scholarly journals Design of an ozone and nitrogen dioxide sensor unit and its long-term operation within a sensor network in the city of Zurich

2017 ◽  
Vol 10 (10) ◽  
pp. 3783-3799 ◽  
Author(s):  
Michael Mueller ◽  
Jonas Meyer ◽  
Christoph Hueglin
Keyword(s):  
Air Quality ◽  
Sensor Network ◽  
Low Cost ◽  
Effective Procedure ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Monitoring Networks ◽  
Term Operation ◽  
Diffusion Tubes ◽  
Over Time

Abstract. This study focuses on the investigation and quantification of low-cost sensor performance in application fields such as the extension of traditional air quality monitoring networks or the replacement of diffusion tubes. For this, sensor units consisting of two boxes featuring NO2 and O3 low-cost sensors and wireless data transfer were engineered. The sensor units were initially operated at air quality monitoring sites for 3 months for performance analysis and initial calibration. Afterwards, they were relocated and operated within a sensor network consisting of six locations for more than 1 year. Our analyses show that the employed O3 and NO2 sensors can be accurate to 2–5 and 5–7 ppb, respectively, during the first 3 months of operation. This accuracy, however, could not be maintained during their operation within the sensor network related to changes in sensor behaviour. For most of the O3 sensors a decrease in sensitivity was encountered over time, clearly impacting the data quality. The NO2 low-cost sensors in our configuration exhibited better performance but did not reach the accuracy level of NO2 diffusion tubes (∼ 2 ppb for uncorrected 14-day average concentrations). Tests in the laboratory revealed that changes in relative humidity can impact the signal of the employed NO2 sensors similarly to changes in ambient NO2 concentration. All the employed low-cost sensors need to be individually calibrated. Best performance of NO2 sensors is achieved when the calibration models also include time-dependent parameters accounting for changes in sensor response over time. Accordingly, an effective procedure for continuous data control and correction is essential for obtaining meaningful data. It is demonstrated that linking the measurements from low-cost sensors to the high-quality measurements from routine air quality monitoring stations is an effective procedure for both tasks provided that time periods can be identified when pollutant concentrations can be accurately predicted at sensor locations.

scholarly journals Design of an ozone and nitrogen dioxide sensor unit and its long-term operation within a sensor network in the city of Zurich

2017 ◽  
Author(s):  
Michael Mueller ◽  
Jonas Meyer ◽  
Christoph Hueglin
Keyword(s):  
Air Quality ◽  
Sensor Network ◽  
Data Transfer ◽  
Low Cost ◽  
Effective Procedure ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Monitoring Networks ◽  
Term Operation ◽  
Diffusion Tubes

Abstract. This study focuses on the investigation and quantification of low-cost sensor performance in application fields such as the extension of traditional air quality monitoring networks or the replacement of diffusion tubes. For this, sensor units consisting of two boxes featuring NO2 and O3 low-cost sensors and wireless data transfer were engineered. The sensor units were initially operated at air quality monitoring sites for three months for performance analysis and initial calibration. Afterwards, they were relocated and operated within a sensor network consisting of six locations for more than one year. Our analyses show that the employed O3 and NO2 sensors can be accurate to 2–5 and 5–7 ppb, respectively, during the first three months of operation. This accuracy, however, could not be maintained during their operation within the sensor network related to changes in sensor behaviour. Hence, the low-cost sensors in our configuration do not reach the accuracy level of NO2 diffusion tubes. Tests in the laboratory revealed that changes in relative humidity can impact the signal of the employed NO2 sensors similarly as changes in ambient NO2 concentration. All the employed low-cost sensors need to be individually calibrated. Best performance of NO2 sensors is achieved when the calibration models include also time dependent parameters accounting for changes in sensor response over time. Accordingly, an effective procedure for continuous data control and correction is essential for obtaining meaningful data. It is demonstrated that linking the measurements from low-cost sensors to the high quality measurements from routine air quality monitoring stations is an effective procedure for both tasks provided that time periods can be identified when pollutant concentrations can be accurately predicted at sensor locations.

scholarly journals LoRa Sensor Network Development for Air Quality Monitoring or Detecting Gas Leakage Events

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6225
Author(s):  
Ernesto González ◽  
Juan Casanova-Chafer ◽  
Alfonso Romero ◽  
Xavier Vilanova ◽  
Jan Mitrovics ◽  
...  
Keyword(s):  
Air Quality ◽  
Sensor Network ◽  
Low Cost ◽  
Background Level ◽  
Early Warning Systems ◽  
Air Pollutant ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Gas Leakage ◽  
Events Detection

