the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Deriving the hygroscopicity of ambient particles using low-cost optical particle counters
Abstract. This study investigates the chemical composition and physical properties of aerosols, which play a crucial role in influencing human health, cloud physics, and local climate. Our focus centers on the hygroscopicity of ambient aerosols, a key property reflecting the ability to absorb moisture from the atmosphere and serve as cloud condensation nuclei. Employing home-built Air Quality Box (AQB) systems equipped with low-cost sensors, we assess the ambient variability of particulate matter (PM) concentrations to determine PM hygroscopicity. The AQB systems effectively captured meteorological parameters and most pollutant concentrations, with high correlations observed compared to Taiwan Environmental Protection Administration (EPA) data. With the application of κ-Köhler equation and certain assumptions, AQB-monitored PM concentrations are converted to dry particle mass concentration, showing improved correlation with EPA data and optical particles counter sensitivity correction. The derived κ values range from 0.15 to 0.29 for integrated fine particles (PM2.5) and 0.05 to 0.13 for coarse particles (PM2.5-10), consistent with results of ionic chromatography analysis for samples from a previous winter campaign nearby. Moreover, the analysis of PM10 division into PM2.5 and PM2.5-10, considering composition heterogeneity, provided improved dry PM10 concentration as the sensitivity coefficients for PM2.5-10 were notedly higher than for PM2.5. Our methodology provides a comprehensive approach to assess ambient aerosol hygroscopicity, offering significant implications for atmospheric modeling, particularly in evaluating aerosol efficiency as cloud condensation nuclei and in radiative transfer calculations. Overall, the AQB systems proved to be effective in monitoring air quality and deriving key aerosol properties, contributing valuable insights into atmospheric science.
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RC1: 'Comment on amt-2024-39', Anonymous Referee #1, 13 Apr 2024
1. You stated that "AQB-monitored PM concentration can be converted to dry particle mass concentration, aligning well with EPA data
after OPC sensitivity correction. The derived hygroscopicity provides the relationship between ambient relative humidity and
particle water content. By dividing PM10 into PM2.5 and PM2.5-10, considering the composition heterogeneity, we achieved more
280 precise dry PM10 concentrations with lower MAPE." Please state differences clearly with 2 different observing packages.2. Provide units for the parameters in the equations.
3. How accurate your hygroscopicity calculation that needs to bediscussed.
4. please provide how did you convert ppm to mass for various species?
5. please provide your final conclusions in an itemized list.
Citation: https://doi.org/10.5194/amt-2024-39-RC1 -
AC1: 'Reply on RC1', H. M. Hung, 03 Jun 2024
The comment was uploaded in the form of a supplement: https://amt.copernicus.org/preprints/amt-2024-39/amt-2024-39-AC1-supplement.pdf
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AC1: 'Reply on RC1', H. M. Hung, 03 Jun 2024
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RC2: 'Comment on amt-2024-39', Anonymous Referee #2, 28 Apr 2024
General Comments:
The normalization and portability of atmospheric pollutant monitoring are crucial for in-depth research into local variations in atmospheric environment and pollution. This manuscript establishes a low-cost air quality monitoring device and applies it to estimate the hygroscopicity parameters of aerosols, comparing the results with site observation data. The study holds certain scientific and application value and aligns with the publication scope of the Atmospheric Measurement Techniques journal. However, there are some scientific and technical issues within the manuscript itself, suggesting major revisions before reconsidered.
Specific Comments:
1) The accuracy and error range of AQB in detecting aerosols and the application of AQB to estimate aerosol hygroscopicity parameters should be two separate research components with a sequential order. However, in this manuscript, the authors often fail to clearly distinguish between the two. The authors adopt a method of setting RH thresholds to classify AQB observation results into dry aerosols and humidified aerosols, and then compares and calibrates the observations of dry aerosols with EPA station data, which is a feasible approach. However, in Sections 3.1, 3.2, and Figures 2 and 3, the authors do not classify or analyze the data based on the RH threshold set by themselves. Meanwhile, as shown in Figure 2(b), the occurrence of RH below 50% during the observation period is rare. Can such a limited amount of data support the examination of the reliability of AQB detection?
2) Introducing aerosol chemical composition observations into a thermodynamic equilibrium model to calculate aerosol hygroscopic growth and comparing it with optical observations is a common research approach. However, contrasting different field experiments conducted at different times (with an 8-year difference) and different underlying surfaces by the authors doesn't have much significance.
3) In Table 1, the hygroscopicity parameter kappa for PM2.5 and PM2.5-10 is smaller than the hygroscopicity parameter kappa for PM10, which is abnormal. PM10 is the sum of PM2.5 and PM2.5-10, and its hygroscopicity should be intermediate between the two.
4) In Section 2.1, the author introduces the photo-ionization detector for monitoring VOCs, but in Figure 1 and subsequent manuscripts, the abbreviation used by the author is NMHC. These two abbreviations are not entirely equivalent.
Citation: https://doi.org/10.5194/amt-2024-39-RC2 -
AC2: 'Reply on RC2', H. M. Hung, 03 Jun 2024
The comment was uploaded in the form of a supplement: https://amt.copernicus.org/preprints/amt-2024-39/amt-2024-39-AC2-supplement.pdf
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AC3: 'Reply on RC2', H. M. Hung, 03 Jun 2024
Publisher’s note: this comment is a copy of AC2 and its content was therefore removed.
Citation: https://doi.org/10.5194/amt-2024-39-AC3
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AC2: 'Reply on RC2', H. M. Hung, 03 Jun 2024
Data sets
Deriving the hygroscopicity of ambient particles using low-cost optical particle counters Wei-Chieh Huang, Hui-Ming Hung, Ching-Wei Chu, Wei-Chun Hwang, and Shih-Chun Candice Lung https://github.com/NTUACLab/Wei-Chieh
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