Growth model of carbon storage and driving force of carbon sequestration capacity of natural secondary forests

He Xiao; Li Haikui; Zhang Yiru; Huang Jinjin

doi:10.12171/j.1000-1522.20210265

Journal of Beijing Forestry University > 2023 > 45(1): 1-10. > DOI: 10.12171/j.1000-1522.20210265

He Xiao, Li Haikui, Zhang Yiru, Huang Jinjin. Growth model of carbon storage and driving force of carbon sequestration capacity of natural secondary forests[J]. Journal of Beijing Forestry University, 2023, 45(1): 1-10. DOI: 10.12171/j.1000-1522.20210265

Citation:

PDF (655 KB)

Growth model of carbon storage and driving force of carbon sequestration capacity of natural secondary forests

Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Key Laboratory of Forest Management and Growth Modelling, National Forestry and Grassland Administration, Beijing 100091, China

More Information

Received Date: July 13, 2021
Revised Date: December 12, 2021
Available Online: January 08, 2023
Published Date: January 24, 2023

Graphical Abstract

Abstract

Abstract

Objective This paper aims to establish a growth model of carbon storage of the natural secondary forests after logging disturbance and the corresponding carbon sink model, so as to analyze the driving effect of different factors on carbon sequestration capacity, which can provide references for quantitative evaluation of carbon sequestration capacity.
Method Totally 111 sample plot data for the natural secondary forests were selected based on the data of the 9th National Forest Inventory in Jilin Province of northeastern China. Based on the Richards theoretical growth equation, taking the average tree carbon storage of the sample plot as the dependent variable and the average age of the sample plot as the independent variable, a carbon storage classified growth model was established by grouping the age and iterative algorithm, and the carbon sink classified growth model was obtained by deriving the age in the carbon storage classified growth model. The coefficient of determination (R²) and the root mean square error (RMSE) were used to evaluate the effect of model fitting. Based on a linear model, taking geographical factors, topographic factors, climate factors, soil factors and forest stand factors as independent variables, the interactions of qualitative and quantitative factors were introduced to analyze the driving force of carbon sequestration capacity.
Result (1) The R² of the natural secondary forest carbon storage classified growth model was 0.965 6, and the RMSE was 2.612 7 kg, this model had a good goodness of fit. (2) The ages of the largest carbon sinks in each level were 8, 10, 13, 17 and 29 years. Calculated at a density of 1 000 plants per hectare, the threshold between levels in 5 years was 1.84 t/ha, and by 30 years increased to 10.78 t/ha, the threshold between each level increased with age. (3) The first-order qualitative, first-order, and second-order quantitative interaction model had the highest explanation for the average tree carbon storage, and R² was 0.919 2. Compared with the main effect model without interaction terms, R² was increased by 0.088 5. (4) For the average tree carbon storage, geographic factors (longitude and latitude), topographic factors (altitude, landform, slope aspect, slope position and slope degree), litter layer thickness, climate factors (annual average temperature, extreme minimum temperature, humidity index, frost-free days), soil factor (soil thickness) and stand factors (age and dominant tree species) had significant effects. For 20 years of carbon density by different classifications, geographic factor (latitude), topographic factors (landform, slope degree and slope aspect), climate factors (extreme maximum temperature, hottest monthly average temperature and humidity index), soil factors (soil type and gravel content) and stand factor (dominant tree species) had significant effects, while factors such as altitude and humus thickness existed in the interaction terms, and their main effects were not significant.
Conclusion The carbon density of different level thresholds of the natural secondary forests is different in varied time periods. With the increase of age, the thresholds between levels continue to increase, but the relative magnitudes in carbon storage between different levels are decreasing. The introduction of interaction could help improve the interpretation degree of forest carbon sequestration. Geographical factors such as latitude, topographical factors such as slope degree and slope aspect, climate factors such as humidity index, and stand factors such as dominant tree species are the key factors affecting the carbon sequestration capacity of the natural secondary forests.
- natural secondary forest,
- growth model,
- carbon sequestration classification,
- influencing factor

