Articles | Volume 14, issue 8
https://doi.org/10.5194/amt-14-5637-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-14-5637-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Aug 2021
Research article |
| 18 Aug 2021
| 18 Aug 2021
Machine learning calibration of low-cost NO2 and PM10 sensors: non-linear algorithms and their impact on site transferability
Grantham Institute – Climate Change and the Environment, Imperial College London, London SW7 2AZ, UK
Department of Physics, Imperial College London, London SW7 2AZ, UK
Data Science Institute, Imperial College London, London SW7 2AZ, UK
Climatic Research Unit, School of Environmental Sciences, University of East Anglia, Norwich NR4 7TJ, UK
Lev Konstantinovskiy
AirPublic Ltd, London, UK
Hannah Gardiner
AirPublic Ltd, London, UK
John Cant
AirPublic Ltd, London, UK
Related authors
Peer Nowack and Duncan Watson-Parris
Atmos. Chem. Phys., 25, 2365–2384, https://doi.org/10.5194/acp-25-2365-2025, https://doi.org/10.5194/acp-25-2365-2025, 2025
Short summary
Short summary
In our article, we review uncertainties in global climate change projections and current methods using Earth observations as constraints, which is crucial for climate risk assessments and for informing society. We then discuss how machine learning can advance the field, discussing recent work that provides potentially stronger and more robust links between observed data and future climate projections. We further discuss the challenges of applying machine learning to climate science.
Jingyu Wang, Gabriel Chiodo, Timofei Sukhodolov, Blanca Ayarzagüena, William T. Ball, Mohamadou Diallo, Birgit Hassler, James Keeble, Peer Nowack, Clara Orbe, and Sandro Vattioni
EGUsphere, https://doi.org/10.5194/egusphere-2025-340, https://doi.org/10.5194/egusphere-2025-340, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
We analyzed the ozone response under elevated CO2 using the data from CMIP6 DECK experiments. We then looked at the relations between ozone response and temperature and circulation changes to identify drivers of the ozone change. The climate feedback of ozone is investigated by doing offline calculations and comparing models with and without interactive chemistry. We find that ozone-climate interactions are important for Earth System Models, thus should be considered in future model development.
Philipp Breul, Paulo Ceppi, and Peer Nowack
EGUsphere, https://doi.org/10.5194/egusphere-2025-221, https://doi.org/10.5194/egusphere-2025-221, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
We explore how Pacific low-level clouds influence projections of regional climate change by adjusting a climate model to enhance low cloud response to surface temperatures. We find significant changes in projected warming patterns and circulation changes, under increased CO2 conditions. Our findings are supported by similar relationships across state-of-the-art climate models. These results highlight the importance of accurately representing clouds for predicting regional climate change impacts.
Xiao Lu, Yiming Liu, Jiayin Su, Xiang Weng, Tabish Ansari, Yuqiang Zhang, Guowen He, Yuqi Zhu, Haolin Wang, Ganquan Zeng, Jingyu Li, Cheng He, Shuai Li, Teerachai Amnuaylojaroen, Tim Butler, Qi Fan, Shaojia Fan, Grant L. Forster, Meng Gao, Jianlin Hu, Yugo Kanaya, Mohd Talib Latif, Keding Lu, Philippe Nédélec, Peer Nowack, Bastien Sauvage, Xiaobin Xu, Lin Zhang, Ke Li, Ja-Ho Koo, and Tatsuya Nagashima
EGUsphere, https://doi.org/10.5194/egusphere-2024-3702, https://doi.org/10.5194/egusphere-2024-3702, 2024
Short summary
Short summary
This study analyzes summertime ozone trends in East and Southeast Asia derived from a comprehensive observational database spanning from 1995 to 2019, incorporating aircraft observations, ozonesonde data, and measurements from 2500 surface sites. Multiple models are applied to attribute to changes in anthropogenic emissions and climate. The results highlight increases in anthropogenic emission are the primary driver of ozone increases both in the free troposphere and at the surface.
Kevin Debeire, Lisa Bock, Peer Nowack, Jakob Runge, and Veronika Eyring
EGUsphere, https://doi.org/10.5194/egusphere-2024-2656, https://doi.org/10.5194/egusphere-2024-2656, 2024
Short summary
Short summary
This study introduces a new method to reduce uncertainty in climate model projections of future precipitation patterns over land. By using advanced causal discovery techniques, our approach improves the reliability of precipitation projections under different global warming scenarios, supporting the development of more effective strategies to address the impacts of climate change.
Sarah Wilson Kemsley, Paulo Ceppi, Hendrik Andersen, Jan Cermak, Philip Stier, and Peer Nowack
Atmos. Chem. Phys., 24, 8295–8316, https://doi.org/10.5194/acp-24-8295-2024, https://doi.org/10.5194/acp-24-8295-2024, 2024
Short summary
Short summary
Aiming to inform parameter selection for future observational constraint analyses, we incorporate five candidate meteorological drivers specifically targeting high clouds into a cloud controlling factor framework within a range of spatial domain sizes. We find a discrepancy between optimal domain size for predicting locally and globally aggregated cloud radiative anomalies and identify upper-tropospheric static stability as an important high-cloud controlling factor.
Hendrik Andersen, Jan Cermak, Alyson Douglas, Timothy A. Myers, Peer Nowack, Philip Stier, Casey J. Wall, and Sarah Wilson Kemsley
Atmos. Chem. Phys., 23, 10775–10794, https://doi.org/10.5194/acp-23-10775-2023, https://doi.org/10.5194/acp-23-10775-2023, 2023
Short summary
Short summary
This study uses an observation-based cloud-controlling factor framework to study near-global sensitivities of cloud radiative effects to a large number of meteorological and aerosol controls. We present near-global sensitivity patterns to selected thermodynamic, dynamic, and aerosol factors and discuss the physical mechanisms underlying the derived sensitivities. Our study hopes to guide future analyses aimed at constraining cloud feedbacks and aerosol–cloud interactions.
Xiang Weng, Grant L. Forster, and Peer Nowack
Atmos. Chem. Phys., 22, 8385–8402, https://doi.org/10.5194/acp-22-8385-2022, https://doi.org/10.5194/acp-22-8385-2022, 2022
Short summary
Short summary
We use machine learning to quantify the meteorological drivers behind surface ozone variations in China between 2015 and 2019. Our novel approaches show improved performance when compared to previous analysis methods. We highlight that nonlinearity in driver relationships and the impacts of large-scale meteorological phenomena are key to understanding ozone pollution. Moreover, we find that almost half of the observed ozone trend between 2015 and 2019 might have been driven by meteorology.
Carl Thomas, Apostolos Voulgarakis, Gerald Lim, Joanna Haigh, and Peer Nowack
Weather Clim. Dynam., 2, 581–608, https://doi.org/10.5194/wcd-2-581-2021, https://doi.org/10.5194/wcd-2-581-2021, 2021
Short summary
Short summary
Atmospheric blocking events are complex large-scale weather patterns which block the path of the jet stream. They are associated with heat waves in summer and cold snaps in winter. Blocking is poorly understood, and the effect of climate change is not clear. Here, we present a new method to study blocking using unsupervised machine learning. We show that this method performs better than previous methods used. These results show the potential for unsupervised learning in atmospheric science.
Alexander Kuhn-Régnier, Apostolos Voulgarakis, Peer Nowack, Matthias Forkel, I. Colin Prentice, and Sandy P. Harrison
Biogeosciences, 18, 3861–3879, https://doi.org/10.5194/bg-18-3861-2021, https://doi.org/10.5194/bg-18-3861-2021, 2021
Short summary
Short summary
Along with current climate, vegetation, and human influences, long-term accumulation of biomass affects fires. Here, we find that including the influence of antecedent vegetation and moisture improves our ability to predict global burnt area. Additionally, the length of the preceding period which needs to be considered for accurate predictions varies across regions.
James Keeble, Birgit Hassler, Antara Banerjee, Ramiro Checa-Garcia, Gabriel Chiodo, Sean Davis, Veronika Eyring, Paul T. Griffiths, Olaf Morgenstern, Peer Nowack, Guang Zeng, Jiankai Zhang, Greg Bodeker, Susannah Burrows, Philip Cameron-Smith, David Cugnet, Christopher Danek, Makoto Deushi, Larry W. Horowitz, Anne Kubin, Lijuan Li, Gerrit Lohmann, Martine Michou, Michael J. Mills, Pierre Nabat, Dirk Olivié, Sungsu Park, Øyvind Seland, Jens Stoll, Karl-Hermann Wieners, and Tongwen Wu
Atmos. Chem. Phys., 21, 5015–5061, https://doi.org/10.5194/acp-21-5015-2021, https://doi.org/10.5194/acp-21-5015-2021, 2021
Short summary
Short summary
Stratospheric ozone and water vapour are key components of the Earth system; changes to both have important impacts on global and regional climate. We evaluate changes to these species from 1850 to 2100 in the new generation of CMIP6 models. There is good agreement between the multi-model mean and observations, although there is substantial variation between the individual models. The future evolution of both ozone and water vapour is strongly dependent on the assumed future emissions scenario.
Abdul Malik, Peer J. Nowack, Joanna D. Haigh, Long Cao, Luqman Atique, and Yves Plancherel
Atmos. Chem. Phys., 20, 15461–15485, https://doi.org/10.5194/acp-20-15461-2020, https://doi.org/10.5194/acp-20-15461-2020, 2020
Short summary
Short summary
Solar geoengineering has been introduced to mitigate human-caused global warming by reflecting sunlight back into space. This research investigates the impact of solar geoengineering on the tropical Pacific climate. We find that solar geoengineering can compensate some of the greenhouse-induced changes in the tropical Pacific but not all. In particular, solar geoengineering will result in significant changes in rainfall, sea surface temperatures, and increased frequency of extreme ENSO events.
