Poor Air Quality Research Articles

Volatile Organic Compounds in Early Childhood Education Facilities: Simultaneous Indoor and Outdoor Measurements in the Haifa Bay Area

Indoor air quality (IAQ) is of great importance, as people spend up to 90% of their time indoors, leading to significant exposure to air pollutants. The IAQ in early childhood education (ECE) facilities is of particular interest since young children are more vulnerable and poor air quality may have possible long-lasting impacts on them. In the present study, simultaneous indoor and outdoor VOC measurements were carried out in three ECE facilities in the Haifa Bay area, Israel. Three sampling campaigns were utilized, each lasted for a minimum of one week, encompassing four consecutive working days and at least one weekend. During working days, sampling was performed during daytime activity hours and at nighttime (off hours). Twenty-three VOCs were identified, quantified, and classified into six chemical groups—aromatic hydrocarbons, aliphatic alkanes, terpenes, alcohols, carbonyls, and “others”. The total outdoor VOC concentration was 23 μg m−3 during the daytime and 22 μg m−3 at night, with carbonyls and aromatic hydrocarbons accounting for ~80% of it. Despite the heterogeneity of the study area, outdoor concentrations depicted a smaller spatial and temporal variability than was observed indoors. In the ECE facilities, the total VOC reached 134 and 204 μg m−3 during the daytime and nighttime, respectively, and were strongly impacted by the air exchange rate. Carbonyls, alcohols, and terpenes were more prevalent indoors, accounting for 77.5–81.1% of the total. Their high indoor/outdoor ratios, especially for formaldehyde and limonene, suggest a significant contribution from indoor emission sources. Exposure calculations were compared to reference values for carcinogenic and non-carcinogenic effects. While the lifetime average daily dose (LADD) did not exceed the available reference values, the upper-limit estimates of continuous lifetime exposure to measured indoor levels indicate that formaldehyde and acetaldehyde surpassed their respective limits by factors of 10 and 3, respectively.

Quantifying the impact of air pollution from coal-fired electricity generation on crop productivity in India.

Air pollution from coal electricity generation is a major driver of poor air quality in India and its effects on human health have been extensively studied. Despite considerable evidence that the same pollution also reduces crop productivity, we lack similar quantitative assessments of coal electricity's crop damages. Here, we estimate rice and wheat crop losses from coal generation's nitrogen dioxide (NO2) emissions using a regression model that combines station-level electricity generation and wind direction, satellite-measured NO2, and its association with crop productivity. Coal emissions impact yields up to 100 km away from power stations. In parts of West Bengal, Madhya Pradesh, and Uttar Pradesh heavily exposed to coal-linked NO2, annual yield losses exceed 10%, equivalent to approximately 6 y worth of average annual yield growth in both rice and wheat in India between 2011 and 2020. While station-specific crop damages (value of lost output) are almost always lower than mortality damages (monetized value of annual premature PM2.5-related deaths), crop damage intensity (crop damage per GWh of electricity generated) is frequently higher than mortality damage intensity (mortality damage/GWh). Rice damage intensity exceeds mortality damage intensity at 58, and wheat damage intensity at 35 of the 144 power stations studied. The stations associated with the largest crop losses differ from those associated with the highest mortality. Co-optimizing for crop gains and mortality reduction slightly increases and meaningfully changes the distribution of social benefits from reducing emissions, highlighting the importance of considering crop losses alongside health impacts when regulating coal electricity emissions in India.

Dataset of microscale atmospheric flow and pollutant concentration large-eddy simulations for varying mesoscale meteorological forcing in an idealized urban environment.

