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It is now recognized that analytical chemistry must also be a target for green principles, in particular chromatographic methods which typically use relatively large volumes of hazardous organic solvents. More generally, high performance liquid chromatography (HPLC) is employed routinely for quality control of complex mixtures in various industries. Acetonitrile and methanol are the most commonly used organic solvents in HPLC, but they generate an impact on the environment and can have a negative effect on the health of analysts. Ethanol offers an exciting alternative as a less toxic, biodegradable solvent for HPLC. In this work we demonstrate that replacement of acetonitrile with ethanol as the organic modifier for HPLC can be achieved without significantly compromising analytical performance. This general approach is demonstrated through the specific example analysis of a complex plant extract. A benchmark method employing acetonitrile for the analysis of Bidens pilosa extract was statistically optimized using the Green Chromatographic Fingerprinting Response (GCFR) which includes factors relating to separation performance and environmental parameters. Methods employing ethanol at 30 and 80°C were developed and compared with the reference method regarding their performance of separation (GCFR) as well as by a new metric, Comprehensive Metric to Compare Liquid Chromatography Methods (CM). The fingerprint with ethanol at 80°C was similar to or better than that with MeCN according to GCFR and CM. This demonstrates that temperature may be used to replace harmful solvents with greener ones in HPLC, including for solvents with significantly different physiochemical properties and without loss in separation performance. This work offers a general approach for the chromatographic analysis of complex samples without compromising green analytical chemistry principles.

It is now recognized that analytical chemistry must also be a target for green principles, in particular chromatographic methods which typically use relatively large volumes of hazardous organic solvents. More generally, high performance liquid chromatography (HPLC) is employed routinely for quality control of complex mixtures in various industries. Acetonitrile and methanol are the most commonly used organic solvents in HPLC, but they generate an impact on the environment and can have a negative effect on the health of analysts. Ethanol offers an exciting alternative as a less toxic, biodegradable solvent for HPLC. In this work we demonstrate that replacement of acetonitrile with ethanol as the organic modifier for HPLC can be achieved without significantly compromising analytical performance. This general approach is demonstrated through the specific example analysis of a complex plant extract. A benchmark method employing acetonitrile for the analysis of Bidens pilosa extract was statistically optimized using the Green Chromatographic Fingerprinting Response (GCFR) which includes factors relating to separation performance and environmental parameters. Methods employing ethanol at 30 and 80°C were developed and compared with the reference method regarding their performance of separation (GCFR) as well as by a new metric, Comprehensive Metric to Compare Liquid Chromatography Methods (CM). The fingerprint with ethanol at 80°C was similar to or better than that with MeCN according to GCFR and CM. This demonstrates that temperature may be used to replace harmful solvents with greener ones in HPLC, including for solvents with significantly different physiochemical properties and without loss in separation performance. This work offers a general approach for the chromatographic analysis of complex samples without compromising green analytical chemistry principles.

Co-generation of energy derived from human movement is not new. Intentionally accumulating energy, from mass urban-mobility, provides opportunities to re-purpose power. However, when mass-mobility is predictable, yet not harnessed, this highlights critical gaps in application of interdisciplinary knowledge. This research highlights a novel application of geostatistical modelling for the built environment with the purpose of understanding where energy harvesting infrastructure should be located. The work presented argues that advanced Geostatistical methods can be implemented as an appropriate method to predict probability distribution, density, clustering of populations and mass-population mobility patterns from large-scale online distributed and heterogeneous data sets published by the Australian Urban Research Infrastructure Network. Where clear urban spatio-behavioural relationships of density and movement can be predicted – understanding such patterns supports cross-disciplinary city planning and decision-making. A data-informed – predictive spatial decision-making framework is proposed – facilitating the endeavour of cogenerating kinetic human energy within a prescribed space. This novel proposition could further sustainability strategies for compact living for cities such as in Perth, Western Australia which is increasingly economically and geographically pressured to densify. This research argues that surveillance data elucidate a capacity to interpret and understand impacts of densification strategies, efficacy of CCTV networks in existing and emerging cities.

