Hi, it’s Rui Ma. Today, it’s my pleasure to introduce our work about air pollution
sensing, on the behalf of my coauthors from both Tsinghua University and Carnegie Mellon
University. Our work focuses on the fine-grained air pollution
inference with mobile sensing systems, where we are using a weather-related deep Autoencoder
Model. According to WHO, air pollution causes 7 million
premature deaths per year, which is a severe global health threat.
These facts emphasize the importance of fine-grained air quality monitoring.
which could benefit the daily protection for city residents, while could also support accurate
air quality control and effective city management for urban administrators.
Nowadays, static stations and mobile systems are both applied for urban air quality sensing.
Official air quality stations are usually deployed statically in many countries for
routine monitoring. However, these high-accurate instruments are with great cost, bulk size
and high environmental requirements. They can barely be deployed with sufficient density
to capture the fine-grained pollution map In recent years, mobile sensing networks are
proposed as a supplementary method by a series of previous work. They utilize air quality
sensors with lower unit prices, and further motivate them by carriers like human and vehicles
to achieve wider range and greater granularity. Although mobile sensing provides higher possibilities
to capture detailed variations, these samples are unable to cover the entire spatial-temporal
space. An inference algorithm is still essential to recover the fine-grained air pollution
field, therefore supporting a comprehensive understanding of the city environment.
Previous algorithms for air pollution inference in traditional environmental area are mostly
based on deterministic models describing physical and chemical processes. However, these models
require abundant prior knowledge like initial states and pollution sources, and suffer from
great computation complexity. With growing amount of observed samples, data-driven
methods are proposed, especially deep learning methods. Nevertheless, deep learning models
are always seen as black boxes and the selection of these models is scarcely backed up with
physical interpretations. On the other hand, samples from mobile systems are irregularly
distributed in time and space, meanwhile the relative positions among mobile sensors are
dynamic. The dependencies in spatiotemporal neighborhoods of a pollution map are hard
to be learned. Above all, while using deep learning methods
in air pollution inference, there are two major challenges. First is the non-interpretability
of deep learning methods. Deep learning models without physical insights may have bad extrapolation
abilities. The second is the irregular samples from mobile sensing. Upon existing machine
learning structures, it is difficult to model the dependencies in spatiotemporal neighborhoods
directly with the irregular samples from mobile sensing.
In this paper, we address these two challenges from two perspectives:
To provide deep learning methods with physical insights, we
We first reveal the physical insights of ConvLSTM with the classical physical model in environmental
field, called atmospheric dispersion model Then customize the ConvLSTM with a weather-related
kernel in its inner structure, which introduces auxiliary information. It also connects the
daily pollution inference models, which enables this algorithm to be quasi real time.
On the other hand, to address the irregular sampling from mobile sensing,
Previous data-driven algorithms are mostly inferring unobserved samples directly from
observed ones. A typical method uses a temporal-spatial predictor
to learn the relationship among a group of fixed samples.
However, since the relative position of samples are dynamic in mobile scenarios, this kind
of inference can not fit into our problem. We address this challenge by introducing the
autoencoder framework from image denoising, then separate the pollution reconstruction
and the sampling process. Instead of directly modeling the relationship between observed
samples and unobserved samples, this framework first compresses information from the observed
samples into a low-dimensional latent vector, then reformulate the whole pollution field.
After that, be imitating the sampling of mobile systems, we could evaluate the inference performance
using our irregular samples. In order to evaluate our proposed algorithm,
we deployed a real-world data collection in a coastal area in Tianjin within half-year
time. The sensing units were installed on 8 static
sites, 12 buses and 3 cruisers. By comparing with existing methods, our method
obtains 15% less error and 7% higher correlations than the second place in offline applications
And 4% less error in quasi real-time applications Benefited from the physical interpretation,
our algorithm also achieves higher robustness against bad conditions, like low sampling
coverage. Here are the conclusions.
Due to the limited time, please find more details in our full paper.
Thanks for your listening!