There are several methods to obtain the in situ thermal transmittance value (U-value) of building envelopes from on-site data, including the three approaches of the progressive average method, average method considering the thermal storage effect, and dynamic method for deriving the U-value from heat flowmeter (HFM) measurements and the four methods with different formulas to analyze infrared thermography (IRT) measurement data. Since each of these methods considers different parameters and the non-steady characteristics of the heat transfer in building walls in their own way, discrepancies may occur among the obtained results. This study evaluates and compares the in situ U-values by using various methods of analyzing HFM and IRT measurement data. Further, by investigating buildings with similar materials and identical stratigraphies, but with different construction years, we analyze the discrepancy between the designed and measured values caused by material deterioration and evaluate the errors according to the analysis method. The percentage deviation between the U-values obtained by the three methods from the HFM data is found to be satisfactory, being within 10%. When compared with the results of the progressive average method, the deviations for the four different IRT-measurement-based methods vary greatly, being in the range of 6–43%.

In the last few decades, an average method which is regulated by ISO 9869-1 has been used to evaluate the in situ thermal transmittance (U-value) and thermal resistance (R-value) of building envelopes obtained from onsite measurements and to verify the validity of newly proposed methods. Nevertheless, only a few studies have investigated the test duration required to obtain reliable results using this method and the convergence characteristics of the results. This study aims to evaluate the convergence characteristics of the in situ values analyzed using the average method. The criteria for determining convergence (i.e., end of the test) using the average method are very strict, mainly because of the third condition, which compares the deviation of two values derived from the first and last periods of the same duration. To shorten the test duration, environmental variables should be kept constant throughout the test or an appropriate period should be selected. The convergence of the in situ U-value and R-value is affected more by the length of the test duration than by the temperature difference if the test environment meets literature-recommended conditions. Furthermore, there is no difference between the use of the U-value and R-value in determining the end of the test.

In the context of remodeling old buildings, enhancing insulation performance in the exterior skin necessitates an accurate assessment of a wall’s thermal performance. The conventional method for determining the thermal transmittance (U-value) of a wall is the heat flow meter (HFM) as outlined in the ISO 9869-1. However, this measurement is susceptible to errors influenced by indoor and outdoor environmental conditions and the wall’s material composition. This study evaluates the U-value of an internally insulated wall, specifically constructed for this purpose, utilizing both the average and dynamic methodologies of an HFM. Furthermore, it introduces a novel estimation method: the extended average method (EXAM). The effectiveness of this proposed method is ascertained by comparing the accuracy and convergence of the U-value estimations with those derived from existing methodologies. Additionally, the study explores the limitations of the HFM by analyzing the heat flow traversing the interior of a wall. The findings revealed that the EXAM method enhanced the precision of U-value estimation in all scenarios. Particularly, in walls with superior insulation, the HFM tended to underestimate the heat flow observed indoors, leading to negative errors. The EXAM method, incorporating considerations of both insulation and structural materials, offers an accurate in situ measurement of the U-value relative to the HFM.

This study examines environmental impact assessment considering filterable particulate matter (FPM) and condensable particulate matter (CPM) to improve the accuracy of the air quality model. Air pollutants and meteorological data were acquired from Korea’s national monitoring station near a residential development area in the target district and background site. Seasonal emissions of PM_2.5, including CPM, were estimated using the California puff (CALPUFF) model, based on Korea’s national emissions inventory. These results were compared with the traditional environmental impact assessment results. For the residential development area, the seasonal PM_2.5 concentration was predicted by considering FPM and CPM emissions in the target area as well as the surrounding areas. In winter and spring, air quality standards were not breached because only FPM was considered. However, when CPM was included in the analysis, the results exceeded the air quality standards. Furthermore, it was predicted that air quality standards would not be breached in summer and autumn, even when CPM is included. In other words, conducting an environmental impact assessment on air pollution including CPM affects the final environmental decision. Therefore, it is concluded that PM_2.5 should include CPM for greater accuracy of the CALPUFF model for environmental impact assessment.

This study analyzes the performance of thermal energy storage tanks and chillers in efficiently operating cooling systems for smart greenhouses in hot, arid climates such as the United Arab Emirates (UAE). The performance of chillers is heavily influenced by outdoor air temperatures, with the coefficient of performance (COP) of chillers decreasing and energy consumption increasing as daytime temperatures rise. This study found that when the outdoor air temperature reached 46.6 °C, the COP of the chiller dropped to 2.18, representing a 24.6% decrease compared to the COP of 2.89 at 35 °C. Conversely, at night, when the outdoor air temperature dropped to 28.3 °C, the chiller’s performance recovered, with the COP rising to 3.67. To address this, a strategy utilizing thermal energy storage tanks to store chilled water at night for use during the day was proposed, compensating for the decline in chiller performance. The results showed that when the thermal energy storage capacity was set at 40%, energy consumption decreased by up to 15%. However, while increasing the thermal energy storage capacity beyond 50% effectively reduced the peak load on the chiller, further increasing it beyond 60% led to a rise in chiller capacity and energy consumption. Therefore, this study concludes that setting the thermal energy storage capacity to no more than 50% is the most effective strategy to maximize chiller performance and reduce energy consumption. These findings provide crucial guidelines for the design and operation of cooling systems, offering valuable strategies to optimize the operation of smart greenhouses in hot climates.

The main purpose of this study was to analyze the Korean PM_2.5 emissions according to the past, present, and future energy mix scenarios from 1970 to 2035, with the aim of identifying a sustainable, future environmentally friendly energy mix scenario for Korea related to PM_2.5 emissions. To calculate the PM_2.5 emissions according to an energy mix plan, we assumed two scenarios: (1) Scenario 1 is based on an energy conversion scenario established by the Korean government’s 7th electric power demand supply program; and (2) Scenario 2 is enhancement of fuel cell usage. In Scenario 1, filterable PM_2.5 (FPM_2.5) emission was calculated as 61,158 ton/year, which includes contributions of anthracite (46.8%), petroleum (39.7%), natural gas (LNG) (10.0%), and LPG (0.1%). In Scenario 2, FPM_2.5 emission was calculated as 36,917 ton/year, which includes contributions of petroleum (47.8%), anthracite (40.3%), bituminous coal (10.1%), and LNG (1.7%). Thus, we concluded that the FPM_2.5 mitigation effect from fuel cell policy enforcement is about 38.13% higher than the Korean national energy conversion policy. PM_2.5 (FPM2.5 + condensable PM2.5 (CPM_2.5)) emissions dramatically increased in both energy mix scenarios so that CPM_2.5 should be considered when estimating PM_2.5 emissions and PM_2.5 reduction.