Lili Xia, Peer J. Nowack, Simone Tilmes, and Alan Robock
Atmos. Chem. Phys., 17, 11913–11928, https://doi.org/10.5194/acp-17-11913-2017, https://doi.org/10.5194/acp-17-11913-2017, 2017
Short summary
Short summary
Ozone is a key air pollutant. We model two geoengineering schemes, stratospheric sulfur injection and solar irradiance reduction, to compare their impacts on atmospheric ozone concentrations. With the nearly identical global mean surface temperature reduction, solar dimming increases global average surface ozone concentration, while sulfate injection decreases it. This difference is due to different stratosphere–troposphere exchange of ozone and tropospheric ozone chemistry in the two scenarios.
Peer Johannes Nowack, Nathan Luke Abraham, Peter Braesicke, and John Adrian Pyle
Atmos. Chem. Phys., 16, 4191–4203, https://doi.org/10.5194/acp-16-4191-2016, https://doi.org/10.5194/acp-16-4191-2016, 2016
Short summary
Short summary
Various forms of solar radiation management (SRM) have been proposed to counteract man-made climate change. However, all these countermeasures could have unintended side-effects. We add a novel perspective to this discussion by showing how atmospheric ozone changes under solar geoengineering could affect UV exposure and air pollution. This would have implications for human health and ecology. Atmospheric composition changes are therefore important to consider in the evaluation of any SRM scheme.
Peer Nowack and Duncan Watson-Parris
Atmos. Chem. Phys., 25, 2365–2384, https://doi.org/10.5194/acp-25-2365-2025, https://doi.org/10.5194/acp-25-2365-2025, 2025
Short summary
Short summary
In our article, we review uncertainties in global climate change projections and current methods using Earth observations as constraints, which is crucial for climate risk assessments and for informing society. We then discuss how machine learning can advance the field, discussing recent work that provides potentially stronger and more robust links between observed data and future climate projections. We further discuss the challenges of applying machine learning to climate science.
Jingyu Wang, Gabriel Chiodo, Timofei Sukhodolov, Blanca Ayarzagüena, William T. Ball, Mohamadou Diallo, Birgit Hassler, James Keeble, Peer Nowack, Clara Orbe, and Sandro Vattioni
EGUsphere, https://doi.org/10.5194/egusphere-2025-340, https://doi.org/10.5194/egusphere-2025-340, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
We analyzed the ozone response under elevated CO2 using the data from CMIP6 DECK experiments. We then looked at the relations between ozone response and temperature and circulation changes to identify drivers of the ozone change. The climate feedback of ozone is investigated by doing offline calculations and comparing models with and without interactive chemistry. We find that ozone-climate interactions are important for Earth System Models, thus should be considered in future model development.
Philipp Breul, Paulo Ceppi, and Peer Nowack
EGUsphere, https://doi.org/10.5194/egusphere-2025-221, https://doi.org/10.5194/egusphere-2025-221, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
We explore how Pacific low-level clouds influence projections of regional climate change by adjusting a climate model to enhance low cloud response to surface temperatures. We find significant changes in projected warming patterns and circulation changes, under increased CO2 conditions. Our findings are supported by similar relationships across state-of-the-art climate models. These results highlight the importance of accurately representing clouds for predicting regional climate change impacts.
Xiao Lu, Yiming Liu, Jiayin Su, Xiang Weng, Tabish Ansari, Yuqiang Zhang, Guowen He, Yuqi Zhu, Haolin Wang, Ganquan Zeng, Jingyu Li, Cheng He, Shuai Li, Teerachai Amnuaylojaroen, Tim Butler, Qi Fan, Shaojia Fan, Grant L. Forster, Meng Gao, Jianlin Hu, Yugo Kanaya, Mohd Talib Latif, Keding Lu, Philippe Nédélec, Peer Nowack, Bastien Sauvage, Xiaobin Xu, Lin Zhang, Ke Li, Ja-Ho Koo, and Tatsuya Nagashima
EGUsphere, https://doi.org/10.5194/egusphere-2024-3702, https://doi.org/10.5194/egusphere-2024-3702, 2024
Short summary
Short summary
This study analyzes summertime ozone trends in East and Southeast Asia derived from a comprehensive observational database spanning from 1995 to 2019, incorporating aircraft observations, ozonesonde data, and measurements from 2500 surface sites. Multiple models are applied to attribute to changes in anthropogenic emissions and climate. The results highlight increases in anthropogenic emission are the primary driver of ozone increases both in the free troposphere and at the surface.
Kevin Debeire, Lisa Bock, Peer Nowack, Jakob Runge, and Veronika Eyring
EGUsphere, https://doi.org/10.5194/egusphere-2024-2656, https://doi.org/10.5194/egusphere-2024-2656, 2024
Short summary
Short summary
This study introduces a new method to reduce uncertainty in climate model projections of future precipitation patterns over land. By using advanced causal discovery techniques, our approach improves the reliability of precipitation projections under different global warming scenarios, supporting the development of more effective strategies to address the impacts of climate change.
Sarah Wilson Kemsley, Paulo Ceppi, Hendrik Andersen, Jan Cermak, Philip Stier, and Peer Nowack
Atmos. Chem. Phys., 24, 8295–8316, https://doi.org/10.5194/acp-24-8295-2024, https://doi.org/10.5194/acp-24-8295-2024, 2024
Short summary
Short summary
Aiming to inform parameter selection for future observational constraint analyses, we incorporate five candidate meteorological drivers specifically targeting high clouds into a cloud controlling factor framework within a range of spatial domain sizes. We find a discrepancy between optimal domain size for predicting locally and globally aggregated cloud radiative anomalies and identify upper-tropospheric static stability as an important high-cloud controlling factor.
Hendrik Andersen, Jan Cermak, Alyson Douglas, Timothy A. Myers, Peer Nowack, Philip Stier, Casey J. Wall, and Sarah Wilson Kemsley
Atmos. Chem. Phys., 23, 10775–10794, https://doi.org/10.5194/acp-23-10775-2023, https://doi.org/10.5194/acp-23-10775-2023, 2023
Short summary
Short summary
This study uses an observation-based cloud-controlling factor framework to study near-global sensitivities of cloud radiative effects to a large number of meteorological and aerosol controls. We present near-global sensitivity patterns to selected thermodynamic, dynamic, and aerosol factors and discuss the physical mechanisms underlying the derived sensitivities. Our study hopes to guide future analyses aimed at constraining cloud feedbacks and aerosol–cloud interactions.
Xiang Weng, Grant L. Forster, and Peer Nowack
Atmos. Chem. Phys., 22, 8385–8402, https://doi.org/10.5194/acp-22-8385-2022, https://doi.org/10.5194/acp-22-8385-2022, 2022
Short summary
Short summary
We use machine learning to quantify the meteorological drivers behind surface ozone variations in China between 2015 and 2019. Our novel approaches show improved performance when compared to previous analysis methods. We highlight that nonlinearity in driver relationships and the impacts of large-scale meteorological phenomena are key to understanding ozone pollution. Moreover, we find that almost half of the observed ozone trend between 2015 and 2019 might have been driven by meteorology.
Carl Thomas, Apostolos Voulgarakis, Gerald Lim, Joanna Haigh, and Peer Nowack
Weather Clim. Dynam., 2, 581–608, https://doi.org/10.5194/wcd-2-581-2021, https://doi.org/10.5194/wcd-2-581-2021, 2021
Short summary
Short summary
Atmospheric blocking events are complex large-scale weather patterns which block the path of the jet stream. They are associated with heat waves in summer and cold snaps in winter. Blocking is poorly understood, and the effect of climate change is not clear. Here, we present a new method to study blocking using unsupervised machine learning. We show that this method performs better than previous methods used. These results show the potential for unsupervised learning in atmospheric science.
Alexander Kuhn-Régnier, Apostolos Voulgarakis, Peer Nowack, Matthias Forkel, I. Colin Prentice, and Sandy P. Harrison
Biogeosciences, 18, 3861–3879, https://doi.org/10.5194/bg-18-3861-2021, https://doi.org/10.5194/bg-18-3861-2021, 2021
Short summary
Short summary
Along with current climate, vegetation, and human influences, long-term accumulation of biomass affects fires. Here, we find that including the influence of antecedent vegetation and moisture improves our ability to predict global burnt area. Additionally, the length of the preceding period which needs to be considered for accurate predictions varies across regions.
James Keeble, Birgit Hassler, Antara Banerjee, Ramiro Checa-Garcia, Gabriel Chiodo, Sean Davis, Veronika Eyring, Paul T. Griffiths, Olaf Morgenstern, Peer Nowack, Guang Zeng, Jiankai Zhang, Greg Bodeker, Susannah Burrows, Philip Cameron-Smith, David Cugnet, Christopher Danek, Makoto Deushi, Larry W. Horowitz, Anne Kubin, Lijuan Li, Gerrit Lohmann, Martine Michou, Michael J. Mills, Pierre Nabat, Dirk Olivié, Sungsu Park, Øyvind Seland, Jens Stoll, Karl-Hermann Wieners, and Tongwen Wu
Atmos. Chem. Phys., 21, 5015–5061, https://doi.org/10.5194/acp-21-5015-2021, https://doi.org/10.5194/acp-21-5015-2021, 2021
Short summary
Short summary
Stratospheric ozone and water vapour are key components of the Earth system; changes to both have important impacts on global and regional climate. We evaluate changes to these species from 1850 to 2100 in the new generation of CMIP6 models. There is good agreement between the multi-model mean and observations, although there is substantial variation between the individual models. The future evolution of both ozone and water vapour is strongly dependent on the assumed future emissions scenario.
