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

Vishal Chaudhary; Pradeep Bhadola; Ajeet Kaushik; Mohammad Khalid; Hidemitsu Furukawa; Ajit Khosla

doi:10.1038/s41598-022-16781-4

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

Sci Rep. 2022 Jul 28;12(1):12949. doi: 10.1038/s41598-022-16781-4.

Authors

Vishal Chaudhary¹, Pradeep Bhadola², Ajeet Kaushik^{3

4}, Mohammad Khalid^{5

6}, Hidemitsu Furukawa⁷, Ajit Khosla⁸

Affiliations

¹ Research Cell and Department of Physics, Bhagini Nivedita College, University of Delhi, New Delhi, 110043, India. chaudhary00vishal@gmail.com.
² Centre for Theoretical Physics and Natural Philosophy, Nakhonsawan Studiorum for Advanced Studies, Mahidol University, Nakhonsawan, 60130, Thailand. bhadola.pradeep@gmail.com.
³ NanoBioTech Laboratory, Health System Engineering, Department of Environmental Engineering, Florida Polytechnic University, Lakeland, FL, 33805, USA.
⁴ School of Engineering, University of Petroleum and Energy Studies (UPES) , Dehradun, Uttarakhand, India.
⁵ Graphene and Advanced 2D Materials Research Group (GAMRG), School of Engineering and Technology, Sunway University, No. 5, Jalan University, Bandar Sunway, 47500, Petaling Jaya, Selangor, Malaysia.
⁶ Sunway Materials Smart Science & Engineering (SMS2E) Research Cluster, Sunway University, No. 5, Jalan Universiti, Bandar Sunway, 47500, Petaling Jaya, Selangor, Malaysia.
⁷ Department of Mechanical Systems Engineering, Graduate School of Science and Engineering, Yamagata University, Yonezawa, Yamagata, 992-8510, Japan.
⁸ School of Advanced Materials and Nanotechnology, Xidian University, Xi'an, 710126, People's Republic of China. ajitkhosla@xidian.edu.cn.

Abstract

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 (NH₃) 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, PM₁₀, NH₃, 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 PM₁₀ (r > 0.40), respectively. Interestingly, the COVID-19 spread shows significant dependence on RH (r > 0.5) and NH₃ (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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Air Pollutants* / analysis
Air Pollution* / analysis
COVID-19* / epidemiology
Cities
Communicable Disease Control
Environmental Monitoring
Humans
India / epidemiology
Particulate Matter / analysis
Risk Factors
SARS-CoV-2

Substances

Air Pollutants
Particulate Matter