Analysis of the effect of dust barriers on particulate matter dispersion from a construction site using CFD simulation.

Urban construction activities generate particulate matter (PM), which poses a severe threat to urban residents' quality of life. The complex topography of urban areas and turbulent air flow make it difficult to predict PM dispersion and establish control measures. This study predicted PM dispersion from construction sites to urban areas under four wind directions using CFD simulations and determined optimal dust barrier heights for reducing PM exposure. This study showed that PM exposure on residents depends on wind directions in the urban area up to 2.3 times, still a flexible approach to determine optimal barrier heights based on stakeholder preferences was effective in all wind directions. Accurate PM dispersion predictions and control measures are crucial for improving urban air quality and residents' well-being.

An effective alerting strategy to facilitate occupants' perception of indoor air quality: By alarming concentration of indoor air pollution.

Previous studies have proven that it is hard for occupants to perceive concentration of indoor air pollution (IAP) and resulting indoor air quality (IAQ) on their own. Therefore, a method is needed to encourage them to turn their attention to actual IAP, in this context, alerting is thus suggested. However, previous studies pose limitations in that they failed to analyze the effects of alerting concentration of IAP on occupants' IAQ perception. To fill the research gap, this study sought to explore a proper strategy to help occupants have a clearer perception of IAQ. A one-month observational experiment was conducted on nine subjects under three scenarios with different alerting strategies. In addition, the visual distance estimation method was used to quantitatively analyze similar tendencies between the subject's perceived IAQ and concentration of IAP for each scenario. The experimental results confirmed that when an alerting notification was not sent, the occupants could not clearly perceive IAQ as the visual distance was the highest at 0.332. On the other hand, when the alerting notification whether the concentration of IAP exceeded the standard or not was sent, the occupants could perceive IAQ relatively clearly as the visual distance was reduced to 0.291 and 0.236. In conclusion, not only installing a monitoring device but also establishing proper alerting strategies on the concentration of IAP is essential to facilitate occupants' IAQ perception and protect occupants' health.

Analysis of ways to reduce potential health risk from ultrafine and fine particles emitted from 3D printers in the makerspace.

Due to the growing maker culture, maker spaces using multiple fused deposition modeling (FDM)-3D printers have spread around the world. However, the 3D printing process is known to cause the release of ultrafine and fine particles, which may have adverse health effects on occupants. Therefore, this experiment-based study was conducted on FDM-3D printers placed in an actual makerspace by the following three scenarios: the number of operating FDM-3D printers, ventilation, and measurement location to compare the concentrations of ultrafine and fine particles. In addition, the deposited dose in alveolar region for ultrafine and fine particles was predicted using a respiratory deposition model to analyze the potential health risk on occupants. As a result, the scenario-based comparison revealed that if the number of operating 3D printers is reduced by less than half, the potential health risk can be decreased by 34.1%, proper ventilation can reduce potential health risk by 55.5%, and working away from the 3D printer can also reduce potential health risk by up to 27.5%. This study analyzed the potential health risk of multiple FDM-3D printers on users in an actual makerspace, and proposed various improvement measures to reduce the potential health risk of ultrafine and fine particles.

Emotional impact, task performance and task load of green walls exposure in a virtual environment.

As the time spent indoors increases significantly due to the Coronavirus Disease 2019 (COVID-19) pandemic, creating an indoor environment to promote the health of occupants has become critical. Although green walls efficiently realize a healthy indoor environment, few studies have analyzed their impact on occupants based on the visual element of green walls. This study measures the emotional impact, task performance, and task load of the subjects according to four virtual experiments (a non-green wall, a freestanding green wall, two freestanding green walls, and a full-sized green wall). The results of the four experiments are as follows: (i) The visual elements of the green wall had an emotional impact on the occupants, which was verified through the Friedman test; (ii) the effect of the visual elements of the green wall on the task performance of the occupants was not verified by the one-way repeated measures analysis of variance (ANOVA); and (iii) the task load of the occupants influenced their task performance, which was verified by the repeated-measures ANOVA. This study can help determine the optimal type and area of green walls by considering their impact on the occupants as well as on the economic and constructional aspects of the indoor space.

Changes in energy consumption according to building use type under COVID-19 pandemic in South Korea.

