AIRAAIRA

Journal of Information Systems and Technology ResearchJournal of Information Systems and Technology Research

Air pollution continues to be a critical environmental issue that negatively impacts human health, ecosystems, and urban sustainability. Therefore, reliable air quality monitoring systems are urgently required to provide real-time and accurate information for both communities and decision-makers. This study presents the design and implementation of an Internet of Things (IoT)-based air quality monitoring system that integrates environmental sensors with an ESP32 microcontroller. A key novelty of this research is the adoption of a dual-cloud architecture, combining ThingSpeak and Blynk, to enhance data accessibility, visualization, and system reliability compared to conventional single-cloud approaches. The Fuzzy Mamdani method is applied to classify air quality levels into three categories: Good, Moderate, and Poor, using input variables of temperature, humidity, and gas concentration. Methodologically, the system was tested under multiple environmental conditions, and fuzzy membership functions and rules were carefully designed to reflect realistic thresholds. The results show that the dual-cloud system enables more robust and flexible monitoring, with faster data synchronization and higher reliability in remote visualization. Quantitatively, the system achieved a 92% expert validation score and demonstrated a 15% improvement in responsiveness compared to previous single-cloud implementations reported in the literature. The discussion highlights that dual-cloud visualization provides an effective solution to overcome downtime risks and single-point failures, while also improving user experience in accessing real-time air quality information. This research contributes to the growing body of work on IoT-based environmental monitoring and can serve as a foundation for future smart city applications.

The study successfully designed and implemented a dual-cloud IoT-based air quality monitoring system utilizing the ESP32 microcontroller, DHT22, and MQ-135 sensors.The integration of ThingSpeak and Blynk platforms enabled real-time dual visualization, while the Fuzzy Mamdani method effectively classified air quality into Good, Moderate, and Poor categories.The findings confirm the practicality and robustness of the proposed system for real-world application.

Future research should explore expanding the system with additional sensors for particulate matter (PM2.5 and PM10) to provide a more comprehensive air quality assessment. Furthermore, integrating machine learning models could enable predictive analysis of air quality trends, allowing for proactive interventions and public health advisories. Finally, investigating the use of blockchain technology for secure data storage and transmission would enhance the systems reliability and trustworthiness, particularly in sensitive environmental monitoring applications. These enhancements would build upon the current systems strengths, offering a more robust and insightful solution for addressing the growing challenges of air pollution in urban environments and beyond, ultimately contributing to the development of smarter, healthier, and more sustainable cities. The integration of these technologies will not only improve the accuracy and reliability of the monitoring system but also empower communities and policymakers with the data needed to make informed decisions about air quality management and public health protection.

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