PMID- 37185799
OWN - NLM
STAT- MEDLINE
DCOM- 20230703
LR  - 20230703
IS  - 1573-2983 (Electronic)
IS  - 0269-4042 (Linking)
VI  - 45
IP  - 7
DP  - 2023 Jul
TI  - Investigating the nonlinear and non-stationary relationship between PM(2.5) and 
      air pollutants by wavelet signal analysis in central Taiwan.
PG  - 5195-5211
LID - 10.1007/s10653-023-01560-5 [doi]
AB  - In recent years, PM(2.5) has become a critical factor as an environmental 
      indicator, causing severe air pollution that has negatively impacted nature and 
      human health. This study used hourly data gathered in central Taiwan from 2015 to 
      2019 and applied spatiotemporal data analysis and wavelet analysis methods to 
      investigate the cross-correlation between PM(2.5) and other air pollutants. 
      Furthermore, it explored the correlation differences between adjacent stations 
      after excluding major environmental factors such as climate and terrain. Wavelet 
      coherence shows that PM(2.5) and air pollutants mostly have a significant 
      correlation at the half-day and one-day frequencies, while the differences 
      between PM(2.5) and PM(10) are only particle size; hence, not only is the 
      correlation the most consistent among all air pollutants but also the lag time is 
      the most negligible. Carbon monoxide (CO) is the primary source pollutant of 
      PM(2.5) as it is also significantly correlated with PM(2.5) at most timescales. 
      Sulfur dioxide (SO(2)) and nitrogen oxide (NO(x)) are related to the generation 
      of secondary aerosols, which are important components of PM(2.5); therefore, the 
      consistency of significant correlations improves as the timescale increases and 
      the lag time becomes amplified. The pollution source mechanism of ozone (O(3)) 
      and PM(2.5) is not identical, so the correlation is lower than for other air 
      pollutants; the lag time is also obviously influenced by the season changes that 
      have significant fluctuations. At stations near the ocean such as Xianxi station 
      and Shulu station, PM(2.5) and PM(10) have a higher correlation in the 24-h 
      frequency, while the SO(2) and PM(2.5) at Sanyi station and Fengyuan station, 
      which are close to industrial areas, have significant correlations in the 24-h 
      frequency. This study hopes to help better understand the impact mechanisms 
      behind different pollutants, and thus construct a better reference for 
      establishing a complete air pollution prediction model in the future.
CI  - (c) 2023. The Author(s), under exclusive licence to Springer Nature B.V.
FAU - Feng, Shan-Non
AU  - Feng SN
AD  - Department of Civil Engineering, National Central University, No. 300, Zhongda 
      Rd., Zhongli District, Taoyuan City, 32001, Taiwan.
FAU - Chen, Yi-Ho
AU  - Chen YH
AD  - Department of Civil Engineering, National Central University, No. 300, Zhongda 
      Rd., Zhongli District, Taoyuan City, 32001, Taiwan.
FAU - Weng, Tzu-Han
AU  - Weng TH
AD  - Department of Civil Engineering, National Central University, No. 300, Zhongda 
      Rd., Zhongli District, Taoyuan City, 32001, Taiwan.
FAU - Lin, Yuan-Chien
AU  - Lin YC
AD  - Department of Civil Engineering, National Central University, No. 300, Zhongda 
      Rd., Zhongli District, Taoyuan City, 32001, Taiwan. yclin@ncu.edu.tw.
LA  - eng
GR  - MOST 108-2625-M-008-002/National Science and Technology Council, Taiwan/
GR  - MOST 108-2119-M-008-003/National Science and Technology Council, Taiwan/
GR  - MOST 111-2119-M-008-004/National Science and Technology Council, Taiwan/
GR  - MOST 110-2634-F-008-008/National Science and Technology Council, Taiwan/
GR  - MOST 111-2119-M-008-006/National Science and Technology Council, Taiwan/
GR  - MOST 108-2636-E-008-004/Young Scholar Fellowship Program/
GR  - MOST 109-2636-E-008-008/Young Scholar Fellowship Program/
GR  - 110-2636-E-008-006/Young Scholar Fellowship Program/
GR  - 111-2636-E-008-014/Young Scholar Fellowship Program/
GR  - MOST 108-2638-E-008-001-MY2/Shackleton Program Grant/
PT  - Journal Article
DEP - 20230425
PL  - Netherlands
TA  - Environ Geochem Health
JT  - Environmental geochemistry and health
JID - 8903118
RN  - 0 (Air Pollutants)
RN  - 31C4KY9ESH (Nitric Oxide)
RN  - 0 (Particulate Matter)
SB  - IM
MH  - Humans
MH  - *Air Pollutants/analysis
MH  - Taiwan
MH  - Wavelet Analysis
MH  - *Air Pollution/analysis
MH  - Nitric Oxide
MH  - Particulate Matter/analysis
OTO - NOTNLM
OT  - Air pollution
OT  - Lag time
OT  - PM2.5
OT  - Wavelet analysis
OT  - Wavelet coherence
EDAT- 2023/05/15 19:12
MHDA- 2023/07/03 06:41
CRDT- 2023/05/15 13:49
PHST- 2022/10/06 00:00 [received]
PHST- 2023/03/29 00:00 [accepted]
PHST- 2023/07/03 06:41 [medline]
PHST- 2023/05/15 19:12 [pubmed]
PHST- 2023/05/15 13:49 [entrez]
AID - 10.1007/s10653-023-01560-5 [pii]
AID - 10.1007/s10653-023-01560-5 [doi]
PST - ppublish
SO  - Environ Geochem Health. 2023 Jul;45(7):5195-5211. doi: 
      10.1007/s10653-023-01560-5. Epub 2023 Apr 25.