Source apportionment and source specific health risk assessment of HMs and PAHs in soils with an integrated framework in a typical cold agricultural region in China.

A new innovative methodology system framework for source apportionment and source-specific risk assessment has been proposed and actively applied to identify the contamination characteristics, oriented sources and health risks associated with contamination levels of Heavy metals (HMs) and Polycyclic Aromatic Hydrocarbons (PAHs) in soils, a typical cold agricultural region in Northeastern China. To achieve this meaningful goal, a large-scale dataset including 1780 top soil samples, 10 HMs and 16 priority PAHs has been organized and collected from a typical study area in China. The total concentrations of the 10 selected HMs in study area range from 0.05 to 2147.40 mg/kg, with an average of 549.25 ± 541.37 mg/kg. The average concentrations of PAHs for (3-6)-rings are 16.60 ± 18.90, 26.40 ± 28.20, 9.51 ± 13.00 and 1.99 ± 5.30 ng/g, respectively. On the base of optimized literature source fingerprints for HM and PAH, a widely used receptor model, positive matrix factorization (PMF) has been applied to apportion the contamination sources HMs and PAHs in soils. Then source-specific health risk of soil HMs and PAHs have been assessed using the probabilistic incremental lifetime cancer risk model incorporated with source apportionment results data. Fertilizer residues/coke oven comprise the primary contamination source contributors of HMs and PAHs with corresponding contributions of 32.23 % and 27.93 % for HMs and 37.94 % for PAHs. Fertilizer/pesticide residues contributes most to the risks of soil HMs (28.8 %), followed by fossil fuel combustion (24.6 %), mining activities (20.2 %), traffic and vehicle emission (16.3 %) and electroplating/dyeing (14.1 %). Meanwhile, the ranking of health risks from the five identified contamination sources of soil PAHs are resident discharge, coal-fired boilers, coke oven emission, gasoline combustion and power plant, with the contribution of 27.1 %, 25.3 %, 17.3 %, 15.5 % and 14.8 %. And relatively, source-specific risk assessment demonstrates fossil fuel and coal combustion contribute the greatest impact to the total risk of HMs and PAHs (61.7 % and 56.1 %), respectively. This study provides a good example of how the source specific health risk assessment can be utilized to reduce the contamination in soils.

A PMF-SSD based approach for the source apportionment and source-specific ecological risk assessment of Le'an river in Jiangxi Province, China.

Identifying the contamination characteristics of trace metals in river and targeting their corresponding potential contamination sources and source-specific ecological risk are of very importance for putting forward effective river environment protection strategies. Here, a detailed investigation was conducted to recognize the contamination and ecological risk characteristics of trace metals in Le'an River. To attain this objective, a PMF-SSD model (Positive Matrix Factorization-Species Sensitivity Distribution) was proposed to evaluate the ecological risk of trace metals in Le'an River. The positive matrix factorization (PMF) was employed to identify the potential source of trace metals in surface water and their corresponding contributions. The ecological risks of the sources were quantitatively calculated by PMF-SSD. In addition, the spatial dissimilarity analysis of the source contribution distributions was also conducted in this study. Results showed that the water environment in Jiangxi were considerably contaminated by trace metals (Cd, Cr, Co, Al, Mn, Cu, Zn and Ni). The concentrations of these trace metals in surface water demonstrated significant spatial variations and the ecological risk lay in high level. Mining activities were identified as the main anthropogenic sources, which should to be strictly regulated.

Male-specific long non-coding RNA testis-specific transcript, Y-linked 15 promotes gastric cancer cell growth by regulating Wnt family member 1/ß-catenin signaling by sponging microRNA let-7a-5p.

