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Brochure 2020
In addition, we have investigated the properties of micro Finally, by exploiting the fine-scale data resolution of
air-quality sensing data and generated a number of micro air-quality sensing, our AirBox project not only can
algorithms for data analysis. For instance, we designed an benefit researches in the computer and environmental
anomaly detection framework (ADF) to detect anomalous sciences, but it can also stimulate interdisciplinary
devices/events in sensing data streams, and we have innovation in public health, risk management, urban
proposed a hybrid model for short-term air-quality planning, atmospheric science, and various other science
forecasting by incorporating data clustering and neural and technology fields. Our project has created a positive
networks. Moreover, by combining real-time sensing data ecosystem in which academia, industry, governments, and
and short-term forecasting results, we have proposed citizens collaborate. It has the potential to facilitate smart
a clean air routing (CAR) algorithm to provide route city design, intelligent environmental governance, and
recommendations for minimal air pollution exposure. Our public-private partnerships for the common good into the
research is of both theoretical and practical value and future.
our findings have been published in prestigious journals.
Our algorithms have been implemented in the AirBox
system, and the results they generate are being used by
governments and research communities.
(a) System architecture (IEEE Access' 17) (b) Anomaly detection (IEEE JIoT' 18)
(c) Hybrid data forecast (IEEE Access' 18) (d) Clean Air Routing (IEEE Access' 19)
Figure 2 : Selected research results from the AirBox project.
"Dust Island - Particulate Matters" documentaryt (2019.01) BBC News: Clickt (2019.11)
25
In addition, we have investigated the properties of micro Finally, by exploiting the fine-scale data resolution of
air-quality sensing data and generated a number of micro air-quality sensing, our AirBox project not only can
algorithms for data analysis. For instance, we designed an benefit researches in the computer and environmental
anomaly detection framework (ADF) to detect anomalous sciences, but it can also stimulate interdisciplinary
devices/events in sensing data streams, and we have innovation in public health, risk management, urban
proposed a hybrid model for short-term air-quality planning, atmospheric science, and various other science
forecasting by incorporating data clustering and neural and technology fields. Our project has created a positive
networks. Moreover, by combining real-time sensing data ecosystem in which academia, industry, governments, and
and short-term forecasting results, we have proposed citizens collaborate. It has the potential to facilitate smart
a clean air routing (CAR) algorithm to provide route city design, intelligent environmental governance, and
recommendations for minimal air pollution exposure. Our public-private partnerships for the common good into the
research is of both theoretical and practical value and future.
our findings have been published in prestigious journals.
Our algorithms have been implemented in the AirBox
system, and the results they generate are being used by
governments and research communities.
(a) System architecture (IEEE Access' 17) (b) Anomaly detection (IEEE JIoT' 18)
(c) Hybrid data forecast (IEEE Access' 18) (d) Clean Air Routing (IEEE Access' 19)
Figure 2 : Selected research results from the AirBox project.
"Dust Island - Particulate Matters" documentaryt (2019.01) BBC News: Clickt (2019.11)
25
