the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 22 Jan 2024
Performance Evaluation of MeteoTracker Mobile Sensor for Outdoor Applications
Abstract. The morphological complexity of urban environments results in a high spatial and temporal variability of the urban microclimate. The consequent demand for highly-resolution atmospheric data remains a challenge for atmospheric research and operational application. The recent widespread availability and increasing adoption of low-cost mobile sensing offers the opportunity to integrate observations from conventional monitoring networks with microclimatic and air pollution data at a finer spatial and temporal scale. So far, the relatively low quality of the measurements and outdoor performance compared to conventional instrumentation has discouraged the full deployment of mobile sensors for routine monitoring. The present study addresses the performance of a commercial mobile sensor, the MeteoTracker (IoTopon Srl), recently launched on the market to quantify the microclimatic characteristics of the outdoor environment. The sensor follows the philosophy of the Internet of Things technology, being low cost, having an automatic data flow via personal smartphones and online data sharing, supporting user-friendly software, and having the potential to be deployed in large quantities. In this paper, the outdoor performance is evaluated through tests aimed at quantifying (i) the intra-sensor variability under similar atmospheric conditions and (ii) the outdoor accuracy compared to a reference weather station under sub-optimal (in fixed location) and optimal (mobile) sensor usage. Data-driven corrections are developed and successfully applied to improve the MeteoTracker data quality. In particular, a recursive method for the simultaneous improvement of relative humidity, dew point, and humidex index proves crucial for increasing the data quality. The results mark an intra-sensor variability in the range of ±0.5 °C for air temperature and ±1.2 % for the corrected relative humidity, both within the declared sensor accuracy. The sensor captures the same atmospheric variability as the reference sensor during both fixed and mobile tests, showing positive biases (overestimation) for both variables. Through the mobile test, the outdoor accuracy is observed between ±0.3 °C to ±0.5 °C for air temperature, between ±3 % and ±5 % for the relative humidity, ranking the MeteoTracker in the real accuracy range of similar commercial sensors from the literature and making it a valid solution for atmospheric monitoring.
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Status: closed
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RC1: 'Comment on amt-2023-256', Anonymous Referee #1, 19 Mar 2024
The proposed contribution presents a detailed investigation of the performance of a commercial device aimed at providing high spatial resolution for microclimate investigation through mobile monitoring, in an IoT philosophy. The device is accessible via smartphone and data can be easily shared among the community on a dedicated platform according to the user available license. The topic is of great relevance given the rising awareness on the implications of complex urban morphology and ecosystem on intra-urban microclimate variability which impacts, among others, urban communities’ resilience. The authors comprehensively explore the reliability of the commercial device object of the study through three experimental steps that provide a clear view of the instrument potentials and limitations. Moreover, the authors implemented a recursive method to correct the relative humidity data that have been proven to significantly increase the reliability of measurements collected through the system under the formulated hypothesis. The methodology is accurately presented and could be of reference to future studies involving the usage of novel mobile systems for microclimate investigations, that are expected to increase in number given the relevance of the topic.
Just minor comments from my side: please check equations numbering in the paper, it seems that the recalled equation 3 in section 4.1 is missing; typo at page 2, line 50 “oOftentimes”, page 3 line 93 “mwith”.
A final comment related to the usability of the system in long-term route monitoring: the lack of internal memory may be a limitation since the device needs to be constantly connected to a smartphone which is feasible, but a backup plan is always recommended. Not sure about the economical and technical implications due to the integration of a minimum internal memory capacity.
Citation: https://doi.org/10.5194/amt-2023-256-RC1 -
AC1: 'Reply on RC1', Francesco Barbano, 19 Mar 2024
We thank the referee for the comment and we are glad for the appreciation received. We have corrected the typos and equation referencing.
We have not explored long-term route monitoring but a few isolated long trips have been made and we have not observed any major limitation either with the sensor battery (it should last for 250 hours according to the manufacturer) or the storage memory. Indeed, data are not stored in the sensor but flow directly into the smartphone application so the limitation is given by the available memory on the smartphone. Nonetheless, this problem is quite limited as the application occupies approximately 50 MB and each data point is <2 kB (which means that for a 500 km track, MT data occupies <17 MB). We have added this information to the manuscript, it could be helpful for the general overview of the sensor. Regarding the use of the MT without a constant connection to a smartphone, there is a version of the sensor named stand-alone where the MT is connected to a cellular/GNSS module, but we have not tested this solution.
