How Smart PID Control Can Improve Indoor Air Quality for Healthier Living

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Indoor air quality has become an increasingly important engineering and public health concern as modern lifestyles require people to spend most of their time inside homes, offices, and other enclosed environments. Fine particulate matter, particularly PM2.5 and PM10, has been consistently associated with respiratory illnesses, cardiovascular diseases, and reduced quality of life, especially among vulnerable populations such as individuals with asthma. Although numerous commercial air purifiers are available, many operate reactively by activating only after pollutant concentrations exceed predefined thresholds. This delay may unnecessarily expose occupants to unhealthy air. The reviewed study explores how integrating Proportional-Integral-Derivative (PID) control into a home air quality monitoring and purification system enables predictive operation that responds before indoor air conditions become unsafe. The research provides valuable insights into the design of intelligent indoor environmental control systems that combine sensing, automation, and engineering control principles to improve residential air quality.

Bibliographic Information

Item Information
Article Title Design and development of a PID-controlled home air quality monitoring and purification system
Authors Josamae Salas and Allain Jessel Macas
Journal Innovation in Engineering
Volume & Issue Volume 2, Issue 2
Publication Year 2025
Pages 106–113
DOI https://doi.org/10.58712/ie.v2i2.38
Publisher Researcher and Lecturer Society
License Creative Commons Attribution 4.0 International (CC BY 4.0)

1. Research Background

  • Indoor air pollution remains a significant public health issue. Fine airborne particles such as PM2.5 and PM10 are among the most common environmental pollutants that negatively affect respiratory health. Individuals with asthma are particularly vulnerable because even short-term exposure to elevated particulate concentrations can trigger symptoms and reduce overall well-being.
  • Asthma continues to affect millions of people. The article highlights asthma as an important health challenge in the Philippines, where respiratory diseases associated with poor air quality impose both medical and socioeconomic burdens. Improving indoor environments therefore represents an important preventive strategy rather than relying solely on medical treatment.
  • Indoor environments deserve greater engineering attention. Since people spend a substantial proportion of their daily lives indoors, maintaining acceptable indoor air quality is essential for creating healthier residential spaces. Smart environmental control systems provide opportunities to continuously monitor pollutant concentrations and automatically initiate corrective actions.
  • Existing monitoring systems often provide passive observation only. Many previously developed indoor air quality systems focus primarily on measuring pollutant concentrations and displaying sensor readings. While these systems improve awareness, they generally lack adaptive control mechanisms capable of responding intelligently to changing environmental conditions.
  • Conventional air purifiers usually operate reactively. Traditional purification systems commonly activate only after pollutant concentrations exceed predefined thresholds. This threshold-based strategy may allow occupants to remain exposed to unhealthy air before purification begins, reducing the effectiveness of indoor environmental protection.
  • Predictive control offers a promising engineering solution. Incorporating a Proportional-Integral-Derivative (PID) controller enables the purification system to analyse real-time sensor information continuously and anticipate deteriorating air quality. Instead of waiting until pollutant levels become unsafe, the controller can initiate purification earlier to minimize exposure duration.
  • The study combines monitoring and intelligent control into a single system. Rather than treating sensing and purification as separate functions, the proposed prototype integrates air quality sensors, microcontroller-based processing, filtration components, and PID control into one coordinated platform capable of continuous monitoring and proactive intervention.
  • The research addresses an important engineering gap. The novelty of the study lies not in developing another air purifier, but in integrating predictive control with real-time indoor air quality monitoring to create a more responsive home purification system that better supports vulnerable individuals living with respiratory conditions.
  • The prototype demonstrates practical engineering implementation. The complete system combines commercially available sensors, Arduino-based embedded control, HEPA filtration, activated carbon filtration, and PID algorithms into an operational prototype tested under controlled indoor conditions, illustrating how automation technologies can improve residential environmental quality.

