The Temporal Variations of PM10 Concentration in Taipei: A Fractal Approach

Ho, Ding-Shun; Juang, Lanin-Chuen; Liao, Yuying; Wang, Cheng-Cai; Lee, Chung‐Kung; Hsu, Ting-Chu; Yang, Shaw-Yang; Yu, Chung-Chin

doi:10.4209/aaqr.2004.07.0004

Cited by 8 publications

(6 citation statements)

References 23 publications

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“…Those measurement results suggested that the highest levels of PM 10 and PM 2.5 appear in spring (mean = 53.3 μg/m 3 for PM 10 ; mean = 34.5 μg/m 3 for PM 2.5 ), which is closely related to the influence of long-range transport of Asian dust and manmade pollutants from Mainland China on Taiwan. However, the PM 10 and PM 2.5 levels at the highway toll station were significantly higher than those obtained by Ho et al (2004) and Chang et al (2010). Fig.…”

Section: Data Quality Assurancementioning

confidence: 66%

“…The PM 10 and PM 2.5 levels at the highway toll station were higher than those obtained by Chen et al (1999) approximately 1.9 and 2.4 times, respectively, indicating that PM 10 and PM 2.5 levels at the highway toll station were significantly higher than those measured at the urban area due to traffic emissions. Ho et al (2004) also demonstrated that mean PM 10 levels at five monitoring stations in the Taipei urban area were about 42.2-49.9 μg/m 3 . The mean values of this five examined stations were nearly equal, indicating that the spatial differences in PM 10 levels in Taipei urban area were rather small.…”

Section: Data Quality Assurancementioning

confidence: 97%

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Influences of Traffic Emissions and Meteorological Conditions on Ambient PM10 and PM2.5 Levels at a Highway Toll Station

Chen

2010

Aerosol Air Qual. Res.

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The PM 10 and PM 2.5 levels at a highway toll station were monitored from October to December 2008. Experimental results show that hourly average PM 10 and PM 2.5 levels at the highway toll station were 10.6-208.4 μg/m 3 and 6.6-187.9μg/m 3 , respectively. Additionally, the PM 2.5 -to-PM 10 ratio at the highway toll station was 0.73, indicating that emissions from traffic sources are dominant in PM 2.5 fraction. At the highway toll station, the time variations of the PM 10 and PM 2.5 levels were not strongly correlated with traffic volumes; however, traffic on the highway markedly elevated ambient PM 10 and PM 2.5 levels. The PM 10 and PM 2.5 levels at the highway toll station are higher than those at monitoring stations in the vicinity to the toll station by factors of 1.3-1.4 and 1.4-1.8 times, respectively. The low wind speeds and low mixing-layer heights lead to relatively high PM 10 and PM 2.5 levels. Moreover, high wind speed also could have resulted in high PM 10 and PM 2.5 levels due to the re-suspension of particulate matter under well dispersed conditions. Measurements indicate that both traffic emissions and meteorological conditions drive PM 10 and PM 2.5 levels at the highway toll station.

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Section: Data Quality Assurancementioning

confidence: 66%

Section: Data Quality Assurancementioning

confidence: 97%

Influences of Traffic Emissions and Meteorological Conditions on Ambient PM10 and PM2.5 Levels at a Highway Toll Station

Chen

2010

Aerosol Air Qual. Res.

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“…Kwon et al (2021) investigated the real‐time air quality monitors and Global Positioning System (GPS) loggers. Ho et al (2004) applied the fractal technique to examine the temporal features of PM 10 emissions in Taipei, Taiwan, and found that the time series dataset had a right‐skewed frequency count and long‐term memory. Krampah et al (2021) explored the temporal variation in total suspended particle (TSP) and PM 10 concentration and their correlation with other meteorological factors in Tarkwa.…”

Section: Short Reviewmentioning

confidence: 99%

“…Air quality indices was studied and taken as the model input concentrations of six pollutants, namely, CO (mgm À3 ), NO 2 , O 3 , PM 10 , PM 2.5 and SO 2 (μgm À3 ), with hourly time series obtained from automated monitoring systems Kwon et al (2021). investigated the realtime air quality monitors and Global Positioning System (GPS) loggers Ho et al (2004). applied the fractal technique to examine the temporal features of PM 10 emissions in Taipei, Taiwan, and found that the time series dataset had a right-skewed frequency count and long-term memory Krampah et al (2021).…”

