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Updated: Jan 10, 2026
Composition and Distribution Analysis of Bioaerosols Under Different Environmental Conditions
Published on: January 7, 2019
Modeling the spatiotemporal distribution of ambient ultrafine particles in Shanghai, China
Yuhao Wei1, Mengxun Rong1, Yihui Ge1
1School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai 200032, China; IRDR ICoE on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health, Fudan University, Shanghai, China.
Background:
The interpretation of epidemiological evidence on ambient ultrafine particles (UFPs) is limited by uncertainties in exposures exposure due to unaccounted spatiotemporal variability.
Objective:
To develop models for estimating the spatiotemporal distribution of ambient UFPs in Shanghai, China.
Methods:
We established a study-specific monitoring network and repeatedly measured on-road UFP across three typical seasons. Using traffic, land use, air pollutants, and meteorological data, we constructed five prediction models based on both linear and nonlinear approaches, in single- and two-stage frameworks.
Results:
Mean (± standard deviation) of on-road UFP concentrations was 26,313 (± 8,932) N∙cm-3, showing pronounced spatial and temporal variations. The Random Forest (RF) one-stage model (R2 = 0.63, root mean squared error (RMSE) = 5463 N∙cm-3) and RF-based two-stage model (R2 = 0.62, RMSE = 5521 N∙cm-3) performed best, followed by Support Vector Regression (SVR) one-stage model (R2 = 0.57, RMSE = 5830 N∙cm-3), SVR-based two-stage model (R2 = 0.53, RMSE = 6146 N∙cm-3) and multiple linear regression model (R2 = 0.39, RMSE = 6975 N∙cm-3). External validation confirmed the superiority of the RF two-stage model.
Conclusions:
The models effectively captured spatiotemporal variation in UFP concentrations, supporting improved exposure assessment and health effect estimation.
