• Journal of Environmental Science International
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• v.12 no.9
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• pp.987-996
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• 2003

In this study, we analyzed the relationship between the time-variation trend of air pollution concentration index and the meteorological conditions with CO(carbon monoxide) concentration and meteorological observation data in high-CO episode days. CO is a representative automobile air pollutant. The results are as follows; 1. Most of the high-CO episode days within 30 classes appeared in winter season. 2. Most of them appeared under the surface weather conditions with east-west high-pressure system. The surface winds in this high-pressure area were very light. 3. The high-CO episode days were due to unusual accumulation within urban atmosphere in the morning. 4. The Atmospheric stabilities were more stable, and then the wind-ventilation conditions were worse than yearly mean atmospheric condition in Daegu.

• Asian Journal of Atmospheric Environment
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• v.9 no.1
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• pp.12-21
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• 2015

Air pollution is a challenging issue in some of the large cities in developing countries. In this regard, data interpretation is one of the most important parts of air quality management. Several methods exist to analyze air quality; among these, we applied the Multilayer Perceptron (MLP) and Radial Basis Function (RBF) methods to predict the hourly air concentration of benzene in 14 districts in the municipality of Tehran. Input data were hourly temperature, wind speed and relative humidity. Both methods determined reliable results. However, the RBF neural network performance was much closer to observed benzene data than the MLP neural network. The correlation determination resulted in 0.868 for MLP and 0.907 for RBF, while the Index of Agreement (IA) was 0.889 for MLP and 0.937 for RBF. The sensitivity analysis related to the MLP neural network indicated that the temperature had the greatest effect on prediction of benzene in comparison with the wind speed and humidity in the study area. The temperature was the most significant factor in benzene production because benzene is a volatile liquid.

• Asian Journal of Atmospheric Environment
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• v.10 no.2
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• pp.67-79
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• 2016

The forecasting of air pollution is an important and popular topic in environmental engineering. Due to health impacts caused by unacceptable particulate matter (PM) levels, it has become one of the greatest concerns in metropolitan cities like Karaj City in Iran. In this study, the concentration of $PM_{2.5}$ was predicted by applying a multilayer percepteron (MLP) neural network, a radial basis function (RBF) neural network and a Markov chain model. Two months of hourly data including temperature, NO, $NO_2$, $NO_x$, CO, $SO_2$ and $PM_{10}$ were used as inputs to the artificial neural networks. From 1,488 data, 1,300 of data was used to train the models and the rest of the data were applied to test the models. The results of using artificial neural networks indicated that the models performed well in predicting $PM_{2.5}$ concentrations. The application of a Markov chain described the probable occurrences of unhealthy hours. The MLP neural network with two hidden layers including 19 neurons in the first layer and 16 neurons in the second layer provided the best results. The coefficient of determination ($R^2$), Index of Agreement (IA) and Efficiency (E) between the observed and the predicted data using an MLP neural network were 0.92, 0.93 and 0.981, respectively. In the MLP neural network, the MBE was 0.0546 which indicates the adequacy of the model. In the RBF neural network, increasing the number of neurons to 1,488 caused the RMSE to decline from 7.88 to 0.00 and caused $R^2$ to reach 0.93. In the Markov chain model the absolute error was 0.014 which indicated an acceptable accuracy and precision. We concluded the probability of occurrence state duration and transition of $PM_{2.5}$ pollution is predictable using a Markov chain method.

• Advances in environmental research
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• v.4 no.4
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• pp.219-231
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• 2015

In recent years, raising air pollutants has become as a big concern, especially in metropolitan cities such as Tehran. Therefore, forecasting the level of pollutants plays a significant role in air quality management. One of the forecasting tools that can be used is an artificial neural network which is able to model the complicated process of air pollution. In this study, we applied two different methods of artificial neural networks, the Multilayer Perceptron (MLP) and Radial Basis Function (RBF), to predict the hourly air concentrations of toluene in Tehran. Hourly temperature, wind speed, humidity and $NO_x$ were selected as inputs. Both methods had acceptable results; however, the RBF neural network produced better results. The coefficient of determination ($R^2$) between the observed and predicted data was 0.9642 and 0.99 for MLP and RBF neural networks, respectively. The results of the mean bias errors (MBE) were 0.00 and -0.014 for RBF and MLP, respectively which indicate the adequacy of the models. The index of agreement (IA) between the observed and predicted data was 0.999 and 0.994 in the RBF and the MLP, respectively which indicates the efficiency of the models. Finally, sensitivity analysis related to the MLP neural network determined that temperature was the most significant factor in air concentration of toluene in Tehran which may be due to the volatile nature of toluene.