By 2050, two-thirds of the world's population will live in urban areas under climate change, exacerbating the environmental and public health risks associated with poor air quality and urban heat island effects. Assessing these risks requires the development of microscale meteorological models that quickly and accurately predict wind velocity and pollutant concentration with high resolution, as the heterogeneity of urban environments leads to complex wind patterns and strong pollutant concentration gradients. Computational Fluid Dynamics (CFD) has emerged as a powerful tool to address this challenge by providing obstacle-resolved flow and dispersion predictions. However, CFD models are very expensive and require intensive computing resources, which can hinder their systematic use in practical engineering applications. They are also subject to significant uncertainties, particularly those arising from the mesoscale meteorological forcing and the internal variability of the atmospheric boundary layer, some of which are aleatory and thereby irreducible. Given these issues, the construction of CFD datasets that account for uncertainty would be an interesting avenue of research for microscale atmospheric science. In this context, we present the PPMLES (Perturbed-Parameter ensemble of MUST Large-Eddy Simulations) dataset, which consists of 200 large-eddy simulations (LES) characterizing the complex interactions between the turbulent airflow, the tracer dispersion, and an idealized urban environment. These simulations reproduce the canonical MUST dispersion field campaign while perturbing the model's mesoscale meteorological forcing parameters. PPMLES includes time series at human height within the built environment to track wind velocity and pollutant release and dispersion over time. PPMLES also includes complete 3-D fields of first- and second-order temporal statistics of the wind velocity and pollutant concentration, with a sub-metric resolution. The uncertainty of the fields induced by the internal variability of the atmospheric boundary layer is also provided. The computation of PPMLES required significant resources, consuming 6 million CPU core hours, equivalent to the emission of approximately 10 tCO2eq of greenhouse gases. This significant computational effort and associated carbon footprint motivates the sharing of the data generated. The added value of the PPMLES dataset is twofold. First, the perturbed-parameter ensemble of LES enables to quantify and understand the effects of the mesoscale meteorological forcing and the internal variability of the atmospheric boundary layer, which has been identified as a major challenge in predicting atmospheric flow and pollutant dispersion in urban environments. Secondly, PPMLES reference data can be used to benchmark models of different levels of complexity, and to extract key information about the physical processes involved to inform more operational modeling approaches, for example through learning surrogate models.

Effect of Pulmonary Rehabilitation on Dyspnoea, Functional Capacity and Thoracic Excursion in Chronic Obstructive Pulmonary Disease: A Case Report

Chronic Obstructive Pulmonary Disease (COPD) is a top cause of death. It is a lung disease that gets worse over time and includes emphysema and chronic bronchitis, causing blocked airflow. Hospitalisations, mortality and exacerbations of COPD are all increased by poor air quality. Significant occupational dosages include excessive pesticide exposure and home air pollution from burning coal, wood, animal dung and crop residues. Besides medical treatments, pulmonary rehabilitation is crucial for patient recovery from hospital to home. This study focuses on a 67-year-old man who visited a respiratory clinic with breathing problems for the past 15 days and chest pain for the past week. He also had a yellowish cough and occasional fever in the last week. With a 5-year history of COPD, he used an inhaler twice daily. His symptoms worsened in winter but stayed the same throughout the day. A chest X-ray confirmed a flare-up of COPD. A two-week pulmonary rehab plan with physical therapy was recommended. Before starting treatment, a baseline assessment was done, including measures like Modified Medical Research Council (mMRC) grading for breathlessness, 6-Minute Walk Distance (6MWD) and chest movement. Following the completion of the instructed pulmonary rehabilitation, significant improvements in dyspnoea grading, 6MWD, and thoracic were found. The findings imply that when modified to each patient’s specific requirements and capabilities, an organised pulmonary rehabilitation program can have an important beneficial therapeutic impact on those suffering from Acute Exacerbation of Chronic Obstructive Pulmonary Disease (AECOPD).

Change point detection to analyze air pollution and its economic effects: an exponentially weighted moving average perspective

Air pollution has a direct impact on every society, leading to consequential effects on the economy of a nation. Poor air quality adversely affects human health, resulting in various economic outcomes such as rising healthcare costs, diminished labor productivity, negative impacts on tourism and living standards, increased regulatory expenses for businesses, and heightened economic disparities. Effective control methods are essential to monitor factors influencing the economy, including air quality. The presence of toxic substances in the air reduces air quality, necessitating its monitoring through indices like PM10. Among statistical process control tools, control charts are the most prominent for efficient change point detection. This study introduces a new process monitoring tool that incorporates additional auxiliary information, if available, alongside the main variable of interest. The proposed methodology ensures detection ability remains robust, even under disturbances in the auxiliary variable. Furthermore, mathematical analyses reveal that many existing statistical quality control tools become special cases of the proposed structure for specific sensitivity parameter values. Evaluated through properties of run length distribution, the proposed chart allows control of the robustness-efficiency balance by adjusting its sensitivity parameter. A practical implementation demonstrates the effectiveness of the chart in monitoring air quality data.

