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海南岛甲烷浓度空间异质性规律解析

  • 刘嘉慧1

    机 构：

    1. 海南大学 生态与环境学院,海口, 570228

    ×
  • ，刘应帅1

    机 构：

    1. 海南大学 生态与环境学院,海口, 570228

    ×
  • ，余瑞1

    机 构：

    1. 海南大学 生态与环境学院,海口, 570228

    ×
  • ，董金玮2,3

    机 构：

    2. 中国科学院地理科学与资源研究所/中国科学院陆地表层格局与模拟重点实验室,北京, 100101

    3. 中国科学院大学,北京, 100049

    ×
  • ，陈明星2,3

    机 构：

    2. 中国科学院地理科学与资源研究所/中国科学院陆地表层格局与模拟重点实验室,北京, 100101

    3. 中国科学院大学,北京, 100049

    ×
  • ，岳天祥2,3

    机 构：

    2. 中国科学院地理科学与资源研究所/中国科学院陆地表层格局与模拟重点实验室,北京, 100101

    3. 中国科学院大学,北京, 100049

    ×
  • ，邓祥征2,3

    机 构：

    2. 中国科学院地理科学与资源研究所/中国科学院陆地表层格局与模拟重点实验室,北京, 100101

    3. 中国科学院大学,北京, 100049

    ×

1. 海南大学 生态与环境学院,海口, 570228；
2. 中国科学院地理科学与资源研究所/中国科学院陆地表层格局与模拟重点实验室,北京, 100101；
3. 中国科学院大学,北京, 100049；

Spatial heterogeneity of methane concentration in Hainan Island

  • LIU Jiahui1

    Affiliation：

    1. College of Ecology and Environment,Hainan University,Haikou 570228

    ×
  • ，LIU Yingshuai1

    Affiliation：

    1. College of Ecology and Environment,Hainan University,Haikou 570228

    ×
  • ，YU Rui1

    Affiliation：

    1. College of Ecology and Environment,Hainan University,Haikou 570228

    ×
  • ，DONG Jinwei2,3

    Affiliation：

    2. Key Laboratory of Land Surface Pattern and Simulation/Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101

    3. University of Chinese Academy of Sciences,Beijing 100049

    ×
  • ，CHEN Mingxing2,3

    Affiliation：

    2. Key Laboratory of Land Surface Pattern and Simulation/Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101

    3. University of Chinese Academy of Sciences,Beijing 100049

    ×
  • ，YUE Tianxiang2,3

    Affiliation：

    2. Key Laboratory of Land Surface Pattern and Simulation/Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101

    3. University of Chinese Academy of Sciences,Beijing 100049

    ×
  • ，DENG Xiangzheng2,3

    Affiliation：

    2. Key Laboratory of Land Surface Pattern and Simulation/Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101

    3. University of Chinese Academy of Sciences,Beijing 100049

    ×

1. College of Ecology and Environment,Hainan University,Haikou 570228；
2. Key Laboratory of Land Surface Pattern and Simulation/Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101；
3. University of Chinese Academy of Sciences,Beijing 100049；

作者简介:

刘嘉慧,女,硕士生,研究方向为生态遥感.jhliu666@outlook.com;