Abdul Malik, Peer J. Nowack, Joanna D. Haigh, Long Cao, Luqman Atique, and Yves Plancherel
Atmos. Chem. Phys., 20, 15461–15485, https://doi.org/10.5194/acp-20-15461-2020, https://doi.org/10.5194/acp-20-15461-2020, 2020
Short summary
Short summary
Solar geoengineering has been introduced to mitigate human-caused global warming by reflecting sunlight back into space. This research investigates the impact of solar geoengineering on the tropical Pacific climate. We find that solar geoengineering can compensate some of the greenhouse-induced changes in the tropical Pacific but not all. In particular, solar geoengineering will result in significant changes in rainfall, sea surface temperatures, and increased frequency of extreme ENSO events.
Lili Xia, Peer J. Nowack, Simone Tilmes, and Alan Robock
Atmos. Chem. Phys., 17, 11913–11928, https://doi.org/10.5194/acp-17-11913-2017, https://doi.org/10.5194/acp-17-11913-2017, 2017
Short summary
Short summary
Ozone is a key air pollutant. We model two geoengineering schemes, stratospheric sulfur injection and solar irradiance reduction, to compare their impacts on atmospheric ozone concentrations. With the nearly identical global mean surface temperature reduction, solar dimming increases global average surface ozone concentration, while sulfate injection decreases it. This difference is due to different stratosphere–troposphere exchange of ozone and tropospheric ozone chemistry in the two scenarios.
Peer Johannes Nowack, Nathan Luke Abraham, Peter Braesicke, and John Adrian Pyle
Atmos. Chem. Phys., 16, 4191–4203, https://doi.org/10.5194/acp-16-4191-2016, https://doi.org/10.5194/acp-16-4191-2016, 2016
Short summary
Short summary
Various forms of solar radiation management (SRM) have been proposed to counteract man-made climate change. However, all these countermeasures could have unintended side-effects. We add a novel perspective to this discussion by showing how atmospheric ozone changes under solar geoengineering could affect UV exposure and air pollution. This would have implications for human health and ecology. Atmospheric composition changes are therefore important to consider in the evaluation of any SRM scheme.
Related subject area
Subject: Gases | Technique: In Situ Measurement | Topic: Data Processing and Information Retrieval
Digitization and calibration of historical solar absorption infrared spectra from the Jungfraujoch site
Direct high-precision radon quantification for interpreting high-frequency greenhouse gas measurements
Resolving the contributions of local emissions to measured concentrations: a method comparison
Intercomparison of fast airborne ozone instruments to measure eddy covariance fluxes: spatial variability in deposition at the ocean surface and evidence for cloud processing
Field assessments on the impact of CO2 concentration fluctuations along with complex-terrain flows on the estimation of the net ecosystem exchange of temperate forests
Multi-instrumental analysis of ozone vertical profiles and total columns in South America: comparison between subtropical and equatorial latitudes
Gridded surface O3, NOx, and CO abundances for model metrics from the South Korean ground station network
Transferability of machine-learning-based global calibration models for NO2 and NO low-cost sensors
Detection and long-term quantification of methane emissions from an active landfill
Working standard gas saving system for in-situ CO2 and CH4 measurements and calculation method for concentrations and their uncertainty
Research of low-cost air quality monitoring models with different machine learning algorithms
New insights from the Jülich Ozone Sonde Intercomparison Experiment: calibration functions traceable to one ozone reference instrument
Identification of spikes in continuous ground-based in situ time series of CO2, CH4 and CO: an extended experiment within the European ICOS Atmosphere network
Data treatment and corrections for estimating H2O and CO2 isotope fluxes from high-frequency observations
Measurements of volatile organic compounds in ambient air by gas-chromatography and real-time Vocus PTR-TOF-MS: calibrations, instrument background corrections, and introducing a PTR Data Toolkit
Development of low-cost air quality stations for next-generation monitoring networks: calibration and validation of NO2 and O3 sensors
Detecting plumes in mobile air quality monitoring time series with density-based spatial clustering of applications with noise
Characterising the methane gas and environmental response of the Figaro Taguchi Gas Sensor (TGS) 2611-E00
Reducing errors on estimates of the carbon uptake period based on time series of atmospheric CO2
Generalized Kendrick analysis for improved visualization of atmospheric mass spectral data
Determination of NOx emission rates of inland ships from onshore measurements
Data quality enhancement for field experiments in atmospheric chemistry via sequential Monte Carlo filters
A flexible algorithm for network design based on information theory
Real-world wintertime CO, N2O, and CO2 emissions of a central European village
Evaluation of two common source estimation measurement strategies using large-eddy simulation of plume dispersion under neutral atmospheric conditions
Machine learning techniques to improve the field performance of low-cost air quality sensors
Estimation of sulfuric acid concentration using ambient ion composition and concentration data obtained with atmospheric pressure interface time-of-flight ion mass spectrometer
Importance of the Webb, Pearman, and Leuning (WPL) correction for the measurement of small CO2 fluxes
Unravelling a black box: an open-source methodology for the field calibration of small air quality sensors
An algorithm to detect non-background signals in greenhouse gas time series from European tall tower and mountain stations
Mobile atmospheric measurements and local-scale inverse estimation of the location and rates of brief CH4 and CO2 releases from point sources
SIBaR: a new method for background quantification and removal from mobile air pollution measurements
The high-frequency response correction of eddy covariance fluxes – Part 2: An experimental approach for analysing noisy measurements of small fluxes
The high-frequency response correction of eddy covariance fluxes – Part 1: An experimental approach and its interdependence with the time-lag estimation
Uncertainty of hourly-average concentration values derived from non-continuous measurements
Emissions relationships in western forest fire plumes – Part 1: Reducing the effect of mixing errors on emission factors
A new method to correct the electrochemical concentration cell (ECC) ozonesonde time response and its implications for “background current” and pump efficiency
Monitoring the compliance of sailing ships with fuel sulfur content regulations using unmanned aerial vehicle (UAV) measurements of ship emissions in open water
High-resolution mapping of urban air quality with heterogeneous observations: a new methodology and its application to Amsterdam
Towards standardized processing of eddy covariance flux measurements of carbonyl sulfide
Integration and calibration of non-dispersive infrared (NDIR) CO2 low-cost sensors and their operation in a sensor network covering Switzerland
Correcting the impact of the isotope composition on the mixing ratio dependency of water vapour isotope measurements with cavity ring-down spectrometers
Correcting high-frequency losses of reactive nitrogen flux measurements
Surface flux estimates derived from UAS-based mole fraction measurements by means of a nocturnal boundary layer budget approach
InnFLUX – an open-source code for conventional and disjunct eddy covariance analysis of trace gas measurements: an urban test case
Accurate measurements of atmospheric carbon dioxide and methane mole fractions at the Siberian coastal site Ambarchik
Traffic-related air pollution near roadways: discerning local impacts from background
Bayesian atmospheric tomography for detection and quantification of methane emissions: application to data from the 2015 Ginninderra release experiment
Evaluating and improving the reliability of gas-phase sensor system calibrations across new locations for ambient measurements and personal exposure monitoring
A novel approach for simple statistical analysis of high-resolution mass spectra
Jamal Makkor, Mathias Palm, Matthias Buschmann, Emmanuel Mahieu, Martyn P. Chipperfield, and Justus Notholt
Atmos. Meas. Tech., 18, 1105–1114, https://doi.org/10.5194/amt-18-1105-2025, https://doi.org/10.5194/amt-18-1105-2025, 2025
Short summary
Short summary
During the years 1950 and 1951, Marcel Migeotte took regular solar measurements in the form of paper rolls at the Jungfraujoch site. These historical spectra proved to be valuable for atmospheric research and needed to be saved for posterity. Therefore, a digitization method which used image-processing techniques was developed to extract them from the historical paper rolls. This allowed them to be saved in a machine-readable format that is easily accessible to the scientific community.
Dafina Kikaj, Edward Chung, Alan D. Griffiths, Scott D. Chambers, Grant Forster, Angelina Wenger, Penelope Pickers, Chris Rennick, Simon O'Doherty, Joseph Pitt, Kieran Stanley, Dickon Young, Leigh S. Fleming, Karina Adcock, Emmal Safi, and Tim Arnold
Atmos. Meas. Tech., 18, 151–175, https://doi.org/10.5194/amt-18-151-2025, https://doi.org/10.5194/amt-18-151-2025, 2025
Short summary
Short summary
We present a protocol to improve confidence in atmospheric radon measurements, enabling site comparisons and integration with greenhouse gas data. As a natural tracer, radon provides an independent check of transport model performance. This standardized method enhances radon’s use as a metric for model evaluation. Beyond UK observatories, it can support broader networks like ICOS and WMO/GAW, advancing global atmospheric research.
Taylor D. Edwards, Yee Ka Wong, Cheol-Jeon Heong, Jonathan M. Wang, Yushan Su, and Greg J. Evans
EGUsphere, https://doi.org/10.5194/egusphere-2024-2488, https://doi.org/10.5194/egusphere-2024-2488, 2024
Short summary
Short summary
We tested a variety of scientific measurements and algorithms for distinguishing the amounts of air pollution that were emitted by a nearby pollutor from background pollution that was already in the air. The results show that machine learning and other statistical algorithms produced accurate estimates of this background pollution. These findings help scientists and regulators understand where pollution comes from, and to improve measurements of pollution from sources like traffic.
Randall Chiu, Florian Obersteiner, Alessandro Franchin, Teresa Campos, Adriana Bailey, Christopher Webster, Andreas Zahn, and Rainer Volkamer
Atmos. Meas. Tech., 17, 5731–5746, https://doi.org/10.5194/amt-17-5731-2024, https://doi.org/10.5194/amt-17-5731-2024, 2024
Short summary
Short summary
The ozone sink into oceans and marine clouds is seldom studied and highly uncertain. Calculations suggest O3 destruction at aqueous surfaces (ocean, droplets) may be strongly accelerated, but field evidence is missing. Here we compare three fast airborne O3 instruments to measure eddy covariance fluxes of O3 over the remote ocean, in clear and cloudy air. We find O3 fluxes below clouds are consistently directed into clouds, while O3 fluxes into oceans are much smaller and spatially variable.
