Development of multi-functional streetscape green infrastructure using a performance index approach

This paper presents a performance evaluation framework for streetscape vegetation. A performance index (PI) is conceived using the following seven traits, specific to the street environments – Pollution Flux Potential (PFP), Carbon Sequestration Potential (CSP), Thermal Comfort Potential (TCP), Noise Attenuation Potential (NAP), Biomass Energy Potential (BEP), Environmental Stress Tolerance (EST) and Crown Projection Factor (CPF). Its application is demonstrated through a case study using fifteen street vegetation species from the UK, utilising a combination of direct field measurements and inventoried literature data. Our results indicate greater preference to small-to-medium size trees and evergreen shrubs over larger trees for streetscaping. The proposed PI approach can be potentially applied two-fold: one, for evaluation of the performance of the existing street vegetation, facilitating the prospects for further improving them through management strategies and better species selection; two, for planning new streetscapes and multi-functional biomass as part of extending the green urban infrastructure

Economic sustainability modeling provides decision support for assessing hybrid poplar-based biofuel development in California

Biofuels are expected to play a major role in meeting California's long-term energy needs, but many factors influence the commercial viability of the various feedstock and production technology options. We developed a spatially explicit analytic framework that integrates models of plant growth, crop adoption, feedstock location, transportation logistics, economic impact, biorefinery costs and biorefinery energy use and emissions. We used this framework to assess the economic potential of hybrid poplar as a feedstock for jet fuel production in Northern California. Results suggest that the region has sufficient suitable croplands (2.3 million acres) and nonarable lands (1.5 million acres) for poplar cultivation to produce as much as 2.26 billion gallons of jet fuel annually. However, there are major obstacles to such large-scale production, including, on nonarable lands, low poplar yields and broad spatial distribution and, on croplands, competition with existing crops. We estimated the production cost of jet fuel to be $4.40 to$5.40 per gallon for poplar biomass grown on nonarable lands and $3.60 to$4.50 per gallon for biomass grown on irrigated cropland; the current market price is $2.12 per gallon. Improved poplar yields, use of supplementary feedstocks at the biorefinery and economic supports such as carbon credits could help to overcome these barriers

Hydrocarbon Bio-Jet Fuel from Bioconversion of Poplar Biomass: Life Cycle Assessment of Site-Specific Impacts

Hydrocarbon drop-in bio-jet fuels could help to reduce greenhouse gas emissions within the aviation sector. Large tracts of land will be required to grow biomass feedstock for this biofuel, and changes to the management of these lands could have substantial environmental impacts. This research uses spatial analysis and life cycle assessment methodologies to investigate potential environmental impacts associated with converting land to grow poplar trees for conversion to bio-jet fuel from different regions within the western United States. Results vary by region and are dependent on land availability, type of land converted, prior land management practices, and poplar growth yields. The conversion of intensively managed cropland to poplar production results in a decrease in fertilizer and a lower annual global warming potential (GWP) (Clarksburg CA region). Bringing unmanaged rangeland into production results in increases in fertilizers, chemical inputs, fuel use, and GWP (Jefferson OR region). Where poplar yields are predicted to be lower, more land is converted to growing poplar to meet feedstock demands (Hayden ID). An increased use of land leads to greater fuel use and GWP. Changes to land use and management practices will drive changes at the local level that need to be understood before developing a drop-in biofuels industry

Isolation and Characterization of a Trace Level Unknown Impurity of Salmeterol by Chromatographic and Spectroscopic Methods

During the process development of Salmeterol, an unknown impurity was detected at 2.08 Relative Retention Time (RRT) at a level of 0.11% by a gradient Reverse Phase-High Performance Liquid Chromatography (RP-HPLC). The impurity was isolated from the salmeterol drug substance using preparative HPLC. The separation was achieved with an Inertsil C8 column, using acetonitrile–trifluroacetic acid buffer pH 2.7 as mobile phase. The isolated impurity was characterized by NMR and MS techniques. The impurity has been characterized as 4-(2-{[6-(4-cyclohexylbutoxy)hexyl]amino}-1-hydroxyethyl)- 2-(hydroxymethyl)phenol. The synthesis of the impurity and its formation was also discussed

Hybrid Poplar based Biorefinery Siting Web Application (HP-BiSWA): An online decision support application for siting hybrid poplar based biorefineries

