Would it be better for partition prediction of heavy metal concentration in soils based on the fusion of XRF and Vis-NIR data?

Heavy metal (HM) contamination in soil necessitates effective methods to diagnose suspected contaminated areas and control rehabilitation processes. The synergistic use of proximal sensors demonstrates significant potential for rapid detection via accurate surveys of soil HM pollution at large scales and high sampling densities, and necessitates the selection of appropriate data mining and modeling methods for early diagnosis of soil pollution. The aim of this study is to evaluate the performance of a subarea model based on geographically partitioned and global models based on high-precision energy dispersive X-ray fluorescence (HD-XRF) and visible near-infrared (vis-NIR) spectra using a random forest model for predicting soil Cu and Pb concentrations. A total of 166 soil samples are acquired from a contaminated plot in Baiyin, Gansu Province, China. The soil samples are subjected to HM analysis and proximal sensor scanning in a laboratory. Vis-NIR spectral data are preprocessed using the Savitzky Golay (SG) and first-order derivative with Savitzky Golay (SGFD) methods. The results show that for predicting Cu and Pb concentrations in soil, the subarea models performs better than the global models in terms of quantitative prediction, based solely on individual HD-XRF data. For the subarea and global models, the R2 values are 0.961 and 0.981, respectively; the RMSE values are 27.8 and 79.6, respectively; and the RPD values are 4.96 and 7.38, respectively. However, making use of the random forest algorithm trained with data fusion obtained from the HD-XRF and vis-NIR sensors, the global model achieves the best predictions for Cu and Pb concentrations via HD-XRF + vis-NIR (SGFD) and HD-XRF + vis-NIR (SG), respectively. The results will provide a new perspective for modeling approaches to rapidly invert HM concentrations based on proximal sensor data fusion within a large scope of the study area.

Effect of different washing solutions on soil enzyme activity and microbial community in agricultural soil severely contaminated with cadmium.

Soil enzyme activities and microbial communities have a good response to the remediation effect of heavy metal-contaminated soils. To evaluate the effect of three commonly used washing agents, ferric chloride (FC), ethylenediaminetetraacetic acid (EDTA) and ethylenediamine-tetra-methylenephosphonic acid (EDTMP) on soil enzyme activities and microbial community in cadmium (Cd)-contaminated agricultural soil were collected from farmland near a non-ferrous metal smelter. The soil enzyme activities, microbial community, chemical forms of Cd and some physicochemical properties of the soil washed with different washing solutions were determined. The results showed that the three washing solutions had moderate removal efficiencies for Cd in the tested soil and the breakdown product of EDTMP has a certain stabilizing effect on Cd. The geometric mean and the integrated total enzyme activity index showed that soil washing with FC and EDTA was more beneficial to the restoration of biochemical functions than that with EDTMP. After soil washing, the Chao1 index of bacteria increased, and the microbial community structure changed. Pearson correlation analysis and redundancy analysis (RDA) indicated that the three washing solutions affected soil enzyme activities and microbial community by altering soil nutrient, total Cd concentration and Cd fractions in soils.

Mercury horizontal spatial distribution in paddy field and accumulation of mercury in rice as well as their influencing factors in a typical mining area of Tongren City, Guizhou, China.

PURPOSE: To make up for the deficiency of the distribution characteristics of mercury (Hg) pollution in soil and rice in a specific area, the relationship between more than ten soil indices and Hg in soil-rice system was analysed, and the main factors affecting mercury accumulation in rice were screened out. So as to provide reliable theoretical and scientific basis for the regulation and safe utilization of Hg-contaminated soil. METHODS: The Hg-polluted area of Siqian Dam, with a paddy field area of 1.34 million square meters, was selected as the research unit. Soil and corresponding rice samples were collected and analysed. Then, common Kriging interpolation was used to explore the spatial distribution differences of mercury content between soil and rice, Pearson correlation analysis and stepwise linear regression were used to analyse the relationship between mercury content and 14 soil indices. RESULTS: In the study area, the total mercury(THg) content in soil and rice was as high as 30.60 mg/kg and 160.19 µg/kg, respectively, and the methyl mercury(MeHg) content was as high as 14.56 µg/kg and 40.32 µg/kg, respectively, indicating that mercury pollution in soil and rice was serious. The horizontal spatial distribution of soil THg and MeHg was different. Flood with its sediment and topography were the main reasons for the uneven distribution of Hg content in the region. The spatial distribution of Hg was different between rice and soil. There was no significant correlation between rice and soil THg, but there was a significant correlation between rice and soil MeHg content. Among the 14 soil indices, available potassium was a vital index affecting the accumulation of Hg in rice, followed by pH, Zn, Mn and Fe. CONCLUSIONS: The results showed that in weakly acidic and fertile soil, the appropriate reduction of soil pH, OM and available Se and Cr contents could inhibit soil Hg methylation, the reduction of potassium fertilizer application could further reduce rice Hg accumulation. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40201-021-00711-z.

Pesticide residues in soils planted with Panax notoginseng in south China, and their relationships in Panax notoginseng and soil.

In this study, 73 samples from soils planted with Panax notoginseng and six P. notoginseng samples were collected in Yunnan Province to investigate the residual levels of six pesticides and their relationships with P. notoginseng and soil. All six pesticides were detected in the soils planted with P. notoginseng located in three regions of Shilin, Kaiyuan, and Yanshan. The detection frequencies of the pesticides in the soils followed the order: quintozene (100%) > iprodione (96%) > procymidone (69%) > chlorothalonil (51%) > pyrimethanil (49%) > pyraclostrobin (29%). The median concentrations of iprodione, pyraclostrobin, pyrimethanil, quintozene, procymidone, and chlorothalonil were 46.40, 6.4, 3.1, 2.86, 2.69, and 0.24 µg/kg, respectively. The mean concentrations of pesticides in the three regions followed the order: Kaiyuan > Shilin > Yanshan, except for iprodione. Furthermore, the concentrations of pesticide residues in soils in each region followed the order: soils never planted with P. notoginseng < soils previously planted with P. notoginseng < soils currently planted with P. notoginseng. The concentration of chlorothalonil in P. notoginseng followed the order: root > stem > leaf, whereas those of the other five pesticides followed the opposite order: root < stem < leaf. There were significant positive correlations between the mean concentrations of pesticides in P. notoginseng and those in the corresponding soils. These results indicate that the rational application of pesticides in P. notoginseng cultivation would be effective for reducing the accumulation of pesticides in P. notoginseng to protect people from the harmful effects of residual pesticides.