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Wireless Sensor Network for Air Quality Monitoring and Control

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (31 October 2019) | Viewed by 46809

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Special Issue Editors

Prof. Dr. Jesus Lozano

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Guest Editor

Industrial Engineering School, University of Extremadura, 06006 Badajoz, Spain
Interests: gas sensors; patter recognition; machine olfaction; electronic nose; intelligent control
Special Issues, Collections and Topics in MDPI journals

Dr. Saverio De Vito

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TERIN-FSD Division, ENEA CR-Portici, P. le E. Fermi 1, 80055 Portici, Italy
Interests: artificial olfaction & vision; smart cyber physical systems & IoT; intelligent sensing; machine learning with application to environmental (air quality) monitoring; energy production; aerospace industry; water management cycle; digital signal processing
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Dr. José Pedro Santos

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Nanosensors and Intelligent System group (NoySI), Instituto de Tecnologías Físicas y de la Información ITEFI-CSIC, 28006 Madrid, Spain
Interests: chemical sensors; nanotechnology; graphene; sensor networks; air quality; electronic noses
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Air pollution is one of the most serious problems in the world. It refers to the contamination of the atmosphere by harmful chemicals or biological materials. There is a need to implement air quality management plans to ensure compliance with the pollution limits established by governments and institutions, so to improve air quality and reduce the severe health impacts causing millions of deaths worldwide. The task of air quality monitoring (AQM) are performed in most cases by reference stations in urban areas, which are costly, bulky and of complex operation and hence not suited for applications where ubiquity and low consumption are required. In addition, spatial and temporal resolution measurements of the order of one meter and one minute respectively are required to determine the actual exposure of each individual to pollution. Exposomics is currently a hot topic, and enhancing our knowledge of it will positively impact on the efficacy and efficiency of our public health systems.

In the field of odors and air quality, some citizens with simple and readily available equipment are increasingly engaged in collecting and processing heterogeneous data, which have traditionally been collected by authorized sources. The development of smart measuring devices with high accuracy, small size, low cost and high granularity can complement and/or in some cases replace official networks in their attempt to measure ambient air quality, but with a greater number of measuring points. In this sense, wireless sensor networks (WSN) play a fundamental role in this approach. The integration of low-cost detection capabilities, machine learning and wireless networking provides the core component of the WSN concept, which foresees a large number of autonomous sensors, known as "specks", working together to monitor different parameters. In its latest manifestation, the integration of WSNs into the emerging IoT and fog computing realm would move to the "Internet scale", with intelligent sensors from different WSNs collaborating to provide new services over networks that are in turn linked over large areas using the common Internet communications infrastructure.

In addition, novel gas sensors with improved features in terms of their ability to sense and sensitivity to pollutant gases, and other features, such as size and consumption, are required for their use in WSNs. In this sense, new materials and nanostructures are postulated as an important direction to explore. On the other hand, signal and data processing are essential elements in gas sensor-based detection systems and networks for the identification (classification) of chemical compounds and the estimation (regression) of the concentration and/or clustering of similar compounds (clustering). Methods should be developed to enhance drift compensation, changes in the measurement environment or sampling conditions, sensor switching or calibration between devices and compensation of the response due to moisture or other interferences.

The aim of this Special Issue is to contribute to the state-of-the-art and present current applications of wireless sensor networks for AQM. This Special Issue welcomes new research results from academia and industry. The Special Issue topics include, but are not limited to:

Architectures of gas sensor networks
Devices for citizen measurements
Electronics for sensor motes
Smart sensors
Novel gas sensors
Sensor data fusion
Artificial intelligence and deep learning for data processing
Prediction and classification from sensor data
Applications of sensor networks for air quality monitoring

Dr. Jesús Lozano
Dr. Saverio De Vito
Dr. José Pedro Santos
Guest Editor

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Keywords

Internet of Things
Air quality monitoring
Wireless sensor networks
Portable gas and particle detectors
Communications systems
Gas sensors
Data and signal processing

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Published Papers (7 papers)

