Article(id=1241116647268864429, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1241116641321350143, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1723132800000, receivedDateStr=2024-08-09, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773834867540, onlineDateStr=2026-03-18, pubDate=1742400000000, pubDateStr=2025-03-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773834867540, onlineIssueDateStr=2026-03-18, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773834867540, creator=13701087609, updateTime=1773834867540, updator=13701087609, issue=Issue{id=1241116641321350143, tenantId=1146029695717560320, journalId=1234093305789726721, year='2025', volume='45', issue='3', pageStart='1185', pageEnd='1776', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773834866123, creator=13701087609, updateTime=1773881366030, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241311676130193619, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1241116641321350143, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241311676130193620, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1241116641321350143, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=1431, endPage=1443, ext={EN=ArticleExt(id=1241116647633768906, articleId=1241116647268864429, tenantId=1146029695717560320, journalId=1234093305789726721, language=EN, title=Meta analysis of heavy metal pollution in agricultural soil in Guangdong Province, columnId=1234106389669409584, journalTitle=China Environmental Science, columnName=Soil Pollution Control, runingTitle=null, highlight=null, articleAbstract=

In this paper, we reviewed the measured data of heavy metals in farmland soils in Guangdong Province since 2010. We applied a meta-analysis approach to combine the data based on “average concentration” “number of sampling points” and “standard deviation”. The results showed that the concentration of lead(Pb, 48.83mg/kg)and mercury(Hg, 0.18mg/kg)in Guangdong Province are among the highest in China, likely influenced by industrial and mining activities. In addition, subgroup analyses explored the effects of spatial distribution and cropping systems across different cities. The results revealed that heavy metal concentrations were higher in the economically developed Pearl River Delta and mineral-rich regions. Notably, some of the most polluted cities include Foshan city(As:18.32mg/kg, Cr:73.5mg/kg, Ni: 35.4mg/kg), Shaoguan city(Pb: 86.61mg/kg), Shenzhen city(Cd: 0.51mg/kg), Meizhou city(Cu: 61.1mg/kg), etc. Among them, farmland soil Cd concentration was significantly correlated with regional industrial output value(r=0.77, P<0.01), with additional impacts from factors such as the local population and energy consumption. The analyses of different farming systems showed that Cd, Pb, Zn, Cr, and Hg concentrations in soils were higher in land and paddy fields; while Cu, Zn, As, and Ni were enriched in orchards and facility-based agriculture, which may be related to the soil physicochemical properties, fertiliser and the geological backgrounds in different farming systems. This study provides province-wide information on the spatial distribution of heavy metals in agricultural soils and cropping systems, which is of great significance to the management and control strategies of heavy metal pollution in agricultural soils in Guangdong Province.

, correspAuthors=Zhong-wei WANG, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=null, fund=null, authors=null, authorsList=Guo-feng HUANG, Zhong-wei WANG, Zhi-yi XIE, Jun LIU, Duo-hong CHEN), CN=ArticleExt(id=1241116651052126832, articleId=1241116647268864429, tenantId=1146029695717560320, journalId=1234093305789726721, language=CN, title=基于Meta分析的广东省农用地土壤重金属污染现状, columnId=1234106394572550190, journalTitle=中国环境科学, columnName=土壤污染与控制, runingTitle=null, highlight=null, articleAbstract=

通过回顾2010年来广东省农田土壤重金属实测数据,利用Meta分析,基于“平均浓度”、“采样点数”、“标准差”计算全省合并效应量.结果表明,广东省重金属中Pb(48.83mg/kg)、Hg(0.18mg/kg)在我国处于较高水平,可能受到工矿活动的影响.此外,采用亚组分析来探讨各地市空间分布以及不同种植制度的影响.各地市分布特征表明,在经济较为发达的珠江三角洲地区以及矿产资源较为丰富的地区含量较高,其中污染最为严重的地区包括佛山(As:18.32mg/kg、Cr:73.5mg/kg、Ni:35.4mg/kg)、韶关(Pb:86.61mg/kg)、深圳(Cd:0.51mg/kg)、梅州(Cu:61.1mg/kg)等,其中农田土壤Cd浓度与地区工业产值显著相关(r=0.77,P<0.01),同时也受到当地人口、能源消耗等区位因素影响.耕作制度亚组分析结果显示,菜地和稻田中富集Cd、Pb、Zn、Cr和Hg;果园、设施农业中富集Cu、Zn、As、Ni,这可能与不同耕作制度中土壤理化性质、施肥施药习惯以及地质背景有关.本研究提供了在全省范围内关于农田土壤重金属空间分布和种植制度的信息,对广东省农田土壤重金属污染管理控制策略具有重要意义.

, correspAuthors=王中伟, authorNote=null, correspAuthorsNote=
* 责任作者,高级工程师,
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黄国锋(1974-),男,广东新会人,高级工程师,博士,主要从事生态环境监测与管理研究.发表论文10余篇..

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黄国锋(1974-),男,广东新会人,高级工程师,博士,主要从事生态环境监测与管理研究.发表论文10余篇..

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黄国锋(1974-),男,广东新会人,高级工程师,博士,主要从事生态环境监测与管理研究.发表论文10余篇..

