Maintaining Indoor Air Quality Research Articles

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.

DATIV-Remote Enhancement of Smart Aerosol Measurement System Using Raspberry Pi-Based Distributed Sensors.

Enclosed public spaces are hotspots for airborne disease transmission. To measure and maintain indoor air quality in terms of airborne transmission, an open source, low cost and distributed array of particulate matter sensors was developed and named Dynamic Aerosol Transport for Indoor Ventilation, or DATIV, system. This system can use multiple particulate matter sensors (PMSs) simultaneously and can be remotely controlled using a Raspberry Pi-based operating system. The data acquisition system can be easily operated using the GUI within any common browser installed on a remote device such as a PC or smartphone with a corresponding IP address. The software architecture and validation measurements are presented together with possible future developments.

A bi-level real-time optimal control strategy for thermal coupled multi-zone dedicated outside air system-assisted HVAC systems

This paper proposes a bi-level optimal control strategy for thermally coupled multi-zone dedicated outside air system (DOAS)-assisted HVAC systems, which integrates the deep reinforcement learning (DRL) algorithm with Lyapunov function-based reward shaping (LRS) and an augmented genetic algorithm. The proposed strategy aims to maintain indoor air quality (IAQ) and thermal comfort while reducing the overall energy cost. On the upper level, the LRS method enhances the convergence of the DRL algorithm, ensuring satisfactory IAQ with reduced electricity cost. On the lower level, the embedding of the pre-trained optimal agent achieves a unidirectional decoupling, which mitigates model dependency and accurately control thermal comfort under thermal coupling. The proposed strategy offers the following advantages: a) It can efficiently maintain IAQ in multiple zones and high energy efficiency by optimizing the supply airflow, in response to real-time environmental changes. b) It can achieve satisfactory thermal comfort by regulating the fan coil unit power, while implicitly reducing the overall energy costs via unidirectional decoupling. c) Experimental results demonstrate that the proposed strategy achieves a maximum energy savings of 33.13 % and electricity savings of 34.14 %, compared with the traditional strategy. d) The proposed strategy exhibits good generalization ability under different test climates and occupancy scenarios.

Carbon dioxide guidelines for indoor air quality: a review.

The importance of building ventilation to protect health has been more widely recognized since the COVID-19 pandemic. Outdoor air ventilation in buildings dilutes indoor-generated air pollutants (including bioaerosols) and reduces resulting occupant exposures. Many countries and organizations have advisory guidelines or mandatory standards for minimum ventilation rates (VRs) to maintain indoor air quality (IAQ). Because directly measuring VRs is often difficult, many IAQ guidelines instead specify indoor concentration limits for carbon dioxide (CO2), using CO2 exhaled by building occupants as an indicator of VR. Although indoor CO2 guidelines are common, the evidence basis for the various CO2 limits has not been clear. To review current indoor CO2 guidelines worldwide and the supportive evidence provided. We identified worldwide CO2-based guidelines for IAQ or ventilation, along with any supportive evidence provided. We excluded occupational guidelines for CO2 levels ≥5000 ppm. Among 43 guidelines identified, 35 set single CO2 concentration limits and eight set multi-tiered limits; 16 mentioned no specific human effect to be controlled, 19 specified only odor dissatisfaction, five specified non-infectious health effects, and three specified airborne infectious disease transmission. The most common indoor CO2 limit was 1000 ppm. Thirteen guidelines specified maximum CO2 limits as extended time-weighted averages, none with evidence linking averaged limits to occupant effects. Of only 18 guidelines citing evidence to support limits set, we found this evidence persuasive for eight. Among these eight guidelines, seven set limits to control odor perception. One provided 17 scientifically-based CO2 limits, for specific example space uses and occupancies, to control long-range COVID-19 transmission indoors. Many current indoor carbon dioxide (CO2) guidelines for indoor air quality specified no adverse effects intended for control. Odor dissatisfaction was the effect mentioned most frequently, few mentioned health, and three mentioned control of infectious disease. Only one CO2 guideline was developed from scientific models to control airborne transmission of COVID-19. Most guidelines provided no supportive evidence for specified limits; few provided persuasive evidence. No scientific basis is apparent for setting one CO2 limit for IAQ across all buildings, setting a CO2 limit for IAQ as an extended time-weighted average, or using any arbitraryone-time CO2 measurement to verify a desired VR.

