OBJECTIVES:Although particulate matter (PM) exposure poses significant public health risks, previous research has focused on limited clinical areas. However, emerging evidence and pathological mechanisms of PM suggest that PM may exert broader systemic effects across a wide range of diseases. Therefore, we aim to identify and prioritize research questions to evaluate health impacts of PM exposure across various clinical specialties. METHODS:A structured collaborative process was conducted between April and November 2024 in South Korea, incorporating systematic literature reviews, multidisciplinary expert discussions, and knowledge-sharing seminars. The primary outcomes were the identification of diseases potentially influenced by PM exposure and the development of corresponding research questions. The literature review synthesized more than 417 publications, including the U.S. Environmental Protection Agency's integrated science assessment materials, a government-issued abstract compendium on PM covering 2010-2019, and studies published from 2020 to 2024 identified via a structured search. These were categorized by exposure duration (short- or long-term) and diseases outcome (incidence or progression). Prioritization was based on three criteria: pathological causality, clinical impact (public health burden), and feasibility using the Korea National Health Insurance Service (K-NHIS). RESULTS:A total of 99 experts from epidemiology, data science, and 14 clinical specialties participated. The experts panel (mean age: 46.1 years; mean professional experience: 20.5 years) identified 211 research questions across 80 diseases. These were classified by disease outcome: disease incidence (short-term, 54; long-term, 64) and progression (short-term, 47; long-term, 46). Notably, several clinical areas such as ophthalmology, dermatology, and otolaryngology were underrepresented. CONCLUSION:This structured, multidisciplinary approach broadened the scope of PM-related clinical research beyond commonly studied clinical area. This scalable framework can be adapted in other regions with similar claims data systems to guide evidence-based research agendas and inform public health policies.

The South Korean government aims to achieve carbon neutrality by 2050. Here, this study analyzed the economic efforts of the net-zero policy by integrating top-down and bottom-up models; The UNIfied Climate Options Nexus (UNICON) within this study aims to create a link between the 26 sectors from the bottom-up model and the 85 sectors of the top-down model. Positive mathematical programming methods were used to ensure consistency between the top-down and bottom-up models within the base year. The study found that the total reduction rate was slightly higher in the integrated model than in the computable general equilibrium (CGE) stand-alone model. In both models, the reduction rate increased when the carbon tax increased, but the marginal reduction rate was considerably lowered, and the reduction rate did not exceed 80% even with the high level of a carbon tax. The technological change of the linked industries in the integrated model showed that the steel industry had the highest emission reduction. When estimating costs for reducing GHGs, results can vary based on the technological changes under consideration.

Sector coupling emerges as a potentially efficient strategy for emission reduction, particularly when the power sector is sufficiently decarbonized. This study aims to explore the effects of sector coupling on the decarbonization of the manufacturing sector. This study also develops the hybrid energy system model by integrating bottom-up energy system models for the power and manufacturing sectors with the computable general equilibrium model. The hybrid model helps to explain the integration of the power, hydrogen, and manufacturing sectors. The manufacturing sector is electrified and integrated with other sectors based on its use of decarbonized electricity and electrolysis hydrogen. With a carbon tax rate of 200,000 Korean won (161 U S. dollars) per ton of carbon dioxide equivalent, the percentage of electricity in the manufacturing sector's energy mix is expected to increase from 30 % to 46 % by 2050 if electrolysis hydrogen is considered. Moreover, emission reduction from sector coupling accounts for more than 50 % of the total emission reduction in the manufacturing sector. If policymakers consider the overall benefit of decarbonizing the power sector and aid the development of power-to-X technologies, sector coupling can contribute the decarbonization of the manufacturing sector significantly.

