Search Results (2,690)

This study investigates the relationship between carbon dioxide (CO₂) emissions and inhalable particulate matter levels with a diameter of at most 2.5 micrometers (PM2.5) on neonatal mortality rates across 88 countries, building upon an extensive amount of literature highlighting the harmful effects of air pollution on health. We categorize the countries based on their levels of CO₂ emissions and PM2.5 pollution as high versus low emitters, showing a statistically significant disparity in the average neonatal mortality rates between countries with high and low levels of CO₂ and PM2.5. Further exploring the underlying factors influencing the neonatal mortality rate within each group with the help of regression analysis, we identified several significant socioeconomic, environmental, and health-related factors affecting the neonatal mortality rate. Our findings highlight urgent public health concerns toward the achievement of the Sustainable Development Goals, particularly SDG 3, at a country level that address health-related issues in varying contexts of environmental pollution. Full article

Particulate matter, particularly PM_2.5, poses a significant threat to public health due to its ability to disperse widely and its detrimental impact on the respiratory and circulatory systems upon inhalation. Consequently, it is imperative to maintain regular monitoring and assessment of particulate matter levels to anticipate air pollution events and promptly mitigate their adverse effects. However, predicting air quality is inherently complex, given the multitude of variables that influence it. Deep learning models, renowned for their ability to capture nonlinear relationships, offer a promising approach to address this challenge, with hybrid architectures demonstrating enhanced performance. This study aims to develop and evaluate a hybrid model integrating Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for forecasting PM_2.5 levels in India, Milan, and Frankfurt. A comparative analysis with established deep learning and machine learning techniques substantiates the superior predictive capabilities of the proposed CNN-RNN model. The findings underscore its potential as an effective tool for air quality prediction, with implications for informed decision-making and proactive intervention strategies to safeguard public health. Full article

The iron and steel industry is one of the main industrial contributors to air pollution. The aim of our study is to analyze modern studies on air pollution by the iron and steel industry, as a result of which the geography and research directions and the degree of development of current issues will be assessed, and the most cited articles and journals will be identified. A review of contemporary research (2018–2024) was conducted on the basis of articles with a digital object identifier (DOI) using machine learning methodologies (VOSviewer software version 1.6.20). The number of articles selected was 80. The heat map of study density clearly showed that the geographic distribution of studies was extremely uneven. A total of 65% of the studies were conducted in China, 9% in Nigeria, 6% in Russia, 3% in Poland, and 3% in Turkey. The remaining 14% of articles represent a series of single studies conducted in 11 countries. The revealed geographical imbalance between countries with developed production and the number of studies conducted in them shows a significant shortcoming in monitoring research. Most of the studies (20%) were devoted to the assessment of multicomponent emissions. A special place among them was occupied by the inventory of emissions using various methods. The next main directions in terms of the number of articles were aimed at studying the toxic metal emissions (19%), at the analysis of organic emissions (19%), at the modeling and forecasting of emissions (18%), and at particulate matter studies (15%). The main features of the articles for each direction are briefly noted. Citation analysis made it possible to compile a rating of articles of greatest scientific interest and the most authoritative journals. Citation network analysis revealed important insights into the structure of scientific communication in the monitoring of atmospheric pollution from the iron and steel industry. The results of our review will contribute to the consolidation of scientists, the identification of gaps in scientific knowledge, and the improvement of environmental policy and technological solutions. Full article

In the present investigation, carbon dioxide (CO₂), carbon monoxide (CO), hydrocarbons (HC), nitric oxides (NO_X), particulate matter (PM), and fuel consumption were measured in a compression ignition internal combustion engine on a road route cycle in Quito, Ecuador. We used premium diesel and a mixture of diesel and cerium oxide at a concentration of 250 ppm. This research aimed to investigate the impact of cerium oxide on fossil fuels in terms of CO₂, CO, HC, NOx, PM, and fuel consumption. Five repetitions were performed for each fuel, and the results obtained were statistically analyzed using control charts. The experimental results showed a 27.1% reduction in PM, a 24.9% increase in NOx, and a 24.2% increase in HC, along with a 1% decrease in fuel consumption compared to the premium diesel case. We observed that the reduction in PM was due to the catalytic action of CeO₂, which enhances carbon oxidation. On the other hand, the increase in NOx was related to the higher temperature in the combustion chamber resulting from the improved thermal efficiency of the engine. This study provides guidelines for controlling air pollutants originating from vehicle emissions in high-altitude (over 2000 masl) road operations using cerium oxide as an additive. Full article

