Data enrichment and calibration for PM 2.5 low-cost optical sensors

Particulate matter (PM) in air has been proven to be hazardous to human health. Until recently, monitoring of air quality has been done by professional agencies. Nowadays, the availability of portable, low cost microsensor devices and the exponential growth of IoT (Internet of Things) in everyday life has enabled widespread monitoring of air quality among all citizens.[1]. For PM measurements, optical sensors measure light scattering by particles carried in an air stream through a light beam, which is converted by computation to equivalent mass concentration. Light scattering is strongly affected by parameters such as particle density, particle hygroscopicity, refraction index, and particle composition [2]. In this study, we measured PM 2.5 by seven AQ MESH low-cost optical sensors and compared the measured data with the ones obtained from the reference monitoring station (SEPA). Could we, by a sequence of low-processing data enrichment and a simple calibration method, reach an accuracy as close as a calibration based on machine learning? To answer this question, we used low-processing data enrichment such as resampling, encoding periodic timerelated features and making a composition of the initial low-cost signal at different time scales. We compared two algorithms for the calibration: multivariate linear regression and random forest. The results gave promising results and encouraged us in researching further about signal low-processing to achieve the required quality of data from low-cost sensor devices monitoring air quality [3].XVI Photonics Workshop : Book of abstracts; March 12-15, 2023; Kopaonik, Serbi

Low-processing data enrichment and calibration for PM2.5 low-cost sensors

Particulate matter (PM) in air has been proven to be hazardous to human health. Here we focused on analysis of PM data we obtained from the same campaign which was presented in our previous study. Multivariate linear and random forest models were used for the calibration and analysis. In our linear regression model the inputs were PM, temperature and humidity measured with low-cost sensors, and the target was the reference PM measurements obtained from SEPA in the same timeframe

A surrogate-assisted measurement correction method for accurate and low-cost monitoring of particulate matter pollutants

Air pollution involves multiple health and economic challenges. Its accurate and low-cost monitoring is important for developing services dedicated to reduce the exposure of living beings to the pollution. Particulate matter (PM) measurement sensors belong to the key components that support operation of these systems. In this work, a modular, mobile Internet of Things sensor for PM measurements has been proposed. Due to a limited accuracy of the PM detector, the measurement data are refined using a two-stage procedure that involves elimination of the non-physical signal spikes followed by a non-linear correction of the responses using a multiplicative surrogate model. The correction layer is derived from the sparse and non-uniform calibration data, i.e., a combination of the measurements from the PM monitoring station and the sensor obtained in the same location over a specified (relatively short) interval. The device and the method have been both demonstrated based on the data obtained during three measurement campaigns. The proposed correction scheme improves the fidelity of PM measurements by around two orders of magnitude w.r.t. the responses for which the post-processing has not been considered. Performance of the proposed surrogate-assisted technique has been favorably compared against the benchmark approaches from the literature

Error Prediction of Air Quality at Monitoring Stations Using Random Forest in a Total Error Framework

Instead of a flag valid/non-valid usually proposed in the quality control (QC) processes of air quality (AQ), we proposed a method that predicts the p-value of each observation as a value between 0 and 1. We based our error predictions on three approaches: the one proposed by the Working Group on Guidance for the Demonstration of Equivalence (European Commission (2010)), the one proposed by Wager (Journal of Machine Learning Research, 15, 1625–1651 (2014)) and the one proposed by Lu (Journal of Machine Learning Research, 22, 1–41 (2021)). Total Error framework enables to differentiate the different errors: input, output, structural modeling and remnant. We thus theoretically described a one-site AQ prediction based on a multi-site network using Random Forest for regression in a Total Error framework. We demonstrated the methodology with a dataset of hourly nitrogen dioxide measured by a network of monitoring stations located in Oslo, Norway and implemented the error predictions for the three approaches. The results indicate that a simple one-site AQ prediction based on a multi-site network using Random Forest for regression provides moderate metrics for fixed stations. According to the diagnostic based on predictive qq-plot and among the three approaches used in this study, the approach proposed by Lu provides better error predictions. Furthermore, ensuring a high precision of the error prediction requires efforts on getting accurate input, output and prediction model and limiting our lack of knowledge about the “true” AQ phenomena. We put effort in quantifying each type of error involved in the error prediction to assess the error prediction model and further improving it in terms of performance and precision

Error Prediction of Air Quality at Monitoring Stations Using Random Forest in a Total Error Framework

