The mortality rate in industrial accidents in South Korea was 11 per 100,000 workers in 2015. It’s five times higher than the OECD average. Economic losses due to industrial accidents continue to grow, reaching 19 trillion won much more than natural disaster losses equivalent to 1.1 trillion won. It requires fundamental changes according to industrial safety management. In this study, We classified the risk of accidents in industrial complex of Ulju-gun using spatial analytics and data mining. We collected 119 data on accident data, factory characteristics data, company information such as sales amount, capital stock, building information, weather information, official land price, etc. Through the pre-processing and data convergence process, the analysis dataset was constructed. Then we conducted geographically weighted regression with spatial factors affecting fire incidents and calculated the risk of fire accidents with analytical model for combining Boosting and CART (Classification and Regression Tree). We drew the main factors that affect the fire accident. The drawn main factors are deterioration of buildings, capital stock, employee number, officially assessed land price and height of building. Finally the predicted accident rates were divided into four class (risk category-alert, hazard, caution, and attention) with Jenks Natural Breaks Classification. It is divided by seeking to minimize each class’s average deviation from the class mean, while maximizing each class’s deviation from the means of the other groups. As the analysis results were also visualized on maps, the danger zone can be intuitively checked. It is judged to be available in different policy decisions for different types, such as those used by different types of risk ratings.

Domestic studies for identification of causality between children exposure to toxic chemicals, such as arsenic (As) and resulted hazardous effects were not implemented. This study was conducted to probabilistically estimate dietary As intake and health risk assessment for young children and all age-specific populations from the consumption of As-contaminated rice of Korea. Arsenic intakes for young children (1 to 6 years old) from As-contaminated rice were higher than other age-specific groups, based on a dose-per-body weight basis. Based on the current EPA cancer slope factor for As, estimated cancer risks (to the skin cancer) associated with dietary intake of As-contaminated rice for 1 to 2 years old group and 3 to years old group are 1.76 per 10,000 and 3.16 per 10,000, respectively, at the 50th percentile. Based on possible reference levels (0.005 mg/kg/day) for children, mean and 95th percentile value of HQ from rice for young children are very below 1.0, which is a regulatory limit of non-carcinogenic risks for human.

Chronic exposure to Arsenic (As) causes significant human health effects including various cancers. Total As concentrations from 300 polished rice samples cultivated near the mining areas in Korea were analyzed to estimate a probabilistic assessment of human health risk from As-contaminated rice. The mean of total As concentrations in rice was 0.09 mg/kg and lognormal distribution model was set for total As concentrations. Human health risk for As in rice was estimated using gender-specific rice consumption data and average daily dose (ADD). While cancer risk (CR) and hazard quotient (HQ) were calculated using oral cancer slope factor (OCSF) and Reference dose (RfD) suggested by the U.S. EPA. Mean of CR posed by total As was 2.16 (for male) and 1.83 (for female) per 10,000. The HQ for general population from rice cultivated near the mining areas in Korea was below 1 as the 50th percentile of general population. However, less than 10% of general population consuming rice cultivated near the mining areas would exceed 1.0. This result is similar with those from each gender-specific group.