Extreme urban heat is a powerful environmental stressor which poses a significant threat to human health and well-being. Exacerbated by the urban heat island phenomenon, heat events are expected to become more intense and frequent as climate change progresses, though we have limited understanding of the impact of such events on vulnerable populations at a neighborhood or census block group level. Focusing on the City of Portland, Oregon, this study aimed to determine which socio-demographic populations experience disproportionate exposure to extreme heat, as well as the level of access to refuge in the form of public cooling centers or residential central air conditioning. During a 2014 heat wave, temperature data were recorded using a vehicle-traverse collection method, then extrapolated to determine average temperature at the census block group level. Socio-demographic factors including income, race, education, age, and English speaking ability were tested using statistical assessments to identify significant relationships with heat exposure and access to refuge from extreme heat. Results indicate that groups with limited adaptive capacity, including those in poverty and non-white populations, are at higher risk for heat exposure, suggesting an emerging concern of environmental justice as it relates to climate change. The paper concludes by emphasizing the importance of cultural sensitivity and inclusion, in combination with effectively distributing cooling centers in areas where the greatest burden befalls vulnerable populations.

The emergence of urban heat as a climate-induced health stressor is receiving increasing attention among researchers, practitioners, and climate educators. However, the measurement of urban heat poses several challenges with current methods leveraging either ground based, in situ observations, or satellite-derived surface temperatures estimated from land use emissivity. While both techniques contain inherent advantages and biases to predicting temperatures, their integration may offer an opportunity to improve the spatial resolution and global application of urban heat measurements. Using a combination of ground-based measurements, machine learning techniques, and spatial analysis, we addressed three research questions: (1) How much do ambient temperatures vary across time and space in a metropolitan region? (2) To what extent can the integration of ground-based measurements and satellite imagery help to predict temperatures? (3) What landscape features consistently amplify and temper heat? We applied our analysis to the cities of Baltimore, Maryland, and Richmond, Virginia, and the District of Columbia using geocomputational machine learning processes on data collected on days when maximum air temperatures were above the 90th percentile of historic averages. Our results suggest that the urban microclimate was highly variable across all of the cities—with differences of up to 10 °C between coolest and warmest locations at the same time—and that these air temperatures were primarily dependent on underlying landscape features. Additionally, we found that integrating satellite data with ground-based measures provided highly accurate and precise descriptions of temperatures in all three study regions. These results suggest that accurately identifying areas of extreme urban heat hazards for any region is possible through integrating ground-based temperature and satellite data.

While there exists extensive assessment of urban heat, we observe myriad methods for describing thermal distribution, factors that mediate temperatures, and potential impacts on urban populations. In addition, the limited spatial and temporal resolution of satellite-derived heat measurements may limit the capacity of decision makers to take effective actions for reducing mortalities in vulnerable populations whose locations require highly-refined measurements. Needed are high resolution spatial and temporal information for urban heat. In this study, we ask three questions: (1) how do urban heat islands vary throughout the day? (2) what statistical methods best explain the presence of temperatures at sub-meter spatial scales; and (3) what landscape features help to explain variation in urban heat islands? Using vehicle-based temperature measurements at three periods of the day in the Pacific Northwest city of Portland, Oregon (USA), we incorporate LiDAR-derived datasets, and evaluate three statistical techniques for modeling and predicting variation in temperatures during a heat wave. Our results indicate that the random forest technique best predicts temperatures, and that the evening model best explains the variation in temperature. The results suggest that ground-based measurements provide high levels of accuracy for describing the distribution of urban heat, its temporal variation, and specific locations where targeted interventions with communities can reduce mortalities from heat events.

