Beyond linear regression: Modeling COVID-19 clinical cases with wastewater surveillance of SARS-CoV-2 for the city of Athens and Ohio University campus.

Wastewater-based surveillance has emerged as a detection tool for population-wide infectious diseases, including coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Infected individuals shed the virus, which can be detected in wastewater using molecular techniques such as reverse transcription-digital polymerase chain reaction (RT-dPCR). This study examined the association between the number of clinical cases and the concentration of SARS-CoV-2 in wastewater beyond linear regression and for various normalizations of viral loads. Viral loads were measured in a total of 446 wastewater samples during the period from August 2021 to April 2022. These samples were collected from nine different locations, with 220 samples taken from four specific sites within the city of Athens and 226 samples from five sites within Ohio University. The correlation between COVID-19 cases and wastewater viral concentrations, which was estimated using the Pearson correlation coefficient, was statistically significant and ranged from 0.6 to 0.9. In addition, time-lagged cross correlation was applied to identify the lag time between clinical and wastewater data, estimated 4 to 7 days. While we also explored the effect on the correlation coefficients of various normalizations of viral loads accounting for procedural loss or amount of fecal material and of estimated lag times, these alternative specifications did not change our substantive conclusions. Additionally, several linear and non-linear regression models were applied to predict the COVID-19 cases given wastewater data as input. The non-linear approach was found to yield the highest R-squared and Pearson correlation and lowest Mean Absolute Error values between the predicted and actual number of COVID-19 cases for both aggregated OHIO Campus and city data. Our results provide support for previous studies on correlation and time lag and new evidence that non-linear models, approximated with artificial neural networks, should be implemented for WBS of contagious diseases.

Lipid Extraction from Spirulina sp. and Schizochytrium sp. Using Supercritical CO2 with Methanol.

Microalgae are one of the most promising feedstocks for biodiesel production due to their high lipid content and easy farming. However, the extraction of lipids from microalgae is energy intensive and costly and involves the use of toxic organic solvents. Compared with organic solvent extraction, supercritical CO2 (SCCO2) has demonstrated advantages through lower toxicity and no solvent-liquid separation. Due to the nonpolar nature of SCCO2, polar organic solvents such as methanol may need to be added as a modifier in order to increase the extraction ability of SCCO2. In this paper, pilot scale lipid extraction using SCCO2 was studied on two microalgae species: Spirulina sp. and Schizochytrium sp. For each species, SCCO2 extraction was conducted on 200 g of biomass for 6 h. Methanol was added as a cosolvent in the extraction process based on a volume ratio of 4%. The results showed that adding methanol in SCCO2 increased the lipid extraction yield significantly for both species. Under an operating pressure of 4000 psi, the lipid extraction yields for Spirulina sp. and Schizochytrium sp. were increased by 80% and 72%, respectively. It was also found that a stepwise addition of methanol was more effective than a one-time addition. In comparison with Soxhlet extraction using methylene chloride/methanol (2:1, v/v), the methanol-SCCO2 extraction demonstrated its high effectiveness for lipid extraction. In addition, the methanol-SCCO2 system showed a high lipid extraction yield after increasing biomass loading fivefold, indicating good potential for scaling up this method. Finally, a kinetic study of the SCCO2 extraction process was conducted, and the results showed that methanol concentration in SCCO2 has the strongest influence on the lipid extraction yield.

Investigation of electrolytic flocculation for microalga Scenedesmus sp. using aluminum and graphite electrodes.

Electrolytic flocculation using non-sacrificial electrodes with flocculants added was studied on harvesting Scenedesmus sp. In order to optimize the operating conditions of the electrolytic flocculation process and to quantify the amount of flocculants added, aluminum electrodes were first used in the process. It was found that under optimal conditions, the microalgae removal efficiency using aluminum electrodes could reach 98.5%, while 34.2 mg L-1 of aluminum ions were released during the process. Different metal electrodes were also studied, but high microalgae removal efficiency was witnessed only using aluminum electrodes, indicating the influence of the aluminum ion in flocculation. When non-sacrificial graphite electrodes were used in the electrolytic flocculation process, the corresponding amount of aluminum sulfate was added so that the aluminum ion concentration in water was also equal to 34.2 mg L-1. The result showed that the microalgae removal efficiency of graphite electrodes could reach above 90% after aluminum sulfate was added. In contrast, using graphite electrodes alone and using the metal salt alone only yielded 22.9% and 7.1% of microalgae removal efficiency, respectively. These results indicated that the presence of metal ions is necessary in the electrolytic flocculation process. The energy consumption of the process was found to be 0.3 kW h m-3 or 0.88 kW h kg-1, which is considered to be low energy consumption. The total cost of the process, including energy and chemicals, was found to be $ 0.21 m-3, proving a cost competitive method in microalgae harvesting.