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Low-Cost Environmental Gas Sensors
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Low-Cost Environmental Gas Sensors

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

Deadline for manuscript submissions: closed (22 September 2023) | Viewed by 7427

Special Issue Editors

Dr. Krishna Naishadham

E-Mail Website
Guest Editor
1. Wi-Sense LLC, Atlanta, GA, USA
2. Georgia Institute of Technology, Atlanta, GA, USA
Interests: gas sensors; nanotechnology; wearable sensors; wireless sensor nodes; microwave resonators; electromagnetics; microwave antennas
Prof. Dr. Jean-Marc Tulliani

E-Mail Website
Guest Editor
Department of Applied Science and Technology, Politecnico di Torino, 10129 Torino, Italy
Interests: development of advanced materials for the construction sector (self-healing and self‐sensing concretes, as well as alkali‐activated materials); physical, microstructural, and mechanical characterization of materials; materials aging and decay
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The health effects of air pollution exposure are widespread, with negative implications for the cardiovascular, respiratory, immune, and nervous systems. Traffic emissions, industrial pollution, and indoor sources such as fuel-burning combustion appliances, cigarette smoke, and building materials release harmful levels of ozone (O3), particulate matter (PM), nitrogen dioxide (NO2), and volatile organic compounds (VOCs) into the environment. The global burden of disease attributable to ambient air pollution is at a historical high, with over 6.6 million deaths per year. Various environmental protection agencies such as the EPA in the US monitor the air quality and inform the public about dangerous levels in their communities. However, these monitors are sparsely located and, thus, measure pollutant levels which may differ substantially from those measured in the patient’s “breathing zone” microenvironment. In order to fill this void in fine-scale air pollution data, low-cost environmental gas sensors are needed for sampling personal microenvironments using wearable or portable devices. The goal of this Special Issue is to provide an overview of the recent progress in the design, development, and application of miniaturized gas sensors for O3, NO2, and VOCs, focusing on improvements in the sensor performance (sensitivity, selectivity, response time, and detection limit) at the low concentration (< 1 ppm) levels needed for human safety.

Original research articles, letters, as well as review papers covering the different experimental and theoretical aspects of environmental gas sensors are invited for submission. The scientific areas of interest include, but are not limited to:

  • Materials used, including metal oxides, carbon nanomaterials, and composite and hybrid materials;
  • Models and computational approaches for the interaction between the analyte and sensor nanostructure;
  • Sensor miniaturization, low-cost, and low-power consumption design;
  • Novel measurement methods (e.g., impedance, resonance, etc., as opposed to common chemiresistive sensors);
  • Sensor array design to measure multiple pollutants simultaneously;
  • Applications such as wearable and smartphone-connected sensors.

Dr. Krishna Naishadham
Prof. Dr. Jean-Marc Tulliani
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • environmental pollution
  • gas sensors
  • metal oxide sensors
  • nanotechnology
  • ozone
  • nitrogen oxide
  • volatile organic compounds
  • sensor array
  • personal exposure
  • microenvironment

Published Papers (4 papers)

