Publication: Detection of PM<inf>2.5</inf> plume movement from IoT ground level monitoring data
Issued Date
2019-09-01
Resource Type
ISSN
18736424
02697491
02697491
Other identifier(s)
2-s2.0-85066437284
Rights
Mahidol University
Rights Holder(s)
SCOPUS
Bibliographic Citation
Environmental Pollution. Vol.252, (2019), 543-552
Suggested Citation
Thongchai Kanabkaew, Preechai Mekbungwan, Sunee Raksakietisak, Kanchana Kanchanasut Detection of PM<inf>2.5</inf> plume movement from IoT ground level monitoring data. Environmental Pollution. Vol.252, (2019), 543-552. doi:10.1016/j.envpol.2019.05.082 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/50903
Research Projects
Organizational Units
Authors
Journal Issue
Thesis
Title
Detection of PM<inf>2.5</inf> plume movement from IoT ground level monitoring data
Abstract
© 2019 Elsevier Ltd In this study, we analysed a data set from 10 low-cost PM2.5 sensors using the Internet of Things (IoT) for air quality monitoring in Mae Sot, which is one of the most vulnerable areas for high PM2.5 concentration in Thailand, during the 2018 burning season. Our objectives were to understand the nature of the plume movement and to investigate possibilities of adopting IoT sensors for near real-time forecasting of PM2.5 concentrations. Sensor data including PM2.5 and meteorological parameters (wind speed and direction) were collected online every 2 min where data were grouped into four zones and averaged every 15 min interval. Results of diurnal profile plot revealed that PM2.5 concentrations were high around early to late morning (3:00–9:00) and gradually reduced till the rest of the day. During the biomass burning period, maximum daily average concentration recorded by the sensors was 280 μg/m3 at Thai Samakkhi while the minimum was 13 μg/m3 at Mae Sot. Lag time concentrations, attributed by biomass burning (hotspots), significantly influenced the formation of PM2.5 while the disappearance of PM2.5 was found to be influenced by moderate wind speed. The PM2.5 concentrations of the next 15 min at the downwind zone (MG) were predicted using lag time concentrations with different wind categories. The next 15 min predictions of PM2.5 at MG were found to be mainly influenced by its lag time concentrations (MG_Lag); with higher wind speed, however, the lag time concentrations from the upwind zones (MS_Lag and TS_Lag) started to show more influence. From this study, we have found that low-cost IoT sensors provide not only real-time monitoring information but also demonstrate great potential as an effective tool to understand the PM2.5 plume movement with temporal variation and geo-specific location. A high-density set of data points obtainable from low-cost IoT sensors revealed its great potential in becoming an effective tool for timely PM2.5 monitoring and plume movement detection.