A comparison of chi-square (X ²) and Log-likelihood (G ²) statistics of 19 adsorption isotherm models—seven two-parameter models (Langmuir, Freundlich, Dubinin–Radushkevich, Temkin, Jovanovic, Harkins–Jura and Halsey) and 12 three-parameter models (Koble–Corrigan, Langmuir–Freundlich, Tóth, Redlich–Peterson, Radke–Prausnitz (three models), Fritz–Schlunder, Jossens, Khan, UNILAN, Vieth–Sladek) have been applied to the experiment of two dyes (Acid Blue 113, Acid Black 1) sorption onto Granular PineCone derived Activated Carbon (GPAC) and three dyes (Acid Blue 80, Acid Red 114, Acid Yellow 117) sorption onto Granular Activated Carbon type Filtrasorb 400 (GAC F400). The study has focused on the assessment of the adequacy and goodness of the fitted models, using two well-known—X ² and G ²—statistics. The results showed that G ² could be better than X ² statistic when the number of model parameters is three.

A reliable forecasting model for each Water Treatment Plant (WTP) influent characteristics is useful for controlling the plant's operation. In this paper Auto-Regressive Integrated Moving Average (ARIMA) and Neural Network Auto-Regressive (NNAR) modeling techniques were applied on a WTP's influent water characteristics time series to make some models for short-term period (to seven days ahead) forecasting. The ARIMA and NNAR models both provided acceptable generalization capability with R2s ranged from 0.44 to 0.91 and 0.45 to 0.92, respectively, for chloride and temperature. Although a more prediction performance was observed for NNAR in comparison with ARIMA for all studied series, the forecasting performance of models was further examined using Time Series Cross-Validation (TSCV) and Diebold-Mariano test. The results showed ARIMA is more accurate than NNAR for forecasting the horizon-daily values for CO2, Cl and Ca time-series. Therefore, despite of the good predictive performance of NNAR, ARIMA may still stands as better alternative for forecasting task of aforementioned series. Thus, as a general rule, not only the predictive performance using R2 statistic but also the forecasting performance of a model using TSCV, are need to be examined and compared for selecting an appropriate forecasting model for WTP's influent characteristics.

In the Gaza strip, diarrhea is one of main reasons for children visiting primary healthcare centers. Hence, we investigate predictors of the diarrheal illness and health care-seeking behavior among different age groups. This community-based cross-sectional survey was conducted from August 2017 to June 2018 among 1857 households. A pretested structured questionnaire included information about socio-demographic, sanitation, hygiene, source of water, diarrheal illness, and seeking healthcare in households was administered to head of household. To achieve representativeness for the five Gaza’s governorates, a cluster random sampling was applied. Of the 1857 household’s heads, 421 (22.7%) reported an episode of diarrhea during the 48 h preceding the interview resulting an overall prevalence rate of 3.8 per 100 individuals. The prevalence of diarrhea was statistical significant greater in males (5.4/100) compared to females (1.3/100) in all age groups (p < 0.05). Socio-demographic, economic, water, sanitation, and hygiene factors were predictors of the diarrheal illness and seeking of non-professional healthcare for diarrhea illness treatment among. A transition behavior from professional to non-professional and vice versa in seeking healthcare in each diarrheal episode was found. We recommend improving the status of water, sanitation, and hygiene in the Gaza strip’s households to reduce diarrhea among the population of Gaza strip. Community sensitization about the importance of seeking care at primary health centers because treatment of children is available for free or in low costs.