To explore the methane production potential and mechanism of anaerobic co-digestion using organic solid residues and residual liquid derived from food waste pretreatment, batch experiments were conducted at 37 ± 0.5°C with varying mass ratios of the substrates. Analytical methods such as detection analysis, gas chromatography, and three-dimensional fluorescence characterization were adopted to study the influence mechanism of key indicators such as total solids, volatile solids (VS), ammonia nitrogen, volatile fatty acids (VFAs), and humus on anaerobic co-digestion. The results showed that when the mass ratio of organic solid residues to residual liquid was 2:1, the methane production efficiency of anaerobic co-digestion was the highest, and the methane production per unit VS was 483.71 mL/g VS. The VFAs in hydrolyzed products were predominantly acetic, propionic, and butyric acids, with trace valeric acid (n-pentanoic acid). Methanogenic activity was significantly inhibited during later stages due to the influence of VFAs and free ammonia. Fluorescence spectral analysis revealed the fluorescence peak of humus shifts from the protein-like peak to the fulvic acid and humic acid peaks during the methane production process; the fluorescence intensity of fulvic acid increased markedly compared with humic acid in the middle stage of digestion, and the fluorescence change of humus reflected the degree of organic matter mineralization. As a characteristic fluorescence substance, humus can be used to characterize the anaerobic digestion process. These findings provide theoretical foundations for food waste resource utilization.

The increasing environmental pollution caused by metal nanoparticles (NPs) has become a major concern, among which the ecological toxicity of zinc oxide (ZnO) NPs attracts particular attention. In this study, the Yellow River Estuary sediment was used as a research focus. A noncontact conductivity method was used to enable real-time, nondestructive monitoring of the growth kinetics of Bacillus licheniformis and Vibrio parahaemolyticus . Combining a noncontact conductivity method with high-throughput sequencing systematically revealed the multilevel toxic effects of ZnO NPs, from individual strains to the microbial community. The results showed that ZnO NPs prolonged the growth lag phase and reduced the maximum metabolic activity of both strains. These inhibitory effects were more pronounced at higher concentrations. At 160 mg/L ZnO NPs, the cell wall integrity of B. licheniformis and V. parahaemolyticus decreased by 21.99% and 12.38%, respectively. In addition, the lactate dehydrogenase release significantly increased ( p < 0.01), and the intracellular reactive oxygen species levels were 15.34 times and 4.27 times those of the control group. Microbial analysis revealed that ZnO NPs significantly reduced the α-diversity index (with Chao and Shannon indices decreasing by 55.35% and 18.11%, respectively) and altered the composition of functional bacteria. This study integrated multiple methods to elucidate multilevel mechanisms, providing critical data and theoretical support for nanomaterial ecological risk assessment in estuarine environments.

Environmental engineering plays a critical role in managing air quality services, which are of daily concern to the public, particularly as climate change alters the factors affecting air quality. Within this context, our study introduces a comprehensive approach that emphasizes predictive models relying on multivariate time-series data. By integrating data from various sources and modalities, we propose a multimodal deep learning method to enhance traditional unimodal models. This study includes a review of existing literature, the preparation of relevant datasets, the development of robust models, and extensive evaluations. The experiments feature a case study focused on air quality services in a subtropical city, aiming to provide insights for improving prediction models. The integrated multimodal approach offers a better understanding of environmental conditions by combining data from automatic air quality monitors, meteorological stations, the European Centre for Medium-Range Weather Forecasts reanalysis data, as well as public welfare information and societal disruption reports. The analysis also considers weather-related alerts, such as typhoon and rainstorm warnings, which lead to school closures and city-wide suspensions. The model incorporates emission sources and upwind areas. Preliminary causality tests confirm that augmented feature space to encompass upstream areas enhances the model analytical capability. Downstream pollution and environmental conditions are significantly influenced by socio-economic activities in upwind areas. Granger causality and Diebold-Mariano tests highlight the importance of public welfare information and societal disruption reports, addressing a critical gap in this field.

