scholarly journals Assessing a low-cost methane sensor quantification system for use in complex rural and urban environments

2018 ◽  
Author(s):  
Ashley Collier-Oxandale ◽  
Michael P. Hannigan ◽  
Joanna Gordon Casey ◽  
Ricardo Piedrahita ◽  
John Ortega ◽  
...  
Keyword(s):  
Air Quality ◽  
Los Angeles ◽  
Oil And Gas ◽  
Spatial Scales ◽  
Low Cost ◽  
Complex Mixture ◽  
Urban Environments ◽  
Monitoring Networks ◽  
Linear Calibration ◽  
Spatial And Temporal Scales

Abstract. Low-cost sensors have the potential to facilitate the exploration of air quality issues on new temporal and spatial scales. Here we evaluate a low-cost sensor quantification system for methane through its use in two different deployments. The first, a one-month deployment along the Colorado Front Range includes sites near active oil and gas operations in the Denver-Julesberg basin. The second deployment in an urban Los Angeles neighborhood, an subject to complex mixture of air pollution sources including oil operations. Given its role as a potent greenhouse gas, new low-cost methods for detecting and monitoring methane may aid in protecting human and environmental health. In this paper, we assess a number of linear calibration models to convert raw sensor signals into ppm concentration values. We also examine different choices that can be made during calibration and data processing, and explore cross-sensitivities that impact this sensor type. The results illustrate the accuracy of the Figaro TGS 2600 sensor when methane is quantified from raw signals using the techniques described. The results also demonstrate the value of these tools for examining air quality trends and events on small spatial and temporal scales as well as their ability to characterize an area – highlighting their potential to provide preliminary data that can inform more targeted measurements or supplement existing monitoring networks.

scholarly journals Assessing a low-cost methane sensor quantification system for use in complex rural and urban environments

2018 ◽  
Vol 11 (6) ◽  
pp. 3569-3594 ◽  
Author(s):  
Ashley Collier-Oxandale ◽  
Joanna Gordon Casey ◽  
Ricardo Piedrahita ◽  
John Ortega ◽  
Hannah Halliday ◽  
...  
Keyword(s):  
Air Quality ◽  
Los Angeles ◽  
Oil And Gas ◽  
Spatial Scales ◽  
Low Cost ◽  
Urban Environments ◽  
Monitoring Networks ◽  
Linear Calibration ◽  
Spatial And Temporal Scales ◽  
Rural And Urban

Abstract. Low-cost sensors have the potential to facilitate the exploration of air quality issues on new temporal and spatial scales. Here we evaluate a low-cost sensor quantification system for methane through its use in two different deployments. The first was a 1-month deployment along the Colorado Front Range and included sites near active oil and gas operations in the Denver-Julesburg basin. The second deployment was in an urban Los Angeles neighborhood, subject to complex mixtures of air pollution sources including oil operations. Given its role as a potent greenhouse gas, new low-cost methods for detecting and monitoring methane may aid in protecting human and environmental health. In this paper, we assess a number of linear calibration models used to convert raw sensor signals into ppm concentration values. We also examine different choices that can be made during calibration and data processing and explore cross sensitivities that impact this sensor type. The results illustrate the accuracy of the Figaro TGS 2600 sensor when methane is quantified from raw signals using the techniques described. The results also demonstrate the value of these tools for examining air quality trends and events on small spatial and temporal scales as well as their ability to characterize an area – highlighting their potential to provide preliminary data that can inform more targeted measurements or supplement existing monitoring networks.

scholarly journals Field Evaluation of Low-Cost PM Sensors (Purple Air PA-II) Under Variable Urban Air Quality Conditions, in Greece

Atmosphere ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 926 ◽  
Author(s):  
Iasonas Stavroulas ◽  
Georgios Grivas ◽  
Panagiotis Michalopoulos ◽  
Eleni Liakakou ◽  
Aikaterini Bougiatioti ◽  
...  
Keyword(s):  
Air Quality ◽  
Urban Forest ◽  
Low Cost ◽  
Field Evaluation ◽  
Urban Environments ◽  
Spatiotemporal Variability ◽  
Monitoring Networks ◽  
Fire Event ◽  
Ambient Relative Humidity ◽  
Reference Measurements

