Environmental risk factor assessment for major respiratory disorders in metropolitan cities of India using VIIRS Suomi Aerosol data and Google Trends.
Diptarshi MitraShiva Reddy KotiPrabhakar Alok VermaSameer SaranPublished in: Environmental sustainability (Singapore) (2021)
This study has investigated the association between the amount of atmospheric aerosols and the occurrences of Asthma, Chronic Obstructive Pulmonary Disease (COPD) and Lung Cancer in Delhi, Mumbai, Chennai, Kolkata and Bengaluru. Aerosol Optical Thickness (AOT) data of Visible Infrared Imaging Radiometer Suite (VIIRS) and Google Trends (GT) have been used to acquire information regarding the abundance of atmospheric aerosols and the occurrences of the respiratory diseases respectively. The result of Granger causality test between AOT and GT has shown that Delhi, Mumbai and Chennai were quite vulnerable to the three respiratory diseases whereas Bengaluru did not display so much vulnerability to these ailments. Kolkata was not so much vulnerable to Asthma but did exhibit susceptibility to the other two diseases. GT is validated by correlating with Annual Morbidity data of Delhi. The result of Granger causality test between Particulate Matter (diameter ≤ 10 μm) (PM 10 ) data and GT validates the result of Granger causality between AOT and GT, and shows the trustworthiness of GT and AOT. Thus, this study also proves the usefulness of VIIRS AOT and GT as dependable sources of information on atmospheric aerosols and prevalence of the respiratory diseases respectively, and the effectiveness of Granger causality test as a tool of analysis in health and geographic information systems (GIS).
Keyphrases
- particulate matter
- chronic obstructive pulmonary disease
- air pollution
- lung function
- electronic health record
- water soluble
- big data
- adverse drug
- health information
- high resolution
- public health
- randomized controlled trial
- mental health
- respiratory tract
- systematic review
- emergency department
- photodynamic therapy
- drinking water
- social media
- cystic fibrosis
- human health
- deep learning
- mass spectrometry
- antibiotic resistance genes