Seasonal Influence on the Performance of Low-Cost NO2 Sensor Calibrations.
Ontology highlight
ABSTRACT:
SUBMITTER: Ratingen SV
PROVIDER: S-EPMC8659619 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Pollutant gases, such as CO, NO2, O3, and SO2 affect human health, and low-cost sensors are an important complement to regulatory-grade instruments in pollutant monitoring. Previous studies focused on one or several species, while comprehensive assessments of multiple sensors remain limited. We conducted a 12-month field evaluation of four Alphasense sensors in Beijing and used single linear regression (SLR), multiple linear regression (MLR), random forest regressor (RFR), and neural network (long short-term memory (LSTM)) methods to calibrate and validate the measurements with nearby reference measurements from national monitoring stations. For performances, CO > O3 > NO2 > SO2 for the coefficient of determination (R2) and root mean square error (RMSE). The MLR did not increase the R2 after considering the temperature and relative humidity influences compared with the SLR (with R2 remaining at approximately 0.6 for O3 and 0.4 for NO2). However, the RFR and LSTM models significantly increased the O3, NO2, and SO2 performances, with the R2 increasing from 0.3-0.5 to >0.7 for O3 and NO2, and the RMSE decreasing from 20.4 to 13.2 ppb for NO2. For the SLR, there were relatively larger biases, while the LSTMs maintained a close mean relative bias of approximately zero (e.g., <5% for O3 and NO2), indicating that these sensors combined with the LSTMs are suitable for hot spot detection. We highlight that the performance of LSTM is better than that of random forest and linear methods. This study assessed four electrochemical air quality sensors and different calibration models, and the methodology and results can benefit assessments of other low-cost sensors.
| S-EPMC7795951 | biostudies-literature
Project description:In earthquake monitoring, an important aspect of the operational effect of earthquake intensity rapid reporting and earthquake early warning networks depends on the density and performance of the deployed seismic sensors. To improve the resolution of seismic sensors as much as possible while keeping costs low, in this article the use of multiple low-cost and low-resolution digital MEMS accelerometers is proposed to increase the resolution through the correlation average method. In addition, a cost-effective MEMS seismic sensor is developed. With ARM and Linux embedded computer technology, this instrument can cyclically store the continuous collected data on a built-in large-capacity SD card for approximately 12 months. With its real-time seismic data processing algorithm, this instrument is able to automatically identify seismic events and calculate ground motion parameters. Moreover, the instrument is easy to install in a variety of ground or building conditions. The results show that the RMS noise of the instrument is reduced from 0.096 cm/s2 with a single MEMS accelerometer to 0.034 cm/s2 in a bandwidth of 0.1-20 Hz by using the correlation average method of eight low-cost MEMS accelerometers. The dynamic range reaches more than 90 dB, the amplitude-frequency response of its input and output within -3 dB is DC -80 Hz, and the linearity is better than 0.47%. In the records from our instrument, earthquakes with magnitudes between M2.2 and M5.1 and distances from the epicenter shorter than 200 km have a relatively high SNR, and are more visible than they were prior to the joint averaging.
| S-EPMC8659451 | biostudies-literature
Project description:CeO2/ZnO-heterojunction-nanorod-array-based chemiresistive sensors were studied for their low-operating-temperature and gas-detecting characteristics. Arrays of CeO2/ZnO heterojunction nanorods were synthesized using anodic electrodeposition coating followed by hydrothermal treatment. The sensor based on this CeO2/ZnO heterojunction demonstrated a much higher sensitivity to NO2 at a low operating temperature (120 °C) than the pure-ZnO-based sensor. Moreover, even at room temperature (RT, 25 °C) the CeO2/ZnO-heterojunction-based sensor responds linearly and rapidly to NO2. This sensor's reaction to interfering gases was substantially less than that of NO2, suggesting exceptional selectivity. Experimental results revealed that the enhanced gas-sensing performance at the low operating temperature of the CeO2/ZnO heterojunction due to the built-in field formed after the construction of heterojunctions provides additional carriers for ZnO. Thanks to more carriers in the ZnO conduction band, more oxygen and target gases can be adsorbed. This explains the enhanced gas sensitivity of the CeO2/ZnO heterojunction at low operating temperatures.
