| Literature DB >> 33153744 |
Mounia Achak1, Soufiane Alaoui Bakri2, Younes Chhiti3, Fatima Ezzahrae M'hamdi Alaoui2, Noureddine Barka4, Wafaa Boumya5.
Abstract
Currently, the apparition of new SARS-CoV, known as SARS-CoV-2, affected more than 34 million people and causing high death rates worldwide. Recently, several studies reported SARS-CoV-2 ribonucleic acid (RNA) in hospital wastewater. SARS-CoV-2 can be transmitted between humans via respiratory droplets, close contact and fomites. Fecal-oral transmission is considered also as a potential route of transmission since several scientists confirmed the presence of SARS-CoV-2 RNA in feces of infected patients, therefore its transmission via feces in aquatic environment, particularly hospital wastewater. Hospitals are one of the important classes of polluting sectors around the world. It was identified that hospital wastewater contains hazardous elements and a wide variety of microbial pathogens and viruses. Therefore, this may potentially pose a significant risk of public health and environment infection. This study reported an introduction about the Physical-chemical and microbiological characterization of hospital wastewater, which can be a route to identify potential technology to reduce the impact of hospital contaminants before evacuation. The presence of SARS-CoV-2 in aqueous environment was reviewed. The knowledge of the detection and survival of SARS-CoV-2 in wastewater and hospital wastewater were described to understand the different routes of SARS-CoV-2 transmission, which is also useful to avoid the outbreak of CoV-19. In addition, disinfection technologies used commonly for deactivation of SARS-CoV-2 were highlighted. It was revealed that, chlorine-containing disinfectants are the most commonly used disinfectants in this field of research. Meanwhile, other efficient technologies must be developed and improved to avoid another wave of the pandemic of COVID-19 infections.Entities:
Keywords: Detection; Disinfection technologies; Hospital wastewater; SARS-CoV-2; Survival; Transmission
Mesh:
Substances:
Year: 2020 PMID: 33153744 PMCID: PMC7585361 DOI: 10.1016/j.scitotenv.2020.143192
Source DB: PubMed Journal: Sci Total Environ ISSN: 0048-9697 Impact factor: 7.963
Macro and micro-pollutants of hospital wastewater and their origin.
| Pollutant substances | Parameter | Origin | Reference |
|---|---|---|---|
| Micro-pollutants | AOX | Sterilization of surgical tools, cleaning activities | |
| Antibiotics | Humans excretion, disposal of unused compounds | ||
| Cytostatics | Humans excretion, some materials that humans deal with them on a daily basis (detergents, shampoos, lotions, cosmetics) | ||
| Hormones (Progesterone, Cortisol, Aldosterone, Testosterone, Estrogen) | Medical applications (radiology, oncology) | ||
| Contrast substances | Sterilization of surgical tools, drugs application, humans excretion | ||
| Phenols | Cleaning and building maintenance | ||
| Detergents | Excreted by oncology patients | ||
| Heavy metals | Diagnostic agents and | ||
| Mercury | Iodinated contrast media (ICM), magnetic resonance imaging (MRI) | ||
| Gadolinium, Silver, nickel, zinc, copper, lead, arsenic | Disinfectants used at diagnosis/examination units and at sterilization units | ||
| Macro-pollutants | Physic-chemical parameters (COD, BOD, TOC, SS, ammonium ions and chloride) | Diagnosis, equipment disinfection, laboratory activities, anesthetics and sterilization products, nutrient solutions used in microbiology laboratories | |
| Microbiological taminants | Atmosphere, soil, medical devices and water employed in the hospital practice | ||
| Viruses | Human fecal matter from infected persons |
Physical-chemical and microbiological characterization of hospital wastewater.
