基于PWEL的乌鲁木齐市空气质量健康指数的构建及验证  

Construction and validation of an air quality health index based on population-weighted exposure level in Urumqi,China

作　　者：付若楠 潘凯[2] 王琛琛[2] 卫泽群 周婧 张婧 日沙来提·塔依尔 张玲[2] 吴顺华[1] FU Ruo-nan;PAN Kai;WANG Chen-chen;WEI Ze-qun;ZHOU Jing;ZHANG Jing;RISHALAITI·Tayier;ZHANG Ling;WU Shun-hua(School of Public Health,Xinjiang Medical University,Urumqi 830054,China;Institute for Health Hazard Factor Surveillance and Control,Center of Disease Control and Prevention of Xinjiang Uygur Autonomous Region)

机构地区：[1]新疆医科大学公共卫生学院,乌鲁木齐830054 [2]新疆维吾尔自治区疾病预防控制中心健康危害因素监测与控制所

出　　处：《环境卫生学杂志》2025年第3期171-177,共7页JOURNAL OF ENVIRONMENTAL HYGIENE

基　　金：国家自然科学基金(82160650);新疆维吾尔自治区自然科学基金(2024D01C67)。

摘　　要：目的基于乌鲁木齐市人口加权大气污染暴露水平(population-weighted exposure level,PWEL)与日门诊就诊人数构建可反映健康危害风险的空气健康指数(air quality health index,AQHI)。方法收集乌鲁木齐市2018—2023年逐日污染物暴露数据(CO、SO_(2)、NO_(2)、PM_(2.5)、PM_(10)、O_(3)-8 h)、空气质量指数(AQI)、同期气象数据(平均温度、平均气压、平均相对湿度、日平均风速)、常住人口分布栅格数据(1 km×1 km)及定点医疗机构日门诊就诊量数据。计算得到逐日PWEL并与日门诊就诊量构建类似泊松广义相加多污染物模型,利用暴露反应关系得到日AQHI,并对其进行验证。结果各污染物2018—2023年逐年PWEL浓度除SO_(2)与O_(3)-8 h外,与原始站点平均浓度相比均有增加。多污染物滞后模型结果显示,NO_(2)移动平均滞后效应lag01 d的影响最大,ER值为47.17%(95%CI:38.06%~56.28%)。PM_(2.5)单日滞后lag7 d产生的影响最大,ER值为2.25%(95%CI:0.26%~4.25%),PM_(10)、SO_(2)、O_(3)对日门诊就诊量的影响均无统计学意义。选择PM_(2.5)-lag7 d和NO_(2)-lag01 d构建AQHI,时序交叉验证结果显示平均绝对误差(MAE)的均数为5.18,均方根误差(RMSE)的均数为5.57。每日AQHI与AQI呈高度正相关(r=0.738,P<0.01),偏相关分析结果显示,AQHI与不同医疗机构类型日门诊就诊量均呈正相关(P<0.01)。结论基于PWEL构建的AQHI对空气污染物程度预报比AQI更为准确,可以反映健康效应。Objective To construct an air quality health index(AQHI)that can reflect health hazards based on population-weighted exposure level(PWEL)and the number of daily outpatient visits in Urumqi,China.Methods Related data of Urumqi in 2018—2023 were collected,including daily pollutant exposure data(CO,SO_(2),NO_(2),PM_(2.5),PM_(10),O_(3)-8 h),air quality index(AQI)meteorological data(mean temperature,mean air pressure,mean relative humidity,and daily mean wind speed),raster data on resident population distribution(1 km×1 km),and the number of daily outpatient visits in designated medical institutions.Daily PWEL and the number of daily outpatient visits were calculated to construct a Poisson generalized additive multi-pollutant model,and daily AQHI was obtained and validated based on exposure-response relationship.Results Compared with the mean concentration of the original site,there were increases in the annual PWEL concentrations of all pollutants except SO_(2)and O_(3)-8 h in 2018—2023.The multi-pollutant lag model showed that the moving average lag01 d of NO_(2)yielded the most substantial effect,with an ER value of 47.17%(95%confidence interval[CI]:38.06%-56.28%).Moreover,the single-day lag of PM_(2.5)-lag7 showed the most significant impact,with an ER value of 2.25%(95%CI:0.26%-4.25%),while PM_(10),SO_(2),and O_(3)showed no significant impact on the number of daily outpatient visits.PM_(2.5)-lag7 and NO_(2)-lag01 were used to construct the AQHI,and the results of time-series cross validation showed a mean absolute error(MAE)of 5.18 and a mean root mean square error(RMSE)of 5.57.Furthermore,daily AQHI was highly positively correlated with AQI(r=0.738,P<0.01),and the partial correlation analysis showed that AQHI was positively correlated with the number of daily outpatient visits in different types of medical institutions(P<0.01).Conclusion AQHI based on PWEL is more accurate than AQI in predicting the degree of air pollution and can reflect the potential health impacts associated with air pollution.

关 键 词：空气污染 空气质量健康指数(AQHI) 日门诊就诊 暴露水平 

分 类 号：R122[医药卫生—环境卫生学] R181.34[医药卫生—公共卫生与预防医学]