Evaluating the performance of low cost chemical sensors for air pollution research

2016 ◽  
Vol 189 ◽  
pp. 85-103 ◽  
Author(s):  
Alastair C. Lewis ◽  
James D. Lee ◽  
Peter M. Edwards ◽  
Marvin D. Shaw ◽  
Mat J. Evans ◽  
...  
Keyword(s):  
Air Pollution ◽  
Electrochemical Sensors ◽  
Low Cost ◽  
Ambient Air ◽  
Boosted Regression Trees ◽  
Supervised Machine Learning ◽  
Learning Approaches ◽  
Individual Parameter ◽  
Air Quality Measurements ◽  
Order Of Magnitude

Low cost pollution sensors have been widely publicized, in principle offering increased information on the distribution of air pollution and a democratization of air quality measurements to amateur users. We report a laboratory study of commonly-used electrochemical sensors and quantify a number of cross-interferences with other atmospheric chemicals, some of which become significant at typical suburban air pollution concentrations. We highlight that artefact signals from co-sampled pollutants such as CO2 can be greater than the electrochemical sensor signal generated by the measurand. We subsequently tested in ambient air, over a period of three weeks, twenty identical commercial sensor packages alongside standard measurements and report on the degree of agreement between references and sensors. We then explore potential experimental approaches to improve sensor performance, enhancing outputs from qualitative to quantitative, focusing on low cost VOC photoionization sensors. Careful signal handling, for example, was seen to improve limits of detection by one order of magnitude. The quantity, magnitude and complexity of analytical interferences that must be characterised to convert a signal into a quantitative observation, with known uncertainties, make standard individual parameter regression inappropriate. We show that one potential solution to this problem is the application of supervised machine learning approaches such as boosted regression trees and Gaussian processes emulation.

MOPGA/Make Air Quality Great Again: Filling in the air quality data gap in Africa using lower-cost RAMP monitors

2020 ◽  
Author(s):  
Ramachandran Subramanian ◽  
Matthias Beekmann ◽  
Carl Malings ◽  
Anais Feron ◽  
Paola Formenti ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Electrochemical Sensors ◽  
Low Cost ◽  
Fine Particulate Matter ◽  
Ambient Air ◽  
Ambient Air Pollution ◽  
Quality Data ◽  
Rural Site ◽  
Data Gap

<p>Ambient air pollution is a leading cause of premature mortality across the world, with an estimated 258,000 deaths in Africa (UNICEF/GBD 2017). These estimated impacts have large uncertainties as many major cities in Africa do not have any ground-based air quality monitoring. The lack of data is due in part to the high cost of traditional monitoring equipment and the lack of trained personnel. As part of the “Make Air Quality Great Again” project under the “Make Our Planet Great Again” framework (MOPGA), we propose filling this data gap with low-cost sensors carefully calibrated against reference monitors.</p><p>Fifteen real-time affordable multi-pollutant (RAMP) monitors have been deployed in Abidjan, Côte d'Ivoire; Accra, Ghana; Kigali, Rwanda; Nairobi, Kenya; Niamey, Niger; and Zamdela, South Africa (near Johannesburg). The RAMPs use Plantower optical nephelometers to measure fine particulate matter mass (PM<sub>2.5</sub>) and four Alphasense electrochemical sensors to detect pollutant gases including nitrogen dioxide (NO<sub>2</sub>) and ozone (O<sub>3</sub>).</p><p>Using a calibration developed in Créteil, France, the deployments thus far reveal morning and evening spikes in combustion-related air pollution. The median hourly NO<sub>2</sub> in Accra and Nairobi for September-October 2019 was about 11 ppb; a similar value was observed across November-December 2019 in Zamdela. However, a previous long-term deployment of the RAMPs in Rwanda showed that, for robust data quality, low-cost sensors must be collocated with traditional reference monitors to develop localized calibration models. Hence, we acquired regulatory-grade PM<sub>2.5</sub>, NO<sub>2</sub>, and O<sub>3</sub> monitors for Abidjan and Accra. We also collocated RAMPs with existing reference monitors in Zamdela, Kigali, Abidjan, and Lamto (a rural site in Côte d'Ivoire). In this talk, we will present results on spatio-temporal variability of collocation-based sensor calibrations across these different cities, source identification, and challenges and plans for future expansion.</p>

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 Ambient Air Quality Measurement with Low-Cost Optical and Electrochemical Sensors: An Evaluation of Continuous Year-Long Operation