The present study is aimed to investigate the regulatory effects and related mechanism of long non-coding RNA testis-specific transcript, Y-linked 15 (TTTY15) in gastric carcinoma (GC) cell proliferation, migration, invasion, apoptosis and epithelial-mesenchymal transition (EMT). TTTY15 expression in GC tissue samples and cells was detected by quantitative real-time PCR (qRT-PCR), and the correlation between TTTY15 expression and GC clinicopathological indicators was analyzed. Cell counting kit-8 (CCK-8), BrdU, flow cytometry and Transwell assays were performed for detecting GC cell proliferation, migration, invasion and apoptosis. Western blot was performed for detecting the expressions of EMT-associated proteins (N-cadherin and E-cadherin), Wnt family member 1 (Wnt1) protein and ß-catenin protein. Bioinformatics analysis was conducted to predict, and RNA immunoprecipitation (RIP) assay and dual-luciferase reporter gene assay were performed to verify the targeted relationships of microRNA let-7a-5p (let-7a-5p) with TTTY15 and Wnt1 mRNA 3'UTR. It was found that TTTY15 expression was significantly up-regulated in GC tissues and cells, and was associated with advanced TNM stage and poor tumor differentiation. TTTY15 overexpression promoted GC cell proliferation, migration and invasion, the expressions of N-cadherin, Wnt1 and ß-catenin protein, and inhibited the apoptosis and E-cadherin expression, while knocking down TTTY15 had the opposite effects. TTTY15 directly targeted let-7a-5p and negatively regulated its expression. Wnt1 was the target gene of let-7a-5p, and TTTY15 could indirectly and positively regulate Wnt1 expression. In conclusion, TTTY15 promotes GC progression, by regulating the let-7a-5p/Wnt1 axis to activate the Wnt/ß-catenin pathway.

Developing an integrated framework for source apportionment and source-specific health risk assessment of PAHs in soils: Application to a typical cold region in China.

Here, a new integrated methodology framework has been proposed for source apportionment and source-oriented risk evaluation, and applied to identify the characteristics, sources and health risks of PAHs in the soils of a typical cold region in Northeastern China. To this end, a large-scale data set containing 1780 soil samples and 16 priority PAHs has been collected from the study area. Two advanced receptor models, positive matrix factorization (PMF) and multivariate curve resolution-weighted alternating least-squares (MCR-WALS), have been comparatively employed to apportion the pollution sources of soil PAHs, with the help of a set of modified literature PAH source fingerprints. Further, the apportionment results have been incorporated into a probabilistic incremental lifetime cancer risk model for assessing the source-specific health risk of soil PAHs. Notably, the PMF and MCR-WALS models have apportioned essentially same results. The coal combustion and gasoline engine are identified as the main contributors of soil PAHs, with contributions of 57.9-58.1% and 25.2-22.2%, respectively. The health risks posed by PAHs in the soils are negligible for both adult and children; relatively, source-oriented risk assessment shows coal combustions make the largest contribution to the total risk of PAHs (56.1%), followed by gasoline engine (22.5%) and coke oven (21.4%).

Source apportionment of heavy metals in sediments and soils in an interconnected river-soil system based on a composite fingerprint screening approach.

Heavy metal pollution has been a global concern and key points of environmental pollution prevention and control due to the growing problems of urbanization and industrialization. Rapidly and correctly apportioning sources of heavy metal is still a great challenge because of the stability of source fingerprint and complex interaction of multiple contaminants and sources. In this study, we perform a combination of optimization of pollution source fingerprint and source apportionment through jointly utilizing two machine classification and screening methods for characterizing the pollution sources of heavy metal in the sediments of an urban river and its surrounding soils. Dominance-based rough set model (DRS), content optimization tools, and multivariate curve resolution-alternating least squares model (MCR-WALS) were employed to screen representative pollution source samples, optimize pollution source fingerprint, and apportion the potential sources of heavy metals, respectively. Further, Support vector machine (SVM) was adopted to correspondence analysis results and pollution fingerprint based on the factor characteristics for achieving source apportionment accurately. Results showed that the pollution source pollution source fingerprints optimized by DRS and optimization tools are more representative and stable, and the results obtained by SVM and MCR-WALS are more accurate comparing with traditional methods. As whole, source apportionment suggested that printing and dyeing, chemical, electroplating, metal processing were the main origins of heavy metals in this area and the proportions of them in sediment and soil pollution sources were 67.05% and 28.43%, respectively. Besides, coal combustion was also the main sources of heavy metal pollution in soils, accounting about 34.16%. Results of the study can advance our knowledge to better understand the characterization of heavy metal pollution in the peri-urban ecosystem and to design effective targeted strategies for reducing heavy metal pollution diffusion.

A coupled optimization of groundwater remediation alternatives screening under health risk assessment: An application to a petroleum-contaminated site in a typical cold industrial region in Northeastern China.

