, olive, peanut, vegetable (soybean), grapeseed, hazelnut, macadamia, almond, sunflower, safflower, flax, walnut, pumpkin seed, and sesame). The oils were heated in a beaker for 20 min at 195–200 °C. Macadamia oil showed the highest BC emission rate of 11.08 (SD = 4.94) µg/min, while peanut oil resulted in the lowest BC emission rate of 0.68 (SD = 0.63) µg/min. Oils including macadamia (11.08 [SD = 4.94] µg/min), coconut (9.85 [SD = 2.20] µg/min), flax (7.93 [SD = 2.46] µg/min), pumpkin (5.65 [SD = 2.22] µg/min), grapeseed (5.43 [SD = 7.85] µg/min), hazelnut (4.65 [SD = 2.48] µg/min) and sesame (4.33 [SD = 2.53] µg/min) were among the high BC-emitting oils (>4 µg/min) at 195 °C, while avocado (3.74 [SD = 3.20] µg/min), olive (3.73 [SD = 1.59] µg/min), corn (2.71 [SD = 2.09] µg/min), almond (2.44 [SD = 1.55] µg/min), walnut (1.76 [SD = 0.56] µg/min), canola (1.58 [SD = 0.90] µg/min), vegetable (1.30 [SD = 1.10] µg/min), safflower (0.92 [SD = 0.56] µg/min), sunflower (0.88 [SD = 0.44] µg/min), and peanut (0.68 [SD = 0.63] µg/min) were among the low BC-emitting oils (<4 µg/min). We present correlations between the BC emission flux from heating these cooking oils and two cooking factors, oil temperature and oil smoke temperature. Despite some exceptions, most of the oils showed that oil temperatures above the smoke point of the oil is an insignificant factor in BC emissions. Copyright © 2024 American Association for Aerosol Research [ABSTRACT FROM AUTHOR]

The International Agency for Research on Cancer (IARC) classifies exposure to fine particulate matter (PM2.5) air pollution as carcinogenic to humans (Group I), most frequently associated with lung cancer. Airborne air pollutants may be associated with other sites of cancer, although few studies have examined this avenue of research. Esophageal cancer mortality rates vary substantially across townships in Taiwan, a fact that suggests environment influence. Therefore, the aim of this study was to investigate the association between long-term exposure to ambient PM2.5 and deaths attributed to esophageal cancer in 66 municipal areas across Taiwan. To conduct this study, annual PM2.5 levels were determined taking into account age-standardized esophageal cancer mortality rates in male and female residents of these municipalities from 2012 to 2021. The annual PM2.5 levels of each municipality were divided into tertiles and computed adjusted risk ratio (RR) using weighted-multiple regression analyses controlling for municipal lung cancer deaths, urbanization level, and physician density. Men residing in those areas with intermediate PM2.5 tertile levels (18.96–25.19 ug/m3) were found to have an adjusted RR of 1.22 (1.15–1.30) and those of residing areas with the highest tertiles levels (25.20–29.48 ug/m3) exhibited an RR of 1.11 (1.051.18). However, in women in the same municipalities, a significant inverse association was found between PM2.5 levels and mortality attributed to esophageal cancer, 0.82 (95% CI = 0.65-1.04) and 0.61 (95% CI = 0.47-0.79), respectively. These findings suggest that long-term exposure to PM2.5 increases the risk of developing esophageal cancer in men in Taiwan. [ABSTRACT FROM AUTHOR]

NO2 is known as one of the most dangerous air pollutants. Even extremely low concentrations of this gas could cause life-threatening diseases such as cancer. Ppb-level detection and measurement of this gas are still serious challenges for researchers. In current work, for the first time, K2CO3/Al2O3 composite material, was employed as solid electrolyte to fabricate a potentiometric sensor for the detection of extremely low NO2 concentrations (in the ppb range) at room temperature (RT). This material was chemically synthesized through a low-cost and facile process and then loaded into appropriate templates, forming rectangular-cube shaped ceramics. A thin layer of gold nano particles was sputtered on the both sides of ceramics' top surface to provide external electrical connections. The sensor's dynamic responses were recorded in the concentrations ranging from 15 to 1500 ppb at RT, and it was observed that its response variations corresponds the Nernst equation. This sensor's limit of detection (LOD) was recorded as 15 ppb which is much lower than that of the standard threshold for safe industrial environments (200 ppb). Tipically, the sensor's response (ΔV = Vgas - Vair, where Vgas and Vair are its voltages in NO2 contaminated air and clean air, respectively) toward 60 ppb NO2 at RT is equal to 14 mV. In the entire evaluated concentration range the rise and recovery times did not exceed 80 s. Therefore, this sensor could be considered as a promising candidate for air quality control applications at RT. [ABSTRACT FROM AUTHOR]

