Fine particulate matter (PM_2.5) is one of the main air pollution problems that occur in major cities around the world. A country’s PM_2.5 can be affected not only by country factors but also by the neighboring country’s air quality factors. Therefore, forecasting PM_2.5 requires collecting data from outside the country as well as from within which is necessary for policies and plans. The data set of many variables with a relatively small number of observations can cause a dimensionality problem and limit the performance of the deep learning model. This study used daily data for five years in predicting PM_2.5 concentrations in eight Korean cities through deep learning models. PM_2.5 data of China were collected and used as input variables to solve the dimensionality problem using principal components analysis (PCA). The deep learning models used were a recurrent neural network (RNN), long short-term memory (LSTM), and bidirectional LSTM (BiLSTM). The performance of the models with and without PCA was compared using root-mean-square error (RMSE) and mean absolute error (MAE). As a result, the application of PCA in LSTM and BiLSTM, excluding the RNN, showed better performance: decreases of up to 16.6% and 33.3% in RMSE and MAE values. The results indicated that applying PCA in deep learning time series prediction can contribute to practical performance improvements, even with a small number of observations. It also provides a more accurate basis for the establishment of PM_2.5 reduction policy in the country.

Increasing precipitation by climate change and the growing number of impervious areas present greater risk of disaster damage in urban areas. Urban green infrastructure can be an effective mitigation alternative in highly developed and concentrated area. This study investigates the effect of various types of urban green infrastructure on mitigating disaster damage in Korea. Tobit model is used to analyze the factors that determine disaster damage. Damage variation is predicted with scenarios of RCP 8.5 and urban green spaces. Seventy-four districts and counties in seven metropolitan areas are defined as the unit and the period from 2005 to 2013 is considered in the analysis. The results indicate that higher urban green ratio, sewer length, financial independence rate, and local government’s budget are relating to lower disaster damage. Based on a precipitation level of RCP 8.5 scenario in 2050, an increase in economic damage is expected to range from 262 to 1086%. However, with an increase in urban green ratio by 10%, increased economic damage is only expected to range from 217 to 1013%. The results suggest that green spaces play important role to mitigate precipitation related disasters. Highly concentrated urban areas need to consider various types of urban green infrastructure to prepare for an increase in precipitation due to climate change.

Marssonina blotch in apples and pear scab are diseases that may affect apple and pear production as well as related industries significantly. The range and scale of the economic influence of such outbreaks should be clarified to ensure the sustainable growth of these industries. This study evaluates the direct and indirect economic effects of outbreaks through a partial equilibrium approach and an input–output model; direct influences are measured on the basis of reduced production, and the estimated costs are 34,926 million Korean Won (mKRW) (US$ 29.79 million) and 11,767 mKRW (US$ 10.04 million) for apples and pears, respectively. The indirect effects are determined according to changes in the quantity of apples and pears supplied to the market; these effects can induce social welfare losses. These costs are estimated to be 305,065 mKRW (US$ 259.08 million) for apples infected with Marssonina blotch, which accounts for 186,628 mKRW (US$ 158.50 million) in producer surplus and 118,437 mKRW (US$ 100.58 million) in consumer surplus and 72,693 mKRW (US$ 61.74 million) for pears infected with scab, which accounts for 44,106 mKRW (US$ 37.46 million) in producer surplus and 28,587 mKRW (US$ 24.28 million) in consumer surplus. The findings from this study can be referenced by the Korean government in designing more effective control measures toward sustainable growth of related industry.