Citation: https://doi.org/10.5194/amt-2023-256-AC1
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AC1: 'Reply on RC1', Francesco Barbano, 19 Mar 2024
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RC2: 'Comment on amt-2023-256', Anonymous Referee #2, 25 Mar 2024
The authors focus on evaluating the metrological performance of a commercial device designed for monitoring microclimate in urban areas with high spatial resolution, compliant with Internet of Things (IoT) principles and developed with low-cost sensors. This device can be connected to a smartphone to record and share data on a dedicated platform. The authors conduct three experimental phases to thoroughly evaluate the reliability of the device and introduce a recursive method to improve the accuracy of the humidity data. The methodology is well presented.
Some minor issues arise:
line 50: please revise the typos
Table 1: please provide the unit of measurement for each variable
Line 135: please explain why you did not consider internal memory
Line 153: it might be useful for the reader to also read the Koppen classification for the three areas considered for the test.
Line 191: if you also look at the data in Table 2, it is clear that there is a summer period when the three test sections were performed. So you wrote that the MTs were placed on the top of the vehicle, as close as possible to the car axis. But what about the factor of proximity to the car body (which can heat up a lot when exposed to sunlight in summer).
Lines 201-207: this section can be moved before section 3.3, which already refers to the comparison with fixed location
Line 207: please check the reference in round brackets: maybe it is sect. 4.4 ?
Figure 6d: Could you add the U.M.?
In Figure 11, the bin width does not seem to be 0.5°C, please check
In the caption of Figure 4, please link the suffix “c” , “d” in the headings of the table with the reference to "measured", "derived" or "corrected".Citation: https://doi.org/10.5194/amt-2023-256-RC2 -
AC2: 'Reply on RC2', Francesco Barbano, 29 Mar 2024
We thank the reviewer for the revision and the comments provided. We are glad to read the investigation is appreciated. We have addressed all the comments, correcting typos and references in the text, and adding the units of measurement where missing. responses to the remaining specific comments are listed below.
line 135: this specific sensor does not have an internal memory and this is a choice made by the manufacturer; now there is a version of this sensor with an external memory that can be attached to the sensor without the need for a smartphone (it is called MeteoTracker stand-alone) but it was realised on the market after all the monitoring activities were completed so we decided to continue with our sensors.
Line 153: we agree; we will add the Koppen classification for the regions
191: Indeed the air in the immediate surroundings of the car can be warmer than the air at the same height. Unfortunately, this effect is almost impossible to remove but we have tried to minimize it by keeping the sensors on the car top away from the major heat sources of the car (engine, brakes, wheels) and where the aerodynamic effect is larger. Also putting the sensors in the front part of the car rooftop maximizes the amount of fresh air that has not interacted with the car before being sampled by the sensor. We will integrate the discussion with these considerations as a best practice for the installation. As a last consideration, for the specific intercomparison test the heating effect of the car is not an important fact as soon as all sensors are impacted the same way.
Lines 201-207: we thank the reviewer for the suggestion but we believe that this paragraph should remain in section 3.3 as it further introduces the need for a comparison with research-grade instrumentation and the difficulties that arise with a non-conventional device like the MT.
Figure 11: thank you, we will correct the indication on bin width
Figure 4: probably the reviewer was suggesting this procedure for Figure 6. Nonetheless, we believe specifying which variable is measured, derived or corrected in each figure containing data plots will help the reader, so we will implement this correction.
Citation: https://doi.org/10.5194/amt-2023-256-AC2
-
AC2: 'Reply on RC2', Francesco Barbano, 29 Mar 2024
Status: closed
-
RC1: 'Comment on amt-2023-256', Anonymous Referee #1, 19 Mar 2024
The proposed contribution presents a detailed investigation of the performance of a commercial device aimed at providing high spatial resolution for microclimate investigation through mobile monitoring, in an IoT philosophy. The device is accessible via smartphone and data can be easily shared among the community on a dedicated platform according to the user available license. The topic is of great relevance given the rising awareness on the implications of complex urban morphology and ecosystem on intra-urban microclimate variability which impacts, among others, urban communities’ resilience. The authors comprehensively explore the reliability of the commercial device object of the study through three experimental steps that provide a clear view of the instrument potentials and limitations. Moreover, the authors implemented a recursive method to correct the relative humidity data that have been proven to significantly increase the reliability of measurements collected through the system under the formulated hypothesis. The methodology is accurately presented and could be of reference to future studies involving the usage of novel mobile systems for microclimate investigations, that are expected to increase in number given the relevance of the topic.
Just minor comments from my side: please check equations numbering in the paper, it seems that the recalled equation 3 in section 4.1 is missing; typo at page 2, line 50 “oOftentimes”, page 3 line 93 “mwith”.