Genetic modifiers of asthma response to air pollution in children: An African ancestry GWAS and PM2.5 polygenic risk score study.

Ambient air pollution (AAP) is linked to asthma outcomes, but predicting individual risk remains challenging. Understanding genetic contributors to AAP sensitivity may help overcome this gap. To determine if single nucleotide polymorphisms (SNPs) are associated with AAP sensitivity in children with asthma. We complete a GWAS in pediatric patients with asthma frequently exposed to AAP, comparing patients with exacerbations following spikes in AAP to patients without this temporal association and calculate a polygenic risk score (PRS) for PM2.5. This PRS was validated using internal data and data from the All of Us cohort. We included 6023 patients in the GWAS, restricted to the African ancestry cohort due to the association between AAP exposure and race. Three loci reached genome-wide significance, including rs111970601, associated with CO sensitivity (odds ratio [OR] 6.58; P=1.63×10-8) and rs9836522 with PM2.5 sensitivity (OR 0.75; P=3,87×10-9), both externally validated. PRS z-scores were associated with increased asthma exacerbations in patients frequently exposed to poor air quality (β=0.15; P=2.67×10⁻⁵). Spirometry data from 4138 patients showed that having a high PRS was associated with lower FVC z-scores in patients frequently exposed to AAP (β=-0.44; P=0.035). External validation confirmed a significant interaction between high PRS and frequent AAP exposure (β=0.30; P=0.012) CONCLUSIONS: We associate specific SNPs with AAP-related asthma exacerbations and introduce a PM2.5 sensitivity PRS, paving the way for future research aimed at protecting genetically predisposed patients.

Racial residential segregation is associated with ambient air pollution exposure after adjustment for multilevel sociodemographic factors: Evidence from eight US-based cohorts.

We examined if racial residential segregation (RRS) - a fundamental cause of disease - is independently associated with air pollution after accounting for other neighborhood and individual-level sociodemographic factors, to better understand its potential role as a confounder of air pollution-health studies. We compiled data from eight large cohorts, restricting to non-Hispanic Black and White urban-residing participants observed at least once between 1999 and 2005. We used 2000 decennial census data to derive a spatial RRS measure (divergence index) and neighborhood socioeconomic status (NSES) index for participants' residing Census tracts, in addition to participant baseline data, to examine associations between RRS and sociodemographic factors (NSES, education, race) and residential exposure to spatiotemporal model-predicted PM2.5 and NO2 levels. We fit random-effects meta-analysis models to pool estimates across adjusted cohort-specific multilevel models. Analytic sample included eligible participants in CHS (N = 3,605), MESA (4,785), REGARDS (22,649), NHS (90,415), NHSII (91,654), HPFS (32,625), WHI-OS (77,680), and WHI-CT (56,639). In adjusted univariate models, a quartile higher RRS was associated with 3.73% higher PM2.5 exposure (95% CI: 2.14%, 5.32%), and an 11.53% higher (95% CI: 10.83%, 12.22%) NO2 exposure on average. In fully adjusted models, higher RRS was associated with 3.25% higher PM2.5 exposure (95% CI: 1.45%, 5.05%; P < 0.05) and 10.22% higher NO2 exposure (95% CI: 6.69%, 13.74%; P < 0.001) on average. Our findings indicate that RRS is associated with the differential distribution of poor air quality independent of NSES or individual race, suggesting it may be a relevant confounder to be considered in future air pollution epidemiology studies.