Environments ◽  
2021 ◽  
Vol 8 (11) ◽  
pp. 114
Author(s):  
Jiří Bílek ◽  
Ondřej Bílek ◽  
Petr Maršolek ◽  
Pavel Buček
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Sensor Network ◽  
Electrochemical Sensors ◽  
Reference Method ◽  
Ambient Air ◽  
Sensor Data ◽  
Dust Particles ◽  
Concentration Changes ◽  
The Impact

Sensor technology is attractive to the public due to its availability and ease of use. However, its usage raises numerous questions. The general trustworthiness of sensor data is widely discussed, especially with regard to accuracy, precision, and long-term signal stability. The VSB-Technical University of Ostrava has operated an air quality sensor network for more than two years, and its large sets of valid results can help in understanding the limitations of sensory measurement. Monitoring is focused on the concentrations of dust particles, NO2, and ozone to verify the impact of newly planted greenery on the reduction in air pollution. The sensor network currently covers an open field on the outskirts of Ostrava, between Liberty Ironworks and the nearby ISKO1650 monitoring station, where some of the worst air pollution levels in the Czech Republic are regularly measured. In the future, trees should be allowed to grow over the sensors, enabling assessment of the green barrier effect on air pollution. As expected, the service life of the sensors varies from 1 to 3 years; therefore, checks are necessary both prior to the measurement and regularly during operation, verifying output stability and overall performance. Results of the PMx sensory measurements correlated well with the reference method. Concentration values measured by NO2 sensors correlated poorly with the reference method, although timeline plots of concentration changes were in accordance. We suggest that a comparison of timelines should be used for air quality evaluations, rather than particular values. The results showed that the sensor measurements are not yet suitable to replace the reference methods, and dense sensor networks proved useful and robust tools for indicative air quality measurements (AQM).

scholarly journals Community Air Sensor Network (CAIRSENSE) project: Evaluation of low-cost sensor performance in a suburban environment in the southeastern United States

2016 ◽  
Author(s):  
Wan Jiao ◽  
Gayle Hagler ◽  
Ronald Williams ◽  
Robert Sharpe ◽  
Ryan Brown ◽  
...  
Keyword(s):  
Air Pollution ◽  
Carbon Monoxide ◽  
Air Quality ◽  
Sulfur Dioxide ◽  
Sensor Network ◽  
Correlation Coefficient ◽  
Low Cost ◽  
Ambient Air ◽  
Sensor Technology ◽  
Sensor Performance

Abstract. Advances in air pollution sensor technology have enabled the development of small and low cost systems to measure outdoor air pollution. The deployment of a large number of sensors across a small geographic area would have potential benefits to supplement traditional monitoring networks with additional geographic and temporal measurement resolution, if the data quality were sufficient. To understand the capability of emerging air sensor technology, the Community Air Sensor Network (CAIRSENSE) project deployed low cost, continuous and commercially-available air pollution sensors at a regulatory air monitoring site and as a local sensor network over a surrounding ~ 2 km area in Southeastern U.S. Co-location of sensors measuring oxides of nitrogen, ozone, carbon monoxide, sulfur dioxide, and particles revealed highly variable performance, both in terms of comparison to a reference monitor as well as whether multiple identical sensors reproduced the same signal. Multiple ozone, nitrogen dioxide, and carbon monoxide sensors revealed low to very high correlation with a reference monitor, with Pearson sample correlation coefficient (r) ranging from 0.39 to 0.97, −0.25 to 0.76, −0.40 to 0.82, respectively. The only sulfur dioxide sensor tested revealed no correlation (r  0.5), step-wise multiple linear regression was performed to determine if ambient temperature, relative humidity (RH), or age of the sensor in sampling days could be used in a correction algorithm to improve the agreement. Maximum improvement in agreement with a reference, incorporating all factors, was observed for an NO2 sensor (multiple correlation coefficient R2adj-orig = 0.57, R2adj-final = 0.81); however, other sensors showed no apparent improvement in agreement. A four-node sensor network was successfully able to capture ozone (2 nodes) and PM (4 nodes) data for an 8 month period of time and show expected diurnal concentration patterns, as well as potential ozone titration due to near-by traffic emissions. Overall, this study demonstrates a straightforward methodology for establishing low-cost air quality sensor performance in a real-world setting and demonstrates the feasibility of deploying a local sensor network to measure ambient air quality trends.