A Mixed Method Study to Explore How Maintenance Personnel Can Enhance Wildfire Smoke Resilience at Long-Term Care Facilities in the US Mountain West

Wildfire activity is increasing around the world, concurrent with climate change, and mitigation strategies for protecting vulnerable populations are desperately needed. Because inhaled particles are deleterious to respiratory health, particularly among older adults with co-morbidities, we engaged maintenance personnel working in long term care facilities located in the Western US. Our objective was to identify opportunities to build resilience during wildfire smoke events. We implemented a virtual workshop that addressed wildfire smoke health impacts as well as strategies to assess and maintain indoor air quality. A total of 24 maintenance personnel attended the virtual workshop and 14 participated in a quantitative survey. Workshop attendees found value in the material and there was enthusiasm for educational resources and enhancing indoor air quality. Four months later, four maintenance staff participated in a follow-up interview. Our qualitative assessment revealed the following themes: awareness and prioritization, application of knowledge, barriers, and educational resources. Access to real-time actionable air quality data was a consistent feature across these themes. Maintenance personnel could play a key role in a facility’s ability to prepare for and respond to wildfire smoke events, and this study highlights potential challenges and opportunities to involving them in resilience building strategies.

Simultaneous thermal zoning and demand control ventilation

Heating, Ventilation, and Air Conditioning systems play a crucial role in controlling indoor air conditions, including temperature, humidity, and CO₂ concentration, to ensure human comfort and safety. Thermal zoning and demand-controlled ventilation have demonstrated enhancements in comfort and energy efficiency. This study aims to develop a mathematical model for a Heating, Ventilation, and Air Conditioning system employing an estimated state-feedback control algorithm to optimize indoor comfort and energy savings while minimizing sensor requirements. The model encompasses dynamic equations governing CO₂ concentration and temperature in a five-room building. Temperature regulation is achieved by adjusting the airflow from a cooling unit with dampers, while CO₂ levels are managed by controlling the proportion of fresh air intake. To showcase the advantages of the proposed control approach, comparisons were made with alternative methods based on response performance, energy consumption, and sensor requirements. The estimated state-feedback control outperformed other approaches, requiring only one temperature and one CO₂ sensor at the mixed return. Additional occupancy sensors in each room are not necessary if an Extended Kalman filter is utilized. This technique exhibits scalability, adaptable to varying output variables and room configurations, and holds promise for broader implementation in the HVAC industry. By optimizing energy consumption and maintaining indoor air quality, this approach aligns with established sustainability metrics, promoting environmental responsibility. Compliance with health and safety audit standards ensures the fulfillment of social targets, while adherence to regulations and laws ensures governance targets are met.

The Performance of Reinforcement Learning for Indoor Climate Control Devices according to the Level of Outdoor Air Particulate Matters

As people spend more than 90% of their time indoors, indoor environmental quality (IEQ) has become an important factor in maintaining a healthy space for the occupants. There are many indoor climate control devices for improving IEQ. However, it is difficult to maintain an appropriate IEQ with changing outdoor air conditions and occupant behavior in a building. This study proposes a reinforcement learning (RL) algorithm to maintain indoor air quality (IAQ) with low energy consumption in a residential environment by optimally operating indoor climate control devices such as ventilation systems, air purifiers, and kitchen hoods. The proposed artificial intelligence algorithm (AI2C2) employs DDQN (double deep Q-network) to determine the optimal operation of various indoor climate control devices, reflecting IAQ and energy consumption via different outdoor levels of particulate matter. This approach considers the outdoor air condition and occupant activities in training the developed algorithm, which are the most significant factors affecting IEQ and building energy performance. A co-simulation platform using Python and EnergyPlus is applied to train and evaluate the model. As a result, the proposed approach reduced energy consumption and maintained good IAQ. The developed RL algorithm for energy and IAQ showed different performances based on the outdoor PM 2.5 level. The results showed the RL-based control can be more effective when the outdoor PM 2.5 level is higher (or unhealthy) compared to moderate (or healthy) conditions.