Dexiong Teng, Jiaojun Zhu, Tian Gao, Fengyuan Yu, Yuan Zhu, Xinhua Zhou, and Bai Yang
Atmos. Meas. Tech., 17, 5581–5599, https://doi.org/10.5194/amt-17-5581-2024, https://doi.org/10.5194/amt-17-5581-2024, 2024
Short summary
Short summary
Dense canopy weakens turbulent mixing, leading to significant CO2 storage (Fs), a key part of net ecosystem exchange (NEE) measured using eddy covariance. Gust-biased Fs measurements complicate NEE estimation in forests with complex terrain. We analyzed gust-induced CO2 fluctuations and their impact on Fs. Fs and its contribution to NEE can be explained by terrain complexity and turbulent mixing. This work highlights how gusts over complex terrain affect the Fs and NEE measurements.
Gabriela Dornelles Bittencourt, Hassan Bencherif, Damaris Kirsch Pinheiro, Nelson Begue, Lucas Vaz Peres, José Valentin Bageston, Douglas Lima de Bem, Francisco Raimundo da Silva, and Tristan Millet
Atmos. Meas. Tech., 17, 5201–5220, https://doi.org/10.5194/amt-17-5201-2024, https://doi.org/10.5194/amt-17-5201-2024, 2024
Short summary
Short summary
The study examines the behavior of ozone at equatorial and subtropical latitudes in South America, in a multi-instrumental analysis. The methodology applied used ozonesondes (SHADOZ/NASA) and satellite data (TIMED/SABER), as well as analysis with ground-based and satellite instruments, allowing a more in-depth study at both latitudes. The main motivation is to understand how latitudinal differences in the observation of ozone content can interfere with the behavior of this trace gas.
Calum Patrick Wilson and Michael John Prather
EGUsphere, https://doi.org/10.5194/egusphere-2024-1173, https://doi.org/10.5194/egusphere-2024-1173, 2024
Short summary
Short summary
We evaluated how well we can infer air pollutant levels (ozone, carbon monoxide, and nitrogen oxides) between air quality stations throughout South Korea, finding good representation in most densely measured cities in spite of intense small-scale emission hotspots. Comparing observed air quality with gridded model output is desirable, and so we created gridded datasets over South Korea using air quality station measurements, which agreed with airborne measurements around Seoul.
Ayah Abu-Hani, Jia Chen, Vigneshkumar Balamurugan, Adrian Wenzel, and Alessandro Bigi
Atmos. Meas. Tech., 17, 3917–3931, https://doi.org/10.5194/amt-17-3917-2024, https://doi.org/10.5194/amt-17-3917-2024, 2024
Short summary
Short summary
This study examined the transferability of machine learning calibration models among low-cost sensor units targeting NO2 and NO. The global models were evaluated under similar and different emission conditions. To counter cross-sensitivity, the study proposed integrating O3 measurements from nearby reference stations, in Switzerland. The models show substantial improvement when O3 measurements are incorporated, which is more pronounced when in regions with elevated O3 concentrations.
Pramod Kumar, Christopher Caldow, Grégoire Broquet, Adil Shah, Olivier Laurent, Camille Yver-Kwok, Sebastien Ars, Sara Defratyka, Susan Warao Gichuki, Luc Lienhardt, Mathis Lozano, Jean-Daniel Paris, Felix Vogel, Caroline Bouchet, Elisa Allegrini, Robert Kelly, Catherine Juery, and Philippe Ciais
Atmos. Meas. Tech., 17, 1229–1250, https://doi.org/10.5194/amt-17-1229-2024, https://doi.org/10.5194/amt-17-1229-2024, 2024
Short summary
Short summary
This study presents a series of mobile measurement campaigns to monitor the CH4 emissions from an active landfill. These measurements are processed using a Gaussian plume model and atmospheric inversion techniques to quantify the landfill CH4 emissions. The methane emission estimates range between ~0.4 and ~7 t CH4 per day, and their variations are analyzed. The robustness of the estimates is assessed depending on the distance of the measurements from the potential sources in the landfill.
Motoki Sasakawa, Noritsugu Tsuda, Toshinobu Machida, Mikhail Arshinov, Denis Davydov, Aleksandr Fofonov, and Boris Belan
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2023-246, https://doi.org/10.5194/amt-2023-246, 2024
Revised manuscript accepted for AMT
Short summary
Short summary
To accurately monitor atmospheric greenhouse gases, stable measurements are needed. Environmental changes like atmospheric pressure can alter device output. We counteract this by measuring standard gases with known concentrations. However, these gases deplete quickly. To address this, we’ve developed a system using ambient air at the site, reducing standard gas consumption. This paper details the system and a method for calculating concentrations.
Gang Wang, Chunlai Yu, Kai Guo, Haisong Guo, and Yibo Wang
Atmos. Meas. Tech., 17, 181–196, https://doi.org/10.5194/amt-17-181-2024, https://doi.org/10.5194/amt-17-181-2024, 2024
Short summary
Short summary
A low-cost multi-parameter air quality monitoring system (LCS) based on different machine learning algorithms is proposed. The LCS can measure particulate matter (PM) and gas pollutants simultaneously. The performance of the different algorithms (RF, MLR, KNN, BP, GA-BP) with the parameters such as R2 and RMSE are compared and discussed. These measurements indicate the LCS based on the machine learning algorithms can be used to predict the concentrations of PM and gas pollution.
Herman G. J. Smit, Deniz Poyraz, Roeland Van Malderen, Anne M. Thompson, David W. Tarasick, Ryan M. Stauffer, Bryan J. Johnson, and Debra E. Kollonige
Atmos. Meas. Tech., 17, 73–112, https://doi.org/10.5194/amt-17-73-2024, https://doi.org/10.5194/amt-17-73-2024, 2024
Short summary
Short summary
This paper revisits fundamentals of ECC ozonesonde measurements to develop and characterize a methodology to correct for the fast and slow time responses using the JOSIE (Jülich Ozone Sonde Intercomparison Experiment) simulation chamber data. Comparing the new corrected ozonesonde profiles to an accurate ozone UV photometer (OPM) as reference allows us to evaluate the time response correction (TRC) method and to determine calibration functions traceable to one reference with 5 % uncertainty.
Paolo Cristofanelli, Cosimo Fratticioli, Lynn Hazan, Mali Chariot, Cedric Couret, Orestis Gazetas, Dagmar Kubistin, Antti Laitinen, Ari Leskinen, Tuomas Laurila, Matthias Lindauer, Giovanni Manca, Michel Ramonet, Pamela Trisolino, and Martin Steinbacher
Atmos. Meas. Tech., 16, 5977–5994, https://doi.org/10.5194/amt-16-5977-2023, https://doi.org/10.5194/amt-16-5977-2023, 2023
Short summary
Short summary
We investigated the application of two automatic methods for detecting spikes due to local emissions in greenhouse gas (GHG) observations at a subset of sites from the ICOS Atmosphere network. We analysed the sensitivity to the spike frequency of using different methods and settings. We documented the impact of the de-spiking on different temporal aggregations (i.e. hourly, monthly and seasonal averages) of CO2, CH4 and CO 1 min time series.
Robbert P. J. Moonen, Getachew A. Adnew, Oscar K. Hartogensis, Jordi Vilà-Guerau de Arellano, David J. Bonell Fontas, and Thomas Röckmann
Atmos. Meas. Tech., 16, 5787–5810, https://doi.org/10.5194/amt-16-5787-2023, https://doi.org/10.5194/amt-16-5787-2023, 2023
Short summary
Short summary
Isotope fluxes allow for net ecosystem gas exchange fluxes to be partitioned into sub-components like plant assimilation, respiration and transpiration, which can help us better understand the environmental drivers of each partial flux. We share the results of a field campaign isotope fluxes were derived using a combination of laser spectroscopy and eddy covariance. We found lag times and high frequency signal loss in the isotope fluxes we derived and present methods to correct for both.
Andrew R. Jensen, Abigail R. Koss, Ryder B. Hales, and Joost A. de Gouw
Atmos. Meas. Tech., 16, 5261–5285, https://doi.org/10.5194/amt-16-5261-2023, https://doi.org/10.5194/amt-16-5261-2023, 2023
Short summary
Short summary
Quantification of a wide range of volatile organic compounds by proton-transfer-reaction mass spectrometry (PTR-MS) can be achieved with direct calibration of only a subset of compounds, characterization of instrument response, and simple reaction kinetics. We characterized our Vocus PTR-MS and developed a toolkit as a guide through this process. A catalytic zero air generator provided the lowest detection limits, and short, frequent calibrations informed variability in instrument response.
Alice Cavaliere, Lorenzo Brilli, Bianca Patrizia Andreini, Federico Carotenuto, Beniamino Gioli, Tommaso Giordano, Marco Stefanelli, Carolina Vagnoli, Alessandro Zaldei, and Giovanni Gualtieri
Atmos. Meas. Tech., 16, 4723–4740, https://doi.org/10.5194/amt-16-4723-2023, https://doi.org/10.5194/amt-16-4723-2023, 2023
Short summary
Short summary
We assessed calibration models for two low-cost stations equipped with O3 and NO2 metal oxide sensors. Environmental parameters had improved accuracy in linear and black box models. Moreover, interpretability methods like SHapley Additive exPlanations helped identify the physical patterns and potential problems of these models in a field validation. Results showed both sensors performed well with the same linear model form, but unique coefficients were required for intersensor variability.
Blake Actkinson and Robert J. Griffin
Atmos. Meas. Tech., 16, 3547–3559, https://doi.org/10.5194/amt-16-3547-2023, https://doi.org/10.5194/amt-16-3547-2023, 2023
Short summary
Short summary
Data collected using air quality instrumentation deployed on automobiles and driven repeatedly in Houston neighborhoods are analyzed using a novel machine learning technique. The aim is to separate large plumes from the rest of the data in order to identify the sources of the highest levels of the pollutants. The number and nature of these plumes are characterized spatially and can be linked to emissions from different types of motor vehicles.