An unprecedented global lockdown has been implemented for controlling the spread of COVID-19 in many countries. These actions are reducing the number of coronics, but with the prolonged COVID-19 outbreak, the restrictions on the activities of people are having a significant impact on all industries. Accordingly, this study aimed to statistically analyze changes in building energy consumption under the COVID-19 pandemic in South Korea, as well as identify the relationship between COVID-19 and building energy consumption according to the building use type. As a result, the average rate of changes in electricity and gas energy consumption decreased -4.46% and -10.35%, respectively, compared to the previous year. The energy consumption in most facilities has tended to decrease while energy consumption in residential facilities increased during COVID-19. The rate of change in building energy consumption had a significantly positive correlation with COVID-19 related factors in various facilities (e.g., neighborhood, religious, educational, and research facilities). Significant findings of this study that social distancing by the COVID-19 outbreak, has changed energy consumption according to building use type indicates the need for new energy systems to effectively manage the energy demand at the community level in the Post COVID-19 era.

Towards environmental sustainability in the local community: Future insights for managing the hazardous pollutants at construction sites.

Although various technologies are being developed in the construction industry, management technologies for achieving environmental sustainability in the local community are still lacking. As such, this study suggests future insights for the development of an automated intelligent environment management system for the promotion of environmental sustainability in the local community, through a systematic review of 1,707 relevant literature. The systematic review was conducted in two steps: (i) quantitative review: keyword co-occurrence and trend analysis; and (ii) qualitative review: a review on monitoring, evaluation, and improvement technologies. As a result, the research level related to the local-level pollutants (noise, vibration, and dust) was found to be quantitatively insufficient, and the limitations of the existing technologies for these pollutants were presented. Eventually, to overcome these limitations, new technologies and application strategies that can be applied to construction sites as future research roadmap to effectively manage the hazardous pollutants were proposed. Furthermore, an intelligent management system should be developed, and the management of environmental complaints is also necessary for environmental sustainability at the local level in the construction industry. As a fundamental study, this study could become a benchmark for future researches dealing with environmental sustainability and hazardous pollutants in the construction industry.

Development of a real-time automated monitoring system for managing the hazardous environmental pollutants at the construction site.

The management of noise, vibration, and dust, which are hazardous pollutants from construction sites, is essential to minimize the health damage of the nearby residents and the economic damage of construction companies due to pollutants from construction sites. For the effective management of hazardous pollutants, their emissions from construction sites must be identified immediately and accurately. Therefore, this study developed a real-time automated monitoring system named "MOnitoring for Noise, Vibration, and Dust (MONVID)" for comprehensively measuring the hazardous environmental pollutants and managing them in real-time. Toward this end, the optimal design of MONVID was planned and customized considering mobility, usability, and economy. Also, for the field application of the developed MONVID, its feasibility was verified by comparing its techno-economic performance with that of the conventional measurement system through experiments. Based on the results of the experiment and performance evaluation, it was concluded that MONVID is a feasible and economical construction pollutant measurement system with reliable technical performance and improved mobility and usability compared to the conventional measurement system. This study has significant contributions to the development of the first platform (including hardware, sensor network, and software) for the integrated real-time automated monitoring of the environmental performance of construction sites.

Multi-criteria decision support system of the photovoltaic and solar thermal energy systems using the multi-objective optimization algorithm.

When the photovoltaic (PV) and solar thermal energy (STE) systems, which share the rooftop area, are installed in the same building, a trade-off problem occurs in terms of the energy, economic, and environmental aspects, and thus, steps need to solve this problem. Therefore, this study aimed to develop a multi-criteria decision support system of the PV and STE systems using the multi-objective optimization algorithm. This system was developed in the following six steps: (i) database establishment; (ii) designing the variables of the PV and STE systems; (iii) development of the analysis engine of the PV and STE systems; (iv) environmental and economic assessment from the life cycle perspective; (v) integrated multi-objective optimization (iMOO) with a genetic algorithm; and (vi) establishment of a multi-criteria decision support system. To verify the robustness and reliability of the developed model, an analysis of "D" City Hall and "I" Airport as target facilities was performed. The optimal PV and STE systems that consider the energy, economic, and environmental aspects at the same time were determined with respect to the 1.23 × 1015 and 1.05 × 1016 installation scenarios, respectively, in terms of effectiveness. The iMOO scores of the existing PV and STE systems installed in "D" City Hall and "I" Airport were 0.358 and 0.346, respectively, whereas those of the optimal solutions were 0.249 and 0.280, showing score improvements. In terms of efficiency, the times required for determining the optimal solutions were 20 and 30 min, respectively. The developed model makes the final decision-maker to find the optimal solution in introducing the PV and STE systems in the early design phase at the same time.

The effects of filters for an intelligent air pollutant control system considering natural ventilation and the occupants.

