the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 30 Mar 2026
Intercomparison of low-cost sensors via simultaneous atmospheric measurements: a case study
Abstract. The adoption of low-cost sensors (LCS) is growing steadily due to their affordability, ease of use, and broad applicability. However, concerns remain regarding their reliability, prompting continued investigations into their performance and proper handling of measurements.
This study uses a three week field campaign in a urban area in central Italy carried out during the winter holiday season. Atmospheric physical and chemical parameters, temperature, relative humidity, pressure, concentration of carbon monoxide (CO), nitric oxide (NO), nitrogen dioxide (NO2), ozone in the form of O3 and OX and particulate matter PM2.5 and PM10, have been measured by three different commercial LCS platform (Vaisala AQT, AirSensEUR and Libelium Smart Environment PRO) in their factory primary calibration, to assess their initial performance. The LCS have been placed in a site close to two meteorological stations hosting standard certified reference instruments, which have been used for the intercomparison process. Additionally a 2B Ozone Monitor, EPA-certified Federal Equivalent Method, has been mounted next to the LCS, to add ozone to the evaluated variables. Due to the absence of a CO reference dataset, only a comparison between LCS has been performed to asses consistency for this measurement.
Meteorological measurements showed high correlation (R ∼ 0.9) across all LCS with the reference data, except for a discrepancy in temperature and relative humidity for AirSensEUR. The concentrations of NO and NO2 exhibited a good correlation (R ≥ 0.75) with reference instrument, although some discrepancies and deviations from the ideal linear relationship were observed. Differently ozone comparison had a good similarity only for Vaisala AQT (R ∼ 0.8), while for the remaining two the differences are noticeable (R ∼ 0.5). CO time series across the three low-cost sensors are almost the same. Finally, both PM values, available from the reference only as daily averages, showed a reasonable level of agreement with the reference instrument for AirSensEUR, albeit with greater variability.
The LCS data acquired in the atmosphere was also analysed in relation to nearby pollution sources. Workday versus holiday daily comparison and wind pollutant correlation have been executed with the aim to evaluate the ability of these LCS to recognize daily patterns and attribute pollutant sources.
Results show the potential information-driven applications of these commercial low-cost sensors, detecting emission patterns during rush hours and holidays.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Atmospheric Measurement Techniques.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.- Preprint
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2026-1262', Anonymous Referee #1, 16 May 2026
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RC2: 'Comment on egusphere-2026-1262', Anonymous Referee #2, 24 Jul 2026
Overall comments
In “Intercomparison of low-cost sensors via simultaneous atmospheric measurements: a case study,” the authors evaluate the performance of three low-cost sensor (LCS) models during a field campaign in central Italy. They also consider the ability of the factory-calibrated LCS to capture pollutant patterns in an area with several pollution sources.
The manuscript contributes useful information about the field performance of three LCS devices in one specific environment. It also provides a valuable perspective about the data quality of off-the-shelf (un-calibrated) sensors and what uses they are appropriate for.
However, the manuscript does not adequately address some areas of uncertainty including (1) the distance between the LCS and reference site and (2) the limited sample size of measurements for the analysis of holiday/weekday patterns and wind direction.
Additionally, while the study aims to assess factory-calibrated sensors, one of the three sensor models (ASE) was calibrated directly against a second sensor (AQT) in the field, undermining independent evaluation of the ASE. There also appear to be some discrepancies in the data presented that should be reconciled (see my specific comments about Table 4 below).
Therefore, I would recommend the following revisions:
Specific comments
Title
To distinguish this paper, consider including in the title:
- the location of measurements
- specify that the paper evaluates factory-calibrated sensors
Abstract
The abstract could use more nuance about the performance results of the sensors. While R statistics show relatively good agreement, the un-corrected sensors have large bias (especially for NO2).
Introduction
Line 55: DeSouza et al. (2022) and Peters et al. (2022) might also be good citations that focus on the role of calibration in achieving air sensor objectives
Line 61: Zimmerman et al. (2018) is also a relevant citation here
Line 65: Best practices typically include calibrating or correcting sensors. It would strengthen the framing to add more justification about why you decided to use un-corrected data from sensors and how it contributes to the aims of the research.
Line 65-69: It would be valuable to add citations that put this study into context – are there other works that test these sensor models?
Materials and methods
Line 88: Please clarify what you mean by black-box. Does this mean a proprietary algorithm?
Line 105: The distance between the sites, especially with known nearby emissions sources, introduces uncertainty into the performance assessment as it is not a true collocation between the LCS and reference. For example, one site could be downwind of airport or highway emissions at a time when the other is not. This could introduce noise (if levels between sites vary differently) or bias (if one site tends to face higher pollution). The authors should include a discussion of this uncertainty and whether sensitivity analyses were performed to understand the impacts of distance between sites.
