Optimizing Indoor Air Quality: Evaluating the Synergistic Impact of Filter Integration and Botanical Solutions

Optimizing Indoor Air Quality: Evaluating the Synergistic Impact of Filter Integration and Botanical Solutions | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Optimizing Indoor Air Quality: Evaluating the Synergistic Impact of Filter Integration and Botanical Solutions Sien Jie Wong, Huiyi Tan, Hong Yee Kek, Mohd Hafiz Dzarfan Othman, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6110064/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 Jun, 2025 Read the published version in Clean Technologies and Environmental Policy → Version 1 posted 8 You are reading this latest preprint version Abstract Indoor air quality is crucial for human health and well-being, directly influencing respiratory function and overall comfort. Poor indoor air quality can lead to various health issues, including respiratory problems, allergies, and the exacerbation of pre-existing conditions, emphasizing the importance of maintaining a healthy indoor environment. This study aims to examine the synergistic impact of filter integration and botanical solution in enhancing air quality. A botanical indoor air biofilter (BIAB) rig that utilises the low-cost Internet of Things approach was developed. The air quality parameters are particulate matter 2.5 (PM2.5) and particulate matter 10 (PM10), volatile organic compounds (VOCs), carbon dioxide (CO 2 ), air temperature and relative humidity (RH). The smart sensors operated on RS485 Modbus protocol was integrated into the BIAB to monitor the real-time fluctuations of air quality parameters. A total of 5 combinations of parametric studies are tested, ranging from the usage of botanical plants, carbon filters, coconut husk, and granular activated carbon (GAC). These combinations were designed to assess the impact of different filtration configurations on the overall effectiveness of the system in reducing air pollutants. Results show that Case 3 (integrating botanical plants and a primary carbon filter) has the highest average reduction rate on PM2.5 with 5.36 µg/m 3 per minute and VOCs with 4.13 µg/m³ per minute, respectively. However, Case 5 (integrating additional GAC) contributes to the highest reduction of PM10 concentration, with an average reduction rate of 5.23 ppm per minute. Botanical Indoor Air Biofilter Particulate Matter Volatile Organic Compound Internet of Things Indoor Air Quality Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Introduction Most people in this modern era tend to live within enclosed spaces (Namieśnik, Górecki et al. 1992 ). According to the National Human Activity Pattern (NHAPS), individuals typically spend around 87% of their daily lives indoors. This indoor lifestyle contributes to various health issues, as highlighted by data from the World Health Organization (WHO) (de Robles and Kramer 2017 , Szczotko, Orych et al. 2022 ). The WHO reports that about 3.2 million premature deaths result from illnesses linked to household air pollution. From 3.2 million people, the disease can be divided into several categories which are 32% are from ischaemic heart disease, 23% are from stroke, 21% are due to lower respiratory infections, 19% are from chronic obstructive pulmonary disease (COPD) and 6% are from lung cancer. Certain air pollutants exhibit immediate health impacts while others may manifest over weeks, months, or even years. The immediate side effects include irritation of the eyes and other common fever symptoms such as a sore throat, headaches, dizziness, and fatigue (Alryalat, Toubasi et al. 2022 ). The chance of having a rapid reaction to indoor air pollution is determined by several factors, including the person's age and any prior medical problems. In certain circumstances, whether a person reacts to a pollutant is determined by individual sensitivity, which varies significantly across people. The long-term consequences may lead to debilitating conditions such as lung disorders, heart disease, and cancer. Interestingly, people often remain unaware that the disease was caused by the air pollutant, as the effects may not manifest rapidly after exposure. The indoor air quality (IAQ) is susceptible to various contaminants, ranging from dust settling on furniture surfaces to the incomplete burning of solid fuels during daily cooking. Among the common pollutants found in indoor air mixtures are particulate matter (PM) and volatile organic compounds (VOCs). PM is characterized as a blend of solid particles merging or binding with liquid droplets in the air. At the same time, VOCs are compounds with high vapor pressure and low water solubility, typically resulting from chemical reactions in household products (Maung, Bishop et al. 2022 ). Particulate matter (PM) is usually classified into two types depending on its size, which are PM2.5 and PM10. PM2.5 has more consequences than PM10, as it has a smaller dimension and can penetrate our respiratory system more easily. PM2.5 in indoor environments primarily originates from typical outdoor sources like motor vehicles, biomass burning, and industrial emissions (Zhang and Cao 2015 , Nadzir, Ooi et al. 2020 ). Additionally, PM10 can have both natural origins, such as rock erosion, volcanic eruptions, and spontaneous combustion of forests, and anthropic origins, including various combustion processes (Jeong-ho and Jeong-min 2018 ). As these tiny particles can penetrate deep into the lungs and even into the bloodstream, PMs are linked to several diseases such as cancer, cardiovascular disease, asthma, and COPD. Developers and researchers actively explore various air purification devices to enhance IAQ in confined spaces. The air purifier is a device designed to clean and filter the harmful chemical substances in the air. With the ongoing impact of the Coronavirus disease 2019 (COVID-19) pandemic globally over the past few years, there is a growing unease among people about the air they breathe, leading to a surge in the purchase of air purifiers. Various commercial air purifiers flooded the market, varying their efficacy in filtering air pollution. According to Grand View Research, the air purifier’s market value is at 12.26 billion US dollars. It is expected to continuously grow in the upcoming years with an annual growth rate of 8.1% from 2022 to 2030 as the results of the Covid-19 pandemic and other airborne diseases increased people’s awareness of the higher IAQ’s importance to their health. The commercial types of air purifiers include ultraviolet air purifier, High-Efficiency Particulate Air (HEPA) air purifiers, activated carbon air purifiers and ionic air purifier. Figure 1 shows the types of commercial air purifiers and their strengths and weaknesses(Grinshpun, Mainelis et al. 2005 , Kujundzic, Matalkah et al. 2006 , Bhave and Yeleswarapu 2020 , Dubey, Rohra et al. 2021 , Ditto, Abbatt et al. 2022 ). UV air purifiers use ultraviolet light to kill or deactivate microorganisms like bacteria and viruses, effectively sanitizing the air. Activated carbon filters capture gases, odours, and VOCs by adsorbing them onto the carbon surface, effectively removing harmful chemicals. Ionic air purifiers release negatively charged ions that attach to positively charged particles, causing them to settle or be captured in a collection plate. HEPA filters are designed to trap small particles such as dust, pollen, and pet dander offering highly effective filtration for particulate matter. Recent studies have demonstrated the efficiency of filter to improve IAQ. Kim & Yeo ( 2020 ) conducted a study on the effect of air flow rate on the filter efficiency to PM2.5 in Korea’s indoor environment. In the study, a simulation was carried out using the particle model to observe and analyse the effect of PM2.5 on the ventilation flow rate and the filtration of PM2.5. To determine how the parameter affected indoor PM2.5 concentrations, MATLAB software was used to analyse a mass balance equation. Based on the result, the author concluded that a high flow rate was preferable for lowering the concentration of PM2.5 indoors, regardless of filter effectiveness. The indoor PM2.5 concentration showed a reduction rate of up to 6–8% depending on the filtration efficiency when the filtration system was operated at a flow rate of 100 m3/h. On the other hand, the indoor PM2.5 concentration decreased by up to 29 to 38% depending on the filter efficiency when the filter system was operating at a flow rate of 600 m 3 /h, which justifies the previous statement. Chung et al. ( 1995 ) and Fu & Liu ( 2017 ) performed a study on the effect on the absorption performance of 40% LiCl solution for selected indoor pollutants which includes toluene, 1,1,1-trichloroethane and carbon dioxide (Chung, Ghosh et al. 1995 , Fu and Liu 2017 ). According to the findings, a LiCl solution could only remove a tiny amount of the carbon dioxide and 1,1,1- trichloroethane and about 20% of the formaldehyde and toluene. The Botanical Indoor Air Biofilter (BIAB) is one such air purifier designed to enhance IAQ (Fleck, Pettit et al. 2020 ). The biofilter relies on the natural processes of the plants, such as absorbing CO 2 and releasing oxygen, as well as the ability of plant roots and rhizosphere microorganisms to break down and absorb air pollutants like VOCs, PM, and other harmful gases. The main removal mechanism of CO 2 and PM2.5 involves plant-leaf interactions (Saucedo-Lucero, Falcón-González et al. 2024 ). Additionally, plants can absorb pollutants through their roots and break them into less harmful compounds through processes like photosynthesis and cellular respiration (Montaluisa-Mantilla, García-Encina et al. 2023 ). This method, known as phytoremediation, is particularly effective in removing VOCs and other hydrophobic pollutants. Passive botanical systems rely on the natural diffusion of air pollutants through the plant components, without any active mechanism to direct the contaminated air to the plants or their substrates (Pettit, Irga et al. 2018 ). These systems have shown significant reductions in VOCs, ranging from 10–90%, within 24 hours in sealed chambers(Llewellyn and Dixon 2011 ). BIAB utilizes multiple filter components, with organic plants as the primary filtration element. A green wall, also known as a living wall or vertical garden, is a vertical structure that is partially or completely covered with vegetation and includes an integrated growing medium, such as soil or a substrate. Green walls are designed to improve indoor or outdoor environmental quality by enhancing aesthetics, reducing CO₂, filtering airborne pollutants, and regulating temperature and humidity levels. Green walls are related to BIAB as they apply the same method for filtering air pollutants by using several botanical plants as filters. Studies by Taemthong and Cheycharoen ( 2022 ) focused on the green wall's ability to reduce carbon dioxide in the classroom. In the study, Epipremnum aureum or Marble Queen or Golden Pothos was chosen as it could reduce the concentration of carbon dioxide. The experiment was done with 13 students, who were the source of the carbon dioxide, as human breathing releases carbon dioxide into the air. The study found that 105 pots of Golden Pothos are needed to absorb 208 ppm of CO 2 in the range of 80 minutes. Another study by Taemthong ( 2021 ) found that with the installation of 150 pots of Golden Pothos was able to reduce CO 2 concentration by 430 ppm within 80 minutes with the presence of 20 students in the class. Pettit, Irga et al. ( 2017 ) investigated the role of the botanical component in the performance of active green wall biofilters for PM removal. Green walls with different plant species exhibited varying particulate matter removal efficiencies, with fern species achieving the highest performance (45.78% for PM 0.3-0.5 and 92.46% for PM 5-10 ). Active botanical filter using Golden Pothos effectively removed low concentrations of acetone, α-pinene, and toluene from indoor air, achieving maximum removal efficiencies of 99.8%, 83.6%, and 71.1% respectively (Montaluisa-Mantilla, Gonçalves et al. 2025 ). The filter’s performance declined with reduced air and nutrient medium recirculation rates, and the absence of the plant component significantly lowered pollutant removal, highlighting the critical role of both plant presence and system design in air purification efficiency. Despite the growing interest in BIAB, limited studies have thoroughly examined their effectiveness in pollutant removal and the underlying mechanisms. Before BIAB can be effectively introduced to the market, significant adjustments and enhancements are required to maximize its performance. This study aims to address these gaps by developing a high-efficiency innovative filtration botanical biofilter rig that integrates multiple factors, including the addition of filters, activated carbon, and botanical plants, to optimize its efficiency in improving IAQ. Furthermore, the study incorporates IoT devices to monitor and assess real-time changes in air pollutant concentrations. This experiment was designed as a pilot study to establish a baseline for the effectiveness of botanical indoor air biofilters. The study also aimed to integrate an affordable IoT approach to monitor and control environmental parameters in real-time, alongside testing the combined effectiveness of plants and carbon materials in air purification. Given the pilot nature of this study, the focus was on exploring the initial feasibility and performance of the biofilter system. The results of this study are intended to represent hot and humid environmental conditions commonly found in Southeast Asian (ASEAN) regions and are not intended to be directly extrapolated to temperate climates or standard indoor conditions as defined by the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE). Methodology 2.1 BIAB Rig Setup The BIAB system was designed and comprised three key components: inlet sensors, a biofilter, and outlet sensors, as illustrated in Fig. 2. The biofilter was positioned centrally within a transparent case, allowing air quality to be assessed systematically. Inlet sensors measured the initial concentrations of pollutants, including PM2.5, PM10, VOCs, and CO 2 , as ventilation fans drew the air into the system. The biofilter facilitated the removal of contaminants as the air passed through, with outlet sensors positioned downstream to capture the final concentrations, validating the air quality improvements. Mechanical ventilation and a submersible pump were activated to initiate the operation of biofilter, ensuring consistent airflow and irrigation. A network router established a centralized IP address to connect all sensors, enabling real-time data acquisition and recording. This setup allowed for simultaneous monitoring of pollutant concentrations, comprehensively evaluating of the BIAB’s performance. Carbon-based materials were integrated into the BIAB system to enhance air purification. The efficiency of air purification was evaluated by analysing the air quality readings and comparing pollutant concentrations measured by inlet sensors before filtration with those detected by outlet sensors after passing through the biofilter. To ensure measurement accuracy and environmental consistency, both the inlet and outlet boxes used in this study were initially prepared as controlled empty enclosures, each equipped with pre-calibrated sensors. The sensors were run continuously for a minimum of 60 minutes to allow the internal conditions of the closed boxes to stabilise. This steady-state phase is critical for establishing a reliable baseline. During this period, temperature, RH, and PM readings were monitored in real-time to confirm the absence of significant fluctuations. Data collection for the experimental phase proceeded only after these readings had stabilised. This approach ensures that both boxes began with equivalent and stable environmental baselines, thereby validating the comparison between inlet and outlet measurements and ruling out baseline calibration errors. 2.2 Air Quality Sensors PM2.5 and PM10 sensors were used to capture the PM concentration. The PM2.5 sensor is designed to measure particulate matter with a size of 2.5µm and smaller, capturing fine particles like dust and smoke that can deeply penetrate the respiratory system. It plays a critical role in assessing air quality and potential health hazards. On the other hand, the PM10 sensor detects particles sized 10µm and smaller, including larger particles like pollen and mould spores. Through a scientific and unique algorithm, these sensors calculate the mass concentration of particles per unit volume based on the equivalent particle size, ensuring reliable and real-time data. PM2.5 and PM10 sensors manufactured by Shandong Renke Technology Co., Ltd. were chosen to be used in the experiment as it can deliver prompt and precise results, allowing for timely monitoring of air quality and facilitating rapid responses to any potential changes or pollution events (Renke 2024). In terms of sensor working methods, Renke Technology Co., Ltd.'s sensors leverage advanced laser scattering measurement principles, which enable them to gauge the size and concentration of suspended particles accurately. Through a scientific and unique algorithm, these sensors calculate the mass concentration of particles per unit volume based on the equivalent particle size, ensuring reliable and real-time data. The reason to use an optical particle sensor was guided by practical and operational considerations commonly encountered in real-time indoor air quality monitoring. While reference-grade methods such as gravimetric sampling, beta attenuation monitors (BAM), and tapered element oscillating microbalances (TEOM) offer more accurate mass-based PM10 measurements, they come with significant trade-offs. Gravimetric methods are time-intensive, requiring manual filter collection, lab analysis, and non-continuous operation. BAM and TEOM instruments, although real-time, are costly, bulky, and sensitive to environmental conditions, making them unsuitable for deployment in dynamic or confined indoor environments. In contrast, the RS-PM-*-2-EX optical sensor provides a non-intrusive, cost-effective solution for continuous monitoring of PM2.5 and estimated PM10 levels. While the PM10 output is derived through algorithmic interpretation of scattered light and not through direct physical sizing, it remains a valuable tool for assessing temporal patterns and environmental changes, especially when rapid or spatially distributed data collection is required. Adding VOCs sensors to the BIAB is essential to understanding how plants and selected filters affect VOCs levels. VOCs are airborne pollutants from various sources such as building materials, paints, furniture, cleaning products, and cosmetics, that harm humans (Rumchev, Brown et al. 2007). Monitoring VOCs helps assess how plants and selected filters can reduce indoor pollutants and improve air quality. Taqu Sensors Technology Co., Ltd. manufacturers the sensors chosen. The sensors continuously measure VOCs concentration, showing the plants’ effectiveness in creating a healthier living environment. This data optimizes the BIAB's efficiency and promotes cleaner, safer indoor air. The VOCs sensor utilized in this study was designed and calibrated by the manufacturer to operate reliably within a broad environmental range. It is capable of functioning between 20 and 80°C and under relative humidity conditions of 0 to 99%. This wide operational tolerance ensures that the sensor remains accurate and stable when deployed in climates similar to those found in ASEAN nations. The VOCs sensor used in this study is a metal oxide semiconductor (MOS)-based device designed to monitor overall VOCs concentration trends under varying environmental conditions. Although it is not calibrated to specific compounds such as fluorene, fluorethane, or phenanthrene, it is effective in detecting overall VOCs changes in real-time. The sensor readings are used here to track relative VOCs dynamics rather than absolute quantification of individual species. Carbon dioxide (CO 2 ) sensors must be integrated into the BIAB system to access the impact of CO 2 concentrations released by the botanical plant. The CO 2 sensor used in the experiment was from Shandong Renke Control Technology Co.,Ltd since it provides high measurement accuracy with quick response (Renke 2024). By monitoring CO 2 levels, the BIAB allows for a precise understanding of the plants' respiratory activities and their role in the carbon cycle. As the plants undergo photosynthesis, they absorb CO 2 and release oxygen, acting as a natural air purifier. The CO 2 sensors continuously measure the CO 2 concentration within the BIAB to provide data to assess the effectiveness of the botanical plants in reducing CO 2 levels and enhancing IAQ. Temperature and humidity sensors are seamlessly integrated into the BIAB system to address important aspects of the performance of biofilter and the health of the botanical plants. Shandong Renke Control Technology Co.,Ltd. produced the temperature and humidity sensor used, which is small and portable, thus making it suitable for installation in a small place to measure temperature and humidity (Renke 2024). Ensuring that the sensors operate within their optimal temperature range is essential for maintaining accurate and reliable measurements. By carefully monitoring temperature changes in real-time, the BIAB can adjust its filtration efficiency, accordingly, optimizing pollutant removal based on the prevailing environmental conditions. Simultaneously, the humidity sensors play a vital role in displaying the humidity level to create a suitable habitat for the plants, preventing excessive humidity or dry conditions that may negatively impact their health and filtration capabilities. Table 1 shows the specifications of the sensors and Fig. 3 shows the arrangement of the sensors in the inlet and outlet sensor boxes. 