• Journal of Korean Society for Atmospheric Environment
• /
• v.13 no.6
• /
• pp.415-426
• /
• 1997

Some arguments have been about over the representativeness of government-run air quality monitoring stations among scholars and non-governmental organizations (NGOs). However, it is not a simple problem to move monitoring stations because of continuity of data and high cost. So it is necessary to complement the monitoring data if it do not represent the ambient air quality properly. The purpose of this study was to evaluate the representativeness of some monitoring stations using passive $NO_2$ samplers and to find a complementary method from linear regression. Two stations were chosen for the evaluation: Shinlim Station was one of the most controversial stations in Seoul and Banpo Station had the best reputation. Air qualities were surveyed at seven points around each monitoring station with consideration of land use and distance. The ratios of the average $NO_2$ levels of the areas to these at the monitoring stations were 1.59 for Shinlim Station and 1.03 for Banpo Station. The differences between the average $NO_2$ levels and those at the monitoring stations were 10.75 ppb for Shilim Station and 0.34 ppb for Banpo Station. The correlation coefficients between the two levels were 0.7668 for Shinlim and 0.7662 for Banpo. The average coefficients of determination $(R^2)$ were 0.61 for Shinlim and 0.61 for Banpo. The Shinlim Station could not represent the air quality of Shinlim-Dong good because it is located in a green area at an outskirt of Shinlim-Dong. But the Banpo Station located in a central residential area of Banpo-Dong showed a fair representativeness. However, air quality turned out to be different with land use such as residential area, green area or road: the air quality data from a monitoring station located at a certain land use should not be interpreted as representing the air quality at any sites around the station. Equations to predict the average $NO_2$ levels of each area from the data from the monitoring stations were presented based on linear regression.

• Journal of Environmental Impact Assessment
• /
• v.20 no.1
• /
• pp.49-59
• /
• 2011

The results of comparing $PM_{10}$ concentration between 'Namsan' and 'Yongsan-gu' air quality monitoring stations show similar values with averaged concentration in the whole Seoul. The correlation factors in both sites were 0.865, 0.828 in 2005, 2006, respectively. For 'Bukhansan' and 'Gangbuk-gu' air quality monitoring stations, different from the results mentioned above, they showed clear differences as altitude changes. PM10 concentration in 'Bukhansan' monitoring stations was 10 ${\mu}g/m^3$ lower than 'Gangbuk-gu' monitoring station which is located near the ground. Also, averaged PM10 concentration in 'Bukhansan' and 'Gangbuk-gu' monitoring stations was lower than that in the whole Seoul. When comparing $NO_2$ concentration between 'Namsan' and 'Yongsan-gu' monitoring stations, $NO_2$ concentration in 'Namsan' monitoring station was lower than 'Yongsan-gu' monitoring station. For $NO_2$ concentration in 'Bukhansan', 'Gangbuk-gu' and 'the whole Seoul', there were the same pattern in 'Gangbuk-gu' and the 'the whole Seoul' and low values in 'Bukhansan' monitoring station. The correlation factors of $NO_2$ concentration in 'Bukhansan' and 'Gangbukgu' was 0.525, 0.549 in 2005, 2006, respectively, which stands for low correlationship.

• Advances in environmental research
• /
• v.7 no.2
• /
• pp.87-107
• /
• 2018

Water quality monitoring network needs periodic evaluations based on environmental demands and financial constraints. We used a genetic algorithm to optimize the existing water quality monitoring stations on the Sefid-Rud River, which is located in the North of Iran. Our objective was to optimize the existing stations for drinking and irrigation purposes, separately. The technique includes two stages called data preparation and the optimization. On the data preparation stage, first the basin was divided into four sections and each section was consisted of some stations. Then, the score of each station was computed using the data provided by the water Research Institute of the Ministry of energy. After that, we applied a weighting method by providing questionnaires to ask the experts to define the significance of each parameter. In the next step, according to the scores, stations were prioritized cumulatively. Finally, the genetic algorithm was applied to identify the best combination. The results indicated that out of 21 existing monitoring stations, 14 stations should remain in the network for both irrigation and drinking purposes. The results also had a good compliance with the previous studies which used dynamic programming as the optimization technique.

