Air Quality Levels Research Articles

Solutions to Detect Air Quality in a Room and Improve It

Abstract: The project focuses on monitoring and improving indoor air quality by implementing an air quality detection system. Poor indoor air quality can have detrimental effects on health and well-being, from breathing difficulties to reduced cognitive function. Therefore, it is necessary to develop methods for effective monitoring and improvement of indoor air quality. In this project, we propose the use of sensors to detect various air pollutants such as particulate matter, volatile organic compounds (VOCs), carbon dioxide (CO2), and humidity levels. Sensor data will be collected and analyzed to assess the current state of air quality. Based on the analysis, appropriate measures will be taken to improve air quality, which may include ventilation modifications, air cleaners or other interventions. The aim of the project is to create a healthier and more comfortable indoor environment for residents by ensuring an optimal level of air quality.

Air Quality Prediction usingKNN and LSTM

The project titled "Air Quality Prediction Using KNN and LSTM" endeavors toaddress the critical issue of air pollutionthrough the application of advanced computational techniques. The project aimsto develop a robust predictive model that can forecast air quality levels based on historical data, meteorological parameters, and relevant environmental features. Leveraging machine learning algorithms such as regression, decision trees, or neural networks, the project seeks to analyze complex relationships within the data and enhance the accuracy of air qualitypredictions. The methodology involves the collection and preprocessing of extensive datasets encompassing pollutant concentrations, weather conditions, and geographicalinformation. The selected machine learning algorithms will be trained on this data to recognize patterns and correlations, enabling the model to make accurate predictions. The project also explores the integration of real- time data streams, satellite imagery, and sensor networks to improve the responsiveness of the predictive model.

Health risk assessment of occupational exposure to heavy metals among green space workers in Iran.

Exposure to heavy metals can result in various adverse health effects. Tehran is rated as one of the world's most polluted cities. Green space workers are continuously exposed to such pollutants in this city. Thus, this study aimed to estimate the health risks caused by exposure to heavy metals among green space workers. Eighty-eight workers and office personnel in two regions with different air quality levels were chosen for sampling. Air samples were collected using the NIOSH-7300 method and analyzed using an Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES) instrument. The hazard quotient (HQ) and the lifetime cancer risk (LTCR) were calculated to assess carcinogenic and non-carcinogenic risk levels. The results revealed that the rank order of heavy metals was determined as Zn, Pb, Mn, Ni, Co, and Cd. Workers were subjected to higher concentrations of Ni, Pb, Zn, and Co than office personnel. Furthermore, the Cd, Co, and Zn exposure levels stood significantly higher in region 6 than in region 14. Non-carcinogenic risk levels for all participants fell within the acceptable range. Moreover, no employee had a carcinogenic risk level within the acceptable range when exposed to Cd. Also, 2.3% of individuals demonstrated Ni's acceptable carcinogenic risk level. Owing unacceptable risk levels, proper interventions are required to minimize occupational exposure to heavy metals. These interventions include optimizing shift schedules, using personal protective equipment, and conducting regular health assessments.

Reengineering Indoor Air Quality Monitoring Systems to Improve End-User Experience.

This paper presents an indoor air quality (IAQ) monitoring system designed for a better end-user experience. The monitoring system consists of elements, from the monitoring sensor to the monitoring interface, designed and implemented by the research team, especially for the proposed monitoring system. The monitoring solution is intended for users who live in houses without automatic ventilation systems. The air quality sensor is designed at a minimum cost and complexity to allow multi-zone implementation without significant effort. The user interface uses a spatial graphic representation that facilitates understanding areas with different air quality levels. Presentation of the outdoor air quality level supports the user's decision to ventilate a space. An innovative element of the proposed monitoring interface is the real-time forecast of air quality evolution in each monitored space. The paper describes the implementation of an original monitoring solution (monitoring device, Edge/Cloud management system, innovative user monitoring interface) and presents the results of testing this system in a relevant environment. The research conclusions show the proposed solution's benefits in improving the end-user experience, justified both by the technical results obtained and by the opinion of the users who tested the monitoring system.

