Spatiotemporal variability of COVID-19 pandemic in relation to air pollution, climate and socioeconomic factors in Pakistan

Khalid Mehmood; Yansong Bao; Muhammad Mohsin Abrar; George P Petropoulos; Saifullah; Ahmad Soban; Shah Saud; Zalan Alam Khan; Shah Masud Khan; Shah Fahad

doi:10.1016/j.chemosphere.2021.129584

Spatiotemporal variability of COVID-19 pandemic in relation to air pollution, climate and socioeconomic factors in Pakistan

Chemosphere. 2021 May:271:129584. doi: 10.1016/j.chemosphere.2021.129584. Epub 2021 Jan 10.

Authors

Affiliations

¹ School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China.
² Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, CMA Key Laboratory for Aerosol-Cloud-Precipitation, Nanjing University of Information Science & Technology, Nanjing 210044, China; School of Atmospheric Physics, Nanjing University of Information Science & Technology, Nanjing 210044, China. Electronic address: ysbao@nuist.edu.cn.
³ National Engineering Laboratory for Improving Quality of Arable Land, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
⁴ Department of Geography, Harokopio University of Athens, El. Venizelou 70, Kallithea, 17671, Athens, Greece.
⁵ Department of Environmental Health, College of Public Health, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia.
⁶ Software Engineering Department Balochistan University of Information Technology, Engineering and Management Sciences (BUITEMS), Pakistan.
⁷ Department of Horticulture, Northeast Agriculture University, Harbin, China.
⁸ Department of Civil Engineering, COMSATS University, Abbotabad, 22010, Pakistan.
⁹ Department of Horticulture, The University of Haripur, Haripur, 22620, Pakistan.
¹⁰ Hainan Key Laboratory for Sustainable Utilization of Tropical Bioresource, College of Tropical Crops,Hainan University, Haikou, 570228, China; Department of Agronomy, The University of Haripur, Khyber Pakhtunkhwa, 21120, Pakistan. Electronic address: shah_fahad80@yahoo.com.

Abstract

Information on the spatiotemporal variability of respirable suspended particulate pollutant matter concentrations, especially of particles having size of 2.5 μm and climate are the important factors in relation to emerging COVID-19 cases around the world. This study aims at examining the association between COVID-19 cases, air pollution, climatic and socioeconomic factors using geospatial techniques in three provincial capital cities and the federal capital city of Pakistan. A series of relevant data was acquired from 3 out of 4 provinces of Pakistan (Punjab, Sindh, Khyber Pakhtunkhwa (KPK) including the daily numbers of COVID-19 cases, PM_2.5 concentration (μgm^-3), a climatic factors including temperature (°F), wind speed (m/s), humidity (%), dew point (%), and pressure (Hg) from June 1 2020, to July 31 2020. Further, the possible relationships between population density and COVID-19 cases was determined. The generalized linear model (GLM) was employed to quantify the effect of PM_2.5, temperature, dew point, humidity, wind speed, and pressure range on the daily COVID-19 cases. The grey relational analysis (GRA) was also implemented to examine the changes in COVID-19 cases with PM_2.5 concentrations for the provincial city Lahore. About 1,92, 819 COVID-19 cases were reported in Punjab, Sindh, KPK, and Islamabad during the study period. Results indicated a significant relationship between COVID-19 cases and PM_2.5 and climatic factors at p < 0.05 except for Lahore in case of humidity (r = 0.175). However, mixed correlations existed across Lahore, Karachi, Peshawar, and Islamabad. The R² value indicates a moderate relationship between COVID-19 and population density. Findings of this study, although are preliminary, offers the first line of evidence for epidemiologists and may assist the local community to expedient for the growth of effective COVID-19 infection and health risk management guidelines. This remains to be seen.

Keywords: COVID-19; Climate factors; Correlation; GLM model; GRA; Geoinformation; PM(2.5).

MeSH terms

Air Pollutants* / analysis
Air Pollution* / analysis
COVID-19*
Cities
Humans
Pakistan / epidemiology
Pandemics
Particulate Matter / analysis
SARS-CoV-2
Socioeconomic Factors

Substances

Air Pollutants
Particulate Matter