Improve Air Quality Research Articles

'Incense is the one that keeps the air fresh': indoor air quality perceptions and attitudes towards health risk.

Air pollution is of significant environmental and public health concern globally. While much research has historically focused on outdoor air pollution, indoor air pollution has been relatively under-explored despite its strong connection with health outcomes, particularly respiratory health. Studies on air pollution exposure mitigation consistently reveal a significant knowledge gap between the understanding of air pollution as a health risk among lay individuals and expert scientists. This study aimed to assess how people define and understand the concept of 'clean air' within their home setting. We adopted a mixed-methods approach which used a guided questionnaire designed to elicit both quantitative and qualitative data, collected as digital voice notes. The total sample (n = 40) comprised data from two socially different sites of science and non-science events. We compared whether the notion of clean air inside homes differs between these two different social contexts and how views and 'sense' of indoor air pollution are formed. The concept of 'place' facilitated fluidity in our explorative analysis. Insights allowed us to assess the extent to which context mediates individuals' perceptions of indoor air pollution and attitudes towards health risk. We found that individuals' insights were embodied in repetitive day-to-day activities (e.g. cleaning and cooking). Three key themes emerged (1) Stimulative Effects, (2) Contextual Conditions, and (3) Risk Attitudes. Sensory perceptions such as sight, smell and temperature primarily motivated participants to assess air quality inside their homes. These perceptions were shaped by contextual conditions, influencing how individuals perceived their health risk and were subsequently motivated to spend personal time considering or seeking information about household air pollution, or improving their home air quality. Our insights revealed that social, geographical, and contextual factors play a crucial role in individuals' understandings of indoor air pollution. These dimensions should be integrated into designs of effective public health risk communication strategies. Our findings highlight that common lay perceptions and practices intended to improve air quality may pose health risks. Therefore, risk communication about household air pollution must extend beyond objective information by considering contextual factors that shape how people interpret and respond to air quality issues. Not applicable.

Modeling and Optimization of NO2 Stations in the Smart City of Barcelona

The growing problem of nitrogen dioxide (NO2) pollution in urban environments is driving cities to adopt smart and sustainable approaches to address this challenge. To quantify and compare the effect of environmental policies, cities must be able to make informed decisions with real-time data that reflect the actual situation. Therefore, the objective of this work is threefold: The first is to study the behavior of the key performance indicator (KPI) of NO2 concentrations per station in Barcelona through exploratory analysis and clustering. The second is to predict NO2 concentration behavior, considering meteorological data. Lastly, a new distribution of current and new stations will be proposed using an optimization algorithm that maximizes the distance between them and covers the largest area of the city. As a result of this study, the importance of the location of measurement points and the need for better distribution in the city are highlighted. These new spatial distributions predict an 8% increase in NO2 concentrations. In conclusion, this study is a comprehensive tool for obtaining an accurate representation of NO2 concentrations in the city, contributing to informed decision-making, helping to improve air quality, and promoting a more sustainable urban environment.

Perforated plate ventilation system and dynamics of infectious respiratory particle transmission

AbstractWith the removal of indoor pollutants and the assurance of air quality emerging as critical research topics, the optimization of the internal environment in offices, where people stay for extended periods, is essential for controlling the spread of infectious respiratory particles. Frequent movements of personnel and the operation of doors and windows within offices significantly impact the mechanisms of droplet transmission, warranting further investigation. This study employs computational fluid dynamics simulations to explore the droplet dispersion characteristics and pollutant removal efficiency of the simplified model of perforated plate ventilation system (PPVS) (the diameter of the air supply openings has been reasonably simplified and uniformly set to 0.02 m) in office settings, as well as the impact of dynamic door operation scenarios on droplet spread and concentrations in breathing zones. To optimize the ventilation system's pollutant removal efficiency, airflow velocities (2.86, 3.18, and 5.00 m/s) are varied, with simulations conducted at the optimal velocity of 3.18 m/s. The effects of continuous door operations, door‐opening directions (towards the office and towards the isolation room), and opening speeds (π/4, π/6, π/8, and π/10 rad/s) are also examined, revealing significant impacts on droplet spread. Results indicate that PPVS effectively reduces indoor pollutant concentrations at all tested airflow velocities, with the optimal speed identified as 3.18 m/s. Additionally, door‐opening direction and speed can significantly influence droplet spread. Opening doors towards isolation rooms at smaller angles (less than 30°) effectively reduces droplet concentrations in personnel breathing zones, thereby mitigating the risk of droplet transmission. Faster door‐opening speeds also contribute to lower droplet concentrations in these zones. This innovative study explores the impacts of PPVS and dynamic door operation dynamics on droplet transmission during respiratory disease outbreaks, providing valuable theoretical insights and technical support for disease prevention and indoor air quality improvement.

