Correcting for filter-based aerosol light absorption biases at the Atmospheric Radiation Measurement program's Southern Great Plains site using photoacoustic measurements and machine learning
<p>Measurement of light absorption of solar radiation by aerosols is vital for assessing direct aerosol radiative forcing, which affects local and global climate. Low-cost and easy-to-operate filter-based instruments, such as the Particle Soot Absorption Photometer (PSAP), that collect aerosol...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2022-08-01
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| Series: | Atmospheric Measurement Techniques |
| Online Access: | https://amt.copernicus.org/articles/15/4569/2022/amt-15-4569-2022.pdf |
| Summary: | <p>Measurement of light absorption of solar radiation by
aerosols is vital for assessing direct aerosol radiative forcing, which
affects local and global climate. Low-cost and easy-to-operate filter-based
instruments, such as the Particle Soot Absorption Photometer (PSAP), that collect aerosols on a filter and measure light attenuation through the
filter are widely used to infer aerosol light absorption. However,
filter-based absorption measurements are subject to artifacts that are
difficult to quantify. These artifacts are associated with the presence of
the filter medium and the complex interactions between the filter fibers and accumulated aerosols. Various correction algorithms have been introduced to correct for the filter-based absorption coefficient measurements toward predicting the particle-phase absorption coefficient (<span class="inline-formula"><i>B</i><sub>abs</sub></span>). However, the inability of these algorithms to incorporate into their formulations the complex matrix of influencing parameters such as particle asymmetry parameter, particle size, and particle penetration depth results in prediction of particle-phase absorption coefficients with relatively low accuracy. The analytical forms of corrections also suffer from a lack of universal applicability: different corrections are required for rural and
urban sites across the world. In this study, we analyzed and compared 3 months of high-time-resolution ambient aerosol absorption data collected
synchronously using a three-wavelength photoacoustic absorption spectrometer (PASS) and PSAP. Both instruments were operated on the same sampling inlet
at the Department of Energy's Atmospheric Radiation Measurement program's Southern Great Plains (SGP) user facility in Oklahoma. We implemented the two most
commonly used analytical correction algorithms, namely, Virkkula (2010) and the average of Virkkula (2010) and Ogren (2010)–Bond et al. (1999) as well as a random forest regression (RFR) machine learning algorithm to predict <span class="inline-formula"><i>B</i><sub>abs</sub></span> values from the PSAP's filter-based measurements. The predicted <span class="inline-formula"><i>B</i><sub>abs</sub></span> was compared against the reference <span class="inline-formula"><i>B</i><sub>abs</sub></span> measured by the PASS. The RFR algorithm performed the best by yielding the lowest root mean square
error of prediction. The algorithm was trained using input datasets from the PSAP (transmission and uncorrected absorption coefficient), a co-located
nephelometer (scattering coefficients), and the Aerosol Chemical Speciation Monitor (mass concentration of non-refractory aerosol particles). A revised
form of the Virkkula (2010) algorithm suitable for the SGP site has been
proposed; however, its performance yields approximately 2-fold errors when compared to the RFR algorithm. To generalize the accuracy and applicability
of our proposed RFR algorithm, we trained and tested it on a dataset of
laboratory measurements of combustion aerosols. Input variables to the
algorithm included the aerosol number size distribution from the Scanning Mobility Particle Sizer, absorption coefficients from the filter-based
Tricolor Absorption Photometer, and scattering coefficients from a
multiwavelength nephelometer. The RFR algorithm predicted <span class="inline-formula"><i>B</i><sub>abs</sub></span> values within 5 % of the reference <span class="inline-formula"><i>B</i><sub>abs</sub></span> measured by the multiwavelength PASS during the laboratory experiments. Thus, we show that machine learning
approaches offer a promising path to correct for biases in long-term
filter-based absorption datasets and accurately quantify their variability
and trends needed for robust radiative forcing determination.</p> |
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| ISSN: | 1867-1381 1867-8548 |