| 陈谊,陈星如,常巧英,范春林.一种基于AHP-E模型的多因子农药残留污染综合评价方法[J].农业环境科学学报,2019,38(2):276-283. |
| 一种基于AHP-E模型的多因子农药残留污染综合评价方法 |
| A multi-factor comprehensive method based on the AHP-E model for evaluating pesticide residue pollution |
| 投稿时间：2018-05-30 修订日期：2018-07-20 |
| DOI：10.11654/jaes.2018-0712 |
| 中文关键词: 农药残留污染 综合评价方法 多因子 层次分析法 熵值法 |
| 英文关键词: pesticide residue pollution comprehensive evaluation method multi-factor analytic hierarchy process entropy method |
| 基金项目:“十二五”国家科技支撑计划项目（2012BAD29B01-2）；虚拟现实技术与系统国家重点实验室开放基金（BUAA-VR-17KF-07）；国家科技基础性工作专项（2015FY111200）；2018年研究生科研能力提升计划项目；国家重点研发计划项目（2018YFC1603602） |
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| 摘要点击次数: 1932 |
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| 中文摘要: |
| 为了能对某地区不同农产品受农药残留污染程度以及不同时间段受农药残留污染程度进行综合定量评价，提出一种基于层次分析法和熵值法AHP-E模型的多因子农药残留污染综合评价方法。该方法首先根据农药残留侦测结果，综合考虑农药毒性、检出频次、检出含量、超标情况多方面因素，选择评价因子；然后应用层次分析法对不同农产品受农药残留污染的程度进行评价，用熵值法对不同时间段受农残污染的程度进行评价。以2013年3月至6月A市6种农产品中农药残留侦测结果为例进行案例研究，对6种农产品受农残污染程度从高到低排序发现，按农残检出率和超标率两个单因子评价指标进行排序的结果分别为黄瓜、苹果、番茄、青椒、芹菜、韭菜和芹菜、韭菜、黄瓜、苹果、番茄、青椒；而用本文综合评价方法计算得出的排序结果为韭菜、芹菜、黄瓜、青椒、番茄、苹果。对不同时间段受农残污染程度进行排序发现，按农残检出率和超标率单指标进行排序的结果均为5月、4月、3月、6月，与本文方法的排序结果一致。对比单因子和多因子评价排序结果发现他们既有联系又有差异，造成差异的原因是单属性评价方法考虑因素单一，而本文提出的综合评价模型能较全面反映农药残留的污染情况，并有效突出不同农产品之间农药残留污染程度的差距。 |
| 英文摘要: |
| To evaluate the degree of pesticide residue pollution in different agricultural products and over different time periods, a comprehensive evaluation method based on the analytic hierarchy process (AHP)-entropy (E)model for pesticide residue pollution was proposed. With this method, the evaluation factors were first selected from the results of pesticide residue detection, in which many factors, including pesticide toxicity, the frequency of which pesticides were detected, the pesticide content, and the level of exceeding the MRL (Maximum Residue Limit), were considered. Then, the AHP was applied to assess the degree of pesticide residue pollution in different agricultural products, and the entropy method was applied to assess the degree of pesticide residue pollution over different time periods. The case study used in this study was from the results of pesticide residue detection on six agricultural products in City A from March to June in 2013. For the degree of pesticide residue pollution in the different agricultural products, the ranking of results from higher to lower by two factors,i.e., the detection rate of pesticide residue and the rate of exceedance of the MRL, were cucumber, apple, tomato, green pepper, celery, leek, and celery, leek, cucumber, apple, tomato, green pepper, respectively. However, the ranking of results from higher to lower by the comprehensive evaluation value proposed by this study was leek, celery, cucumber, green pepper, tomato, apple. For the degree of pesticide residue pollution over different time periods,the ranking of results from higher to lower by both the pesticide residue detection rate and the rate of exceeding the MRL were May, April, March, and June, the ranking of results by the method of this study is also the same with evaluation result by the single-factor. Comparing above ranking results by single-factor and multi-factor evaluation, the same and different aspects were found. The reason for the discrepancy was that the evaluation method based on a single factor had limitations in terms of the completeness of evaluation. Our evaluation model in this study can evaluate the degree of pesticide residue pollution more comprehensively and effectively, and can highlight the difference in degree of pesticide residue pollution among different agricultural products. |
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