Pedestrian exposure to black carbon and PM emissions in urban hot spots: new findings using mobile measurement techniques and flexible Bayesian regression models.

Background: Data from extensive mobile measurements (MM) of air pollutants provide spatially resolved information on pedestrians' exposure to particulate matter (black carbon (BC) and PM mass concentrations).

Objective: We present a distributional regression model in a Bayesian framework that estimates the effects of spatiotemporal factors on the pollutant concentrations influencing pedestrian exposure.

Methods: We modeled the mean and variance of the pollutant concentrations obtained from MM in two cities and extended commonly used lognormal models with a lognormal-normal convolution (logNNC) extension for BC to account for instrument measurement error.

A new method to measure real-world respiratory tract deposition of inhaled ambient black carbon.

In this study, we present the development of a mobile system to measure real-world total respiratory tract deposition of inhaled ambient black carbon (BC). Such information can be used to supplement the existing knowledge on air pollution-related health effects, especially in the regions where the use of standard methods and intricate instrumentation is limited. The study is divided in two parts.