Using A Low-Cost Sensor Array and Machine Learning Techniques to Detect Complex Pollutant Mixtures and Identify Likely Sources

An array of low-cost sensors was assembled and tested in a chamber environment wherein several pollutant mixtures were generated. The four classes of sources that were simulated were mobile emissions, biomass burning, natural gas emissions, and gasoline vapors. A two-step regression and classification method was developed and applied to the sensor data from this array. We first applied regression models to estimate the concentrations of several compounds and then classification models trained to use those estimates to identify the presence of each of those sources. The regression models that were used included forms of multiple linear regression, random forests, Gaussian process regression, and neural networks. The regression models with human-interpretable outputs were investigated to understand the utility of each sensor signal. The classification models that were trained included logistic regression, random forests, support vector machines, and neural networks. The best combination of models was determined by maximizing the F1 score on ten-fold cross-validation data. The highest F1 score, as calculated on testing data, was 0.72 and was produced by the combination of a multiple linear regression model utilizing the full array of sensors and a random forest classification model.</div

Understanding Our Local Environment: Developing Novel Approaches To Quantify and Apportion Ambient VOCs With Low-Cost Sensors

In this dissertation, we demonstrate the application of low-cost air quality sensors to better understanding our local environment. Specifically, my work has focused on the application of arrays of low-cost sensors and methods of analysis that improve our ability to attribute local sources of volatile organic compounds (VOCs). Low-cost sensors have been widely applied to the study of air quality at smaller spatial and temporal scales than was previously feasible. The research that is detailed in Chapter 2 built upon existing low-cost sensor research in order to develop an approach to both quantifying the concentrations of several compounds and also classifying the mixture based on the source that is likely to have emitted the detected gases. This research involved a chamber study where a large sensor array was exposed to complex gas mixtures that simulated realistic pollution sources. These data were used to validate the proposed methodology that involved a two-step process to accomplish the quantification and classification goals. The results of this approach show the feasibility of using low-cost sensors to directly estimate the effect of local sources of VOCs based on their chemical fingerprints.</p

Multidimensional analysis using sensor arrays with deep learning for high-precision and high-accuracy diagnosis

In the upcoming years, artificial intelligence (AI) is going to transform the practice of medicine in most of its specialties. Deep learning can help achieve better and earlier problem detection, while reducing errors on diagnosis. By feeding a deep neural network (DNN) with the data from a low-cost and low-accuracy sensor array, we demonstrate that it becomes possible to significantly improve the measurements' precision and accuracy. The data collection is done with an array composed of 32 temperature sensors, including 16 analog and 16 digital sensors. All sensors have accuracies between 0.5-2.0$^\circ$C. 800 vectors are extracted, covering a range from to 30 to 45$^\circ$C. In order to improve the temperature readings, we use machine learning to perform a linear regression analysis through a DNN. In an attempt to minimize the model's complexity in order to eventually run inferences locally, the network with the best results involves only three layers using the hyperbolic tangent activation function and the Adam Stochastic Gradient Descent (SGD) optimizer. The model is trained with a randomly-selected dataset using 640 vectors (80% of the data) and tested with 160 vectors (20%). Using the mean squared error as a loss function between the data and the model's prediction, we achieve a loss of only 1.47x10$^{-4}$ on the training set and 1.22x10$^{-4}$ on the test set. As such, we believe this appealing approach offers a new pathway towards significantly better datasets using readily-available ultra low-cost sensors.Comment: Corrected typ

Environmental engineering applications of electronic nose systems based on MOX gas sensors

Nowadays, the electronic nose (e-nose) has gained a huge amount of attention due to its ability to detect and differentiate mixtures of various gases and odors using a limited number of sensors. Its applications in the environmental fields include analysis of the parameters for environmental control, process control, and confirming the efficiency of the odor-control systems. The e-nose has been developed by mimicking the olfactory system of mammals. This paper investigates e-noses and their sensors for the detection of environmental contaminants. Among different types of gas chemical sensors, metal oxide semiconductor sensors (MOXs) can be used for the detection of volatile compounds in air at ppm and sub-ppm levels. In this regard, the advantages and disadvantages of MOX sensors and the solutions to solve the problems arising upon these sensors’ applications are addressed, and the research works in the field of environmental contamination monitoring are overviewed. These studies have revealed the suitability of e-noses for most of the reported applications, especially when the tools were specifically developed for that application, e.g., in the facilities of water and wastewater management systems. As a general rule, the literature review discusses the aspects related to various applications as well as the development of effective solutions. However, the main limitation in the expansion of the use of e-noses as an environmental monitoring tool is their complexity and lack of specific standards, which can be corrected through appropriate data processing methods applications

