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A novel method to standardize and automate material flow/waste mapping in economies by integrating mechanistic engineering models and macroeconomic framework is proposed for identifying pathways to transition towards low carbon/zero waste economy.
Venkata Sai Gargeya Vunnava; Shweta Singh. Integrated mechanistic engineering models and macroeconomic input–output approach to model physical economy for evaluating the impact of transition to a circular economy. Energy & Environmental Science 2021, 1 .
AMA StyleVenkata Sai Gargeya Vunnava, Shweta Singh. Integrated mechanistic engineering models and macroeconomic input–output approach to model physical economy for evaluating the impact of transition to a circular economy. Energy & Environmental Science. 2021; ():1.
Chicago/Turabian StyleVenkata Sai Gargeya Vunnava; Shweta Singh. 2021. "Integrated mechanistic engineering models and macroeconomic input–output approach to model physical economy for evaluating the impact of transition to a circular economy." Energy & Environmental Science , no. : 1.
Amidst the growing population, urbanization, globalization, and economic growth, along with the impacts of climate change, decision-makers, stakeholders, and researchers need tools for better assessment and communication of the highly interconnected food–energy–water (FEW) nexus. This study aimed to identify critical periods for water resources management for robust decision-making for water resources management at the nexus. Using a 4610 ha agricultural watershed as a pilot site, historical data (2006–2012), scientific literature values, and SWAT model simulations were utilized to map out critical periods throughout the growing season of corn and soybeans. The results indicate that soil water deficits are primarily seen in June and July, with average deficits and surpluses ranging from −134.7 to +145.3 mm during the study period. Corresponding water quality impacts include average monthly surface nitrate-N, subsurface nitrate-N, and soluble phosphorus losses of up to 0.026, 0.26, and 0.0013 kg/ha, respectively, over the growing season. Estimated fuel requirements for the agricultural practices ranged from 24.7 to 170.3 L/ha, while estimated carbon emissions ranged from 0.3 to 2.7 kg CO2/L. A composite look at all the FEW nexus elements showed that critical periods for water management in the study watershed occurred in the early and late season—primarily related to water quality—and mid-season, related to water quantity. This suggests the need to adapt agricultural and other management practices across the growing season in line with the respective water management needs. The FEW nexus assessment methodologies developed in this study provide a framework in which spatial, temporal, and literature data can be implemented for improved water resources management in other areas.
Val Schull; Sushant Mehan; Margaret Gitau; David Johnson; Shweta Singh; Juan Sesmero; Dennis Flanagan. Construction of Critical Periods for Water Resources Management and Their Application in the FEW Nexus. Water 2021, 13, 718 .
AMA StyleVal Schull, Sushant Mehan, Margaret Gitau, David Johnson, Shweta Singh, Juan Sesmero, Dennis Flanagan. Construction of Critical Periods for Water Resources Management and Their Application in the FEW Nexus. Water. 2021; 13 (5):718.
Chicago/Turabian StyleVal Schull; Sushant Mehan; Margaret Gitau; David Johnson; Shweta Singh; Juan Sesmero; Dennis Flanagan. 2021. "Construction of Critical Periods for Water Resources Management and Their Application in the FEW Nexus." Water 13, no. 5: 718.
Circular economy (CE) offers a pathway towards sustainable, closed-loop resource systems, but widespread adoption across industrial sectors is limited by fragmented knowledge and varied implementation approaches. This article reviews sector-specific challenges and opportunities associated with implementing and measuring the benefits of CE strategies. Literature mapping highlights progress towards CE implementation in food, chemicals, metals, consumer electronics, and building and infrastructure sectors, and towards measuring CE outcomes via systems analysis methods like life cycle assessment (LCA) and material flow analysis (MFA). However, key challenges were also identified that point to future research and demonstration needs. First, research on CE adoption typically exists as case studies that are closely linked to a sector. But literature has not effectively synthesized knowledge gained across domains, particularly understanding underlying barriers to CE and where they occur in product life cycles. Second, research on CE outcomes often applies well-established methods without adapting for unique attributes of CE systems. A key opportunity is in integrative methodological advances, such as expanded use of consequential LCA, development of physical Input–Output tables, and integrating MFA with dynamical models. Finally, regardless of sector, new CE business models are seen as a critical enabler to realize success, but theoretical frameworks in literature are not well-tested in practice. The review also highlights opportunities to harness other emerging trends, such as big data, to provide better information for system modelers and decision-oriented insight to guide CE stakeholders.
Shweta Singh; Callie Babbitt; Gabrielle Gaustad; Matthew J. Eckelman; Jeremy Gregory; Erinn Ryen; Nehika Mathur; Miriam C. Stevens; Abhijeet Parvatker; Raj Buch; Alicia Marseille; Thomas Seager. Thematic exploration of sectoral and cross-cutting challenges to circular economy implementation. Clean Technologies and Environmental Policy 2021, 23, 915 -936.
