Seasonal Variability of GPP and Its Influencing Factors in the Typical Ecosystems in China

doi:10.16258/j.cnki.1674-5906.2022.04.001

Ecology and Environment ›› 2022, Vol. 31 ›› Issue (4): 643-651.DOI: 10.16258/j.cnki.1674-5906.2022.04.001

• Research Articles • Next Articles

Seasonal Variability of GPP and Its Influencing Factors in the Typical Ecosystems in China

JIANG Peng¹^,²(), QIN Mei’ou³, LI Rongping¹^,^*(), MENG Ying², YANG Feiyun⁴, WEN Rihong¹, SUN Pei², FANG Yuan²

1. The Institute of Atmospheric Environment, China Meteorological Administration, Shenyang 110166, P. R. China
2. China meteorological Administration training center of Liaoning, Shenyang 110166, P. R. China
3. Regional Climate Center of Shenyang, Shenyang 110016, P. R. China
4. China Meteorological Administration Training Center, Beijing 100081, P. R. China

Received:2021-09-01 Online:2022-04-18 Published:2022-06-22
Contact: LI Rongping

中国典型生态系统GPP的季节变异及其影响要素

姜鹏¹^,²(), 秦美欧³, 李荣平¹^,^*(), 孟莹², 杨霏云⁴, 温日红¹, 孙沛², 方缘²

1.中国气象局沈阳大气环境研究所,辽宁沈阳 110016
2.中国气象局气象干部培训学院辽宁分院,辽宁沈阳 110016
3.沈阳区域气候中心,辽宁沈阳 110016
4.中国气象局气象干部培训学院,北京 100081

通讯作者: 李荣平
作者简介:姜鹏（1984年生）,男,高级工程师,博士,主要从事生态与农业气象科研与教学工作。E-mail: jiangpenglnqx@163.com
基金资助:
中国气象局沈阳大气环境研究所联合开放基金课题(2021SYIAEKFMS35);中国气象局气象干部培训学院2021年科研项目(2021-13);2020年度辽宁省气象局博士科研专项(D202003);辽宁省气象局科研项目(BA202003);干旱气象科学研究基金项目(IAM202010)

Abstract

Abstract:

Gross primary productivity (GPP) plays an important role in driving the global carbon sinks. Clarifying the variability of GPP and its impact mechanisms is crucial for predicting the future terrestrial carbon balance. However, those mechanisms are still largely unknown in the literature. In this research, we used the observation data from six eddy covariance sites (including DX, NM, HB, CBS, QYZ and DHS) based on ChinaFLUX to represent three types of ecosystems (forest, grassland, and shrublands). We applied multiple statistical methods (e.g., machine learning) to explore the seasonal variability in GPP and its driving factors. The results showed that the seasonal dynamics of GPP displayed an obviously unimodal seasonal cycle in all of the three ecosystems. The GPP in forest was larger than that in grassland and shurbland. The complete subset regression results showed that the soil water conten (C_SW), leaf area index (LAI), and air temperature (t_a) were positively correlated with GPP in grassland ecosystems, while the vapor pressure deficit (D_vp) had negative effects on GPP. t_a and photosynthetically active radiation (R_PA) played a comparatively more important role in driving the seasonal variability of GPP in forest and shurbland ecosystems. Our random forest and variance partitioning analysis revealed that more than 30% of the seasonal variability in GPP can be explained by C_SW in grassland sites. In the forest and shurbland sites, the relative contribution of t_a or R_PA to GPP was greater than 50%. In addition, water conditions (moisture related factors) significantly influenced GPP in QYZ during seasonal droughts and the contributions of water factors were greater than those in the other forest and shurbland sites. Therefore, the driving factors of the seasonal variability in GPP showed strong ecosystem heterogeneities. Among arid and semi-arid grassland ecosystems, the dominant driver of GPP was water availability, but were temperature and radiation in forest and shurland ecosystems. This study improves the existing understanding of the dynamics of GPP and its driving factors in the three typical biomes in China, and also provides a basis for the accurate simulation of China’s carbon cycle.

