key: cord-0936855-294d629e
authors: Mehmood, Khalid; Bao, Yansong; Abrar, Muhammad Mohsin; Petropoulos, George Panagiotis; Saifullah; Soban, Ahmad; Saud, Shah; Khan, Zalan Alam; Khan, Shah Masud; Fahad, Shah
title: Spatiotemporal variability of COVID-19 Pandemic in relation to Air pollution, Climate and Socioeconomic Factors in Pakistan
date: 2021-01-10
journal: Chemosphere
DOI: 10.1016/j.chemosphere.2021.129584
sha: 171eddd2bd863536c63a5aed74ed1dd88e8ce890
doc_id: 936855
cord_uid: 294d629e

Information on the spatiotemporal variability of respirable suspended particulate pollutant matter concentrations, especially of particles having size of 2.5μm 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 ((0)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 rational 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 R(2) 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.

Primary cases of pneumonia of unidentified sources were reported in Wuhan, Hubei Province, 34 China late December 2019. Researchers, however, later identified the Severe Acute Respiratory 19 pandemic in Pakistan. However, this study did not take into account the air pollution 121 especially respirable particle pollutant (PM 2.5 ), and socioeconomic factors include population 122 density which has a significant role in the spread of COVID-19. Looking at these factors, the 123 J o u r n a l P r e -p r o o f present study was planned to investigate the association among COVID-19, respirable suspended 124 particle pollutant (PM 2.5 ), meteorological, and socioeconomic factors.

The present study aims at investigating how the COVID-19 pandemic distributed in three Where "y" represents the response vector, and "y i " indicates ith element. The "b" is the random-168 effects vector. The "Distr" is a definite conditional distribution of y given b. The "μ" shows the 169 conditional mean of y given b, and μ i is its ith element. The "σ 2 " is the dispersion parameter and 170 "w" is the effective observation weight vector, while the "w i " is the weight for observation "i". In Where "i (j)" is the "j" the value in the "∆i" difference data series. " ξ " is called the

Spearman's correlation coefficient (rs) can be calculated using Equation 5:

where "di" denotes the difference between the levels of two parameters and "n" is the number of 189 alternatives.

Also, this study aims to analyze the population density to explore whether any correlation Where "Y" is the dependent variable that denotes the number of cases of COVID-19 X is the 199 independent variable that represents population density "B" is the constant. The GLM offers the dependent variable having non-normal distribution. The GLM results

suggested that COVID-19 cases have a significant relationship with PM 2.5 and climatic factors at 250 p < 0.05 against this hypothesis except for Lahore in the case of humidity (p = 0.175) ( Table 2) . has also been observed that the health danger would be greater for those particles with particle 295 sizes of 2.5 microns or less, which are generally stated to as PM 2.5 .

In the case of Islamabad, a moderate correlation existed for wind speed and humidity suggested The COVID-19 pandemic has produced an enormous health and economic burden over the and COVID-19 data were gathered at the province level.

The R 2 value indicates that the relationship between COVID-19 and population density was *. Correlation is significant at the 0.05 level (2-tailed). 

Relationship between air pollution, climate, and socioeconomic factors on COVID-19 cases 2. Modeling of COVID-19 with PM2.5, climatic, socioeconomic factors in provincial cities 3. Vulnerable PM 2.5 conc. identified for COVID-19

COVID-19 and climatic factors used to predict risky cities in 2021