Poor Air Quality Research Articles

Occurrence of tyre-derived particles in size-segregated aerosol in the urban area of Venice

Benzothiazoles (BTHs), commonly used in various industrial applications, have emerged as significant contaminants due to their widespread usage and potential adverse effects on ecosystems and human health. The aim of this study is to quantify and, for the first time, to characterize the particle size distribution of eight BTHs across twelve fractions of atmospheric aerosol representative of the urban boundary layer. Samples were collected from August to October 2023 in the urban area of Venice, located in the Po Valley, known for its poor air quality. Instrumental analysis was achieved via an UHPLC system coupled with a triple quadrupole mass spectrometer.All target compounds were present in the samples analyzed and most of them tended to distribute mainly in the fine fraction of aerosol (<1.8 μm). An exception is given by BTH-SO3H, which presents a bimodal distribution with maxima centered in the coarse fraction; this may imply a different source compared to other BTHs. BTH-SH is the most abundant benzothiazole derivative, suggesting tyre debris as the main source since it is the main compound used in vulcanizing accelerators in rubber manufacture. Given its mutagenic properties, its high relative abundance could be of concern for human health. Resultsof this work provide valuable insights into the distribution of BTHs in urban aerosols, laying the groundwork for future research aimed at better understanding their sources and environmental pathways, fate and impacts.

Penerapan Data Mining Untuk Klasifikasi Kualitas Udara di Daerah Istimewa Yogyakarta Menggunakan Algoritma C4.5

Air pollution is a global environmental problem that is of serious concern in various regions around the world, including in Indonesia. Poor air quality has negative impacts on human health, ecosystems and economic growth. The purpose of this research is to classify the air quality in the Special Region of Yogyakarta. Classification is done using data mining techniques with the C4.5 algorithm or decision tree. The results of the analysis that has been studied using the C4.5 algorithm used 5822 data and 20 replacement samples with 100 repetitions, so the results of this study obtained 7 decisions or leaves with a success rate of 99.9485% and a failure rate of 0.0515%.

Critical Success Factors of Electric Mobility Transition in Indonesia: A DEMATEL-Based ANP Approach

The high index of poor air quality and the increasing demand for oil fuel pose severe problems for Indonesia. The largest sector contributing to both of these issues is the transportation sector. Electric mobility offers a notable opportunity for decreasing carbon emissions within the transportation domain. Despite the Indonesian government's notable backing of this initiative, the implementation has been slow. Therefore, this study aims to identify the critical factors necessary for a successful transition to electric mobility. This study identifies the factors influencing the transition to e-mobility in the initial stage. Subsequently, these factors are validated and assessed by a panel of nine experts. In the validation phase, data processing involves the use of the Content Validity Index (CVI) and Modified Kappa methods. To assess relationships and prioritize essential success factors, the Decision-making Trial and Evaluation Laboratory-based Analytic Network Process (DEMATEL-based ANP) is applied. This study identifies six dimensions and 24 critical success factors. The political dimension emerges as the most significant, with the top five priorities being government funding, charging infrastructure, value proposition, fuel price, and charging time. The findings can be used to provide recommendations to the government or relevant stakeholders in supporting the shift to electric mobility in Indonesia.

Air pollution and issuance credit spread of municipal investment bond

This paper explores the impact of city-level air pollution on the financing costs of local and regional governments (LRGs) in China. The empirical results demonstrate that cities with poor air quality face higher credit spreads on bonds issued by local financing platforms (LFPs), underscoring the critical role of air quality in determining LRGs financing costs. Air pollution negatively affects the credit rating of municipal investment bonds and increases fiscal pressure on LRGs. However, when LRGs invest more in environmental protection, reduce reliance on tax revenue, enforce stricter environmental regulations, and implement auditing policy for natural resources management, the adverse effect of air pollution on bond credit spreads are mitigated. This study enriches the research on whether and how air pollution affects bond issuance pricing and provides insights into strategies for reducing LFPs financing costs and addressing LRGs debt crises.

