Impact of Nitrogen Dioxide (NO2) Pollution on Asthma: The Case of Louisiana State (2005�20)

This study explores the connection between tropospheric nitrogen dioxide (NO2) vertical column density levels and asthma hospitalization cases in Louisiana from 2005 to 2020. Utilizing NO2 data from NASA抯 Ozone Measurement Instrument (OMI) aboard the Aura satellite, the research integrates these atmospheric measurements with socioeconomic data at the census tract level. This study employs a generalized linear mixed model (GLIMMIX) with a logit link and Beta distribution to analyze the relationship between seasonal NO2 levels and asthma hospitalization cases during winter, fall, spring, and summer. By analyzing OMI data, this research quantifies seasonal variations in NO2 levels and their corresponding impact on asthma hospitalizations. The findings reveal a relationship between NO2 levels and asthma hospitalizations, particularly in communities with high Black and/or low-income populations, with the strongest effects observed during winter. Specifically, the analysis shows that, for each unit increase in NO2 levels, the odds of asthma-related hospitalizations increase by approximately 26.3% (p < 0.0001), with a 95% confidence interval ranging from 23.3% to 29.5%. Assuming a causal link between NO2 and asthma, these findings suggest that reducing NO2 emissions could alleviate healthcare burdens associated with respiratory diseases such as asthma.

Publication Details

Journal
Maryland Shared Open Access Repository (USMAI Consortium)
Published
2024-12-10
DOI
https://doi.org/10.13016/m2ikl2-uign
Primary Topic
German History and Society
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Impact of Nitrogen Dioxide (NO2) Pollution on Asthma: The Case of Louisiana State (2005�20)

Maryland Shared Open Access Repository (USMAI Consortium)
German History and Society
article

Impact of Nitrogen Dioxide (NO2) Pollution on Asthma: The Case of Louisiana State (2005�20)

article en

Abstract

This study explores the connection between tropospheric nitrogen dioxide (NO2) vertical column density levels and asthma hospitalization cases in Louisiana from 2005 to 2020. Utilizing NO2 data from NASA抯 Ozone Measurement Instrument (OMI) aboard the Aura satellite, the research integrates these atmospheric measurements with socioeconomic data at the census tract level. This study employs a generalized linear mixed model (GLIMMIX) with a logit link and Beta distribution to analyze the relationship between seasonal NO2 levels and asthma hospitalization cases during winter, fall, spring, and summer. By analyzing OMI data, this research quantifies seasonal variations in NO2 levels and their corresponding impact on asthma hospitalizations. The findings reveal a relationship between NO2 levels and asthma hospitalizations, particularly in communities with high Black and/or low-income populations, with the strongest effects observed during winter. Specifically, the analysis shows that, for each unit increase in NO2 levels, the odds of asthma-related hospitalizations increase by approximately 26.3% (p < 0.0001), with a 95% confidence interval ranging from 23.3% to 29.5%. Assuming a causal link between NO2 and asthma, these findings suggest that reducing NO2 emissions could alleviate healthcare burdens associated with respiratory diseases such as asthma.

Maryland Shared Open Access Repository (USMAI Consortium)
University of Central Missouri, Louisiana Department of Health
No poverty
Openalex Percentile: Top 45%
German History and Society
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.