Scatter Plot

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:10.5194/amt-11-1233-2018, 2018.

***Please install Igor Pro first before you can run these programs.

Igor Pro 6.x download: https://www.wavemetrics.com/support/demos.htm

Scatter Plot is a handy tool to maximize the efficiency of data visualization in atmospheric science. Many existing generalized data visualization software are great, but they remain unable to fulfill a number 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).

Features:

Data filter (on time scale, numerical data and text marker)

Data masking via GUI

color coding (Z axis)

Batch plotting

***Please install Igor Pro first before you can run these programs.

"IGOR Pro is a scientific data analysis software, numerical computing environment and programming language that runs on Windows or Mac operating systems. It is developed by WaveMetrics Inc., and was originally aimed at time series analysis, but has since then evolved and covers other applications such as curve fitting and image processing. It comes with a fully functional programming language and compiler, but many functions are also accessible through menus. IGOR Pro is primarily known for its graphics capabilities, and like Origin and other similar programs, is often used to generate plots for scientific and other publications. "

Igor Pro Wikipedia page https://en.wikipedia.org/wiki/IGOR_Pro

Adoption in research publications:

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: 10.5194/acp-19-8569-2019, 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.

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: 10.5194/amt-9-4547-2016, 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.http://dx.doi.org/10.1016/j.atmosenv.2016.08.054

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. http://dx.doi.org/10.1016/j.atmosenv.2015.03.010

York regression comparison with other calculation programs

the yield of identical slope and intercept on the person's dataset.