Prediction of Hydrogen Production from Solid Oxide Electrolytic Cells Based on ANN and SVM Machine Learning Methods

In recent years, the application of machine learning methods has become increasingly common in atmospheric science, particularly in modeling and predicting processes that impact air quality. This study focuses on predicting hydrogen production from solid oxide electrolytic cells (SOECs), a technology with significant potential for reducing greenhouse gas emissions and improving air quality. We developed two models using artificial neural networks (ANNs) and support vector machine (SVM) to predict hydrogen production. The input variables are current, voltage, communication delay time, and real-time measured hydrogen production, while the output variable is hydrogen production at the next sampling time. Both models address the critical issue of production hysteresis. Using 50 h of SOEC system data, we evaluated the effectiveness of the ANN and SVM methods, incorporating hydrogen production time as an input variable. The results show that the ANN model is superior to the SVM model in terms of hydrogen production prediction performance. Specifically, the ANN model shows strong predictive performance at a communication delay time ε = 0.01–0.02 h, with RMSE = 2.59 × 10−2, MAPE = 33.34 × 10−2%, MAE = 1.70 × 10−2 Nm3/h, and R2 = 99.76 × 10−2. At delay time ε = 0.03 h, the ANN model yields RMSE = 2.74 × 10−2 Nm3/h, MAPE = 34.43 × 10−2%, MAE = 1.73 × 10−2 Nm3/h, and R2 = 99.73 × 10−2. Using the SVM model, the prediction error values at delay time ε = 0.01–0.02 h are RMSE = 2.70 × 10−2 Nm3/h, MAPE = 44.01 × 10−2%, MAE = 2.24 × 10−2 Nm3/h, and R2 = 99.74 × 10−2, while at delay time ε = 0.03 h they become RMSE = 2.67 × 10−2 Nm3/h, MAPE = 43.44 × 10−2%, MAE = 2.11 × 10−2 Nm3/h, and R2 = 99.75 × 10−2. With this precision, the ANN model for SOEC hydrogen production prediction has positive implications for air pollution control strategies and the development of cleaner energy technologies, contributing to overall improvements in air quality and the reduction of atmospheric pollutants.

Empirical analysis of the influence mechanism of vegetation and environment on negative air ion in warm temperate forest ecosystems

Air pollution presents a significant threat to public health in megacities globally. Negative air ions (NAI), often referred to as “air vitamins,” are recognized for their effectiveness in alleviating the harmful effects of air pollution. Forest ecosystems serve as natural generators of NAI, with both vegetation and environmental conditions playing critical roles in the formation and persistence of NAI. Gaining a comprehensive understanding of how forest ecosystems regulate NAI production is essential for leveraging their potential to enhance air quality. However, the intricate dynamics of forest ecosystems, along with seasonal fluctuations in vegetation and environmental factors, introduce uncertainties in NAI generation. This study utilized long-term observational data to explore the relationships between environmental variables, vegetation photosynthetic capacity (using solar-induced chlorophyll fluorescence, SIF), and NAI concentrations. By employing machine learning algorithms, we analyzed the spatiotemporal distribution of NAI, identifying the key contributing factors and their relative influence within forest ecosystems. The results revealed distinct seasonal variations in NAI levels, with higher values in summer and lower in winter. SIF and PM2.5 primarily influenced NAI through direct effects across seasons, whereas ambient temperature (TA), relative humidity, photosynthetically active radiation (PAR), and soil moisture predominantly impacted SIF on NAI through indirect effects in summer. TA was the primary influencing factor in spring and winter, contributing 28% and 25%, respectively, while PAR played a more significant role in summer and autumn, accounting for 37% and 27%. Vegetation had a greater impact on NAI levels during spring and summer, contributing 66% and 62%, whereas environmental factors dominated in autumn and winter, with contributions of 83% and 89%. This study offers both a theoretical foundation and technical guidance for enhancing the role of forest ecosystems in improving air quality and human living environments.

