Air Quality Conditions Research Articles

Synoptic Variation Drives Genetic Diversity and Transmission Mode of Airborne DNA Viruses in Urban Space.

Airborne viruses are ubiquitous and play critical roles in maintaining ecosystem balance, however, they remain unexplored. Here, it is aimed to demonstrate that highly diverse airborne viromes carry out specific metabolic functions and use different transmission modes under different air quality conditions. A total of 263.5-Gb data are collected from 13 air samples for viral metagenomic analysis. After assembly and curation, a total of 12 484 viral contigs (1.5-184.2 kb) are assigned to 221 genus-level clades belonging to 47 families, 19 orders, and 15 classes. The composition of viral communities is influenced by weather conditions, with the main biomarker being Caudoviricetes. The most dominant viruses in these air samples belong to the dsDNA Caudoviricetes (54.0%) and ssDNA Repensiviricetes (31.2%) classes. Twelve novel candidate viruses are identified at the order/family/genus levels by alignment of complete genomes and core genes. Notably, Caudoviricetes are highly prevalent in cloudy and smoggy air, whereas Repensiviricetesare highly dominant in sunny and rainy air. Diverse auxiliary metabolic genes of airborne viruses are mainly involved in deoxynucleotide synthesis, implying their unique roles in atmosphere ecosystem. These findings deepen the understanding of the meteorological impacts on viral composition, transmission mode, and ecological roles in the air that we breathe.

Spatio-Temporal Monitoring and Assessment of Air Quality and its Impact on Public Health from Geospatial perspective over Haryana, India

Abstract. This study aims to evaluate the air quality of the Haryana state, India by utilizing Sentinel-5P satellite data. This study aims to track, quantify, and analyze the level of air pollutants in several regions of Haryana. By integrating and analyzing diverse satellite datasets, the study provides a comprehensive understanding of air quality conditions in the region and supports informed decision-making for pollution mitigation and environmental sustainability. This also examines the public health and environmental impacts of air pollution, identifies pollution sources, reviews existing policies, and also provides recommendations for prevention of air pollution. The outcomes of this study will improve the understanding of air quality dynamics in Haryana and provide preventive measures towards sustainable environmental practices. This paper presents a detailed assessment of air quality in Haryana, India, covering the period from 2019 to 2023 using satellite datasets. This evaluates the concentrations and spatial distributions of air pollutants across Haryana. The experimental results provide significant variations in pollution levels over the four years, with higher concentrations in 2019 and 2021, and lower levels in 2020 and 2023. Moreover, spatial distribution maps illustrate pollutant patterns, emphasizing the temporal and spatial changes in pollution levels. The results also highlight the ongoing challenge of increasing pollution in certain areas of Haryana and the need for continuous monitoring to improve air quality.

Geographical variations and predictors of coronary artery disease mortality in Turkiye: an environmental and behavioral analysis

ObjectiveThis study aims to analyze the geographical variations and identify key environmental and behavioral predictors of coronary artery disease (CAD) mortality in Turkiye.MethodsA 10-year longitudinal province-level data was used to identify change trajectories of CAD mortality. Environmental determinants (such as air quality and climatic conditions) and behavioral factors of alcohol consumption and smoking were examined for their association with CAD mortality change trajectories using Ordinal Logistic Regression models.ResultsThe study revealed significantly different trajectoriesof CAD mortality across Turkiye. Environmental factors, particularly air quality (Particulate Matter-10 variation) and climatic conditions (humidity and temperature variations), were heavily associated with the level of CAD mortality. Behavioral factors, notably alcohol consumption and smoking, also exhibited a significantly positive association. Humidity, sunlight, and temperature remained as key predictors of CAD after controlling for smoking and alcohol consumption.ConclusionThe study underscores the importance of addressing environmental and lifestyle factors in CAD management and prevention strategies. The findings suggest the necessity for region-specific interventions and public health policies tailored to the unique characteristics of each province in Turkiye. This research contributes to a deeper understanding of the multifactorial nature of CAD mortality, providing valuable insights for future research to investigate causal associations, healthcare planning, and policy-making.Trial registrationOur study has been registered in ClinicalTrials.GOV system with a procotol ID of CAD001.

AirNet: predictive machine learning model for air quality forecasting using web interface

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.