| S-EPMC8705860 | biostudies-literature
Project description:Low-cost air quality sensors (LCSs) have become more widespread due to their low cost and increased capabilities; however, to supplement more traditional air quality networks, the performance of these LCSs needs to be validated. This study focused on NO2 measurements from eight Clarity Node-S sensors and used various environmental factors to calibrate the LCSs. To validate the calibration performance, we calculated the root-mean-square error (RMSE), mean absolute error (MAE), R2, and slope compared to reference measurements. Raw results from six of these sensors were comparable to those reported for other NO2 LCSs; however, two of the evaluated LCSs had RMSE values ~20 ppb higher than the other six LCSs. By applying a sensor-specific calibration that corrects for relative humidity, temperature, and ozone, this discrepancy was mitigated. In addition, this calibration improved the RMSE, MAE, R2, and slope of all eight LCS compared to the raw data. It should be noted that relatively stable environmental conditions over the course of the LCS deployment period benefited calibration performance over time. These results demonstrate the importance of developing LCS calibration models for individual sensors that consider pertinent environmental factors.
| S-EPMC8624883 | biostudies-literature
Project description:ObjectiveAccurate real-time continuous glucose measurements may improve glucose control in the critical care unit. We evaluated the accuracy of the FreeStyle(®) Navigator(®) (Abbott Diabetes Care, Alameda, CA) subcutaneous continuous glucose monitoring (CGM) device in critically ill adults using two methods of calibration.Subjects and methodsIn a randomized trial, paired CGM and reference glucose (hourly arterial blood glucose [ABG]) were collected over a 48-h period from 24 adults with critical illness (mean±SD age, 60±14 years; mean±SD body mass index, 29.6±9.3?kg/m(2); mean±SD Acute Physiology and Chronic Health Evaluation score, 12±4 [range, 6-19]) and hyperglycemia. In 12 subjects, the CGM device was calibrated at variable intervals of 1-6?h using ABG. In the other 12 subjects, the sensor was calibrated according to the manufacturer's instructions (1, 2, 10, and 24?h) using arterial blood and the built-in point-of-care glucometer.ResultsIn total, 1,060 CGM-ABG pairs were analyzed over the glucose range from 4.3 to 18.8?mmol/L. Using enhanced calibration median (interquartile range) every 169 (122-213) min, the absolute relative deviation was lower (7.0% [3.5, 13.0] vs. 12.8% [6.3, 21.8], P<0.001), and the percentage of points in the Clarke error grid Zone A was higher (87.8% vs. 70.2%).ConclusionsAccuracy of the Navigator CGM device during critical illness was comparable to that observed in non-critical care settings. Further significant improvements in accuracy may be obtained by frequent calibrations with ABG measurements.
| S-EPMC3894676 | biostudies-literature
Project description:The use of air sensor technology is increasing worldwide for a variety of applications, however, with significant variability in data quality. The United States Environmental Protection Agency held a workshop in July 2019 to deliberate possible performance targets for air sensors measuring particles with aerodynamic diameters of 10 μm or less (PM10), nitrogen dioxide (NO2), carbon monoxide (CO), and sulfur dioxide (SO2). These performance targets were discussed from the perspective of non-regulatory applications and with the sensors operating primarily in a stationary mode in outdoor environments. Attendees included representatives from multiple levels of government organizations, sensor developers, environmental nonprofits, international organizations, and academia. The workshop addressed the current lack of sensor technology requirements, discussed fit-for-purpose data quality needs, and debated transparency issues. This paper highlights the purpose and key outcomes of the workshop. While more information on performance and applications of sensors is available than in past years, the performance metrics, or parameters used to describe data quality, vary among the studies reports and there is a need for more clear and consistent approaches for evaluating sensor performance. Organizations worldwide are increasingly considering, or are in the process of developing, sensor performance targets and testing protocols. Workshop participants suggested that these new guidelines are highly desirable, would help improve data quality, and would give users more confidence in their data. Given the wide variety of uses for sensors and user backgrounds, as well as varied sensor design features (e.g., communication approaches, data tools, processing/adjustment algorithms and calibration procedures), the need for transparency was a key workshop theme. Suggestions for increasing transparency included documenting and sharing testing and performance data, detailing best practices, and sharing data processing and correction approaches.
| S-EPMC7970457 | biostudies-literature
Project description:Nitrogen dioxide (NO2) is one of the most dangerous air pollutants that can affect human health even at the ppb (part per billion) level. Thus, the superior sensing performance of nitrogen dioxide gas sensors is an imperative for real-time environmental monitoring. Traditional solid-state sensors based on metal-oxide transistors have the drawbacks of high power consumption, high operating temperature, poor selectivity, and difficult integration with other electronics. In that respect, graphene-based gas sensors have been extensively studied as potential replacements. However, their advantages of high sensing efficiency, low power consumption, and simple electronic integration have been countered by their slow response and poor repeatability. Here, we report the fabrication of high-performance ultraviolet (UV)-assisted room temperature NO2 sensors based on chemical vapor deposition-grown graphene. UV irradiation improves the response of the sensor sevenfold with respect to the dark condition attaining 26% change in resistance at 100 ppm NO2 concentration with a practical detection limit below 1 ppm (42.18 ppb). In addition, the recovery time was shortened fivefold to a few minutes and the excellent repeatability. This work may provide a promising and practical method to mass produce room-temperature NO2 gas sensors for real-time environment monitoring due to its simple fabrication process, low cost, and practicality.
