The United States generated approximately 730kg of waste per capita in 2013, which is the highest amount of waste among OECD countries. The waste has adverse effects to human health and the environment. One of the most serious adverse effects is greenhouse gas emissions, especially methane (CH4), which causes global warming. However, the United States' amount of waste generation is not decreasing, and the recycling rate is only 26%, which is lower than other OECD countries. In order to decrease waste generation and greenhouse gas emissions, identifying the causality of the waste generation and greenhouse gas emissions from waste sector should be made a priority. The research objective is to verify whether the Environmental Kuznets Curve relationship is supported for waste generation and GDP across the U.S. Moreover, it also confirmed that total waste generation and recycling of waste influences carbon dioxide emissions from the waste sector. Based on the results, critical insight and suggestions were offered to policymakers, which is the potential way to lower the solid waste and greenhouse gas emissions from the waste sector. This research used annually based U.S. data from 1990 to 2012, and these data were collected from various data sources. To verify the causal relationship, the Granger causality test was applied. The results showed that there is no causality between GDP and waste generation, but total waste and recycling generate significantly increasing and decreasing greenhouse gas emissions from the waste sector, respectively. This implies that waste generation will not decrease even if GDP increases. And, if waste generation decreases or the recycling rate increases, greenhouse gas emission will decrease. Based on these results, increasing the recycling rate is first suggested. The second suggestion is to break the causal relationship between MSW and greenhouse gas emission from the waste sector. The third is that the U.S. government should benchmark a successful case of waste management. Based on the research, it is expected that waste generation and carbon dioxide emission from the waste sector can be decreased more efficiently.

Developing an expert system has been considered as complex and knowledge driven process. This study proposes a nature-inspired metaheuristic regression system that can find appropriate solutions. The system uses a graphical user interface but does not require a mathematical program installation. The user-friendly interface was designed in the MATLAB graphical user interface design environment (GUIDE) and was implemented by MATLAB compiler. The stand-alone system is easy to use and has many functions, including evaluation, use of an opened data file, test set selection, hold-out, cross validation, and prediction to solve many civil engineering problems with simple manipulations on the system interface. Five benchmark functions were used to evaluate the effectiveness of the optimization module. The performance of the proposed regression system was then validated by comparing its solutions obtained for civil engineering problems with those obtained by empirical methods reported previously. Five actua...

This research examined how various green space layouts in an urban area affected the surrounding atmospheric air temperature that contributes to a heat island effect. The aim was to develop an understanding of whether this greenery arrangement had an influence on the urban heat island. Field measurements were taken for three different layouts: greenery surrounding a complex/building, greenery in the center of a complex/building, and greenery distributed over a complex/building. The study showed that in the presence of solar heat, there were significant temperature differences among the three layouts. The study showed that the sites with greenery in the middle of a complex generated the lowest urban heat island effect of the three layouts.

Buildings are the largest consumer of energy in the United States from various sectors that includes transportation, industry, commercial, and residential buildings. Leadership in Energy and Environmental Design (LEED) certification program, home energy rating system (HERS), and American Society of Heating, Refrigerating and Air-conditioning Engineers (ASHRAE) standards are developed to improve the energy efficiency of the commercial and residential buildings. However, these programs, codes, and standards are used before or during the design and construction phases. For this reason, it is challenging to track whether buildings still could be energy efficient post construction. The primary purpose of this study was to detect the anomalies from the energy consumption dataset of LEED institutional buildings. The anomalies are identified using two different data mining techniques, which are clustering, and isolation Forest (iForest). This paper demonstrates an integrated data mining approach that helps in evaluating LEED energy and atmosphere (EA) credits after construction.

Building energy systems are designed to handle both permanent and temporary occupants. Permanent occupants are considered the base energy load while temporary occupants are considered a temporary or additional load. Temporary occupancy is potentially the most difficult to design as the number of temporary occupants varies more significantly than permanent occupants. This case study was designed to investigate the effect of occupancy on energy loads, i.e. the relationship between occupancy and building energy loads. This study estimated the building occupancy by using existing network infrastructure, such as Wi-Fi and wired Ethernet based on the assumption that the number of Wi-Fi connections and the wired Ethernet traffic were used as a proxy for total and stationary occupancy. The relationships were then examined using correlations and regression analyses. The results showed the following: 1. Stationary occupancy was successfully estimated using the network infrastructure; 2. There was a linear relationship between electricity use and total occupancy (and, thus, the use of network infrastructure); 3. Permanent occupants generated a higher impact on the electricity load than the temporary occupants; 4. There was a logarithmic relationship between electricity use and the Ethernet data traffic (a proxy of permanent occupants); and 5. The statistical and qualitative analyses indicated that there was no significant relationship between occupancy and thermal loads, such as cooling and heating loads.