2. Research Objectives

  • To design and develop a home-based indoor air quality monitoring system capable of continuously measuring Air Quality Index (AQI), PM2.5, and PM10 concentrations in real time.
  • To integrate an air purification mechanism consisting of HEPA filtration, activated carbon filtration, and automated fan operation for reducing airborne particulate pollutants.
  • To implement a Proportional-Integral-Derivative (PID) controller that enables predictive air purification by activating the system before pollutant concentrations exceed predefined safety thresholds.
  • To evaluate the performance of the PID-controlled system by comparing its response with a conventional threshold-based purification strategy under simulated indoor pollution conditions.
  • To investigate whether intelligent predictive control improves indoor air quality stability and reduces the duration of exposure to unhealthy particulate concentrations.

3. Why This Research Matters

  • Supports healthier indoor living. The proposed system demonstrates how intelligent environmental monitoring can help maintain safer indoor air conditions, particularly for individuals who are highly sensitive to airborne particulate matter.
  • Advances smart home engineering. By integrating sensors, embedded systems, and automatic control algorithms, the research illustrates how engineering principles can be applied to develop responsive residential environmental management technologies.
  • Enhances preventive healthcare. Reducing exposure to PM2.5 and PM10 before pollutant levels become hazardous may decrease respiratory irritation and improve daily living conditions for asthma patients and other vulnerable groups.
  • Demonstrates practical application of PID control. The study extends the application of classical control engineering beyond industrial automation by showing its effectiveness in consumer-oriented environmental control systems.
  • Promotes intelligent environmental monitoring. Continuous sensing combined with predictive decision-making represents an important step toward more adaptive indoor environmental management technologies capable of responding dynamically to changing conditions.
  • Contributes to sustainable residential technology. Early intervention through predictive purification can improve operational efficiency by maintaining air quality more consistently rather than relying on repeated reactive correction after pollutant concentrations become excessive.
  • Provides a foundation for future smart-building applications. Although developed for residential use, the proposed architecture demonstrates concepts that could support future intelligent air quality management systems in schools, healthcare facilities, offices, and other occupied buildings.

4. Research Methodology

  • Research Type

    The study adopted a system prototyping approach to design, implement, and evaluate an intelligent home air quality monitoring and purification system. The research focused on developing a functional engineering prototype capable of continuously monitoring indoor air quality while automatically regulating purification using a Proportional-Integral-Derivative (PID) controller. Experimental evaluation was conducted to examine the effectiveness of the proposed control strategy under controlled indoor conditions.

  • System Development Approach

    The prototype integrated sensing, embedded control, and air purification into a single platform. The system combined air quality sensors, an Arduino Uno microcontroller, a PID control algorithm, a direct-current (DC) fan, an activated carbon filter, and a High-Efficiency Particulate Air (HEPA) filter. These components worked together to monitor pollutant concentrations continuously and regulate purification automatically according to measured air quality conditions.

  • Experimental Environment

    Experimental testing was conducted inside a controlled bedroom located in Libona, Bukidnon, Philippines. The room measured approximately 3.0 m × 2.2 m, providing a realistic residential environment for evaluating prototype performance. The purifier was positioned beside the bed to simulate practical household operation while maintaining consistent testing conditions throughout the experiments.

  • Pollutant Simulation

    To evaluate system performance under deteriorating indoor air conditions, small quantities of burning paper were used to generate particulate matter. This approach created controlled increases in PM2.5 and PM10 concentrations, allowing the researchers to examine both pollutant detection and purification performance under repeatable experimental conditions.

  • Air Quality Parameters

    The monitoring system continuously measured three primary environmental indicators: Air Quality Index (AQI), PM2.5 concentration, and PM10 concentration. These parameters were selected because they represent widely accepted indicators of indoor air quality and are directly associated with respiratory health risks.

  • Sensing Hardware

    Air Quality Index measurements were obtained using the MQ135 gas sensor, while PM2.5 and PM10 concentrations were measured using the PMS5003 optical dust sensor. Sensor outputs were transmitted to the Arduino Uno microcontroller for continuous processing, visualization, and automatic control.

  • Control Strategy

    A Proportional-Integral-Derivative (PID) controller served as the intelligent decision-making component of the system. Instead of waiting until pollutant concentrations exceeded predefined limits, the controller continuously evaluated historical and real-time sensor data to predict increasing pollution levels and activate the purification process proactively.

