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A Review of the Distribution Coefficient (K_d) of Some Selected Heavy Metals over the Last Decade (2012-2021)

Journal of Geoscience and Environment Protection 2022年第8期10卷 199-242页

作者： Chabi Dari Seidou Tiejun Wang mikouendanandi mouendo rahmat brice espoire Yibin Dai Yutao Zuo Institute of Surface-Earth System Science School of Earth System Science Tianjin University Tianjin China Tianjin Bohai Rim Coastal Earth Critical Zone National Observation and Research Station Tianjin University Tianjin China Tianjin Key Laboratory of Earth Critical Zone Science and Sustainable Development in Bohai Rim Tianjin University Tianjin China

This review synthesizes the methods for estimating the distribution coefficient (Kd) and provides a compilation of Kd values for five heavy metals (As, Pb, Cd, Cu, and Zn) based on research published in the last decade (2012-2021). We used the PRISMA method to ensure the transparency of the collected data. For mono-metal systems (MS), the Kd values ranged from 10-2 to 107 L/kg for Pb, from 10-2 to 106 L/kg for Cd, As, and Zn, and from 10-2 to 105 L/kg for Cu. In competitive systems (CS), the Kd values ranged from 10-2 to 105 L/kg for Cu, and 10-2 to 104 L/kg for Pb, Cd, and Zn, while no Kd value for As was reported under CS. It was found that the Kd values of heavy metals are affected not only by soil chemical and physical properties but also by the nature and characteristics of the metal involved along with experimental conditions. The totals references number of Kd data observation per element metal are represented as follows: Cd 35 (50%), Zn 35 (50%), Pb 33 (47.14%), Cu 33 (47.14%), and As 19 (27.14%). Overall, most research was done 1) on MS rather than CS, 2) on sorption rather than desorption, 3) on soil rather than sediments , and 4) most literature have reported the Kd values, derived from batch method than on column method. Despite significant progress over the past decade towards a better understanding of the variation in Kd values and the effect of factors influencing them to provide important parameters for predicting and controlling toxic metals in soils, additional research is still warranted to the complexity of underlying processes.

关键词： Kd Values Heavy Metals PRISMA Sorption Desorption

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Assessing Change of Lamto Reserve Area Based on the MODIS Time Series Data and Bioclimatic Factors Using BFAST Algorithms

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American Journal of Plant Sciences 2022年第4期13卷 517-540页

作者： Christian Jonathan Anoma Kouassi Dilawar Khan Lutumba Suika Achille James Kehinde Omifolaji mikouendanandi mouendo rahmat brice espoire Kebin Zhang Xiaohui Yang School of Soil and Water Conservation Beijing Forestry University Beijing China School of Ecology and Nature Conservation Beijing Forestry University Beijing China Department of Forestry and Wildlife Management Federal University Dutse Dutse Jigawa State Nigeria School of Economics and Management Beijing Forestry University Beijing China Institute of Desertification Studies Chinese Academy of Forestry Beijing China

Lamto Reserve area is a savannah landscape threatened by periodic drought, and anthropogenic activities leading to natural ecological imbalance. The ecological support services of the landscape had been significantly impacted by the grassland ecosystem. The Breaks for Additive Season and Trend Algorithms have been implemented in R to analyze the land cover/land use dynamic in relation to the climatic driver of Lamto forest from 2000 to 2020. We examine the vegetation state breaks using vegetation phenological patterns, and several time series including the Normalized Difference Vegetation Index and the Enhanced Vegetation Index, were studied utilizing Breaks for Additive Season and Trend. The findings indicate that the phenological changes in the vegetation in 2020 resulted from an increased temperature from (27.7°C) to (32.17°C), and a decrease in precipitation (71.75 millimeters). The analysis of variance ANOVA of the non-parametric Mann-Kendall test reveals a strong correlation between Precipitation/Evapotranspiration Grass (p mperature/Evapotranspiration Grass (p < 0.311), and Temperature/Normalized Difference Vegetation Index (p in vegetation detected by the Breaks for Additive Season and Trend Algorithms were caused by temperature extremes and reduced rainfall.

关键词： Assessing Break Change Detection Land Cover Climate Extreme Vegetation Vulnerability

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