Since the publication of the Report of the World Commission on Environment and Development in 1987, there have been numerous studies on sustainability. These studies created new knowledge and tools for understanding and managing complex coupled human and natural systems. In this Critical Review, we used a topic modeling technique to analyze 12 526 peer-reviewed research articles and identify the research questions and the approaches that were used or developed in each of the studies. These approaches were then classified by function. The analysis revealed twenty-three categories of research questions and seven functional approach classes design for sustainability, modeling of complexity, sustainability indicators, life cycle sustainability assessment, decision making support, sustainability governance, and engagement each of which is described here as an individual approach or tool within a larger sustainability toolbox. The article concludes with a discussion about using the sustainability toolbox as an integrated knowledge system to support transdisciplinary study and decision-making.

Prescribed burning (PB) is practiced throughout the USA, most extensively in the southeast, for the purpose of maintaining and improving the ecosystem and reducing wildfire risk. However, PB emissions contribute significantly to trace gas and particulate matter loads in the atmosphere. In places where air quality is already stressed by other anthropogenic emissions, PB can lead to major health and environmental problems. We developed a PB impact forecasting system to facilitate the dynamic management of air quality by modulating PB activity. In our system, a new decision tree model predicts burn activity based on the weather forecast and historic burning patterns. Emission estimates for the forecast burn activity are input into an air quality model, and simulations are performed to forecast the air quality impacts of the burns on trace gas and particulate matter concentrations. An evaluation of the forecasts for two consecutive burn seasons (2015 and 2016) showed that the modeling system has promising forecasting skills that can be further improved with refinements in burn area and plume rise estimates. Since 2017, air quality and burn impact forecasts are being produced daily with the ultimate goal of incorporating them into the management of PB operations.

Increasing urbanization places cities at the forefront of achieving global sustainability. For cities to become more sustainable, however, the infrastructure on which they rely must also become more productive, efficient and resilient. Unfortunately the current paradigm of urban infrastructure development is fragmented in approach lacking a systems perspective. Urban infrastructure systems are analogous to ecological systems because they are interconnected, complex and adaptive components that exchange material, information and energy among themselves and to and from the environment, and exhibit characteristic scaling properties that can be expressed by Zipf's Law. Analyzing them together as a whole, as one would do for an ecological system, provides a better understanding about their dynamics and interactions, and enables system-level optimization. The adoption of this “infrastructure ecology” approach will result in urban (re)development that requires lower investment of financial and natural resources to build and maintain, is more sustainable (e.g. uses less materials and energy and generates less waste) and resilient, and enables a greater and more equitable opportunities for the creation of wealth and comfort. The 12 guiding principles of infrastructure ecology will provide a set of goals for urban planners, engineers and other decision-makers in an urban system for urban (re)development.

The newly developed weather-based prescribed burn forecasting capability presents new opportunities for dynamic air quality management. Forecasting of burn emissions has been incorporated into the HiRes-2 Air Quality Forecasting System. Forecasts are being produced daily for air quality and the impacts of power plant, traffic and prescribed burn emissions. The ultimate goal is to integrate these air quality forecasts into the burn permitting operations.

Combined with need of the carbon emissions, the feasibility of Moderate or Intense Low-oxygen Dilution (MILD) combustion fueled with hydrogen/methane blends needs to be investigated. This paper discusses the pollutant emissions, the stable operating range and the flame morphology for a jet-induced MILD model combustor. The hydrogen/methane volume ratios range 0:10 to 5:5. The NOx emissions are less than 5 [email protected]%O2 when the hydrogen content is less than 50% by volume in the atmospheric conditions. The calculation using chemical reactor network (CRN) model demonstrates that the effect of heat loss on NOx emissions increases as the adiabatic combustion temperature increases, which is consistent with the experimental results. The maximum OH∗ signal intensity increased at higher hydrogen content, especially when the hydrogen content exceeds 30% by volume. Due to the increase in turbulent burning velocity and the enhancement in the reaction intensity, the reaction zones shrink with increasing hydrogen content. In addition, with increasing hydrogen content, the stable operation range of the combustor becomes narrower, and the stable combustion is not maintained when the hydrogen content exceeds 50% by volume. The findings of the paper help to further understand the effect of hydrogen content on the formation of MILD combustion in the jet-induced combustor.

s are being sought which demonstrate innovative, scientific approaches for site investigation and remediation, including those with a vapor intrusion component. Abstracts should reflect original work that has not previously been published or presented. Abstracts of 300 words or less must be submitted by December 16, 2013 to siteclosure@awma.org. 2349 Marlton Pike W Cherry Hill, NJ 08002 Phone: (856) 665-6666 Room rate: $112

