환경 빅데이터 분석 및 서비스 개발 Ⅵ

홍한움,강성원,이동현,표종철,고길곤,조윤랑,한국진,진대용,강선아 한국환경연구원 2022 사업보고서 Vol.2022 No.-

환경 빅데이터 분석 및 서비스 개발 VII

홍한움,이동현,고길곤,강성원,진대용,김도연,김용건,김충기,김태윤,심창섭,이진희,조예은,조윤랑,주현수,한국진,한상운,한혜진 한국환경연구원 2023 사업보고서 Vol.2023 No.0

시계열자료 눈집방법의 비교연구

홍한움,박민정,조신섭,Hong, Han-Woom,Park, Min-Jeong,Cho, Sin-Sup 한국통계학회 2009 응용통계연구 Vol.22 No.6

In this paper we introduce the time series clustering methods in the time and frequency domains and discuss the merits or demerits of each method. We analyze 15 daily stock prices of KOSPI 200, and the nonparametric method using the wavelet shows the best clustering results. For the clustering of nonstationary time series using the spectral density, the EMD method remove the trend more effectively than the differencing.

환경 빅데이터 분석 및 서비스 개발 Ⅴ

홍한움,이동현,고길곤,진대용,강성원,강선아,김도연 한국환경연구원 2021 사업보고서 Vol.2021 No.-

Ⅰ. Background and Aims of Research □ Continue to build up ‘Environmental Policy monitoring System’ ○ Periodic repetition of environmental policy need identification, timeliness assessment and effectiveness assessment ○ composed of ‘Deep Learning Based Pollution Prediction algorithm’, ‘Real Time Environmental Text Analysis algorithm’ and ‘Issue Based Database’ □ 2021 Research Goal: Strengthening the components of the environmental policy monitoring system ○ Deep Learning Based Pollution Prediction algorithm - Enhancement of uncertainty prediction and causal analysis - Development of an algorithms with better long-term predictive performance in predicting particular matter ○ Issue Based Database - Composition of issue bulletin board for carbon neutrality issues ○ Real Time Environmental Text Analysis algorithm - Analysis of Similarities and Differences in Carbon Neutral Strategies by Country Ⅱ. Deep Learning-based Particular Matter Concentration and Uncertainty Prediction □ Research purpose ○ Prediction of particular matter concentration and uncertainty simultaneously based on deep-learning - Improving the reliability of PM prediction □ Research Method ○ Collect data from Air Korea and Korea Meteorological Administration ○ Missing value preprocessing with spatial interpolation ○ Development of a deep learning algorithm capable of uncertainty prediction - Interpolated Convolutional Neural Network using Monte - Carlo Dropout(ICNN-MCDO) □ Development of algorithms that can estimate uncertainty as well as high prediction performance ○ Applying Monte Carlo Dropout (MCDO) to Interpolated Convolutional Neural Network (ICNN) ○ High prediction performance reflecting spatio temporal characteristics through CNN using multidimensional arrays - High performance in classifying high-concentration of particular matter ○ Estimate predicted values and uncertainties simultaneously even at non-observation points by expressing the entire Republic of Korea as a grid at regular intervals ○ Confirm that there is a positive correlation between the deviation of the actual value and the predict value and the estimated uncertainty ○ Providing a threshold while evaluating uncertainty in prediction of particular matter Ⅲ. Mid- to Long-term Prediction of Ultra-fine Particles Using Graph- GRU: Focusing on South Korea □ Research purpose ○ Considering the characteristics of ultra-fine particles and the seasonal and geographical characteristics of South Korea ○ Development of mid- to long-term (72 hours later) ultra-fine particles prediction model based on Graph Neural Network □ Construction of space-time 3D dataset ○ Input data: Meteorological, air pollution, and Chinese ultrafine dust concentration data - Weather and air pollution data: Satellite-based reanalysis data provided by the European Center for Medium-Range Weather Forecasts - Ultra-fine particles data from China is based on measurement stations □ Establishment of ultra-fine particles prediction model ○ Build with Graph-based model ○ It consists of a stage of combining surrounding information, a graph attention stage, and a GRU stage for time series learning □ Prediction results and application ○ Improved prediction performance compared to other deep learning prediction models ○ The prediction model developed in this study can be used to predict other air pollution and can be used as base data when establishing a preemptive air pollution response plan Ⅳ. LEDS Document Analysis Using Text Analysis □ Research purpose ○ Analysis of similarities and differences in carbon-neutral strategies by country through LEDS document analysis of major countries including South Korea ※ LEDS (Long-term low greenhouse gas Emission Development Strateges ○ Investing the application range and usability of text analysis results - LEDS Document characteristics: Long document length and multiple topics in one document - Prior research for establishing policy document DB and text analysis automation procedure □ Process ○ LEDS document collection → Keyword frequency analysis and related keyword review → Cross-country similarity network and keyword network analysis bt country → result analysis and implication □ Results ○ Differences in national strategies in the LEDS document - South Korea: Emphasis on energy and target alternative resources by sector with a focus on energy management and demand - Japan: Energy efficiency and transportation targets to be reduced. Emphasis on decarbonization keywords - Can be applied as policy analysis data ○ Establishment of case of application of text mining for global policy issue documents - Lay the foundation for digital transformation with examples of quantifying and using atypical data such as text data Ⅴ. Carbon-neutral Issue Based Database □ Built a datamap on Carbon-neutral issue ○ Development of an evaluation model for monitoring the flow and relationship of variables according to the policy input, implementation, and production process - Building a datamap under the theme of ‘carbon neutrality’ following ‘particular matter’ in 2019 and ‘climate change’ in 2020 ○ Close linkage of questions, methodologies and data for policy evaluation and analysis □ The difference between carbon neutrality and greenhouse gas reduction ○ No significant difference in terms of concepts and policy measures ○ Carbon neutrality is focused on goal-oriented discussion while greenhouse gas reduction policy is focused on specific indicators - Carbon neutrality emphasizes strengthening the institutional foundation, changing the reduction target to an absolute value method, realizing regional carbon neutrality and seeking ways to raise public awareness. □ Implication ○ Despite the situation in which an active low-carbon strategy is required, the proposed policy measures are not significantly different from the existing greenhouse gas reduction policies ○ Differences in regional industrial structure, final energy consumption and policy effects - It is necessary to establish a policy that takes into account differences in regional industrial structure and carbon emission levels Ⅵ. The Impact of Air Pollution Long-term Exposure on the Mortality of COPD Patients □ Research purpose ○ Advancement of research on the impact of air pollution long-term exposure on the mortality of COPD patients, conducted from 2019 ○ Refinement of Interpolation Modeling of Air Pollution - Advancement of kriging-based air pollution estimation into a machine learning-based hybrid model □ Data and methodology ○ Interpolation variable: PM₁₀, PM_2.5, O₃, NO₂, SO₂ - Target period: 2009-2018 yr. - 1km × 1km points for the entire area of South Korea ○ Input variables: Air pollution data from Air Korea and Seoul Information Communication Plaza, satellite-based air data, meteorological data - Missing values of satellite data are processed through convolutional neural network analysis □ Results ○ Interpolation modeling performance of pollutant sources (PM₁₀, PM_2.5, O₃, NO₂) excluding SO₂ is good with R² value of 0.8 or higher - The PM_2.5 interpolation model in Seoul has an R² value of 0.92, which is superior to previous studies of 0.75 to 0.90 ○ The interpolation results of this study will be used for subsequent studies of health impact of air pollution Ⅶ. Conclusions and Academic Achievements □ Expanding and deepening the components of the environmental policy monitoring system ○ Reinforcement of environmental policy monitoring system components by adding two particular matter prediction studies of ‘Deep Learning Based Pollution Prediction algorithm’, one study of ‘Real Time Environmental Text Analysis algorithm’ and one study of ‘Issue Based Database’ □ Academic Achievements ○ Develop an algorithm that can simultaneously predict uncertainty while maintaining high predictive performance in particular matter prediction, and develop an algorithm with better mid- to long-term prediction efficiency of 3 days or longer than the existing neural network model by using a graph model ○ Through text mining analysis of Long-term low greenhouse gas Emission Development Strateges documents, we identify low-carbon strategies for each country including South Korea and suggest the need to establish DB for policy documents ○ Promote close linkage of issues, methodologies, and data for carbonneutral policy evaluation and analysis. Development of time series analysis automation program module and analysis of policy effect ○ Lay the foundation for more improved estimation of individual air pollution exposure through the development of improved air pollution interpolation models