Contaminated sites have been recognized as posing serious comprehensive social and environmental issues and have earned worldwide attention. China is becoming one of the largest contaminated sites remediation markets in the world and the contaminated sites in northeastern China need to rehabilitate urgently. However, remediation planning is often hindered by high financial costs resulting from incomplete assessments of pollution and inappropriate remediation plans. In-depth contaminated site assessments can provide the necessary baseline data for remediation alternatives screening. Therefore, risk assessments and remediation decisions will play crucial roles in the rehabilitation and reconstruction of contaminated sites in China. The main objectives of this study were to present a novel method for health risk assessment (HRA) and to demonstrate a multicriteria decision analysis (MCDA) based on this method to select the most suitable remediation alternatives of groundwater and to prioritize management of contaminated site. To demonstrate the HRA and MCDA processes, a typical contaminated site in Longtan, Jilin province, China, was used. The results of this research indicated that Benzene (PhH) and 1,2-Dichloroethylene (1,2-DCE) were the main organic pollutants and the vanillin plant in the north of the site was main pollution source. Pollution migrated from the north to the south and the health risk range in winter was significantly greater than in summer. Four remediation alternatives were proposed on the basis of the HRA results. The MCDA results showed that PRB was the most suitable technology for integrating the relevant environmental, social, economic, and technical aspects required for remediation. This study may help responsible agencies to strengthen local risk-based program screening frameworks for contaminated sites, to promote reconstruction projects, and to increase local public confidence of contaminated sites remediation.

MALAT1 overexpression promotes the growth of colon cancer by repressing ß-catenin degradation.

Colon cancer is one of the most common types of cancer and more than 80% of colon cancer cases are associated with Wnt-ß-catenin signaling activation. Metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) is a multi-functional long non-coding RNA that is overexpressed in many types of cancers, including colon cancer. In this study, MALAT1 and ß-catenin were found to be overexpressed in tumor samples from 62 patients with colon cancer. A positive correlation was identified between MALAT1 levels and ß-catenin protein levels in tumors. MALAT1 was found to upregulate ß-catenin protein levels in HCT116 and LOVO cells without changing the mRNA expression levels. ß-catenin degradation was confirmed to be upregulated in MALAT1-knockdown cells and inhibited in cells overexpressing MALAT1 overexpressing. MALAT1 was then identified as a negative regulator of GSK-3ß; it did so via promotion of H3K27 trimethylation of the promoter region. In conclusion, MALAT1 is an oncogene in colon cancer, which inhibits ß-catenin degradation by upregulating H3K27 trimethylation and repressing GSK-3ß expression.

Characterization and source apportionment of heavy metals in the sediments of Lake Tai (China) and its surrounding soils.

Heavy metals (HMs) in lake sediments have aroused long-term attention due to their persistent threat to the lake ecosystems. Study into the pollution characteristics and potential sources of HMs in lakes, especially those heavily influenced by human activities, can give some insights to prevent and control the environmental risks of HMs. Lake Tai is the China's third largest freshwater lake but has been a drastic example of water pollution in recent decades. Here, we systematically investigated the characterization of pollution, risk and sources of HMs in the sediments of Lake Tai and its surrounding soils using several methods and tools. Specifically, a total of 46 sediments and 578 soils were collected from the study area, and geochemical approaches (geo-accumulation index and enrichment factor) and pollution indexes were jointly used to reveal the contamination characteristics of HMs. Meanwhile, the potential ecological risk index and probabilistic model of health risk assessment were employed to understand the environmental risk of HMs. Further, absolute principle component score-multiple linear regression and positive matrix factorization models were comparatively applied to apportion the sources of HMs. Results showed that various metals including Cd, Cr, As, Hg, Pb, and Cu appeared contamination level in varying degrees both in the sediments and soils. In particular, Cd, As and Hg were identified as the significant contaminants and presented moderate ecological risk. As a whole, the health risk levels of metals were acceptable. Source apportionment suggested that industrial discharge, agricultural activities, atmospheric deposition and natural source were the potential origins of HMs in this area. Among them, anthropogenic activities were the largest dominator of HMs with contribution of >75%. The study will advance our knowledge on HMs in lake and its surrounding soils and provide reference for policy maker to design migration strategies for protecting the lake ecosystems.

Source identification of antibiotic resistance genes in a peri-urban river using novel crAssphage marker genes and metagenomic signatures.