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Acta Scientiae Circumstantiae202141(11):4710-4719., articleTitle=Dynamics of manganese,copper,and zinc along with rainfall partitioning in humid subtropical forests, refAbstract=null)], funds=[Fund(id=1241116661298811347, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241116647268864429, awardId=2024YFE0202000, language=CN, fundingSource=国家重点研发计划(2024YFE0202000), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1241116651505111700, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241116647268864429, xref=null, ext=[AuthorCompanyExt(id=1241116651530277528, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241116647268864429, companyId=1241116651505111700, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Guangdong Ecological and Environmental Monitoring Center, Guangzhou 510308, China), AuthorCompanyExt(id=1241116651547054746, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241116647268864429, companyId=1241116651505111700, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=广东省生态环境监测中心,广东 广州 510308)])], figs=[ArticleFig(id=1241116656127235050, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241116647268864429, language=EN, label=Fig.1, caption=Literature selection flow chart, figureFileSmall=bpRzAZTRAkPhEynIDYcOHA==, figureFileBig=1tYn+JvUFAGv9adinrYaMQ==, tableContent=null), ArticleFig(id=1241116656244675575, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241116647268864429, language=CN, label=图1, caption=文献收集流程示意, figureFileSmall=bpRzAZTRAkPhEynIDYcOHA==, figureFileBig=1tYn+JvUFAGv9adinrYaMQ==, tableContent=null), ArticleFig(id=1241116658182443053, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241116647268864429, language=EN, label=Fig.2, caption=Coefficient of variation of heavy metals in agricultural soil in Guangdong Province, figureFileSmall=D6VT4d6OfOKUrqHwVKYLNQ==, figureFileBig=GdjgPocYsDkLSMemUlKw4Q==, tableContent=null), ArticleFig(id=1241116658429907009, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241116647268864429, language=CN, label=图2, caption=广东省农田土壤重金属变异系数, figureFileSmall=D6VT4d6OfOKUrqHwVKYLNQ==, figureFileBig=GdjgPocYsDkLSMemUlKw4Q==, tableContent=null), ArticleFig(id=1241116658530570315, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241116647268864429, language=EN, label=Fig.3, caption=Spatial variation of heavy metals of agricultural soil in Guangdong Province, figureFileSmall=PP1MHDRjbXjlwdlRok2YbQ==, figureFileBig=b3vmqSvyuSvwogscejxK3w==, tableContent=null), ArticleFig(id=1241116658668982363, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241116647268864429, language=CN, label=图3, caption=广东省各地市农田土壤重金属空间变化情况, figureFileSmall=PP1MHDRjbXjlwdlRok2YbQ==, figureFileBig=b3vmqSvyuSvwogscejxK3w==, tableContent=null), ArticleFig(id=1241116658803200110, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241116647268864429, language=EN, label=Fig.4, caption=Correlation between the output value of secondary industry and Cd of farmland soil in different municipalities of Guangdong Province, figureFileSmall=aVR9n92Mwh465E+oZsyE0g==, figureFileBig=+cLAEzE40XLRAYyUjPN80A==, tableContent=null), ArticleFig(id=1241116658912252027, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241116647268864429, language=CN, label=图4, caption=广东省各地市第二产业产值与农田土壤Cd相关性, figureFileSmall=aVR9n92Mwh465E+oZsyE0g==, figureFileBig=+cLAEzE40XLRAYyUjPN80A==, tableContent=null), ArticleFig(id=1241116659046469775, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241116647268864429, language=EN, label=Fig.5, caption=Changes in humanities and social science parameters in various cities in Guangdong Province(by Year), figureFileSmall=0+Co3rNkptBgoO2145AqDQ==, figureFileBig=cN5Ail4mIhFfOjBm2rDmug==, tableContent=null), ArticleFig(id=1241116659151327392, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241116647268864429, language=CN, label=图5, caption=广东省各地市(各年份)社会经济参数变化情况, figureFileSmall=0+Co3rNkptBgoO2145AqDQ==, figureFileBig=cN5Ail4mIhFfOjBm2rDmug==, tableContent=null), ArticleFig(id=1241116659285545144, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241116647268864429, language=EN, label=Fig.6, caption=Average values of heavy metals in farmland soil under different cultivation types, figureFileSmall=M1aJ1NdIzqMlyXfQ2Sz/6Q==, figureFileBig=oL2FjAzQa8IaTXArjrkSFA==, tableContent=null), ArticleFig(id=1241116659419762894, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241116647268864429, language=CN, label=图6, caption=不同耕作类型下农田土壤重金属平均值, figureFileSmall=M1aJ1NdIzqMlyXfQ2Sz/6Q==, figureFileBig=oL2FjAzQa8IaTXArjrkSFA==, tableContent=null), ArticleFig(id=1241116659533009126, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241116647268864429, language=EN, label=Fig.7, caption=Annual changes in major agricultural indicators in Guangdong Province, figureFileSmall=nBW0HQ8j/v90/oPf2ERjXw==, figureFileBig=fyi5vHrnttC3G/FgrObKjg==, tableContent=null), ArticleFig(id=1241116659633672437, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241116647268864429, language=CN, label=图7, caption=广东省农业主要指标年变化, figureFileSmall=nBW0HQ8j/v90/oPf2ERjXw==, figureFileBig=fyi5vHrnttC3G/FgrObKjg==, tableContent=null), ArticleFig(id=1241116659763695879, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241116647268864429, language=EN, label=Table 1, caption=

Number of heavy metal element cases in each city

, figureFileSmall=null, figureFileBig=null, tableContent=
地市研究数地市研究数
东莞2清远4
云浮1湛江4
佛山4珠海3
广州6肇庆2
惠州2阳江1
汕头3韶关10
江门1梅州1
深圳1其他17
东莞2
), ArticleFig(id=1241116659893719324, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241116647268864429, language=CN, label=表1, caption=

各地市重金属元素研究数

, figureFileSmall=null, figureFileBig=null, tableContent=
地市研究数地市研究数
东莞2清远4
云浮1湛江4
佛山4珠海3
广州6肇庆2
惠州2阳江1
汕头3韶关10
江门1梅州1
深圳1其他17
东莞2
), ArticleFig(id=1241116660015354157, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241116647268864429, language=EN, label=Table 2, caption=

Classification of geo-accumulation index

, figureFileSmall=null, figureFileBig=null, tableContent=
评价等级无污染轻污染中污染强污染严重污染
(Igeo)≤00~11~22~3≥3
), ArticleFig(id=1241116660136988984, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241116647268864429, language=CN, label=表2, caption=

