Based on the operating data of a 40 tCO₂/day (2 megawatt (MW)) class carbon capture and utilization (CCU) pilot plant, the scaled-up 400 tCO₂/day (20 MW) class CCU plant at 500 MW power plant was economically analyzed by applying the levelized cost of energy analysis (LCOE) and CO₂ avoided cost. This study shows that the LCOE and CO₂ avoided cost for 400 tCO₂/day class CCU plant of mineral carbonation technology were 26 USD/MWh and 64 USD/tCO₂, representing low LCOE and CO₂ avoided cost, compared to other carbon capture and storage CCS and CCU plants. Based on the results of this study, the LCOE and CO₂ avoided cost may become lower by the economy of scale, even if the CO₂ treatment capacity of the CCU plant could be extended as much as for similar businesses. Therefore, the CCU technology by mineral carbonation has an economic advantage in energy penalty, power plant construction, and operating cost over other CCS and CCU with other technology.

The general population is exposed to numerous environmental pollutants, and it remains unclear which pollutants affect the brain, accelerating brain aging and increasing the risk of dementia. The Environmental-Pollution-Induced Neurological Effects study is a multi-city prospective cohort study aiming to comprehensively investigate the effect of different environmental pollutants on brain structures, neuropsychological function, and the development of dementia in adults. The baseline data of 3,775 healthy elderly people were collected from August 2014 to March 2018. The eligibility criteria were age ≥50 years and no self-reported history of dementia, movement disorders, or stroke. The assessment included demographics and anthropometrics, laboratory test results, and individual levels of exposure to air pollution. A neuroimaging sub-cohort was also recruited with 1,022 participants during the same period, and brain magnetic resonance imaging and neuropsychological tests were conducted. The first follow-up environmental pollutant measurements will start in 2022 and the follow-up for the sub-cohort will be conducted every 3-4 years. We have found that subtle structural changes in the brain may be induced by exposure to airborne pollutants such as particulate matter 10 μm or less in diameter (PM10), particulate matter 2.5 μm or less in diameter (PM2.5) and Mn10, manganese in PM10; Mn2.5, manganese in PM2.5. PM10, PM2.5, and nitrogen dioxide in healthy adults. This study provides a basis for research involving large-scale, long-term neuroimaging assessments in community-based populations.

This research was to conduct a pilot study for two consecutive days in order to assess fine particulate matter (PM_2.5) exposure of an entire population in a community. We aimed to construct a surveillance system by analyzing the observed spatio-temporal variation of exposure. Guro-gu in Seoul, South Korea, was divided into 2,204 scale grids of 100 m each. Hourly exposure concentrations of PM_2.5 were modeled by the inverse distance weighted method, using 24 sensor-based air monitoring instruments and the indoor-to-outdoor concentration ratio. Population distribution was assessed using mobile phone network data and indoor residential rates, according to sex and age over time. Exposure concentration, population distribution, and population exposure were visualized to present spatio-temporal variation. The PM_2.5 exposure of the entire population of Guro-gu was calculated by population-weighted average exposure concentration. The average concentration of outdoor PM_2.5 was 42.1 µg/m³, which was lower than the value of the beta attenuation monitor measured by fixed monitoring station. Indoor concentration was estimated using an indoor-to-outdoor PM_2.5 concentration ratio of 0.747. The population-weighted average exposure concentration of PM_2.5 was 32.4 µg/m³. Thirty-one percent of the population exceeded the Korean Atmospheric Environmental Standard for PM_2.5 over a 24 h average period. The results of this study can be used in a long-term aggregate and cumulative PM_2.5 exposure assessment, and as a basis for policy decisions on public health management among policymakers and stakeholders.

This study introduces a novel methodology to calculate the carbon dioxide (CO₂) emission reduction related to residual emissions, calculating the CO₂ emission reduction through a 2 MW (40 tCO₂/day) carbon capture and utilization (CCU) plant installed at a 500 MW coal-fired power plant in operation, to evaluate the accuracy, maintainability, and reliability of the quantified reduction. By applying the developed methodology to calculate the CO₂ emission reduction, the established amount of CO₂ reduction in the mineral carbonation was evaluated through recorded measurement and monitoring data of the 2 MW CCU plant at the operating coal-fired plant. To validate the reduction, the accuracy, reproducibility, consistency, and maintainability of the reduction should be secured, and based on these qualifications, it is necessary to evaluate the contribution rate of nationally determined contributions (NDCs) in each country. This fundamental study establishes the concept of CCU CO₂ reduction and quantifies the reduction to obtain the validation of each country for the reduction. The established concept of the CCU in this study can also be applied to other CCU systems to calculate the reduction, thereby providing an opportunity for CCU technology to contribute to the NDCs in each country and invigorate the technology.

Facile detection of indoor semi-volatile organic compounds (SVOCs) is a critical issue to raise an increasing concern to current researchers, since their emissions have impacted the health of humans, who spend much of their time indoors after the recent incessant COVID-19 pandemic outbreaks. Plasmonic nanomaterial platforms can utilize an electromagnetic field to induce significant Raman signal enhancements of vibrational spectra of pollutant molecules from localized hotspots. Surface-enhanced Raman scattering (SERS) sensing based on functional plasmonic nanostructures has currently emerged as a powerful analytical technique, which is widely adopted for the ultra-sensitive detection of SVOC molecules, including phthalates and polycyclic aromatic hydrocarbons (PAHs) from household chemicals in indoor environments. This concise topical review gives updated recent developments and trends in optical sensors of surface plasmon resonance (SPR) and SERS for effective sensing of SVOCs by functionalization of noble metal nanostructures. Specific features of plasmonic nanomaterials utilized in sensors are evaluated comparatively, including their various sizes and shapes. Novel aptasensors-assisted SERS technology and its potential application are also introduced for selective sensing. The current challenges and perspectives on SERS-based optical sensors using plasmonic nanomaterial platforms and aptasensors are discussed for applying indoor SVOC detection.

Human exposure to air pollution is a major public health concern. Environmental policymakers have been implementing various strategies to reduce exposure, including the 10th-day-no-driving system. To assess exposure of an entire population of a community in a highly polluted area, pollutant concentrations in microenvironments and population time–activity patterns are required. To date, population exposure to air pollutants has been assessed using air monitoring data from fixed atmospheric monitoring stations, atmospheric dispersion modeling, or spatial interpolation techniques for pollutant concentrations. This is coupled with census data, administrative registers, and data on the patterns of the time-based activities at the individual scale. Recent technologies such as sensors, the Internet of Things (IoT), communications technology, and artificial intelligence enable the accurate evaluation of air pollution exposure for a population in an environmental health context. In this study, the latest trends in published papers on the assessment of population exposure to air pollution were reviewed. Subsequently, this study proposes a methodology that will enable policymakers to develop an environmental health surveillance system that evaluates the distribution of air pollution exposure for a population within a target area and establish countermeasures based on advanced exposure assessment.