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수목생리학연구실 - 20210105_홍정현

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20210105_홍정현
작성일
2021-01-05
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1. Kousehlar, M., & Widom, E. (2019). Sources of metals in atmospheric particulate matter in Tehran, Iran: Tree bark biomonitoring. Applied Geochemistry, 104, 71-82.

https://doi.org/10.1016/j.apgeochem.2019.03.018

2. Suleiman, A., Tight, M. R., & Quinn, A. D. (2019). Applying machine learning methods in managing urban concentrations of traffic-related particulate matter (PM10 and PM2. 5). Atmospheric Pollution Research, 10(1), 134-144.

https://doi.org/10.1016/j.apr.2018.07.001

3. Just, A. C., Arfer, K. B., Rush, J., Dorman, M., Shtein, A., Lyapustin, A., & Kloog, I. (2020). Advancing methodologies for applying machine learning and evaluating spatiotemporal models of fine particulate matter (PM2. 5) using satellite data over large regions. Atmospheric Environment, 239, 117649.

https://doi.org/10.1016/j.atmosenv.2020.117649
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Key root traits of Poaceae for adaptation to soil water gradients New Phytologist (2020) doi: 10.1111/nph.17093
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1.McCormack, M. Luke, et al. "Predicting fine root lifespan from plant functional traits in temperate trees."New Phytologist195.4 (2012): 823-831. https://doi.org/10.1111/j.1469-8137.2012.04198.x 2.Strand, Allan E., et al. "Irreconcilable differences: fine-root life spans and soil..