地累计指数等级标准(Igeo

, figureFileSmall=null, figureFileBig=null, tableContent=
评价等级无污染轻污染中污染强污染严重污染
(Igeo)≤00~11~22~3≥3
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Average content of heavy metals of agricultural soil in Guangdong Province(mg/kg)

, figureFileSmall=null, figureFileBig=null, tableContent=
重金属CdPbCuZnAsCrNiHg
研究论文数4324373326323222
全省平均值10.2248.8327.5188.8811.6254.6816.910.18
广东省元素背景值20.0563620.70347.38.950.514.40.078
标准值30.41005020030250700.5
常规地区40.2443.1425.2475.9612.3652.6418.760.21
工矿地区41.85165.52103.28314.6129.6258.2425.840.24
), ArticleFig(id=1241116660355092826, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241116647268864429, language=CN, label=表3, caption=

广东省农田土壤重金属平均含量(mg/kg)

, figureFileSmall=null, figureFileBig=null, tableContent=
重金属CdPbCuZnAsCrNiHg
研究论文数4324373326323222
全省平均值10.2248.8327.5188.8811.6254.6816.910.18
广东省元素背景值20.0563620.70347.38.950.514.40.078
标准值30.41005020030250700.5
常规地区40.2443.1425.2475.9612.3652.6418.760.21
工矿地区41.85165.52103.28314.6129.6258.2425.840.24
), ArticleFig(id=1241116660434784621, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241116647268864429, language=EN, label=Table 4, caption=

Comparison of the results of this study with other domestic studies and the current status of soil heavy metal pollution in other countries(mg/kg)

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土壤重金属采样点数/研究数量参考文献
CdPbCuZnAsCrNiHg
广东省农田土壤0.2248.8327.5188.8811.6254.6816.910.18研究数=47本研究
广东省表层土0.14847.18220.70367.84412.69547.32918.9680.165采样点数=1000[25]
广东省农田土壤0.19539.4725.1597.4311.7453.67/0.1442020全国[26]
全国农田土壤0.2431.9128.2686.7310.3558.13/0.111农田土壤综述
全国农田土壤0.2432.0728.3483.2910.7162.1828.170.132019全国[24]
农田土壤综述
全国城市土壤0.2935.2426.58104.2812.9872.5928.690.082021全国[27]
城市土壤综述
全国矿山土壤3.76196.428.3483.2920.5967.328.170.1872个矿区[28]
), ArticleFig(id=1241116660560613759, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241116647268864429, language=CN, label=表4, caption=

本研究结果与国内其他研究及其他国家土壤重金属污染现状比较(mg/kg)

, figureFileSmall=null, figureFileBig=null, tableContent=
土壤重金属采样点数/研究数量参考文献
CdPbCuZnAsCrNiHg
广东省农田土壤0.2248.8327.5188.8811.6254.6816.910.18研究数=47本研究
广东省表层土0.14847.18220.70367.84412.69547.32918.9680.165采样点数=1000[25]
广东省农田土壤0.19539.4725.1597.4311.7453.67/0.1442020全国[26]
全国农田土壤0.2431.9128.2686.7310.3558.13/0.111农田土壤综述
全国农田土壤0.2432.0728.3483.2910.7162.1828.170.132019全国[24]
农田土壤综述
全国城市土壤0.2935.2426.58104.2812.9872.5928.690.082021全国[27]
城市土壤综述
全国矿山土壤3.76196.428.3483.2920.5967.328.170.1872个矿区[28]
), ArticleFig(id=1241116660690637201, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241116647268864429, language=EN, label=Table 5, caption=

Heavy metal content of agricultural soil in various cities of Guangdong Province(mg/kg)

, figureFileSmall=null, figureFileBig=null, tableContent=
城市CdPbCuZnAsCrNiHg
东莞0.2249.7224.2377.2811.0443.4121.970.17
云浮0.02///////
佛山0.3558.5644.20/18.3273.5035.40/
广州0.2847.9646.89135.7918.1570.7420.060.18
惠州0.1545.9116.7457.2112.6240.3114.890.20
汕头0.2253.2121.5494.937.0545.0512.950.21
江门0.2446.50//12.2075.60/0.30
深圳0.5151.2119.8962.26/43.9612.60/
清远0.2853.9522.4175.3910.6330.7412.740.15
湛江0.1824.9519.1449.834.5052.0117.010.16
珠海0.1358.4620.1265.504.5741.6713.700.11
肇庆0.1231.2016.60/12/14.700.13
阳江0.2236.6620.5857.7112.6936.6312.930.30
韶关0.2886.6124.61140.575.2769.2820.050.17
梅州//61.10/////
其他0.1830.1522.1763.3213.9859.5417.920.12
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广东省各地市农田土壤重金属含量(mg/kg)

, figureFileSmall=null, figureFileBig=null, tableContent=
城市CdPbCuZnAsCrNiHg
东莞0.2249.7224.2377.2811.0443.4121.970.17
云浮0.02///////
佛山0.3558.5644.20/18.3273.5035.40/
广州0.2847.9646.89135.7918.1570.7420.060.18
惠州0.1545.9116.7457.2112.6240.3114.890.20
汕头0.2253.2121.5494.937.0545.0512.950.21
江门0.2446.50//12.2075.60/0.30
深圳0.5151.2119.8962.26/43.9612.60/
清远0.2853.9522.4175.3910.6330.7412.740.15
湛江0.1824.9519.1449.834.5052.0117.010.16
珠海0.1358.4620.1265.504.5741.6713.700.11
肇庆0.1231.2016.60/12/14.700.13
阳江0.2236.6620.5857.7112.6936.6312.930.30
韶关0.2886.6124.61140.575.2769.2820.050.17
梅州//61.10/////
其他0.1830.1522.1763.3213.9859.5417.920.12
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Accumulated index of agricultural soil in various cities of Guangdong Province(Igeo

