Combining Dispersion Modeling and Monitoring Data for Community-Scale Air Quality Characterization

Isakov, Vlad; Arunachalam, Saravanan; Baldauf, Richard; Breen, Michael S.; Deshmukh, Parikshit; Hawkins, Andy; Kimbrough, Sue; Krabbe, Stephen; Naess, Brian; Serre, Marc L.; Valencia, Alejandro

doi:10.3390/atmos10100610

Cited by 9 publications

(8 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Jointly with previous work [9,12,14,[45][46][47], our results can support and motivate the development of future low-cost networks and their integration in data fusion applications. According to the literature, North America, Europe, and China harbor most of the current low-cost implementations, with experimental, citizen, and data dissemination purposes [8,48].…”

Section: Discussionsupporting

confidence: 81%

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

et al. 2021

View full text Add to dashboard Cite

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 Aburrá 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 Long Term Ozone Simulation-European Operational Smog (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 selection 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 capability to issue warnings for pollution events 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.

show abstract

Section: Discussionsupporting

confidence: 81%

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

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Jointly with previous work (Johnston, Basford, Bulot, Apetroaie-Cristea, Easton, Davenport, Foster, Loxham, Morris and Cox, 2019;Popoola et al, 2018;Isakov, Arunachalam, Baldauf, Breen, Deshmukh, Hawkins, Kimbrough, Krabbe, Naess, Serre and Valencia, 2019;Ahangar et al, 2019;Schneider et al, 2017;Moltchanov, Levy, Etzion, Lerner, Broday and Fishbain, 2015), our results can support and motivate the development of future low-cost networks and their integration in data fusion applications. According to the literature, North America, Europe, and China concentrate most of the current low-cost implementations, with experimental, citizen, and data dissemination purposes (Kumar and Gurjar, 2019;Morawska, Thai, Liu, Asumadu-Sakyi, Ayoko, Bartonova, Bedini, Chai, Christensen, Dunbabin, Gao, Hagler, Jayaratne, Kumar, Lau, Louie, Mazaheri, Ning, Motta, Mullins, Rahman, Ristovski, Shafiei, Tjondronegoro, Westerdahl and Williams, 2018).…”

Section: Discussion and Commentssupporting

confidence: 87%

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

Lopez-Restrepo¹,

Botero²,

Pinel³

et al. 2020

Preprint

View full text Add to dashboard Cite

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 Aburrá Valley's low-cost network against the official monitoring network. The results show that the PM 2.5 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.

show abstract

“…Jointly with previous work [15,18,25,[37][38][39], our results can support and motivate the development of future low-cost networks and their integration in data fusion applications. According to the literature, North America, Europe, and China concentrate most of the current low-cost implementations, with experimental, citizen, and data dissemination purposes [14,40].…”

Section: Discussionsupporting

confidence: 83%

Data Assimilation as a Tool to Improve Chemical Transport Models Performance in Developing Countries

Restrepo

Botero

Montoya³

et al. 2021

Environmental Sustainability - Preparing for Tomorrow

View full text Add to dashboard Cite

Particulate matter (PM) is one of the most problematic pollutants in urban air. The effects of PM on human health, associated especially with PM of ≤2.5μm in diameter, include asthma, lung cancer and cardiovascular disease. Consequently, major urban centers commonly monitor PM2.5 as part of their air quality management strategies. The Chemical Transport models allow for a permanent monitoring and prediction of pollutant behavior for all the regions of interest, different to the sensor network where the concentration is just available in specific points. In this chapter a data assimilation system for the LOTOS-EUROS chemical transport model has been implemented to improve the simulation and forecast of Particulate Matter in a densely populated urban valley of the tropical Andes. The Aburrá Valley in Colombia was used as a case study, given data availability and current environmental issues related to population expansion. Using different experiments and observations sources, we shown how the Data Assimilation can improve the model representation of pollutants.

show abstract

Combining Dispersion Modeling and Monitoring Data for Community-Scale Air Quality Characterization

Cited by 9 publications

References 30 publications

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

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

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

Data Assimilation as a Tool to Improve Chemical Transport Models Performance in Developing Countries

Contact Info

Product

Resources

About