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This research introduces syN-BEATS, a novel ensemble deep learning model tailored for effective pollutant forecasting under conditions of limited data availability. Based on the N-BEATS architecture, syN-BEATS integrates various configurations with differing numbers of stacks and blocks, effectively combining weak and strong learning approaches. Our experiments show that syN-BEATS outperforms standard models, especially when using Bayesian optimization to fine-tune ensemble weights. The model consistently achieves low relative root mean square errors, proving its capacity for precise pollutant forecasting despite data constraints. A key aspect of this study is the use of data from only one meteorological and one air quality monitoring station per region, simulating environments with restricted monitoring capabilities. By applying this approach in regions with diverse climates and air quality levels, we thoroughly assess the model's flexibility and resilience under different environmental conditions. The results highlight syN-BEATS' ability to support the development of effective health alert systems that can detect specific airborne pollutants, even in areas with limited monitoring infrastructure. This advancement is crucial for enhancing environmental monitoring and public health management in under-resourced areas. [ABSTRACT FROM AUTHOR]

The Chinese government has explicitly promised to peak carbon dioxide emissions by 2030 and strive to become carbon neutral by 2060. As the capital of China, Beijing should play a pilot role in reducing carbon emissions. Researching on the synergistic effect of air pollutants and carbon dioxide emissions reduction can be conducive to the reduction of carbon and pollution, and ultimately promote economic growth and enhance environmental management. Based on the extended Kaya identity and the gray correlation model, this study analyzes the correlation degree of the influencing factors of collaborative emission reduction. The Logarithmic Mean Divisia Index method (LMDI model) is conducted to decompose the driving effects and quantify the collaborative emission reduction effects of main air pollutants and carbon dioxide in Beijing. The results showed a strong correlation (correlation coefficient > 0.6) between carbon dioxide and major air pollution. The energy intensity and energy structure are the main factors to promote the major air pollutants emission reduction in Beijing, while the economic output and population size increase the air pollutant emissions. The average CO2 contribution rate to SO2, NOx, and PM10 from 2010 to 2019 was 9.60, 5.99 and 9.06%, respectively. In general, there is a significant connection between CO2 emissions and the main air pollutants. However, the synergistic emission reduction effect of CO2 and SO2 is greater than that of CO2 and NOx, and CO2 and PM10. Finally, this paper proposes several countermeasures and suggestions for front-end prevention, middle-end control, and collaborative emission reduction based on the findings. [ABSTRACT FROM AUTHOR]

Purpose: Building operations and human activities indoors continuously affect air quality, contaminating the air and sometimes exceeding permissible limits which can be health threatening either in the short or long time. This implies a need for strict awareness and compliance with air quality standards, particularly in workplaces prone to air contaminants emissions. This study aims to evaluate printing-related pollutant concentrations and their effects on indoor air quality (IAQ). The study investigated a printing press's total volatile organic compounds (TVOC), particulate matter (PM), carbon monoxide and carbon dioxide emissions. Design/methodology/approach: This study used mainly an experimental research design supported by physical assessment by identifying the major printing-related pollutants, assessing the existing situation and measuring pollutant concentration levels using literature reviews, walkthrough inspections and experiments, respectively. The measurements were conducted in two scenarios: with and without printing activities, and the results were compared with relevant standards and guidelines. Findings: The outcomes indicate that TVOC concentration reaches 120 ppb during printing and binding activities, exceeding the 75 ppb acceptable limit based on the time-weighted average. The PM2.5 concentrations reach 49 µg/m3 and PM10 up to 150 µg/m3, exceeding acceptable levels given by the US Environmental Protection Agency (EPA), which are 35 µg/m3 and 150 µg/m3 for PM2.5 and PM10, respectively. These high concentrations of TVOC and PM indicate a significant risk to the health of building occupants, particularly those with respiratory conditions. PM concentrations do not exceed permissible levels when no printing or bookbinding occurs, suggesting that printing-related activities can contribute to elevated TVOC and PM concentrations. Social implications: The social implication of the study lies in its ability to promote awareness among workers and improve their well-being which in turn relates to productivity. The study outcome could also encourage businesses to adopt more responsible environmental and social practices as part of corporate social responsibility practices. Originality/value: The study's findings, which highlight the need for improved ventilation in printing halls, have the potential to significantly benefit building system designers, facility managers, policymakers and decision-makers. By providing information and theoretical support, the research can help integrate policies that regulate IAQ by reducing pollutant concentrations. This protects workers' health and helps update and enforce stricter IAQ regulations for industrial operations. [ABSTRACT FROM AUTHOR]

Atmospheric Aerosols are highly abundant in windblown dust events originating in arid and semiarid areas. Aerosols are dangerous to human health when the emission rate is presumably high. Harmattan dust is considered to be amongst the most harmful of all air pollutants due to the toxic effect of the dust constituents. Respiratory infections make up more than 20% of the causes of human mortality and morbidity. This study was carried out to assess the correlation between aerosol loading and respiratory diseases at Zaria, Kaduna State in North-Central Nigeria. The aerosol data were accessed from the National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectra-radiometer (MODIS) platform, while data on respiratory diseases were obtained from Ahmadu Bello University (A.B.U) Teaching Hospital Shika, Zaria, Nigeria from Jan 2009 - Dec 2018. Within that period, 2022 patients were diagnosed with different ranges of respiratory diseases. Out of 2022 516 (25.52%) were adult male, 455 (22.5%) were female, while teenagers were constituted 290 (14.34%), children were 385 (19.04%), and infants were 376 (18.6%). The correlation between aerosol optical depth and the number of cases of respiratory ailments was evaluated. A correlation coefficient of 0.65 was evaluated in the dry season, while in the rainy season the correlation coefficient was -0.55, overall correlation for an inter-annual variation is 0.27 while for the seasonal variation is 0.49. These results suggest that there is a positive correlation between aerosol loading and respiratory cases at Zaria. It also shows that the correlation between the dry seasons is high compared to the rainy season. [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]

Ludhiana, a pollution hot spot in North India, has seen a rapid deterioration in air quality over the years due to urbanization and industrialization. This study interprets the variations of particulate matter (PM) and gaseous pollutants (Nitrogen oxide, Nitrogen dioxide, NOX, Sulphur dioxide, Carbon monoxide, Benzene, Toluene, Ozone, and Ammonia) for the data observed from 2017 to 2023 in Ludhiana. This also covers the analysis focused on capturing the changes that occurred at the times of lockdown imposed during the Coronavirus Disease (COVID-19). The maximum 24-h averaged mass concentration values exceeded the National Ambient Air Quality Standards (NAAQS) of 100 µg/m3 for PM10 concentration and 60 µg/m3 for PM2.5 concentration in 2018 by the factor of 5 and 8. With the onset of the COVID-19 lockdown in 2020 year, PM10 and PM2.5 reached the minimum level while CO, T, O3, and NO2 increased by the factor of 3.9, 1.9, 1.4, and 1.3 from their previous year. This NO2 is a precursor of ozone formation, a higher NO2 to NO ratio observed during the lockdown, confirms the role of nitrogen compounds in the higher ozone formation rate. Based on the NO2/NO ratio, the probability rate of ozone formation determined using survival analysis is observed to be 94% from 2017 to 2023. The local sources' contribution to these air pollutants during Pre-Lockdown, Lockdown, and Post-Lockdown are analyzed using principal component analysis. The impact of the lockdown on ozone concentration sources has been observed. During the Pre- and Post-Lockdown phases, three sources (PC1, PC2, and PC3) were positively identified. Ozone levels are linked to PC3 in these phases, but during the lockdown, a negative loading in PC3 and positive loadings in PC1 and PC2 indicate a decrease in ozone from reduced emissions and an increase from secondary reactions involving nitrogen compounds. Moreover, the Toluene to Benzene concentration ratio is > 2, indicating the source of their origin from industrial emission or other non-traffic sources. Health assessment for the years 2017–2019 reveals a significant decrease in the number of cases of all-cause mortality, ischemic heart disease, stroke, and chronic obstructive pulmonary disease associated with reducing PM2.5 concentrations to national and international standards. [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]

