• Particle and aerosol research
• /
• v.7 no.2
• /
• pp.59-68
• /
• 2011

The objective of the study was to investigate the main factors that contribute the variation of $PM_{10}$ concentration of Seoul and to quantify their effects using generalized additive model (GAM). The analysis was performed with 3 year air pollution data (2004~2006) measured at 27 urban sites and 7 roadside sites in Seoul, a background site in Gangwha and a rural site in Pocheon. The diurnal variation of urban $PM_{10}$ concentrations of Seoul showed a typical bimodal pattern with the same peak times as that of roadside, and the maximum difference of $PM_{10}$ level between urban and roadside was about $14{\mu}g/m^{3}$ at 10 in the morning. The wind direction was found to be a major factor that affects $PM_{10}$ level in all investigated areas. The overall $PM_{10}$ level was reduced when air came from east, but background $PM_{10}$ level in Gangwha was rather higher than the urban $PM_{10}$ level in Seoul, indicating that the $PM_{10}$ level in Gangwha is considerably influenced by that in Seoul metropolitan area. When hourly variations of $PM_{10}$ were analyzed using GAM, wind direction and speed explained about 34% of the variance in the model where the variables were added as a 2-dimensional smoothing function. In addition, other variables, such as diurnal variation, difference of concentrations between roadside and urban area, precipitation, month, and the regression slope of a plot of carbon monooxide versus $PM_{10}$, were found to be major explanatory variables, explaining about 64% of total variance of hourly variations of $PM_{10}$ in Seoul.

• Journal of Environmental Science International
• /
• v.30 no.7
• /
• pp.573-584
• /
• 2021

To analyze the effects of PM₁₀ and PM_2.5 on daily mortality cases, the relations of death counts from natural causes, respiratory diseases, and cardiovascular diseases with PM₁₀ and PM_2.5 concentrations were applied to the generalized additive model (GAM) in this study. From the coefficients of the GAM model, the excessive mortality risks due to an increase of 10 ㎍/m³ in daily mean PM₁₀ and PM_2.5 for each cause were calculated. The excessive risks of deaths from natural causes, respiratory diseases, and cardiovascular diseases were 0.64%, 1.69%, and 1.16%, respectively, owing to PM₁₀ increase and 0.42%, 2.80%, and 0.91%, respectively, owing to PM_2.5 increase. Our result showed that particulate matter posed a greater risk of death from respiratory diseases and is consistent with the cases in Europe and China. The regional distribution of excessive risk of death is 0.24%-0.81%, 0.34%-2.6%, and 0.62%-1.94% from natural causes, respiratory diseases, and cardiovascular diseases, respectively, owing to PM₁₀ increase, and 0.14%-1.02%, 1.07%-3.92%, and 0.22%-1.73% from natural causes, respiratory diseases, and cardiovascular diseases, respectively, owing to PM_2.5 increase. Our results represented a different aspect from the regional concentration distributions. Thus, we saw that the concentration distributions of air pollutants differ from the affected areas and identified the need for a policy to reduce damage rather than reduce concentrations.

• Korean Journal of Ecology and Environment
• /
• v.44 no.2
• /
• pp.195-202
• /
• 2011

This study analyzed and modeled the relationships between the probabilities of two exotic species occurrence (i.e. largemouth bass and blue gill) and environmental factors such as climatic and geographical variables using Generalized Additive Models (GAM), Generalized Liner Models and Classification Tree Analysis (CTA). The most moderate occurrence probability of largemouth bass was predicted using GAM with an area under the curve (ADC) of 0.88 and Kappa of 0.42, while those of blue gill was suggested by using CTA with an AUC of 0.92 and Kappa of 0.44. The most significant environmental variable in terms of changes in deviance for both species was the annual air temperature for the occurrence probability. Dams had stronger effect on the occurrence of largemouth bass than blue gill. Model development and prediction for the occurrence probability of fish species and richness are necessary to prevent further spread of exotic fishes such as largemouth bass and blue gill because they can threaten habitats of native river ecosystem through various mechanisms.

