Radical innovation breakthroughs of biodegradation of plastics by insects:history,present and future perspectives

Insects damaging and penetrating plastic packaged materials has been reported since the 1950s.Radical innovation breakthroughs of plastic biodegradation have been initiated since the discovery of biodegradation of plastics by Tenebrio molitor larvae in 2015 followed by Galleria mellonella in 2017.Here we review updated studies on the insect-mediated biodegradation of plastics.Plastic biodegradation by insect larvae,mainly by some species of darkling beetles(Tenebrionidae)and pyralid moths(Pyralidae)is currently a highly active and potentially transformative area of research.Over the past eight years,publications have increased explosively,including discoveries of the ability of different insect species to biodegrade plastics,biodegradation performance,and the contribution of host and microbiomes,impacts of polymer types and their physic-chemical properties,and responsible enzymes secreted by the host and gut microbes.To date,almost all major plastics including polyethylene(PE),polypropylene(PP),polyvinyl chloride(PVC),polyethylene terephthalate(PET),polyurethane(PUR),and polystyrene(PS)can be biodegraded by T.molitor and ten other insect species representing the Tenebrionidae and Pyralidae families.The biodegradation processes are symbiotic reactions or performed by synergistic efforts of both host and gut-microbes to rapidly depolymerize and biodegrade plastics with hourly half-lives.The digestive ezymens and bioreagents screted by the insects play an essential role in plasatic biodegradation in certain species of Tenebrionidae and Pyralidae families.New research on the insect itself,gut microbiomes,transcriptomes,proteomes and metabolomes has evaluated the mechanisms of plastic biodegradation in insects.We conclude this review by discussing future research perspectives on insect-mediated biodegradation of plastics.

Cloud-Model-Based Feature Engineering to Analyze the Energy-Water Nexus of a Full-Scale Wastewater Treatment Plant

Wastewater treatment plants(WWTPs)are important and energy-intensive municipal infrastructures.High energy consumption and relatively low operating performance are major challenges from the perspective of carbon neutrality.However,water-energy nexus analysis and models for WWTPs have rarely been reported to date.In this study,a cloud-model-based energy consumption analysis(CMECA)of a WWTP was conducted to explore the relationship between influent and energy consumption by clustering its influent’s parameters.The principal component analysis(PCA)and K-means clustering were applied to classify the influent condition using water quality and volume data.The energy consumption of the WWTP is divided into five standard evaluation levels,and its cloud digital characteristics(CDCs)were extracted according to bilateral constraints and golden ratio methods.Our results showed that the energy consumption distribution gradually dispersed and deviated from the Gaussian distribution with decreased water concentration and quantity.The days with high energy efficiency were extracted via the clustering method from the influent category of excessive energy consumption,represented by a compact-type energy consumption distribution curve to identify the influent conditions that affect the steady distribution of energy consumption.The local WWTP has high energy consumption with 0.3613 kW·h·m^(-3)despite low influent concentration and volumes,across four consumption levels from low(I)to relatively high(IV),showing an unsatisfactory operation and management level.The average oxygenation capacity,internal reflux ratio,and external reflux ratio during high energy efficiency days recognized by further clustering were obtained(0.2924-0.3703 kg O_(2)·m^(-3),1.9576-2.4787,and 0.6603-0.8361,respectively),which could be used as a guide for the days with low energy efficiency.Consequently,this study offers a water-energy nexus analysis method to identify influent conditions with operational management anomalies and can be used as an empirical reference for the optimized operation of WWTPs.