本文主要研究内容
作者于庆（2019）在《龙口市北部平原冬小麦种植区重金属遥感监测与污染评价研究》一文中研究指出：近年来,我国频发的重金属污染事件使人们更加重视粮食种植区的重金属污染问题。土壤环境受到重金属污染后,重金属在土壤中难以转化分解,又易于累积,危害性强且持久,根本无法保障安全稳定的农业生产,再加上重金属被作物吸收后又能转化为对人类危害更强的化合物,对人类的生存造成威胁,因此高效地对粮食种植区的重金属进行监测尤为重要。为研究出高效、经济且快速的重金属监测方法并探明粮食种植区的重金属污染状况,本文在国家自然科学基金项目“基于地表参量的污水灌溉区农田生态安全遥感监测研究”(No.41371395)的支持下,以山东省胶东地区的主要粮食产区—龙口市北部平原的冬小麦种植区为研究区,共设立了125个土壤采样点和61个冬小麦采样点,并用地物光谱仪实地测量土壤反射光谱和冬小麦冠层反射光谱。利用野外实测的冬小麦冠层光谱数据、土壤的反射光谱以及实验室重金属的化验数据建立起土壤及冬小麦叶片中的8种重金属(As、Cd、Cr、Cu、Hg、Ni、Pb以及Zn)的高光谱估测模型,并运用地统计学以及多元分析的方法结合多源数据进行各种重金属的来源分析,空间分布分析以及污染状况评价等,主要研究结果如下:(1)利用野外实测土壤光谱对土壤重金属含量进行估测,其中BP神经网络(Back Propagation Neural Network,BPNN)模型对八种重金属的估测能力最强,地理加权回归(Geographically Weighted Regression,GWR)模型和偏最小二乘回归(Partial Least Squares Regression,PLSR)模型次之,逐步多元线性回归(Stepwise Multiple Linear Regression,SMLR)模型效果最差。同时土壤原始反射光谱(Original Spectral Reflectance,R)经各过种光谱变换及信息提取后,估测能力都有不同程度的提高,以光谱反射率的一阶微分(First-order Derivative of Reflectivity,FDR)为自变量建立BPNN模型是重金属As、Cu、Hg、Ni、Pb和Zn的最优模型,以光谱反射率的二阶微分(Second-order Derivative of Reflectivity,SDR)为自变量建立的BPNN模型是重金属Cd和Cr的最优模型。(2)在进行冬小麦叶片中重金属含量估测时,发现BPNN模型对冬小麦叶片重金属的估测能力也是最强的,其中以SDR为自变量建立的BPNN模型是重金属Cd和Zn的最优估测模型,对于重金属As,Ni,Hg以及Pb,以FDR为自变量建立BPNN模型的估测效果最为理想,三边参数(Spectral Parameters of the Three Sides,SPs)—BPNN模型是重金属Cr,Cu的最优模型。(3)对于研究区土壤重金属的空间分布特征及来源,地统计学、多元分析以及多源数据的结合分析表明,Cr、Ni、As有相似的空间分布,西北部含量较低,东北部及南部含量较高,主要来源于土壤母质—花岗岩,并且受地势的影响大;对于重金属Hg,交通排放和工业污染及北部、西部较低的地势导致了重金属Hg在中部城区、北部以及西部部分地区集聚;对于重金属Zn,成土母质以及工农业活动对重金属Zn的富集都产生影响,主要分布在西北以及南部;Pb在研究区西南部以及东北部出现集聚,主要来源于工农业等人为活动,受地质背景影响小;Cd主要分布在研究区中部以及南部,受工业以及地势的影响大;对于重金属Cu,主要分布在西北部以及东南部,受工农业等人为活动影响大。(4)综合单因子污染指数、综合污染指数评价以及潜在生态风险评价结果来看,该研究区的土壤重金属的污染水平依次为Cd>Pb>Hg>Cu>Ni>Zn>Cr>As;近一半的采样点属于污染警戒值以下,部分采样点超出轻微污染水平,只有个别采样点达到中度污染水平,研究区大部分地区的生态风险水平较低,只有西部以及东北部主要处于中等生态风险水平,这些地区的土壤环境质量应该予以足够重视。
Abstract
jin nian lai ,wo guo pin fa de chong jin shu wu ran shi jian shi ren men geng jia chong shi liang shi chong zhi ou de chong jin shu wu ran wen ti 。tu rang huan jing shou dao chong jin shu wu ran hou ,chong jin shu zai tu rang zhong nan yi zhuai hua fen jie ,you yi yu lei ji ,wei hai xing jiang ju chi jiu ,gen ben mo fa bao zhang an quan wen ding de nong ye sheng chan ,zai jia shang chong jin shu bei zuo wu xi shou hou you neng zhuai hua wei dui ren lei wei hai geng jiang de hua ge wu ,dui ren lei de sheng cun zao cheng wei xie ,yin ci gao xiao de dui liang shi chong zhi ou de chong jin shu jin hang jian ce you wei chong yao 。