• International Journal of Computer Science & Network Security
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• v.24 no.3
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• pp.196-200
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• 2024

Weather forecasting is considered to be of utmost important among various important sectors such as flood management and hydro-electricity generation. Although there are various numerical methods for weather forecasting but majority of them are reported to be Mechanistic computationally demanding due to their complexities. Therefore, it is necessary to develop and build models for accurately predicting the weather conditions which are faster as well as efficient in comparison to the prevalent meteorological models. The study has been undertaken to forecast various atmospheric parameters in the city of Bangalore using Naïve Bayes algorithms. The individual parameters analyzed in the study consisted of wind speed (WS), wind direction (WD), relative humidity (RH), solar radiation (SR), black carbon (BC), radiative forcing (RF), air temperature (AT), bar pressure (BP), PM₁₀ and PM_2.5 of the Bangalore city collected from Air Quality Monitoring Station for a period of 5 years from January 2015 to May 2019. The study concluded that Naive Bayes is an easy and efficient classifier that is centered on Bayes theorem, is quite efficient in forecasting the various air pollution parameters of the city of Bangalore.

• Spatial Information Research
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• v.20 no.2
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• pp.25-34
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• 2012

Air quality in Daegu area is lower compared to many other cities, since Daegu is a basin surrounded by mountains. Accordingly, the present study investigated the location of carbon monoxide(CO) monitoring stations for systematic CO pollution management on the basis of the CO emission distribution in Daegu area. In order to achieve this purpose, the location of CO monitoring stations, which can be used for the establishment of CO management, were assessed. Emission map in Daegu area was prepared using numerical map and Clean Air Policy Support System(CAPSS) data supplied by the M inistry of Environment. Average emissions were estimated by dividing emission sources into four subgroups(roadway, apartment, industry, and municipal incineration facility) according to legal division. The CO emission intensities were subdivided into 10, which a high number represents a high emission intensity, and the current monitoring stations were evaluated for the determination of their steps in CO emission intensities. As a result, additional installation of monitoring stations were suggested for the high CO emission areas rather than the low CO emission areas. A systematic CO management strategy would be established by the supplying various principle CO data when the CO monitoring stations are additionally installed at Kukwudong and other six sites on the basis of analyses of data obtained from 1999 to 2007.

• Journal of Korean Society for Atmospheric Environment
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• v.18 no.4
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• pp.305-316
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• 2002

Various air pollutants were measured at Tokchok Island in the Yellow Sea that is about 50 km distant from Incheon since April 1999. This study was undertaken to help understanding the level of air pollution and its distribution characteristics over the Yellow Sea. The geographical characteristics of the air pollution monitoring station and emission inventories of Tokchok Island are introduced. The mass concentrations and chemical compositions of TSP and P $M_{2.5}$ measured until March 2000 are discussed in this paper. The overall average mass concentrations are about 37.2 $\mu\textrm{g}$/㎥ for TSP and 18.7 $\mu\textrm{g}$/㎥ for P $M_{2.5}$ , respectively, which are similar to or a little lower than the values observed in other background sites at Kangwha and Taean. However, they are much lower than those observed at Qingdao in China. The low mass concentration and major anthropogenic ion concentrations in aerosols collected at Tokchok Island show that local sources are not dominant at Tokchok Island. The estimated average fractions of anthropogenic non-sea-salt sulfate to the total sulfate concentration of TSP and P $M_{2.5}$ are greater than 80% for both sizes. It hence suggests that the sulfate be mainly affected by anthropogenic sources. If we consider the average mass ratio of P $M_{2.5}$ to TSP, the mass fractions of anthropogenic species to P $M_{2.5}$ , and the molar ratio of nss S $O_4$$^{2-}$ to total N $O_{3-}$, it may be possible to infer that a part of anthropogenic species measured at Tokchok Island be transported from China. The characteristics of neutralization of nss S $O_4$$^{2-}$ and the particle size of major ions are also discussed.