Poor Indoor Air Quality Linked to Cancer &amp; Other Issues

Poor Indoor Air Quality Linked to Cancer &amp; Other Issues

Integrated Analysis of Indoor Air Quality and Fungal Microbiota in Educational Heritage Buildings: Implications for Health and Sustainability

Indoor air quality is paramount for the health and well-being of individuals, especially in enclosed spaces like office buildings, schools, hospitals, and homes where people spend a significant amount of time. Ensuring good indoor air quality is not only essential for reducing symptoms such as headaches, eye and respiratory irritation, fatigue, and difficulty in concentration, but it is also a key component of sustainable building practices aimed at promoting long-term health and environmental balance. This study aims to explore the impact of the microclimate and fungal microbiota on the health and cognitive performance of occupants in a university classroom, which is part of a cultural heritage building. The research delves into various microclimatic parameters, including temperature, relative humidity, CO2, volatile organic compounds, O2, and particulate matters (PM2.5 and PM10), to understand their influence on the development of microbiota and the manifestation of symptoms associated with Sick Building Syndrome. Over the course of a year-long investigation, microbiological samples were collected, revealing the presence of 19 fungal species, with Cladosporium, Alternaria, and Aureobasidium being the most prevalent genera. These species were found to thrive in an environment characterized by inadequate ventilation, posing potential health risks to occupants, such as allergic reactions and respiratory infections. Microclimatic parameter values such as mean temperature of 22.9 °C and mean relative humidity of 38.5% indicated moderate conditions for fungal proliferation, but occasional high levels of PM2.5 and CO2 indicated periods of poor indoor air quality, negatively influencing the comfort and health of the occupants. The questionnaires completed by 190 students showed that 51.5% reported headaches, 44.2% frequent sneezing, and 43.7% severe fatigue, linking these symptoms to increased levels of CO2 and PM2.5. The novelty of the study lies in the integrated approach to indoor air quality assessment in a heritage educational building, highlighting the need for improved ventilation and air management to enhance health and cognitive performance, while emphasizing sustainable indoor environment management that balances occupant well-being with the preservation of cultural heritage.

Analisis Kualitas Udara dalam Ruangan di Kampus Malalayang Fakultas Kedokteran Universitas Sam Ratulangi Berdasarkan Uji Parameter Fisika

Poor air quality can cause various health problems, including respiratory problems, allergies, stress, and comfort. By testing air quality using physical parameters such as temperature, humidity, and light intensity, this research can help optimize indoor air quality. This study aims to determine air quality based on physical parameter tests in classrooms at the Malalayang Campus, Faculty of Medicine, Sam Ratulangi University. This research method is observational and descriptive. Descriptive research to get an idea of temperature, humidity, and light intensity directly in the room of the Faculty of Medicine, Sam Ratulangi University using tools such as thermohygrometer and luxmeter. The results of this study compare measurements in 3 conditions, namely before class, during class and after class. It was found that the temperature and humidity were relatively increasing, and the lighting decreased in the temporary conditions of the classroom which had an impact on the comfort of students during learning activities. This study concluded that the physical quality of the air with variables of temperature, humidity, and lighting in the room of the Malalayang Campus, Faculty of Medicine, Sam Ratulangi University is not in accordance with the standards set by the Regulation of the Minister of Health of the Republic of Indonesia Number 2 of 2023 concerning the Implementation Regulation of Government Regulation Number 66 of 2014 concerning Environmental Health.

Improved daily PM2.5 estimates in India reveal inequalities in recent enhancement of air quality.

Poor ambient air quality poses a substantial global health threat. However, accurate measurement remains challenging, particularly in countries such as India where ground monitors are scarce despite high expected exposure and health burdens. This lack of precise measurements impedes understanding of changes in pollution exposure over time and across populations. Here, we develop open-source daily fine particulate matter (PM2.5) datasets at a 10-kilometer resolution for India from 2005 to 2023 using a two-stage machine learning model validated on held-out monitor data. Analyzing long-term air quality trends, we find that PM2.5 concentrations increased across most of the country until around 2016 and then declined partly due to favorable meteorology in southern India. Recent reductions in PM2.5 were substantially larger in wealthier areas, highlighting the urgency of air quality control policies addressing all socioeconomic communities. To advance equitable air quality monitoring, we propose additional monitor locations in India and examine the adaptability of our method to other countries with scarce monitoring data.

