This work was performed with financial support from the Regional Government of Castilla y Leon and the FEDER program (projects CLU 2017-09, UIC315 and VA266P20).

All methods described here have been published elsewhere and contain small modifications from the original methods.
1. Wastewater collection and sample pre-processing
NOTE: Due to the low concentrations of SARS-CoV-2 RNA in environmental samples, the implementation of a concentration step is crucial for a successful detection33,34,35. Described here is the first reported method for the detection of SARS-CoV-2 in wastewater36.
<ol>
	<li>Collection and concentration of wastewater sample(s)
	<ol>
		<li>Collect 1 L of 24 h composite wastewater sample. Store the sample at 4 &#176;C and proceed with the protocol within 24 h.</li>
		<li>Homogenize the sample by gently shaking the bottle. Collect 70 mL into a 100 mL centrifugal tube or divide the sample into two centrifugal tubes of 50 mL.</li>
		<li>Spike 20 &#181;L of mengovirus (3.2 x 103 copies/&#181;L) to 70 mL of each sample as an internal control of the concentration process. Alternatively, spike 10 &#181;L of mengovirus (3.2 x 103 copies/&#181;L) into each 50 mL centrifuge tube.</li>
		<li>Homogenize the sample and centrifuge at 700 x g for 10 min to remove large particles and organisms (pellet).</li>
		<li>Use the resulting supernatant for concentration with centrifugal ultrafiltration devices with a cutoff of 10 KDa by centrifuging at 4,000 x g for 40 min at 4 &#176;C. Elute the concentrate by centrifuging at 700 x g for 40 min and inverting the position of the ultrafiltration device.</li>
		<li>Measure the volume of the resulting concentrate. The volume will change depending on the quantity of solids in the sample that clog the centrifugal filters. In this study, the concentrate volumes obtained were between 200-1,200 &#181;L.</li>
		<li>Use the resulting concentrate for RNA extraction using a commercial kit or an in-house method. For the samples used in this study, a specific commercial kit for viral RNA extraction with an elution volume of 50 &#181;L is used.</li>
		<li>Measure the concentration of the extracted RNA with a fluorometric RNA quantification kit. Divide the extracted RNA into aliquots and freeze at -80 &#176;C until further analysis. 
		NOTE: For each RNA isolation procedure set, a negative control of isolation (NCI) should be included, containing only buffers to detect possible contamination during the extraction.</li>
	</ol>
	</li>
	<li>Collection of air samples using a Coriolis compact air sampler
	<ol>
		<li>Choose a sampling strategy according to the research goal by defining the sampling frequency and sampling locations.</li>
		<li>Set the flow rate on the air sampler (here, 50 L/min for 30 min). Please note that a flow rate of more than 200 L/min can degrade viral RNA, so it is recommended to use a flow rate below 200 L/min.</li>
		<li>Place a sterile cone in the air sampler before starting the collection by pulsing the start. Once the sampling is finished, add 5-15 mL of sterile phosphate-buffered saline (PBS) into the cone. Gently vortex or shake the sample by hand for 15 s. Store the samples for up to 24 h at 4 &#176;C or at -80 &#176;C in cryotubes.</li>
		<li>An optional concentration step can be performed. Clean and decontaminate the cones after each experiment using bleach.</li>
		<li>Extract RNA using a commercial kit or an in-house method. For the samples in this study, a specific commercial kit for viral RNA extraction with an elution volume of 50 &#181;L is used.</li>
		<li>Measure the concentration of the extracted RNA with a fluorometric RNA quantification kit. Divide the extracted RNA into aliquots and freeze at -80 &#176;C until further analysis. 
		&#8203;NOTE: For each RNA isolation procedure set, a negative control of isolation (NCI) should be included, containing only buffers to detect possible contamination during the extraction.</li>
	</ol>
	</li>
</ol>
2. Quantification of SARS-CoV-2 RNA by real-time-quantitative polymerase chain reaction (RT-qPCR)
NOTE: The below protocol is according to the CDC 2019-Novel Coronavirus (2019-nCoV) RT-PCR diagnostic panel37. Divide the primer/probe mix into several aliquots to avoid freezing and thawing cycles.
<ol>
	<li>Prepare the reaction mastermix for each target (mengovirus; SARS-CoV-2 [N1 and N2 genes]) as in Table 1, in a clean hood in the reagent setup room. Mix the primers/probe mix and enzyme by inversion five times. Alternatively, light pulse-vortex the primers/probe mix and enzymefive times. 
	NOTE: Keep all reagents cold during preparation and use. In order to minimize freeze-thaw cycles, it is highly recommended to freeze in aliquots.</li>
	<li>Spin down (1,000 x g for 15-30 s) the tubes to collect the contents at the bottom and place the tubes in a cold rack or on ice.</li>
	<li>Label 1.5 mL microcentrifuge tubes for each target. Add to each microcentrifuge tube the amount of each reagent needed (volume per reaction times the number of reactions including the needed controls). Mix by pipetting up and down and centrifuge for 5 s to collect the contents at the bottom.</li>
	<li>Keep the microcentrifuge tubes in a cold rack and dispense 15 &#181;L into strip PCR tubes or a 96-well plate in a cooling rack. Cover the plate and move it to the nucleic acid handling area (keep it in a cooling rack).</li>
	<li>Thaw an aliquot of extracted RNA and gently vortex for 5 s. Pipette 5 &#181;L of RNA (in addition to 5 &#181;L of non-template control [NTC], negative control of isolation [NCI], and positive control [PC]) to each reaction well or tube containing the previously prepared mastermix. Change gloves often as needed to avoid cross-contamination.</li>
