• Journal of the Korean Institute of Landscape Architecture
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• v.47 no.6
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• pp.67-77
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• 2019

The purpose of this study is to analyze how much particulate matter at the center of the urban park is reduced compared to the entrance of the park, where the particulate matter problem is serious. It also endeavored to analyze the relationship between the space closure rate and particulate matter reduction rate in the center of the park through the collection and analysis of experimental data. Seven flat land type urban neighborhood parks in Suseong-gu, Daegu were measured at the same place for three days. The research results are as follows. First, the center of the urban neighborhood park had an average temperature 1.05℃ lower than at the entrance and an average humidity of 2.57% higher. Second, the rate of fine dust reduction was PM1- 17.09%, PM2.5- 17.65%, PM10- 14.99%. As for the reduction rate of particulate matter, the smaller the size of the park, the greater the reduction rate. In addition, the reduction rate at the center of the park was lower on days when particulate matter concentration based on the weather reports was low. The higher the concentration at the park entrance, the higher the reduction rate was. Third, a higher the rate of space closures at the center of the park resulted in a higher effect of particulate matter reduction. Noting this, the relationship between particulate matter reduction and the space closure rate in urban neighborhood parks was clearly shown. We hope to be the basis for more extensive experimental data collection.

• Journal of information and communication convergence engineering
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• v.19 no.4
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• pp.241-247
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• 2021

Particulate matter has emerged as a serious global problem, necessitating highly reliable information on the matter. Therefore, various algorithms have been used in studies to predict particulate matter. In this study, we compared the prediction performance of neural network models that have been actively studied for particulate matter prediction. Among the neural network algorithms, a deep neural network (DNN), a recurrent neural network, and long short-term memory were used to design the optimal prediction model using a hyper-parameter search. In the comparative analysis of the prediction performance of each model, the DNN model showed a lower root mean square error (RMSE) than the other algorithms in the performance comparison using the RMSE and the level of accuracy as metrics for evaluation. The stability of the recurrent neural network was slightly lower than that of the other algorithms, although the accuracy was higher.

• Transactions of the Korean Society of Automotive Engineers
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• v.17 no.3
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• pp.29-34
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• 2009

Diesel particulate filter (DPF) has been developed to optimize engine out emission, especially particulate matter (PM). One of the main important factors for developing the DPF is estimation of soot mass being accumulated inside the DPF. Evaluation of pressure drop over the DPF is a simple way to estimate the accumulated soot mass but its accuracy is known to be limited to certain vehicle operating conditions. The method to compensate drawback is adoption of integrating time history of the engine out PM and burning soot. Present study demonstrates current status of the soot estimation methods including the results from the engine test benches and vehicles.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.25 no.4
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• pp.15-31
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• 2022

The Green Purification Unit System (GPUS) is a green infrastructure facility applicable to the roadside to reduce particulate matter from road traffic. This study introduces two types of GPUS (type1 and type2) and assesses the performance and maintenance costs of each of them. The GPUS's performance analysis used the data collected in November 2021 after the installation of the GPUS type1 and type2 at the study site in Suwon. The changes in the particulate matter concentration near the GPUS were measured. The maintenance cost of GPUS type1 and type2 was assessed by calculating the initial installation cost and the management and repair cost after installation. The results of the performance analysis showed that the GPUS type1, which was manufactured by combining plants and electric dust collectors, had a superior particulate matter reduction performance. In particular, type1 produced a greater effect of particulate matter reduction in the time with a high concentration (50㎍/m³ or higher) of particulate matter due to the operation of electric dust collectors. GPUS type2, which was designed in the form of a plant wall without applying an electric dust collector, showed lower reduction performance than type1 but showed sufficiently improved performance compared to the existing band green area. Meanwhile, the GPUS type1 had three times higher costs for the initial installation than GPUS type2. In terms of costs for managing and repairing, it was evaluated that type1 would be slightly more costly than type2. Finally, this study discussed the applicability of two types of GPUS based on the result of the analysis of their particulate matter performance and maintenance cost at the same time. Since GPUS type2 has a cheaper cost than type1, it could be more economical. However, in the area suffering a high concentration of particulate matter, GPUS type1 would be more effective than type2. Therefore, the choice of GPUS types should rely on the status of particulate matter concentration in the area where GPUS is being installed.