인공지능 딥러닝을 활용한 조류현상 예측기술 개발 및 활용방안

홍한움,조을생,강선아,한국진 한국환경연구원 2020 기본연구보고서 Vol.2020 No.-

Ⅰ. Background and Aims of Research 1. Research outline □ Research title: Development and application of an algal bloom forecast system using artificial intelligence deep learning technology □ Research period: January 1, 2020 ~ December 31, 2020 2. Necessity and purpose of research □ Limitations of the current algal bloom warning system ㅇ The Ministry of Environment and the National Institute of Environmental Research implemented an algal bloom warning system based on the measured values of harmful blue-green algae and the EFDC model. ㅇ Limitations of physics-based models - They have a solid theoretical background but there is a difficulty in securing the detailed data required by the model. - Since algal blooms are living organisms, the law of conservation of mass does not apply to the number of harmful blue-green algae cells. Therefore, the physics-based model has limitations. - Deep learning-based forecasting can be considered as an alternative and a complementary method. Ⅱ. Current Algal Bloom Response Policy 1. Algal bloom warning system □ Year of introduction: 1998 □ Legal basis: Article 21 of the Water Environment Conservation Act □ Target ㅇ 28 branches of water supply sources and hydrophilic activities ㅇ Issuer: Basin Environmental Office and local governments □ Analysis items ㅇ Measured numbers of harmful blue-green algae cells ㅇ Based on water source section - Attention: 1,000 (cells/mL) or more - Alert: 10,000 (cells/mL) or more - Large bloom: 1,000,000 (cells/mL) or more ㅇ Based on hydrophilic activities section - Attention: 20,000 (cells/mL) or more - Alert: 100,000 (cells/mL) or more 2. (Former) Water quality forecast system □ Year of Introduction: 2012 □ Legal basis: Article 21 of the Water Environment Conservation Act □ Target ㅇ 17 branches including 16 barrages and the Bukhan River Sambong-ri of the four major rivers of South Korea ㅇ Issuer: National Institute of Environmental Research □ Analysis items ㅇ Predicted water temperature and chlorophyll-a concentration ㅇ Currently, as the algal bloom warning system and the water quality forecast system are integrated, no forecast is issued although forecasting is performed. □ Providing forecasts for harmful blue-green algae cells ㅇ Twice a week, Monday and Thursday, six branches that are targets of the algal bloom system ㅇ Issuing the predicted number of harmful blue-green algae cells and water temperature predictions 3. Status of the water quality monitoring network □ Legal basis ㅇ Article 22 of the Basic Act on Environmental Policy and Article 9 of the Water Environment Conservation Act □ Organization ㅇ Water quality monitoring network - Target: water quality measurement data in rivers, lakes, agricultural water, urban streams, and industrial rivers - Provided information: water depth, hydrogen ion concentration, dissolved oxygen content, BOD, COD, suspended matter, total nitrogen, total phosphorus, total organic carbon (TOC), water temperature, phenols, electrical conductivity, total coliform group, dissolved total nitrogen, ammonia nitrogen, nitrate nitrogen, dissolved total phosphorus, phosphate phosphorus, chlorophyll a, transparency - Cycle: once a month, once a week for major locations ㅇ Total quantity measurement network - Target: basic data for total amount management in areas subject to the total water pollution rate system - Provided information: water temperature, hydrogen ion concentration, electrical conductivity, dissolved oxygen, BOD, COD, suspended matter, total nitrogen, total phosphorus, TOC, flow rate - Cycle: once a month ㅇ Automatic measurement network - Operated to complement the hand-operated measurements of the water quality monitoring network - Provided information: (Common) water temperature, hydrogen ion concentration, dissolved oxygen content, electrical conductivity, TOC (Optional) Turbidity, chlorophyll a, TN, TP, NH₃-N, NO₃-N, PO₃-P, VOCs (nine types, ten items), phenol, heavy metals, biological monitoring items - Cycle: once a day ㅇ Sediment monitoring network - Purpose: investigation of the physicochemical properties of sediments in public waters subject to water quality conservation of South Korea - Provided information: (Common) water temperature, hydrogen ion concentration, dissolved oxygen content, electrical conductivity, TOC (Optional) maximum depth during collection, surface measurement depth, surface and bottom depth, water temperature, dissolved oxygen content, pH, electrical conductivity, sediment particle size, moisture content, ratio and grade of complete combustion potential, COD, TOC, TN, TN grade, TP, SRP, heavy metals, conservative element concentration - Cycle: (River) twice a year for the first and second halves, (Lake) once a year ㅇ In addition, there are additional measurements of radioactive monitoring networks and biometric networks. Ⅲ. Water Quality Prediction Models 1. Physics-based model □ Example ㅇ EFDC, QUAL2K, WASP, etc. ㅇ The National Institute of Environmental Research is operating an EFDC-based model. □ Organization ㅇ Construct a grid network by dividing the water system into sub-regions and set boundary conditions ㅇ Estimate the water quality in sub-area units within the grid 2. Deep learning algorithm □ Model structure ㅇ Multi-layer perceptron (MLP) - It mimics the neurons and synapses of a neural network. It consists of an input layer, a hidden layer, and an output layer. it has a multi-layered structure with more than one hidden layer. ㅇ Recurrent Neural Network (RNN) - It additionally reflects the feedback effects of previous hidden nodes. - Nowadays, GRU and LSTM models are used. These models utilize the long-term memory based on a simple recurrent neural network. 3. Physics-based model vs. Deep learning algorithm □ Physics-based model ㅇ Based on well-established mathematical/physical laws ㅇ Actual observations are used for model evaluation. ㅇ Prediction can be performed at a more detailed resolution than observed values based on physical equations. ㅇ Disadvantages - Errors due to uncertain initial/boundary conditions - Difficulty in predicting the abnormal phenomena - May not work due to problems such as poor input data, instability of model relations, modeling method, etc. □ Deep learning algorithm ㅇ Establish the relationship between input and output variables through machine learning ㅇ Actual observations are used for model construction. ㅇ Includes error conditions in the model by quantifying the error of the measurements ㅇ Advantages in short-term predictions with greater uncertainties compared to physics-based models ㅇ Disadvantages - Requires a huge amount of data - Cannot be performed at a more detailed resolution than observation resolution - Practical application is limited since the relationship between input and output variables cannot be explained. Ⅳ. Development of an Algal Bloom Forecast Algorithm Based on Deep Learning 1. Data collection and preprocessing □ Model construction target ㅇ Target point: algae observation point in the hydrophilic activity section of the Han River ㅇ Target variable - Direct prediction of the number of harmful blue-green algae cells which is the direct cause of the algal bloom - Differentiated from previous studies that indirectly predicted the algal bloom through chlorophyll a prediction □ Model construction period ㅇ Target period: April 2007 ~ August 2020 ㅇ Data in winter from December to March, which is relatively safe from algal blooms, are excluded. 2. Characteristics of algae data □ Descriptive statistics □ Characteristics ㅇ Extremely right-skewed asymmetric distribution ㅇ Extreme asymmetric distribution is exhibited since algal blooms occur intensively in summer when the temperature is high. ㅇ Because of this, it is difficult to directly predict harmful blue-green algae using physics-based models or traditional statistical models. 3. Development of a predicting algorithm □ RNN model construction ㅇ Target of prediction: the number of harmful blue-green algae cells ㅇ Constructing an LSTM prediction algorithm to utilize the long-term memory information ㅇ Loss function for optimization: least squares function Optimization algorithm: ADAM ㅇ Training data: April 2007 ~ November 2016 Test data: April 2017 ~ June 2020 □ Results ㅇ The increasing and decreasing patterns are well predicted although there is difficulty in predicting using traditional prediction methods due to high data instability, which results from the fact that the hydrophilic activity section is located downstream of the river. ㅇ Well predict the occurrence of the largest extreme value at the same time ㅇ Prediction error Ⅴ. Conclusion and Achievements □ Achievements ㅇ Since the prediction using a physical model is established based on a well-established theory, it is widely used to predict properties of water quality such as water temperature, dissolved oxygen, total phosphorus, and total nitrogen. The prediction using the physical equation based on the law of conservation of mass is well suited for conservative substance. However, there is a limitation in the prediction of algae cells since it is the activity of living organisms. ㅇ Existing algal phenomena prediction studies have not directly predicted the number of harmful blue-green algae cells, which is the direct cause of algal phenomena. It is replaced by using the results of chlorophyll a concentration prediction. ㅇ In this study, a deep learning algorithm based on recurrent neural networks was used as an alternative method to predict the number of harmful blue-green algae cells. It well predicted the increasing or decreasing patterns of algae and the occurrence of abnormal phenomena at the concurrent point. □ Limitations ㅇ Only water quality, upstream water quality, water level, and meteorological information were used as input variables. These variables are already used in the physical model. Taking into account social variables such as population change and the benefits of deep learning analytics can be leveraged to a greater extent. Unstructured information such as satellite images can be additionally considered. ㅇ There is a limitation in the amount of data. In this study, the model was studied using data from a total of 365 weekly data collections from 2007 to 2016, but this amount itself is not sufficient. Whenever new data are added, the predictive model should be updated to increase the prediction efficiency. ㅇ There is a limitation due to the black-box characteristic. The detailed operational process of the prediction model cannot be clearly observed. When implementing a policy, evidence is needed. The black-box characteristic of deep learning prediction models makes it difficult to provide clear evidence. □ Conclusions and suggestions ㅇ Because it is very simple to perform predictions with the model that has already been established, it can be directly used as reference information for current algal bloom forecasts. ㅇ Since predictions using deep learning models and physics-based models both have advantages and disadvantages, it is most desirable to integrate the two prediction methods. Based on the deep learning model, the physical model can be integrated by including the physical equation in the constraint of the objective function. Or, deep learning can be partially performed in the partial module of the physical model prediction.

