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Background: This study aims to evaluate the relationship between the number of visits to a pediatric emergency department due to asthma attacks and air pollutants. Methods: In this ecological study, all pediatric patients who visited the pediatric emergency department of a tertiary hospital in Istanbul with asthma between January 2016 and December 2023 were included. The effect of air pollution on the number of patient visits was analyzed using a negative binomial regression model. Results: Based on the negative binomial model, a one-unit increase in SO2 leads to a 0.020-unit decrease in the logarithm of the number of patient visits (p < 0.05). A one-unit increase in NO leads to a 0.040-unit increase in the logarithm of the number of patient visits (p < 0.05). According to factor analysis, as the levels of NO, NOx, PM2.5, NO2, and PM10 in the air increase, the number of patient visits also increases; however, as the level of SO2 increases, the number of patient visits decreases. Conclusions: Families should be informed about environmental exposures for disease management of children with asthma. The confounding factors may also play a role in SO2 level and the decrease in admissions due to asthma. Further studies are needed in this regard. [ABSTRACT FROM AUTHOR]

Simple Summary: To investigate the effects of air pollutants and bioclimatic variables on tuberculosis in domestic ruminants, we simulated the risk distribution of tuberculosis in cattle, buffaloes, sheep, and goats in China using the maximum entropy ecological niche model, and evaluated the effects of environmental factors. The risk factors that most significantly influenced the prevalence of tuberculosis were the nitrogen dioxide (NO2) level, mean temperature of the coldest quarter, cattle distribution density, sheep distribution density, ozone (O3) level, and precipitation of the driest month. The predicted map of tuberculosis risk in cattle, buffaloes, sheep, and goats indicated that the high-risk regions were mainly distributed in South, North, East, and Northwest China. The findings of this study provide a valuable reference for the prevention and control of tuberculosis in cattle, buffaloes, sheep, and goats in China. Tuberculosis is a zoonotic chronic respiratory infectious disease caused by the Mycobacterium tuberculosis complex. The outbreak and epidemic of tuberculosis can seriously threaten human and veterinary health. To investigate the effects of environmental factors on tuberculosis in domestic ruminants, we collected data regarding the prevalence of tuberculosis in cattle, buffaloes, sheep, and goats in China (1956–2024) from publicly published literature and available databases. We identified the key risk factors among six major air pollutants and 19 bioclimatic variables; simulated the risk distribution of tuberculosis in cattle, buffaloes, sheep, and goats in China using the maximum entropy ecological niche model; and evaluated the effects of environmental factors. The area under the curve of the model was 0.873 (95% confidence interval, 0.851–0.895). The risk factors that most significantly influenced the prevalence of tuberculosis were the nitrogen dioxide (NO2) level, mean temperature of the coldest quarter, cattle distribution density, sheep distribution density, ozone (O3) level, and precipitation of the driest month. The predicted map of tuberculosis risk in cattle, buffaloes, sheep, and goats indicated that the high-risk regions were mainly distributed in South, North, East, and Northwest China. Improved surveillance is needed in these high-risk areas, and early preventive measures must be implemented based on the risk factors identified to reduce the future prevalence of tuberculosis in cattle, buffaloes, sheep, and goats. [ABSTRACT FROM AUTHOR]

As industrialization accelerates, China's industrial development pace has been rapidly increasing. However, this growth has been accompanied by an increase in high-pollution and high-emission industries, leading to the release of a significant amount of air pollutants and exacerbating haze pollution nationwide. This article utilizes the spatial dynamic Durbin model and panel threshold regression model to analyze the impact of the thermal power industry and other high-haze-pollution industries on atmospheric environmental quality. The results indicate a negative correlation between the thermal power industry and other high-haze pollution industries and atmospheric environmental quality. There is a spatial spillover effect of the thermal power industry and other high-haze-pollution industries on air pollution. Environmental regulations have a single-threshold characteristic in their impact on atmospheric quality in the thermal power industry and other high-haze-pollution industries, as does green technology innovation. Additionally, cumulative rainfall has a significant single-threshold effect on the atmospheric environmental quality in regions with the thermal power industry and other high-haze-pollution industries. The article suggests policies for severely polluted areas, including reducing high-haze-pollution enterprises, optimizing industrial structures rationally, strengthening regional cooperation, enhancing regional haze pollution prevention and control coordination mechanisms, increasing the intensity of environmental regulations, utilizing the threshold effect of environmental regulations, promoting green technological innovation and application in heavily polluted areas, and exploring options to improve air pollution through increased rainfall. These recommendations aim to provide reference points for China to further optimize its industrial structure and comprehensively manage haze pollution. [ABSTRACT FROM AUTHOR]

This study explores the fabrication of electret nonwoven structures for high-efficiency air filtration, utilizing the blow spinning technique. In response to the growing need for effective filtration systems, we aimed to develop biodegradable materials capable of capturing fine particulate matter (PM2.5) without compromising environmental sustainability. Polylactic acid (PLA) was used as the primary polymer, with the addition of SiO2 and MoS2 to enhance the fibers' charge retention and filtration performance. The fibers were charged electrostatically to improve particle capture efficiency. The experimental results showed that fibers containing 5% MoS2 exhibited the highest filtration efficiency, surpassing those with SiO2, despite MoS2 being a semiconductor and SiO2 a dielectric. Furthermore, the addition of MoS2 improved the filtration efficiency across a range of particle sizes (0.2–1 µm) while maintaining a manageable pressure drop. These findings suggest that incorporating MoS2 in electret nonwoven structures can significantly improve filtration performance, making it a promising material for advanced air filtration applications. This study contributes to the development of eco-friendly filtration materials with high performance, essential in reducing exposure to airborne pollutants. [ABSTRACT FROM AUTHOR]

The chemistry of atmospheric precipitation serves as an important proxy for discriminating the source(s) of air contaminants in urban environments as well as to discuss the dynamic of atmospheric chemistry exchanges. This approach can be undertaken at time scales varying from single events to seasonal and yearly time frames. Here, we characterized the chemical composition of two single rain episodes (18 July 2018 and 21 February 2019) collected in Wrocław (SW Poland). Our results demonstrated inner variations and seasonality (within the rain event as well as between summer and winter), both in ion concentrations as well as in their potential relations with local air contaminants and scavenging processes. Coupling statistical analysis of chemical parameters with meteorological/synoptic conditions and HYSPLIT back trajectories allowed us to identify three main factors (i.e., principal components; PC) controlling the chemical composition of precipitation, and that these fluctuated during each event: (i) PC1 (40%) was interpreted as reflecting the long-range transport and/or anthropogenic influences of emission sources that included biomass burning, fossil fuel combustion, industrial processes, and inputs of crustal origin; (ii) PC2 (20%) represents the dissolution of atmospheric CO2 and HF into ionic forms; and (iii) PC3 (20%) originates from agricultural activities and/or biomass burning. Time variations during the rain events showed that each factor was more important at the start of the event. The study of both SO42− and Ca2+ concentrations showed that while sea spray inputs fluctuated during both rain events, their overall impact was relatively low. Finally, below-cloud particle scavenging processes were only observed for PM10 at the start of the winter rain episode, which was probably explained by the corresponding low rain intensity and an overlap from local aerosol emissions. Our study demonstrates the importance of multi-time scale approaches to explain the chemical variability in rainwater and both its relation to emission sources and the atmosphere operating processes. [ABSTRACT FROM AUTHOR]

Featured Application: The results of the article can be applied to determine the zones were the maintenance works should be applied on the tank farm. In the context of the increased pollution from different sources and its significant negative effect on the population's health and environment, the article presents a comprehensive analysis of the data series formed by the concentrations of the volatile organic compounds (VOCs) collected in three zones—storage areas in the reservoir park—of a refinery complex in Romania during the maintenance period. Statistical analyses, including parametric and nonparametric tests, were performed to assess the correlation between the studied series and to group them based on some common features. The series were clustered using the raw data, and the series features were extracted after the statistical analysis. The results indicate that the series are not correlated and do not follow the same distribution even though the study zone is not large. The sites' classification based on statistical features is shown to be more relevant from the viewpoint of the emissions level than that provided using the raw series. The Principal Component Analysis (PCA) indicates that the features with the highest contribution on the first two components are maximum, standard deviation, autocorrelation, and partial autocorrelation for Zone 1; average, maximum, minimum, and partial autocorrelation for Zone 2; and skewness, average, maximum, and standard deviation for Zone 3. The study's novelty is two-fold. First, it provides the results of the study performed during the maintenance period of the storage tanks, which was insufficiently investigated in the literature. Secondly, since complete data series are not generally available to the large public, clustering them based on their features provides a clear image of pollution levels and the sites where actions should be taken to reduce it. This investigation offers essential insights that can serve as a background for developing effective air pollutant monitoring strategies and mitigation measures by understanding the emission patterns and identifying the factors that influence VOC levels during the maintenance of storage tanks for highly volatile petroleum products. [ABSTRACT FROM AUTHOR]