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Research

24 pages, 4984 KiB

Open AccessArticle

Correlation between Indoor Environmental Data and Biometric Parameters for the Impact Assessment of a Living Wall in a ZEB Lab

by Francesco Salamone, Benedetta Barozzi, Ludovico Danza, Matteo Ghellere and Italo Meroni

Sensors 2020, 20(9), 2523; https://doi.org/10.3390/s20092523 - 29 Apr 2020

Cited by 8 | Viewed by 3682

Abstract

Users’ satisfaction in indoor spaces plays a key role in building design. In recent years, scientific research has focused more and more on the effects produced by the presence of greenery solutions in indoor environments. In this study, the Internet of Things (IoT) concept is used to define an effective solution to monitor indoor environmental parameters, along with the biometric data of users involved in an experimental campaign conducted in a Zero Energy Building laboratory where a living wall has been installed. The growing interest in the key theory of the IoT allows for the development of promising frameworks used to create datasets usually managed with Machine Learning (ML) approaches. Following this tendency, the dataset derived by the proposed infield research has been managed with different ML algorithms in order to identify the most suitable model and influential variables, among the environmental and biometric ones, that can be used to identify the plant configuration. The obtained results highlight how the eXtreme Gradient Boosting (XGBoost)-based model can obtain the best average accuracy score to predict the plant configuration considering both a selection of environmental parameters and biometric data as input values. Moreover, the XGBoost model has been used to identify the users with the highest accuracy considering a combination of picked biometric and environmental features. Finally, a new Green View Factor index has been introduced to characterize how greenery has an impact on the indoor space and it can be used to compare different studies where green elements have been used. Full article

(This article belongs to the Special Issue Wireless Sensor Network for Air Quality Monitoring and Control)

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19 pages, 7050 KiB

Open AccessArticle

A Secure and Portable Multi-Sensor Module for Distributed Air Pollution Monitoring

by Gyorgy Kolumban-Antal, Vladko Lasak, Razvan Bogdan and Bogdan Groza

Sensors 2020, 20(2), 403; https://doi.org/10.3390/s20020403 - 10 Jan 2020

Cited by 24 | Viewed by 4829

Abstract

Air quality in urban environments has become a central issue of our present society as it affects the health and lives of the population all over the world. The first step in mitigating negative effects is proper measurement of the pollution level. This work presents a portable air pollution measurement system, built from off-the-shelf devices, that is designed to assure user privacy and data authenticity. Data is collected from sensor modules that can be hand carried or installed on vehicles, possibly leading to a vehicular sensor network that may cover a larger area. The main challenge is to provide authenticity for the sensor data while also ensuring user privacy. The proposed system assures authenticity and non-repudiation for the collected data by using group signatures and a blockchain-like structure for secure storage. We use regular key-exchange protocols based on elliptic curve cryptography in order to securely bootstrap a session key, then we benefit from secure tunneling to export data from sensors to the remote server. Post-update tampering is prevented by the use of a blockchain-like structure on the data server. We carry experiments both to determine the computational requirements of the procedures, as well as to measure indicators of air quality on nearby areas. Full article

(This article belongs to the Special Issue Wireless Sensor Network for Air Quality Monitoring and Control)

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19 pages, 3663 KiB

Open AccessArticle

Development of a Network of Accurate Ozone Sensing Nodes for Parallel Monitoring in a Site Relocation Study

by Brandon Feenstra, Vasileios Papapostolou, Berj Der Boghossian, David Cocker and Andrea Polidori

