The Association between Exposure to Fine Particulate Air Pollution and the Trajectory of Internalizing and Externalizing Behaviors during Late Childhood and Early Adolescence: Evidence from the Adolescent Brain Cognitive Development (ABCD) Study.

Background: Exposure to high levels of fine particulate matter (PM) with aerodynamic diameter () via air pollution may be a risk factor for psychiatric disorders during adulthood. Yet few studies have examined associations between exposure and the trajectory of symptoms across late childhood and early adolescence.

Objective: The current study evaluated whether exposure at 9-11 y of age affects both concurrent symptoms as well as the longitudinal trajectory of internalizing and externalizing behaviors across the following 3 y.

Do environmental attitudes and personal characteristics influence how people perceive their exposure to green spaces?

Background: This study explores the relationship between perceived and objective greenspace exposure, and how sociodemographic traits and environmental attitudes influence peoples' perceptions of greenspace.

Methods: We leveraged a cross-sectional survey on greenspace exposure among residents of Denver, CO that ran from November 2019 through April 2021. We measured objective greenspace using the average NDVI (normalized difference vegetation index), average percent vegetation, and median GVI (green view index) within 300, 500, and 1,000 m of participants' residences, and in participant-drawn polygons representing their neighborhoods.

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.

Perceptions of green space usage, abundance, and quality of green space were associated with better mental health during the COVID-19 pandemic among residents of Denver.

Background: The COVID-19 pandemic has impacted both physical and mental health. This study aimed to understand whether exposure to green space buffered against stress and distress during the COVID-19 pandemic while taking into account significant stressors of the pandemic.

Methods: We leveraged a cross-sectional survey on green space exposure and mental health among residents of Denver, CO that ran from November 2019 through January 2021.

Socio-demographic and health vulnerability in prescribed-burn exposed versus unexposed counties near the National Forest System.

Prescribed fire is an increasingly important tool in restoring ecological conditions and reducing uncontrolled wildfire. Prescribed burn techniques could reduce public health impacts associated with wildfire smoke exposure. However, there have been few assessments of the health impacts of prescribed burning, and potential vulnerabilities among populations exposed to smoke from prescribed fires.

Green infrastructure can limit but not solve air pollution injustice.

Outdoor air pollution contributes to millions of deaths worldwide yet air pollution has differential exposures across racial/ethnic groups and socioeconomic status. While green infrastructure has the potential to decrease air pollution and provide other benefits to human health, vegetation alone cannot resolve health disparities related to air pollution injustice. We discuss how unequal access to green infrastructure can limit air quality improvements for marginalized communities and provide strategies to move forward.

Hospital Preparedness, Mitigation, and Response to Hurricane Harvey in Harris County, Texas.

Objective: This case study documents Harris County hospitals' flood preparedness and mitigation efforts before Hurricane Harvey, their collective response experience during Hurricane Harvey, and their lessons learned in the storm's aftermath.

Methods: The case study was constructed using a survey of hospital emergency managers, semi-structured interviews with local agencies involved in public health, emergency management, and health care, and an analysis of news reports and other documents from a variety of government agencies, local organizations, and hospitals themselves.

Results: Harris County hospitals learned their most valuable lessons through their direct and repeated experience with flooding over the years, leading to improved preparedness before Hurricane Harvey.

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.

Extreme Weather and Climate Change: Population Health and Health System Implications.

Extreme weather and climate events, such as heat waves, cyclones, and floods, are an expression of climate variability. These events and events influenced by climate change, such as wildfires, continue to cause significant human morbidity and mortality and adversely affect mental health and well-being. Although adverse health impacts from extreme events declined over the past few decades, climate change and more people moving into harm's way could alter this trend.

Wildland firefighter exposure to smoke and COVID-19: A new risk on the fire line.

Throughout the United States, wildland firefighters respond to wildfires, performing arduous work in remote locations. Wildfire incidents can be an ideal environment for the transmission of infectious diseases, particularly for wildland firefighters who congregate in work and living settings. In this review, we examine how exposure to wildfire smoke can contribute to an increased likelihood of SARS-CoV-2 infection and severity of coronavirus disease (COVID-19).

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.

Hurricane Harvey Hospital Flood Impacts: Accuracy of Federal Emergency Management Agency Flood Hazard Areas in Harris County, Texas.

