首页|期刊导航|环境与职业医学|2019-2024年合肥市关键大气污染物组合与呼吸系统疾病入院量的关联

2019-2024年合肥市关键大气污染物组合与呼吸系统疾病入院量的关联OA

Association between key air pollutant combinations and respiratory disease hospitalizations in Hefei from 2019 to 2024

中文摘要英文摘要

[背景]大气污染是威胁呼吸系统健康的重要环境因素,不同污染物对呼吸系统疾病的影响存在滞后效应差异,且多污染物之间可能存在协同效应.亟需厘清影响呼吸系统疾病的关键大气污染物及其在特异滞后期的交互效应. [目的]识别影响呼吸系统疾病住院的关键污染物,解析其滞后效应特征,并量化多污染物协同作用对呼吸系统疾病入院的影响. [方法]基于合肥市 2019-2024年多个国控站点的大气污染数据、气象数据及呼吸系统疾病住院的逐日数据,采用两阶段分析框架,首先通过分布滞后模型(DLM)构建污染物滞后矩阵,利用套索回归(LASSO)从细颗粒物(PM2.5)、二氧化硫(SO2)、二氧化氮(NO2)、一氧化碳(CO)、臭氧(O3)等污染物中筛选关键变量;随后建立广义可加模型(GAM),并引入乘积交互项与超额相对风险(ERI),以定量评估污染物间的协同效应. [结果]LASSO筛选出 24个非零系数污染物滞后项,其中 NO2、PM2.5 和 SO2 贡献了 66.7%的正向效应,且三者呈现出显著的滞后模式差异.NO2 暴露呈现急性风险,在滞后 0 d(lag0)时的相对风险为 1.040(95%CI:1.023~1.057);PM2.5 和 SO2 表现为延迟效应,最大效应出现在lag7和 lag3,相对风险分别为 1.012(95%CI:1.000 2~1.024)、1.015(95%CI:1.000 4~1.03).NO2 短期暴露(lag0~1)与 PM2.5 中期暴露(lag4~7)存在显著正向交互,两污染物共同处于高暴露水平时的健康风险比独立效应之和增加 4.40%,分层分析显示女性(ERI=5.00%)、儿童(<18岁,ERI=5.50%)和老年人(≥65岁,ERI=3.90%)的污染物交互效应更高. [结论]合肥市大气污染健康效应存在显著的滞后差异和协同机制.建议对 NO2 实施实时预警响应,对 PM2.5 和 SO2 侧重中长期治理,并在 NO2 与 PM2.5 污染物浓度同步升高时采取协同减排措施,同时加强对女性、儿童和老年人等敏感人群的健康防护.

[Background]Air pollution is a major environmental factor threatening respiratory health.Dif-ferent pollutants exhibit varying degrees of lag effects on respiratory diseases,and synergistic ef-fects may exist among multiple pollutants.There is an urgent need to identify the key air pollutants influencing respiratory diseases and their interactive effects at specific lags. [Objective]To identify key pollutants affecting hospital admissions for respiratory diseases,to analyze their lag effect characteristics,and to quantify the impact of multi-pollutant synergistic effects on respiratory disease admissions. [Methods]Daily air pollution data,meteorological data,and respiratory disease hospitalization records were collected from multiple national monitoring stations in Hefei City from 2019 to 2024.A two-stage analytical framework was employed.First,a distributed lag model(DLM)was used to construct pollutant lag matrices,followed by least absolute shrinkage and selection op-erator(LASSO)regression to select key variables among fine particulate matter(PM2.5),sulfur dioxide(SO2),nitrogen dioxide(NO2),carbon monoxide(CO),and ozone(O3).Second,a generalized additive model(GAM)was established,incorporating product interaction terms and excess relative risk(ERI)to quantitatively assess synergistic effects among the selected pollutants. [Results]Through LASSO regression,24 pollutant lag terms with non-zero coefficients were identified,among which NO2,PM2.5,and SO2 accounted for 66.7%of the total positive effects and exhibited distinct lag patterns.Exposure to NO2 showed acute risk,with a relative risk of 1.040(95%CI:1.023,1.057)at lag0.Conversely,PM2.5 and SO2 exhibited delayed effects,with peak impacts observed at lag7(RR=1.012,95%CI:1.000 2,1.024)and lag3(RR=1.015,95%CI:1.000 4,1.03),respectively.Short-term NO2 exposure(lag0-1)exhibited a significant positive interaction with medium-term PM2.5 exposure(lag4-7).When both pollutants coexisted at high exposure levels,the combined health risk exceeded the sum of their independent effects by 4.40%.Stratified analysis showed that the interaction effects of pollutants were higher in females(ERI=5.00%),children(<18 years,ERI=5.50%),and older adults(≥65 years,ERI=3.90%) [Conclusion]There are significant lag heterogeneity and synergistic mechanisms in the health effects of air pollution in Hefei.It is recom-mended to implement real-time early warning responses for NO2,prioritize medium-to-long-term management for PM2.5 and SO2,and adopt coordinated emission reduction measures when NO2 and PM2.5 concentrations rise simultaneously.Concurrently,enhanced health protection should be provided for sensitive populations such as women,children,and the elderly.

刘翔国;余林玲;朱昱;肖长春

合肥市疾病预防控制中心环境卫生科,安徽 合肥 230091合肥市疾病预防控制中心环境卫生科,安徽 合肥 230091合肥市疾病预防控制中心环境卫生科,安徽 合肥 230091合肥市疾病预防控制中心环境卫生科,安徽 合肥 230091

医药卫生

关键污染物呼吸系统疾病住院LASSO回归时间序列分析交互效应

key air pollutantrespiratory disease hospital admissionLASSO regressiontime-series analysisinteraction effect

《环境与职业医学》 2026 (3)

293-301,9

10.11836/JEOM25339

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