Air of the Anthropocene

2021 ◽  
Author(s):  
Francis Pope ◽  
Robin Price
Keyword(s):  
Air Pollution ◽  
Los Angeles ◽  
Low Cost ◽  
Measurement Techniques ◽  
Transformational Change ◽  
Ambient Air ◽  
Expert Advice ◽  
Light Sources ◽  
Anthropogenic Contamination ◽  
Science Collaboration

<p>Anthropogenic contamination of the atmosphere is causing both climate change and air pollution, which respectively represent the greatest long term and short term environmental risks to human and planetary health. The contamination is largely invisible and hence difficult to contextualise for non-expert audiences. This can lead to the problem being ignored; or where it is acknowledged, leading to feelings of helplessness and a lack of agency.</p><p>This project uses digital light painting to visualise and explore responses to particulate matter (PM) air pollution, in a variety of global locations, as a method for both public engagement and campaign work. This photographic technique combines long exposure with light sources digitally controlled by sensors, it builds upon the prior work of electronic pioneer Steve Mann (e.g. Mann et al. 2019) and more recent work visualising wifi strength (Arnall et al. 2013).</p><p>The five year art-science collaboration between Price and Pope has been highly successful. The Air of the Anthropocene project resulted in multiple gallery shows (including Los Angeles, Belfast and Birmingham). The media publicized it heavily, including Source Magazine, New Scientist and the Guardian. The physical art works were acquired by the Arts Council of Northern Ireland’s public collection.</p><p>In this presentation, we will highlight the scientific and aesthetic underpinnings of the use of low cost air pollution sensors for data visualisation through light painting. Locations for visualizations were guided by expert advice from environmental scientists in global locations, including those in Europe, Africa, Asia and South America. In this sense the science informed the art. Also, since the code from the project ended being used by scientists, the art informed the science (e.g. Crilley et al. 2018).</p><p>We will highlight the efficacy of this image making approach as an engagement and advocacy tool, through case studies of its use in field campaigns in Ethiopia (2020) and Kampala (2018), investigating both indoor and outdoor air pollution.  Future possibilities of the approach to air pollution visualization will be discussed. This will include expanding the approach through open sourcing the project and its adaptation beyond lens based techniques into augmented reality camera phone use.</p><p>The projected next phase of the collaboration will work towards empowering interested citizens of the world to make their own creative, aesthetic representations of their environment and use these images as citizen activists to affect transformational change in their own localities. Through adopting open source methodologies it is hoped that sustainability beyond the timescale and budget of the initial project with lasting legacy will be achieved.</p><p> </p><p>Arnall et al, 2013. Immaterials: light painting WiFi. Significance, 10(4). https://doi.org/10.1111/j.1740-9713.2013.00683.x </p><p>Crilley et al, 2018. Evaluation of a low-cost optical particle counter (Alphasense OPC-N2) for ambient air monitoring. Atmospheric Measurement Techniques. https://doi.org/10.5194/amt-11-709-2018 </p><p>Mann et al 2019, June. Making Sensors Tangible with Long-exposure Photography. In The 5th ACM Workshop on Wearable Systems and Applications. https://doi.org/10.1145/3325424.3329668</p>

scholarly journals An improved low-power measurement of ambient NO<sub>2</sub> and O<sub>3</sub> combining electrochemical sensor clusters and machine learning

2019 ◽  
Vol 12 (2) ◽  
pp. 1325-1336 ◽  
Author(s):  
Kate R. Smith ◽  
Peter M. Edwards ◽  
Peter D. Ivatt ◽  
James D. Lee ◽  
Freya Squires ◽  
...  
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Low Power ◽  
Electrochemical Sensors ◽  
Ambient Air ◽  
Boosted Regression Trees ◽  
Power Measurement ◽  
Sensor Data ◽  
Ambient Air Pollution ◽  
Reference Methods

Abstract. Low-cost sensors (LCSs) are an appealing solution to the problem of spatial resolution in air quality measurement, but they currently do not have the same analytical performance as regulatory reference methods. Individual sensors can be susceptible to analytical cross-interferences; have random signal variability; and experience drift over short, medium and long timescales. To overcome some of the performance limitations of individual sensors we use a clustering approach using the instantaneous median signal from six identical electrochemical sensors to minimize the randomized drifts and inter-sensor differences. We report here on a low-power analytical device (< 200 W) that is comprised of clusters of sensors for NO2, Ox, CO and total volatile organic compounds (VOCs) and that measures supporting parameters such as water vapour and temperature. This was tested in the field against reference monitors, collecting ambient air pollution data in Beijing, China. Comparisons were made of NO2 and Ox clustered sensor data against reference methods for calibrations derived from factory settings, in-field simple linear regression (SLR) and then against three machine learning (ML) algorithms. The parametric supervised ML algorithms, boosted regression trees (BRTs) and boosted linear regression (BLR), and the non-parametric technique, Gaussian process (GP), used all available sensor data to improve the measurement estimate of NO2 and Ox. In all cases ML produced an observational value that was closer to reference measurements than SLR alone. In combination, sensor clustering and ML generated sensor data of a quality that was close to that of regulatory measurements (using the RMSE metric) yet retained a very substantial cost and power advantage.

