Removal Step Research Articles

Advanced treatment of landfill leachate through combined Anammox-based biotreatment, O3/H2O2 oxidation, and activated carbon adsorption: technical performance, surrogate-based control strategy, and operational cost analysis

The complexity of landfill leachate makes it difficult to treat it with a single biological/ physical/chemical process. Moreover, the dynamic leachate characteristics pose a challenge for effective process control. Therefore, a combined treatment, consisting of a one-stage partial nitrification-Anammox process, an O3/H2O2 process, and a granular activated carbon filtration (GAC) process, was investigated. Meanwhile, a novel surrogate-based ozone dose control strategy for O3/H2O2 process was evaluated. Results show that this three-stage process offers high removal of total nitrogen (> 90%), COD (chemical oxygen demand, 60–82%), and micropollutants (atrazine, alachlor, carbamazepine, and bisphenol A, > 96%), satisfying discharge requirements. In the combined post-treatment, ozone dosing for COD removal can be real-time controlled by UVA254 reduction monitoring, based on a specific correlation between COD and UVA254 changes. On the other hand, O3/H2O2 pre-treatment controlled at a 50% UVA254 reduction shows to be the optimal point, when adsorption is designed as the main step for COD removal. Cost analysis shows that post-treatment with low (high) organic load i.e., COD ≤ (≥)540 mg/L, a combination with O3/H2O2 (GAC) as the main step appears to be more cost-effective. Therefore, a dynamic operation strategy in response to the leachate change is recommended.

Automated Detection of Nonmelanoma Skin Cancer Based on Deep Convolutional Neural Network.

One of the deadliest diseases is skin cancer, especially melanoma. The high resemblance between different skin lesions such as melanoma and nevus in the skin colour images increases the complexity of identification and diagnosis. An efficient automated early detection system for skin cancer detection is essential in order to save human lives, time, and effort. In this article, an automatic skin lesion classification system using a pretrained deep learning network and transfer learning was proposed. Here, diagnosing melanoma in premature stages, a detection system has been designed which contains the following digital image processing techniques. First, dermoscopy images of skin were taken and this is subjected to a preprocessing step for noise removal and postprocessing step for image enhancement. Then the processed image undergoes image segmentation using k-means and modified k-means clustering. Second, using feature extraction technology, Gray Level Co-occurrence Matrix, and first order statistics, characteristics are extracted. Features are selected on the basis of Harris Hawks optimization (HHO). Finally, various classifiers are used for predicting the stages and efficiency of the proposed work. Measures of well-known quantities, sensitivity, precision, accuracy, and specificity are used in assessing the efficiency of the suggested method, where higher values were obtained. Compared to the current methods, it is found that the classification rate exceeded the output of the current approaches in the performance of the proposed approach.

Evaluating bioinformatics pipelines for population‐level inference using environmental DNA

AbstractEnvironmental DNA is mainly not only used at the interspecific level, to quantify species diversity in ecosystems, but can also be used to quantify intraspecific genetic variability, thus avoiding the need to sample individual tissue. However, errors in the amplification and sequencing of eDNA samples can blur this intraspecific signal and strongly over‐estimate genetic diversity. Existing bioinformatics pipelines therefore need to be tested to evaluate whether reliable levels of intraspecific genetic variability can be derived from eDNA samples. Here, we compare the ability of twelve metabarcoding pipelines to detect intraspecific genetic variability combining five programs. All pipelines have common pre‐processing steps, a processing data step using programs among obiclean; DADA2; SWARM; and LULU. An additional chimera removal step is also investigated based on two programs (VSEARCH or DADA2). The case study was the natural intraspecific variation within Mullus surmuletus in experimental settings. We developed specific primers for this species, located on the mitochondrial D‐loop fragment (barcode MS‐DL06). Thirty‐nine individuals were collected from the Mediterranean Sea, placed into four aquariums, and their DNA was sequenced on this marker to build an intraspecific reference database. After filtering the aquarium water, DNA was extracted, amplified, and sequenced using the primer pair developed. We then quantified the number of true haplotypes returned by each pipeline and its capacity to eliminate most of the erroneous sequences. We show that the program DADA2 with a two‐parent chimeric sequence removal step is the best tool to estimate intraspecific diversity from eDNA. Furthermore, our approach was also able to detect true M. surmuletus haplotypes in two eDNA samples collected in the Mediterranean Sea. We conclude that the combination of an appropriate intrapopulation barcode and a denoising pipeline like DADA2 with a chimeric sequence removal step is promising to make population‐level inference using environmental DNA possible.

