Advertisement

A Temporal Approach for Air Quality Forecast

  • Eric Hsueh-Chan LuEmail author
  • Chia-Yu Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11432)

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.

Keywords

Air quality forecast City dynamics AirBox 

References

  1. 1.
    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)Google Scholar
  2. 2.
    Domańska, D., Łukasik, S.: Handling high-dimensional data in air pollution forecasting tasks. Ecol. Inform. 34, 70–91 (2016)CrossRefGoogle Scholar
  3. 3.
    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)Google Scholar
  4. 4.
    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_37CrossRefGoogle Scholar
  5. 5.
    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)Google Scholar
  6. 6.
    Zhu, J.Y., et al.: pg-causality: identifying spatiotemporal causal pathways for air pollutants with urban big data. IEEE Trans. Big Data (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of GeomaticsNational Cheng Kung UniversityTainan CityTaiwan (R.O.C.)

Personalised recommendations