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Tea plants that have a large leaf area mainly suffer from heavy metal accumulation in the above-ground parts through foliar uptake. With the world rapid industrialization, this pollution in tea is considered a crucial challenge due to its potential health risks. The present study proposes an innovative approach based on visible and near-infrared (Vis-NIR) spectroscopy coupled with chemometrics for the characterization of tea chemical indicators under airborne lead stress, which can be performed fast and in situ. The effects of lead stress on chemical indicators and accumulation in leaves of the two tea varieties at different time intervals and levels of treatment were investigated. In addition, changes in cell structure and leaf stomata were monitored during foliar uptake of aerosol particles by transmission electron microscopy (TEM) and scanning electron microscopy (SEM). The spectral variation was able to classify the tea samples into the Pb treatment groups through the linear discriminant analysis (LDA) model. Two machine learning techniques, namely, partial least squares (PLS) and radial basis function neural network (RBFNN), were evaluated and compared for building the quantitative determination models. The RBFNN models combined with correlation-based feature selection (CFS) and PLS data compression methods were used to optimize the prediction performance. The results demonstrated that the PLS–RBFNN as a non-linear model outperformed the PLS model and provided the R-value of 0.944, 0.952, 0.881, 0.937, and 0.930 for prediction of MDA, starch, sucrose, fructose, glucose, respectively. It can be concluded that the proposed approach has strong application potential in monitoring the quality and safety of plants under airborne heavy metal stress.

Metabolic engineering of cyanobacteria has received much attention as a sustainable strategy to convert CO2 to various longer carbon chain fuels. Pinene has become increasingly attractive since pinene dimers contain high volumetric energy and have been proposed to act as potential aircraft fuels. However, cyanobacteria cannot directly convert geranyl pyrophosphate into pinene due to the lack of endogenous pinene synthase. Herein, we integrated the gene encoding Abies grandis pinene synthase into the model cyanobacterium Synechococcus sp. PCC 7002 through homologous recombination. The genetically modified cyanobacteria achieved a pinene titer of 1.525 ± 0.l45 mg L−1 in the lab-scale tube photobioreactor with CO2 aeration. Specifically, the results showed a mixture of α- and β-pinene (∼33:67 ratio). The ratio of β-pinene in the product was significantly increased compared with that previously reported in the engineered Escherichia coli. Furthermore, we investigated the photoautotrophic growth performances of Synechococcus overlaid with different concentrations of dodecane. The work demonstrates that the engineered Synechococcus is a suitable potential platform for β-pinene production.