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作 者:高圣华[1] 钱乐[1] 邢方潇[1] 韩嘉艺[1] 叶必雄[1] 张岚 GAO Shenghua;QIAN Le;XING Fangxiao;HAN Jiayi;YE Bixiong;ZHANG Lan
机构地区:[1]中国疾病预防控制中心环境与健康相关产品安全所,100050
出 处:《环境卫生学杂志》2021年第5期457-461,467,共6页JOURNAL OF ENVIRONMENTAL HYGIENE
基 金:水体污染控制与治理科技重大专项(2018ZX0752001)。
摘 要:目的通过实验室间比对的能力验证方法,评价我国各级疾控中心实验室生活饮用水中砷的检测能力情况。方法 2018年11月,在全国31个省(自治区、直辖市)以及新疆生产建设兵团共选取不同行政级别疾控中心的158家实验室进行能力验证。每家实验室发放1份砷考核样品和1份砷标准品。对检测结果采用稳健统计迭代算法A进行统计分析,采用z值进行结果判定。结果全国151家实验室上报了检测结果。检测结果为满意值的单位有118家,占78.1%;检测结果为可疑值的单位有6家,占4.0%;检测结果为离群值的单位有27家,占17.9%。省级、省会和地级疾控中心实验室的砷检测能力优于县区疾控中心(χ^(2)=11.5,P<0.05);东部、中部和西部地区疾控中心实验室的砷检测能力水平差异没有统计学意义(χ^(2)=2.84,P> 0.05)。结论我国疾控系统实验室的砷检测能力水平总体较好,部分实验室检测能力有待提高,应加强水质检测能力建设。Objective To evaluate the ability of CDC laboratories at all levels in detecting arsenic in drinking water by proficiency testing based on inter-laboratory comparison. Methods A total of 158 CDC laboratories of different administration levels were selected from 31 provinces(autonomous regions and centrally administered municipalities) and Xinjiang Production and Construction Corps for proficiency testing in November 2018. A standard sample and a testing sample of arsenic were distributed to each laboratory. The robust statistical iterative algorithm A was used to analyze testing results, and the z value was used to evaluate such results. Results A total of 151 laboratories reported their testing results, among which 118 laboratories(78.1%) had satisfactory results, 6 laboratories(4.0%) had suspected results, and 27 laboratories(17.9%) had outliers. The CDC laboratories at the provincial, provincial capital, and prefecture levels had a significantly better arsenic detection ability than those at the county and district levels(χ^(2)= 11.5, P <0.05), and there was no significant difference in arsenic detection ability between the CDC laboratories in eastern, central, and western regions(χ^(2)= 2.84, P >0.05). ConclusionCDC laboratories in China generally have a good arsenic detection ability, and the detection ability of some laboratories needs to be improved. Training of water quality detection should be strengthened.
分 类 号:R123[医药卫生—环境卫生学]
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