Global NEST Journal

An optical sensor for colorimetric detection of Cr(VI) has been synthesized by immobilizing 1,5-diphenylcarbazide (DPC) into alginate/pectin films. The addition of cationic surfactant (CTAB) was used to increase the effectiveness of DPC immobilization by forming hydrophobic-ion pairs. At optimum condition, the films has good selectivity for Cr(VI) compare to other interference metals. The formation of violet color in the films showed its potential application for visual colorimetric detection of Cr(VI). The developed films in this study was found to be simple, rapid and sensitive sensor for direct determination of Cr(VI) with detection limit as low as 0.233 mg L–1 and time response was 8 minutes.

Objective: To obtain the characteristics analysis results of aronia melanocarpa leaves under saline alkali stress state and improve yield and quality of aronia melanocarpa. Methods: Using hyper spectral imaging system to obtain hyper spectral images of aronia melanocarpa leaves under saline alkali stress. The clear hyper spectral image is obtained by the conversion of reflectance, spectral envelope removal, and spectral denoising, and final hyper spectral image is obtained by the normalization of a clearer hyper spectral image. Results: Spectral information of aronia melanocarpa leaves under slight saline alkali stress: in the visible band, leaf reflectance is lower than leaf nutrient rich stress condition; in the near infrared band, the nutrient rich leaves was significantly higher than that of stress leaves. Spectral information of leaves under moderate saline alkali stress: spectral reflectance of different lesion spots for a same leaf in 550-680nm band is: severe > moderate > slight > normal, in the near infrared band is on the contrary; spectral reflectance for different lesion grades in 550-680 nm, severe lesion leaves have highest reflectance, and normal leaves have the lowest reflectance. Under severe saline alkali stress, the leaf spectral information is: there is no significant difference in leaf spectral reflectance between the attachment aphid and the damaged leaf, but the difference is obvious for normal leaves in the band of 450-500 nm, 560-680 nm and 750-900 nm. Comparative results analysis for the three of saline alkali stress degree is: the near infrared band of 560-680 nm and the visible band of 780-900 nm is the sensitive band for the diagnosis of three kinds of stress; slight saline alkali stress has the most significant differences at 550 nm, and 780-900 nm; severe saline alkali stress has at 680 nm and 780-900 nm. Conclusion: The proposed method can analyze hyper spectral image characteristics of aronia melanocarpa leaves under different saline alkali stress the condition of is a kind of plant leaves, which is an efficient method for the analysis of characteristics of plant leaves under saline alkali stress.

In this study, an ecological awareness rating scale and an eco-product purchase decision-making rating scale were developed based on literature and interviews with experts. The ecological awareness variables were subdivided into three aspects: ecological knowledge, ecological emotion, and ecological perception. The eco-product purchase decision-making behavior was also divided into eco-product information gathering behavior and eco-product scheme evaluation behavior. According to consumer decision-making theories, an ecological awareness-ecological product purchase decision-making behavior model was constructed to verify the relationships between variables. Based on regression analysis, the effects of ecological knowledge, ecological emotion, and ecological perception on the two dimensions of consumers’ eco-product purchase decision-making behavior were discussed. It is concluded that ecological awareness has a significant positive impact on the eco-product purchase decision-making behavior, which provides a reference for the development of ecological society and the cultivation of consumers’ ecological consumption habits and methods.

Solid wastes and acid wastewater lead to the enrichment of heavy metals in the soil of mining area. Heavy metal pollution causes the decline of soil quality, ecosystem degradation, crop yield reduction, and even threatens human health. For this reason, the real-time detection method for heavy metal pollution in mining area is studied. Taking a mining area as an example, the data of heavy metal content in mining area soil are collected by PLSR model. Based on the collected data, the real-time detection model of heavy metal pollution in mining area soil based on improved analytic hierarchy process and weighted average method is adopted to real-time detect the heavy metal pollution index in the soil of mining area. The results show that the pollution index of Cu, Zn and Pb in the soil of this mining area belongs to heavy pollution, and the pollution of Cd is relatively small. Among them, the pollution index of Pb is the largest among the four heavy metals, and the pollution is quite serious. The pollution sources of Pb, Zn and Cu in this mining area are the same, and the pollution sources of Cd are different from those of other three heavy metals.