scholarly journals Improving Air Pollutant Metal Oxide Sensor Quantification Practices through: An Exploration of Sensor Signal Normalization, Multi-Sensor and Universal Calibration Model Generation, and Physical Factors Such as Co-Location Duration and Sensor Age

Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 645
Author(s):  
Kristen Okorn ◽  
Michael Hannigan
Keyword(s):  
Metal Oxide ◽  
Low Cost ◽  
Air Pollutant ◽  
Sensor System ◽  
Physical Factors ◽  
Time Averaging ◽  
Calibration Model ◽  
Sensor Signal ◽  
Metal Oxide Sensors ◽  
Signal Normalization

As low-cost sensors have become ubiquitous in air quality measurements, there is a need for more efficient calibration and quantification practices. Here, we deploy stationary low-cost monitors in Colorado and Southern California near oil and gas facilities, focusing our analysis on methane and ozone concentration measurement using metal oxide sensors. In comparing different sensor signal normalization techniques, we propose a z-scoring standardization approach to normalize all sensor signals, making our calibration results more easily transferable among sensor packages. We also attempt several different physical co-location schemes, and explore several calibration models in which only one sensor system needs to be co-located with a reference instrument, and can be used to calibrate the rest of the fleet of sensor systems. This approach greatly reduces the time and effort involved in field normalization without compromising goodness of fit of the calibration model to a significant extent. We also explore other factors affecting the performance of the sensor system quantification method, including the use of different reference instruments, duration of co-location, time averaging, transferability between different physical environments, and the age of metal oxide sensors. Our focus on methane and stationary monitors, in addition to the z-scoring standardization approach, has broad applications in low-cost sensor calibration and utility.

scholarly journals Development of a general calibration model and long-term performance evaluation of low-cost sensors for air pollutant gas monitoring

2019 ◽  
Vol 12 (2) ◽  
pp. 903-920 ◽  
Author(s):  
Carl Malings ◽  
Rebecca Tanzer ◽  
Aliaksei Hauryliuk ◽  
Sriniwasa P. N. Kumar ◽  
Naomi Zimmerman ◽  
...  
Keyword(s):  
Electrochemical Sensors ◽  
Low Cost ◽  
Air Pollutant ◽  
Calibration Model ◽  
Linear Regression Models ◽  
Quadratic Regression ◽  
Neural Network Models ◽  
Calibration Models ◽  
New Models

Abstract. Assessing the intracity spatial distribution and temporal variability in air quality can be facilitated by a dense network of monitoring stations. However, the cost of implementing such a network can be prohibitive if traditional high-quality, expensive monitoring systems are used. To this end, the Real-time Affordable Multi-Pollutant (RAMP) monitor has been developed, which can measure up to five gases including the criteria pollutant gases carbon monoxide (CO), nitrogen dioxide (NO2), and ozone (O3), along with temperature and relative humidity. This study compares various algorithms to calibrate the RAMP measurements including linear and quadratic regression, clustering, neural networks, Gaussian processes, and hybrid random forest–linear regression models. Using data collected by almost 70 RAMP monitors over periods ranging up to 18 months, we recommend the use of limited quadratic regression calibration models for CO, neural network models for NO, and hybrid models for NO2 and O3 for any low-cost monitor using electrochemical sensors similar to those of the RAMP. Furthermore, generalized calibration models may be used instead of individual models with only a small reduction in overall performance. Generalized models also transfer better when the RAMP is deployed to other locations. For long-term deployments, it is recommended that model performance be re-evaluated and new models developed periodically, due to the noticeable change in performance over periods of a year or more. This makes generalized calibration models even more useful since only a subset of deployed monitors are needed to build these new models. These results will help guide future efforts in the calibration and use of low-cost sensor systems worldwide.

