ABSTRACT
Reduced complexity tools that provide a representation of both primarily emitted particulate matter with an aerodynamic diameter less than 2.5 µm (PM2.5), secondarily formed PM2.5, and ozone (O3) allow for a quick assessment of many iterations of pollution control scenarios. Here, a new reduced complexity tool, Pattern Constructed Air Pollution Surfaces (PCAPS), that estimates annual average PM2.5 and seasonal average maximum daily average 8 h (MDA8) O3 for any source location in the United States is described and evaluated. Typically, reduced complexity tools are not evaluated for skill in predicting change in air pollution by comparison with more sophisticated modeling systems. Here, PCAPS was compared against multiple types of emission control scenarios predicted with state-of-the-science photochemical grid models to provide confidence that the model is realistically capturing the change in air pollution due to changing emissions. PCAPS was also applied with all anthropogenic emissions sources for multiple retrospective years to predict PM2.5 chemical components for comparison against routine surface measurements. PCAPS predicted similar magnitudes and regional variations in spatial gradients of measured chemical components of PM2.5. Model performance for capturing ambient measurements was consistent with other reduced complexity tools. PCAPS also did well at capturing the magnitude and spatial features of changes predicted by photochemical transport models for multiple emissions scenarios for both O3 and PM2.5. PCAPS is a flexible tool that provides source-receptor relationships using patterns of air quality gradients from a training data set of generic modeled sources to create interpolated air pollution gradients for new locations not part of the training database. The flexibility provided for both sources and receptors makes this tool ideal for integration into larger frameworks that provide emissions changes and need estimates of air quality to inform downstream analytics, which often includes an estimate of monetized health effects.
ABSTRACT
Climate change is increasing the frequency and severity of extreme heat events. These events affect cities in increasingly abrupt and catastrophic ways; yet, many of the deaths caused by exposure to heat have gone unnoticed or are inaccurately identified, resulting in a lack of urgency in addressing this issue. We aim to address this under-identification of deaths from heat waves in order to better assess heat risk. We investigated death records in New York City from 2010 to 2012 to identify characteristics that vary between deaths officially categorized as caused by heat wave exposure (oHDs) and those possibly caused by heat (pHDs). We found that oHDs were more often black and of a younger age than would typically be expected. We also found that there was a lack of evidence to substantiate that an oHD had occurred, using the NYC official criteria. We conclude that deaths from heat waves are not being accurately recorded, leading to a mis-estimation. Training regarding the collection and interpretation of evidence may improve preparedness for heat events.