Indonesia accounts for more than one third of the world's tropical peatlands. Much of the peatland in Indonesia has been deforested and drained, meaning it is more susceptible to fires, especially during drought and El Niño events. Fires are most common in Riau (Sumatra) and Central Kalimantan (Borneo) and lead to poor regional air quality. Measurements of air pollutant concentrations are sparse in both regions contributing to large uncertainties in both fire emissions and air quality degradation. We deployed a network of 13 low‐cost PM2.5 sensors across urban and rural locations in Central Kalimantan and measured indoor and outdoor PM2.5 concentrations during the onset of an El Niño dry season in 2023. During the dry season (September 1st to October 31st), mean outdoor PM2.5 concentrations were 136 μg m−3, with fires contributing 90 μg m−3 to concentrations. Median indoor/outdoor (I/O) ratios were 1.01 in rural areas, considerably higher than those reported during wildfires in other regions of the world (e.g., USA), indicating housing stock in the region provides little protection from outdoor PM2.5. We combined WRF‐Chem simulated PM2.5 concentrations with the median fire‐derived I/O ratio and questionnaire results pertaining to participants' time spent I/O to estimate 1.62 million people in Central Kalimantan were exposed to unhealthy, very unhealthy and dangerous air quality (>55.4 μg m−3) during the dry season. Our work provides new information on the exposure of people in Central Kalimantan to smoke from fires and highlights the need for action to help reduce peatland fires. Plain Language Summary: More than one third of the world's tropical peatlands are in Indonesia. Much of the peatland in Indonesia has been deforested and drained, meaning it is more susceptible to fires, especially during drought. Fires are most common in Riau (Sumatra) and Central Kalimantan (Borneo) and lead to poor regional air quality. There are not many measurements of air pollution in either region, and this means the air quality impacts of fires are not well understood. We deployed a network of air quality (AQ) sensors across urban and rural Central Kalimantan. The sensors measured the concentration of fine particulate matter (PM2.5), a major component of air pollution that is directly emitted by fires. The AQ sensors were deployed sensors inside and outside of people's homes during the onset of a dry season, when drought occurred (in 2023). Indoor and outdoor PM2.5 concentrations were very similar, indicating housing in the region provides little protection from outdoor PM2.5. We estimate 1.62 million people in Central Kalimantan were exposed to unhealthy, very unhealthy and dangerous AQ in 2023. Our work provides new information on the exposure of people in Central Kalimantan to fire PM2.5 and highlights the need for action to reduce peatland fires. Key Points: Mean outdoor PM2.5 concentrations during the dry season were 136 μg m−3 and fires contributed ∼90 μg m−3 to PM2.5 concentrationsI/O ratios indicate that housing stock provides little protection from outdoor PM2.51.62 million people in Central Kalimantan were exposed to unhealthy, very unhealthy and dangerous air quality (>55.4 μg m−3) during the dry season [ABSTRACT FROM AUTHOR]

The relationship between exposure to ambient air pollutants and emergency attendance for upper gastrointestinal bleeding (UGIB) remains inconclusive. This study examines the association between short‐term exposure to various ambient pollutants and the risk of UGIB emergency attendance. Data on daily UGIB emergency attendance, ambient pollutants, and meteorological conditions in Hong Kong were collected from 2017 to 2022. A time‐series study using a distributed lag non‐linear model to analyze the data, considering lag days. Stratified analysis was performed based on sex, seasons, and the COVID‐19 pandemic period. The burden was quantified using attributable fraction (AF) and number (AN). The study included 31,577 UGIB emergency records. Exposure to high levels of PM2.5 significantly increased the risk of UGIB emergency attendance from lag day 3 (RR: 1.012) to day 6 (RR: 1.008). High NO2 exposure also posed a significant risk from lag day 0 (RR: 1.026) to day 2 (RR: 1.014), and from lag day 5 (RR: 1.013) to day 7 (RR: 1.024). However, there was no association between UGIB and high O3 levels. The attributable burden of high‐concentration NO2 exposure was higher compared to those of PM2.5. Males and elderly individuals (≥65 years) faced a higher risk of UGIB emergencies, particularly during cold seasons. Our study suggests that both PM2.5 and NO2 exposure are associated with an increased risk of emergency attendance for UGIB. Ambient pollutant exposure has a stronger effect on UGIB in males and the elderly, particularly during cold seasons. Plain Language Summary: Our research in Hong Kong from 2017 to 2022 investigated the impact of brief exposure to air pollution on emergency visits for upper gastrointestinal bleeding (UGIB). We examined various types of air pollutants, including fine particulate matter (PM) and ozone, with a specific focus on identifying the most harmful pollutants and their temporal patterns. Our findings demonstrate that even a slight increase in levels of fine PM and nitrogen dioxide can significantly contribute to an upsurge in emergency cases of UGIB. Notably, these effects are more pronounced during colder months and in the elderly. The burden was more severe before the COVID‐19 pandemic. This study underscores the direct influence of air pollution on public health and emphasizes the imperative need to enhance air quality, particularly in densely populated urban areas. Our finding could help to inform public health policies and to promote measures aiming at reducing individuals' exposure to pollutants. Key Points: Short‐term exposure to high concentration of PM2.5 and NO2 significantly raise upper gastrointestinal bleeding (UGIB) emergency visitsThe impact of air pollution on the occurrence of UGIB emergencies is exacerbated by cold weather and among the elderly populationThe results of our study underscore the necessity for policy interventions aimed at enhancing air quality and promoting health safety [ABSTRACT FROM AUTHOR]

Higher concentrations of ambient air pollutants, including PM2.5 and NO2, and other pollutants have been found near active oil and gas wells and may be associated with adverse COVID‐19 outcomes. We assessed whether residential exposure to nearby oil and gas production was associated with higher rates of the respiratory infection COVID‐19 and related mortality using a population‐based ecological study in California. Using gridded population estimates, we estimated area‐level exposure to annual average oil and gas production volume from active wells within 1 kilometer (km) of populated areas within census block groups from 2018 to 2020. We geocoded confirmed cases and associated deaths to assess block group case and mortality rates from COVID‐19 from February 2020 to January 2021. We fit hierarchical Poisson models with individual and area covariates (e.g., age, sex, socioeconomic disadvantage), and included time and other interactions to assess additional variation (e.g., testing, reporting rates). In the first 4 months of the study period (February–May 2020), block groups in the highest tertile of oil and gas production exposure had 34% higher case rates (IRR: 1.34 95% CI: 1.20, 1.49) and 55% higher mortality rates (MRR: 1.52 95%: CI: 1.14, 2.03) than those with no estimated production, after accounting for area‐level covariates. Over the entire study period, we observed moderately higher mortality rates in the highest group (MRR: 1.16 95%: CI: 1.01, 1.33) and null associations for case rates. Plain Language Summary: Exposure to air, noise, light pollution and other localized stressors from active oil and gas drilling may affect the immune and cardiovascular systems and respiratory health, including diseases such as COVID‐19. This study reports on COVID‐19 case and death rates in communities within 10 km of active oil and gas production. We observed that California communities that were located less than 1 km from an actively producing oil and gas well were more likely to have higher COVID‐19 case and death rates in the first 4 months of the pandemic, especially when the level of production was high. Across our entire study time (February 2020–January 2021), we did not observe higher rates of cases or deaths in these communities, except for deaths in communities with the highest amount of production. These results suggest that if there is an effect of oil and gas production on COVID‐19 rates, it may be on the number of people that become severely ill; overall, it suggests that more research may help to understand why different communities had different levels of COVID‐19. Key Points: Using gridded population estimates, we estimated exposure to oil and gas production in areas within 1 km of an active wellWe observed higher reported case and mortality rates in areas with higher estimated oil and gas production from February to May 2020We geocoded COVID‐19 cases and deaths to census block groups and estimated age and sex‐specific mortality rates from February 2020 to January 2021 [ABSTRACT FROM AUTHOR]

