# Innovative Approaches for Minimizing Disinfection
## 4 DBP Minimization Approaches
### Advanced Water Treatment Technologies
#### 4.5.1 Enhanced Coagulation  
Wastewater treatment plants (WWTPs) employ a series of complementary unit operations to remove NOM throughout the treatment process effectively. The typical approach involves combining coagulation/flocculation with filtration-based processes. Various studies have shown that optimizing coagulation and flocculation significantly influences the removal of organic components. Additionally, sand filters and GAC beds are crucial in removing specific NOM fractions. Following coagulation, ultrafiltration (UF) membranes have emerged as promising and robust methods for water quality treatment, capable of operating as hybrid systems. In this context, monitoring parameters to control membrane fouling can complement NOM characterization. Therefore, integrating enhanced coagulation models with expert rules derived from membrane experiments can enhance the performance of DWTPs [31].  
DWTPs have implemented various analytical techniques to identify and eliminate NOM content and have increased the use of sensors to monitor NOM, adjusting treatment processes according to environmental conditions. Characterizing and quantifying NOM at multiple stages is essential for a resilient treatment process, allowing for evaluating treatment efficiency and determination of NOM compound reactivity levels. The waterborne NOM content is monitored using online sensors and analyzers, generating continuous large volumes of data. These data can be processed and integrated into an Environmental Decision Support System (EDSS), providing valuable insights into the capabilities of DWTP processes in reducing NOM and associated DBP formation. EDSS tools, incorporating artificial intelligence (AI), have been developed to manage system complexity and expedite decision making. These tools combine quantitative and qualitative elements, integrating mechanistic and statistical models with other AI techniques. For instance, in a practical scenario, process parameters like turbidity and UV254 were utilized to develop an artificial neural network for predicting oxidant demand in a full-scale DWTP [31].  
The pH fluctuations in DOM play a crucial role in controlling the potential formation of DBPs. DOM in surface waters, transported from watershed areas during runoff events, is a significant source of DBP precursors. Moreover, the physicochemical characteristicsof soil, including pH, ion content, and texture, influence and break down terrestrial DOM within the watershed. Soil pH can vary widely, from highly acidic (pH! 4) to highly alkaline (pH? 10), due to factors like rainfall patterns, temperature changes, organic matter decomposition, ammonia nitrification, and agricultural activities. The chemistry of source water, particularly its pH, impacts the reactivity of DOM with disinfectants like chlorine and influences ozone decomposition rates in AOPs.  
This disruption ultimately hinders the degradation of organic matter and DBP precursors. The diverse organic matter composition presents a notable challenge in determining the removal mechanisms during the oxidation and separation processes mentioned above, necessitating the individual monitoring of the different DOM components during their elimination. In this regard, excitation-emission matrix (EEM) fluorescence spectroscopy and ultraviolet-visible (UV-Vis) spectroscopy, along with size exclusion chromatography (SEC), are valuable techniques that can effectively provide insights into the heterogeneous distribution of organic content in DOM [32].