2.3 Internet of Things The internet-of-things (IoT) was used to develop a low-cost method of monitoring real-time IAQ in a BIAB test rig. IoT enables real-time data collection and analysis of critical pollutants allowing for continuous and precise monitoring. This connectivity facilitates remote access and control, enabling users to track IAQ trends and adjust conditions as needed. The air quality sensors were connected to the Raspberry Pi 4 Model B through USB to RS485 converter. RS485 is a serial communication protocol for reliable long-distance and noise-resistant data transmission. These sensors measure specific air quality parameters, such as PM2.5, PM10, VOCs, or CO2, and transmit the data over the RS485 interface. The sensors output data in the form of electrical signals following the RS485 standard. Since the Raspberry Pi 4 lacks native RS485 ports, a USB to RS485 converter is used. This device acts as an intermediary, converting the RS485 signals from the sensors into USB-compatible signals. It plugs into the Raspberry Pi’s USB port and effectively communicates with the RS485 sensors effectively. The Raspberry Pi 4 serves as the central processing unit for the IoT system. It runs OpenHABian, an open-source smart home automation platform, which collects and processes data from the sensors via the USB to RS485 converter. The network router connects the Raspberry Pi to the internet and other devices. It ensures communication between the Raspberry Pi and remote clients or users, allowing data to be accessed, monitored, and controlled in real-time from anywhere. The concentration of PM 2.5, PM 10, CO 2 and VOC, as well as the air temperature, and relative humidity (RH) were all measured by two sets of air quality sensors installed at the BIAB rig's air inlet and outlet. Figure 4 depicts a schematic diagram of integrating of the real-time IAQ monitoring system. IoT devices enhance data visualization through platforms like dashboards or mobile applications, providing actionable insights for optimizing the rig’s performance. Data was collected once every 2 minutes. In cases where the concentration of air pollutants at the inlet was lower than the outlet concentration, it is essential to conduct a thorough re-evaluation and reconfiguration of the BIAB system before its operation. OpenHABian was used in this study as a free and open-source home automation platform designed to serve as an integration platform and centralization hub for a wide range of IoT devices/protocols. Also, the collected data can be stored in the cloud or in a local digital database (SD card). InfluxDB is a time-series database running on the Raspberry Pi, used to store the collected air quality data efficiently, allowing for timestamped data entries critical for trend analysis. Grafana as the visualization tool connected to InfluxDB can create dynamic dashboards. It queries the stored air quality data from InfluxDB and displays it in graphs, charts, and other formats for by users to easily interpret and analyse. Figure 5 illustrates the OpenHABian system architecture, data structure, and interconnected devices utilized in the botanical biofilter system developed for this study. In this study, a wireless smart control system was used to control the ventilation fan and the submersible pump in the BIAB rig. The biofilter system was equipped with eight brushless DC fans strategically installed on the back panel to ensure a consistent and efficient airflow. Brushless DC fans were chosen for their reliability, energy efficiency, and low noise operation, making them ideal for the continuous operation required in the experimental setup. These fans collectively provided an inlet airflow rate of 110 m³/h, which was essential for drawing contaminated air through the biofilter for purification. The airflow rate was validated using a calibrated digital anemometer (TESTO 405) capable of directly measuring volumetric flow (m³/h). Multiple readings were recorded and averaged to ensure accuracy and reliability. This validated flow rate aligned with the fans’ rated specifications, confirming the consistency of the system’s ventilation performance. The airflow rate was validated to optimize the interaction between the air pollutants and the biofilter components, ensuring effective pollutant removal while maintaining stable system performance. Also, the submersible pump was controlled so that the botanical parts were kept hydrated to prevent any excessive water from being sprayed into the growing medium. The model used in this study is the Sonoff 4CH Rev 2 with Tasmota firmware. This configuration allows OpenHAB to control 250 VAC appliances up to 2200 W via Wi-Fi and Message Queue Telemetry Transport (MQTT). A complete list of IoT and smart control appliances is shown in Table 2. Table 2 List of IoT and smart control appliances Device Specification Ventilation Fan Rated Voltage: 12V Rated Current: 0.18A Diameter: 90mm Speed: 2400RPM Submersible Pump Model: WP-100D Power Supply: 7W Max Flow Rate: 560L/H Wireless Electrical Switch Model: Sonoff 4CH Rev2 Power Supply: 90 ~ 250V AC Maximum current: 10A per channel Network Router Model: TP-link TL WR902AC Power Supply: 5V 2A 2.4 Pollutant generation The source of pollutants was injected into the system by burning one ring of mosquito-repellent coils. The combustion process releases the substances as the active ingredients in the repellent, often pyrethroids, burn and vaporize. The smoke produced contain fine PM2.5, which can penetrate deep into the respiratory system, and VOCs, which include irritants and compounds that contribute to indoor air pollution. In addition, the burning of the coils may also release toxic substances like formaldehyde and acetaldehyde, which are by-products of incomplete combustion. Therefore, the source of pollutants from burning mosquito-repellent coils can significantly impact indoor air quality, making it an important consideration in IAQ monitoring. The same injection of pollutant source will be applied in the subsequent cases. 2.5 Filter Setup Several proposed methods were conducted to examine the efficiency of the filtration part of the BIAB rig. Case 1 is an integrated baseline case without any filtration system. The baseline case in this study represented the scenario where no filtration system was added to the BIAB rig. It served as a reference point to compare and assess the effectiveness of the implemented filtration methods. The initial level of PM and VOCs present in the indoor environment were identified by examining the baseline case without any filtration intervention. Therefore, the extent of air pollution reduction achieved through the subsequent filtration experiments can be quantified. Additionally, the baseline case provided a basis for evaluating the performance of the filtration systems in terms of their ability to improve IAQ compared to untreated conditions. Case 2 used a botanical plant as the filtration system of the BIAB. Previous literature suggested that organic plants can filter different types of VOCs, such as benzene and formaldehyde, from the air (Sokhal and Narayan 2020). However, their effectiveness in filtering PM remains uncertain. Therefore, this method was implemented to investigate whether the presence of the botanical plant could lead to a reduction in the PM concentration. In this study, Golden Pothos was used as it is believed to be able to reduce the VOCs concentration. The present study involved 2-year-old Golden Pothos plants, which are considered adult plants at this stage. By 2 years, Golden Pothos typically reaches full maturity with a well-developed root system and an extensive leaf area, enhancing its air purification abilities. This maturity ensures that the plant can substantial contribution to improving indoor air quality. A study by Sokhal and Narayan (2020) claimed that Golden Pothos effectively remove formaldehyde, benzene, toluene, and xylene from the air. Golden Pothos was more effective at removing formaldehyde, with a removal efficiency of 68.1%. Results from another study by Saucedo-Lucero, Falcón-González et al. (2024) also supported the ability of Golden Pothos to remove air pollutants such as VOCs, CO 2 , and particulate matter. It shows the highest removal efficiency for CO 2 , improving performance as the foliar area increases, and is particularly effective in filtering PM2.5 compared to PM10. While CO removal is notable, especially in later stages, TVOC removal is relatively low. The experimental setup consisted of three large pots (diameter: 17 cm) and three small pots (diameter: 7 cm) of Golden Pothos, positioned directly in front of the filtration fans. This was to ensure a higher circulation of the airflow and the maximum exposure of the air passing through the plants. Placing the pots in front of the fans aimed to facilitate the efficient exchange of air between the indoor environment and the plants, enabling effective interaction between the air pollutants and the botanical filtration system. Case 3 added a primary carbon filter to the biofilter. As botanical plants focus on the filtration of VOCs, carbon filters were introduced to help with the filtration of PM 2.5 and PM 10. Carbon filters were believed to be able to reduce the concentration of PM due to their large surface area. The carbon filter used in this study features a thickness of 4mm ± 1mm and adheres to the G3 filter class as per the EN779:2012 standard, making it well-suited for general air filtration in non-critical environments. This filter includes carbon, which enhances its ability to absorb odours and gases such as VOCs, improving indoor air quality. With a 22g/cm³ density, the filter balances adsorption capacity and airflow resistance, ensuring efficient filtration without causing significant pressure drops. Its average arrestance of synthetic dust falls between 80% and 90%, effectively capturing airborne dust particles. While it may not efficiently trap finer particles like PM2.5, the filter remains highly beneficial for general air quality improvement by addressing larger particulate matter and gaseous pollutants. Case 4 introduced the coconut husk to the filtration component in the BIAB system. Coconut husk was known for its high porosity and natural ability to absorb impurities. According to Pettit, Irga et al. (2018), coconut husk has been utilized in various studies focusing on functional green walls, and the existing literature suggests that it serves as an effective substrate for active green walls. Moreover, it has been demonstrated to be suitable as a packing medium in biofilters. The porosity and fibrous characteristics of coconut husks are one of the main reasons they can trap particles passing through them and act as a barrier to filter out large airborne particles, or PM. Incorporating coconut husk into the filtration process is expected to enhance the removal of PM2.5 and PM10. The porous structure of coconut husk allows for increased surface area and contact time, facilitating the adsorption and retention of airborne pollutants. However, the effectiveness of coconut husk as a filtration medium in the specific context of the BIAB system requires investigation and evaluation. Case 5 included granular activated carbon (GAC) as an additional filtration component in the BIAB system. GAC is known for its high adsorption capacity and effectiveness in removing various contaminants from the air, including VOCs and odorous substances. GAC filters can reduce 90% of the formaldehyde in the air (Ahn, Cho et al. 2021). Formaldehyde, a common VOCs found in indoor environments, is known to be a potent respiratory irritant and a potential carcinogen. The GAC used in this study, with a high iodine value of 900IV to 1500IV, ensures efficient adsorption of a wide range of contaminants, including VOCs and odours (Chaudhary, Bansal et al. 2024). The specified mesh size 20×50 provides an optimal balance for pollutant capture and air permeability. The density of the GAC directly impacts the airflow rate; while a lower density improves airflow through the material, it may slightly reduce the adsorptive capacity (Patel and Mansoor Ahammed 2024). With a minimum hardness of 99%, the GAC maintains excellent structural integrity and durability during use. Additionally, a maximum moisture content of 5% and ash content of 3% ensure consistent performance and minimal interference with its adsorption efficiency. These properties make the GAC ideal for enhancing air purification systems. Therefore, the GAC used in this experiment is maintained dry to ensure optimal adsorption capacity and performance. Incorporating GAC into the filtration process is expected to further enhance the removal of VOCs and improve the overall air quality. The large surface area and porous structure of GAC provide ample contact points for the adsorption of pollutants, effectively trapping them within the filter media. However, the specific performance and suitability of GAC in the context of the BIAB system need to be assessed through experimentation and analysis. The proposed cases were simplified and tabulated in Table 3. 2.6 Pollutant reduction rate calculation To determine the average reduction rate of pollutant concentrations such as PM2.5, PM10, and VOCs in the experiment, the reduction rate was calculated as a measure of how efficiently the filtration system reduced pollutant levels over a specific period. The average reduction rate was computed using Eq. 1. $$\:Average\:Reduction\:Rate=\frac{Initial\:Concentration\:(\mu\:g/m³)\:\:-\:Final\:Concentration\:(\mu\:g/m³)}{Time\:Taken\:\left(Minute\right)}$$ 1 The reduction rate for each pollutant is calculated within a targeted concentration range to evaluate the system’s performance across specific pollutant levels. For PM2.5, the reduction rate is assessed as concentrations decrease from 1000 µg/m³, the maximum range of the PM sensor to 100 µg/m³, which falls within the "satisfactory" category of the Air Quality Index (AQI) (Malhotra, Walia et al. 2024). This reflects the ability of the system to filter fine particulate matter to meet stringent air quality standards. For PM10, the reduction rate is measured as concentrations decline from 1000 µg/m³ to 100 µg/m³ (satisfactory level), demonstrating the efficiency of removing coarser particulate matter. Similarly, the reduction rate for VOCs is evaluated as concentrations decrease from peak value to 1000 ppm, the threshold for a "good" AQI level highlighting the system’s capability to effectively reduce harmful volatile organic compounds (Talati, Shah et al. 2025). These targeted ranges are crucial for assessing the filtration system’s performance in achieving specific air quality improvements and compliance with health and safety guidelines. This calculation quantitatively assesses the rate at which pollutants are removed from the air. The initial and final concentrations were measured using inlet and outlet sensors, respectively, while the time taken 𝑇 refers to the duration over which the reduction occurred. A higher average reduction rate indicates a more efficient filtration process, reflecting the system’s ability to reduce pollutants more effectively within a shorter time. This metric is critical for evaluating the overall performance of the filtration system and is helpful in comparing different filtration configurations or assessing the effectiveness of individual components. Conversely, a lower average reduction rate may suggest inefficiencies or system optimization needs. Results and Discussion 3.1 Air Quality Monitoring A 960-minute (16 hours) data plot, highlighting the most significant concentration changes caused by pollutant injection (mosquito-repellent coils) was presented for each case. The data plots were generated from real-time measurements, sampled at 30-minute intervals. Error bars are included to represent the accuracy specifications of the sensors employed: ±30 µg/m³ for the PM sensor and ± 100 ppm for the VOCs sensor. Inlet and outlet pollutant concentrations were plotted to analyse their variation and visually assess the differences in pollutant levels across the system. Throughout the experiment, temperature and humidity showed minimal variation during the 960-minute data collection period. The temperature fluctuated around 34 ± 0.6°C, while the humidity remained stable at approximately 60 ± 2%. The minimal variation in temperature and humidity during the experiment can likely be attributed to the controlled indoor environment where the study was conducted. Indoor conditions typically offer stable temperature and humidity levels, minimizing external influences that could cause fluctuations. This stability justifies why these parameters did not show significant changes and were not further discussed. Therefore, these parameters were not discussed further. All cases exhibited high initial concentrations, as the initial conditions were intentionally set to ensure a thorough mixing of pollutants within the inlet sensor box. Case 1 (baseline) focuses on evaluating the air quality within the system without the inclusion of any filtration mechanisms. Therefore, the baseline case acts as a reference point to assess the natural state of air pollutant concentrations and provides a basis for comparison with the subsequent cases involving filtration systems. By examining the inlet and outlet measurements of various pollutants, the baseline case offers insights into the initial pollutant levels. It allows for a better understanding of the effectiveness of the filtration systems implemented in subsequent experiments. It provides a comparative analysis to show the impact of filtration on improving air quality within the BIAB system. Figure 6(a) and Fig. 6(b) illustrate the temporal variation of PM2.5 and PM10 concentrations at the system’s inlet and outlet respectively. The concentrations of PM2.5 at both the inlet and outlet increased synchronously, reaching the sensor’s maximum detectable limit of 1000 µg/m³, and began to decline at approximately 470 minutes. In comparison, PM10 concentrations at the inlet and outlet also rose to 1000 µg/m³, with a subsequent decline observed around 490 minutes. The observed decline in PM2.5 and PM10 concentrations can be attributed to the cessation of emissions following the complete combustion of the mosquito-repellent coils. Figure 6(c) presents the VOCs concentrations at the system’s inlet and outlet over time. The initial VOCs concentration of approximately 1000 ppm increased sharply, reaching peak values of 1626 ppm at the inlet and 1611 ppm at the outlet at 402 minutes. The graph shows that the patterns of VOCs concentrations at the inlet and outlet closely resemble each other. This trend similarity indicates that no filtration system has been incorporated into the setup yet. Without any filtration mechanisms in place, the pollutants level remains relatively consistent throughout the observation. The absence of a noticeable deviation between the inlet and outlet concentrations implies that the system does not currently possess the capability to reduce PM and VOCs effectively. This emphasizes the need to explore and implement filtration measures to address the presence of these potentially harmful PM and VOCs. Case 2 focuses on investigating the