Urban mobility and air pollution at the neighbourhood scale in the Megacity of São Paulo, Brazil

Developing countries’ megacities, characterised by dense populations and socioeconomic disparities, often face high levels of pollutants, mainly from vehicle emissions. Poor air quality can lead to a range of public health problems which in turn, need to be addressed by public policies. The main goal of this article is to examine the impact of public policies and the influence of transport modes on air pollution of three districts from the Metropolitan Region of São Paulo, in Brazil: Pinheiros, Parque Dom Pedro II and Taboão da Serra. The method was held through a comparative analysis, which in turn, took into account urban indicators, urban mobility data and pollutants levels (CO and NOx). These data were collected from the 2007 and 2017 São Paulo Metrô Origin and Destination Surveys and the Air Quality Database of the Environmental Company of the State of São Paulo. As key findings, the three study areas’ pollutant concentrations presented a downward trend from 2007 to 2017 as the same time there was an increase in the public transport and non-motorized transport modes. However, it is important to highlight the confluence of the state and federal public policies occuring at the same period such as PRONCOVE, Rodoanel and the Yellow Subway Line. The identified socio-environmental disparities in the urban realm highlight the importance of localised analyses in order to reveal problems and opportunities to get a better response in terms of urban mobility and air quality. Thus, the perpetuation of constant policy’s updates and interdisciplinary collaboration is crucial.

Perspective on Theoretical and Experimental Advances in Atmospheric Photochemistry.

Research that explores the chemistry of Earth's atmosphere is central to the current understanding of global challenges such as climate change, stratospheric ozone depletion, and poor air quality in urban areas. This research is a synergistic combination of three established domains: earth observation, for example, using satellites, and in situ field measurements; computer modeling of the atmosphere and its chemistry; and laboratory measurements of the properties and reactivity of gas-phase molecules and aerosol particles. The complexity of the interconnected chemical and photochemical reactions which determine the composition of the atmosphere challenges the capacity of laboratory studies to provide the spectroscopic, photochemical, and kinetic data required for computer models. Here, we consider whether predictions from computational chemistry using modern electronic structure theory and nonadiabatic dynamics simulations are becoming sufficiently accurate to supplement quantitative laboratory data for wavelength-dependent absorption cross-sections, photochemical quantum yields, and reaction rate coefficients. Drawing on presentations and discussions from the CECAM workshop on Theoretical and Experimental Advances in Atmospheric Photochemistry held in March 2024, we describe key concepts in the theory of photochemistry, survey the state-of-the-art in computational photochemistry methods, and compare their capabilities with modern experimental laboratory techniques. From such considerations, we offer a perspective on the scope of computational (photo)chemistry methods based on rigorous electronic structure theory to become a fourth core domain of research in atmospheric chemistry.

Unraveling Cross-Organ Impacts of Airborne Pollutants: A Multiomics Study on Respiratory Exposure and Gastrointestinal Health.

Poor air quality is increasingly linked to gastrointestinal diseases, suggesting a potential correlation with human intestine health. However, this relationship remains largely unexplored due to limited research. This study used a controlled mouse model exposed to cooking oil fumes (COFs) and metagenomics, transcriptomics, and metabolomics to elucidate interactions between intestine microbiota and host metabolism under environmental stress. Our findings reveal that short-term COF inhalation induces pulmonary inflammation within 3 days and leads to gastrointestinal disturbances, elucidating a pathway connecting respiratory exposure to intestinal dysfunction. The exposure intensity significantly correlates with changes in intestinal tissue integrity, microbial composition, and metabolic function. Extended exposure of 7 days disrupts intestine microbiota and alters tryptophan metabolism, with further changes observed after 14 days, highlighting an adaptive response. These results highlight the vulnerability of intestinal health to airborne pollutants and suggest a pathway through which inhaled pollutants may affect distant organ systems.

