Empirical nitrogen and sulfur critical loads of U.S. tree species and their uncertainties with machine learning-Reference-Cited by-同舟云学术

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Empirical nitrogen and sulfur critical loads of U.S. tree species and their uncertainties with machine learning

Published:2023-01 Issue: Volume:857 Page:159252
ISSN:0048-9697
Container-title:Science of The Total Environment
language:en
Short-container-title:Science of The Total Environment

Author:

Pavlovic Nathan R.,Chang Shih Ying,Huang Jiaoyan,Craig Kenneth,Clark Christopher,Horn Kevin,Driscoll Charles T.

Funder

Electric Power Research Institute

U.S. Environmental Protection Agency

Publisher

Elsevier BV

Subject

Pollution,Waste Management and Disposal,Environmental Chemistry,Environmental Engineering

Reference38 articles.

1. Restoring soil calcium reverses forest decline;Battles;Environ. Sci. Technol. Lett.,2014

2. FOCUS: a pilot study for national-scale critical loads development in the United States;Blett;Sci. Policy,2014

3. **USE 17150**Critical Loads of Atmospheric Deposition to Adirondack Lake Watersheds: A Guide for Policymakers;Burns,2015

4. XGBoost: a scalable tree boosting system;Chen,2016

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1. Projected global sulfur deposition with climate intervention;Global Environmental Change Advances;2024-12

2. Characterizing localized nitrogen sensitivity of tree species and the associated influences of mediating factors;Ecosphere;2024-07

3. Sensitivity of air quality to vehicle ammonia emissions in the United States;Atmospheric Environment;2024-06

4. Atmospheric reduced nitrogen: Sources, transformations, effects, and management;Journal of the Air & Waste Management Association;2024-05-31

5. Sensitive tree species remain at risk despite improved air quality benefits to US forests;Nature Sustainability;2023-08-21

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