| Parameter | [a] | [b] | [c] | [d] | [e] | [f] | [g] | [h] |
|---|---|---|---|---|---|---|---|---|
| pH | 8.6 | 7.28 | 6.94 | 8.2 | 8.28 | 7.5–7.9 | 6.8 | 7.47 |
| Conductivity (mS cm−1) | n.d | n.d | 1468 | n.d | 230 | n.d | n.d | 814 |
| SS (mg L−1) | 138 | 571 | 11 | 236 | n.d | 259–520 | 900 | n.d |
| COD (mg L−1) | 365 | 810 | 710 | 2664 | 1594 | 450–654 | 150 | 441 |
| BOD5 mg L−1 | n.d | 450 | 227 | 1530 | 131 | 220–345 | 80 | 187 |
| TKN (mg L−1) | 94 | 124.1 | 40 | n.d | n.d | 81–120 | 27.6 | n.d |
| NH4+ (mg L−1) | 75 | n.d | n.d | 68 | 2.53 | 18–41 | 16.7 | n.d |
| Total phenols (mg L−1) | 8.4 | 2.26 | n.d | n.d | n.d | n.d | n.d | n.d |
| Ptot (mg L−1) | n.d | 15.1 | n.d | n.d | n.d | 14–19 | n.d | n.d |
| Chlorides (mg L−1) | n.d | n.d | n.d | 359 | n.d | n.d | 110 | 65 |
| AOX (mg L−1) | n.d | 11 | n.d | 1.24 | n.d | n.d | n.d | n.d |
| Fats and oils (mg L−1) | n.d | n.d | 3.5 | n.d | n.d | n.d | n.d | n.d |
| Total coliforms (MPN/100 mL) | 4.16 106 | n.d | n.d | n.d | n.d | n.d | n.d | n.d |
| Ecotoxicity (TU) (μg/L) | 4.8 | n.d | 163 | n.d | n.d | n.d | n.d | n.d |
| Total copper (Cu) (μg/L) | n.d | n.d | 34 | 110 | n.d | n.d | <1.0 | n.d |
| Total mercury (Hg) (μg/L) | n.d | n.d | 2.8 | n.d | n.d | n.d | n.d | n.d |
| Total zinc (Zn) (μg/L) | n.d | n.d | 95 | 536 | n.d | n.d | 0.077 | n.d |
| Total silver (Ag) (μg/L) | n.d | n.d | 8.1 | n.d | n.d | n.d | n.d | n.d |
| Total chromium (Cr) (μg/L) | n.d | n.d | 121 | 4 | n.d | n.d | 0.042 | n.d |
[a] Munoz et al. (2016); [b] Top et al. (2020); [c] Basturka et al. (2020); [d] Emmanuel et al. (2005); [e] Sanaa et al. (2019); [f] Pirsaheb et al. (2015); [g] Meo et al. (2014); [h] Ouardaa et al. (2019).
Fig. 1Collection of domestic, industrial and hospital effluents to wastewater treatment plant.
Range of variability of physical-chemical and microbiological characterization of hospital wastewater and urban wastewater.
| Parameter | Hospital wastewater | Urban wastewater |
|---|---|---|
| pH | 6.9–9.18 | 7.5–8.5 |
| Conductivity (mS cm−1) | 750–1000 | 420–1340 |
| SS (mg L−1) | 120–400 | 120–350 |
| COD (mg L−1) | 450–2300 | 500–600 |
| BOD5 mg L−1 | 150–603 | 100–400 |
| TKN (mg L−1) | 30–100 | 20–70 |
| NH4 (mg L−1) | 10–55 | 12–45 |
| Ptot (mg L−1) | 3–8 | 4–10 |
| Chlorides (mg L−1) | 80–400 | 30–100 |
| Fats and oils (mg L−1) | 13–60 | 50–150 |
| Total detergents (mg L−1) | 3–7.2 | 4–8 |
| Total coliforms (MPN/100 mL) | 106–109 | 107–108 |
| Fecal coliforms (MPN/100 mL) | 103–107 | 106–107 |
| 103–106 | 106–107 | |
| Streptococci (MPN/100 mL) | 103–105 | 103–105 |
Fig. 2Structure of enveloped and nonenveloped SARS-CoV-2.
Fig. 3Transmission of SARS-CoV-2 via respiratory droplets and aerosol.
Influence of environment parameters in survival of SARS-CoV-2.
| Environment factor | Condition | Study area/period | Interpretation | References |
|---|---|---|---|---|
| Temperature | −33.8–26.9 °C | 122 cities (China) 23 January–29 February | Linear relationship with the number of COVID-19 cases in the range of <3 °C and became flat above 3 °C. No evidence supporting that case counts of SARS-CoV-2 could decline when T increase | |
| Increasing ambient daily average temperature up to around 13 °C | 31 provinces of Mainland China | Decrease in the rate of progression of COVID-19 with the arrival of spring and summer in the north hemisphere | ||
| 24.6–28.6 °C | Jakarta Indonesia | Significant correlation with SARS-CoV-2 only at average temperature (°C). Insignificant correlation with minimum and maximum temperature. | ||
| Increase of temperature as spring and summer months | China (North Hemisphere) | Negative association of daily number of SARS CoV-2 cases | ||
| −10–10 °C | 31 provinces of mainland (China, Wuhan) | Strong association of decrease of SARS-CoV-2 incidence at lower and higher temperatures | ||
| High temperature | China cities | Reduced Reproductive number (R values) of COVID-19 in China and USA at high temperature | ||
| 13–24 °C | Wuhan (China) | SARS-CoV-2 survival between 13 and 24 °C. 19 °C lasting about 60 days is conducive to the spread between the vector and humans | ||
| −10-30 °C | South Korea, Japan, Iran, Northern Italy, Northwestern United States, Spain, France | Cold temperature include stabilization of the droplet and enhanced propagation in nasal mucosa | ||
