Sustainable operation is an essential challenge in many municipal wastewater treatment plants. Among many types of wastewater mixed in a sewer, healthcare wastewaters need special attention due to their hazardous substance content, which can be toxic to activated sludge. This study compared the acute inhibitory effects of healthcare wastewaters (HW) and medical laboratory wastewater (MLW) on conventional activated sludge (CAS) and membrane bioreactor (MBR). The sensitivity test showed that nitrifying bacteria (NBs) in MBR sludge have higher resistance to acute toxicity than the CAS. Compared with HW, MLW caused much higher inhibition on both sludges. When the ratio of HW in the tested domestic wastewater was 10%, inhibition of NBs was 39% in AS, while it was 31% in MBR. When the ratio of MLW in the tested domestic wastewater was only 10%, 72% of NBs in AS and 57% of NBs in MBR were already inhibited. The higher resistance of NB in MBR may be explained by the diversity of microorganisms in the MBR operated at high sludge ages. The findings of this study may be used to estimate the acute inhibition effect of HW and MLW discharged directly to the sewage at higher loads. GRAPHICAL ABSTRACT

Accurate and rapid determination of moisture content in waste sludge is essential for optimizing dewatering processes, reducing disposal costs, and minimizing environmental impact. This study investigates the use of Fourier Transform Near-Infrared (FT-NIR) spectroscopy combined with Partial Least Squares Regression (PLS-R) for predicting the moisture content of dewatered sludge. A total of 96 sludge samples, with dry matter contents ranging from 12.4% to 24.6%, were collected from two treatment plants. FT-NIR spectra were acquired over the 800–2500 nm range, and chemometric models were developed to correlate spectral information with gravimetrically determined moisture content. The optimized PLS-R model demonstrated strong predictive performance, achieving a cross-validated coefficient of determination (R²_CV) of 0.87, a root mean square error of cross-validation (RMSECV) of 0.92%, and a residual predictive deviation (RPD) of 2.73. Independent test set validation confirmed the robustness of the model (R²_Test = 0.88, RMSEP = 0.88%, RPD = 2.92), supported by strong calibration results (R²_CT = 0.95, RMSEE = 0.60%, RPD = 4.46). Principal component analysis indicated that spectral variability observed in sludge samples was primarily associated with wastewater treatment plant (WWTP)-specific characteristics, reflecting moisture–organic matter interactions. These results demonstrate that FT-NIR spectroscopy is a promising tool for sludge moisture prediction.

Accurate and rapid determination of moisture content in waste sludge is essential for optimizing dewatering processes, reducing disposal costs, and minimizing environmental impact. This study investigates the use of Fourier Transform Near-Infrared (FT-NIR) spectroscopy combined with Partial Least Squares Regression (PLS-R) for predicting the moisture content of dewatered sludge. A total of 96 sludge samples, with dry matter contents ranging from 12.4% to 24.6%, were collected from two treatment plants. FT-NIR spectra were acquired over the 800–2500 nm range, and chemometric models were developed to correlate spectral information with gravimetrically determined moisture content. The optimized PLS-R model demonstrated strong predictive performance, achieving a cross-validated coefficient of determination (R²CV) of 0.87, a root mean square error of cross-validation (RMSECV) of 0.92%, and a residual predictive deviation (RPD) of 2.73. Independent test set validation confirmed the robustness of the model (R²test = 0.88, RMSEP = 0.88%, RPD = 2.92), supported by strong calibration results (R² = 0.95, RMSEE = 0.60%, RPD = 4.46). Principal component analysis indicated that spectral variability observed in sludge samples was primarily associated with WWTP-specific characteristics, reflecting moisture–organic matter interactions. These results demonstrate that FT-NIR spectroscopy is a promising tool for sludge moisture prediction.

The application of low-field time-domain nuclear magnetic resonance (TD-NMR) to measure water content and assess moisture-related relaxation behavior in sludge samples has been investigated. The results of TD-NMR measurements on 26 dewatered sludge samples revealed a strong correlation between sludge water content and key features of the T₂ distribution curves, including the maximum relaxation time and peak area, demonstrating the potential of the TD-NMR method for estimating sludge moisture content. No consistent relationship was observed between the peaks in T₂ relaxation distribution curves obtained by Inverse Laplace Transform (ILT) and the expected water fraction ratios, apparently because the sludge structure is highly variable from sample to sample. Despite the complex and heterogeneous nature of sludge samples, the direct correspondence between key features of the T₂ relaxation curves and moisture content demonstrates the high potential of TD-NMR as a tool for rapid and reliable moisture monitoring, even in an online device configuration.