AMA StyleShweta Singh, Callie Babbitt, Gabrielle Gaustad, Matthew J. Eckelman, Jeremy Gregory, Erinn Ryen, Nehika Mathur, Miriam C. Stevens, Abhijeet Parvatker, Raj Buch, Alicia Marseille, Thomas Seager. Thematic exploration of sectoral and cross-cutting challenges to circular economy implementation. Clean Technologies and Environmental Policy. 2021; 23 (3):915-936.
Chicago/Turabian StyleShweta Singh; Callie Babbitt; Gabrielle Gaustad; Matthew J. Eckelman; Jeremy Gregory; Erinn Ryen; Nehika Mathur; Miriam C. Stevens; Abhijeet Parvatker; Raj Buch; Alicia Marseille; Thomas Seager. 2021. "Thematic exploration of sectoral and cross-cutting challenges to circular economy implementation." Clean Technologies and Environmental Policy 23, no. 3: 915-936.
Dynamical equations form the basis of design for manufacturing processes and control systems; however, identifying governing equations using a mechanistic approach is tedious. Recently, Machine learning (ML) has shown promise to identify the governing dynamical equations for physical systems faster. This possibility of rapid identification of governing equations provides an exciting opportunity for advancing dynamical systems modeling. However, applicability of the ML approach in identifying governing mechanisms for the dynamics of complex systems relevant to manufacturing has not been tested. We test and compare the efficacy of two white-box ML approaches (SINDy and SymReg) for predicting dynamics and structure of dynamical equations for overall dynamics in a distillation column. Results demonstrate that a combination of ML approaches should be used to identify a full range of equations. In terms of physical law, few terms were interpretable as related to Fick’s law of diffusion and Henry’s law in SINDy, whereas SymReg identified energy balance as driving dynamics.
Renganathan Subramanian; Raghav Rajesh Moar; Shweta Singh. White-box Machine learning approaches to identify governing equations for overall dynamics of manufacturing systems: A case study on distillation column. Machine Learning with Applications 2020, 3, 100014 .
AMA StyleRenganathan Subramanian, Raghav Rajesh Moar, Shweta Singh. White-box Machine learning approaches to identify governing equations for overall dynamics of manufacturing systems: A case study on distillation column. Machine Learning with Applications. 2020; 3 ():100014.
Chicago/Turabian StyleRenganathan Subramanian; Raghav Rajesh Moar; Shweta Singh. 2020. "White-box Machine learning approaches to identify governing equations for overall dynamics of manufacturing systems: A case study on distillation column." Machine Learning with Applications 3, no. : 100014.
Life Cycle Analysis (LCA) has long been utilized for decision making about the sustainability of products. LCA provides information about the total emissions generated for a given functional unit of a product, which is utilized by industries or consumers for comparing two products with regards to environmental performance. However, many existing LCAs utilize data that is representative of an average system with regards to life cycle stage, thus providing an aggregate picture. It has been shown that regional variation may lead to large variation in the environmental impacts of a product, specifically dealing with energy consumption, related emissions and resource consumptions. Hence, improving the reliability of LCA results for decision making with regards to environmental performance needs regional models to be incorporated for building a life cycle inventory that is representative of the origin of products from a certain region. In this work, we present the integration of regionalized data from process systems models and other sources to build regional LCA models and quantify the spatial variations per unit of biodiesel produced in the state of Indiana for environmental impact. In order to include regional variation, we have incorporated information about plant capacity for producing biodiesel from North and Central Indiana. The LCA model built is a cradle-to-gate. Once the region-specific models are built, the data were utilized in SimaPro to integrate with upstream processes to perform a life cycle impact assessment (LCIA). We report the results per liter of biodiesel from northern and central Indiana facilities in this work. The impact categories studied were global warming potential (kg CO2 eq) and freshwater eutrophication (kg P eq). While there were a lot of variations at individual county level, both regions had a similar global warming potential impact and the northern region had relatively lower eutrophication impacts.
Venkata Sai Gargeya Vunnava; Shweta Singh. Spatial Life Cycle Analysis of Soybean-Based Biodiesel Production in Indiana, USA Using Process Modeling. Processes 2020, 8, 392 .
AMA StyleVenkata Sai Gargeya Vunnava, Shweta Singh. Spatial Life Cycle Analysis of Soybean-Based Biodiesel Production in Indiana, USA Using Process Modeling. Processes. 2020; 8 (4):392.
Chicago/Turabian StyleVenkata Sai Gargeya Vunnava; Shweta Singh. 2020. "Spatial Life Cycle Analysis of Soybean-Based Biodiesel Production in Indiana, USA Using Process Modeling." Processes 8, no. 4: 392.