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城市CdPbCuZnAsCrNiHg
东莞1.39-0.12-0.360.12-0.27-0.800.020.54
云浮-2.07///////
佛山2.060.120.51/0.46-0.040.71/
广州1.74-0.170.590.940.44-0.10-0.110.62
惠州0.84-0.23-0.89-0.31-0.08-0.91-0.540.77
汕头1.39-0.02-0.530.42-0.92-0.75-0.740.84
江门1.51-0.22//-0.130.00/1.36
深圳2.60-0.08-0.64-0.19/-0.79-0.78/
清远1.740.00-0.470.09-0.33-1.30-0.760.36
湛江珠海1.100.63-1.110.11-0.70-0.63-0.51-0.12-1.57-1.55-0.54-0.86-0.34-0.660.45-0.09
肇庆0.51-0.79-0.90/-0.15/-0.560.15
阳江1.39-0.56-0.59-0.30-0.07-1.05-0.741.36
韶关1.740.68-0.340.99-1.34-0.13-0.110.54
梅州//0.98/////
其他1.10-0.84-0.49-0.160.07-0.35-0.270.04
全省平均1.18-0.18-0.310.11-0.46-0.61-0.380.63
污染占比(%)92.8623.0823.0850.0016.670.0016.6790.91
), ArticleFig(id=1241116661084901824, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241116647268864429, language=CN, label=表6, caption=

广东省各地市农田土壤地累积指数(Igeo

, figureFileSmall=null, figureFileBig=null, tableContent=
城市CdPbCuZnAsCrNiHg
东莞1.39-0.12-0.360.12-0.27-0.800.020.54
云浮-2.07///////
佛山2.060.120.51/0.46-0.040.71/
广州1.74-0.170.590.940.44-0.10-0.110.62
惠州0.84-0.23-0.89-0.31-0.08-0.91-0.540.77
汕头1.39-0.02-0.530.42-0.92-0.75-0.740.84
江门1.51-0.22//-0.130.00/1.36
深圳2.60-0.08-0.64-0.19/-0.79-0.78/
清远1.740.00-0.470.09-0.33-1.30-0.760.36
湛江珠海1.100.63-1.110.11-0.70-0.63-0.51-0.12-1.57-1.55-0.54-0.86-0.34-0.660.45-0.09
肇庆0.51-0.79-0.90/-0.15/-0.560.15
阳江1.39-0.56-0.59-0.30-0.07-1.05-0.741.36
韶关1.740.68-0.340.99-1.34-0.13-0.110.54
梅州//0.98/////
其他1.10-0.84-0.49-0.160.07-0.35-0.270.04
全省平均1.18-0.18-0.310.11-0.46-0.61-0.380.63
污染占比(%)92.8623.0823.0850.0016.670.0016.6790.91
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基于Meta分析的广东省农用地土壤重金属污染现状
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黄国锋 , 王中伟 * , 谢志宜 , 刘军 , 陈多宏
中国环境科学 | 土壤污染与控制 2025,45(3): 1431-1443
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中国环境科学 | 土壤污染与控制 2025, 45(3): 1431-1443
基于Meta分析的广东省农用地土壤重金属污染现状
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黄国锋 , 王中伟* , 谢志宜, 刘军, 陈多宏
作者信息
  • 广东省生态环境监测中心,广东 广州 510308
  • 黄国锋(1974-),男,广东新会人,高级工程师,博士,主要从事生态环境监测与管理研究.发表论文10余篇..

通讯作者:

* 责任作者,高级工程师,
Meta analysis of heavy metal pollution in agricultural soil in Guangdong Province
Guo-feng HUANG , Zhong-wei WANG* , Zhi-yi XIE, Jun LIU, Duo-hong CHEN
Affiliations
  • Guangdong Ecological and Environmental Monitoring Center, Guangzhou 510308, China
出版时间: 2025-03-20
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通过回顾2010年来广东省农田土壤重金属实测数据,利用Meta分析,基于“平均浓度”、“采样点数”、“标准差”计算全省合并效应量.结果表明,广东省重金属中Pb(48.83mg/kg)、Hg(0.18mg/kg)在我国处于较高水平,可能受到工矿活动的影响.此外,采用亚组分析来探讨各地市空间分布以及不同种植制度的影响.各地市分布特征表明,在经济较为发达的珠江三角洲地区以及矿产资源较为丰富的地区含量较高,其中污染最为严重的地区包括佛山(As:18.32mg/kg、Cr:73.5mg/kg、Ni:35.4mg/kg)、韶关(Pb:86.61mg/kg)、深圳(Cd:0.51mg/kg)、梅州(Cu:61.1mg/kg)等,其中农田土壤Cd浓度与地区工业产值显著相关(r=0.77,P<0.01),同时也受到当地人口、能源消耗等区位因素影响.耕作制度亚组分析结果显示,菜地和稻田中富集Cd、Pb、Zn、Cr和Hg;果园、设施农业中富集Cu、Zn、As、Ni,这可能与不同耕作制度中土壤理化性质、施肥施药习惯以及地质背景有关.本研究提供了在全省范围内关于农田土壤重金属空间分布和种植制度的信息,对广东省农田土壤重金属污染管理控制策略具有重要意义.