A final comment related to the usability of the system in long-term route monitoring: the lack of internal memory may be a limitation since the device needs to be constantly connected to a smartphone which is feasible, but a backup plan is always recommended. Not sure about the economical and technical implications due to the integration of a minimum internal memory capacity.
Citation: https://doi.org/10.5194/amt-2023-256-RC1 -
AC1: 'Reply on RC1', Francesco Barbano, 19 Mar 2024
We thank the referee for the comment and we are glad for the appreciation received. We have corrected the typos and equation referencing.
We have not explored long-term route monitoring but a few isolated long trips have been made and we have not observed any major limitation either with the sensor battery (it should last for 250 hours according to the manufacturer) or the storage memory. Indeed, data are not stored in the sensor but flow directly into the smartphone application so the limitation is given by the available memory on the smartphone. Nonetheless, this problem is quite limited as the application occupies approximately 50 MB and each data point is <2 kB (which means that for a 500 km track, MT data occupies <17 MB). We have added this information to the manuscript, it could be helpful for the general overview of the sensor. Regarding the use of the MT without a constant connection to a smartphone, there is a version of the sensor named stand-alone where the MT is connected to a cellular/GNSS module, but we have not tested this solution.
Citation: https://doi.org/10.5194/amt-2023-256-AC1
-
AC1: 'Reply on RC1', Francesco Barbano, 19 Mar 2024
-
RC2: 'Comment on amt-2023-256', Anonymous Referee #2, 25 Mar 2024
The authors focus on evaluating the metrological performance of a commercial device designed for monitoring microclimate in urban areas with high spatial resolution, compliant with Internet of Things (IoT) principles and developed with low-cost sensors. This device can be connected to a smartphone to record and share data on a dedicated platform. The authors conduct three experimental phases to thoroughly evaluate the reliability of the device and introduce a recursive method to improve the accuracy of the humidity data. The methodology is well presented.
Some minor issues arise:
line 50: please revise the typos
Table 1: please provide the unit of measurement for each variable
Line 135: please explain why you did not consider internal memory
Line 153: it might be useful for the reader to also read the Koppen classification for the three areas considered for the test.
Line 191: if you also look at the data in Table 2, it is clear that there is a summer period when the three test sections were performed. So you wrote that the MTs were placed on the top of the vehicle, as close as possible to the car axis. But what about the factor of proximity to the car body (which can heat up a lot when exposed to sunlight in summer).
Lines 201-207: this section can be moved before section 3.3, which already refers to the comparison with fixed location
Line 207: please check the reference in round brackets: maybe it is sect. 4.4 ?
Figure 6d: Could you add the U.M.?
In Figure 11, the bin width does not seem to be 0.5°C, please check
In the caption of Figure 4, please link the suffix “c” , “d” in the headings of the table with the reference to "measured", "derived" or "corrected".Citation: https://doi.org/10.5194/amt-2023-256-RC2 -
AC2: 'Reply on RC2', Francesco Barbano, 29 Mar 2024
We thank the reviewer for the revision and the comments provided. We are glad to read the investigation is appreciated. We have addressed all the comments, correcting typos and references in the text, and adding the units of measurement where missing. responses to the remaining specific comments are listed below.
line 135: this specific sensor does not have an internal memory and this is a choice made by the manufacturer; now there is a version of this sensor with an external memory that can be attached to the sensor without the need for a smartphone (it is called MeteoTracker stand-alone) but it was realised on the market after all the monitoring activities were completed so we decided to continue with our sensors.
Line 153: we agree; we will add the Koppen classification for the regions
191: Indeed the air in the immediate surroundings of the car can be warmer than the air at the same height. Unfortunately, this effect is almost impossible to remove but we have tried to minimize it by keeping the sensors on the car top away from the major heat sources of the car (engine, brakes, wheels) and where the aerodynamic effect is larger. Also putting the sensors in the front part of the car rooftop maximizes the amount of fresh air that has not interacted with the car before being sampled by the sensor. We will integrate the discussion with these considerations as a best practice for the installation. As a last consideration, for the specific intercomparison test the heating effect of the car is not an important fact as soon as all sensors are impacted the same way.
Lines 201-207: we thank the reviewer for the suggestion but we believe that this paragraph should remain in section 3.3 as it further introduces the need for a comparison with research-grade instrumentation and the difficulties that arise with a non-conventional device like the MT.
Figure 11: thank you, we will correct the indication on bin width
Figure 4: probably the reviewer was suggesting this procedure for Figure 6. Nonetheless, we believe specifying which variable is measured, derived or corrected in each figure containing data plots will help the reader, so we will implement this correction.
Citation: https://doi.org/10.5194/amt-2023-256-AC2
-
AC2: 'Reply on RC2', Francesco Barbano, 29 Mar 2024
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