Air Quality Metrics Research Articles

Seasonality in the incidence of acute sinusitis, air pollutant levels, and climate.

Emerging evidence from epidemiological studies highlights the interaction between air quality metrics and sinusitis occurrence. This study investigates the relationship between acute sinusitis incidence, air pollutant levels, and climatic conditions in Taiwan from 2008 to 2017. This study extracted outpatient claims data from the Longitudinal Health Insurance Database 2010. We computed seasonal incidence rates of acute sinusitis episodes per 1000 population over 120 months, stratifying the data by gender and three age groups: 20-44, 45-64, and 65 years and older. Addressing the seasonal variability inherent in our dataset, we utilized Auto-Regressive Moving Average (ARMA) models to analyze each variable as a univariate time series influenced by its historical values. The analysis reveals that, except relative humidity, all other climatic factors including CO, NO2, SO2, PM10, O3, ambient temperature and rainfall demonstrated significant crude correlations with the rates of acute sinusitis. The ARIMA test suggested that seasonality plays a significant role in influencing sinusitis episodes across all age groups. Specifically, individuals experience significantly higher incidence rates during winter compared to spring. These findings underscore winter as a period with notably higher incidence rates of acute sinusitis, even after adjusting for meteorological and air pollution variables. This study provides comprehensive evidence of the significant associations between acute sinusitis incidence, air quality, and climatic factors in Taiwan.

A Place-Based County-Level Study of Air Quality and Health in Urban Communities

This study investigates the relationships between air quality, social vulnerability, and health outcomes at the census tract-level in Harris County, Texas. Spatial and regression analyses were conducted using sociodemographic data, air quality indicators, including PM2.5, diesel particulate matter (DPM), nitrogen dioxide (NO2), and ozone, and health metrics, such as coronary heart disease, chronic obstructive pulmonary disease (COPD), asthma, and stroke prevalence. The results indicated variability in sociodemographic challenges, air pollution, and health outcomes. Social vulnerability strongly correlated with increased prevalence of respiratory and cardiovascular diseases, notably COPD, asthma, and stroke. The air quality metrics showed significant geospatial variability: PM2.5 and NO2 were concentrated centrally near transportation corridors, DPM was elevated near eastern industrial regions, and ozone peaked in western parts of the county, potentially due to atmospheric transport and photochemical processes. PM2.5 exposure significantly correlated with increased cardiovascular and respiratory health outcomes, particularly at elevated concentrations. In contrast, ozone demonstrated a plateauing effect, increasing the health risks but with a diminishing impact at higher concentrations. The correlations between social vulnerability and air quality were modest, suggesting homogenous distributions of PM2.5, NO2, and DPM across socioeconomically diverse areas, whereas ozone exposure slightly increased with higher social vulnerability. The findings pointed to the complexity of spatial relationships between socioeconomic status, air pollution, and health, highlighting the need for additional monitoring and targeted interventions to improve health outcomes in socio-demographically and economically challenged communities.

Expository Assessment of Air Quality Scenario with Sentinel-5 Precursor TROPOMI Explorer Sensor

Air pollution is the atmospheric state in which the concentration of specific elements has adverse impacts on human health as well as the environment, including global warming, transportation disruptions, acid rain, and ozone layer depletion. Nowadays, a large portion of the world’s population lives in urban areas, where population growth and the increasing number of vehicles have significantly worsened air quality. Clean air is essential for the health and well-being of any region’s environment and its inhabitants. Henceforth, the primary focus of this research endeavor is to meticulously scrutinize the levels of key air pollutants, notably nitrogen dioxide (NO₂) and sulfur dioxide (SO₂), leveraging satellite remote sensing data obtained from TROPOMI EXPLORER across a network of monitoring stations dispersed throughout Lucknow City. Additionally, it aims to meticulously dissect groundbased air quality monitoring data to validate and amalgamate the observations derived from satellite technology. Furthermore, it analyzes the distribution of concentrations of primary air pollutants, encompassing NO₂, SO₂, and PM₁₀, within Lucknow City, juxtaposing them against the stringent benchmarks stipulated by the World Health Organization (WHO) for air quality standards. Moreover, it endeavors to ascertain the deleterious health ramifications of air pollution by correlating air quality metrics with health outcomes among the denizens of Lucknow City through a meticulously crafted questionnaire survey. The scrutiny of satellite imagery unveiled a conspicuous escalation in the concentration of air pollution parameters vis-à-vis the WHO’s prescribed thresholds, portending consequential adverse ramifications for both the environment and human health.