FullText(HTML)

References (35)

References

[1]	Sutherland I J, Gergel S E, Bennett E M. Seeing the forest for its multiple ecosystem services: indicators for cultural services in heterogeneous forests[J]. Ecological Indicators, 2016, 71: 123−133. doi: 10.1016/j.ecolind.2016.06.037
[2]	Kolo H, Kindu M, Knoke T. Optimizing forest management for timber production, carbon sequestration and groundwater recharge[J]. Ecosystem Services, 2020, 44: 101147. doi: 10.1016/j.ecoser.2020.101147
[3]	魏曦, 梁文俊, 毕华兴, 等. 晋西黄土区油松林分结构与水土保持功能的多因子复合关系[J]. 林业科学研究, 2020, 33(3): 39−47. doi: 10.13275/j.cnki.lykxyj.2020.03.005 Wei X, Liang W J, Bi H X, et al. Multifactor relationships between stand structure and soil and water conservation function of Pinus tabulaeformis plantations in the Loess Plateau of Western Shanxi[J]. Forest Research, 2020, 33(3): 39−47. doi: 10.13275/j.cnki.lykxyj.2020.03.005
[4]	龚诗涵, 肖洋, 郑华, 等. 中国生态系统水源涵养空间特征及其影响因素[J]. 生态学报, 2017, 37(7): 2455−2462. Gong S H, Xiao Y, Zheng H, et al. Spatial patterns of ecosystem water conservation in China and its impact factors analysis[J]. Acta Ecologica Sinica, 2017, 37(7): 2455−2462.
[5]	Parker C, Mitchell A, Trivedi M, et al. The little REDD book: a guide to governmental and non-governmental proposals for reducing emissions from deforestation and degradation[M]. Oxford: Global Canopy Programme, 2008.
[6]	牟长城, 杨明, 倪志英, 等. 不同恢复途径对大兴安岭森林沼泽群落结构与生产力的影响[J]. 东北林业大学学报, 2007, 35(5): 27−31. doi: 10.3969/j.issn.1000-5382.2007.05.009 Mu C C, Yang M, Ni Z Y, et al. Effects of different restoration approaches on structure and productivity of forest swamp communities in Daxing’an Mountain[J]. Journal of Northeast Forestry University, 2007, 35(5): 27−31. doi: 10.3969/j.issn.1000-5382.2007.05.009
[7]	United Nations Environment Programme. The state of the world’s forests: forests, biodiversity and people[R/OL]. Rome: FAO, 2020 [2021−06−22]. http://resp.llas.ac.cn/C666/handle/2XK7JSWQ/270906.
[8]	马姜明, 刘世荣, 史作民, 等. 退化森林生态系统恢复评价研究综述[J]. 生态学报, 2010, 30(12): 3297−3303. Ma J M, Liu S R, Shi Z M, et al. A review on restoration evaluation studies of degraded forest ecosystem[J]. Acta Ecologica Sinica, 2010, 30(12): 3297−3303.
[9]	Wu B, Meng X, Ye Q, et al. Method of estimating degraded forest area: cases from dominant tree species from Guangdong and Tibet in China[J]. Forests, 2020, 11(9): 930. doi: 10.3390/f11090930
[10]	张小全, 侯振宏. 森林退化、森林退化、森林管理、植被破坏和恢复的定义与碳计量问题[J]. 林业科学, 2003, 39(4): 140−144. doi: 10.3321/j.issn:1001-7488.2003.04.023 Zhang X Q, Hou Z H. Definitions of forest degradation, forest management, devegetation and revegetation in relations to carbon accounting[J]. Scientia Silvae Sinicae, 2003, 39(4): 140−144. doi: 10.3321/j.issn:1001-7488.2003.04.023
[11]	纪小芳, 鲁建兵, 杨军, 等. 凤阳山针阔混交林碳通量变化特征及其影响因子[J]. 东北林业大学学报, 2019, 47(3): 49−55. doi: 10.3969/j.issn.1000-5382.2019.03.010 Ji X F, Lu J B, Yang J, et al. Carbon flux variation characteristics and its influencing factors in coniferous and broadleaved mixed forest in Fengyang Mountain[J]. Journal of Northeast Forestry University, 2019, 47(3): 49−55. doi: 10.3969/j.issn.1000-5382.2019.03.010
