Analysis of microplastics in a remote region of the Tibetan Plateau: Implications for natural environmental response to human activities.

Microplastics are one of the most valuable indicators reflecting the effects of human activities on natural environment. This study was conducted in a representative remote region of Tibetan Plateau in China, simultaneously analyzing the abundance, compositions and fate of MPs both in water and soil media. MPs were detected in surface water, sediment and soil with abundances ranging from 66.6 to 733.3 number/m3, 20 to 160 items/kg, and 20 to 110 items/kg, respectively. Fibers were the most frequently observed shape in the surface water and sediment, while the dominant shape in the soil was film. The major polymers of MPs in water and soil samples were polypropylene (PP) and polyethylene (PE). Small MPs were the main components with the <500µm fraction accounting for 94.74%, 88.37% and 88.34% of total MP particles in surface water, sediment and soil, respectively. Correlation analysis was further conducted to identify the sources of MPs from different human activities. The night light index was innovatively used to represent population rather than local residents, considering the large number of tourists in this region. It was found that tourism was the main source of MPs in water bodies, while facility agriculture and previous secondary industry are major contributors to soil MPs. A simplified equation set for MP abundance prediction was also formulated related to different industrial features. This study provides an evidence of noticeable MPs associated with human activities even at remote regions, and advances a feasible tool for MPs prediction according to local economic development. CAPSULE: The effect of human activities on natural environment in a remote region was illustrated by evaluating the abundance, compositions and fate of MPs across freshwater and terrestrial environment.

Multi-criteria decision analysis of optimal planting for enhancing phytoremediation of trace heavy metals in mining sites under interval residual contaminant concentrations.

As one of the most cost-effective and sustainable methods for contaminants' removal, sequestration and/or detoxification, phytoremediation has already captured comprehensive attention worldwide. Nevertheless, the accurate effects of various spatial pattern in enhancing phytoremediation efficiency is not yet clear, especially for the polluted mining areas. This study designed nine planting patterns (monocropping, double intercropping and triple intercropping) of three indigenous plant species (Setaria viridis (L.), Echinochloa crus-galli (L.) and Phragmites australis (Cav.) Trin. ex Steud.) to further explore the effects of plants spatial pattern on phytoremediation efficiency. Considering the uncertainties of the residual contaminants' concentration (RCC) caused by soil anisotropy, permeability and land types, the interval transformation was introduced into the plant uptake model to simulate the remediation efficiency. Then multi-criteria decision analysis (MCDA) were applied to optimal the planting patterns, with the help of criteria of (a) the amount of heavy metal absorption; (b) the concentration of residual contaminant in soil; (c) root tolerance of heavy metals; (d) the total investment cost. Results showed that (1) the highest concentrations of Zn, Cd, and Pb of the polluted area were 7320.02, 14.30, 1650.51 mg kg-1 (2) During the 180 days simulation, the highest RMSE of residue trace metals in soil are 3.02(Zn), 2.67(Pb), 2.89(Cd), respectively. (3) The result of IMCDA shows that the planting patterns of Setaria viridis, Echinochloa crus-galli and Phragmites australis in alternative a9 (269 mg kg-1 year-1) had the highest absorption rate of heavy metals compared with a7 (235 mg kg-1 year-1) and a2 (240 mg kg-1 year-1). After 20 years of remediation, the simulated RCC in a9 is far below the national standard, and the root toxicity is 0.12 (EC ≤ EC20). In general, the optimal alternative derived from interval residual contaminant concentration can effectively express the dynamic of contaminant distribution and then can be effectively employed to evaluate the sustainable remediation methods.