余瑞(通信作者),男,博士,副教授,研究方向为生态环境遥感与复杂系统建模.yur@outlook.com

中图分类号:Q148

文献标识码:A

DOI:10.13878/j.cnki.jnuist.2022.04.013

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目录contents

    摘要

    温室气体排放升高对全球气候变暖有重要影响,其浓度分布的空间异质性及与局地气候变化和社会经济的关系是影响温室气体减排策略制定的关键因素.海南岛是国家生态文明建设的试验区,温室气体减排方面也面临着目标考核的压力与责任.本研究使用GHGSat 的CH4浓度产品、气温、降雨、GDP和人口数据集,采用Theil-Sen趋势分析、Mann-Kendall(MK)检验构建地理加权回归(GWR)模型,分析了2020—2021年海南岛CH4浓度变化趋势及规律.结果表明:1)时空尺度上,2020—2021年海南岛CH4浓度整体呈下降趋势,CH4浓度均值为1848.40×10-9 mol/mol,8—11月的CH4浓度低于均值,其余月份均值皆高于均值;海南岛46.42%的区域CH4浓度呈显著减小趋势,主要集中在海南岛的西部及东部地区.2)CH4浓度时空分布的异质性规律方面,GWR模拟结果显示,海南岛CH4浓度与气温回归系数值分布于-114.92~127.80,CH4浓度与降雨回归系数分布集中于-297.40~399.91,而CH4浓度与GDP和人口的回归系数较高,分别集中于-4125.55~4509.07和-1751.43~1556.41,GWR能够揭示像元尺度上CH4浓度分布对气候与社会经济因子的响应规律.3)拟合方法选择上,基于普通最小二乘法(OLS)与GWR方法对CH4浓度与气温、降雨、GDP和人口的拟合结果的R 2分别为0.14和0.83,表明海南岛CH4浓度分布受气候和社会经济的局地效应影响显著,GWR方法在解释CH4浓度分布影响因素方面比OLS的拟合效果更优.本研究揭示了海南岛CH4浓度变化趋势及其空间变异规律,能够为海南CH4浓度时空分布的动态评估提供方法参考.

    Abstract

    Increasing greenhouse gas emissions has become an important factor affecting global warming.The spatial heterogeneity of CH4 concentration and its relationship with local climate and social economy are important factors affecting the ecological policy formulation.Greenhouse gas emission reduction is a challenging and imperative task for Hainan Island,which is a pilot zone for the national ecological protection drive.Here,the trend and driving factors of CH4 concentration in Hainan Island during 2020-2021 were analyzed through the Geographically Weighted Regression (GWR) model,which was established by using GHGSat CH4 product,temperature,rainfall,GDP and population datasets,as well as Theil-Sen trend analysis and Mann-Kendall test.The results showed a slight decreasing trend of CH4 concentration in Hainan Island from 2020 to 2021 with the mean value of 1848.40×10-9 mol/mol;as for its spatial distribution,no obvious variation in 52.95% areas and a significant decrease in 46.42% areas were observed in Hainan Island.In addition,the GWR can reveal the response of CH4 concentration distribution to climate factors on pixel scale,specifically,the regression coefficients between CH4 concentration and temperature ranged from -114.92 to 127.80,and those between CH4 concentration and rainfall ranged from -297.40 to 399.91;the regression coefficients of CH4 concentration with GDP and population were relatively high,ranged at -4125.55-4509.07 and -1751.43-1556.41,respectively,suggesting possible big impacts of GDP and population on CH4 concentration distribution.The R 2 of Ordinary Least Squares (OLS) and GWR for CH4 concentration with temperature,rainfall,GDP and population were 0.14 and 0.83 respectively,indicating that GWR model outperformed the OLS model in explaining the spatial pattern of CH4 concentration.This study revealed the trend and driving factors of CH4 concentration in Hainan Island,thus provides a method guidance for evaluation of the dynamic CH4 concentration in Hainan.

  • 0 引言

  • 近来年,大气中甲烷(CH4)浓度的趋势与动态研究已有不少报道,但其空间分布特征及影响因素仍需探究.CH4是大气成分中体积占比仅次于二氧化碳(CO2)的温室气体,占温室气体总含量的20%以上,其对气候变化的重要性仅次于二氧化碳[1].1999—2006年,全球大气中CH4浓度的增长率几乎为零[2];2007后,大气中的CH4浓度逐渐增长[3].Nisbet等[4]的研究表明,2014—2019年期间全球大气CH4浓度增加率分别为(12.7±0.5)×10-9、(10.1±0.7)×10-9、(7.0±0.7)×10-9、(6.9±0.7)×10-9、(8.7±0.5)×10-9和 (10.0±0.5)×10-9(mol/mol)/a.根据世界气象组织(WMO)的全球大气观测数据的最新分析结果显示[5],现场观测计算的全球平均甲烷浓度在2020年达到(1 889±2)×10-9 mol/mol的新高,比2019年增加了11×10-9 mol/mol.CH4的产生不仅受生物因素影响,还受到非生物因素如气候变化的作用.CH4的增加使得全球变暖加速,进而导致CH4以更快的速度产生[6].但目前对于非生物因素如气候对CH4浓度分布特征的影响还缺乏了解,CH4浓度新一轮增长的驱动力及其成因仍在争论中[7].