• Environmental Analysis Health and Toxicology
• /
• v.26
• /
• pp.9.1-9.9
• /
• 2011

Objectives: In order to evaluate which temperature index is the best predictor for the health impact assessment of heat stress in Korea, several indexes were compared. Methods: We adopted temperature, perceived temperature (PT), and apparent temperature (AT), as a heat stress index, and changes in the risk of death for Seoul and Daegu were estimated with $^1{\circ}C$ increases in those temperature indexes using generalized additive model (GAM) adjusted for the non-temperature related factors: time trends, seasonality, and air pollution. The estimated excess mortality and Akaike's Information Criterion (AIC) due to the increased temperature indexes for the $75^{th}$ percentile in the summers from 2001 to 2008 were compared and analyzed to define the best predictor. Results: For Seoul, all-cause mortality presented the highest percent increase (2.99% [95% CI, 2.43 to 3.54%]) in maximum temperature while AIC showed the lowest value when the all-cause daily death counts were fitted with the maximum PT for the $75^{th}$ percentile of summer. For Daegu, all-cause mortality presented the greatest percent increase (3.52% [95% CI, 2.23 to 4.80%]) in minimum temperature and AIC showed the lowest value in maximum temperature. No lag effect was found in the association between temperature and mortality for Seoul, whereas for Daegu one-day lag effect was noted. Conclusions: There was no one temperature measure that was superior to the others in summer. To adopt an appropriate temperature index, regional meteorological characteristics and the disease status of population should be considered.

• Journal of Environmental Science International
• /
• v.11 no.10
• /
• pp.1061-1068
• /
• 2002

To identify possible associations with concentrations of ambient air pollutants and daily mortality in Busan, this study assessed the effects of air pollution for the time period 1999-2000. Poisson regression analysis by Generalized Additive Model were conducted considering trend, season, meteorology, and day-of-the-week as confounders in a nonparametric approach. Busan had a 10% increase in mortality in persons aged 65 and older(95% Cl : 1.01-1.10) in association with IQR in $NO_2$(lagged 2 days). An increase of $NO_2$(lagged 2days) was associated with a 4% increase in respiratory mortality(Cl : 1.02-1.11) and CO(lagged 1 day) showed a 3% increase(Cl : 1.00-1.07).

• Journal of the Korean Society of Environmental Restoration Technology
• /
• v.25 no.2
• /
• pp.55-67
• /
• 2022

Land use is a critical factor that affects the hydrological characteristics of watersheds, thereby determining the biological condition of streams. This study analyzes the effects of land uses in the watersheds on biological indicators of streams across the Han River basin using a linear model (LM) and generalized additive model (GAM). LULC and biological monitoring data of streams were obtained from the Korean Ministry of Environment. The proportions of urban, agricultural, and forest areas in the watersheds were regressed to the three biological indicators, including diatom, benthic macroinvertebrate, and fish of streams. The estimated LM and GAM models for the biological indicators were then compared, using regression determination R² and AIC values. The results revealed that GAM models performed better than the LM models in explaining the variances of biological indicators of streams, indicating the non-linear relationships between biological indicators and land uses in watersheds. Also, the results suggested that the indicator of macroinvertebrates was the most sensitive indicator to land uses in watersheds. Although non-linear relationships between watershed land uses and biological indicators of streams could vary among biological indicators, it was consistent that streams' biological integrity significantly deteriorated by a relatively low percentage of urban areas. Meanwhile, biological indicators of streams were negatively affected by the relatively high percentage of agricultural areas. The results of this study can be integrated into effective quantitative criteria for the watershed management and land use plans to enhance the biological integrity of streams. In specific, land uses management plans in watersheds may need more close attention to urban land use changes than agricultural land uses to sustain the biological integrity of streams.

• Journal of the Korean Geographical Society
• /
• v.51 no.2
• /
• pp.219-233
• /
• 2016

In social science fields, statistical models are used almost exclusively for causal explanation, and explanatory modeling has been a mainstream until now. In contrast, predictive modeling has been rare in the fields. Hence, we focus on constructing the predictive non-parametric model, instead of the explanatory model. Gangnam-gu, Seoul was chosen as a study area and we collected single-family house sales data sold between 2011 and 2014. We applied non-parametric models proposed in machine learning area including generalized additive model(GAM), random forest, multivariate adaptive regression splines(MARS) and support vector machines(SVM). Models developed recently such as MARS and SVM were found to be superior in predictive power for house price estimation. Finally, spatial autocorrelation was accounted for in the non-parametric models additionally, and the result showed that their predictive power was enhanced further. We hope that this study will prompt methodology for property price estimation to be extended from traditional parametric models into non-parametric ones.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.23 no.1
• /
• pp.34-45
• /
• 2021