Setting up a CFD model to evaluate the impact of green infrastructures on local air quality

Green infrastructures have been pointed out as innovative solutions to deal with current and future challenges related to air pollution and climate change. Although the potential of green infrastructures, such as green walls and green roofs, to mitigate air pollution has been documented, evidence at a local scale is still limited. This work aims to increase knowledge about the potentialities of green infrastructures in improving local air quality, focusing on particulate matter, nitrogen dioxide and ozone pollutants, and by using a local-scale computational fluid dynamics model. The ENVI-met model was applied to a particular hour of a summer day over a built-up environment centred on a main avenue in the city of Lisbon (Portugal). The dimensions of the computational domain are 618 m × 594 m × 143 m, and it contains 184 buildings, with the tallest building being 56 m. In addition to the baseline simulation, modelling was also done considering the application of green walls and green roofs to specific buildings located near the main avenue, together with a green corridor. The overall results show no disturbances exerted by green walls on the turbulent flow dynamics and on the air quality levels when compared to the baseline scenario (without green walls). The integrated scenario, which includes green walls, green roofs and a green corridor, will lead to potential local benefits of green infrastructures on O3 concentrations, followed by variable impacts on NO2 and particulate matter concentrations.

Investigation of indoor noise pollution level and air quality of furniture manufacturers

Indoor air quality has become a more prominent concern since the arrival of the COVID-19 pandemic. Manufacturing industries have always been prone to occupational health risks, which depend on the dynamics of the production shop floors. The furniture industry is one of these sectors with a unique work environment. Although a typical furniture manufacturing facility involves physical, chemical, and noise pollution-producing elements, this industry has been studied relatively less for indoor air quality and noise-related risks. This study investigated nine furniture manufacturing organizations’ indoor air quality and noise pollution levels through comprehensive quantitative techniques. The results of the measurements were compared against reference values set by specific guidelines to explore the degree of occupational health risk associated with the World Health Organization’s (WHO) suggested levels. Repetitive measurements from five pre-designated workstations were taken at each facility. The study’s results indicated that organization size and department were significant factors for PM 2.5 and HCHO parameters, while only department type was substantial for noise exposure levels. However, across all departments and organization sizes, LAeq noise levels were below the safety threshold of 85 dB(A). Most organizations presented a lack of proper use of personal protective equipment and poor ventilation across shop floors.

Portable Air Quality Detector Using DSM501A Dust Sensor and Arduino Uno

Aims: The primary feature of this research is to design a circuit that can analyze the air around the system and determine the quality of the air.
 Study Design: This paper reports on the design and implementation of a portable air pollution analyzer using Arduino Uno, DSM501A dust sensor, LCD, switches, buzzer, and multiple LEDs. The air pollution analyzer system was designed using a combination of hardware and software techniques to measure air pollution properly.
 Place and Period of Study: The research was accomplished by the authors in a group of two students under the grasp of a professor as a part of one of his course capstone projects for the Bachelor of Science in Electrical and Electronic Engineering degree at the American International University Bangladesh (AIUB), Dhaka, Bangladesh. The authors performed their investigative tasks at AIUB from September 2023 to February 2024.
 Methodology: The air pollution analyzer was implemented using an Arduino Uno and dust sensor to generate and determine accurate air quality based on specific air contents. The system that was developed used a PM10 detection system that identified the amount of dust particles in the area. Based on PM10 readings, the air quality was identified. Moreover, the Air Quality Index (AQI) was determined using the PM10 readings. The LCD, buzzer, and multiple LEDs were used to display and indicate the air quality condition and levels.
 Results: The portable air pollution analyzer was successfully tested and found to be accurate and reliable in determining air quality. To indicate different hazardous states of air, red, green, and yellow color LEDs are used. The test outcomes were very satisfactory.
 Conclusion: This system demonstrates the use of a microcontroller in building a simple yet effective portable air quality detector. It can be scaled up for commercial production.

A machine learning approach for unraveling the influence of air quality awareness on travel behavior

Urbanization has escalated air pollution levels with subsequent health implications. This study explores the potential of awareness about air quality levels on travelers’ choices and proposes machine learning models to predict travel mode under exposure to different air quality levels. These models are Random Forest, XGBoost, Naive Bayes (NB), K-Nearest Neighbor, Support Vector Machine (SVM), and Multinomial Logistic Regression (MLR). The models are trained using data from individuals who have an understanding of air quality levels. The trained model is further used to predict travel mode choices when the knowledge of air quality reaches all travelers. Travel modes are aggregated into open/closed modes, private/public modes, and motorized/non-motorized/metro modes to assess the impact of air quality awareness and modal shift. The model evaluation shows that the Random forest (RF) exhibits the highest accuracy and F1 score. The model demonstrates that as air quality worsens, commuters shift their preferences from open modes of transport to closed modes. Similarly, during periods of deteriorating air quality, commuters exhibit a preference for public transportation over private modes. This study emphasizes the crucial role of disseminating air quality information, empowering individuals to make informed travel decisions, and mitigating health risks.

