Development of Phase and Seasonally Dependent Land-Use Regression Models to Predict Atmospheric PAH Levels.

Polycyclic aromatic hydrocarbons (PAHs) are an important class of pollutants in China. The land use regression (LUR) model has been used to predict the selected PAH concentrations and screen the key influencing factors. However, most previous studies have focused on particle-associated PAHs, and research on gaseous PAHs was limited.

[Non-carcinogenic Risk Assessment of Cadmium Exposure Through Drinking Water in Chinese Residents Based on Age-stratification Weight].

Drinking water is an important cadmium (Cd) exposure pathway for residents in China. In order to quantitatively characterize the risk of cadmium exposure through drinking water, the Cd concentrations of three main drinking water types in China were collected via systematic literature review. The probability distribution of the exposure parameters was estimated using regression models.

Key Factors for Improving the Carcinogenic Risk Assessment of PAH Inhalation Exposure by Monte Carlo Simulation.

Monte Carlo simulation (MCS) is a computational technique widely used in exposure and risk assessment. However, the result of traditional health risk assessment based on the MCS method has always been questioned due to the uncertainty introduced in parameter estimation and the difficulty in result validation. Herein, data from a large-scale investigation of individual polycyclic aromatic hydrocarbon (PAH) exposure was used to explore the key factors for improving the MCS method.