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.
作者
付若楠
潘凯
王琛琛
卫泽群
周婧
张婧
日沙来提·塔依尔
张玲
吴顺华
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)
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)。