Energy efficient ethylene purification in a commercially viable ethane-selective MOF

• Cu(BDC)(DMF) using BDC as a cheap ligand was prepared using a simple and easy-to-scale up method. • Cu(BDC) simultaneously meets the requirements of high C 2 H 6 uptake and excellent ethane/ethylene separation selectivity. • Cu(BDC) exhibits excellent thermal stability and could be stored at room temperature for>1 year. • Cu(BDC) shows excellent separation ability with superior regeneration capability. Separating ethane (C 2 H 6 ) from ethylene (C 2 H 4 ) using ethane-selective adsorbents to replace energy-intensive cryogenic distillation technology is an important energy-saving method in the petrochemical industry to obtain high purity C 2 H 4 . Limited to the low separation efficiency in current commercial materials, it is very urgent and promising to fabricate such alkane selective adsorbents using cheap ligands and simple synthesis methods, which have characteristics such as stable structure, high C 2 H 6 uptake, and adsorption selectivity. In this work, we have used one of the cheapest ligands (terephthalic acid, H 2 BDC) to prepare Cu(BDC)(DMF) via a simple and easy-to-scale up solvothermal method. The activated Cu(BDC) exposed diamond-shaped one-dimensional pores form a stronger affinity with C 2 H 6 than C 2 H 4 through the double function of C-H∙∙∙π interactions and C-H∙∙∙O hydrogen bonds. Benefiting from the difference in these interactions, Cu(BDC) exhibited a higher adsorption capacity of C 2 H 6 (51.5 cm 3 /g) compared to C 2 H 4 (45.1 cm 3 /g) at 1 bar and 298 K, which resulted in high adsorption selectivity (2.0) for the C 2 H 6 /C 2 H 4 mixture (50/50, v/v). Breakthrough experiments indicated that Cu(BDC) successfully achieved the removal of C 2 H 6 from C 2 H 6 /C 2 H 4 (50/50 and 10/90, v/v) mixtures to obtain high purity C 2 H 4 (> 99.99%) using a one-step separation process. When compared with the benchmark materials, Fe 2 (O 2 )(dobdc) and NIIC-20, Cu(BDC) had the advantages of much lower preparation cost and enhanced structural stability. These comprehensive properties indicated that Cu(BDC) with a commercially viable price and synthesis process, could be potentially used for the C 2 H 6 removal step during the production of high purity C 2 H 4 .

Synergistic effect of F and triggered oxygen vacancies over F-TiO2 on enhancing NO ozonation

Oxidation-absorption technology is a key step for NOx removal from low-temperature gas. Under the condition of low O3 concentration (O3/NO molar ratio = 0.6), F-TiO2 (F-TiO2), which is cheap and environmentally friendly, has been prepared as ozonation catalysts for NO oxidation. Catalytic activity tests performed at 120°C showed that the NO oxidation efficiency of F-TiO2 samples was higher than that of TiO2 (about 43.7%), and the NO oxidation efficiency of F-TiO2-0.15 was the highest, which was 65.3%. Combined with physicochemical characteristics of catalysts and the analysis of active species, it was found that there was a synergistic effect between F sites and oxygen vacancies on F-TiO2, which could accelerate the transformation of monomolecular O3 into multi-molecule singlet oxygen (1O2), thus promoting the selective oxidation of NO to NO2. The oxidation reaction of NO on F-TiO2-0.15 follows the Eley-Rideal mechanism, that is, gaseous NO reacts with adsorbed O3 and finally form NO2.

Preparation of tambacu protein concentrates using different methodologies

The aim of this study was to develop methodologies to obtain fish protein concentrates from tambacu filleting residues, characterize their physical-chemical composition and evaluate their sensory profile. Concentrates were prepared by one of three methodologies: cooking of the raw material and subsequent drying (FPC1); cooking, drying, lipid removal with ethanol at 70 °C and final drying (FPC2); and three washing steps of the raw material with ethanol at 70 °C and drying (FPC3). FPC2 had a lower final yield (15.5%) and chroma a* (1.77) and chroma b* values (14.12) but higher moisture (5.22%) and protein content (80.39%) and luminosity (74.97), in addition to having the weakest fish taste compared to FPC1 and FPC3. FPC1 had the strongest odour, darkest colour, and strongest fish taste compared to FPC2 and FPC3. Ash, pH values, and water activity were not different between the concentrates. It is concluded that the methodology where lipid removal steps are carried out after cooking and drying (FPC2) is more effective for the removal of lipids and deodorization of tambacu protein concentrate.