Key words: GPP, seasonal variations, influencing factors, typical ecosystems, machine learning, heterogeneous responses

摘要：

总初级生产力（GPP）是陆地生态系统碳汇的重要组分之一,明晰GPP的变化规律及其影响机制,对深入认知生态系统碳循环过程有着重要意义。然而,目前鲜有研究基于多站点通量监测阐明不同类型生态系统GPP的季节变化特征及其主要影响因素。基于ChinaFLUX,沿着东西水热梯度分布的草地样带选择内蒙古温带草原（内蒙古站）、高寒灌丛-草甸（海北站）和高寒草地（当雄站）作为研究对象,沿着南北森林样带选择长白山温带针阔混交林（长白山站）、千烟洲亚热带常绿针叶人工林（千烟洲站）和鼎湖山亚热带常绿针阔混交林（鼎湖山站）作为研究对象,结合机器学习等统计方法,探讨GPP的季节变化特征、主要影响因素以及甄别不同生态系统间的差异性。研究发现,（1）3种典型生态系统GPP的季节动态整体呈单峰分布,且森林生态系统GPP高于草地和灌丛。（2）全子集回归等分析发现,草地生态系统GPP与土壤含水量（C_SW）、叶面积指数（LAI）和气温（t_a）呈正相关,与饱和水汽压差（D_vp）呈负相关,而灌丛和森林生态系统GPP的影响因子主要为t_a和光合有效净辐射（R_PA）,与水分因子的关系较弱。在季节干旱期间,千烟洲站GPP与C_SW和D_vp存在显著的相关关系。（3）方差分解和随机森林结果表明,水分是草地生态系统GPP季节变异的主导因子,其解释比重高于30%;温度是高寒灌丛（海北站）和温带落叶针叶林（长白山）生态系统GPP变异的主导因子,其解释比重接近或高于50%;辐射是亚热带森林生态系统（鼎湖山和千烟洲站）GPP变异的主导因子,其解释比重高于50%。在季节性干旱期间,水分因子对千烟洲站GPP变异的解释比重高于对其他灌丛和森林站点的。因此,在中国不同类型生态系统中,生长季GPP变异的驱动要素存在明显的异质性,其中在干旱和半干旱草地生态系统中,水分（C_SW和D_vp）是GPP变异的主要影响要素,而在森林和灌丛生态系统中,温度或辐射为GPP的主要驱动要素。该研究有助于深入认识GPP的变化趋势及其影响要素,也可为中国碳循环的准确模拟提供数据基础和参数依据。

关键词: GPP, 季节变异, 影响要素, 典型生态系统, 机器学习, 异质性响应

CLC Number:

Q948
X17

JIANG Peng, QIN Mei’ou, LI Rongping, MENG Ying, YANG Feiyun, WEN Rihong, SUN Pei, FANG Yuan. Seasonal Variability of GPP and Its Influencing Factors in the Typical Ecosystems in China[J]. Ecology and Environment, 2022, 31(4): 643-651.

姜鹏, 秦美欧, 李荣平, 孟莹, 杨霏云, 温日红, 孙沛, 方缘. 中国典型生态系统GPP的季节变异及其影响要素[J]. 生态环境学报, 2022, 31(4): 643-651.

