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. 2024 Apr 25;24(9):2729.
doi: 10.3390/s24092729.

Development of a Unified IoT Platform for Assessing Meteorological and Air Quality Data in a Tropical Environment

David Kairuz-Cabrera  1 , Victor Hernandez-Rodriguez  1 , Olivier Schalm  2 , Alain Martinez  1 , Pedro Merino Laso  3   4 , Daniellys Alejo-Sánchez  1
Affiliations

Development of a Unified IoT Platform for Assessing Meteorological and Air Quality Data in a Tropical Environment

David Kairuz-Cabrera et al. Sensors (Basel). .

Abstract

In developing nations, outdated technologies and sulfur-rich heavy fossil fuel usage are major contributors to air pollution, affecting urban air quality and public health.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.

Keywords: Grafana; Node-RED; TTGO TBeam; air quality; internet of things; low-cost sensors; monitoring system; weather station.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Overview of the hardware architecture of the IoT monitoring system.
Figure 2
Figure 2
Flowchart used to collect, process, send, and receive measurements for (a) the HZS-GARP-AQ-04 and (b) the TBeam Receiver.
Figure 3
Figure 3
Software architecture in the Raspberry Pi used to collect and visualize data.
Figure 4
Figure 4
Node-RED flow for data acquisition and process.
Figure 5
Figure 5
The sensor system and the reference system by Libelium deployed on the roof of the building of the Faculty of Electrical Engineering, Santa Clara, Cuba. (a) Front view showing the Libelium system attached at the bottom of the grid. (b) The setup seen in frog perspective showing the IoT sensor system underneath the large solar panel.
Figure 6
Figure 6
Grafana dashboard for the visualization of output variables of the unified IoT platform.
Figure 7
Figure 7
Temperature and relative humidity collected by the remote sensor system and the reference system by Libelium, complemented by meteorological information from the Bresser station.
Figure 8
Figure 8
Dynamic patterns of pollutants observed on 5 and 6 March 2024, just before and after rainfall, following the activation of the unified IoT platform and the Libelium system at the same location.
Figure 9
Figure 9
Relation between the measurements of HZS-GARP-AQ-04 and Libelium Smart Environment Pro for gaseous pollutants.
Figure 10
Figure 10
Relation between the measurements of HZS-GARP-AQ-04 and Libelium Smart Environment Pro for Temperature, RH, and particulate matter.
See this image and copyright information in PMC

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