Influences of Season and Geography on Urban Air Quality Monitoring Stations (AQMS) Representativeness Areas by High Resolution Air Quality Monitoring
Authors: Hohenberger Tilman Leo 
Published: March 2020

&lt;p&gt;Urban air pollution remains a key pressure on public health. With the megatrend of urbanization and its forcing on emissions and exposure, effective monitoring tools in cities are at the center of prevention efforts.&lt;/p&gt;&lt;p&gt;Air Quality Monitoring Stations (AQMS) are traditionally used for regulatory efforts and, increasingly, as publicly available information sources. Facing high levels of air pollution heterogeneity in complex urban environments, a simple spatial approach is often misleading when choosing an AQMS that represents local street-level conditions the best. Model-based calculation of representativeness areas are rare for the urban scale (e.g. Rodriguez et al., 2019), and suffer from short model times, low model correlations and a lack of external validation by observation data. Moreover, as both health impacts and air-pollution episodes are influenced by environmental factors, the sensitivity of representativeness areas to wind impacts and during different seasons are a further point of interest not covered well by previous literature.&lt;/p&gt;&lt;p&gt;For the high-density environment of geographically complex Hong Kong, we used a full year (2019) of high-resolution air quality modelling (ADMS-Urban) data to establish representativeness areas for the territory&amp;#8217;s 16 AQMS. We constructed representativeness areas for key air-pollutants for the full period and based on season and wind speed. We parameterized the effects of wind and geography on the size and shape of the representativeness areas. Furthermore, we validated our findings by a series of week-long outdoor measurements aimed to cover the whole territory of Hong Kong.&lt;/p&gt;&lt;p&gt;Our results show that Hong Kong&amp;#8217;s AQMS network covering the territory well for a PM&lt;sub&gt;2.5&lt;/sub&gt;, PM&lt;sub&gt;10&lt;/sub&gt; and O&lt;sub&gt;3&lt;/sub&gt;, where the mean CSF (hourly Concentration Similarity Frequency with a target of &amp;#177;20%) of each grid-cell to the best matching AQMS lies at around 60%. Both NO&lt;sub&gt;2&lt;/sub&gt; and SO&lt;sub&gt;2&lt;/sub&gt; are less well represented, with a CSF of around 30%. Moreover, we show that representativeness areas calculated from similarity-based metrices as CSF and percentage difference represent the impact of geographical features on pollution dispersion better than correlation-based metrices (R&lt;sup&gt;2&lt;/sup&gt; and ioa). It was further found that AQMS represent upwind areas better than downwind areas, especially in areas exposed to open wind-flow, and that the represented areas change strongly over the course of a year.&lt;/p&gt;&lt;p&gt;In this study, we showcase the ability of high-resolution urban air-pollution modelling to guide the public with information on AQMS representativeness. Furthermore, we report that representativeness areas are non-static, changing with seasons and under the influence of wind. High-resolution urban modelling can further be used to gauge the quality of AQMS networks and assess the need and location of additions to an existing network.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;Rodriguez, D., Valari, M., Payan, S., &amp; Eymard, L. (2019). On the spatial representativeness of NOX and PM10 monitoring-sites in Paris, France. Atmospheric Environment: X, 1, 100010.&lt;/p&gt;