Assessment of PM2.5 penetration based on airflow paths in Korean classrooms

PM2.5 in classrooms adversely affect the health of young children, underscoring the importance of maintaining indoor air quality. In many Korean schools, the airtightness between a corridor and classrooms is not adequately tight. This lack of airtightness implies that corridors, often having high PM2.5 concentrations, significantly contribute to the degradation of classroom air quality. While some studies have acknowledged corridors as a key source of PM2.5 infiltration, few have quantitatively assessed the amount and frequency of PM2.5 entering classrooms through these pathways. This study examined the pattern and quantity of PM2.5 penetrating from the outdoor and corridor into classrooms in a typical Korean school. In south-facing classrooms, much of the infiltrating air originated directly from external walls, with a minimal amount coming from the corridor. However, given the corridor's relatively high PM2.5 concentration and significant penetration, approximately 25% of the total PM2.5 infiltration was attributed to the corridor. In contrast, east- and west-facing classrooms, influenced by Korea's prevalent westerly winds, experienced a pronounced increase in air entering the room, particularly from the corridor. Although the PM2.5 concentration in the corridor was lower than that in the south-facing classroom, the heightened airflow indicated that the corridor was responsible for 79% and 58% of PM2.5 infiltration in east- and west-facing classrooms, respectively. The results indicate that corridors' contribution to classroom PM2.5 levels is substantial; therefore, enhancing the airtightness between corridor and outdoor is crucial to reducing corridor PM2.5 levels. Alternatively, improving the airtightness between classrooms and corridor can effectively mitigate PM2.5 penetration.

Performance Comparison of Indoor Humidity Modeling: A Novel Hybrid Model vs. the Simplified Calculation Model by EN ISO 13788

Ventilation is essential for maintaining indoor air quality and human comfort, particularly in buildings with mechanical ventilation systems. However, modeling the complex relationship between ventilation rate and measurable indoor environmental parameters, such as humidity, poses significant challenges. Current methods for modeling this relationship are often inefficient. For example, data-driven models frequently require sufficient information on ventilation, which may not be available for constant air volume (CAV) systems. On the other hand, simulation programs can be time-consuming and require expert engagement. In this study, we present two simplified calculation models, including a hybrid model developed by the authors, and a well-known model introduced by EN ISO 13788. We compare their performance in evaluating the impact of ventilation rate on indoor humidity. The hybrid model uses a novel analytical method to upgrade a single-input regression model (which considers only outdoor humidity as input) to enable the evaluation of indoor humidity based on both outdoor humidity and ventilation rate. We used the commercial TRNSYS program to model two scenarios: increasing the reference ventilation rate and decreasing the reference ventilation rate. The results showed that both models matched TRNSYS simulations with satisfactory accuracies, but the hybrid model demonstrated superior performance to the EN ISO 13788 model in both scenarios. The hybrid model’s higher accuracy and hybrid features make it better suited for big data analysis and field studies.

Method of test for CO2-based demand control ventilation systems: Benchmarking the state-of-the-art and the undervalued potential of proportional-integral control

Carbon dioxide (CO2) based demand control ventilation (DCV) adjusts a building’s outdoor air ventilation rate in response to indoor CO2 concentration to save energy while maintaining indoor air quality. Packaged heating, ventilation, and air-conditioning systems often contain DCV controllers with embedded proprietary algorithms that lack transparent performance data. A test method was developed to assess the ability of a DCV controller to maintain the indoor CO2 concentration at a setpoint in response to a series of CO2 generation functions that represent three different building occupancy densities and two occupancy schedules. Six commercially available controllers were tested to demonstrate the method and provide directly comparable results. The performance (in terms of CO2 control and damper movement) of each controller tested was compared to the performance of an ideal controller which knows the CO2 generation function. Finally, the performance of a proportional-integral (PI) controller with preset gains was developed and tested to determine the potential maximum performance achievable with this control strategy. The best performing commercially available controller achieved CO2 control (within 75 ppm of the setpoint) approximately 80 % of the time with damper movement slightly less than an ideal controller. However, most of the commercially available controllers had marginal or poor performance for CO2 control and damper movement. Two controllers had damper movement more than three times the ideal controller. Notably, a PI algorithm configured and tested by the research team achieved superior performance with CO2 control 92 % of the time and damper movement 1.5 times the ideal controller.

