Articles | Volume 14, issue 7
https://doi.org/10.5194/amt-14-4879-2021
https://doi.org/10.5194/amt-14-4879-2021
Research article
 | 
10 Jul 2021
Research article |  | 10 Jul 2021

New correction method for the scattering coefficient measurements of a three-wavelength nephelometer

Jie Qiu, Wangshu Tan, Gang Zhao, Yingli Yu, and Chunsheng Zhao

Related authors

A Novel Method to Quantify the Uncertainty Contribution of Aerosol-Radiative Interaction Factors
Bishuo He and Chunsheng Zhao
EGUsphere, https://doi.org/10.5194/egusphere-2024-3441,https://doi.org/10.5194/egusphere-2024-3441, 2024
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Quality Control of Historical Temperature Data for Pure Rotational Raman Lidar Using Density-Based Clustering
Rongzheng Cao, Siying Chen, Wangshu Tan, Yixuan Xie, He Chen, Pan Guo, Rui Hu, Yinghong Yu, Jie Yu, and Shusen Yao
EGUsphere, https://doi.org/10.5194/egusphere-2024-2650,https://doi.org/10.5194/egusphere-2024-2650, 2024
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Theoretical Framework for Measuring Cloud Effective Supersaturation Fluctuations with an Advanced Optical System
Ye Kuang, Jiangchuan Tao, Hanbin Xu, Li Liu, Pengfei Liu, Wanyun Xu, Weiqi Xu, Yele Sun, and Chunsheng Zhao
EGUsphere, https://doi.org/10.5194/egusphere-2024-2698,https://doi.org/10.5194/egusphere-2024-2698, 2024
Short summary
Markedly different impacts of primary emissions and secondary aerosol formation on aerosol mixing states revealed by simultaneous measurements of CCNC, H(/V)TDMA, and SP2
Jiangchuan Tao, Biao Luo, Weiqi Xu, Gang Zhao, Hanbin Xu, Biao Xue, Miaomiao Zhai, Wanyun Xu, Huarong Zhao, Sanxue Ren, Guangsheng Zhou, Li Liu, Ye Kuang, and Yele Sun
Atmos. Chem. Phys., 24, 9131–9154, https://doi.org/10.5194/acp-24-9131-2024,https://doi.org/10.5194/acp-24-9131-2024, 2024
Short summary
Measurement report: Size-resolved mass concentration of equivalent black carbon-containing particles larger than 700 nm and their role in radiation
Weilun Zhao, Ying Li, Gang Zhao, Song Guo, Nan Ma, Shuya Hu, and Chunsheng Zhao
Atmos. Chem. Phys., 23, 14889–14902, https://doi.org/10.5194/acp-23-14889-2023,https://doi.org/10.5194/acp-23-14889-2023, 2023
Short summary

Related subject area

Subject: Aerosols | Technique: In Situ Measurement | Topic: Data Processing and Information Retrieval
Spatial analysis of PM2.5 using a concentration similarity index applied to air quality sensor networks
Rósín Byrne, John C. Wenger, and Stig Hellebust
Atmos. Meas. Tech., 17, 5129–5146, https://doi.org/10.5194/amt-17-5129-2024,https://doi.org/10.5194/amt-17-5129-2024, 2024
Short summary
A novel probabilistic source apportionment approach: Bayesian auto-correlated matrix factorization
Anton Rusanen, Anton Björklund, Manousos I. Manousakas, Jianhui Jiang, Markku T. Kulmala, Kai Puolamäki, and Kaspar R. Daellenbach
Atmos. Meas. Tech., 17, 1251–1277, https://doi.org/10.5194/amt-17-1251-2024,https://doi.org/10.5194/amt-17-1251-2024, 2024
Short summary
Towards a hygroscopic growth calibration for low-cost PM2.5 sensors
Milan Y. Patel, Pietro F. Vannucci, Jinsol Kim, William M. Berelson, and Ronald C. Cohen
Atmos. Meas. Tech., 17, 1051–1060, https://doi.org/10.5194/amt-17-1051-2024,https://doi.org/10.5194/amt-17-1051-2024, 2024
Short summary
Enhancing characterization of organic nitrogen components in aerosols and droplets using high-resolution aerosol mass spectrometry
Xinlei Ge, Yele Sun, Justin Trousdell, Mindong Chen, and Qi Zhang
Atmos. Meas. Tech., 17, 423–439, https://doi.org/10.5194/amt-17-423-2024,https://doi.org/10.5194/amt-17-423-2024, 2024
Short summary
Machine learning approaches for automatic classification of single-particle mass spectrometry data
Guanzhong Wang, Heinrich Ruser, Julian Schade, Johannes Passig, Thomas Adam, Günther Dollinger, and Ralf Zimmermann
Atmos. Meas. Tech., 17, 299–313, https://doi.org/10.5194/amt-17-299-2024,https://doi.org/10.5194/amt-17-299-2024, 2024
Short summary

Cited articles

Anderson, T. L. and Ogren, J. A.: Determining aerosol radiative properties using the TSI 3563 integrating nephelometer, Aerosol Sci. Tech., 29, 57–69, https://doi.org/10.1080/02786829808965551, 1998. 
Anderson, T. L., Covert, D. S., Marshall, S. F., Laucks, M. L., Charlson, R. J., Waggoner, A. P., Ogren, J. A., Caldow, R., Holm, R. L., Quant, F. R., Sem, G. J., Wiedensohler, A., Ahlquist, N. A., and Bates, T. S.: Performance characteristics of a high-sensitivity, three-wavelength, total scatter/backscatter nephelometer, J. Atmos. Ocean. Tech., 13, 967–986, https://doi.org/10.1175/1520-0426(1996)013<0967:PCOAHS>2.0.CO;2, 1996. 
Bond, T. C., Covert, D. S., and Müller, T.: Truncation and angular-scattering corrections for absorbing aerosol in the TSI 3563 nephelometer, Aerosol Sci. Tech., 43, 866–871, https://doi.org/10.1080/02786820902998373, 2009. 
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001. 
Download
Short summary
Considering nephelometers' major problems of a nonideal Lambertian light source and angle truncation, a new correction method based on a machine learning model is proposed. Our method has the advantage of obtaining data with high accuracy while achieving self-correction, which means that researchers can get more accurate scattering coefficients without the need for additional observation data. This method provides a more precise estimation of the aerosol’s direct radiative forcing.