Dynamic Route Planning for Urban Green Mobility: Development of a Web Application Offering Sustainable Route Options to Commuters

Rising travel distances and worsening urban air quality underscore the need for improved route choices to reduce energy consumption and exposure. Whereas traditional route-planning systems focus mainly on distance and time, this study develops a methodology that provides users with wider choices in route selection. The paper presents a web-based tool designed to assist commuters in selecting eco-friendly routes. This tool combines a React.js frontend for intuitive user input with a Go back end that processes real-time data to rank and deliver route suggestions based on air pollution and energy consumption, in addition to distance and time. Real-time application programming interfaces from Mapbox are used for traffic conditions, and the World Air Quality Index is used for air-quality metrics, which are integrated into the GraphHopper multimodal routing engine. As a result, the tool offers five route choices: the shortest route, the fastest route, the route with the least exposure to air pollution (LEAP), the route with the least energy-consumption route (LECR), and a suggested route that considers all four parameters. Applied to Delhi, the tool demonstrates that the LEAP route can reduce exposure by up to 53%, although it may lead to a 42% increase in travel time in Central Delhi. This study is the first to showcase the LECR in India, demonstrating that selecting this route can save up to 28% of energy in South Delhi. Daily variability analysis conducted at different hours revealed that peak-hour times correlate with increased exposure and energy consumption. Although these results are initial and could be further enhanced with better data, the preliminary results are encouraging.

Predictive machine learning and geospatial modeling reveal PM10 hotspots and guide targeted air pollution interventions in Addis Ababa, Ethiopia

Air pollution is a critical twenty-first century environmental and public health challenge that is linked to millions of deaths and ecological harm. Accurate prediction of pollutants such as PM10 is vital for mitigation and urban sustainability. This study combines geospatial modeling with three machine learning algorithms (MLAs), Random Forest (RF), K-Nearest Neighbor (KNN), and Naïve Bayes (NB), to identify PM10 hotspots in Addis Ababa, Ethiopia. PM10 data from 11 stations (August 2021–August 2023) were analyzed alongside 25 atmospheric, climatic, anthropogenic, and pollution source predictors. A concentric zonal approach was used to assess spatial variability across radial distances and directional sectors and was supported by 30 m-resolution satellite imagery, climate data, and local geospatial repositories. The model accuracies were 0.95 (KNN), 0.93 (RF), and 0.88 (NB), with distinct performance trade-offs: RF predicted the largest “Good” PM10 zones (78.98 km2), KNN highlighted the most “UnHealSen” areas (279 km2), and NB predict “Moderate” coverage (311 km2). High PM10 concentrations clustered in eastern and northwestern sectors, aligning with industrial zones and traffic density. The results demonstrate the efficacy of MLAs and geospatial integration in producing high-resolution pollution maps. We advocate for targeted emission controls in hotspots, expanding public transit to reduce vehicular emissions, and incorporating air quality metrics into urban planning. This study advances air quality assessment methods for rapidly urbanizing regions, providing data-driven strategies to combat pollution and enhance ecological resilience in African cities.

