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Imidacloprid (IMI) is existence in the soil environment with a half-life habitually more than hundred days. This study targets to determine, identify and characterize photo-biodegradation bacteria from neonicotinoids (NEOs) contaminated agricultural field soils. The sub-surface soil had a higher level contamination of NEOs, in specifically greater concentration of IMI (3445.2 ± 0.09 μg/g) and thiacloprid (4084.4 ± 0.09 μg/g) has been found. Three bacteria Ralstonia pickettii (PBMS-2), Bacillus cereus (PBMS-3) and Shinella zoogloeoides (PBMS-4) was identified from soil-free stable enrichment cultures. The biodegradability of IMI (50 mg L−1) by three bacteria under different colors of light-emitting diodes (LEDs) with a constant 12 V power supply was tested and found that the blue-LEDs had greatest efficiency in supporting biodegradation of IMI which is called photo-biodegradation. In specific, the rate of photo-biodegradation of IMI by Ralstonia pickettii (87%), Bacillus cereus (80%) and Shinella zoogloeoides (80%) was measured. Besides this study also tested the effect of aeration (rpm), pH, and temperature on photo-biodegradation of IMI. There were seven intermediate metabolites were measured as biodegradation products of IMI under photo-biodegradation conditions and they are; IMI-urea, IMI-desnitro, 6-chloronicotinic acid, 6-hydroxy nicotinic acid, IMI- aminoguanidine, IMI-nitrosoguanidine and 4,5-hydroxy IMI, these metabolites are may non-toxic to the environment. • Neonicotinoids insecticide such as imidacloprid in the soil environment had greater half-life. • The agricultural field sub-surface soil had a higher concentration of IMI (3445.2 ± 0.09 μg/g). • Photo-biodegradation performance is greater than separate photo degradation and biodegradation. • The bacteria of Ralstonia pickettii with blue-LEDs enhanced IMI degradation rate. [ABSTRACT FROM AUTHOR]

Wastewater recycling is the measure with enormous potentiality to achieve carbon neutrality in wastewater treatment plants. High-precision online monitoring can improve the stability of wastewater treatment system and help wastewater recycling. A new water quality prediction CSWLSTM-GPR model, which fused the spatial feature of convolutional neural network (CNN), the temporal feature of sharing-weight long short-term memory (SWLSTM) and the probabilistic reliability of Gaussian process regression (GPR), was applied for monitoring papermaking wastewater treatment system with high-precision point prediction and interval prediction. Compared with SWLSTM-GPR and CLSTM-GPR, RMSE of CSWLSTM-GPR reduced by more than 48.9% on effluent chemical oxygen demand (COD eff), MAE reduced by more than 49.3%, R2 increased by more than 25.14%, R increased by more than 7.07%. And for the effluent suspended solids (SS eff), CSWLSTM-GPR had better predictive results than SWLSTM-GPR and CSWLSTM-GPR. Compared with SWLSTM-GPR, RMSE, MAE, R, R2 of CSWLSTM-GPR on effluent suspended solids (SS eff) were improved by 4.8%, 6.1%, 29.01% and 31.15%, respectively. Simulation results showed convincing comprehensive forecasting ability were obtained and the true values frequently stayed within the water quality range obtained by CSWLSTM-GPR model, which provided important insights for online monitoring, wastewater recycling and carbon neutrality of papermaking industry. • The new model was applied for monitoring papermaking wastewater treatment system. • The new model fused the advantage of Deep learning and Gaussian process regression. • The dropout and sharing weight strategy were used to avoid overfitting and training difficulties. • The new model can improve carbon neutrality of papermaking industry. [ABSTRACT FROM AUTHOR]