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A pilot-scale study was performed to evaluate a coagulant dose which had been optimized for biopolymer (i.e., foulant) removal on subsequent ultrafiltration (UF) fouling, as well as disinfection by-product (DBP) precursor removal. Polyaluminum chloride (PACl) dosages were selected based on a point of diminishing returns for biopolymer removal (0.5 mg/L) and directly compared to that applied at full-scale (6 mg/L). Membrane fouling (reversible and irreversible) was measured as resistance increase over a 48 hour filtration period. DBP formation potential (total trihalomethanes (TTHMs), haloacetic acids (HAA9) and total adsorbable organic halides (AOX)) were measured in both raw and treated waters. Results of the study indicate that application of a PACl dose optimized for biopolymer reduction (0.5 mg/L) resulted in 65% less irreversible UF fouling when compared to 6 mg/L. The addition of PACl prior to the membrane resulted in up to a 14% reduction in DBP precursors relative to the UF membrane alone. A similar level of DBP precursor reduction was achieved for both 0.5 and 6 mg/L dosages. The results have implications for cost savings, which may be realized due to decreased chemical use, as well as increased membrane life associated with lower irreversible fouling rates.

Effective coagulation is essential to achieving drinking water treatment objectives when considering surface water. To minimize settled water turbidity, artificial neural networks (ANNs) have been adopted to predict optimum alum and carbon dioxide dosages at the Elgin Area Water Treatment Plant. ANNs were applied to predict both optimum carbon dioxide and alum dosages with correlation (R2) values of 0.68 and 0.90, respectively. ANNs were also used to developed surface response plots to ease optimum selection of dosage. Trained ANNs were used to predict turbidity outcomes for a range of alum and carbon dioxide dosages and these were compared to historical data. Point-wise confidence intervals were obtained based on error and squared error values during the training process. The probability of the true value falling within the predicted interval ranged from 0.25 to 0.81 and the average interval width ranged from 0.15 to 0.62 NTU. Training an ANN using the squared error produced a larger average interval width, but better probability of a true prediction interval.

The potential application of fluorescence spectroscopy for monitoring of organic matter concentration and character at four water treatment facilities was investigated. Results are presented showing impacts on natural organic matter (NOM) due to intake location on the same water body and from individual unit processes including ozonation, granular-activated carbon filtration, and coagulation/flocculation. For validation and comparison of fluorescence methods, organic matter was quantified and characterized using liquid chromatography-organic carbon detection (LC-OCD). Principal component analysis (PCA) and parallel factors analysis were used for dimensionality reduction and to represent individual organic components observed through fluorescence excitation-emission matrices. Fluorescence results generally agreed with LC-OCD characterization, indicating that complete treatment reduced organic concentrations and preferential removal of humic-like material was associated with coagulation/flocculation. PCA results indicated higher concentrations of humic-like material at the Island water treatment plant intake that was not well reduced by inline polyaluminum chloride coagulation and direct filtration. Through fluorescence spectroscopy, ozonation increased Rayleigh scattering, which is correlated to small colloidal/particulate concentrations. Full-scale results from four water treatment plants presented demonstrate that fluorescence methods can characterize NOM, providing similar identification of trends to LC-OCD, with possible online application and use in real-time water treatment process control.

A pilot-scale study was conducted to evaluate the impact of several biofiltration enhancement strategies in terms of organic removal to reduce disinfection by-product (DBP) formation potential and mitigate ultrafiltration (UF) fouling. Strategies included nutrient addition (nitrogen and phosphorus) to optimize metabolic degradation of organics, use of hydrogen peroxide (H2O2, peroxide) to improve filter run times, and the application of in-line aluminum sulphate (alum) for biopolymer removal. The impact of media type on performance was also examined (anthracite versus granular activated carbon (GAC)). Passive biofiltration (without enhancement) reduced dissolved organic carbon (∼5%), biopolymers (∼20%), and trihalomethane and haloacetic acid precursors (∼20% and ∼12%, respectively) while mitigating UF irreversible fouling (∼60%). Nutrient addition was not observed to enhance biological performance. Addition of 0.5 mg/L hydrogen peroxide decreased head loss by up to 45% without affecting organic removal; however at a dosage of 1 mg/L, it negatively impacted both UF fouling and DBP precursor removal. In-line alum addition prior to biofiltration (

