the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 11 Jun 2026
SPS30 and SEN55 PM2.5 sensor intercomparison and validation through indoor and outdoor measurements in Arba Minch, Ethiopia
Abstract. Ethiopian air pollution is understudied yet highly relevant considering population size and source abundance including solid fuel cooking, small scale waste burning and vehicle fleet. Low cost PM2.5 sensors can be used to mitigate this by mapping pollution exposure. We report on indoor and outdoor measurements with Sensirion sensors SPS30 and SEN55, the former extensively validated in literature, the latter not. We evaluate their use in Arba Minch, population ca 200.000. In addition to sensor inter and intra comparison we benchmark the low-cost sensors against gravimetry. Furthermore, a separate Swedish outdoor dataset is included to extend the range of particle types and loadings and evaluate relative humidity (RH) effects using a reference-equivalent monitor (Palas FIDAS).
We found that the SEN55 consistently reports values 6–10 % higher than the SPS30; once this systematic offset is corrected, the sensor types are functionally identical with high precision (coefficient of variation ≤ 7.7 %, between-sampler uncertainty ≤ 1.7 µg m-3). Both sensors demonstrated high stability across repeated high-concentration events (> 1000 µg m-3). While the SEN55 exhibits digital truncation at 6553.4 µg m-3, we find that measurements beyond the 1000 µg m-3 manufacturer specification remain meaningful and essential for accurate mass estimation in biomass-burning environments. The impact of relative humidity was small and consistent across both sensor types.
Our study shows that the SPS30 and SEN55 – when calibrated under circumstances of use – are stable and accurate instruments (indoor and outdoor accuracy error ≤ 22 %, outdoor expanded uncertainty ≤ 15 % in comparison to gravimetric measurements). Pragmatic, large-scale low-cost monitoring supported by mobile gravimetric validation offers the most viable path toward mitigating air pollution exposure in resource-constrained settings.
- Preprint
(4661 KB) - Metadata XML
- BibTeX
- EndNote
Status: final response (author comments only)
-
RC1: 'Comment on egusphere-2026-2537', Anonymous Referee #1, 13 Jul 2026
-
CC1: 'First reply on RC1 _ Q5', Johannes Dirk Dingemanse, 17 Jul 2026
To facilitate a productive discussion, I (corresponding author) reply already on parts of the RC1 questions. I however do so as community comment, to distinguish this from a later full response on behalf of all authors. This comment is regarding RC1 fifth question: "Appendix A.3: The post-hoc exclusion of SEN1 and SEN2 in Phase 2 is justified only by citation, not by the authors' own data. Please report the affected results with and without these exclusions, and state whether the 12.5% SDT rejection threshold was defined a priori."
In the attached PDF I included a figure (Fig.1) and a table (Table 1) that includes SEN1 and SEN2 data.
SEN1 shows an overall wide variation (Fig. 1). This leads to an r of 0.53, and an RMSE significantly higher than any of the other sensors (Table 1). SEN1, as part of Box1, was the only sensor positioned perpendicular rather than front-facing, and it has seen different orientations versus source and collocated UPAS across the measurement period (the second half, more sensors were added to the same location, so the box was repositioned). We expect this is the reason for this wide variability. After bias correction, the AE is higher than 25% (44%). SEN2 simply has little data (n=6). It is quite linear (r=0.91), and it is following the SEN3 (both were in the same Box2) data quite well (slopes of 1.12 and 1.11, respectively). However, the variation versus number of data points is simply too high to achieve an AE < 25% (39%).
When including the SEN1 and SEN2 data into the metrics for all sensors combined, the linearity is weaker (r 0.87 instead of original 0.93; R2 0.95 instead of 0.97, RMSE 264 instead of 187), but the ultimate slope is similar (1.15) and AE is still < 25% (20%). In other words: SEN1 and SEN2 results add to the random error, but do not alter the conclusions. Given that the SEN1 and SEN2 errors are connected to reasons other than the sensor quality (SEN1: other orientation, SEN2: significant dataloss due to power supply problems), we prefer to leave it out of the main text, as it does not speak to the sensor quality but just to field challenges and choices.