Co-generation of energy derived from human movement is not new. Intentionally accumulating energy, from mass urban-mobility, provides opportunities to re-purpose power. However, when mass-mobility is predictable, yet not harnessed, this highlights critical gaps in application of interdisciplinary knowledge. This research highlights a novel application of geostatistical modelling for the built environment with the purpose of understanding where energy harvesting infrastructure should be located. The work presented argues that advanced Geostatistical methods can be implemented as an appropriate method to predict probability distribution, density, clustering of populations and mass-population mobility patterns from large-scale online distributed and heterogeneous data sets published by the Australian Urban Research Infrastructure Network. Where clear urban spatio-behavioural relationships of density and movement can be predicted – understanding such patterns supports cross-disciplinary city planning and decision-making. A data-informed – predictive spatial decision-making framework is proposed – facilitating the endeavour of cogenerating kinetic human energy within a prescribed space. This novel proposition could further sustainability strategies for compact living for cities such as in Perth, Western Australia which is increasingly economically and geographically pressured to densify. This research argues that surveillance data elucidate a capacity to interpret and understand impacts of densification strategies, efficacy of CCTV networks in existing and emerging cities.

SummaryEnvironmentally extended multiregional input‐output (EE MRIO) tables have emerged as a key framework to provide a comprehensive description of the global economy and analyze its effects on the environment. Of the available EE MRIO databases, EXIOBASE stands out as a database compatible with the System of Environmental‐Economic Accounting (SEEA) with a high sectorial detail matched with multiple social and environmental satellite accounts. In this paper, we present the latest developments realized with EXIOBASE 3—a time series of EE MRIO tables ranging from 1995 to 2011 for 44 countries (28 EU member plus 16 major economies) and five rest of the world regions. EXIOBASE 3 builds upon the previous versions of EXIOBASE by using rectangular supply‐use tables (SUTs) in a 163 industry by 200 products classification as the main building blocks. In order to capture structural changes, economic developments, as reported by national statistical agencies, were imposed on the available, disaggregated SUTs from EXIOBASE 2. These initial estimates were further refined by incorporating detailed data on energy, agricultural production, resource extraction, and bilateral trade. EXIOBASE 3 inherits the high level of environmental stressor detail from its precursor, with further improvement in the level of detail for resource extraction. To account for the expansion of the European Union (EU), EXIOBASE 3 was developed with the full EU28 country set (including the new member state Croatia). EXIOBASE 3 provides a unique tool for analyzing the dynamics of environmental pressures of economic activities over time.

SummaryEnvironmentally extended multiregional input‐output (EE MRIO) tables have emerged as a key framework to provide a comprehensive description of the global economy and analyze its effects on the environment. Of the available EE MRIO databases, EXIOBASE stands out as a database compatible with the System of Environmental‐Economic Accounting (SEEA) with a high sectorial detail matched with multiple social and environmental satellite accounts. In this paper, we present the latest developments realized with EXIOBASE 3—a time series of EE MRIO tables ranging from 1995 to 2011 for 44 countries (28 EU member plus 16 major economies) and five rest of the world regions. EXIOBASE 3 builds upon the previous versions of EXIOBASE by using rectangular supply‐use tables (SUTs) in a 163 industry by 200 products classification as the main building blocks. In order to capture structural changes, economic developments, as reported by national statistical agencies, were imposed on the available, disaggregated SUTs from EXIOBASE 2. These initial estimates were further refined by incorporating detailed data on energy, agricultural production, resource extraction, and bilateral trade. EXIOBASE 3 inherits the high level of environmental stressor detail from its precursor, with further improvement in the level of detail for resource extraction. To account for the expansion of the European Union (EU), EXIOBASE 3 was developed with the full EU28 country set (including the new member state Croatia). EXIOBASE 3 provides a unique tool for analyzing the dynamics of environmental pressures of economic activities over time.