Visualizing microbial dechlorination processes in underground ecosystem by statistical correlation and network analysis approach

Rapid start-up and stable operation of pilot scale denitrification-partial nitritation/anammox process for treating electroplating tail wastewater

This study explored a novel economical and efficient process for treating actual low-ammonia nitrogen electroplating tail wastewater. A pilot scale system of denitrification-partial nitrification/anaerobic ammonium oxidation (DN-PN/A) was constructed and operated for 190 days. The partial nitrification (PN) reactor, filled with zeolite, increased free ammonia concentration beyond the nitrite oxidizing bacteria threshold and successfully supplied NO2--N, with nitrite accumulation rate exceeding 90 %. Over 109 days, the total nitrogen removal rate achieved was 80.2 ± 7.41 %, and the chemical oxygen demand removal rate reached 79.68 ± 9.53 %. The dominant functional bacteria were Nitrosomonas (5.45 %) and Candidatus Anammoxoglobus (28.84 %) in PN reactor and anaerobic ammonium oxidation (Anammox) reactor. This process, characterized by rapid start-up, strong shock resistance, and low cost, alleviates the pressure of ammonium pollution control, promotes the sustainable development of the electroplating industry and has the potential for application in the treatment of other industrial wastewater.

Acidification inhibition, biodechlorination, and biotransformation of chlorinated acetaldehydes on acidogenic sludge and microbial community changes

Chlorinated acetaldehydes (CALs) are typical chlorinated organic compounds that posing a great threat to biological wastewater treatment plants. In this study, volatile batch acid (VFA) tests were employed to investigate the acidification inhibition, biodechlorination, and biotransformation of high-strength CALs on hydrolytic acidification. The results indicated that the optimum parameters were 4 g/L sludge, pH = 8, and glucose as an electron donor. Moreover, the acidification inhibition and biodechlorination showed a strongly positive correlation with the degree of chlorination and CAL concentrations. Extracellular polymeric substances (EPS) decreased dramatically, while DNA increased sharply under higher CAL concentrations, which was the result of cell death caused by the toxicity of the CALs. Additionally, the relative toxicities of the CALs were as follows: trichloroacetaldehyde > dichloroacetaldehyde > chloroacetaldehyde. Furthermore, Excitation-Emission-Matrix (EEM) spectra of EPS revealed that aromatic protein-like substances I interacted with CALs to achieve a slight removal of CALs. The detected products revealed that some of the chlorine atoms and aldehyde groups in the CALs were removed by microbes to certain degree. Moreover, microbial community analysis indicated that the dominant phyla were Actinobacteria, Bacteroidetes, and Synergistetes, which had a stronger tolerance to CALs. Notably, biodechlorination was closely related to a remarkable increase in members of the genus Trichococcus.

Environmental metabolomics with data science for investigating ecosystem homeostasis

A natural ecosystem can be viewed as the interconnections between complex metabolic reactions and environments. Humans, a part of these ecosystems, and their activities strongly affect the environments. To account for human effects within ecosystems, understanding what benefits humans receive by facilitating the maintenance of environmental homeostasis is important. This review describes recent applications of several NMR approaches to the evaluation of environmental homeostasis by metabolic profiling and data science. The basic NMR strategy used to evaluate homeostasis using big data collection is similar to that used in human health studies. Sophisticated metabolomic approaches (metabolic profiling) are widely reported in the literature. Further challenges include the analysis of complex macromolecular structures, and of the compositions and interactions of plant biomass, soil humic substances, and aqueous particulate organic matter. To support the study of these topics, we also discuss sample preparation techniques and solid-state NMR approaches. Because NMR approaches can produce a number of data with high reproducibility and inter-institution compatibility, further analysis of such data using machine learning approaches is often worthwhile. We also describe methods for data pretreatment in solid-state NMR and for environmental feature extraction from heterogeneously-measured spectroscopic data by machine learning approaches.

Noninvasive analysis of metabolic changes following nutrient input into diverse fish species, as investigated by metabolic and microbial profiling approaches

An NMR-based metabolomic approach in aquatic ecosystems is valuable for studying the environmental effects of pharmaceuticals and other chemicals on fish. This technique has also contributed to new information in numerous research areas, such as basic physiology and development, disease, and water pollution. We evaluated the microbial diversity in various fish species collected from Japan’s coastal waters using next-generation sequencing, followed by evaluation of the effects of feed type on co-metabolic modulations in fish-microbial symbiotic ecosystems in laboratory-scale experiments. Intestinal bacteria of fish in their natural environment were characterized (using 16S rRNA genes) for trophic level using pyrosequencing and noninvasive sampling procedures developed to study the metabolism of intestinal symbiotic ecosystems in fish reared in their environment. Metabolites in feces were compared, and intestinal contents and feed were annotated based on HSQC and TOCSY using SpinAssign and network analysis. Feces were characterized by species and varied greatly depending on the feeding types. In addition, feces samples demonstrated a response to changes in the time series of feeding. The potential of this approach as a non-invasive inspection technique in aquaculture is suggested.