Skin lesion segmentation using an improved framework of encoder‐decoder based convolutional neural network

AbstractAutomatic lesion segmentation is a key phase of skin lesion analysis that significantly increases the performance of subsequent classification steps. Segmentation is a highly complex task due to the varying nature of lesions, such as unique shapes, different colors, and structures. In this study, a two‐step system is proposed comprising preprocessing algorithm and lesion segmentation network. The hairlines removal algorithm is designed using morphological operators to eliminate noise artifacts. The resulting output images are fed to the convolutional neural network (CNN) to perform lesion segmentation. The proposed CNN network is a new framework designed from scratch based on encoder‐decoder architecture. The layers are stacked in a unique sequence to perform downsampling and upsampling, generating a high‐resolution segmentation map. Additionally, a Tversky loss function is implemented to reduce the error rate between predicted and target output. In this study, we focus on the challenges related to the extraction of accurate lesion regions and the presence of hairlines in lesion images that can occlude important information resulting in poor segmentation. The proposed model is evaluated on four publicly available datasets, namely ISIC 2016–2018. The mean intersection over union (IoU) obtained for ISIC 2016–2018 and is 87.1%, 77.8%, 85.2%, and 88.8%, respectively. The proposed model demonstrated higher performance as compared to other state‐of‐the‐art methods. The hair removal step aids in the improvement of the overall performance.

UASB Performance and Perspectives in Urban Wastewater Treatment at Sub-Mesophilic Operating Temperature

UASBs present several advantages compared to conventional wastewater treatment processes, including relatively low construction cost facilities, low excess sludge production, plain operation and maintenance, energy generation in the form of biogas, robustness in terms of COD removal efficiency, pH stability, and recovery time. Although anaerobic treatment is possible at every temperature, colder climates lead to lower process performance and biogas production. These factors can be critical in determining the applicability and sustainability of this technology for the treatment of urban wastewater at low operating temperature. The purpose of this study is the performance evaluation of a pilot-scale (2.75 m3) UASB reactor for treatment of urban wastewater at sub-mesophilic temperature (25 °C), below the optimal range for the process, as related to biogas production and organic matter removal. The results show that, despite lower methane production and COD removal efficiency compared to operation under ideal conditions, a UASB can still achieve satisfactory performance, and although not sufficient to grant effluent discharge requirements, it may be used as a pretreatment step for carbon removal with some degree of energy recovery. Options for UASB pretreatment applications in municipal WWTPs are discussed.

Iraqi porcelanite Rocks for Efficient Removal of Safranin Dye from Aqueous Solution

This research includes a study of the ability of Iraqi porcelanite rocks powder to remove the basic Safranine dye from its aqueous process by adsorption. The experiments were carried out at 298Kelvin in order to determine the effect of the starting concentration for Safranin dye, mixing time, pH, and the effect of ionic Strength. The good conditions were perfect for safranine dye adsorption was performed when0.0200g from that adsorbed particles and the removal max percentage was found be 96.86% at 9 mg/L , 20 minutes adsorption time and at PH=8 and in 298 K. The isothermal equilibrum stoichiometric adsorption confirmed, the process data were examined by Langmuir, Freundlich and Temkin adsorption equations at different temperatures (298-338)K.The results of the adsorption were good suited for Freundlich and Langmuir Isotherm more than Temkin. Thermodynamic data found for adsorption processes ΔG0, ΔH0, ΔS0 indicates this is the removal step is through the adsorption operations which is spontaneity, exothermic and indicates a decrease in the randomness of that adsorbed dye molecules. Isotherms obtained from the experiments were identical to (S-curve) in form at Giles' discretion.