Investigating the reliability of estimating real-time air exchange rates in a building by using airborne particles, including PM1.0, PM2.5, and PM10: A case study in Suzhou, China

This study aimed to evaluate the reliability of using airborne particles to estimate the real-time Air Exchange Rate (AER) of buildings, considering particle size and outdoor conditions' impact on the AER estimation accuracy. The study utilized on-site data collection and numerical simulations to analyze the factors affecting the AER prediction accuracy. Results showed that the PM1.0- and PM2.5-based empirical correlation could predict the AER of buildings with a Normalized Mean Error (NME) of less than 10% and a correlation coefficient (r) of over 0.97, outperforming the pressurization method. Fine particles with a diameter under 2.5 μm were found to be a reliable tracer for AER prediction, with a negative correlation between particle size and AER prediction accuracy due to their higher penetration rate. The study also found that outdoor particle levels and pressure differentials positively impacted the accuracy of PM-based AER estimation. These findings have practical applications for maintaining Indoor Air Quality (IAQ) and accurately predicting a building's heat losses.

Sistem Monitoring dan Deteksi Dini Pencemaran Udara Berbasis Internet Of Things (IOT)

The problem of environmental pollution has been a concern for a long time and continues to be an important issue today. One form of pollution that is very detrimental is air pollution, which causes a decrease in air quality and disrupts its normal function. . The ISPU Scale usually consists of several categories that describe the level of air quality, such as the range 1-50 is said to be "Good", the range 51-100 is said to be "Moderate", the range 101-200 is said to be "Unhealthy", the range 201-300 is said to be "Very Unhealthy", and a range of 301+ is said to be "Dangerous". To monitor the level of air pollution, it is necessary to use a tool that can measure air quality accurately. Indoor pollution, as is often the case in office environments, is one of the most serious threats to the health of modern society. A worker can spend up to eight hours a day in the room. The air we breathe everyday comes from the building's air duct system. However, these duct systems not only circulate air, they can also spread contaminants from one room to another. Therefore, it is important to maintain indoor air quality for the comfort and health of workers, because this has a direct impact on their productivity. Based on this, an idea emerged to develop a prototype detector of carbon monoxide gas that is sensitive to indoor air pollution using a microcontroller and web-based. This research begins with analyzing and designing the system, both in terms of hardware and software. After that, the next step involves coding and programming configuration and setup on the Thingspeak web. The last stage is testing tools and systems to ensure that they operate according to the plan that has been set. This system is designed to be able to detect and monitor the level of air pollution, as well as provide a graphical display on Thingspeak. This tool works by using a microcontroller to read the MQ-135 sensor attached to the Ardino Wemos ESP8266. This sensor is used to measure gas levels in a predetermined room. The tool will connect to the Wi-Fi hotspot on the Android device to provide notifications via Thigspeak based on a graph

How Building Energy Use Reacted to Variable Occupancy Pre- and Post- COVID-19 Pandemic—Sensitivity Analysis of 35 Commercial Buildings in Canada

The COVID-19 pandemic and global shutdown and work-from-home order for non-essential businesses and employees led to a substantial decline in energy usage in the commercial building sector. However, the magnitude of decline was not equivalent to what would be expected for unoccupied spaces. The energy performance of low/unoccupied commercial buildings, particularly in the context of new minimum requirements to maintain indoor air quality, is an intriguing research question. In this study, we developed a numerical model that measures electricity usage sensitivity to occupancy (ESTO) where we compare the business-as-usual energy performance with unoccupied energy performance. Two years of COVID-time (in addition to a pre-COVID control year) hourly energy use (electricity (plug loads, lighting, and fans), heating, and cooling) using data from 35 commercial buildings (i.e., buildings with HVAC and other building systems typical of commercial rather than residential buildings) are analyzed to quantify those changes. A change point model is used to assess thermal load intensities, change point temperature, and off-season unoccupied baseloads. Finally, we suggest a generic framework for building scoring based on selected performance parameters. Results indicate that the suggested scoring system is robust and replicable and is reliable for ranking buildings within a given portfolio from best- to worst- performing, thus prioritizing buildings that are best candidates for retrofits.