-
CC2: 'First reply on RC1 _ Q3', Johannes Dirk Dingemanse, 17 Jul 2026
To facilitate a productive discussion, I (corresponding author) reply already on parts of the RC1 questions. I however do so as community comment, to distinguish this from a later full response on behalf of all authors. This comment is regarding RC1 third question: "Section 2.1 and Appendix D: Outdoor 2 is located behind a window (physically indoors) and contributes 36 of the 53 outdoor filter pairs. Indoor placement attenuates concentration peaks and may modify the sampled size distribution, and Appendix D.1 already shows different variability at the two sites. Please report the regression results separately for Outdoor 1 and Outdoor 2 to justify pooling them, and describe the nature of this site in the main text rather than only in the appendix."
In the attached PDF I included a figure (Fig.1) and two tables (Table 1, 2) that show data for locations Outdoor 1 and 2 separately.
The data points for Outdoor 1 and 2 are visibly in the same range and of similar trend (Fig. 1). For each sensor, the confidence intervals (CIs) of Outdoor 1 slopes overlap with those of Outdoor 2 (Table 1), and of both locations they overlap with the CIs for pooled data (manuscript Table 5). Furthermore, the difference between SPS30 and SEN55 runs across both locations (with SEN55 having a lower slope i.e. less of an underestimation). If there is a difference to speak of, it is that at Outdoor 1 slopes are lower than at Outdoor 2. But, also, that the Outdoor 1 data is slightly more variable: accuracy error (AE) and expanded uncertainty (WCM) after correction is higher than the original situation – while those of Outdoor 2 are lower. The higher Outdoor 1 variation also results in the SEN55 versus SPS30 slopes not being significant (CIs overlap).
When applying the slopes based on the pooled location data as correction factors to the sensor data (1.23, 1.21, 1.20 and 1.12 for SPS1, SPS2, SPS3 and SEN1, respectively), and then calculating AE and WCM for each sensor, the maximum AE and WCM for Outdoor 1 are 16% and 22%, and for Outdoor 2 10% and 11% (Table 2): still lower than the 25 % thresholds. Even when applying the averaged SPS30 slope (1.21) as correction to all individual sensors, all resulting AEs (including that of the SEN55) are < 25 %. The WCM for the SEN55 becomes 27 %, which still is well below the threshold for indicative sensors (< 50 %) – but not below that for reference-grade (< 25 %).
In other words, while there might be a difference in sensor to gravimetry for Outdoor 1 and Outdoor 2, we do not have conclusive evidence (non-significant slope differences), using results of the pooled data does not lead to unacceptable accuracy errors for either of the locations, and the main conclusions still stand: the sensors can reach required accuracy after site-specific calibration, SPS30 and SEN55 are similar enough to allow for extrapolating data quality results of the SPS30 to the SEN55, but a better accuracy is reached when taking into account differences between SPS30 and SEN55.
-
AC1: 'Reply on RC1_Q2', Johannes Dirk Dingemanse, 25 Aug 2026
Reaction on RC1 second question: Sections 3.5 and 4.7: The κ-Köhler correction uses κ = 0.3, a continental value, while Section 4.7 itself argues that local biomass-burning aerosol likely has κ ≈ 0.06–0.19. Please recalculate the RH correction with a locally representative κ. This would directly test whether the negative post-correction trend disappears and would strengthen the argument that RH corrections are unnecessary in this environment.
Since we genuinely do not know the hygroscopicity of aerosols in our study area, we can only hypothesize as to what are more locally representative kappas. Table 1 shows trends and p-values when using kappa values of 0.3 (as in the manuscript), 0.19 and 0.06.
Table 1: Slopes, with p-values, of a regression between relative humidity (x) and absolute or relative SEN55 to gravimetry difference (y).