农用地土壤  /  重金属  /  meta分析  /  分布特征

In this paper, we reviewed the measured data of heavy metals in farmland soils in Guangdong Province since 2010. We applied a meta-analysis approach to combine the data based on “average concentration” “number of sampling points” and “standard deviation”. The results showed that the concentration of lead(Pb, 48.83mg/kg)and mercury(Hg, 0.18mg/kg)in Guangdong Province are among the highest in China, likely influenced by industrial and mining activities. In addition, subgroup analyses explored the effects of spatial distribution and cropping systems across different cities. The results revealed that heavy metal concentrations were higher in the economically developed Pearl River Delta and mineral-rich regions. Notably, some of the most polluted cities include Foshan city(As:18.32mg/kg, Cr:73.5mg/kg, Ni: 35.4mg/kg), Shaoguan city(Pb: 86.61mg/kg), Shenzhen city(Cd: 0.51mg/kg), Meizhou city(Cu: 61.1mg/kg), etc. Among them, farmland soil Cd concentration was significantly correlated with regional industrial output value(r=0.77, P<0.01), with additional impacts from factors such as the local population and energy consumption. The analyses of different farming systems showed that Cd, Pb, Zn, Cr, and Hg concentrations in soils were higher in land and paddy fields; while Cu, Zn, As, and Ni were enriched in orchards and facility-based agriculture, which may be related to the soil physicochemical properties, fertiliser and the geological backgrounds in different farming systems. This study provides province-wide information on the spatial distribution of heavy metals in agricultural soils and cropping systems, which is of great significance to the management and control strategies of heavy metal pollution in agricultural soils in Guangdong Province.

