Automated data scanning for dense networks of low-cost air quality instruments: Detection and differentiation of instrumental error and local to regional scale environmental abnormalities

Maryam Alavi-Shoshtari, Jennifer Ann Salmond, Ciprian Doru Giurcaneanu, Georgia Miskell, Lena Weissert, David Edward Williams

Abstract: Highlights•The algorithm output indicates instrumental errors, local and regional variations.•It makes minimal assumptions about data availability and reliability.•It is not restricted to certain data types or network characteristics.•Linear multi-regression models and change-point detection techniques are used.•It is successfully tested on hourly-averaged ozone data of two different networks.