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confidence: 99%

Micro‐ to macro‐scaling analysis of PM_2.5 in sensitive environment of Himalaya, India

Kimothi,

Chilkoti,

Rawat

et al. 2023

Geological Journal

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Fine particulate matter (PM) in the atmosphere has become a significant air contaminant with substantial health consequences. Although airborne remote sensing and ground sensor monitoring can offer air quality datasets containing PM2.5, there are limitations to effectively analysing large long‐term datasets. The research aims to evaluate air quality over the Himalayan region using the multifractal approach for the PM2.5 data time series. Fractal dimension (FD), Hurtz exponent (H), and predictability (PI) are estimated using the rescaled range. PM2.5 is found to have high concentration and frequency throughout the day. The same is found in the night hours during peak tourism months. The hourly PM2.5 time series datasets have shown multifractality. The primary reason for this is emissions produced by vehicles and anthropocentric activities in the region. The H is used to assess the dynamic features of the PM2.5 time series in terms of persistence and self‐correlation. In the context of climate change studies, it is crucial to monitor the spatial distribution and dynamic behaviour of PM2.5 in the Himalayan foothills. This study aims to provide prediction analyses and air quality index (AQI) estimates and demonstrate how PM2.5 concentrations alter the sensitive environment throughout the micro to macro scale. This will help us to build a long‐term strategy for reducing the harmful effect of increasing pollution levels on the ecosystem and human health.

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“…Short-term exposure to PM can cause health problems including respiratory illnesses, pulmonary diseases, heart strokes (Kwon et al, 2002;Pope et al, 2002;Ho et al, 2004;Pope et al, 2004;Miller et al, 2007;Brook and Rajagopalan, 2009;Kazunari et al, 2012). In addition, showed the strongest probable effects increased 7.7% of respiratory disease 1 day after ADS in Taipei.…”

Section: Introductionmentioning

confidence: 99%

Source Apportionment of PM2.5 Particle Composition and Submicrometer Size Distribution during an Asian Dust Storm and Non-Dust Storm in Taipei

Liang

Chang

et al. 2013

Aerosol Air Qual. Res.

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Asian dust storm (ADS) not only increase the coarse particle concentrations, but also bring the fine and ultrafine particles to Taiwan. In this study, A PCA model was applied to identify the potential source categories, obtained through measuring ambient 10-500 nm particle number concentrations, size distributions and composition data, during an ADS and non-dust periods. The three factors estimated with rotational components during an ADS were vehicular emissions (52%), dust storm (24%), and primarily gasoline vehicles (12%). During non-dust periods, the three factors were vehicular emissions, secondary sulfate and nitrate (40%), combustion processes and traffic-related emissions (29%), and road dust (25%). In addition, vehicular emissions and road dust were the main sources (78%) during particulate matter episodes. The results showed that, along with wind direction and wind speed, PM composition and size distribution can be used to determine the locally dominant source categories, and to identify ADS episodes.

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The Temporal Variations of PM10 Concentration in Taipei: A Fractal Approach

Cited by 8 publications

References 23 publications

Influences of Traffic Emissions and Meteorological Conditions on Ambient PM10 and PM2.5 Levels at a Highway Toll Station

Influences of Traffic Emissions and Meteorological Conditions on Ambient PM10 and PM2.5 Levels at a Highway Toll Station

Micro‐ to macro‐scaling analysis of PM_2.5 in sensitive environment of Himalaya, India

Source Apportionment of PM2.5 Particle Composition and Submicrometer Size Distribution during an Asian Dust Storm and Non-Dust Storm in Taipei

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The Temporal Variations of PM10 Concentration in Taipei: A Fractal Approach

Cited by 8 publications

References 23 publications

Influences of Traffic Emissions and Meteorological Conditions on Ambient PM10 and PM2.5 Levels at a Highway Toll Station

Influences of Traffic Emissions and Meteorological Conditions on Ambient PM10 and PM2.5 Levels at a Highway Toll Station

Micro‐ to macro‐scaling analysis of PM2.5 in sensitive environment of Himalaya, India

Source Apportionment of PM2.5 Particle Composition and Submicrometer Size Distribution during an Asian Dust Storm and Non-Dust Storm in Taipei

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Micro‐ to macro‐scaling analysis of PM_2.5 in sensitive environment of Himalaya, India