	<li>Cover the entire reaction plate or tubes and gently vortex. Centrifuge (1,000 x g for 15-30 s) the plate or PCR tubes.</li>
	<li>Start the 25 &#181;L RT-qPCR with the following cycling conditions: reverse transcriptase at 45 &#176;C for 10 min; polymerase activation at 95 &#176;C for 10 min; 45 cycles of denaturation at 95 &#176;C for 15 s; and annealing/extension at 60 &#176;C for 45s.</li>
	<li>Calculate the recovery efficiency of mengovirus controls as reported by Conte et al.38: 
	<img alt="Equation 1" src="/files/ftp_upload/65053/65053eq01.jpg" style="margin: auto;" /> &#215; 100</li>
</ol>
3. Sequencing variants in wastewater and data analysis
NOTE: The described protocol is a modified protocol created by Quick et al.39,40. It uses two sets of primers for SARS-CoV-2 genome amplification by PCR tiling methodology-ARTIC primers and VarSkip primers. A combination of primers is used to guarantee the best genome coverage and to minimize the possibility of novel mutations causing primers of one type to fail. In general, the protocol is divided into three parts: reverse transcription (RT) and amplicon generation, sequencing library preparation, and sequencing and data analysis.
<ol>
	<li>Reverse transcription and amplicon generation
	<ol>
		<li>Ensure the input samples have a known qPCR cycling threshold (Ct) value to correctly dilute them in PCR-grade water. The Ct value is obtained during the quantification with qPCR. The dilutions are as follows: if the Ct value is 12-15, dilute the samples 1:100; if the Ct value is 15-18, dilute the samples 1:10; if the Ct value is 18-35, do not dilute the sample. Samples above a Ct value of 35 have a high chance of not working.</li>
		<li>In a PCR plate, pipette 16 &#181;L of each sample to its position on the plate. Add 4 &#181;L of reverse transcription (RT) mastermix (5x). Cover the plate and start the PCR reaction with the following conditions: 25 &#176;C for 2 min, 55 &#176;C for 20 min, and 95&#176;C for 1 min.</li>
		<li>Prepare 10 &#181;M dilutions of each primer set (ARTIC pool A and pool B, and VarSkip pool A and pool B). Prepare the mastermix for each set of primers (ARTIC set A and B, and VarSkip set A and B) as in Table 2. Four mastermixes will result from this.</li>
		<li>On a new PCR plate, pipette 20 &#181;L of the mastermix to each corresponding well. Add 5 &#181;L of the RT sample to each mix. 
		NOTE: Each sample will have four reaction mixtures-ARTIC pool A and B, and VarSkip pool A and B.</li>
		<li>Cover the plate and start the PCR reaction as follows: polymerase activation at 98 &#176;C for 30 s; 35 cycles of denaturation at 98 &#176;C for 15 s; and annealing/extension at 65 &#176;C for 4 min. Spin down (1,000 x g for 15-30 s) the plate. Combine the ARTIC reaction pools and separately combine the VarSkip reaction pools. Each sample will have two 50 &#181;L amplicon reactions.</li>
		<li>Add 50 &#181;L of bead-based reagent for PCR purification to each well and mix well by pipetting. Incubate at room temperature for 5 min. 
		NOTE: Before every use, mix the PCR purification reagent vigorously to resuspend the beads.</li>
		<li>Place the plate on a magnetic separation stand and wait for the beads to form a pellet and the liquid to clear (~5 min). Pipette and discard the supernatant. Keep the PCR plate on the magnetic stand.</li>
		<li>Maintaining the PCR plate on the magnetic stand, add 200 &#181;L of 80% ethanol to each sample without touching the bead pellet. Remove the ethanol.</li>
		<li>Repeat the previous step. Leave the plate on the magnetic stand uncovered for 30 s to allow the ethanol to evaporate.</li>
		<li>Remove the plate from the magnetic stand and add 15 &#181;L of PCR-grade water to each sample. Resuspend the pellet by pipetting. Incubate at room temperature for 2 min.</li>
		<li>Place the plate back on the magnetic stand and allow the beads to pellet (2 min). Carefully pipette 15 &#181;L of the supernatant from each sample to a new PCR plate.</li>
		<li>Measure the concentration of each sample with a fluorometric RNA quantification kit . Take approximately 50 ng of each sample in 12.5 &#181;L to the next step. If required, dilute the sample in PCR-grade water.</li>
	</ol>
	</li>
	<li>Library preparation
	<ol>
		<li>Add 1.75 &#181;L of reaction bufferfrom the Illumina DNA library preparation kit and 0.75 &#181;L of enzyme mix to each sample. Cover the plate, vortex briefly, and spin down (1,000 x g for 15-30 s). Incubate at 21&#176;C for 5 min and at 65 &#176;C for 5 min.</li>
		<li>In a new plate, pipette 3 &#181;L of PCR-grade water for each sample to a new PCR plate. Add 0.75 &#181;L of the prepared samples, 1.25 &#181;L of barcodes for sequencing library preparation, and 5 &#181;L of T4 DNA ligase mastermix. Mix well by pipetting, briefly spin down (1,000 x g for 15-30 s) in a centrifuge and incubate at 21 &#176;C for 20 min and at 65 &#176;C for 10 min. 
		NOTE: If using less than 25 samples, double all the volumes in this step.</li>
		<li>Pool all the samples together by adding 10 &#181;L of each sample to the same low-binding tube. Take 480 &#181;L to the next step.</li>
		<li>Add 192 &#181;L of bead-based reagent for PCR purification to the pool and mix well by pipetting. Incubate at room temperature for 10 min.</li>
		<li>Place the tube on a magnetic stand and wait for the supernatant to clear and a bead pellet to form. Remove the supernatant. Remove the tube from the magnetic stand.</li>