    For comparison purposes, the researchers also evaluated a conventional threshold-based control strategy in which purification began only after pollutant concentrations exceeded predefined safety limits. This comparison enabled direct assessment of the advantages provided by predictive PID control.

  • Purification Mechanism

    The purification process employed a dual-stage filtration system. Incoming air first passed through an activated carbon sponge that adsorbed gaseous pollutants and odors. The airflow was subsequently directed through a HEPA filter capable of capturing extremely small airborne particles, including fine dust and allergens, before clean air was returned to the indoor environment by a DC fan.

  • Threshold Values

    Control decisions were based on internationally recognized Air Quality Index guidelines. The system used threshold values of AQI = 100, PM2.5 = 35 µg/m³, and PM10 = 155 µg/m³ to determine when purification should begin. These reference values were programmed into the embedded controller to enable automatic decision-making.

  • Prototype Architecture

    The complete prototype integrated sensing hardware, embedded processing, filtration components, visual monitoring through an OLED display, and automatic control into a single compact system. The architecture enabled simultaneous monitoring, decision-making, purification, and user feedback during operation.

  • Performance Evaluation

    Performance evaluation focused on comparing pollutant reduction between PID-controlled operation and conventional threshold-based control. The researchers observed changes in AQI, PM2.5, and PM10 before pollution generation, during pollutant exposure, and throughout the recovery period to determine system responsiveness and purification effectiveness.

  • Data Analysis

    Experimental data were analysed descriptively by examining variations in pollutant concentrations over time. Graphical comparisons between PID-controlled and non-PID operation allowed the researchers to evaluate stability, response behaviour, exposure duration, and recovery performance under identical experimental conditions.

5. Key Findings

Real-Time Monitoring Successfully Detected Indoor Air Quality Changes

The developed prototype successfully monitored Air Quality Index (AQI), PM2.5, and PM10 concentrations continuously using the integrated MQ135 and PMS5003 sensors. Real-time measurements were displayed through the onboard OLED interface, enabling users to observe indoor air quality conditions immediately without requiring external monitoring equipment.

Sensor calibration based on internationally recognized air quality guidelines enabled the system to distinguish between acceptable and unsafe environmental conditions. As pollutant concentrations increased during the experiments, the monitoring subsystem consistently detected these changes and provided the information required for automated purification decisions.

The Dual-Filter Purification System Effectively Reduced Airborne Pollutants

Experimental testing demonstrated that combining an activated carbon sponge with a HEPA filter successfully reduced particulate pollution generated during the simulation. The activated carbon layer removed gaseous contaminants and unpleasant odors, while the HEPA filter captured fine airborne particles before purified air was returned to the room.

Following activation of the purification system, AQI, PM2.5, and PM10 values consistently decreased below their predefined threshold levels. These observations indicate that integrating complementary filtration technologies can improve indoor air purification performance by addressing different categories of airborne contaminants simultaneously.

PID Control Enabled Predictive Rather Than Reactive Purification

The most significant contribution of the study was the incorporation of the PID controller into the purification system. Unlike conventional operation that waits until pollutant concentrations exceed safety thresholds, the PID algorithm continuously analysed sensor information and predicted deteriorating air quality before unsafe conditions fully developed.

Because purification was initiated earlier, the system shortened the period during which indoor air remained above recommended pollutant limits. This proactive behaviour illustrates how classical control engineering principles can improve environmental management by anticipating system changes instead of responding only after undesirable conditions occur.

PID-Controlled Operation Produced More Stable Indoor Air Quality

Both purification strategies ultimately reduced pollutant concentrations following smoke generation; however, the PID-controlled configuration maintained more stable environmental conditions throughout the recovery process. Air quality measurements gradually returned to their baseline values with fewer fluctuations than the conventional threshold-based approach.

The experimental graphs presented in the article clearly show that predictive control minimized oscillations in AQI, PM2.5, and PM10 during purification. Maintaining greater stability is particularly beneficial for sensitive individuals because repeated fluctuations in pollutant concentration may contribute to prolonged exposure.

