时雨, 武迪, 依里帕·依力哈木, 郑彦玲, 张利萍. 乌鲁木齐市呼吸系统疾病空气质量健康指数的构建[J]. 环境与职业医学, 2024, 41(3): 276-281. DOI: 10.11836/JEOM23261
引用本文: 时雨, 武迪, 依里帕·依力哈木, 郑彦玲, 张利萍. 乌鲁木齐市呼吸系统疾病空气质量健康指数的构建[J]. 环境与职业医学, 2024, 41(3): 276-281. DOI: 10.11836/JEOM23261
SHI Yu, WU Di, Yilipa YILIHAMU, ZHENG Yanling, ZHANG Liping. Construction of air quality health index for respiratory diseases in Urumqi[J]. Journal of Environmental and Occupational Medicine, 2024, 41(3): 276-281. DOI: 10.11836/JEOM23261
Citation: SHI Yu, WU Di, Yilipa YILIHAMU, ZHENG Yanling, ZHANG Liping. Construction of air quality health index for respiratory diseases in Urumqi[J]. Journal of Environmental and Occupational Medicine, 2024, 41(3): 276-281. DOI: 10.11836/JEOM23261

乌鲁木齐市呼吸系统疾病空气质量健康指数的构建

Construction of air quality health index for respiratory diseases in Urumqi

  • 摘要:
    背景 空气质量健康指数(AQHI)是根据空气污染和发病率/死亡率时间序列分析暴露-反应系数得出的,有助于了解空气污染对健康的整体短期影响。
    目的 研究乌鲁木齐市空气污染物对呼吸系统疾病的影响,并制定相应的乌鲁木齐市呼吸系统疾病发病风险AQHI。
    方法 收集整理新疆乌鲁木齐市新疆医科大学第一附属医院2017年1月1日—2021年12月31日呼吸系统逐日门诊量数据、同期气象资料(日均气温、日均相对湿度)及乌鲁木齐市大气污染物细颗粒物(PM2.5)、可吸入颗粒物(PM10)、二氧化硫(SO2)、二氧化氮(NO2)、一氧化碳(CO)、臭氧(O3)原始监测数据。采用时间分层病例交叉设计构建以准泊松分布为基础的分布滞后非线性模型。以空气污染物零浓度为基准,使用暴露-反应系数(β值)来量化不同空气污染物对于呼吸系统疾病就医风险的影响,建立AQHI。比较AQHI、空气质量指数(AQI)与呼吸系统疾病发病关联性,评估AQHI预测效果。
    结果 PM10、SO2、NO2、O3浓度每增加10 µg·m−3在累积滞后3d(Lag03)和累积滞后2d(Lag02)时超额就诊风险值最大,发病风险分别增加0.687%(95%CI:0.101%~1.276%)、17.609%(95%CI:3.253%~33.961%)、13.344%(95%CI:8.619%~18.275%)、4.921%(95%CI:1.401%~8.502%),PM2.5、CO滞后效应无统计学意义。依据结果选取PM10、SO2、NO2、O3构建AQHI。结果显示,AQHI每升高一个四分位数间距全人群、不同性别人群、不同年龄人群、不同季节就诊人群呼吸系统疾病就诊风险的超额风险均高于AQI的相应指标值。
    结论 乌鲁木齐市PM10、SO2、NO2、O3对呼吸系统疾病门诊就诊人次有影响,构建的乌鲁木齐市呼吸系统疾病发病风险AQHI与AQI相比预测大气污染对呼吸系统健康的影响更强。

     

    Abstract:
    Background Air quality health index (AQHI) is derived from exposure-response coefficients calculated from air pollution and morbidity/mortality time series, which helps to understand the overall short-term health impacts of air pollution.
    Objective To study the effects of common air pollutants on respiratory diseases in Urumqi and to develop an AQHI for the risk of respiratory diseases in the city.
    Methods The daily outpatient volume data of respiratory diseases from The First Affiliated Hospital of Xinjiang Medical University, meteorological data (daily mean temperature and daily mean relative humidity), and air pollutants fine particulate matter (PM2.5), inhalable particulate matter (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon dioxide (CO), and ozone (O3) in Urumqi City, Xinjiang, China were collected from January 1, 2017 to December 31, 2021. A distributed lag nonlinear model based on quasi-Poisson distribution was constructed by time-stratified case crossover design. Adopting zero concentration of air pollutants as reference, the exposure-response coefficient (β value) was used to quantify the impact of included air pollutants on the risk of seeking medical treatment for respiratory diseases, and the AQHI was established. The association of between AQHI and the incidence of respiratory diseases and between air quality index (AQI) and the incidence of respiratory diseases was compared to evaluate the prediction effect of AQHI.
    Results Each 10 µg·m−3 increase in PM10, SO2, NO2, and O3 concentrations presented the highest excess risk of seeking outpatient services at 3 d cumulative lag (Lag03) and 2d cumulative lag (Lag02), with increased risks of morbidity of 0.687% (95%CI: 0.101%, 1.276%), 17.609% (95%CI: 3.253%, 33.961%), 13.344% (95%CI: 8.619%, 18.275%), and 4.921% (95%CI: 1.401%, 8.502%), respectively. There was no statistically significant PM2.5 or CO lag effect. An AQHI was constructed based on a model containing PM10, SO2, NO2, and O3, and the results showed that the excess risk of respiratory disease consultation for the whole population, different genders, ages, or seasons for each inter-quartile range increase in the AQHI was higher than the corresponding value of AQI.
    Conclusion PM10, SO2, NO2, and O3 impact the number of outpatient visits for respiratory diseases in Urumqi, and the constructed AQHI for the risk of respiratory diseases in Urumqi outperforms the AQI in predicting the effect of air pollution on respiratory health.

     

/

返回文章
返回