Antimicrobial resistance is a growing public health concern, and environment is regarded as an important reservoir and dissemination route for antibiotic resistance genes (ARGs). To prevent and control ARG pollution, it is essential to correctly disentangle source-sink relationship of ARGs in the environment. However, accurately apportioning sources of ARGs is still a big challenge due to the complex interaction of multiple sources and contaminants in the environment with changing dynamics. In this study, we addressed this problem and focused on identifying the potential sources of ARGs in a peri-urban river by jointly utilizing two novel microbial source tracking methods. To attain the objective, sediment/water samples were collected from the peri-urban river and four ARG-associated ecotypes including effluents of sewage treatment plants (STPs), STP influent, chicken manures and pig manures. The high-throughput profilings of ARGs and microbial taxa in the river sediments and the four ecotypes were comprehensively characterized in combination of shotgun sequencing and metagenomic assembly analysis. CrAssphage, a recently-discovered DNA bacteriophage, was employed to track the impact of human fecal pollution on ARGs in the river sediments. Further, SourceTracker, a machine-learning classification tool, was used for quantifying the contributions of potential sources to ARGs in the river sediments based on the metagenomic signatures of ARGs and microbial taxa. In total, 888 ARG subtypes belonging to 29 ARG types were detected across all samples, including mcr-1 and a range of carbapenemases types. Statistical analyses suggested different ecotypes generally had distinct profiles of both ARGs and microbial taxa, while the ARG compositions were significantly correlated with the microbial community. Source tracking with crAssphage showed the presence of ARGs in the river sediments might be largely impacted by the extent of human fecal pollution, which was also confirmed by the analyses of SourceTracker that the discharge from STPs was the largest contributor of ARGs (81.6-92.1%) and microbes (49.3-68.1%) in the river sediments. Results of the study can help us to better understand the characterization of ARGs in the peri-urban ecosystem and to design effective prevention and control strategies for reducing ARG dissemination.

Groundwater pollution and risk assessment based on source apportionment in a typical cold agricultural region in Northeastern China.

Increasing anthropogenic contamination poses a significant threat to groundwater security. Identifying potential contamination sources and apportioning their corresponding contributions are of vital importance for the prevention of contamination and management of groundwater resources. In this study, principal component analysis (PCA), modified grey relational analysis (MGRA), absolute principal component score-multiple linear regression (APCS-MLR), and positive matrix factorization (PMF) receptor modeling technologies were employed to evaluate the groundwater quality and apportion the potential contamination sources in the Lalin river basin, a main grain production district in the northeast of China. The contamination assessment with PCA and MGRA suggested that the groundwater in Lalin river basin was polluted due to human activities. The PCA method identified five and four potential contamination sources in wet and dry seasons, respectively, and the main sources were basically same. The APCS-MLR and PMF methods apportioned the source contributions to each groundwater quality variable. The final results showed that agricultural sources including waste water, agrochemicals and fertilizers were identified as the main sources of groundwater contamination both in wet and dry seasons. In addition, groundwater management strategies learned from the advanced experiences were discussed to protect the groundwater system in that region.

Characterization and source-tracking of antibiotic resistomes in the sediments of a peri-urban river.

The peri-urban rivers are one of the critical interfaces between urban-rural symbiotic ecosystems and appear to be a reservoir of antibiotic resistance genes (ARGs) in the environment. To prevent the transmission risks of ARGs between peri-urban river and human, it is essential to explore the prevalence and source of ARGs in the environment for designing potential mitigation strategies. In this study, we focused on the characterization and source-tracking of ARGs in the sediments of a typical peri-urban river in Beijing, Chaobai River. Twenty-seven ARGs frequently reported in the environment, and two integrons (intI1 and intI2) were detected using high-throughput quantitative PCR. The profile of bacterial community was determined by performing 16S rRNA gene sequencing. Meanwhile, crAssphage, a novel recently-discovered DNA bacteriophage, was employed for tracking the contribution of human fecal pollution to the prevalence of ARGs. Results showed that the targeted ARGs were detected widely in the sediments of Chaobai River. Relatively, the abundances of ARGs in downstream were higher than those in the upstream, likely suggesting a gradient impact of anthropogenic activities along the river. Remarkably, the int1 gene was correlated significantly with most of the ARGs and might be the key factor influencing the shaping of ARGs in the river sediments. However, no significant correlations were observed between the ARGs and selective pressure factors, including antibiotics and metals. Of the identified 1039 genera, Escherichia-Shigella, Bacteroides, Arcobacter, Dechloromonas and Pseudomonas were the top most abundant organisms. Microbial source tracking based on the crAssphage annotation suggested that human sewage might be one of the potential sources of resistance bacteria in the river sediments. The study can advance our knowledge about ARGs in the peri-urban river and provides a management reference for ARG pollution control.