Figures/Tables 6

References 38

[1]	BEER C, REICHSTEIN M, TOMELLERI E, et al., 2010. Terrestrial gross carbon dioxide uptake: Global distribution and covariation with climate[J]. Science, 329(5993): 834-838. DOI URL
[2]	BOOTH B-B-B, JONES C-D, COLLINS M, et al., 2012. High sensitivity of future global warming to land carbon cycle processes[J]. Environmental Research Letters, 7(2): 024002. DOI URL
[3]	CHEN N, ZHANG Y J, ZHU J T, et al., 2019. Temperature-mediated responses of carbon fluxes to precipitation variabilities in an alpine meadow ecosystem on the Tibetan Plateau[J]. Ecology and evolution, 9(6237): 9005-9017. DOI URL
[4]	CHEN N, ZHANG Y J, ZHU J T, et al., 2020a. Multiple-scale negative impacts of warming on ecosystem carbon use efficiency across the Tibetan Plateau grasslands[J]. Global Ecology and Biogeography, 30(2): 398-413. DOI URL
[5]	CHEN N, ZHANG Y J, ZU J X, et al., 2020b. The compensation effects of post-drought regrowth on earlier drought loss across the tibetan plateau grasslands[J]. Agricultural and Forest Meteorology, 281: 107822. DOI URL
[6]	CHEN S P, LIN G H, HUANG J H, et al., 2009. Dependence of carbon sequestration on the differential responses of ecosystem photosynthesis and respiration to rain pulses in a semiarid steppe[J]. Global Change Biology, 15(10): 2450-2461. DOI URL
[7]	DENG Y, WANG X H, WANG K, et al., 2021. Responses of vegetation greenness and carbon cycle to extreme droughts in China[J]. Agricultural and Forest Meteorology, 298-299: 108307.
[8]	DING J Z, YANG T, ZHAO Y T, et al., 2018. Increasingly important role of atmospheric aridity on Tibetan alpine grasslands[J]. Geophysical Research Letters, 45(6): 2852-2859. DOI URL
[9]	FU G, SHEN Z X, ZHANG X Z, 2018. Increased precipitation has stronger effects on plant production of an alpine meadow than does experimental warming in the Northern Tibetan Plateau[J]. Agricultural and Forest Meteorology, 249: 11-21. DOI URL
[10]	HE H L, WANG S Q, ZHANG L, et al., 2019. Altered trends in carbon uptake in China's terrestrial ecosystems under the enhanced summer monsoon and warming hiatus[J]. National Science Review, 6(3): 505-514. DOI URL
[11]	JIANG P P, MEINZER F C, WANG H M, et al., 2020. Below-ground determinants and ecological implications of shrub species' degree of isohydry in subtropical pine plantations[J]. New Phytologist, 226(6): 1656-1666. DOI URL
[12]	KIMM H, GUAN K, GENTINE P, et al., 2020. Redefining droughts for the US Corn Belt: The dominant role of atmospheric vapor pressure deficit over soil moisture in regulating stomatal behavior of Maize and Soybean[J]. Agricultural and Forest Meteorology, 287: 107930. DOI URL
[13]	KONINGS A G, GENTINE P, 2017. Global variations in ecosystem-scale isohydricity[J]. Global change biology, 23(2): 891-905. DOI URL
[14]	LI F, PENG Y F, NATALI S M, et al., 2017a. Warming effects on permafrost ecosystem carbon fluxes associated with plant nutrients[J]. Ecology, 98(11): 2851-2859. DOI URL