Integrated Portable and Stationary Health Impact-Monitoring System for Firefighters.

The multi-layered negative effects caused by pollutants released into the atmosphere as a result of fires served as the stimulus for the development of a system that protects the health of firefighters operating in the affected area. A collaborative network comprising mobile and stationary Internet of Things (IoT) devices that are furnished with gas sensors, along with a remote server, constructs a resilient framework that monitors the concentrations of harmful emissions, characterizes the ambient air quality of the vicinity where the fire transpires, adopting European Air Quality levels, and communicates the outcomes via suitable applications (RESTful APIs and visualizations) to the stakeholders responsible for fire management decision making. Different experimental evaluations adopting separate contexts illustrate the operation of the infrastructure.

Air Quality and Public Health Risk Assessment: A Case of an Industrial Area in Haridwar City, Uttarakhand (India).

Air pollution is a significant issue for a developing country like India and the air quality index (AQI) forecasting helps to predict air quality levels in advance and allows individuals to take precautionary measures to protect their health. The study aimed to forecast the AQI for an industrial area (SIDCUL, Haridwar City) using a time series regression model. Three years of existing AQI data points (post-COVID-19) were collected from the Uttarakhand Pollution Control Board for the SIDCUL area of Haridwar City and tried to know the status of AQI values for the following 12 months. Trend and seasonality components were seen through the decomposition process. Further, the augmented Dickey-Fuller test was applied to check the stationarity of the series before finalizing the best-suited time series model for forecasting the AQI values. With the help of autocorrelation function (ACF)/partial ACF plots, a seasonal autoregressive integrated moving average (ARIMA) (0,1,0) (1,0,0)[12] model was selected with the minimum akaike information criterion (253.143) and mean absolute percentage error (17.42%). The AQI values have also been forecasted for this industrial area (SIDCUL) for the following year. The seasonal ARIMA (0,1,0) (1,0,0)[12] model may be helpful to forecast the AQI values for a nonstationary time series dataset. Research indicates that the air of the SIDCUL area will become moderately polluted and may cause breathing discomfort to asthma patients' health. The scientists might apply this model to other polluted regions of the country so that the public and the government can take preventive measures in advance.

The development of color histogram method to identify air quality index based on sky images

Air quality index measurements in Indonesia are carried out by ministry of environment and forestry (KLHK). The Ministry divides air quality levels into 5 categories, namely good, moderate, unhealthy, very unhealthy and dangerous. In this study, 3 air quality categories were used as primary research data, namely good, moderate and unhealthy because the others, never occurred in Indonesia from the time this research was conducted until its completion. This research develops the color histogram method in order to recognize the shape of an object in an image. First stage in this research is inputting the sky image into the system. Then carry out pre-processing in the form of cropping the image to obtained is only an image of sky. Next, convert the red, green and blue (RGB) colored sky image to Grayscale, then image enhancement, then noise reduction. After that is processed using development of the color histogram method. Refinement of color histogram method has yielded an impressive accuracy level of 90%, validated through the analysis of 30 sky images. The method successfully detected 27 images accurately, while three images posed detection challenges. The findings of this research is color histogram method can be used to identify objects especially air pollution from sky images.

Dynamic Neural Assimilation: a deep learning and data assimilation model for air quality predictions

Ambient air pollution is known to be a serious issue that has an impact on human health and the environment. Assessing air quality is of the utmost importance to protect human health and the environment. Different tools are available, from monitoring stations to complex models. These systems are capable of accurately predicting air quality levels, but they are often computationally very expensive which makes them poorly efficient. In this paper, we developed a novel model called Dynamic Neural Assimilation (DyNA) integrating Recurrent Neural Networks and Data Assimilation methods to derive a physics-informed system capable of accurately forecasting air pollution tendencies and investigating the relationship with industrial statistics. DyNA is trained in historical data and is fine-tuned as soon as new data comes available. We trained and tested the system on real data provided by the air quality monitoring stations located in Italy from the European Environment Agency and simulated results derived from the air quality modelling system Atmospheric Modelling System-Model to support the International Negotiation on atmospheric pollution on a National Italian level. We analysed air pollution data in Italy from the years 2003–2010 and studied its correlation with nearby industries in some regions where monitoring sensors were available.

