Articles | Volume 16, issue 20
https://doi.org/10.5194/amt-16-4723-2023
© Author(s) 2023. 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-16-4723-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 Oct 2023
Research article |
| 20 Oct 2023
| 20 Oct 2023
Development of low-cost air quality stations for next-generation monitoring networks: calibration and validation of NO2 and O3 sensors
Alice Cavaliere
CORRESPONDING AUTHOR
National Research Council – Institute of BioEconomy (CNR–IBE), Via Caproni 8, 50145 Florence, Italy
Lorenzo Brilli
National Research Council – Institute of BioEconomy (CNR–IBE), Via Caproni 8, 50145 Florence, Italy
Bianca Patrizia Andreini
ARPAT, Tuscany Region Environmental Protection Agency, Via Porpora, 22, 50144 Florence, Italy
Federico Carotenuto
National Research Council – Institute of BioEconomy (CNR–IBE), Via Caproni 8, 50145 Florence, Italy
Beniamino Gioli
National Research Council – Institute of BioEconomy (CNR–IBE), Via Caproni 8, 50145 Florence, Italy
Tommaso Giordano
National Research Council – Institute of BioEconomy (CNR–IBE), Via Caproni 8, 50145 Florence, Italy
Marco Stefanelli
ARPAT, Tuscany Region Environmental Protection Agency, Via Porpora, 22, 50144 Florence, Italy
Carolina Vagnoli
National Research Council – Institute of BioEconomy (CNR–IBE), Via Caproni 8, 50145 Florence, Italy
Alessandro Zaldei
National Research Council – Institute of BioEconomy (CNR–IBE), Via Caproni 8, 50145 Florence, Italy
Giovanni Gualtieri
National Research Council – Institute of BioEconomy (CNR–IBE), Via Caproni 8, 50145 Florence, Italy
Related authors
No articles found.
Fabio Giardi, Silvia Nava, Giulia Calzolai, Giulia Pazzi, Massimo Chiari, Andrea Faggi, Bianca Patrizia Andreini, Chiara Collaveri, Elena Franchi, Guido Nincheri, Alessandra Amore, Silvia Becagli, Mirko Severi, Rita Traversi, and Franco Lucarelli
Atmos. Chem. Phys., 22, 9987–10005, https://doi.org/10.5194/acp-22-9987-2022, https://doi.org/10.5194/acp-22-9987-2022, 2022
Short summary
Short summary
The restriction measures adopted to contain the COVID-19 virus offered a unique opportunity to study urban particulate emissions in the near absence of traffic, which is one of the main emission sources in the urban environment. However, the drastic decrease in this source of particulate matter during the months of national lockdown did not lead to an equal decrease in the total particulate load. This is due to the inverse behavior shown by different sources, especially secondary sources.
Related subject area
Subject: Gases | Technique: In Situ Measurement | Topic: Data Processing and Information Retrieval
Direct high-precision radon quantification for interpreting high-frequency greenhouse gas measurements
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
Transferability of machine-learning-based global calibration models for NO2 and NO low-cost sensors
Digitization and calibration of historical solar absorption infrared spectra from the Jungfraujoch site
Detection and long-term quantification of methane emissions from an active landfill
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
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
Machine learning calibration of low-cost NO2 and PM10 sensors: non-linear algorithms and their impact on site transferability
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
Application of open-path Fourier transform infrared spectroscopy (OP-FTIR) to measure greenhouse gas concentrations from agricultural fields
Flexible approach for quantifying average long-term changes and seasonal cycles of tropospheric trace species
The ICAD (iterative cavity-enhanced DOAS) method
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.
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.
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.
Jamal Makkor, Mathias Palm, Matthias Buschmann, Emmanuel Mahieu, Martyn P. Chipperfield, and Justus Notholt
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-93, https://doi.org/10.5194/amt-2024-93, 2024
Revised manuscript accepted for AMT
Short summary
Short summary
During the years 1950 and 1951, Marcel Migeotte took regular solar measurements in form of paper rolls at the Jungfraujoch site. These historical spectra proved 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.
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.
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.
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.
Peer Nowack, Lev Konstantinovskiy, Hannah Gardiner, and John Cant
Atmos. Meas. Tech., 14, 5637–5655, https://doi.org/10.5194/amt-14-5637-2021, https://doi.org/10.5194/amt-14-5637-2021, 2021
Short summary
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.