The Clean Air Act and Local Employment Base Sensitivity

The Clean Air Act, an important part of U.S. federal environmental regulations, is designed to improve air quality in targeted localities. However, its implementation has unexpected impacts on local industrial structure. The authors identify the effect of nonattainment regulations under the Clean Air Act on the sensitivity of county employment to changes in the national economy, providing guidance for local economic development in counties affected by these regulations. The authors use nationwide, county-level, longitudinal data from 1979 to 2019 and adopt a difference-in-differences approach and implement Coarsened Exact Matching to make the treatment and control groups approximate a fully blocked experiment. The findings indicate that nonattainment for ground-level ozone and sulfur dioxide results in decreased employment sensitivity. This effect is seen immediately for ground-level ozone nonattainment, while it is not seen until 25 years for sulfur dioxide nonattainment. Furthermore, these employment effects persist after attainment status has been regained.

Influence of Temperature Variability on the Efficacy of Negative Ions in Removing Particulate Matter and Pollutants: An Experimental Database

Cities globally must make urgent decisions to ensure a sustainable future as rising pollution, particularly PM2.5, poses severe health risks like respiratory and heart diseases. PM2.5’s harmful composition also impacts vegetation and the environment. Immediate government intervention is necessary to mitigate these effects. This study tackles the urgent problem of reducing PM2.5 levels in Medellín’s urban and indoor environments, where pollution presents serious health risks. To explore effective solutions, this research provides new data on the interaction between particulate matter from various pollutants and negative ions under different temperature conditions, offering valuable insights into air quality improvement strategies. Using a high-voltage system, ions bind to pollutants, accelerating their removal. Experiments measured temperature, humidity, formaldehyde, volatile organic compounds, negative ions, and PM2.5 in a 40 cm3 chamber across various conditions. Pollutants tested included cigarette smoke, incense, charcoal, and gasoline at two voltage levels and three temperature ranges. The data, available in CSV format, were based on 36,000 samples and repeated tests for reliability. This resource is designed to support studies investigating particulate matter control in urban and indoor environments, as well as to improve our understanding of negative ion-based air purification processes. The data are publicly available and structured in formats compatible with leading data analysis platforms.

Exploring hybrid models for identifying locations for active mobility pathways using real-time spatial Delphi and GANs

The spatial planning process is considered an extremely complex system, as it comprises different variables that interrelate and interact with each other. Effectively addressing this spatial complexity necessitates a multidisciplinary approach, as unified methodologies may prove insufficient. Specifically, in urban planning, it is increasingly crucial to prioritize bike lanes, bike stations, and pedestrian zones, for functional transportation infrastructures. This approach can enhance cities by improving air quality, reducing emissions, and boosting public health and safety through physical activity and accident prevention. However, implementing these changes requires careful planning, community engagement, and stakeholder collaboration. This paper proposes a hybrid model for identifying optimal locations for bike lanes, bike stations, and pedestrian zones adopting Real-Time Spatial Delphi and Generative Adversarial Networks (GANs). The Real-Time Spatial Delphi is a modified version of the traditional Delphi method that incorporates real-time feedback and visualization of group response in real-time, aiming to achieve a convergence of opinions among experts on the territory. Nevertheless, these judgments are a spatial representation not visible in reality, and with the spread of artificial intelligence models, different implementations can support the planning process, such as the use of GANs. In this case, GANs can be exploited by adopting pre-existing location images resulting from experts’ judgments to illustrate the proposed intervention’s visual impact. To demonstrate the effectiveness of our hybrid model, we apply it to the city of Dublin. The results showcased how the method helps stakeholders, policymakers, and citizens in visualizing the proposed changes and gauging their potential impact with greater precision.

