Source identification with high-temporal resolution data from low-cost sensors using bivariate polar plots in urban areas of Ghana
Collins Gameli Hodoli, Frédéric Coulon, Mohammed Iqbal Mead
Abstract
The emergence of low-cost sensors for atmospheric observations presents a new opportunity for identifying atmospheric emission sources based on high-resolution data reporting. Low-cost sensors have been widely assessed for use in source monitoring and identification of hotspots of key atmospheric species in advanced countries (e.g., for CO, NOx, CO 2 , SO 2 , O 3 , VOCs and PM (PM 10 , PM 2.5 including emerging PM 1 ). In contrast, research in recent years has focused on their utility for real-time monitoring, understanding precision and associated calibration requirements in technologically lagging environments. This leads to limited evidence on the utility of high-resolution data from low-cost sensor networks for air pollution source identification in Ghana and more widely across the African continent. In this paper, we demonstrate the potential of low-cost sensors for emission source apportionment in urban areas of Ghana when used with analytical tools such as sectoral and cluster analysis.