DGCA3QM: DESIGN OF A DUAL GENETIC ALGORITHM BASED AUTOREGRESSION MODEL FOR CORRELATIVE PREDICTION OF AIR QUALITY METRICS

DGCA3QM: DESIGN OF A DUAL GENETIC ALGORITHM BASED AUTOREGRESSION MODEL FOR CORRELATIVE PREDICTION OF AIR QUALITY METRICS

Artificial Intelligence Based Public Air Purifying System

Introduction: Air pollution poses a significant threat to public health and well-being, necessitating innovative solutions to mitigate its adverse effects. This research presents an AI-based Public Air Purifying System (PAPS) designed to enhance outdoor air quality in urban environments, densely polluted locations and other similar locations. The proposed system leverages artificial intelligence algorithms to monitor, analyze, and respond dynamically to pollution levels in real-time. Objectives: A system for real-time surveillance of air quality metrics, including particulate matter and contaminants, using IoT-enabled sensors. Develop a system that autonomously initiates and controls air purifiers in response to pollution metrics, guaranteeing energy efficiency and peak performance. Utilize AI algorithms to enhance airflow distribution for optimal pollutant elimination in various regions. Methods: The proposed technology integrates AI-driven optimization with a sophisticated multi-stage filtration and purification process. The prototype has a modular design with an AI system that adaptively modifies purification levels according to real-time air quality data, guaranteeing efficient and precise pollutant elimination in various settings. Results: The show results in the air pollution ppm values before and after using the purifier. The results show the data according to the current date and time. A steady decrease in PPM values indicates continuous improvement in air quality over time. PPM values fluctuate, initially rising and then stabilizing, indicating dynamic changes in air quality following the process. Conclusions: The AI-Based Public Air Purifying System demonstrates a unique and significant method for addressing air pollution. Its cognitive talents facilitate real-time modifications, although enhancement is required to guarantee dependability and efficacy. With further developments, this technology may significantly improve urban air quality, mitigate health hazards, and foster sustainable living in highly populated regions

Examining the changes in the amounts of PM2.5, PM10 and CO2 in the environment in debarking operations

One of the most time-consuming stages in the production of coniferous trees is debarking the bark of the product. During debarking with a chainsaw apparatus, many air pollutants such as bark residues, dust, sawdust, fuel material, etc. are mixed into the environment. This situation reduces the air quality for the workers. This study aims to investigate the impact of the debarking process on worker health and environmental impacts in coastal pine plantations in Sarıyer region. The air pollution parameters PM2.5, PM10, CO2 and HCHO levels during the debarking process were measured using a Temtop M2000 air quality meter. The results showed that when comparing these values to standard air quality metrics, it's clear that the peak values of PM2.5, PM10, and CO2 (247 μg/m3, 411 μg/m3, and 2549 ppm) result in a "Very Unhealthy" environment. The average values for all three (83.91 μg/m3, 121.69 μg/m3, and 614.19 ppm) are classified as "Unhealthy" for PM2.5, and "Moderate" for PM10 and CO2. The study results emphasize that the log debarking process is associated with bark volume, CO2 release and PM2.5 levels. Therefore, it is vital that workers in harvesting use maximum protective equipment, especially respirators. There is also a need to raise awareness and provide training for forest workers on the use of protective equipment. These findings may contribute to the development of effective policies on the health of workers in wood production and environmental quality.

The Effect of Meteorology and Air Quality to the Covid-19 Cases in Malaysia: A Multivariate Deep Learning Approach

In October 2022, the World Health Organization (WHO) reported that over six hundred million people globally had been infected by the COVID-19 pandemic, leading to six million deaths. Malaysia, like many other countries, has experienced significant economic and societal impacts due to COVID-19. Previous research has identified meteorological conditions and air quality as critical factors influencing the spread of infectious diseases like influenza. In this study, we explore the impact of meteorological and air quality factors on COVID-19 case numbers in Malaysia, focusing on a case study in the Hulu Langat district of Selangor state, utilizing a deep learning approach. Our model, which employs a neural network architecture incorporating both Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN), was trained using multivariate time-series data. This data included meteorological and air quality metrics from the Department of Environment, Malaysia, and COVID-19 case data collected from the Hulu Langat Health Office. We prepared three datasets for predictive modeling: one combining all features, one including only meteorological data, and another with only air quality data. Our results indicate that the CNN model outperformed the LSTM model in terms of prediction accuracy. Furthermore, the dataset incorporating all features resulted in the lowest prediction error, compared to datasets with only meteorological or air quality features. Feature importance analysis showed that air quality factors were the most significant predictors, suggesting that air quality has a greater impact on COVID-19 case numbers than meteorological factors.

Abstract 4142708: DIgital REgistry for CardiomeTabolic conditions – Diabetes Mellitus (DIRECT-DM): A Large, Pragmatic, Multisite, Diverse Longitudinal Registry of Type 2 Diabetes

