Abstract
Recently, air pollution caused by particulate matter that the diameter is less than or equal to 2.5 μg/m3 has become an important issue. It is so tiny that it can go through alveolar microvascular and enter our body. PM2.5 makes a significant impact on human health. Therefore, monitoring and forecasting the air quality is an indispensable task for human society. Nowadays, we can easily acquire Air Quality Indices (AQIs) by installing a small-scale air quality sensor or downloading from some freely authorized databases. However, people demand farther PM2.5 information to plan their route. This research aims to forecast PM2.5 value in the future hours. Previous studies indicated that the air quality varies nonlinearly in urban areas and depends on several factors such as temperature, humidity and wind speed. Therefore, we combine air quality data from AirBox and meteorology data to forecast PM2.5 value. Air quality is a continuous data. If monitored air quality is good at the last time stamp, the next monitored air quality has high possibility to be good at the same location. And air quality may have some regular in the history data. We forecast PM2.5 values via the algorithm similar to weighted average method. It can figure out the time intervals with similar weather condition. Finally, the error is calculated to examine the accuracy of our method. In contrast to a famous method, Pearson’s Correlation Coefficient, our method preforms well and stable with farther forecast.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bouarar, I., et al.: Monitoring and forecasting air quality over china: results from the PANDA modeling system. In: IGAC 2016 Science Conference (International Global Atmospheric Chemistry) (2016)
Domańska, D., Łukasik, S.: Handling high-dimensional data in air pollution forecasting tasks. Ecol. Inform. 34, 70–91 (2016)
Hsieh, H.P., Lin, S.D., Zheng, Y.: Inferring air quality for station location recommendation based on urban big data. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 437–446 (2015)
Lu, X., Wang, Y., Huang, L., Yang, W., Shen, Y.: Temporal-spatial aggregated urban air quality inference with heterogeneous big data. In: Yang, Q., Yu, W., Challal, Y. (eds.) WASA 2016. LNCS, vol. 9798, pp. 414–426. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-42836-9_37
Zheng, Y., et al.: Forecasting fine-grained air quality based on big data. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2267–2276 (2015)
Zhu, J.Y., et al.: pg-causality: identifying spatiotemporal causal pathways for air pollutants with urban big data. IEEE Trans. Big Data (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Lu, E.HC., Liu, CY. (2019). A Temporal Approach for Air Quality Forecast. In: Nguyen, N., Gaol, F., Hong, TP., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2019. Lecture Notes in Computer Science(), vol 11432. Springer, Cham. https://doi.org/10.1007/978-3-030-14802-7_12
Download citation
DOI: https://doi.org/10.1007/978-3-030-14802-7_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-14801-0
Online ISBN: 978-3-030-14802-7
eBook Packages: Computer ScienceComputer Science (R0)