1
|
Yang S, Jiao Y, Dong Q, Li S, Xu C, Liu Y, Sun L, Huang X. Evaluating approach uncertainties of quantitative detection of SARS-CoV-2 in wastewater: Concentration, extraction and amplification. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 951:175285. [PMID: 39102960 DOI: 10.1016/j.scitotenv.2024.175285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Revised: 06/10/2024] [Accepted: 08/02/2024] [Indexed: 08/07/2024]
Abstract
Substantial uncertainties pose challenges to the accuracy of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) quantification in wastewater. We conducted a comprehensive evaluation of two concentration methods, three nucleic acid extraction methods, and the amplification performance of eight primer-probe sets. Our results showed that the two concentration methods exhibited similar recovery rates. Specifically, using a 30 kDa cut-off ultrafilter and a centrifugal force of 2500 g achieved the highest virus recovery rates (27.32 ± 8.06 % and 26.37 ± 7.77 %, respectively), with lower corresponding quantification uncertainties of 29.51 % and 29.47 % in ultrafiltration methods. Similarly, a 15 % PEG concentration with 1.5 M NaCl markedly improved virus recovery (26.76 ± 5.92 % and 28.47 ± 6.74 %, respectively), and reducing variation to 22.16 % and 23.66 % in the PEG precipitation method. Additionally, employing a vigorous bead-beating approach at 6 m/s during viral RNA extraction significantly increased RNA yield, with an efficiency reaching up to 82.18 %. Among the evaluated eight primer-probe sets, the E_Sarbeco primer-probe set provided the most stable and consistent quantitative results across various sample matrices. These findings are crucial for establishing robust viral quantification protocols and enhancing methodological precision for effective wastewater surveillance, enabling sensitive and precise detection of SARS-CoV-2.
Collapse
Affiliation(s)
- Shaolin Yang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 10084, China
| | - Yang Jiao
- Beijing Chaoyang Center for Disease Control and Prevention, Beijing 100021, China
| | - Qian Dong
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 10084, China
| | - Siqi Li
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 10084, China
| | - Chenyang Xu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 10084, China
| | - Yanchen Liu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 10084, China.
| | - Lingli Sun
- Beijing Chaoyang Center for Disease Control and Prevention, Beijing 100021, China.
| | - Xia Huang
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 10084, China.
| |
Collapse
|
2
|
Wang W, Luo L, Li Y, Hong B, Ma Y, Kang K, Wang J. Detection of SARS-CoV-2 using machine learning-enabled paper-assisted ratiometric fluorescent sensors based on target-induced magnetic DNAzyme. Biosens Bioelectron 2024; 255:116272. [PMID: 38581837 DOI: 10.1016/j.bios.2024.116272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 03/28/2024] [Accepted: 04/03/2024] [Indexed: 04/08/2024]
Abstract
The development of an advanced analytical platform with regard to SARS-CoV-2 is crucial for public health. Herein, we present a machine learning platform based on paper-assisted ratiometric fluorescent sensors for highly sensitive detection of the SARS-CoV-2 RdRp gene. The assay involves target-induced rolling circle amplification to generate magnetic DNAzyme, which is then detectable using the paper-assisted ratiometric fluorescent sensor. This sensor detects the SARS-CoV-2 RdRp gene with a visible-fluorescence color response. Moreover, leveraging different fluorescence responses, the ResNet algorithm of machine learning assists in accurately identifying fluorescence images and differentiating the concentration of the SARS-CoV-2 RdRp gene with over 99% recognition accuracy. The machine learning platform exhibits exceptional sensitivity and color responsiveness, achieving a limit of detection of 30 fM for the SARS-CoV-2 RdRp gene. The integration of intelligent artificial vision with the paper-assisted ratiometric fluorescent sensor presents a novel approach for the on-site detection of COVID-19 and holds potential for broader use in disease diagnostics in the future.
Collapse
Affiliation(s)
- Wenhai Wang
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006, Guangdong Province, China.
| | - Lun Luo
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006, Guangdong Province, China
| | - Yanmei Li
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006, Guangdong Province, China
| | - Bin Hong
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006, Guangdong Province, China
| | - Yi Ma
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006, Guangdong Province, China
| | - Keren Kang
- National Engineering Laboratory of Rapid Diagnostic Tests, Guangzhou Wondfo Biotech Co., Ltd., Guangzhou, 510663, China
| | - Jufang Wang
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006, Guangdong Province, China.
