Monitoring of heavy metals contamination of agricultural products and their transfer and bioaccumulation in crops like rice has become a hot topic worldwide over the last two decades. The present study was carried out...Monitoring of heavy metals contamination of agricultural products and their transfer and bioaccumulation in crops like rice has become a hot topic worldwide over the last two decades. The present study was carried out to determine the accumulation of heavy metals in rice fields and their transfer to rice grains. Soil, irrigation water and rice grains samples were gathered in Maga-Pouss, Far-North, Cameroon. Concentrations of six heavy metals (lead, cadmium, zinc, copper, iron and mercury) were evaluated by Atomic Absorption Spectrophotometer (AAS). Mercury was not detected in this study. Average concentrations of metals were in this order (in mg/kg): Fe (188.60 ± 97.06) > Pb (63.63 ± 7.11) > Cd (2.59 ± 0.29) > Zn (1.10 ± 1.05) > Cu (0.80 ± 0.73) in water and Pb (105.50 ± 31.11) > Fe (105.50 ± 31.11) > Cu (45.93 ± 14.39) > Zn (22.52 ± 6.40) > Cd (3.15 ± 0.49) in soil. Water in Maga-Pouss rice fields appears to be more harmful than the soil, notably for lead, cadmium and copper. In rice grains, heavy metals were found in this order (mg/kg): Fe (188.01 ± 82.62) > Cu (27.20 ± 0.00) > Zn (23.61 ± 12.42) > Pb (19.50 ± 19.91) > Cd (2.02 ± 1.05). The mean bioconcentration factor (BCF) of metals from soil to rice grains was in the following order: Fe (2.60) > Zn (1.05) > Cd (0.64) > Cu (0.59) > Pb (0.18). From water to rice grains, the order is: Cu (37.26) > Zn (22.49) > Cd (6.97) > Pb (2.74) > Fe (1.94). Rice field pH and electrical conductivity favored the uptake of lead, copper and cadmium by rice grains. The findings of this study will be good documentation for risk assessment, and decision-making by environmental managers in this region.展开更多
The use of groundwater for drinking water supply to the population is increasingly practiced in the rice cultivation area of Maga. However, there is a lack of knowledge about the hydrochemical characteristics of this ...The use of groundwater for drinking water supply to the population is increasingly practiced in the rice cultivation area of Maga. However, there is a lack of knowledge about the hydrochemical characteristics of this water due to a lack of quality control. This study aims to contribute to the understanding of mineralization processes in order to establish the hydrochemical profile of the water in the area. The methodological approach consisted of collecting fifteen water samples from wells and boreholes during six campaigns for physicochemical analysis, and studying them through methods of interpreting hydrochemical data. The analysis results show that these waters are moderately mineralized. The water facies are mainly of the bicarbonate sodium and potassium type, as well as the bicarbonate calcium and magnesium type. Calculation of saturation indices demonstrates that evaporite minerals show lower degrees of saturation than carbonate minerals, with gypsum, anhydrite, and halite being in a highly undersaturated state. The mineralization of groundwater originates from the dissolution of surrounding rocks on the one hand, and anthropogenic activities involving exchanges between alkalis (Na+ and K+) in the aquifer and alkaline earth (Ca2+ and Mg2+), resulting in the fixation of alkaline earth and the dissolution of alkalis.展开更多
For the deficiency that the traditional single forecast methods could not forecast electronic equipment states, a combined forecast method based on the hidden Markov model（HMM） and least square support vector machin...For the deficiency that the traditional single forecast methods could not forecast electronic equipment states, a combined forecast method based on the hidden Markov model（HMM） and least square support vector machine（LS-SVM） is presented. The multi-agent genetic algorithm（MAGA） is used to estimate parameters of HMM to overcome the problem that the Baum-Welch algorithm is easy to fall into local optimal solution. The state condition probability is introduced into the HMM modeling process to reduce the effect of uncertain factors. MAGA is used to estimate parameters of LS-SVM. Moreover, pruning algorithms are used to estimate parameters to get the sparse approximation of LS-SVM so as to increase the ranging performance. On the basis of these, the combined forecast model of electronic equipment states is established. The example results show the superiority of the combined forecast model in terms of forecast precision,calculation speed and stability.展开更多
