Quantifying the health effects of exposure to non-exhaust road emissions using agent-based modelling (ABM).

This paper provides an agent-based model, entitled TRAPSim, to examine the exposure to non-exhaust emissions (NEEs) and the consequent health effects of driver and pedestrians groups in Seoul. To make the model reproducible and replicable, TRAPSim uses the ODD protocol to demonstrate the details of the agents and parameters, as well as provide the codes alongside the descriptions to avoid possible ambiguity. The model's main parameters are thoroughly tested through sensitivity experiments and are calibrated with the city's air pollution monitoring networks. This paper also provides the instructions to the model, possible artefacts, and the configurations to submit the model on the HPC cluster.•An ODD protocol is used to document the agent-based model TRAPSim.•Sensitivity experiments and calibration are explained.•The step-by-step codes and annotations are attached in the protocol and HPC sections.

Ordinary kriging approach to predicting long-term particulate matter concentrations in seven major Korean cities.

OBJECTIVES: Cohort studies of associations between air pollution and health have used exposure prediction approaches to estimate individual-level concentrations. A common prediction method used in Korean cohort studies is ordinary kriging. In this study, performance of ordinary kriging models for long-term particulate matter less than or equal to 10 µm in diameter (PM10) concentrations in seven major Korean cities was investigated with a focus on spatial prediction ability. METHODS: We obtained hourly PM10 data for 2010 at 226 urban-ambient monitoring sites in South Korea and computed annual average PM10 concentrations at each site. Given the annual averages, we developed ordinary kriging prediction models for each of the seven major cities and for the entire country by using an exponential covariance reference model and a maximum likelihood estimation method. For model evaluation, cross-validation was performed and mean square error and R-squared (R(2)) statistics were computed. RESULTS: Mean annual average PM10 concentrations in the seven major cities ranged between 45.5 and 66.0 µg/m(3) (standard deviation=2.40 and 9.51 µg/m(3), respectively). Cross-validated R(2) values in Seoul and Busan were 0.31 and 0.23, respectively, whereas the other five cities had R(2) values of zero. The national model produced a higher crossvalidated R(2) (0.36) than those for the city-specific models. CONCLUSIONS: In general, the ordinary kriging models performed poorly for the seven major cities and the entire country of South Korea, but the model performance was better in the national model. To improve model performance, future studies should examine different prediction approaches that incorporate PM10 source characteristics.