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.
Cheng-Hsien Lin, Richard H. Grant, Albert J. Heber, and Cliff T. Johnston
Atmos. Meas. Tech., 12, 3403–3415, https://doi.org/10.5194/amt-12-3403-2019, https://doi.org/10.5194/amt-12-3403-2019, 2019
Short summary
Short summary
The open-path FTIR (OP-FTIR) is often used to measure the atmospheric gas composition and concentrations. The OP-FTIR, however, is sensitive to the changed ambient factors, which likely led to quantitative biases. This study developed methods to minimize the effect of the ambient temperature and humidity on N2O/CO2 quantification. These methods can help the users who implement the OP-FTIR to estimate gas fluxes in the agroecosystem achieve more precise and accurate estimations.
David D. Parrish, Richard G. Derwent, Simon O'Doherty, and Peter G. Simmonds
Atmos. Meas. Tech., 12, 3383–3394, https://doi.org/10.5194/amt-12-3383-2019, https://doi.org/10.5194/amt-12-3383-2019, 2019
Short summary
Short summary
We present a flexible method that employs a power series expansion and Fourier series analysis to characterize the average long-term change and seasonal cycle, respectively, from a time series of observations of a trace atmospheric species. This approach maximizes the statistically significant information derived, including non-linear aspects of the long-term trends, without over fitting the data. Generally, a small set of parameter values (e.g., 7 or 8) provides this characterization.
Martin Horbanski, Denis Pöhler, Johannes Lampel, and Ulrich Platt
Atmos. Meas. Tech., 12, 3365–3381, https://doi.org/10.5194/amt-12-3365-2019, https://doi.org/10.5194/amt-12-3365-2019, 2019
Short summary
Short summary
ICAD allows a precise in situ measurement of gases like NO2 in a relatively simple and compact setup. The main advantage in comparison to most other optical methods is that it does not require a stable total light intensity. This allows a simpler and mobile instrument setup and additionally it features no observed cross-interferences. We validated the high quality for an ICAD NO2 instrument in different inter-comparisons with a detection limit of 0.02 ppbv.
Cited articles
Aleixandre, M., Gerboles, M., and Spinelle, L.: Report of the laboratory and in-situ validation of micro-sensors and evaluation of suitability of model equations NO9: CairClipNO2 of CAIRPOL (F), Publications Office of the European Union, Luxembourg, oCLC: 1111194588, 2013. a
Asair: Datasheet AM2305C, https://asairsensors.com/wp-content/uploads/2021/09/Data-Sheet-AM2315C-Humidity-and-Temperature-Module-ASAIR-V1.0.02.pdf, 29 September 2021. a
Aula, K., Lagerspetz, E., Nurmi, P., and Tarkoma, S.: Evaluation of Low-Cost Air Quality Sensor Calibration Models, ACM Transactions on Sensor Networks, 3512889, https://doi.org/10.1145/3512889, 2022. a
Azen, R. and Budescu, D. V.: The Dominance Analysis Approach for Comparing Predictors in Multiple Regression, Psychol. Meth., 8, 129–148, https://doi.org/10.1037/1082-989X.8.2.129, 2003. a
Barcelo-Ordinas, J. M., Ferrer-Cid, P., Garcia-Vidal, J., Ripoll, A., and Viana, M.: Distributed Multi-Scale Calibration of Low-Cost Ozone Sensors in Wireless Sensor Networks, Sensors, 19, 2503, https://doi.org/10.3390/s19112503, 2019. a
Bart, M., Williams, D. E., Ainslie, B., McKendry, I., Salmond, J., Grange, S. K., Alavi-Shoshtari, M., Steyn, D., and Henshaw, G. S.: High Density Ozone Monitoring Using Gas Sensitive Semi-Conductor Sensors in the Lower Fraser Valley, British Columbia, Environ. Sci. Technol., 48, 3970–3977, https://doi.org/10.1021/es404610t, 2014. a