Comparison of multi-criteria analysis methodologies for the prioritization of arsenic-contaminated sites in the southwest of China.

The issue of contaminated sites has been highlighted as an immediate priority in the 13th Five-Year Plan of China. Identification and prioritization of contaminated sites are of key importance for proposing effective strategies for the regional management of contaminated sites. In this study, three advanced multi-attribute methodologies, the risk-based priority methodology, the regional risk assessment methodology, and the dominance-based rough set approach (DRSA), were comparatively employed to screen contaminated sites in, Guangxi, Southwest of China. The results of the three prioritizations show that the highest ranking site identified by the three methods had great agreement. In regard to the screening attributers, while the risk-based prioritization methodology and regional risk assessment methodology allowed a high discrimination in the screening of contaminated sites associated with different attributes, such as farmland, residential areas, contaminant level, number of people, area, storage quality, site service life, and surrounding communities, the DRSA allowed the identification of contamination strength (CS) and contamination potential (CP).

Characterization of antibiotic resistance genes in the sediments of an urban river revealed by comparative metagenomics analysis.

The over-use of antibiotics causes growing concerns about human health risks induced by increasing rates of antimicrobial resistance. Riverine systems are considered generally as a natural reservoir of antibiotic resistance genes (ARGs). In this study, several methods including high-throughput sequencing-based metagenomics approach, statistical analysis and network analysis were applied jointly to characterize the wide-spectrum profile of ARGs in the sediments of an urban river in Beijing. Furthermore, contribution of human activities for the presence of ARGs was identified through comparative studies on the metagenomic profiling of ARGs between the river sediments and pristine niches (remote Antarctic soils and deep sea sediments). In total, 442 ARG subtypes belonging to 22 ARG types were detected in the human-impacted river sediments with an abundance range of 1.1 × 10-1-8.1 × 10-1 copy of ARG per copy of 16S-rRNA gene. The most abundant and diverse ARGs were commonly associated with antibiotics that have been extensively used in that area, likely indicating the spread of ARGs in river environments because of the selective pressure resulting from antibiotic use. As a whole, anthropogenic activities were the dominant contributor of major ARG types, for example, occupying 100% for sulfonamide-ARGs, 97% for beta-lactam-ARGs, 94% for aminoglycoside-ARGs and 64% for tetracycline-ARGs. This study provides insights into the role of human activities in accelerating the dissemination and proliferation of ARGs in urban river environment and draws attention to controlling the use and discharge of antibiotics for protection of public health.

A metagenomic analysis framework for characterization of antibiotic resistomes in river environment: Application to an urban river in Beijing.

River is considered generally as a natural reservoir of antibiotic resistance genes (ARGs) in environments. For the prevention and control of ARG risks, it is critical to comprehensively characterize the antibiotic resistomes and their associations in riverine systems. In this study, we proposed a metagenomic framework for identifying antibiotic resistomes in river sediments from multiple categories, including ARG potential, ARG hosts, pathogenicity potential, co-selection potential and gene transfer potential, and applied it to understand the presence, hosts, and co-occurrence of ARGs in the sediments of an urban river in Beijing. Results showed that a total of 203 ARG subtypes belonging to 21 ARG types were detected in the river sediments with an abundance range of 107.7-1004.1×/Gb, dominated by multidrug, macrolide-lincosamide-streptogramin, bacitracin, quinolone and sulfonamide resistance genes. Host-tracking analysis identified Dechloromonas, Pseudoxanthomonas, Arenimonas, Lysobacter and Pseudomonas as the major hosts of ARGs. A number of ARG-carrying contigs (ACCs) were annotated as fragments of pathogenic bacteria and carried multiple multidrug-ARGs. In addition, various biocide/metal resistance genes (B/MRGs) and mobile genetic elements (MGEs), including prophages, plasmids, integrons and transposons, were detected in the river sediments. More importantly, the co-occurrence analysis via ACCs showed a strong association of ARGs with B/MRGs and MGEs, indicating high potential of co-selection and active horizontal transmission for ARGs in the river environment, likely driven by the frequent impact of anthropogenic activities in that area.

