Highly Accurate and Autonomous Programmable Platform for Providing Air Pollution Data Services to Drivers and Public

Highly Accurate and Autonomous Programmable Platform for Providing Air Pollution Data Services to Drivers and the Public (HAPADS) is ambitious project that will custom design and build a novel smart and autonomous air monitoring platform which, will enable end-users (drivers, transport companies, municipalities and the at-large public) to make information-driven decisions to mitigate air pollution exposure for the people. One of the objectives of the project is the development of novel detectors such as microwave-based NO2 sensor and time-delay-integration (TDI) image-based particulate matter (PM) sensor that can be used both in-vehicle and on-vehicle. Moreover, a programmable multiprocessor hardware for data acquisition and signal processing with parallel edge computing and deep learning algorithms support will be developed together with embedded software with air pollution calibration modeling and multi-objective optimization. Data quality of low-cost sensors is often questionable, therefore existing air quality sensors must be manually calibrated for a given deployment site, making them unsuitable for mobile deployment. HAPADS will devise and implement specialized embedded software for mobile MPs that are automatically self-calibrated for a new deployment location. The mobile sensors can be used to provide the data to public entities to make a pollution map in near real time. The main parts of the project, such as PM sensors or Mobile Platform (in particular implementation of particle detectors in the form of specialized ASIC integrated circuits and the implementation of a modern mobile computing platform with optimized algorithms), will be implemented at GUT. Cooperation with other scientific and research institutions, including reputable universities and institutions from Poland and Norway and institutions specializing in air quality monitoring, will allow the staff to gain further scientific and practical experience and to present the results of their work in the form of high-quality publications.

Details

Project's acronym:

HAPADS

Financial Program Name:

EEA and Norway grants - Applied Research Programme

Organization:

Narodowe Centrum Badań i Rozwoju (NCBR) (The National Centre for Research and Development)

Agreement:

NOR/POLNOR/HAPADS/0049/2019-00 z dnia 2020-10-20

Realisation period:

2020-10-01 - 2024-04-01

Project manager:

dr hab. inż. Marek Wójcikowski

Team members:

researcher dr hab. inż. Waldemar Jendernalik
researcher dr hab. inż. Anna Pietrenko-Dąbrowska
young researcher dr hab. inż. Adrian Bekasiewicz
researcher dr hab. inż. Grzegorz Blakiewicz
researcher dr hab. inż. Jacek Jakusz
Work Package WP2 leader, researcher prof. dr inż. Sławomir Kozieł
researcher dr inż. Miron Kłosowski
Primary Investigator, researcher Marek Wójcikowski
researcher dr hab. inż. Bogdan Pankiewicz

Realised in:

Department of Microelectronic Systems

External institutions participating in project:

Norwegian Institute for Air Research (Norway)
Wroclaw University of Science and Technology (Poland)
Logistics Enhancement Systems and Services Sp. z o.o. (Poland)
AGH University of Science and Technology (Poland)
University of Tromsø – The Arctic University of Norway (Norway)

Project's value:

6 999 075.00 PLN

Request type:

International Research Programmes

Domestic:

International project

Verified by:

Gdańsk University of Technology

Papers associated with that project

results on page:
year:
- selected sort by year newest first
- sort by year oldest first
title:
- selected sort by title A-Z
- sort by title Z-A
citation:
- sort by citation descending
- sort by citation ascending

Filters

total: 3

Year 2024

Data fusion of sparse, heterogeneous, and mobile sensor devices using adaptive distance attention
Publication
- J. Lepioufle
- P. Schneider
- P. D. Hamer
- R. Odegard
- I. Vallejo
- T. Cao
- A. Taherkordi
- M. Wójcikowski
- Environmental Data Science - Year 2024
In environmental science, where information from sensor devices are sparse, data fusion for mapping purposes is often based on geostatistical approaches. We propose a methodology called adaptive distance attention that enables us to fuse sparse, heterogeneous, and mobile sensor devices and predict values at locations with no previous measurement. The approach allows for automatically weighting the measurements according to a priori...

Full text to download in external service

Year 2023

Accurate Lightweight Calibration Methods for Mobile Low-Cost Particulate Matter Sensors
Publication
- P. Jørstad
- M. Wójcikowski
- T. Cao
- J. Lepioufle
- K. Wojtkiewicz
- P. H. Ha
- Year 2023
Monitoring air pollution is a critical step towards improving public health, particularly when it comes to identifying the primary air pollutants that can have an impact on human health. Among these pollutants, particulate matter (PM) with a diameter of up to 2.5 μ m (or PM2.5) is of particular concern, making it important to continuously and accurately monitor pollution related to PM. The emergence of mobile low-cost PM sensors...

Full text to download in external service
Adaptive Wavelet-Based Correction of Non-Anechoic Antenna Measurements
Publication
- A. Bekasiewicz
- V. Waladi
- M. Wójcikowski
- T. Dhaene
- Year 2023
Non-anechoic measurements represent an affordable alternative to evaluation of antenna performance in expensive, dedicated facilities. Due to interferences and noise from external sources of EM radiation, far-field results obtained in non-ideal conditions require additional post-processing. Conventional correction algorithms rely on manual tuning of parameters, which make them unsuitable for reliable testing of prototypes. In this...

Full text available to download

seen 620 times