Wednesday, 10 January 2018
Exhibit Hall 3 (ACC) (Austin, Texas)
Meteorological station data provide accurate point measurements of heat exposure, but temporally and spatially contiguous fields are needed to estimate human exposure for health studies, especially in dense urban areas. In New York City, a unique highly spatially resolved ground level ambient air temperature dataset has been collected through the New York City Community Air Survey (NYCCAS), a neighborhood level air pollution monitoring network. We compared daily minimum and maximum temperatures from NYCCAS against the new 1-km air temperature product that was downscaled from ~12-km North American Land Data Assimilation System (NLDAS) air temperature data. The study focuses on minimum temperatures since the maximum temperatures generated from NYCCAS may be influenced by the lack of standardized shading. Elevated overnight temperatures have been found to be closely associated with adverse health effects. Downscaling was performed using 1-km Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature data to capture and impose the spatial temperature pattern on the 12-km NLDAS air temperatures. Overall, the model compares very well with ground station data showing better performance with lower temperatures, in less densely urbanized areas, and in areas with higher vegetative cover. Specifically, the overnight low temperature correlation coefficients were 0.91 and 0.92, and the mean absolute errors were 2.02 °F (1.12 °C) and 4.18 °F (2.32 °C) for the years 2009 and 2010, respectively. Compared with the 12-km coverage the higher spatial resolution of this 1-km dataset is necessary for applications in high-density urban situations with heterogeneous land cover. In the context of increasing extreme heat health risks in cities this dataset will help improve the effectiveness of public health decision making and messaging.
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