Contamination characteristics and source apportionment of trace metals in soils around Miyun Reservoir.

Due to their toxicity and bioaccumulation, trace metals in soils can result in a wide range of toxic effects on animals, plants, microbes, and even humans. Recognizing the contamination characteristics of soil metals and especially apportioning their potential sources are the necessary preconditions for pollution prevention and control. Over the past decades, several receptor models have been developed for source apportionment. Among them, positive matrix factorization (PMF) has gained popularity and was recommended by the US Environmental Protection Agency as a general modeling tool. In this study, an extended chemometrics model, multivariate curve resolution-alternating least squares based on maximum likelihood principal component analysis (MCR-ALS/MLPCA), was proposed for source apportionment of soil metals and applied to identify the potential sources of trace metals in soils around Miyun Reservoir. Similar to PMF, the MCR-ALS/MLPCA model can incorporate measurement error information and non-negativity constraints in its calculation procedures. Model validation with synthetic dataset suggested that the MCR-ALS/MLPCA could extract acceptable recovered source profiles even considering relatively larger error levels. When applying to identify the sources of trace metals in soils around Miyun Reservoir, the MCR-ALS/MLPCA model obtained the highly similar profiles with PMF. On the other hand, the assessment results of contamination status showed that the soils around reservoir were polluted by trace metals in slightly moderate degree but potentially posed acceptable risks to the public. Mining activities, fertilizers and agrochemicals, and atmospheric deposition were identified as the potential anthropogenic sources with contributions of 24.8, 14.6, and 13.3 %, respectively. In order to protect the drinking water source of Beijing, special attention should be paid to the metal inputs to soils from mining and agricultural activities.

Contamination characteristics, ecological risk and source identification of trace metals in sediments of the Le'an River (China).

Recognizing the pollution characteristics of trace metals in river sediments and targeting their potential sources are of key importance for proposing effective strategies to protect watershed ecosystem health. In this study, a comprehensive investigation was conducted to identify the contamination and risk characteristics of trace metals in sediments of Le'an River which is a main tributary of the largest freshwater lake in China, Poyang Lake. To attain this objective, several tools and models were considered. Geoaccumulation index and enrichment factor were used to understand the general pollution characteristic of trace metals in sediments. Discriminant analysis was applied to identify the spatial variability of sediment metals. Sediment quality guidelines and potential ecological risk index were employed for ecological risk evaluation. Multivariate curve resolution-alternating least square was proposed to extract potential pollution sources, as well as the application of Monte-Carlo simulation for uncertainty analysis of source identification. Results suggested that the sediments in Le'an River were considerably polluted by the investigated trace metals (Cd, Cr, As, Hg, Pb, Cu, Zn and Ni). Sediment concentrations of these metals showed significant spatial variations. The potential ecological risk lay in high level. Comparatively speaking, the metals of Cd, Cu and Hg were likely to result in more harmful effects. Mining activities and the application of fertilizers and agrochemicals were identified as the main anthropogenic sources. To protect the ecological system of Le'an River and Poyang Lake watershed, industrial mining and agricultural activities in this area should to be strictly regulated.

[Adsorption dynamics and breakthrough characteristics based on the fluidization condition].

Few studies on the adsorption dynamics and breakthrough characteristics based on the fluidization condition have been reported. In a fluidized bed adsorption reactor with phenol as the adsorbate and granular activated carbon as the adsorbent, the adsorption efficiency, adsorption dynamic characteristics, adsorption breakthrough curves and adsorption capacities were studied and compared with those of a fixed bed operated under the same conditions. The results showed that the adsorption efficiencies exceeded 93% in 5 min in both the fluidized conditions and fixed conditions at the superficial velocities of 8 mm x s(-1) and 13 mm x s(-1). Meanwhile, the above adsorption reactions fitted to Pseudo-second-order with linear correlation coefficients greater than 0.999. The adsorption capacity of fluidized conditions was 8.77 mg x g(-1) and 24.70 mg x g(-1) at the superficial velocities of 6 mm x s(-1) and 8 mm x s(-1). Generally, the fluidized bed reactor showed a higher adsorption efficiency and greater adsorption capacity than the fixed bed reactor.