Species-specific interaction of trihalomethane (THM) precursors in a scaled-up distribution network using response surface methodology (RSM)

Abstract

Response surface methodology (RSM) with central composite design (CCD) was used to monitor and optimize species-specific interaction of trihalomethane (THM) precursors in a scaled-up distribution network (DN). Independent variables such as applied chlorine (Cl), contact time (t), humic acid (HA) and bromide ions (Br) were analyzed using full factorial CCD. Analysis of variance revealed a good agreement between experimental data and proposed a two-factor interaction model (p = .04, R = 0.7983). As a precursor, Cl and Br interaction with HA affected THMs' speciation. These precursor molecules were perceived least significant as discrete elements but HA: Br and pH product significantly impacted total trihalomethane (TTHM) formation (r = 0.998, p = .007). This mutual interactive fraction was observed pH-dependent and influenced TTHM yield. Dibromochloromethane and bromoform formation was observed pH-dependent provided sufficient Br in the system. Applied chlorine had significant (p = .01), while time had insignificant (p = .75) effect. Multiple response optimization suggested pH range between 6.0 and 7.6 and HA: Br ratio between 1.3 and 5.9 were satisfactory for maintaining TTHM below ≤80 μg/L in DN with 0.88 desirability function (D). Their respective concentration may be minimized by changing precursor's individual concentration and possible combinations.

Comparison of Trihalomethane Formation Using Chlorine-Based Disinfectants Within a Model System; Applications Within Point-of-Use Drinking Water Treatment

“…The increased formation of tTHMs in the presence of NaOCl is likely a result of the higher pH of the disinfectant (11.4 ± 0.1) compared to HOCl (5.6 ± 0.25) or ECAS (3.3 ± 0.16). THMs have shown to have a greater formation affinity in more alkaline conditions (Peters et al, 1980;Urano et al, 1983;Brown et al, 2010;Saidan et al, 2013;Rasheed et al, 2017).…”

“…HOCl ⇋ OCl − +H + pKa = 7.5 (7) (Liu and Margerum, 2001;Heeb et al, 2014) THMs have been shown to have a greater affinity to form at a higher pH and with increased free chlorine concentrations (Peters et al, 1980;Urano et al, 1983;Brown et al, 2010;Saidan et al, 2013;Rasheed et al, 2017). It is known that HOCl is the dominant compound that results in tTHM formation, although, since tTHM formation is base-catalyzed (Yee et al, 2009), there is a trade-off in terms of the effect of pH.…”

“…Minimizing NOM [encompassing dissolved, suspended, particulate organic carbon, or matter which occur in aquatic systems (Demiral et al, 2006)] concentrations in raw water supplies in decentralized pointof-use (POU) drinking water treatment systems, via filtration prior to chlorination, limits THM formation (World Health Organization, 2000). Other considerations such as temperature, pH, and contact time within the distribution network (World Health Organization, 2000) are also known to drive THM formation (Brown et al, 2010(Brown et al, , 2011bRasheed et al, 2017). As the knowledge of the toxicological effects of THMs on human health has increased, so has the need to investigate alternative disinfection techniques such as ozonation (Schlichter et al, 2004;Zhu et al, 2014) and UV sterilization (Carratalà et al, 2016).…”

Comparison of chlorination and chloramination practices on microbial inactivation efficiencies within a scaled‐up water distribution network using central composite design

“…This indicates that the fitted regression equation showed a satisfactorily good fit for the model applicability. It also implies that errors are distributed normally (Sagbas, 2011; Appavoo et al ., 2014; Rasheed et al ., 2018).…”

“…Process optimization is an important step in deciding the optimum range of variables that would maximize the output response (Rasheed et al ., 2018). Based on the variables selected, numerical optimization was performed as an optimizing steps to achieve maximum bacterial inactivation whilst minimum DBPs formation in the system, expressed a desirability value (D), a value closer to 1 is considered to be desirable and significant by the software (Umar et al ., 2011).…”