agricultural soil  /  heavy metal  /  meta-analysis  /  spatial distribution
黄国锋, 王中伟, 谢志宜, 刘军, 陈多宏. 基于Meta分析的广东省农用地土壤重金属污染现状. 中国环境科学, 2025 , 45 (3) : 1431 -1443 .
Guo-feng HUANG, Zhong-wei WANG, Zhi-yi XIE, Jun LIU, Duo-hong CHEN. Meta analysis of heavy metal pollution in agricultural soil in Guangdong Province[J]. China Environmental Science, 2025 , 45 (3) : 1431 -1443 .
重金属(HMs),如镉(Cd)、铬(Cr)、汞(Hg)、铅(Pb)、砷(As)、铜(Cu)、锌(Zn)和镍(Ni),因其低阈值下的高毒性、持久性和稳定性,引起了广泛关注[1-2].土壤是重金属最重要的地球化学汇,排放到环境中的重金属通过大气沉降、地表径流等途径最终沉积到土壤[3-4].农田土壤重金属污染对人类高度敏感,一方面,化肥农药滥用以及工业废气和污水排放造成的重金属积累会影响土壤养分流失和土壤结构和功能的破坏,导致作物减质减产甚至生态失衡[5];另一方面,农田土壤中重金属通过食物链以及环境暴露等途径从土壤转移到人体,从而对人类健康构成威胁[6].现有证据表明,人体摄入或接触重金属后,体内容易产生氧化应激和炎症,最终诱发人体高血压、心律失常和动脉粥样硬化等心血管疾病[2].
目前,我国仅使用世界9%的可耕地来养活全球近20%的人口[7].此前一项调查显示,我国大约1000万hm2的农田受到污染,以及333.3万hm2的耕地因污染而不适合耕种,其中大部分是由重金属污染造成的[8].近年研究发现,水稻和蔬菜很容易从受污染耕地中积累重金属,我国约10%的水稻超过了Cd国家食品安全标准的0.20mg/kg含量限值,这大幅度增加了从农田摄入重金属造成的人体健康风险[9-10].
广东省经历了快速城市化和经济增长,长期高强度的工业发展和城市化对当地土壤环境质量造成了严重影响,大量土壤受到重金属污染[11].珠江三角洲土壤中Cd和Cu超标率达18.77%和12.95%,并受到地理、气候和工业活动的综合影响[12].然而,目前针对广东省农田土壤调查分析以不同地区大田实测为主,由于研究机构独立性、研究对象的敏感性和保密性,我国农田土壤重金属研究目前存在规模小且数据不互通的状态[13].广东省属于亚热带和热带季风气候区,具有温暖湿润的气候条件,加之地形多样,土壤类型丰富,适合多种耕作方式.然而,由于部分耕作方式施肥和耕作管理不当,耕作土壤酸化和重金属污染等问题日益突出[14-16].因此,针对广东省农田土壤空间重金属分布及不同耕作制度下重金属积累情况仍需进一步研究.
Meta分析是一种统计学方法,通过整合多个研究的结果来评估某一影响因素的总体效应,近年来,该技术逐渐应用于环境科学领域[17].Shi等[18]基于Meta分析系统评估了土壤生物量、植物多样性和土壤有机碳等因素的影响.目前广东省农田土壤重金属的空间分布研究主要以国家尺度为主,存在以个别样点值代替区域平均值、样点偏少或未展开有效评价等问题,难以全面反映广东省农田土壤重金属的实际分布特征水平[13].因此,可以通过Meta分析广东省重金属土壤污染的现状以及不同因素对重金属污染的影响,为当地政府部门土壤污染防治提供实质性指导.
本文基于2010年以来可收集到的广东省农田土壤重金属大田监测数据,采用Meta分析,合并各项研究的效应量,通过研究中平均值、标准差、采样点数量对不同研究进行权重分配,最终获得更加精确和可靠的效应值,进一步探讨广东省农田土壤中Cd、Hg、Cr、Ni、Cu、Zn、Hg和As 8种重金属工矿地区与常规地区分布差异及各地市重金属分布特征,同时采用亚组分析对不同耕作方式下农田土壤污染状况进行分析.通过准确掌握广东省各地市农田土壤重金属污染现状,建立农田土壤重金属数据库,结合我省社会经济发展情况及对农田土壤污染精确防治提供数据支持.
首先,在Web of Science和中国知网数据库中,使用"重金属"、"农田土壤"、"广东"和"珠三角"等关键词,对2010~2024年发表的土壤重金属研究论文进行搜索.然后,制定纳入分析的筛选标准,剔除不合格的文章.论文的选择标准如下:①实验类型为农田土壤监测实验,采集农田耕作层土壤样品(0~20cm、0~50cm或表层剖面);②研究中要体现研究所在区域(地市/经纬度)和采样点的具体信息(数量/点位位置);③研究中土壤采集、样品制备及测试要使用国家/行业认证的技术标准及方法;④研究中要列明耕作层土壤重金属含量的平均值和标准偏差或变异系数.图1为文献收集具体流程图.
从每篇文章中提取的数据包括:①论文题目、作者及年份;②研究区域地理位置或经纬度、采样点数量;③该农田目前的种植制度,并将种植制度分为混合(没有具体描述,主要为稻田+菜地)、水稻、蔬菜(田间蔬菜)、果园(果园、茶园等)、设施农业(大棚、温室)、林地;④受该区域影响的主要人为排放,将区域分为二组:正常区(城市、农村地区农田)、工矿地区(采矿冶炼、煤炭发电以及其他工业区周边农田);⑤平均值和标准差/方差/变异系数/元素含量范围.完成数据提取后,在数据库中将Cd、Hg、Cr、Ni、Cu、Zn、Hg和As 8个元素分离为8个文件,然后根据论文主题,材料与方法中对研究区域、点位及周边污染源的具体描述,对相关研究所涉及的工矿地区、广东省各地市和不同耕作制度的相关研究进行了分类和亚组分析.其中,共收集农田土壤Cd研究43项、Pb研究42项、Cu研究37项、Zn研究33项、As研究25项、Cr研究32项、Ni研究32项和Hg研究22项.
根据数据提取结果,广东省农业土壤中重金属的研究案例分布在全市16个地区,按地市计算的研究数如表1所示.
①模型参数:Meta分析类型选择为平均值型,由于不同案例的研究方法、实验环境等存在诸多差异,本研究Meta分析模型选择为随机效应模型,效应量计算方法选择为倒方差法,估计方法为随机效应模型中常用的异质性方差估计方法(Dersimonian-Laird法),敏感性检验采取逐一剔除法.②异常值剔除:在进行Meta分析之前,对数据进行测试,数据存在明显的偏态分布,表明异常值的存在会在一定程度上影响整体分析结果.这可能是由于研究者会倾向于研究工矿地区周边的污染地带,这可能会给农田土壤数据库带来异常高的数值,从而产生发表偏倚.然而,这种极值并非都是异常值,处理数据时应结合实际情况进一步分析,谨慎处理数据[19].因此,为了保证分析结果的可靠性,避免用极值对合并效应值产生偏差,本研究对所有数据进行了检验,以消除异常样本.处理方法为采用箱线图法,将大于平均值1.5倍四分位数间距的数值确定异常值并进行剔除(剔除异常值个数为Cd=15,Pb=9,Cu=7,Zn=8,As=7,Cr=0,Ni=6,Hg=4)[20].值得注意的是,即使剔除了异常值,一些数据集的结果也可能仍然存在显著的发表偏倚[21].最后,本研究通过逐一剔除数据法对合并效应值进行敏感性检验,发现原Meta分析结果未因某些研究的影响而发生显著变化,这表明Meta分析合并效应的结果是稳健的[22].