effectiveness of utilizing botanical plants as the filtration part of improving IAQ. The experiment was setup with three big pots and three small pots of Golden Pothos in front of the filtration fans. Figure 7(a) and Fig. 7(b) show the concentrations at the inlet and outlet of PM2.5 and PM10 against time respectively. Higher initial concentrations of PM2.5, PM10 and VOCs at the outlet compared to the inlet were observed, which may be attributed to several potential factors. As the plant pots were installed, the dust and particles from them might have caused a sudden spike in the outlet concentration as the presence of the airflow caused the dust and particles from the plants and pots to resuspend back into the air. The concentrations of PM2.5 at both the inlet and outlet began to decline at approximately 280 minutes, while PM10 concentrations started to decrease by around 302 minutes. Following the removal of the air pollution source, a consistent downward trend was observed, with inlet and outlet PM2.5 concentrations exhibiting similar patterns and values. Figure 7(b) also indicates almost the same trend as the results obtained for PM2.5, where the difference between the inlet and outlet concentrations of PM10 is minimal and practically imperceptible. This shows that the addition of Golden Pothos has low capability in PM2.5 and PM10 filtration. Further improvements or additional filtration methods may be necessary to achieve a more substantial reduction in PM concentrations. Figure 7(c) illustrates the concentration of inlet and outlet VOCs against time. The highest VOCs concentration recorded for both the inlet and outlet was 1477 ppm and 1481 ppm respectively. As observed in Fig. 7(c), the outlet VOCs had a lower concentration than the inlet VOCs in the first 2 hours. However, after that period, as the day transitions to night, the effectiveness of Golden Pothos declines as it does not reduce the VOC concentration, and there is a slight increase in outlet VOCs from 150 minutes to 480 minutes. This could be the reason for the photosynthetic activity of the plant. During the daytime, plant’s ability to reduce VOCs concentrations is generally more pronounced when they are actively photosynthesizing. This is because the photosynthetic process helps plants metabolize and break down VOCs, improving air quality. However, at night, the photosynthetic activity of plants diminishes as they switch to a different process called respiration. These changes in the process may be relatively lowering the plant's VOCs reduction ability. We can conclude that adding Golden Pothos as a botanical plant will reduce the concentration of VOCs. However, its ability is not significant as it depends on several other factors, such as the light condition. This aligns with the findings of Taemthong & Cheycharoen (2022), which indicate that variations in light intensity supplied to plants can influence their capacity to absorb VOCs. Case 3 utilized adding a primary carbon filter to the previous system inside the biofilter box to improve PM and VOCs filtering. Figure 8(a) and Fig. 8(b) below show the inlet and outlet concentrations of PM2.5 and PM10 against time. The increasing trend of the PM concentration in the graph was due to the injection of air pollution by burning one mosquito repellent coil. It was observed that the outlet PM2.5 concentration was lower than the inlet concentration of PM2.5 right after the experiment started. Once the source of the air pollutant was eliminated, the outlet concentration showed a steeper decline in concentration compared to the inlet. This shows that carbon filter leads to a lower concentration in the outlet compared to the inlet, as the carbon filter acts on filtering the PM2.5. This implies that without continuous pollution input, the filtration system, including the carbon filter, efficiently removes PM2.5 particles from the air. Similarly, the outlet PM10 concentration was lower than the inlet PM10 concentration once the pollution was injected, as observed in Fig. 8(c). This indicates that the addition of the filtration system can reduce PM10 in the air. With respect to PM filtration, it can be concluded that the addition of a carbon filter effectively decreases the proportion of PM2.5 and PM10 passing through the BIAB system. The analysis of VOCs revealed the impact of the effectiveness of botanical plants with the addition of a carbon filter on the filtration system. The variation of inlet and outlet VOCs concentration throughout the experiment is illustrated in Fig. 8(c). It is observed from Fig. 8(c) that the carbon filter significantly reduced the VOCs concentration as the outlet VOCs concentration was kept in a lower value than the inlet. The maximum VOCs concentration recorded at the inlet was 1622 ppm at 108 minutes. In contrast, the maximum VOCs concentration at the outlet was considerably lower, reaching a maximum of 1479 ppm at 416 minutes. Compared to the previous cases, adding a carbon filter improved the ability of the BIAB rig to reduce VOCs in the air. The successful reduction of VOCs concentrations at the outlet highlights the carbon filter’s positive impact on enhancing the performance of the BIAB system’s performance. Case 4 used the coconut husk as an additional filtration layer, working alongside the primary carbon filter. Its purpose was to complement the existing filtration mechanism by targeting specific pollutants and further reducing their concentration in the air. Figure 9(a) and Fig. 9(b) show the PM2.5 and PM10 concentration throughout the experiment by installing coconut husk into the filtration system. The same pollution source had been applied in the experiment. The differences between the inlet and outlet PM2.5 and PM10 are not significant. At some points, the outlet PM concentrations exceed the inlet PM concentration considering that the coconut husk might also contribute to the increasing concentration of PM due to its degradation over time. Incorporating coconut husk as an additional filtration material in the BIAB showed limited improvements in the filtration of PM. While coconut husk has been recognized for its natural filtration properties, the experimental results indicated its impact on PM reduction was relatively modest. Figure 9(c) shows the inlet and outlet VOCs concentrations against time. The graph obtained has almost the same trend as the one without the coconut husk. This shows that coconut husk has limited ability to reduce VOCs. The maximum inlet VOCs concentration obtained is 1522 ppm at 260 minutes, and the maximum outlet concentration is 1476 ppm at 262 minutes, which is almost similar to the one obtained in Case 3 without coconut husk. Case 5 represents a new experimental setup incorporating GAC. Figure 10(a) presents the temporal profiles of PM2.5 concentrations at the inlet and outlet, while Fig. 10(b) depicts the corresponding inlet and outlet concentrations of PM10 over time. The same pollutant source was injected and caused the PM concentration to spike up to the maximum limit of the PM sensor which was 1000 µg/m³. A significant reduction of PM concentrations is observed during the declining phase, as the outlet concentrations of both PM2.5 and PM10 are markedly lower than the corresponding inlet concentrations. It is noted that the addition of GAC slightly improves on PM2.5 and PM10 filtration. Next, the analysis of VOCs revealed the impact of the addition of GAC in the filtration system on the effectiveness of the system. It is observed from Fig. 10(c) that the addition of GAC has a noticeable impact on the reduction of VOCs. The maximum concentration obtained at the inlet sensors is 1626 ppm, while it is only 1374 ppm on the outlet sensors as shown in Fig. 10(c). The inclusion of a carbon filter in the previous case resulted in a reduction in VOCs concentrations. However, the introduction of GAC further elevated the filtration efficiency, leading to enhanced removal of VOCs. The combined use of a carbon filter and GAC proved to be more effective in mitigating VOCs contamination, surpassing the filtration capabilities of the carbon filter alone. 3.2 Pollutant Removal The time for the PM and VOCs concentration to start declining varies for each case since the time for a coil of mosquito repellent to be completely burned might be slightly different. Furthermore, the limitation range of the PM sensor until 1000 µg/m³ resulted in an incomplete observation on the maximum PM concentration recorded and the time of PM concentration started declining. However, the filter efficiency can be compared based on the reduction rate of PM concentration at the outlet. The reduction rate of PM concentration is calculated by determining the time needed to reduce maximum PM concentration recorded (1000 µg/m³) to a satisfactory level (100 µg/m³). The reduction trend of outlet PM2.5 and PM10 concentration from Case 1 to Case 5 are summarized in Fig. 11(a) and Fig. 11(b). Figure 11(a) clearly illustrates that Case 3, which incorporates a botanical plant and a carbon filter, exhibits the most significant decrease in PM2.5 concentration at the outlet, followed by Cases 5 and Case 4. Figure 11(b) demonstrates that Case 3, which incorporates both botanical plants and a carbon filter, exhibits the most prominent decline in PM10 concentration, followed by Cases 4 and 5. Cases 1 and 2 exhibit similar PM2.5 and PM10 reduction trends, which require further clarification through a detailed analysis of their respective reduction rates. Figure 12(a) below summarizes the outlet VOCs concentrations across Cases 1 to 5. Case 1, the baseline scenario, recorded the highest VOCs concentration at 1611 ppm. In contrast, Case 5, which incorporated a full combination of filtration system, exhibited the lowest concentration, peaking at 1374 ppm. The concentrations in Case 2 (1481 ppm), Case 3 (1479 ppm), and Case 4 (1476 ppm) were all lower than the baseline value observed in Case 1. This trend indicates that the introduction of the filtration system leads to a reduction in VOCs concentrations, with varying degrees of efficiency. Figure 12(b) presents the normalized VOCs concentration trends for all cases, allowing direct comparison on a uniform scale. Normalized VOCs concentration is based on the ratio of VOCs concentration at specific time to the initial VOCs concentration in each case. By normalizing the data, the influence of varying initial concentrations is eliminated, enabling a clear assessment of the relative efficiency and behaviour of each case over time. Case 2 (Botanical Plant) exhibits the most significant and consistent reduction in normalized VOCs concentration over time, indicating the highest removal efficiency. Case 4 (BP + CF + Coconut Husk) and Case 5 (BP + CF + CH + GAC) also show substantial reductions, performing better than Case 3 (BP + Carbon Filter) and the Baseline (Case 1) which maintains the highest normalized concentration throughout the period. Overall, the plot highlights that the integration of botanical and natural filtration elements enhances VOCs removal performance, with the botanical plant alone showing the fastest and most efficient reduction trend. 3.3 Pollutant reduction rate The rate of pollutant concentration reduction was calculated for each case to enable a more detailed comparison of the removal efficiencies among the different systems. The reduction rates for PM2.5 and PM10 were determined based on the time required for the outlet concentrations to decrease from 1000 µg/m³ to the satisfactory level of 100 µg/m³. Similarly, the VOCs reduction rate was evaluated based on the time taken for the outlet VOCs concentration to decrease from its peak value back to the satisfactory threshold of 1000 ppm. The calculated reduction rates are summarized in Fig. 13. Reduction rates for PM2.5 and PM10 are very similar in Case 1 and Case 2 showing minimal improvement. This shows that incorporating a botanical plant into the biofilter does not significantly enhance PM filtration. However, when a carbon filter is added (Case 3), there is a significant increase in the reduction rates for both PM2.5 (5.36 µg/m³/min) and PM10 (4.95 µg/m³/min), indicating a strong positive effect of the carbon filter. Introducing a coconut husk in Case 4 slightly lowers the reduction rates (4.59 µg/m³/min for PM2.5 and 4.74 µg/m³/min for PM10) compared to Case 3 but still maintains a better performance than the baseline. Case 5, which combines a botanical plant, carbon filter, coconut husk, and GAC, achieves high reduction rates for both PM2.5 (5.11 µg/m³/min) and PM10 (5.23 µg/m³/min), the latter being the highest among all cases. In Case 1 (Baseline), the average outlet VOCs reduction rate is 3.89 ppm per minute, serving as the initial reference point. When a botanical plant is added in Case 2, the VOCs reduction rate drops to 2.36 ppm/min, indicating that the botanical plant alone may not effectively contribute to VOCs removal and could even interfere slightly with baseline performance. Case 3, which combines the botanical plant with a carbon filter, shows a major improvement, reaching the highest VOCs reduction rate of 4.13 ppm/min. This highlights the strong adsorptive capability of the carbon filter for VOCs. However, when a coconut husk is introduced in Case 4 alongside the carbon filter and botanical plant, the VOCs reduction rate falls sharply to 2.11 ppm/min (the lowest across all cases) suggesting that the coconut husk might compete for adsorption sites or release VOC-like substances that reduce the filtering efficiency. In Case 5, where GAC is added on top of the previous materials, the VOCs reduction rate improves slightly to 2.92 ppm/min but remains lower than the baseline and far below Case 3. This indicates that while GAC provides some recovery in VOCs capture, it does not fully compensate for the negative effect introduced by the coconut husk. Conclusion, Limitation and Recommendation of Future Works The study’s findings exhibited the successful development of the BIAB prototype and the provision of valuable insights into different filtration systems' efficiency in decreasing PM2.5, PM10, and VOC concentrations. Integration of the IoT into the BIAB system allows for real-time monitoring of crucial parameters like PM, VOCs, temperature, humidity, and CO2 levels. This IoT integration enables continuous data collection and transmission from strategically placed sensors, facilitating ongoing air quality analysis. One notable limitation of this study is the reliance on an optical particle sensor (RS-PM-*-2-EX) that estimates PM10 concentrations using proprietary algorithms based on laser light scattering. This type of sensor does not physically separate or directly measure PM10 through inertial or gravimetric methods. As a result, coarse particles larger than 10 µm, such as certain pollen grains and some mould spores, may fall outside the effective detection range of the device. Consequently, the reported PM10 values should be interpreted as algorithmic estimations rather than absolute measurements. Although this limitation does not affect the ability to observe relative trends and fluctuations in particulate matter concentration, it should be taken into account when comparing results with regulatory-grade instruments or standards. Another important limitation involves the upper detection limit of 1000 µg/m³ for the PM sensors used. While this range suffices for many indoor environments, it restricted the ability to capture true peak concentrations following pollutant injection. However, the maximum filtration capacity and potential overload behaviour of the system could not be fully evaluated. Future studies should employ higher-range PM sensors or integrate dilution-based sampling strategies to accurately determine the filtration system's maximum capacity under extreme conditions. It is also acknowledged that the current housing design may allow VOCs and SVOCs to interact with multiple internal surfaces before reaching the VOCs sensor, potentially affecting response accuracy. Future improvements should include redesigned enclosures to provide more direct and unobstructed airflow pathways to the sensor module, minimising surface adsorption and enhancing measurement fidelity.” Several types of filtrations including botanical plants, carbon filters, coconut husk, and GAC, were introduced throughout the studies to validate the capability and efficiency in reducing the concentration of PM2.5, PM10, and VOCs. In this study, the chosen botanical plant, Golden Pothos, shows only a slight improvement in PM10 reduction compared to Case 1 (Baseline), while the PM2.5 reduction rate remains nearly the same. Moreover, the integration of the botanical plant into the filtration system appears to worsen VOCs removal, recording the second lowest VOCs reduction rate (2.36 ppm/min) among all cases. The pollutant removal efficiency of botanical plants is influenced by light intensity, as the photosynthetic process requires sunlight to function effectively. During the daytime, when light levels are higher, botanical plants exhibit an increased ability to absorb pollutants. This is because photosynthesis drives stomatal opening, which enhances gas exchange and allows for greater uptake of airborne pollutants, including certain VOCs. By integrating Golden Pothos and carbon filters, Case 3 demonstrates the highest average reduction rate of PM 2.5 (5.36 µg/m³/min) and VOCs (4.13 ppm/min) while providing second highest PM10 reduction rate (4.95 µg/m³/min) among all cases. The synergy between the natural filtration abilities of Golden Pothos and the adsorption properties of carbon filters significantly enhances the overall efficiency in capturing and eliminating fine particulate matter from the air. Integrating these filtration components forms a highly efficient system for reducing PM concentrations, emphasizing the importance of employing diverse filtration techniques for optimal air purification. In Case 5 , where GAC is added in addition to the botanical plant, carbon filter, and coconut husk, the VOCs reduction rate increases slightly to 2.92 ppm/min. However, this value remains lower than the baseline (3.89 ppm/min) and significantly below the peak performance observed in Case 3 (4.13 ppm/min). This suggests that while GAC contributes positively to VOCs adsorption due to its high surface area and porous structure, it is not sufficient to fully counteract the adverse impact introduced by the coconut husk in Case 4 . The coconut husk may release organic compounds or physically hinder airflow and adsorption dynamics, reducing the overall efficiency of the filtration system. As a result, the addition of GAC only partially restores VOCs removal efficiency, highlighting the importance of material compatibility and interaction in multi-layer biofilter systems. In summary, Case 3 stands out as the most reliable filtration system for reducing PM2.5, PM10 and VOCs. The integration of GAC also improves the filtration efficiency in PM10 obviously. However, the addition of coconut husk in BIAB can lead to a drastic decrease in VOCs filtration efficiency. Through integrating various filtration components, including Golden Pothos, carbon filters, coconut husk, and GAC, Case 5 demonstrates impressive outcomes in decreasing PM2.5 and PM10 concentrations. This study focused on assessing air filtration systems in a controlled environment. The results offer valuable insights into the performance of these systems under such conditions. However, for future research, it is advisable to broaden the scope to environments with active human movement. Including human presence can provide a more realistic portrayal of air quality dynamics and the efficacy of filtration systems in real-world scenarios. Additionally, investigating the influence of human activities on air quality can assist in developing targeted and efficient filtration strategies for optimal IAQ across diverse settings like offices, schools, and public spaces. Examining the interaction between human movements, air circulation, and filtration systems will deepen our understanding of IAQ management and contribute to developing effective strategies for creating healthier indoor environments. Based on the results, future studies will incorporate more extensive replications to further validate and refine the findings, with a particular emphasis on integrating IoT technologies and optimizing plant and carbon material combinations for improved air quality. Future research is recommended to explore the efficacy of various commercial air filters when integrated into the BIAB. This entails assessing the performance of different filter types, including HEPA, activated carbon, electrostatic, and hybrid filters. Comparative experiments should be conducted to gauge their efficiency in filtering PM and VOCs from indoor air. Understanding how different commercial air filters work in conjunction with the BIAB system will offer valuable insights for practical applications, aiding in the selection of filters based on specific IAQ requirements and limitations. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Acknowledgements: The authors would like to acknowledge the financial support from Asia Technological University (ATU) Network under ATU-Net Young Researcher Grant (YRG) with Vot. No. R.J130000.7724.4J714 and Universiti Teknologi Malaysia under UTM Nexus Postgrad Grant with Vot. No. Q.J130000.5324.00L96. Competing interests The authors declare that they have no competing interests. Availability of data and materials Not applicable. Authors' contribution: Sien Jie Wong: Conceptualization, methodology, and writing of the original draft. Huiyi Tan, Hong Yee Kek, Mohd Hafiz Dzarfan Othman; Methodology, supervision, writing, reviewing, and editing: Kok Sin Woon, Xue-Chao Wang,Wan Muhammad Akid Wan Abd Samad: Writing, review, and editing. Keng Yinn Wong: Conceptualization, supervision, methodology, funding acquisition, and writing of the original draft. References Ahn, Y., D.-W. Cho, W. Ahmad, J. Jo, J. Jurng, M. B. Kurade, B.-H. Jeon and J. Choi (2021). "Efficient removal of formaldehyde using metal-biochar derived from acid mine drainage sludge and spent coffee waste." Journal of Environmental Management 298 : 113468. Alryalat, S. A., A. Toubasi, J. Patnaik and M. Kahook (2022). "The impact of air pollution and climate change on eye health: a global review." Reviews on environmental health . Bhave, P. and D. Yeleswarapu (2020). Removal of Indoor Air Pollutants Using Activated Carbon—A Review : 65-75. Chaudhary, P., S. Bansal, B. B. Sharma, S. Saini and A. Joshi (2024). "Waste biomass-derived activated carbons for various energy storage device applications: A review." Journal of Energy Storage 78 : 109996. Chung, T.-W., T. K. Ghosh, A. L. Hines and D. Novosel (1995). "Dehumidification of Moist Air with Simultaneous Removal of Selected Indoor Pollutants by Triethylene Glycol Solutions in a Packed-Bed Absorber." Separation Science and Technology 30 (7-9): 1807-1832. de Robles, D. and S. W. Kramer (2017). "Improving Indoor Air Quality through the Use of Ultraviolet Technology in Commercial Buildings." Procedia Engineering 196 : 888-894. Ditto, J., J. Abbatt and A. Chan (2022). "Gas- and Particle-Phase Amide Emissions from Cooking: Mechanisms and Air Quality Impacts." Environmental science & technology . Dubey, S., H. Rohra and A. Taneja (2021). "Assessing effectiveness of air purifiers (HEPA) for controlling indoor particulate pollution." Heliyon 7 : e07976. Envion (2024). Air Purifier TA500. Fleck, R., T. J. Pettit, A. N. J. Douglas, P. J. Irga and F. R. Torpy (2020). 15 - Botanical biofiltration for reducing indoor air pollution. Bio-Based Materials and Biotechnologies for Eco-Efficient Construction . F. Pacheco-Torgal, V. Ivanov and D. C. W. Tsang, Woodhead Publishing : 305-327. Fu, H.-X. and X. Liu (2017). "Review of the impact of liquid desiccant dehumidification on indoor air quality." Building and Environment 116 . Grand-View-Research (2022). Air Purifier Market Size, Share & Trends Analysis Report By Technology (HEPA, Activated Carbon), By Application (Commercial, Residential), By Coverage Range, By Sales Channel, By Type, By Region, And Segment Forecasts, 2024 - 2030. Grinshpun, S. A., G. Mainelis, M. Trunov, A. Adhikari, T. Reponen and K. Willeke (2005). "Evaluation of ionic air purifiers for reducing aerosol exposure in confined indoor spaces." Indoor Air 15 (4): 235-245. Homedics.com (2024). TotalClean® 5-in-1 UV-C Large Room Air Purifier. Jeong-ho, P. and S. Jeong-min (2018). "Characteristics of PM10, PM2.5 and PM2.5/PM10 Ratio in Air Monitoring Stations in Gyeongnam." 한국환경과학회지 27 (10): 925-931. Kim, J.-H. and M.-S. Yeo (2020). "Effect of Flow Rate and Filter Efficiency on Indoor PM2.5 in Ventilation and Filtration Control." Atmosphere 11 : 1061. Klepeis, N., W. Nelson, W. Ott, J. Robinson, A. Tsang, P. Switzer, J. Behar, S. Hern and W. Engelmann (2001). "The National Human Activity Pattern Survey (NHAPS): A resource for assessing exposure to environmental pollutants." Journal of exposure analysis and environmental epidemiology 11 : 231-252. Kujundzic, E., F. Matalkah, C. Howard, M. Hernandez and S. Miller (2006). "UV Air Cleaners and Upper-Room Air Ultraviolet Germicidal Irradiation for Controlling Airborne Bacteria and Fungal Spores." Journal of occupational and environmental hygiene 3 : 536-546. Llewellyn, D. and M. Dixon (2011). 4.26 - Can Plants Really Improve Indoor Air Quality? Comprehensive Biotechnology (Second Edition) . M. Moo-Young. Burlington, Academic Press : 331-338. Malhotra, M., S. Walia, C.-C. Lin, I. K. Aulakh and S. Agarwal (2024). "A systematic scrutiny of artificial intelligence-based air pollution prediction techniques, challenges, and viable solutions." Journal of Big Data 11 (1): 142. Maung, T., J. Bishop, E. Holt, A. Turner and C. Pfrang (2022). "Indoor Air Pollution and the Health of Vulnerable Groups: A Systematic Review Focused on Particulate Matter (PM), Volatile Organic Compounds (VOCs) and Their Effects on Children and People with Pre-Existing Lung Disease." International Journal of Environmental Research and Public Health 19 : 8752. mobile2go (2024). Mi SmartMi HEPA air purifier. Montaluisa-Mantilla, M. S., P. García-Encina, R. Lebrero and R. Muñoz (2023). "Botanical filters for the abatement of indoor air pollutants." Chemosphere 345 : 140483. Montaluisa-Mantilla, M. S., J. Gonçalves, P. A. García-Encina, R. Lebrero, M. Rico and R. Muñoz (2025). "Harnessing the potential of Epipremnum aureum (Pothos) for indoor air purification in botanical filters." Building and Environment 275 : 112813. Nadzir, M. S. M., M. C. G. Ooi, K. M. Alhasa, M. A. A. Bakar, A. A. A. Mohtar, M. F. F. M. Nor, M. T. Latif, H. H. Abd Hamid, S. H. M. Ali and N. M. Ariff (2020). "The impact of movement control order (MCO) during pandemic COVID-19 on local air quality in an urban area of Klang valley, Malaysia." Aerosol and Air Quality Research 20 (6): 1237-1248. Namieśnik, J., T. Górecki, B. Kozdroń-Zabiega ła and J. Łukasiak (1992). "Indoor air quality (IAQ), pollutants, their sources and concentration levels." Building and Environment 27 (3): 339-356. Patel, M. and M. Mansoor Ahammed (2024). "Characteristics and removal of organic micropollutants in reverse osmosis concentrate from urban wastewater treatment plants: A review." Waste Management Bulletin 2 (4): 249-260. Pettit, T., P. Irga and F. Torpy (2018). "Functional green wall development for increasing air pollutant phytoremediation: Substrate development with coconut coir and activated carbon." Journal of Hazardous Materials 360 . Pettit, T., P. J. Irga, P. Abdo and F. R. Torpy (2017). "Do the plants in functional green walls contribute to their ability to filter particulate matter?" Building and Environment 125 : 299-307. Pettit, T., P. J. Irga and F. R. Torpy (2018). "Towards practical indoor air phytoremediation: A review." Chemosphere 208 : 960-974. Renke (2024). CO2 Sensor. L. Shandong Renke Control Technology Co. Renke (2024). Modbus rtu temperature humidity sensor. L. Shandong Renke Control Technology Co. Renke (2024). PM2.5 PM10 Sensor. L. Shandong Renke Control Technology Co. Rumchev, K., H. Brown and J. Spickett (2007). "Volatile organic compounds: do they present a risk to our health?" Reviews on environmental health 22 (1): 39-56. Saucedo-Lucero, J. O., L. S. Falcón-González, M. Ovando Franco and S. Revah (2024). "Implementation of a botanical bioscrubber for the treatment of indoor ambient air." Journal of Environmental Management 363 : 121414. Sokhal, A. and U. Narayan (2020). "Plants are More Effective in Absorbing Volatile Organic Compounds from the Atmosphere than Air Purifiers." Journal of Student Research 9 (2). Szczotko, M., I. Orych, Ł. Mąka and J. Solecka (2022). "A Review of Selected Types of Indoor Air Purifiers in Terms of Microbial Air Contamination Reduction." Atmosphere 13 : 800. Taemthong, W. (2021). "AIR QUALITY IMPROVEMENT USING ORNAMENTAL PLANTS IN CLASSROOMS." Journal of Green Building 16 : 201-216. Taemthong, W. and N. Cheycharoen (2022). GREEN WALLS CAN IMPROVE INDOOR AIR QUALITY IN THE CLASSROOM . Talati, I., K. J. Shah, O. Patel, I. Tanna, A. Iain, A. D. Oza, A. A. Yadav, M. I. Alshayeb, M. A. Khan and S. Islam (2025). "Study of AQI Monitoring System of Indoor Environment Using Machine Learning Model and IoT Device." ROCZNIK OCHRONA SRODOWISKA 27 : 152-163. Technologies, C. (2024). Importance of activated carbon filter in an air purifier. WHO. (2024). "Air quality, energy and health." Retrieved 10/3, 2024, from https://www.who.int/teams/environment-climate-change-and-health/ air-quality-energy-and-health/sectoral-interventions/household-air-pollution/health-risks#:~:text=Nearly%203.2%20million%20people%20die,caus ed%20by%20household%20air%20pollution&text=Household%20air %20pollution%20is%20caused,in%20and%20around%20the%20household. Zhang, Y.-L. and F. Cao (2015). "Is it time to tackle PM2. 5 air pollutions in China from biomass-burning emissions?" Environmental Pollution 202 : 217-219. Table 1 and 3 Table 1 and 3 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files graphicalabstract.jpg Table1and3.docx Supplementary.docx Cite Share Download PDF Status: Published Journal Publication published 08 Jun, 2025 Read the published version in Clean Technologies and Environmental Policy → Version 1 posted Editorial decision: Accepted 14 May, 2025 Reviews received at journal 11 May, 2025 Reviews received at journal 29 Apr, 2025 Reviewers agreed at journal 27 Apr, 2025 Reviewers agreed at journal 27 Apr, 2025 Reviewers invited by journal 27 Apr, 2025 Submission checks completed at journal 26 Apr, 2025 First submitted to journal 25 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6110064","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":448649598,"identity":"0fdcee9f-42ae-4366-ba3d-8427fa32c8a7","order_by":0,"name":"Sien Jie Wong","email":"","orcid":"","institution":"Universiti Teknologi Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Sien","middleName":"Jie","lastName":"Wong","suffix":""},{"id":448649599,"identity":"f4db7f35-d626-4eae-880d-4a24b76fbaa9","order_by":1,"name":"Huiyi Tan","email":"","orcid":"","institution":"Universiti Teknologi Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Huiyi","middleName":"","lastName":"Tan","suffix":""},{"id":448649600,"identity":"909b887f-1b79-49e8-95e9-a614909757d7","order_by":2,"name":"Hong Yee Kek","email":"","orcid":"","institution":"Universiti Teknologi Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Hong","middleName":"Yee","lastName":"Kek","suffix":""},{"id":448649601,"identity":"152500e9-668e-43c0-bcbb-e53280e74ba9","order_by":3,"name":"Mohd Hafiz Dzarfan Othman","email":"","orcid":"","institution":"Universiti Teknologi Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Mohd","middleName":"Hafiz Dzarfan","lastName":"Othman","suffix":""},{"id":448649602,"identity":"d28bcd0c-6d55-4dce-9d9a-b67148d14050","order_by":4,"name":"Kok Sin Woon","email":"","orcid":"","institution":"The Hong Kong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Kok","middleName":"Sin","lastName":"Woon","suffix":""},{"id":448649603,"identity":"ce0f0d36-2393-41a7-9d5d-0e0aca25579b","order_by":5,"name":"Xue-Chao Wang","email":"","orcid":"","institution":"Beijing Normal University","correspondingAuthor":false,"prefix":"","firstName":"Xue-Chao","middleName":"","lastName":"Wang","suffix":""},{"id":448649604,"identity":"d8a1bbb6-cc17-4c5f-8568-0131bc27ef55","order_by":6,"name":"Wan Muhammad Akid Wan Abd Samad","email":"","orcid":"","institution":"Universiti Teknologi Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Wan","middleName":"Muhammad Akid Wan Abd","lastName":"Samad","suffix":""},{"id":448649605,"identity":"f3b8a84d-b0aa-4b76-9217-370a6c3dd313","order_by":7,"name":"Keng Yinn Wong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyUlEQVRIiWNgGAWjYBACCTBpAOV9IFkL4wy4ZoJaoICZhxgtku29h1/zFNzJMzgOZNi2/YlmEDuDX580z7k0ax6DZ8UGZ4CM3DaD3AbpHPxa5CRyzIx5DA4nbrsBZBCnRf4NkhZLYrRIS/AYP4ZqMX7MSIwWyZ4cM8Y5QC37z5wxY+w5B3SbdFoBXi0Sx88Yf3jz53DizPYe4w8/yuRy+6WTN+DVAgRsUjxQBjiS2Bg4CEYO88cfUAY0vbA/IKRlFIyCUTAKRhYAAHlgR35TUb4/AAAAAElFTkSuQmCC","orcid":"","institution":"Universiti Teknologi Malaysia","correspondingAuthor":true,"prefix":"","firstName":"Keng","middleName":"Yinn","lastName":"Wong","suffix":""}],"badges":[],"createdAt":"2025-02-26 05:53:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6110064/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6110064/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10098-025-03221-w","type":"published","date":"2025-06-08T15:57:40+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82141152,"identity":"72cc5374-3b59-4721-b17e-646f2d521aea","added_by":"auto","created_at":"2025-05-07 06:33:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":204541,"visible":true,"origin":"","legend":"\u003cp\u003eAdvantages and limitations of different types of commercial air purifier (Envion 2024, Homedics.com 2024, mobile2go 2024, Technologies 2024)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6110064/v1/ad7726d108e199d74cdcbfee.png"},{"id":82143555,"identity":"cf2f6654-6767-4580-8961-71e898419750","added_by":"auto","created_at":"2025-05-07 06:41:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":133902,"visible":true,"origin":"","legend":"\u003cp\u003eBotanical indoor air biofilter (BIAB) experimental setup\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6110064/v1/b0ce3b602905d1c2c6d799d6.png"},{"id":82143556,"identity":"7595578b-8dfd-4913-b9a7-cd70709e208b","added_by":"auto","created_at":"2025-05-07 06:41:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":228632,"visible":true,"origin":"","legend":"\u003cp\u003eSensor arrangement in the inlet and outlet sensor box\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6110064/v1/95c3c340e18949369db81f7d.png"},{"id":82145012,"identity":"edbe5145-131c-494c-af73-47a6fdb7d11a","added_by":"auto","created_at":"2025-05-07 06:49:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":538711,"visible":true,"origin":"","legend":"\u003cp\u003eConnection map of BIAB data acquisition and logging\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6110064/v1/ef1ff2e934c3779d43069949.png"},{"id":82145013,"identity":"5fb3e9b5-2273-4c7e-9d69-e10fcb8b2771","added_by":"auto","created_at":"2025-05-07 06:49:50","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":174464,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of OpenHAB network diagram from data acquisition from sensors to the end-users\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6110064/v1/065c6b1e86af9e62aaefa137.png"},{"id":82141169,"identity":"8a749fd1-ed9c-4f42-968e-8bac193fd821","added_by":"auto","created_at":"2025-05-07 06:33:50","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":670041,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6110064/v1/2953c88e26248f35cbde33f1.png"},{"id":82141160,"identity":"84e4e28c-74e0-4327-b8d9-c6cf1c5381cf","added_by":"auto","created_at":"2025-05-07 06:33:50","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":689585,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6110064/v1/92d786ae38d47784135cac20.png"},{"id":82143562,"identity":"232314a2-d8e6-475e-ae72-54608e70b8d7","added_by":"auto","created_at":"2025-05-07 06:41:50","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":650121,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6110064/v1/81b146309b1576ab3cd60788.png"},{"id":82141165,"identity":"a4b46414-3988-4d12-a8e1-8cdcfaa60505","added_by":"auto","created_at":"2025-05-07 06:33:50","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":695110,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-6110064/v1/0523a0ff90ccf341659e15f8.png"},{"id":82143564,"identity":"be8e0f08-a0bd-4f29-9d62-d16d134dca63","added_by":"auto","created_at":"2025-05-07 06:41:50","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":665919,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-6110064/v1/69e3362cbe22191e97cf69c7.png"},{"id":82145016,"identity":"5ec72139-b7bb-4342-82b8-61f59ab8415a","added_by":"auto","created_at":"2025-05-07 06:49:50","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":473582,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-6110064/v1/f866d3a6eb2eaaa17401a661.png"},{"id":82141175,"identity":"765a0ec9-7255-4d72-988a-80ab5bed0968","added_by":"auto","created_at":"2025-05-07 06:33:50","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":530152,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Outlet VOCs concentration against time and (b) normalised outlet VOCs concentration plot\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-6110064/v1/e855a8993f11710eecfd9c84.png"},{"id":82141171,"identity":"3d806cfe-f9c0-497b-957b-8a4ea4a224b4","added_by":"auto","created_at":"2025-05-07 06:33:50","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":491298,"visible":true,"origin":"","legend":"\u003cp\u003eAverage outlet PM 5, PM 10, and VOCs reduction rate\u003c/p\u003e","description":"","filename":"13.png","url":"https://assets-eu.researchsquare.com/files/rs-6110064/v1/8f0aebfd704645cdd96f4ef2.png"},{"id":84242619,"identity":"9d972ac9-d104-4ced-a264-0d7f40fe14e3","added_by":"auto","created_at":"2025-06-09 16:10:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6924189,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6110064/v1/4c18e510-89dc-44df-802f-c01431d54186.pdf"},{"id":82143554,"identity":"9f765ff3-d437-4b6a-bfb5-54584c4d0525","added_by":"auto","created_at":"2025-05-07 06:41:50","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":116816,"visible":true,"origin":"","legend":"","description":"","filename":"graphicalabstract.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6110064/v1/90575443a1f8d74cd579592f.jpg"},{"id":82141159,"identity":"e2b7fb77-b514-44af-a694-4c65c2bb9d98","added_by":"auto","created_at":"2025-05-07 06:33:50","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":316749,"visible":true,"origin":"","legend":"","description":"","filename":"Table1and3.docx","url":"https://assets-eu.researchsquare.com/files/rs-6110064/v1/4150084afa3f4901e30302d5.docx"},{"id":82141156,"identity":"85f0d968-3b83-453b-8ec3-926dbbbda2b6","added_by":"auto","created_at":"2025-05-07 06:33:50","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":128935,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-6110064/v1/fc5f156a63d27ad5a4f4a9a1.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Optimizing Indoor Air Quality: Evaluating the Synergistic Impact of Filter Integration and Botanical Solutions","fulltext":[{"header":"Introduction","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eMost people in this modern era tend to live within enclosed spaces (Namieśnik, G\u0026oacute;recki et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). According to the National Human Activity Pattern (NHAPS), individuals typically spend around 87% of their daily lives indoors. This indoor lifestyle contributes to various health issues, as highlighted by data from the World Health Organization (WHO) (de Robles and Kramer \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Szczotko, Orych et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The WHO reports that about 3.2\u0026nbsp;million premature deaths result from illnesses linked to household air pollution. From 3.2\u0026nbsp;million people, the disease can be divided into several categories which are 32% are from ischaemic heart disease, 23% are from stroke, 21% are due to lower respiratory infections, 19% are from chronic obstructive pulmonary disease (COPD) and 6% are from lung cancer. Certain air pollutants exhibit immediate health impacts while others may manifest over weeks, months, or even years. The immediate side effects include irritation of the eyes and other common fever symptoms such as a sore throat, headaches, dizziness, and fatigue (Alryalat, Toubasi et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The chance of having a rapid reaction to indoor air pollution is determined by several factors, including the person's age and any prior medical problems. In certain circumstances, whether a person reacts to a pollutant is determined by individual sensitivity, which varies significantly across people. The long-term consequences may lead to debilitating conditions such as lung disorders, heart disease, and cancer. Interestingly, people often remain unaware that the disease was caused by the air pollutant, as the effects may not manifest rapidly after exposure.