Experimental analysis was conducted on the indoor air pollutant concentration using natural ventilation and filters. The study targeted two office rooms each of which was occupied by four people, and with the same outdoor environments. A non-woven fabric filter (room A) and an electrostatic filter (room B) were installed on the window frame, and the indoor air pollutant concentration and indoor climate factors were monitored based on the number of occupants and the occupants' activities. The results are as follows: (i) when the number of occupants in each room increased from 0.03-0.06 to 1.53-1.63, room A showed a 60% average PM10 concentration increase while room B showed an opposite result (10% average PM10 concentration decrease), meaning the electrostatic filter's lower resistance to flow contributed to better ventilation and also decreased the influence of the occupants on the indoor air pollutant concentration. A low correlation (0.323-0.350) between the CO2 concentration and the occupants in room B also proved these results; (ii) while the average PM10 concentration in room A was 9 µg/m3 higher than that in room B, the average PM2.5 concentration in room A was higher by only 0.2 µg/m3, which showing that much of the generated or resuspended indoor particulate matter was PM10; and (iii) due to the more frequent heat transfer from outdoors to indoors, room B consumed 23% more heating energy. The results of this study are expected to be used as bases for the establishment of an appropriate management strategy that considers the indoor air pollutant concentration caused by the number of occupants and occupants' activities by combining natural ventilation and filters.

Framework for the mapping of the monthly average daily solar radiation using an advanced case-based reasoning and a geostatistical technique.

For the effective photovoltaic (PV) system, it is necessary to accurately determine the monthly average daily solar radiation (MADSR) and to develop an accurate MADSR map, which can simplify the decision-making process for selecting the suitable location of the PV system installation. Therefore, this study aimed to develop a framework for the mapping of the MADSR using an advanced case-based reasoning (CBR) and a geostatistical technique. The proposed framework consists of the following procedures: (i) the geographic scope for the mapping of the MADSR is set, and the measured MADSR and meteorological data in the geographic scope are collected; (ii) using the collected data, the advanced CBR model is developed; (iii) using the advanced CBR model, the MADSR at unmeasured locations is estimated; and (iv) by applying the measured and estimated MADSR data to the geographic information system, the MADSR map is developed. A practical validation was conducted by applying the proposed framework to South Korea. It was determined that the MADSR map developed through the proposed framework has been improved in terms of accuracy. The developed MADSR map can be used for estimating the MADSR at unmeasured locations and for determining the optimal location for the PV system installation.

A model for evaluating the environmental benefits of elementary school facilities.

In this study, a model that is capable of evaluating the environmental benefits of a new elementary school facility was developed. The model is composed of three steps: (i) retrieval of elementary school facilities having similar characteristics as the new elementary school facility using case-based reasoning; (ii) creation of energy consumption and material data for the benchmark elementary school facility using the retrieved similar elementary school facilities; and (iii) evaluation of the environmental benefits of the new elementary school facility by assessing and comparing the environmental impact of the new and created benchmark elementary school facility using life cycle assessment. The developed model can present the environmental benefits of a new elementary school facility in terms of monetary values using Environmental Priority Strategy 2000, a damage-oriented life cycle impact assessment method. The developed model can be used for the following: (i) as criteria for a green-building rating system; (ii) as criteria for setting the support plan and size, such as the government's incentives for promoting green-building projects; and (iii) as criteria for determining the feasibility of green building projects in key business sectors.

Synthesis and characterization of sulfonated poly(ether sulfone)s containing mesonaphthobifluorene for polymer electrolyte membrane fuel cell.

The novel sulfonated poly(ether sulfone)s containing mesonaphthobifluorene (MNF) moiety were synthesized and characterized their properties. The prepared polymers have highly conjugated aromatic structure due to the MNF group which is an allotrope of carbon and one atom thick planar sheets of sp2-bonded carbon atoms. Poly(ether sulfone)s bearing tetraphenylethylene on polymer backbone were synthesized by polycondensation and followed intra-cyclization from tetraphenylethylene to form MNF by Friedel-craft reaction with Lewis acid (FeCl3). The sulfonation was performed selectively on MNF units with conc. sulfuric acid. The structural properties of the sulfonated polymers were investigated by 1H-NMR spectroscopy. The membranes were studied by ion exchange capacity (IEC), water uptake, and proton conductivity. The synthesized polymer electrolyte membranes showed better thermal and dimensional stabilities owing to the inducted highly conjugated aromatic structure in the polymer backbone. The water uptake of the synthesized membranes ranged from 23-52%, compared with 32.13% for Nafion 211 at 80 degrees C. The synthesized membranes exhibited proton conductivities (80 degrees C, RH 90%) of 74.6-100.4 mS/cm, compared with 102.7 mS/cm for Nafion 211.

Estimation of the monthly average daily solar radiation using geographic information system and advanced case-based reasoning.

