Long-term Association between NO2 and Human Mobility: A Two-year Spatiotemporal Study during the COVID-19 Pandemic in Southeast Asia

Since the COVID-19 pandemic, governments have implemented lockdowns and movement restrictions to contain the disease outbreak. Previous studies have reported a significant positive correlation between NO2 and mobility level during the lockdowns in early 2020. Though NO2 level and mobility exhibited similar spatial distribution, our initial exploration indicated that the decreased mobility level did not always result in concurrent decreasing NO2 level during a two-year time period in Southeast Asia with human movement data at a very high spatial resolution (i.e., Facebook origin-destination data). It indicated that factors other than mobility level contributed to NO2 level decline. Our subsequent analysis used a trained Multi-Layer Perceptron model to assess mobility and other contributing factors (e.g., travel modes, temperature, wind speed) and predicted future NO2 levels in Southeast Asia. The model results suggest that, while as expected mobility has a strong impact on NO2 level, a more accurate prediction requires considering different travel modes (i.e., driving and walking). Mobility shows two-sided impacts on NO2 level: mobility above the average level has a high impact on NO2, whereas mobility at a relatively low level shows negligible impact. The results also suggest that spatio-temporal heterogeneity and temperature also have impacts on NO2 and they should be incorporated to facilitate a more comprehensive understanding of the association between NO2 and mobility in the future study.

The Impact of COVID-19 on NO2 and PM2.5 Levels and Their Associations with Human Mobility Patterns in Singapore

The decline in NO2 and PM2.5 pollutant levels were observed during COVID-19 around the world, especially during lockdowns. Previous studies explained such observed decline with the decrease in human mobility, whilst overlooking the meteorological changes (e.g., rainfall, wind speed) that could mediate air pollution level simultaneously. This pitfall could potentially lead to over-or under-estimation of the effect of COVID-19 on air pollution. Consequently, this study aims to re-evaluate the impact of COVID-19 on NO2 and PM2.5 pollutant level in Singapore, by incorporating the effect of meteorological parameters in predicting NO2 and PM2.5 baseline in 2020 using machine learning methods. The results found that NO2 and PM2.5 declined by a maximum of 38% and 36%, respectively, during lockdown period. As two proxies for change in human mobility, taxi availability and carpark availability were found to increase and decrease by a maximum of 12.6% and 9.8%, respectively, in 2020 from 2019 during lockdown. To investigate how human mobility influenced air pollutant level, two correlation analyses were conducted: one between PM2.5 and carpark availability changes at regional scale and the other between NO2 and taxi availability changes at a spatial resolution of 0.01{degrees}. The NO2 variation was found to be more associated with the change in human mobility, with the correlation coefficients vary spatially across Singapore. A cluster of stronger correlations were found in the South and East Coast of Singapore. Contrarily, PM2.5 and carpark availability had a weak correlation, which could be due to the limit of regional analyses. Drawing to the wider context, the high association between human mobility and NO2 in the South and East Coast area can provide insights into future NO2 reduction policy in Singapore. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/21267165v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@1e0ea3aorg.highwire.dtl.DTLVardef@131be31org.highwire.dtl.DTLVardef@bda881org.highwire.dtl.DTLVardef@181dec5_HPS_FORMAT_FIGEXP M_FIG C_FIG