Air pollution has a significant impact on the housing market, both in terms of property prices and buyer preferences, as well as urban development. Below, we present the main aspects of this impact. These may include a decline in property values in polluted areas, a change in buyer preferences (more buyers are taking environmental factors into account when choosing a home, including air quality—both outdoor and indoor—which translates into increased demand in ‘green’ neighborhoods), the development of energy-efficient and environmentally friendly buildings, the impact on spatial planning and urban policy, health effects, and the rental market. The study showed that air pollution has a significant negative impact on housing prices in Warsaw, particularly in relation to two pollutants: nitrogen dioxide (NO₂) and particulate matter (PM_2.5). As their concentrations decreased, housing prices increased, with the highest price sensitivity observed for smaller flats on the secondary market. The analysis used GRM and OLS statistical models, which confirmed the significance of the relationship between the concentrations of these pollutants and housing prices (per m²). NO₂ had a significant impact on prices in the primary market and on the largest flats in the secondary market, while PM_2.5 affected prices of smaller flats in the secondary market. No significant impact of other pollutants, meteorological factors, or their interaction on housing prices was detected. The study also showed that the primary and secondary markets differ significantly, requiring separate analyses. Attempts to combine them do not allow for the precise identification of key price-determining factors. Full article

PM_2.5 is an air pollutant that has a direct link to increased cardiovascular and respiratory morbidity and mortality, which has been demonstrated in numerous studies. Existing research highlights species-specific variations in the capacity of trees to capture and retain particulate matter (PM). However, a critical gap remains regarding sensitivity analyses of i-Tree Eco model assumptions. Such analyses are crucial for validating the model’s PM deposition estimates against empirically derived efficiencies, a deficiency that the present study addresses. The study consisted of two steps: a tree inventory was carried out at three selected sites, based on which, an ecosystem service analysis was performed using i-Tree Eco, and samples were taken from the leaves of trees at the analysed sites, which were the basis for comparing the data from the i-Tree Eco method and laboratory methods. The study focused on comparing PM_2.5 and PM₁₀ removal estimates derived from both the model and laboratory measurements. The results revealed significant discrepancies between the modelled and laboratory values. A comparison of the average annual PM₁₀ accumulation measured using laboratory methods for individual tree species showed that Tilia sp. achieved 24%, Fraxinus sp. 47.6%, Aesculus sp. 50.77%, and Quercus robur 23.4% of the PM₁₀ uptake efficiency estimated by the i-Tree Eco model. For PM_2.5 uptake, the values obtained through both methods were more consistent. Furthermore, trees growing under more challenging environmental conditions exhibited smaller diameter at breast height (DBH) and lower PM₁₀ and PM_2.5 removal efficiency according to both methods. While I-Tree Eco incorporates tree biophysical characteristics and health status, its methodology currently lacks the resolution to reflect site-specific environmental conditions and local pollutant concentrations at the individual tree level. Therefore, laboratory methods are indispensable for calibrating, validating, and supplementing i-Tree Eco estimates, especially when applied to diverse urban environments. Only the combined application of empirical and model-based methods provides a comprehensive understanding of the potential of urban greenery to improve air quality. Full article