Background: Despite the rising prevalence of T2DM, contemporary information on its longitudinal care and outcomes is limited and largely derived from small, self-selected cohorts. We describe the development and patterns of care of a digital pragmatic cohort of all T2DM patients seeking care in the multisite network of an integrated health system. Methods: DIRECT-DM is a digital registry of T2DM patients seeking care across multiple hospitals and community practices in CT and RI from 2013 to 2023. From 223,932 individuals with T2DM, those with regular care (≥1 visit every 2 years) were included in the longitudinal cohort. Mortality was derived from the CT Death Index, healthcare resource utilization (HRU) was tracked across all care settings, and exposomic drivers of outcomes were linked via zip-code-based community and environmental characteristics, including data drawn from census, surveys, and air quality metrics. In this report, we describe the cohort’s characteristics, patterns of HRU, particularly in those with CV disease and factors associated with HRU using multivariate Poisson regression. Results: DIRECT-DM included 106,881 T2DM individuals with a median age of 63 years (IQR 52-73), 50% women and 16% Black, who were followed for a median of 5.4 years (3.1-7.5), and 9% died. There were 2.3M healthcare visits, including 1.5M hospitalizations, 250K emergency department visits, and 1.9M outpatient visits with 11.4% of CV-related principal diagnoses. In the DIRECT-DM cohort, where 31.5% had established CV disease, the burden of HRU increased with the comorbidities burden, with those having CV disease experiencing the highest HRU (3.7 median visits/year for ASCVD [1.9-6.8] and 4.4 [2.2-8.0] for HF). However, for the same age, sex, and comorbidity profile, lack of health insurance (IRR 0.94; 0.92-0.97), low income (IRR 0.97; 0.96-0.98), and Black vs. White race (IRR 0.98; 0.98-0.99) were associated with lower HRU. Conclusions: DIRECT-DM is a pragmatic T2DM registry in a diverse high-risk population, identifying key factors associated with HRU among T2DM patients. While there was an expected increased HRU in established CV disease, there was a lower-than-expected HRU among racially and socioeconomically disadvantaged populations.

Review of IoT Systems for Air Quality Measurements Based on LTE/4G and LoRa Communications

The issue of air pollution has recently come to light due to rapid urbanization and population growth globally. Due to its impact on human health, such as causing lung and heart diseases, air quality monitoring is one of the main concerns. Improved air pollution forecasting techniques and systems are needed to minimize the human health impact. Systems that fall under the Internet of Things (IoT) topology have been developed to assess and track numerous air quality metrics. This paper presents a review of IoT systems for air quality measurements, where the emphasis is placed on systems with LTE/4G and LoRa communication capabilities. Firstly, an overview of the IoT monitoring system is provided with recent technologies in the market. A critical review is provided of IoT systems regarding air quality using LTE/4G and LoRa communications systems. Lastly, this paper presents a market analysis of commercial IoT devices in terms of the costs, availability of the device, particulate matter each device can measure, etc. A comparative study of these devices is also presented on LTE/4G and possibly LoRa communications systems.

AUTOMATIC COOKER HOOD MONITORING SYSTEM BASED ON INTERNET OF THINGS

Acute Respiratory Tract Infection (ARI) poses a significant health risk, particularly for housewives frequently exposed to kitchen smoke, which contains harmful pollutants like carbon monoxide, potentially leading to serious conditions such as cancer and pneumonia. To address this issue, the development of an effective smoke removal system is crucial. This research employs a Research and Development methodology to design and test an automatic cooker hood that utilizes the MQ-7 sensor for smoke detection, controlling a 12V DC fan through a relay to operate at three adjustable speeds: low, medium, and high, based on real-time smoke intensity. The Aims of this study are to create a monitoring tool that efficiently removes kitchen smoke and provides immediate feedback on air quality. Results indicate that the system functions optimally, with the fan speed successfully adjusting according to detected smoke levels and displaying data on a 16x2 I2C LCD as well as through the Blynk application, offering clear visualization of air quality metrics. This research introduces a Novelty in integrating IoT technology for real-time air quality monitoring in kitchens, enhancing user awareness and intervention capabilities. However, fluctuations in smoke readings from the MQ-7 sensor present a Knowledge Gap, highlighting inconsistencies that affect PPM accuracy. The Implications of this study suggest the potential for improved health outcomes through better indoor air quality management, while future research should explore sensor accuracy enhancements and optimal placement strategies to ensure more reliable measurements, thereby further advancing the effectiveness of the automatic cooker hood system.