Predictive Control Reduced Exposure to Unhealthy Air

Baseline indoor conditions were recorded at approximately AQI 79–81, PM2.5 levels of 6–8 µg/m³, and PM10 levels of 7–9 µg/m³. Following smoke generation, pollutant concentrations increased rapidly and exceeded recommended thresholds, creating an unhealthy indoor environment suitable for evaluating system performance.

The PID-controlled system reduced the duration of exposure above these thresholds by activating purification before pollutant concentrations reached their maximum values. Consequently, occupants would theoretically spend less time breathing contaminated air compared with conventional threshold-based control, highlighting the practical health benefits of predictive automation.

The Prototype Demonstrated Practical Feasibility for Residential Applications

The study demonstrates that intelligent indoor environmental management can be implemented using commercially available hardware components and relatively simple embedded control technologies. Arduino-based processing, widely available air quality sensors, standard filtration materials, and PID control were successfully combined into an operational prototype without requiring highly specialized infrastructure.

Beyond demonstrating technical feasibility, the prototype illustrates how embedded systems, environmental sensing, and automatic control can be integrated into practical residential technologies. The authors further suggest that similar concepts may be extended to schools, healthcare facilities, office buildings, and other indoor environments where maintaining healthy air quality is equally important.

6. Scientific Contribution

  • Introduces an intelligent home air quality management framework by integrating real-time environmental monitoring, embedded control, and automated air purification into a single engineering system capable of operating continuously within residential environments.
  • Demonstrates the practical application of PID control for indoor environmental management. Rather than limiting the controller to conventional industrial process control, the study shows how predictive PID algorithms can improve the performance of household air purification systems.
  • Advances smart environmental monitoring technology through the integration of continuous AQI, PM2.5, and PM10 sensing with automatic decision-making, enabling the purifier to respond intelligently to changing indoor conditions.
  • Validates the effectiveness of combining multiple engineering components, including air quality sensors, Arduino-based embedded systems, HEPA filtration, activated carbon filtration, and PID algorithms within a functional engineering prototype.
  • Provides experimental evidence supporting predictive purification. The comparative evaluation between PID-controlled and conventional threshold-based operation demonstrates that predictive control can shorten exposure to elevated particulate concentrations while maintaining more stable indoor air quality.
  • Bridges embedded systems, environmental engineering, and control engineering. The research illustrates how interdisciplinary engineering approaches can be applied to address indoor environmental challenges through automation and intelligent control technologies.
  • Establishes a foundation for future smart indoor environmental systems. The proposed architecture offers a practical reference for future developments involving intelligent buildings, smart homes, and automated environmental management technologies.

7. Industrial Implications

  • Supports next-generation smart home products. The proposed design demonstrates how intelligent control algorithms can enhance the performance of consumer air purifiers beyond conventional threshold-based operation.
  • Improves residential indoor environmental quality. Integrating predictive purification into home environments can help maintain healthier indoor conditions by reducing occupants' exposure to airborne particulate matter.
  • Enhances embedded system applications. The study illustrates practical implementation of microcontroller-based automation using readily available sensors and embedded hardware suitable for low-cost engineering solutions.
  • Creates opportunities for smart building technologies. The engineering principles demonstrated in the prototype may be adapted for larger intelligent buildings, offices, educational institutions, hospitals, and other enclosed environments requiring continuous air quality management.
  • Supports preventive environmental control. Predictive activation enables engineering systems to intervene before indoor air quality deteriorates substantially, improving overall environmental stability compared with purely reactive operation.
  • Promotes intelligent environmental monitoring. Continuous acquisition and interpretation of air quality data provide a foundation for future Internet of Things (IoT)-enabled environmental management systems capable of autonomous operation.
  • Contributes to digital engineering practice. The integration of sensing technologies, embedded computing, automation, and control engineering reflects current trends toward intelligent cyber-physical systems for residential and commercial applications.
  • Offers potential benefits for healthcare-related environments. Although designed primarily for residential use, similar systems may support indoor air management in healthcare facilities and other locations occupied by individuals with respiratory sensitivities.