The incidence of allergic diseases has increased notably in recent years. The reasons for this increase include air pollution, diet, and infectious factors. This study aims to analyze the interactions between aeroallergens, environmental pollutants, and meteorological factors and their impact on allergenic sensitization in Toledo, Spain. An aerobiological study was conducted over the past 30 years (1994–2023) using a Burkard collector and the SEAIC (Spanish Society of Allergology and Clinical Immunology) methodology. Meteorological data were obtained from the State Meteorological Agency (AEMET) and pollutant data were acquired from the Castilla-La Mancha Air Quality Monitoring Network. Patients presenting with seasonal allergic symptoms at the University Hospital of Toledo were selected for skin testing with various types of airborne pollen. A total of twenty pollen taxa were identified in the Toledo atmosphere, as follows: Cupressaceae (26.53%); Olea europaea (21.62%); Quercus (21.12%); Poaceae (10.30%); Urticaceae (2.58%); Plantago (2.48%); Platanus (2.00%); Amaranthaceae (1.72%); Rumex (1.68%); and Morus, Pistacia, Populus, Artemisia, Fraxinus, Alnus, Carex, and Ericaceae (less than 1% each). The average temperature increased by 1.2 °C, while the level of precipitation remained stable. Among all pollutants, only a moderate increase in ozone levels was observed; however, the concentrations of particulate matter and nitrogen oxides decreased. The prevalence of pollen sensitization in allergic patients ranged from 8% for Pinus nigra to 84% for Phleum pratense. In conclusion, the rise in temperature due to climate change, coupled with high concentrations of pollutants such as ozone, can result in increased concentrations of the main types of wind-borne pollen. Thus, this can lead to a greater sensitivity to pollen and, consequently, more people becoming allergic to pollen. Full article

This study presents real-time measurements of particulate matter (PM₁, PM_2.5, PM₁₀) and carbon dioxide (CO₂) concentrations across five university indoor environments with varying occupancy levels and natural ventilation conditions. CO₂ concentrations frequently exceeded the 1000 ppm guideline, with peak values reaching 3018 ppm and 2715 ppm in lecture spaces, whereas one workshop environment maintained levels well below limits (mean = 668 ppm). PM concentrations varied widely: PM₁₀ reached 541.5 µg/m³ in a carpeted amphitheater, significantly surpassing the 50 µg/m³ legal daily limit, while a well-ventilated classroom exhibited lower levels despite moderate occupancy (PM₁₀ max = 116.9 µg/m³). Elevated PM values were strongly associated with flooring type and occupant movement, not just activity type. Notably, window ventilation during breaks reduced CO₂ concentrations by up to 305 ppm (p < 1 × 10⁻⁴⁷) and PM₁₀ by over 20% in rooms with favorable layouts. These findings highlight the importance of ventilation strategy, spatial orientation, and surface materials in shaping indoor air quality. The study emphasizes the need for targeted, non-invasive interventions to reduce pollutant exposure in historic university buildings where mechanical ventilation upgrades are often restricted. Full article

Climate change and air pollution are associated with a range of health outcomes, including cardiovascular and respiratory disease. Evaluation of the synergic effects of air pollution and increasing natural temperature on mortality is important for understanding their potential joint health effects. In this study, the modification effects of air temperature on the short-term association of ambient fine particulate matter (PM_2.5) and ozone (O₃) with non-accidental death (NAD) and cardiovascular disease (CVD) mortality were evaluated by using the generalized additive model (GAM) combined with the distributed lag nonlinear model (DLNM) in urban areas of Taiyuan, a representative of energy and heavy industrial cities in Northern China. The data on the daily cause-specific death numbers, air pollutants concentrations, and meteorological factors were collected from January 2013 to December 2019, and the temperature was divided into low (<25th percentile), medium (25–75th percentile), and high (>75th percentile) categories. Significant associations of PM_2.5 and O₃ with NAD and CVD mortality were observed in single-effect analysis. A statistically significant increase in the effect estimates of PM_2.5 and O₃ on NAD and CVD mortality was also observed on high-temperature days. But the associations of those were not statistically significant on medium- and low-temperature days. At the same temperature level, the effects of PM_2.5 and O₃ on the CVD mortality were larger than those on NAD (1.74% vs. 1.21%; 1.67% vs. 0.57%), and the elderly and males appeared to be more vulnerable to both higher temperatures and air pollution. The results suggest that the acute effect of PM_2.5 and O₃ on NAD and CVD mortality in urban Taiyuan was enhanced by increasing temperatures, particularly for the elderly and males. It highlights the importance of reducing PM_2.5 and O₃ exposure in urban areas to reduce the public health burden under the situation of global warming. Full article

