| 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.
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