文摘Monitoring of heavy metals contamination of agricultural products and their transfer and bioaccumulation in crops like rice has become a hot topic worldwide over the last two decades. The present study was carried out to determine the accumulation of heavy metals in rice fields and their transfer to rice grains. Soil, irrigation water and rice grains samples were gathered in Maga-Pouss, Far-North, Cameroon. Concentrations of six heavy metals (lead, cadmium, zinc, copper, iron and mercury) were evaluated by Atomic Absorption Spectrophotometer (AAS). Mercury was not detected in this study. Average concentrations of metals were in this order (in mg/kg): Fe (188.60 ± 97.06) > Pb (63.63 ± 7.11) > Cd (2.59 ± 0.29) > Zn (1.10 ± 1.05) > Cu (0.80 ± 0.73) in water and Pb (105.50 ± 31.11) > Fe (105.50 ± 31.11) > Cu (45.93 ± 14.39) > Zn (22.52 ± 6.40) > Cd (3.15 ± 0.49) in soil. Water in Maga-Pouss rice fields appears to be more harmful than the soil, notably for lead, cadmium and copper. In rice grains, heavy metals were found in this order (mg/kg): Fe (188.01 ± 82.62) > Cu (27.20 ± 0.00) > Zn (23.61 ± 12.42) > Pb (19.50 ± 19.91) > Cd (2.02 ± 1.05). The mean bioconcentration factor (BCF) of metals from soil to rice grains was in the following order: Fe (2.60) > Zn (1.05) > Cd (0.64) > Cu (0.59) > Pb (0.18). From water to rice grains, the order is: Cu (37.26) > Zn (22.49) > Cd (6.97) > Pb (2.74) > Fe (1.94). Rice field pH and electrical conductivity favored the uptake of lead, copper and cadmium by rice grains. The findings of this study will be good documentation for risk assessment, and decision-making by environmental managers in this region.
文摘The use of groundwater for drinking water supply to the population is increasingly practiced in the rice cultivation area of Maga. However, there is a lack of knowledge about the hydrochemical characteristics of this water due to a lack of quality control. This study aims to contribute to the understanding of mineralization processes in order to establish the hydrochemical profile of the water in the area. The methodological approach consisted of collecting fifteen water samples from wells and boreholes during six campaigns for physicochemical analysis, and studying them through methods of interpreting hydrochemical data. The analysis results show that these waters are moderately mineralized. The water facies are mainly of the bicarbonate sodium and potassium type, as well as the bicarbonate calcium and magnesium type. Calculation of saturation indices demonstrates that evaporite minerals show lower degrees of saturation than carbonate minerals, with gypsum, anhydrite, and halite being in a highly undersaturated state. The mineralization of groundwater originates from the dissolution of surrounding rocks on the one hand, and anthropogenic activities involving exchanges between alkalis (Na+ and K+) in the aquifer and alkaline earth (Ca2+ and Mg2+), resulting in the fixation of alkaline earth and the dissolution of alkalis.
文摘For the deficiency that the traditional single forecast methods could not forecast electronic equipment states, a combined forecast method based on the hidden Markov model（HMM） and least square support vector machine（LS-SVM） is presented. The multi-agent genetic algorithm（MAGA） is used to estimate parameters of HMM to overcome the problem that the Baum-Welch algorithm is easy to fall into local optimal solution. The state condition probability is introduced into the HMM modeling process to reduce the effect of uncertain factors. MAGA is used to estimate parameters of LS-SVM. Moreover, pruning algorithms are used to estimate parameters to get the sparse approximation of LS-SVM so as to increase the ranging performance. On the basis of these, the combined forecast model of electronic equipment states is established. The example results show the superiority of the combined forecast model in terms of forecast precision,calculation speed and stability.