Air quality and comfort constrained energy efficient operation of multi-zone buildings

Maintaining indoor air quality (IAQ) through effective ventilation is essential for the well-being and productivity of building occupants. Control strategies aimed at improving the efficiency of heating, ventilation and air conditioning (HVAC) systems must jointly determine ventilation and heating and cooling processes. In this paper, we study the problem of minimizing the energy consumption of the HVAC system in a multi-zone building, while meeting thermal comfort and IAQ requirements. We first perform a steady state analysis of the zonal carbon dioxide (CO2) concentration and the temperature dynamics. The resulting expressions are convex in the zonal mass flow rates and zonal temperatures. Guided by the steady state solutions for meeting the thermal comfort constraints, we develop two control policies for improving the energy efficiency of building HVAC systems while jointly satisfying indoor temperature and IAQ constraints. We compare the performance of our proposed approaches with those of multiple baseline approaches which implement separate regimes for controlling zonal temperature and IAQ for a typical work-day in a multi-zone campus building. We have evaluated the performance of our proposed approaches under varying levels of flexibility in zonal temperatures. We have shown that zonal temperature flexibility can result in energy savings up to 32% (for the same control strategies) as compared to the case where no such flexibility is permitted. Our proposed approaches were seen to offer potential savings of nearly 29% compared to the baseline.

Investigation of Microclimate Parameter Assurance in Schools with Natural Ventilation Systems

Slow population growth has limited the construction of new schools, leading to the renovation of existing buildings to achieve energy efficiency goals. While improvements are made to thermal insulation, heating and ventilation systems often remain outdated, presenting challenges in maintaining indoor air quality (IAQ) in schools, where children spend a significant amount of time in densely populated classrooms, and whose health is more affected by IAQ than that of adults. Therefore, this study assessed the possibilities to achieve IAQ requirements in schools ventilated by opening the windows by monitoring the carbon dioxide (CO2) concentration, temperature, and relative humidity (RH) fluctuation. The results of the study have shown that it is not feasible to achieve the defined IAQ parameters in classrooms through window opening alone. The measured CO2 concentration during lessons in many cases exceeded the limit value of 1000 ppm and did not decrease to ambient levels when the windows were opened during the break. Additionally, the internal air temperature dropped below the normative when lessons started, and RH was significantly below the recommended minimum value on all days. It was also found that the use of thermal energy decreases evenly because of inefficient air change leaving no direct economic leverage for the installation of efficient ventilation systems.

Enhancing IAQ, thermal comfort, and energy efficiency through an adaptive multi-objective particle swarm optimizer-grey wolf optimization algorithm for smart environmental control

People in modern societies spend most of their time in buildings, and a considerable amount of energy is used to maintain indoor air quality (IAQ) and thermal comfort levels to ensure people's health and productivity. Thus, to ensure the energy efficiency of buildings, smart environmental control must be implemented through prediction and optimization based on artificial intelligence (AI) algorithms. However, rapid and accurate environmental control based on comprehensive consideration of IAQ, thermal comfort, and energy savings remains to be accomplished. Therefore, this paper describes the development and application of a multi-objective AI algorithm that can be deployed in practical environmental control to rapidly and accurately optimize the IAQ, thermal comfort levels, and energy efficiency of buildings. First, a database of indoor airflow and temperature distribution is established through computational fluid dynamics (CFD) simulations and chamber experiments. Then, with the database, the back-propagation neural network (BPNN) model is created to rapidly predict air pollution concentrations and thermal comfort levels. If these predictions using the BPNN model do not satisfy the setup requirements, an adaptive multi-objective particle swarm optimizer-grey wolf optimization (AMOPSO-GWO) algorithm is used to identify the optimal strategy for maximizing indoor air quality, thermal comfort levels, and energy saving. The results demonstrate that the BPNN-based AMOPSO-GWO algorithm has high predictive accuracy (>90%). The mean reduction in air pollution concentrations, increase in thermal comfort levels, and average energy savings are 31%, 45%, and 35%, respectively.

A Comparative Study to Explore Static and Dynamic Lung Functions in Users and Non-Users of Air Conditioners in Bengaluru.

Air conditioners (AC) have become indispensable in the contemporary World. However, their effects on respiratory health need to be explored further using dynamic and static lung functions. The objectives were: 1. To evaluate and compare dynamic parameters (FEV1, FVC, FEV1/FVC, PEFR, and FEF25-75) in AC users and non-users of Bengaluru. 2. To evaluate and compare airway resistance and specific airway conductance as additional parameters. After applying ATS-ERS criteria for lung disorders, 30 AC users and non-users of Bengaluru, aged 18-40 yrs were assessed using body plethysmography. A significant association of restrictive pattern was seen in AC users (9 AC users out of 60 participants, Chi-square 8.37, P = 0.0038) having an Odd's ratio of 26.95 (CI: 1.4876 to 488.3558, Z = 2.229, P = 0.0258). Airway resistance (Raw) was comparable in both groups further indicating a non-obstructive pathology. AC users had a significant decrease in flow rates (PEFR and FEF25-75) as well as an association with restrictive lung disorder suggesting the need for proper ventilation and hygiene. It is crucial to develop policies to maintain indoor air quality.