Energy use is one of the largest drivers of climate change, but the large share of energy used for space heating and cooling is also driven by climate change. Demand for energy, particularly cooling, is important for long-range infrastructure planning. Urban areas represent a very small proportion of total land, but usually consume the majority of energy. In this work, statistical, top-down approaches are used to model residential and commercial urban energy demand changes in Indiana, a state in the Midwest region of the USA, in 2050 and 2080 under the climate change scenarios of RCP 4.5 and 8.5. By modeling energy demand changes in urban areas in Indiana, we can project the majority of energy demand while placing it in a spatial perspective that is missing from the statewide estimates. Two time periods are used to give an intuitive time stamp and temporal perspective. Results indicate that Indiana’s northernmost cities are expected to show significantly increased residential cooling demand due to climate change by 2080. Indianapolis represents an increasing share of total urban commercial and residential energy use over the next 60 years. Transportation is expected to represent a larger share of energy use as heating demand declines under climate change scenarios.
Liz Wachs; Shweta Singh. Projecting the urban energy demand for Indiana, USA, in 2050 and 2080. Climatic Change 2020, 163, 1949 -1966.
AMA StyleLiz Wachs, Shweta Singh. Projecting the urban energy demand for Indiana, USA, in 2050 and 2080. Climatic Change. 2020; 163 (4):1949-1966.
Chicago/Turabian StyleLiz Wachs; Shweta Singh. 2020. "Projecting the urban energy demand for Indiana, USA, in 2050 and 2080." Climatic Change 163, no. 4: 1949-1966.
The share of wind energy in the US energy supply has been steadily increasing in the last two decades. With new wind energy farms being installed in various states of the country, local and multi-regional economic disruptions are bound to take place. The multi-regional economic impacts of installing new wind farms was determined using the US multi-region input-output (US-MRIO) model that has been developed, also called the USLab. Currently, there is a lack of multi-regional impact assessment of wind energy expansion in the US. In this article, we use the US-MRIO to determine regional and sectoral spill-over effects resulted from installation of wind energy farms in 10 US states. The economic impacts were calculated by feeding the USLab with data obtained from the Jobs and Economic Development Impacts (JEDI) Wind model published by National Renewable Energy Laboratory (NREL). The JEDI wind model provides the change in local economic data such as the number of new jobs created and increase of energy-related products in each region in the final demand and value-added. The data about final demand and value-added change was used with the US-MRIO model to account for the multi-regional economic impact across US due to installation of wind energy farms. The year of wind farm installation was set to 2017 and a US-MRIO for 2017 was generated to calculate the economic impact. The total economic impact was found to be 26 billion dollars of which 3 billion dollars was associated with the states where no new wind energy capacity was installed. Installation of new energy production capacity also results in “change in energy consumption” across US. Using the US-MRIO model and the energy intensity of manufacturing sectors, the energy consumption increase due to addition of wind farms was found to be about 6952 trillion of btu for the total change in economic throughput. Primary metal manufacturing and Machinery manufacturing sectors stood out amongst other manufacturing sectors with considerable change in energy consumption with an increase of 3074 trillion of btu and 1537 trillions of btu.
Futu Faturay; Venkata Sai Gargeya Vunnava; Manfred Lenzen; Shweta Singh. Using a new USA multi-region input output (MRIO) model for assessing economic and energy impacts of wind energy expansion in USA. Applied Energy 2019, 261, 114141 .
AMA StyleFutu Faturay, Venkata Sai Gargeya Vunnava, Manfred Lenzen, Shweta Singh. Using a new USA multi-region input output (MRIO) model for assessing economic and energy impacts of wind energy expansion in USA. Applied Energy. 2019; 261 ():114141.
Chicago/Turabian StyleFutu Faturay; Venkata Sai Gargeya Vunnava; Manfred Lenzen; Shweta Singh. 2019. "Using a new USA multi-region input output (MRIO) model for assessing economic and energy impacts of wind energy expansion in USA." Applied Energy 261, no. : 114141.
As society works to reduce its reliance on fossil fuels, the demand for renewable energy has grown rapidly in recent years. Photovoltaic (PV) solar power is one such source of renewable energy and has seen significant growth over the past several decades. While End of Life (EoL) PV volumes remain relatively small at this time, the number of EoL PVs is estimated to grow dramatically in the near future. This raises concerns about the management of EoL PVs. Effective EoL management methods need to be aggressively developed and implemented in order to prevent large volumes of hazardous wastes from being disposed in landfills. At the same time, PV waste can also be viewed as a potential source of valuable materials. The development of effective processing technologies to isolate these materials will help divert waste away from landfills while reducing dependence on virgin resources and enhancing economic sustainability. This paper proposes the circularization of the PV industry by applying the notion of Life Cycle Symbiosis (LCS), an extension of Industrial Symbiosis (IS). By identifying waste streams that may have value as potential raw material/feedstock, through the development of collaborative business/economic interactions among a diverse set of organizations, we have applied LCS in the context of EoL PVs. The avoided global warming potential (GWP) and ecotoxicity impacts 2750 kg CO2 eq and 32,000 CTUe per metric ton of EoL PVs respectively, while the water savings and electricity savings were 37,735.65 m3 and 3615.11 MJ per metric ton of EoL PVs.
