Modeling air pollutant removal, carbon storage, and CO2 sequestration potential of urban forests in Scotlandville, Louisiana, USA

doi:10.3832/ifor1845-009

Modeling air pollutant removal, carbon storage, and CO₂ sequestration potential of urban forests in Scotlandville, Louisiana, USA

Zhu Hua Ning⁽¹⁾ , Robert Chambers⁽²⁾, Kamran Abdollahi⁽³⁾

iForest - Biogeosciences and Forestry, Volume 9, Issue 6, Pages 860-867 (2016)
doi: https://doi.org/10.3832/ifor1845-009
Published: Sep 22, 2016 - Copyright © 2016 SISEF

Research Articles

Collection/Special Issue: IUFRO RG7.01.00 - Nice (France 2015)
Global Challenges of Air Pollution and Climate Change to Forests
Guest Editors: Elena Paoletti, Pierre Sicard

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Geo-mapping

Understanding an urban forest’s structure, function, and value can promote management decisions that will improve environmental quality and human health. Using i-Tree Eco software and its sampling and data collection protocol, an assessment of the baseline condition, ecological function, and value of the urban forests in Scotlandville (Louisiana, USA) was conducted during 2014. A stratified (by land use type) random sample plot map of the town was generated. Data from 170 field plots located throughout Scotlandville were collected, including tree species, diameter at breast height, total tree height, height to live top, height to crown base, crown width, crown dieback, crown light exposure, percent impervious surface under the tree, and direction and distance to building. Data were then entered into i-Tree Eco v5.0 and analyzed. Modeling results indicated that there are a total of 31 species and an estimated 239.000 trees in Scotlandville with a tree canopy cover of 23.7 percent; the three most common species are Black willow (Salix nigra), Water oak (Quercus nigra), and American elm (Ulmus americana); the overall tree density is 77 trees per hectare and trees with diameters of more than 15 cm (6 inches) constitute 56.5% of the population. The model estimated that annually, the urban forests in Scotlandville remove 96 tons of air pollutants; gross sequestration is about 3.880 tons of carbon and net carbon sequestration is about 3.650 tons. Each year, trees in Scotlandville are estimated to store 88.700 tons of carbon, produce 9.720 tons of oxygen, reduce runoff by 121.200 m³, reduce energy-related costs by $324.000 USD, and provide an additional $52.595 in value by reducing the amount of carbon released by power plants (a reduction of 739 tons of carbon emissions). The structural value for Scotlandville community forest is estimated at $185 million and the annual ecological functional value is estimated at 9 million USD. These results provide baseline information for management recommendations to maximize the ecological benefits provided by trees.

Keywords

Urban Forest, Pollution Removal, Carbon Sequestration, Carbon Storage, Runoff Reduction, Energy Saving, Climate Change

Authors’ address

(1)

Urban Forestry and Natural Resources Department, P.O. Box 11686, Southern University and A&M College, Baton Rouge, LA 70813 (USA)

(2)

USDA Natural Resource Conservation Service. 1126 Little Street, Camden, SC 29020 (USA)

(3)

Urban Forestry and Natural Resources Department, Southern University Agricultural Research and Extension Center, P.O. Box 10771, Southern University and A&M College, Baton Rouge, LA 70813 (USA)

Corresponding author

Zhu Hua Ning
zhu_ning@subr.edu

Citation

Ning ZH, Chambers R, Abdollahi K (2016). Modeling air pollutant removal, carbon storage, and CO₂ sequestration potential of urban forests in Scotlandville, Louisiana, USA. iForest 9: 860-867. - doi: 10.3832/ifor1845-009

Academic Editor

Elena Paoletti

Paper history

Received: Aug 31, 2015
Accepted: Jul 13, 2016

First online: Sep 22, 2016
Publication Date: Dec 14, 2016
Publication Time: 2.37 months

Open Access

This article is distributed under the terms of the Creative Commons Attribution-Non Commercial 4.0 International (https://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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