[12]	魏艳艳. 2009-2018年崇明岛主要土地利用类型植被碳储量变化及其驱动力分析[D]. 上海: 上海师范大学, 2020. Wei Y Y. Analysis on the change and driving force of carbon storage of main land use types in Chongming Island from 2009 to 2018[D]. Shanghai: Shanghai Normal University, 2020.
[13]	Gruba P, Socha J. Exploring the effects of dominant forest tree species, soil texture, altitude, and pH (H₂O) on soil carbon stocks using generalized additive models[J]. Forest Ecology and Management, 2019, 447: 105−114. doi: 10.1016/j.foreco.2019.05.061
[14]	柏广新, 牟长城. 抚育对长白山幼龄次生林群落结构与动态的影响[J]. 东北林业大学学报, 2012, 40(10): 48−55. doi: 10.3969/j.issn.1000-5382.2012.10.012 Bai G X, Mu C C. Effect of thinning on the structure and succession of secondary forest communities in Changbai Mountains of China[J]. Journal of Northeast Forestry University, 2012, 40(10): 48−55. doi: 10.3969/j.issn.1000-5382.2012.10.012
[15]	朱教君, 李凤芹. 森林退化/衰退的研究与实践[J]. 应用生态学报, 2007, 18(7): 1601−1609. doi: 10.3321/j.issn:1001-9332.2007.07.032 Zhu J J, Li F Q. Forest degradation/decline research and practice[J]. Chinese Journal of Applied Ecology, 2007, 18(7): 1601−1609. doi: 10.3321/j.issn:1001-9332.2007.07.032
[16]	Richards F J. A flexible growth function for empirical use[J]. Journal of Experimental Botany, 1959, 10: 290−300. doi: 10.1093/jxb/10.2.290
[17]	李海奎, 法蕾. 基于分级的全国主要树种树高−胸径曲线模型[J]. 林业科学, 2011, 47(10): 83−90. doi: 10.11707/j.1001-7488.20111013 Li H K, Fa L. Height-diameter model for major tree species in China using the classified height method[J]. Scientia Silvae Sinicae, 2011, 47(10): 83−90. doi: 10.11707/j.1001-7488.20111013
[18]	Li H X, Zhao P X. Improving the accuracy of tree-level aboveground biomass equations with height classification at a large regional scale[J]. Forest Ecology and Management, 2013, 289: 153−163. doi: 10.1016/j.foreco.2012.10.002
[19]	唐守正, 郎奎建, 李海奎. 统计和生物数学模型计算(ForStat教程)[M]. 北京: 科学出版社, 2009. Tang S Z, Lang K J, Li H K. Statistics and biomathematical models (ForStat textbook)[M]. Beijing: Science Press, 2009.
[20]	刘晓彤, 李海奎, 曹磊, 等. 广东省森林土壤养分异质性析因[J]. 北京林业大学学报, 2021, 43(2): 90−101. Liu X T, Li H K, Cao L, et al. Analysis on the heterogeneity of forest soil nutrients in Guangdong Province of southern China[J]. Journal of Beijing Forestry University, 2021, 43(2): 90−101.
[21]	何潇, 赵嘉诚, 曹磊, 等. 广东省枫香单木干材生物量生长模型的研建[J]. 西北林学院学报, 2018, 33(6): 236−242. doi: 10.3969/j.issn.1001-7461.2018.06.37 He X, Zhao J C, Cao L, et al. Construction of individual bole biomass growth models for Liquidambar formosana in Guangdong Province[J]. Journal of Northwest Forestry University, 2018, 33(6): 236−242. doi: 10.3969/j.issn.1001-7461.2018.06.37
[22]	雷相东, 符利勇, 李海奎, 等. 基于林分潜在生长量的立地质量评价方法与应用[J]. 林业科学, 2018, 54(12): 116−126. doi: 10.11707/j.1001-7488.20181213 Lei X D, Fu L Y, Li H K, et al. Methodology and applications of site quality assessment based on potential mean annual increment[J]. Scientia Silvae Sinicae, 2018, 54(12): 116−126. doi: 10.11707/j.1001-7488.20181213
[23]	何潇, 曹磊, 徐胜林, 等. 内蒙古大兴安岭林区不同恢复阶段森林生物量特征与影响因素[J]. 北京林业大学学报, 2019, 41(9): 50−58. doi: 10.13332/j.1000-1522.20190030 He X, Cao L, Xu S L, et al. Forest biomass characteristics and influencing factors in different restoration stages in the Daxing’anling forest region of Inner Mongolia, northern China[J]. Journal of Beijing Forestry University, 2019, 41(9): 50−58. doi: 10.13332/j.1000-1522.20190030