Emerging Trends in Eco-Logistics: A Review of Sustainable Practices in Szczecin (Poland) from the Residents’ Perspective

This article offers an interdisciplinary analysis of eco-friendly urban logistics in Szczecin. It includes a review of innovative ecological solutions, results from a survey on residents’ awareness and acceptance and a case study on the implementation of these solutions in Szczecin. The theoretical part discusses the latest trends in eco-logistics, emphasising urban-specific solutions. The empirical section presents survey findings and explores the practical application of eco-friendly practices in Szczecin, analysing the associated benefits and challenges. The respondents demonstrated a high level of awareness and acceptance of city logistics solutions, such as urban pick-up points and electric scooters, which indicates support for sustainable urban transport. The residents of Szczecin appreciate the practical benefits and positive environmental impact of these eco-friendly investments, which they believe enhance city life by improving air quality and mobility. This article aims to provide a detailed perspective on eco-logistics in Szczecin, focusing on both the social and practical aspects of implementing sustainable solutions.

Particulate matter pollution and older adult health: global trends and disparities, 1991–2021

BackgroundParticulate matter pollution (PMP) is a major global health concern, with the older adult being particularly vulnerable. This study aimed to analyze global trends in PMP-related deaths and disability-adjusted life years (DALYs) among the older adult from 1991 to 2021.MethodsUsing data from the Global Burden of Disease Study 2021, we examined the impacts of ambient particulate matter pollution (APMP) and household air pollution from solid fuels (HAP-SF). We analyzed trends across different regions, socioeconomic development levels, age groups, and genders.ResultsAPMP-related older adult deaths increased from 1,745,000 to 3,850,000, and DALYs from 32,000,000 to 70,000,000. However, age-standardized mortality rate decreased from 384 to 337 per 100,000. HAP-SF-related deaths decreased from 2,700,000 to 2,100,000, and DALYs from 54,000,000 to 42,000,000. Age-standardized mortality rate for HAP-SF declined from 580 to 188 per 100,000. High APMP burden was concentrated in Asia, Africa, and the Middle East, while high HAP-SF burden was found in parts of Africa and South Asia. East Asia had the highest APMP-related older adult deaths (1,680,000) with an age-standardized mortality rate (ASMR) of 619 per 100,000. For HAP-SF, South Asia bore the heaviest burden with 1,020,000 deaths and an ASMR of 616 per 100,000. Females consistently experienced higher age-standardized DALYs rate than males for both APMP and HAP-SF across all regions and years. APMP burden showed a weak negative correlation with the Socio-demographic Index (SDI) at the regional level (r = −0.25, p < 0.001) but no significant correlation at the country level. HAP-SF burden exhibited strong negative correlations with SDI at both regional (r = −0.74, p < 0.001) and country levels (r = −0.83, p < 0.001).ConclusionDespite overall improvements, PMP continues to significantly impact older adult health globally, with substantial regional and gender disparities. These findings emphasize the need for targeted interventions, particularly in developing regions, and continued global efforts in air quality improvement and clean energy promotion.

Do government environmental audits reduce air pollution? Evidence obtained from Lanzhou

IntroductionThis study investigates the effectiveness of government environmental audits in mitigating air pollution. Specifically, it focuses on a pilot project conducted in Lanzhou City by the former Ministry of Environmental Protection. The research employs a regression discontinuity design to empirically assess the impact of these audits on air quality.MethodsThe study utilizes a regression discontinuity design to evaluate the impact of the government environmental audits. The primary outcome variable is the Air Quality Index (AQI). The analysis includes extensive robustness checks, such as sensitivity testing for optimal bandwidths of key pollutants, adjusting for precipitation levels, and conducting various robustness tests to ensure the reliability of the results.ResultsThe findings reveal a significant decline in the AQI following the implementation of the environmental audits. The AQI decreased from 31 to 25 during and after the audit period. Additionally, the study observed a notable increase in precipitation levels, suggesting potential environmental improvements. Robustness tests further confirm the reliability of the regression results.DiscussionThe research outcomes demonstrate the effectiveness of government environmental audits in reducing air pollution. By supervising local governments in the implementation of environmental protection policies, these audits contribute to improved air quality. The findings highlight the importance of targeted interventions and policy enforcement in addressing environmental challenges.