Research on smart city public health detection system and improvement technology based on intelligent multi-objective.

With the increase of urban population density, urban sanitation becomes more severe; urban sanitation has important influence on public health. Therefore, in order to realize the detection of public health in smart cities, the research will use cutting-edge scientific and technological methods to improve urban environmental health, so as to promote the realization of public health achievements. This study introduces public health detection and optimizationtechnologies for smart cities. Firstly, a data detection system for urban public health environment was established using sensors and intelligent multi-objective technology to evaluate the water quality, air quality, and noise level of the city. Then, an intelligent garbage management system based on Tensor-flow was constructed to achieve efficient garbage collection and treatment. Finally, an intelligent traffic management system was developed to monitor and regulate urban traffic flow. The results of the simulation experiment demonstrated that the life data detection system was operationally stable, with a high success rate of 98%. Furthermore, its accuracy in detecting residents' living environment data was above 95%, the maximum relative error was only 0.0465, making it a reliable and efficient tool. The waste recycling system achieved a minimum accuracy of 83.6%, the highest accuracy rate was 95.3%, making it capable of sorting and recycling urban waste effectively. Additionally, the smart traffic management system led to a 20% reduction in traffic congestion rates, 20 tonnes less tailpipe emissions and an improvement in public health and well-being. In summary, the plan proposed in this study aims to create a more comfortable, safe, and healthy urban public health environment, while providing theoretical support for environmental health management in smart cities.

Application of machine learning models and landsat 8 data for estimating seasonal pm 2.5 concentrations.

Air pollution is a significant global challenge that affects many cities. In Europe, Bosnia and Herzegovina (BiH) are among the most highly polluted and are mainly affected by air pollution. In this study, we integrate open-source landsat 8 remote sensing products, topographical data, and the limited ground truth PM2.5 data to spatially predict the air quality level across different seasons in Tuzla Canton, BiH by adopting three pre-existing machine learning models, namely XGBoost, K-Nearest Neighbour (KNN) and Naive Bayes (NB). These classification models were implemented based on landsat 8 bands, environmental-derived indices, and topographical variables generated for the study area. Based on the predicted results, the XGBoost model exhibited the highest overall accuracy across all seasons. The predicted model results were used to generate spatial air quality maps. Based on the classification maps, the PM2.5 air quality level predicted for Tuzla Canton in the Winter Season is very unhealthy. The findings conclude that the PM2.5 air quality concentration in Tuzla Canton is relatively unsatisfactory and requires urgent intervention by the government to prevent further deterioration of air quality in Tuzla and other affected cantons in BiH.

Indoor Air Quality and Ventilation Energy in University Classrooms: Simplified Model to Predict Trade-Offs and Synergies

Students and educators spend significant time in learning spaces on university campuses. Energy efficiency has become a concern among facility managers, given the need to maintain acceptable indoor air quality (IAQ) levels during and after the COVID-19 pandemic. This paper investigates the relationship between control and extraneous variables in a university classroom’s total mechanical ventilation (kWh). The model is built using Grasshopper software on Rhino Version 7. Our methodology encompasses (1) an extensive review of recent trends for studying IAQ and energy, (2) selecting parameters for simulation, (3) model configuration on Grasshopper, and finally, (4) a formulation of a pertinent equation to consolidate the relationship between the studied factors and the total mechanical ventilation energy (kWh). Central to this study are two key research questions: (1) What correlations exist between various parameters related to occupancy and IAQ in educational spaces? And (2) how can we optimize energy efficiency in university classrooms? The main contribution of this research is a generated equation representing the annual mechanical ventilation energy consumption based on selected parameters of classroom height, area, occupancy, window location, and ventilation rate of HVAC systems. We find that occupancy and class volume are the two most influential factors directly affecting mechanical ventilation energy consumption. The equation serves as a valuable estimation tool for facility managers, designers, and campus operations to investigate how fluctuations in occupancy can influence ventilation energy consumption in the physical attributes of a university classroom. This enables proactive decision-making, optimizing energy efficiency and resource allocation in real-time to promote sustainable and cost-effective campus operations.

