Comprehensive assessment of enhancing dewaterability of dredged sediments by starch-based flocculant.

Dredged sediment poses significant challenges for transportation and subsequent treatment due to its high water content and large volume. Coagulation, a common method of dewatering, can significantly enhance the dewatering performance of dredged sediment. This study synthesized a cationic starch-based flocculant [starch-3-chloro-2-hydroxypropyl trimethylammonium chloride (St-CTA)] through etherification for the flocculation dewatering of dredged sediment. The effectiveness and mechanism of St-CTA as a dewatering flocculant for dredged sediment were investigated. The results demonstrated that when the dosage of St-CTA was 12 mg g-1 TSS (total suspended solids), the dehydration property of dredged sediment substantially improved, with the specific resistance to filtration (SRF) decreasing by 93.3%, the capillary suction time (CST) by 93.5%, and the water content of the filter cake (WC) by 9.7%. The removal rate of turbidity of the supernatant from the conditioned dredged sediment reached 99.6%, accelerating the settling speed and effectively capturing and separating fine particles from the sediment. St-CTA significantly increased the median particle size (D50), altered the microstructure and extracellular polymeric substances (EPS) of the flocs, and increased the fractal dimension of the flocs, making them more compact and conducive to the formation of drainage channels. These findings confirm the feasibility of using potentially environmentally friendly St-CTA as a rapid dewatering conditioning agent for sediment.

Atmospheric particle-bound polycyclic aromatic compounds over two distinct sites in Pakistan: Characteristics, sources and health risk assessment.

Much attention is drawn to polycyclic aromatic hydrocarbons (PAHs) as an air pollutant due to their toxic, mutagenic and carcinogenic properties. Therefore, to understand the levels, seasonality, sources and potential health risk of PAHs in two distinct geographical locations at Karachi and Mardan in Pakistan, total suspended particle (TSP) samples were collected for over one year period. The average total PAH concentrations were 31.5 ± 24.4 and 199 ± 229 ng/m3 in Karachi and Mardan, respectively. The significantly lower concentration in Karachi was attributed to diffusion and dilution of the PAHs by the influence of clean air mass from the Arabian sea and high temperature, enhancing the volatilization of the particle phase PAHs to the gas phase. Conversely, the higher concentration (~6 times) in Mardan was due to large influence from local and regional emission sources. A clear seasonality was observed at both the sites, with the higher values in winter and post-monsoon due to higher emissions and less scavenging, and lower values during monsoon season due to the dilution effect. Diagnostic ratios and principal component analysis indicated that PAHs in both sites originated from traffic and mixed combustion sources (fossil fuels and biomass). The average total BaP equivalent concentrations (BaPeq) in Karachi and Mardan were 3.26 and 34 ng/m3, respectively, which were much higher than the WHO guideline of 1 ng/m3. The average estimates of incremental lifetime cancer risk from exposure to airborne BaPeq via inhalation indicated a risk to human health from atmospheric PAHs at both sites.

Contrastive Self-Supervised Pre-Training for Video Quality Assessment.

Video quality assessment (VQA) task is an ongoing small sample learning problem due to the costly effort required for manual annotation. Since existing VQA datasets are of limited scale, prior research tries to leverage models pre-trained on ImageNet to mitigate this kind of shortage. Nonetheless, these well-trained models targeting on image classification task can be sub-optimal when applied on VQA data from a significantly different domain. In this paper, we make the first attempt to perform self-supervised pre-training for VQA task built upon contrastive learning method, targeting at exploiting the plentiful unlabeled video data to learn feature representation in a simple-yet-effective way. Specifically, we implement this idea by first generating distorted video samples with diverse distortion characteristics and visual contents based on the proposed distortion augmentation strategy. Afterwards, we conduct contrastive learning to capture quality-aware information by maximizing the agreement on feature representations of future frames and their corresponding predictions in the embedding space. In addition, we further introduce distortion prediction task as an additional learning objective to push the model towards discriminating different distortion categories of the input video. Solving these prediction tasks jointly with the contrastive learning not only provides stronger surrogate supervision signals, but also learns the shared knowledge among the prediction tasks. Extensive experiments demonstrate that our approach sets a new state-of-the-art in self-supervised learning for VQA task. Our results also underscore that the learned pre-trained model can significantly benefit the existing learning based VQA models. Source code is available at https://github.com/cpf0079/CSPT.

Genetic evolution analysis and pathogenicity assessment of porcine epidemic diarrhea virus strains circulating in part of China during 2011-2017.

In recent years, the outbreaks of porcine epidemic diarrhea (PED) caused by the highly virulent porcine epidemic diarrhea virus (PEDV) variants occurred frequently in China, resulting in severe economic impacts to the pork industry. In this study, we selected and analyzed the genetic evolution of 15 PEDV representative strains that were identified in fecal samples of diarrheic piglets in 10 provinces and cities during 2011-2017. The phylogenetic analysis indicated that all the 15 PEDV isolates clustered into G2 genotype associated with the current circulating strains. Compared with the genome of the prototype strain CV777, these strains had 103-120 amino acid mutations in their S proteins, most of which were in the N terminal domain of S1 (S1-NTD). We also found 37 common mutations in all these 15 strains, although these strains shared 96.9-99.7% nucleotide homology and 96.3-99.8% amino acid homology in the S protein compared with the other original pandemic strains. Computational analysis showed that these mutations may lead to remarkable changes in the conformational structure and asparagine (N)-linked glycosylation sites of S1-NTD, which may be associated with the altered pathogenicity of these variant PEDV strains. We evaluated the pathogenicity of the PEDV strain FJzz1 in piglets through oral and intramuscular infection routes. Compared with oral infection, intramuscular infection could also cause typical clinical signs but with a slightly delayed onset, confirming that the variant PEDV isolate FJzz1 was highly pathogenic to suckling piglets. In conclusion, we analyzed the genetic variation and pathogenicity of the emerging PEDV isolates of China, indicating that G2 variant PEDV strains as the main prevalent strains that may mutate continually. This study shows the necessity of monitoring the molecular epidemiology and the etiological characteristics of the epidemic PEDV isolates, which may help better control the PED outbreaks.

[Chemical speciation and risk assessment of heavy metals in the middle part of Yarlung Zangbo surface sediments].

Nine heavy metals (Cd, Co, Ni, Cu, Zn, Pb, Cs, As, Cr) of surface sediments collected from the Middle Part of the Yarlung Zangbo River were analyzed by BCR sequential extraction procedure. Based on speciation distributions of these metals, ecological risk of heavy metals was assessed with methods of risk assessment code (RAC) and sediment quality guideline quotient (SQG-Q). The results show that: (1) Large part of Cr and As belong to residual fraction(87%-96%). Ni, Cu, Co and Cs have similar distribution characteristics (non-residual part accounts for approximate 20%). Correspondingly, mass fraction of Cd exists mainly in acid soluble and reducible faction (65% ) , indicating high ecological risk level. (2) Based on calculation of RAC method, Cd is of high risk to the environment (37. 38%). Accordingly, Co, Ni, Cu and Zn are of low risk, and Pb, Cs, As and Cr pose extreme low risk to the environment. According to evaluation of heavy metal speciation distributions, the potential ecological risk of heavy metals is in the descending order of Cd > Co > Ni > Cu > Zn > Pb > Cs > As > Cr. (3) The SQG index of sediments is 0. 804, indicating that studied river sediment has a moderate potential biological toxicity effect. Given most of heavy metals exists in residual fraction, the sediments of this region pose a low ecological risk to the environment.