		<li>Add 700 &#181;L of the short fragment buffer (SFB) and mix by pipetting. Place back on the magnetic stand and wait for the pellet to form and the liquid to clear (~5 min). Pipette out the supernatant and discard it.</li>
		<li>Repeat the previous step. Leave the tube on the magnetic stand. Add 100 &#181;L of 80% ethanol without touching the bead. Pipette out the ethanol and allow the bead to dry for 30 s. 
		NOTE: Do not allow the bead to over-dry.</li>
		<li>Remove the tube from the magnetic stand and add 35 &#181;L of PCR-grade water. Mix by pipetting and incubate at room temperature for 2 min. Place the tube on the magnetic stand and allow the bead to pellet and the liquid to clear. Pipette 35 &#181;L of the supernatant to a new tube.</li>
		<li>Measure the RNA concentration of the pooled library. Pipette the volume needed to reach an amount of 30-50 ng of RNA and fill it with PCR-grade water to reach a final volume of 30 &#181;L.</li>
		<li>Prepare the adapter ligation reaction mix: 30 &#181;L of the pooled library, 5 &#181;L of Adapter Mix II, 10 &#181;L of the ligation reaction buffer (5x), and 5 &#181;L of T4 DNA ligase. Mix by pipetting and spin-down (1,000 x g for 15-30 s). Incubate at room temperature for 10 min.</li>
		<li>Add 20 &#181;L of the bead-based reagent for PCR purification and mix by pipetting. Incubate for 10 min at room temperature.</li>
		<li>Place the tube on the magnetic stand and wait for the bead pellet to form and the supernatant to become colorless. Carefully pipette out and discard the supernatant.</li>
		<li>Remove from the magnetic stand and add 125 &#181;L of SFB. Mix by pipetting and place back on the magnetic stand to separate the beads. When the liquid is clear, pipette and discard the supernatant.</li>
		<li>Repeat the previous step. Leave the tube open on the magnetic stand for 30 s for some of the leftover liquid to evaporate.</li>
		<li>Remove the tube from the magnetic stand and add 15 &#181;L of the elution buffer. Mix well by pipetting and briefly spin-down (1,000 x g for 15-30 s). Incubate at room temperature for 5 min. Place on the magnetic stand for 2 min.</li>
		<li>Pipette 15 &#181;L of the supernatant into a new low-binding tube. This is the final library. Measure the concentration with a fluorometric DNA quantification kit. 
		NOTE: In the next step, 12 &#181;L is needed. If the concentration is very high and less than 12 &#181;L of the library is needed, fill up to 12 &#181;L with the elution buffer reagent.</li>
	</ol>
	</li>
	<li>Flow cell loading and sequencing
	<ol>
		<li>Insert the flow cell (R9.4.1) into the real-time DNA and RNA sequencing device.</li>
		<li>Prepare the priming mix by adding 30 &#181;L of flush tether (FLT) reagent to a tube of flush buffer (FB) reagent. Vortex to mix and spin down (1,000 x g for 15-30 s).</li>
		<li>Open the priming port cover. Using a 1,000 &#181;L pipette, set to 200 &#181;L and insert the tip into the priming port. Turn the volume setting wheel and increase the volume until the liquid in the tip is seen. Discard the tip. 
		NOTE: Only turn the wheel until a few &#181;L of liquid in the tip is seen. Pulling larger amounts could damage the flow cell.</li>
		<li>Slowly add 800 &#181;L of the priming mix to the priming port, taking care not to introduce bubbles. Wait for 5 min.</li>
		<li>In the meantime, prepare the libraries for loading. Mix 37.5 &#181;L of the sequencing buffer, 25.5 &#181;L of the loading buffer, and 12 &#181;L of the library. 
		&#8203;NOTE: Mix the loading buffer before pipetting. The beads in this reagent settle very quickly.</li>
		<li>Open the port cover. Pipette 200 &#181;L of the priming mix into the priming port. 
		NOTE: While pipetting into the priming port with the cover port open, one will notice small drops coming from the sample port. Pipette slow enough so the sample port can pull the drops in without spilling over.</li>
		<li>Before loading the prepared library, mix well by pipetting. Using a 100 &#181;L pipette set to 75 &#181;L, take the library and pipette drop by drop into the sample port. Do not touch the port and wait for each drop to be absorbed into the port before loading the next drop to avoid overflowing of the liquid.</li>
		<li>Close the cover port and priming port. Open the software and start the sequencing experiment. Select basecalling On. For a library with 96 multiplexed samples, 10-12 h of sequencing is usually enough to generate sufficient data. 
		NOTE: Using the software, one can insert the sample sheet in .csv format. The sample sheet requires the following data: flow_cell_id, position_id, sample_id, experiment_id, flow_cell_product_code, and kit. The flow cell ID is needed (listed on the side of the flow cell) together with the kit used. For the suggested protocol, the LSK109 kit should be selected.</li>
	</ol>
	</li>
	<li>Sequencing data analysis
	<ol>
		<li>Insert the compressed fastq reads into the wf-artic workflow41. Run the workflow separately with two different primer schemes (ARTIC/V4.1 and NEB-VarSkip/v1a). Two &#34;. primertrimmed.rg.sorted.bam&#34; files are created for each sample.</li>
		<li>Merge the BAM files into a single file with the &#34;samtools merge&#34; command42. Insert the merged BAM file into Freyja tool, leaving default settings, to recover relative lineage abundances43.</li>
		<li>To create a FASTA file, insert the merged BAM file into the &#34;samtools mpileup&#34; and &#34;ivar&#34; tools with default settings44,45. For mutation calling, load the FASTA file into the Nextclade tool46,47.</li>
	</ol>
	</li>
</ol>

