research-article

Real-Time Estimation of the Urban Air Quality with Mobile Sensor System

Authors: Yun Wang, Guojie Song, Lun Du, Zhicong LuAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 13, Issue 5

Article No.: 49, Pages 11 - 19

https://doi.org/10.1145/3356584

Published: 24 September 2019 Publication History

Abstract

Recently, real-time air quality estimation has attracted more and more attention from all over the world, which is close to our daily life. With the prevalence of mobile sensors, there is an emerging way to monitor the air quality with mobile sensors on vehicles. Compared with traditional expensive monitor stations, mobile sensors are cheaper and more abundant, but observations from these sensors have unstable spatial and temporal distributions, which results in the existing model could not work very well on this type of data. In this article, taking advantage of air quality data from mobile sensors, we propose an real-time urban air quality estimation method based on the Gaussian Process Regression for air pollution of the unmonitored areas, pivoting on the diffusion effect and the accumulation effect of air pollution. In order to meet the real-time demands, we propose a two-layer ensemble learning framework and a self-adaptivity mechanism to improve computational efficiency and adaptivity. We evaluate our model with real data from mobile sensor system located in Beijing, China. And the experiments show that our proposed model is superior to the state-of-the-art spatial regression methods in both precision and time performances.

References

[1]

Roberto Calandra, Jan Peters, Carl Edward Rasmussen, and Marc Peter Deisenroth. 2014. Manifold gaussian processes for regression. arXiv:1402.5876.

[2]

Leo Breiman. 1996. Bagging predictors. Machine Learning 24, 2 (1996), 123--140.

[3]

J. A. Brian, S. P. Tipper, B. Weaver, and S. E. Bryson. 2009. An Experimental Evaluation of ZCS-DM for the Prediction of Urban Air Quality. Springer, Berlin, 291--304.

[4]

Ling Chen, Yaya Cai, Yifang Ding, Mingqi Lv, Cuili Yuan, and Gencai Chen. 2016. Spatially fine-grained urban air quality estimation using ensemble semi-supervised learning and pruning. In ACM International Joint Conference. 1076--1087.

Digital Library

[5]

Yun Cheng, Xiucheng Li, Zhijun Li, Shouxu Jiang, and Xiaofan Jiang. 2014. Fine-grained air quality monitoring based on gaussian process regression. In International Conference on Neural Information Processing. 126--134.

[6]

Tapiwa M. Chiwewe and Jeofrey Ditsela. 2016. Machine learning based estimation of ozone using spatio-temporal data from air quality monitoring stations. In IEEE International Conference on Industrial Informatics (INDIN’16).

[7]

Giorgio Corani. 2005. Air quality prediction in Milan: Feed-forward neural networks, pruned neural networks and lazy learning. Ecological Modelling 185, 2 (2005), 513--529.

[8]

Aditya Grover, Ashish Kapoor, and Eric Horvitz. 2015. A deep hybrid model for weather forecasting. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.

Digital Library

[9]

Vitor Campanholo Guizilini and Fabio Tozeto Ramos. 2015. A nonparametric online model for air quality prediction. In 29th AAAI Conference on Artificial Intelligence (AAAI’15). 651--657.

[10]

David Hasenfratz. 2015. Enabling Large-Scale Urban Air Quality Monitoring with Mobile Sensor Nodes. Doctoral thesis. ETH-Zürich.

[11]

David Hasenfratz, Olga Saukh, Christoph Walser, Christoph Hueglin, Martin Fierz, Tabita Arn, Jan Beutel, and Lothar Thiele. 2015. Deriving high-resolution urban air pollution maps using mobile sensor nodes. Pervasive and Mobile Computing 16 (2015), 268--285.

Digital Library

[12]

G. Hoek, B. Brunekreef, P. Fischer, and J. Van-Wijnen. 2001. Association between Air pollution and heart failure, arrhythmia, embolism, thrombosis, and other cardiovascular causes of death in a time series study. Epidemiology 12, 3 (2001), 355.

[13]

Hsun Ping Hsieh, Shou De Lin, and Yu Zheng. 2015. Inferring Air quality for station location recommendation based on urban big data. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 437--446.