Objectives: Long-term exposure to air pollution has been associated with higher risk of cardiovascular mortality. Less is known about the association of air pollution with initial development of cardiovascular disease. Herein, the association between low-level exposure to air pollutants and subclinical carotid atherosclerosis in adults without known clinical cardiovascular disease was investigated. Design: Cross-sectional analysis within a prospective cohort study. Setting: The Canadian Alliance for Healthy Hearts and Minds Cohort Study; a pan-Canadian cohort of cohorts. Participants: Canadian adults (n = 6645) recruited between 2014–2018 from the provinces of British Columbia, Alberta, Ontario, Quebec, and Nova Scotia, were studied, for whom averages of exposures to nitrogen dioxide (NO2), ozone (O3), and fine particulate matter (PM2.5) were estimated for the years 2008–2012. Main outcome measure: Carotid vessel wall volume (CWV) measured by magnetic resonance imaging (MRI). Results: In adjusted linear mixed models, PM2.5 was not consistently associated with CWV (per 5 μg/m3 PM2.5; adjusted estimate = -8.4 mm3; 95% Confidence Intervals (CI) -23.3 to 6.48; p = 0.27). A 5 ppb higher NO2 concentration was associated with 11.8 mm3 lower CWV (95% CI -16.2 to -7.31; p<0.0001). A 3 ppb increase in O3 was associated with 9.34 mm3 higher CWV (95% CI 4.75 to 13.92; p<0.0001). However, the coarse/insufficient O3 resolution (10 km) is a limitation. Conclusions: In a cohort of healthy Canadian adults there was no consistent association between PM2.5 or NO2 and increased CWV as a measure of subclinical atherosclerosis by MRI. The reasons for these inconsistent associations warrant further study. [ABSTRACT FROM AUTHOR]

Introduction Methods Results Conclusions Understanding the impact of environmental exposures on disease incidence is important for environmental guidelines, health services management, and advising patients. We aimed to assess the relationship between daily mean temperature and common pollutants with atrial fibrillation (AF) presentations to emergency departments (EDs).The study included consecutive adult patients presenting with AF to EDs from 1/1/2014 to 31/12/2020 with linkage to hospital and emergency discharge diagnosis data. A time series quasi‐Poisson regression with a distributed lag nonlinear model was fitted to assess the association between AF with mean air temperature and five common pollutants, overall and according to sex and region, with adjustment for season, day of the week, long‐term trend, and co‐pollutants.In 82 575 AF presentations to EDs during the study period, mean (standard deviations [SD]) (SD) age was 69.6 (SD 14.7) years and 50.7% were female. AF presentations were associated with elevated levels of Particulate Matter (PM) 2.5 (≥57.7 µg/m3) and nitrous dioxide (NO2) (≥16.5 parts per billion), but not mean air temperature or other pollutants (carbon monoxide, sulphur dioxide, or ozone). The attributable fraction of AF presentations relating to above optimal NO2 and PM 2.5 levels was 7.24% and 3.81% resulting in 854 and 450 annual excess AF presentations, respectively.High levels of NO2 and PM 2.5 are associated with increased risk of AF presentations to EDs. These findings have important implications for environmental policies and advice to patients susceptible to AF presentations to EDs, especially in regions with higher baseline levels of PM 2.5 or NO2. [ABSTRACT FROM AUTHOR]

Background Aim Methods Results Conclusions Air pollutants, such as diesel exhaust particles (DEPs), induce respiratory disease exacerbation with neutrophilic infiltration. Progranulin (PGRN), an epithelial cell and macrophage‐derived secretory protein, is associated with neutrophilic inflammation. PGRN is digested into various derivatives at inflammatory sites and is involved in several inflammatory processes. PGRN and its derivatives likely regulate responses to DEP exposure in allergic airway inflammation.To investigate the role of PGRN and its derivatives in the regulation of responses to DEP exposure in allergic airway inflammation.A murine model of allergic airway inflammation was generated in PGRN‐deficient mice, and they were simultaneously exposed to DEP followed by intranasal administration of full‐length recombinant PGRN (PGRN‐FL) and a PGRN‐derived fragment (FBAC). Inflammatory status was evaluated by bronchoalveolar lavage fluid and histopathologic analyses. Human bronchial epithelial cells were stimulated with DEPs and house dust mites (HDMs), and the effect of FBAC treatment was evaluated by assessing various intracellular signaling molecules, autophagy markers, inflammatory cytokines, and intracellular oxidative stress.DEP exposure exaggerated neutrophilic inflammation, enhanced IL‐6 and CXCL15 secretions, and increased oxidative stress in the murine model; this effect was greater in PGRN‐deficient mice than in wild‐type mice. The DEP‐exposed mice with PGRN‐FL treatment revealed no change in neutrophil infiltration and higher oxidative stress status in the lungs. On the contrary, FBAC administration inhibited neutrophilic infiltration and reduced oxidative stress. In human bronchial epithelial cells, DEP and HDM exposure increased intracellular oxidative stress and IL‐6 and IL‐8 secretion. Decreased nuclear factor erythroid 2‐related factor 2 (Nrf2) expression and increased phosphor‐p62 and LC3B expression were also observed. FBAC treatment attenuated oxidative stress from DEP and HDM exposure.FBAC reduced neutrophilic inflammation exaggerated by DEP exposure in a mouse model of allergic airway inflammation by reducing oxidative stress. PGRN and PGRN‐derived proteins may be novel therapeutic agents in attenuating asthma exacerbation induced by air pollutant exposure. [ABSTRACT FROM AUTHOR]

Air pollution in the interior of our homes is caused by diverse chemical, physical, and biological entities. This review comprehensively explores the current understanding of sources and health impacts of gaseous and particulate pollutants. Trend analysis of indoor air research worldwide revealed a quantum jump of 2.8 times in the number of publications during the last ten years. Indoor air pollutants are innumerable, but only a few are widely prevalent in most households. The qualitative complexity of pollutants translates to different health problems, including respiratory diseases, cardiovascular conditions, cancer, and deaths. There exist wide-scale disparities in the negative impacts among different economic strata, genders, and age groups; children and elderly populations are more vulnerable. In developing countries, pollutants primarily arise from traditional sources, whereas in developed countries, pollutants from non-conventional sources are comparatively significant. Only a few countries have indoor air regulations, policies, monitoring plans and effective enforcement. [ABSTRACT FROM AUTHOR]

Nitrogen dioxide (NO 2) is a significant air pollutant with a notable environmental impact, necessitating precise monitoring. However, most existing NO 2 sensing materials exhibit low sensitivity and require high-temperature operation. In this study, we employed a hydrothermal and co-precipitation method to synthesize a novel ternary nanocomposite, In 2 O 3 /SnO 2 nanoparticles (NPs)/fluorine-doped carbon quantum dots (F-CDs). By varying the mass ratio of In 2 O 3 to SnO 2 NPs, we investigated the gas-sensing performance of the composite towards NO 2. The results demonstrate that when the ratio of SnO 2 and In 2 O 3 reaches to 1 : 2, the 2-In 2 O 3 /SnO 2 /F-CDs gas sensor exhibited the highest response, excellent selectivity, and remarkable stability under ultraviolet (UV) light irradiation at room temperature (∼25 °C). Furthermore, the response of the 2-In 2 O 3 /SnO 2 /F-CDs gas sensor displayed a strong linear relationship with the concentration of NO 2 gas. At a NO 2 gas concentration of 3 ppm, the sensitivity was 238, which is significantly higher than that of In 2 O 3 /SnO 2 NPs and In 2 O 3 sensors. The response time and recovery time were measured at 62 s and 33 s, respectively. Finally, based on experimental characterization results and band structure analysis, we confirmed the promising effects of ternary nanocomposite in enhancing gas sensing. [ABSTRACT FROM AUTHOR]