The authors have no competing economic interests or other conflicts of interest.

Microbial and viral detection and quantification using (RT-)qPCR methods have garnered widespread acceptance due to their remarkable sensitivity. However, these techniques face numerous challenges when analyzing environmental samples. Wastewater samples contain an abundance of inhibitory substances that can skew measurements and generate misleading results. To tackle these limitations and enhance precision, a complex protocol was conceived, designed, and implemented. This protocol was tailored by combining protocols from...

In December 2019, a novel disease called COVID-19 emerged, caused by a previously unknown coronavirus, SARS-CoV-21. The resulting global pandemic has presented a significant challenge to clinical and public health laboratories worldwide, as a large number of individuals require testing to accurately assess virus transmission and prevalence in the community. However, in many regions, achieving the necessary level of testing in a timely and spatially comprehensive manner is economically unfeasible2,3. Current surveillance systems based on individual clinical diagnostics rely heavily on symptom severity and individual reporting, as well as the extent to which these symptoms overlap with existing diseases circulating in the population4,5,6,7,8,9,10. Consequently, a high number of asymptomatic cases contributes to a significant underestimation of disease burden7,11.
Due to these challenges, wastewater-based epidemiology (WBE) for COVID-19 surveillance was proposed as a complementary surveillance strategy. WBE was first described in 200112, and was initially used to trace cocaine and other illegal drugs13. This approach relies on the assumption that it is possible to calculate the initial concentration of any substance that is stable in wastewater and excreted by humans8,12. WBE has been successfully implemented in many countries as a complementary and efficient surveillance system for SARS-CoV-23,8,14,15,16. The majority of methods to detect human viruses in aquatic environments follow these steps: concentration, nucleic acid extraction, amplification of the genomic segment (or segments) chosen, and detection/quantification of the amplified genomic segment3.
Another important environment for the detection and quantification of SARS-CoV-2 is in air samples. Initially, SARS-CoV-2 was thought to be transmitted mainly through close personal contact with respiratory droplets from aerosols generated by an infected person while speaking, sneezing, coughing, singing, or breathing17. However, several studies began to report the presence of SARS-CoV-2 RNA in the air, especially in healthcare facilities and other enclosed spaces18,19,20,21. Evidence of SARS-CoV-2 viability in air samples taken indoors in hospitals and other enclosed spaces has been found when the virus concentration was sufficiently high22,23,24. Outdoor studies have generally found no evidence of SARS-CoV-2, except in crowded outdoor spaces21,25,26,27,28,29. As of now, airborne transmission of SARS-CoV-2 has been recognized as a mode of transmission30,31. A recent review study shows the differences between outdoors, where risks of airborne transmission are minimal outside of crowded areas, and indoors, where larger risks could be present in poorly ventilated environments in which strong sources (i.e., number of infected people) could be present. A recent comprehensive review study has highlighted the substantial differences between the risks of airborne transmission in outdoor versus indoor environments, particularly in crowded areas with poor ventilation. The study indicates that the risk of airborne transmission is minimal in outdoor environments, where there is a larger volume of air available for the dilution and dispersion of virus particles32. These findings have important implications for public health policies and guidelines related to COVID-19. By recognizing the significant differences in transmission risks between indoor and outdoor environments, policymakers can develop more effective strategies to mitigate the spread of the virus and protect public health.
There are a variety of methods and protocols for the detection, quantification, and sequencing of SARS-CoV-2 from different environmental samples. This method article aims to present a combination of well-established protocols that allow laboratories with different capacity levels to perform environmental detection, quantification, and sequencing of viruses from wastewater and air samples.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Adapter+A25+A2:D19+A2:D20+A2+A2:D19</td>
			<td>Oxford Nanopore</td>
			<td>EXP-AMII001</td>
			<td>Sequencing</td>
		</tr>
		<tr>
			<td>AllPrep PowerViral DNA/RNA Kit</td>
			<td>Qiagen</td>
			<td>28000-50</td>
			<td>RNA extraction kit</td>
		</tr>
		<tr>
			<td>AMPure XP</td>
			<td>Beckman Coulter</td>
			<td>A63880</td>
			<td>PCR Purification, NGS Clean-up, PCR clean-up</td>
		</tr>
		<tr>
			<td>ARTIC SARS-CoV-2 Amplicon Panel</td>
			<td>IDT</td>
			<td>10011442</td>
			<td>SARS-CoV-2 genome amplification</td>
		</tr>
		<tr>
			<td>Blunt/TA Ligase Master Mix</td>
			<td>NEB</td>
			<td>M0367S</td>
			<td>Library preparation</td>
		</tr>
		<tr>
			<td>CENTRICON PLUS&#173;70 10KDA.</td>
			<td>Fisher Scientific</td>
			<td>10296062</td>
			<td>Concentration filters</td>
		</tr>
		<tr>
			<td>CORIOLIS COMPACT AIR SAMPLER</td>
			<td>Bertin Technologies</td>
			<td>083-DU001</td>
			<td>Air sampler</td>
		</tr>
		<tr>
			<td>Duran&#160;laboratory bottles</td>
			<td>Merck</td>
			<td>Z305200-10EA</td>
			<td>Sampling Bottles</td>
		</tr>
		<tr>
			<td>Flow Cell (R9.4.1)</td>
			<td>Oxford Nanopore</td>
			<td>FLO-MIN106D</td>
			<td>Sequencing</td>
		</tr>
		<tr>
			<td>General labarotory consumables (tubes, qPCR plates, etc)</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Ligation Sequencing Kit</td>
			<td>Oxford Nanopore</td>
			<td>SQK-LSK109</td>
			<td>Sequencing</td>
		</tr>
		<tr>
			<td>LunaScript&#160;RT SuperMix Kit</td>
			<td>NEB</td>
			<td>E3010</td>
			<td>&#160;cDNA synthesis</td>
		</tr>
		<tr>
			<td>Mengovirus extraction control Kit</td>
			<td>Biom&#233;rieux</td>
			<td>KMG</td>
			<td>Concentration control</td>
		</tr>
		<tr>
			<td>Nalgene General Long-Term Storage Cryogenic Tubes</td>
			<td>Thermofisher</td>
			<td>5011-0012</td>
			<td>Sample storage</td>
		</tr>
		<tr>
			<td>Native Barcoding Expansion 1-12 (PCR-free</td>
			<td>Oxford Nanopore</td>
			<td>EXP-NBD104</td>
			<td>Barcoding</td>
		</tr>
		<tr>
			<td>NEBNext Ultra II End Repair/dA-Tailing Module</td>
			<td>NEB</td>
			<td>E7595</td>
			<td>DNA repair</td>
		</tr>
		<tr>
			<td>NEBNext VarSkip Short SARS-CoV-2 Primer Mixes</td>
			<td>NEB</td>
			<td>E7658</td>
			<td>SARS-CoV-2 genome amplification</td>
		</tr>
		<tr>
			<td>NEBNext&#160;Quick Ligation Reaction Buffer</td>
			<td>NEB</td>
			<td>B6058S</td>
			<td>Sequencing&#160;</td>
		</tr>
		<tr>
			<td>Phosphate buffered saline</td>
			<td>Merck</td>
			<td>P4474</td>
			<td>Collection buffer</td>
		</tr>
		<tr>
			<td>Phosphate-buffered saline (PBS, 1X), sterile-filtered</td>
			<td>Thermofisher</td>
			<td>J61196.AP</td>
			<td>Elution of air samples</td>
		</tr>
		<tr>
			<td>Q5&#160;Hot Start High-Fidelity 2X Master Mix</td>
			<td>NEB</td>
			<td>M0494S</td>
			<td>hot start DNA polymerase</td>
		</tr>
		<tr>
			<td>Qubit RNA HS Assay Kit</td>
			<td>Thermofisher</td>
			<td>Q32852</td>
			<td>RNA quantitation</td>
		</tr>
		<tr>
			<td>SARS-CoV-2 RUO qPCR Primer &#38; Probe Kit</td>
			<td>IDT</td>
			<td>10006713</td>
			<td>Primer-Probe mix and qPCR positive control</td>
		</tr>
		<tr>
			<td>TaqPath 1-Step RT-qPCR Master Mix</td>
			<td>Thermofisher</td>
			<td>A15299</td>
			<td>RT-qPCR kit</td>
		</tr>
	</tbody>
</table>