MACHINE LEARNING METHODS APPLIED IN AIR QUALITY PREDICTION

Air quality is an important environmental component that has a significant influence on public health and well-being. Poor air quality can cause a variety of health problems, including respiratory and cardiovascular disorders. Therefore, there is a growing demand for air quality prediction tools to enable consumers and authorities to take the best decisions and to implement the necessary actions to reduce air pollution. The present paper describes an innovative application that uses machine learning techniques to supply to the users real-time air quality predictions made on past data from their unique location. The Scikit-learn Python package was used to implement five machine learning algorithms, including K-Nearest Neighbors, Random Forest, Gradient Boosting, Support Vector Regression (SVR) and AdaBoost. To achieve robust model performance, compatibility with cross-validation approaches was evaluated. The obtained results indicate that these machine learning techniques are successful at forecasting air quality. The AdaBoost method emerged as the best accurate predictor after extensive investigation, closely followed by Gradient Boosting, SVR, Random Forest, and K-Nearest Neighbors. Furthermore, the investigation also focused on the adapted handling of inaccurate data and providing graphical visualizations to highlight the algorithm's efficacy.

Use of satellite data to increase public health advisories for poor air quality in the sugarcane growing region of Florida

Use of satellite data to increase public health advisories for poor air quality in the sugarcane growing region of Florida

Harnessing Machine Learning Algorithms to Model the Association between Land Use/Land Cover Change and Heatwave Dynamics for Enhanced Environmental Management

As we navigate the fast-paced era of urban expansion, the integration of machine learning (ML) and remote sensing (RS) has become a cornerstone in environmental management. This research, focusing on Silchar City, a non-attainment city under the National Clean Air Program (NCAP), leverages these advanced technologies to understand the urban microclimate and its implications on the health, resilience, and sustainability of the built environment. The rise in land surface temperature (LST) and changes in land use and land cover (LULC) have been identified as key contributors to thermal dynamics, particularly focusing on the development of urban heat islands (UHIs). The Urban Thermal Field Variance Index (UTFVI) can assess the influence of UHIs, which is considered a parameter for ecological quality assessment. This research examines the interlinkages among urban expansion, LST, and thermal dynamics in Silchar City due to a substantial rise in air temperature, poor air quality, and particulate matter PM2.5. Using Landsat satellite imagery, LULC maps were derived for 2000, 2010, and 2020 by applying a supervised classification approach. LST was calculated by converting thermal band spectral radiance into brightness temperature. We utilized Cellular Automata (CA) and Artificial Neural Networks (ANNs) to project potential scenarios up to the year 2040. Over the two-decade period from 2000 to 2020, we observed a 21% expansion in built-up areas, primarily at the expense of vegetation and agricultural lands. This land transformation contributed to increased LST, with over 10% of the area exceeding 25 °C in 2020 compared with just 1% in 2000. The CA model predicts built-up areas will grow by an additional 26% by 2040, causing LST to rise by 4 °C. The UTFVI analysis reveals declining thermal comfort, with the worst affected zone projected to expand by 7 km2. The increase in PM2.5 and aerosol optical depth over the past two decades further indicates deteriorating air quality. This study underscores the potential of ML and RS in environmental management, providing valuable insights into urban expansion, thermal dynamics, and air quality that can guide policy formulation for sustainable urban planning.

Sources and Specified Health Risks of 12 PM2.5-Bound Metals in a Typical Air-Polluted City in Northern China during the 13th Five-Year Plan.