N. Mathur; S. Singh; J.W. Sutherland. Promoting a circular economy in the solar photovoltaic industry using life cycle symbiosis. Resources, Conservation and Recycling 2019, 155, 104649 .
AMA StyleN. Mathur, S. Singh, J.W. Sutherland. Promoting a circular economy in the solar photovoltaic industry using life cycle symbiosis. Resources, Conservation and Recycling. 2019; 155 ():104649.
Chicago/Turabian StyleN. Mathur; S. Singh; J.W. Sutherland. 2019. "Promoting a circular economy in the solar photovoltaic industry using life cycle symbiosis." Resources, Conservation and Recycling 155, no. : 104649.
Machine learning recently has been used to identify the governing equations for dynamics in physical systems. The promising results from applications on systems such as fluid dynamics and chemical kinetics inspire further investigation of these methods on complex engineered systems. Dynamics of these systems play a crucial role in design and operations. Hence, it would be advantageous to learn about the mechanisms that may be driving the complex dynamics of systems. In this work, our research question was aimed at addressing this open question about applicability and usefulness of novel machine learning approach in identifying the governing dynamical equations for engineered systems. We focused on distillation column which is an ubiquitous unit operation in chemical engineering and demonstrates complex dynamics i.e. it's dynamics is a combination of heuristics and fundamental physical laws. We tested the method of Sparse Identification of Non-Linear Dynamics (SINDy) because of it's ability to produce white-box models with terms that can be used for physical interpretation of dynamics. Time series data for dynamics was generated from simulation of distillation column using ASPEN Dynamics. One promising result was reduction of number of equations for dynamic simulation from 1000s in ASPEN to only 13 - one for each state variable. Prediction accuracy was high on the test data from system within the perturbation range, however outside perturbation range equations did not perform well. In terms of physical law extraction, some terms were interpretable as related to Fick's law of diffusion (with concentration terms) and Henry's law (with ratio of concentration and pressure terms). While some terms were interpretable, we conclude that more research is needed on combining engineering systems with machine learning approach to improve understanding of governing laws for unknown dynamics.
Renganathan Subramanian; Shweta Singh. Can Machine Learning Identify Governing Laws For Dynamics in Complex Engineered Systems ? : A Study in Chemical Engineering. 2019, 1 .
AMA StyleRenganathan Subramanian, Shweta Singh. Can Machine Learning Identify Governing Laws For Dynamics in Complex Engineered Systems ? : A Study in Chemical Engineering. . 2019; ():1.
Chicago/Turabian StyleRenganathan Subramanian; Shweta Singh. 2019. "Can Machine Learning Identify Governing Laws For Dynamics in Complex Engineered Systems ? : A Study in Chemical Engineering." , no. : 1.
Indiana’s climate and its manufacturing-heavy economy make it a prime user of energy. In fact, Indiana is the ninth-most energy intensive state per capita in the country. Nearly three-quarters of Indiana’s electricity comes from coal, and 5 percent is generated by renewable sources, though the wind energy sector is growing and coal use is declining. This energy mix makes the Hoosier State the eighth-largest emitter of climate-changing gases, at 183 million metric tons of carbon dioxide (CO2) emitted per year. As global and local climates continue to shift, it is important to know how Indiana’s future energy profile will be affected and what those changes mean for Hoosier families and businesses. This report from the Indiana Climate Change Impacts Assessment (IN CCIA) looks at projected changes to Indiana’s residential and commercial energy demands as the state warms, and to Indiana’s energy supply over the coming century.
Leigh Leigh Raymond, Purdue University; Douglas Douglas Gotham, Purdue University; William William McClain, Purdue University; Sayanti Mukhopadhyay; Roshanak Roshanak Nateghi, School of Industrial Engineering and Division of Environmental and Ecological Engineering, Purdue University; Paul Paul V. Preckel, Purdue University; Peter Peter Schubert, Indiana University Purdue University Indianapolis; Shweta Shweta Singh, Purdue University; Elizabeth Liz Wachs, Purdue University; Melissa Melissa Widhalm, Purdue University; Jeffrey Jeffrey Dukes, Purdue University. Climate Change and Indiana’s Energy Sector: A Report from the Indiana Climate Change Impacts Assessment. Climate Change and Indiana’s Energy Sector: A Report from the Indiana Climate Change Impacts Assessment 2019, 1 .