• Journal of Environmental Science International
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• v.14 no.11
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• pp.1063-1069
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• 2005

In order to investigate the distribution of suspended particulate matter of the surface water in the South Sea of Korea in early winter, the cruise results during 2 to 8 December 2004 were analyzed in relation to the hydrography. The front was formed along the line connecting between Tsushima and Cheju Islands, which divided the water into two water masses; the coastal water with for temperature and for salinity, and the Tsushima Warm Current Water with high temperature and high salinity. In the coastal water the suspended particulte matter was 5.0-6.5 mg/l, while in the oceanic water suspended particulate matter was 4.5-5.0 mg/l. The coastal water showed higher mixing effects, compared to the oceanic area where vertical stratification was clearly formed. These indicate that the distribution of suspended particulate matter was affected by the stratification or mixing of the water column. Also it is suggested that the mixing effects of sea surface cooling and rind play an important role on the distribution of suspended particulate matter in the South Sea of Korea in winter time.

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1208-1215
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• 2017

Recently, people's attention and worries about fine particulate matter have been increasing. Due to the construction and maintenance costs, there are insufficient air quality monitoring stations. As a result, people have limited information about the concentration of fine particulate matter, depending on the location. Studies have been undertaken to estimate the fine particle concentrations in areas without a measurement station. Yet there are limitations in that the estimate cannot take account of other factors that affect the concentration of fine particle. In order to solve these problems, we propose a framework for estimating the concentration of fine particulate matter of a specific area using meteorological data and traffic data. Since there are more grids without a monitor station than grids with a monitor station, we used a domain adversarial neural network based on the domain adaptation method. The features extracted from meteorological data and traffic data are learned in the network, and the air quality index of the corresponding area is then predicted by the generated model. Experimental results demonstrate that the proposed method performs better as the number of source data increases than the method using conditional random fields.

• Resources Recycling
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• v.29 no.1
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• pp.62-69
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• 2020

Manufacturing processes in industrial complexes produce NOx, SOx, VOCs, which cause particulate matter (PM). Therefore, this study analyzed the characteristics of each industrial complex by using scattered public data, matched the existing particulate matter(PM) reduction technology, and proposed an optimized reduction plan. The application of matching technologies and facilities by industrial complexes based on data is able to mitigate NOx, SOx, and VOCs which cause particulate matter in the process in advance. This way can be an effective alternative in order to reduce PM in the manufacturing processes as well as industrial complexes.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.543-545
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• 2019

The AQI index has been developed and used to guide the action of particulate matter. Information on the AQI index can be easily provided to the general public, and various services are provided based on the AQI index. As services are provided, accurate AQI index prediction is needed. In this paper, we design the classification model using the circular neural network to predict the AQI index of particulate matter. For the evaluation of the designed model, compare the AQI index of the actual particulate matter with the predicted value.

• Journal of Environmental Health Sciences
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• v.32 no.6
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• pp.533-538
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• 2006

The nano-particles are known to influence the environmental protection and human health. The relationships between transient vehicle operation and nano-particle emissions are not well-known, especially for diesel passenger vehicles with DPF(Diesel Particulate Filter). In this study, two diesel passenger vehicles were measured on a chassis dynamometer test bench. The particulate matter (PM) emission of these vehicles was investigated by number and mass measurement. The mass of the total PM was evaluated using the standard gravimetric measurement method, and the total number concentrations were measured on a ECE15+EUDC driving cycle using Condensation Particle Counter (CPC). According to the investigation results, total number concentration was $1.14{\times}10^{11}$M and mass concentration was 0.71mg/km. About 99% of total number concentration was emitted during the $0{\sim}400s$ because of engine cold condition. In high temperature and high speed duration, the particulate matter was increased but particle concentration was emitted not yet except initial engine cold condition According to DPF performance deterioration, the particulate matter was emitted 2 times and particle concentration was emitted 32 times. Thus DPF performance deterioration affects particle concentration more than PM.

• Proceedings of the PSK Conference
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• 2003.04a
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• pp.165.1-165.1
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• 2003

This research was designed to examine the presence of mutagenic/carcinogenic compounds in airborne pollutants in diesel particulate matter using an integrated biological approach. Respirable air borne particulate matter (PM2.5: <2.5mm) was collected from diesel engine exhaust using a high-volume sampler equipped with a cascade impactor. (omitted)