Line 109: The statistics named very briefly here are an important part of the study. It would be beneficial to introduce each statistic and the type of information it provides so the reader can more readily interpret the results tables.
Line 110: Why was a 3-sigma filter applied? Were these suspected measurement artifacts? If valid measurements were excluded it could affect the performance results.
Section 2.1.1: The calibration of the ASE sensor using a different sensor (AQT) complicates the performance evaluation results and the framing of the paper. While the paper is stated to examine un-calibrated sensors, in reality the ASE sensor has been calibrated to match the results of the AQT sensor as closely as possible. This would not be possible when buying ASE off the shelf. As a result it is not surprising that the gas performance results for ASE and AQT are very similar. To truly evaluate off-the-shelf performance as the study aims, it would make more sense to evaluate the digital signal directly (does it correlate and show trends properly) or use a correction from the literature. This way the ASE could truly be evaluated independently of the AQT.
Results
Line 181: Is there any evidence in the literature of similar effects on this sensor?
Figure 6: The 1:1 line is in black, not red. Also make sure the x axis labels are not cut off.
Line 185: The agreement does not look so good between SEP and AQT – there is a nonlinear shape in the scatter plot.
Table 3: Similar to my comment above, it is not surprising that AQT and ASE have very similar results because ASE has been calibrated to match AQT. It is difficult to see this as an independent evaluation of ASE.
Table 4: Why are statistics for AQT vs ASE CO comparisons different than Table S2? The AQT vs SEP R value is highest in the table although these two variables appear to have worse agreement in Figure 6 scatter. Please double check that these are the correct statistics for each pair of sensors.
Figure 7: The daily column chart is useful, but it is difficult to assess performance in aggregate. I’d suggest including a supplemental table with summary statistics. This would support the statements on line 195.
Figure 8: Given that the number of holidays is small (5 days), it is difficult to conclude that the difference between days is caused by emissions patterns and not changes in weather that happened to occur on certain days. Did the authors perform any sensitivity analyses to understand the potential influence of weather (e.g. increased dispersion that reduces pollutant concentrations)? Including this and discussing the potential role of weather would strengthen the argument that emission differences were detected.
Figure 8: Specify what the shaded region represents.
Section 3.3 Wind Analysis: Have the authors filtered the wind analysis to ensure a minimum number of data points per wind speed/direction bin? In some cases, the points on the figure might represent only a single moment in time, making generalization difficult. Consider filtering to a minimum number of hours to improve the robustness.
Line 241: The higher concentrations at low wind speeds are also consistent with the buildup of pollution during periods of stagnation overnight
Conclusions
The discussion of sensor performance results could use more clarity and specificity to inform the reader. Currently it is qualitative and discusses some positives (good correlation) and some negatives (poor agreement with ARTA). The authors could consider comparing performance results to benchmarks (such as those from US EPA or defined by the authors) to provide a more definitive judgement of how each sensor performed in terms bias and accuracy.
Line 274: The discussion of how the sensors can be used despite imperfect data is useful. It would be interesting to also consider analyses that would not be appropriate based on the data quality (like comparisons to regulatory or health thresholds).
Minor comments
Decimals should be rounded throughout to something meaningful like 2 places
I would suggest specifying in tables and figure captions the time resolution of the data that were used to generate the figure or statistics.
Check figure captions to ensure the correct colors are used to describe features (like the 1:1 line).
Citation: https://doi.org/10.5194/egusphere-2026-1262-RC2
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- 1
This work by Gentile at al. pesents an interesting intercomparison exercise between low-cost sensors for meteorological and air pollution measurements. The topic is relevant for AMT, particularly in light of the continuously growing interest in low-cost technologies for air quality monitoring and research applications.
In addition to provide insights into the quality and reliability of data from three specific commercial low-cost sensors, the authors also discuss their fitness for purpose by exploring selected use cases, such as the investigation of the typical diel variability of pollutants and source attribution through combined analysis with near-surface wind variability.
The manuscript is generally well organized and easy to follow. I recommend publication after addressing the following technical and minor comments.
Specific Comments
1. Introduction
The following WMO report on the use of low-cost sensors in air quality monitoring networks should be included to strengthen the reference list: https://library.wmo.int/records/item/68924-integrating-low-cost-sensor-systems-and-networks-to-enhance-air-quality-applications
It would be valuable if the authors discuss how their work relates to the findings and recommendations presented in this report.
2. Materials and methods
The Vaisala AQT sensor is not described with the same level of detail as the other LCSs in this section. I recommend providing a proper introduction of the Vaisala AQT sensor.
