Air pollution and mobility patterns in two Ugandan cities during COVID-19 mobility restrictions suggest the validity of air quality data as a measure for human mobility

Abstract

We explored the viability of using air quality as an alternative to aggregated location data from mobile phones in the two most populated cities in Uganda. We accessed air quality and Google mobility data collected from 15^th February 2020 to 10^th June 2021 and augmented them with mobility restrictions implemented during the COVID-19 lockdown. We determined whether air quality data depicted similar patterns to mobility data before, during, and after the lockdown and determined associations between air quality and mobility by computing Pearson correlation coefficients ([Formula: see text]), conducting multivariable regression with associated confidence intervals (CIs), and visualized the relationships using scatter plots. Residential mobility increased with the stringency of restrictions while both non-residential mobility and air pollution decreased with the stringency of restrictions. In Kampala, PM_2.5 was positively correlated with non-residential mobility and negatively correlated with residential mobility. Only correlations between PM_2.5 and movement in work and residential places were statistically significant in Wakiso. After controlling for stringency in restrictions, air quality in Kampala was independently correlated with movement in retail and recreation (- 0.55; 95% CI = - 1.01- - 0.10), parks (0.29; 95% CI = 0.03-0.54), transit stations (0.29; 95% CI = 0.16-0.42), work (- 0.25; 95% CI = - 0.43- - 0.08), and residential places (- 1.02; 95% CI = - 1.4- - 0.64). For Wakiso, only the correlation between air quality and residential mobility was statistically significant (- 0.99; 95% CI = - 1.34- - 0.65). These findings suggest that air quality is linked to mobility and thus could be used by public health programs in monitoring movement patterns and the spread of infectious diseases without compromising on individuals' privacy.

Keywords: Air quality; COVID-19; Human mobility; Infectious diseases; Particulate matter; Transmission.

Fig. 1

Overall ambient air quality temporal variation for Kampala and Wakiso districts

Fig. 2

Change in mobility patterns in Kampala and Wakiso districts relative to the baseline

Fig. 3

Correlation between changes in mobility (relative to the baseline) in Kampala and Wakiso districts and government response stringency index. The baseline is the median value, for the corresponding day of the week, during the five-week period 3rd January 2020–6th February 2020. Associated Pearson correlation coefficients ($R$) and a significance code for the $p$ value ($p$) are shown

Fig. 4

Correlation between fine particulate matter (PM_2.5) in Kampala and Wakiso and government response stringency index. Associated Pearson correlation coefficients ($R$) and a significance code for the $p$ value ($p$) are shown

Fig. 5

Correlation between changes in mobility (relative to the baseline) and fine particulate matter (PM_2.5) in Kampala district. The baseline is the median value, for the corresponding day of the week, during the five-week period 3rd January 2020–6th February 2020. Scatter plots, associated Pearson correlation coefficients ($R$) and $p$ values ($p$) are shown

Fig. 6

Correlation between changes in mobility (relative to the baseline) and fine particulate matter (PM_2.5) in Wakiso district. The baseline is the median value, for the corresponding day of the week, during the five-week period 3rd January 2020–6th February 2020. Scatter plots, associated Pearson correlation coefficients ($R$) and $p$ values ($p$) are shown