Correction
Slope of trend in absolute difference
Slope of trend in relative difference
Kappa 0.3 ~ SEN55 RH
-0.036 (p=0.26)
-0.003 (p=0.026)
Kappa 0.3 ~ ambient RH
-0.07 (p=0.04)
-0.005 (p=0.0003)
Kappa 0.19 ~ SEN55 RH
-0.014 (p=0.66)
-0.001 (p=0.28)
Kappa 0.19 ~ ambient RH
-0.041 (p=0.20)
-0.003 (p=0.02)
Kappa 0.06 ~ SEN55 RH
0.023 (p=0.50)
0.001 (p=0.59)
Kappa 0.06 ~ ambient RH
0.006 (p=0.85)
0.000 (p=0.90)
With κ=0.3, we get a significant negative trend in the relative error when using the SEN55 relative humidity, and a stronger significant negative trend in both absolute and relative error when using ambient humidity. With κ=0.19, with the SEN55 RH there is no significant trend; with ambient RH there is still a significant negative trend (i.e. significant overcorrection). When using 0.06, none of the trends are significant – which is in fact the same as the original data. κ=0.06, under the relative humidities encountered during our study, simply has little effects.
We can find little studies of hygroscopicity over cities with expected similar sources to Arba Minch (common use of biomass fuels, unregulated open waste burning). Over Cairo (Egypt), Christodoulou et al. (2024) find for smallest particle sizes (dry mobility diameters of 30 and 60 nm) 60-80% to be particles with low hygroscopicity (κ 0.08-0.11); most likely black carbon and organics originating from traffic and biomass burning. However, amongst the bigger particles (90, 120, 160 nm), mixtures of atmospheric salts (eg. NH4Cl) with significantly higher hygroscopicity (κ 0.28-0.41) are more present – originating from biomass and waste burning. Haslett et al. (2019) estimate a kappa of 0.22 over West-Africa due to the presence of both black carbon and organics with low hygroscopicity (<=0.1) and inorganic ammonium salts (0.52-0.68). Precursors of those salts, are also released by biomass burning (Andreae, 2019; Tomsche et al., 2023).
From this, we conclude that a kappa of 0.06 (suggesting the presence of only BC and organic aerosols) is unlikely. It might well be that a kappa in the range of 0.15-0.25 is closer to reality. The main points:
- We do not know the hygroscopicity of aerosols over Arba Minch. We prefer to therefore stick to the general κ=0.3, but to add to appendix the calculation at 0.19, and come back to this in the discussion;
- Using the SEN55 RH is more representative than ambient RH. Even under 0.19, ambient RH leads to an overcorrection. Since we found no differences in SEN55 and SPS30 effects to RH, this conclusion expands to the SPS30. This could explain why other studies find that the SPS30 holds out relatively well under higher RH: the sensor internal RH and related particle hygroscopic growth is lower than that of the ambient surroundings.
References
Andreae, M. O.: Emission of trace gases and aerosols from biomass burning – an updated assessment, Atmospheric Chemistry and Physics, 19, 8523–8546, https://doi.org/10.5194/acp-19-8523-2019, 2019.
Christodoulou, A., Bezantakos, S., Bourtsoukidis, E., Stavroulas, I., Pikridas, M., Oikonomou, K., Iakovides, M., Hassan, S. K., Boraiy, M., El-Nazer, M., Wheida, A., Abdelwahab, M., Sarda-Estève, R., Rigler, M., Biskos, G., Afif, C., Borbon, A., Vrekoussis, M., Mihalopoulos, N., Sauvage, S., and Sciare, J.: Submicron aerosol pollution in Greater Cairo (Egypt): A new type of urban haze?, Environment International, 186, 108610, https://doi.org/10.1016/j.envint.2024.108610, 2024.
Haslett, S. L., Taylor, J. W., Deetz, K., Vogel, B., Babić, K., Kalthoff, N., Wieser, A., Dione, C., Lohou, F., Brito, J., Dupuy, R., Schwarzenboeck, A., Zieger, P., and Coe, H.: The radiative impact of out-of-cloud aerosol hygroscopic growth during the summer monsoon in southern West Africa, Atmospheric Chemistry and Physics, 19, 1505–1520, https://doi.org/10.5194/acp-19-1505-2019, 2019.