Understanding the spatial and temporal dynamics of air pollutants is crucial for effective urban air pollution management. This study focuses on the temporal dynamics of air quality monitoring stations (AQMSs) and the association among air pollutants, particularly PM2.5, in Tehran, Iran. Using time series clustering and the Copula model, we analyzed data from 2019 to 2022. We found that the levels and dynamics of O3 and SO2 were similar across most AQMSs and unrelated to geographical positions. CO levels and dynamics were consistent among urban and border AQMSs, with higher concentrations in urban stations. NO2 levels and dynamics varied significantly among northern AQMSs with no relationship geographical positions. PM10 levels and dynamics had a relationship with geographical positions, with western clusters having the highest and northern clusters the lowest concentrations. The dynamics of PM2.5 showed significant relationship among AQMSs in the eastern, southern, and western regions, but not in the north. We also observed that PM10 and O3 levels were higher in warm seasons, whereas CO, SO2, NO2, and PM2.5 levels were higher in cold seasons. Most pollutants, except O3, peaked during traffic hours. Notably, the significant increase in PM2.5 since spring 2021 was primarily due to PM10. Policymakers should focus on these spatial and temporal variations to improve urban air quality and public health outcomes through targeted interventions. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) equipped with a miniaturized sensor package were developed for aerial observations, which realizes aerial observations affordable to scientists in atmospheric science and achieves aerial measurements in high spatial resolution. UAVs are deployed to a variety of aerial detecting tasks in different scientific scenarios including chemical industry parks (CIPs) with hazardous gases emissions, and some places difficult for humans to reach. In this study, UAV sensing technology was deployed to detect air pollutants in a suburb, a CIP and a natural gas plant, respectively. The effects of atmospheric conditions such as the atmospheric boundary layer height, long-distance transport and atmospheric stability on the spatiotemporal variations of the air pollutants vertical profiles were investigated by the UAV. The UAV with the sensor package was deployed to capture the methane (CH4) leakages in a natural gas plant. The spatiotemporal variations of CH4 in both vertical and horizontal directions studied by UAV were employed to calculate accurate CH4 emissions, which is crucial to reducing the emissions of greenhouse gases. The low-cost UAV sensing technology for air pollutants was developed by Dr. Xiaobing Pang, who was funded by the Newton Fellowship in 2009 and worked in the University of York. This article is part of the theme issue 'Celebrating the 15th anniversary of the Royal Society Newton International Fellowship'. [ABSTRACT FROM AUTHOR]

Metal‐organic frameworks (MOFs), an emerging class of porous crystalline material, are identified as a promising candidate for tackling the most formidable global challenges due to their unique and intriguing properties including well‐defined porous crystal structure, large specific surface area, and vast tunable chemical and physical property. Air pollution resulting from various toxic gases, volatile organic compounds, and fine particulate matters has posed a big threat to human health and ecological sustainability. In recent years, various MOFs are studied and shown great potential as active materials for air pollution control in both pollutant detection and air quality remediation. In this review, the most recent research progress for MOFs‐based materials for air pollution control is summarized. The discussion is categorized based on the different approaches for air quality control with a focus on the performance correlations to the structure and functionality of MOFs. In addition, the technical merits and downsides of each approach are discussed based on their application scenarios. Finally, the remaining challenges and future research directions in this field are proposed. [ABSTRACT FROM AUTHOR]

Background: Air quality health index (AQHI), as a developed air quality risk communication tool, has been proved to be more accurate in predicting air quality related health risks than air quality index (AQI) by previous studies. However, the standard method to construct AQHI is summing the excess risks of single-pollutant models directly, which may ignore the joint effect of air pollutant mixtures. Methods: In this study, a new method which could solve the aforementioned problem, Analytic hierarchy process (AHP), was introduced. Based on this method, we constructed the respiratory health related AQHI using years of life lost (YLL) as indicator of health outcome and compared its validity with AQI. Results: There was a correlation between daily AQI and AQHI in 2019 (R2 = 0.830, P < 0.01), and the chi-square test between the two excellent rates showed a statistically significant difference (χ2 = 4.156, P < 0.05). Both AQI and AQHI were correlated with the daily respiratory YLL (P < 0.01), however, the coefficient of AQHI was larger than those of AQI. Conclusions: This study indicated that compared with AQI, the constructed AQHI based on AHP may predict the health risk of air pollution more effectively. AHP may become a new method to construct AQHI which needs to be proved by taking into consideration by more studies. [ABSTRACT FROM AUTHOR]

Background: Previous investigations identified a connection between air pollution and kidney diseases. Nevertheless, there is a lack of comprehensive evidence on the long-term risks posed by air pollution with respect to acute kidney injury (AKI) and AKI-related death. Methods: This prospective cohort analysis included 414,885 UK Biobank (UKB) participants who did not exhibit AKI at the study's outset. AKI was defined based on ICD-10 codes recorded for hospitalized patients. Cox proportional hazards models were used to assess the association between prolonged exposure to air pollutants (particulate matter with diameters of 2.5 micrometers or less (PM2.5), between 2.5 and 10 micrometers (PM2.5−10), and 10 micrometers or less (PM10), along with nitrogen dioxide (NO2) and nitrogen oxides (NOx)) and the risk of AKI and AKI-related death, adjusting for potential confounders including sex, age, ethnicity, education, income, lifestyle factors, and relevant clinical covariates. Restricted cubic splines were applied to evaluate non-linear dose-response relationships, and stratified analyses were performed to explore potential effect modification across subgroups. Results: Over an average follow-up duration of 11.7 years, 14,983 cases of AKI and 326 cases of AKI-related death were diagnosed. Quartile analysis showed individuals exposed to higher levels of these air pollutants had a significantly higher risk of developing AKI and AKI-related death compared to those in the lowest quartile (all P < 0.05). The RCS curves depicting the relationship between PM2.5, PM2.5−10, PM10, NO2, NOx, and the risk of AKI showed a significant departure from linearity (P for non−linearity < 0.05), while the relationships between PM2.5, NO2, NOx, and the risk of AKI-related death did not exhibit a significant departure from linearity (P for non−linearity > 0.05). Sensitivity analyses confirmed the robustness of our findings. Conclusion: Our study reveals a direct association between prolonged air pollution exposure and elevated risks of both AKI and AKI-related death. These findings offer scientific validation for the adoption of environmental and public health measures directed towards the reduction of air pollution. Such initiatives could potentially ease the impact associated with AKI and AKI-related death. [ABSTRACT FROM AUTHOR]

Fires are a significant disturbance in Earth's systems. Smoke aerosols emitted from fires can cause environmental degradation and climatic perturbations, leading to exacerbated air pollution and posing hazards to public health. However, research on the climatic and health impacts of fire emissions is severely limited by the scarcity of air pollution data directly attributed to these emissions. Here, we develop a global daily fire-sourced PM2.5 concentration ([PM2.5]) dataset at a spatial resolution of 0.25° for the period 2000–2023, using the GEOS-Chem chemical transport model driven with two fire emission inventories, the Global Fire Emissions Database version 4.1 with small fires (GFED4.1s) and the Quick Fire Emission Dataset version 2.5r1 (QFED2.5). Simulated all-source [PM2.5] are bias-corrected using a machine learning algorithm, which incorporates ground observations from over 9000 monitoring sites worldwide. Then the simulated ratios between fire- and all-source [PM2.5] at individual grids are applied to derive fire-sourced [PM2.5]. Globally, the average fire-sourced [PM2.5] is estimated to be 1.94 μg m-3 with GFED4.1s and 3.74 μg m-3 with QFED2.5. Both datasets show consistent spatial distributions with regional hotspots in central Africa and widespread decreasing trends over most areas. While the mean levels of fire-sourced [PM2.5] are much larger at low latitudes, fire episodes at the boreal regions can cause comparable PM2.5 levels as in the tropics. This dataset serves as a valuable tool for exploring the impacts of fire-related air pollutants on climate, ecosystems, and public health, enabling accurate assessments and supports for decision-making in environmental management and policy. [ABSTRACT FROM AUTHOR]

