Improved Hyperlocal Air Quality Mapping: Can Traditional Deterministic Modelling Leverage Mobile Monitoring via Hybrid Modelling Approaches?

BACKGROUND AND AIM: Recent advances in mobile monitoring offer excellent opportunities to explore the hyperlocal variation of ambient air pollution. One example is Google Street View (GSV) car equipped with high-quality instruments. Here, some challenges are translating on-road pollution levels to the building façades and the scalability of such mapping at large spatial scales, e.g. national scale. Since "traditional” deterministic modelling has been a reliable method for pollution assessment and mapping, this talk aims to explore whether deterministic modelling, via hybrid modelling, can help address the challenges mentioned above and leverage mobile monitoring for improved pollution mapping. METHODS: Three GSV cars measured hyperlocal levels of nitrogen dioxide (NO2), black carbon (BC) and Ultrafine particles (UFP) on all streets of Amsterdam (N = 46664) and Copenhagen (N = 28499) from October 2018 to March 2020. The measurements were corrected and, among others, compared with pollution estimates from national prediction models, the Danish DEHM-UBM-AirGIS, and the Dutch NSL (National Collaborative Air Quality Programme). Further, model estimates are incorporated with GSV measurements to test and apply hybrid modelling approaches using statistical (e.g. kriging) and machine learning techniques. RESULTS: Overall, Amsterdam's measured pollution levels were relatively higher (e.g. median NO2 = 24 µg/m3) than in Copenhagen (median NO2 = 13 µg/m3). In addition, GSV NO2 measurements correlated moderately (Spearman’s r = 0.50) (N = 7004) with the NSL estimates in Amsterdam, whereas in Copenhagen, the Spearman’s correlation (r) was in the range 0.45 – 0.67 (N = 97 and 58234). CONCLUSIONS: High-quality mobile monitoring offers a great way to study the hyperlocal variation of air pollution. Since the modelled vs measured correlation was moderate to slightly high, combining both datasets may better predict external data. The presentation will reflect on hybrid model development. KEYWORDS: Mobile measurements, Google Street View, deterministic modelling, AirGIS, hybrid model