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. 2017 May 22;14(5):549. doi: 10.3390/ijerph14050549

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© 2017 by the authors.

Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

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The non-linear associations between each predictor and PM_2.5. The non-linear association between (a) Log-PM₁₀ and PM_2.5; (b) aerosol optical thickness (AOT) and PM_2.5; (c) number of the emission plants and PM_2.5; (d) wind and PM_2.5; (e) precipitation and PM_2.5; (f) temperature and PM_2.5; (g) day of year and PM_2.5.

The non-linear associations between each predictor and PM_2.5. The non-linear association between (a) Log-PM₁₀ and PM_2.5; (b) aerosol optical thickness (AOT) and PM_2.5; (c) number of the emission plants and PM_2.5; (d) wind and PM_2.5; (e) precipitation and PM_2.5; (f) temperature and PM_2.5; (g) day of year and PM_2.5.