Statistics-Guided Residual Dictionary Learning for Footprint Noise Removal

The footprint noise is a type of coherent noise that arises from the acquisition geometry. The footprint noise is commonly seen in 3-D seismic volumes and greatly affects the amplitude-based processing and interpretation steps in the seismic exploration workflow. Thus, removal of the footprint noise is necessary for warranting a reliable interpretation output of seismic data processing. However, because footprint noise is usually weak and also spatially coherent, it is inevitable to cause damages to useful signals during its removal steps. Here, we propose a dictionary learning (DL)-based method to effectively remove the footprint noise. We design an algorithm framework to effectively learn the dictionary atoms of the signal waveforms and separate the features of the footprint noise from the learned atoms. Considering the special features of the footprint noise in the dictionary atoms, we propose a statistics-guided way to separate the dictionary atoms into footprint-affected and footprint-free atoms. Then, the footprint-affected atoms are processed via a 2-D median filtering step. The combination between the untouched footprint-free atoms and filtered footprint-affected atoms result in a better dictionary of the signal waveforms and the footprint atoms. We use residual DL to encode the input data by a linear combination of signal atoms and footprint atoms. Removal of footprint atoms and their corresponding sparse coefficients leads to a successful footprint removal. We use both 3-D synthetic and field data examples to demonstrate the effectiveness of the proposed method.

A Comprehensive Review of Vehicle Detection Techniques Under Varying Moving Cast Shadow Conditions Using Computer Vision and Deep Learning

Design of a vision-based traffic analytic system for urban traffic video scenes has a great potential in context of Intelligent Transportation System (ITS). It offers useful traffic-related insights at much lower costs compared to their conventional sensor based counterparts. However, it remains a challenging problem till today due to the complexity factors such as camera hardware constraints, camera movement, object occlusion, object speed, object resolution, traffic flow density, and lighting conditions etc. ITS has many applications including and not just limited to queue estimation, speed detection and different anomalies detection etc. All of these applications are primarily dependent on sensing vehicle presence to form some basis for analysis. Moving cast shadows of vehicles is one of the major problems that affects the vehicle detection as it can cause detection and tracking inaccuracies. Therefore, it is exceedingly important to distinguish dynamic objects from their moving cast shadows for accurate vehicle detection and recognition. This paper provides an in-depth comparative analysis of different traffic paradigm-focused conventional and state-of-the-art shadow detection and removal algorithms. Till date, there has been only one brief survey which highlights the shadow removal methodologies particularly for traffic paradigm. In this paper, a number of research papers containing results of urban traffic scenes have been shortlisted from the last three decades to give a comprehensive overview of the work done in this area. The study reveals that the only way to make a comparative evaluation is to use the existing Highway I, II, and III datasets which are frequently used for qualitative or quantitative analysis of shadow detection or removal algorithms. Furthermore, the paper not only provides cues to solve moving cast shadow problems, but also suggests that even after the advent of Convolutional Neural Network (CNN)-based vehicle detection methods, the problems caused by moving cast shadows persists. Therefore, this paper proposes a hybrid approach which uses a combination of conventional and state-of-the-art techniques as a pre-processing step for shadow detection and removal before using CNN for vehicles detection. The results indicate a significant improvement in vehicle detection accuracies after using the proposed approach.

Implementation of a multi-material mechanism in a laser-based powder bed fusion (PBF-LB) machine

Multi-material additive manufacturing by laser-based powder bed fusion (PBF-LB) combines the advantages of additive manufacturing (AM), such as the ability to build complex structures, with the capability to create an arbitrary material distribution in one manufacturing process. This paper presents the implementation of a multi-material mechanism in a mono-material PBF-LB system. Therefore, the multi-material process and the new powder deposition mechanism are introduced. Special attention is paid to the required process steps of powder recoating and powder removal. To gain further insight into the multi-material process, dependencies between the powder removal and the manufacturing quality are described. Furthermore, the required equipment and the current challenges of multi-material additive manufacturing are highlighted. In addition, results of the material combination consisting of the tool steel 1.2709 and the copper alloy CW106C are shown and discussed.

Meningiomas of the Cerebellopontine Angle: Tips and Pearls for Safe Surgical Resection.

To present our series of cerebellopontine angle (CPA) meningiomas, and to provide some innovative surgical steps for safe removal of these tumors. The clinical, radiological, and surgical characteristics of 32 patients with meningioma in the CPA were retrospectively analyzed. A statistical analysis was performed to determine the factors that affect the incidence of complication. The mean age was 49.5 yr, and 87.5% of patients were men had World Health Organization (WHO) grade I meningioma, while the remaining patients had WHO grade II tumors. The mean tumor volume was 33.98 mL, and gross total resection was performed on 65.6% of the patients. Preoperative tumor size/extension and extent of resection were related with the development of complications. CPA meningiomas are challenging tumors to remove safely. Important risk factors for the development of postoperative complications include tumor size and extent of resection.

