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首页> 外文期刊>Journal of soil & sediments >Machine learning methods for estimation the indicators of phosphogypsum influence in soil
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Machine learning methods for estimation the indicators of phosphogypsum influence in soil

机译：估计土壤中磷石膏影响指标的机器学习方法

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Purpose The full understanding of the effect of mineral waste-based fertilizer in soil is still unrelieved, because of the extreme complex chemical composition and plethora of their action pathways. The purposes of this paper is to quantify the input of PG into the soil ecosystem process, considering the direct effects of PG as a whole on soil environment using of a plethora of chemical, toxicological, and biological tests.Materials and methods Greenhouse experiment includes different PG doses (0, 1%, 3%, 7.5%, 15%, 25%, and 40%) and two-time collection points after treatments-7 and 28 days. For each treatment and each time collection point, we measure (i) soil pH, bioavailable (H(2)0 and NH4COOH-extractable) element content (S, P, K, Na, Mg, Ca, Fe, Zn, Sr, Ba, F); (ii) soil enzyme activities-dehydrogenase, urease, acid phosphatase, FDA; (iii) soil CO2 respiration activity with and without glucose addition; (iv) Eisenia fetida, Sinapis alba, and Avena sativa responses. Finally, we combine the ordinary chemical, toxicology, and biological measuring of soil properties with state-of-the-art mathematical analysis, namely (i) support vector machines (used for prediction), (ii) mutual information test (variable importance tasks), (iii) t-SNE and LLE algorithms (used for unsupervised classification).Results and discussion The results show similarity between the 0%, 1%, and 3% PG treatments in all collection times based on the toxicological and biological properties. Beyond 7.5% PG, some biological test was significantly inhibited in response to trace element stress. Among all tested parameters, soil urease activities, soil respiration activities after glucose addition, S. alba root lengths, and E. fetida survival rates show sensitivity to PG addition. Furthermore, the machine learning algorithms revealed that only several elements (mobile and water-soluble forms of Ca, Ba, Sr, S, and Na; water-soluble F) could be responsible to elevated soil toxicity for those indicators. SVR models were able to predict soil biological and ecotoxicity properties, and increasing numbers of randomly selected training examples from 50 to 90% of initial experimental data significantly improved model performance.Conclusions At this study, we demonstrate benefits of unsupervised machine learning methods for investigating toxicity of man-made substances in soil that can be further applied to risk assessments of various toxins, which are of significant interest to environmental protection.
机译：目的由于对矿物废物的肥料化学成分极其复杂且其作用途径过多，因此至今仍无法完全理解以矿物废物为基础的肥料在土壤中的作用。本文的目的是通过使用大量的化学，毒理学和生物学测试来考虑PG整体对土壤环境的直接影响，从而量化PG在土壤生态系统过程中的投入。材料和方法温室实验包括治疗7天和28天后的PG剂量（0％，1％，3％，7.5％，15％，25％和40％）和两次采集点。对于每种处理方法和每个收集时间点，我们测量（i）土壤pH，生物有效性（H（2）0和NH4COOH可萃取）元素含量（S，P，K，Na，Mg，Ca，Fe，Zn，Sr， Ba，F）; （ii）土壤酶活性-脱氢酶，脲酶，酸性磷酸酶，FDA； （iii）添加和不添加葡萄糖的土壤二氧化碳呼吸活动； （iv）赤子花（Eisenia fetida），白芥（Sinapis alba）和苜蓿（Avena sativa）反应。最后，我们将对土壤特性的常规化学，毒理学和生物学测量与最新的数学分析相结合，即（i）支持向量机（用于预测），（ii）互信息测试（可变重要性任务） ），（iii）t-SNE和LLE算法（用于无监督分类）。结果和讨论结果表明，根据毒理学和生物学特性，在所有收集时间内PG处理的0％，1％和3％相似。除7.5％的PG以外，对痕量元素的应激反应也显着抑制了一些生物学测试。在所有测试参数中，土壤脲酶活性，添加葡萄糖后的土壤呼吸活性，S。alba根长度和E. fetida存活率均显示对PG的敏感性。此外，机器学习算法显示，只有几种元素（Ca，Ba，Sr，S和Na的流动形式和水溶性形式；水溶性F）可能会导致这些指标的土壤毒性升高。 SVR模型能够预测土壤生物学和生态毒性特性，将随机选择的训练实例的数量从初始实验数据的50％增加到90％可以显着提高模型的性能。结论在本研究中，我们证明了无监督机器学习方法在研究毒性方面的优势土壤中的人造物质，可以进一步应用于各种毒素的风险评估，这对环境保护具有重要意义。

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