| In environmental pollution control, the degradation of contaminants are carried out by the cooperation of microbes, and it is an important approach for studying the contaminant degradation process to rapidly and sensitively trace, monitor and indentify the contaminants and the microbial community population, functional activities, variety of metabolites, and etc. in their degradation. Biosensing technology is a new approach for rapid, real-time and on-line detection of analytes based on biometric identification, which has the advantages of high sensitivity, specificity, rapidity, portability and capacity for real-time and on-line detection in complex system in comparison with conventional analytical methods, and provides a new technical platform for the analysis and measurement of contaminants and various biological components or metabolites in relative degradation. It is very significant for the development of environmental detection technology to systematically construct a detection system of biosensing technology in environmental pollution control, to provide timely and accurate biological information for pollution control process, and to solve the real-time and on-line monitoring problems in environmental science and engineering study, and to ultimately realize automation.This dissertation developed a series of biosensing detection methods, for highly sensitive detection of trace contaminants and their degradation metabolites in the environment, and took the composting system for example to construct a set of novel methods based on biosensing technology for the detection of contaminants and the dynamic changing of relative enzymes, microbial community population, functional genes and metabolites in complex environment, which overcame the limitation of conventional analytical methods applied in environmental pollution control in the aspects of real-time and on-line detection, and etc., and favored the thorogh investigation of the microbial mechanism in waste composting process and efficient instruction of the microbial inoculation and composting technique renovation. This dissertation is composed of four sections.The first section describes the research on highly sensitive biosensing detection technology for trace hazardous contaminants and their metabolites in the environment. The enzymes were immobilized on the electrodes through ferrocene-doped electropolymerization, self-assembly by concanavalin A, and other techniques to develop an inhibition-based glucose oxidase sensor and an inhibition-based horseradish peroxidase sensor, for the detection of trace Cr(VI) and Hg(II) in soil, and the detection of phenylhydrazine in Xiangjiang River, respectively, which obtained high sensitivity, reversibility, stability and selectivity. Both of the lower detection limits for Cr(VI) and Hg(II) were 0.49μg L-1, and that for phenylhydrazine was 1.7×10-6 M. The experimental data obtained by enzyme sensors were used to deduce the kinetic models of the catalysis of H2O2 and hydroquinone by horseradish peroxidase and the inhibition by phenylhydrazine, and to carry out the model simulation and parameter estimation. Picloram, a kind of organo-chlorinated pesticide, was conjugated with bovine serum albumin as an artificial antigen which was injected into New Zealand white rabbits to obtain purified antibody. An electrochemical immonosensor based on chitosan/gold nanoparticles (Au NPs) composite membrane was developed for the detection of trace picloram in rice, lettuce, and paddy field water. The immunomenbrane has good selectivity, high sensitivity, disposability, and good reproducibility for fabrication in batch. The lower detection limit was 5 ng mL-1. A sensoring chip was developed based on the ubiquitous reductivity of phenols, which utilized the Au NPs immobilized on the glass slide as the seeds to catalyze the enlargement of Au NPs by the phenol reduction, and then the phenol concentration could be detected through absorption spectroscopy change. The lower detection limit was 7×10-6 M. This method was convenient, low-cost, and highly sensitive. A laccase sensor based on magnetic nanoparticles-laccase conjugates was developed utilizing laccase-catalyzed redox of catechol and the magnetic separation, which was used to detect trace catechol in complex composting system with the concentration as low as 7.5×10-7 M.The second section describes the trace of microbial degrading enzyme activities and biosurfactants in composting system using biosensing technology. Some enzyme activities in composting system are important maturity indexes. An electrochemical dihydronicotinamide adenine dinucleotide (NADH) sensor based on the catalytic growth of Au NPs on electrode was developed