the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 02 May 2024
Calibration of Low-Cost Particulate Matter Sensors PurpleAir: Model Development for Air Quality under High Relative Humidity Conditions
Abstract. The primary source of measurement error from the widely-used particulate matter (PM) PurpleAir sensors is ambient relative humidity (RH). Recently, the U.S. EPA developed a national correction model for PM2.5 concentrations measured by PurpleAir sensors (Barkjohn model). However, their study included few sites in the Southeastern U.S., the most humid region of the country. To provide high-quality spatial and temporal data and inform community exposure risks in this area, our study developed and evaluated PurpleAir correction models for use in the warm-humid climate zones of the U.S. We used hourly PurpleAir data and hourly reference grade PM2.5 data from the EPA Air Quality System database from January 2021 to August 2023. Compared with the Barkjohn model, we found improved performance metrics with error metrics decreasing by 16–23 % when applying a multi linear regression (MLR) model with RH and temperature as predictive variables. We also tested a novel semi-supervised clustering (SSC) method and found that a nonlinear effect between PM2.5 and RH emerges around a RH of 50 % with slightly greater accuracy. Therefore, our results suggested that a clustering approach might be more accurate in high humidity conditions to capture the non-linearity associated with PM particle hygroscopic growth.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
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Supplement
(568 KB) - BibTeX
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- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
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CC1: 'Comment on egusphere-2024-1142', JAMES OUIMETTE, 09 May 2024
Hi,
Thank you for your preprint. I have a couple suggestions that could improve your paper.
Could you please provide a table with the following information about each of the PA sensors used in this study:
PurpleAir ID number; AQS number for the regulatory monitoring site; name of regulatory PM2.5 monitor (e.g., Teledyne T640x, Met One BAM 1020, etc); distance from PurpleAir to regulatory PM2.5 monitor; name of the NOAA site used for relative humidity and temperature data; distance from PurpleAir to NOAA site.
The sites that you chose are characterized by high dew points, resulting from both high RH and high temperatures.
Your graphs comparing RH between the PurpleAir and its corresponding NOAA site is inadequate for assessing whether or not the NOAA site is representative. The best way to show if the PurpleAir and its corresponding NOAA site are sampling similar air is to compare their hourly average dew points. That's because the PurpleAir slightly heats the air sample, resulting in a higher temperature and lower RH compared to the NOAA site. However, the water content and dew point should be the same for the PurpleAir and the NOAA site.
Could you please provide graphs comparing the hourly average dew points for your 21 sites.
Thanks,
Jim Ouimette
Citation: https://doi.org/10.5194/egusphere-2024-1142-CC1 - AC3: 'Reply on CC1', Jennifer Richmond-Bryant, 05 Jul 2024
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RC1: 'Comment on egusphere-2024-1142', Anonymous Referee #1, 11 May 2024
This paper looks for a better PurpleAir correction for sensors in the US southeast. However, they only consider one equation from the literature when much additional work has been done on this topic in the past 4 years. This is not the first paper to look at nonlinear RH correction and the paper would be strengthened by comparing to other corrections in the literature that account for nonlinear RH. I have a number of other specific comments below that I hope the authors will address to strengthen their paper. The editor should also find someone to review that is more familiar with semi-supervised clustering.
Major
- I think this paper would be strengthened by considering other common corrections from the literature especially those that consider nonlinear RH terms (e.g., Wallace https://www.mdpi.com/1424-8220/22/13/4741, Nilson https://amt.copernicus.org/articles/15/3315/2022/amt-15-3315-2022.html, Malings https://www.tandfonline.com/doi/full/10.1080/02786826.2019.1623863)
- Also, can you add a plot showing the RH nonlinearity? You say that the model shows that it shows up around 50% but where does it increase visually? Something like RH on the X axis and Sensor/Monitor on the Y axis (Examples: Zheng https://amt.copernicus.org/articles/11/4823/2018/)
- This paper discusses how the southeast is unique because it is high humidity but it would also be helpful to comment on how particle properties (e.g., composition, size distribution) are different in the south east and how that might impact the performance (e.g., Patel https://amt.copernicus.org/articles/17/1051/2024/, Jaffe https://amt.copernicus.org/articles/16/1311/2023/).
- How does the recent release of the T640 correction impact this work? I agree with Dr. Ouimette that it would be helpful to list all the AQS monitors compared to, I assume some of them are Teledyne T640s.
- Were any of these sensors the alternate PMS5003s? Sear, Kaur, Kelly, https://www.sciencedirect.com/science/article/pii/S0021850223001210 How does this impact your results?
- How much data is excluded for each of the QA methods? (AB channel comparison high, low, etc.)