[15]	LI G Y, HAN H Y, DU Y, et al., 2017b. Effects of warming and increased precipitation on net ecosystem productivity: A long-term manipulative experiment in a semiarid grassland[J]. Agricultural and Forest Meteorology, 232: 359-366. DOI URL
[16]	LI H Q, ZHANG F W, LI Y N, et al., 2016. Seasonal and inter-annual variations in CO₂ fluxes over 10 years in an alpine shrubland on the Qinghai-Tibetan Plateau, China[J]. Agricultural and Forest Meteorology, 228-229: 95-103. DOI URL
[17]	LI P, SAYER E J, JIA Z, et al., 2020. Deepened winter snow cover enhances net ecosystem exchange and stabilizes plant community composition and productivity in a temperate grassland[J]. Global change biology, 26(5): 3015-3027. DOI URL
[18]	LI X Y, LI Y, CHEN A P, et al., 2019. The impact of the 2009/2010 drought on vegetation growth and terrestrial carbon balance in Southwest China[J]. Agricultural and Forest Meteorology, 269-270: 239-248. DOI URL
[19]	LIU R, CIERAAD E, LI Y, et al., 2016. Precipitation pattern determines the inter-annual variation of herbaceous layer and carbon fluxes in a phreatophyte-dominated desert ecosystem[J]. Ecosystems, 19(4): 601-614. DOI URL
[20]	MARTÍNEZ-VILALTA J, POYATOS R, AGUADÉ D, et al., 2014. A new look at water transport regulation in plants[J]. New phytologist, 204(1): 105-115. DOI URL
[21]	MATHENY A M, BOHRER G, STOY P C, et al., 2014. Characterizing the diurnal patterns of errors in the prediction of evapotranspiration by several land-surface models: An NACP analysis[J]. Journal of Geophysical Research: Biogeosciences, 119(7): 1458-1473. DOI URL
[22]	MONTEITH J L, 1995. A reinterpretation of stomatal responses to humidity[J]. Plant, Cell & Environment, 18(4): 357-364.
[23]	PENG S S, PIAO S L, SHEN Z H, et al., 2013. Precipitation amount, seasonality and frequency regulate carbon cycling of a semi-arid grassland ecosystem in Inner Mongolia, China: A modeling analysis[J]. Agricultural and Forest Meteorology, 178-179: 46-55. DOI URL
[24]	SULMAN B N, ROMAN D T, YI K, et al., 2016. High atmospheric demand for water can limit forest carbon uptake and transpiration as severely as dry soil[J]. Geophysical Research Letters, 43(18): 9686-9695. DOI URL
[25]	WANG X F, MA M G, HUANG G H, et al., 2012. Vegetation primary production estimation at maize and alpine meadow over the Heihe River Basin, China[J]. International Journal of Applied Earth Observation and Geoinformation, 17(7): 94-101. DOI URL
[26]	WOHLFAHRT G, HAMMERLE A, HASLWANTER A, et al., 2008. Seasonal and inter-annual variability of the net ecosystem CO₂ exchange of a temperate mountain grassland: Effects of weather and management[J]. Journal of Geophysical Research: Atmospheres, 113(D8): D08110.
[27]	XU M J, WANG H M, WEN X F, et al., 2017. The full annual carbon balance of a subtropical coniferous plantation is highly sensitive to autumn precipitation[J]. Scientific Reports, 7(1): 10025. DOI URL
