Feasibility of using Vis-NIR spectroscopy and PXRF spectrometry to estimate regional soil cadmium concentration.

Conventionally, soil cadmium (Cd) measurements in the laboratory are expensive and time-consuming, involving complex processes of sample preparation and chemical analysis. This study aimed to identify the feasibility of using sensor data of visible near-infrared reflectance (Vis-NIR) spectroscopy and portable X-ray fluorescence spectrometry (PXRF) to estimate regional soil Cd concentration in a time- and cost-saving manner. The sensor data of Vis-NIR and PXRF, and Cd concentrations of 128 surface soils from Yunnan Province, China, were measured. Outer-product analysis (OPA) was used for synthesizing the sensor data and Granger-Ramanathan averaging (GRA) was applied to fuse the model results. Artificial neural network (ANN) models were built using Vis-NIR data, PXRF data, and OPA data, respectively. Results showed that: (1) ANN model based on PXRF data performed better than that based on Vis-NIR data for soil Cd estimation; (2) Fusion methods of both OPA and GRA had higher predictive power (R2) = 0.89, ratios of performance to interquartile range (RPIQ) = 4.14, and lower root mean squared error (RMSE) = 0.06, in ANN model based on OPA fusion; R2 = 0.88, RMSE = 0.06, and RPIQ = 3.53 in GRA model) than those based on either Vis-NIR data or PXRF data. In conclusion, there exists a great potential for the combination of OPA fusion and ANN to estimate soil Cd concentration rapidly and accurately.

Soil physiochemical properties and bacterial community changes under long-term polycyclic aromatic hydrocarbon stress insitu steel plant soils.

In situ soils were collected at two depths in Jinan and Hangzhou steel plants, which both have a long history of operation and polycyclic aromatic hydrocarbons (PAHs) contamination. The richness of 16 S rRNA gene and bacterial community of the soil were determined by real-time PCR and high-throughput sequencing. Soil physicochemical properties, PAHs contamination characteristics, and their interrelationships were also analyzed. In general, the PAHs contamination decreased with increasing soil depths. The physicochemical properties and PAH concentration of soil had synergistic impacts on the composition of the bacterial community. The long-term higher PAHs stress in Hangzhou contaminated soil (982 mg kg-1) increased the bacterial abundance and diversity, while that of Jinan contaminated soil (63 mg kg-1) decreased bacterial abundance and diversity. The pH value, sand content of the soil were positively correlated (P < 0.05) with the bacterial diversity including Simpson, Shannon, Observed_species and Chao1 indexes., and the other soil properties exhibited negative correlations with different strengths. The abundances of Curvibacter, Pseudomonas, Thiobacillus, Lysobacter, and Limnobacter were positively correlated with the PAHs concentration (P < 0.01). Additionally, the network structure of the PAHs-contaminated soils was more complex compared to that of uncontaminated soils, with stronger linkages and correlations between the different bacteria. These findings provide a theoretical basis for microbial remediation of PAHs-polluted soil.

Comprehensive assessment of heavy metal risk in soil-crop systems along the Yangtze River in Nanjing, Southeast China.

Conventional assessment of soil environmental quality commonly focuses on soil heavy metals (HMs), neglecting the HMs in agricultural products. To response this shortcoming, a comprehensive assessment combining both soil environmental quality and agricultural product security for evaluating soil HM impact is urgently required. This comprehensive assessment incorporates not only the HM contents in soil and agricultural product but also soil environmental quality standards, soil elemental background values, and safety standards for HMs in agricultural products. In this study, it was applied to evaluate the potential risk of HMs in soil-crop systems (i.e., soil-vegetable, soil-maize, soil-rice, and soil-wheat systems) along the Yangtze River in Nanjing, Jiangsu Province, Southeast China. Furthermore, 114Cd/110Cd isotope ratio analysis was used to identify the specific contamination sources. The mean concentrations of Cd, As, Hg, Pb, Cu, Zn, and Cr in the surface soils (0-20 cm) were 0.26, 11.07, 0.09, 32.63, 38.57, and 107.92 mg kg-1, respectively, exceeding the corresponding soil background values. Fertilizer and atmospheric deposition were the major anthropogenic sources of HM contamination in crop-growing soils. In addition to the crop type, soil pH and organic matter also influenced the transfer of HMs from soils to the edible parts of crops. Results of comprehensive assessment revealed that approximately 11.1% of paired soil-crop sites were multi-contaminated by HMs, among which paddy soils had the highest potential risk of HMs followed by maize soils, vegetable soils, and wheat soils. To evaluate the potential risk of HMs in arable land, this study provides a novel, scientific and reliable approach via integrating soil environmental quality and agricultural product security.