A correlation study of road dust pollutants, tire wear particles, air quality, and traffic conditions in the Seoul (South Korea)

The interdependence of traffic dynamics and air quality on the concentrations of tire wear particles (TWP), heavy metals, and carbon black (CB) in road dust collected from 15 locations in Seoul, South Korea, was assessed. Pearson correlation, principal component analysis, and network analysis were employed to evaluate the correlations among traffic volume, vehicle speed, air quality parameters (PM10, PM2.5, NOx, and CO), and the concentrations of TWP (5934 to 16,253 mg/kg, average 9581 ± 4086 mg/kg), CB (371–22,287 mg/kg, average 4291 ± 6096 mg/kg), and heavy metals (Cr, Mn, Fe, Ni, Cu, Zn, As, Cd, and Pb) in road dust. The enrichment factors for heavy metals and the pollution index of TWP and CB were also calculated to evaluate the contribution of vehicle-derived particulate substances to road dust contamination. It was found that Cluster B, characterized by traffic-related variables such as traffic volume, vehicle speed, and heavy metals (Zn, Cd, and Pb), was significantly correlated with these pollutants, with correlation coefficients reaching up to 0.933. TWP was identified as a significant mediator in the increase of Zn, Pb, and Cd concentrations linked to traffic activities, contributing to pollution levels that were 2–16 times higher than the geochemical background. The presence of TWP and CB in road dust was identified as an indicator of contamination from traffic-related activities, highlighting the importance of Zn, Pb, and Cd as emerging pollutants that require targeted management strategies.

68 Wildfire smoke simulations and the effects of smoke exposure on cattle acute phase protein response

Abstract The incidence of and acreage burned by wildfires are globally increasing, with predictions of more devastating fires in the future. The impacts of wildfire smoke exposure on have been of research interest over the years, although the scope of the literature is limited. Evaluating the effects of wildfire smoke exposure is challenging because of the unpredictability of fires, limiting the ability of researchers to evaluate this phenomenon. For this study, a wildfire smoke simulation was created to evaluate the effects of smoke exposure on beef cattle health, performance, and behavior. Heifers [n = 8 Angus x Hereford; body weight (BW) = 532 kg; 8 mo of age) were acclimated to individual pens in a completely closed barn for 7 d (d -7 to -1). Heifers were maintained in the barn for 36 d and exposed to wildfire smoke simulation for 7 consecutive days (d 0 to 7) while inside the barn. Heifers were individually fed alfalfa hay ad libitum, and feed intake was recorded daily. Health scores were collected daily from each heifer by 4 trained technicians, and blood samples were collected weekly for evaluation of acute phase proteins, cytokines, hormones, and hematology. Heifer behavior was recorded continuously using 4 cameras for the length of the study. Air quality was monitored daily every 1 min, using 2 air quality monitors. Preliminary data were analyzed using MIXED procedure of SAS. Heifers were considered the experiment unit serving as its own control, where variables were analyzed over time. Significance was set at P ≤ 0.05. Air quality, assessed with PM2.5, changed over time (P < 0.0001), reaching a daily average of 159 µg/m3 during the smoke simulation period. Although the air quality daily average was above the EPA standard (35 µg/m3), the smoke dissipated during the day, leading to wide variation in the 24 h-cycle (7.60 to 433 µg/m3; P < 0.0001), which might not truly represent air quality conditions during wildfires. Regardless, heifer plasma ceruloplasmin concentration increased over time (P = 0.05), reaching a peak during smoke exposure (29 mg/mL), which was maintained (P = 0.39; 27 mg/mL) for 1 wk post-smoke exposure before returning to nadir (23 mg/mL). An increase in acute phase proteins stimulated by smoke exposure has been observed in cattle naturally exposed to wildfires. Therefore, exposure to wildlife smoke might elicit an inflammatory response, potentially leading to reduced cattle performance, which could be accompanied by changes in behavior. This research was supported by the intramural research program of the USDA-NIFA, Award #2023-68008-39173.