  • 气温和水分作为生命活动的主要限制气候因子,其变化与CH4浓度的时空分布联系紧密[8].气温升高影响土壤中缺氧区的泥炭含量,使质量较差的泥炭进入土壤缺氧区,CH4的产量随之减少[9],从而导致局地CH4的浓度下降.Granberg等[10]在德格罗斯托米尔草地覆盖度较高处,发现气温升高对于大气中的CH4浓度具有正向的影响,即高温会导致CH4浓度上升,但在附近的草地覆盖度较低的地方,气温升高对CH4浓度没有显著影响;而当降雨量增加时,草地生态系统CH4的吸收总量减少了61%,导致大气中CH4浓度升高,这种现象和程度也恰好与森林生态系统相似[11-12].然而,在哈佛森林的展望山,降雨对大气中CH4浓度的影响很小,微乎其微甚至接近于没有影响[13-14].由此可见,CH4浓度对降雨的响应也有区域差异性,如Billings等[11]在阿拉斯加两个内陆森林中对土壤浓度进行测量时发现,当降雨量较少时,相对干燥的森林大气中CH4 浓度较低,但在距此森林仅10km的相对潮湿的森林处,较少的降雨量又会导致此处的CH4 浓度较高.

  • 进一步地,表征人类活动强度的社会经济因素也会影响区域CH4浓度分布的空间异质性.Grossman等[15]提出环境库兹涅茨曲线EKC(Environmental Kuznets Curve)假说,认为经济增长与环境污染之间存在一种“倒U”型关系;Adeel-Farooq等[16]在此假说的基础上分析1985—2012年东南亚国家联盟(ASEAN)六国CH4浓度与经济增长关系,发现经济增长会导致CH4浓度增加.然而,不同区域CH4浓度分布对社会经济具有不同的响应特征,且即使在同一地区CH4浓度对社会经济的响应也有所差异.Zoundi[17]和Mert等[18]的研究发现GDP的增加导致区域CH4浓度有所减少.除此之外,人口的数量很大程度上决定了该地区的人类活动.Polag等[19]认为在小区域范围内,人口数量对整个区域CH4浓度产生重大影响,而在某些CH4浓度主要来源于自然和农业的地区,人口数量对区域CH4浓度的影响就极其微小.一般的线性回归模型在拟合CH4 浓度的空间分布时,未能有效考虑上述气候因子与社会经济活动的空间分布对CH4 浓度的局地效应.基于地理学第一定律而提出的地理加权回归(Geographically Weighted Regression,GWR)模型方法,考虑了空间关系的异质性和地理位置对于变量的局地效应,能更为准确地评估潜在的空间差异.探究区域CH4浓度演变趋势的主要驱动因素方面,GWR能更加直观地刻画地理关系的非平稳特征,并能反映出不同自变量的空间估计参数,且进行统计假设检验[20-22].本研究使用Theil-Sen趋势分析、Mann-Kendall(MK)检验,评估了2020—2021年海南岛甲烷的时空演变趋势;通过对甲烷的局部Moran's I分析,确定甲烷的空间自相关性;随后以近两年的海南岛CH4浓度均值作为解释变量,而气候与社会经济因子为自变量构建GWR模型,探究海南岛CH4浓度分布的时空异质性规律.

