Wastewater-based SARS-CoV-2 environmental monitoring for Piedmont, Italy

Hydrological and physicochemical parameters associated with SARS-CoV-2 and pepper mild mottle virus wastewater concentrations for a large-combined sewer system

During COVID-19, surveillance of SARS-CoV-2 in wastewater has been a promising tool for tracking viral infection at the community level. However, in addition to the shedding rates within the community, SARS-CoV-2 concentrations in raw wastewater are influenced by several environmental factors. This study investigated the effects of wastewater characteristics on the viral quantification of SARS-CoV-2 and pepper mild mottle virus (PMMoV) for a large wastewater system with combined sewers. Principal component analysis illustrated that water temperature negatively correlates with SARS-CoV-2 and PMMoV in wastewater, but flow rate and EC are highly correlated with SARS-CoV-2 in spring and winter. The normalization using EC enhanced the correlation with clinical data compared to normalization using pH, flow rate, and raw SARS-CoV-2. The normalization using PMMoV reduced the correlation with clinical data. Multiple linear and random forest (RF) applied to predict the concentrations of SARS-CoV-2 in wastewater, given the confirmed cases and physicochemical parameters. RF regression was the best model to predict SARS-CoV-2 in wastewater (R2=0.8), with the most important variables being the confirmed cases followed by water temperature. RF model is a potent predictor of the presence of SARS-CoV-2 in wastewater. This enhances the degree of reliability between community outbreaks and SARS-CoV-2 monitoring.

Targeted Enrichment Sequencing Utilizing a Respiratory Pathogen Panel for Genomic Wastewater-Based Viral Epidemiology in Uruguay

Human respiratory and enteric viruses are responsible for substantial morbidity and mortality worldwide. Wastewater-based epidemiology utilizing next-generation sequencing serves as an effective tool for monitoring viral circulation dynamics at the community level. However, these complex environmental samples are often laden with other microorganisms and host genomic material, which can hinder the sensitivity of viral detection. To address this limitation, targeted enrichment sequencing is emerging as a preferred strategy, facilitating the acquisition of a more comprehensive understanding of specific pathogens. In this study, we evaluated the performance of a targeted enrichment sequencing panel for 42 excreted respiratory viruses (including Picornaviridae, Adenoviridae, Coronaviridae, Paramyxoviridae, Orthomyxoviridae, Orthoherpesviridae, Pneumoviridae, and Parvoviridae families), known as the Respiratory Pathogen ID/AMR enrichment panel (RPIP), coupled with Explify bioinformatics analysis in 3 sewage samples from Uruguay. RPIP panel successfully identified sequences from frequently circulating viruses, along with some that had not been documented previously. We identified and characterized various viruses, including human Enterovirus (Coxsackievirus A1 and A19), Influenza A-H1N1, and full-length sequences of SARS-CoV-2. Additionally, several other viral pathogens were detected, such as human Bocavirus, human Parechovirus, Enterovirus A71, and Enterovirus D68; however, for these viruses further analysis was limited due to the small genomic regions or low-read coverage obtained. While the RPIP panel necessitates substantial sequencing depth and may introduce bias towards the more predominant strains present in the samples, this approach suggests its viability as a genomic epidemiological tool for assessing respiratory and enteric viruses in wastewater.

Environmental Dissemination of SARS-CoV-2: An Analysis Employing Crassphage and Next-Generation Sequencing Protocols

This study aimed to investigate the dissemination of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in water samples obtained during the coronavirus disease 2019 pandemic period, employing cross-assembly phage (crAssphage) as a fecal contamination biomarker and next-generation sequencing protocols to characterize SARS-CoV-2 variants. Raw wastewater and surface water (stream and sea) samples were collected for over a month in Rio de Janeiro, Brazil. Ultracentrifugation and negatively charged membrane filtration were employed for viral concentration of the wastewater and surface water samples, respectively. Viruses were detected and quantified by (RT-)qPCR applying TaqMan® system protocols. SARS-CoV-2 RNA signals were detected in 92.5% (37/40) of the wastewater samples and in 31.25% (10/32) of the stream water samples, but not in seawater samples. CrAssphage was detected in 100% of the wastewater samples, 93.75% (30/32) of the stream samples, and in 2/4 of the seawater samples. CrAssphage detection and high concentrations in stream surface waters (median 8.95 log10 gc/L) revealed diffuse contamination by domestic wastewater in a region with high sanitary coverage. The correlations detected between SARS-CoV-2 data and the moving averages of clinical cases per capita over the sampling period were moderate to strong when applying a 13-day offset, regardless of normalization by crAssphage data or not. Sequencing of the receptor-binding domain of the spike protein confirmed the detection of SARS-CoV-2, but did not characterize the circulating variant. On the other hand, the whole genome sequencing protocol identified circulation of the Gamma variant, corroborating the sampling period clinical data.