wei yan jiu chu gao xiao 、jing ji ju kuai su de chong jin shu jian ce fang fa bing tan ming liang shi chong zhi ou de chong jin shu wu ran zhuang kuang ,ben wen zai guo jia zi ran ke xue ji jin xiang mu “ji yu de biao can liang de wu shui guan gai ou nong tian sheng tai an quan yao gan jian ce yan jiu ”(No.41371395)de zhi chi xia ,yi shan dong sheng jiao dong de ou de zhu yao liang shi chan ou —long kou shi bei bu ping yuan de dong xiao mai chong zhi ou wei yan jiu ou ,gong she li le 125ge tu rang cai yang dian he 61ge dong xiao mai cai yang dian ,bing yong de wu guang pu yi shi de ce liang tu rang fan she guang pu he dong xiao mai guan ceng fan she guang pu 。li yong ye wai shi ce de dong xiao mai guan ceng guang pu shu ju 、tu rang de fan she guang pu yi ji shi yan shi chong jin shu de hua yan shu ju jian li qi tu rang ji dong xiao mai xie pian zhong de 8chong chong jin shu (As、Cd、Cr、Cu、Hg、Ni、Pbyi ji Zn)de gao guang pu gu ce mo xing ,bing yun yong de tong ji xue yi ji duo yuan fen xi de fang fa jie ge duo yuan shu ju jin hang ge chong chong jin shu de lai yuan fen xi ,kong jian fen bu fen xi yi ji wu ran zhuang kuang ping jia deng ,zhu yao yan jiu jie guo ru xia :(1)li yong ye wai shi ce tu rang guang pu dui tu rang chong jin shu han liang jin hang gu ce ,ji zhong BPshen jing wang lao (Back Propagation Neural Network,BPNN)mo xing dui ba chong chong jin shu de gu ce neng li zui jiang ,de li jia quan hui gui (Geographically Weighted Regression,GWR)mo xing he pian zui xiao er cheng hui gui (Partial Least Squares Regression,PLSR)mo xing ci zhi ,zhu bu duo yuan xian xing hui gui (Stepwise Multiple Linear Regression,SMLR)mo xing xiao guo zui cha 。tong shi tu rang yuan shi fan she guang pu (Original Spectral Reflectance,R)jing ge guo chong guang pu bian huan ji xin xi di qu hou ,gu ce neng li dou you bu tong cheng du de di gao ,yi guang pu fan she lv de yi jie wei fen (First-order Derivative of Reflectivity,FDR)wei zi bian liang jian li BPNNmo xing shi chong jin shu As、Cu、Hg、Ni、Pbhe Znde zui you mo xing ,yi guang pu fan she lv de er jie wei fen (Second-order Derivative of Reflectivity,SDR)wei zi bian liang jian li de BPNNmo xing shi chong jin shu Cdhe Crde zui you mo xing 。