Impacts of Anthropogenic Emissions and Open Biomass Burning in South Asia and Southeast Asia on Air Quality and Meteorology Over Southern China

AbstractThe emissions from South Asia and Southeast Asia significantly impact air quality and meteorological conditions in China. However, the individual or joint contributions of anthropogenic emissions and/or biomass burning from outside China to surface particulate matter with aerodynamic diameters less than 2.5 μm (PM2.5) and aerosol optical depth (AOD) over southern China have not been fully investigated. Here, five experiments were designed to investigate the impacts of these emissions in January (winter), March (pre‐monsoon), and October (post‐monsoon) for 2017. Aerosols from South Asia and Southeast Asia contributed less to southern China during winter and post‐monsoon seasons, whereas the wind patterns and emission intensity during the pre‐monsoon season were conducive to the transport of aerosols. During pre‐monsoon season, the total emissions contributed approximately 5.0 μg m−3 to surface PM2.5 in Xizang Province. Biomass burning in Southeast Asia increased PM2.5 in Yunnan Province by 37.9 μg m−3, while anthropogenic emissions increased it by 8.9 μg m−3. Transboundary aerosols can be transported to Xizang Province and Yunnan Province, primarily influencing PM2.5 below 2 km height. It mainly affected PM2.5 levels above the planetary boundary layer over southeast China. Aerosols from outside China can account for 79.5% and 54.8% AOD in Yunnan Province and southeast China, respectively. These aerosols reduced surface incident solar radiation by approximately 6%, leading to decreases in air temperature, wind speed, and boundary layer height. The findings are only applicable to the pre‐monsoon season.

Comparison of cooking emissions mitigation between automated and manually operated air quality interventions in one-bedroom apartments

We implemented a crossover study design exposing 15 participants to two indoor air quality conditions in the Well Living Lab. The first condition, the Standard Control Condition, resembled the ventilation and air supply of a typical home in the USA with a manually operated stove hood. The second condition, Advanced Control, had an automated: (i) stove hood, (ii) two portable air cleaners (PAC), and (iii) bathroom exhaust. The PM2.5 sensors were placed in the kitchen, living room, bedroom, and bathroom. Once the sensor detected a PM2.5 level of 15 μg/m3 or higher, an air quality intervention (stove hood, PAC or bathroom exhaust) in that space was activated and turned off when the corresponding PM2.5 sensor had three consecutive readings below 6 μg/m3. Advanced Control in the overall apartment reduced PM2.5 concentration by 40% compared to the Standard Control. The PM2.5 concentration difference between Advanced and Standard Control was ~ 20% in the kitchen. This can be attributed to using the stove hood manually in 66.5% of cooking PM2.5 emission events for 323.6 h compared to 88 h stove hood used in automated mode alongside 61.9 h and 33.7 h of PAC use in living room and bedroom, respectively.

Environmental injustice in the air quality for digital platform delivery workers in Bogotá, Colombia, 2021

Air quality is a matter of interest for public health due to its rapid deterioration in low- and middle-income countries and the effects of polluted air on the health of populations. To explore the air quality conditions in which digital platform delivery workers carry out their work, evaluating the localities of Kennedy and Usaquén in Bogotá, 2021. We developed a mixed parallel convergent study based on four sources of information: 1) Ethnographic observation in five commercial locations of the two localities; 2) Monitoring of PM10 and PM2.5 in 56 delivery routes using a low-cost sensor; 3) Daily logs of the routes to support the device data interpretation, and 4) A semi-structured interview applied to the drivers to explore their danger perception during the routes. We identified elements causing environmental injustice among digital platform delivery workers between the two study locations. The routes made by the delivery drivers in the locality of Kennedy registered higher concentrations of PM10 and PM2.5, compared to the values observed in Usaquén. The sources of air pollution identified by the delivery drivers through ethnographic observation and the router logbook showed the worst parameters in Kennedy. We evidenced that air quality, urban equipment, road infrastructure, mobile sources, and geospatial location are elements that mark the presence of environmental injustice for the digital platform delivery drivers in the studied localities. To reduce this inequity, it is necessary for digital delivery platforms and the district government to implement strategies that reduce the exposure and emission of air pollutants to protect the health of digital platform delivery workers.

A PM2.5 pollution-level adaptive air filtration system based on elastic filters for reducing energy consumption

Exacerbated by human activities and natural events, air pollution poses severe health risks, requiring effective control measures to ensure healthy living environments. Traditional filtration systems that employ high-efficiency particulate air (HEPA) filters are capable of effectively removing particulate matter (PM) in indoor environments. However, these systems often work without considering the fluctuations in air pollution levels, leading to high energy consumption. This study proposed a novel PM2.5 pollution-level adaptive air filtration system that combined elastic thermoplastic polyurethane (TPU) filters and an Internet of Things (IoT) system. The developed system can effectively adjust its filtration performance (i.e., pressure drop and PM2.5 filtration efficiency) in response to real-time air quality conditions by mechanically altering the structures of TPU filters. Furthermore, while operating in varied pollution conditions, the proposed system demonstrated remarkable reductions in pressure drop without notably compromising the pollution control capability. Finally, the energy consumption of the pollution-level adaptive air filtration system was estimated when applied in mechanical ventilation systems in different cities (Hong Kong, Beijing, and Xi’an) with various pollution conditions. The results revealed that, compared to a traditional fixed system, the annual energy consumption could be reduced by up to ∼26.4 % in Hong Kong.