Microplastics (MPs) are omnipresent particles that receive special attention because of their persistent nature and their potential impact on human disease and on the environment. Most MPs are generated by the degradation of larger plastic items such as clothing, car tires, and discarded plastic materials. In indoor environments, where human beings spend most of their time, aerial MP levels are higher, and the majority are fibers produced from textiles. Airborne MPs indoors are a greater potential danger to humans than MPs ingested in food and drink. Fragments small enough to remain substantially suspended in the air column, the small airborne microparticles that are measured as PM10 and PM2.5, become available for assimilation by human beings through respiration, potentially producing various health problems. Larger MPs act by ingestion and skin contact. MPs can carry microorganisms and micropollutants adsorbed to their surfaces, facilitating their uptake and survival within the human body. Indoor airborne MPs thus represent emerging pollutants of fast-growing concern that are especially important as potential invaders of the human respiratory system, reaching the alveoli of the lungs and finally entering the circulatory system and other tissues. Since this direct human exposure to MP contamination via indoor air is so important, we discuss in this article the ways in which MP concentration and dispersal in indoor air can be affected by air turbulence that is induced by anthropogenic objects such as air conditioners, filters, and purifiers. Much evidence is equivocal and further research is necessary. [ABSTRACT FROM AUTHOR]

Air pollution is a pervasive global issue affecting human health, ecosystems, and the environment. This paper reviews the evolution and implementation of air quality legislation in Australia and Canada, providing a comparative analysis of their approaches to managing air pollution. Both countries have established robust legal frameworks to address air quality issues, involving governmental policies, regulatory mechanisms, and collaborative efforts among stakeholders. Australia began its air quality regulation at the state level in the 1950s, evolving into a national framework with the National Environment Protection Measure for Ambient Air Quality in 1998. In contrast, Canada centralized its efforts with the Clean Air Act in 1971, complemented by the National Air Pollution Surveillance program. Key findings reveal that, while both nations have achieved significant improvements in air quality, challenges remain in addressing the broader impacts of air pollution, such as climate change and health-related economic burdens. Australia's air quality standards are generally more stringent than Canada's for particulate matter but more relaxed for sulfur dioxide, nitrogen dioxide, and ozone. Both countries employ advanced air quality monitoring and reporting systems, with Australia's Air Quality Index and Canada's Air Quality Health Index providing critical public health information. The study highlights the need for continuous improvement and a more integrated approach to air quality management. By examining the legislative and regulatory landscapes of Australia and Canada, this paper offers valuable insights for other countries striving to enhance their air quality governance and mitigate the adverse effects of air pollution on a global scale. [ABSTRACT FROM AUTHOR]

Background: The tricellular tight junction protein angulin-1/lipolysis-stimulated lipoprotein receptor (LSR) is linked to numerous signal transduction pathways that govern gene expression, epithelial cell function, and morphogenesis. The effect of titanium dioxide (TiO2) on LSR and asthma remains unknown. The objective of the present study was to evaluate the impact of TiO2 on LSR expression in asthma. Methods: A TiO2-induced animal model of asthma was established using BALB/c mice and cell lines using normal human bronchial epithelial (NHBE) lung cells and we examined LSR, RAGE, and TGFβ expression using this model. Additionally, we analyzed plasma-LSR concentrations and their correlation with clinical variables in asthma patients and control subjects. Results: The LSR concentrations in patients with asthma were lower compared to controls, and were correlated with lung function and inflammatory cell ratio. In NHBE cells treated with Derp1, LSR protein expression was reduced and changed by exposure to TiO2, whereas TGFβ expression was increased and changed. In mouse lungs, LSR expression was significantly reduced in OVA mice and changed in OVA/TiO2 mice. Conclusion: Circulating LSR levels were decreased and correlated with clinical variables in patients with asthma, and they were influenced by TiO2 exposure in mice, suggesting the potential involvement of LSR in asthma pathogenesis. [ABSTRACT FROM AUTHOR]

The role of urban vegetation in urban air quality is usually assessed by considering only the pollutant removal capacity of the plants. This study aims to show, for the first time, the effects of vegetation on air pollutant concentrations through its effects on meteorology, separately from its biogenic emissions. It also investigates how air quality changes when only biogenic emissions are altered by using plants with different emission factors, as well as the potential effects of introducing new vegetation into urban areas. These assessments were conducted using atmospheric modelling systems currently employed for air quality forecasting and planning, configured specifically for the cities of Bologna and Milan. Simulations were performed for two representative months, July and January, to capture summer and winter conditions, respectively. The variability in air concentrations of ozone (O3), nitrogen dioxide (NO2), and particulate matter (PM10) within the municipal boundaries was assessed monthly. When evaluating the impact of future vegetation, changes in temperature, wind speed, and relative humidity were also considered. The results indicate that vegetation influences air quality more significantly through changes in meteorological conditions than through biogenic emissions. Changes in biogenic emissions result in similar behaviours in O3 and PM10 concentrations, with the latter being affected by the changes in the concentrations of secondary biogenic aerosols formed in the atmosphere. Changes in NO2 concentrations are controlled by the changes in O3 concentrations, increasing where O3 concentrations decrease, and vice versa, as expected in highly polluted areas. Meteorologically induced vegetation effects also play a predominant role in depositions, accounting for most of the changes; however, the concentrations remain high despite increased deposition rates. Therefore, understanding only the removal characteristics of vegetation is insufficient to quantify its effects on urban air pollution. [ABSTRACT FROM AUTHOR]

Motor vehicles emit a large amount of air pollutants. NOx and particulate matter (PM) account for 53.2% and 74.7%, respectively, of vehicle emissions in China. Using the technical guidelines for compiling road vehicle emission inventories, the emission factors based on the onboard diagnostics (OBD) system of heavy-duty diesel vehicles are obtained. The trajectory of heavy-duty diesel vehicles is corrected using big data interpolation, and the correction coefficients for different vehicle speeds are fitted to calculate the corresponding correction factors. Simultaneously, the Weather Research and Forecasting model is used for the meteorological correction of emissions, a heavy-duty diesel vehicle emission inventory under the community multiscale air quality model is established, and the distribution characteristics of pollution emissions from heavy-duty diesel vehicles in Chengdu are analyzed at the time and space levels. Overall, the pollutant gasses emitted by heavy-duty diesel vehicles in Chengdu are largely concentrated at the city center. In 2023, the total annual emissions of the pollutants NOx, CO, fine PM, and volatile organic compounds from heavy-duty diesel vehicles in Chengdu were 10,590.60, 28,852.90, 686.18, and 657.60 tons, respectively. NOx and CO have the highest proportions among the major pollutants, accounting for 70.7% and 26%, respectively. [ABSTRACT FROM AUTHOR]

Atmospheric pollution caused by aerosols deteriorates air quality, increasing public health risks. Anthropogenic aerosols are usually located within the atmospheric boundary layer (ABL), which presents a daytime evolution that determines the air pollutants' vertical mixing of those produced near the surface and, therefore, their ground-level concentration from local sources. Precise and complete characterization of the mixing layer is of crucial importance for numerical weather forecasting and climate models, but traditional methods such as radiosounding present some spatial and temporal limitations. Better resolutions have been obtained using lidar, which provides the aerosol vertical distribution. A particular type of lidar, the ceilometer, has demonstrated continuous measurement capabilities, providing vertical profiles with sub-minute time resolution and several-meter spatial resolution. Advanced methods, such as the recently developed STRATfinder algorithm, are required to estimate the ABL height in the presence of residual layers. More complex situations occur due to the advection of aerosols (e.g., due to long-range transport of desert dust, volcanic eruptions, or pyrocloud convection), producing a lofted layer in the free troposphere that may remain decoupled from the local ABL but can also be mixed. Aerosol-based methods for determination of the ABL height are challenging in those situations. The main objective of this research is the assessment of the impact of Saharan dust intrusions on the ABL using ceilometer signals, over a period of four years, 2020–2023. The ABL height database, obtained from ceilometer measurements every hour, is analyzed based on the most frequent synoptic patterns. A reduction in the ABL height was obtained from high dust load days (1576 ± 876 m) with respect to low dust load days (1857 ± 914 m), although it was still higher than clean days (1423 ± 772 m). This behavior is further studied discriminating by season and synoptic patterns. These results are relevant for health advice during Saharan dust intrusion days. [ABSTRACT FROM AUTHOR]

This study intended to understand whether teachers' perceptions of indoor air quality (IAQ) during classes aligned with the real levels of air pollutants and comfort parameters. For this purpose, an IAQ monitoring survey based on low-cost sensors using a multi-parameter approach was carried out in nine classrooms (a total of 171 monitored classes) in a Portuguese school. In each monitored class, the perception of IAQ reported by the teacher was assessed using a scale from 1 (very bad IAQ) to 10 (very good IAQ). Several exceedances regarding national legislation were found, with temperature being the parameter with a higher percentage of exceedance in all the studied classrooms (46%), followed by PM10 (32%), and then CO2 (27%). Temperature was found to be the only environmental parameter that was significantly associated with lower IAQ perception reported by the teachers, highlighting that typical pollutants such as CO2 (which can be identified as stuffy air) did not contribute to the teachers' perceptions. [ABSTRACT FROM AUTHOR]

