Predictive modeling of haloacetonitriles under uniform formation conditions

The formation and control of disinfection by-products by two-step chlorination for sewage effluent: Role of organic chloramine decomposition among molecular weight fractions

With the increasing discharge of wastewater effluent to natural waters, there is an urgent need to achieve both pathogenic microorganism inactivation and the mitigation of disinfection by-products (DBPs) during disinfection. Studies have shown that two-step chlorination, which injected chlorine disinfectant by splitting into two portions, was more effective in inactivating Escherichia coli than one-step chlorination under same total chlorine consumption and contact time. In this study, we observed a substantial reduction in the formation of five classes of CX3R-type DBPs, especially highly toxic haloacetonitriles (HANs), during two-step chlorination of secondary effluent when the mass ratio of chlorine-to-nitrogen exceeded 2. The shift of different chlorine species (free chlorine, monochloramine and organic chloramine) verified the decomposition of organic chloramines into monochloramine during second chlorination stage. Notably, the organic chloramines generated from the low molecular weight (< 1 kDa) fraction of dissolved organic nitrogen in effluent organic matter tended to decompose during the second step chlorination leading to the mitigation of HAN formation. Furthermore, the microbiological analysis showed that two-step chlorinated effluent had a slightly lower ecological impact on surface water compared to one-step chlorination. This work provided more information about the two-step chlorination for secondary effluent, especially in terms of organic chloramine transformation and HAN control.

Application of artificial intelligence in (waste)water disinfection: Emphasizing the regulation of disinfection by-products formation and residues prediction

Water/wastewater ((waste)water) disinfection, as a critical process during drinking water or wastewater treatment, can simultaneously inactivate pathogens and remove emerging organic contaminants. Due to fluctuations of (waste)water quantity and quality during the disinfection process, conventional disinfection models cannot handle intricate nonlinear situations and provide immediate responses. Artificial intelligence (AI) techniques, which can capture complex variations and accurately predict/adjust outputs on time, exhibit excellent performance for (waste)water disinfection. In this review, AI application data within the disinfection domain were searched and analyzed using CiteSpace. Then, the application of AI in the (waste)water disinfection process was comprehensively reviewed, and in addition to conventional disinfection processes, novel disinfection processes were also examined. Then, the application of AI in disinfection by-products (DBPs) formation control and disinfection residues prediction was discussed, and unregulated DBPs were also examined. Current studies have suggested that among AI techniques, fuzzy logic-based neuro systems exhibit superior control performance in (waste)water disinfection, while single AI technology is insufficient to support their applications in full-scale (waste)water treatment plants. Thus, attention should be paid to the development of hybrid AI technologies, which can give full play to the characteristics of different AI technologies and achieve a more refined effectiveness. This review provides comprehensive information for an in-depth understanding of AI application in (waste)water disinfection and reducing undesirable risks caused by disinfection processes.

Disinfection By-Products Formation from Chlor(am)ination of Algal Organic Matter of Chlorella sorokiniana

Eutrophication in water reservoirs releases algal organic matter (AOM), which is an important precursor of disinfection by-products (DBPs) formed during water treatment. Chlorella sorokiniana is a microalgae which flourishes under conditions of high light intensity and temperature, thus its prevalence in algal blooms is expected to increase with climate change. However, Chlorella sorokiniana AOM has not been previously investigated as a DBP precursor. In this context, this study evaluated the effect of AOM concentration, humic acid (HA), and pH on DBP formation from chlor(am)ination of AOM Chlorella sorokiniana. DBP yields determined by linear regression for trichloromethane (TCM) and chloral hydrate (CH) were 57.9 and 46.0 µg·mg DOC-1 in chlorination, while the TCM, CH, dichloroacetonitrile (DCAN), 1,1,1-trichloropropanone (1,1,1-TCP), and chloropicrin (CPN) concentrations were 33.6, 29.8, 16.7, 2.1, and 1.2 µg·mg DOC-1 in chloramination. Chloramination reduced the formation of TCM and CH but increased CPN, DCAN, and 1,1,1-TCP yields. AOM Chlorella sorokiniana showed a higher DBP formation than 9 of 11 algae species previously investigated in the literature. At basic pH, the concentration of TCM increased while the concentration of other DBP classes decreased. Bromide was effectively incorporated into the AOM structure and high values of bromine incorporation factor were found for THM (1.81-1.89) and HAN (1.32) at 1.5 mg Br·L-1. Empirical models predicted successfully the formation of THM and HAN (R2 > 0.86). The bromide concentration had more impact in the model on the DBP formation than AOM and HA. These results provide the first insights into the DBP formation from AOM chlor(am)ination of Chlorella sorokiniana.