Preterm birth (PTB) is a major global public health concern with substantial impacts on neonatal morbidity and mortality. There is a growing body of evidence linking maternal exposure to fine particulate matter (PM_2.5) with PTB, and national birth cohort data from the Middle East remains sparse. We analyzed 3,839,531 singleton live births in Iran from 2013 to 2018. Monthly PM_2.5 concentrations during pregnancy were estimated using validated spatiotemporal models. Associations between prenatal PM_2.5 exposure and multiple PTB subtypes, moderate to late (MPTB), very (VPTB), and extremely preterm birth (EPTB), were assessed using multivariable logistic regression. A 10 μg/m³ increase in PM_2.5 was associated with increased odds of PTB (odds ratio [OR] = 1.048, 95% confidence interval [CI]: 1.044–1.051), MPTB (OR = 1.046, 95% CI: 1.042–1.049), VPTB (OR = 1.059, 95% CI: 1.048–1.070), and EPTB (OR = 1.064, 95% CI: 1.047–1.081), respectively. Age- and trimester-stratified analyses showed greater exposure-related risks among mothers aged 25–34 and during mid-pregnancy. We observed consistent evidence for a J-shaped exposure–risk pattern in overall and subgroup populations, suggesting a PM_2.5 threshold near 40 μg/m³. From 2013 to 2018, 6716 (95% CI: 5336–8678) PTB cases, representing 2.7% (95% CI: 2.2–3.5%) of total PTB, were attributable to PM_2.5 exposure exceeding the WHO first-stage interim target (IT1, 35 μg/m³). Our results suggested improved ambient PM_2.5 quality may substantially reduce PTB burden in Iran. Full article

Urban particulate matter (PM) pollution critically impacts public health and climate. However, traditional ground-based monitoring fails to resolve vertical PM distribution, limiting understanding of transport and stratification-coupled mechanisms. Vertical profiles collected by an unmanned aerial vehicle (UAV) over Hangzhou, a core megacity in China’s Yangtze River Delta, reveal the spatiotemporal heterogeneity and multi-scale drivers of regional PM pollution during two intensive ten-day campaigns capturing peak pollution scenarios (winter: 17–26 January 2019; summer: 21–30 August 2019). Results show stark seasonal differences: winter PM₁ and PM_2.5 averages were 2.6- and 2.7-fold higher (p < 0.0001) than summer. Diurnal patterns were bimodal in winter and unimodal (single valley) in summer. Vertically consistent PM₁ and PM_2.5 distributions featured sharp morning (08:00) concentration increases within specific layers (winter: 250–325 m; summer: 350–425 m). Analysis demonstrates multi-scale coupling of synoptic systems, boundary layer processes, and vertical wind structure governing pollution. Key mechanisms include a winter “Transport-Accumulation-Reactivation” cycle driven by cold air, and summer typhoon circulation influences. We identify hygroscopic growth triggered by inversion-high humidity coupling and sea-breeze-driven secondary aerosol formation. Leveraging UAV-based vertical profiling over Hangzhou, this study pioneers a three-dimensional dissection of layer-coupled PM dynamics in the Yangtze River Delta, offering a scalable paradigm for aerial–ground networks to achieve precision stratified control strategies in megacities. Full article

Background/Objectives: As older adults spend increasing amounts of time indoors, concerns are rising about the neurological effects of indoor air pollution. This study examined associations between indoor air pollutants and structural brain changes in community-dwelling older adults in Seoul and Incheon, South Korea. A purposive sample of 23 individuals aged ≥65 years was recruited. Internet of Things (IoT)-based devices were installed in participants’ homes to continuously monitor indoor concentrations of PM₁₀, PM_2.5, and CO₂ for over two months. All participants underwent 3T brain magnetic resonance imaging (MRI), and brain structure metrics were analyzed using multiple linear regression models with and without adjustment for estimated total intracranial volume (eTIV). Hierarchical clustering was also performed based on exposure and neuroanatomical characteristics. Brain MRI indicators included cortical surface area, cortical thickness in six regions, and volumes of seven subcortical structures including the hippocampus and amygdala. Higher CO₂ concentrations were significantly associated with lower hippocampal volumes in both hemispheres (left: −2.83, −0.88, −1.02 mm³; right: −3.29, −0.86, −0.99 mm³; p ≤ 0.05). Elevated PM_2.5 levels were associated with reduced bilateral amygdala volume (−283.24 mm³ left; −292.37 mm³ right) and right hippocampal volume (−544.55 mm³; p ≤ 0.05). Cluster analysis showed that, before eTIV adjustment, Group C exhibited the lowest subcortical volumes. After adjustment, Group A showed the smallest cortical surface area, and Group D had the lowest subcortical volumes. These findings suggest that indoor air pollutants, including PM₁₀, PM_2.5, and CO₂, may be associated with structural brain alterations in older adults, supporting the need for age-specific indoor air quality standards and residential monitoring systems. Full article