Conscious walk: integrating soundscape, biodiversity, and air quality metrics for holistic urban environmental assessment

The impact of environmental noise, poor biodiversity, and air pollution on urban populations poses a significant challenge to public health. Addressing these concerns requires a comprehensive approach that considers the perceptual experiences of citizens and objective environmental data. The Conscious Walks introduce a hybrid methodology designed to address the perceptive impact of citizens on their environment. Conscious Walks are citizen-science participative projects that combine the active involvement of citizens with scientific data collection. Implemented and tested in Barcelona, Conscious Walks provide a unique opportunity to assess urban soundscapes, and this contribution specifically evaluates the soundscapes of the Ciutadella Park in Barcelona for three consecutive years. The gathered data is carefully cross-referenced with information on air quality and biodiversity obtained during the walks. The novelty of this research lies in the development of integrated indicators that comprehensively capture the interplay between biodiversity, noise pollution, and air pollution. These indicators provide an understanding of urban environmental quality, offering valuable insights for sustainable urban planning and policy-making. The results presented in this contribution underscore the significance of adopting holistic methodologies to address the complex challenges posed by urban environments, emphasizing the need for integrated indicators in assessing the multifaceted aspects of urban sustainability.

Dynamics of atmospheric emissions and meteorological variables in Bangladesh from pre-to post-COVID-19 lockdown

Dynamics of atmospheric emissions and meteorological variables in Bangladesh from pre-to post-COVID-19 lockdown

Home Air Quality Monitoring System

Abstract: The quality of indoor air is a critical determinant of health and well-being, particularly Given the considerable amount of time individuals invest indoors. Recognizing the pivotal role of air quality, this paper introduces a novel Home Air Quality Monitoring System (HAQMS) designed to provide real-time, accurate assessments of air quality within residential environments. The HAQMS integrates advanced sensors and IoT (Internet of Things) technologies to detect and quantify a wide range of air pollutants, including particulate matter (PM2.5 and PM10), volatile organic compounds (VOCs), carbon dioxide (CO2), carbon monoxide (CO), and ozone (O3).The system architecture is delineated into three primary components: the sensor array for pollutant detection, a data processing unit employing advanced algorithms for real-time data analysis, and a user interface for displaying air quality metrics and providing health recommendations. Utilizing machine learning techniques, the system not only reports currentair quality but also predicts future air quality levels based on historical data and trend analysis. This predictive feature is pivotal for proactive measures in maintaining indoor air quality.

Revitalizing IoT-based air quality monitoring system for major cities in Indonesia

An IoT-based air quality monitoring system is a technology that integrates with the internet to monitor and measure numerous air quality metrics in real-time. CO levels, dust particle levels, temperature, and humidity are the air quality characteristics that must be monitored. The air quality monitoring system in its current state requires further development, such as challenges to acquiring accurate and real-time data and difficulty in accessing reliable information. Poor air quality causes various health problems, respiratory, vision, heart disease, and even cancer. The development of air pollution producers continues to increase along with the number of oil-fueled vehicles, industries operated using petroleum-fueled engines, power plants that use energy from coal, gas and petroleum. This study presents an IoT-based air quality parameter monitoring system solution that is connected with the Blynk platform and can be accessible in real time, in an effort to assist the SDGs program, which is mandated by the global community through the UN. The research technique employed is Analysis, Design, Development, Implementation, and Evaluation. The study successfully presented an IoT-based air quality monitoring system connected with the Blynk platform, which showed great accuracy in measurement 94.34% (CO), 81.15% (dust), 99.14% (temperature), and 96.84% (humidity). These results advance urban air quality monitoring and inform sustainable technology development, contributing to environmental and health-related SDGs.

Data enrichment toolchain: A use-case for correlation analysis of air quality, traffic, and meteorological metrics in Madrid’s smart city

Data enrichment toolchain: A use-case for correlation analysis of air quality, traffic, and meteorological metrics in Madrid’s smart city

Research on CC-SSBLS Model-Based Air Quality Index Prediction

Establishing reliable and effective prediction models is a major research priority for air quality parameter monitoring and prediction and is utilized extensively in numerous fields. The sample dataset of air quality metrics often established has missing data and outliers because of certain uncontrollable causes. A broad learning system based on a semi-supervised mechanism is built to address some of the dataset’s data-missing issues, hence reducing the air quality model prediction error. Several air parameter sample datasets in the experiment were discovered to have outlier issues, and the anomalous data directly impact the prediction model’s stability and accuracy. Furthermore, the correlation entropy criteria perform better when handling the sample data’s outliers. Therefore, the prediction model in this paper consists of a semi-supervised broad learning system based on the correlation entropy criterion (CC-SSBLS). This technique effectively solves the issue of unstable and inaccurate prediction results due to anomalies in the data by substituting the correlation entropy criterion for the mean square error criterion in the BLS algorithm. Experiments on the CC-SSBLS algorithm and comparative studies with models like Random Forest (RF), Support Vector Regression (V-SVR), BLS, SSBLS, and Categorical and Regression Tree-based Broad Learning System (CART-BLS) were conducted using sample datasets of air parameters in various regions. In this paper, the root mean square error (RMSE) and mean absolute percentage error (MAPE) are used to judge the advantages and disadvantages of the proposed model. Through the experimental analysis, RMSE and MAPE reached 8.68 μg·m−3 and 0.24% in the Nanjing dataset. It is possible to conclude that the CC-SSBLS algorithm has superior stability and prediction accuracy based on the experimental results.