Understanding the ability of low-cost MOx sensors to quantify ambient VOCs

Volatile organic compounds (VOCs) present a unique challenge in air quality research given their importance to human and environmental health, and their complexity to monitor resulting from the number of possible sources and mixtures. New technologies, such as low-cost air quality sensors, have the potential to support existing air quality measurement methods by providing data in high time and spatial resolution. These higher-resolution data could provide greater insight into specific events, sources, and local variability. Furthermore, given the potential for differences in selectivities for sensors, leveraging multiple sensors in an array format may even be able to provide insight into which VOCs or types of VOCs are present. During the FRAPPE and DISCOVER-AQ monitoring campaigns, our team was able to co-locate two sensor systems, using metal oxide (MOx) VOC sensors, with a proton-transfer-reaction quadrupole mass spectrometer (PTR-QMS) providing speciated VOC data. This dataset provided the opportunity to explore the ability of sensors to estimate specific VOCs and groups of VOCs in real-world conditions, e.g., dynamic temperature and humidity. Moreover, we were able to explore the impact of changing VOC compositions on sensor performance as well as the difference in selectivities of sensors in order to consider how this could be utilized. From this analysis, it seems that systems using multiple VOC sensors are able to provide VOC estimates at ambient levels for specific VOCs or groups of VOCs. It also seems that this performance is fairly robust in changing VOC mixtures, and it was confirmed that there are consistent and useful differences in selectivities between the two MOx sensors studied. While this study was fairly limited in scope, the results suggest that there is the potential for low-cost VOC sensors to support highly resolved ambient hydrocarbon measurements. The availability of this technology could enhance research and monitoring for public health and communities impacted by air toxics, which in turn could support a better understanding of exposure and actions to reduce harmful exposure.</p

Low-power techniques for wireless gas sensing network applications: pulsed light excitation with data extraction strategies

Aquesta tesi està enfocada en dues línies d'investigació. La primera aborda el desenvolupament d'una metodologia basada en llum polsada per modulació de sensors químic-resistius per a l'extracció d'informació del senyal transitòri, i la segona planteja la implementació d'una xarxa sense fils de sensors (WSN) basada en tecnologia LoRa per al monitoratge de la qualitat de l'aire (AQM) i la detecció d'esdeveniments de fuita de gasos. Aquest document està estructurat en quatre capítols organitzats de la següent manera: el Capítol 1 presenta l'estat de l'art, una introducció als mecanismes de millora de l'comportament dels sensors químic-resistius, així com una introducció a la implementació de xarxes sense fils de sensors per a la monitorització de la qualitat de l'aire; el Capítol 2 està compost pels dos articles publicats relacionats amb la metodologia basada en la modulació utilitzant llum polsada per a l'extracció d'informació del senyal transitòria de sensors químic-resistius; el Capítol 3 presenta l'article publicat relacionat amb la implementació d'una WSN per a AQM; el Capítol 4 presenta les conclusions derivades dels resultats obtinguts durant el desenvolupament de el projecte de tesi i les recomanacions per al treball futur associat a la continuïtat dels principals resultats d'aquesta tesiLa presente tesis está enfocada en dos líneas de investigación, La primera aborda el desarrollo de una metodología basada en luz pulsada para modulación de sensores químico-resistivos para la extracción de información de la señal transitoria; y la segunda plantea la implementación de una red inalámbrica de sensores (WSN) basada en tecnología LoRa para la monitorización de la calidad del aire (AQM) y la detección de eventos de fuga de gases. Este documento está estructurado en cuatro capítulos organizados de la siguiente forma: el Capítulo 1 presenta el estado del arte, una introducción a los mecanismos de mejora del comportamiento de los sensores químico-resistivos, así como una introducción a la implementación de redes inalámbricas de sensores para la monitorización de la calidad del aire; el Capítulo 2 está compuesto por los dos artículos publicados relacionados con la metodología basada en la modulación utilizando luz pulsada para la extracción de información de la señal transitoria de sensores químico-resistivos; el Capítulo 3 presenta el artículo publicado relacionado con la implementación de una WSN para AQM; el Capítulo 4 presenta las conclusiones derivadas de los resultados obtenidos durante el desarrollo de el proyecto de tesis y las recomendaciones para el trabajo futuro asociado a la continuidad de los principales resultados de esta tesis.The present thesis project is focused in two different yet related research lines. The first one addresses the development of a pulsed light-based chemiresistive sensor modulation methodology for transient information extraction. The second research line developed deals with the implementation of a LoRa-based portable, scalable, low-cost, and low power Wireless Sensor Network (WSN) for Air Quality Monitoring (AQM) and gas leakage events detection. This document is structured in four Chapters organized as follows: Chapter 1 presents the state of the art, an introduction to sensing performance enhancement and transient data extraction methods, as well as an introduction to the implementation of WSN for AQM; Chapter 2 is composed of the two published paper related to the pulsed light modulation methodology for transient information extraction; Chapter 3 presents the published paper related to the implementation of a LoRa-based WSN for AQM; Chapter 4 states the conclusions derived from the results obtained during this thesis project and the recommendations for the future work associated to the continuity of this thesis findings

