Applicability and limitation of compost maturity evaluation indicators: A review

被引:24
|
作者
Kong, Yilin [1 ]
Zhang, Jing [1 ]
Zhang, Xuanshuo [1 ,2 ]
Gao, Xia [1 ]
Yin, Jie [1 ]
Wang, Guoying [1 ]
Li, Jiaming [1 ]
Li, Guoxue [1 ,2 ]
Cui, Zhongliang [1 ]
Yuan, Jing [1 ]
机构
[1] China Agr Univ, Coll Resources & Environm Sci, Beijing Key Lab Farmland Soil Pollut Prevent & Rem, Beijing, Peoples R China
[2] Suzhou China Agr Univ, Organ Recycling Inst, Suzhou 215128, Peoples R China
基金
中国国家自然科学基金;
关键词
Compost; Maturity evaluation indicator; Applicability; Limitation; DISSOLVED ORGANIC-MATTER; CATION-EXCHANGE CAPACITY; MUNICIPAL SOLID-WASTE; SULFUR-COMPOUNDS VSCS; SEED-GERMINATION TEST; SEWAGE-SLUDGE; PIG-MANURE; CHICKEN MANURE; FLUORESCENCE EXCITATION; GASEOUS EMISSIONS;
D O I
10.1016/j.cej.2024.151386
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Composting is a resource treatment method that uses aerobic microorganisms to convert organic solid waste into stable humus and applied as organic fertilizer. The maturity evaluation of compost is of great significance for its safe application in farmland. Presently, lots of methods have been used to evaluate the compost maturity, including physical indicators, chemical indicators and biological indicators, as well as spectroscopic indicators, computer vision and machine learning. However, these indicators all have limitations. This review systematically delineates and analyzes the role, connotation, applicability, and constraints of various compost maturity evaluation indices. Physical indicators offer ease of operation and qualitative assessment, while quantitative evaluation necessitates machine learning and computer vision approaches. Chemical indicators allow for quantitative maturity assessment, yet evaluation outcomes are substantially influenced by compost raw material properties, necessitating a comprehensive evaluation system incorporating multiple chemical indices and raw material categorization. Although the seed germination index is currently an authoritative indicator for evaluating compost maturity, the absence of standardized seeds and normalized methods impedes the comparison of research results. The spectroscopic indexes are the high demands on detection instruments and data analysis capabilities. Machine learning presents a promising avenue for objective evaluation in the future. However, the development of machine learning models and algorithms necessitates more standardized data training sets to enhance accuracy. In light of the pressing need for maturity evaluation, establishing a more standardized and universal maturity evaluation system or developing rapid, accurate, and cost-effective detection indicators is imperative. This study analyzed compost maturity evaluation methods in order to give technical assistance for accurate and quantitative compost maturity evaluation as well as safe manure return to the field.
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页数:19
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