지속가능발전목표 이행 방안 마련 연구

홍한움,김호석,강선아,강지은 한국환경연구원 2021 기본연구보고서 Vol.2021 No.-

Ⅰ. Introduction 1. Research Necessity and Purpose □ The Need for Research ㅇ Agreement of the 2030 Agenda for Sustainable Development, at the UN General Assembly in September 2015. - There were 17 Sustainable Development Goals (SDGs) which were agreed upon to achieve integrated sustainable development goals in all sectors. - There have been national efforts to achieve these SDGs. The Committee for Sustainable Development under the Ministry of Environment established the Korean Sustainable Development Goals (K-SDGs) and monitored the UN Sustainable Development Indicators in Statistics Korea. ㅇ There have been changes made to the conditions for implementing the SDGs since the 2020 COVID pandemic. It is necessary to prepare an implementation plan that reflects changes in circumstances and challenges. - Large-scale financial commitment of a Korean New Deal to overcome the pandemic with a wide range of economic, social, and environmental impacts. - Reorganization of the national sustainable development policy system by establishing the “4th Basic Plan for Sustainable Development” in 2020. - Reorganizing the green growth system after the Korean government declared its intent to achieve carbon neutrality ㅇ There is a lack of an integrated perspective on the current implementation of sustainable development in Korea. - It is desirable to carry out integrated implementation in various fields, such as securing financial resources, financial planning, a reflection of SDGs in sectoral policies and national plans, setting targets and plans for national SDGs, continuous monitoring, and support for the development of developing countries. However, currently, there is an emphasis on setting targets and achieving indicators. □ Research Purpose ㅇ The implementation plan for sustainable development can be prepared using two main aspects. - Financial aspect: The Korean New Deal project promoted to overcome the COVID-19 crisis and stimulate the economy, is set to be consistent with sustainable development. - Policy coherence aspect: Utilization of the checklist to maintain coherent sustainable development policies proposed by the OECD, diagnosis of the current status of Korea, and suggestion for a direction of implementation. Ⅱ. Trends of Sustainable Development Implementations 1. Trend of UN SDG Implementation □ Structure of the UN SDGs ㅇ 5 principles, and 17 goals □ High-Level Political Forum of the UN ㅇ Inspection of the implementation status of member states through high-level political forums - One-year cycle: Under the auspices of the UN Economic and Social Council, the forum is held for eight days every year, including ministerial-level meetings. - Four-year cycle: Under the General Assembly, there are meetings among heads of states and governments. □ Limitations of the UN SDGs Implementation System ㅇ Member-states are recommending voluntary implementation, without binding them under the international legal system and institutional arrangements (Woo, Kim, and Kim, 2020). ㅇ It is recommended to submit a Voluntary National Review report once every four years, to inspect the implementation of member states. However, although there are recommendations for the report format, member states are submitting reports based on their interpretation without any systematic structure. □ Trend of Korea's UN SDG Implementation ㅇ Main inspection results of global SDG indicators under the auspices of Statistics Research Institute (Source: Statistics Research Institute (2021), 「SDG Implementation Report of the Republic of Korea 2021」) - High relative poverty rate among the elderly - Differences in securing food safety based on income levels. Inflation of agricultural, livestock, and seafood prices - Need to improve informatization capability and usability for the vulnerable - Low decision-making power for women compared to developed countries - Increase in greenhouse gas (GHG) emissions and hazardous waste - Need for sustainable management for the entire ecosystem - Increased number of reports and suspected cases of child violence and abuse - Although the ratio of ODA to GNI increased significantly over the past decade, Korea fell short of its commitment by 0.2% to the international community. ㅇ Ranked 28th out of a total of 193 countries in the 2021 Sustainability Evaluation of the Republic of Korea of the Sustainable Development Solutions Network (SDSN), an advisory body of the United Nations. 2. National Sustainable Development Goals □ Korea's Sustainable Development Implementation System ㅇ The Committee for Sustainable Development under the Ministry of Environment set the Korean Sustainable Development Goals (K-SDGs) that localized the UN SDGs considering the Korean situation, to implement sustainable development. - Currently, the K-SDGs system has been established through the “4th National Basic Plan for Sustainable Development (2021-2040)” - It consists of 17 goals, 119 targets, and 236 indicators ㅇ Publication of the ‘National Sustainability Report’ to confirm the implementation of national sustainability - Based on Articles 13 and 14 of Chapter 3 of ‘Sustainable Development Act’ - The current national sustainability report focuses on simple monitoring of K-SDGs indicators. 3. Implications □ Current Target-Oriented Implementation System ㅇ Both the implementations of UN SDGs and national sustainable development only presented simple target systems, as of 2021, and there is no guideline to control financial commitment and develop integrated governance for a specific implementation. ㅇ The UN SDGs are not legally bound to international law; as the Korean Committee for Sustainable Development, which is responsible for national sustainable development, belongs to the Minister of Environment, due to its low status, there are limits in serving a leading role in sustainable development, such as financial control and policy coherence. - In countries like Germany, Japan, and Finland, the Prime Ministers of the respective countries lead the government committees. - Norway pursues an integrated approach based on budget processes to implement sustainable development. ㅇ There is a need to strengthen the implementation of sustainable development in terms of fiscal commitment and strengthened policy coherence. Ⅲ. Measures to Strengthen Sustainability: Fiscal Commitment 1. Overview □ The Need for Connecting with the Korean New Deal to Strengthen Fiscal Commitment ㅇ To overcome the economic recession crisis caused by the COVID-19 pandemic, the Korean government is promoting the Korean New Deal. ㅇ Based on ‘The Korean New Deal 2.0’ in 2021, a large-scale investment plan with a total project cost of KRW 220 trillion won which includes the national budget of KRW 114.1 trillion won will be implemented by 2025. ㅇ The UN SDGs have the system to achieve integration in all economic, social, and environmental fields. If the large-scale finance of the Korean New Deal can be used to exert a positive impact on all economic, social, and environmental sectors and can be used to achieve the integrated SDGs, it can greatly contribute to the implementation of sustainable development. 2. Connection between the Korean New Deal and Sustainable Development □ Connectivity Analysis between Representative Tasks of the Korean New Deal and Sustainable Development ㅇ As both the Korean New Deal and sustainable development seek to achieve the integration of society, environment, and economy, the promotion of the Korean New Deal directly or indirectly affects the achievement of sustainable development. ㅇ Primary identification of UN SDGs on which the 10 representative tasks of the Korean New Deal 1.0 and the 5 representative tasks of the Korean New Deal 2.0 exert direct impacts. - As for detailed tasks excluding in the representative tasks, the detailed project details, specific budget (input) plans, and reports on the corresponding detailed project progresses in the National Report Conference are excluded; Therefore, in this analysis excluded detailed tasks not included in the representative tasks because they have low driving forces for for actively supporting the corresponding projects through large-scale new deal projects, compared to representative tasks. ㅇ Identification of SDGs on which the Green New Deal-related representative tasks exert impacts, based on the first identified 10 targets - Utilization of the nexus dialogues visualization tool which is provided by the UN Environment Management Group ㅇ Identify sustainable development targets on which the Korean New Deal is likely to have a negative impact - Utilize the SDGs interconnection analysis and visualization tool (Version 4.0, as of Sep. 2021) of the Institute for Global Environmental Strategies (IGES), the global environmental research institute. - Identify targets on which 10 sub-tasks with the possibility of a direct impact from the Korean New Deal, are likely to only expert a negative impact ※ The trade-off relationship between targets can appear not only when there is actual negative causation, but also when there is competition for the same