Tomsche, L., Piel, F., Mikoviny, T., Nielsen, C. J., Guo, H., Campuzano-Jost, P., Nault, B. A., Schueneman, M. K., Jimenez, J. L., Halliday, H., Diskin, G., DiGangi, J. P., Nowak, J. B., Wiggins, E. B., Gargulinski, E., Soja, A. J., and Wisthaler, A.: Measurement report: Emission factors of NH3 and NHx for wildfires and agricultural fires in the United States, Atmospheric Chemistry and Physics, 23, 2331–2343, https://doi.org/10.5194/acp-23-2331-2023, 2023.
Citation: https://doi.org/10.5194/egusphere-2026-2537-AC1 -
AC2: 'Reply on RC1_Q1', Johannes Dirk Dingemanse, 05 Sep 2026
Reaction on RC1 first comment: Section 4.4.2 and Table 6: The indoor evaluation uses a UPAS sampler (expanded uncertainty ~12%, not a reference method), and the authors' own plume calculation shows that 0.3 m displacement changes concentrations by ~16%, which matches the observed inter-unit slope spread of 1.01–1.25. This suggests the slope variability might be dominated by spatial gradients rather than sensor response. Please provide an error budget separating sensor error, UPAS uncertainty, and spatial gradients, and qualify the term "validation" in the abstract accordingly.
We would like to start by stating that we have phrased our text too strongly. Where we stated in lines 471-474 that “[t]he observed indoor slope variations (1.01 – 1.25) are likely attributable to local spatial heterogeneity” with as argument the Gaussian plume exercise, phrasing it as 'possibly' would have been better, as we have no way of knowing which part of the existing errors are due to the UPAS, due to the sensors, and due to spatial heterogeneity. We meant the Gaussian plume model exercise to serve merely as illustration that, if it were due to spatial heterogeneity, this order of magnitude would not be surprising. What we are however going to add here, thanks to RC2_Q6, is a referral to what we shared in the results, that sensors located on the same box had very similar slopes (section 3.3.2, lines 331-334). That is a support of existing spatial heterogeneity originating from observations, while the Gaussian plume model ‘merely’ is a validation of the possibility.
We however do not know how to provide an error budget in which we separate sensor error, UPAS uncertainty, and spatial gradients. Spatial gradients might have a consistent bias (suggested by the slope variations but similar slopes on same boxes), but will also have a random error nature due to the variability of plumes. UPAS uncertainty should be a random error (and hence not be part of slope variation across sensors), as all sensors were matched with the same UPAS instruments. Sensors however had different moments of data loss, so potentially part of a UPAS error (if exhibited to one side with sensor x having data loss and to another side with sensor y having data loss) runs over in slope variations. Sensor error might have a partial consistent bias (leading to slope variation), but the extent of this bias should be limited to 5% or less as suggested by precision results. As the slope variation is significantly larger than the sensor precision error, we think it likely that spatial heterogeneity plays an important role (which is why we comment on it), but we do not feel we have sufficient evidence to quantify specific parts of an error budget.
Citation: https://doi.org/10.5194/egusphere-2026-2537-AC2 -
AC3: 'Reply on RC1_Q4', Johannes Dirk Dingemanse, 05 Sep 2026
Reply to RC1 question 4: Section 4.7 and Appendix C.1.3: The self-heating of the SEN55 lowers the internal RH, but indoor biomass-burning aerosol is enriched in semi-volatile organic compounds. Please discuss whether internal heating could evaporate SVOCs before optical detection, which might partly explain the indoor (1.14) versus outdoor (1.21) slope difference.