In this study, we examined the association between telemedicine use before a disaster and utilization of emergency or hospital services for ambulatory care sensitive conditions post-disaster. Difference-in-differences analyses were conducted in 2020‒2021 to assess pre- to post-fire changes in emergency or hospital utilization for 5 ambulatory care sensitive conditions: asthma, diabetes, hypertension, coronary artery disease, and heart failure across all Kaiser Permanente Santa Rosa patients (N=108,113) based on telemedicine utilization before the 2017 Tubbs wildfire. Inverse probability of treatment weighting was employed for cohort balancing across telemedicine familiar status. Utilization for any ambulatory care sensitive condition increased from 9.03% pre-fire to 9.45% post-fire across the full cohort. Telemedicine familiarity (ref: not familiar) was associated with decreased absolute risk in pre- to post-fire inpatient and emergency department utilization for 4 conditions: asthma (absolute risk= -1.59%, 95% CI= -2.02%, -1.16%), diabetes (absolute risk= -0.68%, 95% CI= -0.89%, -0.47%), hypertension (absolute risk= -2.07%, 95% CI= -2.44%, -1.71%), and coronary artery disease (absolute risk= -0.43%, 95% CI= -0.61%, -0.24%). Telemedicine familiarity was associated with decreased relative change in pre- to post-fire utilization for 5 conditions: asthma (RRR=0.70, 95% CI=0.64, 0.75), diabetes (RRR=0.54, 95% CI=0.47, 0.63), hypertension (RRR=0.57, 95% CI=0.52, 0.62), heart failure (RRR=0.64, 95% CI=0.50, 0.82), and coronary artery disease (RRR=0.56, 95% CI=0.47, 0.67). Similar results were seen among patients residing in evacuation zones. Telemedicine familiarity pre-fire was associated with decreased inpatient and emergency department utilization for certain ambulatory care sensitive conditions for 1-year post-fire. These results suggest a role for telemedicine in preventing unnecessary emergency and hospital utilization following disasters.

Reducing exposure to degraded air quality is essential for building healthy cities. Although air quality and population vary at fine spatial scales, current regulatory and public health frameworks assess human exposures using county- or city-scales. We build on a spatial analysis technique, dasymetric mapping, for allocating urban populations that, together with emerging fine-scale measurements of air pollution, addresses three objectives: (1) evaluate the role of spatial scale in estimating exposure; (2) identify urban communities that are disproportionately burdened by poor air quality; and (3) estimate reduction in mobile sources of pollutants due to local tree-planting efforts using nitrogen dioxide. Our results show a maximum value of 197% difference between cadastrally-informed dasymetric system (CIDS) and standard estimations of population exposure to degraded air quality for small spatial extent analyses, and a lack of substantial difference for large spatial extent analyses. These results provide the foundation for improving policies for managing air quality, and targeting mitigation efforts to address challenges of environmental justice.

This study investigated environmental justice by developing and comparing four contemporary urban segregation typologies (persistent exclusivity, persistent disadvantage, recent gentrification, and recent displacement-hosting) with historical Homeowners’ Loan Corporation (HOLC) zones and 104 variables spanning the natural and built environments, pollution, environmental hazards, health, and disability. We applied principal component analysis to the 2010 and 2020 American Community Survey and the US Census data to derive neighborhood typology indices for 342 tracts in Portland, Oregon. Using Spearman’s correlation and end-member comparisons, we assessed relationships among the typologies, HOLC zones, and environmental and health outcomes. HOLC zones predicted persistently exclusive areas in the urban core but not current injustices elsewhere. Persistently exclusive neighborhoods, both within and outside HOLC zones, had more tree canopy, less dense development, lower disaster risks, better preventive care, and fewer diseases than other areas, but more air pollution exposure. Recently gentrified areas had more transit and community resources but worse heat exposure, pollution, and impervious surface coverage and aligned strongly with HOLC C and D zones. Recent displacement-hosting neighborhoods were cleaner and greener but lack transit and community resources, while exhibiting weak health outcomes. Persistently disadvantaged areas, often outside HOLC zones, faced the most severe conditions, including minimal access to nature, aging infrastructure, the highest pollution and hazard exposure, and the worst health outcomes and disability rates. Our study offers insights into the spatial mechanisms of urban inequities and well-being, underscoring the need to look beyond redlining to fully understand and address today’s social and environmental injustices.