Urban Air Quality Modeling Using Low-Cost Sensor Network and Data Assimilation in the Aburra Valley, Colombia

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Santiago Lopez-Restrepo , Andres Yarce , Nicolas Pinel , O. Lucia Quintero , Arjo Segers , Arnold W. Heemink 


The use of low air quality networks has been increasing in recent years to study urban pollution dynamics. Here we show the evaluation of the operational Aburra Valley's low-cost network against the official monitoring network. The results show that the PM2.5 low-cost measurements are very close to those observed by the official network. Additionally, the low-cost allows a higher spatial representation of the concentrations across the valley. We integrate low-cost observations with the chemical transport model LOTOS-EUROS using data assimilation. Two different configurations of the low-cost network were assimilated: using the whole low-cost network (255 sensors), and a high-quality using just the sensors with a correlation factor greater than 0.8 with respect to the official network (115 sensors). The official stations were also assimilated to compare the more dense low-cost network's impact on the model performance. Both simulations assimilating the low-cost model outperform the model without assimilation and assimilating the official network. The model capability to predict high concentration events' warnings is also improved by assimilating the low-cost network with respect to the other simulations. Finally, the simulation using the high-quality configuration has lower error values than using the complete low-cost network, showing that it is essential to consider the quality and location and not just the total number of sensors. Our results suggest that with the current advance in low-cost sensors, it is possible to improve model performance with low-cost network data assimilation.




Applied Mathematics, Atmospheric Sciences, Environmental Monitoring


Citizen Scientists, low-cost network, Chemistry Transport Model, Data assimilation, Particulate Matter, low-cost network Chemistry Transport Model, Particulate Matter Citizen Scientists, low-cost sensor network, Chemistry Transport Model, Particulate Matter


Published: 2020-10-29 12:12

Last Updated: 2020-10-30 10:43

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