Sensors 2020, 20(1), 16; https://doi.org/10.3390/s20010016 - 18 Dec 2019

Cited by 5 | Viewed by 4473

Abstract

Recent technological advances in both air sensing technology and Internet of Things (IoT) connectivity have enabled the development and deployment of remote monitoring networks of air quality sensors. The compact size and low power requirements of both sensors and IoT data loggers allow for the development of remote sensing nodes with power and connectivity versatility. With these technological advancements, sensor networks can be developed and deployed for various ambient air monitoring applications. This paper describes the development and deployment of a monitoring network of accurate ozone (O₃) sensor nodes to provide parallel monitoring in an air monitoring site relocation study. The reference O₃ analyzer at the station along with a network of three O₃ sensing nodes was used to evaluate the spatial and temporal variability of O₃ across four Southern California communities in the San Bernardino Mountains which are currently represented by a single reference station in Crestline, CA. The motivation for developing and deploying the sensor network in the region was that the single reference station potentially needed to be relocated due to uncertainty that the lease agreement would be renewed. With the implication of siting a new reference station that is also a high O₃ site, the project required the development of an accurate and precise sensing node for establishing a parallel monitoring network at potential relocation sites. The deployment methodology included a pre-deployment co-location calibration to the reference analyzer at the air monitoring station with post-deployment co-location results indicating a mean absolute error (MAE) < 2 ppb for 1-h mean O₃ concentrations. Ordinary least squares regression statistics between reference and sensor nodes during post-deployment co-location testing indicate that the nodes are accurate and highly correlated to reference instrumentation with R² values > 0.98, slope offsets < 0.02, and intercept offsets < 0.6 for hourly O₃ concentrations with a mean concentration value of 39.7 ± 16.5 ppb and a maximum 1-h value of 94 ppb. Spatial variability for diurnal O₃ trends was found between locations within 5 km of each other with spatial variability between sites more pronounced during nighttime hours. The parallel monitoring was successful in providing the data to develop a relocation strategy with only one relocation site providing a 95% confidence that concentrations would be higher there than at the current site. Full article

(This article belongs to the Special Issue Wireless Sensor Network for Air Quality Monitoring and Control)

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16 pages, 6791 KiB

Open AccessArticle

Development of an AI Model to Measure Traffic Air Pollution from Multisensor and Weather Data

by Hai-Bang Ly, Lu Minh Le, Luong Van Phi, Viet-Hung Phan, Van Quan Tran, Binh Thai Pham, Tien-Thinh Le and Sybil Derrible

Sensors 2019, 19(22), 4941; https://doi.org/10.3390/s19224941 - 13 Nov 2019

Cited by 79 | Viewed by 6816

Abstract

Gas multisensor devices offer an effective approach to monitor air pollution, which has become a pandemic in many cities, especially because of transport emissions. To be reliable, properly trained models need to be developed that combine output from sensors with weather data; however, many factors can affect the accuracy of the models. The main objective of this study was to explore the impact of several input variables in training different air quality indexes using fuzzy logic combined with two metaheuristic optimizations: simulated annealing (SA) and particle swarm optimization (PSO). In this work, the concentrations of NO₂ and CO were predicted using five resistivities from multisensor devices and three weather variables (temperature, relative humidity, and absolute humidity). In order to validate the results, several measures were calculated, including the correlation coefficient and the mean absolute error. Overall, PSO was found to perform the best. Finally, input resistivities of NO₂ and nonmetanic hydrocarbons (NMHC) were found to be the most sensitive to predict concentrations of NO₂ and CO. Full article

(This article belongs to the Special Issue Wireless Sensor Network for Air Quality Monitoring and Control)

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19 pages, 3474 KiB

Open AccessArticle

Estimation of PM₁₀ Levels and Sources in Air Quality Networks by Digital Analysis of Smartphone Camera Images Taken from Samples Deposited on Filters

by Selena Carretero-Peña, Lorenzo Calvo Blázquez and Eduardo Pinilla-Gil

Sensors 2019, 19(21), 4791; https://doi.org/10.3390/s19214791 - 4 Nov 2019

Cited by 5 | Viewed by 3348

Abstract

This paper explores the performance of smartphone cameras as low-cost and easily accessible tools to provide information about the levels and origin of particulate matter (PM) in ambient air. We tested the concept by digital analysis of the images of daily PM₁₀ (particles with diameters 10 µm and smaller) samples captured on glass fibre filters by high-volume aerosol samplers at urban and rural locations belonging to the air quality monitoring network of Extremadura (Spain) for one year. The images were taken by placing the filters inside a box designed to maintain controlled and reproducible light conditions. Digital image analysis was carried out by a mobile colour-sensing application using red, green, blue/hue, saturation, value/hue, saturation, luminance (RGB/HSV/HSL) parameters, that were processed through statistical procedures, directly or transformed to greyscale. The results of the study show that digital image analysis of the filters can roughly estimate the concentration of PM₁₀ within an air quality network, based on a significant linear correlation between the concentration of PM₁₀ measured by an official gravimetric method and the colour parameters of the filters’ images, with better results in the case of the saturation parameter (S_HSV). The methodology based on digital analysis can discriminate urban and rural sampling locations affected by different local particle-emitting sources and is also able to identify the presence of remote sources such as Saharan dust outbreaks in both urban and rural locations. The proposed methodology can be considered as a useful complement to the aerosol sampling equipment of air quality network field units for a quick estimation of PM₁₀ in the ambient air, through a simple, accessible and low-cost procedure, with further miniaturization potential. Full article