Bigi, A., Mueller, M., Grange, S. K., Ghermandi, G., and Hueglin, C.: Performance of NO, NO2 low cost sensors and three calibration approaches within a real world application, Atmos. Meas. Tech., 11, 3717–3735, https://doi.org/10.5194/amt-11-3717-2018, 2018. a, b
Bisignano, A., Carotenuto, F., Zaldei, A., and Giovannini, L.: Field calibration of a low-cost sensors network to assess traffic-related air pollution along the Brenner highway, Atmos. Environ., 275, 119008, https://doi.org/10.1016/j.atmosenv.2022.119008, 2022. a, b
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001. a
Brilli, L., Carotenuto, F., Andreini, B. P., Cavaliere, A., Esposito, A., Gioli, B., Martelli, F., Stefanelli, M., Vagnoli, C., Venturi, S., Zaldei, A., and Gualtieri, G.: Low-Cost Air Quality Stations' Capability to Integrate Reference Stations in Particulate Matter Dynamics Assessment, Atmosphere, 12, 1065, https://doi.org/10.3390/atmos12081065, 2021. a
Burgués, J. and Marco, S.: Low Power Operation of Temperature-Modulated Metal Oxide Semiconductor Gas Sensors, Sensors, 18, 339, https://doi.org/10.3390/s18020339, 2018. a
Camalier, L., Cox, W., and Dolwick, P.: The effects of meteorology on ozone in urban areas and their use in assessing ozone trends, Atmos. Environ., 41, 7127–7137, https://doi.org/10.1016/j.atmosenv.2007.04.061, 2007. a
Cavaliere, A.: Code and dataset for: Development of Low-Cost Air Quality Stations for Next Generation Monitoring Networks: Calibration and Validation of NO2 and O3 Sensors, Zenodo [code and data set], https://doi.org/10.5281/zenodo.7826791, 2023. a
Cavaliere, A., Carotenuto, F., Di Gennaro, F., Gioli, B., Gualtieri, G., Martelli, F., Matese, A., Toscano, P., Vagnoli, C., and Zaldei, A.: Development of Low-Cost Air Quality Stations for Next Generation Monitoring Networks: Calibration and Validation of PM2.5 and PM10 Sensors, Sensors, 18, 2843, https://doi.org/10.3390/s18092843, 2018. a
Chakraborty, S., Mittermaier, S., Carbonelli, C., and Servadei, L.: Explainable AI for Gas Sensors, in: 2022 IEEE Sensors, 1–4, IEEE, https://doi.org/10.1109/SENSORS52175.2022.9967180, 2022. a
Claveau, C., Giraudon, M., Coville, B., Saussac, A., Eymard, L., Turcati, L., and Payan, S.: Performance comparison between electrochemical and semiconductors sensors for the monitoring of O3, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2022-75, 2022. a
Concas, F., Mineraud, J., Lagerspetz, E., Varjonen, S., Liu, X., Puolamäki, K., Nurmi, P., and Tarkoma, S.: Low-Cost Outdoor Air Quality Monitoring and Sensor Calibration: A Survey and Critical Analysis, ACM Transactions on Sensor Networks, 17, 1–44, https://doi.org/10.1145/3446005, 2021. a, b, c, d, e
Cook, R. D.: Detection of Influential Observation in Linear Regression, Technometrics, 19, 15–18, https://doi.org/10.1080/00401706.1977.10489493, 1977. a
Cordero, J. M., Borge, R., and Narros, A.: Using statistical methods to carry out in field calibrations of low cost air quality sensors, Sensor. Actuator. B, 267, 245–254, https://doi.org/10.1016/j.snb.2018.04.021, 2018. a
De Vito, S., Piga, M., Martinotto, L., and Di Francia, G.: CO, NO2 and NOx urban pollution monitoring with on-field calibrated electronic nose by automatic bayesian regularization, Sensor. Actuator. B, 143, 182–191, https://doi.org/10.1016/j.snb.2009.08.041, 2009. a
De Vito, S., Di Francia, G., Esposito, E., Ferlito, S., Formisano, F., and Massera, E.: Adaptive machine learning strategies for network calibration of IoT smart air quality monitoring devices, Pattern Recogn. Lett., 136, 264–271, https://doi.org/10.1016/j.patrec.2020.04.032, 2020. a