The reviewer raises a valid point that where internal heating would affect relative humidity, it might also affect other volatile components. However, first we would like to point out that if such an effect would exist, potentially enhanced under the indoor conditions in our study, it would not partly explain the indoor versus outdoor slope difference – it would in fact increase it. The slope is related to the following relation: gravimetry = sensor * slope. We chose for this (rather than sensor = gravimetry * slope) to let the slope directly represent a correction factor. A slope of 1.14 versus 1.21 implies hence that indoor less correction is needed, i.e. there is less underestimation. If there would be an enhanced loss of particle mass through SVOC evaporation, it would imply that in reality there would even be less underestimation, i.e. the ‘true’ slope (if not for evaporation) should be <1.14. That would increase the difference between indoor and outdoor slope, rather than partly explain it.
More importantly, we expect any enhanced VOC evaporation due to internal temperature (TSEN55) to ambient temperature (Treal) differences to be negligible. Our regression result (Treal = 1.27*TSEN55 – 8.0, see manuscript Eq. (C4)) represents for the temperature range of 200C to 300C a temperature offset of 2.60C to 0.10C, i.e. internal temperature increases more pronounced under lower temperatures, and negligible under higher temperatures (also visible in manuscript Figure C1). The residence time of air inside the sensor is less than a minute. VOC mass evaporation takes generally higher temperature differences or time scales. Volatility of compounds is often measured with a thermal denuder, where temperatures well above ambient temperature are used to see effects under short (< minute) residence times. Jeon et al. (2023) present mass fraction remaining across temperatures of 30 to 2000C, where within the 30 to 400C range even for the most volatile fraction the loss is below 5%. Under the circumstances of our study (only below 300C modest temperature increase, with <1 minute residence time) we expect no significant SVOC evaporation due to raised internal temperatures.
Reference
Jeon, J., Chen, Y., & Kim, H. (2023). Influences of meteorology on emission sources and physicochemical properties of particulate matter in Seoul, Korea during the heating period. Atmospheric Environment, 303, 119733. https://doi.org/10.1016/j.atmosenv.2023.119733
Citation: https://doi.org/10.5194/egusphere-2026-2537-AC3 -
AC4: 'Reply on RC1 _ General', Johannes Dirk Dingemanse, 05 Sep 2026
We would like to thank RC1 for the constructive comments. We feel honored that the comments do not only concern the main text but also the appendix, showing a genuine interest in increasing the quality of the totality of our work. We have answered the five comments across five replies. Where the replies focus on 'giving answers' (and less on what we will change to the manuscript), we will make sure to implement clarifications regarding the points raised by the reviewer in the revised manuscript.
The only part of comments that we did not react on yet, was the part of RC1 fifth question, "whether the 12.5% SDT rejection threshold was defined a priori". In all honesty, we implemented this threshold during data analysis, because before the study we did not anticipate such low concentrations that the gravimetry analysis would pose a significant error. We settled for 12.5% as a compromise between losing data points and having a large gravimetric error, with 12.5% being a 'natural' number as 'half of the 25% accuracy error metric'. However, we deliberately picked a threshold in terms of uncertainty (SDT) rather than some concentration threshold or from specific days or something else, as to limit subjective bias from this removal.
Citation: https://doi.org/10.5194/egusphere-2026-2537-AC4
-
CC1: 'First reply on RC1 _ Q5', Johannes Dirk Dingemanse, 17 Jul 2026
-
RC2: 'Comment on egusphere-2026-2537', Anonymous Referee #2, 25 Aug 2026
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2026/egusphere-2026-2537/egusphere-2026-2537-RC2-supplement.pdf
-
AC5: 'Reply on RC2 _ Major comments', Johannes Dirk Dingemanse, 05 Sep 2026
We want to thank RC2 for the constructive comments. We are glad to see the comments cover both main text and appendices - we are aware that is quite a read, so we thank RC2 for taking the time in helping to improve the quality of the totality of our work. We very much appreciate RC2 for not only pointing out where things can be clarified or improved, but also suggesting remedies.
Below, we answer the four “major comments”. To limit the length of this post, we only copy per comment RC2's header (and not the full text). In a second reply we answer the four “Minor comments”.
RC2_1: I recommend distinguishing calibration performance from independent validation
As for clarification to which observations were used for deriving correction factors or subsequent evaluation: as the reviewer suspected, all observations have been used in deriving correction factors. There has indeed not been an independent validation. We will follow the suggestion to consistently describe any presented accuracy metric as performance after correction.
