Characteristics and Source Apportionment of Ambient Volatile Organic Compounds in a Science Park in Central Taiwan

Cheng, Jen-Hsuan; Hsieh, Ming-Jui; Chen, Kang-Shin

doi:10.4209/aaqr.2015.02.0114

Cited by 19 publications

(11 citation statements)

References 28 publications

(42 reference statements)

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“…The benzene-to-toluene (B/T) ratio is often applied as an index for determining the emission sources of BTEX compounds (Buczynska et al, 2009; Cheng et al, 2016; Elbir et al, 2007; Legay et al, 2011; Parra et al, 2008b). A ratio exceeding 0.5 suggests that the source of benzene is not only related to traffic but also other sources (Jiang et al, 2017; Yurdakul et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Characteristics and health effects of BTEX in a hot spot for urban pollution

Dehghani

Fazlzadeh

Sorooshian

et al. 2018

Ecotoxicology and Environmental Safety

187

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This study reports a spatiotemporal characterization of toluene, benzene, ethylbenzene, and xylenes concentrations (BTEX) in an urban hot spot in Iran, specifically at an bus terminal region in Shiraz. Sampling was carried out according to NIOSH Compendium Method 1501. The inverse distance weighting (IDW) method was applied for spatial mapping. The Monte Carlo simulation technique was applied to evaluate carcinogenic and non-carcinogenic risk owing to BTEX exposure. The highest average BTEX concentrations were observed for benzene in the morning (at 7:00–9:00A.M. local time) (26.15±17.65μg/m3) and evening (at 6:00–8:00P.M. local time) (34.44±15.63μg/m3). The benzene to toluene ratios in the morning and evening were 2.02 and 3.07, respectively. The main sources of BTEX were gas stations and a municipal solid waste transfer station. The inhalation lifetime cancer risk (LTCR) for benzene in the morning and evening were 1.96×10−4 and 2.49×10−4, respectively, which exceeds the recommended value by US EPA and WHO. The hazard quotient (HQ) of all these pollutants was less than 1. The results of this work have implications for public health near ‘hot spots’ such as IKBT where large populations are exposed to carcinogenic emissions.

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Section: Introductionmentioning

confidence: 99%

Characteristics and health effects of BTEX in a hot spot for urban pollution

Dehghani

Fazlzadeh

Sorooshian

et al. 2018

Ecotoxicology and Environmental Safety

187

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“…The least squares method is applied to determine the parameters of the empirical CDF equation. The parameters of Equation (9) for different return periods (5,10,20,30,40,50,60,75, and 100 years) and different durations (1,8,16,24,48, and 96 h) are calculated.…”

Section: Empirical Pm 25 Cdf Functionmentioning

confidence: 99%

“…The heterogeneity of extreme pollutant concentrations represents a different level of risk to people's health [23]. The emissions were from local sources, such as traffic and industrial or agricultural activities, and from outside Taiwan [24,25]. A highly developed industry and a large population density distinguish the western plain of Taiwan.…”

Section: Heterogeneity Of Air Pollutionmentioning

confidence: 99%

Establishment of Regional Concentration–Duration–Frequency Relationships of Air Pollution: A Case Study for PM2.5

Chu

Ali

2020

IJERPH

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Poor air quality usually leads to PM2.5 warnings and affects human health. The impact of frequency and duration of extreme air quality has received considerable attention. The extreme concentration of air pollution is related to its duration and annual frequency of occurrence known as concentration–duration–frequency (CDF) relationships. However, the CDF formulas are empirical equations representing the relationship between the maximum concentration as a dependent variable and other parameters of interest, i.e., duration and annual frequency of occurrence. As a basis for deducing the extreme CDF relationship of PM2.5, the function assumes that the extreme concentration is related to the duration and frequency. In addition, the spatial pattern estimation of extreme PM2.5 is identified. The regional CDF identifies the regional extreme concentration with a specified duration and return period. The spatial pattern of extreme air pollution over 8 h duration shows the hotspots of air quality in the central and southwestern areas. Central and southwestern Taiwan is at high risk of exposure to air pollution. Use of the regional CDF analysis is highly recommended for efficient design of air quality management and control.

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“…These VOCs can cause severe cases of poisoning and even damage the liver or hematopoietic system. Benzene, toluene, ethylbenzene, and xylene, collectively known as BTEX, serve as indicators of various pollution sources (automobiles and motorbikes included) since the composition ratios of these four substances in air pollution remain stable [13,14].…”

Section: Introductionmentioning

confidence: 99%

Study on Air Pollution Behavior of VOCs with Photochemical Monitoring Stations Using EGARCH Model in Southern Taiwan

Kuo

2021

Atmosphere

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This study adopted the exponential generalized autoregressive conditional heteroscedasticity (EGARCH) model to examine the 10 ozone precursors of the highest concentrations among the 54 that were assessed over a number of years at the four photochemical assessment monitoring stations (PAMSs) in the Kaohsiung–Pingtung Area in Taiwan. First, the 10 ozone precursors, which were all volatile organic compounds (VOCs), were analyzed using the factor analyses in multiple statistical analyses that had the most significant impact on the area’s ozone formation: mobile pollution factor, which included 1,2,4-Trimethylbenzene (C9H12), toluene (C7H8), and Isopropyl benzene (C9H12). Then, taking into consideration that the number sequences might be affected by standardized residuals, this study applied the vector autoregressive moving average-EGARCH (VARMA-EGARCH) model to analyze the correlation between the three VOCs under different polluting activities. The VARMA-EGARCH model in this research included dummy variables representing changing points of variance structures in the variance formula to predict the conditional variance. This process proved able to effectively estimate the relevant coefficients of the three VOCs’ dynamic conditions that changed with time. The model also helped to prevent errors from occurring when estimating the conditional variance. Based on the testing results, this study determined the VARMA(2,1)-EGARCH(1,0) as the most suitable model for exploring the correlation between the three VOCs and meteorological phenomena, as well as the interplay between them in regard to interaction and formation. With the most representative of the three, toluene (TU), as the dependent variable and 1,2,4-Trimethylbenzene (TB) and Isopropyl benzene (IB) as the independent variables, this study found it impossible to calculate the TU concentration with TB and IB concentrations in the same period; estimations of TB and IB concentrations with a period of lag time were required because TU was mainly contributed by automobiles and motorcycles in Kaohsiung. TB and IB resulted from other stationary pollution sources in the region besides cars and motorbikes. When TU was evenly distributed and stayed longer in the atmosphere, the TB and IB concentrations were lower, so distribution conditions and concentrations could not be used to effectively estimate the concentration of toluene. This study had to wait until the next period, or when stationary pollution sources started producing TB and IB of higher concentrations during the daytime, in order to estimate the TU concentrations in a better photochemical situation.

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Characteristics and Source Apportionment of Ambient Volatile Organic Compounds in a Science Park in Central Taiwan

Cited by 19 publications

References 28 publications

Characteristics and health effects of BTEX in a hot spot for urban pollution

Characteristics and health effects of BTEX in a hot spot for urban pollution

Establishment of Regional Concentration–Duration–Frequency Relationships of Air Pollution: A Case Study for PM2.5

Study on Air Pollution Behavior of VOCs with Photochemical Monitoring Stations Using EGARCH Model in Southern Taiwan

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