\u003c/p\u003e \u003cp\u003eThe indoor air quality (IAQ) is susceptible to various contaminants, ranging from dust settling on furniture surfaces to the incomplete burning of solid fuels during daily cooking. Among the common pollutants found in indoor air mixtures are particulate matter (PM) and volatile organic compounds (VOCs). PM is characterized as a blend of solid particles merging or binding with liquid droplets in the air. At the same time, VOCs are compounds with high vapor pressure and low water solubility, typically resulting from chemical reactions in household products (Maung, Bishop et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Particulate matter (PM) is usually classified into two types depending on its size, which are PM2.5 and PM10. PM2.5 has more consequences than PM10, as it has a smaller dimension and can penetrate our respiratory system more easily. PM2.5 in indoor environments primarily originates from typical outdoor sources like motor vehicles, biomass burning, and industrial emissions (Zhang and Cao \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Nadzir, Ooi et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Additionally, PM10 can have both natural origins, such as rock erosion, volcanic eruptions, and spontaneous combustion of forests, and anthropic origins, including various combustion processes (Jeong-ho and Jeong-min \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). As these tiny particles can penetrate deep into the lungs and even into the bloodstream, PMs are linked to several diseases such as cancer, cardiovascular disease, asthma, and COPD.\u003c/p\u003e \u003cp\u003eDevelopers and researchers actively explore various air purification devices to enhance IAQ in confined spaces. The air purifier is a device designed to clean and filter the harmful chemical substances in the air. With the ongoing impact of the Coronavirus disease 2019 (COVID-19) pandemic globally over the past few years, there is a growing unease among people about the air they breathe, leading to a surge in the purchase of air purifiers. Various commercial air purifiers flooded the market, varying their efficacy in filtering air pollution. According to Grand View Research, the air purifier\u0026rsquo;s market value is at 12.26\u0026nbsp;billion US dollars. It is expected to continuously grow in the upcoming years with an annual growth rate of 8.1% from 2022 to 2030 as the results of the Covid-19 pandemic and other airborne diseases increased people\u0026rsquo;s awareness of the higher IAQ\u0026rsquo;s importance to their health. The commercial types of air purifiers include ultraviolet air purifier, High-Efficiency Particulate Air (HEPA) air purifiers, activated carbon air purifiers and ionic air purifier. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the types of commercial air purifiers and their strengths and weaknesses(Grinshpun, Mainelis et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2005\u003c/span\u003e, Kujundzic, Matalkah et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2006\u003c/span\u003e, Bhave and Yeleswarapu \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Dubey, Rohra et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Ditto, Abbatt et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). UV air purifiers use ultraviolet light to kill or deactivate microorganisms like bacteria and viruses, effectively sanitizing the air. Activated carbon filters capture gases, odours, and VOCs by adsorbing them onto the carbon surface, effectively removing harmful chemicals. Ionic air purifiers release negatively charged ions that attach to positively charged particles, causing them to settle or be captured in a collection plate. HEPA filters are designed to trap small particles such as dust, pollen, and pet dander offering highly effective filtration for particulate matter.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eRecent studies have demonstrated the efficiency of filter to improve IAQ. Kim \u0026amp; Yeo (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) conducted a study on the effect of air flow rate on the filter efficiency to PM2.5 in Korea\u0026rsquo;s indoor environment. In the study, a simulation was carried out using the particle model to observe and analyse the effect of PM2.5 on the ventilation flow rate and the filtration of PM2.5. To determine how the parameter affected indoor PM2.5 concentrations, MATLAB software was used to analyse a mass balance equation. Based on the result, the author concluded that a high flow rate was preferable for lowering the concentration of PM2.5 indoors, regardless of filter effectiveness. The indoor PM2.5 concentration showed a reduction rate of up to 6\u0026ndash;8% depending on the filtration efficiency when the filtration system was operated at a flow rate of 100 m3/h.\u003c/p\u003e \u003cp\u003eOn the other hand, the indoor PM2.5 concentration decreased by up to 29 to 38% depending on the filter efficiency when the filter system was operating at a flow rate of 600 m\u003csup\u003e3\u003c/sup\u003e/h, which justifies the previous statement. Chung et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1995\u003c/span\u003e) and Fu \u0026amp; Liu (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) performed a study on the effect on the absorption performance of 40% LiCl solution for selected indoor pollutants which includes toluene, 1,1,1-trichloroethane and carbon dioxide (Chung, Ghosh et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1995\u003c/span\u003e, Fu and Liu \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). According to the findings, a LiCl solution could only remove a tiny amount of the carbon dioxide and 1,1,1- trichloroethane and about 20% of the formaldehyde and toluene.\u003c/p\u003e \u003cp\u003eThe Botanical Indoor Air Biofilter (BIAB) is one such air purifier designed to enhance IAQ (Fleck, Pettit et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The biofilter relies on the natural processes of the plants, such as absorbing CO\u003csub\u003e2\u003c/sub\u003e and releasing oxygen, as well as the ability of plant roots and rhizosphere microorganisms to break down and absorb air pollutants like VOCs, PM, and other harmful gases. The main removal mechanism of CO\u003csub\u003e2\u003c/sub\u003e and PM2.5 involves plant-leaf interactions (Saucedo-Lucero, Falc\u0026oacute;n-Gonz\u0026aacute;lez et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Additionally, plants can absorb pollutants through their roots and break them into less harmful compounds through processes like photosynthesis and cellular respiration (Montaluisa-Mantilla, Garc\u0026iacute;a-Encina et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This method, known as phytoremediation, is particularly effective in removing VOCs and other hydrophobic pollutants. Passive botanical systems rely on the natural diffusion of air pollutants through the plant components, without any active mechanism to direct the contaminated air to the plants or their substrates (Pettit, Irga et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These systems have shown significant reductions in VOCs, ranging from 10\u0026ndash;90%, within 24 hours in sealed chambers(Llewellyn and Dixon \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). BIAB utilizes multiple filter components, with organic plants as the primary filtration element. A green wall, also known as a living wall or vertical garden, is a vertical structure that is partially or completely covered with vegetation and includes an integrated growing medium, such as soil or a substrate. Green walls are designed to improve indoor or outdoor environmental quality by enhancing aesthetics, reducing CO₂, filtering airborne pollutants, and regulating temperature and humidity levels. Green walls are related to BIAB as they apply the same method for filtering air pollutants by using several botanical plants as filters.\u003c/p\u003e \u003cp\u003eStudies by Taemthong and Cheycharoen (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) focused on the green wall's ability to reduce carbon dioxide in the classroom. In the study, Epipremnum aureum or Marble Queen or Golden Pothos was chosen as it could reduce the concentration of carbon dioxide. The experiment was done with 13 students, who were the source of the carbon dioxide, as human breathing releases carbon dioxide into the air. The study found that 105 pots of Golden Pothos are needed to absorb 208 ppm of CO\u003csub\u003e2\u003c/sub\u003e in the range of 80 minutes. Another study by Taemthong (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) found that with the installation of 150 pots of Golden Pothos was able to reduce CO\u003csub\u003e2\u003c/sub\u003e concentration by 430 ppm within 80 minutes with the presence of 20 students in the class. Pettit, Irga et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) investigated the role of the botanical component in the performance of active green wall biofilters for PM removal. Green walls with different plant species exhibited varying particulate matter removal efficiencies, with fern species achieving the highest performance (45.78% for PM\u003csub\u003e0.3-0.5\u003c/sub\u003e and 92.46% for PM\u003csub\u003e5-10\u003c/sub\u003e). Active botanical filter using Golden Pothos effectively removed low concentrations of acetone, α-pinene, and toluene from indoor air, achieving maximum removal efficiencies of 99.8%, 83.6%, and 71.1% respectively (Montaluisa-Mantilla, Gon\u0026ccedil;alves et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The filter\u0026rsquo;s performance declined with reduced air and nutrient medium recirculation rates, and the absence of the plant component significantly lowered pollutant removal, highlighting the critical role of both plant presence and system design in air purification efficiency.\u003c/p\u003e \u003cp\u003eDespite the growing interest in BIAB, limited studies have thoroughly examined their effectiveness in pollutant removal and the underlying mechanisms. Before BIAB can be effectively introduced to the market, significant adjustments and enhancements are required to maximize its performance. This study aims to address these gaps by developing a high-efficiency innovative filtration botanical biofilter rig that integrates multiple factors, including the addition of filters, activated carbon, and botanical plants, to optimize its efficiency in improving IAQ. Furthermore, the study incorporates IoT devices to monitor and assess real-time changes in air pollutant concentrations. This experiment was designed as a pilot study to establish a baseline for the effectiveness of botanical indoor air biofilters. The study also aimed to integrate an affordable IoT approach to monitor and control environmental parameters in real-time, alongside testing the combined effectiveness of plants and carbon materials in air purification. Given the pilot nature of this study, the focus was on exploring the initial feasibility and performance of the biofilter system. The results of this study are intended to represent hot and humid environmental conditions commonly found in Southeast Asian (ASEAN) regions and are not intended to be directly extrapolated to temperate climates or standard indoor conditions as defined by the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003e2.1 BIAB Rig Setup\u003c/h2\u003e\n \u003cp\u003eThe BIAB system was designed and comprised three key components: inlet sensors, a biofilter, and outlet sensors, as illustrated in Fig.\u0026nbsp;2. The biofilter was positioned centrally within a transparent case, allowing air quality to be assessed systematically. Inlet sensors measured the initial concentrations of pollutants, including PM2.5, PM10, VOCs, and CO\u003csub\u003e2\u003c/sub\u003e, as ventilation fans drew the air into the system. The biofilter facilitated the removal of contaminants as the air passed through, with outlet sensors positioned downstream to capture the final concentrations, validating the air quality improvements. Mechanical ventilation and a submersible pump were activated to initiate the operation of biofilter, ensuring consistent airflow and irrigation. A network router established a centralized IP address to connect all sensors, enabling real-time data acquisition and recording. This setup allowed for simultaneous monitoring of pollutant concentrations, comprehensively evaluating of the BIAB’s performance. Carbon-based materials were integrated into the BIAB system to enhance air purification. The efficiency of air purification was evaluated by analysing the air quality readings and comparing pollutant concentrations measured by inlet sensors before filtration with those detected by outlet sensors after passing through the biofilter.\u003c/p\u003e\n \u003cp\u003eTo ensure measurement accuracy and environmental consistency, both the inlet and outlet boxes used in this study were initially prepared as controlled empty enclosures, each equipped with pre-calibrated sensors. The sensors were run continuously for a minimum of 60 minutes to allow the internal conditions of the closed boxes to stabilise. This steady-state phase is critical for establishing a reliable baseline. During this period, temperature, RH, and PM readings were monitored in real-time to confirm the absence of significant fluctuations. Data collection for the experimental phase proceeded only after these readings had stabilised. This approach ensures that both boxes began with equivalent and stable environmental baselines, thereby validating the comparison between inlet and outlet measurements and ruling out baseline calibration errors.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\"\u003e\n \u003ch2\u003e2.2 Air Quality Sensors\u003c/h2\u003e\n \u003cp\u003ePM2.5 and PM10 sensors were used to capture the PM concentration. The PM2.5 sensor is designed to measure particulate matter with a size of 2.5µm and smaller, capturing fine particles like dust and smoke that can deeply penetrate the respiratory system. It plays a critical role in assessing air quality and potential health hazards. On the other hand, the PM10 sensor detects particles sized 10µm and smaller, including larger particles like pollen and mould spores. Through a scientific and unique algorithm, these sensors calculate the mass concentration of particles per unit volume based on the equivalent particle size, ensuring reliable and real-time data. PM2.5 and PM10 sensors manufactured by Shandong Renke Technology Co., Ltd. were chosen to be used in the experiment as it can deliver prompt and precise results, allowing for timely monitoring of air quality and facilitating rapid responses to any potential changes or pollution events (Renke 2024). In terms of sensor working methods, Renke Technology Co., Ltd.'s sensors leverage advanced laser scattering measurement principles, which enable them to gauge the size and concentration of suspended particles accurately. Through a scientific and unique algorithm, these sensors calculate the mass concentration of particles per unit volume based on the equivalent particle size, ensuring reliable and real-time data.\u003c/p\u003e\n \u003cp\u003eThe reason to use an optical particle sensor was guided by practical and operational considerations commonly encountered in real-time indoor air quality monitoring. While reference-grade methods such as gravimetric sampling, beta attenuation monitors (BAM), and tapered element oscillating microbalances (TEOM) offer more accurate mass-based PM10 measurements, they come with significant trade-offs. Gravimetric methods are time-intensive, requiring manual filter collection, lab analysis, and non-continuous operation. BAM and TEOM instruments, although real-time, are costly, bulky, and sensitive to environmental conditions, making them unsuitable for deployment in dynamic or confined indoor environments. In contrast, the RS-PM-*-2-EX optical sensor provides a non-intrusive, cost-effective solution for continuous monitoring of PM2.5 and estimated PM10 levels. While the PM10 output is derived through algorithmic interpretation of scattered light and not through direct physical sizing, it remains a valuable tool for assessing temporal patterns and environmental changes, especially when rapid or spatially distributed data collection is required.\u003c/p\u003e\n \u003cp\u003eAdding VOCs sensors to the BIAB is essential to understanding how plants and selected filters affect VOCs levels. VOCs are airborne pollutants from various sources such as building materials, paints, furniture, cleaning products, and cosmetics, that harm humans (Rumchev, Brown et al. 2007). Monitoring VOCs helps assess how plants and selected filters can reduce indoor pollutants and improve air quality. Taqu Sensors Technology Co., Ltd. manufacturers the sensors chosen. The sensors continuously measure VOCs concentration, showing the plants’ effectiveness in creating a healthier living environment. This data optimizes the BIAB's efficiency and promotes cleaner, safer indoor air. The VOCs sensor utilized in this study was designed and calibrated by the manufacturer to operate reliably within a broad environmental range. It is capable of functioning between 20 and 80°C and under relative humidity conditions of 0 to 99%. This wide operational tolerance ensures that the sensor remains accurate and stable when deployed in climates similar to those found in ASEAN nations. The VOCs sensor used in this study is a metal oxide semiconductor (MOS)-based device designed to monitor overall VOCs concentration trends under varying environmental conditions. Although it is not calibrated to specific compounds such as fluorene, fluorethane, or phenanthrene, it is effective in detecting overall VOCs changes in real-time. The sensor readings are used here to track relative VOCs dynamics rather than absolute quantification of individual species.\u003c/p\u003e\n \u003cp\u003eCarbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e) sensors must be integrated into the BIAB system to access the impact of CO\u003csub\u003e2\u003c/sub\u003e concentrations released by the botanical plant. The CO\u003csub\u003e2\u003c/sub\u003e sensor used in the experiment was from Shandong Renke Control Technology Co.,Ltd since it provides high measurement accuracy with quick response (Renke 2024). By monitoring CO\u003csub\u003e2\u003c/sub\u003e levels, the BIAB allows for a precise understanding of the plants' respiratory activities and their role in the carbon cycle. As the plants undergo photosynthesis, they absorb CO\u003csub\u003e2\u003c/sub\u003e and release oxygen, acting as a natural air purifier. The CO\u003csub\u003e2\u003c/sub\u003e sensors continuously measure the CO\u003csub\u003e2\u003c/sub\u003e concentration within the BIAB to provide data to assess the effectiveness of the botanical plants in reducing CO\u003csub\u003e2\u003c/sub\u003e levels and enhancing IAQ.\u003c/p\u003e\n \u003cp\u003eTemperature and humidity sensors are seamlessly integrated into the BIAB system to address important aspects of the performance of biofilter and the health of the botanical plants. Shandong Renke Control Technology Co.,Ltd. produced the temperature and humidity sensor used, which is small and portable, thus making it suitable for installation in a small place to measure temperature and humidity (Renke 2024). Ensuring that the sensors operate within their optimal temperature range is essential for maintaining accurate and reliable measurements. By carefully monitoring temperature changes in real-time, the BIAB can adjust its filtration efficiency, accordingly, optimizing pollutant removal based on the prevailing environmental conditions. Simultaneously, the humidity sensors play a vital role in displaying the humidity level to create a suitable habitat for the plants, preventing excessive humidity or dry conditions that may negatively impact their health and filtration capabilities. Table\u0026nbsp;1 shows the specifications of the sensors and Fig.\u0026nbsp;3 shows the arrangement of the sensors in the inlet and outlet sensor boxes.