AMA StyleLeigh Leigh Raymond, Purdue University, Douglas Douglas Gotham, Purdue University, William William McClain, Purdue University, Sayanti Mukhopadhyay, Roshanak Roshanak Nateghi, School of Industrial Engineering and Division of Environmental and Ecological Engineering, Purdue University, Paul Paul V. Preckel, Purdue University, Peter Peter Schubert, Indiana University Purdue University Indianapolis, Shweta Shweta Singh, Purdue University, Elizabeth Liz Wachs, Purdue University, Melissa Melissa Widhalm, Purdue University, Jeffrey Jeffrey Dukes, Purdue University. Climate Change and Indiana’s Energy Sector: A Report from the Indiana Climate Change Impacts Assessment. Climate Change and Indiana’s Energy Sector: A Report from the Indiana Climate Change Impacts Assessment. 2019; ():1.
Chicago/Turabian StyleLeigh Leigh Raymond, Purdue University; Douglas Douglas Gotham, Purdue University; William William McClain, Purdue University; Sayanti Mukhopadhyay; Roshanak Roshanak Nateghi, School of Industrial Engineering and Division of Environmental and Ecological Engineering, Purdue University; Paul Paul V. Preckel, Purdue University; Peter Peter Schubert, Indiana University Purdue University Indianapolis; Shweta Shweta Singh, Purdue University; Elizabeth Liz Wachs, Purdue University; Melissa Melissa Widhalm, Purdue University; Jeffrey Jeffrey Dukes, Purdue University. 2019. "Climate Change and Indiana’s Energy Sector: A Report from the Indiana Climate Change Impacts Assessment." Climate Change and Indiana’s Energy Sector: A Report from the Indiana Climate Change Impacts Assessment , no. : 1.
Phosphorus (P) recovery from waste is a crucial sustainability challenge both for meeting the growing food demand and maintaining water quality effected by P run-off. Several new technologies are under development to address this issue, however each technology presents a trade-off in terms of cost vs benefits. Energy consumption forms one such critical trade-off as energy intensive technologies can lead to unintended consequences. However, the energy assessment of waste treatment and nutrient recovery systems are limited in literature and mostly focused on empirical analysis. This limits the design optimization of these systems for improving energy utilization. In this work, this critical gap of rigorous energy assessment of waste recovery technologies has been addressed by proposing the method of entropy generation analysis (EGA) as a tool for evaluating the design of waste recovery systems in order to improve energy utilization. To demonstrate the method of EGA for waste recovery technologies, the method was applied on a sequential Anaerobic Digester (AD)-Ion Exchange (IE) based side stream process used for extraction of P from waste water system which is the first step in recovery of nutrient from waste. EGA was performed by assuming a black box model for the technology and thermodynamic parameters were calculated using mechanism based approach by considering biochemical reactions in AD, stream flow analysis in ASPEN PLUS and batch IE experiments. The study successfully shows how to account for entropy generation in processes like AD and IE using mechanisms that result in entropy generation leading to energy losses. Results from the study indicate that the entropy generated from the heat exchange system to maintain the operating temperature for AD and IE was leading cause of entropy generation which provides guidance for targeting design improvements to design cleaner technologies in future. Such insights about mechanism behind energy losses is not feasible using widely used energy analysis methods and the method of total exergy destruction analysis which combines the total losses for a subsystem but does not elucidate exact causes of losses, thus EGA provides a distinct advantage for guiding the design of technologies to minimize energy losses.
Venkata Sai Gargeya Vunnava; Shweta Singh. Entropy generation analysis of sequential Anaerobic Digester Ion-Exchange technology for Phosphorus extraction from waste. Journal of Cleaner Production 2019, 221, 55 -62.
AMA StyleVenkata Sai Gargeya Vunnava, Shweta Singh. Entropy generation analysis of sequential Anaerobic Digester Ion-Exchange technology for Phosphorus extraction from waste. Journal of Cleaner Production. 2019; 221 ():55-62.
Chicago/Turabian StyleVenkata Sai Gargeya Vunnava; Shweta Singh. 2019. "Entropy generation analysis of sequential Anaerobic Digester Ion-Exchange technology for Phosphorus extraction from waste." Journal of Cleaner Production 221, no. : 55-62.