RC2_2: The conclusions concerning concentrations above 1000 µg m-3 should be narrowed
We agree with the reviewer that we cannot verify the absolute accuracy of measurements at shorter timeranges than the filter measurements, and as such also not those peaking concentrations. We cannot state up to what degree a measurement point of 10,345 µg m-3 or a 10-minute average concentration of 4,802 µg m-3 is accurate. We will rephrase it accordingly. However, we do not merely want to leave it at “We don’t know anything about it”. Whether there are little errors, or whether bigger errors cancel each other out to some extent at filter-averaging level, the result is the same: at filter-averaged level the sensors exhibit accurate enough readings, for which those beyond 1000 µg m-3 are required. Furthermore, while the agreement at 10-minute averaged level between the sensors cannot speak to the accuracy, it at least contains the relevant information that data at levels >1000 µg m-3 or even >6000 µg m-3 are not random noise, but a signal to physical circumstances. And, while we cannot speak to the full range of concentration levels measured at raw-data level, we can speak towards the full range of filter-averaged or 24-hour averaged concentration levels (which we will rephrase accordingly).
RC2_3: The novelty statement should be reconsidered
We thank the reviewer for seeing the clear value in the Ethiopian field settings and other aspects, and agree to remove the claim that we are the first to compare SEN55 with reference-equivalent instrumentation. This claim, even if true, is less relevant than the value for Ethiopian conditions. We would just as soon claim that despite our study, reference comparisons in other parts of the worlds are still relevant, making the claim of being the first somewhat redundant.
We do however want to note that reference-equivalent validations under ambient conditions are limited. For the SPS30 there are some, like the one referred to by RC2, and some others we referred to in our manuscript. However, regarding the SEN55 we could not find such studies. RC2 refers to two, which we evaluated after that suggestion.
- The reference “Field testing of low-cost particulate matter sensors for Digital Twin applications in nanomanufacturing processes" uses a TSI OPS 3330, which while certainly research-grade, is not reference-equivalent in the sense that it is confirmed to be equivalent to gravimetric PM2.5 measurements. The OPS measures number concentrations, so any derived mass concentration will still have an inherent uncertainty regarding particle composition and morphology. Furthermore, the evaluation of this study under conditions of specific manufacturing processes certainly leaves room for studies validating under ambient conditions.
- The reference “Monitoring and Ensuring Worker Health in Controlled Environments Using Economical Particle Sensors” validates the SEN55 with a Temtop M2000, which has a same measurement principle (light scatter / optical), and we could not find any certification or documentation stating it to be reference-equivalent.
RC2_4: Clarify the statistical independence of the sensor-gravimetry comparisons
We completely agree with the reviewer: the ‘All SPS30’ and ‘All SEN55’ were all datapoints of any SPS30 or SEN55 combined, but that contains datapoints connected to the same filter measurement. We did not explicitly handle this dependency in any way, and go even further than the reviewer by not only acknowledging this, but to state that this is incorrect. Since we at one hand present evidence that suggests spatial heterogeneity, we cannot on the other hand presume to present a single representative correction factor. We will remove this from our analysis. In line with this, we will also edit Table 7 to not show single accuracy and bias values, but instead ranges covering all individual sensor values.
However, for ease of reading, instead of referring to all underlying individual slopes, we do want at certain points to refer in-text to ‘average slopes’ (which will simply be the average of the individual slopes). This will not be to claim any statistical significance (also not with any confidence intervals and other statistical metrics), but just to conveniently summarize them. With that, we believe we can still make following claims (with minor textual adjustments as implementation of this comment):
- Section 3.3.2 “The averaged slopes for SPS30 and SEN55 are respectively 1.14 and 1.15. When correcting data of all sensors with a slope of 1.14, accuracy errors for individual sensors range from 15 to 22%. In other words: while individual correction factors result in higher data quality, the accuracy error with a same correction for all sensors (SPS30 and SEN55 alike) is still within the NIOSH range of acceptable values (< 25 %).