| |
Collapse
|
3
|
Xu X, Deng Y, Ding J, Tang Q, Lin Y, Zheng X, Zhang T. High-resolution and real-time wastewater viral surveillance by Nanopore sequencing. WATER RESEARCH 2024; 256:121623. [PMID: 38657304 DOI: 10.1016/j.watres.2024.121623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 03/27/2024] [Accepted: 04/15/2024] [Indexed: 04/26/2024]
Abstract
Wastewater genomic sequencing stands as a pivotal complementary tool for viral surveillance in populations. While long-read Nanopore sequencing is a promising platform to provide real-time genomic data, concerns over the sequencing accuracy of the earlier Nanopore versions have somewhat restrained its widespread application in wastewater analysis. Here, we evaluate the latest improved version of Nanopore sequencing (R10.4.1), using SARS-CoV-2 as the model infectious virus, to demonstrate its effectiveness in wastewater viral monitoring. By comparing amplicon lengths of 400 bp and 1200 bp, we revealed that shorter PCR amplification is more suitable for wastewater samples due to viral genome fragmentation. Utilizing mock wastewater samples, we validated the reliability of Nanopore sequencing for variant identification by comparing it with Illumina sequencing results. The strength of Nanopore sequencing in generating real-time genomic data for providing early warning signals was also showcased, indicating that as little as 0.001 Gb of data can provide accurate results for variant prevalence. Our evaluation also identified optimal alteration frequency cutoffs (>50 %) for precise mutation profiling, achieving >99 % precision in detecting single nucleotide variants (SNVs) and insertions/deletions (indels). Monitoring two major wastewater treatment plants in Hong Kong from September 2022 to April 2023, covering over 4.5 million population, we observed a transition in dominant variants from BA.5 to XBB lineages, with XBB.1.5 being the most prevalent variants. Mutation detection also highlighted the potential of wastewater Nanopore sequencing in uncovering novel mutations and revealed links between signature mutations and specific variants. This study not only reveals the environmental implications of Nanopore sequencing in SARS-CoV-2 surveillance but also underscores its potential in broader applications including environmental health monitoring of other epidemic viruses, which could significantly enhance the field of wastewater-based epidemiology.
Collapse
Affiliation(s)
- Xiaoqing Xu
- Environmental Microbiome Engineering and Biotechnology Laboratory, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong Special Administrative Region
| | - Yu Deng
- Environmental Microbiome Engineering and Biotechnology Laboratory, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong Special Administrative Region
| | - Jiahui Ding
- Environmental Microbiome Engineering and Biotechnology Laboratory, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong Special Administrative Region
| | - Qinling Tang
- Environmental Microbiome Engineering and Biotechnology Laboratory, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong Special Administrative Region
| | - Yunqi Lin
- Environmental Microbiome Engineering and Biotechnology Laboratory, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong Special Administrative Region
| | - Xiawan Zheng
- Environmental Microbiome Engineering and Biotechnology Laboratory, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong Special Administrative Region
| | - Tong Zhang
- Environmental Microbiome Engineering and Biotechnology Laboratory, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong Special Administrative Region; School of Public Health, The University of Hong Kong, Pokfulam Road, Hong Kong Special Administrative Region.
| |
Collapse
|
4
|
Gholipour S, Shamsizadeh Z, Halabowski D, Gwenzi W, Nikaeen M. Combating antibiotic resistance using wastewater surveillance: Significance, applications, challenges, and future directions. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 908:168056. [PMID: 37914125 DOI: 10.1016/j.scitotenv.2023.168056] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 10/19/2023] [Accepted: 10/21/2023] [Indexed: 11/03/2023]
Abstract
The global increase of antibiotic resistance (AR) and resistant infections call for effective surveillance methods for understanding and mitigating (re)-emerging public health risks. Wastewater surveillance (WS) of antibiotic resistance is an emerging, but currently under-utilized decision-support tool in public health systems. Recent years have witnessed an increase in evidence linking antibiotic resistance in wastewaters to that of the community. To date, very few comprehensive reviews exist on the application of WS to understand AR and resistant infections in population. Current and emerging AR detection methods, and their merits and limitations are discussed. Wastewater surveillance has several merits relative to individual testing, including; (1) low per capita testing cost, (2) high spatial coverage, (3) low requirement for diagnostic equipment, and (4) detection of health threats ahead of real outbreaks. The applications of WS as an early warning system and decision support tool to understand and mitigate AR are discussed. Wastewater surveillance could be a tool of choice in low-income settings lacking resources and diagnostic facilities for individual testing. To demonstrate the utility of WS, empirical evidence from field case studies is presented. However, constraints still exist, including; (1) lack of standardized protocols, (2) the clinical utility and sensitivity of WS-based data, (3) uncertainties in relating WS data to pathogenic and virulent bacteria, and (4) whether or not AR in stools and ultimately wastewater represent the complete human resistome. Finally, further prospects are presented, include knowledge gaps on; (1) development of low-cost biosensors for AR, (2) development of WS protocols (sampling, processing, interpretation), (3) further pilot scale studies to understand the opportunities and limits of WS, and (4) development of computer-based analytical tools to facilitate rapid data collection, visualization and interpretation. Therefore, the present paper discusses the principles, opportunities, and constraints of wastewater surveillance applications to understand AR and safeguard public health.