[24]	李江, 陈宏伟, 冯弦. 云南热区几种阔叶人工林碳储量的研究[J]. 广西植物, 2003, 23(4): 294−298. doi: 10.3969/j.issn.1000-3142.2003.04.002 Li J, Chen H W, Feng X. Carbon stock and rate of carbon sequestration assessment of hardwood plantations in tropical Yunnan, China[J]. Guihaia, 2003, 23(4): 294−298. doi: 10.3969/j.issn.1000-3142.2003.04.002
[25]	郑立生, 李海奎. 模型形式和地域对落叶松地上生物量预测的影响[J]. 林业资源管理, 2013, 2: 83−88. doi: 10.3969/j.issn.1002-6622.2013.03.019 Zheng L S, Li H K. Effects of model form and region on prediction for aboveground biomass of Larix[J]. Forest Resources Management, 2013, 2: 83−88. doi: 10.3969/j.issn.1002-6622.2013.03.019
[26]	李娜. 川西亚高山森林植被生物量及碳储量遥感估算研究[D]. 雅安: 四川农业大学, 2008. Li N. Application of remote sensing model in biomass estimation and carbon storage of the subalpine coniferous forest in western Sichuan Province[D]. Ya’an: Sichuan Agricultural University, 2008.
[27]	Latta G, Temesgen H, Adams D, et al. Analysis of potential impacts of climate change on forests of the United States Pacific Northwest[J]. Forest Ecology and Management, 2010, 259(4): 720−729. doi: 10.1016/j.foreco.2009.09.003
[28]	Khan D, Muneer M A, Nisa Z U, et al. Effect of climatic factors on stem biomass and carbon stock of Larix gmelinii and Betula platyphylla in Daxing’anling Mountain of Inner Mongolia, China[J/OL]. Advances in Meteorology, 2019: 5692574 [2021−07−14]. http://doi.org/10.1155/2019/5692574.
[29]	Aguirre A, Río M D, Ruiz-Peinado R, et al. Stand-level biomass models for predicting C stock for the main Spanish pine species[J]. Forest Ecosystems, 2021, 8: 29. doi: 10.1186/s40663-021-00308-w
[30]	Baraloto C, Rabaud S, Molto Q, et al. Disentangling stand and environmental correlates of aboveground biomass in Amazonian forests[J]. Global Change Biology, 2011, 17(8): 2677−2688. doi: 10.1111/j.1365-2486.2011.02432.x
[31]	Ali A, Sanaei A, Li M S, et al. Impacts of climatic and edaphic factors on the diversity, structure and biomass of species-poor and structurally-complex forests[J]. Science of the Total Environment, 2020, 706: 135719. doi: 10.1016/j.scitotenv.2019.135719
[32]	Santiago-Garcia R J, Finegan B, Bosque-Perez N A. Soil is the main predictor of secondary rain forest estimated aboveground biomass across a Neotropical landscape[J]. Biotropica, 2019, 51: 10−17. doi: 10.1111/btp.12621
[33]	Bennett A C, Penman T D, Arndt S K, et al. Climate more important than soils for predicting forest biomass at the continental scale[J]. Ecography, 2020, 43(11): 1692−1705. doi: 10.1111/ecog.05180
[34]	Bengough A G. Root growth and function in relation to soil structure, composition, and strength[M]//de Kroon H, Visser E J W. Root ecology: ccological studies (analysis and synthesis). Berlin: Springer, 2003: 168.
[35]	Lindner M, Fitzgerald J B, Zimmermann N E, et al. Climate change and European forests: what do we know, what are the uncertainties, and what are the implications for forest management?[J]. Journal of Environmental Management, 2014, 146: 69−83.