• 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.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.3
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• pp.1879-1887
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• 2013

To investigate the level of genotoxicity over Bangkok atmosphere, $PM_{10}$ samples were collected at the Klongchan Housing Authority (KHA), Nonsree High School (NHS), Watsing High School (WHS), Electricity Generating Authority of Thailand (EGAT), Chokchai 4 Police Station (CPS), Dindaeng Housing Authority (DHA) and Badindecha High School (BHS). For all monitoring stations, each sample covered a period of 24 hours taken at a normal weekday every month from January-December 2006 forming a database of 84 individual air samples (i.e. $12{\times}7=84$). Atmospheric concentrations of low molecular weight PAHs (i.e. phenanthrene, anthracene, pyrene and fluoranthene) were measured in $PM_{10}$ at seven observatory sites operated by the pollution control department of Thailand (PCD). The mutagenicity of extracts of the samples was compared in Salmonella according to standard Ames test method. The dependence of the effects on sampling time and on sampling location was investigated with the aid of a calculation of mutagenic index (MI). This MI was used to estimate the increase in mutagenicity above background levels (i.e. negative control) at the seven monitoring sites in urban area of Bangkok due to anthropogenic emissions within that area. Applications of the AMES method showed that the average MI of $PM_{10}$ collected at all sampling sites were $1.37{\pm}0.10$ (TA98; +S9), $1.24{\pm}0.08$ (TA98; -S9), $1.45{\pm}0.10$ (TA100; +S9) and $1.30{\pm}0.09$ (TA100; -S9) with relatively less variations. Analytical results reconfirm that the particulate PAH concentrations measured at PCD air quality monitoring stations are moderately low in comparison with previous results observed in other countries. In addition, the concept of incremental lifetime particulate matter exposure (ILPE) was employed to investigate the potential risks of exposure to particulate PAHs in Bangkok atmosphere.

• Journal of Environmental Science International
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• v.11 no.12
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• pp.1253-1259
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• 2002

This study analyzes the surface ozone, NO and $NO_2$ concentration data from 1997 to 1999 in Daegu. It investigates effect on precursor during high-ozone episode days. The high-ozone episode is defined when a daily maximum ozone concentration is higher than 100 ppb(ambient air quality standard of Korea) in at least one station among six air quality monitoring stations. The frequency of episodes is 13 days(33 hours). The frequency is the highest in May and September, and the area with the highest frequency is Nowondong and Manchondong. The average value of daily maximum ozone concentration with high ozone episode is 81.6 ppb, and that of 8-hour average ozone concentration is 58.6 ppb. It means that ozone pollution is continuous and wide-ranging in Daegu. The daily variation of NO, $NO_2$ and $O_3$ in high-ozone episodes are inversely proportional one another. Nowondong an industrial area, is affected by pollutants that are emitted from the primary sources, while Manchondong a residential area, is affected by the advection of $O_3$ or by the primary pollutants like VOCs.

• Journal of Korean Society for Atmospheric Environment
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• v.32 no.4
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• pp.422-434
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• 2016

This study was conducted to understand roles of $NO_x(=NO+NO_2)$ on high $O_3$ episodes at an urban monitoring station in Seoul. Concentrations of NO, $NO_2$, $NO_y$ and $O_3$ were measured intensively at KIST monitoring station which located at urban center in Seoul metropolitan area during May 18~June 13, 2015. Sampling period was planed because high $O_3$ and PM occurred frequently during from late spring to early summer months in Seoul. The experimental site locates in NW from center of Seoul and is surrounded by residential area. Belt highway of the city runs from north to west side nearby experimental site. Vehicle exhaust emissions due to heavy traffic influenced $NO_x$ concentration at the site during northwesterly wind. Specific $NO_2$ concentration was measured by Blue Light photolytic converter, and it was compared to $NO_2$ concentration measured by molybedenum converter. $[NO_2]_{phtolysis}$ was usually lower than $[NO_2]_{molybedenum}$ during the experiment period; however their diurnal variations were very similar. The linear relationship between these $NO_2$ concentrations was found to be $[NO_2]_{phtolysis}$=0.64 $[NO_2]_{molybedenum}$ - 2.6, $r^2$=0.83 during May 16~8, 2015. The difference between $NO_2$ by molybdenum converter and by photolytic converter (${\Delta}NO_2=[NO_2]_{molybedenum}-[NO_2]_{phtolysis}$) accounted for residual $NO_y$ which can represent $NO_z$ (=$NO_y-NO_x$). $O_3$ concentration showed typical daily trend which has maximum at late afternoon and minimum during the night. $O_3$ increased at a rate of 7 ppb/hr since 8 am. and reached the maximum concentration (~80 ppb) at 3 pm.. The diurnal pattern of $O_3$ was inversely related with that of $NO_2$, suggesting that the formation of $O_3$ was the result of photochemical activity of $NO_2$.