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Research

17 pages, 9477 KiB  
Article
Design of a Portable Analyzer to Determine the Net Exchange of CO2 in Rice Field Ecosystems
by Mirko Bonilla-Cordova, Lena Cruz-Villacorta, Ida Echegaray-Cabrera, Lia Ramos-Fernández and Lisveth Flores del Pino
Sensors 2024, 24(2), 402; https://0-doi-org.brum.beds.ac.uk/10.3390/s24020402 - 09 Jan 2024
Viewed by 815
Abstract
Global warming is influenced by an increase in greenhouse gas (GHG) concentration in the atmosphere. Consequently, Net Ecosystem Exchange (NEE) is the main factor that influences the exchange of carbon (C) between the atmosphere and the soil. As a result, agricultural ecosystems are [...] Read more.
Global warming is influenced by an increase in greenhouse gas (GHG) concentration in the atmosphere. Consequently, Net Ecosystem Exchange (NEE) is the main factor that influences the exchange of carbon (C) between the atmosphere and the soil. As a result, agricultural ecosystems are a potential carbon dioxide (CO2) sink, particularly rice paddies (Oryza sativa). Therefore, a static chamber with a portable CO2 analyzer was designed and implemented for three rice plots to monitor CO2 emissions. Furthermore, a weather station was installed to record meteorological variables. The vegetative, reproductive, and maturation phases of the crop lasted 95, 35, and 42 days post-sowing (DPS), respectively. In total, the crop lasted 172 DPS. Diurnal NEE had the highest CO2 absorption capacity at 10:00 a.m. for the tillering stage (82 and 89 DPS), floral primordium (102 DPS), panicle initiation (111 DPS), and flowering (126 DPS). On the other hand, the maximum CO2 emission at 82, 111, and 126 DPS occurred at 6:00 p.m. At 89 and 102 DPS, it occurred at 4:00 and 6:00 a.m., respectively. NEE in the vegetative stage was −25 μmolCO2 m2 s−1, and in the reproductive stage, it was −35 μmolCO2 m2 s−1, indicating the highest absorption capacity of the plots. The seasonal dynamics of NEE were mainly controlled by the air temperature inside the chamber (Tc) (R = −0.69), the relative humidity inside the chamber (RHc) (R = −0.66), and net radiation (Rn) (R = −0.75). These results are similar to previous studies obtained via chromatographic analysis and eddy covariance (EC), which suggests that the portable analyzer could be an alternative for CO2 monitoring. Full article
(This article belongs to the Special Issue Low-Cost Environmental Gas Sensors)
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27 pages, 9438 KiB  
Article
Response Surface Modeling of the Steady-State Impedance Responses of Gas Sensor Arrays Comprising Functionalized Carbon Nanotubes to Detect Ozone and Nitrogen Dioxide
by Krishna Naishadham, Gautam Naishadham, Nelson Cabrera and Elena Bekyarova
Sensors 2023, 23(20), 8447; https://0-doi-org.brum.beds.ac.uk/10.3390/s23208447 - 13 Oct 2023
Cited by 1 | Viewed by 907
Abstract
Carbon nanotube (CNT) sensors provide a versatile chemical platform for ambient monitoring of ozone (O3) and nitrogen dioxide (NO2), two important airborne pollutants known to cause acute respiratory and cardiovascular health problems. CNTs have shown great potential for use [...] Read more.
Carbon nanotube (CNT) sensors provide a versatile chemical platform for ambient monitoring of ozone (O3) and nitrogen dioxide (NO2), two important airborne pollutants known to cause acute respiratory and cardiovascular health problems. CNTs have shown great potential for use as sensing layers due to their unique properties, including high surface to volume ratio, numerous active sites and crystal facets with high surface reactivity, and high thermal and electrical conductivity. With operational advantages such as compactness, low-power operation, and easy integration with electronics devices, nanotechnology is expected to have a significant impact on portable low-cost environmental sensors. Enhanced sensitivity is feasible by functionalizing the CNTs with polymers, metals, and metal oxides. This paper focuses on the design and performance of a two-element array of O3 and NO2 sensors comprising single-walled CNTs functionalized by covalent modification with organic functional groups. Unlike the conventional chemiresistor in which the change in DC resistance across the sensor terminals is measured, we characterize the sensor array response by measuring both the magnitude and phase of the AC impedance. Multivariate response provides higher degrees of freedom in sensor array data processing. The complex impedance of each sensor is measured at 5 kHz in a controlled gas-flow chamber using gas mixtures with O3 in the 60–120 ppb range and NO2 between 20 and 80 ppb. The measured data reveal response change in the 26–36% range for the O3 sensor and 5–31% for the NO2 sensor. Multivariate optimization is used to fit the laboratory measurements to a response surface mathematical model, from which sensitivity and selectivity are calculated. The ozone sensor exhibits high sensitivity (e.g., 5 to 6 MΩ/ppb for the impedance magnitude) and high selectivity (0.8 to 0.9) for interferent (NO2) levels below 30 ppb. However, the NO2 sensor is not selective. Full article