The photovoltaic (PV) system is considered an unlimited source of clean energy, whose amount of electricity generation changes according to the monthly average daily solar radiation (MADSR). It is revealed that the MADSR distribution in South Korea has very diverse patterns due to the country's climatic and geographical characteristics. This study aimed to develop a MADSR estimation model for the location without the measured MADSR data, using an advanced case based reasoning (CBR) model, which is a hybrid methodology combining CBR with artificial neural network, multiregression analysis, and genetic algorithm. The average prediction accuracy of the advanced CBR model was very high at 95.69%, and the standard deviation of the prediction accuracy was 3.67%, showing a significant improvement in prediction accuracy and consistency. A case study was conducted to verify the proposed model. The proposed model could be useful for owner or construction manager in charge of determining whether or not to introduce the PV system and where to install it. Also, it would benefit contractors in a competitive bidding process to accurately estimate the electricity generation of the PV system in advance and to conduct an economic and environmental feasibility study from the life cycle perspective.

A decision support model for improving a multi-family housing complex based on CO2 emission from electricity consumption.

The number of deteriorated multi-family housing complexes in South Korea continues to rise, and consequently their electricity consumption is also increasing. This needs to be addressed as part of the nation's efforts to reduce energy consumption. The objective of this research was to develop a decision support model for determining the need to improve multi-family housing complexes. In this research, 1664 cases located in Seoul were selected for model development. The research team collected the characteristics and electricity energy consumption data of these projects in 2009-2010. The following were carried out in this research: (i) using the Decision Tree, multi-family housing complexes were clustered based on their electricity energy consumption; (ii) using Case-Based Reasoning, similar cases were retrieved from the same cluster; and (iii) using a combination of Multiple Regression Analysis, Artificial Neural Network, and Genetic Algorithm, the prediction performance of the developed model was improved. The results of this research can be used as follows: (i) as basic research data for continuously managing several energy consumption data of multi-family housing complexes; (ii) as advanced research data for predicting energy consumption based on the project characteristics; (iii) as practical research data for selecting the most optimal multi-family housing complex with the most potential in terms of energy savings; and (iv) as consistent and objective criteria for incentives and penalties.

Economic and environmental evaluation model for selecting the optimum design of green roof systems in elementary schools.

Green-roof systems offer various benefits to man and nature, such as establishing ecological environments, improving landscape and air quality, and offering pleasant living environments. This study aimed to develop an optimal-scenario selection model that considers both the economic and the environmental effect in applying GRSs to educational facilities. The following process was carried out: (i) 15 GRSs scenarios were established by combining three soil and five plant types and (ii) the results of the life cycle CO(2) analyses with the GRSs scenarios were converted to an economic value using certified emission reductions (CERs) carbon credits. Life cycle cost (LCC) analyses were performed based on these results. The results showed that when considering only the currently realized economic value, the conventional roof system is superior to the GRSs. However, the LCC analysis that included the environmental value, revealed that compared to the conventional roof system, the following six GRSs scenarios are superior (cost reduction; reduction ratio; in descending order): scenarios 13 ($195,229; 11.0%), 3 ($188,178; 10.6%), 8 ($181,558; 10.3%), 12 ($130,464; 7.4%), 2 ($124,566; 7.0%), and 7 ($113,931; 6.4%). Although the effect is relatively small in terms of cost reduction, environmental value attributes cannot be ignored in terms of the reduction ratio.

Integrated model for assessing the cost and CO2 emission (IMACC) for sustainable structural design in ready-mix concrete.

Cost has traditionally been considered the most important factor in the decision-making process. Recently, along with the consistent interest in environmental problems, environmental impact has also become a key factor. Accordingly, there is a need to develop a method that simultaneously reflects the cost and environmental impact in the decision-making process. This study proposed an integrated model for assessing the cost and CO(2) emission (IMACC) at the same time. IMACC is a model that assesses the cost and CO(2) emission of the various structural-design alternatives proposed in the structural-design process. To develop the IMACC, a standard on assessing the cost and CO(2) emission generated in the construction stage was proposed, along with the CO(2) emission factors in the structural materials, based on such materials' strengths. Moreover, using the economic and environmental scores that signify the cost and CO(2) emission reduction ratios, respectively, a method of selecting the best design alternative was proposed. To verify the applicability of IMACC, practical application was carried out. Structural designs were assessed, each of which used 21, 24, 27, and 30 MPa ready-mix concrete (RMC). The use of IMACC makes it easy to verify what the best design is. Results show the one that used 27 MPa RMC was the best design. Therefore, the proposed IMACC can be used as a tool for supporting the decision-making process in selecting the best design alternative.