[28]	YU G R, ZHANG L M, SUN X M, et al., 2008. Environmental controls over carbon exchange of three forest ecosystems in eastern China[J]. Global Change Biology, 14(11): 2555-2571. DOI URL
[29]	ZHANG T, ZHANG Y J, XU M J, et al., 2016. Ecosystem response more than climate variability drives the inter-annual variability of carbon fluxes in three Chinese grasslands[J]. Agricultural and Forest Meteorology, 225: 48-56. DOI URL
[30]	ZHANG T, ZHANG Y J, XU M J, et al., 2018. Water availability is more important than temperature in driving the carbon fluxes of an alpine meadow on the Tibetan Plateau[J]. Agricultural and Forest Meteorology, 256-257: 22-31. DOI URL
[31]	ZHU J T, ZHANG Y J, JIANG L, 2017. Experimental warming drives a seasonal shift of ecosystem carbon exchange in Tibetan alpine meadow[J]. Agricultural and Forest Meteorology, 233: 242-249. DOI URL
[32]	ZU J X, ZHANG Y J, HUANG K, et al., 2018. Biological and climate factors co-regulated spatial-temporal dynamics of vegetation autumn phenology on the Tibetan Plateau[J]. International Journal of Applied Earth Observation and Geoinformation, 69: 198-205. DOI URL
[33]	柴曦, 李英年, 段呈, 等, 2018. 青藏高原高寒灌丛草甸和草原化草甸CO₂通量动态及其限制因子[J]. 植物生态学报, 42(1): 6-19. DOI
	CHAI X, LI Y N, DUAN C, et al., 2018. CO₂ flux dynamics and its limiting factors in the alpine shrub-meadow and steppe-meadow on the Qinghai-Xizang Plateau[J]. Chinese Journal of Plant Ecology, 42(1): 6-19. DOI
[34]	耿晓东, 旭日, 魏达, 2017. 多梯度增温对青藏高原高寒草甸温室气体通量的影响[J]. 生态环境学报, 26(3): 445-452.
	GENG X D, XU R, WEI D, 2017. Response of greenhouse gases flux to multi-level warming in an alpine meadow of Tibetan Plateau[J]. Ecology and Environmental Sciences, 26(3): 445-452.
[35]	李夏子, 郭春燕, 韩国栋, 2013. 气候变化对内蒙古荒漠化草原优势植物物候的影响[J]. 生态环境学报, 22(1): 50-57.
	LI X Z, GUO C Y, HAN G D, 2013. Impacts of climate change on phenological phases of dominant grass species in the desert steppe in Inner Mongolia[J]. Ecology and Environmental Sciences, 22(1): 50-57.
[36]	孟莹, 姜鹏, 方缘, 2020. 大气水分亏缺对中国两种典型草地生态系统总初级生产力的影响[J]. 生态学杂志, 39(11): 99-108.
	MENG Y, JIANG P, FANG Y, 2020. Contrasting impacts of vapor pressure deficit on gross primary productivity in two typical grassland ecosystems in China[J]. Chinese Journal of Ecology, 39(11): 99-108.
[37]	尹茜茜, 乐旭, 周浩, 等, 2020. 全球FLUXNET站点总初级生产力的年际变化及其主导因子解析[J]. 大气科学学报, 43(6): 1106-1114.
	YIN X X, YUE X, ZHOU H, et al., 2020. Interannual variability of gross primary productivity at global FLUXNET sites and its driving factors[J]. Transactions of Atmospheric Sciences, 43(6): 1106-1114.
[38]	于贵瑞, 张雷明, 孙晓敏, 2014. 中国陆地生态系统通量观测研究网络 (ChinaFLUX) 的主要进展及发展展望[J]. 地理科学进展, 33(7): 903-917. DOI
	YU G R, ZHANG L M, SUN X M, 2014. Progresses and prospects of Chinese terrestrial ecosystem flux observation and research network (ChinaFLUX)[J]. Progress in Geography, 33(7): 903-917.