quantification and whole genome characterization of sars-cov-2 rna in wastewater and air samples

Wastewater-based epidemiology has emerged as a promising and efficacious surveillance system for SARS-CoV-2 and other infectious diseases in many nations. The process typically involves wastewater concentration, nucleic acid extraction, amplification of selected genomic segments, and detection and quantification of the amplified genomic segment. This methodology can similarly be leveraged to detect and quantify infectious agents, such as SARS-CoV-2, in air samples. Initially, SARS-CoV-2 was presumed to spread primarily through close personal contact with droplets generated by an infected individual while speaking, sneezing, coughing, singing, or breathing. However, a growing number of studies have reported the presence of SARS-CoV-2 RNA in the air of healthcare facilities, establishing airborne transmission as a viable route for the virus. This study presents a composite of established protocols to facilitate environmental detection, quantification, and sequencing of viruses from both wastewater and air samples.

The results summarized in Table 3 show examples of the detection and quantification of SARS-CoV-2 RNA in wastewater and air samples using the method described in this article. Wastewater samples were collected from wastewater treatment plants in Spain and Slovenia and were considered positive if the Ct was less than 40 in at least two of the three replicates, with quantification considered valid if the Ct had a variation of less than 5%. In Spain and Portugal, indoor and outdoor air samples were collecte...

Watch this Scientific Journal Video about Quantification and Whole Genome Characterization of SARS-CoV-2 RNA in Wastewater and Air Samples at JoVE.com

Quantification and Whole Genome Characterization of SARS-CoV-2 RNA in Wastewater and Air Samples

This protocol aims to quantify SARS-CoV-2 RNA in wastewater and air samples to be used for wastewater-based epidemiology studies and to assess the exposure risk to SARS-CoV-2 in indoor and outdoor aerosols. This protocol also describes a tiled amplicon long-template sequencing approach for SARS-CoV-2 whole genome characterization.

Wastewater-based epidemiology has emerged as a promising and efficacious surveillance system for SARS-CoV-2 and other infectious diseases in many nations. ...

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1.0K Views.  University of Valladolid. This protocol aims to quantify SARS-CoV-2 RNA in wastewater and air samples to be used for wastewater-based epidemiology studies and to assess the exposure risk to SARS-CoV-2 in indoor and outdoor aerosols. This protocol also describes a tiled amplicon long-template sequencing approach for SARS-CoV-2 whole genome characterization.