with fast response and high sensitivity, which could detect NADH in acidic buffers with the concentration as low as 2.5×10-7 M, and maintained the accuracy in 3 months. A sensoring chip was developed based on the catalytic growth of Au/Ag core/shell nanoparticles by NADH reduction, and then NADH concentration could be detected through absorption spectroscopy change with the lower detection limit of 1.56×10-5 M. An electrochemical enzyme sensor for rapid and simultaneous detection of the lignin-degrading peroxidase activities was developed based on the substrate redox catalyzed by lignin peroxidase (LiP) and manganese peroxidase (MnP). LiP and MnP activities could be detected in the range of 8.1429.79 U L-1 and 0.0851.37 U L-1 , respectively. This assay is more rapid, sensitive and precise than conventional spectrophotometric assays, free from interference of turbidity and UV- and visible-light-absorbing substances in compost extract. Rhamnolipid, a biosurfactant secreted by the common bacteria in compost, Pseudomonas aeruginose, in fermentation metabolism, can improve the microenvironment in compost. The artificial antigen and antibody of rhamnolipid were fabricated, and a chromogenic enzyme-linked immuno test paper for dirhamnolipid based on 3,3′-diaminobenzidine (DAB) was developed, which could detect dirhamnolipid with the concentration as low as 0.05 mg L-1 by unaided eye. This method was convenient, fast, and reproducible.The third section focuses on the trace of microbial community dynamics and the functional genes in composting system based on genosensing technology. Some high- efficiency contaminant degrading microbes contain relative functional genes. LiP is a key enzyme in lignin degradation by fungi. The gene probes for lip in Phanerochaete chrysosporium was designed and synthesized, and an enzyme-linked electrochemical DNA sensor based on the sandwich hybridization recognition of lip was developed. This sensor, combined with polymerase chain reaction (PCR) and restriction enzyme digestion, succeeded in measuring lip gene fragments from Phanerochaete chrysosporium genome extraction samples with the lower detection limit of 0.03 nM. It could discriminate satisfactorily against mismatched nucleic acid samples of similar lengths. Cellobiohydrolase (CBH) is a key enzyme in cellulose degradation. The gene probes for cbh2 in Trichoderma reesei was designed and synthesized, and magnetic Au/Si/Fe core/shell nanoparticles were fabricated by catalytic growth of Au NPs on the surface. An electrochemical nano DNA sensor based on the competitive hybridization recognition of cbh2 was developed, with the lower detection limit of 10-13 M. Another cbh2 genosensor was developed by the deposition of multi-walled carbon nanotubes-poly(1-ethyl-(3-dimethyllaminopropyl)carbodiimide hydrochloride) complex membrane on electrode surface. The experimental parameters were optimized, and the genosensor was characterized by scanning electron microscopy (SEM) and electrochemistry. The 16S rDNA/rRNA sequences of bacteria are highly conservative, and can be used for species identification. An in vitro 16S rRNA quantification method for Pseudomonas aeruginose based on molecular beacon was developed for the detection of 16S rRNA from total RNA isolation samples. The hybridization reaction required 30 min, and the method had high specificity, free from cross-pollution by nucleic acid, and required no isolation or purification procedures for bacteria total RNA.The fourth section focused on the data resolution of quantification of contaminants and relative degradation capabilities in complex environment system. The complex components in real environmental samples, various interferents, and coexistence of multiple analytes take uncertainty to environmental analysis. Due to the good performance of artificial neural networks (ANNs) in signal resolution, this dissertation combined ANNs with electrochemical enzyme biosensing technology in the electrochemical detection of the lignin-degrading peroxidase activities in Phanerochaete chrysosporium inoculated compost, and the quantitive analysis of catechol by laccase sensor. These methods were more accurate, sensitive and robust than linear regression models. Cation exchange capacity (CEC) is an important index of the soil buffering capacity to maintain cationic nutrients and pollutants against leaching to the subsurface layers. A pedotransfer function based on radial basis function neural networks (RBFN) was developed and applied into the estimation of CEC different regions and different horizons, from soil physico-chemical properties, which showed superiority in large scale data simulation.
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