出版社：Thai Society of Higher Eduction Institutes on Environment
摘要：Particle number count concentrations ([PNC]) is a new metric unit that can be used to quantify
the characteristics of particles in the atmosphere. This study was conducted to explore the
variability of [PNC] and the relationship between the factors that influenced this variation. The
[PNC], gases (SO2 and NOx), and meteorological factors (wind speed, wind direction, humidity,
pressure and temperature) data were gathered for a six months period from the Institute of Ocean
and Earth Sciences (IOES) Station, Kelantan, Malaysia by using a particle counter (GRIMM,
model EDM180), EcoTech EC9805T Series and EcoTech EC9841T Series for gases and Lasteem
Model LSI for meteorological measurements. The [PNC] data were categorised into fine particle
number count concentrations (FPNC0.25–0.99 and FPNC1.0 -2.49) with diameters of 0.25–0.99 µm,
1.0 -2.49 µm and coarse particles number count concentrations (CPNC 2.5–10) with diameters of
2.5–10 µm. The particle number concentration were measured and reported in number count/
particles at the entire size or number in every litre of air flow that pumped into the instruments
(EDM180, GRIMM). The concentration of FPNC was found higher (maximum of 5,826,380
counts/L) compared to CPNC (maximum of 818 counts/litre). An artificial intelligent technique
(boosted regression trees (BRT) algorithm) was constructed from multiple regression models,
and the best iteration of the BRT model was performed by optimising prediction performance.
The analysis revealed that the significant variation in the FPNC was largely influenced by SO2
(46.53%), Julian day (13.71%) and wind direction (10.50%). In contrast, the CPNC was
primarily influenced by wind speed (22.33%), wind direction (18.89%), Julian day (18.17%)
and pressure (11.38%).
关键词：Particle number count concentrations; Fine particles; Coarse particles; Boosted;
regression trees; Coastal area