Particulate matter concentrations and health risks associated with cow hides singeing in Abattoirs in Benin City, Nigeria

Introduction: The singeing of animals is one of the major sources of human exposure to air pollutants in abattoirs especially in developing countries and it can cause various adverse health effects among the workers. This study assessed the concentrations of particulate matter in selected abattoirs and examined the associated health risks among the workers in Benin City, Nigeria. Materials and methods: Measurements of Particulate Matter (PM2.5 and PM10) were performed in three areas in the abattoirs for three months, and the noncarcinogenic risks of exposure to PM2.5 and PM10 were determined via Hazard-Quotients (HQs) and Hazard Indices (HIs). The occurrence and relationship between the reported risk factors and health effects among abattoir workers were also examined using the modified American Thoracic Standard (ATS) respiratory symptoms questionnaire. Results: The levels of PM2.5 and PM10 in the hide roasting areas were significantly higher (p&gt;0.05) than those at other sampling points across the abattoirs. The indoor and outdoor ratio (I/O) of the particulates was greater than 1.0 in almost all the abattoirs. The concentrations of PM2.5 and PM10 were higher than the permissible air quality standards. The HQ values exceeded 1.0 in all the sampling areas, implying that exposure to particulate concentrations could result in adverse health effects in abattoirs. The chi-square test revealed significant associations between reported risk factors such as: age, work experience, duration of exposure, and use of Personal Protection Equipment (PPE) and health effects (dry cough, eye irritation, asthma, difficulty breathing, sore throat, and chest pain) among abattoir workers. Conclusion: This study revealed that hide roasting at abattoirs results in poor air quality in the abattoir environment and contributes to adverse health effects among workers.

Visualization microclimate scenarios of Classical Chinese Garden in Suzhou, China

The intensification of urbanization worldwide, particularly in China, has led to significant challenges in maintaining sustainable urban environments, primarily due to the Urban Heat Island (UHI) effect. This effect exacerbates urban thermal stress, leading to increased energy consumption, poor air quality, and heightened health risks. In response, urban green spaces are recognized for their role in ameliorating urban heat and enhancing environmental resilience. This paper has studied the microclimate regulation effects of three representative classical gardens in Suzhou—the Humble Administrator’s Garden, the Lingering Garden and the Canglang Pavilion. It aims to explore the specific impacts of water bodies, vegetation and architectural features on the air temperature and relative humidity within the gardens. With the help of Geographic Information System (GIS) technology and the Inverse Distance Weighted (IDW) spatial interpolation method, this study has analyzed the microclimate regulation mechanisms in the designs of these traditional gardens. The results show that water bodies and lush vegetation have significant effects on reducing temperature and increasing humidity, while the architectural structures and rocks have affected the distribution and retention of heat to some extent. These findings not only enrich our understanding of the role of the design principles of classical gardens in climate adaptability but also provide important theoretical basis and practical guidance for the design of modern urban parks and the planning of sustainable urban environments. In addition, the study highlights GIS-based spatial interpolation as a valuable tool for visualizing and optimizing thermal comfort in urban landscapes, providing insights for developing resilient urban green spaces.

AeroGlan: A Smart and Sustainable Plant Species Estimator For Organic And Localized Air Filtering