• Atmosphere
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• v.30 no.4
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• pp.439-454
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• 2020

This study investigates the change in the fine particulate matter (PM2.5) concentration and World Health Organization (WHO) air quality index (AQI) in East Asia (EA) under Shared Socioeconomic Pathways (SSPs). AQI is an indicator of increasing levels about health concern, divided into six categories based on PM2.5 annual concentrations. Here, we utilized the ensemble results of UKESM1, the climate model operated in Met Office, UK, for the analysis of long-term variation during the historical (1950~2014) and future (2015~2100) period. The results show that the spatial distributions of simulated PM2.5 concentrations in present-day (1995~2014) are comparable to observations. It is found that most regions in EA exceeded the WHO air quality guideline except for Japan, Mongolia regions, and the far seas during the historical period. In future scenarios containing strong air quality (SSP1-2.6, SSP5-8.5) and medium air quality (SSP2-4.5) controls, PM2.5 concentrations are substantially reduced, resulting in significant improvement in AQI until the mid-21st century. On the other hand, the mild air pollution controls in SSP3-7.0 tend to lead poor AQI in China and Korea. This study also examines impact of increased in PM2.5 concentrations on downward shortwave energy at the surface. As a result, strong air pollution controls can improve air quality through reduced PM2.5 concentrations, but lead to an additional warming in both the near and mid-term future climate over EA.

• Advances in environmental research
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• v.3 no.3
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• pp.207-216
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• 2014

A study was conducted to determine the phytotoxic effect of mercury on seed germination and seedling growth of an important arid legume tree Albizia lebbeck. The seeds germination and seedling growth performance of A. lebbeck responded differently to mercuric chloride treatment (1 mM, 3 mM, 5 mM and 7 mM) as compared to control. Seed germination of A. lebbeck was significantly (p < 0.05) affected by mercury treatment at 1 mM. Root growth of A. lebbeck was not significantly affected by mercury treatment at 1 mM, and 3 mM. Shoot and root length of A. lebbeck were significantly (p < 0.05) affected by 5 mM concentration of mercury treatment. Increase in concentration of mercury treatment at 5 mM and 7 mM significantly (p < 0.05) reduced seedling dry weight of A. lebbeck. The treatment of mercury at 1 mM decreased high percentage of seed germination (22%), seedling length (10%), root length (21.85%) and seedling dry weight (9%). Highest decrease in seed germination (51%), seedling (34%), root length (48%) and seedling dry weight (41%) of A. lebbeck occurred at 7 mM mercury treatment. A. lebbeck showed high percentage of tolerance (78.14%) to mercury at 1 mM. However, 7 mM concentration of mercury produced lowest percentage of tolerance (51.65%) in A. lebbeck. The seed germination potential and seedling vigor index (SVI) clearly decreased with the higher level of mercury. Plantation of A. lebbeck in mercury-polluted area will help in reducing the burden of mercury pollution. A. lebbeck can serve better in coordinating in land management programs in metal contaminated areas. The identification of the toxic concentration of metals and tolerance indices of A. lebbeck would also be helpful for the establishment of air quality standard.

• Journal of the Korean Institute of Landscape Architecture
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• v.49 no.5
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• pp.1-11
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• 2021

The purpose of this study is to find out the main factors affecting air quality in urban physical space factors, and provide clues for environmental improvement. Nine monitoring stations in China's industrial city, Changchun, collected AQI concentration data from January 1, 2018 to December 31, 2019. This paper analyzes the types and distribution characteristics of urban physical facilities within a radius of 300m with the detection station as the center. The monitoring station is divided into three groups, and the difference in floating dust concentration among the three groups in different seasons is analyzed. The results show that AQI concentration is the highest in spring and winter, followed by summer, and the lowest in autumn. The place with the highest concentrations of AQI in spring are F (93.00), D (91.10), I (89.20), in summer are D (69.05), A (67.89), B (84.44), in autumn are I (62.80), G (60.84), D (53.27), D (53.27), in winter are I (95.82), H (95.60), f (94.04). Through SPSS analysis, it shows that the air index in a space with a diameter of 600 meters is related to forest land, grassland, bare land, water space, tree height, building area (average value), and building volume (average value). According to the statistical analysis results of spring and winter with the most serious pollution, forest land area (43,637m², 15.44%) and water surface area (18,736m², 6.63%) accounted for the majority, and group 1 (A, B, C) with the least average building area (448m², 0.17%) and average building volume (10,201m²) had the lowest pollution concentration. On the contrary, group 2 (D, E, F) had the highest AQI concentration, with less or no woodland (1,917m², 0.68%) and water surface area (0m², 0%), and the highest average building area (1,056m², 0.37%) and average building volume (17,470m³). It is confirmed that the characteristics of the area with the highest AQI concentration are that the more the site ratio of tree height above 12m, the smaller the site ratio of bare land, and the lower the pollution degree. On the contrary, the larger the area of bare land, the higher the pollution degree. By analyzing the characteristics of nine monitoring stations in Changchun, it can be seen that the air quality brought by the physical characteristics of urban space is closely related to the above factors.