Video: Quantification and Whole Genome Characterization of SARS-CoV-2 RNA in Wastewater and Air Samples

Air-sampled Filter Analysis for Endotoxins and DNA Content

Outdoor aerosol research commonly uses particulate matter sampled on filters. This procedure enables various characterizations of the collected particles to be performed in parallel. The purpose of the method presented here is to obtain a highly accurate and reliable analysis of the endotoxin and DNA content of bio-aerosols extracted from filters. The extraction of high molecular weight organic molecules, such as lipopolysaccharides, from sampled filters involves shaking the sample in a pyrogen-free water-based medium. The subsequent analysis is based on an enzymatic reaction that can be detected using a turbidimetric measurement. As a result of the high organic content on the sampled filters, the extraction of DNA from the samples is performed using a commercial DNA extraction kit that was originally designed for soils and modified to improve the DNA yield. The detection and quantification of specific microbial species using quantitative polymerase chain reaction (q-PCR) analysis are described and compared with other available methods.

Two complementary analyses of atmospheric biological particles from air sampled filters are described herein: the extraction and detection of endotoxin, and of DNA.

Outdoor aerosol research commonly uses particulate matter sampled on filters. This procedure enables various characterizations of the collected particles ...

Simultaneous DNA-RNA Extraction from Coastal Sediments and Quantification of 16S rRNA Genes and Transcripts by Real-time PCR

Real Time Polymerase Chain Reaction also known as quantitative PCR (q-PCR) is a widely used tool in microbial ecology to quantify gene abundances of taxonomic and functional groups in environmental samples. Used in combination with a reverse transcriptase reaction (RT-q-PCR), it can also be employed to quantify gene transcripts. q-PCR makes use of highly sensitive fluorescent detection chemistries that allow quantification of PCR amplicons during the exponential phase of the reaction. Therefore, the biases associated with &#39;end-point&#39; PCR detected in the plateau phase of the PCR reaction are avoided. A protocol to quantify bacterial 16S rRNA genes and transcripts from coastal sediments via real-time PCR is provided. First, a method for the co-extraction of DNA and RNA from coastal sediments, including the additional steps required for the preparation of DNA-free RNA, is outlined. Second, a step-by-step guide for the quantification of 16S rRNA genes and transcripts from the extracted nucleic acids via q-PCR and RT-q-PCR is outlined. This includes details for the construction of DNA and RNA standard curves. Key considerations for the use of RT-q-PCR assays in microbial ecology are included.

A protocol to quantify bacterial 16S rRNA genes and transcripts from coastal sediments via real-time PCR is provided. The methodology includes the co-extraction of DNA and RNA; preparation of DNA-free RNA; and 16S rRNA gene and transcript quantification via RT-q-PCR, including standard curve construction.

Real Time Polymerase Chain Reaction also known as quantitative PCR (q-PCR) is a widely used tool in microbial ecology to quantify gene abundances of ...

Comparison of Scale in a Photosynthetic Reactor System for Algal Remediation of Wastewater

An experimental methodology is presented to compare the performance of two different sized reactors designed for wastewater treatment. In this study, ammonia removal, nitrogen removal and algal growth are compared over an 8-week period in paired sets of small (100 L) and large (1,000 L) reactors designed for algal remediation of landfill wastewater. Contents of the small and large scale reactors were mixed before the beginning of each weekly testing interval to maintain equivalent initial conditions across the two scales. System characteristics, including surface area to volume ratio, retention time, biomass density, and wastewater feed concentrations, can be adjusted to better equalize conditions occurring at both scales. During the short 8-week representative time period, starting ammonia and total nitrogen concentrations ranged from 3.1-14 mg NH3-N/L, and 8.1-20.1 mg N/L, respectively. The performance of the treatment system was evaluated based on its ability to remove ammonia and total nitrogen and to produce algal biomass. Mean &#177; standard deviation of ammonia removal, total nitrogen removal and biomass growth rates were 0.95&#177;0.3 mg NH3-N/L/day, 0.89&#177;0.3 mg N/L/day, and 0.02&#177;0.03 g biomass/L/day, respectively. All vessels showed a positive relationship between the initial ammonia concentration and ammonia removal rate (R2=0.76). Comparison of process efficiencies and production values measured in reactors of different scale may be useful in determining if lab-scale experimental data is appropriate for prediction of commercial-scale production values.

An experimental methodology is presented to compare the performance of small (100 L) and large (1,000 L) scale reactors designed for algae remediation of landfill wastewater. System characteristics, including surface area to volume ratio, retention time, biomass density, and wastewater feed concentrations, can be adjusted based on application.

An experimental methodology is presented to compare the performance of two different sized reactors designed for wastewater treatment. In this study, ...

Collection and Extraction of Occupational Air Samples for Analysis of Fungal DNA

Traditional methods of identifying fungal exposures in occupational environments, such as culture and microscopy-based approaches, have several limitations that have resulted in the exclusion of many species. Advances in the field over the last two decades have led occupational health researchers to turn to molecular-based approaches for identifying fungal hazards. These methods have resulted in the detection of many species within indoor and occupational environments that have not been detected using traditional methods. This protocol details an approach for determining fungal diversity within air samples through genomic DNA extraction, amplification, sequencing, and taxonomic identification of fungal internal transcribed spacer (ITS) regions. ITS sequencing results in the detection of many fungal species that are either not detected or difficult to identify to species level using culture or microscopy. While these methods do not provide quantitative measures of fungal burden, they offer a new approach to hazard identification and can be used to determine overall species richness and diversity within an occupational environment.

Determining the fungal diversity within an environment is a method utilized in occupational health studies to identify health hazards. This protocol describes DNA extraction from occupational air samples for amplification and sequencing of fungal ITS regions. This approach detects many fungal species that can be overlooked by traditional assessment methods.

Traditional methods of identifying fungal exposures in occupational environments, such as culture and microscopy-based approaches, have several ...