Dekking, F. M., Kraaikamp, C., Lopuhaä, H. P., and Meester, L. E.: A Modern Introduction to Probability and Statistics, Springer Texts in Statistics, Springer London, London, https://doi.org/10.1007/1-84628-168-7, 2005. a
Di Lonardo, S., Zaldei, A., Toscano, P., Matese, A., Gioli, B., Rocchi, L., Vagnoli, C., De Filippis, T., Gualtieri, G., and Martelli, F.: The SensorWebBike for air quality monitoring in a smart city, in: IET Conference on Future Intelligent Cities, 2, Institution of Engineering and Technology, London, UK, https://doi.org/10.1049/ic.2014.0043, 2014. a
Draper, N. R. and Smith, H.: Applied regression analysis, Wiley series in probability and statistics, Wiley, New York, 3rd edn., ISBN 978-0-471-17082-2, 1998. a
Esposito, E., Salvato, M., Vito, S. D., Fattoruso, G., Castell, N., Karatzas, K., and Francia, G. D.: Assessing the Relocation Robustness of on Field Calibrations for Air Quality Monitoring Devices, in: Sensors and Microsystems, Lecture Notes in Electrical Engineering, edited by: Leone, A., Forleo, A., Francioso, L., Capone, S., Siciliano, P., and Di Natale, C., Springer International Publishing, Cham, 457, 303–312, https://doi.org/10.1007/978-3-319-66802-4_38, 2018. a
Fine, G. F., Cavanagh, L. M., Afonja, A., and Binions, R.: Metal Oxide Semi-Conductor Gas Sensors in Environmental Monitoring, Sensors, 10, 5469–5502, https://doi.org/10.3390/s100605469, 2010. a
Gäbel, P., Koller, C., and Hertig, E.: Development of Air Quality Boxes Based on Low-Cost Sensor Technology for Ambient Air Quality Monitoring, Sensors, 22, 3830, https://doi.org/10.3390/s22103830, 2022. a, b
Gu, K., Qiao, J., and Lin, W.: Recurrent Air Quality Predictor Based on Meteorology- and Pollution-Related Factors, IEEE T. Ind. Inform., 14, 3946–3955, https://doi.org/10.1109/TII.2018.2793950, 2018. a
Han, P., Mei, H., Liu, D., Zeng, N., Tang, X., Wang, Y., and Pan, Y.: Calibrations of Low-Cost Air Pollution Monitoring Sensors for CO, NO2, O3, and SO2, Sensors, 21, 256, https://doi.org/10.3390/s21010256, 2021. a
Han, S., Bian, H., Feng, Y., Liu, A., Li, X., Zeng, F., and Zhang, X.: Analysis of the Relationship between O3, NO and NO2 in Tianjin, China, Aerosol Air Qual. Res., 11, 128–139, https://doi.org/10.4209/aaqr.2010.07.0055, 2011. a, b
Holstius, D. M., Pillarisetti, A., Smith, K. R., and Seto, E.: Field calibrations of a low-cost aerosol sensor at a regulatory monitoring site in California, Atmos. Meas. Tech., 7, 1121–1131, https://doi.org/10.5194/amt-7-1121-2014, 2014. a
Idrees, Z. and Zheng, L.: Low cost air pollution monitoring systems: A review of protocols and enabling technologies, Journal of Industrial Information Integration, 17, 100123, https://doi.org/10.1016/j.jii.2019.100123, 2020. a
Johnson, N. E., Bonczak, B., and Kontokosta, C. E.: Using a gradient boosting model to improve the performance of low-cost aerosol monitors in a dense, heterogeneous urban environment, Atmos. Environ., 184, 9–16, https://doi.org/10.1016/j.atmosenv.2018.04.019, 2018. a, b
Karagulian, F.: New Challenges in Air Quality Measurements, in: Air Quality Networks, Environmental Informatics and Modeling, edited by: De Vito, S., Karatzas, K., Bartonova, A., and Fattoruso, G., Springer International Publishing, Cham, https://doi.org/10.1007/978-3-031-08476-8_1, 1–18, 2023. a
Karagulian, F., Barbiere, M., Kotsev, A., Spinelle, L., Gerboles, M., Lagler, F., Redon, N., Crunaire, S., and Borowiak, A.: Review of the Performance of Low-Cost Sensors for Air Quality Monitoring, Atmosphere, 10, 506, https://doi.org/10.3390/atmos10090506, 2019. a, b
