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| 1 | 水源地周边土壤重金属分布特征及潜在风险-以深圳市为例显示文摘以深圳市水源地周边三种不同类型土壤(赤红壤､红壤､水稻田土)为研究对象,选择具有较高环境含量且毒性较强的Zn､Pb､As为目标,探究其在三种土壤剖面淋溶层､淀积层和母质层(A、B和C层)中的分布特征及赋存形态,同时分析重金属含量､赋存形态与土壤理化性质的相关性,并利用潜在生态危害指数法和潜在迁移指数法从重金属全量和赋存形态两个角度对土壤重金属的生态风险水平进行评估.结果表明:土壤重金属Zn､Pb､As在三类土壤各层中含量较高,但均低于当地土壤环境质量背景值,重金属含量受当地土壤成岩母质影响较大.形态分析表明三种重金属在三类土壤中均以残渣态为主,但红壤中可还原态Pb含量较高,在低pH时易转化为弱酸可溶态,而后释放并迁移.相关性分析表明Zn､Pb的全量与有机质含量呈极显著正相关,可还原态Zn与pH呈显著正相关,Pb和As的弱酸可溶态与可还原态显著正相关,黏粒和粉粒含量对Pb和As的形态分布造成不同程度的影响.三种重金属潜在生态危害级别均为轻微,对水源地安全潜在生态风险影响较小;重金属迁移能力大小在不同土类中依次为红壤土>赤红壤>水稻田土,元素本身迁移能力强弱依次为Zn>As>Pb;赤红壤和水稻田土A层Zn迁移能力最强,B层As最强,C层Zn､Pb､As均较弱;红壤中A层迁移能力Zn最强,B层Zn､Pb､As均较强,C层Zn､Pb､As均较弱. | 周睿 秦超 任何军 赵妍 | 2022 | 中国环境科学2022，42，6： | 5 |
| 2 | Proximal sensor-enhanced soil mapping in complex soil-landscape areas of Brazil显示文摘Portable X-ray fluorescence(pXRF) spectrometry and magnetic susceptibility(MS) via magnetometer have been increasingly used with terrain variables for digital soil mapping. However, this methodology is still emerging in many countries with tropical soils. The objective of this study was to use proximal soil sensor data associated with terrain variables at varying spatial resolutions to predict soil classes using the Random Forest(RF) algorithm. The study was conducted on a 316-ha area featuring highly variable soil classes and complex soil-landscape relationships in Minas Gerais State, Brazil. The overall accuracy and Kappa index were evaluated using soils that were classified at 118 sites, with 90 being used for modeling and 28 for validation. Digital elevation models(DEMs) were created at 5-, 10-, 20-, and 30-m resolutions using contour lines from two sources. The resulting DEMs were processed to generate 12 terrain variables. Total Fe, Ti, and SiO_(2) contents were obtained using pXRF, with MS determined via a magnetometer. Soil class prediction was performed using the RF algorithm. The quality of the soil maps improved when using only the five most important covariates and combining proximal sensor data with terrain variables at different spatial resolutions. The finest spatial resolution did not always provide the most accurate maps. The high soil complexity in the area prevented highly accurate predictions. The most important variables influencing the soil mapping were MS, Fe, and Ti. Proximal sensor data associated with terrain information were successfully used to map Brazilian soils at variable spatial resolutions. | Sérgio H.G.SILVA David C.WEINDORF Wilson M.FARIA Leandro C.PINTO Michele D.MENEZES Luiz R.G.GUILHERME Nilton CURI | 2021 | Pedosphere2021，31，4： | 1 |
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