In the summer of 2017, we designed, implemented, and tested a computer framework to collect, analyze, and visualize air quality data for concentrations of fine particulate matter (PM2.5). Our project had three phases: (1) construction and testing of an outdoor/indoor low-cost portable device based on the PM sensor (Plantower PMS5003) and Arduino technology for data collection and data storage for short-term experiments, (2) building a datawarehouse for the collection and aggregation of the PM2.5 data from multiple sources and locations within Kamloops, and (3) analysis and visualization of the PM2.5 data.
In the first phase, we tested the performance of the PMS5003 sensor in a series of experiments in two Kamloops locations (low-traffic TRU campus and high-traffic intersection). We designed several experiments to test the responsiveness of the PMS5003 sensor to PM2.5 pollution from vehicles. The collected data show some differences in PM2.5 levels at the intersection with and without heavy traffic and with the varied background pollution (before and during the wildfires in BC). However, we have observed that the data are influenced by several environmental variables, such as wind, temperature, and relative humidity. In the second phase, we obtained several data sets for a multi-sensor analysis of the PM2.5 data from two sources in Kamloops: the two government-run stations and the 22 citizen-run PurpleAir units (based on PMS5003 sensors). We collected data from May 1 until August 23, 2017, stored the data in a database, and prepared the data for further analysis (marking missing data and errors). In the third phase, we created several programs for data aggregation and visualization. We used the database from Phase 2 and produced a series of graphs to compare the PM2.5 concentrations measured by multiple sensors located in various locations within the Kamloops area.
Our study was exploratory and had several limitations: the PM concentrations vary greatly according to location and environmental factors; the sensors use different approaches for data collection, the data are reported at multiple averaging times; and numerous data are missing from the citizen-run devices. The results provided in this report represent a first step towards an integrated framework for PM2.5 data analysis from environmental monitoring instruments (government stations) and from a network of low cost sensors used by the citizen scientists.