Road Traffic Infrastructure Construction and Air Pollution Based on the Perspective of Spatial Spillover

Road traffic infrastructure construction is widely regarded as a solution to urban congestion and air pollution. Given the frequent use of non-highway roads for inter-city travel, it is plausible that such infrastructure projects could also improve air quality in neighboring regions. However, the spatial spillover effects of these projects on air pollution remain underexplored. This paper investigates the spatial impacts of road traffic infrastructure construction on urban air pollution. Using a spatial panel regression model, we analyze data from 273 cities in a period spanning 2008 to 2021. Our findings show that road infrastructure construction significantly reduces local air pollution, with results robust to concerns about endogeneity. Furthermore, we identify notable spatial spillover effects, primarily concentrated in adjacent cities. These findings suggest that the accessibility between central cities and their neighboring areas plays a critical role in shaping traffic patterns, air quality, and sustainable urban development. As a result, coordinated regional planning for road infrastructure could serve as an effective strategy to mitigate urban air pollution and promote sustainable growth.

A comprehensive simultaneous allocation algorithm of charging stations and vehicle to grid operation in radial networks

The widespread adoption of electric vehicles (EVs) helps improve air quality by minimizing pollutants and promoting sustainable transportation practices. The integration of a substantial fleet of EVs leads to an increase in the installation of charging stations. The unplanned allocation of these loads impacts the distribution systems, such as increasing power loss. This paper introduces an algorithm that proposes an optimal allocation strategy for charging stations (CSs) in a distribution system. The allocation process aims to minimize the total apparent energy losses and ensure that the system voltage profile remains within limits. Load profile, charging and discharging profiles of CSs are considered. Vehicle-to-Grid (V2G) mode is one of the merits of integrating EVs in the grid, so this feature has been used to maintain system voltage stability and minimize energy loss. Power rating and locations of V2G mode are optimally determined to guarantee power quality indices. A hybrid algorithm of genetic algorithm (GA) and Self-Adaptive Multi-Population Elitist JAYA (SAMPE-JAYA) is developed to simultaneously allocate CSs and V2G in the systems. The proposed algorithm is verified on standard systems, IEEE 33, and 69-bus systems. The proposed algorithm is verified on standard IEEE 33-bus and 69-bus systems. V2G integration with CSs results in a reduction of energy losses by 6.33% and 22.25%, respectively, and voltage deviation improvements to 7.61% and 7.88% for the two systems.

Elevated Perspectives: Unraveling Cardiovascular Dynamics in High-Altitude Realms.

High-altitude regions pose distinctive challenges for cardiovascular health because of decreased oxygen levels, reduced barometric pressure, and colder temperatures. Approximately 82 million people live above 2400 meters, while over 100 million people visit these heights annually. Individuals ascending rapidly or those with pre-existing cardiovascular conditions are particularly vulnerable to altitude-related illnesses, including Acute Mountain Sickness (AMS) and Chronic Mountain Sickness (CMS). The cardiovascular system struggles to adapt to hypoxic stress, which can lead to arrhythmias, systemic hypertension, and right ventricular failure. Pathophysiologically, high-altitude exposure triggers immediate increases in cardiac output and heart rate, often due to enhanced sympathetic activity. Over time, acclimatisation involves complex changes, such as reduced stroke volume and increased blood volume. The pulmonary vasculature also undergoes significant alterations, including hypoxic pulmonary vasoconstriction and vascular remodelling, contributing to conditions, like pulmonary hypertension and high-altitude pulmonary edema. Genetic adaptations in populations living at high altitudes, such as gene variations linked to hypoxia response, further influence these physiological processes. Regarding cardiovascular disease risk, stable coronary artery disease patients generally do not face significant adverse outcomes at altitudes up to 3500 meters. However, those with unstable angina or recent cardiac interventions should avoid high-altitude exposure to prevent exacerbation. Remarkably, high-altitude living correlates with reduced cardiovascular mortality rates, possibly due to improved air quality and hypoxia-induced adaptations. Additionally, there is a higher incidence of congenital heart disease among children born at high altitudes, highlighting the profound impact of hypoxia on heart development. Understanding these dynamics is crucial for managing risks and improving health outcomes in high-altitude environments.