[1]	Mei Xuesong, Dong Lingbo, Chen Guanmou. Driving factors of carbon sink in natural Larix gmelinii forests based on structural equation models[J]. Journal of Beijing Forestry University, 2024, 46(9): 1-10. DOI: 10.12171/j.1000-1522.20230284
[2]	Wu Yan, Li Xinyu, Zhang Yiting, Ding Bo, Zhang Yunlin, Fu Yuhong, Liu Xun. Litter carbon, nitrogen, and phosphorus stoichiometric characteristics and their influencing factors of Pinus massoniana plantation with different age groups in karst region of southwestern China[J]. Journal of Beijing Forestry University, 2024, 46(2): 87-94. DOI: 10.12171/j.1000-1522.20220052
[3]	Xu Chao, Long Ting, Wu Xinlei, Chen Jie, Liang Yanjun, Li Jingwen. Reintroducing effects and influencing factors of Taxus cuspidata population[J]. Journal of Beijing Forestry University, 2020, 42(8): 34-42. DOI: 10.12171/j.1000-1522.20190423
[4]	Gao Yan, Zhang Yuqing, Qin Shugao, Zhang Jutao, Liu Zhen. Landscape pattern change and its influencing factors of sand-binding vegetation[J]. Journal of Beijing Forestry University, 2020, 42(4): 102-112. DOI: 10.12171/j.1000-1522.20190061
[5]	He Xiao, Cao Lei, Xu Shenglin, Li Haikui. Forest biomass characteristics and influencing factors in different restoration stages in the Daxing’anling forest region of Inner Mongolia, northern China[J]. Journal of Beijing Forestry University, 2019, 41(9): 50-58. DOI: 10.13332/j.1000-1522.20190030
[6]	ZHOU Wen-jun, SHA Li-qing, ZHANG Yi-ping, SONG Qing-hai, LIU Yun-tong, DENG Yun, DENG Xiao-bao. Characteristics and influencing factors of soil dissolved organic carbon and nitrogen in a tropical seasonal rainforest in Xishuangbanna,Southwest China.[J]. Journal of Beijing Forestry University, 2016, 38(9): 34-41. DOI: 10.13332/j.1000-1522.20150238
[7]	LI Ning, CHEN Li-hua, YANG Yuan-jun.. Factors influencing root tensile properties of Pinus tabuliformis and Larix principis-rupprechtii.[J]. Journal of Beijing Forestry University, 2015, 37(12): 77-84. DOI: 10.13332/j.1000-1522.20150131
[8]	CHEN Chong, LI Ji-yue, , WANG Yu- tao. Variation of stem sap flow of Salix matsudana and its impact factors.[J]. Journal of Beijing Forestry University, 2008, 30(4): 82-88.
[9]	GUO Hong-wu, WANG Jin-lin, LI Chun-sheng, YAN Hao-Peng. Light-induced discoloration and influencing factors of dyed veneer after painted.[J]. Journal of Beijing Forestry University, 2008, 30(4): 22-27.
[10]	JIAO Wen-jun, ZHU Qing-ke, ZHANG Yu-qing, WU Xiu-qin, WANG Na. Distribution of biotic crusts and its influencing factors in the grain-for-green land of the loess region, northern Shaanxi Province[J]. Journal of Beijing Forestry University, 2007, 29(1): 102-107. DOI: 10.13332/j.1000-1522.2007.01.018