土壤地累积指数(Igeo)是衡量土壤重金属污染的常见手段,其主要反映土壤受人为活动的影响.计算公式为:
式中:Ci为计算所得重金属i的含量,mg/kg;系数k为考虑不同自然地质过程影响的修正系数,通常取值为1.5;Si为广东省土壤中重金属背景值,数据来源自《中国土壤元素背景值》[23].污染等级划分如表2所示.
本研究使用微软Excel 2019进行数据预处理和统计分析,相关数据用Origin 2022绘制.Meta分析采用Review Manager 5.4软件进行.
本次研究用Meta分析得到的平均值代表全省的农田土壤重金属含量平均值(表3).农田土壤重金属Cd积累量最大,含量为0.22mg/kg,为背景值的3.9倍.同时,相比于中国农田土壤污染风险管控标准(GB15618-2018)[24]中Cd筛选值仅相差0.18mg/kg,存在一定的农田土壤污染风险,需要尽快开展风险控制与源头防控工作.此外,农田土壤中的Hg和Zn浓度分别为0.18,88.88mg/kg,较背景值上涨231%和188%,其与农田土壤污染风险管控标准相差0.32和111.12mg/kg,考虑到其增长速率,同样需要对这两种元素进行合理管控与修复.其他5种重金属浓度低于农田土壤污染风险管控标准的筛选值,且在农田土壤中与背景值相比,未发现明显上升.
本次研究根据农田土壤周边土地利用类型,将数据分为常规地区和工矿地区进行了亚组分析,进一步解析农田土壤重金属变化的因素.其中,工矿地区附近的农田土壤中重金属含量远高于常规地区附近的农田土壤(表3),尤其是Cd、Pb、Zn、Cu.其中Cd浓度为常规地区农田土壤重金属含量的7.7倍,而Pb、Cu和Zn浓度均为常规地区约4倍,表明工矿业活动对周边农田土壤重金属输入和积累造成了巨大的影响,这与Huang等[25]对全国农田土壤的研究一致.此外,工矿地区Cd、Pb、Cu和Zn平均值均高于农田土壤污染风险管控标准的筛选值,存在农田土壤污染风险.此外,本研究通过亚组分析的常规地区的平均值与剔除异常值后的全省平均值相近,表明本研究中计算的广东省农田土壤平均值能较为准确的反应广东省农田土壤中重金属的平均含量,能有效消除不同研究之间的发布偏倚问题.
本研究进一步分析8种农田重金属的变异系数(CV),结果表明Cd(平均值:0.83)、Cu(0.66)和Hg(0.64)的变异系数高于0.5,这表明3种重金属主要到人为活动的影响.
表4所示,将广东省农田土壤重金属平均值与其他农田土壤研究进行进一步对比.本研究的广东省农田土壤重金属含量平均值整体与广东省其他两项研究数值上差异较小,表明本次meta分析结果的有效性.相较于广东省其他两项研究,本研究农田土壤中Cd、Hg明显增加,这表明近年来人为活动对这两种重金属在农田土壤累积具有较大贡献.此外,相较于我国农田土壤重金属,广东省农田土壤中Pb、Hg浓度高于全国平均,一方面,人口高密度地区(广州、深圳及珠三角等地区)密集的人为排放引起农田土壤中这两种重金属快速累积;另一方面,广东省快速发展的城市化、工业化,导致工业废气、废水的大量排放,对周边农田土壤造成影响.同时,本研究发现矿山周边土壤中Cd、Pb和Hg浓度较高,这与广东省农田土壤污染特征相近,表明广东省农田土壤可能受到相关矿业活动的影响.
剔除异常值的重金属数据后,对广东省农田土壤重金属含量进行各地市亚组分析,得到广东省各地市的农田土壤重金属含量平均值(表5图3).根据广东省农田土壤重金属平均含量统计,8种重金属含量最高的地区分别为Cd 0.51mg/kg(深圳市)、Pb 86.61mg/kg(韶关市)、Cu 61.1mg/kg(梅州市)、Zn 140.57mg/kg(韶关市)、As 18.32mg/kg(佛山市)、Cr 73.50mg/kg(佛山市)、Ni 35.40mg/kg(佛山市)、Hg 0.3mg/kg(江门市、阳江市).其中深圳市Cd和梅州市Cu平均效应浓度高于农田筛选值.
以各地市的土壤背景值为标准,计算广东省各地市农田土壤8种重金属的地质累积指数(Igeo)(表6).广东省农田土壤地累积指数全省平均值依次为:Cd>Hg>Zn>Pb>Cu>Ni>As>Cr,其中Cd、Zn和Hg全省平均土壤地累积指数分别为1.18、0.11、0.63,表明这3种金属元素受到人类活动的影响较大.此外,结合表3可知,地累计指数还反应了各种重金属的累积程度,重金属Cd、Hg和Zn在广东省地区污染占比分别为92.86%、90.91%和50%,达轻度污染及以上,其中佛山、广州、江门等珠三角城市,各项重金属累积程度较高.总体来看,广东各地市农田土壤中Cd和Hg的Igeo值较高,而各地市累积程度占比也表明农田土壤Cd和Hg的污染较重,这与各地市农田土壤重金属的平均含量分布结果一致.
现有研究表明,土壤中Cd主要来自于工业排放,这与本文的研究结果高度一致[30].农田土壤较高的Cd浓度主要分布在深圳、佛山、广州、韶关(图3).根据图4,广东省农田土壤重金属Cd浓度变化与各地市第二产业(工矿业)整体变化趋势相近(r=0.77,p<0.01).同时,本研究观察到较高的Pb、Cu、Zn浓度主要分布在韶关、梅州、佛山和广州等地,与Cd的分布区域相似.可能的污染成因包括两类,一方面韶关、梅州等矿产资源大市,以铅锌矿采冶为主的工业活动在当地快速发展,大规模开采、采矿废石堆放、选矿及洗矿过程产生大量酸性矿山废水导致大量Cu、Zn和Pb进入土壤[31-32];另一方面佛山、广州和深圳等珠三角地区是广东省工业制造业的密集地区,分布众多电子产品、金属制造和印染工厂[33-34],Zhang等[35]的研究表明染料、媒染剂以及印染工艺大量使用的氧化剂中含有Cd、Cu、Pb和Zn等重金属,可能污染周边土壤环境.
本研究发现阳江、江门两市农田土壤中存在较高浓度的Hg. Pacyna等[36]指出,化石燃料燃烧是Hg的主要来源,约占人为Hg排放的45%.阳江和江门分别拥有广东省最大,全国排名第三、第四的火力发电厂台山发电厂和阳西发电厂,总装机容量均高达500万kw,大规模的燃煤发电造成的废气排放可能对当地农田土壤带来重金属Hg污染[37].对于Ni、Cr、As等重金属,通常归因为自然成因[38-39],然而,在佛山、广州和江门等工业制造业密集地区农田土壤仍有较高检出,这表明工业排放对这几种重金属的积累不容忽视.例如,Hu等[38]的研究表明,土壤中的As可能来自于金属冶炼和化石燃料燃烧等工业活动.