Weather is one of the important factors in the agricultural industry as it affects the price, production, and quality of crops. Upland crops are directly exposed to the natural environment because they are mainly grown in mountainous areas. Therefore, it is necessary to provide accurate weather for upland crops. This study examined the effectiveness of 12 forest soil factors to interpolate the weather in mountainous areas. The daily temperature and precipitation were collected by the Korea Meteorological Administration between January 2009 and December 2018. The Generalized Additive Model (GAM), Kriging, and Random Forest (RF) were considered to interpolate. For evaluating the interpolation performance, automatic weather stations were used as training data and automated synoptic observing systems were used as test data for cross-validation. Unfortunately, the forest soil factors were not significant to interpolate the weather in the mountainous areas. GAM with only geography aspects showed that it can interpolate well in terms of root mean squared error and mean absolute error. The significance of the factors was tested at the 5% significance level in GAM, and the climate zone code (CLZN_CD) and soil water code B (SIBFLR_LAR) were identified as relatively important factors. It has shown that CLZN_CD could help to interpolate the daily average and minimum daily temperature for upland crops.

• Journal of the Korean Society of Environmental Restoration Technology
• /
• v.19 no.6
• /
• pp.19-30
• /
• 2016

The projection of climate-related range shift is critical information for conservation planning of Korean fir (Abies koreana E. H. Wilson). We first modeled the distribution of Korean fir under current climate condition using five single-model species distribution models (SDMs) and the pre-evaluation weighted ensemble method and then predicted the distributions under future climate conditions projected with HadGEM2-AO under four $CO_2$ emission scenarios, the Representative Concentration Pathways (RCP) 2.6, 4.5, 6.0 and 8.5. We also investigated the predictive uncertainty stemming from five individual algorithms and four $CO_2$ emission scenarios for better interpretation of SDM projections. Five individual algorithms were Generalized linear model (GLM), Generalized additive model (GAM), Multivariate adaptive regression splines (MARS), Generalized boosted model (GBM) and Random forest (RF). The results showed high variations of model performances among individual SDMs and the wide range of diverging predictions of future distributions of Korean fir in response to RCPs. The ensemble model presented the highest predictive accuracy (TSS = 0.97, AUC = 0.99) and predicted that the climate habitat suitability of Korean fir would increase under climate changes. Accordingly, the fir distribution could expand under future climate conditions. Increasing precipitation may account for increases in the distribution of Korean fir. Increasing precipitation compensates the negative effects of increasing temperature. However, the future distribution of Korean fir is also affected by other ecological processes, such as interactions with co-existing species, adaptation and dispersal limitation, and other environmental factors, such as extreme weather events and land-use changes. Therefore, we need further ecological research and to develop mechanistic and process-based distribution models for improving the predictive accuracy.

• Journal of Environmental Health Sciences
• /
• v.34 no.2
• /
• pp.124-130
• /
• 2008

This study investigated the age dependencies in ambient air pollution-associated asthma hospitalization from 2003 to 2005 in Seoul. For all ages and the age groups of 0-14, 15-64, and 65+years, the Generalized Additive Model (GAM) was used to estimate the relative risks of daily asthma hospitalization associated with changes in particulate matter and ozone. The time-trends, seasonal variances, day effects, temperature, humidity, and pressure at sea level were controlled in the models. Significant associations were observed between asthma hospitalization and the levels of $PM_{10}$ and $O_3$. The relative risks (RRs) of asthma hospitalization for every 10 unit increases in $PM_{10}({\mu}g/m^3)$ and $O_3$(ppb) were 1.008 (95% CI 1.005-1.012), and 1.012 (95% CI 1.003-1.020), respectively. Evaluated over $10\;{\mu}g/m^3$ increase in $PM_{10}$, we found the relative risks of asthma hospitalization to be 1.009 (95% CI 1.004-1.014) in 0-14 age group, and 1.015 (95% CI 1.008-1.022) in 65+ age group. Considering 10 ppb increase in $O_3$, those were 1.014 (95% CI 1.003-1.024) in 0-14 age group, and 1.025 (95% CI 1.009-1.041) in 65+ age group. It was concluded that current levels of ambient air pollution in Seoul make a significant contribution to the variation in daily asthma hospitalization. Further reduction in air pollution is necessary to protect the health of the community, especially that of the higher risky groups including children and elderly population.