Applications of Nanostructured Materials as Gas Sensors

2013 ◽  
Vol 201 ◽  
pp. 131-158 ◽  
Author(s):  
Ravi Chand Singh ◽  
Manmeet Pal Singh ◽  
Hardev Singh Virk
Keyword(s):  
Metal Oxide ◽  
Gas Sensors ◽  
Tin Dioxide ◽  
Gas Sensing ◽  
Dominant Role ◽  
Low Cost ◽  
Critical Role ◽  
Health And Safety ◽  
Chemical Route ◽  
Metal Oxide Sensors

Gas detection instruments are increasingly needed for industrial health and safety, environmental monitoring, and process control. To meet this demand, considerable research into new sensors is underway, including efforts to enhance the performance of traditional devices, such as resistive metal oxide sensors, through nanoengineering. The resistance of semiconductors is affected by the gaseous ambient. The semiconducting metal oxides based gas sensors exploit this phenomenon. Physical chemistry of solid metal surfaces plays a dominant role in controlling the gas sensing characteristics. Metal oxide sensors have been utilized for several decades for low-cost detection of combustible and toxic gases. Recent advances in nanomaterials provide the opportunity to dramatically increase the response of these materials, as their performance is directly related to exposed surface volume. Proper control of grain size remains a key challenge for high sensor performance. Nanoparticles of SnO2have been synthesized through chemical route at 5, 25 and 50°C. The synthesized particles were sintered at 400, 600 and 800°C and their structural and morphological analysis was carried out using X-ray diffraction (XRD) and transmission electron microscopy (TEM). The reaction temperature is found to be playing a critical role in controlling nanostructure sizes as well as agglomeration. It has been observed that particle synthesized at 5 and 50°C are smaller and less agglomerated as compared to the particles prepared at 25°C. The studies revealed that particle size and agglomeration increases with increase in sintering temperature. Thick films gas sensors were fabricated using synthesized tin dioxide powder and sensing response of all the sensors to ethanol vapors was investigated at different temperatures and concentrations. The investigations revealed that sensing response of SnO2nanoparticles is size dependent and smaller particles display higher sensitivity. Table of Contents

scholarly journals Development of a General Calibration Model and Long-Term Performance Evaluation of Low-Cost Sensors for Air Pollutant Gas Monitoring

2018 ◽  
Author(s):  
Carl Malings ◽  
Rebecca Tanzer ◽  
Aliaksei Hauryliuk ◽  
Sriniwasa P. N. Kumar ◽  
Naomi Zimmerman ◽  
...  
Keyword(s):  
Linear Models ◽  
Low Cost ◽  
Air Pollutant ◽  
Superior Performance ◽  
Calibration Model ◽  
Quadratic Regression ◽  
Calibration Models ◽  
Using Data ◽  
One Year

Abstract. Assessing the intra-city spatial distribution and temporal variability of air quality can be facilitated by a dense network of monitoring stations. However, the cost of implementing such a network can be prohibitive if traditional high-quality, expensive monitoring systems are used. To this end, the Real-time Affordable Multi-Pollutant (RAMP) monitor has been developed, which can measure up to five gases including the criteria pollutant gases carbon monoxide (CO), nitrogen dioxide (NO2), and ozone (O3), along with temperature and relative humidity. This study compares various algorithms to calibrate the RAMP measurements including linear and quadratic regression, clustering, neural networks, Gaussian processes, and random forests. Using data collected by more than sixty RAMP monitors over periods ranging up to eighteen months, it was found that quadratic regression models or a hybrid of random forest and linear models tend to be the most effective calibration models overall. In specific cases, other types of models can have comparable or even superior performance. Furthermore, generalized calibration models may be used instead of individual models with only a small reduction in overall performance. For long-term deployments, it is recommended that new models be developed each year, due to the noticeable change in performance when models for one year were used for processing data collected in the subsequent year. This makes annually-developed generalized calibration models even more useful since only a subset of deployed monitors are needed to build these models. These results will help guide future efforts in the calibration and use of low-cost sensor systems worldwide.

scholarly journals Applications and Limitations of Quantifying Speciated and Source-Apportioned VOCs with Metal Oxide Sensors

