Assessing temporal correlation in environmental risk factors to design efficient area-specific COVID-19 regulations: Delhi based case study

• Published in: Scientific reports Vol. 12; no. 1; pp. 12949 - 16
• Main Authors: Chaudhary, Vishal, Bhadola, Pradeep, Kaushik, Ajeet, Khalid, Mohammad, Furukawa, Hidemitsu, Khosla, Ajit
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 28-07-2022; Nature Publishing Group; Nature Portfolio
• Subjects: 639/705; 692/699; 704/172; Air Pollutants - analysis; Air Pollution - analysis; Ammonia; Case studies; Cities; Communicable Disease Control; Coronaviruses; COVID-19; COVID-19 - epidemiology; Disease hot spots; Environmental conditions; Environmental Monitoring; Environmental risk; Epidemiology; Humanities and Social Sciences; Humans; India - epidemiology; Latency; multidisciplinary; Outbreaks; Pandemics; Particulate matter; Particulate Matter - analysis; Public health; Regression analysis; Relative humidity; Risk Factors; SARS-CoV-2; Science; Science (multidisciplinary); Severe acute respiratory syndrome coronavirus 2; Temporal variations; Transportation planning; Cities; India
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-022-16781-4

Amid ongoing devastation due to Serve-Acute-Respiratory-Coronavirus2 (SARS-CoV-2), the global spatial and temporal variation in the pandemic spread has strongly anticipated the requirement of designing area-specific preventive strategies based on geographic and meteorological state-of-affairs. Epidemiological and regression models have strongly projected particulate matter (PM) as leading environmental-risk factor for the COVID-19 outbreak. Understanding the role of secondary environmental-factors like ammonia (NH
3
) and relative humidity (RH), latency of missing data structuring, monotonous correlation remains obstacles to scheme conclusive outcomes. We mapped hotspots of airborne PM
2.5
, PM
10
, NH
3
, and RH concentrations, and COVID-19 cases and mortalities for January, 2021-July,2021 from combined data of 17 ground-monitoring stations across Delhi. Spearmen and Pearson coefficient correlation show strong association (p-value < 0.001) of COVID-19 cases and mortalities with PM
2.5
(r > 0.60) and PM
10
(r > 0.40), respectively. Interestingly, the COVID-19 spread shows significant dependence on RH (r > 0.5) and NH
3
(r = 0.4), anticipating their potential role in SARS-CoV-2 outbreak. We found systematic lockdown as a successful measure in combatting SARS-CoV-2 outbreak. These outcomes strongly demonstrate regional and temporal differences in COVID-19 severity with environmental-risk factors. The study lays the groundwork for designing and implementing regulatory strategies, and proper urban and transportation planning based on area-specific environmental conditions to control future infectious public health emergencies.