Collapse
Affiliation(s)
- Sahar Gholipour
- Department of Environmental Health Engineering, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Zahra Shamsizadeh
- Department of Environmental Health Engineering, School of Health, Larestan University of Medical Sciences, Larestan, Iran
| | - Dariusz Halabowski
- University of Lodz, Faculty of Biology and Environmental Protection, Department of Ecology and Vertebrate Zoology, Lodz, Poland
| | - Willis Gwenzi
- Universität Kassel, Fachbereich Ökologische Agrarwissenschaften Fachgebiet Grünlandwissenschaft und Nachwachsende Rohstoffe, Steinstr. 19, 37249 Witzenhausen, Germany; Leibniz-Institut für Agrartechnik und Bioökonomie e.V. Max-Eyth-Allee 100, D-14469 Potsdam, Germany.
| | - Mahnaz Nikaeen
- Department of Environmental Health Engineering, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran; Environment Research Center, Research Institute for Primordial Prevention of Non-Communicable Diseases, Isfahan University of Medical Sciences, Isfahan, Iran.
| |
Collapse
|
5
|
Ding J, Xu X, Deng Y, Zheng X, Zhang T. Comparison of RT-ddPCR and RT-qPCR platforms for SARS-CoV-2 detection: Implications for future outbreaks of infectious diseases. ENVIRONMENT INTERNATIONAL 2024; 183:108438. [PMID: 38232505 DOI: 10.1016/j.envint.2024.108438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 01/08/2024] [Accepted: 01/09/2024] [Indexed: 01/19/2024]
Abstract
The increased frequency of human infectious disease outbreaks caused by RNA viruses worldwide in recent years calls for enhanced public health surveillance for better future preparedness. Wastewater-based epidemiology (WBE) is emerging as a valuable epidemiological tool for providing timely population-wide surveillance for disease prevention and response complementary to the current clinical surveillance system. Here, we compared the analytical performance and practical applications between predominant molecular detection methods of RT-qPCR and RT-ddPCR on SARS-CoV-2 detection in wastewater surveillance. When pure viral RNA was tested, RT-ddPCR exhibited superior quantification accuracy at higher concentration levels and achieved more sensitive detection with reduced variation at low concentration levels. Furthermore, RT-ddPCR consistently demonstrated more robust and accurate measurement either in the background of the wastewater matrix or with the presence of mismatches in the target regions of the consensus assay. Additionally, by detecting mock variant RNA samples, we found that RT-ddPCR outperformed RT-qPCR in virus genotyping by targeting specific loci with signature mutations in allele-specific (AS) assays, especially at low levels of allele frequencies and concentrations, which increased the possibility for sensitive low-prevalence variant detection in the population. Our study provides insights for detection method selection in the WBE applications for future infectious disease outbreaks.
Collapse
Affiliation(s)
- Jiahui Ding
- Environmental Microbiome Engineering and Biotechnology Laboratory, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong Special Administrative Region
| | - Xiaoqing Xu
- Environmental Microbiome Engineering and Biotechnology Laboratory, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong Special Administrative Region
| | - Yu Deng
- Environmental Microbiome Engineering and Biotechnology Laboratory, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong Special Administrative Region
| | - Xiawan Zheng
- Environmental Microbiome Engineering and Biotechnology Laboratory, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong Special Administrative Region
| | - Tong Zhang
- Environmental Microbiome Engineering and Biotechnology Laboratory, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong Special Administrative Region.
| |
Collapse
|
6
|
Xu X, Deng Y, Ding J, Shi X, Zheng X, Wang D, Yang Y, Liu L, Wang C, Li S, Gu H, Poon LLM, Zhang T. Refining detection methods for emerging SARS-CoV-2 mutants in wastewater: A case study on the Omicron variants. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 904:166215. [PMID: 37591380 DOI: 10.1016/j.scitotenv.2023.166215] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 08/07/2023] [Accepted: 08/08/2023] [Indexed: 08/19/2023]
Abstract
COVID-19 is an ongoing public health threat worldwide driven by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Wastewater surveillance has emerged as a complementary tool to clinical surveillance to control the COVID-19 pandemic. With the emergence of new variants of SARS-CoV-2, accumulated mutations that occurred in the SARS-CoV-2 genome raise new challenges for RT-qPCR diagnosis used in wastewater surveillance. There is a pressing need to develop refined methods for modifying primer/probes to better detect these emerging variants in wastewater. Here, we exemplified this process by focusing on the Omicron variants, for which we have developed and validated a modified detection method. We first modified the primers/probe mismatches of three assays commonly used in wastewater surveillance according to in silico analysis results for the mutations of 882 sequences collected during the fifth-wave outbreak in Hong Kong, and then evaluated them alongside the seven original assays. The results showed that five of seven original assays had better sensitivity for detecting Omicron variants, with the limits of detection (LoDs) ranging from 1.53 to 2.76 copies/μL. UCDC-N1 and Charité-E sets had poor performances, having LoDs higher than 10 copies/μL and false-positive/false-negative results in wastewater testing, probably due to the mismatch and demonstrating the need for modification of primer/probe sequences. The modified assays exhibited higher sensitivity and specificity, along with better reproducibility in detecting 81 wastewater samples. In addition, the sequencing results of six wastewater samples by Illumina also validated the presence of mismatches in the primer/probe binding sites of the three assays. This study highlights the importance of re-configuration of the primer-probe sets and refinements for the sequences to ensure the diagnostic effectiveness of RT-qPCR detection.