| S-EPMC6732984 | biostudies-literature
Project description:Magnetic Resonance Imaging (MRI) is unparalleled in its ability to visualize anatomical structure and function non-invasively with high spatial and temporal resolution. Yet to overcome the low sensitivity inherent in inductive detection of weakly polarized nuclear spins, the vast majority of clinical MRI scanners employ superconducting magnets producing very high magnetic fields. Commonly found at 1.5-3 tesla (T), these powerful magnets are massive and have very strict infrastructure demands that preclude operation in many environments. MRI scanners are costly to purchase, site, and maintain, with the purchase price approaching $1 M per tesla (T) of magnetic field. We present here a remarkably simple, non-cryogenic approach to high-performance human MRI at ultra-low magnetic field, whereby modern under-sampling strategies are combined with fully-refocused dynamic spin control using steady-state free precession techniques. At 6.5 mT (more than 450 times lower than clinical MRI scanners) we demonstrate (2.5 × 3.5 × 8.5) mm(3) imaging resolution in the living human brain using a simple, open-geometry electromagnet, with 3D image acquisition over the entire brain in 6 minutes. We contend that these practical ultra-low magnetic field implementations of MRI (<10 mT) will complement traditional MRI, providing clinically relevant images and setting new standards for affordable (<$50,000) and robust portable devices.
| S-EPMC4606787 | biostudies-other
Project description:Air pollution is one of the major threats to human health. The monitoring of toxic NO2 gas in urban air emission pollution is becoming increasingly important. Thus, the development of an NO2 sensor with low power consumption, low cost, and high performance is urgent. In this paper, a planar structural micro hotplate gas sensor based on an AlN ceramic substrate with an annular Pt film heater was designed and prepared by micro-electro-mechanical system (MEMS) technology, in which Pt/Nb/In2O3 composite semiconductor oxide was used as the sensitive material with a molar ratio of In:Nb = 9:1. The annular thermal isolation groove was designed around the heater to reduce the power consumption and improve the thermal response rate. Furthermore, the finite element simulation analysis of the thermal isolation structure of the sensor was carried out by using ANSYS software. The results show that a low temperature of 94 °C, low power consumption of 150 mW, and low concentration detection of 1 to 10 ppm NO2 were simultaneously realized for the Nb-doped In2O3-based gas sensor. Our findings provide a promising strategy for the application of In2O3-based sensors in highly effective and low concentration NO2 detection.
| S-EPMC6749266 | biostudies-literature
Project description:Integration of low-cost air quality sensors with the internet of things (IoT) has become a feasible approach towards the development of smart cities. Several studies have assessed the performance of low-cost air quality sensors by comparing their measurements with reference instruments. We examined the performance of a low-cost IoT particulate matter (PM10 and PM2.5) sensor in the urban environment of Santiago, Chile. The prototype was assembled from a PM10-PM2.5 sensor (SDS011), a temperature and relative humidity sensor (BME280) and an IoT board (ESP8266/Node MCU). Field tests were conducted at three regulatory monitoring stations during the 2018 austral winter and spring seasons. The sensors at each site were operated in parallel with continuous reference air quality monitors (BAM 1020 and TEOM 1400) and a filter-based sampler (Partisol 2000i). Variability between sensor units (n?=?7) and the correlation between the sensor and reference instruments were examined. Moderate inter-unit variability was observed between sensors for PM2.5 (normalized root-mean-square error 9-24%) and PM10 (10-37%). The correlations between the 1-h average concentrations reported by the sensors and continuous monitors were higher for PM2.5 (R2 0.47-0.86) than PM10 (0.24-0.56). The correlations (R2) between the 24-h PM2.5 averages from the sensors and reference instruments were 0.63-0.87 for continuous monitoring and 0.69-0.93 for filter-based samplers. Correlation analysis revealed that sensors tended to overestimate PM concentrations in high relative humidity (RH?>?75%) and underestimate when RH was below 50%. Overall, the prototype evaluated exhibited adequate performance and may be potentially suitable for monitoring daily PM2.5 averages after correcting for RH.
| S-EPMC7010625 | biostudies-literature