Avian species, as part of the higher-order vertebrates and situated at the upper trophic levels of the food chain, are particularly vulnerable to environmental pollutants owing to their potential for bioenrichment. This physiological characteristic renders birds as sensitive indicators of ecosystem health and contaminant exposure. In this study, sampling was done in the Saunders’s Gulls nesting grounds and feeding regions in the Lijin Field of the Yellow River Delta Nature Reserve in May 2018 and April 2019. Deceased individuals of Saunders’s Gulls were collected, totaling 15 nestlings (8 females and 7 males) and 3 adult female Saunders’s Gulls. We determined the concentrations of cadmium (Cd), iron (Fe), nickel (Ni), manganese (Mn), copper (Cu), chromium (Cr), and lead (Pb) in various tissues—including the heart, sternum, muscles, kidneys, lungs, intestines, and liver—of Saunders’s Gulls across different age and sex groups. Tissues were processed by drying and grinding, hydrogen peroxide digestion, and metal concentrations in different tissues were compared using atomic absorption spectrometry. Pearson correlation analysis and principal component analysis were used to understand the effects of different metals on gulls of different ages and sexes. Our findings indicated that, on average, Fe was the most abundant metal in all tissues of Saunders’s Gulls. The content of Fe in all tissues of female Saunders’s Gull nestlings ranged from 625.919 to 1,352.175 mg/kg, and in female adult Saunders’s Gulls, the content of Fe in the tissues confined 1,167.484–2,969.771 mg/kg. Notably, Ni was the least abundant in the intestines of adult birds, ranging from 0.926 to 1.572 mg/kg. We observed higher Fe concentrations in adult birds compared with nestlings, with the liver being the primary site of Fe accumulation in both female nestlings and adults. The study revealed that different tissues of Saunders’s Gulls have varying capacities for metal accumulation, with the liver, kidneys, and muscles showing the highest levels. Additionally, we found that different age and sex groups exhibit distinct metal concentration profiles in their tissues. Juvenile gulls were more likely to accumulate metals such as Mn, Cu, and Fe, whereas adult gulls were more affected by Cr and Pb. This research contributes valuable insights into metal pollution assessment in the Yellow River Delta wetlands, aiding in the ecological monitoring and management of metal pollution in this region.

Algal blooms not only lead to significant water pollution but also produce algal organic matter (AOM) from algal cells, which serves as precursors for disinfection byproducts. To address the inefficiency of existing algae removal technologies in eliminating AOM, this study investigates the feasibility of a combined technique utilizing chitosan quaternary ammonium salt (HTCC) and activated carbon (AC) for simultaneous algae removal and AOM elimination. First, the release of AOM during algae removal by standalone HTCC was examined. Second, AC with optimal AOM removal efficiency was selected, followed by evaluating the performance of the HTCC–AC combined technique in algae removal and AOM elimination. The disinfection byproduct formation potential before and after treatment was analyzed. Mechanism insights were elucidated through characterization techniques including Zeta potential, scanning electron microscope, Brunauer–Emmett–Teller, and FT-IR. Results indicated that standalone HTCC treatment poses a risk of AOM release, with released AOM originating from both intracellular and extracellular algal components. Wood-based activated carbon exhibited the highest AOM adsorption capacity at 6.43 mg/g. The HTCC–AC combined technique achieved removal efficiencies of 97.32%, 96.11%, and 91.13% for algal density, turbidity, and dissolved organic carbon, respectively. Notably, trihalomethane formation decreased by 94.56% compared with the control group, resulting in a posttreatment THM ratio of 0.15, significantly below the 1.0 limit specified in drinking water standards. The mechanisms of algal removal through the combined technique involve adsorption bridging and charge neutralization, while the removal of AOM occurs via π–π conjugation and hydrogen bonding interactions.