Collapse
Affiliation(s)
- Xiaoqing Xu
- Environmental Microbiome Engineering and Biotechnology Laboratory, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Yu Deng
- Environmental Microbiome Engineering and Biotechnology Laboratory, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Jiahui Ding
- Environmental Microbiome Engineering and Biotechnology Laboratory, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Xianghui Shi
- Environmental Microbiome Engineering and Biotechnology Laboratory, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Xiawan Zheng
- Environmental Microbiome Engineering and Biotechnology Laboratory, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Dou Wang
- Environmental Microbiome Engineering and Biotechnology Laboratory, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Yu Yang
- Environmental Microbiome Engineering and Biotechnology Laboratory, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Lei Liu
- Environmental Microbiome Engineering and Biotechnology Laboratory, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Chunxiao Wang
- Environmental Microbiome Engineering and Biotechnology Laboratory, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Shuxian Li
- Environmental Microbiome Engineering and Biotechnology Laboratory, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Haogao Gu
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Sassoon Road, Hong Kong, China
| | - Leo L M Poon
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Sassoon Road, Hong Kong, China; HKU-Pasteur Research Pole, The University of Hong Kong, Sassoon Road, Hong Kong, China
| | - Tong Zhang
- Environmental Microbiome Engineering and Biotechnology Laboratory, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China.
| |
Collapse
|
7
|
Fu S, Zhang Y, Wang R, Deng Z, He F, Jiang X, Shen L. Longitudinal wastewater surveillance of four key pathogens during an unprecedented large-scale COVID-19 outbreak in China facilitated a novel strategy for addressing public health priorities-A proof of concept study. WATER RESEARCH 2023; 247:120751. [PMID: 37918201 DOI: 10.1016/j.watres.2023.120751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 10/12/2023] [Accepted: 10/17/2023] [Indexed: 11/04/2023]
Abstract
Wastewater-based epidemiology (WBE) is a promising tool for monitoring the spread of SARS-CoV-2 and other pathogens, providing a novel public health strategy to combat disease. In this study, we first analysed nationwide reports of infectious diseases and selected Salmonella, norovirus, and influenza A virus (IAV) as prioritized targets apart from SARS-CoV-2 for wastewater surveillance. Next, the decay rates of Salmonella, norovirus, and IAV in wastewater at various temperatures were established to obtain corrected pathogen concentrations in sewage. We then monitored the concentrations of these pathogens in wastewater treatment plant (WWTP) influents in three cities, establishing a prediction model to estimate the number of infected individuals based on the mass balance between total viral load in sewage and individual viral shedding. From October 2022 to March 2023, we conducted multipathogen wastewater surveillance (MPWS) in a WWTP serving one million people in Xi'an City, monitoring the concentration dynamics of SARS-CoV-2, Salmonella, norovirus, and IAV in sewage. The infection peaks of each pathogen were different, with Salmonella cases and sewage concentration declining from October to December 2022 and only occasionally detected thereafter. The SARS-CoV-2 concentration rapidly increased from December 5th, peaked on December 26th, and then quickly decreased until the end of the study. Norovirus and IAV were detected in wastewater from January to March 2023, peaking in February and March, respectively. We used the prediction models to estimate the rate of SARS-CoV-2 infection in Xi'an city, with nearly 90 % of the population infected in urban regions. There was no significant difference between the predicted and actual number of hospital admissions for IAV. We also accurately predicted the number of norovirus cases relative to the reported cases. Our findings highlight the importance of wastewater surveillance in addressing public health priorities, underscoring the need for a novel workflow that links the prediction results of populations with public health interventions and allocation of medical resources at the community level. This approach would prevent medical resource panic squeezes, reduce the severity and mortality of patients, and enhance overall public health outcomes.
