Solar Water Disinfection: A Probabilistic Methodology for Extracting Exposure Period from Time Series of Insolation Data

Published: 01-08-2020| Version 1 | DOI: 10.17632/7m68cr5r5b.1
Ekene Nwankwo,
Jonah Agunwamba


The operation of solar disinfection (SODIS) systems require a variable exposure period depending upon a number of process parameters: cloud coverage, air temperature, water turbidity, and pathogen concentration. The uncertainty arising from variable exposure period can be resolved using probability methods. The study presents a probabilistic methodology for extracting exposure period from times series of insolation data. To do this, the exposure period was interpreted as the number of consecutive exposure days until the occurrence of a day whose solar irradiance is higher than the disinfection threshold. This interpretation is consistent with the underlying physical processes that govern geometric distribution. Representative values of monthly exposure periods can be selected for any locations at a given probability of exceedance, assuming geometric distribution. The 3600 data samples are contained in the folder “ The 3600 Data Samples for the 300 Locations, January to December”. The excel workbooks in the data sample folder are named by the latitude, longitude, and month for which the data represent. “Result of Anderson-Darling Goodness-of-Fit Test and Model Validation” contains a table of results obtained from the Anderson-Darling goodness-of-fit test and model validation for the 3600 data samples collected for different locations and months (300 locations multiplied by 12 months). The same results were reproduced in the form of maps in “Goodness of Fit Maps”, “Observed vs Predicted Exposure Period Maps”, and “Reliability Maps” for easy comprehension. The observed exposure period at 5% exceedance probability (k5) was evaluated as the 95th percentile of observed exposure periods. Correct prediction implies that the predicted k5 is equal to the observed k5 for a particular month and location. Overprediction implies that the predicted exposure period is greater than the observed exposure period. Underprediction implies that the predicted exposure period is less than the observed exposure period. The reliability of each predicted exposure period was computed by drawing with replacement 10,000 samples of consecutive days equal to the predicted exposure period from the validation data and counting the number samples which contains at least one threshold day. A 95% chance of complete disinfection is guaranteed if the proportion of these samples with at least one threshold day is ≥ 0.95.