8. Research Limitations

  • The prototype was evaluated within a single controlled bedroom environment. Additional testing in larger spaces and under more diverse environmental conditions would provide a broader understanding of system performance.
  • Indoor pollution was generated using a controlled paper-burning procedure to simulate particulate contamination. Other common indoor pollution sources, such as cooking emissions, tobacco smoke, or household chemicals, were not investigated in this study.
  • The performance evaluation focused primarily on particulate matter represented by AQI, PM2.5, and PM10 measurements. Other indoor air pollutants were outside the scope of the current investigation.
  • The prototype employed an Arduino Uno microcontroller and the selected sensor configuration. Alternative embedded platforms or higher-performance controllers were not examined within this research.
  • The study compared predictive PID control with conventional threshold-based activation only. Other intelligent control techniques or optimization algorithms were not included in the experimental comparison.
  • System evaluation emphasized engineering functionality and operational behaviour rather than long-term durability, maintenance requirements, or lifecycle performance during prolonged residential use.
  • The research focused on demonstrating prototype feasibility rather than commercialization, manufacturing optimization, or large-scale deployment considerations.

9. Future Research Opportunities

  • Evaluate the proposed system in larger residential spaces, commercial buildings, hospitals, classrooms, and public facilities to examine its performance under different occupancy conditions.
  • Investigate the integration of Internet of Things (IoT) technologies to enable remote monitoring, cloud-based data storage, and mobile application control for real-time indoor air quality management.
  • Compare PID control with other intelligent control techniques, including fuzzy logic, adaptive control, artificial intelligence, machine learning, and model predictive control, to determine the most effective strategy for indoor environmental regulation.
  • Expand the sensing capability by incorporating additional environmental parameters such as carbon dioxide, volatile organic compounds (VOCs), temperature, humidity, and carbon monoxide to provide a more comprehensive assessment of indoor air quality.
  • Investigate long-term operational performance, filter degradation, maintenance requirements, and energy consumption under continuous residential operation.
  • Develop smart-home integration that enables communication with other intelligent household devices, including ventilation systems, smart windows, and building automation platforms.
  • Explore the use of higher-performance embedded processors and edge-computing technologies to improve data processing speed, control precision, and system scalability.
  • Evaluate user acceptance, usability, and human-centered design aspects to better understand how occupants interact with intelligent air quality management systems in daily life.
  • Assess the economic feasibility of large-scale implementation by analysing manufacturing costs, operational expenses, maintenance requirements, and potential healthcare savings associated with improved indoor air quality.
  • Investigate the applicability of the proposed engineering framework for specialized environments requiring strict environmental control, including laboratories, pharmaceutical facilities, clean rooms, and healthcare institutions.

10. Potential for Public Policy Citation (Overton)

This study demonstrates moderate potential for future citation in public policy documents because it addresses an increasingly important issue at the intersection of environmental engineering, public health, and smart residential technology. Rather than proposing a national air quality policy or regulatory framework, the research presents an engineering solution that supports healthier indoor environments through intelligent monitoring and predictive purification. Such technological evidence can inform policymakers seeking practical approaches to reduce indoor exposure to particulate pollution.

The findings may provide useful technical references for government agencies responsible for environmental health, housing development, smart-city initiatives, and indoor environmental quality standards. In particular, the demonstrated integration of continuous monitoring, embedded control, and automated purification aligns with growing interest in intelligent building technologies that promote healthier living environments while supporting sustainable urban development.

Potential policy relevance includes guidance documents related to indoor environmental quality, healthy housing programs, healthcare facility management, school environmental standards, and smart-building strategies. However, because the prototype was evaluated only in a controlled residential setting and represents an engineering proof-of-concept, additional large-scale validation would strengthen its suitability for direct incorporation into technical standards or nationwide policy recommendations.

11. Who Should Read This Paper?

  • Researchers working in indoor environmental engineering.
  • Control systems and automation engineers.
  • Embedded systems researchers.
  • Electronics engineering students.
  • Mechanical and environmental engineers.
  • Graduate students studying smart building technologies.
  • Developers of IoT-based environmental monitoring systems.
  • Manufacturers of residential air purification equipment.
  • Healthcare professionals interested in environmental health technologies.
  • Smart home technology developers.
  • Building services engineers.
  • Government agencies involved in indoor environmental quality initiatives.
  • Educators teaching embedded systems, automation, environmental monitoring, and control engineering.

