Articles | Volume 17, issue 6
https://doi.org/10.5194/amt-17-1651-2024
https://doi.org/10.5194/amt-17-1651-2024
Research article
 | 
19 Mar 2024
Research article |  | 19 Mar 2024

Hybrid instrument network optimization for air quality monitoring

Nishant Ajnoti, Hemant Gehlot, and Sachchida Nand Tripathi

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Cited articles

Ajnoti, N., Gehlot, H., and Tripathi, S. N.: Hybrid instrument network optimization for air quality monitoring, Version v1, Zenodo [code], https://doi.org/10.5281/zenodo.10795963, 2024. 
Araki, S., Iwahashi, K., Shimadera, H., Yamamoto, K., and Kondo, A.: Optimization of air monitoring networks using chemical transport model and search algorithm, Atmos. Environ., 122, 22–30, https://doi.org/10.1016/j.atmosenv.2015.09.030, 2015. 
Brienza, S., Galli, A., Anastasi, G., and Bruschi, P.: A Low-Cost Sensing System for Cooperative Air Quality Monitoring in Urban Areas, Sensors, 15, 12242–12259, https://doi.org/10.3390/s150612242, 2015. 
Castell, N., Dauge, F. R., Schneider, P., Vogt, M., Lerner, U., Fishbain, B., Broday, D., and Bartonova, A.: Can commercial low-cost sensor platforms contribute to air quality monitoring and exposure estimates?, Environ. Int., 99, 293–302, https://doi.org/10.1016/j.envint.2016.12.007, 2017. 
Cormen, T. H., Leiserson, C. E., Rivest, R. L., and Stein, C.: Introduction to algorithms, MIT press, ISBN 9780262046305, 2022. 
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Short summary
This research focuses on the optimal placement of hybrid instruments (sensors and monitors) to maximize satisfaction function considering population, PM2.5 concentration, budget, and other factors. Two algorithms are developed in this study: a genetic algorithm and a greedy algorithm. We tested these algorithms on various regions. The insights of this work aid in quantitative placement of air quality monitoring instruments in large cities, moving away from ad hoc approaches.