Background:Evidence on whether long-term exposure to air pollution increases the mortality risk in patients with chronic obstructive pulmonary disease (COPD) is limited. Objectives:We aimed to investigate the associations of long-term exposure to particulate matter with diameter <10 mu m (PM10) and nitrogen dioxide (NO2) with overall and disease-specific mortality in COPD patients. Design:We conducted a nationwide retrospective cohort study of 121,423 adults > 40 years diagnosed with COPD during 1 January to 31 December 2009. Methods:Exposure to PM10 and NO2 was estimated for residential location using the ordinary kriging method. We estimated the risk of overall mortality associated with 1-, 3-, and 5-years average concentrations of PM10 and NO2 using Cox proportional hazards models and disease-specific mortality using the Fine and Gray method adjusted for age, sex, income, body mass index, smoking, comorbidities, and exacerbation history. Results:The adjusted hazard ratios (HRs) for overall mortality associated with a 10 mu g/m(3) increase in 1-year PM10 and NO2 exposures were 1.004 [95% confidence interval (CI) = 0.985, 1.023] and 0.993 (95% CI = 0.984, 1.002), respectively. The results were similar for 3- and 5-year exposures. For a 10-mu g/m(3) increase in 1-year PM10 and NO2 exposures, the adjusted HRs for chronic lower airway disease mortality were 1.068 (95% CI = 1.024, 1.113) and 1.029 (95% CI = 1.009, 1.050), respectively. In stratified analyses, exposures to PM10 and NO2 were associated with overall mortality in patients who were underweight and had a history of severe exacerbation. Conclusion:In this large population-based study of patients with COPD, long-term PM10 and NO2 exposures were not associated with overall mortality but were associated with chronic lower airway disease mortality. PM10 and NO2 exposures were both associated with an increased risk of overall mortality, and with overall mortality in underweight individuals and those with a history of severe exacerbation.

Harmful algal blooms (HABs) can cause serious problems for aquatic ecosystems and human health, as well as massive social costs. Therefore, continuous monitoring and prevention are required. Water quality management is an important task to minimize such algae, and future occurrences can be accurately predicted through optimal water resource management. In this study, we developed a convolutional neural network model using eight water quality variables and four weather variables to predict the concentration of chlorophyll-a in four major Korean rivers. In addition, Deep SHAP was applied to aid in policy decision-making and identify the influence on variables affecting chlorophyll-a. This integrated prediction model showed a 38.01 % reduction in root mean square error and 36.16 % improvement in R-squared compared to the long short-term memory (LSTM) model. This demonstrated the effectiveness of the proposed integrated prediction approach. Furthermore, despite simultaneously predicting HABs at all monitoring stations and training 394 times faster than LSTM-based models, the proposed method exhibited a significant improvement in efficiency and elucidated variable influences that existing models failed to explain. The proposed integrated prediction model can predict HAB spread, identify variable influences to aid decision-makers, and effectively implement preemptive responses, thus reducing economic losses and preserving aquatic ecosystems.

Harmful algal bloom (HAB) can lead to severe problems in aquatic ecosystems and human health. Therefore, it requires constant monitoring and prevention. Water quality management is one of the most important tasks to minimize this phenomenon, and its future occurrence can be accurately predicted through optimal water resource management. This study developed a convolutional neural network model that can predict chlorophyll-a in the Four Major Rivers of South Korea through one deep learning model, using multiple parameters that consider eight types of water quality data and four types of weather data. This integrated prediction model showed a reduction of 38.01% in root mean square error (RMSE) and an improvement of 36.16% in R-squared compared to the long short-term memory (LSTM) model used in previous predictions of chlorophyll-a. This demonstrates the effectiveness of integrated prediction. Furthermore, by performing training 394 times faster than the LSTM-based model despite simultaneously predicting the HAB of all monitoring stations, the integrated prediction model showed a greatly improved efficiency. Through the integrated prediction model presented in this study, the proliferation of HAB can be mitigated, and pre-emptive measures can be more effectively implemented, thereby contributing to reducing economic losses and preserving aquatic ecosystems.

In this paper, we propose a real-time prediction model that can respond to particulate matters (PM) in the air, which are an indication of poor air quality. The model applies interpolation to air quality and weather data and then uses a Convolutional Neural Network (CNN) to predict PM concentrations. The interpolation transforms the irregular spatial data into an equally spaced grid, which the model requires. This combination creates the interpolated CNN (ICNN) model that we use to predict PM10 and PM2.5 concentrations. The PM10 and PM2.5 evaluation results show an effective prediction performance with an R-squared higher than 0.97 and a root mean square error (RMSE) of approximately 16% of the standard deviation. Furthermore, both PM10 and PM2.5 prediction models forecast high concentrations with high reliability, with a probability of detection higher than 0.90 and a critical success index exceeding 0.85. The proposed ICNN prediction model achieves a high prediction performance using spatio-temporal information and presents a new direction in the prediction field.