(This article belongs to the Special Issue Low-Cost Environmental Gas Sensors)
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29 pages, 4331 KiB  
Article
From Raising Awareness to a Behavioural Change: A Case Study of Indoor Air Quality Improvement Using IoT and COM-B Model
by Rameez Raja Kureshi, Dhavalkumar Thakker, Bhupesh Kumar Mishra and Jo Barnes
Sensors 2023, 23(7), 3613; https://0-doi-org.brum.beds.ac.uk/10.3390/s23073613 - 30 Mar 2023
Cited by 4 | Viewed by 2589
Abstract
The topic of indoor air pollution has yet to receive the same level of attention as ambient pollution. We spend considerable time indoors, and poorer indoor air quality affects most of us, particularly people with respiratory and other health conditions. There is a [...] Read more.
The topic of indoor air pollution has yet to receive the same level of attention as ambient pollution. We spend considerable time indoors, and poorer indoor air quality affects most of us, particularly people with respiratory and other health conditions. There is a pressing need for methodological case studies focusing on informing households about the causes and harms of indoor air pollution and supporting changes in behaviour around different indoor activities that cause it. The use of indoor air quality (IAQ) sensor data to support behaviour change is the focus of our research in this paper. We have conducted two studies—first, to evaluate the effectiveness of the IAQ data visualisation as a trigger for the natural reflection capability of human beings to raise awareness. This study was performed without the scaffolding of a formal behaviour change model. In the second study, we showcase how a behaviour psychology model, COM-B (Capability, Opportunity, and Motivation-Behaviour), can be operationalised as a means of digital intervention to support behaviour change. We have developed four digital interventions manifested through a digital platform. We have demonstrated that it is possible to change behaviour concerning indoor activities using the COM-B model. We have also observed a measurable change in indoor air quality. In addition, qualitative analysis has shown that the awareness level among occupants has improved due to our approach of utilising IoT sensor data with COM-B-based digital interventions. Full article
(This article belongs to the Special Issue Low-Cost Environmental Gas Sensors)
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20 pages, 6597 KiB  
Article
Calibration of SO2 and NO2 Electrochemical Sensors via a Training and Testing Method in an Industrial Coastal Environment
by Sofía Ahumada, Matias Tagle, Yeanice Vasquez, Rodrigo Donoso, Jenny Lindén, Fredrik Hallgren, Marta Segura and Pedro Oyola
Sensors 2022, 22(19), 7281; https://0-doi-org.brum.beds.ac.uk/10.3390/s22197281 - 26 Sep 2022
Cited by 1 | Viewed by 2342
Abstract
Low-cost sensors can provide inaccurate data as temperature and humidity affect sensor accuracy. Therefore, calibration and data correction are essential to obtain reliable measurements. This article presents a training and testing method used to calibrate a sensor module assembled from SO2 and [...] Read more.
Low-cost sensors can provide inaccurate data as temperature and humidity affect sensor accuracy. Therefore, calibration and data correction are essential to obtain reliable measurements. This article presents a training and testing method used to calibrate a sensor module assembled from SO2 and NO2 electrochemical sensors (Alphasense B4 and B43F) alongside air temperature (T) and humidity (RH) sensors. Field training and testing were conducted in the industrialized coastal area of Quintero Bay, Chile. The raw responses of the electrochemical (mV) and T-RH sensors were subjected to multiple linear regression (MLR) using three data segments, based on either voltage (SO2 sensor) or temperature (NO2). The resulting MLR equations were used to estimate the reference concentration. In the field test, calibration improved the performance of the sensors after adding T and RH in a linear model. The most robust models for NO2 were associated with data collected at T < 10 °C (R2 = 0.85), while SO2 robust models (R2 = 0.97) were associated with data segments containing higher voltages. Overall, this training and testing method reduced the bias due to T and HR in the evaluated sensors and could be replicated in similar environments to correct raw data from low-cost electrochemical sensors. A calibration method based on training and sensor testing after relocation is presented. The results show that the SO2 sensor performed better when modeled for different segments of voltage data, and the NO2 sensor model performed better when calibrated for different temperature data segments. Full article
(This article belongs to the Special Issue Low-Cost Environmental Gas Sensors)
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