Introduction: Human health is significantly compromised by air pollution, especially by local air quality. The majority of our society spends their lives in a confined geographical location, which if subjected to air pollution can expose them to long-term air contamination. It is also possible that poor air quality can pose serious health risks, especially to susceptible individuals thereby impacting their lifestyle. Air quality can be improved with appropriate plantation, but they are underutilized. Various air purification devices have been developed in response to the everincreasing air pollution level. Method: However, artificial means of air purification are not very viable in terms of cost, accessibility to society, and reliable tools to purify air. This research integrates traditional solutions with modern technology to counter air purification by selectively using plant species and placing them in desired locations suitable for urban settings. The study aims to measure the constituents of various air pollutants spanning across regions to identify and accumulate pollution data using IoTbased smart devices, remit, and feed this information to cloud-based storage for further processing. In addition, advanced predictive intelligence is utilized to determine the plant species that can suffice the need for air purification through organic means in a given geographical zone resulting in enhancement of Air Quality (AQ), with minimal cost, prolonged shelf life, future proof and non-detrimental consequences. Results: Implementation outcome gives a promising outcome. Accurate readings of various air pollutants are aggregated. Suitable trees are identified to tackle these pollutants and their absorbing capacity is determined. Various predictive methods are employed and the random forest model recorded the best results. The sensory units of the model successfully captured the pollutant data and any major fluctuations were reported. The prediction pipeline recorded a mean precision, recall, and f-score value of about 0.95, 0.92, and 0.94 respectively while the mean accuracy of 0.965 was also noted. The observed training and validation accuracy with our model were 0.96 and 0.93 respectively. Conclusion: Hence, the proposed ‘AeroGlan’ model may be locally applied as an air pollutants monitoring device and also to suggest suitable plant species required to counter air contamination in that locality.

Assessing the impact of air quality on solar energy production

In studying fires and other natural disasters, air quality is often used to assess their severity. This study explores the relationship between air quality and solar energy production, focusing on how air pollutants affect solar output. We analyze four air quality indicators—ozone (O₃), nitrogen dioxide (NO₂), carbon monoxide (CO), and sulfur dioxide (SO₂)—and their effects on photovoltaic performance using data analysis and geographic information systems. This research highlights the importance of understanding this connection to improve solar panel placement and efficiency. Hypothesis testing confirms a negative correlation between poor air quality and solar energy production.

Estimation of neighborhood scale PM2.5 impacts in rural towns in the Purepecha region of Mexico.

The impact of cooking with solid fuels on neighborhood-scale PM2.5 concentrations in rural towns and communities is poorly quantified due to the lack of credible ground-level monitoring sites and spatial heterogeneity at a scale that is below the resolution of remote sensing GEOS-Chem hybrid models. Emissions of PM2.5 from use of open fires for cooking in rural Mexico are known to cause poor indoor air quality. The effectiveness of different intervention strategies to reduce such pollution exposures also varies because of different local building densities and source intensities. In this study, the effectiveness of stove intervention strategies on the neighborhood-scale PM2.5 concentrations were evaluated in a village Cucuchucho, located in the Purepecha highlands of Mexico. The Quick Urban & Industrial Complex (QUIC) is deployed in the assessment. The model's performance in simulating interactions between pollutants and flow around building structures is validated through comparison with a water channel experiment, which shows good quantitative agreement. The case study simulation results demonstrate that upstream households contributed ∼30% of concentrations, and current trends will not meet WHO air quality guidelines or interim targets. The magnitude of neighborhood-scale PM2.5 concentrations depends on the intervention and community structure. Based on these simulations, a statistical model is presented to estimate ambient neighborhood PM2.5 pollution concentrations for more communities at a regional level. The statistical model allows neighborhood PM2.5 pollution to be included in estimates of health burdens from household pollution in Mexico using readily accessible community parameters.

The health effects of poor air quality.

Smoke, particularly from wildfires and other combustion sources, is a significant contributor to air pollution, comprising a complex mixture of particulate matter and gaseous pollutants. Prolonged exposure to smoke can exacerbate respiratory diseases, such as asthma and chronic obstructive pulmonary disease, leading to increased ED visits and hospitalizations. This article examines the significant health risks associated with air pollution, particularly chronic diseases and acute respiratory conditions, and discusses the emergency treatment of acute respiratory distress from exposure.

