Previous studies in building energy assessment clearly state that to meet sustainable energy goals, existing buildings, as well as new buildings, will need to improve their energy efficiency. Thus, meeting energy goals relies on retrofitting existing buildings. Most building energy models are bottom-up engineering models, meaning these models calculate energy demand of individual buildings through their physical properties and energy use for specific end uses (e.g., lighting, appliances, and water heating). Researchers then scale up these model results to represent the building stock of the region studied.

Studies reveal that there is a lack of information about the building stock and associated modeling tools and this lack of knowledge affects the assessment of building energy efficiency strategies. Literature suggests that the level of complexity of energy models needs to be limited. Accuracy of these energy models can be elevated by reducing the input parameters, alleviating the need for users to make many assumptions about building construction and occupancy, among other factors. To mitigate the need for assumptions and the resulting model inaccuracies, the authors argue buildings should be described in a regional stock model with a restricted number of input parameters. One commonly-accepted method of identifying critical input parameters is sensitivity analysis, which requires a large number of runs that are both time consuming and may require high processing capacity.

This paper utilizes the Energy, Carbon and Cost Assessment for Buildings Stocks (ECCABS) model, which calculates the net energy demand of buildings and presents aggregated and individual- building-level, demand for specific end uses, e.g., heating, cooling, lighting, hot water and appliances. The model has already been validated using the Swedish, Spanish, and UK building stock data. This paper discusses potential improvements to this model by assessing the feasibility of using stepwise regression to identify the most important input parameters using the data from UK residential sector. The paper presents results of stepwise regression and compares these to sensitivity analysis; finally, the paper documents the advantages and challenges associated with each method.

Construction waste management has become extremely important due to stricter disposal and landfill regulations, and a lesser number of available landfills. There are extensive works done on waste treatment and management of the construction industry. Concepts like deconstruction, recyclability, and Design for Disassembly (DfD) are examples of better construction waste management methods. Although some authors and organizations have published rich guides addressing the DfD's principles, there are only a few buildings already developed in this area. This study aims to find the challenges in the current practice of deconstruction activities and the gaps between its theory and implementation. Furthermore, it aims to provide insights about how DfD can create opportunities to turn these concepts into strategies that can be largely adopted by the construction industry stakeholders in the near future.

The United State generates the most waste among OECD countries, and there are adverse effects of the waste generation. One of the most serious adverse effects is greenhouse gas, especially CH4, which causes global warming. However, the amount of waste generation is not decreasing, and the United State recycling rate, which could reduce waste generation, is only 26%, which is lower than other OECD countries. Thus, waste generation and greenhouse gas emission should decrease, and in order for that to happen, identifying the causes 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 waste influences carbon dioxide emissions from the waste sector. The annual-based U.S. data from 1990 to 2012 were used. The data were collected from various data sources, and the Granger causality test was applied for identifying the causal relationships. The results showed that there is no causality between GDP and waste generation, but total waste and recycling generation significantly cause positive and negative greenhouse gas emissions from the waste sector, respectively. This implies that the waste generation will not decrease even if GDP increases. And, if waste generation decreases or recycling rate increases, the greenhouse gas emission will decrease. Based on these results, it is expected that the waste generation and carbon dioxide emission from the waste sector can decrease more efficiently.

As the construction continue to be a leading industry in the number of injuries and fatalities annually, several organizations and agencies are working avidly to ensure the number of injuries and fatalities is minimized. The Occupational Safety and Health Administration (OSHA) is one such effort to assure safe and healthful working conditions for working men and women by setting and enforcing standards and by providing training, outreach, education and assistance. Given the large databases of OSHA historical events and reports, a manual analysis of the fatality and catastrophe investigations content is a time consuming and expensive process. This paper aims to evaluate the strength of unsupervised machine learning and Natural Language Processing (NLP) in supporting safety inspections and reorganizing accidents database on a state level. After collecting construction accident reports from the OSHA Arizona office, the methodology consists of preprocessing the accident reports and weighting terms in order to apply a data-driven unsupervised K-Means-based clustering approach. The proposed method classifies the collected reports in four clusters, each reporting a type of accident. The results show the construction accidents in the state of Arizona to be caused by falls (42.9%), struck by objects (34.3%), electrocutions (12.5%), and trenches collapse (10.3%). The findings of this research empower state and local agencies with a customized presentation of the accidents fitting their regulations and weather conditions. What is applicable to one climate might not be suitable for another; therefore, such rearrangement of the accidents database on a state based level is a necessary prerequisite to enhance the local safety applications and standards.

