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广东省佛山市高明区空气质量因素与呼吸系统疾病死亡的时间序列分析 被引量:2

Time series analysis of air quality factors and death of respiratory system diseases in Gaoming district,Foshan city
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摘要 目的分析广东省佛山市高明区空气质量对呼吸系统疾病死亡的影响,为开展疾病气象预警服务提供相关方法。方法对2015年1月1日至2020年6月30日广东省佛山市高明区居民呼吸系统疾病每日死亡人数、日均气温和湿度,以及该地区空气中二氧化硫、二氧化氮、一氧化碳、臭氧浓度和细颗粒物(PM2.5)、PM10等变化情况进行描述,利用分布滞后非线性模型对各类空气污染物浓度与呼吸系统疾病死亡进行时间序列分析。结果高温、高湿能增加居民呼吸系统疾病短期死亡风险;低温、低湿能增加居民呼吸系统疾病死亡风险并且存在滞后效应,且女性高于男性,≥65岁居民高于小于65岁居民。综合空气污染物二氧化硫、臭氧浓度,PM2.5、PM10因素后高温能增加呼吸系统疾病短期死亡风险。结论当广东省佛山市高明区日均气温骤升或持续低温时建议居民尽量避免外出活动并做好健康防护,同时,有关部门应强化风险预报,提供防病防害指引。 Objective This paper analyzes the influence of air quality on the death of respiratory system diseases and providing weather forecasts service of diseases in Gaoming District,Foshan City.Methods The average daily deaths and air factors such as temperature,relative humidity,SO_(2),NO_(2),CO,O_(3),PM2.5,PM10 from January 1,2015 to June,302020 is statistical described.Time series analysis of air pollutant concentration and death from respiratory diseases was carried out using distributed lag nonlinear model.Results High temperature and humidity can increase the effect of short-term deaths.Low temperature humidity can increase a delayed death effect of the respiratory system diseases.The number of females was higher than that of males,and the number of residents aged≥65 was higher than that of residents aged less than 65.Combined with SO_(2),O_(3),PM2.5 and PM10,high temperature can increase the risk of short-term death from respiratory diseases.Conclusion It is suggested that when the daily average temperature rises sharply or the low temperature continues,the residents should avoid outdoor activities and take good health protection.The relevant departments should strengthen risk forecast and provide defense guidelines.
作者 黄学敏 郑卓灵 HUANG Xuemin;ZHENG Zhuoling(Center of Disease Control Gaoming District Foshan,Foshan,Guangdong 528500,China)
出处 《现代医药卫生》 2021年第20期3420-3425,共6页 Journal of Modern Medicine & Health
基金 广东省佛山市卫生和健康局医学科研课题项目(20200408)。
关键词 空气质量因素 呼吸系统疾病死亡 时间序列分析 广东 Air quality factors Respiratory system diseases deaths Time series analysis Guangdong
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