The continuous monitoring of PM2.5 (including 12 metal elements) was conducted in Jinan, a city with poor air quality in China, during the 13th Five-Year Plan (2016-2020). Positive matrix factorization (PMF) was used to identify emission sources of PM2.5-bound metals, and the health risks of the metals and their emission sources were assessed. During the study period, the concentration of most metals showed a decreasing trend (except Al and Be), and a significant seasonal difference was found: winter > fall > spring > summer. The PMF analysis showed that there were four main sources of PM2.5-bound metals, and their contributions to the total metals (TMs) were dust emissions (54.3%), coal combustion and industrial emissions (22.3%), vehicle emissions (19.3%), and domestic emissions (4.1%). The results of the health risk assessment indicated that the carcinogenic risk of metals (Cr and As) exceeded the acceptable level (1 × 10-6), which was of concern. Under the influence of emission reduction measures, the contribution of emission sources to health risks changes dynamically, and the emission sources that contribute more to health risks were coal combustion and industrial emissions, as well as vehicle emissions. In addition, our findings suggest that a series of emission reduction measures effectively reduced the health risk from emission sources of PM2.5-bound metals.

Investigating User Feedback for Learning Space Design in Primary Schools of Shandong Province, China

A well-designed learning environment is crucial for enhancing both the physical and mental health of students, which in turn improves their learning outcomes. However, many classrooms in China, particularly in rural areas, were constructed and designed several decades ago, so it is essential to redesign these learning spaces to align with the requirements of 21st Century education. This study aims to develop the stimulation, individuality, and naturalness (SIN) theoretical framework for identifying the learning environment of current classroom by examining the full range of sensory effects experienced by individuals. This study conducted qualitative interviews with 72 students and 18 class teachers to explore major issues with their existing learning spaces in four primary schools in Shandong Province of China. The results show that high temperatures and poor air quality are frequently raised by respondents, which directly impact students’ learning experience. This finding confirms naturalness likely underpins human comfort. Teachers and students felt that the classroom should be improved from the aspects of crowded space, imperceptible decoration, congestion and monotonous layouts. The study emphasised the important factors that designers and policymakers should consider to promote a comfortable, efficient, and healthy learning environment.

What’s bad for the environment is also bad for the market: the impact of air quality on the mispricing of dual-listed shares in China

ABSTRACT Examining the influence of air quality on the mispricing of dual-listed companies in the China stock market, this study shows that poor air quality exacerbates the premium between A-share and H-share prices, and that persistent air quality degradation intensifies this effect. Our findings suggest that air pollution-induced cognitive impairments among local investors contribute to A-share overpricing in mainland China, widening the price gap with H-shares traded in Hong Kong. Our results reveal a connection between financial markets and the environment, providing support for green finance initiatives that emphasize the negative market impacts of environmental harm.

Source attribution of carbon monoxide over Northern India during crop residue burning period over Punjab

National Capital Territory of Delhi and its satellite cities suffer from poor air quality during the post-monsoon months of October–November. In this study, a novel attempt is made to estimate the contribution of different emission sources (industrial, residential, power generation, transportation, biomass burning, photochemical production, lateral transport, etc.) towards the criteria air pollutant carbon monoxide (CO) concentration over North India. Multiple simulations of the WRF-Chem model with a tagged tracer approach with different inputs (6 anthropogenic emission inventories and 3 biomass burning emission inventories) were used. The model performance was evaluated against the MOPITT retrieved CO surface concentration. Analysis of model simulated CO over North India suggests that anthropogenic emissions contribute around 32–49% to surface CO concentration while crop residue burning contributes 27–44% of which 80% originates from Punjab. For Delhi, the contribution from anthropogenic sources is dominant (53–77%) of which 10–28% is from the domestic sector and 14–55% is from the transport sector. Agricultural waste burning contributes about 15–30% to Delhi's surface CO concentration (of which 75% originates from Punjab). Crop residue burning emission is a chief source of CO over Punjab with a contribution of about 56–76%. The results suggest that industrial, transport, and domestic sector activities are more responsible for increased CO levels over New Delhi and surrounding regions than crop residue burning over Punjab. Furthermore, critical meteorological parameters like 10 m wind speed, boundary layer height, 2 m temperature, total precipitation, and relative humidity were evaluated against CO concentration to understand their impact on CO distribution. Results conclude that deteriorating air quality over the North Indian region is caused by a combination of prevailing meteorological factors (such as slow winds, shallow mixing layer, and cold temperatures) and man-made emissions.