This study assessed the impact of chemical coagulation using alum on the removal of three endocrine-disrupting compounds (EDCs; bisphenol A, clofibric acid and estriol) and nine pharmaceutically active compounds (PhACs; acetaminophen, carbamazepine, diclofenac, gemfibrozil, ketoprofen, naproxen, pentoxifylline, sulfamethoxazole and sulfachloropyridazine). The impact on natural organic matter (NOM) fractions as determined using liquid chromatography–organic carbon detection (LC–OCD; total dissolved organic carbon (DOC), hydrophobic DOC, biopolymers, humic substances, building blocks, low molecular weight neutrals and acids) was also examined. Three test surface waters were included: Lake Ontario, Grand River and Otonabee River water (Ontario, Canada). Gemfibrozil concentrations were reduced in both Otonabee and Grand River waters. Reductions were noted for carbamazepine and (inconsistently) for acetaminophen, and estrone appeared to increase in concentration in Grand River water with increasing alum doses. NOM removal was primarily attributed to the humic fraction, with small reductions in biopolymers in all of the waters studied.

Bench-scale tests were conducted to evaluate enhanced coagulation as a method for removing natural organic matter (NOM) from a surface water to reduce the formation of disinfection by-products (DBPs). Aluminium sulphate (alum) and two polyaluminium chloride (PACl) coagulants were used, as well as alum with pH depression. Using a PACl coagulant alone or alum with pH depression was shown to attain 35% removal of TOC at lower dosages (31 and 29 mg/L, respectively) when compared to the use of alum alone (43 mg/L). In addition to TOC and UV254, a fluorescence excitation–emission matrix (FEEM) approach and liquid chromatography–organic carbon detection (LC-OCD) were used to further characterize the removal of NOM in both untreated and filtered waters. Principal component analysis of FEEM was able to identify the presence of humic-like substances (HS), protein-like substances (PS), and colloidal/particulate matter (CPM); HS were found to have a close correlation with TOC and UV254. LC-OCD enabled the quantitative detection of hydrophobic and hydrophilic DOC; the latter was further separated into five components, the largest of which was HS. Strong linear correlations were calculated between TOC, UV254, HS, and hydrophilic DOC (r2 > 0.96); these parameters were also found to be closely correlated with the formation of trihalomethanes (THMs, r2 > 0.78) and haloacetic acids (HAAs, r2 > 0.92). Linear correlations with THMs and HAAs indicated that FEEM and LC-OCD provide good measures of DBP precursors when compared with TOC and UV254.

Filtration is the final physical barrier preventing the passage of microbial pathogens into public drinking water. Proper pre-treatment via coagulation is essential for maintaining good particle removal during filtration. To improve filter performance at the Elgin Area WTP, artificial neural network (ANN) models were applied to optimize pre-filtration processes in terms of settled water turbidity and alum dosage. ANNs were successfully developed to predict future settled water turbidity based on seasonal raw water variables and chemical dosages, with correlation (R2) values ranging from 0.63 to 0.79. Additionally, inverse-process ANNs were developed to predict the optimal alum dosage required to achieve desired settled water turbidity, with correlation (R2) values ranging from 0.78 to 0.89.

Synthetic water matrices containing ammonia, bicarbonate, hydrogen peroxide, and natural organic matter were studied to identify the effects of these compounds (individually and in combination) on bromate formation. Ammonia alone was observed to significantly reduce bromate formation through the sequestering of brominated intermediates as bromamines. Natural organic matter reacted quickly with bromamines, which could impair the ability of ammonia to block bromate formation. Bicarbonate was observed to generally promote bromate formation in otherwise pure water, but bicarbonate worked synergistically with ammonia to reduce bromate formation by a greater factor than ammonia alone, due to OH radical scavenging. Experiments showed that hydrogen peroxide lowered the effectiveness of ammonia to block bromate formation.