resources or a limited budget. Most of the lists in Table 5 are understood to have appeared in terms of resource or budget competition, rather than because there are actual negative causal effects on the implementation of the representative tasks of the Korean New Deal. 3. Direction for Fiscal Commitment via the Korean New Deal for Sustainable Development □ The necessity for strengthening the fields in need of supplementation through the SDG performance confirmation in Korea. ㅇ Projects related to GHG reduction via the expansion of eco-friendly mobility in the environment and energy field have already dominated the Korean New Deal. ㅇ In terms of the implementation of sustainable development, it is desirable to supplement the weak areas other than green energy under the Korean New Deal system. - Necessity for the reinforcement of land, water, and marine ecosystem conservation related to SDGs 14 and 15 - Necessity for supplementing the sustainable agriculture sector - Although cleaner and safer water management is included in the detailed implementation tasks of the Green New Deal, there should be efforts such as implementing representative tasks or clearer budget investment plan, considering the inspection results of Korea's sustainable development implementation. □ Sustainable Development Targets to be Further Strengthened ㅇ It is necessary to further strengthen 'Target 4.4: More training for technical and vocational skills, and Target 9.3: Increase access to financial services and markets for SMEs.' - In terms of budget competition, the current representative tasks of the Korean New Deal could have negative impacts. - Considering that the Korean New Deal aims to stimulate the economy through job creation and lead the future industry, it can be said that those targets have high priorities; it is desirable to promote the corresponding fields by putting at a higher priority than the current level in the Korean New Deal. Ⅳ. Measures to Strengthen Sustainability: Policy Coherence 1. Current Sustainable Development Policy System □ Korea's Sustainable Development Governance System ㅇ It is difficult to see that the current Korean policy environment is integrated based on the SDGs. - Currently, the Committee for Sustainable Development under the Ministry of Environment oversees establishing and monitoring the Basic Plan for National Sustainable Development Goals. · The Committee for Sustainable Development was launched under the jurisdiction of the Prime Minister when the Framework Act on Sustainable Development was enacted in 2007 but was downgraded and is now under the Minister of Environment, in the process of changing the Framework Act on Sustainable Development to the Sustainable Development Act, along with the enactment of the Framework Act on Low Carbon, Green Growth in 2010. - In the process of promoting the Committee on Carbon Neutrality under the current government, there was a process of integrating climate and environment-related committees such as the Committee on Green Growth, the National Council on Climate and Air Quality, and the Special Committee on the Prevention of Fine Dust, but the Committee for Sustainable Development was excluded. - In terms of sustainability monitoring, the K-SDGs indicator monitoring of the Committee for Sustainable Development under the Ministry of Environment, and the global indicator monitoring of Statistics Korea, are not being operated without integration. 2. Review of Consistent Policy Recommendations Proposed by OECD □ Review of Policy Coherence ㅇ 1 Inclusive political commitment and leadership by top-level politicians - Although the sustainable development keyword can be found in the 10 pledges of each party for the 21st National Assembly election, it is difficult to view it as a pledge in terms of policy coherence. - The national inspection of sustainable development is limited to the inspection for the evaluation of individual indicators without considering enhanced policy coherence. ㅇ 2 Strategic vision to lead the government and stakeholders - The pursuit for sustainable development under the current national sustainable development goal system of the Committee for Sustainable Development - In addition, the Korea New Deal Comprehensive Plan, the 2050 Carbon Neutral Strategy, and the Comprehensive Basic Plan for International Development Cooperation can be considered as national comprehensive plans closely related to the achievement of sustainable development. ㅇ 3 Policy integration to manage synergies and offsets between policies - There is no upper-level governance in charge of linkage and integration between policies. - There is no detail related to the integrated guideline for sustainable development, in the Korean New Deal or the Framework Act on Carbon Neutrality - Some indicators of the Basic Plan for Sustainable Development review the budget to understand the level of goal achievement, but it cannot be considered that the government budget is currently managed by integrating SDGs. ㅇ 4 Policy integration to manage synergies and offsets between policies - The Committee for Sustainable Development takes responsibility for the coordination system for sustainable development. - Although sustainability is reviewed through the national sustainable development goals, it is judged that they are not functioning to enhance policy coherence. ㅇ 5 Strengthen coordinated implementation in policy coherence related to local governments - Pursue a sustainable community through Local Agenda 21 - Regionally balanced New Deal is also included in the Korean New Deal system - As for the Framework Act on Carbon Neutrality, there are measures to maintain the cooperative relationship between the central and local governments. ㅇ 6 Strengthen coordinated implementation in policy coherence related to local governments - The Committee for Sustainable Development operates Korea Major Groups and other Stakeholders (K-MGoS); when establishing the basic plan, there is a process of public debate through the national SDG forum or general public survey. - It was pointed out that there was an insufficient accommodation of stakeholder opinions when establishing the Korean New Deal and the Framework Act on Carbon Neutrality. ㅇ 7 Analysis and report of positive and negative, and international impacts of policies - The current monitoring system for inspecting sustainable development is difficult to see as an impact assessment on policies. Occasionally there have been reports and studies evaluating the positive and negative impacts of policies from the perspective of sustainable development, which has been researched by national research institutes and universities. However, there has been no regular evaluation, as of 2021. ㅇ 8 Establish a system to analyze the impact of policies, and to observe and evaluate quantitatively and qualitatively - There has been inspection on sustainability: the National Sustainability Report’ of the Committee for Sustainable Development, and the ‘SDG Implementation Report of Republic of Korea’ of Statistics Korea. However, it is difficult to see that the report results have been utilized for improving the Korean government’s policy coherence. - Reports are transparently disclosed, but accessibility is poor. - Absence of an external independent auditor to evaluate policy consistency 3. Tasks to Strengthen Policy Coherence ㅇ An integrated vision and strategy for the implementation of the SDGs should come first - Korea's Sustainable development seems to be implemented under the ‘Basic Plan for Sustainable Development’ of the Committee for Sustainable Development under the Ministry of Environment, but international development-related details are under the ‘Basic Plan for International Development Cooperation’ and carbon neutrality related details are under the Committee on Carbon Neutrality; committees in charge of different details are dispersed. - Under poor governance, the SDG targets are highly likely to negatively affect each other in terms of resource and budget competition. ㅇ It is necessary to establish a consistent policy evaluation system rather than the current simple indicator monitoring. - An evaluation system is required to evaluate positive and negative impacts of policies from the perspective of sustainable development ㅇ It is necessary to re-elevate the downgraded Commission on Sustainable Development to the Presidential-level Commission Ⅴ. Conclusion □ Fiscal commitment and policy coherence must be strengthened for the fundamental implementation of sustainable development ㅇ There is a limit to achieving sustainable development under the current system confirming the implementation only through simple indicator monitoring. ㅇ As the Committee for Sustainable Development belongs to the Ministry of Environment, it is difficult to coordinate budget investment and policy coherence at the current status. ㅇ It is necessary for the large-scale financial investment via the Korean New Deal to be induced in a direction consistent with sustainable development. ㅇ It is necessary to establish integrated governance that can coordinate budget investment and policy coherence.