Lin, Y., Dong, W., and Chen, Y.: Calibrating Low-Cost Sensors by a Two-Phase Learning Approach for Urban Air Quality Measurement, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2, 1–18, https://doi.org/10.1145/3191750, 2018. a, b
Lundberg, S. and Lee, S.-I.: A Unified Approach to Interpreting Model Predictions, Advances in neural information processing systems, arXiv, 2, https://doi.org/10.48550/ARXIV.1705.07874, 2017. a, b
Lundberg, S. and Lee, S.-I.: SHAP, https://shap.readthedocs.io/en/latest/ (last access: 16 June 2022), 2022. a
Maag, B., Saukh, O., Hasenfratz, D., and Thiele, L.: Pre-Deployment Testing, Augmentation and Calibration of Cross-Sensitive Sensors., in: EWSN, 169–180, 2016. a
Maag, B., Zhou, Z., and Thiele, L.: A Survey on Sensor Calibration in Air Pollution Monitoring Deployments, IEEE Internet Things, 5, 4857–4870, https://doi.org/10.1109/JIOT.2018.2853660, 2018. 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
Masson, N., Piedrahita, R., and Hannigan, M.: Approach for quantification of metal oxide type semiconductor gas sensors used for ambient air quality monitoring, Sensor. Actuator. B, 208, 339–345, https://doi.org/10.1016/j.snb.2014.11.032, 2015. a, b
Maxim Integrated: Datasheet DS18B20, https://datasheets.maximintegrated.com/en/ds/DS18B20.pdf, last access: 29 September 2021. a
Mead, M., Popoola, O., Stewart, G., Landshoff, P., Calleja, M., Hayes, M., Baldovi, J., McLeod, M., Hodgson, T., Dicks, J., Lewis, A., Cohen, J., Baron, R., Saffell, J., and Jones, R.: 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
Meng, X., Liu, C., Chen, R., Sera, F., Vicedo-Cabrera, A. M., Milojevic, A., Guo, Y., Tong, S., Coelho, M. D. S. Z. S., and Saldiva, P. H. N.: Short Term Associations of Ambient Nitrogen Dioxide with Daily Total, Cardiovascular, and Respiratory Mortality: Multilocation Analysis in 398 Cities, BMJ, n534, https://doi.org/10.1136/bmj.n534, 2021. a
Miech, J., Stanton, L., Gao, M., Micalizzi, P., Uebelherr, J., Herckes, P., and Fraser, M.: Calibration of Low-Cost NO2 Sensors through Environmental Factor Correction, Toxics, 9, 281, https://doi.org/10.3390/toxics9110281, 2021. a
Mijling, B., Jiang, Q., de Jonge, D., and Bocconi, S.: Field calibration of electrochemical NO2 sensors in a citizen science context, Atmos. Meas. Tech., 11, 1297–1312, https://doi.org/10.5194/amt-11-1297-2018, 2018. a
Morawska, L., Thai, P. K., Liu, X., Asumadu-Sakyi, A., Ayoko, G., Bartonova, A., Bedini, A., Chai, F., Christensen, B., Dunbabin, M., Gao, J., Hagler, G. S., Jayaratne, R., Kumar, P., Lau, A. K., Louie, P. K., Mazaheri, M., Ning, Z., Motta, N., Mullins, B., Rahman, M. M., Ristovski, Z., Shafiei, M., Tjondronegoro, D., Westerdahl, D., and Williams, R.: Applications of low-cost sensing technologies for air quality monitoring and exposure assessment: How far have they gone?, Environ. Int., 116, 286–299, https://doi.org/10.1016/j.envint.2018.04.018, 2018. a
Mueller, M., Meyer, J., and Hueglin, C.: Design of an ozone and nitrogen dioxide sensor unit and its long-term operation within a sensor network in the city of Zurich, Atmos. Meas. Tech., 10, 3783–3799, https://doi.org/10.5194/amt-10-3783-2017, 2017. a
Narayana, M. V., Jalihal, D., and Nagendra, S. M. S.: Establishing A Sustainable Low-Cost Air Quality Monitoring Setup: A Survey of the State-of-the-Art, Sensors, 22, 394, https://doi.org/10.3390/s22010394, 2022. a, b, c, d
Nowack, P., Konstantinovskiy, L., Gardiner, H., and Cant, J.: Machine learning calibration of low-cost NO2 and PM10 sensors: non-linear algorithms and their impact on site transferability, Atmos. Meas. Tech., 14, 5637–5655, https://doi.org/10.5194/amt-14-5637-2021, 2021. a, b, c
