RESUMEN
Addressing the challenge of mapping hyperlocal air pollution in areas without local monitoring, we evaluated unsupervised transfer learning-based land-use regression (LUR) models developed using mobile monitoring data from other cities: CORrelation ALignment (Coral) and its inverse distance-weighted modification (IDW_Coral). These models mitigated domain shifts and transferred patterns learned from mobile air quality monitoring campaigns in Copenhagen and Rotterdam to estimate annual average air pollution levels in Amsterdam (50m road segments) without involving any Amsterdam measurements in model development. For nitrogen dioxide (NO2), IDW_Coral outperformed Copenhagen and Rotterdam LUR models directly applied to Amsterdam, achieving MAE (4.47 µg/m3) and RMSE (5.36 µg/m3) comparable to a locally fitted LUR model (AMS_SLR) developed using Amsterdam mobile measurements collected for 160 days. IDW_Coral yielded an R2 of 0.35, similar to that of the AMS_SLR based on 20 collection days, suggesting a minimum requirement of 20-day mobile monitoring to capture city-specific insights. For ultrafine particles (UFP), IDW_Coral's citywide predictions strongly correlated with previously published mixed-effect models fitted with 160-day Amsterdam measurements (Pearson correlation of 0.71 for UFP and 0.72 for NO2). IDW_Coral demands no direct measurements in the target area, showcasing its potential for large-scale applications and offering significant economic efficiencies in executing mobile monitoring campaigns.
Asunto(s)
Contaminantes Atmosféricos , Contaminación del Aire , Monitoreo del Ambiente , Monitoreo del Ambiente/métodos , Material Particulado , Dióxido de Nitrógeno/análisis , CiudadesRESUMEN
Role of Ultra-fine Particles (UFPs) in causing adverse health effects among large population across the world, attributable to household smoke, is being increasingly recognized. However, there is very little theoretical perspective available on the complex behavior of the UFP metrics with respect to controlling factors, such as ventilation rate and particle emission rate from the combustion sources. This numerical study examines through coagulation dynamics, the dependence of UFP metrics, viz., number (PN), mass (PM(0.1)) and surface area (PA(0.1)) concentrations below 0.1 µm diameter, on ventilation and the number emission rate from household smoke. For strong sources, the steady-state concentrations of these metrics are found to increase initially with increasing Air Exchange Rate (AER), reach a peak value and then decrease. Counter correlations are seen between UFP metric and PM(2.5) concentrations. The concepts of Critical Air Exchange Rate (CAER) and Half-Value Air Exchange Rate (HaVAER) have been introduced which indicate a feasibility of mitigation of PM(0.1) and PA(0.1), unlike PN, by ventilation techniques. The study clearly brings forth complex differential behavior of the three UFP metrics. The results are further discussed.