analgesic
发表于 2025-3-21 16:08:09
书目名称Recent Trends in Modelling of Environmental Contaminants影响因子(影响力)<br> http://impactfactor.cn/if/?ISSN=BK0823480<br><br> <br><br>书目名称Recent Trends in Modelling of Environmental Contaminants影响因子(影响力)学科排名<br> http://impactfactor.cn/ifr/?ISSN=BK0823480<br><br> <br><br>书目名称Recent Trends in Modelling of Environmental Contaminants网络公开度<br> http://impactfactor.cn/at/?ISSN=BK0823480<br><br> <br><br>书目名称Recent Trends in Modelling of Environmental Contaminants网络公开度学科排名<br> http://impactfactor.cn/atr/?ISSN=BK0823480<br><br> <br><br>书目名称Recent Trends in Modelling of Environmental Contaminants被引频次<br> http://impactfactor.cn/tc/?ISSN=BK0823480<br><br> <br><br>书目名称Recent Trends in Modelling of Environmental Contaminants被引频次学科排名<br> http://impactfactor.cn/tcr/?ISSN=BK0823480<br><br> <br><br>书目名称Recent Trends in Modelling of Environmental Contaminants年度引用<br> http://impactfactor.cn/ii/?ISSN=BK0823480<br><br> <br><br>书目名称Recent Trends in Modelling of Environmental Contaminants年度引用学科排名<br> http://impactfactor.cn/iir/?ISSN=BK0823480<br><br> <br><br>书目名称Recent Trends in Modelling of Environmental Contaminants读者反馈<br> http://impactfactor.cn/5y/?ISSN=BK0823480<br><br> <br><br>书目名称Recent Trends in Modelling of Environmental Contaminants读者反馈学科排名<br> http://impactfactor.cn/5yr/?ISSN=BK0823480<br><br> <br><br>
generic
发表于 2025-3-21 23:27:00
https://doi.org/10.1007/978-81-322-1783-1Acid Mine Drainage; Environmental Radioactivity; Geophysical Subsurface Imaging; Industrial Waste Water
transplantation
发表于 2025-3-22 02:36:07
Debashish SenguptaDiscusses important aspects with pertinent and recent case studies.Elucidates various aspects of modelling and interpretation of the environmental contaminants and subsequent remedial actions.Helps to
interpose
发表于 2025-3-22 05:06:56
Radiometric Studies and Base Line Calibrations for NORM and TENORM Studies,s even have much higher background radiation, and are known as very high background radiation areas (VHBRAs). In this chapter, applications of radioactivity measurements are demonstrated with case studies for various studies, namely characterization of the area as background or HBRAs or VHBRAs, envi
placebo-effect
发表于 2025-3-22 12:33:33
Arsenic Fate and Transport in the Groundwater-Soil-Plant System: An Understanding of Suitable Rice nd arsenate respectively during uptake by plants. Environmental factor such as soil flooding condition is also one of the potential factors influencing arsenic accumulation in rice by increasing As mobility in the soil–rhizosphere. In biological factors, rice genotype, which controls the root aerati
赞成你
发表于 2025-3-22 15:09:17
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捕鲸鱼叉
发表于 2025-3-22 17:03:23
Very Low-Frequency Electromagnetic Method: A Shallow Subsurface Investigation Technique for Geophyss measured, and interpretation procedures are discussed in detail. Finally, the efficacy of the VLF method for groundwater investigation, mineral investigation, and landslide and subsurface pollution monitoring studies has been demonstrated. Even though the VLF method is a rapid technique for subsur
一美元
发表于 2025-3-22 23:24:36
Treatment of Industrial Wastewater,n design of experiment was used to design 15 sets of experiments, to be used in RSM optimization, with the help of Design Expert software version 8.0.6.1. After the optimization under three imposed constraints, the values for the process variables giving optimum results were: catalyst dose, 1.5 g/L,
Defraud
发表于 2025-3-23 03:48:30
Applications of Remote Sensing, Geographic Information System and Geostatistics in the Study of Arss utilizing Remote Sensing data may help in understanding the nature of distribution of arsenic-contaminated areas. GIS helps capturing, retrieving and analysing geospatial data. It recreates a real-world scenario and helps in analysing and creating new relevant information. In arsenic affected area
oncologist
发表于 2025-3-23 08:51:24
,Modeling Radon Behavior for Characterizing and Forecasting Geophysical Variables at the Atmosphere–ration can be a good tracer of stability of the boundary layer (BL) (the lowest part of the atmosphere). In particular, this led to working out diagnostic models for the estimation of the mixing depth from radon data and prognostic neural network (NN) models for forecasting its future behavior, thus