PLUME Dashboard: A free and open-source mobile air quality monitoring dashboard. (February 2023)

• Record Type: Journal Article
• Authors: Kelly, Chris; Fawkes, Julian; Habermehl, Rachel; de Ferreyro Monticelli, Davi; Zimmerman, Naomi
• Is Part Of: Environmental modelling & software. Volume 160(2023)
• Publication Date: 2023-02
• Subjects: Mobile monitoring -- Air pollution -- Peak detection -- Open-source Python script -- Data interpretation -- Data visualization; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2022.105600
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: The deployment of a mobile air quality monitoring laboratory requires advanced real-time instrument monitoring and data management software, which can be prohibitively expensive. In this work we present the PLUME Dashboard: a software package built in Python designed specifically for mobile air quality monitoring purposes. It aims to provide a free and open-source alternative to comparable commercial packages, thus reducing the barrier to entry of conducting such research. This paper outlines the development of the PLUME Dashboard and justifies the design choices that were made while also providing thorough documentation and explanation for how the software works. Functionality includes real-time data display, real-time peak identification, baseline subtraction, real-time air quality and self-sampling alerts (based on wind direction and vehicle speed), and post-processing tools such as peak identification and map merging with GPS data. The functionality of PLUME Dashboard is tested using real-world data collected in Toronto and Vancouver Canada. Graphical abstract: Highlights: Open-source Python script for air pollution mobile monitoring. Integration with Modbus-TCP data loggers to stream real-time data. Real-time data processing tools: peak identification, threshold exceedance alerting. Post-processing tools: baseline detection, GPS map merging, peak identification.