Abstract:
Applying Automation and Visual Analytics to Air Pollutant Measurement and Research Data
Large dynamic data from a variety of sources are becoming more prevalent in research and industry with many applications requiring frequent data processing, automation, and intuitive visualizations to help facilitate communication, analysis, and proactive decision making. Having conducted research with eddy covariance using 10 Hz data, and analyzing years of continuous ambient air monitoring data, I have spent countless hours manually performing and repeating data preparation and analysis – all the while asking myself: Is there a better way? Join me on my journey from painfully slow manual analysis to fully automated data processing.
This talk will showcase some examples that have utilized Python and SQL to automate processing of continuously growing data sets, featuring tools such as Tableau and Grafana which generate dynamic visualizations that update automatically, enabling more efficient dissemination and interpretation of monitoring data.
Bio:
Dr. Jonathan Wang is a senior scientist at the Ontario Ministry of the Environment, Conservation and Parks (MECP). He completed his graduate studies at the University of Toronto. He first undertook a Masters in environmental chemistry, focused on quantifying greenhouse gas fluxes, and his PhD in chemical engineering involved investigating the impacts of traffic emissions on urban air quality. Since joining MECP, he has worked both on the ambient air quality monitoring and mobile industrial monitoring teams. Recently he has shifted towards data science and engineering, leading the ministry towards more streamlined and efficient approaches for data process automation, reporting, and dynamic visualizations.