Evidence regarding the cognitive effects of air pollution is inconsistent. This study aimed to quantitatively analyze the relationship between air pollutants and cognitive abilities using the Mini-Mental State Examination (MMSE). We systematically searched for studies published until July 1, 2023, and assessed heterogeneity with Cochran’s Q test and I² statistics, while publication bias was evaluated using Funnel plots and Egger’s test. Out of 380 studies, 17 were included in the meta-analysis. Results demonstrated significant relationship among long-term exposure to PM2.5, O3, and cognitive decline. The pooled RR of cognitive impairment per 10μg/m3 increase were 1.17 for PM2.5 and 1.07 for PM10. Furthermore, there was a significant association between cognitive decline and long-term exposure to PM2.5 (β = -0.30; I2 = 95.30%) and PM10 (β = -0.15; I2 = 87.50%). Our research suggests that long-term exposure to air pollution, especially PM2.5, is a risk factor for cognitive impairment. [ABSTRACT FROM AUTHOR]

Air pollution has become a major health concern, particularly for vulnerable populations such as the elderly, children, and pregnant women. Studies have reported a strong association between prenatal exposure to air pollutants and adverse pregnancy outcomes, including lower birth weight, reduced fetal growth, and an increased frequency of preterm births. This review summarizes the harmful effects of air pollutants, such as particulate matter, on pregnancy and outlines the mechanistic details associated with these adverse outcomes. Particulate pollutant matter may be able to cross the placenta barrier, and alterations in placental functions are central to the detrimental effects of these pollutants. In addition to associations with preeclampsia and gestational hypertension, air pollutants also induce oxidative stress, inflammation, and epigenetic alteration in the placenta. These pollutants can also affect placental homeostasis and endocrine function, contributing to pregnancy complications and possible transgenerational effects. Prenatal air pollution exposure has been linked to reduced cognitive and motor function in infants and newborns, increasing the predisposition to autism spectrum disorders and other neuropsychiatric disorders. This review also summarizes the use of various animal models to study the harmful effects of air pollution on pregnancy and postnatal outcomes. These findings provide valuable insight into the molecular events associated with the process and can aid in risk mitigation and adopting safety measures. Implementing effective environmental protocols and taking appropriate steps may reduce the global disease burden, particularly for developing nations with poor regulatory compliance and large populations of pregnant women. [ABSTRACT FROM AUTHOR]

Background: Over the past two decades, the incidence of thyroid disorders has been steadily increasing. There is evidence to suggest that air pollution may be one of the etiological factors of thyroid diseases. This comprehensive review aimed to examine the evidence related to air pollutants and thyroid disorders and thyroid hormones levels from an epidemiological perspective. Methods: The scoping review adopted a systematic approach to search for, identify, and include peer-reviewed articles published in English. We performed a comprehensive search of three databases-PubMed, Embase, and Web of Science to identify relevant literature on the relationship between air pollution [particulate matter, nitrogen oxide, carbon monoxide (CO), ozone (O3), sulfur dioxide (SO2)] exposure and thyroid disorders, including hypothyroidism, congenital hypothyroidism (CH), thyroid nodules, thyroid cancer, autoimmune thyroid diseases, as well as thyroid hormone levels, such as thyroid-stimulating hormone (TSH), free triiodothyronine (FT3), and free thyroxine (FT4). Articles published until August 1, 2023, were included. Results: A total of 3,373 studies were retrieved, and among them, 25 studies covering eight different air pollutants were relevant. The most frequently studied air pollutants in this review included fine particulate matter (with fine particulate matter (PM2.5), n=21; inhalable particles (PM10), n=10; PM10-2.5, n=1) and nitrogen oxides (with NO2, n=13; NOx, n=3). The thyroid disorders and thyroid hormone levels most commonly associated with evidence of air pollution exposure were hypothyroidism (n=7) and TSH (n=12). Conclusions: Despite variations in study designs and exposure assessments, the findings consistently highlight the substantial health risks that air pollution, particularly PM2.5, poses to thyroid health, especially among vulnerable populations. Given that our study was limited to epidemiological investigations and the increasing prevalence of toxic substances in the environment, there is an urgent need for further research to elucidate the mechanisms by which these pollutants disrupt thyroid function and contribute to the development of thyroid diseases. [ABSTRACT FROM AUTHOR]

Background: Air pollution may exacerbate diabetes-related indicators; however, the longitudinal associations between air pollutant concentrations and glycemic markers remain unclear. In this prospective cohort study, we examined the longitudinal associations between air pollution and glycemic indicators among older individuals with normoglycemia at baseline and determined whether these associations differed according to changes in physical activity levels. Methods: Overall, 1,856 participants (mean age, 70.9 years) underwent baseline and 4-year follow-up surveys. We used linear mixed-effect models to examine the associations between previous 1-year exposures to air pollutants and glycemic indicators. We further investigated associations between previous 5-year exposures to air pollutants and glycemic indicators after the inverse probability of treatment weighting (IPTW). We explored effect modifications by the level of physical activity maintenance and changes in metabolic equivalent of task (METs) for physical activity. Results: Levels of particulate matter with aerodynamic diameters ≤ 10 μm (PM10) and ≤ 2.5 μm, and nitrogen dioxide (NO2) were significantly associated with increased fasting blood glucose, Hemoglobin A1c, insulin, and homeostatic model assessment for insulin resistance (HOMA-IR) values. After IPTW, the associations remained significant for PM10 and NO2. The positive associations of NO2 with insulin and HOMA-IR remained significant in the maintained inactive group, but not in the maintained moderate-to-vigorous active group. The positive associations of PM10 or NO2 with insulin and HOMA-IR remained significant in the group with increased METs, but not in those with decreased METs. In the post-hoc analysis of non-linear relationships between an increase in METs and glycemic indicators, insulin and HOMA-IR remarkably increased in the higher PM10 and NO2 exposure group from the point of 12,000 and 13,500 METs-min/week increase, respectively. Conclusions: We demonstrated longitudinal associations between air pollution exposures and increased insulin resistance in older individuals. Maintaining moderate-to-vigorous physical activity may mitigate the adverse effects of air pollution on insulin resistance. In older individuals dwelling in highly polluted areas, an increase of less than 12,000 METs-min/week may be beneficial for insulin resistance. Highlights: Why did we undertake this study? Air pollution exposures may increase the risk of diabetes mellitus; however, the impacts of changes in physical activity on the associations between air pollution exposures and glycemic indicators remained unclear. What is the specific question(s) we wanted to answer? We investigated the associations of previous 1-year and 5-year air pollution exposures with glycemic indicators in older individuals with normoglycemia and explored the potential impacts of changes in physical activity levels on the associations. What did we find? Previous 1-year exposures to PM10 and NO2 were associated with elevated fasting blood glucose, Hemoglobin A1c, insulin, and insulin resistance. Previous 5-year exposures to PM10 and NO2 were associated with increased insulin resistance. The adverse effects of NO2 existed in those who maintained inactive but not in those with maintained moderate-to-vigorous activity. What are the implications of our findings? Our findings underscore the detrimental impact of air pollution on glycemic health and emphasize that maintaining moderate to vigorous physical activity may mitigate the adverse effects of air pollution on insulin resistance in older individuals. [ABSTRACT FROM AUTHOR]