We have developed models to predict the incidence of atopic dermatitis using regression analysis and artificial neural networks (ANN). Initially, the prediction models were created using various inputs, including air pollutants (SO2, CO, O3, NO2, and PM10), meteorological factors (temperature, humidity, wind speed, and precipitation), population rates, and clinical data from South Korea, referred to as the average model. Subsequently, we developed models that use sex and age as variables instead of population rates, named the sex and age model. Both sets of models were designed to forecast incidence rates on a nationwide scale (NW), as well as for 16 administrative districts (AD) in South Korea, which includes seven metropolitan areas and nine provinces. We found that SO2 significantly affected the incidence rate, and the inclusion of regional variables in the AD models helped account for regional variations in incidence rates. The average models generally provided accurate predictions of incidence rates, with SO2 chosen as the key independent variable in the regression models for the five air pollutants studied. The R2 values for the average models using regression are 0.70 for the NW model and 0.89 for the AD model. Among the ANN-based models, the R2 values are 0.84 for the NW model and 0.90 for the AD model, this indicated a slightly higher predictive accuracy. For the sex and age models, we differentiated between children under 10 years of age and those older. In these models, ANN demonstrated greater accuracy than regression, with R2 values of 0.95, 0.92, 0.96, and 0.92 for the sex and age NW model under 10 years old, sex and age AD model under 10 years old, sex and age NW model over 10 years old, and sex and age AD model over 10 years old, respectively. [ABSTRACT FROM AUTHOR]

The aim of this study is to assess the contribution of selected air pollutants from the residential sector to the total emissions in Poland from 1990 to 2023 and to evaluate their impact on the incidence of selected diseases among children and adolescents aged 0 to 18 years. The study used data on emissions of selected pollutants (sulfur dioxide, nitrogen oxides, total suspended particulate matter, particulate matter PM2.5 and PM10, polycyclic aromatic hydrocarbons) obtained from the National Centre for Emission Balancing and Management (KOBIZE). The analyses also included data on the incidence of selected diseases among children and adolescents, obtained from the Silesian Voivodeship Office in Katowice, Health Department, and the National Health Fund (asthma, hypertension, allergic contact dermatitis caused by substances introduced into the body, allergic food-induced inflammation of the stomach, small intestine, and colon). The data covered the period from 1990 to 2023. The obtained results clearly indicate that emissions from the municipal and domestic sector have a significant impact on the overall emissions of air pollutants such as nitrogen oxides, sulfur dioxide and total suspended particulate matter. Studies have often shown very strong and statistically significant connections between the concentration of analyzed air pollutants and the occurrence of selected diseases in children and adolescents aged 0-18. [ABSTRACT FROM AUTHOR]

Assessing the effects of air pollutants, including aerosols, on trees is important for protecting forests in the future. This study determined the adsorption of particles on trees after 1- or 2-year long-term exposure (for 1 or 2 h/day) to submicron-scale ammonium sulfate (AS) particles using a field-emission scanning electron microscope (FE-SEM). Energy-dispersive X-ray spectroscopy (EDX) was also used to distinguish particles resulting from exposure from those present on the leaves under natural conditions prior to the 1- or 2-year exposure. We found submicron-sized AS particles were deposited on the leaf surfaces of four tree species after long-term exposure in a growth chamber < 70% humidity. These particles occurred as individual deposits without aggregation on the abaxial and adaxial surfaces. The particle shape deposited on the leaf surface in short-term (3–30 min) exposures in a growth chamber < 70% humidity was spherical with no corners, whereas that in long-term exposures was nonspherical flattened, angular, or irregular. Few micrometers was also observed, differing from 300 to 600 nm in diameter at exposure. These differences could be caused by the possibility that the particles have been deposited for a long time or that the humidity on the leaf surface has caused them to deliquescence and change shape after deposition. We hypothesized that these particle changes facilitate the uptake of AS into the leaf interior. [ABSTRACT FROM AUTHOR]

