PM10-bound microplastics and trace metals: A public health insight from the Korean subway and indoor environments.

Inhalable airborne microplastics (MPs) presented in indoor and outdoor environments, can deeply penetrate the lungs, potentially triggering inflammation and respiratory illnesses. The present study aims to evaluate human health risks from respirable particulate matter (PM)-bound trace metals and MPs in indoor (SW- subway and IRH- indoor residential houses) and outdoor (OD) environments. This research provides an initial approach to human respiratory tract (HRT) mass depositions of PM10-bound total MPs and nine specific MP types to predict potential human health threats from inhalation exposure.

Adverse impacts of Asian dust events on human health and the environment-A probabilistic risk assessment study on particulate matter-bound metals and bacteria in Seoul, South Korea.

This study aimed to assess the impact of Asian dust (AD) on the human health and the environment. Particulate matter (PM) and PM-bound trace elements and bacteria were examined to determine the chemical and biological hazards associated with AD days and compared with non-AD days in Seoul. On AD days, the mean PM10 concentration was ∼3.

Self-optimization of training dataset improves forecasting of cyanobacterial bloom by machine learning.

Data-driven model (DDM) prediction of aquatic ecological responses, such as cyanobacterial harmful algal blooms (CyanoHABs), is critically influenced by the choice of training dataset. However, a systematic method to choose the optimal training dataset considering data history has not yet been developed. Providing a comprehensive procedure with self-based optimal training dataset-selecting algorithm would self-improve the DDM performance.

Challenges of diet planning for children using artificial intelligence.

Background/objectives: Diet planning in childcare centers is difficult because of the required knowledge of nutrition and development as well as the high design complexity associated with large numbers of food items. Artificial intelligence (AI) is expected to provide diet-planning solutions via automatic and effective application of professional knowledge, addressing the complexity of optimal diet design. This study presents the results of the evaluation of the utility of AI-generated diets for children and provides related implications.

Factors Associated with Frequency of Peanut Consumption in Korea: A National Population-Based Study.

Household peanut exposure via skin in infants with impaired skin barrier function is a risk factor for peanut allergy development. The aim of this study is to investigate the peanut consumption of Koreans using national representative data. We used data from the Korean National Health and Nutrition Examination Survey 2012-2016, consisting of data from 17,625 adults who complete the survey.