Air Quality Index Prediction Using Machine Learning

Air pollution poses a significant threat to public health, particularly in rapidly developing countries like India, where urbanization and industrialization contribute to diverse sources of pollutants. This research employs machine learning techniques to comprehensively analyse and predict air quality levels across different regions of India. Extensive air quality data, encompassing various pollutants and temporal variations, is collected and compiled. State-ofthe-art machine learning models are developed to identify key influencing factors, including industrial emissions, vehicular traffic, agricultural practices, and meteorological conditions. The models are evaluated for accuracy and reliability in predicting air quality levels. The study aims to provide valuable insights into the complex dynamics of air pollution in the Indian context. By understanding the contributing factors and predicting future air quality levels, this research contributes to the development of targeted and effective air quality management strategies. Keywords: Machine Learning, Air Pollution, AQI.

Assessment of Air Pollution by Aerosols over a Coal Open-Mine Influenced Region in Southwestern Romania

The mining activity in open-pit coal sites exerts a high pressure over the surrounding environment and on the health of people working and living nearby. Using data form a total of six field campaigns, this study aimed to capture the level of exploitation activities in the residential area close to the coal open-mine in terms of mass concentrations and total carbon fractions (TC) of ambient PM10, PM2.5 samples. Sampling campaigns were performed between spring of 2018 and spring of 2020, covered both working (full time, part-time) and non-working days at the coal exploitation, and captured also the seasonal variations of different fractions of PM and of their carbonaceous fraction. Fine fraction was also analyzed based on the aerosol Angstrom exponent, determined from measurements of the aerosol scattering coefficient at 450 nm, 550 nm and 700 nm by a nephelometer. The mass concentrations of PM and TC showed significant variations from full time working days to non-working days. The air quality (AQ) level is determined by the air quality index (AQI) specific for PM10. In situ measurements clearly indicated that AQIlocal gives better image on the level of air pollution than the AQIestim from AQ monitoring stations in the region, closest to the Matasari site. Insights into the morphology and composition of particles were obtained by scanning electron microscopy (SEM) and energy dispersive X-ray (EDX/EDS) and Fourier transform infrared spectroscopy (FTIR) analyses. Major elements that were identified C, O, Si, Ca, K, S, Cu, Ni, Fe, Mg, Ti have both crustal and anthropogenic origin, in various proportions.

Objectively measured the impact of ambient air pollution on physical activity for older adults

BackgroundAir pollution poses a significant health risk to the human population, especially for vulnerable groups such as the elderly, potentially discouraging their engagement in physical activity. However, there is a lack of sufficient objective and longitudinal data in current research on how air pollution affects physical activity among older adults. With these gaps, we aimed to explore the relationship between air pollution and objective measurement-based physical activity among older adults by engaging in a longitudinal study design.MethodsA total of 184 older adults were recruited from three cities with varying levels of air quality. Mean daily minutes of physical activity were measured with 7 consecutive days of accelerometer monitoring (ActiGraph GT3X-BT). Corresponding air pollution data including daily PM2.5 (µg/m3), PM10 (µg/m3) and air quality index (AQI) were sourced from the China National Environmental Monitoring Centre at monitor locations close to older adults’ addresses. Associations between air quality and physical activity were estimated using a fixed effect model, adjusting for average daytime temperature, rain, age and weight.ResultsAQI and PM2.5 were observed to exhibit significant, inverse, and linear associations with mean daily walk steps, minutes of light physical activity (LPA), moderate physical activity (MPA) and moderate-to-vigorous physical activity (MVPA) in the single variable models. A one-level increase in AQI corresponded to a decline in 550.04 steps (95% [CI] = -858.97, -241.10; p < 0.001), 10.43 min (95% [CI] = -17.07, -3.79; p < 0.001), 4.03 min (95% [CI] = -7.48, -0.59; p < 0.001) and 4.16 min (95% [CI] = -7.77, -0.56; p < 0.001) in daily walking steps, LPA, MPA, and MVPA, respectively. A one-level increase in PM2.5 correlated with a decline in daily walk steps, LPA, MPA and MVPA by 361.85 steps (95% [CI] = -516.53, -207.16; p < 0.001), 8.97 min (95% [CI] = -12.28, -5.66; p < 0.001), 3.73 min (95% [CI] = -5.46, -2.01; p < 0.001,) and 3.79 min (95% [CI] = -5.59, -1.98; p < 0.001), respectively. However, PM10 displayed a significant negative association exclusively with LPA, with one-level increase in PM10 resulting in a 3.7-minute reduction in LPA (95% [CI] = -6.81, -0.59, p < 0.05).ConclusionAir pollution demonstrates an inverse association with physical activity levels among older adults, potentially discouraging their engagement in physical activity. Different air quality indicators may exert varying impacts on physical activity. Future studies are warranted to enhance policy interventions aimed at reducing air pollution and promoting physical activity.