- Figure 2 seems to show a wider range of RH with more noise over time. Is this due to seasonal differences or because the RH sensor performance is changing over time?
- Did you consider whether sensor age had any impacts on your results? (e.g., deSouza https://pubs.rsc.org/en/content/articlehtml/2023/ea/d2ea00142j)
- “However, DP was excluded as a predictor in our study, because collinearity was found between DP, RH, and T when testing for variance inflation factor. This collinearity is attributed to the direct physical relationship between RH and T” I don’t understand what this is saying? T and RH weren’t significantly colinear?
- Random withholding is likely not a good test of your model. It would likely be fairer to withhold by site or state. I think it isn’t surprising that the model you built for your dataset is a better fit than a model built on another dataset. This is likely something to mention in the limitations.
- Table 2 this is interesting basically if the RH is high add 5 ug/m3 to the concentration? This difference doesn’t seem to be reflected in Figure 6. Is there a typo?
- Citations should be checked for accuracy throughout see a few specific comments below.
- While the results are significantly different, they are not largely different. You might consider adding evaluation of performance by AQI category to further strengthen your findings (e.g., https://www.mdpi.com/1424-8220/22/24/9669, https://amt.copernicus.org/articles/15/3315/2022/amt-15-3315-2022.html )
Minor
- A study conducted in 2016 (AQ-SPEC, 2016), evaluating about twelve low-cost PM2.5 sensors showed an overall good agreement between PM2.5 PurpleAir sensors and two reference monitors with a R2 of 78 % and 90 % (Wallace et al., 2021). - Is this citation correct? It seems like the beginning and ending of the sentence are citing 2 different things.
- Lunden, M. M.; Parworth, C. L.; Barkjohn, K. K.; Holder, A. L.; Frederick, S. G.; Clements, A. L. Correction and Accuracy of PurpleAir PM 2.5 Measurements for Extreme Wildfire Smoke. 2022. https://doi.org/10.3390/s22249669. – This citation is incorrect
- Line 45, 269: Why are there superscript numbers? Check for this throughout
- Figure 1: Is the number of counties by state relevant to the story you are telling?
- Line 270: “For all the four fitted models, average concentration of 8.80 μg m-3 , with an SD varying between 4.71- 4.84 μg m-3 were obtained, whereas Model Bj provided and a higher MAE than the four developed models with a mean of 7.67 μg m-3 and a SD of 6.08 μg m-3 .” -A little unclear if the first and second part of this sentence are comparing the same thing.
- “Zheng et al. (Zheng et al., 2018) found an R2 280 value of 66 % for a 1-h averaging period after applying an MLR calibration equation to compare three PA sensors” – This is not a paper about PurpleAirs it is a paper about custom built Plantower PMS3003 sensors
- I don’t think R2 is usually reported as a Percentage?
- What is R in Table 1? Just the root of R2?
- Figure 4: I think this plot would be easier to interpret if both plots used the same color scale.
- This is a personal preference so take or leave, but I would always put the monitor on the X axis and the Sensor on the Y since the monitor is the independent variable. This is also the recommendation in the EPA performance targets.
- Figure 7: Is there an assumed T and RH for the lines on this plot?
Citation: https://doi.org/10.5194/egusphere-2024-1142-RC1 - AC1: 'Reply on RC1', Jennifer Richmond-Bryant, 05 Jul 2024
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RC2: 'Comment on egusphere-2024-1142', Anonymous Referee #2, 28 May 2024
General comments:
This paper provided the evaluation of PurpleAir correction using the warm, humid climate zones data and aimed to improve the EEPA Barkjohn model. It provides helpful information about improved performance metrics and avoids collinearity using DP, RH and T. However, the multilinear regression has been used before. There is no significant scientific insight gained with the new parameters. Several suggestions to strengthen this paper:
- Consider other correction methods and explain what can provide the best insight of the Purpleair data.
- Typically, the low-cost sensors measure the PM base on the optical size, and it is unclear how they can accurately predict the aerodynamic size and get the correct PM2.5. The conversion of particle aerodynamic size to optical size, or vice versa, is not straightforward because it depends on several factors, including the particle's shape, density, and refractive index. Are the FRM/FEM monitors filter-based measurements? How does the linear regression provide reliable information?
Specific comments:
Line 127-129, Please explain how to determine the detection range for PurpleAir? The reference used 1.15-2.55? This paper used 1.5? Why not 1.6? or 1.75?
Line 131, What is the difference between the two channels? Should we expect them to agree in a certain percentage at low and high concentrations?
Line 141, For each site, how much data remained? Does this data cleaning cause any bias in the data collection?
Section 2.4.2, the equations are confusing. Will the beta 2 in equation 2 be the same as the beta 2 in equation 3?