No specifications are provided regarding measurement uncertainty, precision, or stability for the three sensors. These characteristics are usually reported in the instrument manuals. Please include them in Table 1 for each measured variable.
Please specify what Ox stands for.
Line 85: Please clarify what is meant by “open source” with reference to the ASE. Does this represent an added value compared to the other sensors?
Line 106: More details on the site setup (including ITAF and ARTA) are needed. Please specify sampling heights above ground level and (for your site) presence of nearby obstacles.
Table 1: Include declared measurement uncertainty, precision, and stability. Add the acronyms used throughout the manuscript (AQT, SEP, ASE). Ensure consistent use of sensor naming (acronyms vs full names). Include the measurement principle/sensor type for each parameter
Line 108: Please add definitions and formulas for the statistical indicators used.
Line 114: Specify that daily averages are only calculated for PM10 and PM2.5.
Line 115: Clarify what is meant by “the two subsequent analyses”.
Section 2.1.1
Equation (1): Which temperature (T) and relative humidity (RH) data are used? If they are taken from ASE, discuss how the detected inaccuracies in T and RH retrieval could affect gas concentration estimates.
Line 132 (and throughout the manuscript): Please reconsider the use of excessive significant digits when reporting statistical indicators.
Section 3.1.1
Line 142: The phrase “(from 0.8767… respectively)” is unclear. Please specify how the “average” is calculated.
Figure 3: The comparison between ITAF and ASE/SEP shows evident non-linearity, which implies a concentration-dependent bias. This should be clearly emphasized. Moreover, the use of a linear model to assess the performance of the low cost sensors for RH should be critically discussed, as it may not be consistent with the observed behavior.
Section 3.1.2
Line 160: In addition to SEP NO₂, NO also appears to be poorly reproduced by SEP.
Line 170: The statement “The bias for AQT and ASE are generally low” is not supported by Fig. 5. The figure shows large biases (e.g., >25 ppb for NO2 at higher concentrations) for both AQT and ASE. Similar issues are observed for NO.
Figure 4: Some fixed values appear in the OM205 time series. Please clarify their origin and whether these values were excluded from the comparison analysis.
Figure 5: The NO correlation appears strongly influenced by a few high-concentration data points. I suggest repeating the analysis limiting NO values to 0–30 ppb and discuss differences (if any). In the O3 AQT plot there are some fix values for OM205 at around 20 ppb. I think they should be removed.
Section 3.1.3
Line 201: Please specify which differences are being referred to.
I recommend including a summary table for PM2.5 and PM10 comparisons, reporting mean differences (with min–max range) and standard deviation of differences
Section 3.2
Line 206: Better introduce Figures 8 and 9, clearly explaining their content.
Figure 8: If the evening NO peak is attributed to traffic emissions, why do NO2 and CO not decrease during holidays compared to weekdays? The NO2/NO ratio changes between weekdays and holidays. Could this indicate changes in emission sources?
The diurnal ozone peak is likely influenced by vertical mixing and entrainment from higher atmospheric layers under conditions of strong atmospheric mixing. This interpretation is supported by Fig. 10, where ozone behaves differently compared to primary pollutants. Including wind speed data from ITAF in the plot would help disentangle the role of boundary layer dynamics.
Section 3.3
Line 248: The attribution to traffic emissions from the E80 corridor appears still consistent with south-west winds. Have the authors considered differences in traffic flow directions between morning and evening rush hours?
Line 250: Please clarify what is meant by “This is expected for the NOx/Ozone daily cycle.”
Lines 252–255: It appears that AQT results are more consistent with ASE calibrated data, while SEP aligns better with ASE raw data. Please add some discussions about this observation.
Conclusions
In general, it should be interesting that you critically discuss the performance of the three sensors with the characteristics provided by the manufacturer in the manual/data sheet in terms of declared measurement uncertainties.
Line 265: The concentration-dependent bias observed for NO and NO2 (particularly for AQT and ASE) should be explicitly mentioned.
Line 270: Please provide possible explanations for the different behavior observed in PM10 (overestimation) versus PM2.5 (underestimation). Additional information on PM composition at the site would be valuable if available: the authors should discuss whether their findings can be generalized to environments with different PM composition.
Technical Comments
The reference list formatting is inconsistent and should be standardized.
Line 3: “Harwey” (year missing) should be verified; it does not appear to be a peer-reviewed reference. Please also check formatting issues in other references (e.g., “Organization”, “of Science et al.”).
In several cases, references are written as “XXXX at al., yyyy”. The correct format should be “XXXX et al. (yyyy)”.
Figure 5: Some axis labels are partially obscured. Please shift the SEP O3 plot to align more clearly with the ASE Ox plot.
Figures 8–9: Explain what the shaded areas represent in the captions. Ensure that color schemes are accessible to color-blind readers