Recent advances in particle sensor technologies have led to an increased development and utilization of low-cost, compact, particulate matter (PM) monitors. These devices can be deployed in dense monitoring networks, enabling an improved characterization of the spatiotemporal variability in ambient levels and exposure. However, the reliability of their measurements is an important prerequisite, necessitating rigorous performance evaluation and calibration in comparison to reference-grade instrumentation. In this study, field evaluation of Purple Air PA-II devices (low-cost PM sensors) is performed in two urban environments and across three seasons in Greece, in comparison to different types of reference instruments. Measurements were conducted in Athens (the largest city in Greece with nearly four-million inhabitants) for five months spanning over the summer of 2019 and winter/spring of 2020 and in Ioannina, a medium-sized city in northwestern Greece (100,000 inhabitants) during winter/spring 2019–2020. The PM2.5 sensor output correlates strongly with reference measurements (R2 = 0.87 against a beta attenuation monitor and R2 = 0.98 against an optical reference-grade monitor). Deviations in the sensor-reference agreement are identified as mainly related to elevated coarse particle concentrations and high ambient relative humidity. Simple and multiple regression models are tested to compensate for these biases, drastically improving the sensor’s response. Large decreases in sensor error are observed after implementation of models, leading to mean absolute percentage errors of 0.18 and 0.12 for the Athens and Ioannina datasets, respectively. Overall, a quality-controlled and robustly evaluated low-cost network can be an integral component for air quality monitoring in a smart city. Case studies are presented along this line, where a network of PA-II devices is used to monitor the air quality deterioration during a peri-urban forest fire event affecting the area of Athens and during extreme wintertime smog events in Ioannina, related to wood burning for residential heating.

scholarly journals Low-Cost Air Quality Sensors: One-Year Field Comparative Measurement of Different Gas Sensors and Particle Counters with Reference Monitors at Tušimice Observatory

Atmosphere ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 492 ◽  
Author(s):  
Petra Bauerová ◽  
Adriana Šindelářová ◽  
Štěpán Rychlík ◽  
Zbyněk Novák ◽  
Josef Keder
Keyword(s):  
Air Quality ◽  
Gas Sensors ◽  
Low Cost ◽  
Monitoring Network ◽  
Field Testing ◽  
Ambient Air ◽  
Monitoring Networks ◽  
Comparative Measurement ◽  
One Year ◽  
Pm2.5 And Pm10

With attention increasing regarding the level of air pollution in different metropolitan and industrial areas worldwide, interest in expanding the monitoring networks by low-cost air quality sensors is also increasing. Although the role of these small and affordable sensors is rather supplementary, determination of the measurement uncertainty is one of the main questions of their applicability because there is no certificate for quality assurance of these non-reference technologies. This paper presents the results of almost one-year field testing measurements, when the data from different low-cost sensors (for SO2, NO2, O3, and CO: Cairclip, Envea, FR; for PM1, PM2.5, and PM10: PMS7003, Plantower, CHN, and OPC-N2, Alphasense, UK) were compared with co-located reference monitors used within the Czech national ambient air quality monitoring network. The results showed that in addition to the given reduced measurement accuracy of the sensors, the data quality depends on the early detection of defective units and changes caused by the effect of meteorological conditions (effect of air temperature and humidity on gas sensors and effect of air humidity with condensation conditions on particle counters), or by the interference of different pollutants (especially in gas sensors). Comparative measurement is necessary prior to each sensor’s field applications.

Low-Cost Air Quality Sensor Network in Munich

2020 ◽  
Author(s):  
Daniel Zollitsch ◽  
Jia Chen ◽  
Florian Dietrich ◽  
Benno Voggenreiter ◽  
Luca Setili ◽  
...  
Keyword(s):  
Air Quality ◽  
Real Time ◽  
Air Pollutants ◽  
Low Cost ◽  
Environmental Parameters ◽  
Calibration Method ◽  
Sensitivity Function ◽  
Machine Learning Algorithms ◽  
Urban Air ◽  
Monitoring Networks