This study investigates the temporal and spatial variations in PM2.5 concentrations in Dhaka, Bangladesh, from 2001 to 2023 and evaluates the impact of meteorological factors and the effectiveness of mitigation strategies on air pollution. Using satellite and ground-based data, this study analyzed the seasonal trends, daily fluctuations, and the influence of COVID-19 lockdown measures on air quality. Our findings reveal a persistent increase in PM2.5 levels, particularly during winter, with concentrations frequently exceeding WHO guidelines. Our analysis suggests significant correlations between meteorological conditions and PM2.5 concentration, highlighting the significant role of meteorological conditions, such as rainfall, humidity, and temperature, in modulating PM2.5 levels. Our analysis found that PM2.5 levels exhibited a significant inverse correlation with relative humidity (r = −0.72), rainfall (r = −0.69), and temperatures (r = −0.79), highlighting the role of meteorological conditions in mitigating pollution levels. Additionally, the study underscores the temporary improvements in air quality during lockdown periods, demonstrating the potential benefits of sustained emission control measures. The research emphasizes the need for comprehensive and multi-faceted air quality management strategies, including stringent vehicular and industrial emissions regulations, enhancement of urban green spaces, and public awareness campaigns to mitigate the adverse health impacts of PM2.5 pollution in Dhaka. [ABSTRACT FROM AUTHOR]

Objective: This study examines the association between major urban pollutants and the long-term decline of non-idiopathic pulmonary fibrosis interstitial lung disease [non-IPF ILD]. Materials and methods: A total of 41 patients with non-IPF ILD were analyzed from 2011 to 2020, correlating disease long-term decline with concentrations of key pollutants [SO2, CO, NO2, O3, PM2.5, and PM10] in Madrid. The likelihood of meeting severity criteria was assessed using a generalized linear model, considering the average pollutant levels during severe episodes. Results: At diagnosis, the average age of patients was 62.95 ± 13.13 years, with 47.6% women. The study found no significant association between pollution levels and the probability of meeting severity criteria for non-IPF ILD. The odds ratios were as follows: OR SO2 = 0.92 [0.82–1.03], p = 0.16; OR CO = 0.99 [0.97–1.05], p = 0.70; OR NO2 = 0.97 [0.92–1.03], p = 0.38; OR PM2.5 = 0.79 [0.54–1.17], p = 0.24; OR PM10 = 1.1 [0.94–1.28], p = 0.21; OR O3 = 0.97 [0.92–1.01], p = 0.20. Conclusions: Our study suggests that, within the cohort of 41 patients with non-IPF ILD enrolled in this study, urban air pollutants in Madrid are not significantly linked to increased long-term decline of non-IPF ILD. This is one of the first studies to explore the impact of various urban pollutants on a diverse cohort of non-IPF ILD patients, including rare conditions like LAM and histiocytosis X. Further research with larger sample sizes and comprehensive exposure assessments is recommended. [ABSTRACT FROM AUTHOR]

Background: The effect of air pollution, a major global health issue, on the immune system, particularly on white blood cell (WBC) counts, remains underexplored. Methods: This study utilized data from 54,756 participants in the Korean National Health and Nutrition Examination Survey to investigate the effects of short- (day of examination and 7-day averages), mid- (30- and 90-day averages), and long-term (one-, three-, and five-year averages) air pollutant exposure on WBC counts. We assessed exposure to particulate matter (PM10, PM2.5), sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), and carbon monoxide (CO). Results: Linear regression with log-transformed WBC counts, adjusted for confounders, showed that PM10 was positively associated with long-term exposure, PM2.5 was negatively associated with short- and mid-term exposures, SO2 was consistently negatively associated with short- and mid-term exposures, NO2 and CO were positive across most periods, and O3 was negatively associated with short- and mid-term exposures. Logistic regression analysis confirmed these findings, showing that short- and mid-term exposure to PM10, PM2.5, and SO2 was negatively associated with the risk of belonging to the high-WBC group, while long-term exposure to PM10, PM2.5, NO2, and CO showed positive associations with risk. Conclusions: Our findings highlight the time- and pollutant-specific associations between air pollution exposure and WBC counts, underscoring air pollution's potential impact on systemic inflammation. [ABSTRACT FROM AUTHOR]

Air pollution is a pervasive global challenge with profound implications for public health. This review explores the intricate relationship between air pollution and atrial fibrillation (AF), a prevalent cardiac arrhythmia associated with significant morbidity and mortality. Drawing on a comprehensive analysis of the existing literature, this review synthesizes current evidence linking various air pollutants, including particulate matter, nitrogen dioxide, ozone, and carbon monoxide, to the development and exacerbation of AF. The review delves into the role of air pollution as a global health issue alongside its specific sources, such as traffic-related emissions and industrial pollutants. It also examines the underlying mechanisms through which air pollution may contribute to the pathogenesis of AF, encompassing oxidative stress, inflammation, and autonomic nervous system dysregulation. In addition, it explores the impact of individual pollutants and the results of meta-analyses. It considers the results of vulnerable populations, including sex differences between the individuals and those with pre-existing cardiovascular conditions, who may be disproportionately affected. We also address critical research gaps in this area. Overall, air pollution has been increasingly recognized as a significant trigger for AF, with evidence linking exposure to particulate matter and gaseous pollutants to an increased incidence in short- as well as long-term exposure, highlighting the need for targeted public health interventions and further research to mitigate its cardiovascular impact. [ABSTRACT FROM AUTHOR]

Background/Objectives: Ambient air pollution is a significant public health concern, known to affect cardiovascular health adversely. Research has identified both long-term and short-term cardiovascular risks associated with various air pollutants, including those linked to acute coronary syndromes. However, the observed effects are rather small, with most data sourced from highly polluted regions. Methods: This study utilized a prospective registry database, documenting 12,581 myocardial infarction (MI) events in Styria, Austria from January 2007 to December 2015. Pollutants analyzed included particulate matter (PM2.5, PM10) and gases, such as NO2, CO, SO2, O3 and NOx. We employed generalized linear models to examine the interaction of each of these pollutants on the daily incidence of MI. Additionally, we conducted separate analyses for patients with specific comorbidities: diabetes mellitus (DM), arterial hypertension (HTN), heart failure with reduced ejection fraction (HFrEF), chronic obstructive pulmonary disease (COPD) and current smokers. Results: No significant associations were identified between any of the pollutants and MI incidence, both in the overall cohort and in patient subgroups with DM, HTN, HFrEF or COPD. However, among active smokers, we observed a decreased relative risk of MI associated with elevated levels of NO2, CO, SO2 and NOx on the day of MI (p < 0.01 for all pollutants). Conversely, an increased MI risk was associated with rising ozone levels (p = 0.0027). This counterintuitive finding aligns with previously published data and may suggest a new dimension to the "smoker's paradox". Conclusions: In regions with low pollution levels, air pollutants pose only minor or insignificant short-term risks for myocardial infarction. Active smokers exhibit an altered response to ambient air pollution. [ABSTRACT FROM AUTHOR]

Overtraffic is one of the main keys to air pollution in urban areas. The aim of the present work is to review the approaches and explore the potentiality of AI in reducing traffic pollution in urban areas, ranging over three main areas: the optimization of traffic lights timing to reduce delays, the use of AI-powered drones to monitor pollution levels in real-time, and the use of fixed AI-based sensors to detect the levels of pollutants in the air with the use of AI models to identify patterns in the collected data and predict air quality in near-real time. Some attention was also dedicated to possible problems arising from privacy protection and data security, and the case study of the Piemonte area and of the city of Turin in the north–west of Italy is presented: the current situation is depicted, and possible local future applications of AI are explored. The use of AI has proven to be very promising in all three areas, particularly in the field of optimization of traffic lights' timing and coordination in increasingly larger traffic networks. [ABSTRACT FROM AUTHOR]

