• Asian Journal of Atmospheric Environment
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• v.4 no.1
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• pp.1-8
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• 2010

The effectiveness of the government regulation on tail-pipe emission for diesel vehicles issued in 2003 in Tokyo was evaluated in this study. Variations in annual average concentrations of polycyclic aromatic hydrocarbons (PAHs) and nitro-PAHs associated with airborne particulate matter were investigated in connection with the variation in airborne elemental carbon (EC) concentration in downtown Tokyo, Japan in 2006-2007 and in 1997-1998. The annual average concentrations of EC, seven different PAHs, and 1-nitropyrene were found to have decreased significantly from 1997-1998 to 2006-2007. The most prominent decrease in atmospheric concentration was observed for 1-nitropyrene, which is a representative nitro-PAH originating from diesel vehicles. This indicated that the government control has worked to considerably reduce both atmospheric mutagens and airborne particulate matter. In contrast, the concentrations of two nitro-PAHs, 2-nitrofluoranthene and 2-nitropyrene, remained the same. These nitro-PAHs are known to be formed by atmospheric nitration of their parent PAHs, and this result suggested factors other than the concentration of parent PAHs and $NO_2$ affects the degree of atmospheric formation of nitro-PAHs.

• Journal of Korean Society for Atmospheric Environment
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• v.34 no.5
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• pp.651-658
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• 2018

Particulate matter (PM) emitted from fossil fuel-combustion facilities can be classified as either filterable or condensable PM. The U.S. Environmental Protection Agency (EPA) defined condensable PM as material that is in the phase of vapor at the stack temperature of the sampling location which condenses, reacts upon cooling and dilution in the ambient air to form solid or liquid in a few second after the discharge from the stack. Condensable PM passed through the filter media and it is typically ignored. But condensable PM was defined as a component of primary PM. This study investigates the change of condensable PM according to the variation in the sulfur dioxide of combustion gas. Domestic oil boilers were used as the source of emission ($SO_2$) and the level of $SO_2$ concentration (0, 50, 80, and 120 ppm) was adjusted by diluting general light oil and marine gas oil (MGO) that contains sulfur less than 0.5%. Condensable PM was measured as 2.72, 6.10, 8.38, and $13.34mg/m^3$ when $SO_2$ concentration in combustion gas were 0, 50, 80, and 120 ppm respectively. The condensable PM tended to increase as the concentration of $SO_2$ increased. Some of the gaseous air pollutants emitted from the stack should be considered precursors of condensable PM. The gas phase pollutants which converted into condensable PM should reduced for condensable PM control.

• Journal of Environmental Health Sciences
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• v.43 no.3
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• pp.185-193
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• 2017

Objectives: An on-site study was conducted in order to quantify indoor exposure levels and the emission rate of particulate matter for domestic poultry buildings. Materials and methods: Three types of poultry building (caged layer house, broiler house, and layer house with manure belt) as classified by mode of manure treatment and ventilation were investigated in this study. Nine sites per each poultry building were selected and visited for measuring exposure levels and emission rate of particulate matter. Total dust and respirable dust among the particulate matter were analyzed based on the weight method. Emission rates were estimated by dividing emission amount, which was calculated through multiplying indoor concentration ($mg/m^3$), by the ventilation rate ($m^3/h$), into indoor area ($m^2$) and number of poultry reared in the poultry building. Results: Mean exposure levels for total dust and respirable dust in the poultry buildings were $3.91({\pm}1.99)mg/m^3$ and $1.99({\pm}0.89)mg/m^3$, respectively. The emission rates of particulate matter in the poultry buildings were estimated as $4.75({\pm}1.22)mg\;head^{-1}h^{-1}$ and $64.39({\pm}24.95)g\;m^{-2}h^{-1}$ for total dust and $0.58({\pm}0.23)mg\;head^{-1}h^{-1}$ and $7.52({\pm}2.51)mg\;m^{-2}h^{-1}$ for respirable dust, respectively. The distribution patterns for total dust and respirable dust were similar regardless of poultry building type. Among poultry buildings, broiler house showed the highest exposure level and emission rate of total dust and respirable dust, followed by layer house with manure belt and caged layer house. Conclusions: The finding that the broiler house showed the highest exposure level and emission rate of particulate matter can be attributed to sawdust utilized as bedding material, which can be dispersed into the air by movements of the chickens. Thus, a work environmental management solution for optimally reducing dust concentrations is necessary for broiler houses.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.1
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• pp.56-62
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• 2021

High forecast accuracy is required as social issues on particulate matter increase. Therefore, many attempts are being made using machine learning to increase the accuracy of particulate matter prediction. However, due to problems with the distribution of imbalance in the concentration and various characteristics of particulate matter, the learning of prediction models is not well done. In this paper, to solve these problems, a binary classification model was proposed to predict the concentration of particulate matter needed for prediction by dividing it into two classes based on the value of 80㎍/㎥. Four classification algorithms were utilized for the binary classification of PM10. Classification algorithms used logistic regression, decision tree, SVM, and MLP. As a result of performance evaluation through confusion matrix, the MLP model showed the highest binary classification performance with 89.98% accuracy among the four models.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.1
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• pp.9-17
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• 2022

As the COVID-19 pandemic situation worsens, the time spent indoors increases, and the exposure to indoor environmental pollution such as indoor air pollution and noise also increases, causing problems such as deterioration of human health, stress, and discord between neighbors. This paper designs and implements a system that measures and monitors indoor air quality and noise, which are representative evaluation criteria of the indoor environment. The system proposed in this paper consists of a particulate matter measurement subsystem that measures and corrects the concentration of particulate matters to monitor indoor air quality, and a noise measurement subsystem that detects changes in sound and converts it to a sound pressure level. The concentration of indoor particulate matters is measured using a laser-based light scattering method, and an error caused by temperature and humidity is compensated in this paper. For indoor noise measurement, the voltage measured through a microphone is basically measured, Fourier transform is performed to classify it by frequency, and then A-weighting is performed to correct loudness equality. Then, the RMS value is obtained, high-frequency noise is removed by performing time-weighting, and then SPL is obtained. Finally, the equivalent noise level for 1 minute and 5 minutes are calculated to show the indoor noise level. In order to classify noise into direct impact sound and air transmission noise, a piezo vibration sensors is mounted to determine the presence or absence of direct impact transmitted through the wall. For performance evaluation, the error of particulate matter measurement is analyzed through TSI's AM510 instrument. and compare the noise error with CEM's noise measurement system.

