Association between key air pollutant combinations and respiratory disease hospitalizations in Hefei from 2019 to 2024
- VernacularTitle: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
- Author:
Xiangguo LIU
1
;
Linling YU
1
;
Yu ZHU
1
;
Changchun XIAO
1
Author Information
- Publication Type:Selectedarticle
- Keywords: key air pollutant; respiratory disease hospital admission; LASSO regression; time-series analysis; interaction effect
- From: Journal of Environmental and Occupational Medicine 2026;43(3):293-301
- CountryChina
- Language:Chinese
-
Abstract:
Background Air pollution is a major environmental factor threatening respiratory health. Different pollutants exhibit varying degrees of lag effects on respiratory diseases, and synergistic effects 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 operator (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:
1.0002 , 1.024) and lag3 (RR=1.015, 95%CI:1.0004 , 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 recommended 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.
