Modeling transport of arsenic through modified granular natural siderite filters for arsenic removal

Groundwater arsenic (As) contamination is a hot issue,which is severe health concern worldwide.Recently,many Fe-based adsorbents have been used for As removal from solutions.Modified granular natural siderite (MGNS),a special hybrid Fe(II)/Fe(III) system,had higher adsorption capacity for As(III) than As(V),but the feasibility of its application in treating high-As groundwater is still unclear.In combination with transport modeling,laboratory column studies and field pilot tests were performed to reveal both mechanisms and factors controlling As removal by MGNS-filled filters.Results show that weakly acid pH and discontinuous treatment enhanced As(III) removal,with a throughput of 8700 bed volumes (BV) of 1.0 mg/L As(III) water at breakthrough of 10 mg/L As at pH 6.Influent HCO3^- inhibited As removal by the filters.Iron mineral species,SEM and XRD patterns of As-loading MGNS show that the important process contributing to high As(III) removal was the mineral transformation from siderite to goethite in the filter.The homogeneous surface diffusion modeling (HSDM) shows that competition between As(III) and HCO3^- with adsorption sites on MGNS was negligible.The inhibition of HCO3^- on As(III) removal was connected to inhibition of siderite dissolution and mineral transformation.Arsenic loadings were lower in field pilot tests than those in the laboratory experiments,showing that high concentrations of coexisting anions (especially HCO3^-- and SiO4^4-),high pH,low EBCT,and low groundwater temperature decreased As removal.It was suggested that acidification and aeration of high- As groundwater and discontinuous treatment would improve the MGNS filter performance of As removal from real high-As groundwater.

Density estimation-based method to determine sample size for random sample partition of big data

Random sample partition(RSP)is a newly developed big data representation and management model to deal with big data approximate computation problems.Academic research and practical applications have confirmed that RSP is an efficient solution for big data processing and analysis.However,a challenge for implementing RSP is determining an appropriate sample size for RSP data blocks.While a large sample size increases the burden of big data computation,a small size will lead to insufficient distribution information for RSP data blocks.To address this problem,this paper presents a novel density estimation-based method(DEM)to determine the optimal sample size for RSP data blocks.First,a theoretical sample size is calculated based on the multivariate Dvoretzky-Kiefer-Wolfowitz(DKW)inequality by using the fixed-point iteration(FPI)method.Second,a practical sample size is determined by minimizing the validation error of a kernel density estimator(KDE)constructed on RSP data blocks for an increasing sample size.Finally,a series of persuasive experiments are conducted to validate the feasibility,rationality,and effectiveness of DEM.Experimental results show that(1)the iteration function of the FPI method is convergent for calculating the theoretical sample size from the multivariate DKW inequality;(2)the KDE constructed on RSP data blocks with sample size determined by DEM can yield a good approximation of the probability density function(p.d.f);and(3)DEM provides more accurate sample sizes than the existing sample size determination methods from the perspective of p.d.f.estimation.This demonstrates that DEM is a viable approach to deal with the sample size determination problem for big data RSP implementation.

Privacy-preserving human activity sensing:A survey

With the prevalence of various sensors and smart devices in people’s daily lives,numerous types of information are being sensed.While using such information provides critical and convenient services,we are gradually exposing every piece of our behavior and activities.Researchers are aware of the privacy risks and have been working on preserving privacy while sensing human activities.This survey reviews existing studies on privacy-preserving human activity sensing.We first introduce the sensors and captured private information related to human activities.We then propose a taxonomy to structure the methods for preserving private information from two aspects:individual and collaborative activity sensing.For each of the two aspects,the methods are classified into three levels:signal,algorithm,and system.Finally,we discuss the open challenges and provide future directions.