| Literature DB >> 33921482 |
Muhammad Adeel1, Tahir Farooq2, Noman Shakoor1, Sunny Ahmar3, Sajid Fiaz4, Jason C White5, Jorge L Gardea-Torresdey6, Freddy Mora-Poblete3, Yukui Rui1.
Abstract
Given the known presence of SARS-Cov-2 in wastewater, stemming disease spread in global regions where untreated effluent in the environment is common will experience additional pressure. Though development and preliminary trials of a vaccine against SARS-CoV-2 have been launched in several countries, rapid and effective alternative tools for the timely detection and remediation of SARS-CoV-2 in wastewater, especially in the developing countries, is of paramount importance. Here, we propose a promising, non-invasive technique for early prediction and targeted detection of SARS-CoV-2 to prevent current and future outbreaks. Thus, a combination of nanotechnology with wastewater-based epidemiology and artificial intelligence could be deployed for community-level wastewater virus detection and remediation.Entities:
Keywords: SARS-Cov-2; epidemic; nanoscience; remediation; wastewater
Year: 2021 PMID: 33921482 PMCID: PMC8069490 DOI: 10.3390/nano11040991
Source DB: PubMed Journal: Nanomaterials (Basel) ISSN: 2079-4991 Impact factor: 5.076
Figure 1Worldwide detection of SARS-CoV-2 in wastewater until January 2021.
Detailed reports on the detection of SARS-CoV-2 RNA in wastewater.
| Country | City/County | Specimen Source | Detection Method | Percentage Detection | Concentration (Copies/L) | Reference |
|---|---|---|---|---|---|---|
| Australia | Brisbane, Queensland | Untreated wastewater | RT-qPCR c, sequencging | 22% (2/9) | 1.2 × 102 | [ |
| Canada | Ottawa, | PCS a | RT-ddPCR, | 90.6–92.7% ( | Not available | [ |
| PGS b | 79.2–82.3% ( | Not available | ||||
| China | Beijing | Wastewater | RT-qPCR | Not available | Not available | [ |
| Finland | Helsinki | Wastewater | RT-qPCR | Pellte (78–89%) | Varied according to assay | [ |
| Supernatent (59–100%) | ||||||
| France | Paris | Treated wastewater | RT-qPCR | 100% (23/23) | >106.5 | [ |
| Untreated wastewater | RT-qPCR | 75% (6/8) | ~105 | |||
| Germany | North Rhine-Westphalia | Solid phase wastewater d | RT-qPCR, sequencing | Not available | 25 copies/mL | [ |
| Aqueous phase wastewater e | 1.8 copies/mL | |||||
| Italy | Milan, Rome | Untreated wastewater | RT-qPCR | 50% (6/12) | Not available | [ |
| Japan | Yamanashi | Secondary-treated wastewater f | RT-qPCR | 20% | 2.4 × 103 | [ |
| Netherlands | Amsterdam, Utrecht, Amersfoort, | Untreated wastewater | RT-qPCR | 58% (14/24) | Not available | [ |
| Pakistan | 38 districts | Untreated wastewater | RT-qPCR | 27% (21/78) | Not available | [ |
| USA | Massachusett, | Untreated wastewater | RT-qPCR, sequencing | 71% (10/14) | >2 × 105 | [ |
| Bozeman, Montana | Untreated wastewater | RT-PCR, sequencing | 100% (7/7) | >3 × 104 | [ |
a Primary clairified sludge, b Post grit solid, c Polymerase chain reaction, d Additional processing by using biological/chemical methods, e Municipal wastewater treatment plants, f River water in Yamanashi Prefecture, Japan.
Figure 2Schematic illustration of primary sources of SARS-CoV-2 in the water system and proposed removal mechanism.
Figure 3A model representing a combinatory approach for a rapid detection and remediation of SARS-CoV-2 in wastewater.