Near Real Time Modeling of Weather, Air Pollution, and Health Outcome Indicators in New York CityEPA Grant Number: R833623
Title: Near Real Time Modeling of Weather, Air Pollution, and Health Outcome Indicators in New York City
Investigators: Ito, Kazuhiko , Matte, Thomas , Nadas, Arthur , Thurston, George D.
Institution: New York University School of Medicine , New York City Department of Health and Mental Hygiene
EPA Project Officer: Nolt-Helms, Cynthia
Project Period: December 1, 2007 through November 30, 2010 (Extended to November 30, 2011)
Project Amount: $494,552
RFA: Development of Environmental Health Outcome Indicators (2006) RFA Text | Recipients Lists
Research Category: Health Effects , Health
We will develop models to predict acute respiratory morbidity (including asthma exacerbation) using near-real time weather, ambient air pollution, and respiratory emergency department (ED) visits in New York City (NYC). We will take advantage of a unique syndromic surveillance system that monitors ED visits daily by the NYCDOHMH. We will systematically characterize the sequence of events among weather conditions, air pollution buildup, and health effects indicators. Using sub-area analysis, we will also determine spatial and neighborhood/socio-economic factors that influence the prediction power and efficiency of the models. We will estimate model uncertainties by computing prediction errors of candidate models in a series of real-time validation tests. Overall, this proposed project will create a framework to model, in near real time, acute health outcome indicators of environmental exposures in a large metropolitan area.
Our initial model development involves analyses of past weather, air pollution data, and respiratory and asthma ED visits. The main air pollutants of interest are particulate matter less than 2.5 μm (PM2.5) and ozone, but other gaseous air pollutants (carbon monoxide, sulfur dioxide, and nitrogen dioxide) will also be considered. Continuous hourly-average PM2.5 and gaseous pollutants data are available for multiple sampling locations in NYC, allowing modeling of spatial variation in air pollution and morbidity. While past models in air pollution epidemiology focused on conservatively estimating risks for air pollutants while adjusting for weather, day-of-week, and temporal trends treated as independent variables, we will instead explore alternative models that consider weather-air pollution interactions and characterize the sequence of events that predict adverse respiratory system effects. We will also test the incorporation of the existing air pollution episode prediction (forecasting) models into our health effects models. Our aim is to develop health effects models that are useful for predictions of air pollution health impact days based on real-time environmental and health data, and, ultimately, for use in public health impact prevention programs. We will also examine the influence of special events (e.g., Asian dust storms, Canadian forest fires) to evaluate how they impact health and the model predictions. Back-trajectory analysis will be conducted to identify/confirm specific source impacts. Temporal relationships across health outcomes (e.g., ED visits and mortality) in the past data will also be examined to test models’ applicability for avertive/preventive measures. We will develop candidate alternative models in the first half of the project based on past data, and then independently test our models on a prospective real-time basis in the 2nd half of the project. Thus, we will be able to conduct real-world tests of alternative predictive models, and thereby characterize model uncertainty associated with these models.
The proposed project will identify temporal structure of associations between weather, air pollution and their adverse acute effects. The prediction models to be developed will be effective tools to: (1) measure health impacts of weather and air pollution; (2) help detection of unusual events (e.g., bio-terrorism); and, (3) provide real-time means to predict and reduce health risks in response to developing meteorological and air pollution exposures. The existing air pollution epidemiological models can only estimate risks years after data have been collected. The proposed project will develop a novel framework for real-time modeling of acute effects from weather and air pollution concentrations. Such models will be directly useful for risk management and prevention.