The far-UVC (222 nm) system has emerged as a solution for controlling airborne transmission, yet its effect on indoor air quality, particularly concerning positioning, remains understudied. In this study, we examined the impact of far-UVC lamp position on the disinfection and secondary contaminant formation in a small office. We employed a three-dimensional computational fluid dynamics (CFD) model to integrate UV intensity fields formed by different lamp positions (ceiling-mounted, wall-mounted, and stand-alone types) along with the air quality model. Our findings reveal that the ceiling-mounted type reduces human exposure to airborne pathogens by up to 80% compared to scenarios without far-UVC. For all the lamp positions, O3 concentration in the breathing zone increases by 4–6 ppb after one hour of operation. However, it should be noted that a high concentration zone (> 25 ppb) forms near the lamp when it is turned on. Moreover, ventilation plays a crucial role in determining human exposure to airborne pathogens and secondary contaminants. Increasing the ventilation rate from 0.7 h−1 to 4 h−1 reduces airborne pathogen and secondary contaminant concentrations by up to 90%. However, caution is warranted as higher ventilation rates can lead to elevated O3 indoors, especially under conditions of high outdoor O3 concentrations. [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]

Ground level ozone (O3) is a highly oxidising gas with very reactive properties, harmful at high levels, generated by complex photochemical reactions when 'primary' pollutants from combustion of fossil materials react with sunlight. Thus, its concentration serves as an indicator of the activity of other air pollutants and plays a key role in Air Quality monitoring systems in smart cities. To increase its spatial sampling resolution over the city map, ozone low cost sensors are an interesting alternative, but they have a lack of accuracy. In this context, artificial intelligence techniques, in particular ensemble machine learning methods, can improve the raw readings from these sensors taking into account additional environmental information. In this paper, we analyse, propose and compare different techniques, reducing the estimation error in around 94 %, achieving the best results using the Gradient Boosting algorithm and outperforming the related work using sensor approximately 10 times less expensive. [ABSTRACT FROM AUTHOR]

The interaction between physical activity (PA) and ambient particulate matters (PMs) on cognition is rarely investigated. Our study aimed to assess the interactions of PA and PMs on cognitive function in older adults. Our study comprised 3937 Chinese community-dwelling older adults. Cognition was evaluated using the Mini-Mental State Examination. PA information was gathered using the International Physical Activity Questionnaire. The data of PMs were obtained from China High Air Pollutants (CHAP). Linear regressions model and interaction plots were applied to assess and visualize the interaction of PA and PMs on cognition, respectively. Bayesian kernel machine regression (BKMR) method was employed to visualize discernible thresholds for the interaction. PMs were negatively associated with MMSE scores (PM 1 : β = −0.40, 95 % CI: −0.58, −0.28; PM 2.5 : β = −0.46, 95 % CI: −0.64, −0.29; PM 10 : β = −0.44, 95 % CI: −0.61, −0.26), and PA was positively affiliated with MMSE scores (β = 0.18, 95 % CI: −0.01, 0.38). Interaction plots and BKMR demonstrated that adverse connotations of PMs with MMSE increased with the elevated PA levels, and the positive associations of PA with MMSE scores were attenuated by increased PMs (all P interaction < 0.20). Discernible thresholds for the interaction between PMs and PA on MMSE were found. Our findings suggest that PA should not be taken at higher PMs concentrations, and that low level of PA could be performed in PMs polluted environment to improve cognitive function. Further experimental and cohort researches are required to reproduce our discovery. [Display omitted] • Physical activity showed a positive association with cognitive function. • Particulate matters showed negative associations with cognitive function. • Particulate matters attenuated physical activity's cognitive benefit. • Physical activity enhanced particulate matters' cognitive effect. • Our study showed discernible thresholds for the interaction above. [ABSTRACT FROM AUTHOR]

Abstract\nNOVELTY STATEMENTThis 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.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]

With the rise of healthy lifestyles, sports arenas have become important exercise venues for people. However, indoor air quality issues are becoming increasingly prominent, posing a potential threat to the health of athletes. Traditional indoor air quality monitoring systems often suffer from problems such as unreasonable layout of monitoring points and inaccurate data processing, making it difficult to effectively cope with complex indoor environments. This article proposes an intelligent monitoring system for indoor air quality in sports venues based on an improved cohesive hierarchical clustering algorithm to address these issues. The system optimizes the layout of monitoring points, combines real-time data collection on the Android platform, and accurately processes non smooth data using an improved cohesive hierarchical clustering algorithm based on BiLSTM. The experimental results show that the algorithm not only improves the accuracy of monitoring point selection, but also optimizes the number of monitoring points and effectively supplements missing data. In addition, the algorithm significantly reduces computational complexity and improves computational efficiency while ensuring clustering quality. Application testing shows that the indoor air environment monitoring system constructed in this article can obtain and analyze data in real time, effectively control the concentration of key pollutants such as PM2.5 and carbon dioxide, and create a healthier sports environment for sports venues. This study provides new ideas and methods for indoor air quality monitoring, which has important practical application value. [ABSTRACT FROM AUTHOR]

Long-range transport (LRT) of air pollutants from a range of sources can substantially enhance background pollution levels, especially in urbanized regions, which can exacerbate high-pollution episodes. In the Aburrá Valley (AV), Colombia, and other cities in northern South America, biomass burning (BB), dust, and volcanic degassing have been identified as sources of long-range aerosol transport. However, the impact of these sources on air quality and the characterization of these sources have yet to be thoroughly studied. This work investigates the influence of these sources on the chemical composition of PM 2.5 during annual and intra-annual high-load aerosol events in the AV. We identified, tracked, and meteorologically characterized LRT events and evaluated their influence on PM 2.5 concentration and chemical composition. We found that the LRT of aerosols from BB, dust, and volcanic degassing influenced approximately 13 %, 8 %, and 13 % of days in the year, respectively. We applied the positive-matrix-factorization (PMF) statistical model to quantify PM 2.5 concentrations and chemical compositions for the different LRT event types (e.g., BB). For BB events, we identified large contributions from organic carbon (OC1 and OC2), F- , and secondary aerosol tracers (SO42- and NO3-). For dust LRT events, crustal mineral components, along with Ti and Ca, were the primary contributors to aerosol composition, while SO42- , Na, Al, and Ca were the primary contributors during volcanic events. The concentrations of some ions and toxic heavy metals (Cr, Mn, Cd, and Ni) were also elevated during BB and volcanic-degassing events. BB contributed the most to PM 2.5 levels during the LRT events (∼11 µgm-3), while contributions from aerosols arising from dust and volcanic events were also substantial (<7 µgm-3). Our study identifies the Orinoco and the Middle Magdalena Valley as sizable sources of BB aerosols and Nevado del Ruiz as a source of volcanic aerosols. Additionally, we found that African dust reached the Andean region via the Caribbean route. As a result, we identify the need for future chemical-transport modeling studies in the region and new support strategies to manage internal and external pollution sources that degrade air quality in the AV and the surrounding region. [ABSTRACT FROM AUTHOR]

In the field of conservation of cultural heritage, one must always consider the environmental conditions in which the works of art are located and the level of atmospheric pollution to which they are exposed, especially in the case of monuments stored outdoors. The present study is focused on the detection and the quantification of polycyclic aromatic hydrocarbons (PAHs) in black crust samples from the Monumental Cemetery of Milan (Italy), and the assessment of their sources through the analysis of the distributions of the different compounds in the samples, together with the use of diagnostic ratios. Six black crust samples taken from funerary monuments were analyzed. Fourteen polycyclic aromatic hydrocarbons were identified (naphthalene, acenaphthylene, acenaphthene, fluorene, phenanthrene, anthracene, fluoranthene, pyrene, chrysene, benzo[a]anthracene, benzo[b]fluoranthene, benzo[k]fluoranthene, benzo[a]pyrene, indeno[1,2,3-cd]pyrene) by high-performance liquid chromatography with a diode-array detector (HPLC–DAD), with a total concentration from 0.72 to 3.81 μg/g (mean of 1.87 μg/g). The known carcinogenic benzo[a]pyrene accounted for 5–10% of the total polycyclic aromatic hydrocarbons in the samples analyzed, with concentrations up to 0.20 μg/g. Moreover, the study of the distribution and diagnostic ratios allowed us to confirm that anthropogenic sources such as traffic and the proximity of the train station are the major causes of the degradation of the monuments contained in this Cemetery. [ABSTRACT FROM AUTHOR]