Adil Shah, Olivier Laurent, Luc Lienhardt, Grégoire Broquet, Rodrigo Rivera Martinez, Elisa Allegrini, and Philippe Ciais
Atmos. Meas. Tech., 16, 3391–3419, https://doi.org/10.5194/amt-16-3391-2023, https://doi.org/10.5194/amt-16-3391-2023, 2023
Short summary
Short summary
As methane (CH4) contributes to global warming, more CH4 measurements are required to better characterise source emissions. Hence, we tested a cheap CH4 sensor for 338 d of landfill sampling. We derived an excellent CH4 response model in a stable environment. However, different types of air with the same CH4 level had diverse sensor responses. We characterised temperature and water vapour response but could not replicate field sampling. Thus, other species may cause sensor interactions.
Theertha Kariyathan, Ana Bastos, Julia Marshall, Wouter Peters, Pieter Tans, and Markus Reichstein
Atmos. Meas. Tech., 16, 3299–3312, https://doi.org/10.5194/amt-16-3299-2023, https://doi.org/10.5194/amt-16-3299-2023, 2023
Short summary
Short summary
The timing and duration of the carbon uptake period (CUP) are sensitive to the occurrence of major phenological events, which are influenced by recent climate change. This study presents an ensemble-based approach for quantifying the timing and duration of the CUP and their uncertainty when derived from atmospheric CO2 measurements with noise and gaps. The CUP metrics derived with the approach are more robust and have less uncertainty than when estimated with the conventional methods.
Mitchell W. Alton, Harald J. Stark, Manjula R. Canagaratna, and Eleanor C. Browne
Atmos. Meas. Tech., 16, 3273–3282, https://doi.org/10.5194/amt-16-3273-2023, https://doi.org/10.5194/amt-16-3273-2023, 2023
Short summary
Short summary
Mass spectrometric measurements of atmospheric composition routinely detect hundreds of different ions of varying chemical composition, creating challenges for visualization and data interpretation. We present a new analysis technique to facilitate visualization, while providing greater chemical insight. Additionally, it can aid in identifying the chemical composition of ions. A graphical user interface for performing the analysis is introduced and freely available, enabling broad applications.
Kai Krause, Folkard Wittrock, Andreas Richter, Dieter Busch, Anton Bergen, John P. Burrows, Steffen Freitag, and Olesia Halbherr
Atmos. Meas. Tech., 16, 1767–1787, https://doi.org/10.5194/amt-16-1767-2023, https://doi.org/10.5194/amt-16-1767-2023, 2023
Short summary
Short summary
Inland shipping is an important source of nitrogen oxides (NOx). The amount of emitted NOx depends on the characteristics of the individual vessels and the traffic density. Ship emissions are often characterised by the amount of emitted NOx per unit of burnt fuel, and further knowledge about fuel consumption is needed to quantify the total emissions caused by ship traffic. In this study, a new approach to derive absolute emission rates (in g s−1) from onshore measurements is presented.
Lenard L. Röder, Patrick Dewald, Clara M. Nussbaumer, Jan Schuladen, John N. Crowley, Jos Lelieveld, and Horst Fischer
Atmos. Meas. Tech., 16, 1167–1178, https://doi.org/10.5194/amt-16-1167-2023, https://doi.org/10.5194/amt-16-1167-2023, 2023
Short summary
Short summary
Field experiments in atmospheric chemistry provide insights into chemical interactions of our atmosphere. However, high data coverage and accuracy are needed to enable further analysis. In this study, we explore a statistical method that combines knowledge about the chemical reactions with information from measurements to increase the quality of field experiment datasets. We test the algorithm for several applications and discuss limitations that depend on the specific variable and the dynamics.
Rona L. Thompson and Ignacio Pisso
Atmos. Meas. Tech., 16, 235–246, https://doi.org/10.5194/amt-16-235-2023, https://doi.org/10.5194/amt-16-235-2023, 2023
Short summary
Short summary
Atmospheric networks are used for monitoring air quality and greenhouse gases and can provide essential information about the sources and sinks. The design of the network, specifically where to place the observations, is a critical question in order to maximize the information provided while minimizing the cost. Here, a novel method of designing atmospheric networks is presented with two examples, one on monitoring sources of methane and the second on monitoring fossil fuel emissions of CO2.
László Haszpra, Zoltán Barcza, Zita Ferenczi, Roland Hollós, Anikó Kern, and Natascha Kljun
Atmos. Meas. Tech., 15, 5019–5031, https://doi.org/10.5194/amt-15-5019-2022, https://doi.org/10.5194/amt-15-5019-2022, 2022
Short summary
Short summary
A novel approach is used for the determination of greenhouse gas (GHG) emissions of small rural settlements, which may significantly differ from those of urban regions and have hardly been studied yet. Among other results, it turned out that wintertime nitrous oxide emission is significantly underestimated in the official emission inventories. Given the large number of such settlements, the underestimation may also distort the national total emission values reported to international databases.
Anja Ražnjević, Chiel van Heerwaarden, and Maarten Krol
Atmos. Meas. Tech., 15, 3611–3628, https://doi.org/10.5194/amt-15-3611-2022, https://doi.org/10.5194/amt-15-3611-2022, 2022
Short summary
Short summary
We evaluate two widely used observational techniques (Other Test Method (OTM) 33A and car drive-bys) that estimate point source gas emissions. We performed our analysis on high-resolution plume dispersion simulation. For car drive-bys we found that at least 15 repeated measurements were needed to get within 40 % of the true emissions. OTM 33A produced large errors in estimation (50 %–200 %) due to its sensitivity to dispersion coefficients and underlying simplifying assumptions.
Tony Bush, Nick Papaioannou, Felix Leach, Francis D. Pope, Ajit Singh, G. Neil Thomas, Brian Stacey, and Suzanne Bartington
Atmos. Meas. Tech., 15, 3261–3278, https://doi.org/10.5194/amt-15-3261-2022, https://doi.org/10.5194/amt-15-3261-2022, 2022
Short summary
Short summary
Poor air quality is a human health risk which demands high-spatiotemporal-resolution monitoring data to manage. Low-cost air quality sensors present a convenient pathway to delivering these needs, compared to traditional methods, but bring methodological challenges which can limit operational ability. In this study within Oxford, UK, we develop machine learning methods to improve the quality of low-cost sensors for NO2, PM10 (particulate matter) and PM2.5 and demonstrate their effectiveness.
Lisa J. Beck, Siegfried Schobesberger, Mikko Sipilä, Veli-Matti Kerminen, and Markku Kulmala
Atmos. Meas. Tech., 15, 1957–1965, https://doi.org/10.5194/amt-15-1957-2022, https://doi.org/10.5194/amt-15-1957-2022, 2022
Short summary
Short summary
Sulfuric acid is known to be a main compound in atmospheric new particle formation. Yet, its concentration is very low, which leads to challenges in detecting it. In our study, we derive the sulfuric acid concentration from measurements of ambient ions with a mass spectrometer. Our validation shows that the theoretical approach using the bisulfate ion and its clusters with H2SO4 captures the sulfuric acid concentration very well during daytime.
Katharina Jentzsch, Julia Boike, and Thomas Foken
Atmos. Meas. Tech., 14, 7291–7296, https://doi.org/10.5194/amt-14-7291-2021, https://doi.org/10.5194/amt-14-7291-2021, 2021
Short summary
Short summary
Very small CO2 fluxes are measured at night in Arctic regions. If the sensible heat flux is not close to zero under these conditions, the WPL correction will take values on the order of the flux. A special quality control is proposed for these cases.
Seán Schmitz, Sherry Towers, Guillermo Villena, Alexandre Caseiro, Robert Wegener, Dieter Klemp, Ines Langer, Fred Meier, and Erika von Schneidemesser
Atmos. Meas. Tech., 14, 7221–7241, https://doi.org/10.5194/amt-14-7221-2021, https://doi.org/10.5194/amt-14-7221-2021, 2021
Short summary
Short summary
The last 2 decades have seen substantial technological advances in the development of low-cost air pollution instruments. This study introduces a seven-step methodology for the field calibration of low-cost sensors with user-friendly guidelines, open-access code, and a discussion of common barriers. Our goal with this work is to push for standardized reporting of methods, make critical data processing steps clear for users, and encourage responsible use in the scientific community and beyond.
Alex Resovsky, Michel Ramonet, Leonard Rivier, Jerome Tarniewicz, Philippe Ciais, Martin Steinbacher, Ivan Mammarella, Meelis Mölder, Michal Heliasz, Dagmar Kubistin, Matthias Lindauer, Jennifer Müller-Williams, Sebastien Conil, and Richard Engelen
Atmos. Meas. Tech., 14, 6119–6135, https://doi.org/10.5194/amt-14-6119-2021, https://doi.org/10.5194/amt-14-6119-2021, 2021
Short summary
Short summary
We present a technical description of a statistical methodology for extracting synoptic- and seasonal-length anomalies from greenhouse gas time series. The definition of what represents an anomalous signal is somewhat subjective, which we touch on throughout the paper. We show, however, that the method performs reasonably well in extracting portions of time series influenced by significant North Atlantic Oscillation weather episodes and continent-wide terrestrial biospheric aberrations.
