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    廖芬, 杨柳, 李强, 等. 不同生物质来源生物炭品质的因子分析与综合评价[J]. 华南农业大学学报, 2019, 40(3): 29-37. doi: 10.7671/j.issn.1001-411X.201806028
    引用本文: 廖芬, 杨柳, 李强, 等. 不同生物质来源生物炭品质的因子分析与综合评价[J]. 华南农业大学学报, 2019, 40(3): 29-37. doi: 10.7671/j.issn.1001-411X.201806028
    shu
    LIAO Fen, YANG Liu, LI Qiang, et al. Factor analysis and comprehensive evaluation for quality of biochar derived from different biomass[J]. Journal of South China Agricultural University, 2019, 40(3): 29-37. doi: 10.7671/j.issn.1001-411X.201806028
    Citation: LIAO Fen, YANG Liu, LI Qiang, et al. Factor analysis and comprehensive evaluation for quality of biochar derived from different biomass[J]. Journal of South China Agricultural University, 2019, 40(3): 29-37. doi: 10.7671/j.issn.1001-411X.201806028
    shu

    不同生物质来源生物炭品质的因子分析与综合评价

    Factor analysis and comprehensive evaluation for quality of biochar derived from different biomass

      摘要
    • 摘要:
      目的  建立一套适合生物炭品质评价的方法，探求影响生物炭品质的主要影响因子。
      方法  选用8种不同生物质材料，在3种温度下制备并获得24种生物炭材料(Y1~Y24)，测定16项相关品质指标，采用隶属函数法对各项指标数据进行转化，采用软件SPSS19.0进行因子分析，采用四次方最大旋转法获得因子载荷矩阵，计算样品每个公因子分值与相应权重之积的累加和，得到综合评价分值。对16项指标进行相关性分析和因子分析，建立基于因子分析的生物炭品质的综合评价体系，并根据综合评价得分对生物炭进行优良度排序。
      结果  24种生物炭16个品质指标经因子分析，提取了5个特征根>1的公因子，累计方差贡献率达到77.910%，第1公因子以C含量、阳离子交换量(CEC)和pH贡献率较大，达到31.090%，第2公因子以比表面积和孔容贡献率较大，达到19.878%，第3公因子以H原子含量贡献率较大，达到12.819%，第4公因子以磷、钾含量贡献率较大，达到7.479%，第5公因子以NH4+−N吸附量贡献率较大，达到6.643%。
      结论  因子分析方法可以作为评价生物炭品质的方法，根据因子分析评价方法，确定影响生物炭品质最关键的因子是化学性质因子(C含量、C/N比、C/H比、pH、CEC)、物理性质因子(比表面积和孔容)、活化能量因子(H原子含量)、营养因子(P和K含量)和氨态氮吸附能力因子。

       

      Abstract:
      Objective  To establish a set of method suitable for quality evaluation of biochar and to explore the main factors influencing biochar quality.
      Method  Toally 24 biochars (Y1−Y24) were prepared from eight biomass materials under three different temperatures. Sixteen quality indexes of biochars were measured, and the data were converted using the subordinate function. SPSS 19.0 was used for factor analysis. Factor loading matrix was obtained using the biquadratic maximum rotation method. The comprehensive evaluation scores were calculated. Correlation analysis and factor analysis were performed for the 16 indexes, and the comprehensive evaluation system of biochar quality was established based on factor analysis. The biochars were ranked using the comprehensive evaluation scores.
      Result  Through factor analysis of 16 quality indexes of 24 biochars, we extracted five common factors with eigenvalues above one and their cumulative variance contribution was 77.910%. The first common factor consisted of carbon content, cation exchange capacity (CEC) and pH, with variance contribution of 31.090%. The second common factor consisted of specific surface area and pore volume, with variance contribution of 19.878%. The third common factor consisted of hydrogen atom content with variance contribution of 12.819%. The fourth common factor consisted of phosphorus and potassium contents with variance contribution of 7.479%. The fifth common factor consisted of maximum adsorption capacity of NH4+-N with variance contribution of 6.643%.
      Conclusion  Factor analysis is a good statistical method for evaluating biochar quality. The key factors affecting biochar quality include chemical characteristic factors (C content, C/N ratio, C/H ratio, pH and CEC), physical characteristic factors (specific surface area and pore volume), active energy factor (H atom content), nutrition factor (P and K contents) and maximum adsorption capacity of NH4+-N.

       

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