Table 1, the parameters from each model have a very high precision. Is it realistic to include such high precision?
Figure 4 the data plotted seemed to be from two groups. One follows 1:1 line, and the other one follows 2:1 line. Cluster 2 still has the 2:1 group. Is there any other reason for this 2:1 group?
Citation: https://doi.org/10.5194/egusphere-2024-1142-RC2 - AC2: 'Reply on RC2', Jennifer Richmond-Bryant, 05 Jul 2024
Interactive discussion
Status: closed
-
CC1: 'Comment on egusphere-2024-1142', JAMES OUIMETTE, 09 May 2024
Hi,
Thank you for your preprint. I have a couple suggestions that could improve your paper.
Could you please provide a table with the following information about each of the PA sensors used in this study:
PurpleAir ID number; AQS number for the regulatory monitoring site; name of regulatory PM2.5 monitor (e.g., Teledyne T640x, Met One BAM 1020, etc); distance from PurpleAir to regulatory PM2.5 monitor; name of the NOAA site used for relative humidity and temperature data; distance from PurpleAir to NOAA site.
The sites that you chose are characterized by high dew points, resulting from both high RH and high temperatures.
Your graphs comparing RH between the PurpleAir and its corresponding NOAA site is inadequate for assessing whether or not the NOAA site is representative. The best way to show if the PurpleAir and its corresponding NOAA site are sampling similar air is to compare their hourly average dew points. That's because the PurpleAir slightly heats the air sample, resulting in a higher temperature and lower RH compared to the NOAA site. However, the water content and dew point should be the same for the PurpleAir and the NOAA site.
Could you please provide graphs comparing the hourly average dew points for your 21 sites.
Thanks,
Jim Ouimette
Citation: https://doi.org/10.5194/egusphere-2024-1142-CC1 - AC3: 'Reply on CC1', Jennifer Richmond-Bryant, 05 Jul 2024
-
RC1: 'Comment on egusphere-2024-1142', Anonymous Referee #1, 11 May 2024
This paper looks for a better PurpleAir correction for sensors in the US southeast. However, they only consider one equation from the literature when much additional work has been done on this topic in the past 4 years. This is not the first paper to look at nonlinear RH correction and the paper would be strengthened by comparing to other corrections in the literature that account for nonlinear RH. I have a number of other specific comments below that I hope the authors will address to strengthen their paper. The editor should also find someone to review that is more familiar with semi-supervised clustering.
Major
- I think this paper would be strengthened by considering other common corrections from the literature especially those that consider nonlinear RH terms (e.g., Wallace https://www.mdpi.com/1424-8220/22/13/4741, Nilson https://amt.copernicus.org/articles/15/3315/2022/amt-15-3315-2022.html, Malings https://www.tandfonline.com/doi/full/10.1080/02786826.2019.1623863)
- Also, can you add a plot showing the RH nonlinearity? You say that the model shows that it shows up around 50% but where does it increase visually? Something like RH on the X axis and Sensor/Monitor on the Y axis (Examples: Zheng https://amt.copernicus.org/articles/11/4823/2018/)
- This paper discusses how the southeast is unique because it is high humidity but it would also be helpful to comment on how particle properties (e.g., composition, size distribution) are different in the south east and how that might impact the performance (e.g., Patel https://amt.copernicus.org/articles/17/1051/2024/, Jaffe https://amt.copernicus.org/articles/16/1311/2023/).
- How does the recent release of the T640 correction impact this work? I agree with Dr. Ouimette that it would be helpful to list all the AQS monitors compared to, I assume some of them are Teledyne T640s.
- Were any of these sensors the alternate PMS5003s? Sear, Kaur, Kelly, https://www.sciencedirect.com/science/article/pii/S0021850223001210 How does this impact your results?
- How much data is excluded for each of the QA methods? (AB channel comparison high, low, etc.)
- Figure 2 seems to show a wider range of RH with more noise over time. Is this due to seasonal differences or because the RH sensor performance is changing over time?
- Did you consider whether sensor age had any impacts on your results? (e.g., deSouza https://pubs.rsc.org/en/content/articlehtml/2023/ea/d2ea00142j)
- “However, DP was excluded as a predictor in our study, because collinearity was found between DP, RH, and T when testing for variance inflation factor. This collinearity is attributed to the direct physical relationship between RH and T” I don’t understand what this is saying? T and RH weren’t significantly colinear?
- Random withholding is likely not a good test of your model. It would likely be fairer to withhold by site or state. I think it isn’t surprising that the model you built for your dataset is a better fit than a model built on another dataset. This is likely something to mention in the limitations.