Wastewater Irrigation Impacts on Soil Hydraulic Conductivity: Coupled Field Sampling and Laboratory Determination of Saturated Hydraulic Conductivity

Since the early 1960s, an alternative wastewater discharge practice at The Pennsylvania State University has been researched and its impacts monitored. Rather than discharging treated wastewater to a stream, and thereby directly impacting the stream quality, the effluent is applied to forested and cropped land managed by the University. Concerns related to reductions in soil hydraulic conductivity occur when considering wastewater reuse. The methodology described in this manuscript, matching soil sample size with the size of the laboratory-based hydraulic conductivity measurement apparatus, provides the benefits of a relatively rapid collection of samples with the benefits of controlled laboratory boundary conditions. The results suggest that there may have been some impact of wastewater reuse on the soil&#39;s ability to transmit water at deeper depths in the depressional areas of the site. Most of the reductions in the soil hydraulic conductivity in the depressions appear to be related to the depth from which the sample was collected, and by inference, associated with the soil structural and textural differences.

Here we present a methodology which matches a soil sample size and a hydraulic conductivity measurement device to prevent the so-called wall flow along the inside of the soil container from being erroneously included in water flow measurements. Its use is demonstrated with samples collected from a wastewater irrigation site.

Since the early 1960s, an alternative wastewater discharge practice at The Pennsylvania State University has been researched and its impacts monitored. ...

Improving Infrared Spectroscopy Characterization of Soil Organic Matter with Spectral Subtractions

Soil organic matter (SOM) underlies numerous soil processes and functions. Fourier transform infrared (FTIR) spectroscopy detects infrared-active organic bonds that constitute the organic component of soils. However, the relatively low organic matter content of soils (commonly &#60; 5% by mass) and absorbance overlap of mineral and organic functional groups in the mid-infrared (MIR) region (4,000-400 cm-1) engenders substantial interference by dominant mineral absorbances, challenging or even preventing interpretation of spectra for SOM characterization. Spectral subtractions, a post-hoc mathematical treatment of spectra, can reduce mineral interference and enhance resolution of spectral regions corresponding to organic functional groups by mathematically removing mineral absorbances. This requires a mineral-enriched reference spectrum, which can be empirically obtained for a given soil sample by removing SOM. The mineral-enriched reference spectrum is subtracted from the original (untreated) spectrum of the soil sample to produce a spectrum representing SOM absorbances. Common SOM removal methods include high-temperature combustion (&#39;ashing&#39;) and chemical oxidation. Selection of the SOM removal method carries two considerations: (1) the amount of SOM removed, and (2) absorbance artifacts in the mineral reference spectrum and thus the resulting subtraction spectrum. These potential issues can, and should, be identified and quantified in order to avoid fallacious or biased interpretations of spectra for organic functional group composition of SOM. Following SOM removal, the resulting mineral-enriched sample is used to collect a mineral reference spectrum. Several strategies exist to perform subtractions depending on the experimental goals and sample characteristics, most notably the determination of the subtraction factor. The resulting subtraction spectrum requires careful interpretation based on the aforementioned methodology. For many soil and other environmental samples containing substantial mineral components, subtractions have strong potential to improve FTIR spectroscopic characterization of organic matter composition.

SOM underlies many soil functions and processes, but its characterization by FTIR spectroscopy is often challenged by mineral interferences. The described method can increase the utility of SOM analysis by FTIR spectroscopy by subtracting mineral interferences in soil spectra using empirically obtained mineral reference spectra.

Soil organic matter (SOM) underlies numerous soil processes and functions. Fourier transform infrared (FTIR) spectroscopy detects infrared-active organic ...

Enrichment and Detection of Clostridium perfringens Toxinotypes in Retail Food Samples

Clostridium perfringens (C. perfringens) is a prolific toxin producer and causes a wide range of diseases in various hosts. C. perfringens is categorized into five different toxinotypes, A through E, based on the carriage of four major toxin genes. The prevalence and distribution of these various toxinotypes is understudied, especially their pervasiveness in American retail food. Of particular interest to us are the type B and D strains, which produce epsilon toxin, an extremely lethal toxin suggested to be the environmental trigger of multiple sclerosis in humans. To evaluate the presence of different C. perfringens toxinotypes in various food samples, we developed an easy method to selectively culture these bacteria without the use of an anaerobic container system only involving three culturing steps. Food is purchased from local grocery stores and transported to the laboratory under ambient conditions. Samples are minced and inoculated into modified rapid perfringens media (RPM) and incubated overnight at 37 &#176;C in a sealed, airtight conical tube. Overnight cultures are inoculated onto a bottom layer of solid Tryptose Sulfite Cycloserine (TSC) agar, and then overlaid with a top layer of molten TSC agar, creating a &#34;sandwiched&#34;, anaerobic environment. Agar plates are incubated overnight at 37 &#176;C and then evaluated for appearance of black, sulfite-reducing colonies. C. perfringens-suspected colonies are removed from the TSC agar using sterile eye droppers, and inoculated into RPM and sub-cultured overnight at 37 &#176;C in an airtight conical tube. DNA is extracted from the RPM subculture, and then analyzed for the presence of C. perfringens toxin genes via polymerase chain reaction (PCR). Depending on the type of food sampled, typically 15&#8211;20% of samples test positive for C. perfringens.

Enrichment and Detection of Clostridium perfringens Toxinotypes in Retail Food Samples

The objective of this protocol is to detect different Clostridium perfringens toxinotypes in locally purchased foods, particularly epsilon toxin producing strain types B and D, without the use of anaerobic chambers.

Clostridium perfringens (C. perfringens) is a prolific toxin producer and causes a wide range of diseases in various hosts. C. perfringens is categorized ...