<p>As the number of official monitoring stations for measuring urban air pollutants such as nitrogen oxides (NOx), particulate matter (PM) or ozone (O<sub>3</sub>) in most cities is quite small, it is difficult to determine the real human exposure to those pollutants. Therefore, several groups have established spatially higher resolved monitoring networks using low-cost sensors to create a finer concentration map [1-3].</p><p>We are currently establishing a low-cost, but high-accuracy network in Munich to measure the concentrations of NOx, PM, O<sub>3</sub>, CO and additional environmental parameters. For that, we developed a compact stand-alone sensor systems that requires low power, automatically measures the respective parameters every minute and sends the data to our server. There the raw data is transferred into concentration values by applying the respective sensitivity function for each sensor. These functions are determined by calibration measurements prior to the distribution of the sensors.</p><p>In contrast to the other existing networks, we will apply a recurring calibration method using a mobile high precision calibration unit (reference sensor) and machine learning algorithms. The results will be used to update the sensitivity function of each single sensor twice a week.  With the help of this approach, we will be able to create a calibrated real-time concentration map of air pollutants in Munich.</p><p>[1] Bigi et al.: Performance of NO, NO<sub>2</sub> low cost sensors and three calibration approaches within a real world application, Atmos. Meas. Tech., 11, 3717–3735, 2018</p><p>[2] Popoola et al., “Use of networks of low cost air quality sensors to quantify air quality in urban settings,” Atmos. Environ., 194, 58–70, 2018</p><p>[3] Schneider et al.: Mapping urban air quality in near real-time using observations from low-cost sensors and model information, Environ. Int., 106, 234–247, 2017</p>

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.

Airborne Hyperspectral Trace Gas Sensors as Testbeds for Geostationary Air Quality Mission Validation

2020 ◽  
Author(s):  
Scott Janz ◽  
Matthew Kowalewski ◽  
Lok Lamsal ◽  
Laura Judd ◽  
Caroline Nowlan ◽  
...  
Keyword(s):  
Air Quality ◽  
Spatial Scales ◽  
Lower Troposphere ◽  
Weather Conditions ◽  
Trace Gas ◽  
Radiometric Calibration ◽  
Temporal Scales ◽  
Vertical Column ◽  
Spatial And Temporal Scales ◽  
Forecast Models

<p>Next generation air quality sensors are currently planned to launch within the next couple of years. The Tropospheric Emissions: Monitory of Pollution (TEMPO-United States) and Geostationary Environment Monitoring Sensor (GEMS-South Korea) are two such missions that will probe the boundary layer/lower troposphere at unprecedented spatial and temporal scales. These missions are designed to provide constraints on chemical forecast models and specifically to answer the question: "What are the temporal and spatial variations of emissions of gases and aerosols important for air quality and climate?" In preparation for these missions a number of airborne air quality field missions have been performed to collect data at similar spatial and temporal scales, and during relevant seasonal air quality episodes including fires. This data is being used to improve the trace gas retrieval algorithms and explore the unique spatial scales and diurnal patterns that will be encountered when the geostationary experiments are operational. This overview will present details of two of the instruments used during these campaigns, the GeoCAPE Airborne Simulator (GCAS) and the Geostationary Trace Gas and Aerosol Sensor Optimization (GeoTASO) instruments. Maintained at the Goddard Space Flight Center's Radiometric Calibration and Development Facility (RCDF), these instruments are similar in design and sensitivty to what will be measured on-orbit by the TEMPO and GEMS sensors. Results of the retrieval of high spatial resolution nitrogen dioxide and formaldehyde will presented. Examples of vertical column retrievals will be presented under various source/weather conditions as well as the uncertainties that result from both instrument and radiative transfer assumptions.</p>

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 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 The BErkeley Atmospheric CO<sub>2</sub> Observation Network: initial evaluation

2016 ◽  
Author(s):  
Alexis A. Shusterman ◽  
Virginia Teige ◽  
Alexander J. Turner ◽  
Catherine Newman ◽  
Jinsol Kim ◽  
...  
Keyword(s):  
Greenhouse Gas ◽  
Urban Areas ◽  
Atmospheric Transport ◽  
Spatial Scales ◽  
Low Cost ◽  
Cost Effective ◽  
Urban Environments ◽  
Spatial Density ◽  
World Population ◽  
Observation Network

Abstract. With the majority of the world population residing in urban areas, attempts to monitor and mitigate greenhouse gas emissions must necessarily center on cities. However, existing carbon dioxide observation networks are ill-equipped to resolve the specific intra-city emission phenomena targeted by regulation. Here we describe the design and implementation of the BErkeley Atmospheric CO2 Observation Network (BEACO2N), a distributed CO2 monitoring instrument that utilizes low-cost technology to achieve unprecedented spatial density throughout and around the city of Oakland, California. We characterize the network in terms of four performance parameters–cost, reliability, precision, and bias–and find the BEACO2N approach to be sufficiently cost-effective and reliable while nonetheless providing high-quality atmospheric observations. First results from the initial installation successfully capture hourly, daily, and seasonal CO2 signals relevant to urban environments on spatial scales that cannot be accurately represented by atmospheric transport models alone, demonstrating the utility of high-resolution surface networks in urban greenhouse gas monitoring efforts.

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