| 21 cities/provinces in Italy, 21 cities/provinces in Japan, and 51 other countries around the world | Temperature alone could not correlate with the SARS-CoV-2 case counts very well. Combination of several meteorological factors could describe the epidemic trend much better than single-factor models. | |||
| Wuhan (China) | Negative associations with daily death counts of COVID-19 patients | |||
| Humidity | 75%–93% | Jakarta Indonesia | Insignificant correlation with SARS-CoV-2 and humidity | |
| Increase of humidity as spring and summer months | China (North Hemisphere) | Negative association of daily number of SARS-CoV-2 cases | ||
| 30–95% | South Korea, Japan, Iran, Northern Italy, Northwestern United States, Spain, France | Low humidity include stabilization of the droplet and enhanced propagation in nasal mucosa | ||
| 50%–80% | Wuhan (China) | 75% humidity is conducive to the survival of the coronavirus | ||
| High humidity | China cities | Reduced Reproductive number (R values) of COVID-19 in China and USA | ||
| Wuhan (China) | Negative associations with daily death counts of COVID-19 patients | |||
| Air pollution | PM2.5, PM10, CO, NO2, SO2 and O3 | Wuhan (China) | Mortality counts of COVID-19 are negatively associated with PM2.5 and PM10 | |
| PM2.5, PM10, CO, NO2, SO2 and O3 | 120 (China) January 23–29 February | Short-term exposure to air pollutants (PM2.5, PM10, CO, NO2, O3) is associated with increased risk of COVID-19 | ||
| PM2.5, PM10, CO, NO2, SO2 and O3 | Prefectures (China), provinces (Italy), Counties (U.S) | Positive significant correlations between COVID-19 infections and air quality variables in each country and concluded that higher mortality was also correlated with poor air quality, namely, with high PM2.5, CO, NO2 values | ||
| PM2.5, PM10, NO2 and O3 | Italian provinces | Long-term exposure to air pollution may represent a favorable context for the spread of the virus | ||
| PM2.5 | United States | Small increase in long-term exposure to PM2.5 leads to a large increase in the COVID-19 death rate | ||
| Ultraviolet radiation | UV-B radiation | China cities | Higher transmission risks for COVID-19 | |
| UV radiation | 224 cities (China) | Spreadability of COVID-19 would not change with increasing UV exposure | ||
| UV radiation | 173 countries and five continents | Ultraviolet (UV) radiation has a statistically significant effect on daily COVID-19 growth rates | ||
| UV radiation | Jakarta (Indonesia) | Sunlight exposure correlated significantly with recovery from Covid-19. However, sunlight exposure did not correlate significantly with the occurrence of and death from Covid-19. |
Fig. 4Fecal transmission of SARS-CoV-2.
Detection of SARS-CoV-2 in wastewater and hospital wastewater.
| Wastewater sampling | Wastewater type | Study area | Virus concentration method | Detection method | Finding and interpretation | Reference |
|---|---|---|---|---|---|---|
| Untreated wastewater (nine samples) | Sewage, | Netherlands (Hague, Utrecht, Apeldoorn, Amersfoort, Schiphol, Tilburg) | Ultrafiltration | RT-qPCR: | Sensitivity of the primer/probe sets of N1 = N3 > N2 on SARS-CoV-2 RNA. Positive rate (58%). | |
| Untreated wastewater | Swage, | Australia (Southeast Queensland) | Electronegative membranes | RT-qPCR: | N_Sarbeco assay produced positive signals for two wastewater samples (12 copies/100 mL) with 22% positive rate, while the same samples were negative when tested using NIID_2019-nCOV_N. N_Sarbeco assay is more sensitive than the NIID_2019-nCOV_N assay. | |
| Untreated wastewater | Wastewater treatment plant | U.S.A (Bozeman, Montana) | Ultrafiltration | RT-qPCR: | Number of COVID-19 cases should have a linear relationship with viral RNA copies in wastewater. 98.5% SARS-CoV-2 genome sequence from the wastewater | |
| Untreated wastewater | Raw and filtered swage | U.S.A (Massachusetts) | PEG precipitation | RT-qPCR: | Presence of SARS-CoV-2 at high titers (>2 105 copies/L) in the period from March 18–25. Viral titers observed were significantly higher than expected based on clinically confirmed cases as of March 25. | |
