This study examines the effects of surrounding outdoor environmental characteristics in multi-use public facilities that are used by the susceptible population, on the concentration and distribution of indoor airborne bacteria. For this study, areas were divided into ‘factory area,’ ‘city area,’ and ‘forest area.’ The research was conducted from October 2017 to April 2018, and the selected target facilities were daycare centers, hospitals, postpartum care centers, and nursing homes for the elderly. In order to measure airborne bacteria, indoor air samples were collected using a six-stage viable particulate cascade impactor, and airborne bacteria samples were collected using MCE (Mixed cellulose esters) filters. Moreover, the outdoor airborne bacteria concentration was also measured to determine the concentration ratio (I/O ratio) of the total indoor airborne bacteria and total outdoor airborne bacteria concentrations. The results showed that the total outdoor airborne bacteria concentration was highest in the city area, with 74.2 ± 60.0 CFU/m3, and the lowest in the factory area, with 45.9 ± 35.8 CFU/m3. Furthermore, the distribution of the total outdoor airborne bacteria concentrations significantly differed across each surrounding environment (p < 0.05). On the other hand, no statistically significant difference in total indoor airborne bacteria concentrations, according to surrounding environments, was observed (p > 0.05). These findings suggest that the concentration of outdoor airborne bacteria differs across surrounding environments, unlike that of the indoor airborne bacteria.

Recently, measuring instruments for SHM of structures has been developed. In general, the wireless transmission of sensor signals, compared to its wired counterpart, is preferable due to the absence of triboelectric noise and elimination of the requirement of a cumbersome cable. However, in extreme environments, the sensor may be less sensitive to temperature changes and to the distance between the sensor and data logger. This may compromise on the performance of the sensor and instrumentation. Therefore, in this paper, free vibration experiments were conducted using wireless MEMS sensors at an actual site. Measurement was assessed in time and frequency domain by changing the temperature variation at(- 8℃, - 12℃ and - 16℃) and the communication distance (20m, 40m, 60m, 80m).

In this study, we analyzed the factors affecting the concentration of airborne asbestos fiber in the indoor and outdoor environment of a slate roofing house, and performed a health risk assessment of residents living in houses with slate roofs. Sampling was conducted at ten houses with slate roofs on 3 different days under different weather conditions. A high flow rate pump was used for sampling. The specimen was assessed using a phase-contrast microscope. The degree of risk of exposure to asbestos was assessed using EPA’s carcinogen risk assessment method. Asbestos fiber concentrations for slate roofing houses were 2.43 fiber/L inside and 2.46 fiber/L outside, respectively. The correlation between the indoor and outdoor asbestos fiber concentration was 0.486. But on both sides, the asbestos fiber concentrations did not exceed the standard (10 fiber/L) for ambient air in Korea. The factors affecting the concentration of asbestos fiber were year of construction (p<0.05), total roof area (p<0.05) and average wind velocity (p<0.01). According to EPA’s ELCR (Excess Lifetime Cancer Risk) on air pollution substances, a level of 1.0E-04~1.0E-06 should be maintained. However, the ELCR level of 6 out of 10 houses was over 1.0E-04. Therefore, a risk management plan for residents of slate roofing houses must be prepared immediately.

This study started with the issue that the criteria for the general image scale used in outdoor color planning are vague and the vocabulary and expressive area of the scale are not accurate. As an exterior color planning guideline, it is required to suggest a sensible image scale, which is an appropriate assessment tool for outdoor environment. We identified psychological attributes of colors and set the color image scale axis as “Soft–Hard” in a vertical and “Warm–Cool” in a horizontal. 26 sensibility languages were finally selected for the image scale through literature review, pre-analysis, and expert evaluation. All types of colors extracted from analyzing over 600 photo images were categorized according to 26 sensibility languages. 3 colors by each sensibility language were finally selected and arranged in the color image scale in accord with the outdoor environment of the apartment houses. The significance of this study is that wide range of color position in the color image scale could be utilized as an effective guideline for actual color planning by upgrading the existing color image scale to a higher level with the color reflecting sensibility language. The result also implies that the development of diversified color palette is required especially for the exterior color planning of apartment houses, and the systematical color design process should be established in order to improve the quality of color environment.

Automation and robotization has been required in construction for several decades and construction industry has become one of the important research areas in the field of service robotics. Especially in the steel construction, automatic recognition of structural steel members in the stockyard is emphasized. However, since the pose of steel frame in the stockyard is site dependent and also the stockyard is usually in the outdoor environment, it is difficult to determine the pose automatically. This paper adopts the recognition method based on the augmented reality to cope with this problem. Particularly focusing on the light condition of the outdoor environment, we formulated the optimization problem with the constraint and suggested the methodology to evaluate the optimal camera arrangement. From simulation results, sub-optimal solution for the position of the camera can be obtained.

The most important factors relating to the indoor air environment are temperature, airflow, humidity, and contaminant concentration. A sensitivity analysis of indoor environment factors was carried out to grasp influences along with changes of atmospheric conditions. An integrated multizone model was used to predict these sensitivities. This model was applied to an apartment with six zones. Airflow rates are influenced very seriously by changes of wind direct or wind velocity, but are influenced very slightly by changes of outdoor air temperature and are not influenced at all by changes of outdoor air humidity or contaminant concentration. Indoor air temperatures are influenced very directly by changes of outdoor air temperature, but are influenced very slightly by wind direction or wind velocity and are not influenced at all by changes of outdoor air humidity or contaminant concentration. Indoor air humidities are influenced very directly by changes of outdoor air humidity, but are not influenced at all by changes of outdoor air contaminant concentration and have little or no influence by changes of wind direction, wind velocity, or outdoor air temperature. Indoor air contaminant concentrations are influenced very seriously by changes of wind direct or wind velocity, but are influenced somewhat by changes of outdoor air contaminant concentration and are influenced very slightly by changes of outdoor air temperature and are not influenced at all by changes of outdoor air humidity.