\u003c/p\u003e\n \u003cdiv\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\"\u003e\n \u003ch2\u003e2.3 Internet of Things\u003c/h2\u003e\n \u003cp\u003eThe internet-of-things (IoT) was used to develop a low-cost method of monitoring real-time IAQ in a BIAB test rig. IoT enables real-time data collection and analysis of critical pollutants allowing for continuous and precise monitoring. This connectivity facilitates remote access and control, enabling users to track IAQ trends and adjust conditions as needed. The air quality sensors were connected to the Raspberry Pi 4 Model B through USB to RS485 converter. RS485 is a serial communication protocol for reliable long-distance and noise-resistant data transmission. These sensors measure specific air quality parameters, such as PM2.5, PM10, VOCs, or CO2, and transmit the data over the RS485 interface. The sensors output data in the form of electrical signals following the RS485 standard. Since the Raspberry Pi 4 lacks native RS485 ports, a USB to RS485 converter is used. This device acts as an intermediary, converting the RS485 signals from the sensors into USB-compatible signals. It plugs into the Raspberry Pi’s USB port and effectively communicates with the RS485 sensors effectively. The Raspberry Pi 4 serves as the central processing unit for the IoT system. It runs OpenHABian, an open-source smart home automation platform, which collects and processes data from the sensors via the USB to RS485 converter. The network router connects the Raspberry Pi to the internet and other devices. It ensures communication between the Raspberry Pi and remote clients or users, allowing data to be accessed, monitored, and controlled in real-time from anywhere.\u003c/p\u003e\n \u003cp\u003eThe concentration of PM 2.5, PM 10, CO\u003csub\u003e2\u003c/sub\u003e and VOC, as well as the air temperature, and relative humidity (RH) were all measured by two sets of air quality sensors installed at the BIAB rig's air inlet and outlet. Figure\u0026nbsp;4 depicts a schematic diagram of integrating of the real-time IAQ monitoring system. IoT devices enhance data visualization through platforms like dashboards or mobile applications, providing actionable insights for optimizing the rig’s performance. Data was collected once every 2 minutes. In cases where the concentration of air pollutants at the inlet was lower than the outlet concentration, it is essential to conduct a thorough re-evaluation and reconfiguration of the BIAB system before its operation.\u003c/p\u003e\n \u003cp\u003eOpenHABian was used in this study as a free and open-source home automation platform designed to serve as an integration platform and centralization hub for a wide range of IoT devices/protocols. Also, the collected data can be stored in the cloud or in a local digital database (SD card). InfluxDB is a time-series database running on the Raspberry Pi, used to store the collected air quality data efficiently, allowing for timestamped data entries critical for trend analysis. Grafana as the visualization tool connected to InfluxDB can create dynamic dashboards. It queries the stored air quality data from InfluxDB and displays it in graphs, charts, and other formats for by users to easily interpret and analyse. Figure\u0026nbsp;5 illustrates the OpenHABian system architecture, data structure, and interconnected devices utilized in the botanical biofilter system developed for this study.\u003c/p\u003e\n \u003cp\u003eIn this study, a wireless smart control system was used to control the ventilation fan and the submersible pump in the BIAB rig. The biofilter system was equipped with eight brushless DC fans strategically installed on the back panel to ensure a consistent and efficient airflow. Brushless DC fans were chosen for their reliability, energy efficiency, and low noise operation, making them ideal for the continuous operation required in the experimental setup. These fans collectively provided an inlet airflow rate of 110 m³/h, which was essential for drawing contaminated air through the biofilter for purification. The airflow rate was validated using a calibrated digital anemometer (TESTO 405) capable of directly measuring volumetric flow (m³/h). Multiple readings were recorded and averaged to ensure accuracy and reliability. This validated flow rate aligned with the fans’ rated specifications, confirming the consistency of the system’s ventilation performance. The airflow rate was validated to optimize the interaction between the air pollutants and the biofilter components, ensuring effective pollutant removal while maintaining stable system performance. Also, the submersible pump was controlled so that the botanical parts were kept hydrated to prevent any excessive water from being sprayed into the growing medium. The model used in this study is the Sonoff 4CH Rev 2 with Tasmota firmware. This configuration allows OpenHAB to control 250 VAC appliances up to 2200 W via Wi-Fi and Message Queue Telemetry Transport (MQTT). A complete list of IoT and smart control appliances is shown in Table\u0026nbsp;2.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eList of IoT and smart control appliances\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDevice\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSpecification\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVentilation Fan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRated Voltage: 12V\u003c/p\u003e\n \u003cp\u003eRated Current: 0.18A\u003c/p\u003e\n \u003cp\u003eDiameter: 90mm\u003c/p\u003e\n \u003cp\u003eSpeed: 2400RPM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSubmersible Pump\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel: WP-100D\u003c/p\u003e\n \u003cp\u003ePower Supply: 7W\u003c/p\u003e\n \u003cp\u003eMax Flow Rate: 560L/H\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWireless Electrical Switch\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel: Sonoff 4CH Rev2\u003c/p\u003e\n \u003cp\u003ePower Supply: 90 ~ 250V AC\u003c/p\u003e\n \u003cp\u003eMaximum current: 10A per channel\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNetwork Router\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel: TP-link TL WR902AC\u003c/p\u003e\n \u003cp\u003ePower Supply: 5V 2A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\"\u003e\n \u003ch2\u003e2.4 Pollutant generation\u003c/h2\u003e\n \u003cp\u003eThe source of pollutants was injected into the system by burning one ring of mosquito-repellent coils. The combustion process releases the substances as the active ingredients in the repellent, often pyrethroids, burn and vaporize. The smoke produced contain fine PM2.5, which can penetrate deep into the respiratory system, and VOCs, which include irritants and compounds that contribute to indoor air pollution. In addition, the burning of the coils may also release toxic substances like formaldehyde and acetaldehyde, which are by-products of incomplete combustion. Therefore, the source of pollutants from burning mosquito-repellent coils can significantly impact indoor air quality, making it an important consideration in IAQ monitoring. The same injection of pollutant source will be applied in the subsequent cases.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\"\u003e\n \u003ch2\u003e2.5 Filter Setup\u003c/h2\u003e\n \u003cp\u003eSeveral proposed methods were conducted to examine the efficiency of the filtration part of the BIAB rig. Case 1 is an integrated baseline case without any filtration system. The baseline case in this study represented the scenario where no filtration system was added to the BIAB rig. It served as a reference point to compare and assess the effectiveness of the implemented filtration methods. The initial level of PM and VOCs present in the indoor environment were identified by examining the baseline case without any filtration intervention. Therefore, the extent of air pollution reduction achieved through the subsequent filtration experiments can be quantified. Additionally, the baseline case provided a basis for evaluating the performance of the filtration systems in terms of their ability to improve IAQ compared to untreated conditions.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCase 2\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eused a botanical plant as the filtration system of the BIAB. Previous literature suggested that organic plants can filter different types of VOCs, such as benzene and formaldehyde, from the air (Sokhal and Narayan 2020). However, their effectiveness in filtering PM remains uncertain. Therefore, this method was implemented to investigate whether the presence of the botanical plant could lead to a reduction in the PM concentration. In this study, Golden Pothos was used as it is believed to be able to reduce the VOCs concentration. The present study involved 2-year-old Golden Pothos plants, which are considered adult plants at this stage. By 2 years, Golden Pothos typically reaches full maturity with a well-developed root system and an extensive leaf area, enhancing its air purification abilities. This maturity ensures that the plant can substantial contribution to improving indoor air quality. A study by Sokhal and Narayan (2020) claimed that Golden Pothos effectively remove formaldehyde, benzene, toluene, and xylene from the air. Golden Pothos was more effective at removing formaldehyde, with a removal efficiency of 68.1%. Results from another study by Saucedo-Lucero, Falcón-González et al. (2024) also supported the ability of Golden Pothos to remove air pollutants such as VOCs, CO\u003csub\u003e2\u003c/sub\u003e, and particulate matter. It shows the highest removal efficiency for CO\u003csub\u003e2\u003c/sub\u003e, improving performance as the foliar area increases, and is particularly effective in filtering PM2.5 compared to PM10. While CO removal is notable, especially in later stages, TVOC removal is relatively low. The experimental setup consisted of three large pots (diameter: 17 cm) and three small pots (diameter: 7 cm) of Golden Pothos, positioned directly in front of the filtration fans. This was to ensure a higher circulation of the airflow and the maximum exposure of the air passing through the plants. Placing the pots in front of the fans aimed to facilitate the efficient exchange of air between the indoor environment and the plants, enabling effective interaction between the air pollutants and the botanical filtration system.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCase 3\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eadded a primary carbon filter to the biofilter. As botanical plants focus on the filtration of VOCs, carbon filters were introduced to help with the filtration of PM 2.5 and PM 10. Carbon filters were believed to be able to reduce the concentration of PM due to their large surface area. The carbon filter used in this study features a thickness of 4mm ± 1mm and adheres to the G3 filter class as per the EN779:2012 standard, making it well-suited for general air filtration in non-critical environments. This filter includes carbon, which enhances its ability to absorb odours and gases such as VOCs, improving indoor air quality. With a 22g/cm³ density, the filter balances adsorption capacity and airflow resistance, ensuring efficient filtration without causing significant pressure drops. Its average arrestance of synthetic dust falls between 80% and 90%, effectively capturing airborne dust particles. While it may not efficiently trap finer particles like PM2.5, the filter remains highly beneficial for general air quality improvement by addressing larger particulate matter and gaseous pollutants.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCase 4\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eintroduced the coconut husk to the filtration component in the BIAB system. Coconut husk was known for its high porosity and natural ability to absorb impurities. According to Pettit, Irga et al. (2018), coconut husk has been utilized in various studies focusing on functional green walls, and the existing literature suggests that it serves as an effective substrate for active green walls. Moreover, it has been demonstrated to be suitable as a packing medium in biofilters. The porosity and fibrous characteristics of coconut husks are one of the main reasons they can trap particles passing through them and act as a barrier to filter out large airborne particles, or PM. Incorporating coconut husk into the filtration process is expected to enhance the removal of PM2.5 and PM10. The porous structure of coconut husk allows for increased surface area and contact time, facilitating the adsorption and retention of airborne pollutants. However, the effectiveness of coconut husk as a filtration medium in the specific context of the BIAB system requires investigation and evaluation.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCase 5\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eincluded granular activated carbon (GAC) as an additional filtration component in the BIAB system. GAC is known for its high adsorption capacity and effectiveness in removing various contaminants from the air, including VOCs and odorous substances. GAC filters can reduce 90% of the formaldehyde in the air (Ahn, Cho et al. 2021). Formaldehyde, a common VOCs found in indoor environments, is known to be a potent respiratory irritant and a potential carcinogen. The GAC used in this study, with a high iodine value of 900IV to 1500IV, ensures efficient adsorption of a wide range of contaminants, including VOCs and odours (Chaudhary, Bansal et al. 2024). The specified mesh size 20×50 provides an optimal balance for pollutant capture and air permeability. The density of the GAC directly impacts the airflow rate; while a lower density improves airflow through the material, it may slightly reduce the adsorptive capacity (Patel and Mansoor Ahammed 2024). With a minimum hardness of 99%, the GAC maintains excellent structural integrity and durability during use.\u003c/p\u003e\n \u003cp\u003eAdditionally, a maximum moisture content of 5% and ash content of 3% ensure consistent performance and minimal interference with its adsorption efficiency. These properties make the GAC ideal for enhancing air purification systems. Therefore, the GAC used in this experiment is maintained dry to ensure optimal adsorption capacity and performance. Incorporating GAC into the filtration process is expected to further enhance the removal of VOCs and improve the overall air quality. The large surface area and porous structure of GAC provide ample contact points for the adsorption of pollutants, effectively trapping them within the filter media. However, the specific performance and suitability of GAC in the context of the BIAB system need to be assessed through experimentation and analysis. The proposed cases were simplified and tabulated in Table\u0026nbsp;3.\u003c/p\u003e\n \u003cdiv\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003e2.6 Pollutant reduction rate calculation\u003c/h2\u003e\n \u003cp\u003eTo determine the average reduction rate of pollutant concentrations such as PM2.5, PM10, and VOCs in the experiment, the reduction rate was calculated as a measure of how efficiently the filtration system reduced pollutant levels over a specific period. The average reduction rate was computed using Eq.\u0026nbsp;1.\u003c/p\u003e\n \u003cdiv id=\"Equ1\"\u003e\n \u003cdiv id=\"FileID_Equ1\" name=\"EquationSource\"\u003e$$\\:Average\\:Reduction\\:Rate=\\frac{Initial\\:Concentration\\:(\\mu\\:g/m³)\\:\\:-\\:Final\\:Concentration\\:(\\mu\\:g/m³)}{Time\\:Taken\\:\\left(Minute\\right)}$$\u003c/div\u003e\n \u003cdiv\u003e1\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eThe reduction rate for each pollutant is calculated within a targeted concentration range to evaluate the system’s performance across specific pollutant levels. For PM2.5, the reduction rate is assessed as concentrations decrease from 1000 µg/m³, the maximum range of the PM sensor to 100 µg/m³, which falls within the \"satisfactory\" category of the Air Quality Index (AQI) (Malhotra, Walia et al. 2024). This reflects the ability of the system to filter fine particulate matter to meet stringent air quality standards. For PM10, the reduction rate is measured as concentrations decline from 1000 µg/m³ to 100 µg/m³ (satisfactory level), demonstrating the efficiency of removing coarser particulate matter. Similarly, the reduction rate for VOCs is evaluated as concentrations decrease from peak value to 1000 ppm, the threshold for a \"good\" AQI level highlighting the system’s capability to effectively reduce harmful volatile organic compounds (Talati, Shah et al. 2025). These targeted ranges are crucial for assessing the filtration system’s performance in achieving specific air quality improvements and compliance with health and safety guidelines.\u003c/p\u003e\n \u003cp\u003eThis calculation quantitatively assesses the rate at which pollutants are removed from the air. The initial and final concentrations were measured using inlet and outlet sensors, respectively, while the time taken 𝑇 refers to the duration over which the reduction occurred. A higher average reduction rate indicates a more efficient filtration process, reflecting the system’s ability to reduce pollutants more effectively within a shorter time. This metric is critical for evaluating the overall performance of the filtration system and is helpful in comparing different filtration configurations or assessing the effectiveness of individual components. Conversely, a lower average reduction rate may suggest inefficiencies or system optimization needs.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results and Discussion","content":"\u003cdiv id=\"Sec10\"\u003e\n \u003ch2\u003e3.1 Air Quality Monitoring\u003c/h2\u003e\n \u003cp\u003eA 960-minute (16 hours) data plot, highlighting the most significant concentration changes caused by pollutant injection (mosquito-repellent coils) was presented for each case. The data plots were generated from real-time measurements, sampled at 30-minute intervals. Error bars are included to represent the accuracy specifications of the sensors employed: ±30 µg/m³ for the PM sensor and ± 100 ppm for the VOCs sensor. Inlet and outlet pollutant concentrations were plotted to analyse their variation and visually assess the differences in pollutant levels across the system. Throughout the experiment, temperature and humidity showed minimal variation during the 960-minute data collection period. The temperature fluctuated around 34 ± 0.6°C, while the humidity remained stable at approximately 60 ± 2%. The minimal variation in temperature and humidity during the experiment can likely be attributed to the controlled indoor environment where the study was conducted. Indoor conditions typically offer stable temperature and humidity levels, minimizing external influences that could cause fluctuations. This stability justifies why these parameters did not show significant changes and were not further discussed. Therefore, these parameters were not discussed further. All cases exhibited high initial concentrations, as the initial conditions were intentionally set to ensure a thorough mixing of pollutants within the inlet sensor box.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCase 1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(baseline) focuses on evaluating the air quality within the system without the inclusion of any filtration mechanisms. Therefore, the baseline case acts as a reference point to assess the natural state of air pollutant concentrations and provides a basis for comparison with the subsequent cases involving filtration systems. By examining the inlet and outlet measurements of various pollutants, the baseline case offers insights into the initial pollutant levels. It allows for a better understanding of the effectiveness of the filtration systems implemented in subsequent experiments. It provides a comparative analysis to show the impact of filtration on improving air quality within the BIAB system. Figure 6(a) and Fig. 6(b) illustrate the temporal variation of PM2.5 and PM10 concentrations at the system’s inlet and outlet respectively. The concentrations of PM2.5 at both the inlet and outlet increased synchronously, reaching the sensor’s maximum detectable limit of 1000 µg/m³, and began to decline at approximately 470 minutes. In comparison, PM10 concentrations at the inlet and outlet also rose to 1000 µg/m³, with a subsequent decline observed around 490 minutes. The observed decline in PM2.5 and PM10 concentrations can be attributed to the cessation of emissions following the complete combustion of the mosquito-repellent coils. Figure 6(c) presents the VOCs concentrations at the system’s inlet and outlet over time. The initial VOCs concentration of approximately 1000 ppm increased sharply, reaching peak values of 1626 ppm at the inlet and 1611 ppm at the outlet at 402 minutes. The graph shows that the patterns of VOCs concentrations at the inlet and outlet closely resemble each other. This trend similarity indicates that no filtration system has been incorporated into the setup yet. Without any filtration mechanisms in place, the pollutants level remains relatively consistent throughout the observation. The absence of a noticeable deviation between the inlet and outlet concentrations implies that the system does not currently possess the capability to reduce PM and VOCs effectively. This emphasizes the need to explore and implement filtration measures to address the presence of these potentially harmful PM and VOCs.