Identification of High Personal PM2.5 Exposure during Real Time Commuting in the Taipei Metropolitan Area

Explainable geospatial-artificial intelligence models for the estimation of PM2.5 concentration variation during commuting rush hours in Taiwan

PM2.5 concentrations are higher during rush hours at background stations compared to the average concentration across these stations. Few studies have investigated PM2.5 concentration and its spatial distribution during rush hours using machine learning models. This study employs a geospatial-artificial intelligence (Geo-AI) prediction model to estimate the spatial and temporal variations of PM2.5 concentrations during morning and dusk rush hours in Taiwan. Mean hourly PM2.5 measurements were collected from 2006 to 2020, and aggregated into morning (7 a.m.-9 a.m.) and dusk (4 p.m.-6 p.m.) rush-hour mean concentrations. The Geo-AI prediction model was generated by integrating kriging interpolation, land-use regression, machine learning, and a stacking ensemble approach. A forward stepwise variable selection method based on the SHapley Additive exPlanations (SHAP) index was used to identify the most influential variables. The performance of the Geo-AI models for morning and dusk rush hours had accuracy scores of 0.95 and 0.93, respectively and these results were validated, indicating robust model performance. Spatially, PM2.5 concentrations were higher in southwestern Taiwan for morning rush hours, and suburban areas for dusk rush hours. Key predictors included kriged PM2.5 values, SO2 concentrations, forest density, and the distance to incinerators for both morning and dusk rush hours. These PM2.5 estimates for morning and dusk rush hours can support the development of alternative commuting routes with lower concentrations.

An ensemble mixed spatial model in estimating long-term and diurnal variations of PM2.5 in Taiwan

Meteorology, human activities, and other emission sources drive diurnal cyclic patterns of air pollution. Previous studies mainly focused on the variation of PM_2.5 concentrations during daytime rather than nighttime. In addition, assessing the spatial variations of PM_2.5 in large areas is a critical issue for environmental epidemiological studies to clarify the health effects from PM_2.5 exposures. In terms of air pollution spatial modelling, using only a single model might lose information in capturing spatial and temporal correlation between predictors and pollutant levels. Hence, this study aimed to propose an ensemble mixed spatial model that incorporated Kriging interpolation, land-use regression (LUR), machine learning, and stacking ensemble approach to estimate long-term PM_2.5 variations for nearly three decades in daytime and nighttime. Three steps of model development were applied: 1) linear based LUR and Hybrid Kriging-LUR were used to determine influential predictors; 2) machine learning algorithms were used to enhance model prediction accuracy; 3) predictions from the selected machine learning models were fitted and evaluated again to build the final ensemble mixed spatial model. The results showed that prediction performance increased from 0.514 to 0.895 for daily, 0.478 to 0.879 for daytime, and 0.523 to 0.878 for nighttime when applying the proposed ensemble mixed spatial model compared with LUR. Results of overfitting test and extrapolation ability test confirmed the robustness and reliability of the developed models. The distance to the nearest thermal power plant, density of soil and pebbles fields, and funeral facilities might affect the variation of PM_2.5 levels between daytime and nighttime. The PM_2.5 level was higher in daytime compared with nighttime with little difference, revealing the importance of estimating nighttime PM_2.5 variations. Our findings also clarified the emission sources in daytime and nighttime, which serve as valuable information for air pollution control strategies establishment.

Did air pollution continue to affect bike share usage in Seoul during the COVID-19 pandemic?

INTRODUCTION

The role of cycling has become more important in the urban transport system during the Covid-19 pandemic. As public transport passengers have tried to avoid crowded vehicles due to safety concerns, a rapid surge of cycling activities has been noted in many countries. This implies that more cyclists might be exposed to air pollution, potentially leading to health problems in cities like Seoul where the level of air pollution is high.

METHODS

We utilised three years of bike sharing programme (Ddareungi) data in Seoul and time series models to examine the changes in the relationship between particulate concentration (PM_2.5) and total daily cycling duration before and during the pandemic.

RESULTS

We find that cyclists reacted less to the PM_2.5 level during the pandemic, potentially due to the lack of covid-secure travel modes. Specifically, our results show significant negative associations between concentrations of PM_2.5 and total daily cycling duration before the pandemic (year 2018 and 2019). However, this association became insignificant in 2020.

CONCLUSIONS

Building comprehensive cycling infrastructure that can reduce air pollution exposure of cyclists and improving air quality alert systems could help build a more resilient city for the future.

Contribution of Aerosol Sources to Health Impacts

Atmospheric aerosol is one of the major leading environmental risk factors for human health worldwide, potentially causing several million premature deaths per year [...]