Chlorinated nucleotides and analogs as potential disinfection byproducts in drinking water

Identification of emerging disinfection byproducts (DBPs) of health relevance is important to uncover the health risk of drinking water observed in epidemiology studies. In this study, mutagenic chlorinated nucleotides were proposed as potential DBPs in drinking water, and the formation and transformation pathways of these DBPs in chlorination of nucleotides were carefully investigated. A total of eleven chlorinated nucleotides and analogs were provisionally identified as potential DBPs, such as monochloro uridine/cytidine/adenosine acid and dichloro cytidine acid, and the formation mechanisms involved chlorination, decarbonization, hydrolysis, oxidation and decarboxylation. The active sites of nucleotides that reacted with chlorine were on the aromatic heterocyclic rings of nucleobases, and the carbon among the two nitrogen atoms in the nucleobases tended to be transformed into carboxyl group or be eliminated, further forming ring-opening or reorganization products. Approximately 0.2-4.0 % (mol/mol) of these chlorinated nucleotides and analogs finally decomposed to small-molecule aliphatic DBPs, primarily including haloacetic acids, trichloromethane, and trichloroacetaldehyde. Eight intermediates, particularly chlorinated imino-D-ribose and imino-D-ribose, were tentatively identified in chlorination of uridine. This study provides the first set of preliminary evidence for indicating the promising occurrence of chlorinated nucleotides and analogs as potential toxicological-relevant DBPs after disinfection of drinking water.

Developing an Integrated "Regression-QMRA method" to Predict Public Health Risks of Low Impact Developments (LIDs) for Improved Planning

Worldwide Low Impact Developments (LIDs) are used for sustainable stormwater management; however, both the stormwater and LIDs carry microbial pathogens. The widespread development of LIDs is likely to increase human exposure to pathogens and risk of infection, leading to unexpected disease outbreaks in urban communities. The risk of infection from exposure to LIDs has been assessed via Quantitative Microbial Risk Assessment (QMRA) during the operation of these infrastructures; no effort is made to evaluate these risks during the planning phase of LID treatment train in urban communities. We developed a new integrated "Regression-QMRA method" by examining the relationship between pathogens' concentration and environmental variables. Applying of this methodology to a planned LID train shows that the predicted disease burden of diarrhea from Campylobacter is highest (i.e. 16.902 DALYs/1000 persons/yr) during landscape irrigation and playing on the LID train, followed by Giardia, Cryptosporidium, and Norovirus. These results illustrate that the risk of microbial infection can be predicted during the planning phase of LID treatment train. These predictions are of great value to municipalities and decision-makers to make informed decisions and ensure risk-based planning of stormwater systems before their development.

Adsorption modeling of organic compounds (OCs) by carbon nanotubes (CNTs): role of OC and CNT properties on the linear solvation energy relationship

This study evaluated a comprehensive database for the adsorption of polar and nonpolar organic compounds (OCs) by carbon nanotubes (CNTs) and to use the linear solvation energy relationship (LSER) technique for developing predictive adsorption models of OCs by multi-walled carbon nanotubes (MWCNTs) and single-walled carbon nanotubes (SWCNTs). The results showed that coefficient of determinations (R²) values for all compounds are higher variability in the 200 g/mol molecular weight cutoff (74-99%). When the molecular weight cutoff of all OCs is higher than 200 g/mol, the trend of their R² values is decreased (less than 70%). Among all adsorbate descriptor coefficients, V and B terms are the most significant descriptors (p-values ≤ 0.05) in LSER equations for adsorption of low molecular weight polar and nonpolar OCs by both CNTs. Besides, K_OW normalization of all K_d values did not have significant impact on the regression of the LSER model, indicating that hydrophobic interactions are not sole mechanism for the adsorption of OCs on CNTs. Lastly, SWCNTs exhibited higher polar OCs uptake than MWCNTs, which was attributed to more polar surface of SWCNTs as suggested by its high oxygen content (%10).