Despite significant progress in recent decades, air pollution remains the leading environmental cause of premature death in Europe. Urban populations are particularly exposed to high concentrations of air pollutants, such as particulate matter smaller than 10 µm (PM10). Understanding the spatiotemporal variations of PM10 is essential for developing effective control strategies. This study aimed to enhance PM10 prediction models by integrating landscape metrics as ecological indicators into our previous models, assessing their significance in monthly average PM10 concentrations, and analyzing their correlations with PM10 air pollution across European urban landscapes during heating (cold) and non-heating (warm) seasons. In our previous research, we only calculated the proportion of land uses (PLANDs), but according to our current research hypothesis, landscape metrics have a significant impact on PM10 air quality. Therefore, we expanded our independent variables by incorporating landscape metrics that capture compositional heterogeneity, including the Shannon diversity index (SHDI), as well as metrics that reflect configurational heterogeneity in urban landscapes, such as the Mean Patch Area (MPA) and Shape Index (SHI). Considering data from 1216 European air quality (AQ) stations, we applied the Random Forest model using cross-validation to discover patterns and complex relationships. Climatological factors, such as monthly average temperature, wind speed, precipitation, and mean sea level air pressure, emerged as key predictors, particularly during the heating season when the impact of temperature on PM10 prediction increased from 5.80% to 22.46% at 3 km. Landscape metrics, including the SHDI, MPA, and SHI, were significantly related to the monthly average PM10 concentration. The SHDI was negatively correlated with PM10 levels, suggesting that heterogeneous landscapes could help mitigate pollution. Our enhanced model achieved an R² of 0.58 in the 1000 m buffer zone and 0.66 in the 3000 m buffer zone, underscoring the utility of these variables in improving PM10 predictions. Our findings suggest that increased urban landscape complexity, smaller patch sizes, and more fragmented land uses associated with PM10 sources such as built-up areas, along with larger and more evenly distributed green spaces, can contribute to the control and reduction of PM10 pollution. [ABSTRACT FROM AUTHOR]

Nitrogen dioxide (NO2) and particulate matter of 2.5 microns (PM2.5) are air pollutants that impact health, especially among vulnerable populations with respiratory disease. This study identifies factors influencing indoor NO2 and PM2.5 in low-income households of older adults with asthma who use gas stoves in Lowell, Massachusetts. Environmental sampling was conducted in 73 homes, measuring NO2, PM2.5, fractional stove-use, temperature, and humidity for 5–7 days. Participants were recruited between December 2020 and July 2022. Questionnaires were used to collect data on factors influencing indoor NO2 and PM2.5 concentrations. Daily outdoor NO2 and PM2.5 concentrations were obtained from a United States Environmental Protection Agency (EPA) monitoring station. Paired t-tests were conducted between indoor and outdoor NO2 and PM2.5 concentrations, and linear regression was used to evaluate factors influencing indoor NO2 and PM2.5 concentrations. The average indoor concentration for NO2 and PM2.5 were 21.8 (GSD = 2.1) ppb and 16.2 (GSD = 2.7) µg/m3, respectively. Indoor NO2 and PM2.5 concentrations exceeded outdoor concentrations significantly. In multiple regression models, season and pilot light stove use significantly predicted indoor NO2. Season and air freshener use for 6–7 days/week significantly predicted indoor PM2.5. Season-influenced higher indoor concentrations are likely due to reduced ventilation in colder months in the Northeast U.S. [ABSTRACT FROM AUTHOR]

As one of the most common air pollutants, fine particulate matter (PM2.5) increases the risk of diseases in various systems, including the urinary system. In the present study, we exposed male and female C57BL/6J mice to PM2.5 for 8 weeks. Examination of renal function indices, including creatinine (CRE), blood urea nitrogen (BUN), uric acid (UA), and urinary microalbumin, indicated that the kidneys of female mice, not male mice, underwent early renal injury, exhibiting glomerular hyperfiltration. Meanwhile, pathological staining showed that the kidneys of female mice exhibited enlarged glomerulus that filled the entire Bowman's capsule in the female mice. Afterward, we explored the potential causes and mechanisms of glomerular hyperfiltration. Variations in mRNA levels of key genes involved in the renin–angiotensin system (RAS) and kallikrein–kinin system (KKS) demonstrated that PM2.5 led to elevated glomerular capillary hydrostatic pressure in female mice by disturbing the balance between the RAS and KKS, which in turn increased the glomerular filtration rate (GFR). In addition, we found that PM2.5 increased blood glucose levels in the females, which enhanced tubular reabsorption of glucose, attenuated macular dense sensory signaling, induced renal hypoxia, and affected adenosine triphosphate (ATP) synthesis, thus attenuating tubuloglomerular feedback (TGF)-induced afferent arteriolar constriction and leading to glomerular hyperfiltration. In conclusion, this study indicated that PM2.5 induced glomerular hyperfiltration in female mice by affecting RAS/KKS imbalances, as well as the regulation of TGF; innovatively unveiled the association between PM2.5 subchronic exposure and early kidney injury and its gender dependence; enriched the toxicological evidence of PM2.5 and confirmed the importance of reducing ambient PM2.5 concentrations. [ABSTRACT FROM AUTHOR]

Air pollutants have both acute and chronic impacts on human health, affecting multiple systems and organs. While PM2.5 exposure is commonly assumed to be strongly associated with all respiratory diseases, this relationship has not been systematically analyzed. This study employed a two-sample Mendelian randomization approach to investigate the effects of PM2.5 on eight common lung diseases, using data from GWAS. Additionally, multivariable Mendelian randomization was applied to assess the direct effects of various air pollutants and the mediating roles of common factors such as BMI and smoking. At a significance threshold of 5×10−8, PM2.5 showed a significant causal relationship with both asthma and COPD. When the screening threshold was relaxed to 5× 10−6, this exposure continued to demonstrate significant associations not only with asthma and COPD, but also with other respiratory diseases, including pneumonia, emphysema/chronic bronchitis, and lung cancer. In the multivariable Mendelian randomization analysis, which controlled for smoking and bacterial infections, the association with pneumonia became non-significant, while the relationships with the other four diseases persisted. This study provides a systematic exploration of the relationship between PM2.5 and eight pulmonary diseases from a new perspective, deepening our understanding of the impact of air pollution on health and laying the foundation for future efforts to mitigate these effects. [ABSTRACT FROM AUTHOR]

Activated carbon is widely known for its porous structure and diverse surface functional groups, making it an effective adsorbent for removing various organic and inorganic pollutants from air and water. However, as a filtration material in air conditioning systems, activated carbon can also provide favorable conditions for microbial growth, potentially leading to the proliferation of microorganisms on its surface. These microorganisms, along with their metabolic products, can be released into indoor environments, posing potential health risks. This study employs molecular simulation to investigate the adsorption and release mechanisms of microorganisms and their volatile organic compound (VOC) metabolic products on activated carbon. Peptidoglycan (PDG) (as a representative bacterial cell wall component) and p-xylene (as a representative microbial metabolic product) were used as model compounds. The adsorption behavior of these compounds was simulated on activated carbon under different environmental conditions, including varying temperatures. The study found that activated carbon has a higher affinity for peptidoglycan than for p-xylene; at 303.15 K, the diffusion coefficients of peptidoglycan and p-xylene in activated carbon are 0.842 × 10−9 m2/s and 0.587 × 10−8 m2/s, respectively. Temperature plays an important role in affecting adsorption capacity; when the temperature rises by 10 K, the diffusion coefficients of peptidoglycan and p-xylene in activated carbon increase by 32.8% and 34.3%, respectively. These insights contribute to the development of efficient and health-conscious air purification materials, offering theoretical and practical guidance for optimizing the use of activated carbon in HVAC systems. [ABSTRACT FROM AUTHOR]

Prefabricated timber buildings offer a low-carbon approach that can help reduce the environmental impact of the building and construction sectors. However, construction materials such as manufactured timber products can emit a range volatile organic compounds (VOCs) that are potentially hazardous to human health. We evaluated 24 years (2000–2024) of peer-reviewed publications of VOCs within prefabricated timber buildings. Studies detected hazardous air pollutants such as formaldehyde, benzene, toluene, and acetaldehyde (indoor concentration ranges of 3.4–94.9 µg/m3, 1.2–19 µg/m3, 0.97–28 µg/m3, and 0.75–352 µg/m3, respectively), with benzene concentrations potentially exceeding World Health Organization indoor air quality guidelines for long/short term exposure. Most studies also detected terpenes (range of 1.8–232 µg/m3). The highest concentrations of formaldehyde and terpenes were in a prefabricated house, and the highest of benzene and toluene were in a prefabricated office building. Paradoxically, the features of prefabricated buildings that make them attractive for sustainability, such as incorporation of manufactured timber products, increased building air tightness, and rapid construction times, make them more prone to indoor air quality problems. Source reduction strategies, such as the use of low-VOC materials and emission barriers, were found to substantially reduce levels of certain indoor pollutants, including formaldehyde. Increasing building ventilation rate during occupancy is also an effective strategy for reducing indoor VOC concentrations, although with the repercussion of increased energy use. Overall, the review revealed a wide range of indoor VOC concentrations, with formaldehyde levels approaching and benzene concentrations potentially exceeding WHO indoor air quality guidelines. The paucity of evidence on indoor air quality in prefabricated timber buildings is notable given the growth in the sector, and points to the need for further evaluation to assess potential health impacts. [ABSTRACT FROM AUTHOR]

