With 7 million estimated deaths each year, the World Health Organization (WHO) identified air pollution is now the world’s largest single environmental health risk. However, it remains a challenging question in air quality management regarding how to address the exposure and health risk among the population.

Traditional methods take an average metric to evaluate the exposure and health effects among the population, which is insufficient to address the risks regarding health effects in susceptible subgroups and individuals exposed to high exposure. Using Monte-Carlo technique and stochastic exposure models, this thesis research examined the application of subgroup stratification in the health assessment and inter-individual variability in the exposure assessment, both of whi...[ Read more ]

With 7 million estimated deaths each year, the World Health Organization (WHO) identified air pollution is now the world’s largest single environmental health risk. However, it remains a challenging question in air quality management regarding how to address the exposure and health risk among the population.

Traditional methods take an average metric to evaluate the exposure and health effects among the population, which is insufficient to address the risks regarding health effects in susceptible subgroups and individuals exposed to high exposure. Using Monte-Carlo technique and stochastic exposure models, this thesis research examined the application of subgroup stratification in the health assessment and inter-individual variability in the exposure assessment, both of which quantifies the distribution and variation of risks. In particular, this thesis research has identified several key factors for data prioritization in practice via sensitivity analysis, case studies and field measurements.

Subgroup stratification uses stratified demographics and incidence to estimate health effects among different subgroups. Monte-Carlo sensitivity analysis highlighted the importance of obtaining representative demographics for subgroups in health assessment, particularly in areas with rapidly changing demographics.

Stochastic exposure models simulate the movement of individuals across time and space and their exposure in various microenvironments (e.g., outdoors, indoors residence, in-vehicle). Different sampling methods were compared via sensitivity analysis, and the impacts of key input factors were evaluated. Daily activity patterns and residential air exchange rates were identified to significantly influence the exposure estimates.

A field measurement was conducted to quantify PM_2.5 and CO exposure concentrations in 13 selected microenvironments during public transportation in Hong Kong, which complemented our understanding of the magnitude and variations in the exposure concentration during transportation activities. The demonstration case studies conducted for the Pearl River Delta (PRD) region and Hong Kong give insights on the implications of subgroup stratification and inter-individual variability in risk assessment and air quality management.

This thesis research has made an attempt to estimate health risks in susceptible subgroups and individuals exposed to high exposure. The results provide insights on the need and priority in data collection and risk management. Future work is needed to integrate various sources of information for the development of health and exposure assessment based on locally measured data.