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.

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 Long-term behavior and stability of calibration models for NO and NO<sub>2</sub> low cost sensors

2022 ◽  
Author(s):  
Horim Kim ◽  
Michael Müller ◽  
Stephan Henne ◽  
Christoph Hüglin
Keyword(s):  
Electrochemical Sensors ◽  
Low Cost ◽  
Air Pollutant ◽  
Coefficient Of Determination ◽  
Monitoring Networks ◽  
Pollution Levels ◽  
Pollutant Concentrations ◽  
Air Quality Measurements ◽  
One Year

Abstract. Low-cost sensors are considered as exhibiting great potential to complement classical air quality measurements in existing monitoring networks. However, the use of low-cost sensors poses some challenges. In this study, the behavior and performance of electrochemical sensors for NO and NO2 were determined over a longer operating period in a real-world deployment. After careful calibration of the sensors, based on co-location with reference instruments at a rural traffic site during six months and by using robust linear regression and random forest regression, the coefficient of determination of both types of sensors were high (R2 > 0.9) and the root mean square error (RMSE) of NO and NO2 sensors were about 6.8 ppb and 3.5 ppb, respectively, for 10-minute mean concentrations. The RMSE of the NO2 sensors, however, more than doubled, when the sensors were deployed without re-calibration for a one-year period at other site types (including urban background locations), where the range and the variability of air pollutant concentrations differed from the calibration site. This indicates a significant effect of the re-location of the sensors on the quality of their data. During deployment, we found that the NO2 sensors are capable of distinguishing general pollution levels, but they proved unsuitable for accurate measurements, mainly due to significant biases. In order to investigate the long-term stability of the original calibration, the sensors were re-installed at the calibration site after deployment. Surprisingly, the coefficient of determination and the RMSE of the NO sensor remained almost unchanged after more than one year of operation. In contrast, the performance of the NO2 sensors clearly deteriorated as indicated by a higher RMSE (about 7.5 ppb, 10-minute mean concentrations) and a lower coefficient of determination (R2 = 0.59).

scholarly journals Deployment, Calibration, and Cross-Validation of Low-Cost Electrochemical Sensors for Carbon Monoxide, Nitrogen Oxides, and Ozone for an Epidemiological Study

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4214
Author(s):  
Christopher Zuidema ◽  
Cooper S. Schumacher ◽  
Elena Austin ◽  
Graeme Carvlin ◽  
Timothy V. Larson ◽  
...  
Keyword(s):  
Carbon Monoxide ◽  
Exposure Assessment ◽  
Epidemiological Study ◽  
Electrochemical Sensors ◽  
Low Cost ◽  
Puget Sound ◽  
Ambient Air ◽  
Ambient Air Pollution ◽  
Calibration Models ◽  
Study Participants

We designed and built a network of monitors for ambient air pollution equipped with low-cost gas sensors to be used to supplement regulatory agency monitoring for exposure assessment within a large epidemiological study. This paper describes the development of a series of hourly and daily field calibration models for Alphasense sensors for carbon monoxide (CO; CO-B4), nitric oxide (NO; NO-B4), nitrogen dioxide (NO2; NO2-B43F), and oxidizing gases (OX-B431)—which refers to ozone (O3) and NO2. The monitor network was deployed in the Puget Sound region of Washington, USA, from May 2017 to March 2019. Monitors were rotated throughout the region, including at two Puget Sound Clean Air Agency monitoring sites for calibration purposes, and over 100 residences, including the homes of epidemiological study participants, with the goal of improving long-term pollutant exposure predictions at participant locations. Calibration models improved when accounting for individual sensor performance, ambient temperature and humidity, and concentrations of co-pollutants as measured by other low-cost sensors in the monitors. Predictions from the final daily models for CO and NO performed the best considering agreement with regulatory monitors in cross-validated root-mean-square error (RMSE) and R2 measures (CO: RMSE = 18 ppb, R2 = 0.97; NO: RMSE = 2 ppb, R2 = 0.97). Performance measures for NO2 and O3 were somewhat lower (NO2: RMSE = 3 ppb, R2 = 0.79; O3: RMSE = 4 ppb, R2 = 0.81). These high levels of calibration performance add confidence that low-cost sensor measurements collected at the homes of epidemiological study participants can be integrated into spatiotemporal models of pollutant concentrations, improving exposure assessment for epidemiological inference.