국가 지속가능성 이행과제 간 연관관계 분석방안 연구

홍한움 ( Hanwoom Hong ),강선아 ( Suna Kang ),김도연 ( Doyeon Kim ) 한국환경연구원 2019 수시연구보고서 Vol.2019 No.-

For the integrated achievement of society, environment and economy, 17 SDGs and 169 targets were selected from the UN agenda. Since the SDGs and the targets are related to each other in a mutually reinforcing or conflicting manner, an analysis of the interlinkages among the SDGs should precede in order to achieve social, environmental and economic integration. The methodology for analysis could be classified into two types; the qualitative analysis through suggestion of analysis framework and the quantitative analysis method through network analysis. Through qualitative analysis, we can figure out the reliable interlinkages. Through network analysis, it is possible to grasp the relationships among SDGs and among detailed targets at a glance through the network diagram and several centrality statistics. In addition, it is possible to understand which goals or targets need to be focused on for efficient and integrated sustainable development through the statistics of degree centrality, eigenvector centrality, and betweenness centrality. As South Korea’s first National Plan for Sustainable Development and Implementation Plan (’06 ~ ’10) was finalized in 2006 and the first master plan period terminated in 2011, South Korea has strengthened its social equity, climate change response measures and sustainability of environmental resources. In 2011, “The Second National Sustainable Development Plan (’11 ~ ’15)” was established. “The Third National Sustainable Development Plan (’16 ~ ’35)”, then, was established in 2016. In this study, we analyzed the report of the Third National Sustainable Development Plan based on text mining and analyzed the interlinkages among the implementation tasks and among the detailed implementation tasks. The analytical methodology and analysis models investigated in this study can also be applied to analyze the linkage among K-SDGs established in December 2018. From the draft level, the K-SDGs have held various expert groups. Using these expert groups and the methodology of this study, we can expect to analyze the correlations among reliable K-SDGs.