Evaluating the impact of evolving green and grey urban infrastructure on local particulate pollution around city square parks

The relationship between green and grey urban infrastructure, local meteorological conditions, and traffic-related air pollution is complex and dynamic. This case study examined the effect of evolving morphologies around a city square park in Dublin and explores the twin impacts of local urban development (grey) and maturing parks (green) on particulate matter (PM) pollution. A fixed air quality monitoring campaign and computational fluid dynamic modelling (ENVI-met) were used to assess current (baseline) and future scenarios. The baseline results presented the distribution of PM in the study area, with bimodal (PM2.5) and unimodal (PM10) diurnal profiles. The optimal vegetation height for air quality within the park also differed by wind direction with 21 m vegetation optimal for parallel winds (10.45% reduction) and 7 m vegetation optimal for perpendicular winds (30.36% reduction). Increased building heights led to higher PM2.5 concentrations on both footpaths ranging from 25.3 to 37.0% under perpendicular winds, whilst increasing the height of leeward buildings increased PM2.5 concentrations by up to 30.9% under parallel winds. The findings from this study provide evidence of the importance of more in-depth analysis of green and grey urban infrastructure in the urban planning decision-making process to avoid deteriorating air quality conditions around city square parks.

Design industrial 5.1 air quality monitoring system and develop smart city infrastructure

In order to meet the requirements of urban construction and further urbanization of the country, the author proposes an industrial 5.1 air quality monitoring system to develop smart city infrastructure. The author utilizes a wireless network of lighting nodes to solve the cost and positioning accuracy issues of perception nodes covering a large area, achieving real-time alarm of urban air quality status and location of pollution occurrence. The author also adopted the latest design concept of monitoring systems combined with cloud platform interfaces, breaking the closed design of traditional IoT systems and enabling better utilization of air quality data. The test results indicate that: The communication distance of CC2530 can be maintained at around 70m under normal power, while the spacing between urban street lights is approximately 30m, which fully meets the project requirements. After two days of testing, the system alarm function and various functions are running normally. ConclusionThe key parts of the system have been tested and simulated, and ideal results have been obtained.

Long-term trends and probability distribution functions of air pollutant concentrations in the megacity of São Paulo

Air quality conditions in many urban areas have improved in the last decades as a consequence of air pollution control policies and regulations. Mitigation strategies have been successful in reducing the concentration of primary pollutants like inhalable particulate matter (PM10), but the control of secondary pollutants like tropospheric ozone (O3) is still challenging in megacities like the Metropolitan Area of São Paulo (MASP) in Brazil. To support the development of effective mitigation strategies, it is crucial to characterize the statistical behavior of air pollutant concentrations and its long-term evolution. Probability Density Functions (PDF) can be useful to model site-specific air quality conditions, providing estimates for the frequency of extreme pollution events and exceedance of air quality standards. The current study aims to characterize which PDF model better fits and characterizes the variability of PM10 and O3 concentrations in the MASP. For that, daily maximum moving average concentrations were analyzed between 2000 and 2023, characterizing the long-term trends and the frequency of exceedance of air quality standards. PM10 concentrations followed a lognormal PDF, with an expected value of 31 ± 15 µg.m-3. O3 followed a Gamma PDF, with an expected value of 68 ± 25 µg.m-3. A consistent long-term decrease was observed for PM10 (-1.04 ± 0.09 µg.m-3.yr-1), while O3 showed an increasing trend of 0.51 ± 0.04 µg.m-3.yr-1 in the summer. In recent years (2021-2023), the probability of exceedance of the World Health Organization guideline was 17.4 and 11.0%, respectively, for PM10 and O3. In 2020, a statistically significant increase in O3 expected values was observed, possibly associated with changes in the emission patterns of precursors due to the mobility restrictions imposed by the COVID-19 pandemic.