• Journal of Advanced Marine Engineering and Technology
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• v.23 no.4
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• pp.543-551
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• 1999

Diesel engine is a major source of the air pollution. In general the concentrations of these pollu-tants in diesel engine exhaust differ from values calculated assuming chemical equibrium. Thus the detailed chemical mechanisms by which these pollutions form and the kinetic of these process-es are important in determining emission levels. In this study the computer program has been developed to calculate the required thermodynam-ic properties of combustion products(10 spacies) for both equilibrium and non-equilibrium in cylin-der for diesel engines. Nitric oxide emissions are calculated by using the extended Zeldovich Kinet-ic mechanism with a steady state assumption for the N concentration and equilibrium values used for H, O, $O_2$ and OH concentrations. By the results it is confirmed that developed simulations program with the NO prediction model is validated against residual mass fraction combustion index of Wiebe's functions pre-mixed com-bustion ration fuel injection timing.

• Journal of Korean Society for Atmospheric Environment
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• v.21 no.5
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• pp.495-505
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• 2005

This study is to estimate source contribution by air pollutantion types (point, area, line) over Seoul metropolitan area. The Air Pollution Model (TAPM) and the highly resolved anthropogenic and biogenic gridded emissions ($1km{\times}1km$) were applied to simulate $SO_2,\;NO_2,\;O_3\;and\;PM_{10}$ concentrations by seasons and contribution was estimated by their source types (point, area, line). The results showed that the simulated concentrations of secondary pollutant agreed well with observed values with an index of agreement (IOA) over 0.4, whereas IOAs over 0.3 were observed for most primary pollutants. The contributions of each source types by seasons were similar. The point source contribution was the highest for $SO_2$ at medium level ranged from $55.1\%\;to\;61.5\%$. But the contribution from area source during for the spring and summer increased as the concentration level increased. The line source contribution was the highest for $NO_2$ at all levels ranged from $68.3\%\;to\;93.1\%$. The results indicate that $SO_2$ emissions should be mainly controlled from point source, as well as area source at higher level concentration. Also, $NO_2\;and\;PM_{10}$ to from line source should be controlled.

• Journal of Korean Society of Forest Science
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• v.87 no.3
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• pp.404-414
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• 1998

The purpose of this study was to investigate the influence of air pollution using epiphytic lichens on forest trees around Ulsan and Onsan industrial complex from April to June, 1997. The distribution of lichens was investigated at 58 sampled sites. In this study, 16 kinds of epiphytic lichens were recorded, of them, Lepraria sp. having a tolerance to air pollution showed the highest frequency as 30.85%, and in order of Lecanora strobilina(26.18%) and Parmelia austrosinensis(13.42%) unknown to tolerance of air pollution. The number of lichens was gradually decreased around industrial complex, where so - called "lichen desert" was detected. As being distant from the industrial complex, the mean degrees of cover were increased. The degrees of cover in the investigated sites ranged from I to V. According to $SO_2$ concentration limiting lichen's growth, the pattern of distribution of Cladonia sp., Dirinaria applanata, Parmelia austrosinensis, Lepraia sp. and Lecanora strobilina were dissimilar by sensitivity to air pollution Especially Lepraria sp. and Lecanora strobilina were widely distributed to degree of cover from I to V, and as the both had a similar distribution pattern, it could be inferred that Lecanora strobilina had also a tolerance to air pollution. The IAP values ranging from 0 to 64.3 were arranged into six groups and the investigated area was delineated into six IAP zones to represent degree of air environment. It was a high IAP value as being distant from industrial complex. The mare IAP increased, the more number of kinds of lichens increased. It was confirmed that the number of species, coverage and IAP value of epiphytic lichens showed a tendency to decrease of urban area and industrial complex.