Ambient air pollution exposure was associated with an increased risk of incident cancer, but few previous studies have focused on the associations between ambient air pollution and nasopharyngeal carcinoma (NPC). Thus, our goal is to examine whether exposure to ambient air pollution in Hangzhou, which includes sulfur dioxide (SO2), nitrogen dioxide (NO2), and inhalable particles (PM10), will have an impact the risk of incident cancer. We collected data on daily ambient air pollution data, climate, and daily incidence of NPC in Hangzhou from Jan 1, 2013, to Dec 31, 2022. We applied a generalized additive model (GAM) based on the Poisson distribution to investigate the effect of ambient air pollution on the risk of incident NPC. The effects of ambient air pollution exposure on NPC were also discussed in subgroups by age, gender, region, and season. A total of 3121 NPC incident cases were included during the study period. We discovered that the risk of incident NPC was increased by 0.75% (95% CI: 0.01–1.58), 0.36% (95% CI: 0.03–0.69), and 0.14% (95% CI: 0.01–0.28) for every 1 μg/m3 increase in the concentration of SO2, NO2, and PM10, respectively. These pollutants continued to have a substantial impact on the risk of incident NPC even after controlling for other ambient air pollutants. A noteworthy affirmative connection was a significant positive correlation between SO2 and NPC in male, warm season, urban areas, and elderly subgroups. In contrast to SO2, there was a significant positive correlation between PM10 and NPC in female, warm season, rural areas, non-elderly, and elderly subgroups. The association between NO2 and NPC was significantly positively correlated in male, female, rural areas, and elderly subgroups. In conclusion, our study's findings demonstrated that exposure to airborne SO2, NO2, and PM10 can negatively impact the risk of incident NPC. [ABSTRACT FROM AUTHOR]

This research investigates the concentrations, sources, and health risks of polycyclic aromatic hydrocarbons (PAHs) and nitrated PAHs (NPAHs) in particulate matter with an aerodynamic diameter of 10 m or less (PM ) from critical urban centers in northern Mexico: Metropolitan Monterrey Area (MMA), Chihuahua (CHI), and Ciudad Juárez (CDJ). Advanced gas chromatography-mass spectrometry (GC-MS and GC-NCI-MS) revealed significant PAHs concentrations, with levels in MMA reaching 108.89 ± 99.90 ng/m , CHI at 100.69 ± 122.60 ng/m and CDJ at 73.26 ± 90.85 ng/m . Significantly, 3-nitrofluoranthene (3N-FLA) and 1-nitropyrene (1N-PYR), known for their potent toxicity, were among the most prominent NPAHs, with total concentrations in MMA, CHI, and CDJ at 470.32 pg/m , 247.26 pg/m , and 193.20 pg/m , respectively. Source apportionment using diagnostic ratios (DRs) and principal component analysis (PCA) indicated that biomass burning, vehicular emissions, and industrial activities were the primary sources of MMA. At the same time, CHI and CDJ were influenced more by industrial and diesel emissions. Health risk assessments based on benzo[a]pyrene equivalent (BaPeq) concentrations and excess cancer risk (ECR) demonstrated moderate to significant cancer risks, with CDJ exhibiting the highest NPAHs-related risk. This study makes several significant contributions: it presents the first analysis of PAHs and NPAHs levels in these urban areas, identifies key emission sources, and quantifies associated health risks, providing essential data for developing targeted public health policies and environmental regulations. [ABSTRACT FROM AUTHOR]

