Eliminating collinearity observed in chemical mass balance analysis using multi linear regression and synthetic receptor source profile dataset
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s12046-023-02403-w.pdf
Reference43 articles.
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2. Muyemeki L, Burger R, Piketh S J, Language B, Beukes J P and Van P G 2021 Source apportionment of ambient PM10−2.5 and PM2.5 for the Vaal Triangle, South Africa. S. Afr. J. Sci. 117(5): 1–11. https://doi.org/10.17159/SAJS.2021/8617
3. Song L, Dai Q, Feng Y and Hopke P K 2021 Estimating uncertainties of source contributions to 598 PM2.5 using moving window evolving dispersion normalized PMF. Environ. Pollut. 286: 117576. https://doi.org/10.1016/j.envpol.2021.117576
4. Argyropoulos G and Samara C 2011 Development and application of a robotic chemical mass balance model for source apportionment of atmospheric particulate matter. Environ. Model. Softw. 26(4): 469–481. https://doi.org/10.1016/j.envsoft.2010.10.010
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