Correction of in-situ portable X-ray fluorescence (PXRF) data of soil heavy metal for enhancing spatial prediction.

Heavy metal data measured by portable X-ray fluorescence (PXRF), especially by in-situ PXRF, are usually affected by multiple soil factors, such as soil moisture (SM), soil organic matter (SOM), and soil particle size (SPZ). Thus, a correction may be needed. However, traditionally-used correction methods, such as non-spatial linear regression (LR), cannot effectively correct the spatially non-stationary influences of the related soil factors on PXRF analysis. Moreover, these correction methods are not robust to outliers. In this study, robust geographically weighted regression (RGWR) was used to correct in-situ and ex-situ PXRF data of soil Pb in a peri-urban agricultural area of Wuhan City, China. The accuracy of the corrected PXRF data by RGWR was compared with those by non-spatial and spatial but non-robust methods (i.e., LR and GWR). In addition, to find an appropriate method of using the corrected PXRF data for more accurate spatial prediction, we compared robust ordinary kriging with the corrected PXRF data as part of hard data (ROK-CPXRF) and robust ordinary cokriging with the corrected PXRF data as auxiliary soft data (RCoK-CPXRF). Results showed that (i) RGWR obtained higher correction accuracy than LR and GWR on both the in-situ and ex-situ PXRF data; (ii) the accuracy of the RGWR-corrected in-situ PXRF data was increased nearly to that of the RGWR-corrected ex-situ PXRF data; (iii) given the same amount of sample data, ROK-CPXRF obtained higher prediction accuracy than RCoK-CPXRF. It is concluded that the methods suggested in this study may largely promote the application of in-situ PXRF technique for rapid and accurate soil heavy metal investigation in large-scale areas.

Main controlling factors and forecasting models of lead accumulation in earthworms based on low-level lead-contaminated soils.

To explore the main controlling factors in soil and build a predictive model between the lead concentrations in earthworms (Pbearthworm) and the soil physicochemical parameters, 13 soils with low level of lead contamination were used to conduct toxicity experiments using earthworms. The results indicated that a relatively high bioaccumulation factor appeared in the soils with low pH values. The lead concentrations between earthworms and soils after log transformation had a significantly positive correlation (R2 = 0.46, P < 0.0001, n = 39). Stepwise multiple linear regression analysis derived a fitting empirical model between Pbearthworm and the soil physicochemical properties: log(Pbearthworm) = 0.96log(Pbsoil) - 0.74log(OC) - 0.22pH + 0.95, (R2 = 0.66, n = 39). Furthermore, path analysis confirmed that the Pb concentrations in the soil (Pbsoil), soil pH, and soil organic carbon (OC) were the primary controlling factors of Pbearthworm with high pathway parameters (0.71, - 0.51, and - 0.49, respectively). The predictive model based on Pbearthworm in a nationwide range of soils with low-level lead contamination could provide a reference for the establishment of safety thresholds in Pb-contaminated soils from the perspective of soil-animal systems.

Determination and evaluation of heavy metals in soils under two different greenhouse vegetable production systems in eastern China.

The evaluation of heavy metals (HMs) in greenhouse soils is crucial for both environmental monitoring and human health; thus, it is imperative to determine their concentrations, identify their sources and assess their potential risks. In this study, eight metals (As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn) in 167 surface soils were investigated in two representative greenhouse vegetable systems of China: perennial solar greenhouse (SG) and seasonal plastic greenhouse (PG). The results indicated accumulations of Cd, Cu, Hg and Zn in the SG soils and Cd, Pb, Hg and Zn in the PG soils, with higher concentrations than the background values. In particular, Cd and Hg exhibited high levels of pollution under both GVP systems due to their positive Igeo values. Principle component analysis (PCA) and correlation analysis suggested that Cd, Cu, Hg and Zn in the SG soils and Cd, Hg and Zn in the PG soils were mainly related to intensive farming practices; Pb in the PG soils was significantly affected by atmospheric deposition. The results showed that soil characteristics, in particular soil organic matter, total nitrogen and total phosphorus, exerted significant influence on Hg, Cu, Cd, and Zn under the SG system. However, the HMs in the PG soils were weakly affected by soil properties. Overall, this study provides comparative research on the accumulation, potential risks and sources of HMs in two typical greenhouse soils in China, and our findings suggest that, Cd and Hg in both greenhouse soils could potentially represent environmental problems.