To investigate the population distribution characteristics of elderly osteoporosis fracture patients in Hebei Province and analyze the effects of air pollutants on elderly osteoporosis fractures, We retrospectively collected 18,933 cases of elderly osteoporosis fractures from January 1, 2019, to December 31, 2022, from four hospitals in Hebei Province. The average age was 76.44 ± 7.58 years, predominantly female (13,189 patients, 69.66%). The number of hospitalized patients increased progressively from 2019 to 2022. The Distribution Lag Nonlinear Model (DLNM) showed that the cumulative lagged effects of PM2.5 and PM10 on the number of hospitalized elderly osteoporosis fracture patients exhibited a bimodal distribution, with the Relative Risk (RR) reaching its peak at a 1-day lag (PM2.5: RR = 1.032, 95% CI: 1.019, 1.045; PM10: RR = 1.022, 95% CI: 1.014, 1.029). Similarly, the cumulative lagged effect of NO2 displayed a bimodal pattern, with the RR peaking at a 12-day lag (RR = 1.138, 95% CI: 1.101, 1.187). The single-day lag effect of SO2 was statistically significant from day 9 to day 12, reaching its maximum at day 11 (RR = 1.054, 95% CI: 1.032, 1.71). PM2.5, PM10, NO2, and SO2 increase the risk of osteoporosis fractures in the elderly, including single-day and cumulative lag effects. Further studies are needed to explore the molecular mechanisms behind this relationship. [ABSTRACT FROM AUTHOR]

Air pollution monitoring and modeling are the most important focus of climate and environment decision-making organizations. The development of new methods for air quality prediction is one of the best strategies for understanding weather contamination. In this research, different air quality parameters were forecasted, including Carbon Monoxide (CO), Nitrogen Monoxide (NO), Nitrogen Dioxide (NO2), Ozone (O3), Sulphur Dioxide (SO2), Fine Particles Matter (PM2.5), Coarse Particles Matter (PM10), and Ammonia (NH3). Hourly datasets were collected for air quality monitoring stations near Delhi, India, from November 25, 2020 to January 24, 2023. In this context, five intelligent models were developed, including Long Short-Term Memory (LSTM), Bidirectional Long-Short Term Memory (Bi-LSTM), Gated Recurrent Unit (GRU), Multilayer Perceptron (MLP), and Extreme Gradient Boosting (XGBoost). The modelling results revealed that Bi-LSTM model had the best predictability performance for forecasting CO with (R2 = 0.979), NO with (R2 = 0.961), NO2 with (R2 = 0.956), SO2 with (R2 = 0.955), PM10 with (R2 = 0.9751) and NH3 with (R2 = 0.971). Meanwhile, GRU and LSTM models performed better in forecasting O3 and PM2.5 with (R2 = 0.9624) and (R2 = 0.973), respectively. The current research provides illuminating visuals highlighting the potential of deep learning to comprehend air quality modeling, enabling improved environmental decisions. [ABSTRACT FROM AUTHOR]

Background: Ambient air pollution is a known risk factor for several chronic health conditions, including pulmonary dysfunction. In recent years, studies have shown a positive association between exposure to air pollutants and the incidence, morbidity, and mortality of a COVID-19 infection, however the time period for which air pollution exposure is most relevant for the COVID-19 outcome is still not defined. The aim of this study was to analyze the difference in association when varying the time period of air pollution exposure considered on COVID-19 infection within the same cohort during the first wave of the pandemic in 2020. Methods: We conducted a cross-sectional study analyzing the association between long- (10- and 2-years) and short-term (28 days, 7 days, and 2 days) exposure to NO2 and PM2.5 on SARS-CoV-2 incidence, morbidity, and mortality at the level of county during the first outbreak of the pandemic in spring 2020. Health data were extracted from the German national public health institute (Robert-Koch-Institute) and from the German Interdisciplinary Association for Intensive Care and Emergency Medicine. Air pollution data were taken from the APExpose dataset (version 2.0). We used negative binomial models, including adjustment for risk factors (age, sex, days since first COVID-19 case, population density, socio-economic and health parameters). Results: We found that PM2.5 and NO2 exposure 28 days before COVID-19 infection had the highest association with infection, morbidity as well as mortality, as compared to long-term or short-term (2 or 7 days) air pollutant exposure. A 1 μg/m3 increase in PM2.5 was associated with a 31.7% increase in incidence, a 20.6% need for ICU treatment, a 23.1% need for mechanical ventilation, and a 55.3% increase in mortality; an increase of 1 μg/m3 of NO2 was associated with an increase for all outcomes by 25.2 – 29.4%. Conclusions: Our findings show a positive association between PM2.5 and NO2 exposure and the clinical course of a SARS-CoV2 infection, with the strongest association to 28 days of exposure to air pollution. This finding provides an indication as to the primary underlying pathophysiology, and can therefore help to improve the resilience of societies by implementing adequate measures to reduce the air pollutant impact on health outcomes. Trial registration: Not applicable. [ABSTRACT FROM AUTHOR]

