Development of a Real-Tıme IoT-Based Portable Partıculate Matter Monıtorıng Devıce Usıng PMS5003 Sensor

Lina Warlina^1,*, Sri Listyarini¹, Mohamad Afendee Mohamed², Wan Suryani Wan Awang²,

Roslan Umar³, Aceng Sambas^2,4,5

¹Faculty of Science and Technology, Universitas Terbuka, Tangerang Selatan 15437, Indonesia

²Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin, Campus Besut, 22200 Terengganu, Malaysia

³East Coast Environmental Research Institute (ESERI), Universiti Sultan Zainal Abidin, Gong Badak 21300, Malaysia

⁴Department of Mechanical Engineering, Universitas Muhammadiyah Tasikmalaya, Tamansari Gobras 46196 Tasikmalaya, Indonesia

⁵Artificial Intelligence for Sutainability and Islamic Research Center (AISIR), Universiti Sultan Zainal Abidin, Gongbadak, Terengganu 21300, Malaysia

Emails: warlina@ecampus.ut.ac.id; listyarini@ecampus.ut.ac.id; mafedee@unisza.edu.my; suryani@unisza.edu.my; roslan@unisza.edu.my; acengsambas@unisza.edu.my

Abstract

Particulate Matter (PM) concentration significantly affects public health, exacerbating respiratory conditions and contributing to environmental challenges. This study presents a real-time Internet of Things (IoT)-based portable particulate matter monitoring device utilizing the PMS5003 sensor. The device measures PM1.0, PM2.5, and PM10 concentrations and uploads the data to the cloud at 15-second intervals for real-time visualization. A two-week observational study in South Tangerang, Indonesia, revealed peak PM2.5 and PM10 levels of 218 µg/m³ and 232 µg/m³, respectively, on weekdays, compared to a weekend low of 19.76 µg/m³ for PM2.5. Variations were influenced by anthropogenic factors, including vehicular and industrial activity. Data analysis showed a 78% reduction in PM2.5 levels during weekends, highlighting the impact of human activity on air quality. These findings underscore the impact of anthropogenic activities on air quality and demonstrate the effectiveness of IoT-based systems in environmental monitoring. The study highlights the potential for such technology to support data-driven strategies for pollution management and public health improvement.

Keywords: Particulate Matter; Internet of Things; Air Quality Monitoring; PMS5003