Pramod Kumar, Grégoire Broquet, Camille Yver-Kwok, Olivier Laurent, Susan Gichuki, Christopher Caldow, Ford Cropley, Thomas Lauvaux, Michel Ramonet, Guillaume Berthe, Frédéric Martin, Olivier Duclaux, Catherine Juery, Caroline Bouchet, and Philippe Ciais
Atmos. Meas. Tech., 14, 5987–6003, https://doi.org/10.5194/amt-14-5987-2021, https://doi.org/10.5194/amt-14-5987-2021, 2021
Short summary
Short summary
This study presents a simple atmospheric inversion modeling framework for the localization and quantification of unknown CH4 and CO2 emissions from point sources based on near-surface mobile concentration measurements and a Gaussian plume dispersion model. It is applied for the estimate of a series of brief controlled releases of CH4 and CO2 with a wide range of rates during the TOTAL TADI-2018 experiment. Results indicate a ~10 %–40 % average error on the estimate of the release rates.
Blake Actkinson, Katherine Ensor, and Robert J. Griffin
Atmos. Meas. Tech., 14, 5809–5821, https://doi.org/10.5194/amt-14-5809-2021, https://doi.org/10.5194/amt-14-5809-2021, 2021
Short summary
Short summary
This paper describes the development of a new method used to estimate background from mobile monitoring time series. The method is tested on a previously published dataset, applied to an extensive mobile dataset, and compared with other previously published techniques used to estimate background. The results suggest that the method is a promising framework for background estimation.
Toprak Aslan, Olli Peltola, Andreas Ibrom, Eiko Nemitz, Üllar Rannik, and Ivan Mammarella
Atmos. Meas. Tech., 14, 5089–5106, https://doi.org/10.5194/amt-14-5089-2021, https://doi.org/10.5194/amt-14-5089-2021, 2021
Short summary
Short summary
Vertical turbulent fluxes of gases measured by the eddy covariance (EC) technique are subject to high-frequency losses. There are different methods used to describe this low-pass filtering effect and to correct the measured fluxes. In this study, we analysed the systematic uncertainty related to this correction for various attenuation and signal-to-noise ratios. A new and robust transfer function method is finally proposed.
Olli Peltola, Toprak Aslan, Andreas Ibrom, Eiko Nemitz, Üllar Rannik, and Ivan Mammarella
Atmos. Meas. Tech., 14, 5071–5088, https://doi.org/10.5194/amt-14-5071-2021, https://doi.org/10.5194/amt-14-5071-2021, 2021
Short summary
Short summary
Gas fluxes measured by the eddy covariance (EC) technique are subject to filtering due to non-ideal instrumentation. For linear first-order systems this filtering causes also a time lag between vertical wind speed and gas signal which is additional to the gas travel time in the sampling line. The effect of this additional time lag on EC fluxes is ignored in current EC data processing routines. Here we show that this oversight biases EC fluxes and hence propose an approach to rectify this bias.
László Haszpra and Ernő Prácser
Atmos. Meas. Tech., 14, 3561–3571, https://doi.org/10.5194/amt-14-3561-2021, https://doi.org/10.5194/amt-14-3561-2021, 2021
Short summary
Short summary
Most of the tall-tower greenhouse gas observatories apply a single gas analyzer for the sequential sampling of several intakes along the tower. The non-continuous sampling at each intake introduces excess uncertainty to the calculated hourly-average concentrations used in several applications. Based on real-world measurements, the paper systematically assesses this type of uncertainty.
Robert B. Chatfield, Meinrat O. Andreae, ARCTAS Science Team, and SEAC4RS Science Team
Atmos. Meas. Tech., 13, 7069–7096, https://doi.org/10.5194/amt-13-7069-2020, https://doi.org/10.5194/amt-13-7069-2020, 2020
Short summary
Short summary
Forest burning affects air pollution and global climate. A NASA aircraft studied fire emissions including the Rim Fire near Yosemite. We found frequent confusions between the actual fire emission factors and other effects on the air samples. Effects on CO2 and CO can originate far upwind; the gases can mix variably into a smoke plume. We devised a theory of constant features in plumes. A statistical mixed-effects analysis of a co-emitted tracers model disentangles such mixing from fire effects.
Holger Vömel, Herman G. J. Smit, David Tarasick, Bryan Johnson, Samuel J. Oltmans, Henry Selkirk, Anne M. Thompson, Ryan M. Stauffer, Jacquelyn C. Witte, Jonathan Davies, Roeland van Malderen, Gary A. Morris, Tatsumi Nakano, and Rene Stübi
Atmos. Meas. Tech., 13, 5667–5680, https://doi.org/10.5194/amt-13-5667-2020, https://doi.org/10.5194/amt-13-5667-2020, 2020
Short summary
Short summary
The time response of electrochemical concentration cell (ECC) ozonesondes points to at least two distinct reaction pathways with time constants of approximately 20 s and 25 min. Properly considering these time constants eliminates the need for a poorly defined "background" and allows reducing ad hoc corrections based on laboratory tests. This reduces the uncertainty of ECC ozonesonde measurements throughout the profile and especially in regions of low ozone and strong gradients of ozone.
Fan Zhou, Liwei Hou, Rui Zhong, Wei Chen, Xunpeng Ni, Shengda Pan, Ming Zhao, and Bowen An
Atmos. Meas. Tech., 13, 4899–4909, https://doi.org/10.5194/amt-13-4899-2020, https://doi.org/10.5194/amt-13-4899-2020, 2020
Short summary
Short summary
On 15 July 2019, using an unmanned aerial vehicle (UAV), maritime authorities ferreted out a sailing ship whose fuel sulfur content (FSC) failed to meet Chinese regulations. This was the first time that a sailing ship had been caught for having failed the FSC regulations in China. The UAV system, method, and monitoring result utilized are discussed in this paper. We recommend that emissions from sailing ships be monitored more often in the open water in the future.
Bas Mijling
Atmos. Meas. Tech., 13, 4601–4617, https://doi.org/10.5194/amt-13-4601-2020, https://doi.org/10.5194/amt-13-4601-2020, 2020
Short summary
Short summary
Many cities are experimenting with networks of low-cost sensors, complementary to their reference stations. Often the observations are published as dots on a map, as spatial interpolation is far from trivial. A new methodology to assimilate observations of different accuracy in a generic urban-air-quality model is introduced. It can be used for mapping local air quality based on reference measurements only or as a framework to integrate low-cost measurements next to official measurements.
Kukka-Maaria Kohonen, Pasi Kolari, Linda M. J. Kooijmans, Huilin Chen, Ulli Seibt, Wu Sun, and Ivan Mammarella
Atmos. Meas. Tech., 13, 3957–3975, https://doi.org/10.5194/amt-13-3957-2020, https://doi.org/10.5194/amt-13-3957-2020, 2020
Short summary
Short summary
Biosphere–atmosphere gas exchange (flux) measurements of carbonyl sulfide (COS) are becoming popular for estimating biospheric photosynthesis. To compare COS flux measurements across different measurement sites, we need standardized protocols for data processing. We analyze how various data processing steps affect the calculated COS flux and how they differ from carbon dioxide (CO2) flux processing steps, and we aim to settle on a set of recommended protocols for COS flux calculation.
Michael Müller, Peter Graf, Jonas Meyer, Anastasia Pentina, Dominik Brunner, Fernando Perez-Cruz, Christoph Hüglin, and Lukas Emmenegger
Atmos. Meas. Tech., 13, 3815–3834, https://doi.org/10.5194/amt-13-3815-2020, https://doi.org/10.5194/amt-13-3815-2020, 2020
Yongbiao Weng, Alexandra Touzeau, and Harald Sodemann
Atmos. Meas. Tech., 13, 3167–3190, https://doi.org/10.5194/amt-13-3167-2020, https://doi.org/10.5194/amt-13-3167-2020, 2020
Short summary
Short summary
We find that the known mixing ratio dependence of laser spectrometers for water vapour isotope measurements varies with isotope composition. We have developed a scheme to correct for this isotope-composition-dependent bias. The correction is most substantial at low mixing ratios. Stability tests indicate that the first-order dependency is a constant instrument characteristic. Water vapour isotope measurements at low mixing ratios can now be corrected by following our proposed procedure.
Pascal Wintjen, Christof Ammann, Frederik Schrader, and Christian Brümmer
Atmos. Meas. Tech., 13, 2923–2948, https://doi.org/10.5194/amt-13-2923-2020, https://doi.org/10.5194/amt-13-2923-2020, 2020
Short summary
Short summary
With recent technological advances it is now possible to measure the exchange of trace gases between the land surface and the atmosphere. When using the so-called eddy-covariance method, certain corrections need to be applied to account for attenuation in the flux signal. These losses were found to be setup- and site-specific and can be up to 38 % for reactive nitrogen fluxes. We evaluated five different methods and recommend using an empirical version with locally measured cospectra.
Martin Kunz, Jost V. Lavric, Rainer Gasche, Christoph Gerbig, Richard H. Grant, Frank-Thomas Koch, Marcus Schumacher, Benjamin Wolf, and Matthias Zeeman
Atmos. Meas. Tech., 13, 1671–1692, https://doi.org/10.5194/amt-13-1671-2020, https://doi.org/10.5194/amt-13-1671-2020, 2020
Short summary
Short summary
The nocturnal boundary layer (NBL) budget method enables the quantification of gas fluxes between ecosystems and the atmosphere under nocturnal stable stratification, a condition under which standard approaches struggle. However, up to now the application of the NBL method has been limited by difficulties in obtaining the required measurements. We show how an unmanned aircraft system (UAS) equipped with a carbon dioxide analyser can make this method more accessible.
Marcus Striednig, Martin Graus, Tilmann D. Märk, and Thomas G. Karl
Atmos. Meas. Tech., 13, 1447–1465, https://doi.org/10.5194/amt-13-1447-2020, https://doi.org/10.5194/amt-13-1447-2020, 2020
Short summary
Short summary
The current work summarizes a long-term effort to provide an open-source code for the analysis of turbulent fluctuations of trace gases in the atmosphere by eddy covariance and disjunct eddy covariance, with a special focus on reactive gases that participate in atmospheric chemistry. The performance of the code is successfully evaluated based on measurements of minute fluxes of non-methane volatile organic compounds into the urban atmosphere.