서울 미세먼지(PM10) 농도의 시공간 통계분석 활용방안 연구

홍한움 ( Hanwoom Hong ) 한국환경연구원 2018 한국환경정책평가연구원 기초연구보고서 Vol.2018 No.-

As the social interest in particulate matter (PM10) increases, studies on particulate matter have been carried out in various fields, but statistical methodology has been used limitedly. PM10 data is spatio-temporal data having time dependence and spatial dependence simultaneously. In recent decades, statistical techniques for statistical analysis of spatio-temporal data have been developed in statistical field, and the results of study with respect to applying spatio-temporal statistics to PM10 data have been published. The purpose of this study is to predict the risk of PM10 concentration based on Seoul metropolitan city using the recent spatio-temporal statistical methods. First, we introduce the spatio-temporal statistical model and investigate its advantages and disadvantages compared with the physical model which is widely used for PM10 prediction. The challenging tasks in carrying out spatio-temporal statistical analysis can be the fact that time dependence and space dependence should be matched. The aforementioned time dependence and space dependence should be balanced so as to avoid overfitting or underfitting problems. Since the optimal time resolution for a given spatial resolution is not known yet, the actual analysis must be applied to various temporal resolutions. To that end, overseas case study applying the spatio-temporal statistical model to the air quality was examined and applied to PM10 of Seoul metropolitan city. The analytical coverage is based on the PM10 data of Seoul metropolitan city in 2016, and fit the model for 1 hour, 3 hours, and 8 hours time windows. The least stable data of April, the most stable of July and neutral of October were targeted to be analyzed. The results show that the proposed method is in good agreement with the prediction of maximum value and the prediction of VaR (Value at Risk). Spatio-temporal statistical models are suitable to expand to the national level and can be used for receptor-based research. It is expected that effective analysis will be possible if the observatory is located in the widespread agricultural and fishing villages.

하·폐수 방류수 수질 준수 평가방법의 합리화 방안 연구

조을생,홍한움,임동순,황보은,전동진,최원진 한국환경연구원 2020 기본연구보고서 Vol.2020 No.-

Ⅰ. Background and Aims of Research □ The issue that the criteria for determining whether the pollutant level exceeds the water quality standard, which is measured by the Water-TMS (Tele-Monitoring System) in real time are one of the main causes of water data manipulation and the excessive regulations has been raised. □ Although water quality measurement values of treated sewage and wastewater in Korea are generated in real time by the Water-TMS, they are not used in the total water pollution management system, and the criteria for determining whether the discharge standards have been exceeded are different, causing confusion for treatment facility operators and the lack of consistency in water pollutant management policy. □ Therefore, this study aims to review the adequacy of the current criteria for determining whether water pollutants monitored by the Water-TMS in real time meet the water quality standards, and to suggest measures for the improvement of the current criteria and a linkage with the TPLMS (Total Water Pollution Load Management system). Ⅱ. Research Materials and Methods □ This study analyzed the current status of sewage and wastewater treatment facilities, treatment facilities subject to the Water-TMS installation using wastewater related statistics and reviewed the status of policy implementation and legal system in Korea and abroad. □ The distribution of treated wastewater effluent measured in the year 2019 was analyzed for all sewage and wastewater treatment facilities with the Water-TMS based on six scenarios of the criteria for wastewater effluent standard compliance evaluation. □ The national economic impact on the changes in the amount of effluent charges according to six scenarios of the criteria for wastewater effluent standard compliance evaluation was analyzed by Input-Output analysis. □ Lastly, in order to examine how to use the Water-TMS measurement data in the TPLMS, the reliability of the water quality TMS measurement device was reviewed and the manual analysis value of the TPLMS were compared with the water-TMS measurement data. Ⅲ. Results and Conclusions □ compared to the current 3-hour moving average as the criteria for wastewater effluent standard compliance evaluation, the 24-hour moving average shows that more stringent water quality management can be achieved for the high concentration of wastewater effluent. ㅇ When the concentration of the treated effluent becomes higher to the point of exceeding the water quality standard within 24 hours, if 3-hour moving average is used, it will be no longer considered as exceeding the standard after exceeding for three consecutive times; however, if 24-hour moving average is used, it will be considered as exceeding for 24 consecutive times. □ We suggest that exceeding the effluent standard for six or eight consecutive times be the criterion for taking administrative measures. ㅇ According to the current criterion, when the 3 hour-moving average exceeds the wastewater effluent standard, the time that the operator can respond is only about one hour, considering the water-TMS measurement cycle and the time of sample input. ㅇ However, most of domestic sewage and wastewater treatment plants are biological treatment facilities, and at least a hydraulic residence time (on average about 6-12 hours) is required to return from malfunctioning to normal operation. □ According to the analysis of the economic ripple effect, applying the 24-hour moving average as the criteria for wastewater effluent standard compliance evaluation shows the improvement of the economic indicators in terms of the output, added value, employment, and employment due to the reduced basic effluent charge and excess effluent charge compared to the current 3-hour moving average. □ It is necessary to prevent confusion among treatment facility operators and improve the consistency of regulatory standards by applying real-time Water-TMS measurements that reflect changes in sample properties to the TPLMS. ㅇ The reliability of Water-TMS has been improved, as in the reinforcement of regulatory standards related to QA/QC, and revision of the enforcement regulations to prevent water quality TMS manipulation is being promoted. ㅇ As a result of the analysis of errors between water quality TMS measurements and manual analysis value for the TPLMS, up to the 50th percentile, the measured Water-TMS values are lower than those manually measured during inspection, and up to the 90th percentile, the error is within 0.05.

