Cairo University

MTPR Journal

 

ASTHMA EARLY WARNING SYSTEM IN NEW YORK CITY (AEWSNYC) USING REMOTE SENSING APPROACHES

& doi: https://doi.org/10.1142/9789814317511_0029
YASSER HASSEBO and ZAHIDUR RAHMAN
Mathematics Engineering Program Department, LaGuardia Community College of the City University of New York, USA


Vol./Issue: 11 , id: 332

Asthma is estimated to affect approximately 17.3 million Americans, including 5 million children less than 18 years of age. Of these 5 million children, 1.3 million are less than 5 years of age. Asthma is a major public health problem in NYC particularly in Bronx. 12.5% of new Yorkers have been diagnosed with asthma. 300,000 children in NYC have been diagnosed with asthma up to year of 2000. NYC children were almost twice as likely to be hospitalized due to asthma attacks as the average of US child in 2000. Queens county's diesel pollution risk ranks as the 10th unhealthiest in the US compared to over than 3000 counties. Asthma symptoms are consistent with exposure to a high level of a respiratory irritant gas, smoke fume, vapor, aerosol, particulate matter (PM10 and PM2.5), and dust. Some types these environmental gaseous such as sulfur dioxide (SO2), nitrogen dioxide (NO2), and ozone (O3) can exacerbate preexisting respiratory symptoms in the short-term. Control of air pollution related diseases such as asthma, cancer, and bronchitis is difficult and inefficient due to the uncertainty in the air pollution transportation. Asthma control relies on air pollution detection and reduction. Asthma control can be improved by applying spatial tools such as Remote Sensing (RS), Geographical Information Systems (GIS). The project long-term goal is to develop a model to predict an Asthma Early Warning System for NYC (AEWSNYC), using two approaches: (1) satellite data error correction collaboratively with (2) Ground-based multiwavelength lidar measurements and NASA back trajectory tools. The proposed method can be used to create an efficient asthma control model globally.