(This article belongs to the Special Issue Wireless Sensor Network for Air Quality Monitoring and Control)

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17 pages, 5610 KiB

Open AccessArticle

Wireless Sensor Network Combined with Cloud Computing for Air Quality Monitoring

by Patricia Arroyo, José Luis Herrero, José Ignacio Suárez and Jesús Lozano

Sensors 2019, 19(3), 691; https://doi.org/10.3390/s19030691 - 8 Feb 2019

Cited by 104 | Viewed by 12017

Abstract

Low-cost air pollution wireless sensors are emerging in densely distributed networks that provide more spatial resolution than typical traditional systems for monitoring ambient air quality. This paper presents an air quality measurement system that is composed of a distributed sensor network connected to a cloud system forming a wireless sensor network (WSN). Sensor nodes are based on low-power ZigBee motes, and transmit field measurement data to the cloud through a gateway. An optimized cloud computing system has been implemented to store, monitor, process, and visualize the data received from the sensor network. Data processing and analysis is performed in the cloud by applying artificial intelligence techniques to optimize the detection of compounds and contaminants. This proposed system is a low-cost, low-size, and low-power consumption method that can greatly enhance the efficiency of air quality measurements, since a great number of nodes could be deployed and provide relevant information for air quality distribution in different areas. Finally, a laboratory case study demonstrates the applicability of the proposed system for the detection of some common volatile organic compounds, including: benzene, toluene, ethylbenzene, and xylene. Principal component analysis, a multilayer perceptron with backpropagation learning algorithm, and support vector machine have been applied for data processing. The results obtained suggest good performance in discriminating and quantifying the concentration of the volatile organic compounds. Full article

(This article belongs to the Special Issue Wireless Sensor Network for Air Quality Monitoring and Control)

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13 pages, 4695 KiB

Open AccessArticle

A Wireless Gas Sensor Network to Monitor Indoor Environmental Quality in Schools

by Alvaro Ortiz Perez, Benedikt Bierer, Louisa Scholz, Jürgen Wöllenstein and Stefan Palzer

Sensors 2018, 18(12), 4345; https://doi.org/10.3390/s18124345 - 9 Dec 2018

Cited by 53 | Viewed by 9580

Abstract

Schools are amongst the most densely occupied indoor areas and at the same time children and young adults are the most vulnerable group with respect to adverse health effects as a result of poor environmental conditions. Health, performance and well-being of pupils crucially depend on indoor environmental quality (IEQ) of which air quality and thermal comfort are central pillars. This makes the monitoring and control of environmental parameters in classes important. At the same time most school buildings do neither feature automated, intelligent heating, ventilation, and air conditioning (HVAC) systems nor suitable IEQ monitoring systems. In this contribution, we therefore investigate the capabilities of a novel wireless gas sensor network to determine carbon dioxide concentrations, along with temperature and humidity. The use of a photoacoustic detector enables the construction of long-term stable, miniaturized, LED-based non-dispersive infrared absorption spectrometers without the use of a reference channel. The data of the sensor nodes is transmitted via a Z-Wave protocol to a central gateway, which in turn sends the data to a web-based platform for online analysis. The results show that it is difficult to maintain adequate IEQ levels in class rooms even when ventilating frequently and that individual monitoring and control of rooms is necessary to combine energy savings and good IEQ. Full article

(This article belongs to the Special Issue Wireless Sensor Network for Air Quality Monitoring and Control)

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