NOX and PM10 Bayesian concentration estimates using high-resolution numerical simulations and ground measurements over Paris, France

Air quality over cities is mainly monitored by in-situ surface measurements. However, these stations are too sparse to properly capture the inhomogeneity of pollutant concentrations over urban areas. The need for high-resolution concentration estimate has grown in recent years, together with the awareness of the harmful effects of air pollution. In this study, we develop a Bayesian scheme that combines the high-resolution (3 × 3 m2) Particulate Micro SWIFT SPRAY numerical air quality simulations (PMSS) with operational surface measurements. The goal is to improve NOX and PM10 PMSS concentrations estimates over monitoring stations and within their vicinity. For this purpose, we simulate pollutant concentrations over the city of Paris for ten days over the period of March 2016. The Bayesian model provides an enhanced estimate of pollutant concentration in space and time. At the monitoring stations location, these estimates are characterized by lower temporal dispersion compared to the simulated data. Within the vicinity of the monitor stations, enhanced concentration estimates are closer to observations. For NOX, the improvement is stronger and occurs in a larger area for urban background stations than for traffic stations. Overall, NOX improvement is higher than PM10 improvement. The initial PMSS model prediction is more biased for NOX than for PM10 due to large uncertainties in NOX emissions over the traffic network. Keywords: Urban air pollution, Surface monitoring networks, High-resolution modeling, Spatial representativeness, Bayesian modeling

Abstract licence: CC BY-NC-ND

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