Prevalence of indoor air pollutants from First Nation homes in North Central British Columbia, Canada

ABSTRACT Poor indoor air quality poses significant health risks. This study addresses the gap in knowledge regarding the prevalence of indoor air pollutants in remote and rural First Nation communities in north-central British Columbia, Canada. Dust samples from 75 homes were collected and analysed for house dust mites, pet allergens, mould antigens, and bacterial endotoxins. Indoor air quality parameters, including carbon monoxide, carbon dioxide, particulate matter, temperature, and humidity, were measured. A detailed questionnaire on household characteristics and potential pollutant sources was administered. Homes exhibited exposure to multiple pollutants, with wood stove smoke identified as a primary source. Felis domesticus (cat allergen) and Canis familiaris (dog allergen) were prevalent, with detectable levels in 64% and 60% of homes, respectively. Bacterial endotoxins were present in all households. One-third of homes exceeded recommended thresholds for 3 or more pollutants. This study provides critical insights into the prevalence and magnitude of indoor air pollutants, contributing to a broader initiative to characterise respiratory health in First Nations communities. While many homes in First Nations communities had acceptable air quality, one-third of homes exceeded thresholds for 3 or more pollutants. The results can guide ongoing community efforts to address housing concerns and advocate for increased federal funding.

Design and application of CNN for emission detection through thermal imagery

Motorcycle exhaust emissions (EE) that do not meet regulatory standards present a significant environmental and public health issue, particularly given the rising number of motorcycles in densely populated areas. These emissions release pollutants such as carbon monoxide (CO), hydrocarbons (HC), and nitrogen oxides (NOx), which contribute to poor air quality and have adverse effects on human health. Traditional emission testing methods using gas analyzers, while commonly used, face limitations such as sensitivity to environmental fluctuations, the necessity for frequent recalibration, and an intensive testing process requiring specialized expertise. This study addresses these issues by developing an innovative method for emission detection using Convolutional Neural Networks (CNN) applied to thermal images of motorcycle exhausts. The research method involves five key stages: data acquisition, dataset formation, CNN model design and training, model testing, and validation. Thermal images were gathered from 27 motorcycles, representing various brands and engine configurations common in Indonesia, and each image set included 100 samples for both emission-compliant and non-compliant categories. By analyzing thermal patterns, the CNN model was trained to accurately detect combustion patterns indicative of emission status based on the lambda value. This approach enables the model to generalize across different motorcycle models, offering practical adaptability for widespread implementation. The results demonstrate that the CNN model delivers high predictive accuracy, precision, recall, and F1-score, making it a robust tool for assessing motorcycle emission compliance. This CNN-based approach provides a practical solution for real-time, large-scale emission monitoring and regulatory enforcement, reducing dependency on conventional methods. Its scalability and adaptability position it as a valuable advancement in emission monitoring technology, with significant potential for supporting environmental standards and improving air quality management

Survey of Association Between Indoor CO and CO2 Levels and Symptoms of Sick Building Syndrome Among Employees at Alang-Alang Lebar and Plaju Health Centers

Poor air quality and exceptionally high CO and CO2 pollutants can be contributing factors to Sick Building Syndrome (SBS). This study evaluated the relationship between individual factors and CO and CO2 levels with Sick Building Syndrome (SBS) symptoms in health center employees. 74 respondents work in the room of Alang-Alang Lebar and Plaju Health Center. Then, the respondents will fill out a questionnaire related to SBS symptoms. The rooms in the selected health centers will be measured for CO and CO2 levels. The results of filling out the questionnaire will be analyzed to determine whether SBS or not, and the results of measuring CO and CO2 levels will also be categorized as normal or not. Univariate and bivariate analyses related to individual employee factors, SBS symptoms, and CO and CO2. The results of 74 respondents showed that 36 respondents (48.6%) experienced SBS. The highest SBS symptoms were red, itchy, dry, or watery eyes (75%), and symptoms of dizziness or headache (71.4%). The highest CO level measurement result was seven ppm, the lowest was six ppm, while the highest CO2 level was 561 ppm, and the lowest was 498 ppm. These measurement results are still within the normal limits of the standards set. Bivariate test results showed that individual factors, CO and CO2, are not significantly related to SBS symptoms (p &gt; 0.05).