환경 빅데이터 분석 및 서비스 개발 Ⅳ

강성원,진대용,홍한움,고길곤,임예지,강선아,김도연 한국환경연구원 2020 사업보고서 Vol.2020 No.-

Ⅰ. Background and Aims of Research ❏ We continue to build up ‘Environmental Policy monitoring System’ dedicated to periodically identify environmental policy needs and assess timeliness and effectiveness of environmental policy as we did last year ㅇ Environmental Policy monitoring System apply prediction accuracy and real-time data collection-analysis-diffusion capability of Machine learning to environmental policy research ㅇ Our ‘Environmental Policy monitoring System’ consists of three components: ‘Deep Learning Based Pollution Prediction algorithm’, ‘Real Time Environmental Text Analysis algorithm’, ‘Issue Based Database’ - Deep Learning Based Pollution algorithm: Periodically update various pollution prediction - Real Time Environmental Text Analysis algorithm: Periodically summarise environment related text data and sentiment analysis ㆍText summary: abstract keywords and keyword network from texts produced by environmental policy provider and environmental policy consumers ㆍSentiment analysis: Real-time collection and sentiment analysis of SNS related to all subfield of environment - Issue Based Database: Key environmental issue network connected with data analysis for each issue updating real-time ㅇ Policy need Identification: Detect environment policy areas and regions in need of intervention from the predictions of ‘Deep Learning Based Pollution Prediction algorithm’, the text analysis results of ‘Real Time Environmental Text Analysis algorithm’, and the data analysis results of ‘Issue Based Database’ ㅇ Timeliness assessment: check if the temporal pattern of keywords analysis result on policy provider text and the temporal pattern of keywords analysis results on policy consumer are consistent ㅇ Effectiveness assessment: Check Pollution improvement, SNS Sentiment improvement, and Environmental Issue improvement after policy execution ❏ In 2020, we tried to improve interpretability of ‘Environmental Policy monitoring System’ ㅇ While utilizing the advantage of deep learning we found in period 1(2017~2019), we tried to reduce complexity and strengthen interpretability ㅇ In period 1, we focused on ‘apply everything related to big data analysis to Environment policy research’ From 2020, we are going to focus on ‘Environmental Policy Research using large scale data’ ㅇ Regrading methodology, we stick to machine learning in period 1. From 2020, we are going to be more flexible and try to include traditional frequentist and Bayesian statistical methods ㆍWe are going to use simpler models to improve our understanding on feature importance ㆍWe are going to build models capable of longer -term prediction and models with more interpretability ❏ In 2020, we build four algorithms for ‘Environment Policy Monitoring System’ and perform two independent researches ㅇ For ‘Environment Policy Monitoring System’, we expand the methodology and scope of previous components - We added two fine particle estimation algorithms in `Fine particle high concentration event prediction’ and ‘PM_2.5 estimation and prediction using Graph-GRU algorithm’ - In ‘Environmental text sentiment analysis algorithm’, we expanded sentiment analysis of environment related SNS to all subfield of environment - In ‘Climate change issue based database’, we constructed new issue based database on climate change ㅇ On two important issues that cannot be integrated to ‘Environment Policy Monitoring System’, we did independent research - In ‘The impact of air pollution long-term exposure to mortality of COPD patients’,we estimated the effect of long-term exposure of air pollution on the death risk of COPD patients using NHI (National Health Insurance) Data - In ‘Solar electricity generation prediction’, we constructed an RNN based algorithm predicting solar electricity generation of F1 power plant Ⅱ. Fine Particle High Concentration Event Prediction ❏ We built a quantile regression based prediction algorithm to predict ‘extremely bad(76+)’ event of PM_2.5 in 25 air pollution monitoring station in Seoul ㅇ We adjusted quantile regression model to analyze data with extreme values - We applied LASSO variable selection method to Extreme Conditional Quantile Regression Model ❏ We predicted 4 hour average(6 periods per day) PM_2.5 pollution in Seoul using air pollution data and weather data ㅇ For independent variables, we used contemporary and 1 time earlier CO, SO₂, NO₂, O₃, PM₁₀ pollution, rainfall, temperature, humidity, wind strength, wind direction and 1 time earlier PM_2.5 pollution ㅇ We train our model with data from Jan. 1st. 2015 to Mar. 4th. 2018 and test with data from Mar. 4th. 2018 to May. 29th. 2020 ❏ We were able to achieve 89.0%~100.0% Sensitivity while limiting False Positive at 6.0%~17.1%, depending on the station. ㅇ Except for Gandonggu(88.9%), our algorithm achieve sensitivity higher than 90% ❏ The sensitivity of our model was higher by more than 11.3%p, compared to the sensitivity of models based on random forest, supporting vector machine and GRU ㅇ Sensitivity of Ganseogu: our algorithm 92.3% > GRU 81.0% > SVM 73.3% > RandomForest 65% ❏ The probability of ‘extremely bad’ event of PM_2.5 pollution increases when contemporary CO, O₃, PM₁₀ pollution, wind direction and 1 time ahead PM_2.5 pollution. - We applied LASSO variable selection method to Extreme Conditional Quantile Regression Model ❏ We predicted 4 hour average(6 periods per day) PM_2.5 pollution in Seoul using air pollution data and weather data ㅇ For independent variables, we used contemporary and 1 time earlier CO, SO₂, NO₂, O₃, PM₁₀ pollution, rainfall, temperature, humidity, wind strength, wind direction and 1 time earlier PM_2.5 pollution ㅇ We train our model with data from Jan. 1st. 2015 to Mar. 4th. 2018 and test with data from Mar. 4th. 2018 to May. 29th. 2020 ❏ We were able to achieve 89.0%~100.0% Sensitivity while limiting False Positive at 6.0%~17.1%, depending on the station. ㅇ Except for Gandonggu(88.9%), our algorithm achieve sensitivity higher than 90% ❏ The sensitivity of our model was higher by more than 11.3%p, compared to the sensitivity of models based on random forest, supporting vector machine and GRU ㅇ Sensitivity of Ganseogu: our algorithm 92.3% > GRU 81.0% > SVM 73.3% > RandomForest 65% ❏ The probability of ‘extremely bad’ event of PM_2.5 pollution increases when contemporary CO, O₃, PM₁₀ pollution, wind direction and 1 time ahead PM_2.5 pollution. ❏ The probability of ‘extremely bad’ event of PM_2.5 pollution decreases when contemporary and 1 time ahead rainfall, windspeed increases Ⅲ. PM_2.5 Estimation and Prediction Using Graph-GRU Algorithm ❏ We built Graph-GRU albright utilizing weather and geography information to prediction PM_2.5 pollution ㅇ We construct a 3 dimension dataset consists of temporal and spatial data - Our dependent variable is 3 hour average PM2.5 concentration of 0.125° × 0.125° Grid (12.5km x 12.5km) containing air-pollution monitoring stations in South Korea - Our independent variables are weather data, air pollution data and height data ㆍWe trained our model with data from Jan. 1st. 2015 to Dec. 31th. 2015, validated with data from Jan. 1st. 2016 to Dec. 21th. 2016, and tested with data from Jan. 1st. 2017 to Dec. 21th. 2017, ㅇ We used Graph-GRU algorithm - For node attributes, we used weather data, spacial data, and air pollution data - To measure edge attributes, we constructed a function evaluating impact of air pollutant emission between air pollution monitoring stations ㆍThis function evaluates impact of air pollutant emission between air pollution monitoring stations using distance, wind speed, and wind direction - For adjacency Matrix, we used threshold of distance of 300km or height of 1,200m ❏ RMSE of our model in 3 hour~72 hour prediction was 4.05μg/m³ ~ 11.49μg/m³ ㅇ Temporal information reduced RMSE by 0.12μg/m³, and spacial information reduced RMSE by 0.16μg/m³ Ⅳ. Environmental Text Sentiment Analysis Algorithm ❏ ‘Environmental text sentiment analysis algorithm’ periodically performs collection-analysis-result distribution on