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To explore the strong tolerance of bacteria to Hg pollution, aquatic Rheinheimera tangshanensis (RTS-4) was separated from industrial sewage, with a maximum Hg(II) tolerant concentration of 120 mg/L and a maximum Hg(II) removal rate of 86.72 ± 2.11%, in 48 h under optimum culture conditions. The Hg(II) bioremediation mechanisms of RTS-4 bacteria are as follows: (1) the reduction of Hg(II) through Hg reductase encoded by the mer operon; (2) the adsorption of Hg(II) through the production of extracellular polymeric substances (EPSs); and (3) the adsorption of Hg(II) using dead bacterial biomass (DBB). At low concentrations [Hg(II) ≤ 10 mg/L], RTS-4 bacteria employed Hg(II) reduction and DBB adsorption to remove Hg(II), and the removal percentages were 54.57 ± 0.36% and 45.43 ± 0.19% of the total removal efficiency, respectively. At moderate concentrations [10 mg/L < Hg(II) ≤ 50 mg/L], all three mechanisms listed above coexisted, with the percentages being 0.26 ± 0.01%, 81.70 ± 2.31%, and 18.04 ± 0.62% of the total removal rate, respectively. At high concentrations [Hg(II) > 50 mg/L], the bacteria primary employed EPS and DBB adsorption to remove Hg(II), where the percentages were 19.09 ± 0.04% and 80.91 ± 2.41% of the total removal rate, respectively. When all three mechanisms coexisted, the reduction of Hg(II) occurred within 8 h, the adsorption of Hg(II) by EPSs and DBB occurred within 8-20 h and after 20 h, respectively. This study provides an efficient and unused bacterium for the biological treatment of Hg pollution.

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Microbial remediation is vital for improving heavy metal-polluted water. In this work, two bacterial strains, K1 (Acinetobacter gandensis) and K7 (Delftiatsuruhatensis), with high tolerance to and strong oxidation of arsenite [As(III)], were screened from industrial wastewater samples. These strains tolerated 6800 mg/L As(III) in a solid medium and 3000 mg/L (K1) and 2000 mg/L (K7) As(III) in a liquid medium; arsenic (As) pollution was repaired through oxidation and adsorption. The As(III) oxidation rates of K1 and K7 were the highest at 24 h (85.00 ± 0.86%) and 12 h (92.40 ± 0.78%), respectively, and the maximum gene expression levels of As oxidase in these strains were observed at 24 and 12 h. The As(III) adsorption efficiencies of K1 and K7 were 30.70 ± 0.93% and 43.40 ± 1.10% at 24 h, respectively. The strains exchanged and formed a complex with As(III) through the -OH, -CH3, and C]O groups, amide bonds, and carboxyl groups on the cell surfaces. When the two strains were co-immobilized with Chlorella, the adsorption efficiency of As(III) improved (76.46 ± 0.96%) within 180 min, thereby exhibiting good adsorption and removal effects of other heavy metals and pollutants. These results outlined an efficient and environmentally friendly method for the cleaner production of industrial wastewater.

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Based on the development of the concept of a resource-saving and environmentally friendly society, needing to develop low-carbon and sustainable urban transportation. Most of the pollutants come from the emissions of motor vehicle exhaust. Therefore, this paper analyzes the relationship between driving behavior and traffic emissions, to constrain driver behavior to reduce pollutant emissions. The GPS data are preprocessed by using Navicat for data integration, data screening, data sorting, etc., and then, the speed data are cleaned by using a combination of box-and-line plots and linear interpolation in SPSS. Second, this paper uses principal component analysis (PCA) to downsize 12 indicators such as average speed, average acceleration, and maximum speed and then adopts K-MEANS and K-MEDOIDS methods to cluster the driver's behavioral indicators, selects the aggregation method based on the clustering indexes optimally, and analyzes the driver's driving state by using the symbolic approximation aggregation method; finally, according to the above research results and combined with the MOVES traffic emission model to analyze the relationship between the driver's driving mode, driving state, and traffic emissions, the decision tree can be used to predict the unknown driving mode of the driver to estimate the degree of its emissions.