Atmosphere ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1383
Author(s):  
Kristen Okorn ◽  
Michael Hannigan
Keyword(s):  
Metal Oxide ◽  
Oil And Gas ◽  
Low Cost ◽  
Positive Matrix ◽  
Metal Oxide Sensors ◽  
Volatile Organic ◽  
Oil And Gas Operations ◽  
Sensor Signals ◽  
Benzene Toluene ◽  
Voc Mixtures

While low-cost air quality sensor quantification has improved tremendously in recent years, speciated hydrocarbons have received little attention beyond total lumped volatile organic compounds (VOCs) or total non-methane hydrocarbons (TNMHCs). In this work, we attempt to use two broad response metal oxide VOC sensors to quantify a host of speciated hydrocarbons as well as smaller groups of hydrocarbons thought to be emanating from the same source or sources. For sensors deployed near oil and gas facilities, we utilize artificial neural networks (ANNs) to calibrate our low-cost sensor signals to regulatory-grade measurements of benzene, toluene, and formaldehyde. We also use positive matrix factorization (PMF) to group these hydrocarbons along with others by source, such as wet and dry components of oil and gas operations. The two locations studied here had different sets of reference hydrocarbon species measurements available, helping us determine which specific hydrocarbons and VOC mixtures are best suited for this approach. Calibration fits on the upper end reach above R2 values of 0.6 despite the parts per billion (ppb) concentration ranges of each, which are magnitudes below the manufacturer’s prescribed detection limits for the sensors. The sensors generally captured the baseline trends in the data, but failed to quantitatively estimate larger spikes that occurred intermittently. While compounds with high variability were not suited for this method, its success with several of the compounds studied represents a crucial first step in low-cost VOC speciation. This work has important implications in improving our understanding of the links between health and environment, as different hydrocarbons will have varied consequences in the human body and atmosphere.

scholarly journals Developing a Low-Cost Wearable Personal Exposure Monitor for Studying Respiratory Diseases Using Metal–Oxide Sensors

2019 ◽  
Vol 19 (18) ◽  
pp. 8252-8261 ◽  
Author(s):  
Kyle R. Mallires ◽  
Di Wang ◽  
Vishal Varun Tipparaju ◽  
Nongjian Tao
Keyword(s):  
Metal Oxide ◽  
Respiratory Diseases ◽  
Low Cost ◽  
Personal Exposure ◽  
Metal Oxide Sensors

scholarly journals Use of electrochemical sensors for measurement of air pollution: correcting interference response and validating measurements

2017 ◽  
Author(s):  
Eben S. Cross ◽  
David K. Lewis ◽  
Leah R. Williams ◽  
Gregory R. Magoon ◽  
Michael L. Kaminsky ◽  
...  
Keyword(s):  
Air Quality ◽  
Electrochemical Sensors ◽  
Low Cost ◽  
Air Pollutant ◽  
Sensor System ◽  
Time Interval ◽  
High Dimensional Model Representation ◽  
Integrated Sensor ◽  
Project Data ◽  
Interference Response

Abstract. The environments in which we live, work, breathe, and play are subject to enormous variability in air pollutant concentrations. To adequately characterize air quality, measurements must be fast (real-time), scalable, and reliable (with known accuracy, precision, and stability over time). Low-cost AQ sensor technologies offer new opportunities for fast and distributed measurements, but a persistent characterization gap remains when it comes to evaluating sensor performance under realistic environmental sampling conditions. This limits our ability to inform stakeholders about pollution sources and inspire policy makers to address environmental justice air quality issues. In this paper, initial results obtained with a recently developed low-cost air quality sensor system are reported. In this project, data were acquired with the ARISense integrated sensor package over a 4-month time interval during which the sensor system was co-located with a state-operated (Massachusetts, USA) air quality monitoring station equipped with reference instrumentation measuring the same pollutant species. This paper focuses on validating electrochemical sensor measurements of CO, NO, NO2, and O3. Through the use of High Dimensional Model Representation (HDMR), we show that interference effects derived from changing environmental conditions and the ambient-gas concentration mix encountered at an urban neighborhood site can be effectively modelled for the Alphasense CO-B4, NO-B4, NO2-B43F, and Ox-B421 sensors, improving the credibility of air pollutant measurements made with these sensors.

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