Machine learning techniques to improve the field performance of low-cost air quality sensors

Abstract

Abstract. Low-cost air quality sensors offer significant potential for enhancing urban air quality networks by providing higher-spatiotemporal-resolution data needed, for example, for evaluation of air quality interventions. However, these sensors present methodological and deployment challenges which have historically limited operational ability. These include variability in performance characteristics and sensitivity to environmental conditions. In this work, we investigate field “baselining” and interference correction using random forest regression methods for low-cost sensing of NO2, PM10 (particulate matter) and PM2.5. Model performance is explored using data obtained over a 7-month period by real-world field sensor deployment alongside reference method instrumentation. Workflows and processes developed are shown to be effective in normalising variable sensor baseline offsets and reducing uncertainty in sensor response arising from environmental interferences. We demonstrate improvements of between 37 % and 94 % in the mean absolute error term of fully corrected sensor datasets; this is equivalent to performance within ±2.6 ppb of the reference method for NO2, ±4.4 µg m−3 for PM10 and ±2.7 µg m−3 for PM2.5. Expanded-uncertainty estimates for PM10 and PM2.5 correction models are shown to meet performance criteria recommended by European air quality legislation, whilst that of the NO2 correction model was found to be narrowly (∼5 %) outside of its acceptance envelope. Expanded-uncertainty estimates for corrected sensor datasets not used in model training were 29 %, 21 % and 27 % for NO2, PM10 and PM2.5 respectively.

Smart Multi-Sensor Calibration of Low-Cost Particulate Matter Monitors

“…Interestingly, the KNN method presents results close to the best despite its simplicity, although with a higher standard deviation, which can represent unwanted performance variability with small perturbation to the training and test data. These results are in line with other results reported in the literature relating appealing results of ensemble methods in calibrating air quality sensors [ 32 , 33 , 34 , 36 , 37 ], confirming the nonlinear behavior of these devices.…”

The impact of COVID-19 public health restrictions on particulate matter pollution measured by a validated low-cost sensor network in Oxford, UK

“…This sensor was chosen for a variety of reasons, firstly, the unit cost of the OPC-N3 is relatively low (∼£250) when compared to reference grade OPC instruments. Secondly, it is currently the low-cost OPC sensor with the most independent testing and validation in the scientific literature [ 31 , 45 ]. Thirdly, access to raw sensor information (voltages) available remotely enables high-fidelity, independent data analysis and data quality assurance processes to be undertaken.…”

“…Sensor data were calibrated using an extension to the techniques previously described by Bush et al [ 31 ] which proposed a simple and flexible machine learning (ML) method to attenuate for sensor baseline offset and multiple environmental interferences acting upon an OPC sensor signal. The method incorporates a combination of re-weighted regression (adaptive iteratively reweighted Penalized Least Squares - AIRPLS) [ 52 ] and a random forest (RF) regression [ 53 ] to limit, respectively, sensor offset and environmental interferences (including other sensor characteristics such as sample flow rate and raw electrode voltage readings).…”

“…This study investigates changes in PM (PM 10 and PM 2.5 ) concentrations for successive phases of COVID-19-related restrictions using high-spatial resolution data generated by a validated low-cost sensor network in Oxford, UK, The validation is achieved using a Random Forest Machine Learning technique for field calibration, achieving high levels of confidence in the data developed in the authors’ previous work [ 31 ]. Comparison of changes in PM occurring at multiple locations in this real world natural experimental study will inform prospective city-level air quality interventions and therefore present opportunities to benefit human health.…”

A negative emission internal combustion engine vehicle?

“…It is clear that there are a huge range of values that all of these parameters could take, and the author encourages interested readers to make their own choices. Typical ambient air pollution concentrations are measured of order parts per billion (ppb) (Bush et al, 2022) and typical vehicle emissions are measured of order parts per million (ppm) (Leach et al, 2020), three orders of magnitude difference. Therefore, as a starting point, two cases are considered, a typical case, and an extreme casethe extreme case designed to give the ICEV as good as possible chance at emitting less pollutant emissions than it takes in.…”