A Gas Sensor for the Selective Detection of Volatile Organic Compounds

Volatile Organic Compounds (VOCs) are an important class of air pollutant because many of them are harmful to human health. VOCs are typically present at very low concentrations – parts-per-billion (ppb) and lower – which makes their detection a significant challenge. Standard measurement techniques, such as Gas Chromatography (GC), are typically sensitive and selective, but are limited by their large size, high cost and complexity to operate. Such factors restrict deployment for practical applications, including measurement across air quality networks. In contrast, gas sensors are typically small, inexpensive and easily deployed, but are limited by poor selectivity. This work aims to establish the extent to which gas sensors can be used to achieve sensitive and selective detection of VOCs. Based on the processes of adsorption and desorption, it examines how temperature control of functionalised silica adsorbents can be used to produce discriminable signals of VOCs. In this context, the VOCs of interest were benzene, toluene, ethyl benzene, para-xylene (BTEX), due to their proven toxicity and prevalence in human environments. With the aim of producing a potentially deployable device, a novel sensing platform was designed and constructed. The main features of this Adsorption Device were an aluminium channel, a peltier module heating unit, flow path control and photoionisation detector (PID). In addition to unmodified silica, this thesis presents six modified silica adsorbents with amino, chloro, (n8) alkyl, fluoroalkyl, phenyl and chlorophenyl functionality. Analysis of BTEX vapours with the seven silica adsorbents indicated adsorption was physical (physisorption) and desorption was readily reversible between 25 and 100 °C. Adsorption was influenced by the strength of adsorbent-vapour interaction, which could be increased by introducing delocalised electron density (phenyl and chlorophenyl silica), but modification could not compensate for any significant loss of surface area and pore volume that occurred. PID responses during adsorption and desorption were found to be discriminable from each other. Vapour desorption was examined with different heating profiles, which were found to initiate distinct response patterns. Principle Component Analysis (PCA) of Adsorption Device data indicated that the responses were sufficiently discriminable that they could be offer a means of vapour selectivity. Tests of the Adsorption Device indicate that selective detection of individual and dual component BTEX vapours is achievable in the ppb concentration range and with a cycle time of 10 minutes. Classification algorithms based on the Adsorption Device output were found to be at least as accurate as previously published research. This work presents significant progress towards to the development of a selective and practical sensor for air quality applications

