Effects of bioaugmentation of the composite microbial culture CES-1 on a full scale textile dye wastewater treatment process were investigated in terms of water quality, sludge reduction, dynamics of microbial community structures and their functional genes responsible for degradation of azo dye, and other chemicals. The removal efficiencies for Chemical Oxygen Demand (COD), Total Nitrogen (T-N), Total Phosphorus (T-P), Suspended Solids (SS), and color intensity (96.4%, 78.4, 83.1, 84.4, and 92.0, respectively) 300-531 days after the augmentation were generally improved after bioaugmentation. The denitrification linked to T-N removal appeared to contribute to the concomitant COD removal that triggered a reduction of sludge (up to 22%) in the same period of augmentation. Azo dye and aromatic compound degradation and other downstream pathways were highly metabolically interrelated. Augmentation of CES-1 increased microbial diversity in the later stages of augmentation when a strong microbial community selection of , , , sp., and sp. occurred. Herein, there might be a possibility that the CES-1 augmentation could facilitate the indigenous microbial community successions so that the selected communities made the augmentation successful. The metagenomic analysis turned out to be a reasonable and powerful tool to provide with new insights and useful biomarkers for the complex environmental conditions, such as the full scale dye wastewater treatment system undergoing bioaugmentation.
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http://dx.doi.org/10.3390/microorganisms9071503 | DOI Listing |
Nutrients
January 2025
Department of Maternal and Child Health, School of Public Health, Peking University, Beijing 100191, China.
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College of Engineering and Applied Sciences, Nanjing University, Nanjing 210023, China.
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January 2025
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View Article and Find Full Text PDFSensors (Basel)
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National Engineering Laboratory for Efficient Utilization of Soil and Fertilizer Resources, College of Resources and Environment, Shandong Agricultural University, Tai'an 271018, China.
Agricultural land classification plays a pivotal role in food security and ecological sustainability, yet achieving accurate large-scale mapping remains challenging. This study presents methodological innovations through a multi-level feature enhancement framework that transcends traditional time series analysis. Using Shandong Province, northern China's agricultural heartland, as a case study, we first established a foundation with time series red-edge vegetation indices (REVI) from Sentinel-2 imagery, uniquely combining the normalized difference red edge index (NDRE) and plant senescence reflectance index (PSRI).
View Article and Find Full Text PDFSensors (Basel)
January 2025
National Institute of Natural Hazards, Beijing 100085, China.
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