During the few last years, indoor and outdoor Air Quality Monitoring (AQM) has gained a lot of interest among the scientific community due to its direct relation with human health. The Internet of Things (IoT) and, especially, Wireless Sensor Networks (WSN) have given rise to the development of wireless AQM portable systems. This paper presents the development of a LoRa (short for long-range) based sensor network for AQM and gas leakage events detection. The combination of both a commercial gas sensor and a resistance measurement channel for graphene chemoresistive sensors allows both the calculation of an Air Quality Index based on the concentration of reducing species such as volatile organic compounds (VOCs) and CO, and it also makes possible the detection of NO2, which is an important air pollutant. The graphene sensor tested with the LoRa nodes developed allows the detection of NO2 pollution in just 5 min as well as enables monitoring sudden changes in the background level of this pollutant in the atmosphere. The capability of the system of detecting both reducing and oxidizing pollutant agents, alongside its low-cost, low-power, and real-time monitoring features, makes this a solution suitable to be used in wireless AQM and early warning systems.

scholarly journals Robust statistical calibration and characterization of portable low-cost air quality monitoring sensors to quantify real-time O<sub>3</sub> and NO<sub>2</sub> concentrations in diverse environments

2021 ◽  
Vol 14 (1) ◽  
pp. 37-52
Author(s):  
Ravi Sahu ◽  
Ayush Nagal ◽  
Kuldeep Kumar Dixit ◽  
Harshavardhan Unnibhavi ◽  
Srikanth Mantravadi ◽  
...  
Keyword(s):  
Air Quality ◽  
Low Cost ◽  
Metric Learning ◽  
Quality Monitoring ◽  
Series Data ◽  
Air Quality Monitoring ◽  
Monitoring Networks ◽  
Calibration Algorithm ◽  
Two Phases ◽  
Non Parametric

Abstract. Low-cost sensors offer an attractive solution to the challenge of establishing affordable and dense spatio-temporal air quality monitoring networks with greater mobility and lower maintenance costs. These low-cost sensors offer reasonably consistent measurements but require in-field calibration to improve agreement with regulatory instruments. In this paper, we report the results of a deployment and calibration study on a network of six air quality monitoring devices built using the Alphasense O3 (OX-B431) and NO2 (NO2-B43F) electrochemical gas sensors. The sensors were deployed in two phases over a period of 3 months at sites situated within two megacities with diverse geographical, meteorological and air quality parameters. A unique feature of our deployment is a swap-out experiment wherein three of these sensors were relocated to different sites in the two phases. This gives us a unique opportunity to study the effect of seasonal, as well as geographical, variations on calibration performance. We report an extensive study of more than a dozen parametric and non-parametric calibration algorithms. We propose a novel local non-parametric calibration algorithm based on metric learning that offers, across deployment sites and phases, an R2 coefficient of up to 0.923 with respect to reference values for O3 calibration and up to 0.819 for NO2 calibration. This represents a 4–20 percentage point increase in terms of R2 values offered by classical non-parametric methods. We also offer a critical analysis of the effect of various data preparation and model design choices on calibration performance. The key recommendations emerging out of this study include (1) incorporating ambient relative humidity and temperature into calibration models; (2) assessing the relative importance of various features with respect to the calibration task at hand, by using an appropriate feature-weighing or metric-learning technique; (3) using local calibration techniques such as k nearest neighbors (KNN); (4) performing temporal smoothing over raw time series data but being careful not to do so too aggressively; and (5) making all efforts to ensure that data with enough diversity are demonstrated in the calibration algorithm while training to ensure good generalization. These results offer insights into the strengths and limitations of these sensors and offer an encouraging opportunity to use them to supplement and densify compliance regulatory monitoring networks.

scholarly journals Robust statistical calibration and characterization of portable low-cost air quality monitoring sensors to quantify real-time O<sub>3</sub> and NO<sub>2</sub> concentrations in diverse environments

2020 ◽  
Author(s):  
Ravi Sahu ◽  
Ayush Nagal ◽  
Kuldeep Kumar Dixit ◽  
Harshavardhan Unnibhavi ◽  
Srikanth Mantravadi ◽  
...  
Keyword(s):  
Air Quality ◽  
Low Cost ◽  
Metric Learning ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Monitoring Networks ◽  
Local Calibration ◽  
Calibration Models ◽  
Calibration Methods ◽  
Calibration Algorithm

