Land use regression (LUR) models are widely used in epidemiological and environmental
studies to estimate humans' exposure to air pollution within urban areas. However, the early …

The present paper reviews the conceptual framework and development of the Bayesian
Maximum Entropy (BME) approach. BME has been considered as a significant breakthrough …

Background: There is increasing evidence that long-term exposure to fine particulate matter
[PM≤ 2.5 μ m in aerodynamic diameter (PM 2.5)] may adversely impact cognitive …

Background: Recent studies suggest that exposure to traffic-related air pollutants, including
particulate matter (PM), is associated with autism spectrum disorder (autism). Methods …

Exposure to wildfire smoke causes adverse health outcomes, suggesting the importance of
accurately estimating smoke concentrations. Geostatistical methods can combine observed …

China experiences severe particulate matter (PM) pollution problems closely linked to its
rapid economic growth. Advancing the understanding and characterization of …

Merging multisensor aerosol optical depth (AOD) products is an effective way to produce
more spatiotemporally complete and accurate AOD products. A spatiotemporal statistical …

Internet of Things (IoT) enables the seamless integration of sensors, actuators, and
communication devices for real-time applications. IoT systems require good quality sensor …

Knowledge of particulate matter concentrations< 2.5 μm in diameter (PM2. 5) across the
United States is limited due to sparse monitoring across space and time. Epidemiological …

This research develops a framework for integrated energy and exposure to ambient
pollution (iEnEx) assessment addressing energy, health, and equity as an interconnected …