scholarly journals The Relocation Problem of Field Calibrated Low-Cost Sensor Systems in Air Quality Monitoring: A Sampling Bias

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6198
Author(s):  
Georgi Tancev ◽  
Céline Pascale
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Data Science ◽  
Professional Competence ◽  
Low Cost ◽  
Environmental Parameters ◽  
Sampling Bias ◽  
Quality Monitoring ◽  
Sensor Systems ◽  
Air Quality Monitoring

This publication revises the deteriorated performance of field calibrated low-cost sensor systems after spatial and temporal relocation, which is often reported for air quality monitoring devices that use machine learning models as part of their software to compensate for cross-sensitivities or interferences with environmental parameters. The cause of this relocation problem and its relationship to the chosen algorithm is elucidated using published experimental data in combination with techniques from data science. Thus, the origin is traced back to insufficient sampling of data that is used for calibration followed by the incorporation of bias into models. Biases often stem from non-representative data and are a common problem in machine learning, and more generally in artificial intelligence, and as such a rising concern. Finally, bias is believed to be partly reducible in this specific application by using balanced data sets generated in well-controlled laboratory experiments, although not trivial due to the need for infrastructure and professional competence.

scholarly journals Developing Relative Humidity and Temperature Corrections for Low-Cost Sensors Using Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3338
Author(s):  
Ivan Vajs ◽  
Dejan Drajic ◽  
Nenad Gligoric ◽  
Ilija Radovanovic ◽  
Ivan Popovic
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Relative Humidity ◽  
Low Cost ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Lower Accuracy ◽  
Wide Range ◽  
The Impact ◽  
Monitoring Stations

Existing government air quality monitoring networks consist of static measurement stations, which are highly reliable and accurately measure a wide range of air pollutants, but they are very large, expensive and require significant amounts of maintenance. As a promising solution, low-cost sensors are being introduced as complementary, air quality monitoring stations. These sensors are, however, not reliable due to the lower accuracy, short life cycle and corresponding calibration issues. Recent studies have shown that low-cost sensors are affected by relative humidity and temperature. In this paper, we explore methods to additionally improve the calibration algorithms with the aim to increase the measurement accuracy considering the impact of temperature and humidity on the readings, by using machine learning. A detailed comparative analysis of linear regression, artificial neural network and random forest algorithms are presented, analyzing their performance on the measurements of CO, NO2 and PM10 particles, with promising results and an achieved R2 of 0.93–0.97, 0.82–0.94 and 0.73–0.89 dependent on the observed period of the year, respectively, for each pollutant. A comprehensive analysis and recommendations on how low-cost sensors could be used as complementary monitoring stations to the reference ones, to increase spatial and temporal measurement resolution, is provided.

scholarly journals Urban Air Quality Monitoring with Networked Low-Cost Sensor-Systems

Proceedings ◽  
2017 ◽  
Vol 1 (4) ◽  
pp. 573 ◽  
Author(s):  
Michele Penza ◽  
Domenico Suriano ◽  
Valerio Pfister ◽  
Mario Prato ◽  
Gennaro Cassano
Keyword(s):  
Air Quality ◽  
Low Cost ◽  
Urban Air Quality ◽  
Quality Monitoring ◽  
Urban Air ◽  
Sensor Systems ◽  
Air Quality Monitoring

scholarly journals Relevance of Drift Components and Unit-to-Unit Variability in the Predictive Maintenance of Low-Cost Electrochemical Sensor Systems in Air Quality Monitoring

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3298
Author(s):  
Georgi Tancev
Keyword(s):  
Air Quality ◽  
Anomaly Detection ◽  
Low Cost ◽  
Predictive Maintenance ◽  
Quality Monitoring ◽  
Calibration Data ◽  
Sensor Systems ◽  
Air Quality Monitoring ◽  
Measurement Results ◽  
Unsupervised Anomaly Detection