Digital Library

[14]

Arnaud Jutzeler,Jason Jingshi Li, B. Faltings. 2014. A region-based model for estimating urban air pollution. In National Conference on Artificial Intelligence.

[15]

Yuncheng Li, Jifei Huang, and Jiebo Luo. 2015. Using user generated online photos to estimate and monitor air pollution in major cities. In International Conference on Internet Multimedia Computing and Service. 79.

Digital Library

[16]

Yong Liu, Huaicheng Guo, Guozhu Mao, and Pingjian Yang. 2008. A Bayesian hierarchical model for urban air quality prediction under uncertainty. Atmospheric Environment 42, 36 (2008), 8464--8469.

[17]

Anastasia Moumtzidou, Symeon Papadopoulos, Stefanos Vrochidis, Ioannis Kompatsiaris, Konstantinos Kourtidis, George Hloupis, Ilias Stavrakas, Konstantina Papachristopoulou, and Christodoulos Keratidis. 2016. Towards Air Quality Estimation Using Collected Multimodal Environmental Data. Springer International Publishing.

[18]

Duy Nguyen-Tuong, Matthias Seeger, and Jan Peters. 2008. Local Gaussian process regression for real time online model learning.Advances in Neural Information Processing Systems 22, 2009 (2008), 1193--1200.

[19]

Inakwu O. A. Odeh, A. B. McBratney, and D. J. Chittleborough. 1995. Further results on prediction of soil properties from terrain attributes: Heterotopic cokriging and regression-kriging. Geoderma 67, 3 (1995), 215--226.

[20]

M. A. Oliver and R. Webster. 1990. Kriging: A method of interpolation for geographical information systems. International Journal of Geographical Information Science 4, 3 (1990), 313--332.

[21]

W. Pfender, R. Graw, W. Bradley, M. Carney, and L. Maxwell. 2006. Use of a complex air pollution model to estimate dispersal and deposition of grass stem rust urediniospores at landscape scale. Agricultural and Forest Meteorology 139, 1 (2006), 138--153.

[22]

Carl Edward Rasmussen. 2006. Gaussian Processes for Machine Learning. Springer.

Digital Library

[23]

Carl Edward Rasmussen and Hannes Nickisch. 2010. Gaussian processes for machine learning (GPML) toolbox. Journal of Machine Learning Research 11, 6 (2010), 3011--3015.

Digital Library

[24]

Ana Russo, Frank Raischel, and Pedro G. Lind. 2013. Air quality prediction using optimal neural networks with stochastic variables. Atmospheric Environment 79, 7 (2013), 822--830.

[25]

Edward Snelson and Zoubin Ghahramani. 2005. Sparse Gaussian processes using pseudo-inputs. In International Conference on Neural Information Processing Systems. 1257--1264.

[26]

Oliver Steinki and Ziad Mohammad. 2015. Introduction to Ensemble Learning. Social Science Electronic Publishing.

[27]

Bingsheng Wang and Jinjun Xiong. 2014. Novel geospatial interpolation analytics for general meteorological measurements. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1553--1562.

Digital Library

[28]

Andrew Gordon Wilson. 2014. Covariance Kernels for Fast Automatic Pattern Discovery and Extrapolation with Gaussian Processes. University of Cambridge.

[29]

Andrew Gordon Wilson and Ryan Prescott Adams. 2013. Gaussian process kernels for pattern discovery and extrapolation. Microtome Publishing 13, 4 (2013), 129--135.

[30]

Yun Xiang. 2014. Mobile Sensor Network Design and Optimization for Air Quality Monitoring. The University of Michigan.

[31]

Y. Zhang. 2008. Online-coupled meteorology and chemistry models: History, current status, and outlook. Atmospheric Chemistry and Physics 8, 11 (2008), 2895--2932.

[32]

Yang Zhang, Marc Bocquet, Vivien Mallet, Christian Seigneur, and Alexander Baklanov. 2012. Real-time air quality forecasting, part I: History, techniques, and current status. Atmospheric Environment 60, 32 (2012), 632--655.