Assessment of environmental factors influencing SARS-CoV-2 in Vietnam's surface water across two years of clinical data

Wastewater-based epidemiology (WBE) is an effective, non-invasive method for monitoring the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by tracking viral prevalence in water. This study aimed to investigate the presence of SARS-CoV-2 in surface water in Vietnam over two years. One-step quantitative reverse transcription polymerase chain reaction (qRT-PCR) assays were employed to quantify SARS-CoV-2 and its variant-specific mutation sites (G339D/E484A) and pepper mild mottle virus (PMMoV) from a total of 315 samples (105 samples per site) to compare with reported Coronavirus disease 2019 (COVID-19) cases and environmental factors. SARS-CoV-2 was detected in 38 % (40/105), 43 % (45/105), and 39 % (41/105) of water samples from Sites A, B, and C, respectively, with concentrations of 3.0-5.6 log10 copies/L. PMMoV concentrations were 5.1-8.9 log10 copies/L. SARS-CoV-2 levels were higher in winter compared with summer. There was a strong positive association between the mutant type and SARS-CoV-2 concentrations (Spearman's rho = 0.77, p < 0.01). The mean concentrations of mutant and nonmutant types were 2.3 and 1.8 log10 copies/L, respectively. Peaks in SARS-CoV-2 concentrations preceded reported COVID-19 cases by 2-4 weeks, with the highest association observed at a 4-week delay (Pearson's correlation coefficient: 0.46-0.53). Environmental factors, including temperature, pH, and electrical conductivity, correlated negatively with SARS-CoV-2 (Spearman's rho = -0.21, -0.28, and -0.21, respectively, p < 0.05), whereas average rainfall, humidity, and dissolved oxygen correlated positively (Spearman's rho = 0.20, 0.27, and 0.51, respectively, p < 0.05). These correlations highlight the significance of environmental variables in understanding viral prevalence in water. Our findings confirmed the utility of WBE as an early warning system for long-term monitoring. Future research should incorporate environmental factors to improve prediction accuracy for clinical cases and other waterborne diseases.

Wastewater surveillance for viral pathogens: A tool for public health

A focus on water quality has intensified globally, considering its critical role in sustaining life and ecosystems. Wastewater, reflecting societal development, profoundly impacts public health. Wastewater-based epidemiology (WBE) has emerged as a surveillance tool for detecting outbreaks early, monitoring infectious disease trends, and providing real-time insights, particularly in vulnerable communities. WBE aids in tracking pathogens, including viruses, in sewage, offering a comprehensive understanding of community health and lifestyle habits. With the rise in global COVID-19 cases, WBE has gained prominence, aiding in monitoring SARS-CoV-2 levels worldwide. Despite advancements in water treatment, poorly treated wastewater discharge remains a threat, amplifying the spread of water-, sanitation-, and hygiene (WaSH)-related diseases. WBE, serving as complementary surveillance, is pivotal for monitoring community-level viral infections. However, there is untapped potential for WBE to expand its role in public health surveillance. This review emphasizes the importance of WBE in understanding the link between viral surveillance in wastewater and public health, highlighting the need for its further integration into public health management.

Assessment of crAssphage as a biological variable for SARS-CoV-2 data normalization in wastewater surveillance

AIMS

This study aimed to assess the use of cross-assembled phage (crAssphage) as an endogenous control employing a multivariate normalization analysis and its application as a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) data normalizer.