We present a high-resolution atmospheric inversion system combining a Lagrangian Particle Dispersion Model (LPDM) and the Weather Research and Forecasting model (WRF), and test the impact of assimilating meteorological observation on transport accuracy. A Four Dimensional Data Assimilation (FDDA) technique continuously assimilates meteorological observations from various observing systems into the transport modeling system, and is coupled to the high resolution CO₂ emission product Hestia to simulate the atmospheric mole fractions of CO₂. For the Indianapolis Flux Experiment (INFLUX) project, we evaluated the impact of assimilating different meteorological observation systems on the linearized adjoint solutions and the CO₂ inverse fluxes estimated using observed CO₂ mole fractions from 11 out of 12 communications towers over Indianapolis for the Sep.-Nov. 2013 period. While assimilating WMO surface measurements improved the simulated wind speed and direction, their impact on the planetary boundary layer (PBL) was limited. Simulated PBL wind statistics improved significantly when assimilating upper-air observations from the commercial airline program Aircraft Communications Addressing and Reporting System (ACARS) and continuous ground-based Doppler lidar wind observations. Wind direction mean absolute error (MAE) decreased from 26 to 14 degrees and the wind speed MAE decreased from 2.0 to 1.2 m s_-1, while the bias remains small in all configurations (< 6 degrees and 0.2 m s_-1). Wind speed MAE and ME are larger in daytime than in nighttime. PBL depth MAE is reduced by ~10%, with little bias reduction. The inverse results indicate that the spatial distribution of CO₂ inverse fluxes were affected by the model performance while the overall flux estimates changed little across WRF simulations when aggregated over the entire domain. Our results show that PBL wind observations are a potent tool for increasing the precision of urban meteorological reanalyses, but that the impact on inverse flux estimates is dependent on the specific urban environment.

The INFLUX experiment has taken multiple approaches to estimate the carbon dioxide (CO₂) flux in a domain centered on the city of Indianapolis, Indiana. One approach, Hestia, uses a bottom-up technique relying on a mixture of activity data, fuel statistics, direct flux measurement and modeling algorithms. A second uses a Bayesian atmospheric inverse approach constrained by atmospheric CO₂ measurements and the Hestia emissions estimate as a prior CO₂ flux. The difference in the central estimate of the two approaches comes to 0.94 MtC (an 18.7% difference) over the eight-month period between September 1, 2012 and April 30, 2013, a statistically significant difference at the 2-sigma level. Here we explore possible explanations for this apparent discrepancy in an attempt to reconcile the flux estimates. We focus on two broad categories: 1) biases in the largest of bottom-up flux contributions and 2) missing CO₂ sources. Though there is some evidence for small biases in the Hestia fossil fuel carbon dioxide (FFCO2) flux estimate as an explanation for the calculated difference, we find more support for missing CO₂ fluxes, with biological respiration the largest of these. Incorporation of these differences bring the Hestia bottom-up and the INFLUX inversion flux estimates into statistical agreement and are additionally consistent with wintertime measurements of atmospheric ¹⁴CO₂. We conclude that comparison of bottom-up and top-down approaches must consider all flux contributions and highlight the important contribution to urban carbon budgets of animal and biotic respiration. Incorporation of missing CO₂ fluxes reconciles the bottom-up and inverse-based approach in the INFLUX domain.

We assess the detectability of city emissions via a tower-based greenhouse gas (GHG) network, as part of the Indianapolis Flux (INFLUX) experiment. By examining afternoon-averaged results from a network of carbon dioxide (CO₂), methane (CH₄), and carbon monoxide (CO) mole fraction measurements in Indianapolis, Indiana for 2011–2013, we quantify spatial and temporal patterns in urban atmospheric GHG dry mole fractions. The platform for these measurements is twelve communications towers spread across the metropolitan region, ranging in height from 39 to 136 m above ground level, and instrumented with cavity ring-down spectrometers. Nine of the sites were deployed as of January 2013 and data from these sites are the focus of this paper. A background site, chosen such that it is on the predominantly upwind side of the city, is utilized to quantify enhancements caused by urban emissions. Afternoon averaged mole fractions are studied because this is the time of day during which the height of the boundary layer is most steady in time and the area that influences the tower measurements is likely to be largest. Additionally, atmospheric transport models have better performance in simulating the daytime convective boundary layer compared to the nighttime boundary layer. Averaged from January through April of 2013, the mean urban dormant-season enhancements range from 0.3 ppm CO₂ at the site 24 km typically downwind of the edge of the city (Site 09) to 1.4 ppm at the site at the downwind edge of the city (Site 02) to 2.9 ppm at the downtown site (Site 03). When the wind is aligned such that the sites are downwind of the urban area, the enhancements are increased, to 1.6 ppm at Site 09, and 3.3 ppm at Site 02. Differences in sampling height affect the reported urban enhancement by up to 50%, but the overall spatial pattern remains similar. The time interval over which the afternoon data are averaged alters the calculated urban enhancement by an average of 0.4 ppm. The CO₂ observations are compared to CO₂ mole fractions simulated using a mesoscale atmospheric model and an emissions inventory for Indianapolis. The observed and modeled CO₂ enhancements are highly correlated (r² = 0.94), but the modeled enhancements prior to inversion average 53% of those measured at the towers. Following the inversion, the enhancements follow the observations closely, as expected. The CH₄ urban enhancement ranges from 5 ppb at the site 10 km predominantly downwind of the city (Site 13) to 21 ppb at the site near the landfill (Site 10), and for CO ranges from 6 ppb at the site 24 km downwind of the edge of the city (Site 09) to 29 ppb at the downtown site (Site 03). Overall, these observations show that a dense network of urban GHG measurements yield a detectable urban signal, well-suited as input to an urban inversion system given appropriate attention to sampling time, sampling altitude and quantification of background conditions.

