Missing data imputation strategies: A comparative analysis of hourly and daily pollution models

Epidemiological studies have consistently demonstrated the association between daily pollution exposure and disease outcomes. To estimate daily exposure, hourly pollution data are commonly aggregated, but missing data pose a significant challenge to this approach. To overcome this issue, some researchers have developed various models to impute missing hourly data. Alternatively, directly modelling pollution exposure on a daily basis is possible, thereby avoiding the computational burden of hourly pollution modelling. However, the performance of these two modelling strategies remains unclear. This study conducts a comparative assessment between hourly and daily modelling strategies for the purpose of estimating daily pollution exposure. Utilizing data derived from Guangzhou city, the analysis encompasses diverse scenarios of data absence. The outcomes consistently highlight the superior performance of daily pollution models in terms of mitigated bias and diminished root mean square error (RMSE) values.