Understanding the ability of low-cost MOx sensors to quantify ambient VOCs

Volatile organic compounds (VOCs) present a unique challenge in air quality research given their importance to human and environmental health, and their complexity to monitor resulting from the number of possible sources and mixtures. New technologies, such as low-cost air quality sensors, have the potential to support existing air quality measurement methods by providing data in high time and spatial resolution. These higher-resolution data could provide greater insight into specific events, sources, and local variability. Furthermore, given the potential for differences in selectivities for sensors, leveraging multiple sensors in an array format may even be able to provide insight into which VOCs or types of VOCs are present. During the FRAPPE and DISCOVER-AQ monitoring campaigns, our team was able to co-locate two sensor systems, using metal oxide (MOx) VOC sensors, with a proton-transfer-reaction quadrupole mass spectrometer (PTR-QMS) providing speciated VOC data. This dataset provided the opportunity to explore the ability of sensors to estimate specific VOCs and groups of VOCs in real-world conditions, e.g., dynamic temperature and humidity. Moreover, we were able to explore the impact of changing VOC compositions on sensor performance as well as the difference in selectivities of sensors in order to consider how this could be utilized. From this analysis, it seems that systems using multiple VOC sensors are able to provide VOC estimates at ambient levels for specific VOCs or groups of VOCs. It also seems that this performance is fairly robust in changing VOC mixtures, and it was confirmed that there are consistent and useful differences in selectivities between the two MOx sensors studied. While this study was fairly limited in scope, the results suggest that there is the potential for low-cost VOC sensors to support highly resolved ambient hydrocarbon measurements. The availability of this technology could enhance research and monitoring for public health and communities impacted by air toxics, which in turn could support a better understanding of exposure and actions to reduce harmful exposure.</p

Real-Time Water Quality Monitoring with Chemical Sensors

Water quality is one of the most critical indicators of environmental pollution and it affects all of us. Water contamination can be accidental or intentional and the consequences are drastic unless the appropriate measures are adopted on the spot. This review provides a critical assessment of the applicability of various technologies for real-time water quality monitoring, focusing on those that have been reportedly tested in real-life scenarios. Specifically, the performance of sensors based on molecularly imprinted polymers is evaluated in detail, also giving insights into their principle of operation, stability in real on-site applications and mass production options. Such characteristics as sensing range and limit of detection are given for the most promising systems, that were verified outside of laboratory conditions. Then, novel trends of using microwave spectroscopy and chemical materials integration for achieving a higher sensitivity to and selectivity of pollutants in water are described

An Approximation for Metal-Oxide Sensor Calibration for Air Quality Monitoring Using Multivariable Statistical Analysis

Good air quality is essential for both human beings and the environment in general. The three most harmful air pollutants are nitrogen dioxide (NO2), ozone (O-3) and particulate matter. Due to the high cost of monitoring stations, few examples of this type of infrastructure exist, and the use of low-cost sensors could help in air quality monitoring. The cost of metal-oxide sensors (MOS) is usually below EUR 10 and they maintain small dimensions, but their use in air quality monitoring is only valid through an exhaustive calibration process and subsequent precision analysis. We present an on-field calibration technique, based on the least squares method, to fit regression models for low-cost MOS sensors, one that has two main advantages: it can be easily applied by non-expert operators, and it can be used even with only a small amount of calibration data. In addition, the proposed method is adaptive, and the calibration can be refined as more data becomes available. We apply and evaluate the technique with a real dataset from a particular area in the south of Spain (Granada city). The evaluation results show that, despite the simplicity of the technique and the low quantity of data, the accuracy obtained with the low-cost MOS sensors is high enough to be used for air quality monitoring.The researchers would like to thank the University of Cadiz for the grant obtained through its "Programa de Fomento e Impulso de la actividad de Investigacion y Transferencia". The authors would also like to thank to the Environmental Technology researching group and Acoustic Engineering Laboratory researching group, TEP-181 and TEP-195, respectively, for the access to the devices and data of the EcoBici Project (number G-GI3002/IDIC). Alfonso J. Bello acknowledges the support received from the 2014-2020 ERDF Operational Program and by the Department of Economy, Knowledge, and Business and the University of the Regional Government of Andalusia, Spain, under grant: FEDER-UCA18-107519