Accurately identifying microplastics (MPs) poses a challenge because environmental and human activities, like thermal oxidation (common in wildfires, urban fires, and waste burning) and mechanical abrasion (common in riverine and marine environments), chemically modify the plastic structure. Conventional Fourier transform infrared (FTIR) libraries lack the accuracy to identify these plastics because degradation alters their spectra-thermal oxidation adds new peaks, and abrasion causes characteristic peaks to fade. This creates a knowledge gap, leading to MP misidentification and flawed risk assessments for plastic pollution. To address this, we developed a new, environmentally relevant spectral library that significantly improved identification, increasing match rates by 7.3% for thermally oxidized plastics and by 23.8% for mechanically abraded plastics. We did this by subjecting seven commercial polymers (polypropylene [PP], polystyrene, rayon, low-density polyethylene [LDPE], linear low-density polyethylene, high-density polyethylene, and polyethylene terephthalate) to controlled thermal oxidation (100-200°C for 1-24 h) and by exposing PP and LDPE preproduction pellets to 40 days of mechanical abrasion (33 rpm; 3.41 rad s - 1). We then collected attenuated total reflectance (ATR)-FTIR spectra from these altered plastics and incorporated them into two newly developed spectral libraries. We tested these new libraries on 62 environmental MP samples (thermally oxidized) and 15 lab-generated particles (abraded), confirming a significant improvement in identification accuracy. Our findings underscore the importance of incorporating spectra obtained from plastics exposed to environmentally weathering processes into FTIR libraries, which represent a scientific advancement and a practical tool for more accurate MP identification and monitoring in environmental research.

This study proposes a novel hybrid model for short-term urban water demand (UWD) forecasting in the Punjab region of India, coupling support vector regression (SVR) with wavelet transform (WT) and boosting ensemble techniques. The WTs are applied as a preprocessing step to decompose the water demand time series into multiple frequency components capturing both long-term trends and short-term variations. By integrating wavelet analysis with boosting ensemble, the accuracy and robustness of the SVR models were significantly improved. The model’s performance was assessed for lead times of 1, 3, and 5 days ahead using the coefficient of determination ( R 2 ), root mean square error, and mean absolute error. Results showed that boosting consistently enhanced the correlation between observed and predicted UWD values. Among the models evaluated, the wavelet-boosting SVR outperformed others, providing the most accurate forecasts. This highlights the potential of wavelet-based boosting techniques for improving UWD prediction and aiding in efficient water resource management.

The levels of particulate matter (PM10) in Karachi, Pakistan, are hazards to public health and environmental degradation. In this study, two statistical techniques, land use regression (LUR) and Pearson Correlation Coefficient (PCC), have been applied to detect PM10 and Moderate Resolution Imaging Spectroradiometer (MODIS) along with four meteorological parameters. The average values of observed PM10-MODIS aerosol optical depth (AOD)-predicted PM10 have been analyzed with PCC correlation. Results, the M4 and M7 models are more reliable, where coefficient of determination (R2 > 0.6) and root mean square error (RMSE) = 2–14. PCC showed a strong positive correlation with significant levels among observed PM10-MODIS AOD-Predicted PM10. The concentrations of PM10 and AOD approximately increased by 182%, and 208%, respectively, and dropped by 28% to 30%, during COVID-19 (2020). In conclusion, utilize remote sensing with hybrid modeling to mitigate and monitor air pollutants in regions lacking ground-based air quality data resources.

The prevalence of microplastics (MPs) in the environment is an escalating global concern, driven by their increasing abundance and potential adverse effects. Their detrimental environmental consequences and risks to public health necessitate urgent action to mitigate their impacts. To better understand MP accumulation within the environment, estimate their impacts, and develop effective mitigation strategies, it is imperative to advance our knowledge of MPs’ environmental fate and how this fate is linked with plastics’ intrinsic properties. We highlight the current research on MPs’ accumulation within different environmental sinks and identify the critical knowledge gaps in understanding the flux of MPs between these sinks. We also discuss how the intrinsic properties of MPs, including polymeric backbone, structure, chemical additives, crystallinity, molecular weight, and hydrophilicity, influence their environmental behavior and degradation pathways. Among these properties, the polymeric backbone and functional groups serve as primary drivers of MP stability when exposed to external physical and chemical factors, thereby influencing the pathway and rate of MP degradation. Other intrinsic properties and environmental conditions mostly impact the rate of MP degradation. As MPs undergo environmental degradation, changes in their intrinsic characteristics can lead to further fragmentation. This process potentially enhances environmental harm due to the resulting particles’ increased surface area, greater environmental mobility, and higher potential for contaminant transport. These factors collectively contribute to the complex dynamics of MPs’ fate and impact on the environment.