Reconceiving Domestic Burning Controls: Air Quality Alerts, Behavioural Responsive Regulation, and Designing for Compliance.

Domestic combustion emissions pose a growing risk to public health, especially in the UK. Existing responses are polarised, with government advocating use of lower emission fuels and stoves while clean air campaigners call for blanket bans on burning. However, each approach is limited in its ability to control these emissions. An alternative can be found in the U.S.A., where 'burn alert' systems require stove and fireplace users to avoid lighting during periods of actual or projected poor air quality. Given the effectiveness of these regimes, the current study designs and evaluates the effectiveness and acceptability of a burn alert system in the UK for the first time, drawing on the theoretical perspective of behavioural responsive regulation. Fifty participants were recruited to use the system over 2 weeks in winter. The findings illustrate that a voluntary burn alert system can dissuade burning among users. Of those in receipt of an alert, 74% reduced burning frequency or burned for a shorter duration. In total, the alert system prevented at least 178 hours of burning for this group. Qualitative findings show that the consistency of the behavioural response is influenced by technical, structural, and environmental factors, providing key insight into how UK-based burn alert systems could be modified to increase the consistency of compliance in future. The overall conclusion is that burn alerts could be introduced in the UK and beyond, as a means of reducing domestic combustion emissions and their associated public health risks.

Disruption of outdoor activities caused by wildfires increases disease circulation.

Although climate change poses a well-established risk to human health, present-day health impacts, particularly those resulting from climate-induced behavioral changes, are under-quantified. Analyzing the U.S. West Coast wildfires of September 2020, we found that poor air quality drives people indoors, increasing the circulation of airborne pathogens like COVID-19. Indoor masking rates as low as 10% can mitigate this risk, offering a clear path to enhance public health responses during wildfires.

Skyrocketing pollution: assessing the environmental fate of July 4th fireworks in New York City.

Pyrotechnic displays often lead to significant increases in poor air quality. The widespread environmental fate-involving air, water, and spatial-temporal analyses-of fireworks-produced pollutants has seldom been investigated. This study examined the environmental fate of pollutants from the largest fireworks event in the U.S.: Macy's Fourth of July Fireworks show in New York City (NYC). Real-time PM2.5 and gravimetric PM2.5 and PM10 were collected at locations along the East River of NYC. Airborne particles were assayed for trace elements (X-ray fluorescence) and organic and elemental carbon (OC/EC). River water samples were evaluated by ICP-MS for heavy-metal water contamination. Spatial-temporal analyses were created using PM2.5 concentrations reported by both EPA and PurpleAir monitoring networks for NYC and 5 other major metropolitan areas. The fireworks event resulted in large increases in PM2.5 mass concentrations at the river-adjacent sampling locations. While background control PM2.5 was 10-15 µg/m3, peak real-time PM2.5 levels exceeded 3000 µg/m3 at one site and 1000 µg/m3 at two other locations. The integrated gravimetric PM2.5 and PM10 concentrations during the fireworks event ranged from 162 to 240 µg/m3 and 252 to 589 µg/m3, respectively. Zn, Pb, Sb, and Cu more than doubled in river water samples taken after the event, while S, K, Ba, Cu, Mg, Fe, Sr, Ti, and Zn increased in airborne PM2.5 from the fireworks. Data from hyperlocal monitoring networks for NYC and other metropolitan areas yielded similar, but generally smaller, increases in PM2.5 levels. Fireworks shows have been associated with environmental contamination. This comprehensive analysis considers the fate of pollutants from the largest annual U.S. pyrotechnic show through air, water, and hyperlocal temporal characterization.