METHODS AND RESULTS

A total of 188 twelve-hour composite raw sewage samples were obtained from eight wastewater treatment plants (WWTP) during a 1-year monitoring period. Employing the N1 and N2 target regions, SARS-CoV-2 RNA was detected in 94% (177) and 90% (170) of the samples, respectively, with a global median of 5 log10 genomic copies per liter (GC l-1). CrAssphage was detected in 100% of the samples, ranging from 8.29 to 10.43 log10 GC l-1, with a median of 9.46 ± 0.40 log10 GC l-1, presenting both spatial and temporal variabilities.

CONCLUSIONS

Although SARS-CoV-2 data normalization employing crAssphage revealed a correlation with clinical cases occurring during the study period, crAssphage normalization by the flow per capita per day of each WWTP increased this correlation, corroborating the importance of normalizing wastewater surveillance data in disease trend monitoring.

Wastewater-Based Epidemiology for SARS-CoV-2 in Northern Italy: A Spatiotemporal Model

The study investigated the application of Wastewater-Based Epidemiology (WBE) as a tool for monitoring the SARS-CoV-2 prevalence in a city in northern Italy from October 2021 to May 2023. Based on a previously used deterministic model, this study proposed a variation to account for the population characteristics and virus biodegradation in the sewer network. The model calculated virus loads and corresponding COVID-19 cases over time in different areas of the city and was validated using healthcare data while considering viral mutations, vaccinations, and testing variability. The correlation between the predicted and reported cases was high across the three waves that occurred during the period considered, demonstrating the ability of the model to predict the relevant fluctuations in the number of cases. The population characteristics did not substantially influence the predicted and reported infection rates. Conversely, biodegradation significantly reduced the virus load reaching the wastewater treatment plant, resulting in a 30% reduction in the total virus load produced in the study area. This approach can be applied to compare the virus load values across cities with different population demographics and sewer network structures, improving the comparability of the WBE data for effective surveillance and intervention strategies.

Effectiveness of environmental surveillance of SARS-CoV-2 as an early-warning system: Update of a systematic review during the second year of the pandemic

The aim of this updated systematic review was to offer an overview of the effectiveness of environmental surveillance (ES) of SARS-CoV-2 as a potential early-warning system (EWS) for COVID-19 and new variants of concerns (VOCs) during the second year of the pandemic. An updated literature search was conducted to evaluate the added value of ES of SARS-CoV-2 for public health decisions. The search for studies published between June 2021 and July 2022 resulted in 1,588 publications, identifying 331 articles for full-text screening. A total of 151 publications met our inclusion criteria for the assessment of the effectiveness of ES as an EWS and early detection of SARS-CoV-2 variants. We identified a further 30 publications among the grey literature. ES confirms its usefulness as an EWS for detecting new waves of SARS-CoV-2 infection with an average lead time of 1-2 weeks for most of the publication. ES could function as an EWS for new VOCs in areas with no registered cases or limited clinical capacity. Challenges in data harmonization and variant detection require standardized approaches and innovations for improved public health decision-making. ES confirms its potential to support public health decision-making and resource allocation in future outbreaks.

Estimating the prevalence of COVID-19 cases through the analysis of SARS-CoV-2 RNA copies derived from wastewater samples from North Dakota

The SARS-CoV-2 virus was first detected in December 2019, which prompted many researchers to investigate how the virus spreads. SARS-CoV-2 is mainly transmitted through respiratory droplets. Symptoms of the SARS-CoV-2 virus appear after an incubation period. Moreover, the asymptomatic infected individuals unknowingly spread the virus. Detecting infected people requires daily tests and contact tracing, which are expensive. The early detection of infectious diseases, including COVID-19, can be achieved with wastewater-based epidemiology, which is timely and cost-effective. In this study, we collected wastewater samples from wastewater treatment plants in several cities in North Dakota and then extracted viral RNA copies. We used log-RNA copies in the model to predict the number of infected cases using Quantile Regression (QR) and K-Nearest Neighbor (KNN) Regression. The model's performance was evaluated by comparing the Mean Absolute Percentage Error (MAPE). The QR model performs well in cities where the population is >10000 . In addition, the model predictions were compared with the basic Susceptible-Infected-Recovered (SIR) model which is the golden standard model for infectious diseases.