  • 1 研究地区与研究方法

  • 1.1 数据来源

  • 海南岛是我国第二大岛屿,其气候类型为典型的热带海岛季风气候,全年平均气温在22~26℃之间,年均降雨量达1 600mm[23].本研究所使用的甲烷数据来源于温室气体卫星GHGSat (https://pulse.ghgsat.com)的甲烷月合成数据产品.GHGSat目前有三颗用于单站测量温室气体排放的在轨气体传感卫星,以2km的高空间分辨率对大气中的CH4浓度实行以每月均值为基础的每周监测和更新.基于前述气候变化因子与CH4浓度空间分布的影响机制,本研究选取2020年4月至2021年11月的CH4浓度数据,解析海南岛CH4浓度空间分布规律及影响因素,该数据集经相关项目的前期研究做了系统整理[24-25].气温和降雨数据来源于国家科技基础条件平台——国家地球系统科学数据中心黄土高原分中心(http://loess.geodata.cn),该数据集是根据CRU发布的全球0.5°气候数据集以及WorldClim发布的全球高分辨率气候数据集,通过Delta空间降尺度方案在中国地区降尺度生成的,并且使用496个独立气象观测点数据进行验证,整体精度较高.GDP和人口分布数据集来源于中国科学院地理科学与资源研究所资源环境科学与数据中心(http://www.resdc.cn),该数据集为1km网格的空间分布数据,运用栅格空间计算反映出GDP数据在全国范围内的详细空间分布状况和每平方千米网格范围内的人口数.

  • 1.2 研究方法

  • 1.2.1 Theil-Sen趋势分析和MK检验

  • 本研究使用Sen趋势和MK检验对2020—2021年海南岛CH4浓度进行逐网格化的分析.Sen趋势分析是一种对序列进行趋势性描述的非参数统计计算方法,此方法能有效地增强抗噪性,计算公式为

  • β=mdian⁡yj-yij-i,∀i<j
    (1)

  • 式中:median为CH4浓度中位数;yi、yj分别为时间序列第i年和第j年的CH4 浓度值,i,j的取值范围为[1,n],n为时间序列长度;β指计算n(n-1)/2个数据组合的斜率的中位数,β＞0表示序列为上升趋势,β＜0表示下降趋势.

  • MK方法是一种非参数统计检验方法,常用于时间序列数据的趋势检验中.该方法不但可以检验时间序列的变化趋势,还可以检验时间序列是否发生了突变.对于时间序列CH4,MK趋势检验的统计量如下:

  • S=∑i=1n-1 ∑j=i+1n sign⁡yj-yi
    (2)

  • 式中,sign是符号函数,其定义如下:

  • sign⁡yj-yi=1, yj-yi>00, yj-yi=0-1, yj-yi<0
    (3)

  • 统计量S的方差为

  • Var⁡(S)=n(n-1)(2n+5)18
    (4)
  • Zc=S-1Var⁡(S), S>0,0, S=0,S+1Var⁡(S), S<0,
    (5)

  • Zc值用于趋势显著性检验,其中Z 1-α/2为标准正态方差,α为显著性检验水平.不同置信度99%、95%、90%所对应的Z 1-α/2值不同,分别为2.576、1.96、1.645,如当| Zc |＞1.96,表示在α=0.05水平下变化显著.结合Sen趋势分析的β值,β＞0,| Zc |＞1.96,表示显著增加;β＜0,| Zc|＞1.96,表示显著减少.

  • 1.2.2 地理加权回归

  • 本研究使用地理加权回归(GWR)模型,分析海南岛CH4浓度变化的驱动因素.GWR是Brunsdon等[20]基于地理学第一定律提出的局部回归分析方法.较传统的全局回归模型,GWR模型以局部抽样点为基础进行分析解算,将数据的空间位置信息嵌入回归参数,揭示随空间位置变化的参数估计量的空间异质性,其模型的表达式如下:

  • y-i=β0ui,vi+∑k=1m βkui,vixik+εi
    (6)

  • 式中, y-i为CH4浓度多年的平均值,xik 为自变量的值,在本研究中为多年的平均气温和多年的平均降雨量, ui,vi 为回归点i的坐标, β0ui,vi为常数项, βkui,vi为点i的回归系数,εi为随机误差.