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCase 2\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003efocuses on investigating the effectiveness of utilizing botanical plants as the filtration part of improving IAQ. The experiment was setup with three big pots and three small pots of Golden Pothos in front of the filtration fans. Figure 7(a) and Fig. 7(b) show the concentrations at the inlet and outlet of PM2.5 and PM10 against time respectively. Higher initial concentrations of PM2.5, PM10 and VOCs at the outlet compared to the inlet were observed, which may be attributed to several potential factors. As the plant pots were installed, the dust and particles from them might have caused a sudden spike in the outlet concentration as the presence of the airflow caused the dust and particles from the plants and pots to resuspend back into the air. The concentrations of PM2.5 at both the inlet and outlet began to decline at approximately 280 minutes, while PM10 concentrations started to decrease by around 302 minutes. Following the removal of the air pollution source, a consistent downward trend was observed, with inlet and outlet PM2.5 concentrations exhibiting similar patterns and values. Figure 7(b) also indicates almost the same trend as the results obtained for PM2.5, where the difference between the inlet and outlet concentrations of PM10 is minimal and practically imperceptible. This shows that the addition of Golden Pothos has low capability in PM2.5 and PM10 filtration. Further improvements or additional filtration methods may be necessary to achieve a more substantial reduction in PM concentrations. Figure 7(c) illustrates the concentration of inlet and outlet VOCs against time. The highest VOCs concentration recorded for both the inlet and outlet was 1477 ppm and 1481 ppm respectively. As observed in Fig. 7(c), the outlet VOCs had a lower concentration than the inlet VOCs in the first 2 hours. However, after that period, as the day transitions to night, the effectiveness of Golden Pothos declines as it does not reduce the VOC concentration, and there is a slight increase in outlet VOCs from 150 minutes to 480 minutes. This could be the reason for the photosynthetic activity of the plant. During the daytime, plant’s ability to reduce VOCs concentrations is generally more pronounced when they are actively photosynthesizing. This is because the photosynthetic process helps plants metabolize and break down VOCs, improving air quality. However, at night, the photosynthetic activity of plants diminishes as they switch to a different process called respiration. These changes in the process may be relatively lowering the plant's VOCs reduction ability. We can conclude that adding Golden Pothos as a botanical plant will reduce the concentration of VOCs. However, its ability is not significant as it depends on several other factors, such as the light condition. This aligns with the findings of Taemthong \u0026amp; Cheycharoen (2022), which indicate that variations in light intensity supplied to plants can influence their capacity to absorb VOCs.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCase 3\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eutilized adding a primary carbon filter to the previous system inside the biofilter box to improve PM and VOCs filtering. Figure 8(a) and Fig. 8(b) below show the inlet and outlet concentrations of PM2.5 and PM10 against time. The increasing trend of the PM concentration in the graph was due to the injection of air pollution by burning one mosquito repellent coil. It was observed that the outlet PM2.5 concentration was lower than the inlet concentration of PM2.5 right after the experiment started. Once the source of the air pollutant was eliminated, the outlet concentration showed a steeper decline in concentration compared to the inlet. This shows that carbon filter leads to a lower concentration in the outlet compared to the inlet, as the carbon filter acts on filtering the PM2.5. This implies that without continuous pollution input, the filtration system, including the carbon filter, efficiently removes PM2.5 particles from the air. Similarly, the outlet PM10 concentration was lower than the inlet PM10 concentration once the pollution was injected, as observed in Fig. 8(c). This indicates that the addition of the filtration system can reduce PM10 in the air. With respect to PM filtration, it can be concluded that the addition of a carbon filter effectively decreases the proportion of PM2.5 and PM10 passing through the BIAB system. The analysis of VOCs revealed the impact of the effectiveness of botanical plants with the addition of a carbon filter on the filtration system. The variation of inlet and outlet VOCs concentration throughout the experiment is illustrated in Fig. 8(c). It is observed from Fig. 8(c) that the carbon filter significantly reduced the VOCs concentration as the outlet VOCs concentration was kept in a lower value than the inlet. The maximum VOCs concentration recorded at the inlet was 1622 ppm at 108 minutes. In contrast, the maximum VOCs concentration at the outlet was considerably lower, reaching a maximum of 1479 ppm at 416 minutes. Compared to the previous cases, adding a carbon filter improved the ability of the BIAB rig to reduce VOCs in the air. The successful reduction of VOCs concentrations at the outlet highlights the carbon filter’s positive impact on enhancing the performance of the BIAB system’s performance.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCase 4\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eused the coconut husk as an additional filtration layer, working alongside the primary carbon filter. Its purpose was to complement the existing filtration mechanism by targeting specific pollutants and further reducing their concentration in the air. Figure 9(a) and Fig. 9(b) show the PM2.5 and PM10 concentration throughout the experiment by installing coconut husk into the filtration system. The same pollution source had been applied in the experiment. The differences between the inlet and outlet PM2.5 and PM10 are not significant. At some points, the outlet PM concentrations exceed the inlet PM concentration considering that the coconut husk might also contribute to the increasing concentration of PM due to its degradation over time. Incorporating coconut husk as an additional filtration material in the BIAB showed limited improvements in the filtration of PM. While coconut husk has been recognized for its natural filtration properties, the experimental results indicated its impact on PM reduction was relatively modest. Figure 9(c) shows the inlet and outlet VOCs concentrations against time. The graph obtained has almost the same trend as the one without the coconut husk. This shows that coconut husk has limited ability to reduce VOCs. The maximum inlet VOCs concentration obtained is 1522 ppm at 260 minutes, and the maximum outlet concentration is 1476 ppm at 262 minutes, which is almost similar to the one obtained in Case 3 without coconut husk.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCase 5\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003erepresents a new experimental setup incorporating GAC. Figure 10(a) presents the temporal profiles of PM2.5 concentrations at the inlet and outlet, while Fig. 10(b) depicts the corresponding inlet and outlet concentrations of PM10 over time. The same pollutant source was injected and caused the PM concentration to spike up to the maximum limit of the PM sensor which was 1000 µg/m³. A significant reduction of PM concentrations is observed during the declining phase, as the outlet concentrations of both PM2.5 and PM10 are markedly lower than the corresponding inlet concentrations. It is noted that the addition of GAC slightly improves on PM2.5 and PM10 filtration. Next, the analysis of VOCs revealed the impact of the addition of GAC in the filtration system on the effectiveness of the system. It is observed from Fig. 10(c) that the addition of GAC has a noticeable impact on the reduction of VOCs. The maximum concentration obtained at the inlet sensors is 1626 ppm, while it is only 1374 ppm on the outlet sensors as shown in Fig. 10(c). The inclusion of a carbon filter in the previous case resulted in a reduction in VOCs concentrations. However, the introduction of GAC further elevated the filtration efficiency, leading to enhanced removal of VOCs. The combined use of a carbon filter and GAC proved to be more effective in mitigating VOCs contamination, surpassing the filtration capabilities of the carbon filter alone.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003e3.2 Pollutant Removal\u003c/h2\u003e\n \u003cp\u003eThe time for the PM and VOCs concentration to start declining varies for each case since the time for a coil of mosquito repellent to be completely burned might be slightly different. Furthermore, the limitation range of the PM sensor until 1000 µg/m³ resulted in an incomplete observation on the maximum PM concentration recorded and the time of PM concentration started declining. However, the filter efficiency can be compared based on the reduction rate of PM concentration at the outlet. The reduction rate of PM concentration is calculated by determining the time needed to reduce maximum PM concentration recorded (1000 µg/m³) to a satisfactory level (100 µg/m³). The reduction trend of outlet PM2.5 and PM10 concentration from Case 1 to Case 5 are summarized in Fig. 11(a) and Fig. 11(b).\u003c/p\u003e\n \u003cp\u003eFigure 11(a) clearly illustrates that Case 3, which incorporates a botanical plant and a carbon filter, exhibits the most significant decrease in PM2.5 concentration at the outlet, followed by Cases 5 and Case 4. Figure 11(b) demonstrates that Case 3, which incorporates both botanical plants and a carbon filter, exhibits the most prominent decline in PM10 concentration, followed by Cases 4 and 5. Cases 1 and 2 exhibit similar PM2.5 and PM10 reduction trends, which require further clarification through a detailed analysis of their respective reduction rates.\u003c/p\u003e\n \u003cp\u003eFigure 12(a) below summarizes the outlet VOCs concentrations across Cases 1 to 5. Case 1, the baseline scenario, recorded the highest VOCs concentration at 1611 ppm. In contrast, Case 5, which incorporated a full combination of filtration system, exhibited the lowest concentration, peaking at 1374 ppm. The concentrations in Case 2 (1481 ppm), Case 3 (1479 ppm), and Case 4 (1476 ppm) were all lower than the baseline value observed in Case 1. This trend indicates that the introduction of the filtration system leads to a reduction in VOCs concentrations, with varying degrees of efficiency.\u003c/p\u003e\n \u003cp\u003eFigure 12(b) presents the normalized VOCs concentration trends for all cases, allowing direct comparison on a uniform scale. Normalized VOCs concentration is based on the ratio of VOCs concentration at specific time to the initial VOCs concentration in each case. By normalizing the data, the influence of varying initial concentrations is eliminated, enabling a clear assessment of the relative efficiency and behaviour of each case over time. Case 2 (Botanical Plant) exhibits the most significant and consistent reduction in normalized VOCs concentration over time, indicating the highest removal efficiency. Case 4 (BP + CF + Coconut Husk) and Case 5 (BP + CF + CH + GAC) also show substantial reductions, performing better than Case 3 (BP + Carbon Filter) and the Baseline (Case 1) which maintains the highest normalized concentration throughout the period. Overall, the plot highlights that the integration of botanical and natural filtration elements enhances VOCs removal performance, with the botanical plant alone showing the fastest and most efficient reduction trend.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003e3.3 Pollutant reduction rate\u003c/h2\u003e\n \u003cp\u003eThe rate of pollutant concentration reduction was calculated for each case to enable a more detailed comparison of the removal efficiencies among the different systems. The reduction rates for PM2.5 and PM10 were determined based on the time required for the outlet concentrations to decrease from 1000 µg/m³ to the satisfactory level of 100 µg/m³. Similarly, the VOCs reduction rate was evaluated based on the time taken for the outlet VOCs concentration to decrease from its peak value back to the satisfactory threshold of 1000 ppm. The calculated reduction rates are summarized in Fig. 13.\u003c/p\u003e\n \u003cp\u003eReduction rates for PM2.5 and PM10 are very similar in Case 1 and Case 2 showing minimal improvement. This shows that incorporating a botanical plant into the biofilter does not significantly enhance PM filtration. However, when a carbon filter is added (Case 3), there is a significant increase in the reduction rates for both PM2.5 (5.36 µg/m³/min) and PM10 (4.95 µg/m³/min), indicating a strong positive effect of the carbon filter. Introducing a coconut husk in Case 4 slightly lowers the reduction rates (4.59 µg/m³/min for PM2.5 and 4.74 µg/m³/min for PM10) compared to Case 3 but still maintains a better performance than the baseline. Case 5, which combines a botanical plant, carbon filter, coconut husk, and GAC, achieves high reduction rates for both PM2.5 (5.11 µg/m³/min) and PM10 (5.23 µg/m³/min), the latter being the highest among all cases.\u003c/p\u003e\n \u003cp\u003eIn Case 1 (Baseline), the average outlet VOCs reduction rate is 3.89 ppm per minute, serving as the initial reference point. When a botanical plant is added in Case 2, the VOCs reduction rate drops to 2.36 ppm/min, indicating that the botanical plant alone may not effectively contribute to VOCs removal and could even interfere slightly with baseline performance. Case 3, which combines the botanical plant with a carbon filter, shows a major improvement, reaching the highest VOCs reduction rate of 4.13 ppm/min. This highlights the strong adsorptive capability of the carbon filter for VOCs. However, when a coconut husk is introduced in Case 4 alongside the carbon filter and botanical plant, the VOCs reduction rate falls sharply to 2.11 ppm/min (the lowest across all cases) suggesting that the coconut husk might compete for adsorption sites or release VOC-like substances that reduce the filtering efficiency. In Case 5, where GAC is added on top of the previous materials, the VOCs reduction rate improves slightly to 2.92 ppm/min but remains lower than the baseline and far below Case 3. This indicates that while GAC provides some recovery in VOCs capture, it does not fully compensate for the negative effect introduced by the coconut husk.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Conclusion, Limitation and Recommendation of Future Works","content":"\u003cp\u003eThe study\u0026rsquo;s findings exhibited the successful development of the BIAB prototype and the provision of valuable insights into different filtration systems' efficiency in decreasing PM2.5, PM10, and VOC concentrations. Integration of the IoT into the BIAB system allows for real-time monitoring of crucial parameters like PM, VOCs, temperature, humidity, and CO2 levels. This IoT integration enables continuous data collection and transmission from strategically placed sensors, facilitating ongoing air quality analysis. One notable limitation of this study is the reliance on an optical particle sensor (RS-PM-*-2-EX) that estimates PM10 concentrations using proprietary algorithms based on laser light scattering. This type of sensor does not physically separate or directly measure PM10 through inertial or gravimetric methods. As a result, coarse particles larger than 10 \u0026micro;m, such as certain pollen grains and some mould spores, may fall outside the effective detection range of the device. Consequently, the reported PM10 values should be interpreted as algorithmic estimations rather than absolute measurements. Although this limitation does not affect the ability to observe relative trends and fluctuations in particulate matter concentration, it should be taken into account when comparing results with regulatory-grade instruments or standards.\u003c/p\u003e \u003cp\u003eAnother important limitation involves the upper detection limit of 1000 \u0026micro;g/m\u0026sup3; for the PM sensors used. While this range suffices for many indoor environments, it restricted the ability to capture true peak concentrations following pollutant injection. However, the maximum filtration capacity and potential overload behaviour of the system could not be fully evaluated. Future studies should employ higher-range PM sensors or integrate dilution-based sampling strategies to accurately determine the filtration system's maximum capacity under extreme conditions. It is also acknowledged that the current housing design may allow VOCs and SVOCs to interact with multiple internal surfaces before reaching the VOCs sensor, potentially affecting response accuracy. Future improvements should include redesigned enclosures to provide more direct and unobstructed airflow pathways to the sensor module, minimising surface adsorption and enhancing measurement fidelity.\u0026rdquo;\u003c/p\u003e \u003cp\u003eSeveral types of filtrations including botanical plants, carbon filters, coconut husk, and GAC, were introduced throughout the studies to validate the capability and efficiency in reducing the concentration of PM2.5, PM10, and VOCs. In this study, the chosen botanical plant, Golden Pothos, shows only a slight improvement in PM10 reduction compared to Case \u003cspan refid=\"FPar5\" class=\"InternalRef\"\u003e1\u003c/span\u003e (Baseline), while the PM2.5 reduction rate remains nearly the same. Moreover, the integration of the botanical plant into the filtration system appears to worsen VOCs removal, recording the second lowest VOCs reduction rate (2.36 ppm/min) among all cases. The pollutant removal efficiency of botanical plants is influenced by light intensity, as the photosynthetic process requires sunlight to function effectively. During the daytime, when light levels are higher, botanical plants exhibit an increased ability to absorb pollutants. This is because photosynthesis drives stomatal opening, which enhances gas exchange and allows for greater uptake of airborne pollutants, including certain VOCs.\u003c/p\u003e \u003cp\u003eBy integrating Golden Pothos and carbon filters, Case \u003cspan refid=\"FPar2\" class=\"InternalRef\"\u003e3\u003c/span\u003e demonstrates the highest average reduction rate of PM 2.5 (5.36 \u0026micro;g/m\u0026sup3;/min) and VOCs (4.13 ppm/min) while providing second highest PM10 reduction rate (4.95 \u0026micro;g/m\u0026sup3;/min) among all cases. The synergy between the natural filtration abilities of Golden Pothos and the adsorption properties of carbon filters significantly enhances the overall efficiency in capturing and eliminating fine particulate matter from the air. Integrating these filtration components forms a highly efficient system for reducing PM concentrations, emphasizing the importance of employing diverse filtration techniques for optimal air purification. In Case \u003cspan refid=\"FPar4\" class=\"InternalRef\"\u003e5\u003c/span\u003e, where GAC is added in addition to the botanical plant, carbon filter, and coconut husk, the VOCs reduction rate increases slightly to 2.92 ppm/min. However, this value remains lower than the baseline (3.89 ppm/min) and significantly below the peak performance observed in Case \u003cspan refid=\"FPar2\" class=\"InternalRef\"\u003e3\u003c/span\u003e (4.13 ppm/min). This suggests that while GAC contributes positively to VOCs adsorption due to its high surface area and porous structure, it is not sufficient to fully counteract the adverse impact introduced by the coconut husk in Case \u003cspan refid=\"FPar3\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The coconut husk may release organic compounds or physically hinder airflow and adsorption dynamics, reducing the overall efficiency of the filtration system. As a result, the addition of GAC only partially restores VOCs removal efficiency, highlighting the importance of material compatibility and interaction in multi-layer biofilter systems. In summary, Case \u003cspan refid=\"FPar2\" class=\"InternalRef\"\u003e3\u003c/span\u003e stands out as the most reliable filtration system for reducing PM2.5, PM10 and VOCs. The integration of GAC also improves the filtration efficiency in PM10 obviously. However, the addition of coconut husk in BIAB can lead to a drastic decrease in VOCs filtration efficiency. Through integrating various filtration components, including Golden Pothos, carbon filters, coconut husk, and GAC, Case \u003cspan refid=\"FPar4\" class=\"InternalRef\"\u003e5\u003c/span\u003e demonstrates impressive outcomes in decreasing PM2.5 and PM10 concentrations.