Development and Field Validation of Low-Cost Metal Oxide Nanosensors for Tropospheric Ozone Monitoring in Rural Areas

Abstract

This work describes the technical features and the performance of two different types of metal-oxide semiconductor sensors, based on ZnO:Ga thin films and SnO2-G nanofibrous layers, for tropospheric ozone monitoring in ambient air. These nanostructures were tested and compared with commercial metal-oxide semiconductor sensors under controlled laboratory conditions and in a field campaign during summer 2021 in Monfragüe National Park (western Spain). The paper also details the design of the electronic device developed for this purpose. A machine learning algorithm based on Support Vector Regression (SVR) allowed the conversion of the resistive values into ozone concentration, which was evaluated afterward. The results showed that the manufactured sensors performed similarly to the commercial sensors in terms of R2 (0.94 and 0.95) and RMSE (5.21 and 4.83 μg∙m−3). Moreover, a novel uncertainty calculation based on European guides for air quality sensor testing was conducted, in which the manufactured sensors outperformed the commercial ones.

Keywords

• metal-oxide sensor
• tin oxide nanofibers
• graphene
• air pollution
• machine learning