Photocatalytic nitrogen oxides (NOx) oxidation highly depends on the adsorption activation mode of pollutant molecules and the efficient generation of reactive oxygen species (ROS). Previous research underscores the construction of reactive active site synergistic mechanisms for enhancing reactant adsorption and activation. Herein, Bi‐metal is introduced onto the halogen intercalation by synergistically utilizing the strong electronegativity of Cl atoms and oxygen vacancy sites to promote directional charge transfer, forming an electronically localized layer of Cl and unsaturation‐charged Bi sites, which can change the activation mode of reactants and promote ROS formation. The OVs‐Bi4TaO8Cl/Bi with Bi–Cl synergistic sites immobilized on ceramic foam exhibited nitric oxide (NO) conversion efficiency of 89.0% with great stability (82.1% after 200 min) and extremely lowed toxic nitrogen dioxide (NO2) conversion (0.1%). The OVs‐Bi4TaO8Cl/Bi can convert NO into nitrosyl anion (NO−) during the adsorption activation process, thus effectively oxidizing it into the nitrate (NO3−) with NO2 inhibition. This study provides new insights into the development of effective photocatalysts with synergistic active sites for air pollutant purification. [ABSTRACT FROM AUTHOR]

Background: Although there have been many studies on the relationship between ambient air pollution and cognitive functioning in developed countries, there are no studies focusing on the interaction between ambient air pollution and social activities. This study aims to examine interactive effects of ambient air pollution and social activities on cognitive function in Chinese middle-aged and older. Methods: This study used nationally representative longitudinal survey data of China Health and Retirement Longitudinal Study (CHARLS) 2013, 2015 and 2018. The study explored the additive interaction effects of air pollutants and social activities on cognitive function in middle-aged and older adults by constructing mixed linear regression analyses containing interaction terms, as well as constructing additive interaction analyses with dummy variables containing four unordered categories that were partitioned according to median. In addition, the study further explored the interaction between air pollution and different types of social activities through an interaction term between air pollution and different types of social activities. Results: In the model fully adjusted for covariates such as age, sex, region, we found significant coefficients on the interaction term between PM2.5, O3 and social activities on cognitive function (PM2.5, β = -0.018, 95%CI: -0.029, -0.006; O3, β = 0.017, 95%CI: 0.007, 0.027). In the interaction analysis by constructing dummy variables, we found a significant antagonistic effect between PM2.5 and social activities (SI = 0.730, 95%CI: 0.674, 0.785), a possible antagonistic effect between NO2 and social activities (SI = 0.697, 95%CI: 0.648, 0.747), and a possible synergistic effect between O3 and social activities (SI = 1.769, 95%CI: 0.648, 0.747). In addition, the study found significant interactions between simple interaction, leisure and recreational, and intellectual participation social activities and air pollution. Conclusion: Our study demonstrated an antagonistic effect of PM2.5 and social activities on cognitive function in middle-aged and older Chinese adults. [ABSTRACT FROM AUTHOR]

The changes in global air pollutant concentrations influenced by the COVID-19 lockdown have been widely investigated. The lack of clarity regarding the individual contributions to restricted human activities (i.e., transportation) has limited the understanding of the health impacts of the lockdown. In this study, an efficient chemical transport model (GEOS-Chem) was employed to simulate the concentration changes in air pollutants (PM2.5, NO2, and O3) associated with emission reductions in land transportation and the corresponding health benefits. The simulated results suggested that transportation-related PM2.5, NO2, and O3 reduced by 20%, 36%, and 55%, respectively. The reduction in O3 concentrations presented regional variations, with percentages ranked as follows: China (67%) > India (56%) > Europe (−81%) > the US (−86%), indicating the various intensities of secondary transformations with spatial relevance. The health benefits were also simulated, and the all-caused mortalities were estimated to be 63,547 (95% CI: 47,597, 79,497), 52,685 (95% CI: 32,310, 73,059), and 231,980 (95% CI: 210,373, 253,586) for the reduced concentration of PM2.5, NO2, and O3 globally, respectively. Transportation-related O3 reduction contributed the largest proportion (∼67%) to global health benefits, further emphasizing the global relevance and severity of O3 pollution. Our study confirms that the health benefits of transportation emission reduction during the COVID-19 lockdown were considerable and provides relevant simulated data as supporting evidence. We suggest that further coordinated efforts to restrict certain pollutants worldwide should focus on controlling the global O3 concentrations to protect people from severe O3 exposure. [ABSTRACT FROM AUTHOR]