Cited By

Cited by

Periodical cited type(34)

1.	孙丽，张颖，李文彬，包红光，孙迎坤. 青岛市3种常绿灌木滞尘量与叶微观特征及光合作用等的相关性分析. 西北林学院学报. 2024(04): 232-241 .
2.	裴云霞，洪慧，包美玲，邓俊，陈岷轩，张强. 农业环境损害鉴定中受体植物的损害因素判别及损害程度分析. 中国司法鉴定. 2024(04): 40-48 .
3.	贺丹，李朝梅，华超，李思洁，雷雅凯，张曼. 郑州市10种园林植物叶片滞尘与富集重金属的能力. 西北林学院学报. 2023(01): 230-237 .
4.	张碧媛，李智琦，阮琳，潘勇军，陈国财，代色平，冯娴慧. 2种常用的植物滞纳能力测定方法对比研究. 林业与环境科学. 2023(01): 112-119 .
5.	罗建平，王宁，宋菲菲，魏汉博，原白玉，唐钰鑫. 大庆市6种绿化树种对SO_2、NO_2的消减及滞尘效应. 生态学报. 2023(11): 4561-4569 .
6.	张翠，马瑞，谭立佳，杜婉倩，刘涵科. 兰州市10种常用园林绿化树种叶表面微结构对其滞尘量的影响. 甘肃农业大学学报. 2023(04): 192-200+211 .
7.	廖慧敏，师凤起，李明，朱逸龙. 长沙市典型园林植物叶片的滞尘等级与模式识别研究. 生态环境学报. 2022(01): 110-116 .
8.	贺丹，汪安印，李紫萱，王翼飞，李朝梅，雷雅凯，李永华，董娜琳. 郑州市常绿树种滞尘能力与叶片生理结构的响应. 福建农业学报. 2022(02): 203-212 .
9.	李晓璐，叶锦东，章剑，周毅烈，袁楚阳，于慧，张天然，黄芳，张贵豪，邵锋. 乔木滞留大气颗粒物能力及其与叶表面微结构关系. 中国城市林业. 2022(03): 22-28+120 .
10.	王军梦，汪安印，王翼飞，贺丹，李永华，董娜琳. 不同污染程度下树种滞尘能力与叶表微形态关系研究. 林业调查规划. 2022(05): 16-21+37 .
11.	孟畅，彭洋，赵杨，王秀荣，肖枫. 2种叶型膏桐幼苗的形态结构和光合特性. 林业科学. 2022(12): 32-41 .
12.	岳晨，李广德，席本野，曹治国. 叶片大气颗粒物滞纳能力评估方法的定量对比. 环境科学. 2021(01): 114-126 .
13.	徐立人，刘宠，张军，柳俊明，王立成，李清泉，杨敏生，李彦慧. 单叶刺槐半同胞子代叶片的滞尘能力及叶表SEM特征分析. 西部林业科学. 2021(01): 124-131 .
14.	杨克彤，陈国鹏，李广，汤东，张凯. 兰州市常见阔叶树种对大气颗粒物吸滞能力的评估. 东北林业大学学报. 2021(05): 84-89 .
15.	刘宇，张楠，王晓立，周力行，韩浩章. 冬季苏北8种常绿乔木吸滞颗粒物能力与叶表微结构关系. 西北林学院学报. 2021(03): 80-87+127 .
16.	王薇，张蕾. 基于CiteSpace的城市环境中细颗粒物研究进展的可视化分析. 生态环境学报. 2021(06): 1321-1332 .
17.	谢长坤，郭健康，梁安泽，汪静，姜睿原，车生泉. 园林植物表面对大气颗粒物削减过程研究进展. 世界林业研究. 2021(05): 38-43 .
18.	吴桂香，徐成林，刘杰，杨燕飞. 城市道路植物叶面滞尘的微观效应研究. 昆明理工大学学报(自然科学版). 2021(06): 109-115 .