[33]

Yu Zheng, Xiuwen Yi, Ming Li, Ruiyuan Li, Zhangqing Shan, Eric Chang, and Tianrui Li. 2015. Forecasting fine-grained air quality based on big data. In ACM SIGKDD International Conference. 2267--2276.

Digital Library

[34]

Julie Yixuan Zhu, Chenxi Sun, and Victor O. K. Li. 2015. Granger-causality-based air quality estimation with spatio-temporal (S-T) heterogeneous big data. In Computer Communications Workshops. 612--617.

Cited By

Choi HChoi JHwang JLee KLee DPark N(2023)Climate modeling with neural advection–diffusion equationKnowledge and Information Systems10.1007/s10115-023-01829-265:6(2403-2427)Online publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1007/s10115-023-01829-2
Krupp B(2022)Fine-Grained Air Quality Monitoring with Low-Cost Sensors and IoT: Trends, Challenges, and Future Directions2022 7th International Conference on Smart and Sustainable Technologies (SpliTech)10.23919/SpliTech55088.2022.9854310(1-6)Online publication date: 5-Jul-2022
https://doi.org/10.23919/SpliTech55088.2022.9854310
Hu KGuo XGong XHu YLin J(2022)Spatial-Temporal Air Quality Inference based on Matrix FactorizationICC 2022 - IEEE International Conference on Communications10.1109/ICC45855.2022.9839163(5092-5097)Online publication date: 16-May-2022
https://doi.org/10.1109/ICC45855.2022.9839163

Index Terms

Real-Time Estimation of the Urban Air Quality with Mobile Sensor System
1. Information systems
  1. Information systems applications
    1. Data mining

Recommendations

Improving network lifetime with mobile wireless sensor networks

Sensors are used to monitor and control the physical environment. In mobile sensor networks, nodes can self-propel via springs, wheels, or they can be attached to transporters, such as vehicles. Sensors have limited energy supply and the sensor network ...
A cell-based sensor deployment strategy with improved coverage for mobility-assisted hybrid wireless sensor networks

The deployment of wireless sensor devices is one of the most fundamental and important issues in wireless sensor network applications, and coverage is a chief consideration in deployment requirements. This paper proposes a hexagonal cell-based sensor ...
An energy-balanced swept-coverage mechanism for mobile WSNs

Coverage is one of the most important issues in Wireless Sensor Networks (WSNs). In literature, many coverage mechanisms have been proposed and employed mobile sensors to cover (heal) the coverage holes in the monitoring region. Consider that there are ...

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 13, Issue 5

October 2019

258 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3364623

Editors:
Charu Aggarwal
IBM T. J. Watson Research, USA
,
Xindong Wu
Minginglamp Academy of Sciences, China

Issue’s Table of Contents

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 September 2019

Accepted: 01 June 2019

Revised: 01 March 2019

Received: 01 April 2017

Published in TKDD Volume 13, Issue 5

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Funding Sources

National Natural Science Foundation of China

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
322
Total Downloads

Downloads (Last 12 months)26
Downloads (Last 6 weeks)0

Reflects downloads up to 26 Jul 2024

Other Metrics

View Author Metrics

Citations

Cited By

Choi HChoi JHwang JLee KLee DPark N(2023)Climate modeling with neural advection–diffusion equationKnowledge and Information Systems10.1007/s10115-023-01829-265:6(2403-2427)Online publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1007/s10115-023-01829-2
Krupp B(2022)Fine-Grained Air Quality Monitoring with Low-Cost Sensors and IoT: Trends, Challenges, and Future Directions2022 7th International Conference on Smart and Sustainable Technologies (SpliTech)10.23919/SpliTech55088.2022.9854310(1-6)Online publication date: 5-Jul-2022
https://doi.org/10.23919/SpliTech55088.2022.9854310
Hu KGuo XGong XHu YLin J(2022)Spatial-Temporal Air Quality Inference based on Matrix FactorizationICC 2022 - IEEE International Conference on Communications10.1109/ICC45855.2022.9839163(5092-5097)Online publication date: 16-May-2022
https://doi.org/10.1109/ICC45855.2022.9839163

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View Issue’s Table of Contents