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Practical Use of Metal Oxide Semiconductor Gas Sensors for Measuring Nitrogen Dioxide and Ozone in Urban Environments.

ABSTRACT: The potential of inexpensive Metal Oxide Semiconductor (MOS) gas sensors to be used for urban air quality monitoring has been the topic of increasing interest in the last decade. This paper discusses some of the lessons of three years of experience working with such sensors on a novel instrument platform (Small Open General purpose Sensor (SOGS)) in the measurement of atmospheric nitrogen dioxide and ozone concentrations. Analytic methods for increasing long-term accuracy of measurements are discussed, which permit nitrogen dioxide measurements with 95% confidence intervals of 20.0 μ g m - 3 and ozone precision of 26.8 μ g m - 3 , for measurements over a period one month away from calibration, averaged over 18 months of such calibrations. Beyond four months from calibration, sensor drift becomes significant, and accuracy is significantly reduced. Successful calibration schemes are discussed with the use of controlled artificial atmospheres complementing deployment on a reference weather station exposed to the elements. Manufacturing variation in the attributes of individual sensors are examined, an experiment possible due to the instrument being equipped with pairs of sensors of the same kind. Good repeatability (better than 0.7 correlation) between individual sensor elements is shown. The results from sensors that used fans to push air past an internal sensor element are compared with mounting the sensors on the outside of the enclosure, the latter design increasing effective integration time to more than a day. Finally, possible paths forward are suggested for improving the reliability of this promising sensor technology for measuring pollution in an urban environment.

SUBMITTER: Peterson PJD

PROVIDER: S-EPMC5539513 | biostudies-other | 2017 Jul

REPOSITORIES: biostudies-other

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Project description:Typical low-cost electrochemical sensors for ozone (O3) are also highly responsive to nitrogen dioxide (NO2). Consequently, a single sensor's response to O3 is indistinguishable from its response to NO2. Recently, a method for quantifying O3 concentrations became commercially available (Alphasense Ltd., Essex, UK): collocating a pair of sensors, a typical oxidative gas sensor that responds to both O3 and NO2 (model OX-B431) and a second similar sensor that filters O3 and responds only to NO2 (model NO2-B43F). By pairing the two sensors, O3 concentrations can be calculated. We calibrated samples of three NO2-B43F sensors and three OX-B431 sensors with NO2 and O3 exclusively and conducted mixture experiments over a range of 0-1.0 ppm NO2 and 0-125 ppb O3 to evaluate the ability of the paired sensors to quantify NO2 and O3 concentrations in mixture. Although the slopes of the response among our samples of three sensors of each type varied by as much as 37%, the individual response of the NO2-B43F sensors to NO2 and OX-B431 sensors to NO2 and O3 were highly linear over the concentrations studied (R2 ≥ 0.99). The NO2-B43F sensors responded minimally to O3 gas with statistically non-significant slopes of response. In mixtures of NO2 and O3, quantification of NO2 was generally accurate with overestimates up to 29%, compared to O3, which was generally underestimated by as much as 187%. We observed changes in sensor baseline over 4 days of experiments equivalent to 34 ppb O3, prompting an alternate method of calculating concentrations by baseline-correcting sensor signal. The baseline-correction method resulted in underestimates of NO2 up to 44% and decreases in the underestimation of O3 up to 107% for O3. Both methods for calculating gas concentrations progressively underestimated O3 concentrations as the ratio of NO2 signal to O3 signal increased. Our results suggest that paired NO2-B43F and OX-B431 sensors permit quantification of NO2 and O3 in mixture, but that O3 concentration estimates are less accurate and precise than those for NO2.

Project description:Varroosis is a dangerous and difficult to diagnose disease decimating bee colonies. The studies conducted sought answers on whether the electronic nose could become an effective tool for the efficient detection of this disease by examining sealed brood samples. The prototype of a multi-sensor recorder of gaseous sensor signals with a matrix of six semiconductor gas sensors TGS 823, TGS 826, TGS 832, TGS 2600, TGS 2602, and TGS 2603 from FIGARO was tested in this area. There were 42 objects belonging to 3 classes tested: 1st class-empty chamber (13 objects), 2nd class-fragments of combs containing brood sick with varroosis (19 objects), and 3rd class-fragments of combs containing healthy sealed brood (10 objects). The examination of a single object lasted 20 min, consisting of the exposure phase (10 min) and the sensor regeneration phase (10 min). The k-th nearest neighbors algorithm (kNN)-with default settings in RSES tool-was successfully used as the basic classifier. The basis of the analysis was the sensor reading value in 270 s with baseline correction. The multi-sensor MCA-8 gas sensor signal recorder has proved to be an effective tool in distinguishing between brood suffering from varroosis and healthy brood. The five-time cross-validation 2 test (5 × CV2 test) showed a global accuracy of 0.832 and a balanced accuracy of 0.834. Positive rate of the sick brood class was 0.92. In order to check the overall effectiveness of baseline correction in the examined context, we have carried out additional series of experiments-in multiple Monte Carlo Cross Validation model-using a set of classifiers with different metrics. We have tested a few variants of the kNN method, the Naïve Bayes classifier, and the weighted voting classifier. We have verified with statistical tests the thesis that the baseline correction significantly improves the level of classification. We also confirmed that it is enough to use the TGS2603 sensor in the examined context.

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Practical Use of Metal Oxide Semiconductor Gas Sensors for Measuring Nitrogen Dioxide and Ozone in Urban Environments.

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