Combining statistical, structural, and linguistic features for keyword extraction from web pages

<abstract> <p>Keywords are commonly used to summarize text documents. In this paper, we perform a systematic comparison of methods for automatic keyword extraction from web pages. The methods are based on three different types of features: statistical, structural and linguistic. Statistical features are the most common, but there are other clues in web documents that can also be used. Structural features utilize styling codes like header tags and links, but also the structure of the web page. Linguistic features can be based on detecting synonyms, semantic similarity of the words and part-of-speech tagging, but also concept hierarchy or a concept graph derived from Wikipedia. We compare different types of features to find out the importance of each of them. One of the key results is that stop word removal and other pre-processing steps are the most critical. The most successful linguistic feature was a pre-constructed list of words that had no synonyms in <italic>WordNet</italic>. A new method called <italic>ACI‑rank</italic> is also compiled from the best working combination.</p> </abstract>

Speciation analysis of both inorganic and organic 129I in seawater and its application in the study of the marine iodine cycle.

A complete protocol is presented for the speciation analysis of 129I for both inorganic and organic iodine in seawater using coprecipitation and solid-phase extraction (SPE) combined with accelerator mass spectrometry (AMS). By modifying the iodide separation process and adding a crossover removal step, the improved coprecipitation method significantly reduces the cross-contamination of iodide and iodate to less than 0.05% in the speciation analysis of inorganic 129I, with the separation efficiencies of about 95% and 93% for iodide and iodate, respectively. The SPE-DOI method for the dissolved organic 129I (DO129I) analysis was developed, whereby we report the first direct observation of DO129I/DO127I atom ratios in seawater in this paper. 129I species in seawater from Tokyo Bay were analysed. The 129I results demonstrated that our protocol for speciation analysis of 129I is reliable and provided new insights into understanding the iodine cycle.

Proceedings of the 2019 Viral Clearance Symposium, Session 6: Virus-Retentive Filtration.

Manufactures of therapeutic biological products are required to incorporate downstream unit operations that can inactivate or remove potential viral contaminants from the manufacturing process. The viral filtration unit operation is one of the most robust virus removal steps incorporated into downstream manufacturing processes. The primary virus removal mechanism is size exclusion by a network of nanopores in the viral filter membrane. Even though the mechanism of virus removal is size based, there are many operational parameters that can affect virus retention. This article summarizes the current understanding of the operation and validation parameters of this unit operation.

Optimization of GLP-1 Coated Micelle Formulation for Pancreatic Beta-cell-selective Induction of Autophagy

Background and Hypothesis: Type 1 Diabetes (T1D) is a progressive disease in which the insulin-producing beta-cells of the pancreas are damaged by autoimmune attack, often associated with accumulation of reactive oxygen species (ROS) in the early pathogenesis of disease. Autophagy is an endogenous response to protect against ROS-induced damage and promote beta-cell survival. Previous research has demonstrated that beta-cell autophagy is impaired in T1D, making autophagy a potential therapeutic target. We hypothesize that rapamycin, an mTOR inhibitor that stimulates autophagy, can be selectively delivered to and internalized by islet beta-cells through the encapsulation of drug in GLP-1 coated polymeric micelles.
 Project Methods: We first validated and optimized the formulation of the GLP-1 coated micelles by evaluating key steps in the process: initial solvent removal and dissolution of GLP-1-conjugated lipid components. The solvent removal step involved assessment of evaporation and lyophilization, and the dissolution step involved determining solubility of conjugated lipid in methanol, chloroform, and a mixture of the two solvents.
 Results: We utilized high-performance liquid chromatography to validate solubility in a 70:30 mixture of methanol and chloroform, and we have determined that yield of conjugated lipid in the final micelle solution can be increased from 4% to 80% by using lyophilization as the solvent removal technique in place of evaporation.
 Potential Impact: The next step in this project will be to treat beta-cells with GLP-1 coated micelles containing rapamycin in vitro and to evaluate uptake and action of rapamycin within the beta-cell. The overall aim of this project is to establish the use of a targeted nanoparticle drug system in the preclinical setting with the goal of future T1D treatment application.

