The present study investigated potential effectiveness of certain chemical candidates for controlling internal phosphorus loading, and for delineating inactivation pattern in sediment depths of an eutrophic pond under...The present study investigated potential effectiveness of certain chemical candidates for controlling internal phosphorus loading, and for delineating inactivation pattern in sediment depths of an eutrophic pond under simulated mesocosm condition. Chemical administration （@ 30 mg/dm3） resulted in phosphate precipitation from water column concomitant with inactivation in sediments, under specific pH range and/or redox regime. The alum-lime combination dosing wrought the maximum reduction in orthophosphate （65.6%） and soluble reactive phosphate （71.9%） in water plus the utmost increment in sediment-P （0.257 ppm）. The inactivated P forms typically exhibited a downhill concentration gradient with highest sequestration in the uppermost sediment stratum. Ironbound P displayed the highest mobility while calcium- and aluminum- bound P behaved almost immune to internal feedback dynamics. The combo-treatment was established as the most effective phosphate scavenging and confiscating agent, to be adopted as chemical remediation regime for de-eutrophication, restoration and rehabilitation of the water body.展开更多
The increased production and price of rare earth elements(REEs) are indicative of their importance and of growing global attention. More accurate and practical exploration procedures are needed for REEs, and for other...The increased production and price of rare earth elements(REEs) are indicative of their importance and of growing global attention. More accurate and practical exploration procedures are needed for REEs, and for other geochemical resources. One such procedure is a multivariate approach. In this study, five classifiers, including multilayer perceptron(MLP), Bayesian, k-Nearest Neighbors(KNN), Parzen, and support vector machine(SVM),were applied in supervised pattern classification of bulk geochemical samples based on REEs, P, and Fe in the Kiruna type magnetite-apatite deposit of Se-Chahun,Central Iran. This deposit is composed of four rock types:(1) High anomaly(phosphorus iron ore),(2) Low anomaly(metasomatized tuff),(3) Low anomaly(iron ore), and(4)Background(iron ore and others). The proposed methods help to predict the proper classes for new samples from the study area without the need for costly and time-consuming additional studies. In addition, this paper provides a performance comparison of the five models. Results show that all five classifiers have appropriate and acceptable performance. Therefore, pattern classification can be used for evaluation of REE distribution. However, MLP and KNN classifiers show the same results and have the highest CCRs in comparison to Bayesian, Parzen, and SVM classifiers. MLP is more generalizable than KNN and seems to be an applicable approach for classification and predictionof the classes. We hope the predictability of the proposed methods will encourage geochemists to expand the use of numerical models in future work.展开更多
文摘The present study investigated potential effectiveness of certain chemical candidates for controlling internal phosphorus loading, and for delineating inactivation pattern in sediment depths of an eutrophic pond under simulated mesocosm condition. Chemical administration （@ 30 mg/dm3） resulted in phosphate precipitation from water column concomitant with inactivation in sediments, under specific pH range and/or redox regime. The alum-lime combination dosing wrought the maximum reduction in orthophosphate （65.6%） and soluble reactive phosphate （71.9%） in water plus the utmost increment in sediment-P （0.257 ppm）. The inactivated P forms typically exhibited a downhill concentration gradient with highest sequestration in the uppermost sediment stratum. Ironbound P displayed the highest mobility while calcium- and aluminum- bound P behaved almost immune to internal feedback dynamics. The combo-treatment was established as the most effective phosphate scavenging and confiscating agent, to be adopted as chemical remediation regime for de-eutrophication, restoration and rehabilitation of the water body.
文摘The increased production and price of rare earth elements(REEs) are indicative of their importance and of growing global attention. More accurate and practical exploration procedures are needed for REEs, and for other geochemical resources. One such procedure is a multivariate approach. In this study, five classifiers, including multilayer perceptron(MLP), Bayesian, k-Nearest Neighbors(KNN), Parzen, and support vector machine(SVM),were applied in supervised pattern classification of bulk geochemical samples based on REEs, P, and Fe in the Kiruna type magnetite-apatite deposit of Se-Chahun,Central Iran. This deposit is composed of four rock types:(1) High anomaly(phosphorus iron ore),(2) Low anomaly(metasomatized tuff),(3) Low anomaly(iron ore), and(4)Background(iron ore and others). The proposed methods help to predict the proper classes for new samples from the study area without the need for costly and time-consuming additional studies. In addition, this paper provides a performance comparison of the five models. Results show that all five classifiers have appropriate and acceptable performance. Therefore, pattern classification can be used for evaluation of REE distribution. However, MLP and KNN classifiers show the same results and have the highest CCRs in comparison to Bayesian, Parzen, and SVM classifiers. MLP is more generalizable than KNN and seems to be an applicable approach for classification and predictionof the classes. We hope the predictability of the proposed methods will encourage geochemists to expand the use of numerical models in future work.