Analyzing the fluctuations of particulate matter (PM) concentrations and their scaling correlation structures are useful for air quality management. Multifractal characterization of PM2.5 and PM10 of three cities in India wase considered using the detrended fluctuation procedure from 2018 to 2021. The cross-correlation of PM concentration in a multifractal viewpoint using the multifractal cross-correlation analysis (MFCCA) framework is proposed in this study. It was observed that PM2.5 was more multifractal and complex than PM10 at all the locations. The PM–gaseous pollutant (GP) and PM–meteorological variable (MV) correlations across the scales were found to be weak to moderate in different cities. There was no definite pattern in the correlation of PM with different meteorological and gaseous pollutants variables. The nature of correlation in the pairwise associations was found to be of diverse and mixed nature across the time scales and locations. All the time series exhibited multifractality when analyzed pairwise using multifractal cross-correlation analysis. However, there was a reduction in multifractality in individual cases during PM–GP and PM–MV paired analyses. The insights gained into the scaling behavior and cross-correlation structure from this study are valuable for developing prediction models for PMs by integrating them with machine learning techniques. [ABSTRACT FROM AUTHOR]

This study explores the development and performance of photocatalytic cementitious composites modified with nano-TiO2 to address urban air quality and sustainability challenges. Nine mortar series were prepared, incorporating binders with varying carbon footprints and mass contents across different series. The interplay between the fundamental (abrasion resistance) and functional (air purification efficiency) properties of the composites' surfaces and interfaces was investigated. The photocatalytic removal of airborne pollutants, specifically nitrogen oxides (NOx) and ozone (O3), was evaluated under simulated environmental conditions. The variations in binder composition influenced the composites' overall initial carbon footprint and air purification efficiency. The assessment revealed a possible net decrease in carbon emissions over the life cycle of the composite due to the removal of ozone (greenhouse gas) and its precursor—NOx, highlighting the potential of photocatalytic cementitious composites for dual environmental benefits in an urban environment, emphasizing the critical role of surface and interface engineering in achieving carbon-negative composites. [ABSTRACT FROM AUTHOR]

The 2015 Tianjin Port chemical explosion highlighted the severe environmental and structural impacts of industrial disasters. This study presents an Adaptive Weighted Coherence Ratio technique, a novel approach for assessing such damage using synthetic aperture radar (SAR) data. Our method overcomes limitations in traditional techniques by incorporating temporal and spatial weighting factors—such as distance from the explosion epicenter, pre- and post-event intervals, and coherence quality—into a robust framework for precise damage classification. This approach effectively captures extreme damage scenarios, including crater formation in inner blast zones, which are challenging for conventional coherence scaling. Through a detailed analysis of the Tianjin explosion, we reveal asymmetric damage patterns influenced by high-rise buildings and demonstrate the method's applicability to other industrial disasters, such as the 2020 Beirut explosion. Additionally, we introduce a technique for estimating crater dimensions from coherence profiles, enhancing assessment in severely damaged areas. To support structural analysis, we model air pollutant dispersal using HYSPLIT simulations. This integrated approach advances SAR-based damage assessment techniques, providing rapid reliable classifications applicable to various industrial explosions, aiding disaster response and recovery planning. [ABSTRACT FROM AUTHOR]

Air pollution is a growing concern in rural areas where agricultural production can be reduced by it. This article analyses data obtained as part of a research project. The aim of this study is to understand the influence of atmospheric pressure, air temperature, air relative humidity, longitude and latitude of the location, and indoor and outdoor environment on local rural workplace diversity of air pollutants such as carbon monoxide (CO) and suspended particulate matter (SPM), as well as the contribution of these variables to changes in such air pollutants. The focus is on four topics: motivation, innovation and creativity, leadership, and social responsibility. Furthermore, this study developed an artificial neural network (ANN) model to predict CO and SPM concentrations in the air based on data collected from the mentioned inputs. The related sensors were assembled on an Arduino Mega 2560 board to form a field-portable device to detect air pollutants and meteorological parameters. The sensors included an MQ7 sensor for CO concentration measurement, a Sharp GP2Y1010AU0F dust sensor for SPM concentration measurement, a DHT11 sensor for air temperature and air relative humidity measurement, and a BMP180 sensor for air pressure measurements. The longitude and latitude of the location were measured using a smartphone. Measurements were conducted from 20 December 2021 to 16 July 2022. Results showed that the overall average outdoor CO and SPM concentrations were 10.97 ppm and 231.14 μg/m3 air, respectively. The overall average indoor concentrations were 12.21 ppm and 233.91 μg/m3 air for CO and SPM, respectively. Results showed that the ANN model demonstrated acceptable performance in predicting CO and SPM in both the training and testing phases, exhibiting a coefficient of determination (R2) of 0.575, a root mean square error (RMSE) of 1.490 ppm, and a mean absolute error (MAE) of 0.994 ppm for CO concentrations when applying the testing dataset. For SPM concentrations, the R2, RMSE, and MAE using the test dataset were 0.497, 30.301 μg/m3 air, and 23.889 μg/m3 air, respectively. The most influential input variable was air pressure, with contribution rates of 22.88% and 22.82% in predicting CO and SPM concentrations, respectively. The acceptable performance of the developed ANN model provides potential advances in air quality management and agricultural planning, enabling a more accurate and informed decision-making process regarding air pollution. The results of short-term estimation of CO and SPM concentrations suggest that the accuracy of the ANN model needs to be improved through more comprehensive data collection or advanced machine learning algorithms to improve the prediction results of these two air pollutants. Moreover, as even lower cost devices can predict CO and SPM concentrations, this study could lead to the development some kind of virtual sensor, as other air pollutants can be estimated from measurements of particulate matters. [ABSTRACT FROM AUTHOR]

The weather research and forecasting (WRF) model is frequently used to investigate the meteorological field around nuclear installations. The configuration of physical process parameterization schemes in the WRF model has a significant impact on the accuracy of the simulation results. Consequently, carrying out a pre-experiment to quickly obtain the optimal combination of parameterization schemes is essential before conducting meteorological parameter research. To obtain the optimal combination of physical process parameterization schemes from the planetary boundary layer (PBL), land surface (LSF), microphysical (MP), long-wave (LW), and short-wave (SW) radiation processes of the WRF model for simulating the near-surface meteorological variables near a nuclear power plant in Sanshan Town, Fuqing City, Fujian Province, China on 4 June 2019 were observed. Orthogonal experimental design (OED), a comprehensive evaluation method based on the CRiteria Import Through Intercriteria Correlation (CRITIC) weight analysis, and comprehensive balance method were employed for the first time to conduct the research. The sensitivity of meteorological variables to physical processes was first discussed. The findings revealed that the PBL scheme configuration had a profound impact on simulating wind fields. Furthermore, the LSF scheme configuration had a significant influence on simulating near-surface temperature and relative humidity, which was much greater than that of other physical processes. In addition, the choice of the radiation scheme had a significant impact on how the temperature was distributed close to the ground and how the wind field was simulated. Furthermore, the configuration of the MP scheme was found to exert a certain influence on the simulation of relative humidity; however, it demonstrated a weak influence on other meteorological variables. Secondly, The MYNN3 scheme for PBL process, the NoahMP scheme for LSF process, the WSM5 scheme for MP process, the RRTMG scheme for LW process, and the Dudhia scheme for SW process are found to be the comprehensive optimal physical process parameterization scheme combination for simulating meteorological variables in the research area selected in this study. As evident from the findings, the use of the OED method to obtain the combinations of the optimal physical process parameterization scheme could successfully reproduce the wind field, temperature, and relative humidity in the current study. Thus, this method appears to be highly reliable and effective for use in the WRF models to explore the optimal combinations of the physical process parameterization scheme, which could provide theoretical support to quickly analyzing accurate meteorological field data for longer periods and contribute to deeply investigating the migration and diffusion behavior of airborne pollutants in the atmosphere. [ABSTRACT FROM AUTHOR]

The Meteorological Optical Range (MOR) is a measurement of atmospheric visibility. Visibility impairment has been linked to increased aerosol levels in the air. This study conducted statistical analyses using meteorological, air pollutant concentration, and MOR data collected in Mexico City from August 2014 to December 2015 to determine the factors contributing to haze occurrence (periods when MOR < 10,000 m), defined using a light scatter sensor (PWS100). The outcomes revealed seasonal patterns in PM2.5 and relative humidity (RH) for haze occurrence along the year. PM2.5 levels during hazy periods in the dry season were higher compared to the wet season, aligning with periods of poor air quality (PM2.5 > 45 μg/m3). Pollutant-to-CO ratios suggested that secondary aerosols' production, led by SO2 conversion to sulfate particles, mainly impacts haze occurrence during the dry season. Meanwhile, during the rainy season, the PWS100 registered haze events even with PM2.5 values close to 15 μg/m3 (considered good air quality). The broadened distribution of extinction efficiency during the wet period and its correlation with RH suggest that aerosol water vapor uptake significantly impacts visibility during this season. Therefore, attributing poor visibility strictly to poor air quality may not be appropriate for all times and locations. [ABSTRACT FROM AUTHOR]