Racial and Socioeconomic Disparities in Pediatric Asthma Emergency Care

The aim of this paper is to build a model to compare the relative impact of race and ethnicity and insurance type on pediatric Emergency Department utilization for asthma. A binary logistic regression model using relevant variables from 2022 NHIS survey data was conducted to compare asthma ED visit odds ratios of racial and ethnic minorities and publicly insured children. The results suggest that insurance type has slightly greater influence over asthma ED visits than race and ethnicity. Publicly insured children had the highest unadjusted odds ratio (1.88 [CI 95% 1.09-3.24)]) followed by Non-Hispanic Black children (1.80 [0.92-3.52]) and Hispanic children (1.51 [0.80-2.86]). The data are possibly biased toward exclusion of well-controlled asthmatics that may affect the resulting odds ratios. More socioeconomic variables would provide a more complete analysis. The paper has direct implications for health service providers and policymakers. Providers are encouraged to provide equitable, culturally competent care to asthmatics and policymakers are encouraged to embrace policies that promote better access for Medicaid beneficiaries, improved air quality and housing conditions for low-income children, and better racial representation in the provider workforce. The paper collates many existing sources of research on asthma disparities as a backdrop for examination of recent survey data pertaining to differences in asthma ED utilization between populations. It supports existing theories and work being done to mitigate poor asthma outcomes in children that are exacerbated by biological and systemic factors.

Impact of Outdoor Air Pollutants Exposure on the Severity and Outcomes of Community-Acquired Pneumonia in Gabes Region, Tunisia.

Background Acute community-acquired pneumonia (CAP) is considered the leading cause of infectious death worldwide. Air pollution and prolonged exposure to airborne contaminants have been implicated in various respiratory conditions, including asthma and chronic obstructive pulmonary disease (COPD). However, the specific impact of air pollution on pneumonia, particularly CAP, remains underexplored. Given the rising levels of urban air pollution and its potential health ramifications, our study aimed to examine the association between exposure to outdoor air pollution and severity as well as the outcomes of pneumonia cases requiring hospitalization. Methodology A cohort analytical study with retrospective data collection was carried out in the pulmonology department of the Gabès University Hospital between January and October 2022. We compared levels of particulate matter less than or equal to 10µm in aerodynamic diameter (PM10),sulfur dioxide (SO2),ozone (O3), moisture and ambient temperature with severity and outcomes ofpneumonia requiring hospitalization. The choice of these specific pollutants and environmental factors was based on their established impact on respiratory health and their prevalence in the study region. Results Increased sulfur dioxide (SO2) levels were associated with increased use of non-invasive ventilation (NIV) (r = 0.400). Higher levels of particulate matter (PM10) were significantly associated with the development of lung abscesses. Similarly, increased humidity and ambient temperature were strongly correlated with the development of lung abscesses. Increased air SO2 levels were correlated with a higher CURB65 score (r = 0.299). High outdoor SO2 levels and increasing moisture content were associated with increased Pneumonia Severity Index (PSI) score (r = 0.303 and = 0.310, respectively). Higher levels of PM10 were associated with an increased risk of pleural effusion, a serious complication of pneumonia. Finally, higher ambient temperatures were correlated with more extensive opacities on chest X-rays (r = 0.706), suggesting the severity of pneumonia. Conclusion This study highlights the significant associations between environmental factors and various clinical parameters in pneumonia patients. The findings underscore the importance of considering environmental exposures, such as air quality and weather conditions, in understanding and managing the severity of pneumonia.

Indoor Environmental Quality and Effectiveness of Portable Air Cleaners in Reducing Levels of Airborne Particles during Schools’ Reopening in the COVID-19 Pandemic

Educational buildings tend to fail in the contagion containment of airborne infectious diseases because of the high number of children, for several hours a day, inside enclosed environments that often have inadequate indoor air quality (IAQ) conditions. This study aimed to assess indoor environmental quality and test the effectiveness of portable air cleaners (PACs) in alleviating airborne particle levels in schools of Central–Southern Spain during the period of reopening after the lockdown due to the COVID-19 outbreak. To accomplish this, three sampling campaigns were organized from September to December 2020 to consistently monitor temperature and relative humidity, carbon dioxide, and particulate matter in nineteen classrooms (seven school buildings). Results showed that although the recommendation of maintaining the windows open throughout the day seemed to be effective in promoting, in general, proper ventilation conditions (based on CO2 levels). For the colder campaigns, this practice caused notorious thermal comfort impairment. In addition, a great number of the surveyed classrooms presented levels of PM2.5 and PM10, attributable to outdoor and indoor sources, which exceeded the current WHO guideline values. Moreover, considering the practice of having the windows opened, the installation of 1 unit of PACs per classroom was insufficient to ensure a reduction in particle concentration to safe levels. Importantly, it was also found that children of different ages at different education levels can be exposed to significantly different environmental conditions in their classrooms; thus, the corrective measures to employ in each individual educational setting should reflect the features and needs of the target space/building.