• Journal of Environmental Science International
• /
• v.25 no.5
• /
• pp.673-681
• /
• 2016

The adequacy of urban air quality monitoring networks in the largest metropolitan city, Seoul was evaluated using multivariate analysis for $SO_2$, $NO_2$, CO, PM10, and $O_3$. Through cluster analysis for 5 air pollutants concentrations, existing monitoring stations are seen to be clustered mostly by geographical locations of the eight zones in Seoul. And the stations included in the same cluster are redundantly monitoring air pollutants exhibiting similar atmospheric behavior, thus it can be seen that they are being operated inefficiently. Because monitoring stations groups representing redudancy were different depending on measurement items and several pollutants are being measured at the same time in each air monitoring station, it is seemed to be not easy to integrate or transmigrate stations. But it may be proposed as follows : the redundant stations can be integrated or transmigrated based on ozone of which measures are increasing in recent years and alternatively the remaining pollutants other than the pollutant exhibiting similar atmospheric behavior with nearby station's can be measured. So it is considered to be able to operate air quality monitoring networks effectively and economically in order to improve air quality.

• Journal of Korean Society on Water Environment
• /
• v.33 no.6
• /
• pp.680-688
• /
• 2017

Water quality monitoring for flow rates and BOD/COD/T-N/T-P/SS/TOC concentrations has been conducted in Nakdong river tributaries since 2011. In this study concentrations and loading rates of BOD, T-P, and TOC were analyzed to evaluate water quality monitoring stations using accumulated data at 206 tributary monitoring stations in Nakdong river 2012 ~ 2016. Average concentration ranges for 206 monitoring stations were 0.3 ~ 6.4 mg/L, 0.025 ~ 1.562 mg/L, and 0.6 ~ 10.7 mg/L for BOD, T-P, and TOC, respectively. Additionally, average loading rate ranges were 0.96 ~ 46,040 kg/d, 0.087 ~ 1,834 kg/d, and 1.51 ~ 80,425 kg/d for BOD, T-P, and TOC, respectively. Average concentration for BOD, T-P, and TOC at each monitoring station was evaluated using ambient water quality standards of rivers and water quality regulation level for medium-sized management areas. Average loading rate and specific loading rate (loading rate/drainage basin area) for BOD, T-P, and TOC at each monitoring station was considered to evaluate monitoring stations using suggested classification (BOD, TOC: -1, 1 ~ 10, 10 ~ 100, 100 ~ 1,000, and 1,000 ~ kg/d; T-P: -0.1. 0.1 ~ 1, 1 ~ 10, 10 ~ 100, and 100 ~ kg/d) Using results of this study, various water quality status maps were provided, and three evaluation methods were suggested to determine priority monitoring stations in Nakdong river for rational water quality control and tributaries basin management.

• Journal of Korean Society on Water Environment
• /
• v.21 no.6
• /
• pp.651-658
• /
• 2005

The Non-parametric Analysis is powerful in data test especially for the non- normality water quality data. The data at three monitoring stations of the Tam-Jin River were evaluated for their normality using Skewness, Q-Q plot and Shapiro-Willks tests. Various constituent of water quality data including temperature, pH, DO, SS, BOD, COD, TN and TP in the period of January 1994 to December 2004 were used as dataset. Shapiro-Willks normality test was carried out for a test 5% significance level. Most water quality data except DO at monitoring stations 1 and 2 showed that data does not normally distributed. It is indicating that non-parametric method must be used for a water quality data. Therefore, a homogeneity was conducted by Mann-Whitney U test (p<0.05). Two stations were paired in three pairs of such stations. Differences between stations 1, 2 and stations 1, 3 for pH, BOD, COD, TN and TP were meaningful, but Tam-Jin 2 and 3 stations did not meaningful. In addition, a narrow gap of the water quality ranges is not a difference. Categories in which all three pairs of stations (1 and 2, 2 and 3, 1 and 3) in the Tam-Jin River showed difference in water quality were analyzed on TN and TP. The results of in this research suggest a right analysis in the homogeneity test of water quality data and a reasonable management of pollutant sources.

• Journal of Environmental Science International
• /
• v.26 no.8
• /
• pp.927-938
• /
• 2017

This study was conducted to determine correlations and similarity between the ozone and $PM_{10}$ data of 19 air quality monitoring stations in Busan from 2013 to 2016, using correlation and cluster analyses. Ozone concentrations ranged from $0.0278{\pm}0.0148ppm$ at Gwangbok to $0.0378{\pm}0.017ppm$ at Taejongdae and were high in suburban areas, such as Yongsuri and Gijang, as well as in coastal areas, such as Jaw, Gwangan, Taejongdae and Noksan. $PM_{10}$ concentrations ranged from $37.2{\pm}25.0ug/m^3$ at Gijang to $58.3{\pm}32.2ug/m^3$ at and Jangrim. $PM_{10}$ concentrations were high in the west, exceeding the annual ambient air quality standard of $50ug/m^3$. Positive correlations were observed for ozone at most stations, ranging from 0.61 between Taejongdae and Sujeong to 0.92 between Bugok and Myeongjang. The correlation coefficients of $PM_{10}$ between stations ranged from 0.62 between Jangrim and Jaw to 0.9 between Gwangbok and Sujeong. Yeonsan, Daeyeon, and Myeongjang were highly correlated with other stations, so they needed to be reviewed for redundancy. Ozone monitoring stations were initially divided into two sections, north-western areas and suburban-coastal areas. The suburban-coastal areas were subsequently divided into three sections. $PM_{10}$ monitoring stations were initially divided into western and remaining areas, and then the remaining areas were subsequently divided into three sections.