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 Empiric Unsupervised Drifts Correction Method of Electrochemical Sensors for in Field Nitrogen Dioxide Monitoring

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3581
Author(s):  
Rachid Laref ◽  
Etienne Losson ◽  
Alexandre Sava ◽  
Maryam Siadat
Keyword(s):  
Nitrogen Dioxide ◽  
Electrochemical Sensors ◽  
Low Cost ◽  
Correction Method ◽  
Climatic Conditions ◽  
Estimation Accuracy ◽  
Calibration Model ◽  
Monitoring Networks ◽  
Correction Model ◽  
Almost All

This paper investigates the long term drift phenomenon affecting electrochemical sensors used in real environmental conditions to monitor the nitrogen dioxide concentration [NO2]. Electrochemical sensors are low-cost gas sensors able to detect pollutant gas at part per billion level and may be employed to enhance the air quality monitoring networks. However, they suffer from many forms of drift caused by climatic parameter variations, interfering gases and aging. Therefore, they require frequent, expensive and time-consuming calibrations, which constitute the main obstacle to the exploitation of these kinds of sensors. This paper proposes an empirical, linear and unsupervised drift correction model, allowing to extend the time between two successive full calibrations. First, a calibration model is established based on multiple linear regression. The influence of the air temperature and humidity is considered. Then, a correction model is proposed to solve the drift related to age issue. The slope and the intercept of the correction model compensate the change over time of the sensors’ sensitivity and baseline, respectively. The parameters of the correction model are identified using particle swarm optimization (PSO). Data considered in this work are continuously collected onsite close to a highway crossing Metz City (France) during a period of 6 months (July to December 2018) covering almost all the climatic conditions in this region. Experimental results show that the suggested correction model allows maintaining an adequate [NO2] estimation accuracy for at least 3 consecutive months without needing any labeled data for the recalibration.

Quantification of an Active Substance in a Tablet by NIR and Raman Spectroscopy

1998 ◽  
Vol 6 (1) ◽  
pp. 279-289 ◽  
Author(s):  
Ingela Jedvert ◽  
Mats Josefson ◽  
Frans Langkilde
Keyword(s):  
Raman Spectroscopy ◽  
Active Substance ◽  
Reference Method ◽  
Spectroscopic Technique ◽  
Calibration Model ◽  
Baseline Correction ◽  
Spectroscopic Techniques ◽  
Long Term Stability ◽  
Calibration Models

Spectroscopic techniques in combination with chemometrics give opportunities to analyse tablets without time-consuming sample preparation. The aim of the present study was to develop a method to quantify the active substance, isosorbide-5-mononitrate, in Imdur® 120 mg tablets either by NIR diffuse reflectance or Raman spectroscopy. The calibration set was selected to simulate, with the available samples, as closely as possible a full factorial design with three factors. The reference method was liquid chromatography (LC). Calibration models with different baseline correction methods, different parts of wavelength range and different measures of weights have been evaluated. The calibration model found for each spectroscopic technique is discussed. The accuracy for the spectroscopic techniques were equal in merit to the LC method. Both the NIR and the Raman calibrations also showed a good long-term stability. With the baseline correction methods used for the spectra, it was possible to analyse tablets after 1.5 years. In conclusion it is possible to quantify Imdur® 120 mg with either NIR or Raman spectroscopy.

On-line identification of silkworm pupae gender by short-wavelength near infrared spectroscopy and pattern recognition technology

2021 ◽  
pp. 096703352199974
Author(s):  
Yue Ma ◽  
Yichao Xu ◽  
Hui Yan ◽  
Guozheng Zhang
Keyword(s):  
Discriminant Analysis ◽  
Short Wavelength ◽  
Near Infrared ◽  
Low Cost ◽  
Calibration Model ◽  
Linear Discriminant ◽  
Line Identification ◽  
Silkworm Pupae ◽  
Calibration Models ◽  
On Line