Physical input–output tables (PIOTs) were first conceptualized in the 1990s but have not been widely adopted. However, with the increased emphasis on building a circular economy and understanding the resource nexus, PIOTs will become critical for optimizing resource flows and restructuring economies to close material loops. This necessitates a focus on improved methodologies for PIOT development to allow wider adoption. In this work, we propose and demonstrate a modular bottom-up approach for constructing PIOTs from process engineering models. The method was tested on a PIOT for nitrogen with a subset of sectors in Illinois (USA) and compared with a nitrogen PIOT developed earlier for the same time period, finding equal or higher confidence in sector balances. While the method has high initial costs, its suitability for automation enables it to allow the fast creation of PIOTs where technical coefficient matrices reflect underlying physical processes and relationships within and between sectors, thus capturing accurately the physical structure of the economy. We also demonstrate how the method can be extended for the creation of regional input coefficient matrices. While not implemented here, the method can potentially be used for the creation of hybrid IO tables, trend analysis through time series and combined with non-survey methods to fill data gaps. This will allow combining the strengths of complementary methodologies for constructing PIOTs and standardization of methods for better reliability.
Liz Wachs; Shweta Singh. A modular bottom-up approach for constructing physical input–output tables (PIOTs) based on process engineering models. Journal of Economic Structures 2018, 7, 26 .
AMA StyleLiz Wachs, Shweta Singh. A modular bottom-up approach for constructing physical input–output tables (PIOTs) based on process engineering models. Journal of Economic Structures. 2018; 7 (1):26.
Chicago/Turabian StyleLiz Wachs; Shweta Singh. 2018. "A modular bottom-up approach for constructing physical input–output tables (PIOTs) based on process engineering models." Journal of Economic Structures 7, no. 1: 26.
Xinyu Liu; Shweta Singh; Erin L. Gibbemeyer; Brooke E. Tam; Robert A. Urban; Bhavik R. Bakshi. The carbon-nitrogen nexus of transportation fuels. Journal of Cleaner Production 2018, 180, 790 -803.
AMA StyleXinyu Liu, Shweta Singh, Erin L. Gibbemeyer, Brooke E. Tam, Robert A. Urban, Bhavik R. Bakshi. The carbon-nitrogen nexus of transportation fuels. Journal of Cleaner Production. 2018; 180 ():790-803.
Chicago/Turabian StyleXinyu Liu; Shweta Singh; Erin L. Gibbemeyer; Brooke E. Tam; Robert A. Urban; Bhavik R. Bakshi. 2018. "The carbon-nitrogen nexus of transportation fuels." Journal of Cleaner Production 180, no. : 790-803.
Nitrogen (N) presents an important challenge for sustainability. Human intervention in the global nitrogen cycle has been pivotal in providing goods and services to society. However, release of N beyond its intended societal use has many negative health and environmental consequences. Several systems modeling approaches have been developed to understand the tradeoffs between the beneficial and harmful effects of N. These efforts include life cycle modeling, integrated management practices and sustainability metrics for individuals and communities. However, these approaches do not connect economic and ecological N flows in physical units throughout the system, which could better represent these trade-offs for decision-makers. Physical Input-Output Table (PIOT) based models present a viable complementary solution to overcome this limitation. We developed a N-PIOT for Illinois representing the interdependence of sectors in 2002, using N mass units. This allows studying the total N flow required to produce a certain amount of N in the final product. An Environmentally Extended Input Output (EEIO) based approach was used to connect the physical economic production to environmental losses; allowing quantification of total environmental impact to support production in Illinois. A bottom up approach was used to develop the N-PIOT using Material Flow Analysis (MFA) tracking N flows associated with top 3 commodities (Corn, Soybean and Wheat). These three commodities cover 99% of N fertilizer use in Illinois. The PIOT shows that of all the N inputs to corn farming the state exported 68% of N embedded in useful products, 9% went to animal feed manufacturing and only 0.03% was consumed directly within the state. Approximately 35% of N input to soybean farming ended up in animal feed manufacturing. Release of N to the environment was highest from corn farming, at about 21.8% of total N fertilizer inputs, followed by soybean (9.2%) and wheat farming (4.2%). The model also allowed the calculation of life cycle N use efficiency for N based on physical flows in the economy. Hence, PIOTs prove to be a viable tool for developing a holistic approach to manage disrupted biogeochemical cycles, since these provide a detailed insight into physical flows in economic systems and allow physical coupling with ecological N flows.
Shweta Singh; Jana E. Compton; Troy R. Hawkins; Daniel J. Sobota; Ellen J. Cooter. A Nitrogen Physical Input-Output Table (PIOT) model for Illinois. Ecological Modelling 2017, 360, 194 -203.
AMA StyleShweta Singh, Jana E. Compton, Troy R. Hawkins, Daniel J. Sobota, Ellen J. Cooter. A Nitrogen Physical Input-Output Table (PIOT) model for Illinois. Ecological Modelling. 2017; 360 ():194-203.
Chicago/Turabian StyleShweta Singh; Jana E. Compton; Troy R. Hawkins; Daniel J. Sobota; Ellen J. Cooter. 2017. "A Nitrogen Physical Input-Output Table (PIOT) model for Illinois." Ecological Modelling 360, no. : 194-203.