China’s divergent fine particulate matter (PM_2.5) and surface ozone (O₃) pollution trends pose critical threats to sustainable development. This study quantifies the spatiotemporal evolution of health burdens (premature deaths) and economic costs across 333 cities during 2015–2023, integrating the Global Exposure Mortality Model (for PM_2.5) and Log-linear Exposure-Response Model (for O₃) with income- and age-adjusted Value of Statistical Life. The results revealed an 11% decrease in PM_2.5-attributable premature deaths, but this benefit was partially offset (60%) by an 87% increase in O₃-related deaths. Furthermore, the per capita economic loss from O₃ exposure increased by 154%, far exceeding China’s 79% growth in per capita disposable income. Decomposition analysis revealed that while diverging exposure levels primarily drove differential PM_2.5- and O₃-related impacts, this disparity was significantly amplified by population aging. These findings underscore the need for air quality strategies to both sustain PM_2.5 reduction achievements and implement rigorous O₃ controls, while integrating pollution considerations into public health frameworks with special emphasis on protecting vulnerable populations. Full article

Particulate matter (PM), a complex mixture of solid particles and liquid droplets, originates from both natural sources, such as sand, pollen, and marine salts, and anthropogenic activities, including vehicle emissions and industrial processes. While PM itself is not inherently toxic in all its forms, it often acts as a carrier of xenobiotic toxicants, such as heavy metals and organic pollutants, which adhere to its surface. This combination can result in synergistic toxic effects, significantly enhancing the potential harm to biological systems. Due to its small size and composition, PM can penetrate deep into the respiratory tract, acting as a physical “shuttle” that facilitates the distribution and bioavailability of toxic substances to distant organs. The omnipresence of PM in the environment leads to unavoidable and constant exposure, contributing to increased morbidity and mortality rates, particularly among vulnerable populations like the elderly, children, and individuals with pre-existing health conditions. This exposure also imposes a substantial financial burden on healthcare systems, as treating PM-related illnesses requires significant medical resources and leads to higher healthcare costs. Addressing these challenges necessitates effective mitigation strategies, including reducing PM exposure, improving air quality, and exploring novel approaches such as AI-based exposure prediction and nutritional interventions to protect public health and minimize the adverse effects of PM pollution. Full article

Students and educators spend considerable time in indoor learning spaces on university campuses, where indoor air quality (IAQ), of which particulate matter (PM) is an important component, is a critical concern that architecture students must address. However, IAQ is seldom monitored and very rarely, if at all, reported in these spaces. We used a novel living lab approach to provide third-year students of building services with a hands-on learning activity. During a two-week monitoring period, students designed, assembled, and operated low-cost PM sensors using Arduino platforms. The data analysis showed hotspots where the IAQ was consistently compromised and showed repetitive patterns in time. Workshop and laboratory areas repeatedly recorded the highest PM levels in 15 min sampling events distributed over daily two-hour segments, averaging 43.3 and 47.9 μg/m³ PM₁₀, respectively, with maxima of 118.6 and 119.9 μg/m³ PM₁₀. These measurements would have qualified as ‘moderate’ IAQ if sustained over a full day. A distinct weekly pattern was discovered, with Mondays being worse. The results demonstrated a new practical approach to monitoring the building’s IAQ at minimal cost while obtaining reproducible data. This tool provided educators with a valuable teaching tool that provided students with a deeper understanding of indoor air pollution. Full article