Friedemann Reum, Mathias Göckede, Jost V. Lavric, Olaf Kolle, Sergey Zimov, Nikita Zimov, Martijn Pallandt, and Martin Heimann
Atmos. Meas. Tech., 12, 5717–5740, https://doi.org/10.5194/amt-12-5717-2019, https://doi.org/10.5194/amt-12-5717-2019, 2019
Short summary
Short summary
We present continuous in situ measurements of atmospheric CO2 and CH4 mole fractions at the new station Ambarchik, located in northeastern Siberia. We describe the site, measurements and quality control, characterize the signals in comparison with data from Barrow, Alaska, and show which regions the measurements are sensitive to. Ambarchik data are available upon request.
Nathan Hilker, Jonathan M. Wang, Cheol-Heon Jeong, Robert M. Healy, Uwayemi Sofowote, Jerzy Debosz, Yushan Su, Michael Noble, Anthony Munoz, Geoff Doerksen, Luc White, Céline Audette, Dennis Herod, Jeffrey R. Brook, and Greg J. Evans
Atmos. Meas. Tech., 12, 5247–5261, https://doi.org/10.5194/amt-12-5247-2019, https://doi.org/10.5194/amt-12-5247-2019, 2019
Short summary
Short summary
Increased interest in monitoring air quality near roadways, combined with traffic's often unclear contribution to elevated concentrations, has created a need for better interpretation of these data. Using 2 years of measurements collected during a near-road monitoring project in Canada, this paper contrasts three methods for estimating the fraction of roadside pollution resulting from on-road traffic. Robustness of these methods was compared with tandem measurements at background locations.
Laura Cartwright, Andrew Zammit-Mangion, Sangeeta Bhatia, Ivan Schroder, Frances Phillips, Trevor Coates, Karita Negandhi, Travis Naylor, Martin Kennedy, Steve Zegelin, Nick Wokker, Nicholas M. Deutscher, and Andrew Feitz
Atmos. Meas. Tech., 12, 4659–4676, https://doi.org/10.5194/amt-12-4659-2019, https://doi.org/10.5194/amt-12-4659-2019, 2019
Short summary
Short summary
Despite extensive research, emission detection and quantification of greenhouse gases (GHGs) remain an open problem. This article presents a novel statistical framework for detecting and quantifying methane emissions and showcases its efficacy on data collected from different instruments in the 2015 Ginninderra controlled-release experiment. The developed techniques can be used to aid GHG emission reduction schemes by, for example, detecting and quantifying leaks from carbon storage facilities.
Sharad Vikram, Ashley Collier-Oxandale, Michael H. Ostertag, Massimiliano Menarini, Camron Chermak, Sanjoy Dasgupta, Tajana Rosing, Michael Hannigan, and William G. Griswold
Atmos. Meas. Tech., 12, 4211–4239, https://doi.org/10.5194/amt-12-4211-2019, https://doi.org/10.5194/amt-12-4211-2019, 2019
Short summary
Short summary
Low-cost air quality sensors are enabling people to collect data to better understand their local environment and potential exposures. However, there is some concern regarding how reliable the calibrations of these sensors are in new and different environments. To explore this issue, our team colocated sensors at three different sites with high-quality monitoring instruments to compare to. We explored the transferability of calibration models as well as approaches to improve reliability.
Yanjun Zhang, Otso Peräkylä, Chao Yan, Liine Heikkinen, Mikko Äijälä, Kaspar R. Daellenbach, Qiaozhi Zha, Matthieu Riva, Olga Garmash, Heikki Junninen, Pentti Paatero, Douglas Worsnop, and Mikael Ehn
Atmos. Meas. Tech., 12, 3761–3776, https://doi.org/10.5194/amt-12-3761-2019, https://doi.org/10.5194/amt-12-3761-2019, 2019
Short summary
Short summary
Recent advancements in atmospheric mass spectrometry provide large amounts of new information but at the same time present considerable challenges for the data analysis, for example, in high-resolution peak identification and separation. To address these problems, this study presents a simple and novel method, which succeeds in analyzing both synthetic and ambient datasets. We believe it will become a powerful approach in the data analysis of mass spectra.
Cited articles
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, https://doi.org/10.1201/9780429469275-8, 2001. a, b
Breiman, L. and Friedman, J. H.: Predicting multivariate responses in multiple linear regression, J. Roy. Stat. Soc.-B, 59, 3–54, https://doi.org/10.1111/1467-9868.00054, 1997. a
Casey, J. G. and Hannigan, M. P.: Testing the performance of field calibration techniques for low-cost gas sensors in new deployment locations: across a county line and across Colorado, Atmos. Meas. Tech., 11, 6351–6378, https://doi.org/10.5194/amt-11-6351-2018, 2018. a, b
Casey, J. G., Collier-Oxandale, A., and Hannigan, M.: Performance of artificial neural networks and linear models to quantify 4 trace gas species in an oil and gas production region with low-cost sensors, Sensor.
Actuat. B-Chem., 283, 504–514, https://doi.org/10.1016/j.snb.2018.12.049, 2019. a
Castell, N., Dauge, F. R., Schneider, P., Vogt, M., Lerner, U., Fishbain, B., Broday, D., and Bartonova, A.: Can commercial low-cost sensor platforms contribute to air quality monitoring and exposure estimates?, Environ. Int., 99, 293–302, https://doi.org/10.1016/j.envint.2016.12.007, 2017. a, b, c, d
Cross, E. S., Williams, L. R., Lewis, D. K., Magoon, G. R., Onasch, T. B., Kaminsky, M. L., Worsnop, D. R., and Jayne, J. T.: Use of electrochemical sensors for measurement of air pollution: correcting interference response and validating measurements, Atmos. Meas. Tech., 10, 3575–3588, https://doi.org/10.5194/amt-10-3575-2017, 2017. a
De Vito, S., Esposito, E., Salvato, M., Popoola, O., Formisano, F., Jones, R., and Di Francia, G.: Calibrating chemical multisensory devices for real world applications: An in-depth comparison of quantitative machine learning approaches, Sensor. Actuat. B-Chem., 255, 1191–1210, https://doi.org/10.1016/j.snb.2017.07.155, 2018. a, b, c
De Vito, S., Esposito, E., Formisano, F., Massera, E., Auria, P. D., and Di Francia, G.: Adaptive Machine learning for Backup Air Quality Multisensor Systems continuous calibration, 2019 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN), 26–29 May 2019, Fukuoka, Japan, 1–4, https://doi.org/10.1109/isoen.2019.8823250, 2019. a
Dormann, C. F., Elith, J., Bacher, S., Buchmann, C., Carl, G., Carré, G., Marquéz, J. R., Gruber, B., Lafourcade, B., Leitão, P. J., Münkemüller, T., Mcclean, C., Osborne, P. E., Reineking, B., Schröder, B., Skidmore, A. K., Zurell, D., and Lautenbach, S.: Collinearity: A review of methods to deal with it and a simulation study evaluating their performance, Ecography, 36, 27–46, https://doi.org/10.1111/j.1600-0587.2012.07348.x, 2013. a
Eilenberg, S. R., Subramanian, R., Malings, C., Hauryliuk, A., Presto, A. A., and Robinson, A. L.: Using a network of lower-cost monitors to identify the influence of modifiable factors driving spatial patterns in fine particulate matter concentrations in an urban environment, J. Expo. Sci. Env. Epid., 30, 949–961, https://doi.org/10.1038/s41370-020-0255-x, 2020. a
Esposito, E., De Vito, S., Salvato, M., Bright, V., Jones, R. L., and Popoola, O.: Dynamic neural network architectures for on field stochastic calibration of indicative low cost air quality sensing systems, Sensor.
