Impact of Urbanization on Temperature Variation in Big Cities: Measuring Health Risk While Targeting Vulnerable Population

Densely populated cities are experiencing Urban Heat Island (UHI) effects and localized hotspots. Cities, such as New York can form heat islands all year round. This is primarily due to land surface modifications, radiative trapping in urban canyons and lack of cooling through evapotranspiration caused by displaced trees and vegetation. UHI refers to an increase in air and surface temperature in cities compared to surrounding suburban and rural areas. Large scale environmental forcing can cause subdivisions of UHI throughout a city. The combined of environmental forcing effects lead to the formation of hot pockets within the cities at micro-scale. The adverse effect of UHI in highly dense populated cities ends in a higher number of emergency hospital admissions and heat-related illnesses. Studying UHI phenomenon and temperature variations within cities becomes even more important when global Earth temperature is on the rise. To better understand UHI within Manhattan Island in New York, an exploratory study was done using a three-month field campaign to measure high resolution (3m above the ground) spatial and temporal temperature variations within Manhattan\u27s urban setting. A street-level air temperature and humidity dataset with high resolution spatial and temporal components were created for the island of Manhattan, suitable for use by the urban health and modeling communities. It consists of a set of pedestrian measurements over the course of two summers converted into anomaly maps, and a set of ten light-post mounted installations measuring air temperature, relative humidity, and illumination at three-minute intervals over three months. These high time resolution temperature measurements and three months of the ‘model weather analysis data’ output of temperature and relative humidity were used to predict temperature variability from weather forecasts. This study shows that regression of weather variables can predict the amplitude of spatial and temporal variation in temperature within a city for different days. The amplitude of spatial variations was dependent on temperature and low-level lapse rate. Temporal variations were dependent on temperature, low level and mid-level lapse rates. This study puts the attention toward high resolution near surface air temperature analysis and offers a new look at surface thermal properties to find the impact effect of weather model data on air surface temperature. The application of this study is most suitable for forecast modelers who are looking to study the impact of weather and micro-scale climate on surface air temperature using weather variables. To further complete this study by looking at the impact of UHI on human health; a quantitative study was completed analyzing satellite imagery of the five boroughs of New York City (NYC). The influence of different surface types on mitigating UHI effect is investigated by looking at consistent physical properties of the urban system through a framework to highlight environmental and social vulnerabilities. The factors of interest include people, the environment, building and infrastructure. The satellite study revealed that increased levels of urbanization, with no methods of heat mitigation, resulted in higher average temperatures. Results show, neighborhoods of Manhattan, Queens and the Bronx are at the greatest risk of vulnerability and should be targeted for policy changes, implementation of green infrastructures and vegetation coverage to counteract the heating effects. Neighborhoods which need to be prioritized for urban planning due to high environmental risk in NYC include Harlem, Upper Manhattan, East Harlem, Elmhurst, Jamaica, Ridgewood, Bedford, University height and Woodlawn