Instead of a flag valid/non-valid usually proposed in the quality control (QC) processes of air quality (AQ), we proposed a method that predicts the p-value of each observation as a value between 0 and 1. We based our error predictions on three approaches: the one proposed by the Working Group on Guidance for the Demonstration of Equivalence (European Commission (2010)), the one proposed by Wager (Journal of MachineLearningResearch, 15, 1625–1651 (2014)) and the one proposed by Lu (Journal of MachineLearningResearch, 22, 1–41 (2021)). Total Error framework enables to differentiate the different errors: input, output, structural modeling and remnant. We thus theoretically described a one-site AQ prediction based on a multi-site network using Random Forest for regression in a Total Error framework. We demonstrated the methodology with a dataset of hourly nitrogen dioxide measured by a network of monitoring stations located in Oslo, Norway and implemented the error predictions for the three approaches. The results indicate that a simple one-site AQ prediction based on a multi-site network using Random Forest for regression provides moderate metrics for fixed stations. According to the diagnostic based on predictive qq-plot and among the three approaches used in this study, the approach proposed by Lu provides better error predictions. Furthermore, ensuring a high precision of the error prediction requires efforts on getting accurate input, output and prediction model and limiting our lack of knowledge about the “true” AQ phenomena. We put effort in quantifying each type of error involved in the error prediction to assess the error prediction model and further improving it in terms of performance and precision

Accurate Lightweight Calibration Methods for Mobile Low-Cost Particulate Matter Sensors

Monitoring air pollution is a critical step towards improving public health, particularly when it comes to identifying the primary air pollutants that can have an impact on human health. Among these pollutants, particulate matter (PM) with a diameter of up to 2.5 μm (or PM2.5) is of particular concern, making it important to continuously and accurately monitor pollution related to PM. The emergence of mobile low-cost PM sensors has made it possible to monitor PM levels continuously in a greater number of locations. However, the accuracy of mobile low-cost PM sensors is often questionable as it depends on geographical factors such as local atmospheric conditions. This paper presents new calibration methods for mobile low-cost PM sensors that can correct inaccurate measurements from the sensors in real-time. Our new methods leverage Neural Architecture Search (NAS) to improve the accuracy and efficiency of calibration models for mobile low-cost PM sensors. The experimental evaluation shows that the new methods reduce accuracy error by more than 26% compared with the state-of-the-art methods. Moreover, the new methods are lightweight, taking less than 2.5 ms to correct each PM measurement on Intel Neural Compute Stick 2, an AI-accelerator for edge devices deployed in air pollution monitoring platforms

Insertion of Functional Proteins into Bilayer Lipid Membrane using a Cell-Free Expression System

International audienceThe incorporation of well-conformed membrane proteins within lipid bilayer is an important challenge because these proteins play a major role in every living cell and are key factors in cell-cell interaction, signal transduction and transport of ions and nutrients. To insert an integral membrane protein in a lipid bilayer it is important to separate the lipid bilayer from the supporting solid substrate in order to minimize interactions of the protein with the substrate and to provide adequate space for the protein incorporation.We chose to produce two kind of lipid bilayer membrane, a suspended membrane in which alpha hemolysin nanopores were produced and incorporated and a tethered Bilayer Lipid Membrane (tBLM), spaced from the surface by a tethering molecule as a polyethylene glycol (PEG), for the incorporation of a transmembrane protein like Aquaporin Z.The two membrane proteins were produced directly on the top of the lipid bilayers using a cell-free expression system, without any purification. This alternative technique is not affected by cell physiology and allows producing membrane proteins in a correct conformation without toxicity limitation, protein aggregation or misfolding. To demonstrate that these proteins produced with this cell free expression system are inserted and functional in a lipid bilayer, we used Quartz Crystal Microbalance with Dissipation monitoring (QCM-D), Atomic Force Microscopy (AFM), Surface Plasmon Resonance (SPR) and ion current recording experimentations

A surrogate-assisted measurement correction method for accurate and low-cost monitoring of particulate matter pollutants

Air pollution involves multiple health and economic challenges. Its accurate and low-cost monitoring is important for developing services dedicated to reduce the exposure of living beings to the pollution. Particulate matter (PM) measurement sensors belong to the key components that support operation of these systems. In this work, a modular, mobile Internet of Things sensor for PM measurements has been proposed. Due to a limited accuracy of the PM detector, the measurement data are refined using a two-stage procedure that involves elimination of the non-physical signal spikes followed by a non-linear correction of the responses using a multiplicative surrogate model. The correction layer is derived from the sparse and non-uniform calibration data, i.e., a combination of the measurements from the PM monitoring station and the sensor obtained in the same location over a specified (relatively short) interval. The device and the method have been both demonstrated based on the data obtained during three measurement campaigns. The proposed correction scheme improves the fidelity of PM measurements by around two orders of magnitude w.r.t. the responses for which the post-processing has not been considered. Performance of the proposed surrogate-assisted technique has been favorably compared against the benchmark approaches from the literature