The Caspian region represents a complex and unique system of terrestrial, coastal, and aquatic environments, marked by an exceptional landscape and biological diversity. This diversity, however, is increasingly threatened by substantial anthropogenic pressures. One notable impact of this human influence is the rising concentration of pollutants atypical for the atmosphere. Advances in science and technology now make it possible to detect certain atmospheric pollutants using remote Earth observation techniques, specifically through data from the Sentinel-5 satellite, which provides continuous insights into atmospheric contamination. This article investigates the dynamics of atmospheric pollution in the southwestern part of the Caspian Sea basin using Sentinel-5P satellite data and the cloud-computing capabilities of the Google Earth Engine (GEE) platform. The study encompasses an analysis of concentrations of seven key pollutants: nitrogen dioxide (NO2), formaldehyde (HCHO), carbon monoxide (CO), ozone (O3), sulfur dioxide (SO2), methane (CH4), and the Aerosol Index (AI). Spatial and temporal variations in pollution fields were examined for the Caspian region and the basins of the seven rivers (key areas) flowing into the Caspian Sea: Sunzha, Sulak, Ulluchay, Karachay, Atachay, Haraz, and Gorgan. The research methodology is based on the use of data from the Sentinel-5 satellite, SRTM DEM data on absolute elevations, surface temperature data, and population density data. Data processing is performed using the Google Earth Engine cloud-computing platform and the ArcGIS software suite. The main aim of this study is to evaluate the spatiotemporal variability of pollutant concentration fields in these regions from 2018 to 2023 and to identify the primary factors influencing pollution distribution. The study's findings reveal that the Heraz and Gorgan River basins have the highest concentrations of nitrogen dioxide and Aerosol Index levels, marking these basins as the most vulnerable to atmospheric pollution among those assessed. Additionally, the Gorgan basin exhibited elevated carbon monoxide levels, while the highest ozone concentrations were detected in the Sunzha basin. Our temporal analysis demonstrated a substantial influence of the COVID-19 pandemic on pollutant dispersion patterns. Our correlation analysis identified absolute elevation as a key factor affecting pollutant distribution, particularly for carbon monoxide, ozone, and aerosol indices. Population density showed the strongest correlation with nitrogen dioxide distribution. Other pollutants exhibited more complex distribution patterns, influenced by diverse mechanisms associated with local emission sources and atmospheric dynamics. [ABSTRACT FROM AUTHOR]

The study investigates methods to enhance the reliability of NO2 monitoring using low-cost electrochemical sensors to measure gaseous pollutants in air by addressing the impacts of temperature and relative humidity. The temperature within a plastic container was controlled using an internal mica heater, an external hot air blower, or cooling packs, while relative humidity was adjusted using glycerine solutions. Findings indicated that the auxiliary electrode signal is susceptible to temperature and moderately affected by relative humidity. In contrast, the working electrode signal is less affected by temperature and relative humidity; however, adjustments are still required to determine gas concentrations accurately. Tests involving on/off cycles showed that the auxiliary electrode signal experiences exponential decay before stabilizing, requiring the exclusion of initial readings during monitoring activities. Additionally, calibration experiments in zero air allowed the determination of the compensation factor nT across different temperatures and humidity levels. These results highlight the importance of compensating for temperature and humidity effects to improve the accuracy and reliability of NO2 measurements using low-cost electrochemical sensors. This refinement makes the calibration applicable across a broader range of environmental conditions. However, the experiments also show a lack of repeatability in the zero air calibration. [ABSTRACT FROM AUTHOR]

Background: Although computational models are advancing air quality prediction, achieving the desired performance or accuracy of prediction remains a gap, which impacts the implementation of machine learning (ML) air quality prediction models. Several models have been employed and some hybridized to enhance air quality and air quality index predictions. The objective of this paper is to systematically review machine and deep learning techniques for spatiotemporal air prediction challenges. Methods: In this review, a methodological framework based on PRISMA flow was utilized in which the initial search terms were defined to guide the literature search strategy in online data sources (Scopus and Google Scholar). The inclusion criteria are articles published in the English language, document type (articles and conference papers), and source type (journal and conference proceedings). The exclusion criteria are book series and books. The authors' search strategy was complemented with ChatGPT-generated keywords to reduce the risk of bias. Report synthesis was achieved by keyword grouping using Microsoft Excel, leading to keyword sorting in ascending order for easy identification of similar and dissimilar keywords. Three independent researchers were used in this research to avoid bias in data collection and synthesis. Articles were retrieved on 27 July 2024. Results: Out of 374 articles, 80 were selected as they were in line with the scope of the study. The review identified the combination of a machine learning technique and deep learning techniques for data limitations and processing of the nonlinear characteristics of air pollutants. ML models, such as random forest, and decision tree classifier were among the commonly used models for air quality index and air quality predictions, with promising performance results. Deep learning models are promising due to the hyper-parameter components, which consist of activation functions suitable for nonlinear spatiotemporal data. The emergence of low-cost devices for data limitations is highlighted, in addition to the use of transfer learning and federated learning models. Again, it is highlighted that military activities and fires impact the O3 concentration, and the best-performing models highlighted in this review could be helpful in developing predictive models for air quality prediction in areas with heavy military activities. Limitation: This review acknowledges methodological challenges in terms of data collection sources, as there are equally relevant materials on other online data sources. Again, the choice and use of keywords for the initial search and the creation of subsequent filter keywords limit the collection of other relevant research articles. [ABSTRACT FROM AUTHOR]

Data on particulate matter, gaseous pollutants, and AQI values from three cities (Haikou, Sanya, and Danzhou) between January 2018 and December 2022 were obtained in order to analyze the spatiotemporal distribution characteristics of air pollution, the correlation between pollutants with meteorological conditions, and the potential sources in Hainan Island. The spatiotemporal distribution's characteristics demonstrated that the annual mean concentrations of SO2, NO2, CO, O3, PM10 and PM2.5 were 4.34 ± 1.11 μg m−3, 9.87 ± 1.87 μg m−3, 0.51 ± 0.06 mg m−3, 73.04 ± 6.36 μg m−3, 27.31 ± 3.63 μg m−3, and 14.01 ± 2.02 μg m−3, respectively. The yearly mean concentrations were trending downward in the past few years and were below the National Ambient Air Quality Standard (NAAQS) Grade II. Summer was the season with the lowest concentrations of all pollutants (3.84 μg m−3, 7.34 μg m−3, 0.42 mg m−3, 52.80 μg m−3, 18.67 μg m−3 and 8.67 μg m−3 for SO2, NO2, CO, O3, PM10 and PM2.5, respectively), and afternoons were the time with the lowest concentrations of pollutants (except for 78.04 μg m−3 for O3). The influence of meteorological conditions on pollutants was examined: there was a prominent positive correlation between temperature and O3 in summer, and relative humidity largely influenced the concentrations of PM. The pollution in Hainan was affected more by regional transport; according to the backward trajectory results, Hainan is susceptible to air masses from Guangdong and Fujian to the northeast, the Indochina Peninsula to the southwest, and the South China Sea to the southeast. The results of PSCF and CWT analyses indicated that Guangdong, Jiangxi, Hunan, and Fujian were the primary potential sources of PM2.5 and O3. [ABSTRACT FROM AUTHOR]

Atmospheric pollutants' real-time changes and the internal interactions among various data make it challenging to efficiently predict concentration variations. In order to extract more information from the time series of pollutants and improve the accuracy of prediction models, we propose a type of Multilayer Perceptron model based on wavelet decomposition, named Wavelet Transform-based Multilayer Perceptron (WTMP) model. This model decomposes pollutant data through overlapping discrete wavelet transforms to extract non-stationarity and nonlinear dependencies in the time series. It combines the decomposed data with static covariate information such as data collection time and inputs them into an improved Multilayer Perceptron (MLP) model, reconstructing and outputting the prediction results. Finally, the model is validated using atmospheric pollutant data collected at a specific location in Ruian City, Zhejiang Province, China. The results indicate that the model performs well with minimal prediction errors. [ABSTRACT FROM AUTHOR]

