A global sensitivity analysis of solar virus inactivation modeling
Bibliographic record
Abstract
Waterborne pathogens related to the lack of safe drinking water and surface water contamination pose a substantial threat to human health. Sunlight-mediated inactivation of waterborne pathogens has been widely studied in natural surface waters, and it has been leveraged as a low-cost approach to disinfection for drinking water and wastewater treatment. Solar-driven disinfection systems can inactivate virus in water through two major mechanisms: direct endogenous mechanism causes damage to viral components (e.g. DNA/RNA, proteins) upon their absorption of sunlight photons (mostly UVB); indirect exogenous inactivation refers to the viral component damage caused by reactive intermediates, whose production is sensitized by external chromophores upon their absorption of sunlight photons (UVA and visible light). The solar virus inactivation process is affected by a wide range of factors, including sunlight irradiance, water absorbance, concentrations of photosensitizers, and water depth, among others.\nTo elucidate the relative importance of environmental, water quality, photo-reactivity and engineering design parameters in solar virus inactivation in treatment systems, this study adapted and combined the aqueous photochemistry model, APEX, with the sunlight irradiance modeling program, SMARTS, to include different independent factors in a mathematical framework. The uncertainty of each parameter was characterized and incorporated into the Monte Carlo simulation of the integrated mechanistic model for three virus species (MS2 bacteriophage, phiX174 bacteriophage, and human adenovirus) and two water types (natural surface water, waste stabilization pond water), and a global sensitivity analysis was performed to quantitatively apportion the uncertainty of solar virus inactivation rate constant to different sources.\nThis work demonstrated that environmental (location, diurnal and seasonal motion of the sun) and engineering design parameters (water depth) significantly outweigh water quality and photo-reactivity parameters in the determination of virus inactivation rate constants. System reliability and efficiency of a solar-driven disinfection system can be improved by optimizing its geometry configuration for sunlight exposure. Further Monte Carlo simulation of a 3D continuous stirred-tank reactor model coupled with the integrated solar virus inactivation model was performed to investigate the effect of different designs on the virus removal rate of a maturation pond system. Results showed that increasing the hydraulic retention time and the hydraulic efficiency are more cost-effective strategies than reducing pond depth for the improvement of virus removal. The analysis also revealed the trade-off between the solar virus removal performance and the diurnal fluctuation of effluent quality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".