Harnessing Regression Analysis and Machine Learning to Unveil the Market Dynamics and Environmental Impact of New Energy Vehicles

This study leverages regression analysis and machine learning algorithms to systematically investigate the growth patterns of the global New Energy Vehicle (NEV) market, evaluate the effects of international policy frameworks on NEVs, and quantify the environmental advantages of NEVs in urban settings. Through the analysis of worldwide market data, case studies on international policies, and urban air quality metrics, this research delineates the pivotal contributions of NEVs to mitigating urban pollution and advancing environmental sustainability. The findings provide empirical evidence and strategic recommendations for policymakers, the automotive industry, and environmental organizations, aimed at promoting the sustainable development of the global NEV market.

Deterministic and probabilistic occupant-centric control's impacts on the indoor environment in free-running households

PurposeOccupant behavior can lead to considerable uncertainties in thermal comfort and air quality within buildings. To tackle this challenge, the use of probabilistic controls to simulate occupant behavior has emerged as a potential solution. This study seeks to analyze the performance of free-running households by examining adaptive thermal comfort and CO2 concentration, both crucial variables in indoor air quality. The investigation of indoor environment dynamics caused by the occupants' behavior, especially after the COVID-19 pandemic, became increasingly important. Specifically, it investigates 13 distinct window and shading control strategies in courtyard houses to identify the factors that prompt occupants to interact with shading and windows and determine which control approach effectively minimizes the performance gap.Design/methodology/approachThis paper compares commonly used deterministic and probabilistic control functions and their effects on occupant comfort and indoor air quality in four zones surrounding a courtyard. The zones are differentiated by windows facing the courtyard. The study utilizes the energy management system (EMS) functionality of EnergyPlus within an algorithmic interface called Ladybug Tools. By modifying geometrical dimensions, orientation, window-to-wall ratio (WWR) and window operable fraction, a total of 465 cases are analyzed to identify effective control scenarios. According to the literature, these factors were selected because of their potential significant impact on occupants’ thermal comfort and indoor air quality, in addition to the natural ventilation flow rate. Additionally, the Random Forest algorithm is employed to estimate the individual impact of each control scenario on indoor thermal comfort and air quality metrics, including operative temperature and CO2 concentration.FindingsThe findings of the study confirmed that both deterministic and probabilistic window control algorithms were effective in reducing thermal discomfort hours, with reductions of 56.7 and 41.1%, respectively. Deterministic shading controls resulted in a reduction of 18.5%. Implementing the window control strategies led to a significant decrease of 87.8% in indoor CO2 concentration. The sensitivity analysis revealed that outdoor temperature exhibited the strongest positive correlation with indoor operative temperature while showing a negative correlation with indoor CO2 concentration. Furthermore, zone orientation and length were identified as the most influential design variables in achieving the desired performance outcomes.Research limitations/implicationsIt’s important to acknowledge the limitations of this study. Firstly, the potential impact of air circulation through the central zone was not considered. Secondly, the investigated control scenarios may have different impacts on air-conditioned buildings, especially when considering energy consumption. Thirdly, the study heavily relied on simulation tools and algorithms, which may limit its real-world applicability. The accuracy of the simulations depends on the quality of the input data and the assumptions made in the models. Fourthly, the case study is hypothetical in nature to be able to compare different control scenarios and their implications. Lastly, the comparative analysis was limited to a specific climate, which may restrict the generalizability of the findings in different climates.Originality/valueOccupant behavior represents a significant source of uncertainty, particularly during the early stages of design. This study aims to offer a comparative analysis of various deterministic and probabilistic control scenarios that are based on occupant behavior. The study evaluates the effectiveness and validity of these proposed control scenarios, providing valuable insights for design decision-making.