Since 2006, a team of forecasters in Georgia (USA) has been using the high–resolution air quality forecasting system (Hi–Res) as an aid for making ozone (O3) and fine particulate matter (PM2.5) forecasts. Here, we examine Hi–Res's O3 and PM2.5 forecasting performance for the Atlanta metropolitan area during the summers of 2006–2009. A classificatory evaluation approach was adopted. The spatial synoptic classification (SSC) calendar for Atlanta was used to cluster the forecasting days into typical summer weather types of dry moderate, dry tropical, moist moderate, moist tropical, and a transition class. The forecasting days were also classified according to emissions conditions as special weekdays (Monday and Friday), typical weekdays and weekends/holidays. Evaluation of forecasts during 2006– 2009 shows that O3 performance was worse on moist days and better on dry days. This is an important concern for forecasters since a sizeable number of days that exceeded the National Ambient Air Quality Standard (NAAQS) for O3 were observed under moist tropical weather type during the period. On the other hand, PM2.5 performance during 2006–2008 was opposite – worse on dry days, especially on dry tropical days, and better on moist days. This too is a concern since higher concentrations of PM2.5 were observed to occur on dry days. In 2009, PM2.5 forecasting performance on dry days was improved significantly by integrating a new secondary organic aerosol (SOA) module into the system. As a result, the differences in PM2.5 forecasting performance between dry and moist days were diminished. Other results of this study, suggest that a relatively larger forecasting error on weekends/holidays may be due to higher uncertainties in emission estimates on those days. To a lesser extent, this was also true on special weekdays because of the greater variations in rush hour emissions relative to typical weekdays.

Understanding ozone response to its precursor emissions is crucial for effective air quality management practices. This nonlinear response is usually simulated using chemical transport models, and the modeling results are affected by uncertainties in emissions inputs. In this study, a high ozone episode in the southeastern United States is simulated using the Community Multiscale Air Quality (CMAQ) model. Uncertainties in ozone formation and response to emissions controls due to uncertainties in emission rates are quantified using the Monte Carlo method. Instead of propagating emissions uncertainties through the original CMAQ a reduced form of CMAQ is formulated using directly calculated first- and second-order sensitivities that capture the nonlinear ozone concentration-emission responses. This modification greatly reduces the associated computational cost. Quantified uncertainties in modeled ozone concentrations and responses to various emissions controls are much less than the uncertainties in emissions inputs. Average uncertainties in modeled ozone concentrations for the Atlanta area are less than 10% (as measured by the inferred coefficient of variance [ICOV]) even when emissions uncertainties are assumed to vary between a factor of 1.5 and 2. Uncertainties in the ozone responses generally decrease with increased emission controls. Average uncertainties (ICOV) in emission-normalized ozone responses range from 4 to 22%, with the smaller being associated with controlling of the relatively certain point nitrogen oxide (NOx) emissions and the larger resulting from controlling of the less certain mobile NOx emissions. These small uncertainties provide confidence in the model applications, such as in performance evaluation, attainment demonstration, and control strategy development.

In this paper, an intelligent decision-making framework (DMF) is developed to help decision makers identify cost-effective ozone control policies. High concentrations of ozone at the ground level continue to be a serious problem in numerous U. S. cities. Our DMF searches for dynamic and targeted control policies that require a lower total reduction of emissions than current control strategies based on the "trial and error" approach typically employed by state government decision makers. Our DMF utilizes a rigorous stochastic dynamic programming (SDP) formulation and incorporates an atmospheric chemistry module to model how ozone concentrations change over time. Within the atmospheric chemistry module, methods from design and analysis of computer experiments are employed to create SDP state transition equation metamodels, and critical dimensionality reduction is conducted to reduce the state-space dimension in solving our SDP problem. Results are presented from a prototype DMF for the Atlanta metropolitan region.

A computer simulation model, the Regional Simulator (RSim), was constructed to project how land-use changes affect the quality of water, air, noise, and habitat of species of special concern. RSim was designed to simulate these environmental impacts for five counties in Georgia that surround and include Fort Benning. The model combines existing data and modeling approaches to simulate the effects of land-cover changes on: nutrient export by hydrological unit; peak 8-h average ozone concentrations; noise caused by small arms and blasts; and habitat changes for the rare Red-cockaded Woodpecker (Picoides borealis) and gopher tortoise (Gopherus polyphemus). The model also includes submodules for urban growth, new urbanization influenced by existing roads, nonurban land cover transitions, and a new military training area under development at Fort Benning. The model was run under scenarios of business as usual (BAU) and greatly increased urban growth for the region. The projections show that the effects of high urban growth will likely differ from those of BAU for noise and nitrogen and phosphorus loadings to surface water, but not for peak airborne ozone concentrations, at least in the absence of associated increases in industry and transportation use or technology changes. In both scenarios, no effects of urban growth are anticipated for existing populations of the federally endangered Red-cockaded Woodpecker. In contrast, habitat for gopher tortoise in the five-county region is projected to decline by 5 and 40% in the BAU and high urban growth scenarios, respectively. RSim is designed to assess the relative environmental impacts of planned activities both inside and outside military installations and to address concerns related to encroachment and transboundary influences.

