Low-cost sensors and crowd-sourced data: Observations of siting impacts on a network of air-quality instruments

Low-cost sensors offer the possibility of gathering high temporal and spatial resolution crowd-sourced data-sets that have the potential to revolutionize the ways in which we understand individual and population exposure to air pollution. However, one of the challenges associated with crowd-sourced data ('citizen science'), often from low-cost sensors, is that citizens may use sites strongly affected by local conditions, limiting the wider significance of the data. This paper examines results from a low-cost network measuring ground-level ozone to evaluate the impact of siting on data quality. Locations at both reference stations and at private homes or research centers were used, and thought of as a typical 'crowd-sourced' network. Two instruments were co-located at each site to determine intra-site variability and evaluated by standard performance statistics and local-scale activity logs. The wider application of the data for both regional Inter-site variability was evaluated to show-case the wider value and usefulness of crowd-sourced data. Analysis of intra-site variability showed little differences at most sites (<5 ppb). Large differences in intra-site variability were detected when sensors were exposed to direct sunlight (causing thermal variations within the instrument) and proximity to large emission sources. Shortterm local activities, such as lawn-mowing, were identifiable in the data, but had minimal impact on standard reporting time-scales, and so did not pose as being significant limitations or errors. Inter-site evaluation demonstrated that dense networks of low-cost sensors can add value to existing networks, with minimal impact on the overall data-set quality. Sensors located in crowd-sourced locations nearby to regulatory analyzers were able to capture similar trends and concentrations, supporting their ability to report on wider conditions. Thus crowd-sourced approaches to monitoring (with suitable calibration and data quality control checks) may be an appropriate method for increasing the temporal and spatial resolution of air quality networks. (C) 2016 Elsevier B.V. All rights reserved.