The nature of smart factories to help manufacturing firms reducing environmental emissions has attracted the widespread attention of governments and industries. However, some research also worried that if smart factories were not effectively constructed, they may increase firms' environmental emissions. To address this concern, we use PSM-progressive DID model to analyze the relationships between the construction of smart factories and environmental emissions, based on 144 Chinese listed manufacturing firms. The main findings are as follow. First, the construction of smart factories can lead to the short-term increase of 7.55% GHG emissions (1.001 tCO2e) and 4.12% air pollutants cost (1.011 $) per $M operation cost for firms. Second, the negative impact of smart factory construction on GHG emissions can be partially explained by physical technologies. Third, mimetic institution (industrial maturity of environment management system) can reduce the negative impact of smart factory construction, but coercive institution (government regulation) and normative institution (social media attention) have no significant moderating effect. With these findings, this study provides a clear understanding of how the construction of smart factories influences firms' environmental sustainability and accordingly offers insights for business considering environmental objectives in smart factory development. [ABSTRACT FROM AUTHOR]

Air is an essential human necessity, and inhaling filthy air poses a significant health risk. One of the most severe hazards to people's health is air pollution, and appropriate precautions should be taken to monitor and anticipate its quality in advance. Among all the countries, the air quality in India is decreasing daily, which is a matter of concern to the health department. Many studies use machine learning and Deep learning methods to predict atmospheric pollutant levels, prioritizing accuracy over interpretability. Many research studies confuse researchers and readers about how to proceed with further research. This paper aims to give every detail of the considered air pollutants and brief about the techniques used, their advantages, and challenges faced during pollutant prediction, which leads to a better understanding of the techniques before starting any research related to air pollutant prediction. This paper has given numerous prospective questions on air pollution that piqued the study's interest. This study discussed various machine and deep learning methods and optimization techniques. Despite all the discussed machine learning and deep learning techniques, the paper concluded that more datasets, better learning techniques, and a variety of suggestions would enhance interpretability while maintaining high accuracy for air pollution prediction. The purpose of this review is also to reveal how a family of neural network algorithms has helped researchers across the globe to predict air pollutant(s). [ABSTRACT FROM AUTHOR]

Air is one of the most significant elements of the environment. The increasing global air pollution crisis poses an unavoidable threat to human health, environmental sustainability, ecosystems, and the earth's climate. Air pollution has been referred to as a silent killer due to its insidious nature. Its indirect impact on human health further underscores its dangerous effects. Early detection of air quality can potentially save millions of lives globally. A unique and transformative approach can harness the power of machine learning to combat air pollution. This research presents a manual and web-based automatic prediction system that provides real-time alerts on air quality status and can help prevent premature deaths, chronic diseases, and other health problems. Air pollutants, including carbon monoxide (CO), ozone (O3), nitrogen dioxide (NO2), and particulate matter (PM 2.5), are used in this study for feature analysis and extraction. The system utilizes publicly available data from 23,463 different cities worldwide. Data preprocessing was performed before feeding the data into the machine learning models for feature correlation and evaluation. The proposed research uses various machine learning models to predict air quality, including Random Forest (100%), Logistic Regression (79%), Decision Tree (100%), Support Vector Machine (93%), Linear SVC (98%), K-Nearest Neighbor (99%), and Multinomial Naïve Bayes (52%). A user-friendly Django-based web interface offers an accessible platform for users to monitor air quality in real-time, based on the two best-performing models: Random Forest and Decision Tree techniques. [ABSTRACT FROM AUTHOR]

Road traffic is an important source of urban air pollutants. Due to increasingly strict controls of exhaust emissions from road traffic, their contribution to the total emissions has strongly decreased over time in high-income countries. In contrast, non-exhaust emissions from road vehicles are not yet legislated and now make up the major proportion of road traffic emissions in many countries. Brake wear, which occurs due to friction between brake linings and their rotating counterpart, is one of the main non-exhaust sources contributing to particle emissions. Since the focus of brake wear emission has largely been on particulate pollutants, little is currently known about gaseous emissions such as volatile organic compounds from braking and their fate in the atmosphere. This study investigates the oxidative ageing of gaseous brake wear emissions generated with a pin-on-disc tribometer, using an oxidation flow reactor. The results demonstrate, for the first time, that the photooxidation of gaseous brake wear emissions can lead to formation of secondary particulate matter, which could amplify the environmental impact of brake wear emissions. [ABSTRACT FROM AUTHOR]

In this study, the emissions of criteria pollutants and greenhouse gases from mobile sources in the main streets of the city of Valledupar were estimated. A vehicular characterization of the streets studied was carried out, and the IVE model was implemented to estimate the emissions generated by automobiles; subsequently, scenarios of atmospheric transport routes of pollutants were analyzed with the HYSPLIT model. The results showed that in the case of pollutant criteria, motorcycles emitted the highest amount of CO and PM10, and private cars the highest SOx and NOx emissions. For GHGs, private cars emitted the highest amount of CO2 and N2O, while motorcycles emitted the highest amount of CH4. The trajectory scenarios for the days of the study were carried out during the peak hours of vehicular circulation, implementing meteorological data from the WRF model. The trajectories were evaluated in rainy periods of 2021 and 2022 and in the dry period of 2022. In the rainy period, it was observed that the transport routes of the pollutants were directed in a greater proportion towards the NE direction and the dry period towards the SE direction. With this, it was possible to determine which areas of the city could be the most affected by vehicle emissions from the roads studied. [ABSTRACT FROM AUTHOR]

Atopic dermatitis (AD) is a common chronic inflammatory skin disease characterized by dry skin, recurrent itching, and eczema-like lesions, which can significantly reduce the quality of life. In recent years, the global prevalence of AD has been increasing, with a particularly marked rise in China. Research has shown that air pollutants can damage the skin barrier through mechanisms like oxidative stress, thereby increasing the risk of AD. Various exposure factors, including diet and allergens, also significantly influence the onset and progression of AD through different mechanisms. Furthermore, psychological factors such as anxiety and stress can exacerbate AD through neuroendocrine regulation. Obesity is closely associated with AD, particularly among children and adolescents. Given the diversity of factors and mechanisms influencing AD, a comprehensive approach to primary prevention that considers multiple factors is warranted. [ABSTRACT FROM AUTHOR]

Self-similar and generally scaling laws are pointed out in time series issued from different types of data. The analysis of these structures has been conducted via many advanced mathematical tools such as wavelets. In this paper, we propose to exploit some self-similar type models for the modeling of time series issued from air quality and/or pollution data, by using wavelet multifractal techniques. The applied models are shown to involve self-similar, multi-scaling and also noised structures. The resulting models are applied empirically on a sample of data issued from air pollution factors in the northwestern region of Tabuk governorate in Saudi Arabia. Some of the chemicals and materials are essential components of many air pollution factors such as PM10 and PM2.5 particles. [ABSTRACT FROM AUTHOR]

Black carbon (BC) is a crucial air pollutant that contributes to short-lived climate forcing and adverse health impacts. BC emissions have rapidly declined over the past three decades and it is important to uncover the major factors behind this decline. Herein, the temporal trends in BC emissions were compiled from 146 detailed sources from 1960 to 2019. Results revealed that the major emission sources were residential solid fuel usage, coke production and brick production. Furthermore, 96.9% of the emission reduction from 3.03 Tg in 1995 to 1.02 Tg in 2019 was attributed to these three sources. It was determined that the transition in residential energy/stove usage, phasing-out of beehive coke ovens and brick kiln upgrading were the most important drivers leading to this reduction and will continue to play a key role in future emission mitigation. In addition, this study identified the need to address emissions from coal used in vegetable greenhouses and the commercial sector, and diesel consumption in on/off-road vehicles. [ABSTRACT FROM AUTHOR]

