RESUMO
The rapid growth of industrialization and urbanization in China has led to an increase in soil heavy metal pollution, which poses a serious threat to ecosystem safety and human health. The advancement of spectral technology offers a way to rapidly and non-destructively monitor soil heavy metal content. In order to explore the potential of rice leaf spectra to indirectly estimate soil heavy metal content. We collected farmland soil samples and measured rice leaf spectra in Xushe Town, Yixing City, Jiangsu Province, China. In the laboratory, the heavy metals Cd and As were determined. In order to establish an estimation model between the pre-processed spectra and the soil heavy metals Cd and As content, a genetic algorithm (GA) was used to optimise the partial least squares regression (PLSR). The model's accuracy was evaluated and the best estimation model was obtained. The results showed that spectral pre-processing techniques can extract hidden information from the spectra. The first-order derivative of absorbance was more effective in extracting spectral sensitive information from rice leaf spectra. The GA-PLSR model selects only about 10% of the bands and has better accuracy in spectral modeling than the PLSR model. The spectral reflectance of rice leaves has the capacity to estimate Cd content in the soil (relative percent difference [RPD] = 2.09) and a good capacity to estimate As content in the soil (RPD = 2.97). Therefore, the content of the heavy metals Cd and As in the soil can be estimated indirectly from the spectral data of rice leaves. This study provides a reference for future remote sensing monitoring of soil heavy metal pollution in farmland that is quantitative, dynamic, and non-destructive over a large area.
RESUMO
Aims: The high salinity of soil, nutrient scarcity, and poor aggregate structure limit the exploitation and utilization of coastal mudflat resources and the sustainable development of saline soil agriculture. In this paper, the effects of applying exogenous organic acids combined with biological substrate on the composition and diversity of soil bacterial community were studied in moderately saline mudflats in Jiangsu Province. Methods: A combination of three exogenous organic acids (humic acid, fulvic acid, and citric acid) and four biological substrates (cottonseed hull, cow manure, grass charcoal, and pine needle) was set up set up on a coastal saline mudflat planted with a salt-tolerant forage grass, sweet sorghum. A total of 120 kg ha-1 of organic acids and 5,000 kg ha-1 of substrates were used, plus two treatments, CK without application of organic acids and substrates and CK0 in bare ground, for a total of 14 treatments. Results: No significant difference was found in the alpha diversity of soil bacterial community among all treatments (p ≥ 0.05), with the fulvic acid composite pine needle (FPN) treatment showing the largest increase in each index. The beta diversity differed significantly (p < 0.05) among all treatments, and the difference between citric acid-grass charcoal (CGC) and CK treatments was greater than that of other treatments. All treatments were effective in increasing the number of bacterial ASVs and affecting the structural composition of the community. Citric acid-cow manure (CCM), FPN, and CGC treatments were found to be beneficial for increasing the relative abundance of Proteobacteria, Chloroflexi, and Actinobacteria, respectively. By contrast, all treatments triggered a decrease in the relative abundance of Acidobacteria. Conclusion: Among the 12 different combinations of exogenous organic acid composite biomass substrates applied to the coastal beach, the CGC treatment was more conducive to increasing the relative abundance of the salt-tolerant bacteria Proteobacteria, Chloroflexi and Actinobacteria, and improving the community structure of soil bacteria. The FPN treatment was more conducive to increase the species diversity of the soil bacterial community and adjust the species composition of the bacterial community.