Bus stop design strategy based on visual comfort level in Bandung City

High sunlight and poor air quality in the city of Bandung affect the level of visual comfort. In designing bus stops, space is needed that has visual comfort so that the facilities are maintained and can function well. This research was conducted to determine visual comfort as measured using the Daylight Factor (DF) at bus stops in Bandung. The method used is a quantitative experiment using Drajmarsh and Ladybug software. To identify a bus stop design with stable visual comfort, analysis was conducted at various times and places. The results of the study are guidelines for creating publicly accessible spaces, such as bus stops, in Bandung that make utilization of natural light from the sun from morning to night. This light is crucial for visual information, thermal comfort, and building energy efficiency.

Assessment of seasonal variations in dry eye syndrome prevalence among office workers

Background: Dry eye syndrome (DES) is prevalent among office workers and influenced by various environmental and individual factors. This study assesses the prevalence, seasonal variations, symptom severity, and environmental influences on DES among office workers. Aims and Objectives: The study aimed to assess the prevalence of DES among office workers in Warangal, Telangana, India, and to evaluate the impact of seasonal variations, symptom severity, and environmental factors on DES. Materials and Methods: A cross-sectional study was conducted with 100 office workers aged 25–55 years old. Data were collected through questionnaires on DES symptoms, environmental conditions, and demographics. The prevalence of DES was calculated, and seasonal variations were analyzed. Symptom severity was recorded on a scale from 1 to 10. Environmental factors such as humidity, air-conditioning use, screen time, and indoor air quality were examined for their correlation with DES prevalence. Results: The overall prevalence of DES was 45%, with females showing a higher prevalence (50%) compared to males (37.5%). Seasonal variations showed the highest DES prevalence in winter (40%) and the lowest in summer (16%). Symptom severity scores were highest in winter (6.5 overall) and lowest in summer (3.8 overall). Low humidity in winter was associated with a 55% prevalence of DES, especially among females (65%). High air-conditioning use in summer correlated with a lower DES prevalence (12.5%). Increased screen time (&gt;6 h/day) resulted in a 60% prevalence of DES, with seasonal peaks in winter (65%). Poor indoor air quality was reported by 70% of participants and linked to a 50% prevalence of DES. Conclusion: The study highlights significant gender differences, seasonal variations, and environmental factors impacting DES prevalence and severity among office workers. Interventions focusing on improving workplace conditions, managing screen time, and enhancing indoor air quality may mitigate DES symptoms.

Assessments and application of low-cost sensors to study indoor air quality in layer facilities

Indoor poultry facilities often experience poor air quality due to intensive farming and restricted ventilation. Monitoring the air quality in these barns is crucial considering the health of both the birds and producers. Advancements in sensor technologies have led to the development of low-cost sensors (LCS) that can continuously monitor air pollutants. Even though most poultry facilities in Canada are indoors due to harsh winter weather conditions, there is a lack of indoor air quality (IAQ) studies. This study aimed to evaluate the field performance of the LCS network in a table egg farm in Canada, where the sensors were designed specifically for operating in dusty poultry facilities continuously. The LCS monitored IQA parameters such as particulate matter (PM), carbon dioxide (CO2), relative humidity, and temperature in real-time. By implementing a correction factor, the sensor data resulted in an agreement range of 80 ± 20% with a reference instrument. The study observed that PM concentration exceeded several thousand μg/m3, with PM10 at 5.5 × 104 ± 2.2 × 104 and PM2.5 at 6.3 × 103 ± 2.3 × 103, which was found to be most affected by the chicken activity and light regime. The IAQ parameters also exhibited a complex intercorrelation with each other, as well as the outdoor temperature and the building ventilation rate. Sensors were able to make observations that were found only with research-grade instruments in previous studies. Overall, the study showcases the potential of the LCS network as an affordable solution for environmental monitoring in poultry facilities.