Evaluation of the Relative Health Risk Impact of Atmospheric PCDD/Fs in PM2.5 in Taiwan

Hung, Ngo Tuan; Ting, Hsu Wei; Hsien, Kai

doi:10.4209/aaqr.2018.03.0094

Cited by 15 publications

(4 citation statements)

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“…Compared with previous study 21 , it was also showed the similar result with the relative risk of mortality between the highest and lowest concentrations of PCDD/Fs and PM 2.5 . Difference of this study was higher correlation with relative risk of mortality in PM 2.5 .…”

Section: Ci=1supporting

confidence: 70%

Characteristics of PCDD/Fs in PM2.5 From Emission Stacks and The Nearby Ambient Air in Taiwan

Pan

Liou

Chang

et al. 2020

Preprint

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This study aimed to find the characteristics of polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) in fine particulate matter from different stationary emission sources (coal-fired boiler, CFB; municipal waste incinerator, MWI; electric arc furnace, EAF) in Taiwan and the relationship between PM2.5 and PM2.5-bound PCDD/Fs with Taiwanese mortality risk. PM2.5 was quantified using gravimetry and corresponding chemical analyses were done for PM2.5-bound chemicals. Mortality risks of PM2.5 exposure and PCDD/Fs exposure were calculated using Poisson regression. The highest concentration of PM2.5 (0.53±0.39 mg/Nm3) and PCDD/Fs (0.206±0.107 ng I-TEQ/Nm3) was found in CFB and EAF, respectively. Higher proportions of PCDDs over PCDFs were observed in the flue gases of CFB and MWI whereas it was reversed in EAF. For ambient air, PCDD/F congeners around the stationary sources were dominated by PCDFs in vapor phase. Positive matrix factorization (PMF) analysis found that the sources of atmosphere PCDD/Fs were 14.6% from EAF (r=0.81), 52.6% from CFB (r=0.74), 18.0% from traffic (r=0.85) and 14.8% from MWI (r=0.76). For the dioxin congener distribution, PCDDs were dominant in flue gases of CFB and MWI, PCDFs were dominant in EAF. It may be attributed to the different formation mechanisms among wastes incineration, steel-making, and coal-burning processes.

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Section: Ci=1supporting

confidence: 70%

Characteristics of PCDD/Fs in PM2.5 From Emission Stacks and The Nearby Ambient Air in Taiwan

Pan

Liou

Chang

et al. 2020

Preprint

Self Cite

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“…The mean PCDD/F concentrations measured at an industrial park were significantly higher than those measured in other areas (Hung et al, 2018) and high gas fraction of PCDD/F that particulate matter in Taiwan (Lee et al, 2018). The higher chlorinated PCDD/Fs were primarily in the particle phase, and the fraction of particle phase PCDD/Fs increased with a decrease in temperature (Zhu et al, 2017).…”

Section: Airborne Concentration Stimulation Of Six Speciesmentioning

confidence: 75%

Airborne Air Toxics Characteristics and Inhalation Health Risk Assessment of a Metropolitan Industrial Complex

Tsai

Chung

et al. 2019

Aerosol Air Qual. Res.

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Air toxics, also well-known as hazardous air pollutants (HAPs), have significant health effects on human health and are of great concern. This paper studied a number of hazardous air pollutants in an industrial and metropolitan complex area in order to determine their ambient abundance and potential health impacts. The target pollutants in this study are benzene, formaldehyde, 1,3-butadiene, arsenic, 2,3,7,8-TCDD, and diesel particulate matter (DPM). A cancer risk assessment was conducted to determine the health effects of exposure to the six HAPs by using the AERMOD model. Results indicated that the emission of benzene, formaldehyde, 1,3-butadiene, arsenic and DPM was 184.5; 227.3; 68.0; 238, and 316 ton year-1 , respectively, and the emission of 2,3,7,8-TCDD was 4,994 mg-TEQ year-1. Benzene (86%), formaldehyde (69%), and 1,3-butadiene (77%) were mainly emitted from on-road mobile sources. Arsenic (70%) and 2,3,7,8-TCDD (about 100%) were mainly emitted from stationary sources and DPM was emitted from diesel engines, port operations and ocean-going vessels. Spatial air toxic distribution indicated that the highest concentration of DMP, benzene, formaldehyde, and 1.3-butadiene occurred on the highway and in the downtown district due to their high traffic volume. DPM occupied more than 80% of total cancer risk in the region, followed by 1,3-butadiene, benzene, formaldehyde, arsenic, and 2,3,7,8-TCDD. In the industrial and residential complex area, about 99% of the cancer risk stemmed from on-road vehicles and port operations due to hazardous air pollutant emissions, especially DPM. The control scenario was made huge efforts to reduce the emission, however the results indicated only reduced the overall cancer risk assessment by 10%-15%. Policy makers have to think carefully about whether implementing the kind of emissions regulations simulated in this control scenario will need to be enhanced with additional measures to further reduce the risk of air pollution for human health.

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“…It has been indicated that PM2.5 concentration is associated with mortality from cardiovascular disease, myocardial infarction, stroke, and respiratory disease [3], which has been calculated as leading to the premature deaths of more than 50,000 annually in the US [4], [5]. Moreover, according to research in Taiwan, a significantly higher mortality risk was found for people with higher exposure rates of PM2.5 [6]. Air quality also has an influence on human activities and health related decisions.…”

Section: Introductionmentioning

confidence: 99%

Why is Short-Time PM2.5 Forecast Difficult? The Effects of Sudden Events

et al. 2020

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The existing forecast models for PM2.5 concentration can be classified into long term and short term models depending on whether the forecast is performed for the next few hours or days. However, short term forecast models feature narrow forecast time and are thus vulnerable in their sensitivity to soaring variations in air quality, defined as sudden events. The purpose of this work is to investigate the causes behind these sudden events. The PM2.5 data were obtained from monitoring devices deployed in Taichung as a part of the Airbox project. The data were fed into the current short-term forecast model to forecast air quality for the next hour. Event timing was detected by feeding the forecast result as an input to the sudden event detection model. We then combined the filtered timing with factors in environment and human activities. With the application of Hierarchy Clustering, the clustering result was analyzed to find the causes of sudden events. In the springtime and summertime, unexpected changes in rainfall and temperature were critical for forecast models. Moreover, unanticipated changes in the intensity of rainfall and wind are important in the autumn and winter. For human activities, crowds of commuters, tourists, and pilgrims also have influence on unusual air quality. By carefully considering the effects of sudden events, we believe that the response ability of short time forecast can improve significantly in the near future.

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Evaluation of the Relative Health Risk Impact of Atmospheric PCDD/Fs in PM2.5 in Taiwan

Cited by 15 publications

References 0 publications

Characteristics of PCDD/Fs in PM2.5 From Emission Stacks and The Nearby Ambient Air in Taiwan

Characteristics of PCDD/Fs in PM2.5 From Emission Stacks and The Nearby Ambient Air in Taiwan

Airborne Air Toxics Characteristics and Inhalation Health Risk Assessment of a Metropolitan Industrial Complex

Why is Short-Time PM2.5 Forecast Difficult? The Effects of Sudden Events

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