Two-Dimensional Visualization and Quantification of Labile, Inorganic Plant Nutrients and Contaminants in Soil

We describe a method for two-dimensional (2D) visualization and quantification of the distribution of labile (i.e., reversibly adsorbed) inorganic nutrient (e.g., P, Fe, Mn) and contaminant (e.g., As, Cd, Pb) solute species in the soil adjacent to plant roots (the &#8216;rhizosphere&#8217;) at sub-millimeter (~100 &#181;m) spatial resolution. The method combines sink-based solute sampling by the diffusive gradients in thin films (DGT) technique with spatially resolved chemical analysis by laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). The DGT technique is based on thin hydrogels with homogeneously distributed analyte-selective binding phases. The variety of available binding phases allows for the preparation of different DGT gel types following simple gel fabrication procedures. For DGT gel deployment in the rhizosphere, plants are grown in flat, transparent growth containers (rhizotrons), which enable minimal invasive access to a soil-grown root system. After a pre-growth period, DGT gels are applied to selected regions of interest for in situ solute sampling in the rhizosphere. Afterwards, DGT gels are retrieved and prepared for subsequent chemical analysis of the bound solutes using LA-ICP-MS line-scan imaging. Application of internal normalization using 13C and external calibration using matrix-matched gel standards further allows for the quantification of the 2D solute fluxes. This method is unique in its capability to generate quantitative, sub-mm scale 2D images of multi-element solute fluxes in soil-plant environments, exceeding the achievable spatial resolution of other methods for measuring solute gradients in the rhizosphere substantially. We present the application and evaluation of the method for imaging multiple cationic and anionic solute species in the rhizosphere of terrestrial plants and highlight the possibility of combining this method with complementary solute imaging techniques.

This protocol presents a workflow for sub-mm 2D visualization of multiple labile inorganic nutrient and contaminant solute species using diffusive gradients in thin films (DGT) combined with mass spectrometry imaging. Solute sampling and high-resolution chemical analysis are described in detail for quantitative mapping of solutes in the rhizosphere of terrestrial plants.

We describe a method for two-dimensional (2D) visualization and quantification of the distribution of labile (i.e., reversibly adsorbed) inorganic ...

Determination of Total Lipid and Lipid Classes in Marine Samples

Lipids are largely composed of carbon and hydrogen and, therefore, provide a greater specific energy than other organic macromolecules in the sea. Being carbon- and hydrogen-rich they are also hydrophobic and can act as a solvent and absorption carrier for organic contaminants and thus can be drivers of pollutant bioaccumulation in marine ecosystems. Their hydrophobic nature facilitates their isolation from seawater or biological specimens: marine lipid analysis begins with sampling and then extraction in non-polar organic solvents, providing a convenient method for their separation from other substances in an aquatic matrix.
If seawater has been sampled, the first step usually involves separation into operationally defined &#39;dissolved&#39; and &#39;particulate&#39; factions by filtration. Samples are collected and lipids isolated from the sample matrix typically with chloroform for truly dissolved matter and colloids, and with mixtures of chloroform and methanol for solids and biological specimens. Such extracts may contain several classes from biogenic and anthropogenic sources. At this time, total lipids and lipid classes may be determined. Total lipid can be measured by summing individually determined lipid classes which customarily have been chromatographically separated. Thin-layer chromatography (TLC) with flame ionization detection (FID) is regularly used for the quantitative analysis of lipids from marine samples. TLC-FID furnishes synoptic lipid class information and, by summing classes, a total lipid measurement.
Lipid class information is especially useful when combined with measurements of individual components e.g., fatty acids and/or sterols, after their release from lipid extracts. The wide variety of lipid structures and functions means they are used broadly in ecological and biogeochemical research assessing ecosystem health and the degree of influence by anthropogenic impacts. They have been employed to measure substances of dietary value to marine fauna (e.g., aquafeeds and/or prey), and as an indicator of water quality (e.g., hydrocarbons).

This protocol is for the determination of lipids in seawater and biological specimens. Lipids in filtrates are extracted with chloroform or mixtures of chloroform and methanol in the case of solids. Lipid classes are measured by rod thin-layer chromatography with flame ionization detection and their sum gives the total lipid content.

Lipids are largely composed of carbon and hydrogen and, therefore, provide a greater specific energy than other organic macromolecules in the sea. Being ...

Source and Route of Pyrrolizidine Alkaloid Contamination in Tea Samples

Toxic pyrrolizidine alkaloids (PAs) are found in tea samples, which pose a threat to human health. However, the source and route of PA contamination in tea samples have remained unclear. In this work, an adsorbent method combined with UPLC-MS/MS was developed to determine 15 PAs in the weed Ageratum conyzoides L., A. conyzoides rhizospheric soil, fresh tea leaves, and dried tea samples. The average recoveries ranged from 78%-111%, with relative standard deviations of 0.33%-14.8%. Fifteen pairs of A. conyzoides and A. conyzoides rhizospheric soil&#160;samples and 60 fresh tea leaf samples were collected from the Jinzhai tea garden in Anhui Province, China, and analyzed for the 15 PAs. Not all 15 PAs were detected in fresh tea leaves, except for intermedine-N-oxide (ImNO) and senecionine (Sn). The content of ImNO (34.7 &#181;g/kg) was greater than that of Sn (9.69 &#181;g/kg). In addition, both ImNO and Sn were concentrated in the young leaves of the tea plant, while their content was lower in the old leaves. The results indicated that the PAs in tea were transferred through the path of PA-producing weeds-soil-fresh tea leaves in tea gardens.

The present protocol describes the contamination of pyrrolizidine alkaloids (PAs) in tea samples from PA-producing weeds in tea gardens.

Toxic pyrrolizidine alkaloids (PAs) are found in tea samples, which pose a threat to human health. However, the source and route of PA contamination in ...