SARS-CoV-2 RNA in Wastewater and Bivalve Mollusk Samples of Campania, Southern Italy

SARS-CoV-2 can be detected in the feces of infected people, consequently in wastewater, and in bivalve mollusks, that are able to accumulate viruses due to their ability to filter large amounts of water. This study aimed to monitor SARS-CoV-2 RNA presence in 168 raw wastewater samples collected from six wastewater treatment plants (WWTPs) and 57 mollusk samples obtained from eight harvesting sites in Campania, Italy. The monitoring period spanned from October 2021 to April 2022, and the results were compared and correlated with the epidemiological situation. In sewage, the ORF1b region of SARS-CoV-2 was detected using RT-qPCR, while in mollusks, three targets-RdRp, ORF1b, and E-were identified via RT-dPCR. Results showed a 92.3% rate of positive wastewater samples with increased genomic copies (g.c.)/(day*inhabitant) in December-January and March-April 2022. In the entire observation period, 54.4% of mollusks tested positive for at least one SARS-CoV-2 target, and the rate of positive samples showed a trend similar to that of the wastewater samples. The lower SARS-CoV-2 positivity rate in bivalve mollusks compared to sewages is a direct consequence of the seawater dilution effect. Our data confirm that both sample types can be used as sentinels to detect SARS-CoV-2 in the environment and suggest their potential use in obtaining complementary information on SARS-CoV-2.

Recent progress on wastewater-based epidemiology for COVID-19 surveillance: A systematic review of analytical procedures and epidemiological modeling

On March 11, 2020, the World Health Organization declared the coronavirus disease 2019 (COVID-19), whose causative agent is the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), a pandemic. This virus is predominantly transmitted via respiratory droplets and shed via sputum, saliva, urine, and stool. Wastewater-based epidemiology (WBE) has been able to monitor the circulation of viral pathogens in the population. This tool demands both in-lab and computational work to be meaningful for, among other purposes, the prediction of outbreaks. In this context, we present a systematic review that organizes and discusses laboratory procedures for SARS-CoV-2 RNA quantification from a wastewater matrix, along with modeling techniques applied to the development of WBE for COVID-19 surveillance. The goal of this review is to present the current panorama of WBE operational aspects as well as to identify current challenges related to it. Our review was conducted in a reproducible manner by following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for systematic reviews. We identified a lack of standardization in wastewater analytical procedures. Regardless, the reverse transcription-quantitative polymerase chain reaction (RT-qPCR) approach was the most reported technique employed to detect and quantify viral RNA in wastewater samples. As a more convenient sample matrix, we suggest the solid portion of wastewater to be considered in future investigations due to its higher viral load compared to the liquid fraction. Regarding the epidemiological modeling, the data-driven approach was consistently used for the prediction of variables associated with outbreaks. Future efforts should also be directed toward the development of rapid, more economical, portable, and accurate detection devices.

Wastewater early warning system for SARS-CoV-2 outbreaks and variants in a Coruña, Spain

Wastewater-based epidemiology has been widely used as a cost-effective method for tracking the COVID-19 pandemic at the community level. Here we describe COVIDBENS, a wastewater surveillance program running from June 2020 to March 2022 in the wastewater treatment plant of Bens in A Coruña (Spain). The main goal of this work was to provide an effective early warning tool based in wastewater epidemiology to help in decision-making at both the social and public health levels. RT-qPCR procedures and Illumina sequencing were used to weekly monitor the viral load and to detect SARS-CoV-2 mutations in wastewater, respectively. In addition, own statistical models were applied to estimate the real number of infected people and the frequency of each emerging variant circulating in the community, which considerable improved the surveillance strategy. Our analysis detected 6 viral load waves in A Coruña with concentrations between 10³ and 10⁶ SARS-CoV-2 RNA copies/L. Our system was able to anticipate community outbreaks during the pandemic with 8-36 days in advance with respect to clinical reports and, to detect the emergence of new SARS-CoV-2 variants in A Coruña such as Alpha (B.1.1.7), Delta (B.1.617.2), and Omicron (B.1.1.529 and BA.2) in wastewater with 42, 30, and 27 days, respectively, before the health system did. Data generated here helped local authorities and health managers to give a faster and more efficient response to the pandemic situation, and also allowed important industrial companies to adapt their production to each situation. The wastewater-based epidemiology program developed in our metropolitan area of A Coruña (Spain) during the SARS-CoV-2 pandemic served as a powerful early warning system combining statistical models with mutations and viral load monitoring in wastewater over time.