Hybrid poplar has potential as feedstock for the production of bioenergy and bio-based products. Planning effective placement of hybrid poplar based biorefinery facilities takes tremendous time and financial resources. A hybrid poplar based biorefinery siting application (HP-BiSWA), a web-enabled refinery siting application, was developed so that potential stakeholders may quickly assess available resources at selected locations and provide information relating to the economic competitiveness and financial risks associated with construction and operation. At present, the tool supports evaluation of the potential for hybrid poplar based jet fuel and acetic acid production based on user-specified conditions. The application is generally expandable to other feedstock, technology, and product types. HP-BiSWA uses various modules (i.e. 3PG-crop growth model and farm budget application) and services (i.e. parcel service, transportation routing service, crop service, soil and weather services) to retrieve and estimate information to optimize and select potential parcels for poplar cultivation, and ultimately determine net revenue for biofuel production under selected decision options.To demonstrate the utility of HP-BiSWA, a case study analysis was performed for Centralia, WA based on a 380 ML/yr (100 MGY) jet fuel biorefinery using two scenarios: (1) a 175 km feedstock supply radius with 50% pastureland use, and (2) a 225 km feedstock supply radius with 25% pastureland use. The case study captures the interactive effects on biorefinery performance with changes in feedstock supply area and available water, power, and other resources for operation of the biorefinery

Hybrid Poplar based Biorefinery Siting Web Application (HP-BiSWA): An online decision support application for siting hybrid poplar based biorefineries

Hybrid poplar has potential as feedstock for the production of bioenergy and bio-based products. Planning effective placement of hybrid poplar based biorefinery facilities takes tremendous time and financial resources. A hybrid poplar based biorefinery siting application (HP-BiSWA), a web-enabled refinery siting application, was developed so that potential stakeholders may quickly assess available resources at selected locations and provide information relating to the economic competitiveness and financial risks associated with construction and operation. At present, the tool supports evaluation of the potential for hybrid poplar based jet fuel and acetic acid production based on user-specified conditions. The application is generally expandable to other feedstock, technology, and product types. HP-BiSWA uses various modules (i.e. 3PG-crop growth model and farm budget application) and services (i.e. parcel service, transportation routing service, crop service, soil and weather services) to retrieve and estimate information to optimize and select potential parcels for poplar cultivation, and ultimately determine net revenue for biofuel production under selected decision options.To demonstrate the utility of HP-BiSWA, a case study analysis was performed for Centralia, WA based on a 380 ML/yr (100 MGY) jet fuel biorefinery using two scenarios: (1) a 175 km feedstock supply radius with 50% pastureland use, and (2) a 225 km feedstock supply radius with 25% pastureland use. The case study captures the interactive effects on biorefinery performance with changes in feedstock supply area and available water, power, and other resources for operation of the biorefinery

Design of a GIS-Based Web Application for Simulating Biofuel Feedstock Yields

Short rotation woody crops (SRWC), such as hybrid poplar, have the potential to serve as a valuable feedstock for cellulosic biofuels. Spatial estimates of biomass yields under different management regimes are required for assisting stakeholders in making better management decisions and to establish viable woody cropping systems for biofuel production. To support stakeholders in their management decisions, we have developed a GIS-based web interface using a modified 3PG model for spatially predicting poplar biomass yields under different management and climate conditions in the U.S. Pacific Northwest region. The application is implemented with standard HTML5 components, allowing its use in a modern browser and dynamically adjusting to the client screen size and device. In addition, cloud storage of the results makes them accessible on any Internet-enabled device. The web interface appears simple, but is powerful in parameter manipulation and in visualizing and sharing the results. Overall, this application comprises dynamic features that enable users to run SRWC crop growth simulations based on GIS information and contributes significantly to choosing appropriate feedstock growing locations, anticipating the desired physiological properties of the feedstock and incorporating the management and policy analysis needed for growing hybrid poplar plantations

Evaluating Optical Remote Sensing Methods for Estimating Leaf Area Index for Corn and Soybean