  • 关于模型参数的估计,GWR是根据所有观测结果对回归点i的空间邻近度进行加权计算的.距离回归点近的拥有较大的权重,对局部参数估计的影响也较大.可通过以下方程求解:

  • β^ui,vi=XTWui,viX-1XTWui,viy
    (7)

  • 式中, β^ui,vi 为βkui,vi的无偏估计,X为自变量抽样矩阵,W为空间权重矩阵.根据地理学第一定律“距离越近,空间权重越高”,可通过任意值域为[0,1]关于空间位置单调减函数进行权重计算,称为核函数(Kernel Function).本研究用二次(Bi-square)核函数,计算公式如下:

  • wij=1-dij/b22, dij⩽b,0, 其他
    (8)

  • 式中,dij为点j与回归点i的空间距离或邻近度量,b为带宽(Bandwidth).带宽是确定空间权重wij的重要参数,带宽的确定也是GWR模型解算的必要程序.带宽的估算有三种方法:交叉验证法(Cross Validation,CV)、赤池信息准则法(Akachi Information Criterion,AIC)和自定义方法(Bandwidth_Parameter).AIC法相较另外两种方法更能选出最优带宽,其表达式如下:

  • AICc=2nln⁡(σ^)+nln⁡(2π)+nn+tr⁡(S)n-2-tr⁡(S)
    (9)

  • 式中,AICc 是校正的AIC值, σ^为残差的标准差估计,tr(S) 为帽子矩阵S的迹,是关于带宽b的函数.当AIC值最小时的带宽即为最优带宽.

  • 2 结果与分析

  • 2.1 海南岛CH4浓度、气候与社会经济因子的空间异质性

  • 2020—2021年海南岛CH4浓度的空间分布和空间变化趋势如图1所示.CH4浓度的月均值为(1 712~1 900)×10-9 mol/mol,数值主要集中在(1 824~1 848)×10-9 mol/mol和(1 848~1 871)×10-9mol/mol.中部地区CH4浓度范围为(1 824~1 848)×10-9 mol/mol,该区域占海南岛全岛面积比为28.39%,但GHGSat在中部山区,如五指山国家自然保护区、霸王岭国家自然保护区等区域没有CH4浓度数据;中部地区与沿海地区的交接处,CH4浓度数值主要集中在(1 848~1 871)×10-9 mol/mol,此区域占全岛面积比为27.82%;CH4浓度较高的区域是海南岛的北部和东西沿海区域,浓度范围为(1 878~1 900)×10-9 mol/mol,此区域占全岛面积比为22.94%.通过Sen趋势对海南岛CH4浓度数据序列进行逐像素点的趋势性分析,Zc值大于1.96的像素点表示其具有显著变化,Zc值小于或等于1.96的像素点表示无显著变化.结果发现,仅0.63%的区域CH4浓度呈显著增加趋势,且零星分布于西部少数地区;约46.42%的区域CH4浓度呈显著减小趋势,主要分布于海南岛的西部及东部地区;约52.95%的区域CH4浓度无明显变化,并杂乱分布于海南岛各个市县及沿海地区.总体来说,海南岛CH4浓度的空间变化趋势的异质性明显,CH4浓度整体呈现显著减少和无明显变化.在气候因子的空间分布方面,海南岛年均气温的空间分布主要呈现南高北低的状态.三亚市位于海南岛最南端,此地气温常年处于全岛最高,而海口市由于城市效应,气温与其他地区相比也较高,中部地区由于遍布热带雨林如尖峰岭、吊罗山等,此处气温与其他地区相比较低.海南岛的降雨则与气温的空间分布不同,呈现出北部地区高、西部地区低的状态,降雨主要集中在东北部地区,且这些地区的降雨量与西南地区的降雨量具有较大的差值.在社会经济的空间分布方面,海南岛的GDP和人口呈现出较为相似的空间分布,海口市作为海南岛的省会城市,承载大量的人口数量,GDP和人口数值为全岛最高,且远远高于其他地区.