On February 28, 2007, a severe smoke event caused by prescribed forest fires occurred in Atlanta, GA. Later smoke events in the southeastern metropolitan areas of the United States caused by the Georgia-Florida wild forest fires further magnified the significance of forest fire emissions and the benefits of being able to accurately predict such occurrences. By using preburning information, we utilize an operational forecasting system to simulate the potential air quality impacts from two large February 28th fires. Our "forecast" predicts that the scheduled prescribed fires would have resulted in over 1 million Atlanta residents being potentially exposed to fine particle matter (PM2.5) levels of 35 microg m(-3) or higher from 4 p.m. to midnight. The simulated peak 1 h PM2.5 concentration is about 121 microg m(-3). Our study suggests that the current air quality forecasting technology can be a useful tool for helping the management of fire activities to protect public health. With postburning information, our "hindcast" predictions improved significantly on timing and location and slightly on peak values. "Hindcast" simulations also indicated that additional isoprenoid emissions from pine species temporarily triggered by the fire could induce rapid ozone and secondary organic aerosol formation during late winter. Results from this study suggest that fire induced biogenic volatile organic compounds emissions missing from current fire emissions estimate should be included in the future.

An air quality forecasting system was developed to aid the operational ozone and PM2.5 forecasting in Atlanta, Georgia. The system is based on three dimensional models for weather and air quality prediction and provides high resolution locally. A preliminary evaluation shows that the system has the potential of producing reliable forecasts.

In this paper, we present a Decision-Making Framework (DMF) for reducing ozone pollution in the metropolitan Atlanta region. High ground-level concentrations of ozone continue to be a serious problem in several US cities, and Atlanta is one of the most serious of these cases. In contrast to the "trial and error" approach utilized by state government decision-makers, our DMF searches for dynamic and focused control strategies that require a lower total reduction of emissions than current control strategies. Our DMF utilizes a rigorous stochastic dynamic programming formulation and includes an Atmospheric Chemistry Module to represent how ozone concentrations change over time. This paper focuses on the procedures within the Atmospheric Chemistry Module. Using the US EPA's Urban Airshed Model for Atlanta, we use mining and metamodeling tools to develop a computationally efficient representation of the relevant ozone air chemistry. The proposed approach is able to effectively model changes in ozone concentrations over a 24-hour period.

Land-use change and land-management practices affect a variety of ecological processes. Land-use impacts on ecological processes include local extirpations, introductions of new species, changes in land-cover extent, changes in juxtaposition of land-cover types, changes to disturbance regimes, changes in vegetation structure and composition, and effects on air, water, and light quality, and noise pollution. Effects of land-use changes on ecological processes are discussed with special reference to changes in rural environments. Our premise is that better understanding of ecological processes improves land management. Mitigation strategies are presented with respect to management of initial ecological conditions, of the changes themselves, and of the altered system. The paper focuses on proactive environmental management efforts and identifies key research issues as (1) quantifying land-use legacies, (2) determining conditions under which land use modifies impacts of other stressors, (3) identifying conditions under which deleterious impacts can be avoided, (4) understanding cumulative impacts of land-use change, (5) improving our understanding of how land use alters resistance or susceptibility to invasion and impacts of pollutants, (6) crafting socioeconomically reasonable incentives for restoring or reducing effects of land-use practices, and (7) accelerating the integration of social and ecological sciences

ozone prediction has become an important activity in many U.S. ozone nonattainment areas. In this study, we describe the ozone prediction program in the Atlanta metropolitan area and analyze the performance of this program during the 1999 ozone-forecasting season. From May to September, a team of 10 air quality regulators, meteorologists, and atmospheric scientists made a daily prediction of the next-day maximum 8-hr average ozone concentration. The daily forecast was made aided by two linear regression models, a 3-dimensional air quality model, and the no-skill ozone persistence model. The team's performance is compared with the numerical models using several numerical indicators. Our analysis indicated that (1) the team correctly predicted next-day peak ozone concentrations 84% of the time, (2) the two linear regression models had a better performance than a 3-dimensional air quality model, (3) persistence was a strong predictor of ozone concentrations with a performance of 78%, and (4) about half of the team's wrong predictions could be prevented with improved meteorological predictions.

Urban Airshed Model-Version IV (UAM-IV) simulations on 7–8 July, 1988 for the Atlanta, Georgia, nonattainment area are used to investigate how recent changes in the National Ambient Air Quality Standard (NAAQS) and changes in boundary concentrations may affect attempts to comply with the standard through local emissions reductions. According to model results, the recently promulgated 8 h NAAQS at a level of 0.08 ppmv will require larger emission reductions to comply with the standard than those that are necessary to comply with the previous 1 h/0.12 ppmv NAAQS. Regardless of the form of the NAAQS or the magnitude of the concentrations of O3 and its precursors at the model domain boundary, UAM-IV simulations for Atlanta predict that NOx (NO+NO2) emission reductions are more effective than volatile organic compound reductions in mitigating O3 pollution. Moreover, the simulations indicate that NOx emission reductions greater than 60–75% would be required to demonstrate attainment under either form of the standard, even if boundary concentrations of O3 and its precursors were substantially reduced. Further research is necessary to determine if this weak response to emission controls is truly representative of the real atmosphere, or is a result of the meteorological conditions specific to this episode, or is an artifact of the UAM-IV model or its inputs.