The chief aim of this research is to investigate the causality of air pollutants and adverse pregnancy outcomes. Two-sample Mendelian randomization was conducted, employing genetic variants connected with air pollution as instrumental variables. Sixteen adverse pregnancy outcomes were extracted as the main outcome measures from the genome-wide association study (GWAS). The inverse-variance weighted (IVW) method was conducted as the primary analysis method. This study found that there were causal association between NO2 and pre-eclampsia (weighted median: OR = 1.30, 95% CI = [1.03–1.64], p = 0.029) and between PM2.5 and placental abruption (IVW: OR = 10.94, 95% CI = [1.28–93.45], p = 0.029). There were potential causal relationships between NO2 and gestational hypertension (IVW: OR = 1.14, 95% CI = [0.99–1.30], p = 0.060); NO2 and placental abruption (IVW: OR = 1.97, 95% CI = [0.90–4.28], p = 0.089); NOx and fetal growth restriction (IVW: OR = 0.06, 95% CI = [0.99–1.12], p = 0.089); PM2.5 and slow fetal growth and fetal malnutrition (MR–Egger: OR = 54,240.95, 95% CI = [2.08–1,411,757,729.46], p = 0.059); PM10 and hyperemesis gravidarum (MR–Egger: OR = 0.12, 95% CI = [0.02–0.97], p = 0.086); PM10 and preterm birth (weighted median: OR = 1.60, 95% CI = [0.95–2.70], p = 0.075); and PM10 and spontaneous abortion (weighted median: OR = 1.60, 95% CI = [0.95–2.70], p = 0.075). There was no pleiotropy, but there was some heterogeneity. In conclusion, air pollution has a causal effect on several adverse pregnancy outcomes. [ABSTRACT FROM AUTHOR]

Individuals affected by neurodegenerative diseases, including Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS), are dramatically increasing worldwide. Thus, several efforts are being made to develop strategies for stopping or slowing the spread of these illnesses. Although causative genetic variants linked to the onset of these diseases are known, they can explain only a small portion of cases. The etiopathology underlying the neurodegenerative process in most of the patients is likely due to the interplay between predisposing genetic variants and environmental factors. Epigenetic mechanisms, including DNA methylation, are central candidates in translating the effects of environmental factors in genome modulation, and they play a critical role in the etiology of AD, PD, and ALS. Among the main environmental exposures that have been linked to an increased risk for these diseases, accumulating evidence points to the role of heavy metals, pesticides, and air pollutants. These compounds could trigger neurodegeneration through different mechanisms, mainly neuroinflammation and the induction of oxidative stress. However, increasing evidence suggests that they are also capable of inducing epigenetic alterations in neurons. In this article, we review the available literature linking exposure to metals, pesticides, and air pollutants to DNA methylation changes relevant to neurodegeneration. [ABSTRACT FROM AUTHOR]

Air pollutants are usually formed by easily spreading small molecules, representing a severe problem for human health, especially in urban centers. Despite the efforts to stem their diffusion, many diseases are still associated with exposure to these molecules. The present study focuses on modeling and designing two-dimensional systems called Layered Double Hydroxides (LDHs), which can potentially trap these molecules. For this purpose, a Density Functional Theory (DFT) approach has been used to study the role of the elemental composition of LDHs, the type of counterion, and the ability of these systems to intercalate NO2 and SO2 between the LDH layers. The results demonstrated how the counterion determines the different possible spacing between the layers, modulating the internalization capacity of pollutants and determining the stability degree of the system for a long-lasting effect. The variations in structural properties, the density of states (DOS), and the description of the charge transfer have been reported, thus allowing the investigation of aspects that are difficult to observe from an experimental point of view and, at the same time, providing essential details for the effective development of systems that can counteract the spread of air pollutants. [ABSTRACT FROM AUTHOR]

Copper oxide nanoparticles (CuO NPs) have seen increasing use across various industries, raising significant concerns about their potential toxicity and the exacerbation of pre-existing conditions like asthma. Asthma, a chronic inflammatory condition of the airways, can be triggered or worsened by environmental factors such as allergens, air pollutants, and chemicals, including nanoparticles. This study aimed to investigate the pulmonary toxicity induced by CuO NPs and their impact on asthma, with a particular focus on the role of thioredoxin-interacting protein (TXNIP). Using an ovalbumin (OVA)-induced asthma model, we found that CuO NP exposure led to significant increases in inflammatory cell infiltration, cytokine production, airway hyperresponsiveness, OVA-specific immunoglobulin (Ig)E levels, and mucus production. These pathological changes were closely associated with the upregulation of TXNIP-related signaling pathways, including phosphorylated apoptosis signal-regulating kinase (p-ASK)1, the Bax/Bcl-2 ratio, and cleaved caspase-3 activation. Complementary in vitro experiments using NCI-H292 respiratory epithelial cells showed that CuO NP treatment enhanced TXNIP signaling and increased mRNA expression and the production of inflammatory cytokines. Notably, TXNIP knockdown significantly attenuated these CuO NP-induced effects. In conclusion, our findings suggest that CuO NP exposure not only induces pulmonary toxicity but also exacerbates asthma, primarily through the activation of the TXNIP signaling pathway. [ABSTRACT FROM AUTHOR]

The increasing number of vehicles operating in Poland, especially passenger vehicles, justifies the need to conduct air pollution emission tests in the context of the impact of vehicles on the natural environment. Firstly, this article reviews the publications related to air pollutant emissions and passenger vehicles traveling on Polish roads. However, it presents a special method using advanced research equipment to determine air pollutant emissions. The above research methods are justified in implementing clean transport zones. Real Driving Emissions represent an essential procedure in the implementation of clean transport zones in Poland, verifying the actual emissions of air pollutants and modeling this phenomenon using the results of real air pollutant emissions. The results of this research state that establishing a link between a vehicle's air pollutant emissions and its age can support making transport or delivery planning more sustainable and choosing less carbon-intensive means of transport to reduce the negative impact of transport on the environment. The scientific novelty of the proposed solutions is the verification of the actual emissions of Euro 6 vehicles and the modeling of air pollutant emissions as a function of speed and acceleration. The research results are included in this article and will become input data for further analysis in examining the impact of vehicle operating age on air pollution emissions. Consequently, the novelty of the present research also lies in its focus on the verification of the impact of operating age, particularly in the context of vehicles exceeding 15 years of age, on air pollutant emissions. By establishing a correlation between a vehicle's air pollutant emissions and its operating age, it becomes possible to make transport or delivery planning more sustainable. Furthermore, the selection of less carbon-intensive means of transport can contribute to reducing the negative impact of transport on the environment. [ABSTRACT FROM AUTHOR]

Particulate pollutants, particularly PM2.5 and PM10, pose serious threats to human health and environmental quality. Therefore, effectively mitigating and reducing the concentrations of these pollutants is crucial for human survival and development. In this study, we analyzed the distribution characteristics of air particulate pollutants in a typical high-latitude city, extracted urban forest areas from high-resolution remote sensing images, and examined the changing characteristics of PM concentration and the relationship between landscape pattern indexes and PM at different scales. The results showed that the concentrations of PM2.5 and PM10 were highest in winter and lowest in summer. At the small scales of 0.5 km × 0.5 km to 1.5 km × 1.5 km, PM concentration decreased with the decrease in PARA (Perimeter–Area Ratio). At the mesoscales of 2 km × 2 km to 2.5 km × 2.5 km, both PARA and CIRCLE (Related Circumscribing Circle) were highly significant (p < 0.001) correlated with PM concentration. At the large scales of 3 km × 3 km to 4 km × 4 km, PARA and PAFRAC (Perimeter–Area Fractal Dimension) were positively correlated with PM concentration. Our study indicates that reducing the complexity of forest patches in small-scale planning can help mitigate particulate air pollution. In the medium scale of urban forest planning, the more regular the forest patch shape and the more similar the patch shape to the strip, the better PM can be alleviated, while in large-scale planning, increasing the forest area and making the patches more normalized and simplified can reduce PM concentration. Moreover, reducing the complexity of forest patches can significantly mitigate PM pollution at all scales. The results of this research provide theoretical support and guidance for improving air quality in urban forest planning at different scales. [ABSTRACT FROM AUTHOR]

Increasing studies are indicating a potential association between ambient air pollution exposure and fasting blood glucose (FBG), an indicator of prediabetes and diabetes. However, there is inconsistency within the existing literature. The aim of this study was to summarize the associations of exposures to particulate matters (PMs) (with aerodynamic diameters of ≤1 μm (PM1), ≤2.5 μm (PM2.5), and ≤10 μm (PM10), respectively) and gaseous pollutants (sulfur dioxide (SO2), nitrogen dioxide (NO2) and ozone (O3)) with FBG based on the existing epidemiological research for a better understanding of the relationship between air pollution and diabetes. Up to 2 July 2024, we performed a comprehensive literature retrieval from various electronic databases (PubMed, Web of Science, Scopus, and Embase). Random-effect and fixed-effect models were utilized to estimate the pooled percent changes (%) and 95% confidence intervals (CIs). Then, subgroup meta-analyses and meta-regression analyses were applied to recognize the sources of heterogeneity. There were 33 studies eligible for the meta-analysis. The results showed that for each 10 μg/m3 increase in long-term exposures to PM1, PM2.5, PM10, and SO2, the pooled percent changes in FBG were 2.24% (95% CI: 0.54%, 3.96%), 1.72% (95% CI: 0.93%, 2.25%), 1.19% (95% CI: 0.41%, 1.97%), and 0.52% (95% CI:0.40%, 0.63%), respectively. Long-term exposures to ambient NO2 and O3 were not related to alterations in FBG. In conclusion, our findings support that long-term exposures to PMs of various aerodynamic diameters and SO2 are associated with significantly elevated FBG levels. [ABSTRACT FROM AUTHOR]