参数 Parameters	站点 Sites
参数 Parameters	长白山站 CBS station	千烟洲站 QYZ station	鼎湖山站 DHS station	内蒙古站 NM station	当雄站 DX station	海北站 HB station
位置 Location	42°24′N, 128°05′E	26°44′N, 115°03′E	23°10′N, 112°34′E	43°38′N, 116°42′E	30°41′N, 91°01′E	37°36′N, 101°19′E
海拔 Elevation/m	738.00	102.00	300.00	1100.00	4333.00	3200.00
植被类型 Ecosystem types	温带针阔混交林	亚热带常绿针叶人工林	亚热带常绿阔叶林	温带草原	高寒草甸	高寒灌丛-草甸
雨热是否同步 Asynchronous	是	否	是	是	是	是
树冠高度 h_(canopy)/m	26.00	12.00	20.00	0.45	0.05	0.60/0.05
土壤有机质质量分数 w_{(soil organic matter)}/(g∙kg^-1)	62.50	21.40	47.4	14.30	18.50	106.7

参数 Parameters	站点 Sites
参数 Parameters	长白山站 CBS station	千烟洲站 QYZ station	鼎湖山站 DHS station	内蒙古站 NM station	当雄站 DX station	海北站 HB station
位置 Location	42°24′N, 128°05′E	26°44′N, 115°03′E	23°10′N, 112°34′E	43°38′N, 116°42′E	30°41′N, 91°01′E	37°36′N, 101°19′E
海拔 Elevation/m	738.00	102.00	300.00	1100.00	4333.00	3200.00
植被类型 Ecosystem types	温带针阔混交林	亚热带常绿针叶人工林	亚热带常绿阔叶林	温带草原	高寒草甸	高寒灌丛-草甸
雨热是否同步 Asynchronous	是	否	是	是	是	是
树冠高度 h_(canopy)/m	26.00	12.00	20.00	0.45	0.05	0.60/0.05
土壤有机质质量分数 w_{(soil organic matter)}/(g∙kg^-1)	62.50	21.40	47.4	14.30	18.50	106.7

站点 Sites	参数 Parameters	t_a	D_vp	R_PA	C_SW	LAI
DX站 DX Station	回归系数	—	-0.27^**	—	5.53^***	0.53^***
	相对权重	—	20.75	—	63.58	15.66
	调整r²	0.48
NM站 NM Station	回归系数	0.14^***	-0.66^***	—	15.34^***	1.09^***
	相对权重	25.59	6.19	—	35.59	32.63
	调整r²	0.56
HB站 HB Station	回归系数	0.35^***	—	0.03^***	—	0.86^***
	相对权重	47.26	—	14.48	—	38.25
	调整r²	0.65
CBS站 CBS Station	回归系数	0.48^***	-1.37^***	0.01^***	-1.90^**	0.27^***
	相对权重	58.85	2.40	12.70	2.68	23.37
	调整r²	0.70
QYZD站 QYZD Station	回归系数	0.10^***	-2.13^***	0.01^***	8.88^***	—
	相对权重	22.62	14.34	59.63	3.41	—
	调整r²	0.69
QYZN站 QYZN Station	回归系数	0.21^***	—	0.01^***	4.23^***	—
	相对权重	45.52	—	53.15	1.42	—
	调整r²	0.84
DHSD站 DHSD Station	回归系数	0.10^***	—	0.01^***	—	—
	相对权重	35.54	—	64.46	—	—
	调整r²	0.65
DHSW站 DHSW Station	回归系数	0.04^***	—	0.01^***	—	—
	相对权重	21.95	—	78.05	—	—
	调整r²	0.55

站点 Sites	参数 Parameters	t_a	D_vp	R_PA	C_SW	LAI
DX站 DX Station	回归系数	—	-0.27^**	—	5.53^***	0.53^***
	相对权重	—	20.75	—	63.58	15.66
	调整r²	0.48
NM站 NM Station	回归系数	0.14^***	-0.66^***	—	15.34^***	1.09^***
	相对权重	25.59	6.19	—	35.59	32.63
	调整r²	0.56
HB站 HB Station	回归系数	0.35^***	—	0.03^***	—	0.86^***
	相对权重	47.26	—	14.48	—	38.25
	调整r²	0.65
CBS站 CBS Station	回归系数	0.48^***	-1.37^***	0.01^***	-1.90^**	0.27^***
	相对权重	58.85	2.40	12.70	2.68	23.37
	调整r²	0.70
QYZD站 QYZD Station	回归系数	0.10^***	-2.13^***	0.01^***	8.88^***	—
	相对权重	22.62	14.34	59.63	3.41	—
	调整r²	0.69
QYZN站 QYZN Station	回归系数	0.21^***	—	0.01^***	4.23^***	—
	相对权重	45.52	—	53.15	1.42	—
	调整r²	0.84
DHSD站 DHSD Station	回归系数	0.10^***	—	0.01^***	—	—
	相对权重	35.54	—	64.46	—	—
	调整r²	0.65
DHSW站 DHSW Station	回归系数	0.04^***	—	0.01^***	—	—
	相对权重	21.95	—	78.05	—	—
	调整r²	0.55

Seasonal Variability of GPP and Its Influencing Factors in the Typical Ecosystems in China