One-Year Surveillance of SARS-CoV-2 and Rotavirus in Water Matrices from a Hot Spring Area

The pandemic of Coronavirus Disease 2019 (COVID-19) caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is still impacting not only on human health but also all economic activities, especially in those related to tourism. In this study, in order to characterize the presence of SARS-CoV-2 in a hot spring park in Uruguay, swimming pools water, wastewater, and surface water from this area were analyzed by quantitative PCR. Wastewater from Salto city located next to the hydrothermal spring area was also evaluated as well as the presence of Rotavirus (RV). Overall, SARS-CoV-2 was detected in 13% (13/102) of the analyzed samples. Moreover, this virus was not detected in any of the samples from the swimming pools water and was present in 18% (3/17) of wastewater samples from the hotels area showing the same trend between the titer of SARS-CoV-2 and the number of infected people in Salto city. SARS-CoV-2 was also detected in wastewater samples (32% (11/34)) from Salto city, detecting the first positive sample when 105 persons were positive for SARS-CoV-2. Rotavirus was detected only in 10% (2/24) of the wastewater samples analyzed in months when partial lockdown measures were taken, however, this virus was detected in nearly all wastewater samples analyzed when social distancing measures and partial lockdown were relaxed. Wastewater results confirmed the advantages of using the detection and quantification of viruses in this matrix in order to evaluate the presence of these viruses in the population, highlighting the usefulness of this approach to define and apply social distancing. This study suggests that waters from swimming pools are not a source of infection for SARS-CoV-2, although more studies are needed including infectivity assays in order to confirm this statement.

Data modelling recipes for SARS-CoV-2 wastewater-based epidemiology

Wastewater based epidemiology is recognized as one of the monitoring pillars, providing essential information for pandemic management. Central in the methodology are data modelling concepts for both communicating the monitoring results but also for analysis of the signal. It is due to the fast development of the field that a range of modelling concepts are used but without a coherent framework. This paper provides for such a framework, focusing on robust and simple concepts readily applicable, rather than applying latest findings from e.g., machine learning. It is demonstrated that data preprocessing, most important normalization by means of biomarkers and equal temporal spacing of the scattered data, is crucial. In terms of the latter, downsampling to a weekly spaced series is sufficient. Also, data smoothing turned out to be essential, not only for communication of the signal dynamics but likewise for regressions, nowcasting and forecasting. Correlation of the signal with epidemic indicators requires multivariate regression as the signal alone cannot explain the dynamics but - for this case study - multiple linear regression proofed to be a suitable tool when the focus is on understanding and interpretation. It was also demonstrated that short term prediction (7 days) is accurate with simple models (exponential smoothing or autoregressive models) but forecast accuracy deteriorates fast for longer periods.

Simultaneous Detection of SARS-CoV-2 and Influenza Virus in Wastewater of Two Cities in Southeastern Germany, January to May 2022

Dependent on the excretion pattern, wastewater monitoring of viruses can be a valuable approach to characterizing their circulation in the human population. Using polyethylene glycol precipitation and reverse transcription-quantitative PCR, the occurrence of RNA of SARS-CoV-2 and influenza viruses A/B in the raw wastewater of two treatment plants in Germany between January and May 2022 was investigated. Due to the relatively high incidence in both exposal areas (plant 1 and plant 2), SARS-CoV-2-specific RNA was determined in all 273 composite samples analyzed (concentration of E gene: 1.3 × 10⁴ to 3.2 × 10⁶ gc/L). Despite a nation-wide low number of confirmed infections, influenza virus A was demonstrated in 5.2% (concentration: 9.8 × 10² to 8.4 × 10⁴ gc/L; plant 1) and in 41.6% (3.6 × 10³ to 3.0 × 10⁵ gc/L; plant 2) of samples. Influenza virus B was detected in 36.0% (7.2 × 10² to 8.5 × 10⁶ gc/L; plant 1) and 57.7% (9.6 × 10³ to 2.1 × 10⁷ gc/L; plant 2) of wastewater samples. The results of the study demonstrate the frequent detection of two primary respiratory viruses in wastewater and offer the possibility to track the epidemiology of influenza by wastewater-based monitoring.