Improving efficiency is an important option for reducing manufacturing sector emissions. More efficient technologies reduce energy use and emissions. However, efficiency improvements can induce unexpected effects known as rebound effects. Although previous studies have analyzed these effects, these studies fail to precisely evaluate the rebound effects due to improvements in technology efficiency. Bottom-up models are appropriate for analyzing efficiency improvements at the technology level, but they face limitations in exploring output changes because they usually assume that demand is given. In contrast, top-down models are appropriate for observing output changes in an efficiency-improving sector and in the rest of the economy, but they face limitations in explicitly describing technological changes. This study therefore constructs a hybrid model to overcome the limitations of both types of models and evaluates the impacts of climate policy in Korea’s manufacturing sector when rebound effects are considered. The expected emissions reduction due to new technology adoption in the manufacturing sector is 23.8 million tons CO2eq without rebound effects, but when rebound effects are included, the actual emission reduction (12.7 million tons CO2eq) is about 50% of the expected amount.

This paper explores new estimates of the number of veterans and the value of veterans' benefits-both cash benefits and land grants-from the Revolution to 1900. Benefits, it turns out, varied substantially from war to war. The veterans of theWar of 1812, in particular, received a smaller amount of benefits than did the veterans of the other nineteenth century wars. A number of factors appear to account for the differences across wars. Some are familiar from studies of other government programs: the previous history of veterans' benefits, the wealth of the United States, the number of veterans relative to the population, and the lobbying efforts of lawyers and other agents employed by veterans. Some are less familiar. There were several occasions, for example, when public attitudes toward the war appeared to influence the amount of benefits. Perhaps the most important factor, however, was the state of the federal treasury. When the federal government ran a surplus, veterans were likely to receive additional benefits; when it ran a deficit, veterans' hopes for additional benefits went unfilled. Veterans' benefits were, to use the terms a bit freely, more like a luxury than a necessity.

We present a new monthly index of the yields on junk bonds (high risk, high yield bonds) for the period 1910–1955. This index supplements the indexes of government bond yields, and Aaa and Baa corporate bond yields economic historians have relied on previously to describe the long-term risk spectrum. First, we describe our sources and methods. Then we show that our junk bond index contains information that is not in the closest alternative, and suggest some ways that the junk bond index could be used to enrich our understanding of the turbulent middle years of the twentieth century.

Although taxes were raised substantially in the United States during World War I, recourse was had to five bond issues, the famous Liberty loans, to finance the bulk of war expenditures. The Secretary of the Treasury, William Gibbs McAdoo, hoped to create a broad market for the Liberty bonds and to limit their yields by following an aggressive policy of ‘capitalizing patriotism’. He called on everyone from Wall Street bankers to the Boy Scouts to volunteer for campaigns to sell the bonds. The campaigns have become legendary. Some of the nation's best-known artists were recruited to draw posters depicting the contribution to the war effort to be made by buying bonds, and giant bond rallies featuring Hollywood stars were organized. These efforts, however, enjoyed limited success. The yields on the Liberty bonds were kept low mainly by making the bonds tax exempt and by making sure that a large proportion of them were purchased directly or indirectly by the Federal Reserve, turning the Federal Reserve into an engine of inflation. Patriotism proved to be a weak, although not powerless, offset to normal market forces.

We present a new monthly index of the yield on junk (high yield) bonds from 1910-1955. We then use the index to reexamine some of the main debates about the financial history of the interwar years. A close look at junk bond yields: (1) strengthens the view that the decline in lending standards in the late 1920s was modest at best: (2) casts doubt on the view that the banking crisis that began in 1930 disrupted financial markets because banks liquidated their holdings of risky bonds; (3) strengthens the view that the cost of capital rose substantially in the early 1930s and remained high for the rest of the decade; (4) casts doubt on the view that financial markets entered a liquidity trap in the second half of the 1930s; and (5) strengthens the case for believing that junk bond yields contain some information useful for making economic forecasts.