The purpose of this study is to investigate the effect of air pollutants and noise on the prevalence of childhood asthma in Tehran, Iran. The standardized questionnaire was completed by one of the parents of children aged 6–7 years or by adolescents aged 13–14 years. The asthma prevalence in ages 6-7 and 13–14 was found to be 8.8% and 17.44%, respectively. A significant positive association was observed between “ever wheezing” and monoxide carbon (CO) concentration (OR=1.84, 1.05-3.25 in 13–14 years), the occurrence of 4 to 12 wheezing attacks and sulfur dioxide (SO2) concentration (Odds Ratio [OR]=1.39, 1.04-1.91) and particulate matter less than 2.5 micron (PM2.5) concentration (OR=1.38, 1.05-1.98 and OR=1.13, 0.98-1.39 in 6-7 and 13–14 years, respectively), as well as one night per week of sleep disturbances and nitrogen dioxide (NO2) concentration (OR=1.09, 1.03-1.16 in 6–7 years, respectively). It was also found that there was a significant interaction between the noise level and particulate matter less than 10 microns (PM10) level. Based on the findings, exposure to certain outdoor air pollutants and noise can affect the prevalence of asthma symptoms in residents of Tehran. The simultaneous presence of air pollutants and noise has an aggravating effect on the prevalence of asthma symptoms. Therefore, controlling sources of pollutants for reducing asthma symptoms is suggested. [ABSTRACT FROM AUTHOR]

The study was conducted on ambient ozone (O3), the most phyto-toxic air pollutant, and its effects on the growth and quality of okra (Abelmoschus esculentus) and peas (Pisum sativaum) grown in Northern Pakistan during the summer and winter of 2018. Okra was subjected to ambient O3 levels ranging from 43 to 63 ppb during the summer, with a mean O3 concentration of 55 ppb, while peas experienced lower winter concentrations of 15–25 ppb, with a mean O3 concentration of 19 ppb. The results indicated significant impacts on the growth and nutritional quality of crops, especially okra. Anti-ozonant ethylene diurea (EDU) was used for soil drenching to protect okra and green peas from O3 damage. Okra showed notable enhancements of 20%, 20%, 29%, and 13% in ash, protein, fiber, and non-fiber carbohydrates (NFE), respectively. Increase in plant height, leaf numbers, pod length, and dry weight was observed in EDU-treated okra plants. Conversely, peas exhibited less variation, although melioration was observed in plant height, pod numbers, length, and weight with EDU treatment. It was concluded that the concentration of ambient O3 in Peshawar is toxic enough to cause significant damage to crop growth and production. The stark difference in O3 impact during different seasons suggests that higher summer concentrations could severely compromise crop quality. This elicits significant concerns regarding food security in South Asia, especially for summer crops that can jeopardize future food security. It is recommended that further research be conducted on the effects of O3 on other regional crops to assess fully its implications for agricultural sustainability in the area. [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]

The rate of rocket launches is accelerating, driven by the rapid global development of the space industry. Rocket launches emit gases and particulates into the stratosphere, where they impact the ozone layer via radiative and chemical processes. We create a three‐dimensional per‐vehicle inventory of stratospheric emissions, accounting for flight profiles and all major fuel types in active use (solid, kerosene, cryogenic and hypergolic). In 2019, stratospheric (15–50 km) rocket launch emissions were 5.82 Gg CO2 ${\mathrm{C}\mathrm{O}}_{2}$, 6.38 Gg H2 ${\mathrm{H}}_{2}$O, 0.28 Gg black carbon, 0.22 Gg nitrogen oxides, 0.50 Gg reactive chlorine and 0.91 Gg particulate alumina. The geographic locations of launch sites are preserved in the inventory, which covers all active launch sites in 2019. We also report the emissions data from contemporary vehicles that were not launched in 2019, so that users have freedom to construct their own launch activity scenarios. A subset of the inventory—stratospheric emissions for successful launches in 2019—is freely available and formatted for direct use in global chemistry‐climate or Earth system models. Plain Language Summary: Many governments and companies have expressed bold ambitions to grow their presence in space. However, rocket launches throw out a stream of air pollutants from their burnt fuel as they pass through the stratosphere, which is where the protective ozone layer resides. Currently, launch operators do not have to measure the impacts of their activities on the ozone layer. We gather together all the publicly available information on rocket launches in 2019, from 18 active spaceports worldwide, and make some careful assumptions to convert each rocket's fuel to its burnt fuel products left in the atmosphere. We encourage modeling groups to use our inventory for studies on how rocket launches may impact the ozone layer. Key Points: We compile a comprehensive emissions inventory of all rocket launches in 2019 at 18 active spaceportsIt itemizes chemically and radiatively active species that are produced by the main rocket fuels (kerosene, cryogenic, solid and hypergolic)We discuss the inventory's uncertainties and its usage in global models to study the impacts of rocket launches on the ozone layer [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]

The Comprehensive Air Quality Model with extensions (CAMx) is an open-source, state-of-the-science photochemical grid model that addresses tropospheric air pollution (ozone, particulates, air toxics) over spatial scales ranging from neighborhoods to continents. CAMx has been in continuous development for over 25 years and has been used by numerous entities ranging from government to industry to academia to support regulatory actions and scientific research addressing a variety of air quality issues. Here, we describe the technical formulation of CAMx v7.20, publicly released in May 2022. To illustrate an example of regional and seasonal model performance for predicted ozone and fine particulate matter (PM2.5), we summarize a model evaluation from a recent 2016 national-scale CAMx application over nine climate zones contained within the conterminous US. We show that statistical performance for warm season maximum 8 h ozone is consistently within benchmark statistical criteria for bias, gross error, and correlation over all climate zones, and often near statistical goals. Statistical performance for 24 h PM2.5 and constituents fluctuate around statistical criteria with more seasonal and regional variability that can be attributed to different sources of uncertainty among PM2.5 species (e.g., weather influences, chemical treatments and interactions, emissions uncertainty, and ammonia treatments). We close with a mention of new features and capabilities that are planned for the next public releases of the model in 2024 and beyond. [ABSTRACT FROM AUTHOR]

Scarlet fever (SF) is an acute respiratory transmitted disease that primarily affects children. The influence of meteorological factors and air pollutants on SF in children has been proved, but the relevant evidence in Northwest China is still lacking. Based on the weekly reported cases of SF in children in Lanzhou, northwest China, from 2014 to 2018, we used geographical detectors, distributed lag nonlinear models (DLNM), and bivariate response models to explore the influence of meteorological factors and air pollutants with SF. It was found that ozone (O3), carbon monoxide (CO), sulfur dioxide (SO2), temperature, pressure, water vapor pressure and wind speed were significantly correlated with SF based on geographical detectors. With the median as reference, the influence of high temperature, low pressure and high pressure on SF has a risk effect (relative risk (RR) > 1), and under extreme conditions, the dangerous effect was still significant. High O3 had the strongest effect at a 6-week delay, with an RR of 5.43 (95%CI: 1.74,16.96). The risk effect of high SO2 was strongest in the week of exposure, and the maximum risk effect was 1.37 (95%CI: 1.08,1.73). The interactions showed synergistic effects between high temperatures and O3, high pressure and high SO2, high nitrogen dioxide (NO2) and high particulate matter with diameter of less than 10 μm (PM10), respectively. In conclusion, high temperature, pressure, high O3 and SO2 were the most important factors affecting the occurrence of SF in children, which will provide theoretical support for follow-up research and disease prevention policy formulation. [ABSTRACT FROM AUTHOR]