- Table 2 this is interesting basically if the RH is high add 5 ug/m3 to the concentration? This difference doesn’t seem to be reflected in Figure 6. Is there a typo?
- Citations should be checked for accuracy throughout see a few specific comments below.
- While the results are significantly different, they are not largely different. You might consider adding evaluation of performance by AQI category to further strengthen your findings (e.g., https://www.mdpi.com/1424-8220/22/24/9669, https://amt.copernicus.org/articles/15/3315/2022/amt-15-3315-2022.html )
Minor
- A study conducted in 2016 (AQ-SPEC, 2016), evaluating about twelve low-cost PM2.5 sensors showed an overall good agreement between PM2.5 PurpleAir sensors and two reference monitors with a R2 of 78 % and 90 % (Wallace et al., 2021). - Is this citation correct? It seems like the beginning and ending of the sentence are citing 2 different things.
- Lunden, M. M.; Parworth, C. L.; Barkjohn, K. K.; Holder, A. L.; Frederick, S. G.; Clements, A. L. Correction and Accuracy of PurpleAir PM 2.5 Measurements for Extreme Wildfire Smoke. 2022. https://doi.org/10.3390/s22249669. – This citation is incorrect
- Line 45, 269: Why are there superscript numbers? Check for this throughout
- Figure 1: Is the number of counties by state relevant to the story you are telling?
- Line 270: “For all the four fitted models, average concentration of 8.80 μg m-3 , with an SD varying between 4.71- 4.84 μg m-3 were obtained, whereas Model Bj provided and a higher MAE than the four developed models with a mean of 7.67 μg m-3 and a SD of 6.08 μg m-3 .” -A little unclear if the first and second part of this sentence are comparing the same thing.
- “Zheng et al. (Zheng et al., 2018) found an R2 280 value of 66 % for a 1-h averaging period after applying an MLR calibration equation to compare three PA sensors” – This is not a paper about PurpleAirs it is a paper about custom built Plantower PMS3003 sensors
- I don’t think R2 is usually reported as a Percentage?
- What is R in Table 1? Just the root of R2?
- Figure 4: I think this plot would be easier to interpret if both plots used the same color scale.
- This is a personal preference so take or leave, but I would always put the monitor on the X axis and the Sensor on the Y since the monitor is the independent variable. This is also the recommendation in the EPA performance targets.
- Figure 7: Is there an assumed T and RH for the lines on this plot?
Citation: https://doi.org/10.5194/egusphere-2024-1142-RC1 - AC1: 'Reply on RC1', Jennifer Richmond-Bryant, 05 Jul 2024
-
RC2: 'Comment on egusphere-2024-1142', Anonymous Referee #2, 28 May 2024
General comments:
This paper provided the evaluation of PurpleAir correction using the warm, humid climate zones data and aimed to improve the EEPA Barkjohn model. It provides helpful information about improved performance metrics and avoids collinearity using DP, RH and T. However, the multilinear regression has been used before. There is no significant scientific insight gained with the new parameters. Several suggestions to strengthen this paper:
- Consider other correction methods and explain what can provide the best insight of the Purpleair data.
- Typically, the low-cost sensors measure the PM base on the optical size, and it is unclear how they can accurately predict the aerodynamic size and get the correct PM2.5. The conversion of particle aerodynamic size to optical size, or vice versa, is not straightforward because it depends on several factors, including the particle's shape, density, and refractive index. Are the FRM/FEM monitors filter-based measurements? How does the linear regression provide reliable information?
Specific comments:
Line 127-129, Please explain how to determine the detection range for PurpleAir? The reference used 1.15-2.55? This paper used 1.5? Why not 1.6? or 1.75?
Line 131, What is the difference between the two channels? Should we expect them to agree in a certain percentage at low and high concentrations?
Line 141, For each site, how much data remained? Does this data cleaning cause any bias in the data collection?
Section 2.4.2, the equations are confusing. Will the beta 2 in equation 2 be the same as the beta 2 in equation 3?
Table 1, the parameters from each model have a very high precision. Is it realistic to include such high precision?
Figure 4 the data plotted seemed to be from two groups. One follows 1:1 line, and the other one follows 2:1 line. Cluster 2 still has the 2:1 group. Is there any other reason for this 2:1 group?
Citation: https://doi.org/10.5194/egusphere-2024-1142-RC2 - AC2: 'Reply on RC2', Jennifer Richmond-Bryant, 05 Jul 2024
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Martine E. Mathieu-Campbell
Chuqi Guo
Andrew P. Grieshop
Jennifer Richmond-Bryant
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(1176 KB) - Metadata XML
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Supplement
(568 KB) - BibTeX
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- Final revised paper