Nuvolone, D., Petri, D., and Voller, F.: The Effects of Ozone on Human Health, Environ. Sci. Pollut. Res., 25, 8074–8088, https://doi.org/10.1007/s11356-017-9239-3, 2018. a
Pancholi, P., Kumar, A., Bikundia, D. S., and Chourasiya, S.: An observation of seasonal and diurnal behavior of O3–NOx relationships and local/regional oxidant (OX = O3 + NO2) levels at a semi-arid urban site of western India, Sustain. Environ. Res., 28, 79–89, https://doi.org/10.1016/j.serj.2017.11.001, 2018. a
Peterson, P., Aujla, A., Grant, K., Brundle, A., Thompson, M., Vande Hey, J., and Leigh, R.: Practical Use of Metal Oxide Semiconductor Gas Sensors for Measuring Nitrogen Dioxide and Ozone in Urban Environments, Sensors, 17, 1653, https://doi.org/10.3390/s17071653, 2017. a
Piedrahita, R., Xiang, Y., Masson, N., Ortega, J., Collier, A., Jiang, Y., Li, K., Dick, R. P., Lv, Q., Hannigan, M., and Shang, L.: The next generation of low-cost personal air quality sensors for quantitative exposure monitoring, Atmos. Meas. Tech., 7, 3325–3336, https://doi.org/10.5194/amt-7-3325-2014, 2014. a
Rai, A. C., Kumar, P., Pilla, F., Skouloudis, A. N., Di Sabatino, S., Ratti, C., Yasar, A., and Rickerby, D.: End-user perspective of low-cost sensors for outdoor air pollution monitoring, Sci. Total Environ., 607-608, 691–705, https://doi.org/10.1016/j.scitotenv.2017.06.266, 2017. a
Sahu, R., Nagal, A., Dixit, K. K., Unnibhavi, H., Mantravadi, S., Nair, S., Simmhan, Y., Mishra, B., Zele, R., Sutaria, R., Motghare, V. M., Kar, P., and Tripathi, S. N.: Robust statistical calibration and characterization of portable low-cost air quality monitoring sensors to quantify real-time O3 and NO2 concentrations in diverse environments, Atmos. Meas. Tech., 14, 37–52, https://doi.org/10.5194/amt-14-37-2021, 2021. a
Sales-Lérida, D., Bello, A. J., Sánchez-Alzola, A., and Martínez-Jiménez, P. M.: An Approximation for Metal-Oxide Sensor Calibration for Air Quality Monitoring Using Multivariable Statistical Analysis, Sensors, 21, 4781, https://doi.org/10.3390/s21144781, 2021. 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
Schmitz, S., Towers, S., Villena, G., Caseiro, A., Wegener, R., Klemp, D., Langer, I., Meier, F., and von Schneidemesser, E.: Unravelling a black box: an open-source methodology for the field calibration of small air quality sensors, Atmos. Meas. Tech., 14, 7221–7241, https://doi.org/10.5194/amt-14-7221-2021, 2021. a
Seabold, S. and Perktold, J.: statsmodels: Econometric and statistical modeling with python, in: 9th Python in Science Conference, Austin, Texas, 28–30 June 2021, https://doi.org/10.25080/Majora-92bf1922-011, 2010. a
Sensortech, S. G. X.: Datasheet MiCS-2714, https://www.sgxsensortech.com/content/uploads/2014/08/1107_Datasheet-MiCS-2714.pdf (last access: 29 September 2021), a. a
Sensortech, S. G. X.: Datasheet MiCS-2614, https://sensorsandpower.angst-pfister.com/fileadmin/products/datasheets/188/MOS-Ozone-MiCS-2614_1620-21530-0006-E-0714.pdf (last access: 29 September 2021), b. a
Shekhar, S., Bhagat, S., Kunjithapatham, S., and Kolluri, B. K.: Dominance-Analysis, https://github.com/dominance-analysis/dominance-analysis, (last access: 29 September 2022), 2019. a
Smets, K., Verdonk, B., and Jordaan, E. M.: Evaluation of performance measures for SVR hyperparameter selection, in: 2007 International Joint Conference on Neural Networks, IEEE, 637–642, 2007. a
Spiess, A.-N. and Neumeyer, N.: An evaluation of R2 as an inadequate measure for nonlinear models in pharmacological and biochemical research: a Monte Carlo approach, BMC Pharmacology, 10, 6, https://doi.org/10.1186/1471-2210-10-6, 2010. a