19.	陈胜楠，陈左司南，张志强. 北京山区油松和元宝槭冠层气孔导度特征及其环境响应. 植物生态学报. 2021(12): 1329-1340 .
20.	王琴，冯晶红，黄奕，王鹏程，谢梦婷，万好，苏泽琳，王仁鹏，王征洋，余刘思. 武汉市15种阔叶乔木滞尘能力与叶表微形态特征. 生态学报. 2020(01): 213-222 .
21.	童凌云，何婉璎，裘璐函，陈健，刘美华. 基于层次分析法的杭州市8种园林植物林分环境质量评价. 浙江林业科技. 2020(01): 56-62 .
22.	苏维，刘苑秋，赖胜男，古新仁，刘青，龚鹏. 南昌市8种乔木叶片性状对叶表滞留颗粒物的影响. 西北林学院学报. 2020(04): 61-67 .
23.	刘开琳，李学敏，万翔，刘淑娟，李菁菁，徐先英，刘虎俊. 民勤植物园3种灌木的叶面微结构及其滞尘能力研究. 中国农学通报. 2020(26): 62-68 .
24.	孙应都，陈奇伯，李艳梅，杨思莹. 昆明市6个绿化树种叶表微结构与滞尘能力的关系研究. 西南林业大学学报(自然科学). 2019(03): 78-85 .
25.	张俊叶，邹明，刘晓东，王林，朱晨晨，俞元春. 南京城市森林植物叶面颗粒物的含量特征. 环境污染与防治. 2019(07): 837-843 .
26.	林星宇，李海梅，李彦华，姜月梅. 八种乔木滞尘效益及其与叶表面特征关系. 北方园艺. 2019(17): 94-101 .
27.	林星宇，李海梅，李彦华，刘志科. 灌木滞尘能力与重金属含量间的关系. 江苏农业科学. 2019(15): 180-183 .
28.	姜霞，侯贻菊，刘延惠，舒德远，崔迎春，李成龙，杨冰，丁访军. 3种木樨科树种叶片滞尘效应动态变化及其与叶片特征的关系. 江苏农业科学. 2019(16): 150-154 .
29.	林星宇，李彦华，李海梅，李士美. 乔木对不同粒径颗粒物吸滞作用研究. 福建农业学报. 2019(08): 912-919 .
30.	阿丽亚·拜都热拉，甄敬，潘存德，张中远，胡梦玲，喀哈尔·扎依木. 乌鲁木齐市河滩快速路林带内颗粒物浓度变化特征. 新疆农业大学学报. 2019(05): 378-384 .
31.	林星宇，李海梅，李彦华，郑茗月. 5种灌木的滞尘效益研究. 现代农业科技. 2018(02): 150-151+155 .
32.	赵文君，侯贻菊，舒德远，刘延惠，崔迎春，丁访军. 贵阳市木兰科树种叶片滞尘效应及影响因素. 贵州林业科技. 2018(02): 19-24 .
33.	李艳梅，陈奇伯，王邵军，孙应都，杨淏舟，杨思莹. 昆明市主要绿化树种叶片滞尘能力的叶表微形态学解释. 林业科学. 2018(05): 18-29 .
34.	朱济友，于强，刘亚培，覃国铭，李金航，徐程扬，何韦均. 植物功能性状及其叶经济谱对城市热环境的响应. 北京林业大学学报. 2018(09): 72-81 . 本站查看

Other cited types(26)

Get Citation

PDF

XML

Article views (1067) PDF downloads (250) Cited by(60)

Turn off MathJax

Article Contents

Abstract

References

Growth model of carbon storage and driving force of carbon sequestration capacity of natural secondary forests