Air Quality Legislation in Australia and Canada—A Review

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

Transforming urban air quality: Green infrastructure strategies for the urban centers of Ethiopia

Urban green infrastructure (GI) plays a crucial role in improving air quality by removing pollutants and reducing emissions from structures. However, in Ethiopia, inadequate GI planning, largely due to limited awareness among planners and policymakers, can undermine the benefits of GI and worsen urban air quality issues. In this study, we demonstrate how the GI strategy approach can enhance air quality and assess the negative impacts of biogenic volatile organic compounds (BVOCs) emitted by certain tree species in Ethiopia, using Hawassa as a case study. We utilized a customized i-Tree Eco model to estimate annual pollutant removal and BVOC emissions and applied the Kriging method in ArcGIS to map their spatial distribution. In Hawassa, GI systems removed 274.2 t of pollutants annually, valued at $1.79 million, with SO2 being the most and CO the least removed pollutants. Air pollution removal was highest during the dry season (37.4%) compared to the long (29.7%) and short rainy seasons (32.9%). Conversely, trees emitted 35.78 t of BVOCs annually, with monoterpene and isoprene being nearly equal contributors. Eucalyptus genus, Casuarina equisetifolia, and Schinus molle species were the top BVOC emitters despite their low population percentages in the study area. While GI types such as urban parks and institutional compounds are effective at pollutant removal, they also exhibit higher BVOC emissions. Our findings highlight the need for optimized species selection, improved GI planning, and enhanced policy support to maximize GI effectiveness, providing valuable insights for planners and policymakers in integrating GI into urban spatial planning.

Revolutionizing urban emission tracking: Enhanced vehicle ratios via remote sensing techniques

This study presents research that examines the use of several remote sensing methods to quantify automobile emissions in China. The research provides a unified data processing technique, using methodologies such as plume chasing (PCH) and point sampling (P-sampling) to measure vehicle emissions accurately. This method accurately calculates the ratios of gaseous combustion, namely NOx/CO2, in different driving scenarios and verifies the technique using controlled vehicle emissions measurements. The results demonstrate the method’s ability to identify variations in emission levels linked to engine operation and alterations in after-treatment systems. This provides significant information for monitoring emissions in metropolitan areas such as Wuhan, China. The suggested method has substantial promise for improving air quality initiatives in China.

Estimating monthly NO2, O3, and SO2 concentrations via an ensemble three-stage procedure with downscaled satellite remote sensing data and ground measurements

The gaseous air pollutants nitrogen dioxide (NO2), ozone (O3), and sulfur dioxide (SO2), in addition to particulate matter (PM2.5), are known to be associated with many adverse health effects. However, exposure estimates may not be available in rural or mountainous areas without monitoring stations. In this study, we retrieved satellite remote sensing data for NO2, O3, and SO2 from the Ozone Monitoring Instrument (OMI) L3 products. Together with ground measurements (air monitoring stations and meteorological and land use data), we estimated the monthly NO2, O3, and SO2 concentrations with a spatial resolution of 3×3 km across Taiwan from 2005 to 2019. A three-stage estimation procedure was utilized: in Stage 1, an ensemble generalized additive model (GAM) and machine learning method were used to determine the spatiotemporal variations; in Stage 2, the remote sensing data were downscaled; and in Stage 3, the downscaled concentrations were reused in the Stage 1 procedure for fine-tuning estimations. We obtained overall leave-one-out cross-validation (LOOCV) R2 values ranging from 0.927-0.950, 0.704-0.721, and 0.601-0.716, and root-mean-square-errors (RMSEs) ranging from 1.59-2.28, 3.81-4.18, and 0.67-1.32 ppb for NO2, O3, and SO2, respectively, via the random forest procedure. The annual NO2 and SO2 concentrations greatly improved from 2005-2019, especially in the western residential area of Taiwan. However, despite these improvements in air quality, the annual O3 concentrations tended to increase from 2015–2019. This might be due to the complex mixtures of precursors (e.g., NO2), atmospheric circulation, barriers of the Central Mountain Ridge, and increasing ground temperatures over the past decade. The proposed multistage estimation procedure performed well over the whole island with complex terrain and topography. The study outcomes may provide epidemiological information for long-term ambient exposure estimates and guidance for future administrative policies.

