TY - GEN
T1 - A low maintenance particle pollution sensing system using the Minimum Airflow Particle Counter (MAPC)
AU - Van Kessel, Ted
AU - Muralidhar, Ramachandran
AU - Chang, Josephine B.
AU - Wang, Jun Song
AU - Schappert, Michael
AU - Hamann, Hendrik F.
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/1
Y1 - 2017/7/1
N2 - The Minimum Airflow Particle Counter (MAPC) is a portable, low-power, low-cost, wireless optical counter which has been specifically designed for ultra-low-maintenance operation in heavily polluted environments. When exposed continuously to air with high particulate matter concentrations, the primary mode of failure for particle counters is a build-up of dust within the instrument. The MAPC circumvents this failure mode by severely restricting airflow through the system, enabling an estimated 5-year maintenance cycle. Such a long operational lifetime makes this instrument particularly suitable for IOT applications such as environmental air quality monitoring and pollutant source attribution using spatially distributed wireless sensor networks. Here, we present the theory of operation, instrument design, and collected data from a two-month field deployment in Beijing. We find that the MAPC performs comparably to other low-cost optical counters, but with a significantly enhanced maintenance-free operational lifetime.
AB - The Minimum Airflow Particle Counter (MAPC) is a portable, low-power, low-cost, wireless optical counter which has been specifically designed for ultra-low-maintenance operation in heavily polluted environments. When exposed continuously to air with high particulate matter concentrations, the primary mode of failure for particle counters is a build-up of dust within the instrument. The MAPC circumvents this failure mode by severely restricting airflow through the system, enabling an estimated 5-year maintenance cycle. Such a long operational lifetime makes this instrument particularly suitable for IOT applications such as environmental air quality monitoring and pollutant source attribution using spatially distributed wireless sensor networks. Here, we present the theory of operation, instrument design, and collected data from a two-month field deployment in Beijing. We find that the MAPC performs comparably to other low-cost optical counters, but with a significantly enhanced maintenance-free operational lifetime.
KW - Air pollution monitoring
KW - inverse problem
KW - IOT
KW - optical particle counter
KW - PM2.5
KW - source attribution
UR - https://www.scopus.com/pages/publications/85047753942
U2 - 10.1109/BigData.2017.8258501
DO - 10.1109/BigData.2017.8258501
M3 - Conference contribution
AN - SCOPUS:85047753942
T3 - Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
SP - 4577
EP - 4582
BT - Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
A2 - Nie, Jian-Yun
A2 - Obradovic, Zoran
A2 - Suzumura, Toyotaro
A2 - Ghosh, Rumi
A2 - Nambiar, Raghunath
A2 - Wang, Chonggang
A2 - Zang, Hui
A2 - Baeza-Yates, Ricardo
A2 - Baeza-Yates, Ricardo
A2 - Hu, Xiaohua
A2 - Kepner, Jeremy
A2 - Cuzzocrea, Alfredo
A2 - Tang, Jian
A2 - Toyoda, Masashi
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th IEEE International Conference on Big Data, Big Data 2017
Y2 - 11 December 2017 through 14 December 2017
ER -