ASSESSING POPULATION SENSITIVITY TO URBAN AIR POLLUTION USING GOOGLE TRENDS AND REMOTE SENSING DATASETS

Abstract. This study demonstrates relationship between remote sensing satellite retrieved fien aerosol concentration and web-based search volumes of air quality related keywords. People’s perception of urban air pollution can verify policy effectiveness and gauge acceptability of policies. As a serious health issue in Asian cities, population may express concern or uncertainty for air pollution risk by performing search on the web to seek answers. A ‘social sensing’ approach that monitors such search queries, may assess people’ perception about air pollution as a risk. We hypothesize that trend and volume of searches show impact of air pollution on general population. The objectives of this research are to identify those atmospheric conditions under which relative search volume (RSV) obtained from Google Trends shows correlation with measured fine aerosol concentration, and to compare search volume sensitivity to rise in aerosol concentration in seven Asian megacities. We considered weekly relative search volumes from Google Trends (GT) for a four year period from January, 2015 to December, 2018 representing diverse PM_2.5 concentrations. Search volumes for keywords corresponding to perception of air quality (‘air pollution’) and health effects (‘cough’ and ‘asthma’) were considered. To represent PM_2.5 we used fine aerosol indicator developed in an earlier research. The results suggest that tendency to search for ‘air pollution’ and ‘cough’ occurs when AirRGB R is in excess and temperature is below the baseline values. Consistent with this, in cities with high baseline concentrations, sensitivity to rise in AirRGB R is also comparatively lower. The result of this study can used as an indirect measure of awareness in the form of perception and sensitivity of population to air quality. Such an analysis could be useful for forecasting health risks specially in cities lacking dedicated services.