Influence of local meteorology and gaseous pollutant emissions on atmospheric nanoparticle concentrations in a pedestrian way in urban region

The roadside environment is one of the major sources of nanoparticle emission in the urban regions. The complex mixture of different pollutants makes it hard to understand the dynamics and behavior of nanoparticles. The study aims to analyze the dynamics of atmospheric nanoparticles ranging from 10 to 1000 nm along with local meteorological conditions and gaseous pollutants in a pedestrian way at a busy street in Delhi, a megalopolis in India, during all five major seasons of the study area. Nucleation mode particles were higher during the spring season, whereas the contribution of Aitken mode particles dominated the total particle number concentrations in the rest of the seasons. Due to the influence of vehicular sources, rush hour concentrations were higher than during non-rush hours. Thus, the diurnal pattern in particle concentration strongly coincided with emissions associated with the vehicular flow. During the winter season, the average total particle number concentration was observed to be maximum (4.1 × 104 cm-3) with a higher surface area of particles of 3.5 × 10-3 mm2m-3. Compared to the monsoon season, the concentration of NOX was 5 times higher in winter. The boundary layer height in the study region ranged from 600 to 2400 m during different seasons, and the maximum ventilation coefficient was observed to be >3000 m2s-1 during summer. Precipitation reduced the concentration of particles by half, from 2.2 x 104 to 1.1 x 104 cm-3, due to wet scavenging. The study revealed that the concentrations of particles depend not only on primary emissions but also are influenced by local meteorology and other co-emitted pollutants. Understanding the dynamics of atmospheric nanoparticles in urban roadside environments as outlined in this study is crucial to devise necessary mitigation measures for people residing near the road in order to reduce health impact and improve air quality.

PM2.5 Induces Developmental Neurotoxicity in Cortical Organoids

There is mounting evidence implicating the potential neurotoxic effects of PM2.5 during brain development, as it has been observed to traverse both the placental barrier and the fetal blood-brain barrier. However, the current utilization of 2D cell culture and animal models falls short in providing an accurate representation of human brain development. Consequently, the precise mechanisms underlying PM2.5-induced developmental neurotoxicity in humans remain obscure. To address this research gap, we constructed three-dimensional (3D) cortical organoids that faithfully recapitulate the initial stages of human cerebral cortex development. Our goal is to investigate the mechanisms of PM2.5-induced neurotoxicity using 3D brain organoids that express cortical layer proteins. Our findings demonstrate that exposure to PM2.5 concentrations of 5 μg/mL and 50 μg/mL induces neuronal apoptosis and disrupts normal neural differentiation, thereby suggesting a detrimental impact on neurodevelopment. Furthermore, transcriptomic analysis revealed PM2.5 exposure induced aberrations in mitochondrial complex I functionality, which is reminiscent of Parkinson's syndrome, potentially mediated by misguided axon guidance and compromised synaptic maintenance. This study is a pioneering assessment of the neurotoxicity of PM2.5 pollution on human brain tissues based on 3D cortical organoids, and the results are of great significance in guiding the formulation of the next air pollution prevention and control policies in China to achieve the sustainable improvement of air quality and to formulate pollution abatement strategies that can maximize the benefits to public health.