• The Korean Journal of Applied Statistics
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• v.26 no.1
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• pp.93-109
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• 2013

Geostatistical data or point-referenced data have the information on the monitoring stations of interest where the observations are measured. Practical geostatistical data are obtained from a wide variety of observational monitoring networks that are mainly operated by the Korean government. When we analyze geostatistical data and predict the expectations at unobservable locations, we can improve the reliability of the prediction by utilizing some relevant spatial data obtained from different observational monitoring networks and blend them with the measurements of our main interest. In this paper, we consider the hierarchical spatial linear model that enables us to link spatial variables from different resources but with similar patterns and guarantee the precision of the prediction. We compare the proposed model to a classical linear regression model and simple kriging in terms of some information criteria and one-leave-out cross-validation. Real application deals with Sulfur Dioxide($SO_2$) measurements from the urban air pollution monitoring network and wind speed data from the surface observation network.

• Journal of Korean Society for Atmospheric Environment
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• v.34 no.4
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• pp.567-587
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• 2018

A severe haze event occurred in October 2015 in Gwangju, Korea. In this study, the driving chemical species and the formation mechanisms of $PM_{2.5}$ pollution were investigated to better understand the haze event. Hourly concentrations of $PM_{2.5}$, organic and elemental carbon, water-soluble ions, and elemental constituents were measured at the air quality intensive monitoring station in Gwangju. The haze event occurred was attributed to a significant contribution (72.3%) of secondary inorganic species concentration to the $PM_{2.5}$, along with the contribution of organic aerosols that were strongly attributed to traffic emissions over the study site. MODIS images, weather charts, and air mass backward trajectories supported the significant impact of long-range transportation (LTP) of aerosol particles from northeastern China on haze formation over Gwangju in October 2015. The driving factor for the haze formation was stagnant atmospheric flows around the Korean peninsula, and high relative humidity (RH) promoted the haze formation at the site. Under the high RH conditions, $SO{_4}^{2-}$ and $NO_3{^-}$ were mainly produced through the heterogenous aqueous-phase reactions of $SO_2$ and $NO_2$, respectively. Moreover, hourly $O_3$ concentration during the study period was highly elevated, with hourly peaks ranging from 79 to 95ppb, suggesting that photochemical reaction was a possible formation process of secondary aerosols. Over the $PM_{2.5}$ pollution, behavior and formation of secondary ionic species varied with the difference in the impact of LTP. Prior to October 19 when the influence of LTP was low, increasing rate in $NO_3{^-}$ was greater than that in $NO_2$, but both $SO_2$ and $SO{_4}^{2-}$ had similar increasing rates. While, after October 20 when the impact of haze by LTP was significant, $SO{_4}^{2-}$ and $NO_3{^-}$ concentrations increased significantly more than their gaseous precursors, but with greater increasing rate of $NO_3{^-}$. These results suggest the enhanced secondary transformation of $SO_2$ and $NO_2$ during the haze event. Overall, the result from the study suggests that control of anthropogenic combustion sources including vehicle emissions is needed to reduce the high levels of nitrogen oxide and $NO_3{^-}$ and the high $PM_{2.5}$ pollution occurred over fall season in Gwangju.

• Journal of Korean Society for Atmospheric Environment
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• v.24 no.2
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• pp.143-152
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• 2008

This study investigates the variation and spatial distribution of nitrogen dioxide($NO_2$) concentrations measured with passive diffusive samplers at 40 curbside points in Daejeon. Average $NO_2$ concentration was $39.8{\pm}18.0\;ppb$ (n=1,127) and the significant difference in concentrations by regional groups (Dong-gu, Jung-gu, Seo-gu, Daedeok-gu, Yuseong-gu) was not observed. The frequency distribution of $NO_2$ concentration was found to be a normal distribution with the high frequency in the concentration range of 30 to 40 ppb (20 to 25%). Average $NO_2$ concentration measured during the rainy periods was lower than that measured during the non-rainy periods and the decrease of concentration by rainfall was about 16% (7 ppb). The variation of $NO_2$ concentrations measured by passive diffusive samplers during the sampling period was similar to that continuously measured at the air quality monitoring station.