中国典型生态系统GPP的季节变异及其影响要素

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[3]	ZHANG Lin, QI Shi, ZHOU Piao, WU Bingchen, ZHANG Dai, ZHANG Yan. Study on Influencing Factors of Soil Organic Carbon Content in Mixed Broad-leaved and Coniferous Forests Land in Beijing Mountainous Areas [J]. Ecology and Environment, 2023, 32(3): 450-458.
[4]	HE Yanhu, GONG Zhenjie, WU Haibin, CAI Yanpeng, YANG Zhifeng, CHEN Xiaohong. Spatiotemporal Evolution of Urban Eco-efficiency and Its Influencing Factors in Guangdong-Hong Kong-Macao Greater Bay Area [J]. Ecology and Environment, 2023, 32(3): 469-480.
[5]	HAO Jinhu, WEI Wei, LI Shengnan, MA Muyuan, LI Xiaoxia, YANG Hongguo, JIANG Qiyu, CHAI Peidong. GEE Based Evaluation of the Spatial-temporal Pattern and Drivers of Long-term Water Body in Beijing-Tianjin-Hebei [J]. Ecology and Environment, 2023, 32(3): 556-566.
[6]	ZHANG Li, LI Cheng, TAN Haoze, WEI Jiayi, CHENG Jiong, PENG Guixiang. Reduction Effect and Influencing Factors of Typical Urban Woodlands on Atmospheric Particulate Matter in Guangzhou [J]. Ecology and Environment, 2023, 32(2): 341-350.
[7]	YUAN Linjiang, LI Mengbo, LENG Gang, ZHONG Bingbing, XIA Dapeng, WANG Jinghua. Synergistic Effect of Sulfate Reduction and Ammonia Oxidation in Anaerobic Environment [J]. Ecology and Environment, 2023, 32(1): 207-214.
[8]	SU Yongsong, SONG Song, CHEN Ye, YE Ziqiang, ZHONG Runfei, WANG Zhaoyao. Temporal and Spatial Characteristics of Net Anthropogenic Nitrogen Input and Its Influencing Factors in the Pearl River Delta [J]. Ecology and Environment, 2022, 31(8): 1599-1609.
[9]	ZHAO Anzhou, TIAN Xinle. Spatiotemporal Evolution and Influencing Factors of Vegetation Coverage in the Loess Plateau from 1986 to 2021 Based on GEE Platform [J]. Ecology and Environment, 2022, 31(11): 2124-2133.
[10]	LI Liangliang, DAI Liangyu, GAO Weichang, ZHANG Shuyi, LIU Taoze. The Occurrence Characteristics and Influencing Factors of Residual Mulching Film of Typical Farmland with Plastic Film in Guizhou Province [J]. Ecology and Environment, 2022, 31(11): 2189-2197.
[11]	LI Shengzeng, HAO Saimei, TAN Luyao, ZHANG Huaicheng, XU Biao, GU Shumao, PAN Guang, WANG Shuyan, YAN Huaizhong, ZHANG Guiqin. Characteristics of Spatiotemporal Variation, and Factors Influencing Secondary Components in PM_2.5 in Ji'nan [J]. Ecology and Environment, 2022, 31(1): 100-109.
[12]	LI Shaoning, TAO Xueying, LI Xiuhong, ZHAO Na, XU Xiaotian, LU Shaowei. Research Progress of Beneficial Biogenic Volatile Organic Compounds Released from Plants [J]. Ecology and Environment, 2022, 31(1): 187-195.
[13]	CAI Yang, LI Wei, ZUO Xueyan, CUI Lijuan, LEI Yinru, ZHAO Xinsheng, ZHAI Xiajie, LI Jing, PAN Xu. Distribution Characteristics and Influencing Factors of PAHs in Yancheng Coastal Wetland Soil [J]. Ecology and Environment, 2021, 30(6): 1249-1259.
[14]	TIAN Yichao, YANG Tang, XU Xin. Temporal and Spatial Distribution Characteristics and Influencing Factors of Net Primary Productivity of Vegetation in Typical Basin Entering the Sea in Beibu Gulf [J]. Ecology and Environment, 2021, 30(5): 938-948.