\u003c/p\u003e \u003cp\u003eThis study focused on assessing air filtration systems in a controlled environment. The results offer valuable insights into the performance of these systems under such conditions. However, for future research, it is advisable to broaden the scope to environments with active human movement. Including human presence can provide a more realistic portrayal of air quality dynamics and the efficacy of filtration systems in real-world scenarios. Additionally, investigating the influence of human activities on air quality can assist in developing targeted and efficient filtration strategies for optimal IAQ across diverse settings like offices, schools, and public spaces. Examining the interaction between human movements, air circulation, and filtration systems will deepen our understanding of IAQ management and contribute to developing effective strategies for creating healthier indoor environments.\u003c/p\u003e \u003cp\u003eBased on the results, future studies will incorporate more extensive replications to further validate and refine the findings, with a particular emphasis on integrating IoT technologies and optimizing plant and carbon material combinations for improved air quality. Future research is recommended to explore the efficacy of various commercial air filters when integrated into the BIAB. This entails assessing the performance of different filter types, including HEPA, activated carbon, electrostatic, and hybrid filters. Comparative experiments should be conducted to gauge their efficiency in filtering PM and VOCs from indoor air. Understanding how different commercial air filters work in conjunction with the BIAB system will offer valuable insights for practical applications, aiding in the selection of filters based on specific IAQ requirements and limitations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to acknowledge the financial support from Asia Technological University (ATU) Network under ATU-Net Young Researcher Grant (YRG) with Vot. No. R.J130000.7724.4J714 and Universiti Teknologi Malaysia under UTM Nexus Postgrad Grant with Vot. No. Q.J130000.5324.00L96.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contribution:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSien Jie Wong: Conceptualization, methodology, and writing of the original draft.\u003c/p\u003e\n\u003cp\u003eHuiyi Tan, Hong Yee Kek, Mohd Hafiz Dzarfan Othman; Methodology, supervision, writing, reviewing, and editing:\u003c/p\u003e\n\u003cp\u003eKok Sin Woon, Xue-Chao Wang,Wan Muhammad Akid Wan Abd Samad: Writing, review, and editing.\u003c/p\u003e\n\u003cp\u003eKeng Yinn Wong: Conceptualization, supervision, methodology, funding acquisition, and writing of the original draft.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAhn, Y., D.-W. Cho, W. Ahmad, J. Jo, J. Jurng, M. B. Kurade, B.-H. Jeon and J. Choi (2021). \u0026quot;Efficient removal of formaldehyde using metal-biochar derived from acid mine drainage sludge and spent coffee waste.\u0026quot; \u003cu\u003eJournal of Environmental Management\u003c/u\u003e \u003cstrong\u003e298\u003c/strong\u003e: 113468.\u003c/li\u003e\n\u003cli\u003eAlryalat, S. A., A. Toubasi, J. Patnaik and M. Kahook (2022). \u0026quot;The impact of air pollution and climate change on eye health: a global review.\u0026quot; \u003cu\u003eReviews on environmental health\u003c/u\u003e.\u003c/li\u003e\n\u003cli\u003eBhave, P. and D. Yeleswarapu (2020). Removal of Indoor Air Pollutants Using Activated Carbon\u0026mdash;A Review\u003cstrong\u003e: \u003c/strong\u003e65-75.\u003c/li\u003e\n\u003cli\u003eChaudhary, P., S. Bansal, B. B. Sharma, S. Saini and A. Joshi (2024). \u0026quot;Waste biomass-derived activated carbons for various energy storage device applications: A review.\u0026quot; \u003cu\u003eJournal of Energy Storage\u003c/u\u003e \u003cstrong\u003e78\u003c/strong\u003e: 109996.\u003c/li\u003e\n\u003cli\u003eChung, T.-W., T. K. Ghosh, A. L. Hines and D. Novosel (1995). \u0026quot;Dehumidification of Moist Air with Simultaneous Removal of Selected Indoor Pollutants by Triethylene Glycol Solutions in a Packed-Bed Absorber.\u0026quot; \u003cu\u003eSeparation Science and Technology\u003c/u\u003e \u003cstrong\u003e30\u003c/strong\u003e(7-9): 1807-1832.\u003c/li\u003e\n\u003cli\u003ede Robles, D. and S. W. Kramer (2017). \u0026quot;Improving Indoor Air Quality through the Use of Ultraviolet Technology in Commercial Buildings.\u0026quot; \u003cu\u003eProcedia Engineering\u003c/u\u003e \u003cstrong\u003e196\u003c/strong\u003e: 888-894.\u003c/li\u003e\n\u003cli\u003eDitto, J., J. Abbatt and A. Chan (2022). \u0026quot;Gas- and Particle-Phase Amide Emissions from Cooking: Mechanisms and Air Quality Impacts.\u0026quot; \u003cu\u003eEnvironmental science \u0026amp; technology\u003c/u\u003e.\u003c/li\u003e\n\u003cli\u003eDubey, S., H. Rohra and A. Taneja (2021). \u0026quot;Assessing effectiveness of air purifiers (HEPA) for controlling indoor particulate pollution.\u0026quot; \u003cu\u003eHeliyon\u003c/u\u003e \u003cstrong\u003e7\u003c/strong\u003e: e07976.\u003c/li\u003e\n\u003cli\u003eEnvion (2024). Air Purifier TA500.\u003c/li\u003e\n\u003cli\u003eFleck, R., T. J. Pettit, A. N. J. Douglas, P. J. Irga and F. R. Torpy (2020). 15 - Botanical biofiltration for reducing indoor air pollution. \u003cu\u003eBio-Based Materials and Biotechnologies for Eco-Efficient Construction\u003c/u\u003e. F. Pacheco-Torgal, V. Ivanov and D. C. W. Tsang, Woodhead Publishing\u003cstrong\u003e: \u003c/strong\u003e305-327.\u003c/li\u003e\n\u003cli\u003eFu, H.-X. and X. Liu (2017). \u0026quot;Review of the impact of liquid desiccant dehumidification on indoor air quality.\u0026quot; \u003cu\u003eBuilding and Environment\u003c/u\u003e \u003cstrong\u003e116\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eGrand-View-Research (2022). Air Purifier Market Size, Share \u0026amp; Trends Analysis Report By Technology (HEPA, Activated Carbon), By Application (Commercial, Residential), By Coverage Range, By Sales Channel, By Type, By Region, And Segment Forecasts, 2024 - 2030.\u003c/li\u003e\n\u003cli\u003eGrinshpun, S. A., G. Mainelis, M. Trunov, A. Adhikari, T. Reponen and K. Willeke (2005). \u0026quot;Evaluation of ionic air purifiers for reducing aerosol exposure in confined indoor spaces.\u0026quot; \u003cu\u003eIndoor Air\u003c/u\u003e \u003cstrong\u003e15\u003c/strong\u003e(4): 235-245.\u003c/li\u003e\n\u003cli\u003eHomedics.com (2024). TotalClean\u0026reg; 5-in-1 UV-C Large Room Air Purifier.\u003c/li\u003e\n\u003cli\u003eJeong-ho, P. and S. Jeong-min (2018). \u0026quot;Characteristics of PM\u0026lt;sub\u0026gt;10\u0026lt;/sub\u0026gt;, PM\u0026lt;sub\u0026gt;2.5\u0026lt;/sub\u0026gt; and PM\u0026lt;sub\u0026gt;2.5\u0026lt;/sub\u0026gt;/PM\u0026lt;sub\u0026gt;10\u0026lt;/sub\u0026gt; Ratio in Air Monitoring Stations in Gyeongnam.\u0026quot; \u003cu\u003e한국환경과학회지\u003c/u\u003e \u003cstrong\u003e27\u003c/strong\u003e(10): 925-931.\u003c/li\u003e\n\u003cli\u003eKim, J.-H. and M.-S. Yeo (2020). \u0026quot;Effect of Flow Rate and Filter Efficiency on Indoor PM2.5 in Ventilation and Filtration Control.\u0026quot; \u003cu\u003eAtmosphere\u003c/u\u003e \u003cstrong\u003e11\u003c/strong\u003e: 1061.\u003c/li\u003e\n\u003cli\u003eKlepeis, N., W. Nelson, W. Ott, J. Robinson, A. Tsang, P. Switzer, J. Behar, S. Hern and W. Engelmann (2001). \u0026quot;The National Human Activity Pattern Survey (NHAPS): A resource for assessing exposure to environmental pollutants.\u0026quot; \u003cu\u003eJournal of exposure analysis and environmental epidemiology\u003c/u\u003e \u003cstrong\u003e11\u003c/strong\u003e: 231-252.\u003c/li\u003e\n\u003cli\u003eKujundzic, E., F. Matalkah, C. Howard, M. Hernandez and S. Miller (2006). \u0026quot;UV Air Cleaners and Upper-Room Air Ultraviolet Germicidal Irradiation for Controlling Airborne Bacteria and Fungal Spores.\u0026quot; \u003cu\u003eJournal of occupational and environmental hygiene\u003c/u\u003e \u003cstrong\u003e3\u003c/strong\u003e: 536-546.\u003c/li\u003e\n\u003cli\u003eLlewellyn, D. and M. Dixon (2011). 4.26 - Can Plants Really Improve Indoor Air Quality? \u003cu\u003eComprehensive Biotechnology (Second Edition)\u003c/u\u003e. M. Moo-Young. Burlington, Academic Press\u003cstrong\u003e: \u003c/strong\u003e331-338.\u003c/li\u003e\n\u003cli\u003eMalhotra, M., S. Walia, C.-C. Lin, I. K. Aulakh and S. Agarwal (2024). \u0026quot;A systematic scrutiny of artificial intelligence-based air pollution prediction techniques, challenges, and viable solutions.\u0026quot; \u003cu\u003eJournal of Big Data\u003c/u\u003e \u003cstrong\u003e11\u003c/strong\u003e(1): 142.\u003c/li\u003e\n\u003cli\u003eMaung, T., J. Bishop, E. Holt, A. Turner and C. Pfrang (2022). \u0026quot;Indoor Air Pollution and the Health of Vulnerable Groups: A Systematic Review Focused on Particulate Matter (PM), Volatile Organic Compounds (VOCs) and Their Effects on Children and People with Pre-Existing Lung Disease.\u0026quot; \u003cu\u003eInternational Journal of Environmental Research and Public Health\u003c/u\u003e \u003cstrong\u003e19\u003c/strong\u003e: 8752.\u003c/li\u003e\n\u003cli\u003emobile2go (2024). Mi SmartMi HEPA air purifier.\u003c/li\u003e\n\u003cli\u003eMontaluisa-Mantilla, M. S., P. Garc\u0026iacute;a-Encina, R. Lebrero and R. Mu\u0026ntilde;oz (2023). \u0026quot;Botanical filters for the abatement of indoor air pollutants.\u0026quot; \u003cu\u003eChemosphere\u003c/u\u003e \u003cstrong\u003e345\u003c/strong\u003e: 140483.\u003c/li\u003e\n\u003cli\u003eMontaluisa-Mantilla, M. S., J. Gon\u0026ccedil;alves, P. A. Garc\u0026iacute;a-Encina, R. Lebrero, M. Rico and R. Mu\u0026ntilde;oz (2025). \u0026quot;Harnessing the potential of Epipremnum aureum (Pothos) for indoor air purification in botanical filters.\u0026quot; \u003cu\u003eBuilding and Environment\u003c/u\u003e \u003cstrong\u003e275\u003c/strong\u003e: 112813.\u003c/li\u003e\n\u003cli\u003eNadzir, M. S. M., M. C. G. Ooi, K. M. Alhasa, M. A. A. Bakar, A. A. A. Mohtar, M. F. F. M. Nor, M. T. Latif, H. H. Abd Hamid, S. H. M. Ali and N. M. Ariff (2020). \u0026quot;The impact of movement control order (MCO) during pandemic COVID-19 on local air quality in an urban area of Klang valley, Malaysia.\u0026quot; \u003cu\u003eAerosol and Air Quality Research\u003c/u\u003e \u003cstrong\u003e20\u003c/strong\u003e(6): 1237-1248.\u003c/li\u003e\n\u003cli\u003eNamieśnik, J., T. G\u0026oacute;recki, B. Kozdroń-Zabiega ła and J. Łukasiak (1992). \u0026quot;Indoor air quality (IAQ), pollutants, their sources and concentration levels.\u0026quot; \u003cu\u003eBuilding and Environment\u003c/u\u003e \u003cstrong\u003e27\u003c/strong\u003e(3): 339-356.\u003c/li\u003e\n\u003cli\u003ePatel, M. and M. Mansoor Ahammed (2024). \u0026quot;Characteristics and removal of organic micropollutants in reverse osmosis concentrate from urban wastewater treatment plants: A review.\u0026quot; \u003cu\u003eWaste Management Bulletin\u003c/u\u003e \u003cstrong\u003e2\u003c/strong\u003e(4): 249-260.\u003c/li\u003e\n\u003cli\u003ePettit, T., P. Irga and F. Torpy (2018). \u0026quot;Functional green wall development for increasing air pollutant phytoremediation: Substrate development with coconut coir and activated carbon.\u0026quot; \u003cu\u003eJournal of Hazardous Materials\u003c/u\u003e \u003cstrong\u003e360\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003ePettit, T., P. J. Irga, P. Abdo and F. R. Torpy (2017). \u0026quot;Do the plants in functional green walls contribute to their ability to filter particulate matter?\u0026quot; \u003cu\u003eBuilding and Environment\u003c/u\u003e \u003cstrong\u003e125\u003c/strong\u003e: 299-307.\u003c/li\u003e\n\u003cli\u003ePettit, T., P. J. Irga and F. R. Torpy (2018). \u0026quot;Towards practical indoor air phytoremediation: A review.\u0026quot; \u003cu\u003eChemosphere\u003c/u\u003e \u003cstrong\u003e208\u003c/strong\u003e: 960-974.\u003c/li\u003e\n\u003cli\u003eRenke (2024). CO2 Sensor. L. Shandong Renke Control Technology Co.\u003c/li\u003e\n\u003cli\u003eRenke (2024). Modbus rtu temperature humidity sensor. L. Shandong Renke Control Technology Co.\u003c/li\u003e\n\u003cli\u003eRenke (2024). PM2.5 PM10 Sensor. L. Shandong Renke Control Technology Co.\u003c/li\u003e\n\u003cli\u003eRumchev, K., H. Brown and J. Spickett (2007). \u0026quot;Volatile organic compounds: do they present a risk to our health?\u0026quot; \u003cu\u003eReviews on environmental health\u003c/u\u003e \u003cstrong\u003e22\u003c/strong\u003e(1): 39-56.\u003c/li\u003e\n\u003cli\u003eSaucedo-Lucero, J. O., L. S. Falc\u0026oacute;n-Gonz\u0026aacute;lez, M. Ovando Franco and S. Revah (2024). \u0026quot;Implementation of a botanical bioscrubber for the treatment of indoor ambient air.\u0026quot; \u003cu\u003eJournal of Environmental Management\u003c/u\u003e \u003cstrong\u003e363\u003c/strong\u003e: 121414.\u003c/li\u003e\n\u003cli\u003eSokhal, A. and U. Narayan (2020). \u0026quot;Plants are More Effective in Absorbing Volatile Organic Compounds from the Atmosphere than Air Purifiers.\u0026quot; \u003cu\u003eJournal of Student Research\u003c/u\u003e \u003cstrong\u003e9\u003c/strong\u003e(2).\u003c/li\u003e\n\u003cli\u003eSzczotko, M., I. Orych, Ł. Mąka and J. Solecka (2022). \u0026quot;A Review of Selected Types of Indoor Air Purifiers in Terms of Microbial Air Contamination Reduction.\u0026quot; \u003cu\u003eAtmosphere\u003c/u\u003e \u003cstrong\u003e13\u003c/strong\u003e: 800.\u003c/li\u003e\n\u003cli\u003eTaemthong, W. (2021). \u0026quot;AIR QUALITY IMPROVEMENT USING ORNAMENTAL PLANTS IN CLASSROOMS.\u0026quot; \u003cu\u003eJournal of Green Building\u003c/u\u003e \u003cstrong\u003e16\u003c/strong\u003e: 201-216.\u003c/li\u003e\n\u003cli\u003eTaemthong, W. and N. Cheycharoen (2022). \u003cu\u003eGREEN WALLS CAN IMPROVE INDOOR AIR QUALITY IN THE CLASSROOM\u003c/u\u003e.\u003c/li\u003e\n\u003cli\u003eTalati, I., K. J. Shah, O. Patel, I. Tanna, A. Iain, A. D. Oza, A. A. Yadav, M. I. Alshayeb, M. A. Khan and S. Islam (2025). \u0026quot;Study of AQI Monitoring System of Indoor Environment Using Machine Learning Model and IoT Device.\u0026quot; \u003cu\u003eROCZNIK OCHRONA SRODOWISKA\u003c/u\u003e \u003cstrong\u003e27\u003c/strong\u003e: 152-163.\u003c/li\u003e\n\u003cli\u003eTechnologies, C. (2024). Importance of activated carbon filter in an air purifier.\u003c/li\u003e\n\u003cli\u003eWHO. (2024). \u0026quot;Air quality, energy and health.\u0026quot; Retrieved 10/3, 2024, from https://www.who.int/teams/environment-climate-change-and-health/\u003cbr\u003eair-quality-energy-and-health/sectoral-interventions/household-air-pollution/health-risks#:~:text=Nearly%203.2%20million%20people%20die,caus\u003cbr\u003eed%20by%20household%20air%20pollution\u0026amp;text=Household%20air\u003cbr\u003e%20pollution%20is%20caused,in%20and%20around%20the%20household.\u003c/li\u003e\n\u003cli\u003eZhang, Y.-L. and F. Cao (2015). \u0026quot;Is it time to tackle PM2. 5 air pollutions in China from biomass-burning emissions?\u0026quot; \u003cu\u003eEnvironmental Pollution\u003c/u\u003e \u003cstrong\u003e202\u003c/strong\u003e: 217-219.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1 and 3","content":"\u003cp\u003eTable 1 and 3 are available in the Supplementary Files section.\u003c/p\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"clean-technologies-and-environmental-policy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ctep","sideBox":"Learn more about [Clean Technologies and Environmental Policy](https://www.springer.com/journal/10098)","snPcode":"10098","submissionUrl":"https://submission.nature.com/new-submission/10098/3","title":"Clean Technologies and Environmental Policy","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Botanical Indoor Air Biofilter, Particulate Matter, Volatile Organic Compound, Internet of Things, Indoor Air Quality","lastPublishedDoi":"10.21203/rs.3.rs-6110064/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6110064/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIndoor air quality is crucial for human health and well-being, directly influencing respiratory function and overall comfort. Poor indoor air quality can lead to various health issues, including respiratory problems, allergies, and the exacerbation of pre-existing conditions, emphasizing the importance of maintaining a healthy indoor environment. This study aims to examine the synergistic impact of filter integration and botanical solution in enhancing air quality. A botanical indoor air biofilter (BIAB) rig that utilises the low-cost Internet of Things approach was developed. The air quality parameters are particulate matter 2.5 (PM2.5) and particulate matter 10 (PM10), volatile organic compounds (VOCs), carbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e), air temperature and relative humidity (RH). The smart sensors operated on RS485 Modbus protocol was integrated into the BIAB to monitor the real-time fluctuations of air quality parameters. A total of 5 combinations of parametric studies are tested, ranging from the usage of botanical plants, carbon filters, coconut husk, and granular activated carbon (GAC). These combinations were designed to assess the impact of different filtration configurations on the overall effectiveness of the system in reducing air pollutants. Results show that Case 3 (integrating botanical plants and a primary carbon filter) has the highest average reduction rate on PM2.5 with 5.36 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e per minute and VOCs with 4.13 \u0026micro;g/m\u0026sup3; per minute, respectively. However, Case 5 (integrating additional GAC) contributes to the highest reduction of PM10 concentration, with an average reduction rate of 5.23 ppm per minute.\u003c/p\u003e","manuscriptTitle":"Optimizing Indoor Air Quality: Evaluating the Synergistic Impact of Filter Integration and Botanical Solutions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-07 06:33:45","doi":"10.21203/rs.3.rs-6110064/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accepted","date":"2025-05-14T14:17:52+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-11T13:30:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-29T21:07:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"182809903680629363984691322870883219766","date":"2025-04-27T15:43:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"309446764198911581613741730198559283028","date":"2025-04-27T15:15:38+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-27T14:22:11+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-26T09:30:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"Clean Technologies and Environmental Policy","date":"2025-04-25T13:37:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"clean-technologies-and-environmental-policy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ctep","sideBox":"Learn more about [Clean Technologies and Environmental Policy](https://www.springer.com/journal/10098)","snPcode":"10098","submissionUrl":"https://submission.nature.com/new-submission/10098/3","title":"Clean Technologies and Environmental Policy","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"30052012-93df-4d7e-9a9d-eed8edd660df","owner":[],"postedDate":"May 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-09T16:03:19+00:00","versionOfRecord":{"articleIdentity":"rs-6110064","link":"https://doi.org/10.1007/s10098-025-03221-w","journal":{"identity":"clean-technologies-and-environmental-policy","isVorOnly":false,"title":"Clean Technologies and Environmental Policy"},"publishedOn":"2025-06-08 15:57:40","publishedOnDateReadable":"June 8th, 2025"},"versionCreatedAt":"2025-05-07 06:33:45","video":"","vorDoi":"10.1007/s10098-025-03221-w","vorDoiUrl":"https://doi.org/10.1007/s10098-025-03221-w","workflowStages":[]},"version":"v1","identity":"rs-6110064","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6110064","identity":"rs-6110064","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}