Evaluation of Model-Based PM Estimates for Exposure Assessment during Wildfire Smoke Episodes in the Western U.S.

Investigating the health impacts of wildfire smoke requires data on people's exposure to fine particulate matter (PM) across space and time. In recent years, it has become common to use machine learning models to fill gaps in monitoring data. However, it remains unclear how well these models are able to capture spikes in PM during and across wildfire events.

Investigating Use of Low-Cost Sensors to Increase Accuracy and Equity of Real-Time Air Quality Information.

U.S. Environmental Protection Agency (EPA) air quality (AQ) monitors, the "gold standard" for measuring air pollutants, are sparsely positioned across the U.

Daily PM concentration estimates by county, ZIP code, and census tract in 11 western states 2008-2018.

We created daily concentration estimates for fine particulate matter (PM) at the centroids of each county, ZIP code, and census tract across the western US, from 2008-2018. These estimates are predictions from ensemble machine learning models trained on 24-hour PM measurements from monitoring station data across 11 states in the western US. Predictor variables were derived from satellite, land cover, chemical transport model (just for the 2008-2016 model), and meteorological data.

The Prevalence of Noonan Spectrum Disorders in Pediatric Patients with Pulmonary Valve Stenosis.

Objective: To investigate the prevalence of Noonan spectrum disorders in a pediatric population with pulmonary valve stenosis (PVS) and explore other characteristics of Noonan spectrum disorders associated with PVS.

Study Design: A retrospective medical record review was completed for patients with a diagnosis of PVS seen at the Children's Hospital Colorado Cardiology clinic between 2009 and 2019. Syndromic diagnoses, genotypes, cardiac characteristics, and extracardiac characteristics associated with Noonan spectrum disorders were recorded; statistical analysis was conducted using R.

Improving accuracy of air pollution exposure measurements: Statistical correction of a municipal low-cost airborne particulate matter sensor network.

Low-cost air quality sensors can help increase spatial and temporal resolution of air pollution exposure measurements. These sensors, however, most often produce data of lower accuracy than higher-end instruments. In this study, we investigated linear and random forest models to correct PM measurements from the Denver Department of Public Health and Environment (DDPHE)'s network of low-cost sensors against measurements from co-located U.

Associations between respiratory health and ozone and fine particulate matter during a wildfire event.

Wildfires have been increasing in frequency in the western United States (US) with the 2017 and 2018 fire seasons experiencing some of the worst wildfires in terms of suppression costs and air pollution that the western US has seen. Although growing evidence suggests respiratory exacerbations from elevated fine particulate matter (PM) during wildfires, significantly less is known about the impacts on human health of ozone (O) that may also be increased due to wildfires. Using machine learning, we created daily surface concentration maps for PM and O during an intense wildfire in California in 2008.