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This paper refers to a survey conducted in Zhengzhou section of the Yellow River for the purpose of investigating heavy metal pollution characteristics and of exploring the pollution sources. Monitoring was thereby carried on August 29-31, 2022 along the riparian zone collecting samples of the river sediments and analyzing the concentrations of nine kinds of heavy metal, including Cd, Pb, Cu, Zn, As, Hg, Cr, Ni and Mn. Then the Tessier five-step sequential extraction method was used for the morphological analysis of the heavy metals; the ground accumulation index (Igeo) was used to evaluate heavy-metal pollution degree, with Nemero pollution index being applied to the comprehensive assessment of the pollution situation; and the principal component analysis (PCA) and absolute factor analysis-multiple linear regression analysis model (APCS-MLR) were used to analyze the source of the pollutants. Consequently, the survey showed that both of the mean concentrations of Pb and Cd in the sediments of the riverine zone of Zhengzhou section of the Yellow River exceeded the soil background value of heavy-metal in Henan Province. Results of the speciation analysis showed that the environmental activities of Mn, Cd, Pb and Hg were relatively high; the evaluation of geo-accumulation index (Igeo) indicated that Cd had resulted in slight to moderate pollution in the sediments of the River; and the evaluation results of the Nemero pollution index showed that all the above-mentioned heavy-metal elements were on the safety level. Result of the source apportionment suggested that tannery industry, agricultural activities and automobile parts production, as well as the natural source were the major heavy-metal pollution sources in the riparian zone of Zhengzhou section of the Yellow River. [ABSTRACT FROM AUTHOR]

In view of problems of poor accuracy and dependence on source coordinates of existing underground positioning algorithm, an underground positioning method based on GMapping algorithm and fingerprint map construction was proposed. The signal characteristics of unknown position is matched with signal distribution map, and the optimal positioning coordinates is selected, thereby improving accuracy of the underground positioning; by combining with GMapping algorithm, problem of high maintenance cost of fingerprint map construction process is avoided, and searching and matching efficiency of the algorithm is optimized. Actual test results show that the average positioning error of the proposed method is 57.7 cm, which can meet requirements of underground positioning.

Microbial necromass carbon (MNC) is a crucial source for stable soil carbon pool, and understanding its response to carbon inputs from both aboveground (litter) and belowground (roots) in subtropical forest soils is essential for assessing soil carbon stocks in global ecosystems. In a Castanopsis carlesii plantation at the Sanming Forest Ecosystem National Observation and Research Station in Fujian Province, we conducted an experiment with five treatments, including root removal (NR), aboveground litter removal (NL), no litter input (removals of both aboveground litter and roots, NI), double aboveground litter addition (DL), and control (CK). After seven years, we collected soil samples in the 0-10 cm soil layer to examine changes in MNC content and its contribution to soil organic carbon (SOC). Results showed that NR treatment reduced MNC, bacterial necromass carbon (BNC), and fungal necromass carbon (FNC) by 15.9%, 20.2%, and 14.5%, respectively, while other treatments did not induce significant changes. The NR, NL, NI, and DL treatments did not affect the contributions of BNC, FNC, and MNC to SOC. Correlation and path analyses revealed that litter and root carbon input treatments could alter the MNC content directly or indirectly through changing soil available substrates and microbial community structure. Our results suggested that roots exert a stronger influence on the maintenance of MNC than aboveground carbon source in the mid-subtropical plantations. [ABSTRACT FROM AUTHOR]

In view of problems of low illumination, a lot of dust and complex electromagnetic environment in coal mine underground, and images' quality captured by underground monitoring equipment would be reduced in transferring process caused by underground noises, an underground monitoring image sharpening device based on embedded system was designed. The device uses wavelet threshold algorithm and DSP embedded system, and it can effectively restrain noise, meanwhile keep details characteristics and peak point of original images, which greatly enhances monitoring image clarity.