Wastewater-based epidemiology: A Brazilian SARS-COV-2 surveillance experience

Since 2020, developed countries have rapidly shared both publicly and academically relevant wastewater surveillance information. Data on SARS-CoV-2 circulation is pivotal for guiding public health policies and improving the COVID-19 pandemic response. Conversely, low- and middle-income countries, such as Latin America and the Caribbean, showed timid activities in the Wastewater-Based Epidemiology (WBE) context. In these countries, isolated groups perform viral wastewater monitoring, and the data are unevenly shared or accessible to health agencies and the scientific community. This manuscript aims to highlight the relevance of a multiparty effort involving research, public health, and governmental agencies to support usage of WBE methodology to its full potential during the COVID-19 pandemic as part of a joint One Health surveillance approach. Thus, in this study, we explored the results obtained from wastewater surveillance in different regions of Brazil as a part of the COVID-19 Wastewater Monitoring Network ANA (National Water Agency), MCTI (Ministry of Science, Technology, and Innovations) and MS (Ministry of Health). Over the epidemiological weeks of 2021 and early 2022, viral RNA concentrations in wastewater followed epidemiological trends and variations. The highest viral loads in wastewater samples were detected during the second Brazilian wave of COVID-19. Corroborating international reports, our experience demonstrated usefulness of the WBE approach in viral surveillance. Wastewater surveillance allows hotspot identification, and therefore, early public health interventions. In addition, this methodology allows tracking of asymptomatic and oligosymptomatic individuals, who are generally underreported, especially in emerging countries with limited clinical testing capacity. Therefore, WBE undoubtedly contributes to improving public health responses in the context of this pandemic, as well as other sanitary emergencies.

Acquired insights from the long-term surveillance of SARS-CoV-2 RNA for COVID-19 monitoring: The case of Monterrey Metropolitan Area (Mexico)

Wastewater-based epidemiology offers a time- and cost-effective way to monitor SARS-CoV-2 spread in communities and therefore represents a complement to clinical testing. WBE applicability has been demonstrated in a number of cases over short-term periods as a method for tracking the prevalence of SARS-CoV-2 and an early-warning tool for predicting outbreaks in the population. This study reports SARS-CoV-2 viral loads from wastewater treatment plants (WWTPs) and hospitals over a 6-month period (June to December 2020). Results show that the overall range of viral load in positive tested samples was between 1.2 × 103 and 3.5 × 106 gene copies/l, unveiling that secondary-treated wastewaters mirrored the viral load of influents. The interpretation suggests that the viral titers found in three out of four WWTPs were associated to clinical COVID-19 surveillance indicators preceding 2-7 days the rise of reported clinical cases. The median wastewater detection rate of SARS-CoV-2 was one out of 14,300 reported new cases. Preliminary model estimates of prevalence ranged from 0.02 to 4.6% for the studied period. This comprehensive statistical and epidemiological analysis demonstrates that the applied wastewater-based approach to COVID-19 surveillance is in general consistent and feasible, although there is room for improvements.

Link between SARS-CoV-2 emissions and airborne concentrations: Closing the gap in understanding

The airborne transmission of SARS-CoV-2 remains surprisingly controversial; indeed, health and regulatory authorities still require direct proof of this mode of transmission. To close this gap, we measured the viral load of SARS-CoV-2 of an infected subject in a hospital room (through an oral and nasopharyngeal swab), as well as the airborne SARS-CoV-2 concentration in the room resulting from the person breathing and speaking. Moreover, we simulated the same scenarios to estimate the concentration of RNA copies in the air through a novel theoretical approach and conducted a comparative analysis between experimental and theoretical results. Results showed that for an infected subject's viral load ranging between 2.4 × 106 and 5.5 × 106 RNA copies mL-1, the corresponding airborne SARS-CoV-2 concentration was below the minimum detection threshold when the person was breathing, and 16.1 (expanded uncertainty of 32.8) RNA copies m-3 when speaking. The application of the predictive approach provided concentrations metrologically compatible with the available experimental data (i.e. for speaking activity). Thus, the study presented significant evidence to close the gap in understanding airborne transmission, given that the airborne SARS-CoV-2 concentration was shown to be directly related to the SARS-CoV-2 emitted. Moreover, the theoretical analysis was shown to be able to quantitatively link the airborne concentration to the emission.