从广东省区位因素分析,佛山、惠州和东莞等珠三角城市主要以第二产业为主(贡献率>50%),韶关、梅州、阳江、湛江、肇庆、清远等第一产业贡献相对较高(图5(a)).其中,韶关、梅州为广东重要农业种植区,但当地同时位于重要的金属硫化物矿带上,长期的矿冶活动加剧了当地部分农业土壤重金属污染.广东省自2019年第二产业贡献经济快速上升,第一产业贡献也处于历年最高水平(图5(b)),意味着广东省工农业活动近年快速增加,这需要进一步采取适当的环境管理和污染控制措施,在推动经济发展的同时减少对环境的负面影响[40].广州、深圳、汕头、佛山、东莞和中山等地,人口密度大,能源(供电量)消耗大,密集的人类活动带来了较高的地区能耗,导致周边农田土壤受到较高的环境压力[41](图5(c)).同时,根据图5(d)可知广东省历年日均能耗总量逐年升高,尽管煤炭日均消耗量逐年下滑,但其他如焦炭、汽油和柴油等化石能源仍处于较高水平.研究表明,煤炭等化石能源燃烧造成的重金属输入仍是土壤重金属的最重要来源.目前,全省火力发电能源转换效能约43%,全省总体仍处于高能耗、低转换的资源利用现状,仍需大力发展非化石能源替代.
通过对研究文章中表述的农田种植制度,对农田土壤不同耕作制度下重金属浓度进行亚组分析.结果表明,不同耕作制度对重金属积累存在一定的影响,并未发现各种植类型效应浓度高于农田土壤筛选值情况.总体来说,Cd、Pb、Zn、Cr和Hg在混合模式(稻田+菜地)、稻田和菜地中含量相对较高.本文分析认为,一方面,稻田需水量大,重金属可通过污水灌溉等方式进入稻田.研究表明,长期利用受污水灌溉的稻田土壤中土壤Cu、Zn最大超标倍数可达标准值的1.58~5.61倍[42],其他研究也证实农田土壤Cd、Pb和Hg含量可能受到污水灌溉的显著影响[43-44].另一方面,广东省地处华南地区,气候上高温多雨,露天菜地在强降雨下容易流失养分、滋生蚊虫,因此田间需肥、需药量大,从而导致大量重金属通过农田投入品进入菜地土壤[10,45-46].在一项持续25a的长期施肥研究表明,连续施用肥料对土壤中Cd、Pb和Cr的积累具有显著正向作用[47].在除草剂中可检测出Pb和Cr等重金属成分,表明施用农药也会导致土壤重金属积累[44].此外,农药的过量使用可能会导致土壤酸化,导致土壤Cd活性增加,间接导致农田作物重金属富集[48].除人为和气候因素以外,地质背景也是稻田土污染重要原因,广东省稻田主要分布在红壤区,如广东韶关、湛江及梅州一带,由于强烈的自然风化与淋溶作用,土壤酸化及有机质含量低,导致重金属活性较高.同时,水稻的重金属吸收与积累能力较强,其Cd的富集系数远远高于小麦、玉米等粮食作物,导致稻米中Cd的超标率较高[49].根据图7(a),广东是全国稻谷生产的重要省份之一,2022年广东粮食产量为1291.5万t,其中稻谷产量达到1108.6万t,显示出水稻在广东粮食生产中的主导地位.此外,广东省蔬菜产量在2022年达到最高值,总产量增至3854.7万t,是华南地区乃至全国重要的蔬菜供应基地.综上所述,广东省稻田、菜地等土地利用类型的重金属累积需重点关注.
同时,本研究发现Cd、Cu、Zn、As和Ni在果园、设施农业(大棚)土壤中含量相对较高.已有研究表明,Cu、Zn和Cd作为农药中杀菌剂的主要成分以及化肥、有机肥的主要元素广泛应用于农业生产中[50-51].据估计,在中国,每年约有37466.9t Cu和130423.5t Zn通过畜禽粪便和化肥投入农田[50].同时,在果园,设施农业(大棚)等耕作模式下,农户为了快速提高产量,往往过量施用化肥、农药及畜禽粪便,过量的施用农田投入品将导致重金属累积[52-53].研究表明,大棚种植年限越长,土壤重金属积累程度越高,其中Cu、Zn和Cd增幅可达到129.14%、204.17%和161.11%[54].广东省农业第三次普查表明,2016年广东省的温室种植面积较2006年末增长了180.3%,大棚种植面积增长了78.7%,室内农业快速发展.综上,广东省快速增长的设施农业与土壤重金属累积矛盾逐步体现,需尽快开展对果园、设施农业等特殊耕作模式的重金属污染特征分析及风险评估.
广东省畜禽产量逐步上升,而前文表明畜禽粪污是农田重金属输入的主要途径[50].虽然,广东省化肥、农药施用强度(年施用量/农作物播种面积)自2015年来逐年递减,但目前化肥(约450kg/hm2)和农药(约16.5kg/hm2)施用强度仍处于世界较高水平,分别为发达国家生产安全限值的2倍和2.4倍,超量的化肥、农药输入到环境中,造成农田土壤重金属超标[55-56].因此,广东省仍需进一步落实化肥减量增效,有机肥替代以及推进种养循环等相关工作.在林地中,整体重金属浓度较低,这可能是该土地利用模式下,植被覆盖率高、土壤有机质丰富,植物通过根系吸收土壤养分,同时也吸收一定量重金属元素,在生长过程中富集重金属,从而导致土壤中重金属浓度降低[57-58].
3.1 广东省重金属平均值为Cd(0.22mg/kg)、Pb(48.83mg/kg)、Cu(27.51mg/kg)、Zn(88.88mg/kg)、As(11.62mg/kg)、Cr(54.68mg/kg)、Ni(16.91mg/kg)、Hg(0.18mg/kg),其中Pb、Hg在我国处于较高水平,可能受采矿冶炼、煤炭燃烧等工业排放影响.
3.2 8种重金属呈现出东部区域较高,西部较低的特点,突出表现为Cd、Pb、Cu、Zn、Hg在经济较为发达的珠江三角洲地区以及矿产资源较为丰富的地区含量较高,其中污染最为严重的地区包括佛山、广州、韶关、深圳、梅州等.目前,广东省内广州、深圳、汕头、佛山、东莞和中山等地,人口密度大,能源(供电量)消耗大,密集的人类活动带来了较高的地区能耗,导致周边农田土壤受到较高的环境压力.全省总体仍处于高能耗、低转换的资源利用现状,仍需大力发展非化石能源替代.
3.3 耕作制度亚组分析结果显示,在蔬菜田,Cd、Pb、Zn、Cr和Hg土壤累积较高.在果园、设施农业,Cu、Zn、As、Ni等累积较高.目前,广东省化肥、农药使用强度仍处于较高水平.
  • 国家重点研发计划(2024YFE0202000)
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2025年第45卷第3期
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  • 接收时间:2024-08-09
  • 首发时间:2026-03-18
  • 出版时间:2025-03-20
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  • 收稿日期:2024-08-09
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    广东省生态环境监测中心,广东 广州 510308

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2种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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