| Wastewater | Untreated wastewater | France (Paris) | Ultracentrifugation | RT-qPCR: | Increase of genome units in raw wastewater accurately followed the increase of human COVID-19 cases. SARS-CoV-2 concentration exceeds 3.16 106 and less 105 with 100% and 75% of positive rate for untreated and tread wastewater respectively. | |
| Treated wastewater | France (Paris) | Ultracentrifugation | RT-qPCR: | |||
| Wastewater | Influent, secondary and tertiary treated effluent | Spain (Murcia, Cartagena, Molina de Segura, Lorca, Cieza, Totana) | Aluminum hydroxide adsorption-precipitation | RT-qPCR: | From 42 influent 35 are positive for SARS-CoV-2 RNA. | |
| Wastewater | Untreated wastewater (Amsterdam Airport Schiphol) | Holland (Haarlemmermeer, Netherlands) | Not available | RT-qPCR: | Samples tested positive for virus RNA 4 days after the first cases of coronavirus disease 2019 (COVID-19) were identified in the Netherlands on Feb 27, 2020 | |
| Wastewater | Swage | Italy (Milan, Rome) | PEG-dextran | Nested RT-PCR | SARS-CoV-2 RNA detected in 6/12 (50%) of wastewater samples. | |
| Wastewater | Influent, secondary treated effluent | Japan (Yamanashi) | Electronegative membranes | RT-qPCR: | SARS-CoV-2 RNA was detected in one of five secondary-treated wastewater samples (2.4 × 103 copies/L) by N_Sarbeco qPCR assay following the Electronegative membranes method. | |
| Nested RT-PCR: | ARS-CoV-2 RNA was not detected in any of the five influent and three river water samples tested with N_Sarbeco, NIID_2019-nCOV_N, CDC-N1, and CDC-N2assays and two nested PCR (ORF1a and S protein) assays. | |||||
| Hospital wastewater (Hospital of Zhejiang University) | Sewage pools | China (Shanghai) | Not available | Not available | Three sewage samples from the inlets of sewage disinfection pool were positive for SARS-CoV-2 RNA (Cycle threshold value 29.37, 30.58, and 32.42). | |
| Hospital wastewater (Jinyintan Hospital, Huoshenshan Hospital, Wuchang Fangcang Hospital) | Wastewater (adjusting tank) | China (Wuhan) | PEG precipitation | RT-qPCR: | SARS-CoV-2 in Jinyintan Hospital was only detected in water from the adjusting tank (255 copies/L), but undetected in other tanks and effluents. |
Comparison of disinfection technologies in hospital wastewater (Chen et al., 2014).
| Parameter | Cl2 | NaOCl | ClO2 | O3 | UV |
|---|---|---|---|---|---|
| Concentration (g/L) | 10 | 10–15 | 2–5 | 10 | 300 g/m2 |
| Time (min) | 10–30 | 10–30 | 10–20 | 5–10 | 10s |
| Reliability | Good | Better | Good | Good | Better |
| Effects to endospore | Bad | Bad | Good | Better | Better |
| Secondary pollution | Yes | Yes | Yes | No | No |
| Toxicity of disinfection | More | More | More | Less | No |
| Energy consumption | Lower | Low | Lower | High | Lower |
| Influence of pH to disinfection | More | More | More | Little | No |
| Application | Water supply plant and sewage treatment | Seldom used | Medium scale projects, especially in hospital wastewater treatment | Advanced waste treatment in water supply plant and industrial water consumption | Large-scale municipal water supply and wastewater treatment in food industry |
Disinfection of SARS-CoV by different technologies.
| Virus removed | Wastewater sampling | Disinfection technology | Detection method | Inactivation rate (%) | Result and interpretation | Reference |
|---|---|---|---|---|---|---|
| SARS-CoV | Hospital wastewater (Xiao Tang Shan Hospital) | Chlorine | RT-qPCR | 100 | 10 mg L−1 of chlorine resulted in 100% inactivation of SARS-CoV. | |
| Human coronavirus NL63 (HCoV-NL63), human coronavirus OC43 (HCoV-OC43) | Aqueous virus suspensions | Adsorption using chitosan nano/microspheres (CHI-NS/MS) | RT-qPCR | 92 | 5 mg of modified CHI-NS/MS was mixed with the virus suspension and incubated at 23 °C for 30 min with mixing. RT-qPCR analysis revealed the decrease of virions (92%). | |
| SARS-CoV-2 | Hospital wastewater (Wuchang Cabin Hospital) | Sodium hypochlorite | RT-qPCR | Not available | Absence of SARS-CoV-2 viral RNA in the effluent of septic tanks after disinfection with 800 g/m3of sodium hypochlorite. | |
| SARS-CoV-2 | Hospital wastewater (Jinyintan Hospital, Huoshenshan Hospital, Wuchang Fangcang Hospital) | Adjusting tank, Moving Bed Biofilm Reactor (MBBR)-sedimentation-disinfection (sodium hypochlorite) | RT-qPCR | Not available | 255 copies/L of SARS-CoV-2 viral RNA were detected only in adjusting tank of Jinyintan Hospital. |