- Section 4.4.2 “Average slopes of 1.14 (SPS30) and 1.15 (SEN55) align with a reanalysis of previous findings in Arba Minch (1.13; Appendix G).”
- Section 4.4.3 “Compositional differences likely drive the on average lower slopes (lesser underestimation) observed indoors (1.14) compared to outdoors (1.21). This shift mirrors laboratory findings where the SPS30 reports higher values for wood smoke than for salts or mineral dust (Tryner et al., 2020). However, whereas Tryner et al. reported wood smoke overestimation (slope 0.6) and high variation across aerosols (0.6 – 2.1), our sensors continued to underestimate concentrations and had a less pronounced difference for indoor biomass conditions compared to outdoor conditions.”
And we believe claims from conclusion and abstract are not affected:
- Conclusion: “After applying site-specific correction factors, the sensors met sampling guidelines for accuracy, with outdoor errors below 11 % and indoor errors below 22 %.”
- Abstract: “Our study shows that the SPS30 and SEN55 – when calibrated under circumstances of use – are stable and accurate instruments (indoor and outdoor accuracy error ≤ 22 %, outdoor expanded uncertainty ≤ 15 % in comparison to gravimetric measurements).”
Citation: https://doi.org/10.5194/egusphere-2026-2537-AC5 -
AC6: 'Reply on RC2 _Minor comments', Johannes Dirk Dingemanse, 05 Sep 2026
In post AC5 we replied to RC2 Major comments (comments 1 to 4). In this post, we reply to RC2 Minor comments (comments 5 to 8).
RC2_5: Please use "reference-equivalent" cautiously.
We will implement this comment where applicable, to make sure that we do not claim to establish any formal equivalence.
RC2_6: Clarify the interpretation of the indoor sensor-to-sensor differences
We agree with the reviewer that, albeit we make the observation of 'similar slopes for sensors on similar boxes' in the results section, we do not come back to this in the discussion, while that would be relevant. We will add it accordingly, to section 4.4.2, as further (better) support of the statement that “[t]he observed indoor slope variations (1.01 – 1.25) are likely attributable to local spatial heterogeneity”.
RC2_7: Clarify the RH analysis and sensor self-heating
We agree with the reviewer that the analysis of a difference between ambient and internal temperatures is somewhat ‘hidden’ in our (lengthy) appendices, and that it can be referred to more clearly in the main text discussion. We will add it more extensively to section 4.7.
RC2_8: Additional context on the high-concentration measurement conditions should be provided
We can certainly add more information on the experimental setup of the indoor measuremens, to either the main text or at least Appendix section A.2. To already provide it here: both kitchens are university campus restaurant kitchens, with therefore regular cooking throughout the day. Kitchen 1 is a relatively large kitchen (approximately 8 * 4 meters surface area), with multiple cooking places across the area. Kitchen 2 is smaller (2 * 3 meters), with two cooking places. Cooking is conducted with wood, with no dedicated ventilation. Both kitchens have only natural ventilation through door openings and space between the wall and the roof (which can be seen in Figures A4 and A5). The boxes with sensors were at a height of 2 meters above ground level, which also represents the height above the cooking stove. Horizontal distance between the sensors and closest cooking stoves were three and one meters for respectively kitchens 1 and 2.
Citation: https://doi.org/10.5194/egusphere-2026-2537-AC6
-
AC5: 'Reply on RC2 _ Major comments', Johannes Dirk Dingemanse, 05 Sep 2026
Interactive computing environment
Data and code for SPS30 SEN55 PM2.5 indoor outdoor validation, Arba Minch, Ethiopia Johannes Dirk Dingemanse https://doi.org/10.17605/OSF.IO/EC3T8
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 244 | 129 | 29 | 402 | 16 | 9 |
- HTML: 244
- PDF: 129
- XML: 29
- Total: 402
- BibTeX: 16
- EndNote: 9
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
Please see the attached file for detailed comments.