Collapse
Affiliation(s)
- Songzhe Fu
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, School of Medicine, Northwest University, Xi'an 710069, China.
| | - Yixiang Zhang
- CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institutes for Biological Sciences (SIBS), Chinese Academy of Sciences, Shanghai, China; University of Chinese Academy of Sciences, Shanghai, China
| | - Rui Wang
- College of Marine Science and Environment, Dalian Ocean University, Dalian, China; Key Laboratory of Environment Controlled Aquaculture (KLECA), Ministry of Education, Dalian 116023, China
| | - Zhiqiang Deng
- The Collaboration Unit for Field Epidemiology of State Key Laboratory for Infectious Disease Prevention and Control, Nanchang Center for Disease Control and Prevention, Nanchang, China
| | - Fenglan He
- The Collaboration Unit for Field Epidemiology of State Key Laboratory for Infectious Disease Prevention and Control, Nanchang Center for Disease Control and Prevention, Nanchang, China
| | - Xiaotong Jiang
- College of Marine Science and Environment, Dalian Ocean University, Dalian, China; Key Laboratory of Environment Controlled Aquaculture (KLECA), Ministry of Education, Dalian 116023, China
| | - Lixin Shen
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, School of Medicine, Northwest University, Xi'an 710069, China.
| |
Collapse
|
8
|
Xu X, Deng Y, Ding J, Zheng X, Wang C, Wang D, Liu L, Gu H, Peiris M, Poon LLM, Zhang T. Wastewater genomic sequencing for SARS-CoV-2 variants surveillance in wastewater-based epidemiology applications. WATER RESEARCH 2023; 244:120444. [PMID: 37579567 DOI: 10.1016/j.watres.2023.120444] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 08/01/2023] [Accepted: 08/02/2023] [Indexed: 08/16/2023]
Abstract
Wastewater-based epidemiology (WBE) has been widely used as a complementary approach to SARS-CoV-2 clinical surveillance. Wastewater genomic sequencing could provide valuable information on the genomic diversity of SARS-CoV-2 in the surveyed population. However, reliable detection and quantification of variants or mutations remain challenging. In this study, we used mock wastewater samples created by spiking SARS-CoV-2 variant standard RNA into wastewater RNA to evaluate the impacts of sequencing throughput on various aspects such as genome coverage, mutation detection, and SARS-CoV-2 variant deconvolution. We found that wastewater datasets with sequencing throughput greater than 0.5 Gb yielded reliable results in genomic analysis. In addition, using in silico mock datasets, we evaluated the performance of the adopted pipeline for variant deconvolution. By sequencing 86 wastewater samples covering more than 6 million people over 7 months, we presented two use cases of wastewater genomic sequencing for surveying COVID-19 in Hong Kong in WBE applications, including the replacement of Delta variants by Omicron variants, and the prevalence and development trends of three Omicron sublineages. Importantly, the wastewater genomic sequencing data were able to reveal the variant trends 16 days before the clinical data did. By investigating mutations of the spike (S) gene of the SARS-CoV-2 virus, we also showed the potential of wastewater genomic sequencing in identifying novel mutations and unique alleles. Overall, our study demonstrated the crucial role of wastewater genomic surveillance in providing valuable insights into the emergence and monitoring of new SARS-CoV-2 variants and laid a solid foundation for the development of genomic analysis methodologies for WBE of other novel emerging viruses in the future.
Collapse
Affiliation(s)
- Xiaoqing Xu
- Department of Civil Engineering, Environmental Microbiome Engineering and Biotechnology Laboratory, Center for Environmental Engineering Research, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China
| | - Yu Deng
- Department of Civil Engineering, Environmental Microbiome Engineering and Biotechnology Laboratory, Center for Environmental Engineering Research, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China
| | - Jiahui Ding
- Department of Civil Engineering, Environmental Microbiome Engineering and Biotechnology Laboratory, Center for Environmental Engineering Research, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China
| | - Xiawan Zheng
- Department of Civil Engineering, Environmental Microbiome Engineering and Biotechnology Laboratory, Center for Environmental Engineering Research, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China
| | - Chunxiao Wang
- Department of Civil Engineering, Environmental Microbiome Engineering and Biotechnology Laboratory, Center for Environmental Engineering Research, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China
| | - Dou Wang
- Department of Civil Engineering, Environmental Microbiome Engineering and Biotechnology Laboratory, Center for Environmental Engineering Research, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China
| | - Lei Liu
- Department of Civil Engineering, Environmental Microbiome Engineering and Biotechnology Laboratory, Center for Environmental Engineering Research, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China
| | - Haogao Gu
- Li Ka Shing Faculty of Medicine, School of Public Health, The University of Hong Kong, Sassoon Road, Hong Kong SAR, China
| | - Malik Peiris
- Li Ka Shing Faculty of Medicine, School of Public Health, The University of Hong Kong, Sassoon Road, Hong Kong SAR, China; HKU-Pasteur Research Pole, The University of Hong Kong, Sassoon Road, Hong Kong SAR, China
| | - Leo L M Poon
- Li Ka Shing Faculty of Medicine, School of Public Health, The University of Hong Kong, Sassoon Road, Hong Kong SAR, China; HKU-Pasteur Research Pole, The University of Hong Kong, Sassoon Road, Hong Kong SAR, China
| | - Tong Zhang
- Department of Civil Engineering, Environmental Microbiome Engineering and Biotechnology Laboratory, Center for Environmental Engineering Research, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China.