To compare the flood impacts experienced by Harris County, Texas, hospitals with Federal Emergency Management Agency (FEMA) flood hazard areas and Hurricane Harvey's inundation boundary. One year following Hurricane Harvey, we created a novel data set of Hurricane Harvey's flood impacts in Harris County hospitals. We then mapped the hospital flood impact data in ArcGIS alongside FEMA flood hazard areas and Hurricane Harvey's inundation boundary to classify each hospital's location in high flood-risk areas and in areas purportedly affected by Hurricane Harvey.

Putting Co-Exposures on Equal Footing: An Ecological Analysis of Same-Scale Measures of Air Pollution and Social Factors on Cardiovascular Disease in New York City.

Epidemiologic evidence consistently links urban air pollution exposures to health, even after adjustment for potential spatial confounding by socioeconomic position (SEP), given concerns that air pollution sources may be clustered in and around lower-SEP communities. SEP, however, is often measured with less spatial and temporal resolution than are air pollution exposures (i.e.

Machine learning models accurately predict ozone exposure during wildfire events.

Epidemiologists use prediction models to downscale (i.e., interpolate) air pollution exposure where monitoring data is insufficient.

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.

Wildfire smoke exposure under climate change: impact on respiratory health of affected communities.

Purpose Of Review: In this review, we describe the current status of the literature regarding respiratory health related to wildfire smoke exposure, anticipated future impacts under a changing climate, and strategies to reduce respiratory health impacts of wildfire smoke.

Recent Findings: Recent findings confirm associations between wildfire smoke exposure and respiratory health outcomes, with the clearest evidence for exacerbations of asthma. Although previous evidence showed a clear association between wildfire smoke and chronic obstructive pulmonary disease, findings from recent studies are more mixed.

It's not easy assessing greenness: A comparison of NDVI datasets and neighborhood types and their associations with self-rated health in New York City.

Growing evidence suggests that exposure to greenness benefits health, but studies assess greenness differently. We hypothesize greenness-health associations vary by exposure assessment method. To test this, we considered four vegetation datasets (three Normalized Difference Vegetation Index datasets with different spatial resolutions and a finely-resolved land cover dataset), and six aggregation units (five radial buffer sizes and self-described neighborhoods) of each dataset.

Is All Urban Green Space the Same? A Comparison of the Health Benefits of Trees and Grass in New York City.

Living near vegetation, often called "green space" or "greenness", has been associated with numerous health benefits. We hypothesized that the two key components of urban vegetation, trees and grass, may differentially affect health. We estimated the association between near-residence trees, grass, and total vegetation (from the 2010 High Resolution Land Cover dataset for New York City (NYC)) with self-reported health from a survey of NYC adults (n = 1281).

Differential respiratory health effects from the 2008 northern California wildfires: A spatiotemporal approach.

We investigated health effects associated with fine particulate matter during a long-lived, large wildfire complex in northern California in the summer of 2008. We estimated exposure to PM2.5 for each day using an exposure prediction model created through data-adaptive machine learning methods from a large set of spatiotemporal data sets.

Critical Review of Health Impacts of Wildfire Smoke Exposure.

Background: Wildfire activity is predicted to increase in many parts of the world due to changes in temperature and precipitation patterns from global climate change. Wildfire smoke contains numerous hazardous air pollutants and many studies have documented population health effects from this exposure.

Objectives: We aimed to assess the evidence of health effects from exposure to wildfire smoke and to identify susceptible populations.

Temperature deviation index and elderly mortality in Japan.

Few studies have examined how the precedence of abnormal temperatures in previous neighboring years affects the population's health. In the present study, we attempted to quantify the health effects of abnormal weather patterns by creating a metric called the temperature deviation index (TDI) and estimated the effects of TDI on mortality in Japan. We used data from 47 prefectures in Japan to compute the TDI on days between May and September from 1966 to 2010.

Spatiotemporal prediction of fine particulate matter during the 2008 northern California wildfires using machine learning.

Estimating population exposure to particulate matter during wildfires can be difficult because of insufficient monitoring data to capture the spatiotemporal variability of smoke plumes. Chemical transport models (CTMs) and satellite retrievals provide spatiotemporal data that may be useful in predicting PM2.5 during wildfires.