  • 图1 2020—2021年海南岛CH4浓度(a)、气温(b)、降雨(c)、GDP(d)和人口(e)空间分布及CH4浓度变化趋势(f)

  • Fig.1 Spatial distribution of CH4 concentration (a),temperature (b),rainfall (c),GDP (d),population (e) and trend of CH4 concentration (f) in Hainan Island during2020-2021

  • 2.2 海南岛CH4浓度分布的空间异质性拟合

  • 将2020—2021年的CH4浓度均值作为因变量,平均气温、降雨、GDP和人口值作为自变量,利用普通最小二乘法(OLS)进行全局回归分析,结果如表1所示.CH4浓度与这4个自变量的相关系数值P ＜0.01,且气温和人口的回归系数为正值,降雨和GDP的回归系数为负值,表明气温和人口与CH4浓度存在正相关关系,而降雨和GDP与CH4浓度存在负相关关系,但绝对值相差较小,表明这4个变量与CH4浓度的线性关系不显著,且模型调整后的R2仅为0.14,表明模型整体拟合度较差,利用该模型不能拟合气温、降雨、GDP和人口对CH4浓度的影响.由于OLS方法得出的R2较小,无法求证气温、降雨、GDP和人口这4个自变量与CH4浓度的关系,本文利用GWR模型,以局部抽样点的方式,将多月的平均气温、降雨、GDP和人口作为自变量,海南岛多月平均CH4浓度作为因变量,分析海南岛CH4浓度与局地气温、降雨、GDP和人口的关系.GWR模型结果显示AICc值为209 545.1,AICc值最小时的带宽为最优带宽,回归系数与全局的OLS模型相比,R2达到了0.83,拟合效果提升了0.69,GWR模型在海南岛CH4浓度的空间变异规律分析的解释上要优于OLS模型.

  • 表1 OLS模型和GWR模型结果

  • Table1 Results of OLS model and GWR model

  • 图2 2020—2021年海南岛CH4浓度局部莫兰指数(a)和GWR模型拟合结果标准化残差的莫兰指数(b)

  • Fig.2 Local Moran index of CH4 concentration in Hainan Island during2020-2021 (a) and Moran index of normalized residual of GWR model fitting results (b)

  • CH4浓度由于受到相邻像元之间交互和空间扩散作用的影响,相邻地区之间的CH4浓度存在较强的空间依赖关系.针对具有空间异质性的海南岛CH4浓度,本研究使用GeoDa对其进行空间自相关性检验,得到单变量局部Moran's I散点图(图2a),可见CH4浓度在4个象限都有分布,主要集中在第二、第三象限,第二象限为平面区域的低—高型聚集区,第三象限为平面区域的低—低型聚集区,从局部回归的角度来看,CH4浓度较低的区域在空间上更易聚集.从差异的角度来看,第一、第三象限皆有分布,整体呈现出高高、低低的聚集分布状态,说明CH4浓度的空间差异较小,且Moran's I为0.71,说明CH4浓度相似的属性聚集在一起,具有显著的空间正自相关性.图2b显示了空间栅格点标准化残差的Moran's I检验结果,Moran's I值为-0.004,接近于0,说明标准化残差在空间上服从随机分布,同时也说明GWR模型适用于海南岛CH4时空演变的分析.