Boosting K+ Capacitive Storage in Dual‐Doped Carbon Crumples with B–N Moiety via a General Protic‐Salt Synthetic Strategy

AbstractThe heteroatom co‐doped carbonaceous anodes have readily attracted great attention in potassium‐ion batteries (PIBs), owing to their augmented carbon interlayer distances and increased K+ storage sites to induce enhanced capacity value. Nevertheless, the synergistic effect of dual‐doped heteroatoms is still unclear and lacks systematic explorations. In addition, traditional synthetic routes are cumbersome with template removal step, which are normally deficient in product scalability. Herein, a generic protic‐salt strategy is devised to realize sulfur‐, phosphorus‐ or boron‐nitrogen dual‐doped carbon (SNC, PNC, or BNC) via varying the types of protic precursors (e.g., the acid). Throughout comprehensive instrumental probing and theoretical simulation, it is identified that the presence of B–N moiety can harvest high adsorption capability of K+ and hence exhibit more obvious pseudo‐capacitance behavior than bare N‐doped carbon counterpart. As a PIB anode, the BNC electrode displays an impressive reversible capacity (360.5 mAh g−1 at 0.1 A g−1) and cycle stability (125.4 mAh g−1 at 1 A g−1 after 3000 cycles). In situ/ex situ characterizations further reveal the origin of the excellent electrochemical properties of the BNC electrode. Such a tailorable protic‐salt derived anode material offers new possibilities to advance PIB devices.

Fabrication of Sophisticated Microstructures using Weak Support in 3D Printing

AbstractProjection‐based stereolithography (PSL) is one of the most competitive 3D printing technologies in fabricating complex microstructures because of its high accuracy and remarkable efficiency. Due to the layer‐by‐layer forming manner, some structures with cantilever features tend to collapse in the PSL process, for which support structures are required. However, the design, adding, and post‐treatment of traditional support structures can be extremely time‐consuming, and the manual removal step is always destructive and sometimes impossible, thus not suitable for sophisticated microstructures. Here a simple 3D printing method is proposed to fabricate sophisticated microstructures rapidly and precisely using weak support. Grayscale projection is used to produce partially cured photopolymer as weak support, which fills the lower space of overhanging structures and is constrained by a fully cured wall structure. The weak support is sufficient to support the printed parts while easy to remove because of incomplete photocured state. To determine the curing state of weak support, curing characteristics of the resin are measured and the relationship between curing energy and support effectiveness is investigated. Improvement in efficiency and applicability is observed in fabricating sophisticated microstructures using weak support method, which gives a promising solution to fabricate fragile microstructures with inaccessible overhang characteristics.

Evaluation of multiple analytical methods for SARS-CoV-2 surveillance in wastewater samples

In this study, 14 virus concentration protocols based on centrifugation, filtration, polyethylene glycol (PEG) precipitation and ultrafiltration were tested for their efficacy for the quantification of SARS-CoV-2 in wastewater samples. These protocols were paired with four RNA extraction procedures resulting in a combination of 50 unique approaches. Bovine respiratory syncytial virus (BRSV) was used as a process control and seeded in each wastewater sample subjected to all 50 protocols. The recovery of BRSV obtained through the application of 50 unique approaches ranged from <0.03 to 64.7% (±1.6%). Combination of centrifugation as the solid removal step, ultrafiltration (Amicon-UF-15; 100 kDa cut-off; protocol 9) as the primary virus concentration method, and Zymo Quick-RNA extraction kit provided the highest BRSV recovery (64.7 ± 1.6%). To determine the impact of prolonged storage of large wastewater sample volume (900 mL) at −20 °C on enveloped virus decay, the BRSV seeded wastewaters samples were stored at −20 °C up to 110 days and analyzed using the most efficient concentration (protocol 9) and extraction (Zymo Quick-RNA kit) methods. BRSV RNA followed a first-order decay rate (k = 0.04/h with r2 = 0.99) in wastewater. Finally, 21 wastewater influent samples from five wastewater treatment plants (WWTPs) in southern Maryland, USA were analyzed between May to August 2020 to determine SARS-CoV-2 RNA concentrations. SARS-CoV-2 RNA was quantifiable in 17/21 (81%) of the influent wastewater samples with concentration ranging from 1.10 (±0.10) × 104 to 2.38 (±0.16) × 106 gene copies/L. Among the RT-qPCR assays tested, US CDC N1 assay was the most sensitive followed by US CDC N2, E_Sarbeco, and RdRp assays. Data presented in this study may enhance our understanding on the effective concentration and extraction of SARS-CoV-2 from wastewater.