| |
Collapse
|
9
|
Wen J, Duan L, Wang B, Dong Q, Liu Y, Huang J, Yu G. Stability and WBE biomarkers possibility of 17 antiviral drugs in sewage and gravity sewers. WATER RESEARCH 2023; 238:120023. [PMID: 37150064 PMCID: PMC10149109 DOI: 10.1016/j.watres.2023.120023] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 03/31/2023] [Accepted: 04/28/2023] [Indexed: 05/09/2023]
Abstract
Wastewater-based epidemiology (WBE) is a promising technique for monitoring the rapidly increasing use of antiviral drugs during the COVID-19 pandemic. It is essential to evaluate the in-sewer stability of antiviral drugs in order to determine appropriate biomarkers. This study developed an analytical method for quantification of 17 typical antiviral drugs, and investigated the stability of target compounds in sewer through 4 laboratory-scale gravity sewer reactors. Nine antiviral drugs (lamivudine, acyclovir, amantadine, favipiravir, nevirapine, oseltamivir, ganciclovir, emtricitabine and telbivudine) were observed to be stable and recommended as appropriate biomarkers for WBE. As for the other 8 unstable drugs (abacavir, arbidol, ribavirin, zidovudine, ritonavir, lopinavir, remdesivir and efavirenz), their attenuation was driven by adsorption, biodegradation and diffusion. Moreover, reaction kinetics revealed that the effects of sediments and biofilms were regarded to be independent in gravity sewers, and the rate constants of removal by biofilms was directly proportional to the ratio of surface area against wastewater volume. The study highlighted the potential importance of flow velocity for compound stability, since an increased flow velocity significantly accelerated the removal of unstable biomarkers. In addition, a framework for graded evaluation of biomarker stability was proposed to provide reference for researchers to select suitable WBE biomarkers. Compared with current classification method, this framework considered the influences of residence time and different removal mechanisms, which additionally screened four antiviral drugs as viable WBE biomarkers. This is the first study to report the stability of antiviral drugs in gravity sewers.
Collapse
Affiliation(s)
- Jiaqi Wen
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; Beijing Key Laboratory for Emerging Organic Contaminants Control, Beijing Laboratory for Environmental Frontier Technologies, China
| | - Lei Duan
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; Beijing Key Laboratory for Emerging Organic Contaminants Control, Beijing Laboratory for Environmental Frontier Technologies, China
| | - Bin Wang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; Beijing Key Laboratory for Emerging Organic Contaminants Control, Beijing Laboratory for Environmental Frontier Technologies, China
| | - Qian Dong
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Yanchen Liu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Jun Huang
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; Beijing Key Laboratory for Emerging Organic Contaminants Control, Beijing Laboratory for Environmental Frontier Technologies, China
| | - Gang Yu
- State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China; Beijing Key Laboratory for Emerging Organic Contaminants Control, Beijing Laboratory for Environmental Frontier Technologies, China; Advanced Interdisciplinary Institute of Environment and Ecology, Beijing Normal University at Zhuhai, 519087, China.
| |
Collapse
|
10
|
Yang K, Guo J, Møhlenberg M, Zhou H. SARS-CoV-2 surveillance in medical and industrial wastewater-a global perspective: a narrative review. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:63323-63334. [PMID: 36988799 PMCID: PMC10049894 DOI: 10.1007/s11356-023-26571-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 03/16/2023] [Indexed: 05/11/2023]
Abstract
The novel coronavirus SARS-CoV-2 has spread at an unprecedented rate since late 2019, leading to the global COVID-19 pandemic. During the pandemic, being able to detect SARS-CoV-2 in human populations with high coverage quickly is a huge challenge. As SARS-CoV-2 is excreted in human excreta and thus exposed to the aqueous environment through sewers, the goal is to develop an ideal, non-invasive, cost-effective epidemiological method for detecting SARS-CoV-2. Wastewater surveillance has gained widespread interest and is increasingly being investigated as an effective early warning tool for monitoring the spread and evolution of the virus. This review emphasizes important findings on SARS-CoV-2 wastewater-based epidemiology (WBE) in different continents and techniques used to detect SARS-CoV-2 in wastewater during the period 2020-2022. The results show that WBE is a valuable population-level method for monitoring SARS-CoV-2 and is a valuable early warning alert. It can assist policymakers in formulating relevant policies to avoid the negative impacts of early or delayed action. Such strategy can also help avoid unnecessary wastage of medical resources, rationalize vaccine distribution, assist early detection, and contain large-scale outbreaks.