Coupled indoor air quality and dynamic thermal modelling to assess the potential impacts of standalone HEPA filter units in classrooms

The quality of the classroom environment, including ventilation, air quality and thermal conditions, has an important impact on children’s health and academic achievement. The use of portable HEPA filter air cleaners is widely suggested as a strategy to mitigate exposure to particulate matter and airborne viruses. However, there is a need to quantify the relative benefits of such devices including the impacts on energy use. We present a simple coupled dynamic thermal and air quality model and apply it to naturally ventilated classrooms, representative of modern and Victorian era construction. We consider the addition of HEPA filters with, and without, reduced opening of windows, and explore concentrations of carbon dioxide (CO2), particulate matter PM2.5, airborne viral RNA, classroom temperature and energy use. Results indicate the addition of HEPA filters was predicted to reduce PM2.5 by 40–60 % and viral RNA by 30–50 % depending on the classroom design and window opening behaviour. The energy cost of running HEPA filters is likely to be only 1 %–2 % of the classroom heating costs. In scenarios when HEPA filters were on and window opening was reduced (to account for the additional clean air delivery rate of the filters), the heating cost was predicted to be reduced by as much as − 13 %, and these maximum reductions grew to − 46 % in wintertime simulations. In these scenarios the HEPA filters result in a notable reduction in PM2.5 and viral RNA, but the CO2 concentration is significantly higher. The model provides a mechanism for exploring the relative impact of ventilation and air cleaning strategies on both exposures and energy costs, enabling an understanding of where trade-offs lie.

A Review of In-Flight Thermal Comfort and Air Quality Status in Civil Aircraft Cabin Environments

The civil aircraft cabin is enclosed and highly occupied, making it susceptible to a decline in indoor environmental quality. The environmental quality of civil aircraft cabins not only depends on objective factors such as temperature, relative humidity, and the presence of air pollutants such as carbon dioxide (CO2), carbon monoxide (CO), ozone (O3), particle matter (PM), and volatile organic compounds (VOCs) but also the subjective factors pertaining to the perceptions and health symptoms of passengers and crew. However, few studies have thoroughly examined the air quality and thermal comfort parameters that are measured during in-flight testing in airplane cabins, as well as the passengers’ subjective perceptions. In order to evaluate the in-flight thermal comfort and air quality status, this study conducted a review of the recent literature to compile data on primary categories, standard limits, and distribution ranges of in-flight environmental factors within civil aircraft cabins. Following a search procedure outlined in this paper, 54 papers were selected for inclusion. Utilizing the Monte Carlo method, the Predicted Mean Vote (PMV) distributions under different exercise intensities and clothing thermal resistance were measured with the in-cabin temperature and humidity from in-flight tests. Recommendations based on first-hand data were made to maintain the relative humidity in the cabin below 40%, ensure wind speed remains within the range of 0–1 m/s, and regulate the temperature between 25–27 °C (for summer) and 22–27 °C (for winter). The current estimated cabin air supply rate generally complies with the requirements of international standards. Additionally, potential carcinogenic and non-carcinogenic risks associated with formaldehyde, benzene, tetrachloroethylene, and naphthalene were calculated. The sorted data of in-flight tests and the evaluation of the subjective perception of the occupants provide an evaluation of current cabin thermal comfort and air quality status, which can serve as a reference for optimizing indoor environmental quality in future generations of civil aircraft cabins.