Aerosol Vertical Structure and Optical Properties during Two Dust and Haze Episodes in a Typical Valley Basin City, Lanzhou of Northwest China

The vertical profiles of aerosol optical properties are vital to clarify their transboundary transport, climate forcing and environmental health influences. Based on synergistic measurements of multiple advanced detection techniques, this study investigated aerosol vertical structure and optical characteristics during two dust and haze events in Lanzhou of northwest China. Dust particles originated from remote deserts traveled eastward at different altitudes and reached Lanzhou on 10 April 2020. The trans-regional aloft (~4.0 km) dust particles were entrained into the ground, and significantly modified aerosol optical properties over Lanzhou. The maximum aerosol extinction coefficient (σ), volumetric depolarization ratio (VDR), optical depth at 500 nm (AOD500), and surface PM10 and PM2.5 concentrations were 0.4~1.5 km−1, 0.15~0.30, 0.5~3.0, 200~590 μg/m3 and 134 μg/m3, respectively, under the heavy dust event, which were 3 to 11 times greater than those at the background level. The corresponding Ångström exponent (AE440–870), fine-mode fraction (FMF) and PM2.5/PM10 values consistently persisted within the ranges of 0.10 to 0.50, 0.20 to 0.50, and 0.20 to 0.50, respectively. These findings implied a prevailing dominance of coarse-mode and irregular non-spherical particles. A severe haze episode stemming from local emissions appeared at Lanzhou from 30 December 2020 to 2 January 2021. The low-altitude transboundary transport aerosols seriously deteriorated the air quality level in Lanzhou, and aerosol loading, surface air pollutants and fine-mode particles strikingly increased during the gradual strengthening of haze process. The maximum AOD500, AE440–870nm, FMF, PM2.5 and PM10 concentrations, and PM2.5/PM10 were 0.65, 1.50, 0.85, 110 μg/m3, 180 μg/m3 and 0.68 on 2 January 2021, respectively, while the corresponding σ and VDR at 0.20–0.80 km height were maintained at 0.68 km−1 and 0.03~0.12, implying that fine-mode and spherical small particles were predominant. The profile of ozone concentration exhibited a prominent two-layer structure (0.60–1.40 km and 0.10–0.30 km), and both concentrations at two heights always remained at high levels (60~72 μg/m3) during the entire haze event. Conversely, surface ozone concentration showed a significant decrease during severe haze period, with the peak value of 20~30 μg/m3, which was much smaller than that before haze pollution (~80 μg/m3 on 30 December). Our results also highlighted that the vertical profile of aerosol extinction coefficient was a good proxy for evaluating mass concentrations of surface particulate matters under uniform mixing layers, which was of great scientific significance for retrieving surface air pollutants in remote desert or ocean regions. These statistics of the aerosol vertical profiles and optical properties under heavy dust and haze events in Lanzhou would contribute to investigate and validate the transboundary transport and radiative forcing of aloft aerosols in the application of climate models or satellite remote sensing.

Assessing multi-domain impact of IAQ and noise on productivity with portable air cleaners through physiological signals

The office environment's indoor air quality (IAQ) and noise levels are critical factors that affect productivity and overall health. However, a research gap still exists on multi-domain impact of IAQ and noise on human physiological signals. The research aims to establish a comprehensive understanding of the multi-domain impact by analyzing physiological signals such as Electroencephalogram (EEG), Electrodermal Activity (EDA), eye blinks, jaw clenching, and heart rate. 30 human subjects participated in a series of tests in various indoor environments with portable air cleaner configurations. The study incorporated four scenarios with baseline condition and two noise levels. By comparing the different scenarios, the separate and combined impacts of IAQ and noise on office productivity were assessed. Meanwhile, physiological signals were collected and analyzed alongside productivity tests, which included typing and addition tasks, to determine the relationship between IAQ, noise, and office productivity. We discovered a strong relationship between EEG power and productivity, indicating that EEG power was a valuable indicator for evaluating indoor environmental quality, especially the multi-domain effects of IAQ and noise. When using the air cleaner, the relative power in the Theta, Alpha, and Beta bands indicated increased concentration and activity levels in the brain. Additionally, the increasing number of jaw clenching suggested that participants became more anxious and nervous in noisy environments. We also found that there were no gender differences for the analysis of EEG power, providing insights for future research. These findings have implications for smart control and built environment aimed at enhancing office workers' well-being and productivity.