Provably Outlier-resistant Semi-parametric Regression for Transferable Calibration of Low-cost Air-quality Sensors

TL;DR

This paper introduces RESPIRE, a robust semi-parametric calibration method for low-cost air-quality sensors that improves transferability across sites, seasons, and sensors, reducing calibration costs and enhancing reliability.

Contribution

The paper proposes RESPIRE, a novel outlier-resistant semi-parametric calibration technique for low-cost air-quality sensors, with provable robustness and explainability, applicable across diverse deployment conditions.

Findings

RESPIRE outperforms baseline calibration methods in cross-site and cross-season scenarios.

The method effectively detects and flags model overfitting instances.

Empirical results demonstrate improved prediction accuracy across multiple deployment settings.

Abstract

We present a case study for the calibration of Low-cost air-quality (LCAQ) CO sensors from one of the largest multi-site-multi-season-multi-sensor-multi-pollutant mobile air-quality monitoring network deployments in India. LCAQ sensors have been shown to play a critical role in the establishment of dense, expansive air-quality monitoring networks and combating elevated pollution levels. The calibration of LCAQ sensors against regulatory-grade monitors is an expensive, laborious and time-consuming process, especially when a large number of sensors are to be deployed in a geographically diverse layout. In this work, we present the RESPIRE technique to calibrate LCAQ sensors to detect ambient CO (Carbon Monoxide) levels. RESPIRE offers specific advantages over baseline calibration methods popular in literature, such as improved prediction in cross-site, cross-season, and cross-sensor settings. RESPIRE offers a training algorithm that is provably resistant to outliers and an explainable model with the ability to flag instances of model overfitting. Empirical results are presented based on data collected during an extensive deployment spanning four sites, two seasons and six sensor packages. RESPIRE code is available at https://github.com/purushottamkar/respire.