Evaluation of Ambient Air Quality Levels at Various Locations Within Lead City University, Ibadan

BACKGROUND: Air quality in university environment is strongly affected by the student population explosion, climatic conditions and industrial activity within the institution. Monitoring the main air pollutants such as carbon dioxides, carbon monoxide and particulate matter may help control the most polluted areas of the institution and take measures to reduce the pollution.Universities are big metropolitan institutions with sizable populations of students, employees, and visitors. However, university settings can contribute to air pollution, with diverse activities such as lab work, cooking in dorms, and vehicle traffic, among others, causing interior and ambient air pollution. It is impossible to estimate how much air pollution affects the health and happiness of students without embarking on this type of research work. PURPOSE/AIM: Evaluation of meteorology parameters (i.e. temperature and relative humidity (RH)) and ambient air quality (CO, CO2 and particulate matter (PM2.5)) level at various locations within Lead City University, Ibadan is essential. METHODOLOGY: Ambient concentrations of carbon monoxide (CO), carbon dioxide (CO2), Relative humidity (RH), Temperature (TEMP), and Particulate Matter (PM2.5) were measured in 15 locations across Lead City University over a two-week period (between 25th of June 2023 and 3rd of August 2023) with Bosean air quality detector -T-201. RESULTS: Morning temperatures in all the locations measured ranging from 23.7°C to 29.2°C while afternoon temperature fluctuates more significantly, with the lowest recorded at 27.2°C and the highest at a notably warmer 35.8°C in all the locations measured. The morning RH levels ranging from 63.8% to 74.7% while afternoon RH values, ranging from 58.2% to 63.4%. The finding also shows that afternoon CO2 levels range from 468.5 ppm to 971.6 ppm, with Location 13 having an unusually high average. Morning CO levels ranging from 4.1 ppm to 49 ppm, with location 13 showing the most highest figure of 184.2 ppm. CO2 and CO levels are mostly within acceptable ranges as recommended by World Health Organization (WHO) that CO2 concentration levels in school buildings should kept below 1000 ppm and CO be below an average of nine parts per million (ppm) for any eight-hour period, and below 25 ppm for any one-hour period as an indicative benchmark of good indoor air quality (IAQ). Morning RH values range from 63.8% to 74.7%, with Location 1 having the highest average while afternoon RH values are between 58.9% and 67.7%, with Location 6 having the highest average. The relatively narrow variance in RH indicates that the dataset predominantly represents conditions with moderate humidity levels. Morning PM levels vary from 8.9 to 17.1µg/m³, suggesting diverse air quality conditions across the samples. In the afternoon, PM concentrations display a broader range, from 8.9 to an exceptionally high 436.1 in Location 13. Interestingly, all the air pollutants measured are still within the USA EPA permissible level of the National Ambient Air Quality Standard for PM2.5, which is 35 μg/m3, and CO, which is 40 mg/m3 and CO2 between 400 - 1,000ppm CONCLUSION: Variability in these parameters has implications for human health therefore, adequate ventilation and pollution control measures is thereby recommended for the university management in order to improve indoor air quality.

Impact of vegetation cover and land surface temperature on the seasonal tropospheric NO2 level variation from satellite observation

The Southeast Asian Region is one of the world’s regions that has historically had poor air quality conditions due to excess fossil fuel usage, coal combustion, and intensive deforestation activities. As the hub of economic activity, Indonesia’s Jakarta and Palembang cities have drawn significant attention from rural areas, leading to unrestrained urbanization in the past decade. Therefore, this study aimed to integrate the two urban centers of Indonesia, Jakarta and Palembang. It analyzed the variation of nitrogen dioxide (NO2) concentration, vegetation cover, and land surface temperature (LST) across all seasons from 2021 to 2023. We retrieved the tropospheric NO2 concentration data from the ozone monitoring instrument (OMI) aboard Aura Satellite, and obtained LST and the normalized difference vegetation index (NDVI) from Landsat 8 OLI images. We then used a Pearson correlation analysis to determine the relationship between the NO2 concentration, NDVI, and LST. The findings showed that Jakarta had higher levels of air pollution than Palembang, with rising LST and NO2 levels. During the study period, the mean NO2 concentration in Jakarta and Palembang was 3.25 × 1015 mol/cm2 and 0.87 × 1015 mol/cm2, respectively. While, the mean LST values in Jakarta and Palembang were 33.97 °C and 27.17 °C, respectively. The correlation results indicated a negative relationship between NO2 level and NDVI (r = −0.378, p < 0.01), while it obtained a positive association with LST (r = 0.421, p < 0.01). These results can be a chance for the authority to formulate a new strategy for mitigating air pollution, especially for NO2 pollutants in urban areas.