Articles | Volume 17, issue 13
Atmos. Chem. Phys., 17, 8211–8230, 2017
https://doi.org/10.5194/acp-17-8211-2017
Atmos. Chem. Phys., 17, 8211–8230, 2017
https://doi.org/10.5194/acp-17-8211-2017

Research article 06 Jul 2017

Research article | 06 Jul 2017

Estimating daily surface NO2 concentrations from satellite data – a case study over Hong Kong using land use regression models

Jasdeep S. Anand and Paul S. Monks Jasdeep S. Anand and Paul S. Monks
  • Atmospheric Chemistry Group, Department of Chemistry, University of Leicester, University Road, Leicester, LE1 7RH, UK

Abstract. Land use regression (LUR) models have been used in epidemiology to determine the fine-scale spatial variation in air pollutants such as nitrogen dioxide (NO2) in cities and larger regions. However, they are often limited in their temporal resolution, which may potentially be rectified by employing the synoptic coverage provided by satellite measurements. In this work a mixed-effects LUR model is developed to model daily surface NO2 concentrations over the Hong Kong SAR during the period 2005–2015. In situ measurements from the Hong Kong Air Quality Monitoring Network, along with tropospheric vertical column density (VCD) data from the OMI, GOME-2A, and SCIAMACHY satellite instruments were combined with fine-scale land use parameters to provide the spatiotemporal information necessary to predict daily surface concentrations. Cross-validation with the in situ data shows that the mixed-effects LUR model using OMI data has a high predictive power (adj. R2 = 0. 84), especially when compared with surface concentrations derived using the MACC-II reanalysis model dataset (adj. R2 = 0. 11). Time series analysis shows no statistically significant trend in NO2 concentrations during 2005–2015, despite a reported decline in NOx emissions. This study demonstrates the utility in combining satellite data with LUR models to derive daily maps of ambient surface NO2 for use in exposure studies.

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Short summary
Previous investigations into Chinese urban air quality have been hampered by a lack of available data. In this work we present a new statistical modelling technique, in which sparse ground-based measurements of nitrogen dioxide (NO2) are combined with satellite data and other parameters (e.g. road networks) to create high-resolution maps of daily surface NO2 concentrations over Hong Kong. These maps can be used to estimate population exposure, and to identify at-risk groups.
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