A unified empirical modeling approach for particulate matter and NO2 in a coastal city in China

Modeling air pollutants on a fine spatiotemporal scale is necessary for health studies that focus on critical short-term exposure windows. A unified empirical modeling approach is useful for health studies; however, it is unclear whether this approach can be used in a coastal city for air pollutants driven by local emissions and regional meteorological factors. An advanced empirical modeling approach was used to develop exposure models from October 2012 to December 2019, for particulate matter with aerodynamic diameters less than or equal to 2.5 and 10 mu m (PM2.5 and PM10) and nitrogen dioxide (NO2) in the coastal city of Shanghai, China. Air pollutant concentrations were obtained from daily measurements at 55 administrative monitoring sites that were integrated into three-day average concentrations. Data on a large array of geographic variables were collected, and their dimensions were reduced using the partial least squares regression method. A geostatistical model using the land use regression approach in a universal kriging framework was developed to estimate short-term exposure concentrations. The prediction ability of the models were determined by leave-one (site)-out cross-validation (LOOCV) and external validation (EV). Compared to the LOOCV results, the EV results for PM2.5 and PM10 were consistently reliable, but the EV for NO2 had a larger root mean squared error. The temporal random effects involved in the model structure were interpreted using sensitivity analyses. This affected the short-term PM2.5 and PM10 model predictions. This unified empirical modeling approach was successfully used for particulate matter in Shanghai, where air pollution is affected by complex regional and meteorological conditions. These exposure models are going to be applied for making exposure predictions at residential locations for short-term exposure predictions in the Growth trajectories and air pollution (GAAP) study in Shanghai that focuses on maternal and early life exposure to air pollutants.