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The SBE19 Brussels - BAMB-CIRCPATH "Building as Material Banks - A Pathway for a Circular held in Brussels on 5 to 7 of February 2019, is an initiative of the Consortium of the H2020 BAMB Project together with the Sustainable Built Environment (SBE) series of conferences. Being within the SBE series, this event gathers the support of CIB International Council for Research and Innovation in Building and Construction, iiSBE International Initiative for a Sustainable Built Environment, the United Nations Environment Programme, and FIDIC International Federation of Consulting Engineers. The goal of this series of regional and international conferences is to disseminate innovative policies and developments in the field of sustainable urban environment to a broad international audience of specialists in policy, design, construction and operation of buildings and related infrastructure. info:eu-repo/semantics/publishedVersion

The SBE19 Brussels - BAMB-CIRCPATH "Building as Material Banks - A Pathway for a Circular held in Brussels on 5 to 7 of February 2019, is an initiative of the Consortium of the H2020 BAMB Project together with the Sustainable Built Environment (SBE) series of conferences. Being within the SBE series, this event gathers the support of CIB International Council for Research and Innovation in Building and Construction, iiSBE International Initiative for a Sustainable Built Environment, the United Nations Environment Programme, and FIDIC International Federation of Consulting Engineers. The goal of this series of regional and international conferences is to disseminate innovative policies and developments in the field of sustainable urban environment to a broad international audience of specialists in policy, design, construction and operation of buildings and related infrastructure. info:eu-repo/semantics/publishedVersion

ABSTRACTThe California generation fleet manages the existing variability and uncertainty in the demand for electric power (load). When wind power is added, the dispatchable generators manage the variability and uncertainty of the net load (load minus wind power). The variability and uncertainty of the load and the net load are compared when 8790 MW of wind power are added to the California power system, a level expected when California achieves its 33% renewable portfolio standard, using a data set of 26,296 h of synchronous historic load and modeled historic wind power output. Variability was calculated as the rate of change in power generated by wind farms or consumed by the load from 1 h to the next (MW/h). Uncertainty was calculated as the 1 h ahead forecast error [MW] of the wind power or of the load. The data show that wind power adds no additional variability than is already present in the load variability. However, wind power adds additional uncertainty through increased forecast errors in the net load compared with the load. Forecast errors in the net load increase 18.7% for negative forecast errors (actual less than forecast) and 5.4% for positive forecast errors (actual greater than forecast). The increase in negative forecast errors occurs only during the afternoon hours when negative load forecasts and positive wind forecasts are strongly correlated. Managing the integration of wind power in the California power system should focus on reducing wind power forecast uncertainty for wind ramp ups during the afternoon hours. Copyright © 2013 John Wiley & Sons, Ltd.

ABSTRACTThe California generation fleet manages the existing variability and uncertainty in the demand for electric power (load). When wind power is added, the dispatchable generators manage the variability and uncertainty of the net load (load minus wind power). The variability and uncertainty of the load and the net load are compared when 8790 MW of wind power are added to the California power system, a level expected when California achieves its 33% renewable portfolio standard, using a data set of 26,296 h of synchronous historic load and modeled historic wind power output. Variability was calculated as the rate of change in power generated by wind farms or consumed by the load from 1 h to the next (MW/h). Uncertainty was calculated as the 1 h ahead forecast error [MW] of the wind power or of the load. The data show that wind power adds no additional variability than is already present in the load variability. However, wind power adds additional uncertainty through increased forecast errors in the net load compared with the load. Forecast errors in the net load increase 18.7% for negative forecast errors (actual less than forecast) and 5.4% for positive forecast errors (actual greater than forecast). The increase in negative forecast errors occurs only during the afternoon hours when negative load forecasts and positive wind forecasts are strongly correlated. Managing the integration of wind power in the California power system should focus on reducing wind power forecast uncertainty for wind ramp ups during the afternoon hours. Copyright © 2013 John Wiley & Sons, Ltd.

SESAMe v3.0, a spatially explicit multimedia fate model with 50 × 50 km(2) resolution, has been developed for China to predict environmental concentrations of benzo[a]pyrene (BaP) using an atmospheric emission inventory for 2007. Model predictions are compared with environmental monitoring data obtained from an extensive review of the literature. The model performs well in predicting multimedia concentrations and distributions. Predicted concentrations are compared with guideline values; highest values with some exceedances occur mainly in the North China Plain, Mid Inner Mongolia, and parts of three northeast provinces, Xi'an, Shanghai, and south of Jiangsu province, East Sichuan Basin, middle of Guizhou and Guangzhou. Two potential future scenarios have been assessed using SESAMe v3.0 for 2030 as BaP emission is reduced by (1) technological improvement for coal consumption in energy production and industry sectors in Scenario 1 (Sc1) and (2) technological improvement and control of indoor biomass burning for cooking and indoor space heating and prohibition of open burning of biomass in 2030 in Scenario 2 (Sc2). Sc2 is more efficient in reducing the areas with exceedance of guideline values. Use of SESAMe v3.0 provides insights on future research needs and can inform decision making on options for source reduction.