The gender identification of silkworm pupae is a critical step in the sericulture industry's breeding process. In this study, a low cost, short-wavelength (815-1075 nm) near infrared (NIR) spectrometer combined with multivariate spectra evaluation methods was used to establish calibration models for the on-line identification of female and male pupae of eight silkworm varieties (Hibiscus, Jingsong, 932, Xiang Hui, 7532×Xiang Hui, Haoyue B, Jingsong B, and 7532). The diffuse reflection short-wavelength spectra were recorded, and then principal component analysis (PCA), linear discriminant analysis (LDA), and partial least squares discriminant analysis (PLSDA) were tested for calibration model development. The PCA and LDA results showed, that spectral differences between the female and male silkworm pupae existed, however, the two evaluation techniques could not separate the female and male silkworm pupae with the required accuracy. The PLSDA calibration models, on the other hand, could separate the pupae according to their gender with the necessary prediction accuracy of >98.44%. Thus, it has been proved, that a low-cost, short-wavelength range NIR spectrometer in combination with a PLSDA calibration routine can be successfully applied for the reliable on-line identification of female and male silkworm pupae.

scholarly journals Use of the sulfide minerals pyrite and chalcopyrite as electrochemical sensors in non-aqueous solutions. The potentiometric titration of weak acids in alcohols

2009 ◽  
Vol 7 (4) ◽  
pp. 900-908 ◽  
Author(s):  
Ljiljana Mihajlović ◽  
Snežana Nikolić-Mandić ◽  
Branislav Vukanović ◽  
Ranđel Mihajlović
Keyword(s):  
Potentiometric Titration ◽  
Potassium Hydroxide ◽  
Electrochemical Sensors ◽  
Low Cost ◽  
Sodium Methylate ◽  
Weak Acids ◽  
Long Term Stability ◽  
Tert Butanol ◽  
Nernstian Slope

AbstractNatural monocrystalline pyrite and chalcopyrite were examined as new indicator electrodes for the potentiometric titration of weak acids in tert-butanol and iso-propanol. The electrodes investigated demonstrated a linear dynamic response for p-toluenesulfonic acid concentrations in the range from 0.1 to 0.001 M, with a Nernstian slope of 48 mV per decade for pyrite in tert-butanol. Sodium methylate, potassium hydroxide and tetrabutylammonium hydroxide (TBAH) proved to be suitable titrating agents. The response time was less than 12 s and the lifetime of the electrodes was higher than 1 year. The advantages of the electrodes are long-term stability, rapid response, reproducibility, easy preparation and low cost.

scholarly journals Chlorophyll Assessment and Sensitive Wavelength Exploration for Tea (Camellia sinensis) Based on Reflectance Spectral Characteristics

HortScience ◽  
2008 ◽  
Vol 43 (5) ◽  
pp. 1586-1591 ◽  
Author(s):  
Xiao-li Li ◽  
Yong He
Keyword(s):  
Latent Variables ◽  
Low Cost ◽  
Spectral Characteristics ◽  
Tea Plant ◽  
Nondestructive Method ◽  
Calibration Model ◽  
Analysis Techniques ◽  
Calibration Models ◽  
Chlorophyll Index

A nondestructive method for the determination of chlorophyll index for the tea plant based on reflectance spectral characteristics was investigated. Spectral data were collected from 184 samples with a spectroradiometer in a field experiment. Multivariate analysis techniques, including partial least squares (PLS) and multiple linear regression (MLR), were used for developing calibration models for the determination of chlorophyll index of the tea plant. The best calibration model was achieved using the PLS technique with a correlation coefficient (r) of 0.95, a se of prediction of 3.40, and a bias of 1.9e−06. When the model was used for predicting the unknown samples, good performance was also obtained with r of 0.91, se of calibration of 4.77, and bias of 0.02. Sensitive wavelengths were selected through loading analysis of latent variables in the optimal PLS model, and the validity of these wavelengths was proved by MLR and statistical analysis. Three fingerprint wavelengths (488, 695, and 931 nm) were determined and could potentially be used for developing a simple, low-cost, and efficient instrument for the measurement of chlorophyll index. The results proved the feasibility of reflectance spectra for measurement of chlorophyll index of the tea plant.

Sign in / Sign up

Export Citation Format

Share Document