• Korean Journal of Ecology and Environment
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• v.49 no.4
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• pp.245-257
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• 2016

Although rivers cover only 0.5% of the total land area on the Earth, they are windows that show the integrated effects of watershed biogeochemistry. Studies on the loads and properties of riverine carbon have been conducted because they are directly linked with drinking water quality, and because regional or global net ecosystem production (NEP) can be overestimated, unless riverine carbon loads are subtracted. Globally, ${\sim}0.8-1.5Pg\;yr^{-1}$ and ${\sim}0.62-2.1Pg\;yr^{-1}$ of carbon are transported from terrestrial ecosystems to the ocean via rivers and from inland waters to the atmosphere, respectively. Concentrations, ${\delta}^{13}C$, and fluorescence spectra of riverine carbon have been investigated in South Korea to understand the spatiotemporal changes in the sources. Precipitation as well as land use/land cover can strongly influence the composition of riverine carbon, thus shifting the ratios among DIC, DOC, and POC, which could affect the concentrations, loads, and the degradability of adsorbed organic and inorganic toxic materials. A variety of analyses including $^{14}C$ and high resolution mass spectroscopy need to be employed to precisely define the sources and to quantify the degradability of riverine carbon. Long-term data on concentrations of major ions including alkalinity and daily discharge have been used to show direct evidence of ecosystem changes in the US. The current database managed by the Korean government could be improved further by integrating the data collected by individual researchers, and by adding the major components ions including DIC, DOC, and POC into the database.

• Korean Journal of Remote Sensing
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• v.36 no.6_3
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• pp.1711-1720
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• 2020

Particulate matter (PM) that has been artificially generated during the recent of rapid industrialization and urbanization moves and disperses according to weather conditions, and adversely affects the human skin and respiratory systems. The purpose of this study is to predict the PM10 concentration in Seoul using meteorological factors as input dataset for multiple linear regression (MLR), support vector machine (SVM), and random forest (RF) models, and compared and evaluated the performance of the models. First, the PM10 concentration data obtained at 39 air quality monitoring sites (AQMS) in Seoul were divided into training and validation dataset (8:2 ratio). The nine meteorological factors (mean, maximum, and minimum temperature, precipitation, average and maximum wind speed, wind direction, yellow dust, and relative humidity), obtained by the automatic weather system (AWS), were composed to input dataset of models. The coefficients of determination (R2) between the observed PM10 concentration and that predicted by the MLR, SVM, and RF models was 0.260, 0.772, and 0.793, respectively, and the RF model best predicted the PM10 concentration. Among the AQMS used for model validation, Gwanak-gu and Gangnam-daero AQMS are relatively close to AWS, and the SVM and RF models were highly accurate according to the model validations. The Jongno-gu AQMS is relatively far from the AWS, but since PM10 concentration for the two adjacent AQMS were used for model training, both models presented high accuracy. By contrast, Yongsan-gu AQMS was relatively far from AQMS and AWS, both models performed poorly.