12. Final Thoughts

This research presents a practical example of how classical control engineering can be applied to improve indoor environmental quality through intelligent automation. By integrating real-time air quality sensing, embedded processing, dual-stage air filtration, and a Proportional-Integral-Derivative (PID) controller into a single prototype, the study demonstrates a systematic approach to maintaining healthier indoor environments. Rather than relying solely on reactive purification after pollutant concentrations exceed safety thresholds, the proposed system introduces predictive control capable of responding earlier to changing air quality conditions.

One of the principal strengths of the study is its successful integration of several established engineering technologies into a functional prototype that was experimentally evaluated under controlled conditions. The comparison between PID-controlled operation and conventional threshold-based control provides convincing evidence that predictive automation can improve system stability while reducing the duration of exposure to elevated particulate concentrations. Although the investigation was limited to a residential prototype, the engineering concepts presented are sufficiently general to inspire future developments in intelligent environmental management systems.

Overall, this article makes a valuable contribution to the fields of embedded systems, environmental monitoring, and smart residential technologies. Its emphasis on practical implementation, intelligent control, and user-oriented design demonstrates how interdisciplinary engineering can address real-world environmental challenges. As smart homes, Internet of Things (IoT) technologies, and intelligent building systems continue to evolve, the concepts explored in this research are likely to remain relevant for future innovations in healthier indoor living environments.

Suggested Citation

UNP–Teknomekanik Style

Salas, J., & Macas, A. J. (2025). Design and development of a PID-controlled home air quality monitoring and purification system. Innovation in Engineering, 2(2), 106–113. DOI: https://doi.org/10.58712/ie.v2i2.38

APA (7th Edition)

Salas, J., & Macas, A. J. (2025). Design and development of a PID-controlled home air quality monitoring and purification system. Innovation in Engineering, 2(2), 106–113. https://doi.org/10.58712/ie.v2i2.38

IEEE Style

J. Salas and A. J. Macas, "Design and development of a PID-controlled home air quality monitoring and purification system," Innovation in Engineering, vol. 2, no. 2, pp. 106–113, 2025, doi: 10.58712/ie.v2i2.38 .

Harvard Style

Salas, J. & Macas, A.J., 2025. Design and development of a PID-controlled home air quality monitoring and purification system. Innovation in Engineering, 2(2), pp.106–113. Available at: https://doi.org/10.58712/ie.v2i2.38 .

Vancouver Style

Salas J, Macas AJ. Design and development of a PID-controlled home air quality monitoring and purification system. Innovation in Engineering. 2025;2(2):106–113. Available from: https://doi.org/10.58712/ie.v2i2.38

Chicago (Author–Date)

Salas, Josamae, and Allain Jessel Macas. 2025. "Design and Development of a PID-Controlled Home Air Quality Monitoring and Purification System." Innovation in Engineering 2 (2): 106–113. https://doi.org/10.58712/ie.v2i2.38 .

MLA (9th Edition)

Salas, Josamae, and Allain Jessel Macas. "Design and Development of a PID-Controlled Home Air Quality Monitoring and Purification System." Innovation in Engineering, vol. 2, no. 2, 2025, pp. 106–113. https://doi.org/10.58712/ie.v2i2.38 .

Editorial Note

Editorial Note: This blog post is an independent scholarly review intended for educational and scientific communication purposes. It summarizes and discusses the published article in the author's own words while providing full attribution to the original publication, consistent with the principles of the CC BY 4.0 license.

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A comprehensive scholarly review of a PID-controlled home air quality monitoring and purification system published in Innovation in Engineering. Learn how predictive PID control improves indoor air quality, enhances smart home automation, and supports healthier residential environments.

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Discoverability

  • Primary Category: Environmental Engineering
  • Secondary Category: Control Systems Engineering
  • Technology Area: Smart Home Technologies
  • Research Domain: Embedded Systems and Automation
  • Engineering Application: Indoor Air Quality Monitoring
  • Target Audience: Researchers, engineers, graduate students, smart-home developers, healthcare technology practitioners, policymakers, and industry professionals.
  • Recommended Reading Time: 12–15 minutes.
  • Primary Search Intent: Informational and Educational.
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