Kairuz-Cabrera David, Hernandez-Rodriguez Victor, Schalm Olivier, Martinez Alain, Laso Pedro Merino, Alejo-Sánchez Daniellys
Faculty of Electrical Engineering, Central University Marta Abreu of Las Villas (UCLV), Santa Clara 54830, Cuba.
Antwerp Maritime Academy (AMA), 2030 Antwerp, Belgium.
Sensors (Basel). 2024 Apr 25;24(9):2729. doi: 10.3390/s24092729.
In developing nations, outdated technologies and sulfur-rich heavy fossil fuel usage are major contributors to air pollution, affecting urban air quality and public health. In addition, the limited resources hinder the adoption of advanced monitoring systems crucial for informed public health policies. This study addresses this challenge by introducing an affordable internet of things (IoT) monitoring system capable of tracking atmospheric pollutants and meteorological parameters. The IoT platform combines a Bresser 5-in-1 weather station with a previously developed air quality monitoring device equipped with Alphasense gas sensors. Utilizing MQTT, Node-RED, InfluxDB, and Grafana, a Raspberry Pi collects, processes, and visualizes the data it receives from the measuring device by LoRa. To validate system performance, a 15-day field campaign was conducted in Santa Clara, Cuba, using a Libelium Smart Environment Pro as a reference. The system, with a development cost several times lower than Libelium and measuring a greater number of variables, provided reliable data to address air quality issues and support health-related decision making, overcoming resource and budget constraints. The results showed that the IoT architecture has the capacity to process measurements in tropical conditions. The meteorological data provide deeper insights into events of poorer air quality.
在发展中国家，过时的技术和高硫重质化石燃料的使用是空气污染的主要原因，影响着城市空气质量和公众健康。此外，资源有限阻碍了采用对制定明智的公共卫生政策至关重要的先进监测系统。本研究通过引入一种能够跟踪大气污染物和气象参数的经济适用型物联网（IoT）监测系统来应对这一挑战。该物联网平台将一台Bresser五合一气象站与一个先前开发的配备Alphasense气体传感器的空气质量监测设备相结合。树莓派利用MQTT、Node-RED、InfluxDB和Grafana，收集、处理并可视化通过LoRa从测量设备接收到的数据。为了验证系统性能，在古巴圣克拉拉进行了为期15天的实地测试，将一款Libelium智能环境专业版设备作为参考。该系统的开发成本比Libelium低几倍，且能测量更多变量，它提供了可靠的数据来解决空气质量问题并支持与健康相关的决策制定，克服了资源和预算限制。结果表明，该物联网架构有能力在热带条件下处理测量数据。气象数据为空气质量较差的事件提供了更深入的见解。
