Software Open Access

Scatter Plot

Cheng WU

Version 20200818


New features:

1)  Batch plotting- overlaid plots

In overlaid mode, all traces are combined in a single plot, as shown below. Each trace will be assigned to a specified color according to the rainbow color table. Batch plotting can be applied on following scales: Year; Year/Month; Month; Year/Season; Season; YYYY/MM/DD; Day of week; Hour of day; text data (User defined, Weekday/weekend, Daytime/nighttime). Regressed slopes, intercepts, R2 and other metrics can be found in the csv file if “Export txt for batch plot” was selected.

Demo video of overlaid plots:

2) weekday/weekend and daytime/nighttime quick selection.

3) Daytime/nighttime and weekend/weekday options are provided in Text by list data filtering.

4) The mask wave can be exported to a csv file for further use. Click “Export mask as txt” to export. Demo video:




Scatter Plot is a handy tool to maximize the efficiency of data visualization in atmospheric science. Many existing generalized data visualization software had been extensively used, but they remain unable to fulfill a number of specify research purposes in atmospheric science. That becomes the motivation of Scatter Plot development. The program includes Deming and York algorithm for linear regression, which considers uncertainties in both X and Y, and is more realistic for atmospheric applications.  Scatter Plot is Igor based, and packed with a variety of useful features for data analysis and graph plotting, including batch plotting, data masking via GUI, color coding in Z-axis, data filtering and grouping on different time scales (year, season, month, hour, day of week, etc).  


For more details regarding the evaluation and application of Scatter Plot, please refer to 

Wu, C. and Yu, J. Z.: Evaluation of linear regression techniques for atmospheric applications: the importance of appropriate weighting, Atmos. Meas. Tech., 11, 1233-1250, doi:, 2018.


Please cite this paper if Scatter Plot is used in your publication.


The latest version of the program can be found on my website:




Adoption in research publications:

Sun, J. Y., Wu, C.*, Wu, D.*, Cheng, C., Li, M., Li, L., Deng, T., Yu, J. Z., Li, Y. J., Zhou, Q., Liang, Y., Sun, T., Song, L., Cheng, P., Yang, W., Pei, C., Chen, Y., Cen, Y., Nian, H., and Zhou, Z.*: Amplification of black carbon light absorption induced by atmospheric aging: temporal variation at seasonal and diel scales in urban Guangzhou, Atmos. Chem. Phys., 20, 2445-2470, doi:, 2020.

Sun, T., Wu, C.*, Wu, D.*, Liu, B., Sun, J. Y., Mao, X., Yang, H., Deng, T., Song, L., Li, M., Li, Y. J., and Zhou, Z.*: Time-resolved black carbon aerosol vertical distribution measurements using a 356-m meteorological tower in Shenzhen, Theor. Appl. Climatol., doi: , 2020.

Liu, B., Wu, C., Ma, N., Chen, Q., Li, Y., Ye, J., Martin, S. T., and Li, Y. J.*: Vertical profiling of fine particulate matter and black carbon by using unmanned aerial vehicle in Macau, China, Sci. Total. Environ., 136109, doi:, 2020.

Wu, C*., Wu, D., and Yu, J. Z*.: Estimation and Uncertainty Analysis of Secondary Organic Carbon Using One‐Year of Hourly Organic and Elemental Carbon Data. J. Geophys. Res.-Atmos, 124, 2774-2795 doi:, 2019

Ji, D., Gao, W., Maenhaut, W., He, J., Wang, Z., Li, J., Du, W., Wang, L., Sun, Y., Xin, J., Hu, B., and Wang, Y.: Impact of air pollution control measures and regional transport on carbonaceous aerosols in fine particulate matter in urban Beijing, China: insights gained from long-term measurement, Atmos. Chem. Phys., 19, 8569-8590, doi:, 2019.

Liu, B., He, M. M., Wu, C., Li, J., Li, Y., Lau, N. T., Yu, J. Z., Lau, A. K. H., Fung, J. C. H., Hoi, K. I., Mok, K. M., Chan, C. K., and Li, Y. J*.: Potential exposure to fine particulate matter (PM2.5) and black carbon on jogging trails in Macau, Atmos. Environ., 198, 23-33, doi:, 2019.

Wang, N. and Yu, J. Z.: Size distributions of hydrophilic and hydrophobic fractions of water-soluble organic carbon in an urban atmosphere in Hong Kong, Atmos. Environ., 166, 110-119, doi: 10.1016/j.atmosenv.2017.07.009, 2017.

Cheng, C., Li, M.*, Chan, C. K., Tong, H., Chen, C., Chen, D., Wu, D., Li, L., Wu, C., Cheng, P., Gao, W., Huang, Z., Li, X., Zhang, Z., Fu, Z., Bi, Y., and Zhou, Z.*: Mixing state of oxalic acid containing particles in the rural area of Pearl River Delta, China: implications for the formation mechanism of oxalic acid, Atmos. Chem. Phys., 17, 9519-9533, doi:, 2017.

Wu, C., Huang, X. H. H., Ng, W. M., Griffith, S. M., and Yu, J. Z.: Inter-comparison of NIOSH and IMPROVE protocols for OC and EC determination: Implications for inter-protocol data conversion, Atmos. Meas. Tech. doi:, 2016.

Zhou, Y., Huang, X. H. H., Griffith, S. M., Li, M., Li, L., Zhou, Z., Wu, C., Meng, J., Chan, C. K., Louie, P. K. K., and Yu, J. Z.: A field measurement based scaling approach for quantification of major ions, organic carbon, and elemental carbon using a single particle aerosol mass spectrometer, Atmos. Environ., 143, 300-312, 2016.

Adoption in research publications (without acknowledgment):

Qiao, T., Zhao, M., Xiu, G., and Yu, J.: Seasonal variations of water soluble composition (WSOC, Hulis and WSIIs) in PM1 and its implications on haze pollution in urban Shanghai, China, Atmos. Environ., 123, Part B, 306-314, 2015.



List of programs I developed:


Histogram and Boxplot


RT-ECOC raw data processor

Benchtop Sunset ECOC analyzer data processor

DRI 2001A data Sorter

SMPS Toolkit

Mie Scattering

Aethalometer data correction




Scatter Plot是一个方便的工具,可以最大限度地提高大气科学中数据可视化的效率。 虽然有许多现有的通用数据可视化软件,但不能满足许多大气科学特定的研究目的,所以我开发自己的程序。 本程序包括WODR, Deming和York算法进行线性回归,这三种算法考虑了X和Y都包含不确定性(观测误差),对大气的应用而言更加客观地反映真实情况。它是基于Igor的,并且包含大量用于数据分析和图形绘图的有用功能,包括批量绘图,通过图形界面实现数据掩蔽,Z轴的颜色编码,根据数据或字符串进行过滤和分组。

有关Scatter Plot的评估和应用的更多细节,请参阅(如果你在文章中用到了本软件,请引用以下文章)

Wu, C. and Yu, J. Z.: Evaluation of linear regression techniques for atmospheric applications: the importance of appropriate weighting, Atmos. Meas. Tech., 11, 1233-1250, doi:, 2018.







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