• Journal of Environmental Health Sciences
• /
• v.41 no.2
• /
• pp.90-101
• /
• 2015

Objective: This study was conducted to develop a chemical oxygen demand (COD) regression model using water quality monitoring data (January, 2014) obtained from the Han River auto-monitoring stations. Methods: Surface water quality data at 198 sampling stations along the six major areas were assembled and analyzed to determine the spatial distribution and clustering of monitoring stations based on 18 WQPs and regression modeling using selected parameters. Statistical techniques, including combined genetic algorithm-multiple linear regression (GA-MLR), cluster analysis (CA) and principal component analysis (PCA) were used to build a COD model using water quality data. Results: A best GA-MLR model facilitated computing the WQPs for a 5-descriptor COD model with satisfactory statistical results ($r^2=92.64$,$Q{^2}_{LOO}=91.45$,$Q{^2}_{Ext}=88.17$). This approach includes variable selection of the WQPs in order to find the most important factors affecting water quality. Additionally, ordination techniques like PCA and CA were used to classify monitoring stations. The biplot based on the first two principal components (PCs) of the PCA model identified three distinct groups of stations, but also differs with respect to the correlation with WQPs, which enables better interpretation of the water quality characteristics at particular stations as of January 2014. Conclusion: This data analysis procedure appears to provide an efficient means of modelling water quality by interpreting and defining its most essential variables, such as TOC and BOD. The water parameters selected in a COD model as most important in contributing to environmental health and water pollution can be utilized for the application of water quality management strategies. At present, the river is under threat of anthropogenic disturbances during festival periods, especially at upstream areas.

• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.1191-1193
• /
• 2003

Water quantity and quality monitoring at Taipei Watershed Management Bureau (WRATB) is not only a daily business but also a long term job. WRATB is responsible for providing high quality drinking water to about four millions population in Taipei. The quality of drinking water provided by WRATB is among one of the best in Taiwan. The total area is 717 square kilometers. The water resource pollution is usually divided into two categories, point source pollution and nonpoint source pollution. Garbage disposal is the most important component of the point source pollution, especially those by tourist during holidays and weekends. Pesticide pollution, fertilizer pollution, and natural pollution are the major contributions for nonpoint source pollution. The objective of this paper is to implement remote sensing, geographic information systems, and global positioning systems to monitor water quantity and water quality at WRATB. There are 12 water quality monitoring stations and four water gauge stations at WRATB. The coordinates of the 16 stations were determined by GPS devices and created into the base maps. MapObjects and visual BASIC were implemented to create application modules for water quality and quantity monitoring. Water quality of the two major watersheds at WRATB was put on Internet for public review monthly. The GIS software, ArcIMS, can put location maps and attributes of all 16 stations on Internet for general public review and technical implementations at WRATB. Inquiry and statistic charts automatic manipulations for the past 18 years are also available. Garbage disposal by community and tourist were also managed by GIS and GPS. The storage, collection, and transportation of garbage were reviewed by ArcMap file format. All garbage cart and garbage can at WRATB can be displayed on the base maps. Garbage disposal by tourist during holidays and weekends can be managed by a PDA with a GPS device and a digital camera. Man power allocation for tourist garbage disposal management can be done in an integration of GIS and GPS. Monitoring of water quality and quantity at WRATB can be done on Internet and by a PDA.

• Journal of Korean Society for Atmospheric Environment
• /
• v.23 no.5
• /
• pp.526-538
• /
• 2007

Since air quality monitoring data sets are important base for developing of air quality management strategies including policy making and policy performance assessment, the environmental protection authorities need to organize and operate monitoring network properly. Air quality monitoring network of Busan, consisting of 18 stations, was allocated under unscientific and irrational principles. Thus the current state of air quality monitoring networks was reassessed the effect and appropriateness of monitoring objectives such as population protection and sources surveillance. In the process of the reassessment, a GIS-based decision support system was constructed and used to simulate air quality over complex terrain and to conduct optimization analysis for air quality monitoring network with multi-objective. The maximization of protection capability for population appears to be the most effective and principal objective among various objectives. The relocation of current monitoring stations through optimization analysis of multi-objective appears to be better than the network building for maximization of population protection capability. The decision support system developed in this study on the basis of GIS-based database appear to be useful for the environmental protection authorities to plan and manage air quality monitoring network over complex terrain.