Methodology is developed for linking the urban metabolism (UM) to global environmental stresses on the carbon (C) cycle, nitrogen (N) cycle, and biodiversity loss. UM variables are systematically mapped to the drivers of carbon, nitrogen, and biodiversity impacts. Change in mean species abundance is used as metric of biodiversity loss, by adopting the dose‐response relationships from the GLOBIO model. The main biodiversity drivers related to UM included here are land‐use change (LUC) and atmospheric N deposition. The methodology is demonstrated by studying the nexus for Shanghai in 2006, based on energy and soybean consumption. Results for Shanghai show a strong nexus between C, N, and biodiversity impact due to electricity consumption and energy used in manufacturing industries and construction. Prioritization of the shift away from coal energy will therefore lead to lowering the urban growth impact on all three dimensions. Road transportation, domestic aviation, and the metal industry impact only the C footprint highly, whereas district energy impacts only biodiversity loss highly, showing a weak nexus. Among the global impacts of soybean consumption in Shanghai on biodiversity loss (due to LUC only), the highest impact occurs in Uruguay (0.52%) followed by Brazil (0.05%) and Argentina (0.02%). The local impact on biodiversity loss (i.e., within China) of soybean consumption in Shanghai is 1.03%. However, the methodology and results are limited due to the partial inclusion of drivers, a carbon footprint based on carbon dioxide emissions only, and limitations of biodiversity loss models. Potential to overcome methodological limitations is discussed.
Shweta Singh; Christopher Kennedy. The Nexus of Carbon, Nitrogen, and Biodiversity Impacts from Urban Metabolism. Journal of Industrial Ecology 2017, 22, 853 -867.
AMA StyleShweta Singh, Christopher Kennedy. The Nexus of Carbon, Nitrogen, and Biodiversity Impacts from Urban Metabolism. Journal of Industrial Ecology. 2017; 22 (4):853-867.
Chicago/Turabian StyleShweta Singh; Christopher Kennedy. 2017. "The Nexus of Carbon, Nitrogen, and Biodiversity Impacts from Urban Metabolism." Journal of Industrial Ecology 22, no. 4: 853-867.
This paper develops a tool for estimating energy-related CO2 emissions from the world's cities based on regression models. The models are developed considering climatic (heating-degree-days) and urban design (land area per person) independent variables. The tool is applied on 3646 urban areas for estimating impacts on urban emissions of a) global transitioning to Electric Vehicles, b) urban density change and c) IPCC climate change scenarios. Results show that urban density decline can lead to significant increase in energy emissions (upto 346% in electricity & 428% in transportation at 2% density decline by 2050). Among the IPCC climate scenarios tested, A1B is the most effective in reducing growth of emissions (upto 12% in electricity & 35% in heating). The tool can further be improved by including more data in the regression models along with inclusion of other relevant emissions and climatic variables.
Shweta Singh; Chris Kennedy. Estimating future energy use and CO2 emissions of the world's cities. Environmental Pollution 2015, 203, 271 -278.
AMA StyleShweta Singh, Chris Kennedy. Estimating future energy use and CO2 emissions of the world's cities. Environmental Pollution. 2015; 203 ():271-278.
Chicago/Turabian StyleShweta Singh; Chris Kennedy. 2015. "Estimating future energy use and CO2 emissions of the world's cities." Environmental Pollution 203, no. : 271-278.
Shweta Singh; Bhavik R. Bakshi. Footprints of carbon and nitrogen: Revisiting the paradigm and exploring their nexus for decision making. Ecological Indicators 2015, 53, 49 -60.
AMA StyleShweta Singh, Bhavik R. Bakshi. Footprints of carbon and nitrogen: Revisiting the paradigm and exploring their nexus for decision making. Ecological Indicators. 2015; 53 ():49-60.
Chicago/Turabian StyleShweta Singh; Bhavik R. Bakshi. 2015. "Footprints of carbon and nitrogen: Revisiting the paradigm and exploring their nexus for decision making." Ecological Indicators 53, no. : 49-60.
Shweta Singh; Erin L. Gibbemeyer; Bhavik R. Bakshi. N footprint and the nexus between C and N footprints. Assessing and Measuring Environmental Impact and Sustainability 2015, 195 -220.
AMA StyleShweta Singh, Erin L. Gibbemeyer, Bhavik R. Bakshi. N footprint and the nexus between C and N footprints. Assessing and Measuring Environmental Impact and Sustainability. 2015; ():195-220.
Chicago/Turabian StyleShweta Singh; Erin L. Gibbemeyer; Bhavik R. Bakshi. 2015. "N footprint and the nexus between C and N footprints." Assessing and Measuring Environmental Impact and Sustainability , no. : 195-220.