The ‘Hestia Project’ uses a bottom-up approach to quantify fossil fuel CO₂(FFCO₂) emissions spatially at the building/street level and temporally at the hourly level. Hestia FFCO₂ emissions are provided in the form of a group of sector-specific vector layers with point, line, and polygon sources to support carbon cycle science and climate policy. Application to carbon cycle science, in particular, requires regular gridded data in order to link surface carbon fluxes to atmospheric transport models. However, the heterogeneity and complexity of FFCO₂ sources within regular grids is sensitive to spatial resolution. From the perspective of a data provider, we need to find a balance between resolution and data volume so that the gridded data product retains the maximum amount of information content while maintaining an efficient data volume.

The Shannon entropy determines the minimum bits that are needed to encode an information source and can serve as a metric for the effective information content. In this paper, we present an analysis of the Shannon entropy of gridded FFCO₂ emissions with varying resolutions in four Hestia study areas, and find: (1) the Shannon entropy increases with smaller grid resolution until it reaches a maximum value (the max-entropy resolution); (2) total emissions (the sum of several sector-specific emission fields) show a finer max-entropy resolution than each of the sector-specific fields; (3) the residential emissions show a finer max-entropy resolution than the commercial emissions; (4) the max-entropy resolution of the onroad emissions grid is closely correlated to the density of the road network. These findings suggest that the Shannon entropy can detect the information effectiveness of the spatial resolution of gridded FFCO₂ emissions. Hence, the resolution-entropy relationship can be used to assist in determining an appropriate spatial resolution for urban CO₂ flux studies. We conclude that the optimal spatial resolution for providing Hestia total FFCO₂ emissions products is centered around 100 m, at which the FFCO₂ emissions data can not only fully meet the requirement of urban flux integration, but also be effectively used in understanding the relationships between FFCO₂ emissions and various social-economic variables at the U.S. census block group level.

The estimation of energy demand (by power plants) has traditionally relied on historical energy use data for the region(s) that a plant produces for. Regression analysis, artificial neural network and Bayesian theory are the most common approaches for analysing these data. Such data and techniques do not generate reliable results. Consequently, excess energy has to be generated to prevent blackout; causes for energy surge are not easily determined; and potential energy use reduction from energy efficiency solutions is usually not translated into actual energy use reduction. The paper highlights the weaknesses of traditional techniques, and lays out a framework to improve the prediction of energy demand by combining energy use models of equipment, physical systems and buildings, with the proposed data mining algorithms for reverse engineering. The research team first analyses data samples from large complex energy data, and then, presents a set of computationally efficient data mining algorithms for reverse engineering. In order to develop a structural system model for reverse engineering, two focus groups are developed that has direct relation with cause and effect variables. The research findings of this paper includes testing out different sets of reverse engineering algorithms, understand their output patterns and modify algorithms to elevate accuracy of the outputs.

Atmospheric CO₂ inversions estimate surface carbon fluxes from an optimal fit to atmospheric CO₂ measurements, usually including prior constraints on the flux estimates. Eleven sets of carbon flux estimates are compared, generated by different inversions systems that vary in their inversions methods, choice of atmospheric data, transport model and prior information. The inversions were run for at least 5 yr in the period between 1990 and 2010. Mean fluxes for 2001–2004, seasonal cycles, interannual variability and trends are compared for the tropics and northern and southern extra-tropics, and separately for land and ocean. Some continental/basin-scale subdivisions are also considered where the atmospheric network is denser. Four-year mean fluxes are reasonably consistent across inversions at global/latitudinal scale, with a large total (land plus ocean) carbon uptake in the north (−3.4 Pg C yr^-1 (±0.5 Pg C yr^-1 standard deviation), with slightly more uptake over land than over ocean), a significant although more variable source over the tropics (1.6 ± 0.9 Pg C yr^-1) and a compensatory sink of similar magnitude in the south (−1.4 ± 0.5 Pg C yr^-1) corresponding mainly to an ocean sink. Largest differences across inversions occur in the balance between tropical land sources and southern land sinks. Interannual variability (IAV) in carbon fluxes is larger for land than ocean regions (standard deviation around 1.06 versus 0.33 Pg C yr[superscript −1] for the 1996–2007 period), with much higher consistency among the inversions for the land. While the tropical land explains most of the IAV (standard deviation ~ 0.65 Pg C yr^-1), the northern and southern land also contribute (standard deviation ~ 0.39 Pg C yr^-1). Most inversions tend to indicate an increase of the northern land carbon uptake from late 1990s to 2008 (around 0.1 Pg C yr^-1, predominantly in North Asia. The mean seasonal cycle appears to be well constrained by the atmospheric data over the northern land (at the continental scale), but still highly dependent on the prior flux seasonality over the ocean. Finally we provide recommendations to interpret the regional fluxes, along with the uncertainty estimates.