• Journal of Environmental Health Sciences
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• v.20 no.4
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• pp.1-9
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• 1994

In order to investigate the regional level of air pollutants at Kangwha island situated on the western coast in Korea, the suspended particulate matter samples were collected by using the low volume air sampler on ten interval from March 1992 to February 1993 and the mass concentration of suspended particulate matter (SPM) and the chemical composition of water-soluble major ionic components in SPM samples were measured. During the sampling period, the average concentration of SPM under diameter 10 $\mu$m was found to be 48 $\mu$g/m$^3$ (+ 12). The seasonal concentration of SPM was showed in order of spring>fall>winter>summer. It was considered that higher concentration on spring than other season was affected by the long-range transport of Yellow sand particulate from China continent and lower concentration on summer by the washout and rainout effect of large rainfall. The content of water-soluble component in SPM samples was founded to be about 31% (14.69 $\mu$g/m$^3$) and 65% was unknown or unanalyzed. The content of cationic component showed in order of NH$_4^+$ (44.6%)>Na$^+$ (21.2%)>K$^+$ (14.7%)>Ca$^{2+}$ (13.6%)>Mg$^{2+}$ (5.9 %) and the content of anionic component SO$_4^{2-}$ (62.5%)>NO$_3^-$ (22.3%)>Cl$^-$ (15.2%), respectively. This fact indicates that ammonium and sulfate ion of water-soluble component in SPM sample were dominant in this region. From the chemical composition of water-soluble component, the most of Na$^+$, Mg$^{2+}$ and Cl$^-$ were originated from seawater source but K$^+$, Ca$^{2+}$ and SO$_4^{2-}$ were originated from other non-marine source. The contribution of seasalt to the composition of precipitation was 23%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.1
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• pp.8-14
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• 2020

The human impact of particulate matter are revealed and demand for improved forecast accuracy is increasing. Recently, efforts is made to improve the accuracy of PM10 predictions by using machine learning, but prediction performance is decreasing due to the particulate matter data with a large rate of low concentration occurrence. In this paper, separation prediction model by concentration is proposed to improve the accuracy of PM10 particulate matter forecast. The low and high concentration prediction model was designed using the weather and air pollution factors in Cheonan, and the performance comparison with the prediction models was performed. As a result of experiments with RMSE, MAPE, correlation coefficient, and AQI accuracy, it was confirmed that the predictive performance was improved, and that 20.62% of the AQI high-concentration prediction performance was improved.

• Journal of environmental and Sanitary engineering
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• v.24 no.3
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• pp.37-54
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• 2009

The study has analyzed impacts and intensity of weather that affect $PM_{10}$ concentration based on PM10 forecast conducted by the city of Seoul in order to identify ways to improve the accuracy of PM10 forecast. Variables that influence $PM_{10}$ concentration include not only velocity and direction of the wind and rainfalls, but also those including secondary particulate matter, which were identified to greatly influence the concentration in complicated manner as well. In addition, same variables were found to have different impacts depending on seasons and conditions of other variables. The study found out that improving accuracy of $PM_{10}$ concentration forecast face some limits as it is greatly influenced by the weather. As an estimation, this study assumed that basic research units and artificially estimated pollutant emissions, study on mechanisms of secondary particulate matter productions, observatory compliment, and enhanced forecaster's expertise are needed for better forecast.

• Transactions of the Korean Society of Automotive Engineers
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• v.7 no.7
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• pp.113-121
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• 1999

Diesel particulate matter is known to be one of the major harmful emissions produced by diesel engines. Diesel particulates are subject to diesel emission regulations and have lately become the focus in the diesel emission control technology. Thus, the aftertreatment system is adopted at the diesel engine exhaust to reduce the particulate emission. Although this benefit is recognized, it is not clear how the aftertreatment system influences quantitatively the particle size distribution distribution. In this study, the particle size distributions of diesel exhaust were measured using the scanning mobility particle sizer with and without the aftertreatment system. There results showed that the diesel particulate filter and plasm system reduced the number of emitted particles by more than 90% and about 80% respectivley in the particle size range of 20nm∼600nm. On the other hand no significant effect of the diesel oxidation catalyst on the particle number concentration was detected.

• Transactions of the Korean Society of Automotive Engineers
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• v.21 no.2
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• pp.98-103
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• 2013

Conventional method to estimate mass of particulate matter accumulated in diesel particulate filter is to use pressure difference between upstream and downstream of the filter. Then measured pressure difference should be compared that of clean condition which is no particulate matter accumulated in DPF. During regeneration soot oxidation is also estimated by same method. This methodology, however, has demerit on accuracy because of pressure difference deviation of clean DPFs and pressure difference caused by non-carbon based PM which is different from that of caused by carbon based PM. This study suggests new methodology to estimate accumulated soot oxidation rate through exhaust gas characteristics during regeneration. Results, more high accuracy of soot oxidation was obtained by analysis of relationship between fuel mass and concentration of carbon dioxide and oxygen.