Background: Long-term air pollution exposure and inflammation are considered to be associated with cognitive decline. However, whether air pollution exposure related cognitive decline is dependent on inflammation remains uncertain. Materials and methods: The present study collected data from China Health and Retirement Longitudinal Study (CHARLS) at baseline in 2011, with a follow up period in 2015. Concentration of air pollutants (particles with diameters ≤ 1.0 μm [PM1], ≤ 2.5 μm [PM2.5], ≤ 10 μm [PM10], nitrogen dioxide [NO2] and ozone [O3]) were obtained from China High Air Pollutants (CHAP) dataset. Hypersensitive C-reactive protein (hs-CRP), a systemic inflammation marker, was measured in blood of subjects and cognitive function was assessed by standardized questionnaire. Results: A total of 6434 participants were included in the study. Lower exposure to PM2.5, PM1, PM10 and NO2 were associated with mitigated cognitive decline. The odds ratios (ORs) for air pollutants changes and cognitive decline and 95% confidence intervals (CIs) were as follows: PM2.5-0.934(0.925, 0.943), PM1- 0.945 (0.935,0.955), PM10-0.977(0.972,0.982) and NO2-0.962(0.950,0.975), respectively. Hs-CRP showed no significant correlation with cognitive decline or change in levels of air pollution. The interaction regression analyses, both unadjusted and adjusted, did not uncover any significant correlation between hs-CRP and air pollution with respect to cognitive decline. Bootstrap test exhibited no significant mediating effect of hs-CRP on the relationship between any air pollutants and cognitive decline, the indirect effects of hs-CRP in conjunction with exposure to different air pollutants were all found to be non-significant, with the following bootstrap CIs and p-values: PM2.5-1.000([1.000,1.000], P = 0.480),PM1-1.000([1.000,1.000], P = 0.230),PM10-1.000([1.000,1.000], P = 0.650), O3-1.000([1.000,1.000], P = 0.470), ΔNO2-1.000([1.000,1.000], P = 0.830). Conclusion: Ambient air pollution exposure was linked to cognitive decline independent of hs-CRP level. [ABSTRACT FROM AUTHOR]

Background: Mast cells are implicated in the pathogenesis and severity of asthma in children and adults. The release of proinflammatory mediators and cytokines from activated mast cells (MC) is associated with Type 2 (T2) cell-skewed inflammation. Methods: We obtained the airway tissues of Balb/c mice with or without intra-tracheal diesel exhaust particles (DEP) instillation to measure the extent of tryptase+ MCs infiltration and interleukin (IL)-33 expression. Cultured human mast cells (HMC-1) were stimulated with DEP to determine the role of aryl hydrocarbon receptor (AhR) in mediating the synthesis and release of IL-33 and type-2 cytokines. Results: In the control animals, most of the MC accumulated in the submucosal vessels without expression of IL-33. Intra-tracheal DEP installation increased the number of IL-33+ MC infiltrating in the epithelial and sub-epithelial areas of mice. Human MC exposed to DEP upregulated mRNA and protein expression of IL-33. These effects were abolished by knockdown of expression of the AhR or AhR nuclear translocator (ARNT) by small interfering (si)RNA transfection. DEP also activated nuclear factor-kappa B (NF-κB) to facilitate nuclear translocation of the AhR. DEP increased MC migration and induced the synthesis and release of IL-4, IL-5, and IL-13 in MCs, and these effects were abolished by anti-ST2 antibodies. Conclusions: Airborne pollutants may activate MCs to produce IL-33 via the AhR/NF-κB pathway, leading to type 2 cytokines production and enhancing MC airway epithelium-shifted migration through the autocrine or paracrine IL-33/ST2 axis. [ABSTRACT FROM AUTHOR]