Collapse
Affiliation(s)
- Kaiwen Yang
- College of Medical Technology, Chengdu University of Traditional Chinese Medicine, Liutai Road 1166, Wenjiang, Chengdu, 610000, China
| | - Jinlin Guo
- College of Medical Technology, Chengdu University of Traditional Chinese Medicine, Liutai Road 1166, Wenjiang, Chengdu, 610000, China
| | - Michelle Møhlenberg
- Department of Biomedicine, Høegh-Guldbergs Gade 10, Building 1115, DK-8000, Aarhus C, Denmark
| | - Hao Zhou
- College of Medical Technology, Chengdu University of Traditional Chinese Medicine, Liutai Road 1166, Wenjiang, Chengdu, 610000, China.
| |
Collapse
|
11
|
Zheng X, Wang M, Deng Y, Xu X, Lin D, Zhang Y, Li S, Ding J, Shi X, Yau CI, Poon LLM, Zhang T. A rapid, high-throughput, and sensitive PEG-precipitation method for SARS-CoV-2 wastewater surveillance. WATER RESEARCH 2023; 230:119560. [PMID: 36623382 PMCID: PMC9803703 DOI: 10.1016/j.watres.2022.119560] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 12/27/2022] [Accepted: 12/30/2022] [Indexed: 06/17/2023]
Abstract
The effective application of wastewater surveillance is dependent on testing capacity and sensitivity to obtain high spatial resolution testing results for a timely targeted public health response. To achieve this purpose, the development of rapid, high-throughput, and sensitive virus concentration methods is urgently needed. Various protocols have been developed and implemented in wastewater surveillance networks so far, however, most of them lack the ability to scale up testing capacity or cannot achieve sufficient sensitivity for detecting SARS-CoV-2 RNA at low prevalence. In the present study, using positive raw wastewater in Hong Kong, a PEG precipitation-based three-step centrifugation method was developed, including low-speed centrifugation for large particles removal and the recovery of viral nucleic acid, and medium-speed centrifugation for the concentration of viral nucleic acid. This method could process over 100 samples by two persons per day to reach the process limit of detection (PLoD) of 3286 copies/L wastewater. Additionally, it was found that the testing capacity could be further increased by decreasing incubation and centrifugation time without significantly influencing the method sensitivity. The entire procedure uses ubiquitous reagents and instruments found in most laboratories to obtain robust testing results. This high-throughput, cost-effective, and sensitive tool will promote the establishment of nearly real-time wastewater surveillance networks for valuable public health information.
Collapse
Affiliation(s)
- Xiawan Zheng
- Environmental Microbiome Engineering and Biotechnology Lab, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China
| | - Mengying Wang
- Environmental Microbiome Engineering and Biotechnology Lab, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China
| | - Yu Deng
- Environmental Microbiome Engineering and Biotechnology Lab, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China
| | - Xiaoqing Xu
- Environmental Microbiome Engineering and Biotechnology Lab, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China
| | - Danxi Lin
- Environmental Microbiome Engineering and Biotechnology Lab, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China
| | - Yulin Zhang
- Environmental Microbiome Engineering and Biotechnology Lab, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China
| | - Shuxian Li
- Environmental Microbiome Engineering and Biotechnology Lab, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China
| | - Jiahui Ding
- Environmental Microbiome Engineering and Biotechnology Lab, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China
| | - Xianghui Shi
- Environmental Microbiome Engineering and Biotechnology Lab, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China
| | - Chung In Yau
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Sassoon Road, Hong Kong SAR, China
| | - Leo L M Poon
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Sassoon Road, Hong Kong SAR, China
| | - Tong Zhang
- Environmental Microbiome Engineering and Biotechnology Lab, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China; Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macau SAR, China.
| |
Collapse
|
12
|
Zheng X, Li S, Deng Y, Xu X, Ding J, Lau FTK, In Yau C, Poon LLM, Tun HM, Zhang T. Quantification of SARS-CoV-2 RNA in wastewater treatment plants mirrors the pandemic trend in Hong Kong. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 844:157121. [PMID: 35787900 PMCID: PMC9249664 DOI: 10.1016/j.scitotenv.2022.157121] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 06/12/2022] [Accepted: 06/28/2022] [Indexed: 05/09/2023]
Abstract
Wastewater-based epidemiology (WBE) for the SARS-CoV-2 virus in wastewater treatment plants (WWTPs) has emerged as a cost-effective and unbiased tool for population-level testing in the community. In the present study, we conducted a 6-month wastewater monitoring campaign from three WWTPs of different flow rates and catchment area characteristics, which serve 28 % (2.1 million people) of Hong Kong residents in total. Wastewater samples collected daily or every other day were concentrated using ultracentrifugation and the SARS-CoV-2 virus RNA in the supernatant was detected using the N1 and E primer sets. The results showed significant correlations between the virus concentration and the number of daily new cases in corresponding catchment areas of the three WWTPs when using 7-day moving average values (Kendall's tau-b value: 0.227-0.608, p < 0.001). SARS-CoV-2 virus concentration was normalized to a fecal indicator using PMMoV concentration and daily flow rates, but the normalization did not enhance the correlation. The key factors contributing to the correlation were also evaluated, including the sampling frequency, testing methods, and smoothing days. This study demonstrates the applicability of wastewater surveillance to monitor overall SARS-CoV-2 pandemic dynamics in a densely populated city like Hong Kong, and provides a large-scale longitudinal reference for the establishment of the long-term sentinel surveillance in WWTPs for WBE of pathogens which could be combined into a city-wide public health observatory.