(2)zai jin hang dong xiao mai xie pian zhong chong jin shu han liang gu ce shi ,fa xian BPNNmo xing dui dong xiao mai xie pian chong jin shu de gu ce neng li ye shi zui jiang de ,ji zhong yi SDRwei zi bian liang jian li de BPNNmo xing shi chong jin shu Cdhe Znde zui you gu ce mo xing ,dui yu chong jin shu As,Ni,Hgyi ji Pb,yi FDRwei zi bian liang jian li BPNNmo xing de gu ce xiao guo zui wei li xiang ,san bian can shu (Spectral Parameters of the Three Sides,SPs)—BPNNmo xing shi chong jin shu Cr,Cude zui you mo xing 。(3)dui yu yan jiu ou tu rang chong jin shu de kong jian fen bu te zheng ji lai yuan ,de tong ji xue 、duo yuan fen xi yi ji duo yuan shu ju de jie ge fen xi biao ming ,Cr、Ni、Asyou xiang shi de kong jian fen bu ,xi bei bu han liang jiao di ,dong bei bu ji na bu han liang jiao gao ,zhu yao lai yuan yu tu rang mu zhi —hua gang yan ,bing ju shou de shi de ying xiang da ;dui yu chong jin shu Hg,jiao tong pai fang he gong ye wu ran ji bei bu 、xi bu jiao di de de shi dao zhi le chong jin shu Hgzai zhong bu cheng ou 、bei bu yi ji xi bu bu fen de ou ji ju ;dui yu chong jin shu Zn,cheng tu mu zhi yi ji gong nong ye huo dong dui chong jin shu Znde fu ji dou chan sheng ying xiang ,zhu yao fen bu zai xi bei yi ji na bu ;Pbzai yan jiu ou xi na bu yi ji dong bei bu chu xian ji ju ,zhu yao lai yuan yu gong nong ye deng ren wei huo dong ,shou de zhi bei jing ying xiang xiao ;Cdzhu yao fen bu zai yan jiu ou zhong bu yi ji na bu ,shou gong ye yi ji de shi de ying xiang da ;dui yu chong jin shu Cu,zhu yao fen bu zai xi bei bu yi ji dong na bu ,shou gong nong ye deng ren wei huo dong ying xiang da 。(4)zeng ge chan yin zi wu ran zhi shu 、zeng ge wu ran zhi shu ping jia yi ji qian zai sheng tai feng xian ping jia jie guo lai kan ,gai yan jiu ou de tu rang chong jin shu de wu ran shui ping yi ci wei Cd>Pb>Hg>Cu>Ni>Zn>Cr>As;jin yi ban de cai yang dian shu yu wu ran jing jie zhi yi xia ,bu fen cai yang dian chao chu qing wei wu ran shui ping ,zhi you ge bie cai yang dian da dao zhong du wu ran shui ping ,yan jiu ou da bu fen de ou de sheng tai feng xian shui ping jiao di ,zhi you xi bu yi ji dong bei bu zhu yao chu yu zhong deng sheng tai feng xian shui ping ,zhe xie de ou de tu rang huan jing zhi liang ying gai yu yi zu gou chong shi 。
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论文作者分别是来自山东师范大学的于庆，发表于刊物山东师范大学2019-07-06论文，是一篇关于遥感反演论文,光谱论文,龙口市论文,冬小麦种植区论文,重金属论文,污染评价论文,山东师范大学2019-07-06论文的文章。本文可供学术参考使用，各位学者可以免费参考阅读下载，文章观点不代表本站观点，资料来自山东师范大学2019-07-06论文网站，若本站收录的文献无意侵犯了您的著作版权，请联系我们删除。
标签：遥感反演论文; 光谱论文; 龙口市论文; 冬小麦种植区论文; 重金属论文; 污染评价论文; 山东师范大学2019-07-06论文;