Airborne fine dust pollution poses a significant threat to both respiratory and skin health, yet the skin's physiological response to such exposure has been underexplored. This study investigates the impact of fine dust on skin cells, focusing on their metabolic activity, inflammatory responses, and aging-related changes. We found that exposure to fine dust model compounds led to dose-dependent cytotoxicity, with PM2.5-Ions exhibiting higher toxicity compared to PM10-PAHs. Human epithelial keratinocytes (HEKn) showed heightened sensitivity to fine dust, marked by increased inflammation, particularly with elevated IL-8 expression in response to PM2.5-Ions. Additionally, fine dust exposure resulted in reduced cell density, slower proliferation, and decreased migration, notably at higher concentrations of PM2.5-Ions. These changes are indicative of accelerated aging processes, including compromised cell function and structural integrity. Live cell imaging and correlation analyses highlighted significant links between metabolic activity, cell morphology, and IL-8 secretion. These findings provide critical insights into the differential impacts of fine dust components on skin cells, emphasizing the potential acceleration of aging processes and underscoring the need for further research on cellular responses to environmental stress and the development of protective measures against urban fine dust exposure. Overall, this study, which contributes to addressing the skin health risks posed by air pollutants, could be actively used in environmental science, dermatology, and public health. [ABSTRACT FROM AUTHOR]

This study investigates the effect of the low emission zone (LEZ), designed to restrict old diesel vehicles, on air quality in Seoul, Republic of Korea, using the regression discontinuity in time (RDiT) approach. While previous studies have examined LEZ impacts using traditional econometric models such as time series and panel data approaches, our research uniquely integrates high-frequency daily weather data to better control for confounding environmental variables and captures time-of-day effects on pollutant concentrations. Our findings reveal that the LEZ policy effectively reduced NO2 and SO2 concentrations by 4.7% and 11.6%, respectively. Notably, during daytime hours, when traffic is heaviest, NO2, SO2, and PM10 concentrations decreased by 7.1%, 14.8%, and 13.6%, respectively. These results suggest that the observed improvements can be attributed not only to reduced diesel vehicle registrations but also to significant declines in overall traffic volume. [ABSTRACT FROM AUTHOR]

Electrostatic fields are crucial for achieving the highly efficient filtration of airborne pollutants. However, the dissipation of static charges over time, especially under humid conditions, limits their practical application. In this study, we present a self-charging air filter (SAF) powered by a triboelectric nanogenerator (TENG). This SAF is integrated into a commercial mask, termed SAFM, which can effectively capture and degrade airborne pollutants without requiring an external power source. By leveraging the triboelectric effect during breathing, the TENG within the SAFM continuously replenishes static charges, maintaining the triboelectric field. The system employs a cellulose aerogel/Ti3C2Tx composite as the electron donor and an esterified cellulose-based electrospun nanofiber as the electron acceptor. Remarkably, the triboelectric field significantly enhances filtration performance, with the SAF achieving up to 95.7% filtration efficiency for particulate matter as small as 0.3 μm. This work underscores the potential of TENG-powered triboelectric fields in the development of multifunctional, human-machine interactive facemasks. [ABSTRACT FROM AUTHOR]

The frequency of extreme wildfire events (EWEs) is expected to increase due to climate change, leading to higher levels of atmospheric pollutants being released into the air, which could cause significant short-term impacts on human health (both for the population and firefighters) and on visibility. This study aims to gain a better understanding of the effects of EWEs' smoke on air quality, its short-term impacts on human health, and how it reduces visibility by applying a modelling system to the Portuguese EWEs of October 2017. The Weather Research and Forecasting Model was combined with a semi-empirical fire spread algorithm (WRF-SFIRE) to simulate particulate matter smoke dispersion and assess its impacts based on up-to-date numerical approaches. Hourly simulated particulate matter values were compared to hourly monitored values, and the WRF-SFIRE system demonstrated accuracy consistent with previous studies, with a correlation coefficient ranging from 0.30 to 0.76 and an RMSE varying between 215 µg/m3 and 418 µg/m3. The estimated daily particle concentration levels exceeded the European air quality limit value, indicating a potential strong impact on human health. Health indicators related to exposure to particles were estimated, and their spatial distribution showed that the highest number of hospital admissions (>300) during the EWE, which occurred downwind of the fire perimeters, were due to the combined effect of high smoke pollution levels and population density. Visibility reached its worst level at night, when dispersion conditions were poorest, with the entire central and northern regions registering poor visibility levels (with a visual range of less than 2 km). This study emphasises the use of numerical models to predict, with high spatial and temporal resolutions, the population that may be exposed to dangerous levels of air pollution caused by ongoing wildfires. It offers valuable information to the public, civil protection agencies, and health organisations to assist in lessening the impact of wildfires on society. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of air quality and climate variability during the first wave of COVID-19 associated with accelerated transmission and lethality in Wuhan in China and four European metropolises (Milan, Madrid, London, and Bucharest). For the period 1 January–15 June 2020, including the COVID-19 pre-lockdown, lockdown, and beyond periods, this study used a synergy of in situ and derived satellite time-series data analyses, investigating the daily average inhalable gaseous pollutants ozone (O3), nitrogen dioxide (NO2), and particulate matter in two size fractions (PM2.5 and PM10) together with the Air Quality Index (AQI), total Aerosol Optical Depth (AOD) at 550 nm, and climate variables (air temperature at 2 m height, relative humidity, wind speed, and Planetary Boundary Layer height). Applied statistical methods and cross-correlation tests involving multiple datasets of the main air pollutants (inhalable PM2.5 and PM10 and NO2), AQI, and aerosol loading AOD revealed a direct positive correlation with the spread and severity of COVID-19. Like in other cities worldwide, during the first-wave COVID-19 lockdown, due to the implemented restrictions on human-related emissions, there was a significant decrease in most air pollutant concentrations (PM2.5, PM10, and NO2), AQI, and AOD but a high increase in ground-level O3 in all selected metropolises. Also, this study found negative correlations of daily new COVID-19 cases (DNCs) with surface ozone level, air temperature at 2 m height, Planetary Boundary PBL heights, and wind speed intensity and positive correlations with relative humidity. The findings highlight the differential impacts of pandemic lockdowns on air quality in the investigated metropolises. [ABSTRACT FROM AUTHOR]

Air pollution, greenspace and noise are interrelated environmental factors with the potential to influence human health outcomes. Research has measured these exposures in diverse ways across the globe, but no study has yet performed a country-wide analysis of air pollution, greenspace, and noise in England. This study examined cross-sectional PM2.5, NO2, greenness, accessible greenspace, road noise, and rail noise exposure data at all residential postcodes in England (n = 1,227,681). Restricted cubic spline models were fitted between each environmental exposure and a measure of socioeconomic status, the Index of Multiple Deprivation (IMD) rank. Population-weighted exposures by IMD deciles, urbanicity, and region were subsequently estimated. Restricted cubic spline models were also fitted between greenness and each other environmental exposure in the study. The results show some evidence of inequalities in exposure to air pollutants, greenspace, and noise across England. Notably, there is a socioeconomic gradient in greenness, NO2, PM2.5, and road noise in London. In addition, NO2, PM2.5, and road noise exposure decrease as greenness increases in urban areas. Concerningly, almost all air pollution estimates in our study exceed international health guidelines. Further research is needed to elucidate the socioeconomic patterns and health impacts of air pollution, greenspace, and noise over time. [ABSTRACT FROM AUTHOR]

There is a pressing need for tools that can rapidly predict PM 2.5 concentrations and assess health impacts under various emission scenarios, aiding in the selection of optimal mitigation strategies. Traditional chemical transport models (CTMs) like CMAQ are accurate but computationally intensive, limiting practical scenario analysis. To address this, we propose a novel method integrating a conditional U-Net surrogate model with health impact assessments, enabling swift estimation of PM 2.5 concentrations and related health effects. The U-Net model was trained with 2019 South Korean PM 2.5 data, including precursor emissions and boundary conditions. Our model showed high accuracy and significant efficiency, reducing processing times while maintaining reliability. By combining this surrogate model with the EPA's BenMAP-CE tool, we estimated potential premature deaths under various emission reduction scenarios in South Korea, extending projections to 2050 to account for demographic changes. Additionally, we assessed the required PM 2.5 emission reductions needed to counteract the increase in premature deaths due to an aging population. This integrated framework offers an efficient, user-friendly tool that bridges complex air quality modeling with practical policy evaluation, supporting the development of effective strategies to reduce PM 2.5 -related health risks and estimate economic benefits. [ABSTRACT FROM AUTHOR]