Collapse
Affiliation(s)
- Xiawan Zheng
- Environmental Microbiome Engineering and Biotechnology Lab, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Shuxian Li
- Environmental Microbiome Engineering and Biotechnology Lab, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Yu Deng
- Environmental Microbiome Engineering and Biotechnology Lab, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Xiaoqing Xu
- Environmental Microbiome Engineering and Biotechnology Lab, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Jiahui Ding
- Environmental Microbiome Engineering and Biotechnology Lab, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Frankie T K Lau
- Drainage Services Department, The Government of the Hong Kong Special Administrative Region of the People's Republic of China, Wanchai, Hong Kong, China
| | - Chung In Yau
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Sassoon Road, Hong Kong, China
| | - Leo L M Poon
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Sassoon Road, Hong Kong, China
| | - Hein M Tun
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Sassoon Road, Hong Kong, China
| | - Tong Zhang
- Environmental Microbiome Engineering and Biotechnology Lab, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China.
| |
Collapse
|
13
|
Xu X, Deng Y, Ding J, Zheng X, Li S, Liu L, Chui HK, Poon LLM, Zhang T. Real-time allelic assays of SARS-CoV-2 variants to enhance sewage surveillance. WATER RESEARCH 2022; 220:118686. [PMID: 35679788 PMCID: PMC9148393 DOI: 10.1016/j.watres.2022.118686] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 05/25/2022] [Accepted: 05/27/2022] [Indexed: 05/21/2023]
Abstract
To effectively control the ongoing outbreaks of fast-spreading SARS-CoV-2 variants, there is an urgent need to add rapid variant detection and discrimination methods to the existing sewage surveillance systems established worldwide. We designed eight assays based on allele-specific RT-qPCR for real-time allelic discrimination of eight SARS-CoV-2 variants (Alpha, Beta, Gamma, Delta, Omicron, Lambda, Mu, and Kappa) in sewage. In silico analysis of the designed assays for identifying SARS-CoV-2 variants using more than four million SARS-CoV-2 variant sequences yielded ∼100% specificity and >90% sensitivity. All assays could sensitively discriminate and quantify target variants at levels as low as 10 viral RNA copy/µL with minimal cross-reactivity to the corresponding nontarget genotypes, even for sewage samples containing mixtures of SARS-CoV-2 variants with differential abundances. Integration of this method into the routine sewage surveillance in Hong Kong successfully identified the Beta variant in a community sewage. Complete concordance was observed between the results of viral whole-genome sequencing and those of our novel assays in sewage samples that contained exclusively the Delta variant discharged by a clinically diagnosed COVID-19 patient living in a quarantine hotel. Our assays in this method also provided real-time discrimination of the newly emerging Omicron variant in sewage two days prior to clinical test results in another quarantine hotel in Hong Kong. These novel allelic discrimination assays offer a rapid, sensitive, and specific way for detecting multiple SARS-CoV-2 variants in sewage and can be directly integrated into the existing sewage surveillance systems.
Collapse
Affiliation(s)
- Xiaoqing Xu
- Environmental Microbiome Engineering and Biotechnology Lab, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China
| | - Yu Deng
- Environmental Microbiome Engineering and Biotechnology Lab, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China
| | - Jiahui Ding
- Environmental Microbiome Engineering and Biotechnology Lab, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China
| | - Xiawan Zheng
- Environmental Microbiome Engineering and Biotechnology Lab, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China
| | - Shuxian Li
- Environmental Microbiome Engineering and Biotechnology Lab, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China
| | - Lei Liu
- Environmental Microbiome Engineering and Biotechnology Lab, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China
| | - Ho-Kwong Chui
- Environmental Protection Department, The Government of Hong Kong SAR, Tamar, Hong Kong SAR, China
| | - Leo L M Poon
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Sassoon Road, Hong Kong SAR, China
| | - Tong Zhang
- Environmental Microbiome Engineering and Biotechnology Lab, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China.
| |
Collapse
|