Spinelle, L., Aleixandre, M., and Gerboles, M.: Protocol of evaluation and calibration of low-cost gas sensors for the monitoring of air pollution, Publications Office of the European Union, Luxembourg, https://doi.org/10.2788/9916, 2013. 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. Actuator. B, 215, 249–257, https://doi.org/10.1016/j.snb.2015.03.031, 2015. a, b, c
Spinelle, L., Gerboles, M., Aleixandre, M., and Bonavitacola, F.: Evaluation of metal oxides sensors for the monitoring of O3 in ambient air at ppb level, Chem. Engineer. Trans., 54, 319–324, https://doi.org/10.3303/CET1654054, 2016. a
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. Actuator. B, 238, 706–715, https://doi.org/10.1016/j.snb.2016.07.036, 2017. a, b
TeamHG-Memex: Eli5, https://eli5.readthedocs.io/en/latest/ (last access: 25 October 2022), 2022. a
Vega García, M. and Aznarte, J. L.: Shapley additive explanations for NO2 forecasting, Ecol. Inform., 56, 101039, https://doi.org/10.1016/j.ecoinf.2019.101039, 2020. a
Wang, A., Machida, Y., deSouza, P., Mora, S., Duhl, T., Hudda, N., Durant, J. L., Duarte, F., and Ratti, C.: Leveraging Machine Learning Algorithms to Advance Low-Cost Air Sensor Calibration in Stationary and Mobile Settings, Atmos. Environ., 301, 119692, https://doi.org/10.1016/j.atmosenv.2023.119692, 2023. a
Wang, C., Yin, L., Zhang, L., Xiang, D., and Gao, R.: Metal Oxide Gas Sensors: Sensitivity and Influencing Factors, Sensors, 10, 2088–2106, https://doi.org/10.3390/s100302088, 2010. a
Wei, P., Ning, Z., Ye, S., Sun, L., Yang, F., Wong, K., Westerdahl, D., and Louie, P.: Impact Analysis of Temperature and Humidity Conditions on Electrochemical Sensor Response in Ambient Air Quality Monitoring, Sensors, 18, 59, https://doi.org/10.3390/s18020059, 2018. a
Williams, D. E., Henshaw, G. S., Bart, M., Laing, G., Wagner, J., Naisbitt, S., and Salmond, J. A.: Validation of low-cost ozone measurement instruments suitable for use in an air-quality monitoring network, Meas. Sci. Technol., 24, 065803, https://doi.org/10.1088/0957-0233/24/6/065803, 2013. a
World Health Organization: WHO global air quality guidelines: particulate matter (PM2.5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide, World Health Organization, Geneva, https://apps.who.int/iris/handle/10665/345329 (last access: 26 September 2022), section: xxi, 273 pp., 2021. a
Yang, J.: Fast TreeSHAP: Accelerating SHAP Value Computation for Trees, arXiv, 3, https://doi.org/10.48550/ARXIV.2109.09847, 2021. a
Yang, J.: FastTreeSHAP, https://github.com/linkedin/FastTreeSHAP (last access: 29 November 2022), 2022. a
Zaldei, A., Camilli, F., De Filippis, T., Di Gennaro, F., Di Lonardo, S., Dini, F., Gioli, B., Gualtieri, G., Matese, A., Nunziati, W., Rocchi, L., Toscano, P., and Vagnoli, C.: An integrated low-cost road traffic and air pollution monitoring platform for next citizen observatories, Transport. Res. Proced., 24, 531–538, https://doi.org/10.1016/j.trpro.2017.06.002, 2017. a
Zauli-Sajani, S., Marchesi, S., Pironi, C., Barbieri, C., Poluzzi, V., and Colacci, A.: Assessment of air quality sensor system performance after relocation, Atmos. Pollut. Res., 12, 282–291, https://doi.org/10.1016/j.apr.2020.11.010, 2021. a
Zimmerman, N., Presto, A. A., Kumar, S. P. N., Gu, J., Hauryliuk, A., Robinson, E. S., Robinson, A. L., and Subramanian, R.: 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
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.
We assessed calibration models for two low-cost stations equipped with O3 and NO2 metal oxide...