Bhavik R. Bakshi; Umberto Berardi; Thomas Brecheisen; Heriberto Cabezas; Siwanat Chairakwongsa; Lidija Čuček; Richard C. Darton; Luca De Benedetto; Urmila Diwekar; Tarsha Eason; Rafiqul Gani; Carina L. Gargalo; Erin L. Gibbemeyer; Alejandra González-Mejía; Arjen Y. Hoekstra; Boon Hooi Hong; Bing Shen How; Vikas Khanna; Jiří Jaromír Klemeš; Zdravko Kravanja; Hon Loong Lam; Zainuddin Abdul Manan; Rajib Mukherjee; Michael Narodoslawsky; Alberto Quaglia; Debalina Sengupta; Subhas K. Sikdar; Gürkan Sin; Shweta Singh; Thomas Theis; Leisha Vance; Sharifah Rafidah Wan Alwi; George G. Zaimes. Contributors. Assessing and Measuring Environmental Impact and Sustainability 2015, 1 .
AMA StyleBhavik R. Bakshi, Umberto Berardi, Thomas Brecheisen, Heriberto Cabezas, Siwanat Chairakwongsa, Lidija Čuček, Richard C. Darton, Luca De Benedetto, Urmila Diwekar, Tarsha Eason, Rafiqul Gani, Carina L. Gargalo, Erin L. Gibbemeyer, Alejandra González-Mejía, Arjen Y. Hoekstra, Boon Hooi Hong, Bing Shen How, Vikas Khanna, Jiří Jaromír Klemeš, Zdravko Kravanja, Hon Loong Lam, Zainuddin Abdul Manan, Rajib Mukherjee, Michael Narodoslawsky, Alberto Quaglia, Debalina Sengupta, Subhas K. Sikdar, Gürkan Sin, Shweta Singh, Thomas Theis, Leisha Vance, Sharifah Rafidah Wan Alwi, George G. Zaimes. Contributors. Assessing and Measuring Environmental Impact and Sustainability. 2015; ():1.
Chicago/Turabian StyleBhavik R. Bakshi; Umberto Berardi; Thomas Brecheisen; Heriberto Cabezas; Siwanat Chairakwongsa; Lidija Čuček; Richard C. Darton; Luca De Benedetto; Urmila Diwekar; Tarsha Eason; Rafiqul Gani; Carina L. Gargalo; Erin L. Gibbemeyer; Alejandra González-Mejía; Arjen Y. Hoekstra; Boon Hooi Hong; Bing Shen How; Vikas Khanna; Jiří Jaromír Klemeš; Zdravko Kravanja; Hon Loong Lam; Zainuddin Abdul Manan; Rajib Mukherjee; Michael Narodoslawsky; Alberto Quaglia; Debalina Sengupta; Subhas K. Sikdar; Gürkan Sin; Shweta Singh; Thomas Theis; Leisha Vance; Sharifah Rafidah Wan Alwi; George G. Zaimes. 2015. "Contributors." Assessing and Measuring Environmental Impact and Sustainability , no. : 1.
Nitrogen is indispensable for sustaining human activities through its role in the production of food, animal feed, and synthetic chemicals. This has encouraged significant anthropogenic mobilization of reactive nitrogen and its emissions into the environment resulting in severe disruption of the nitrogen cycle. This paper incorporates the biogeochemical cycle of nitrogen into the 2002 input-output model of the U.S. economy. Due to the complexity of this cycle, this work proposes a unique classification of nitrogen flows to facilitate understanding of the interaction between economic activities and various flows in the nitrogen cycle. The classification scheme distinguishes between the mobilization of inert nitrogen into its reactive form, use of nitrogen in various products, and nitrogen losses to the environment. The resulting inventory and model of the US economy can help quantify the direct and indirect impacts or dependence of economic sectors on the nitrogen cycle. This paper emphasizes the need for methods to manage the N cycle that focus not just on N losses, which has been the norm until now, but also include other N flows for a more comprehensive view and balanced decisions. Insight into the N profile of various sectors of the 2002 U.S. economy is presented, and the inventory can also be used for LCA or Hybrid LCA of various products. The resulting model is incorporated in the approach of Ecologically-Based LCA and available online.
Shweta Singh; Bhavik R. Bakshi. Accounting for the Biogeochemical Cycle of Nitrogen in Input-Output Life Cycle Assessment. Environmental Science & Technology 2013, 47, 9388 -9396.
AMA StyleShweta Singh, Bhavik R. Bakshi. Accounting for the Biogeochemical Cycle of Nitrogen in Input-Output Life Cycle Assessment. Environmental Science & Technology. 2013; 47 (16):9388-9396.
Chicago/Turabian StyleShweta Singh; Bhavik R. Bakshi. 2013. "Accounting for the Biogeochemical Cycle of Nitrogen in Input-Output Life Cycle Assessment." Environmental Science & Technology 47, no. 16: 9388-9396.