Abstract. Rising awareness of the health risks posed by elevated levels of ground-level O3 and NO2 have led to an increased demand for affordable and dense spatio-temporal air quality monitoring networks. Low-cost sensors used as a part of Internet of Things (IoT) platforms offer an attractive solution with greater mobility and lower maintenance costs, and can supplement compliance regulatory monitoring stations. These commodity low-cost sensors have reasonably high accuracy but require in-field calibration to improve precision. In this paper, we report the results of a deployment and calibration study on a network of seven air quality monitoring devices built using the Alphasense O3 (OX-B431) and NO2 (NO2-B43F) electrochemical gas sensors. The sensors were deployed at sites situated within two mega-cities with diverse geographical, meteorological and air quality parameters – Faridabad (Delhi National Capital Region) and Mumbai, India. The deployment was done in two phases over a period of three months. A unique feature of our deployment is a swap-out experiment wherein four of these sensors were relocated to different sites in the two deployment phases. Such a diverse deployment with sensors switching places gives us a unique opportunity to ablate the effect of seasonal, as well as geographical variations on calibration performance. We perform an extensive study of more than a dozen parametric as well as non-parametric calibration algorithms and find local calibration methods to offer the best performance. We propose a novel local calibration algorithm based on metric-learning that offers, across deployment sites and phases, an average R2 coefficient of 0.873 with respect to reference values for O3 calibration and 0.886 for NO2 calibration. This represents an upto 9 % increase over R2 values offered by classical local calibration methods. In particular, our proposed model far outperforms the default calibration models offered by the gas sensor manufacturer. We also offer a critical analysis of the effect of various data preparation and model design choices on calibration performance. The key recommendations emerging out of this study include (1) incorporating ambient relative humidity and temperature as free parameters (or features) into all calibration models, (2) assessing the relative importance of various features with respect to the calibration task at hand, by using an appropriate feature weighing or metric learning technique, (3) the use of local (or even hyper-local) calibration techniques such as k-NN that seem to offer the best performance in high variability conditions such as those encountered in field deployments, (4) performing temporal smoothing over raw time series data, say by averaging sensor signals over small windows, but being careful to not do so too aggressively, and (5) making all efforts at ensuring that data with enough diversity is demonstrated to the calibration algorithm while training to ensure good generalization. These results offer insights into the strengths and limitations of these sensors, and offer an encouraging opportunity at using them to supplement and densify compliance regulatory monitoring networks.

scholarly journals Forecasting Model Validation of Particulate Air Pollution by Low Cost Sensors Data

2019 ◽  
Vol 11 (2) ◽  
pp. 63-68 ◽  
Author(s):  
Nicoletta Lotrecchiano ◽  
Filomena Gioiella ◽  
Aristide Giuliano ◽  
Daniele Sofia
Keyword(s):  
Air Quality ◽  
Urban Areas ◽  
Low Cost ◽  
Emission Control ◽  
Quality Monitoring ◽  
Forecasting Model ◽  
Air Quality Monitoring ◽  
Monitoring Networks ◽  
Human Beings ◽  
Monitoring Stations

Environmental pollution in urban areas may be mainly attributed to the rapid industrialization and increased growth of vehicular traffic. As a consequence of air quality deterioration, the health and welfare of human beings are compromised. Air quality monitoring networks usually are used not only to assess the pollutant trend but also in the effective set-up of preventive measures of atmospheric pollution. In this context, monitoring can be a valid action to evaluate different emission control scenarios; however, installing a high space-time resolution monitoring network is still expensive. Merge of observations data from low-cost air quality monitoring networks with forecasting models can contribute to improving significantly emission control scenarios. In this work, a validation algorithm of the forecasting model for the concentration of small particulates (PM10 and PM2.5) is proposed. Results showed a satisfactory agreement between the PM concentration forecast values and the measured data from 3 air quality monitoring stations. Final average RMSE values for all monitoring stations are equal to about 4.5 µg/m3.

Low-cost energy-efficient air quality monitoring system using sensor network

2021 ◽  
Vol 13 (4) ◽  
pp. 204
Author(s):  
Mare Srbinovska ◽  
Aleksandra Krkoleva Mateska ◽  
Vesna Andova ◽  
Maja Celeska Krstevska ◽  
Tomislav Kartalov
Keyword(s):  
Air Quality ◽  
Sensor Network ◽  
Monitoring System ◽  
Energy Efficient ◽  
Low Cost ◽  
Quality Monitoring ◽  
Air Quality Monitoring

Low-cost energy-efficient air quality monitoring system using sensor network

2021 ◽  
Vol 13 (4) ◽  
pp. 204
Author(s):  
Maja Celeska Krstevska ◽  
Mare Srbinovska ◽  
Tomislav Kartalov ◽  
Vesna Andova ◽  
Aleksandra Krkoleva Mateska
Keyword(s):  
Air Quality ◽  
Sensor Network ◽  
Monitoring System ◽  
Energy Efficient ◽  
Low Cost ◽  
Quality Monitoring ◽  
Air Quality Monitoring
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