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浏览/检索结果: 共11条，第1-10条 帮助 限定条件 专题：地理科学与资源研究所    第一署名单位    第一作者单位    通讯作者单位     已选(0)清除 条数/页：5101520253035404550556065707580859095100   排序方式：请选择作者升序作者降序题名升序题名降序发表日期升序发表日期降序提交时间升序提交时间降序 Water-soluble chitosan enhances phytoremediation efficiency of cadmium by Hylotelephium spectabile in contaminated soils 期刊论文 CARBOHYDRATE POLYMERS, 2020, 卷号: 246, 页码: 9 作者:  Guo, Junmei;  Yang, Jun;  Yang, Junxing;  Zheng, Guodi;  Chen, Tongbin 收藏  |  浏览/下载：150/0  |  提交时间：2021/03/18 Water-soluble chitosan  Contaminated soil  Phytoremediation  Hylotelephium spectabile  FTIR spectra   Passivation of lead and cadmium and increase of the nutrient content during sewage sludge composting by phosphate amendments 期刊论文 ENVIRONMENTAL RESEARCH, 2020, 卷号: 185, 页码: 9 作者:  Zheng, Guodi;  Wang, Xiankai;  Chen, Tongbin;  Yang, Jun;  Yang, Junxing 收藏  |  浏览/下载：65/0  |  提交时间：2021/03/23 Sewage sludge  Composting  Heavy metals  Phosphorus speciation  X-ray absorption near-edge structure   Effects and interactions of cadmium and zinc on root morphology and metal translocation in two populations ofHylotelephium spectabile(Boreau) H. Ohba, a potential Cd-accumulating species 期刊论文 ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2020, 页码: 12 作者:  Guo, Junmei;  Guo, Yue;  Yang, Jun;  Yang, Junxing;  Zheng, Guodi 收藏  |  浏览/下载：51/0  |  提交时间：2021/03/18 Cadmium  Zinc  Root morphology  Metal translocation  Hylotelephium spectabile(Boreau) H  Ohba   Identifying factors that influence soil heavy metals by using categorical regression analysis: A case study in Beijing, China 期刊论文 FRONTIERS OF ENVIRONMENTAL SCIENCE & ENGINEERING, 2020, 卷号: 14, 期号: 3, 页码: 14 作者:  Yang, Jun;  Wang, Jingyun;  Qiao, Pengwei;  Zheng, Yuanming;  Yang, Junxing 收藏  |  浏览/下载：4/0  |  提交时间：2020/05/19 Soil  Heavy metal  Influencing factor  Categorical regression  Identification method   Identifying factors that influence soil heavy metals by using categorical regression analysis:A case study in Beijing,China 期刊论文 环境科学与工程前沿(英文), 2020, 卷号: 14.0, 期号: 003, 页码: 1 作者:  Jun Yang;  Jingyun Wang;  Pengwei Qiao;  Yuanming Zheng;  Junxing Yang 收藏  |  浏览/下载：4/0  |  提交时间：2021/03/16 Soil  Heavy  metal  Influencing  factor  Categorical  regression  Identification  method   Effects of environmental governance in mining areas: The trend of arsenic concentration in the environmental media of a typical mining area in 25 years 期刊论文 CHEMOSPHERE, 2019, 卷号: 235, 页码: 849-857 作者:  Zhao, Chen;  Yang, Jun;  Zheng, Yuanming;  Yang, Junxing;  Guo, Guanghui 收藏  |  浏览/下载：29/0  |  提交时间：2020/03/23 Arsenic mining area  Environmental management measures  Effectiveness assessment  Contaminated environmental media  Contaminant concentration change   Emission characteristics of VOCs and potential ozone formation from a full-scale sewage sludge composting plant 期刊论文 SCIENCE OF THE TOTAL ENVIRONMENT, 2019, 卷号: 659, 页码: 664-672 作者:  Nie, Erqi;  Zheng, Guodi;  Gao, Ding;  Chen, Tongbin;  Yang, Junxing 收藏  |  浏览/下载：74/0  |  提交时间：2019/05/22 Sewage sludge  Composting  Volatile organic compounds  Potential ozone formation   SOURCE TRACING OF SOIL HEAVY METALS BASED ON SPATIAL STRUCTURE CHARACTERISTICS: A CASE STUDY OF SEWAGE IRRIGATION AREA IN SOUTHEAST SUBURB OF BEIJING 期刊论文 FRESENIUS ENVIRONMENTAL BULLETIN, 2019, 卷号: 28, 期号: 8, 页码: 5847-5855 作者:  Wang, Jingyun;  Yang, Jun;  Zheng, Yuanming;  Yang, Junxing;  Lei, Mei 收藏  |  浏览/下载：24/0  |  提交时间：2020/03/23 soil  heavy metal  pollution sources  spatial structure  geostatistics   Oxygen Monitoring Equipment for Sewage-Sludge Composting and Its Application to Aeration Optimization 期刊论文 SENSORS, 2018, 卷号: 18, 期号: 11, 页码: 9 作者:  Zheng, Guodi;  Wang, Yuewei;  Wang, Xiankai;  Yang, Junxing;  Chen, Tongbin 收藏  |  浏览/下载：33/0  |  提交时间：2019/05/23 compost  oxygen concentration  detection  monitor  aeration optimization   Method for identifying outliers of soil heavy metal data 期刊论文 ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2018, 卷号: 25, 期号: 13, 页码: 12868-12875 作者:  Yang, Jun;  Wang, Jingyun;  Zheng, Yuanming;  Lei, Mei;  Yang, Junxing 收藏  |  浏览/下载：26/0  |  提交时间：2019/05/23 Soil heavy metal  Outlier data  Checkout method  Cross-validation  Prediction accuracy