SESAMe v3.0, a spatially explicit multimedia fate model with 50 × 50 km(2) resolution, has been developed for China to predict environmental concentrations of benzo[a]pyrene (BaP) using an atmospheric emission inventory for 2007. Model predictions are compared with environmental monitoring data obtained from an extensive review of the literature. The model performs well in predicting multimedia concentrations and distributions. Predicted concentrations are compared with guideline values; highest values with some exceedances occur mainly in the North China Plain, Mid Inner Mongolia, and parts of three northeast provinces, Xi'an, Shanghai, and south of Jiangsu province, East Sichuan Basin, middle of Guizhou and Guangzhou. Two potential future scenarios have been assessed using SESAMe v3.0 for 2030 as BaP emission is reduced by (1) technological improvement for coal consumption in energy production and industry sectors in Scenario 1 (Sc1) and (2) technological improvement and control of indoor biomass burning for cooking and indoor space heating and prohibition of open burning of biomass in 2030 in Scenario 2 (Sc2). Sc2 is more efficient in reducing the areas with exceedance of guideline values. Use of SESAMe v3.0 provides insights on future research needs and can inform decision making on options for source reduction.

Исследование ландшафтов всегда было традиционным научным направлением географии. В России подобная направленность исследований остаётся актуальной, несмотря на то, что термины «геоэкология» и «ландшафтная экология» сегодня более распространены в англоязычном научном сообществе. Наш краткий обзор показывает значительное ускорение антропогенных ландшафтных изменений в Европе, Центральной Азии и азиатской части России за последние пять десятилетий. Ландшафтные исследования в антропоцене должны быть направлены на достижение и сохранение устойчивости ландшафта при его высокой производительности, что включает в себя прекращение деградации ландшафтов, развитие культурных и сохранение природных ландшафтов. Чистая вода и чистый воздух, плодородные и здоровые почвы для производства продуктов питания и других экосистемных услуг, а также биологически разнообразная зеленая среда являются атрибутами ландшафтов, обеспечивающих выживание и благополучие населения. Дисциплинарные и междисциплинарные исследования должны генерировать знания, инновации и правила принятия действенных решений. Генерация знаний в глобализованном мире основана на сборе больших массивов данных и моделировании сценариев. Международные длительные полевые опыты и системы агроэкологического мониторинга будут предоставлять данные для экосистемных моделей и систем поддержки принимаемых решений. Landscape research has been a traditional scientific discipline of geography. This is still the case in Russia, whilst the terms geo-ecology and landscape ecology have become established in the English speaking scientific community. Our short review reveals huge and accelerating anthropogenic landscape transformations in Europe, Central Asia and Asian Russia since the end the 1960s. Landscape research in the Anthropocene has to focus on achieving landscape sustainability at high productivity. This includes halting landscape degradation, developing cultural landscapes, and maintaining semi-natural landscapes. Clean water and air, fertile and healthy soils for food and other ecosystem services and a green and bio-diverse environment are attributes of landscapes for the survival and well-being of humans. Research has to generate knowledge, innovations and decision rules by disciplinary, interdisciplinary and trans-disciplinary work. Knowledge generation in a globalized world is based on big data gathering and scenario modelling. International long-term experiments and agri-environmental monitoring systems will deliver data for ecosystem models and decision support systems.

Исследование ландшафтов всегда было традиционным научным направлением географии. В России подобная направленность исследований остаётся актуальной, несмотря на то, что термины «геоэкология» и «ландшафтная экология» сегодня более распространены в англоязычном научном сообществе. Наш краткий обзор показывает значительное ускорение антропогенных ландшафтных изменений в Европе, Центральной Азии и азиатской части России за последние пять десятилетий. Ландшафтные исследования в антропоцене должны быть направлены на достижение и сохранение устойчивости ландшафта при его высокой производительности, что включает в себя прекращение деградации ландшафтов, развитие культурных и сохранение природных ландшафтов. Чистая вода и чистый воздух, плодородные и здоровые почвы для производства продуктов питания и других экосистемных услуг, а также биологически разнообразная зеленая среда являются атрибутами ландшафтов, обеспечивающих выживание и благополучие населения. Дисциплинарные и междисциплинарные исследования должны генерировать знания, инновации и правила принятия действенных решений. Генерация знаний в глобализованном мире основана на сборе больших массивов данных и моделировании сценариев. Международные длительные полевые опыты и системы агроэкологического мониторинга будут предоставлять данные для экосистемных моделей и систем поддержки принимаемых решений. Landscape research has been a traditional scientific discipline of geography. This is still the case in Russia, whilst the terms geo-ecology and landscape ecology have become established in the English speaking scientific community. Our short review reveals huge and accelerating anthropogenic landscape transformations in Europe, Central Asia and Asian Russia since the end the 1960s. Landscape research in the Anthropocene has to focus on achieving landscape sustainability at high productivity. This includes halting landscape degradation, developing cultural landscapes, and maintaining semi-natural landscapes. Clean water and air, fertile and healthy soils for food and other ecosystem services and a green and bio-diverse environment are attributes of landscapes for the survival and well-being of humans. Research has to generate knowledge, innovations and decision rules by disciplinary, interdisciplinary and trans-disciplinary work. Knowledge generation in a globalized world is based on big data gathering and scenario modelling. International long-term experiments and agri-environmental monitoring systems will deliver data for ecosystem models and decision support systems.