scholarly journals Impacts of Modifiable Factors on Ambient Air Pollution: A Case Study of COVID-19 Shutdowns

2020 ◽  
Author(s):  
Rebecca Tanzer-Gruener ◽  
Jiayu Li ◽  
s. rose eilenberg ◽  
Allen Robinson ◽  
Albert Presto
Keyword(s):  
Air Pollution ◽  
Real Time ◽  
Low Cost ◽  
Ambient Air ◽  
Ambient Air Pollution ◽  
Activity Levels ◽  
Industrial Emissions ◽  
Modifiable Factors ◽  
Business As Usual ◽  
The Impact

Modifiable sources of air pollution such as traffic, cooking, and electricity generation emissions can be modulated either by changing activity levels or source intensity. Although air pollution regulations typically target reducing emission factors rather than altering activity, the COVID-19 related closures offered a novel opportunity to observe and quantify the impact of activity levels of modifiable factors on ambient air pollution in real-time. We use data from a network of twenty-seven low-cost Real-time Affordable Multi-Pollutant (RAMP) sensor packages deployed throughout urban and suburban Pittsburgh along with data from EPA regulatory monitors. The RAMP locations were divided into four site groups based on land use (High Traffic, Urban Residential, Suburban Residential, and Industrial). Concentrations of PM2.5, CO, and NO2 following the COVID-related closures at each site group were compared to measurements from “business as usual” periods in March 2019 and 2020. Overall, PM2.5 concentrations decreased across the domain by 3 μg/m3. Intra-day variabilities of the pollutants were computed to attribute pollutant enhancements to specific emission sources (i.e. traffic and industrial emissions). There was no significant change in the industrial related intra-day variability of PM2.5 at the Industrial sites following the COVID-related closures. The morning rush hour induced CO and NO2 concentrations at the High Traffic sites were reduced by 57% and 43%, respectively, which is consistent with the observed reduction in commuter traffic (~50%). The morning rush hour PM2.5 enhancement from traffic emissions fell from ~1.5 μg/m3 to ~0 μg/m3 across all site groups. This translates to a reduction of 0.125 μg/m3 in the daily average PM2.5 concentration. If PM2.5 National Ambient Air Quality Standards (NAAQS) are tightened these calculations shed light on to what extent reductions in traffic related emissions are able to aid in meeting more stringent regulations.

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 Impacts of Modifiable Factors on Ambient Air Pollution: A Case Study of COVID-19 Shutdowns

2020 ◽  
Author(s):  
Rebecca Tanzer-Gruener ◽  
Jiayu Li ◽  
s. rose eilenberg ◽  
Allen Robinson ◽  
Albert Presto
Keyword(s):  
Air Pollution ◽  
Real Time ◽  
Low Cost ◽  
Ambient Air ◽  
Ambient Air Pollution ◽  
Activity Levels ◽  
Industrial Emissions ◽  
Modifiable Factors ◽  
Business As Usual ◽  
The Impact

Modifiable sources of air pollution such as traffic, cooking, and electricity generation emissions can be modulated either by changing activity levels or source intensity. Although air pollution regulations typically target reducing emission factors rather than altering activity, the COVID-19 related closures offered a novel opportunity to observe and quantify the impact of activity levels of modifiable factors on ambient air pollution in real-time. We use data from a network of twenty-seven low-cost Real-time Affordable Multi-Pollutant (RAMP) sensor packages deployed throughout urban and suburban Pittsburgh along with data from EPA regulatory monitors. The RAMP locations were divided into four site groups based on land use (High Traffic, Urban Residential, Suburban Residential, and Industrial). Concentrations of PM2.5, CO, and NO2 following the COVID-related closures at each site group were compared to measurements from “business as usual” periods in March 2019 and 2020. Overall, PM2.5 concentrations decreased across the domain by 3 μg/m3. Intra-day variabilities of the pollutants were computed to attribute pollutant enhancements to specific emission sources (i.e. traffic and industrial emissions). There was no significant change in the industrial related intra-day variability of PM2.5 at the Industrial sites following the COVID-related closures. The morning rush hour induced CO and NO2 concentrations at the High Traffic sites were reduced by 57% and 43%, respectively, which is consistent with the observed reduction in commuter traffic (~50%). The morning rush hour PM2.5 enhancement from traffic emissions fell from ~1.5 μg/m3 to ~0 μg/m3 across all site groups. This translates to a reduction of 0.125 μg/m3 in the daily average PM2.5 concentration. If PM2.5 National Ambient Air Quality Standards (NAAQS) are tightened these calculations shed light on to what extent reductions in traffic related emissions are able to aid in meeting more stringent regulations.

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