Increasing tree cover and high-albedo surfaces reduces heat-related ER visits in Los Angeles, CA.
Scott C SheridanEdith B de GuzmanDavid P EisenmanDavid J SailorJonathan ParfreyLaurence S KalksteinPublished in: International journal of biometeorology (2024)
There is an urgent need for strategies to reduce the negative impacts of a warming climate on human health. Cooling urban neighborhoods by planting trees and vegetation and increasing albedo of roofs, pavements, and walls can mitigate urban heat. We used synoptic climatology to examine how different tree cover and albedo scenarios would affect heat-related morbidity in Los Angeles, CA, USA, as measured by emergency room (ER) visits. We classified daily meteorological data for historical summer heat events into discrete air mass types. We analyzed those classifications against historical ER visit data to determine both heat-related and excess morbidity. We used the Weather Research and Forecasting model to examine the impacts of varied tree cover and albedo scenarios on meteorological outcomes and used these results with standardized morbidity data algorithms to estimate potential reductions in ER visits. We tested three urban modification scenarios of low, medium, and high increases of tree cover and albedo and compared these against baseline conditions. We found that avoiding 25% to 50% of ER visits during heat events would be a common outcome if the urban environment had more tree cover and higher albedo, with the greatest benefits occurring under heat events that are moderate and those that are particularly hot and dry. We conducted these analyses at the county level and compared results to a heat-vulnerable, working-class Los Angeles community with a high concentration of people of color, and found that reductions in the rate of ER visits would be even greater at the community level compared to the county.
Keyphrases
- heat stress
- climate change
- human health
- estrogen receptor
- endoplasmic reticulum
- healthcare
- breast cancer cells
- risk assessment
- mental health
- electronic health record
- machine learning
- big data
- air pollution
- type diabetes
- metabolic syndrome
- skeletal muscle
- artificial intelligence
- high intensity
- escherichia coli
- protein kinase
- candida albicans