In California, wildfire risk and severity have grown substantially in the last several decades. Research has characterized extensive adverse health impacts from exposure to wildfire-attributable fine particulate matter (PM _2.5 ), but few studies have quantified long-term outcomes, and none have used a wildfire-specific chronic dose-response mortality coefficient. Here, we quantified the mortality burden for PM _2.5 exposure from California fires from 2008 to 2018 using Community Multiscale Air Quality modeling system wildland fire PM _2.5 estimates. We used a concentration-response function for PM _2.5 , applying ZIP code–level mortality data and an estimated wildfire-specific dose-response coefficient accounting for the likely toxicity of wildfire smoke. We estimate a total of 52,480 to 55,710 premature deaths are attributable to wildland fire PM _2.5 over the 11-year period with respect to two exposure scenarios, equating to an economic impact of $432 to $456 billion. These findings extend evidence on climate-related health impacts, suggesting that wildfires account for a greater mortality and economic burden than indicated by earlier studies.

Cooling centers have played a significant role in reducing the risks of adverse health impacts of extreme heat exposure. However, there have been no comparative studies investigating cooling center preparedness in terms of population coverage, location efficiency, and population coverage disparities among different subpopulation groups. Using a catchment area method with a 0.8 km walking distance, we compared three aspects of cooling center preparedness across twenty-five cities in the U.S. We first calculated the percentage of the population covered by a single cooling center for each city. Then, the extracted values were separately compared to the city’s heat indexes, latitudes, and spatial patterns of cooling centers. Finally, we investigated population coverage disparities among multiple demographics (age, race/ethnicity) and socioeconomic (insurance, poverty) subpopulation groups by comparing the percentage of population coverage between selected subpopulation groups and reference subpopulation groups. Our results showed that cooler cities, higher latitude cities, and cities with dispersed cooling centers tend to be more prepared than warmer cities, lower latitude cities, and cities with clustered cooling centers across the U.S. Moreover, older people (≥65) had 9% lower population coverage than younger people (≤64). Our results suggest that the placement of future cooling centers should consider both the location of other nearby cooling centers and the spatial distribution of subpopulations to maximize population coverage and reduce access disparities among several subpopulations.

Prescribed burning can mitigate extreme wildfire risk and reduce total smoke emissions. Yet prescribed burns’ emissions may also contribute to smoke exposures in nearby communities. Incorporating public health considerations into forest management planning efforts may help reduce prescribed burn-related exposure impacts. We present a methodological framework linking landscape ecology, air-quality modelling and health impact assessment to quantify the air-quality and health impacts of specific management strategies. We apply this framework to six forest management scenarios proposed for a landscape in the Central Sierra, California. We find that moderate amounts of prescribed burning can decrease wildfire-specific PM_2.5 exposures and reduce asthma-related health impacts in the surrounding region; however, the magnitude of that benefit levels off under scenarios with additional prescribed burning because of the added treatment-related smoke burdens. This framework can be applied to other fire-prone landscapes to incorporate public health considerations into forest management planning.

Background: Previous extreme heat and human health studies have investigated associations either over time (e.g. case-crossover or time series analysis) or across geographic areas (e.g. spatial models), which may limit the study scope and regional variation. Our study combines a case-crossover design and spatial analysis to identify: 1) the most vulnerable counties to extreme heat; and 2) demographic and socioeconomic variables that are most strongly and consistently related to heat-sensitive health outcomes (cardiovascular disease, dehydration, heat-related illness, acute renal disease, and respiratory disease) across 67 counties in the state of Florida, U. S over 2008–2012. Methods: We first used a case-crossover design to examine the effects of air temperature on daily counts of health outcomes. We employed a time-stratified design with a 28-day comparison window. Referent periods were extracted from ±7, ±14, or ± 21 days to address seasonality. The results are expressed as odds ratios, or the change in the likelihood of each health outcome for a unit change in heat exposure. We then spatially examined the case-crossover extreme heat and health odds ratios and county level demographic and socioeconomic variables with multiple linear regression or spatial lag models. Results: Results indicated that southwest Florida has the highest risks of cardiovascular disease, dehydration, acute renal disease, and respiratory disease. Results also suggested demographic and socioeconomic variables were significantly associated with the magnitude of heat-related health risk. The counties with larger populations working in farming, fishing, mining, forestry, construction, and extraction tended to have higher risks of dehydration and acute renal disease, whereas counties with larger populations working in installation, maintenance, and repair workers tended to have lower risks of cardiovascular, dehydration, acute renal disease, and respiratory disease. Finally, our results showed that high income counties consistently have lower health risks of dehydration, heat-related illness, acute renal disease, and respiratory disease. Conclusions: Our study identified different relationships with demographic/socioeconomic variables for each heat-sensitive health outcome. Results should be incorporated into vulnerability or risk indices for each health outcome.