  • 2.3 海南岛CH4浓度对气候与社会经济因子的空间异质性的响应

  • 气候与社会经济因子对海南岛CH4浓度的影响具有显著的差异性.较OLS模型,GWR模型可以直接反映出不同气候因子和社会经济因子对CH4浓度的作用尺度及其差异,图3进一步描述了由GWR模型计算出气温、降雨、GDP和人口回归系数的空间分布,在95%的置信水平下,气温回归系数中有24 211个像元通过了显著性检验,占比88.83%,降雨回归系数中有26 525个像元通过了显著性检验,占比88.83%,GDP回归系数中24 038个像元通过了显著性检验,占比88.20%,人口回归系数中22 861个像元通过了显著性检验,占比83.88%.气温对CH4浓度的回归系数取值范围为-114.92~127.80,且系数主要集中在-3.55~12.63区间,此范围的区域的面积占比为40.09%,在海南岛中部和南部较为集中,回归系数呈现“北高南低”的空间梯度分布特征;系数的高值区和低值区的回归系数范围也相差较大,回归系数中正值占比57.82%,说明在海南岛气温对CH4浓度存在正向的影响的区域超过50%.降雨对CH4浓度的回归系数取值范围为-297.40~399.91,低值区-29.42~33.48的区域面积占比较大,约37.94%,这些区域遍布海南岛且在西部和北部更为集中,影响模式呈现由低值向四周辐射且逐渐增高的特点,且与高值区相比,低值区变化范围较小,回归系数中正值和负值的像素点占总像素点的比较为接近.GDP对CH4浓度的回归系数取值范围为-4 125.55~4 509.07,回归系数值主要集中在-299.23~615.02,占比高达97.63%,GDP对海南岛CH4浓度分布的影响较为集中,人口对CH4浓度的回归系数取值范围为-1 751.43~1 556.41,回归系数值主要集中在-207.77~142.47,此范围占比也高达97.13%.综上,人口对海南岛CH4浓度的影响与GDP对海南岛CH4浓度的影响在空间分布范围上较为为相似,而降雨对CH4浓度的影响与气温对CH4浓度的影响具有较大的空间差异.

  • 图3 海南岛CH4浓度与气温(a)、降雨(b)、GDP(c)和人口(d)的GWR回归系数

  • Fig.3 GWR regression coefficients of CH4 concentration with temperature (a),rainfall (b), GDP (c) and population (d) in Hainan Island

  • 3 讨论

  • 3.1 海南岛CH4浓度时空分异规律

  • 气候与社会经济因素共同影响着CH4浓度的时空分布.通过对2020—2021年海南岛CH4浓度的变化趋势进行分析,总体的CH4浓度呈现下降趋势,近两年均值为1 848.40×10-9 mol/mol,最低值为1 712×10-9 mol/mol,最高值为1 900×10-9 mol/mol.CH4浓度在空间分布上总体呈现出北高南低的特点,与冯敏玉等[26]基于AIRS数据探究中国长江下游六省CH4浓度空间分布的结论一致,同时也肯定了王红梅等[27]所发现的中国CH4浓度的高值区分布在东北三省的结论.CH4浓度空间分布的成因方面,海南岛中部隆起,山脉聚集,主要有尖峰岭、霸王岭、五指山、黎母山、吊罗山5大热带森林分布区,广泛地分布着热带雨林、热带季雨林以及常绿落叶阔叶混交林等植被类型[28],此处土壤水热状况良好,能吸收较多CH4,从而可能减少CH4 的排放[29],且随着海拔的逐渐升高,CH4浓度会显著降低[30],本文中海南岛中部山区无CH4浓度数据,但山区周围CH4浓度整体较低,也符合上述规律.另外,据海南省统计局2021年统计年鉴[31],海南岛的牲畜养殖主要集中于海南岛文昌、澄迈、儋州等沿海地区,畜牧活动较为频繁[32-33],且沿海地区牲畜总量占海南岛全岛牲畜饲养总量85.64%,反刍动物如牛、羊等的瘤胃微生物发酵碳水化合物可产生CH4[34],导致海南岛边缘的CH4浓度上升,整体呈现较高水平.总体而言,海南岛大气中甲烷浓度具有明显的时空变化特征[35].

