Journal ArticleDOI
Fine-Grained Air Pollution Inference with Mobile Sensing Systems: A Weather-Related Deep Autoencoder Model
Rui Ma,Ning Liu,Xiangxiang Xu,Yue Wang,Hae Young Noh,Pei Zhang,Lin Zhang +6 more
- Vol. 4, Iss: 2, pp 1-21
TLDR
This work proposes a deep autoencoder framework based inference algorithm that better captures the spatiotemporal dependencies in the pollution map with unevenly distributed samples than other real-time approaches, and presents a weather-related ConvLSTM to enable quasi real- time applications.Abstract:
Air pollution is a global health threat. Except static official air quality stations, mobile sensing systems are deployed for urban air pollution monitoring to achieve larger sensing coverage and greater sampling granularity. However, the data sparsity and irregularity also bring great challenges for pollution map recovery. To address these problems, we propose a deep autoencoder framework based inference algorithm. Under the framework, a partially observed pollution map formed by the irregular samples are input into the model, then an encoder and a decoder work together to recover the entire pollution map. Inside the decoder, we adopt a convolutional long short-term memory (ConvLSTM) model by revealing its physical interpretation with an atmospheric dispersion model, and further present a weather-related ConvLSTM to enable quasi real-time applications. To evaluate our algorithm, a half-year data collection was deployed with a real-world system on a coastal area including the Sino-Singapore Tianjin Eco-city in north China. With the resolution of 500 m x 500 m x 1 h, our offline method is proved to have high robustness against low sampling coverage and accidental sensor errors, obtaining 14.9% performance improvement over existing methods. Our quasi real-time model better captures the spatiotemporal dependencies in the pollution map with unevenly distributed samples than other real-time approaches, obtaining 4.2% error reduction.read more
Citations
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The rise of low-cost sensing for managing air pollution in cities
Prashant Kumar,Lidia Morawska,Claudio Martani,George Biskos,George Biskos,George Biskos,Marina Neophytou,Silvana Di Sabatino,Margaret Bell,Leslie Norford,Rex Britter +10 more
TL;DR: In this article, the authors illustrate the drivers behind current rises in the use of low-cost sensors for air pollution management in cities, whilst addressing the major challenges for their effective implementation.
Journal ArticleDOI
A Big Data and Artificial Intelligence Framework for Smart and Personalized Air Pollution Monitoring and Health Management in Hong Kong
TL;DR: An AI and big data framework to estimate and forecast air quality in high temporal-spatial resolution and real-time and an intervention study to determine if smart information presented via the proposed visualized platform will induce personal behavioural change are proposed.
Posted Content
Deep-MAPS: Machine Learning based Mobile Air Pollution Sensing
TL;DR: In this article, a machine learning-based mobile air pollution sensing framework, called Deep-MAPS, is proposed to perform spatial inference of PM2.5 concentrations in Beijing (3,025 km2, 19 Jun-16 Jul 2018).
Journal ArticleDOI
Virtual Sensing and Sensors Selection for Efficient Temperature Monitoring in Indoor Environments.
TL;DR: In this paper, a black-box virtual sensing framework is developed for temperature monitoring in an open space office, where a set of physical sensors is deployed at uneven locations. But, the proposed framework can be adapted to any indoor environment.
Journal ArticleDOI
Deep-AIR: A Hybrid CNN-LSTM Framework for Fine-grained Air Pollution Estimation and Forecast in Metropolitan Cities
TL;DR: In this paper , a hybrid CNN-LSTM structure was proposed to capture the spatio-temporal features, and 1x1 convolution layers were added to enhance the learning of temporal and spatial interaction.
References
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Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising 1 criterion
TL;DR: This work clearly establishes the value of using a denoising criterion as a tractable unsupervised objective to guide the learning of useful higher level representations.
Posted Content
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
TL;DR: This paper proposes the convolutional LSTM (ConvLSTM) and uses it to build an end-to-end trainable model for the precipitation nowcasting problem and shows that it captures spatiotemporal correlations better and consistently outperforms FC-L STM and the state-of-the-art operational ROVER algorithm.
Journal ArticleDOI
Fully coupled “online” chemistry within the WRF model
Georg Grell,Steven E. Peckham,Rainer Schmitz,Stuart A. McKeen,Gregory J. Frost,William C. Skamarock,Brian Eder +6 more
TL;DR: The WRF/Chem model is statistically better skilled in forecasting O3 than MM5/Chem, with no appreciable differences between models in terms of bias with the observations, and consistently exhibits better skill at forecasting the O3 precursors CO and NOy at all of the surface sites.
Journal ArticleDOI
Review of the Governing Equations, Computational Algorithms, and Other Components of the Models-3 Community Multiscale Air Quality (CMAQ) Modeling System
Daewon W. Byun,Kenneth L. Schere +1 more
TL;DR: The Models-3 CMAQ system as mentioned in this paper is a community multiscale air quality modeling system that includes a meteorological modeling system for the description of atmospheric states and motions, emission models for man-made and natural emissions that are injected into the atmosphere, and a chemistry-transport modelling system for simulation of the chemical transformation and fate.
Journal ArticleDOI
A review of land-use regression models to assess spatial variation of outdoor air pollution
Gerard Hoek,Rob Beelen,Kees de Hoogh,Danielle Vienneau,John S. Gulliver,Paul Fischer,David J. Briggs +6 more
TL;DR: Land-use regression (LUR) models have been increasingly used in the past few years to assess the health effects of long-term average exposure to outdoor air pollution as mentioned in this paper.