A proactive 2-stage indoor CO2-based demand-controlled ventilation method considering control performance and energy efficiency

This paper presents a novel method, named proactive 2-stage demand-controlled ventilation (P2S-DCV) method, to maintain indoor air quality (IAQ) and reduce the energy consumption of multi-zone ventilation systems. The proposed P2S-DCV method applies a proactive control scheme, which predicts future indoor CO2 concentration and supplies proper ventilation to each zone. The method includes two stages. In Stage I, a DNN prediction model is established to predict the future CO2 concentration to calculate the corresponding demand airflow. In Stage II, a reinforcement learning method is designed to achieve rapid and accurate control, and further reduce the energy consumption by optimizing the fan pressure and damper positions. A 5-zone ventilation system is established to validate the proposed P2S-DCV method. The experiment verifies that: a) it can maintain comfortable IAQ via predicting the change of future indoor CO2 and applying effective ventilation control in advance; b) it can improve the control performance, the accuracy is maintained within 8 % (satisfied the ASHRAE Standards), and the control time is maintained within minutes. It can reduce the regulating time by 83.62 % compared with ASHRAE Ratio method, and up to 51.68 % compared with PID method; c) it can reduce the fan energy consumption by 16.4 % compared with ASHRAE Ratio method, and up to 21.8 % compared with PID method; d) it has good generalization ability for various IAQ requirements and ventilation systems with different topologies.

Reliability of the In Silico Prediction Approach to In Vitro Evaluation of Bacterial Toxicity.

Several pathogens that spread through the air are highly contagious, and related infectious diseases are more easily transmitted through airborne transmission under indoor conditions, as observed during the COVID-19 pandemic. Indoor air contaminated by microorganisms, including viruses, bacteria, and fungi, or by derived pathogenic substances, can endanger human health. Thus, identifying and analyzing the potential pathogens residing in the air are crucial to preventing disease and maintaining indoor air quality. Here, we applied deep learning technology to analyze and predict the toxicity of bacteria in indoor air. We trained the ProtBert model on toxic bacterial and virulence factor proteins and applied them to predict the potential toxicity of some bacterial species by analyzing their protein sequences. The results reflect the results of the in vitro analysis of their toxicity in human cells. The in silico-based simulation and the obtained results demonstrated that it is plausible to find possible toxic sequences in unknown protein sequences.

A Potential Solution for Solid Particulate Matter Reduction in Large Indoor Spaces

Air filtration is an essential process in indoor air conditioning and its physical removal of particulate matter is critical for enhancing indoor air quality, especially in arid regions including United Arab Emirates. In such regions, meeting indoor air quality standard is challenging during sporadic sandstorms when common air conditioning systems are unable to maintain indoor air quality properly. Such inability occurs either due to air infiltration through building’s fenestrations exposing indoor air to excessive particulate matter or the failure of inlet air filters after rapid clogging and high pressure drops. Such failure may be observed frequently in buildings with frequent openings such as public buildings and warehouses. Aerosolized pathogenic microorganisms, e.g., SARS-CoV-2 virus, can be modelled through air particle matter and be removed to a certain degree. In addition, the recent global pandemic raised more awareness towards the necessity of particulate matter filtration in indoor environment. Employing independent air filtration units might be a great solution for intermittent or emergency situations, when primary or additional air filtration process is required to attain proper indoor air quality. The main objective of this paper is to attempt designing, manufacturing, and utilizing an easy to set portable filtration unit and to assist buildings’ existing air conditioning systems in airborne dust particle elimination. The unit is designed and manufactured with additional feature accommodating easy installation of commercially available filters for further performance studies. The unit was equipped with all necessary performance monitoring sensors to detect key parameters such as air velocity, pressure differential, temperature, humidity, and particulate matter before and after filtration. The results revealed interesting information associated with the performance of commercially available filters and the feasibility of such independent filtration units.