  • 3.2 海南岛CH4浓度与气候、社会经济因子空间关联效应

  • CH4浓度与气候因子空间分布上相关联.本文通过GWR分析了海南岛CH4浓度与气温、降雨、GDP和人口的空间异质性关系,CH4浓度与气候因子的回归系数变化值小于与社会经济的回归系数变化值(图3),但CH4浓度与气温和降雨的回归系数的分布范围较为分散,全岛各个地区的回归系数值大小呈现交错分布,即使是相距较近的地区,其大气中的CH4浓度对气温和降雨的响应也有所差异,总体上来看,海南岛大部分区域大气中CH4浓度的变化与不同气候因子的响应不尽相同,这与Blankinship等[36]在加州一年一度的草原上,CH4浓度对气温升高与对降雨升高的现象出现不同程度的反应的研究结论一致,即当降雨量增加50%时,土壤对CH4的吸收会减少60%.但Itoh等[37]在日本季风气候的中部柏树林山坡进行回归试验却发现在不同的水分梯度中,湿润土壤对CH4的吸收较低,因此大气中CH4的含量较高,而海南岛同属于季风气候,海南的土壤对CH4响应也是如此[38],更加证实了此研究中降雨与甲烷排放量呈负相关这一观点.海南岛地处热带地区北缘,海拔较高,含有丰富的热带雨林,较少的降雨量会导致热带雨林在雨季保持CH4的净汇,再加上热带雨林的高水分含量土壤会限制CH4的吸收,从而保证大气中CH4浓度相对稳定,不会出现急剧下降的情况[13].

  • 社会经济因素对CH4浓度空间分布的影响呈现出相对聚集的特征.CH4浓度与GDP的回归系数值分布范围较为集中,全岛各个地区的回归系数值基本集中在-299.23~615.02范围内(图3),只有极少数的区域出现较大或较小值的情况,表明海南岛GDP对海南岛各个地区的CH4浓度影响无显著的空间差异,这可能是化工与畜牧产品国内国际贸易抵消的影响.此结果与Bilgili等[39]和López-Menéndez等[40]的结论一致,即当国内生产总值和实质性经济发生变化的时候,大气中CH4浓度也会随机发生改变.CH4浓度和人口的回归系数的分布范围与GDP的分布范围较为相似并且呈现出相似的规律,但人口的回归系数值所集中的区域和变化范围较小,但总体来说,人口数量对海南岛CH4浓度的空间分布依旧存在着较为明显的影响,这与Polag等[19]发现大气甲烷浓度随着人口的增长而上升的结论一致.

  • 4 结论

  • 近年来,海南岛CH4浓度在时空尺度上呈现为略微下降的趋势,且社会经济因子如GDP和人口比气候因子如气温和降雨对CH4浓度的时空关联性更强.海南岛CH4浓度主要呈现显著下降趋势和无明显变化,显著减小的区域集中在海南岛的西部及东部地区,占比46.42%;无明显变化区域主要分布在海南岛边缘及中部零散地区,占比52.95%.应用OLS和GWR模型的结果显示,GWR模型的R2为0.83,而OLS模型的R2仅为0.14,考虑局地信息的GWR模型能更准确地拟合CH4与气候与社会经济因子的空间异质性关系.总体而言,海南岛CH4浓度对GDP和人口的响应更为集中,区域间的CH4浓度与气温、降雨、GDP和人口之间既呈现了正的相关关系也呈现了负的相关关系,但在大多数区域呈现正相关关系,GDP的回归系数变化值较大,约为8 634.62.本研究揭示了海南岛CH4浓度的时空趋势变化以及CH4浓度的空间变异规律分析,但是由于GHGSat产品也受数据不确定性的影响,高分辨率高精度大气CH4浓度数据产品的制备与验证工作,仍是今后大气CH4浓度分布特征及其变异规律分析的重要基础.

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  • 基本信息

    中图分类号: Q148
    文献标识码: A
    DOI: 10.13878/j.cnki.jnuist.2022.04.013

    基金信息

    国家自然科学基金(72104065)；
    海南省自然科学基金(321RC472)；
    国家重点研发计划(2016YFA0602500)；

    引用信息

    稿件历史

    收稿日期: 2022-01-20

  • 参考文献

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