Actuat. B-Chem., 231, 701–713, https://doi.org/10.1016/j.snb.2016.03.038, 2016. a
European Environment Agency: Air quality in Europe – 2019 report, available at: http://www.eea.europa.eu/publications/air-quality-in-europe-2012 (last access: 1 November 2020), 2019. a
Green, D. C., Fuller, G. W., and Baker, T.: Development and validation of the volatile correction model for PM10 – An empirical method for adjusting TEOM measurements for their loss of volatile particulate matter, Atmos. Environ., 43, 2132–2141, https://doi.org/10.1016/j.atmosenv.2009.01.024, 2009. a
Hagan, D. H., Isaacman-VanWertz, G., Franklin, J. P., Wallace, L. M. M., Kocar, B. D., Heald, C. L., and Kroll, J. H.: Calibration and assessment of electrochemical air quality sensors by co-location with regulatory-grade instruments, Atmos. Meas. Tech., 11, 315–328, https://doi.org/10.5194/amt-11-315-2018, 2018. a, b, c, d, e
Hagler, G. S., Williams, R., Papapostolou, V., and Polidori, A.: Air Quality Sensors and Data Adjustment Algorithms: When Is It No Longer a Measurement?, Environ. Sci. Technol., 52, 5530–5531, https://doi.org/10.1021/acs.est.8b01826, 2018. a
Hoerl, A. E. and Kennard, R. W.: Ridge Regression: Biased Estimation for Nonorthogonal Problems, Technometrics, 12, 55–67, https://doi.org/10.1080/00401706.2000.10485983, 1970. a
James, G., Witten, D., Hastie, T., and Tibshirani, R.: An Introduction to Statistical Learning, Springer Science+Business Media, New York, https://doi.org/10.1007/978-1-4614-7138-7, 2013. a
Jiao, W., Hagler, G., Williams, R., Sharpe, R., Brown, R., Garver, D., Judge, R., Caudill, M., Rickard, J., Davis, M., Weinstock, L., Zimmer-Dauphinee, S., and Buckley, K.: Community Air Sensor Network (CAIRSENSE) project: evaluation of low-cost sensor performance in a suburban environment in the southeastern United States, Atmos. Meas. Tech., 9, 5281–5292, https://doi.org/10.5194/amt-9-5281-2016, 2016. a
Keller, C. A. and Evans, M. J.: Application of random forest regression to the calculation of gas-phase chemistry within the GEOS-Chem chemistry model v10, Geosci. Model Dev., 12, 1209–1225, https://doi.org/10.5194/gmd-12-1209-2019, 2019. a
Lewis, A. C., Lee, J. D., Edwards, P. M., Shaw, M. D., Evans, M. J., Moller, S. J., Smith, K. R., Buckley, J. W., Ellis, M., Gillot, S. R., and White, A.: Evaluating the performance of low cost chemical sensors for air pollution research, Faraday Discuss., 189, 85–103, https://doi.org/10.1039/c5fd00201j, 2016. a, b, c, d
Lewis, A. C., von Schneidermesser, E., and Peltier, R. E.: Low-cost sensors for the measurement of atmospheric composition: overview of topic and future applications, Tech. rep., World Meteorological Organization, available at: https://www.ccacoalition.org/en/resources/low-cost-sensors-measurement-atmospheric-composition-overview-topic-and-future (last access: 1 November 2020), 2018. a, b, c, d
Liu, H. Y., Schneider, P., Haugen, R., and Vogt, M.: Performance assessment of a low-cost PM2.5 sensor for a near four-month period in Oslo, Norway, Atmosphere, 10, 41, https://doi.org/10.3390/atmos10020041, 2019. a
Malings, C., Tanzer, R., Hauryliuk, A., Kumar, S. P. N., Zimmerman, N., Kara, L. B., Presto, A. A., and R. Subramanian: Development of a general calibration model and long-term performance evaluation of low-cost sensors for air pollutant gas monitoring, Atmos. Meas. Tech., 12, 903–920, https://doi.org/10.5194/amt-12-903-2019, 2019. a, b, c, d, e, f, g, h, i
Malings, C., Tanzer, R., Hauryliuk, A., Saha, P. K., Robinson, A. L., Presto, A. A., and Subramanian, R.: Fine particle mass monitoring with low-cost sensors: Corrections and long-term performance evaluation, Aerosol Sci. Tech., 54, 160–174, https://doi.org/10.1080/02786826.2019.1623863, 2020. a
Mansfield, L., Nowack, P., Kasoar, M., Everitt, R., Collins, W. J., and Voulgarakis, A.: Can we predict climate change from short-term simulations using machine learning?, npj Climate and Atmospheric Science, 3, 44, https://doi.org/10.1038/s41612-020-00148-5, 2020. a
Masson, N., Piedrahita, R., and Hannigan, M.: Quantification method for electrolytic sensors in long-term monitoring of ambient air quality, Sensors, 15, 27283–27302, https://doi.org/10.3390/s151027283, 2015. a, b, c
Mead, M. I., Popoola, O. A., Stewart, G. B., Landshoff, P., Calleja, M., Hayes, M., Baldovi, J. J., McLeod, M. W., Hodgson, T. F., Dicks, J., Lewis, A., Cohen, J., Baron, R., Saffell, J. R., and Jones, R. L.: The use of electrochemical sensors for monitoring urban air quality in low-cost, high-density networks, Atmos. Environ., 70, 186–203, https://doi.org/10.1016/j.atmosenv.2012.11.060, 2013. a, b, c, d, e, f
Moltchanov, S., Levy, I., Etzion, Y., Lerner, U., Broday, D. M., and Fishbain, B.: On the feasibility of measuring urban air pollution by wireless distributed sensor networks, Sci. Total Environ., 502, 537–547, https://doi.org/10.1016/j.scitotenv.2014.09.059, 2015. a
Munir, S., Mayfield, M., Coca, D., Jubb, S. A., and Osammor, O.: Analysing the performance of low-cost air quality sensors, their drivers, relative benefits and calibration in cities – a case study in Sheffield, Environ. Monit. Assess., 191, 94, https://doi.org/10.1007/s10661-019-7231-8, 2019. a, b
Nowack, P. and Konstantinovskiy, L.: Code in support of Nowack et al. (2021) in Atmospheric Measurement Techniques (Version 2), Zenodo [code], https://doi.org/10.5281/zenodo.5215849, 2021. a
Nowack, P., Braesicke, P., Haigh, J., Abraham, N. L., Pyle, J., and Voulgarakis, A.: Using machine learning to build temperature-based ozone parameterizations for climate sensitivity simulations, Environ. Res. Lett., 13, 104016, https://doi.org/10.1088/1748-9326/aae2be, 2018. a, b, c, d
Nowack, P., Ong, Q. Y. E., Braesicke, P., Haigh, J. D., Luke, A., Pyle, J., and Voulgarakis, A.: Machine learning parameterizations for ozone: climate model transferability, in: Conference Proceedings of the 9th International Workshop on Climate Informatics, 2–4 October 2019, Paris, France, 263–268, 2019. a, b, c
Nowack, P., Runge, J., Eyring, V., and Haigh, J. D.: Causal networks for climate model evaluation and constrained projections, Nat. Commun., 11, 1415, https://doi.org/10.1038/s41467-020-15195-y, 2020. a
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E.: Scikit-learn: Machine learning in Python, J. Mach. Learn. Res., 12, 2825–2830, 2011. a, b, c, d, e
Popoola, O. A., Stewart, G. B., Mead, M. I., and Jones, R. L.: Development of a baseline-temperature correction methodology for electrochemical sensors and its implications for long-term stability, Atmos. Environ., 147, 330–343, https://doi.org/10.1016/j.atmosenv.2016.10.024, 2016. a
Rai, A. C. and Kumar, P.: Summary of air quality sensors and recommendations for application, Ref. Ares, p. 65, available at: https://www.iscapeproject.eu/wp-content/uploads/2017/09/iSCAPE_D1.5_Summary-of-air-quality-sensors-and-recommendations-for-application.pdf (last access: 1 November 2020), 2018. a, b, c, d, e
Runge, J., Heitzig, J., Petoukhov, V., and Kurths, J.: Escaping the curse of dimensionality in estimating multivariate transfer entropy, Phys. Rev. Lett., 108, 258701, https://doi.org/10.1103/PhysRevLett.108.258701, 2012. a
Runge, J., Nowack, P., Kretschmer, M., Flaxman, S., and Sejdinovic, D.: Detecting and quantifying causal associations in large nonlinear time series datasets, Science Advances, 5, eaau4996, https://doi.org/10.1126/sciadv.aau4996, 2019. a
Sadighi, K., Coffey, E., Polidori, A., Feenstra, B., Lv, Q., Henze, D. K., and Hannigan, M.: Intra-urban spatial variability of surface ozone in Riverside, CA: viability and validation of low-cost sensors, Atmos. Meas. Tech., 11, 1777–1792, https://doi.org/10.5194/amt-11-1777-2018, 2018. a
Sayahi, T., Garff, A., Quah, T., Lê, K., Becnel, T., Powell, K. M., Gaillardon, P. E., Butterfield, A. E., and Kelly, K. E.: Long-term calibration models to estimate ozone concentrations with a metal oxide sensor, Environ. Pollut., 267, 115363, https://doi.org/10.1016/j.envpol.2020.115363, 2020. a
Sherwen, T., Chance, R. J., Tinel, L., Ellis, D., Evans, M. J., and Carpenter, L. J.: A machine-learning-based global sea-surface iodide distribution, Earth Syst. Sci. Data, 11, 1239–1262, https://doi.org/10.5194/essd-11-1239-2019, 2019. a
Spinelle, L., Gerboles, M., Villani, M. G., Aleixandre, M., and Bonavitacola, F.: Field calibration of a cluster of low-cost available sensors for air quality monitoring. Part A: Ozone and nitrogen dioxide, Sensor. Actuat. B-Chem., 215, 249–257, https://doi.org/10.1016/j.snb.2015.03.031, 2015. a, b, c
Spinelle, L., Gerboles, M., Villani, M. G., Aleixandre, M., and Bonavitacola, F.: Field calibration of a cluster of low-cost commercially available sensors for air quality monitoring. Part B: NO, CO and CO2, Sensor. Actuat. B-Chem., 238, 706–715, https://doi.org/10.1016/j.snb.2016.07.036, 2017. a
Tanzer, R., Malings, C., Hauryliuk, A., Subramanian, R., and Presto, A. A.: Demonstration of a low-cost multi-pollutant network to quantify intra-urban spatial variations in air pollutant source impacts and to evaluate environmental justice, Int. J. Environ. Res. Pub. He., 16, 2523, https://doi.org/10.3390/ijerph16142523, 2019. a
Vikram, S., Collier-Oxandale, A., Ostertag, M. H., Menarini, M., Chermak, C., Dasgupta, S., Rosing, T., Hannigan, M., and Griswold, W. G.: Evaluating and improving the reliability of gas-phase sensor system calibrations across new locations for ambient measurements and personal exposure monitoring, Atmos. Meas. Tech., 12, 4211–4239, https://doi.org/10.5194/amt-12-4211-2019, 2019.
a
Zimmerman, N., Presto, A. A., Kumar, S. P. N., Gu, J., Hauryliuk, A., Robinson, E. S., Robinson, A. L., and R. Subramanian: A machine learning calibration model using random forests to improve sensor performance for lower-cost air quality monitoring, Atmos. Meas. Tech., 11, 291–313, https://doi.org/10.5194/amt-11-291-2018, 2018. a, b, c, d, e, f, g, h, i, j
Short summary
Machine learning (ML) calibration techniques could be an effective way to improve the performance of low-cost air pollution sensors. Here we provide novel insights from case studies within the urban area of London, UK, where we compared the performance of three ML techniques to calibrate low-cost measurements of NO2 and PM10. In particular, we highlight the key issue of the method-dependent robustness in maintaining calibration skill after transferring sensors to different measurement sites.
Machine learning (ML) calibration techniques could be an effective way to improve the...