As key components of low-cost sensor systems in air quality monitoring, electrochemical gas sensors have recently received a lot of interest but suffer from unit-to-unit variability and different drift components such as aging and concept drift, depending on the calibration approach. Magnitudes of drift can vary across sensors of the same type, and uniform recalibration intervals might lead to insufficient performance for some sensors. This publication evaluates the opportunity to perform predictive maintenance solely by the use of calibration data, thereby detecting the optimal moment for recalibration and improving recalibration intervals and measurement results. Specifically, the idea is to define confidence regions around the calibration data and to monitor the relative position of incoming sensor signals during operation. The emphasis lies on four algorithms from unsupervised anomaly detection—namely, robust covariance, local outlier factor, one-class support vector machine, and isolation forest. Moreover, the behavior of unit-to-unit variability and various drift components on the performance of the algorithms is discussed by analyzing published field experiments and by performing Monte Carlo simulations based on sensing and aging models. Although unsupervised anomaly detection on calibration data can disclose the reliability of measurement results, simulation results suggest that this does not translate to every sensor system due to unfavorable arrangements of baseline drifts paired with sensitivity drift.

scholarly journals Low-Cost Outdoor Air Quality Monitoring and Sensor Calibration

2021 ◽  
Vol 17 (2) ◽  
pp. 1-44
Author(s):  
Francesco Concas ◽  
Julien Mineraud ◽  
Eemil Lagerspetz ◽  
Samu Varjonen ◽  
Xiaoli Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Environmental Monitoring ◽  
Low Cost ◽  
Quality Monitoring ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Great Promise ◽  
Air Quality Monitoring ◽  
Monitoring Stations

The significance of air pollution and the problems associated with it are fueling deployments of air quality monitoring stations worldwide. The most common approach for air quality monitoring is to rely on environmental monitoring stations, which unfortunately are very expensive both to acquire and to maintain. Hence, environmental monitoring stations are typically sparsely deployed, resulting in limited spatial resolution for measurements. Recently, low-cost air quality sensors have emerged as an alternative that can improve the granularity of monitoring. The use of low-cost air quality sensors, however, presents several challenges: They suffer from cross-sensitivities between different ambient pollutants; they can be affected by external factors, such as traffic, weather changes, and human behavior; and their accuracy degrades over time. Periodic re-calibration can improve the accuracy of low-cost sensors, particularly with machine-learning-based calibration, which has shown great promise due to its capability to calibrate sensors in-field. In this article, we survey the rapidly growing research landscape of low-cost sensor technologies for air quality monitoring and their calibration using machine learning techniques. We also identify open research challenges and present directions for future research.

scholarly journals 15 - Stationary and Mobile Low-Cost Gas Sensor-Systems for Air Quality Monitoring Applications

2015 ◽  
Author(s):  
D. Suriano ◽  
M. Prato ◽  
V. Pfister ◽  
G. Cassano ◽  
G. Camporeale ◽  
...  
Keyword(s):  
Air Quality ◽  
Gas Sensor ◽  
Low Cost ◽  
Quality Monitoring ◽  
Sensor Systems ◽  
Air Quality Monitoring ◽  
Monitoring Applications

scholarly journals A concept of the air quality monitoring system in the city of Lublin with machine learning methods to detect data outliers

2019 ◽  
Vol 252 ◽  
pp. 03009 ◽  
Author(s):  
Tomasz Cieplak ◽  
Tomasz Rymarczyk ◽  
Robert Tomaszewski
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Monitoring System ◽  
Low Cost ◽  
Quality Monitoring ◽  
Spatiotemporal Variability ◽  
Air Quality Monitoring ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
The City

This paper presents a concept of the air quality monitoring system design and describes a selection of data quality analysis methods. A high level of industrialisation affects the risk of natural disasters related to environmental pollution such ase.g.air pollution by gases and clouds of dust (carbon monoxide, sulphur oxides, nitrogen oxides). That is why researches related to the monitoring this type of phenomena are extremely important. Low-cost air quality sensors are more commonly used to monitor air parameters in urban areas. These types of sensors are used to obtain an image of the spatiotemporal variability in the concentration of air pollutants. Aside from their low price , which is important from a point of view of the economic accessibility of society, low-cost sensors are prone to produce erroneous results compared to professional air quality monitors. The described study focuses on the analysis of outliers as particularly interesting for further analysis, as well as modelling with machine learning methods for air quality assessment in the city of Lublin.

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