The leaf area index (LAI) is a key crop biophysical variable influencing many vegetation processes. Spatial LAI estimates are essential to develop and improve spatial modeling tools to monitor vegetation conditions at large regional scales. Numerous optical remote sensing methods have been explored to retrieve crop-specific LAI at a regional scale using satellite observations. However, a major challenge is selecting a method that performance well under various conditions without local scale calibration. As such, we assessed the performance of existing statistical and physical approaches, developed based on parametric, non-parametric and radiative transfer model (RTM)-look-up-table based inversion, using field observations from two geographically distant locations and Landsat 5, 7, and 8 satellite observations. These methods were implemented for corn and soybeans cultivated at two locations in the U.S (i.e., Mead, Nebraska, and Bushland, Texas). The evaluation metrics (i.e., Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R2)) were used to study the performance of each method, and then the methods were ranked based on these metrics. Our study showed that overall parametric methods outperformed other methods. The RMSE (MAE) for the top five methods was less than 1.3 (0.95) for corn and 1.0 (0.8) for soybeans, irrespective of location. Even though they outperformed, parametric methods exhibited inconsistency in their performance. For instance, the SR_CA_cross method ranked 1 for corn, however, it performed poorly for soybean (ranked 15). The non-parametric methods showed moderate accuracy partly due to the availability of a smaller number of observations for training. The RTM-LUT inversion physical-based approach was found to perform reasonably well RMSE (MAE) less than 1.5 (1.0) consistently irrespective of location and crop, implying that this approach is more suitable for regional-scale LAI estimation. The results of this study highlighted the drawbacks and advantages of available optical remote sensing approaches to estimate LAI for corn and soybean crops using Landsat imagery. These results are of interest for remote sensing and modeling communities developing spatial-scale approaches to model and monitor agricultural vegetation

Higher US crop prices trigger little area expansion so marginal land for biofuel crops is limited

By expanding energy biomass production on marginal lands that are not currently used for crops, food prices increase and indirect climate change effects can be mitigated. Studies of the availability of marginal lands for dedicated bioenergy crops have focused on biophysical land traits, ignoring the human role in decisions to convert marginal land to bioenergy crops. Recent history offers insights about farmer willingness to put non-crop land into crop production. The 2006-09 leap in field crop prices and the attendant 64% gain in typical profitability led to only a 2% increase in crop planted area, mostly in the prairie states. At this rate, a doubling of expected profitability from biomass crops would expand cropland supply by only 3.2%. Yet targets for cellulosic ethanol production in the US Energy Independence and Security Act imply boosting US planted area by 10% or more with perennial biomass crops. Given landowner reluctance to expand crop area with familiar crops in the short run, large scale expansion of the area in dedicated bioenergy crops will likely be difficult and costly to achieve.Marginal land Cellulosic ethanol Supply elasticity

Evaluating Optical Remote Sensing Methods for Estimating Leaf Area Index for Corn and Soybean

The leaf area index (LAI) is a key crop biophysical variable influencing many vegetation processes. Spatial LAI estimates are essential to develop and improve spatial modeling tools to monitor vegetation conditions at large regional scales. Numerous optical remote sensing methods have been explored to retrieve crop-specific LAI at a regional scale using satellite observations. However, a major challenge is selecting a method that performance well under various conditions without local scale calibration. As such, we assessed the performance of existing statistical and physical approaches, developed based on parametric, non-parametric and radiative transfer model (RTM)-look-up-table based inversion, using field observations from two geographically distant locations and Landsat 5, 7, and 8 satellite observations. These methods were implemented for corn and soybeans cultivated at two locations in the U.S (i.e., Mead, Nebraska, and Bushland, Texas). The evaluation metrics (i.e., Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R2)) were used to study the performance of each method, and then the methods were ranked based on these metrics. Our study showed that overall parametric methods outperformed other methods. The RMSE (MAE) for the top five methods was less than 1.3 (0.95) for corn and 1.0 (0.8) for soybeans, irrespective of location. Even though they outperformed, parametric methods exhibited inconsistency in their performance. For instance, the SR_CA_cross method ranked 1 for corn, however, it performed poorly for soybean (ranked 15). The non-parametric methods showed moderate accuracy partly due to the availability of a smaller number of observations for training. The RTM-LUT inversion physical-based approach was found to perform reasonably well RMSE (MAE) less than 1.5 (1.0) consistently irrespective of location and crop, implying that this approach is more suitable for regional-scale LAI estimation. The results of this study highlighted the drawbacks and advantages of available optical remote sensing approaches to estimate LAI for corn and soybean crops using Landsat imagery. These results are of interest for remote sensing and modeling communities developing spatial-scale approaches to model and monitor agricultural vegetation