SNS text regarding environment ㅇ We expanded climate change sentiment analysis algorithm we build in 2018~2019 to all subfield of environments, and trained with new 140 thousands SNS text training data labeled by semi-supervised learning to improve accuracy ㅇ We constructed web based user interface to visualize sentiment analysis results over time ❏ To improve accuracy, we increase the size of our training data from 50 thousand to 180 thousand using semi-supervised learning ㅇ We collected 6.5 million SNS text, and applied 2018~2019 version climate change sentiment analysis algorithm. We collected 140 thousands cases with high positive/negative sentiment score, which we added to training data. We kept 10 thousand cases for testing ㅇ With this new training data, the accuracy of sentiment analysis improved by 1%p: From 78.7% to 79.7% ❏ Sensitivity of our newly trained model was 66~92%, and Recall of our newly trained model was 73%~81%, according to subfield ㅇ ‘Waste’ field had the lowest Sensitivity and Recall. This field needs supervised learning approach to improve overall accuracy ❏ We built web based user interface to visualize sentiment analysis results with user option of keyword search and period choice ㅇ Our user interface also abstract keyword network of SNS of negative sentiment, which should give insights on the cause of negative sentiment Ⅴ. Climate Change Issue Based Database ❏ We built a datamap on Climate change consists of hierarchically organized climate change issue network and data analysis linked to each issue in the network. This datamap is capable of real-time data analysis update ㅇ We build issue collection module to extract issues from text data and data analysis module to link data analysis with extracted issues ❏ Issue collection module execute ‘Climate change text collection → Topic Extraction → Issue Identification → Issue Network Organization’ process ㅇ Text Collection: Reports from government sponsored research institutes/ Formal speeches from higher-ranking official/Press Release from government/Assembly meeting transcripts/Materials from Climate Change From in Assembly/DBpia academic paper abstracts/NAVER paper articles of 12 major papers from 2012 to 2019 ㅇ Topic Extraction: Apply Correlated Topic Model to extract 10 topics and correlation between topics ㅇ Issue Identification: Derive issues from key sentences extracted from TextRank algorithm ㅇ Issue Network Organization: Assign each issue to topics and organize issues according to the relationship between topics - We deduced relationship between topics combining three sources of informations - (1) Correlation coefficient from Correlated Topic Model (2) Similarity of time series frequency pattern from Dynamic Time Warping (3) Specialist Survey - We re-categorized 10 CTM topics into 5 Categories: Climate Issue cooperation/Climate Change adaptation/Greenhouse Gas Reduction /Energy and Environment/Urban Environment and Citizen ❏ Data analysis module attach data source and data analysis result to each issue in Climate Change Issue Network - We linked each data analysis to data source so that we can update data analysis in real-time Ⅵ. The Impact of Air Pollution Long-Term Exposure on the Mortality of COPD Patients ❏ We estimate the impact of 1-year and 5-year air pollution exposure on the mortality of COPD patients ㅇ We analyzed medical data of COPD patients older than 40 diagnosed from 2009 to 2018 ㅇ We used kriging to convert air pollution monitoring station data to small local district (Up. Myun.Dong) data and applied Cox Proportional hazard model to small local district data ❏ We combined NHI(National Health Insurance) individual patient data and air-pollution monitoring station data ㅇ For air pollution exposure variable, we used 1-year and 5 year average of small local district PM₁₀, O₃, NO₂ pollution - For PM₁₀ and NO₂, We used daily average. For O₃, we used average of maximum 8 hours for each day (We converted the unit of O₃ and NO₂ from ppb to ㎍/m3) ㅇ From NHI individual medical data, we obtained gender, age, income percentile, CCI, COPD exacerbation, smoking status ㅇ For dependent variable, we used dummy variable assigning 1 for death and 0 for survival ❏ We found that COPD patients exposed higher O₃ 1-year or 5-year had higher risk of death ㅇ Hazard ratio of 1 year O₃ exposure was estimated as 1.003. Hazard ratio of 5 year O₃ exposure was estimated as 1.004 Ⅶ. Solar Electricity Generation Prediction ❏ We developed an LSTM algorithm predicting electricity generation of Yung -am F1 solar power plant ㅇ We predicted hourly electricity generation and 12 hour average of electricity generation. The electricity generation was non-stationary time-series ❏ For independent variables, we used electricity generation and weather data with 1 lag ㅇ For weather data, we used hourly temperature, rainfall, humidity, solar insolation, Total Cloud amount from Mokpo weather monitoring station ㅇ We used data from Jan. 01.2017 to Jun. 30. 2018 for training, and data from Jun. 30. 2018 to Jun. 30. 2019 for testing ❏ We built and RNN based LSTM algorithm and compared RMSE with ARIMA model and 3-lag moving average ❏ The RMSE of our model was 36.9% of standard deviation in 1 hour prediction and 51.1% in 12 hours average prediction ㅇ The RMSE to standard deviation of our model was 71% of the RMSE to standard deviation ratio of 3-lag moving average, and 45% of RMSE to standard deviation of ARIMA model Ⅷ. Conclusion and Suggestions ❏ Summarizing, we improved ‘Environmental Policy Monitoring System’ and added some new results ㅇ We supplemented and improved components of ‘Environmental Policy Monitoring System’ - ‘Deep Learning Based Pollution Prediction algorithm’: We improved interpretability and extended prediction lag ㆍ ‘Fine particle high concentration event prediction’: We built a Quantile regression model which can produce coefficient estimates for independent variables and is capable of prediction as accurate as machine learning algorithm ㆍ ‘PM_2.5 estimation and prediction using Graph-GRU algorithm’: We achieved 7.64g/m³ 12 hour prediction RMSE, which is equivalent to 1 hour prediction RMSE of our CNN based algorithm in 2019 - ‘Real Time Environmental Text Analysis algorithm ’: We expanded realtime sensitivity analysis and keyword network abstraction of negative sentiment for all environmental policy subfield - ‘Issue Based Database’: We improve policy monitoring scope from fine particle issue (2019) to climate change (2020) ㅇ We developed three new algorithms and one new issue based database - Fine particle high concentration event prediction quantile regression model, PM_2.5 pollution prediction Graph-GRU model, RNN based Solar electricity generation prediction algorithm/ climate change datamap ㅇ We expanded scope of environmental text analysis: Real-time environmental text analysis web interface ❏ For policy application, we strengthen environmental policy monitoring capability, quantify the health risk of air pollution, and provided items for the infrastructure of renewable energy ㅇ Environmental Policy monitoring: We improved policy need identification and information generation for precautionary policy intervention - Now our ‘Real Time Environmental Text Analysis algorithm’ is capable of identifying subfield of environment regarding which general public has negative sentiment. - Now our ‘Climate Change datamap’ is capable of real-time assessment of climate change issues - Now our ‘Deep Learning Based Pollution Estimation algorithm’ is capable of extending prediction lag of PM_2.5 and providing basic causality analysis for high concentration event of PM_2.5 ㆍGraph-GRU extended prediction lag. We can use this time for preventive policy intervention ㆍQuantile Regression model can be used policy evaluation tool by extending control variables and policy related variables ㅇ We provided quantified risk of air pollution on COPD patients, which can be used to quantify benefits of air-pollution reduction policy ㅇ We provided solar electricity generation prediction algorithm, which can be used for renewable energy smart-grid infrastructure