Multistage scenario-based interval-stochastic programming for planning water resources allocation

In this study, a multistage scenario-based interval-stochastic programming (MSISP) method is developed for water-resources allocation under uncertainty. MSISP improves upon the existing multistage optimization methods with advantages in uncertainty reflection, dynamics facilitation, and risk analysis. It can directly handle uncertainties presented as both interval numbers and probability distributions, and can support the assessment of the reliability of satisfying (or the risk of violating) system constraints within a multistage context. It can also reflect the dynamics of system uncertainties and decision processes under a representative set of scenarios. The developed MSISP method is then applied to a case of water resources management planning within a multi-reservoir system associated with joint probabilities. A range of violation levels for capacity and environment constraints are analyzed under uncertainty. Solutions associated different risk levels of constraint violation have been obtained. They can be used for generating decision alternatives and thus help water managers to identify desired policies under various economic, environmental and system-reliability conditions. Besides, sensitivity analyses demonstrate that the violation of the environmental constraint has a significant effect on the system benefit

Inexact fuzzy-stochastic constraint-softened programming - A case study for waste management

In this study, an inexact fuzzy-stochastic constraint-softened programming method is developed for municipal solid waste (MSW) management under uncertainty, The developed method can deal with multiple uncertainties presented in terms of fuzzy sets, interval values and random variables. Moreover, a number of violation levels for the system constraints are allowed. This is realized through introduction of violation variables to soften system constraints, such that the model's decision space can be expanded under demanding conditions. This can help generate a range of decision alternatives under various conditions, allowing in-depth analyses of tradeoffs among economic objective, satisfaction degree, and constraint-violation risk. The developed method is applied to a case study of planning a MSW management system. The uncertain and dynamic information can be incorporated within a multi-layer scenario tree; revised decisions are permitted in each time period based on the realized values of uncertain events. Solutions associated with different satisfaction degree levels have been generated, corresponding to different constraint-violation risks. They are useful for supporting decisions of waste flow allocation and system-capacity expansion within a multistage context. (C) 2008 Elsevier Ltd. All rights reserved

Interval-Parameter Robust Minimax-regret Programming and Its Application to Energy and Environmental Systems Planning

In this study, an interval-parameter robust minimax-regret programming method is developed and applied to the planning of energy and environmental systems. Methods of robust programming, interval-parameter programming, and minimax-regret analysis are incorporated within a general optimization framework to enhance the robustness of the optimization effort. The interval-parameter robust minimax-regret programming can deal with uncertainties expressed as discrete intervals, fuzzy sets, and random variables. It can also be used for analyzing multiple scenarios associated with different system costs and risk levels. In its solution process, the fuzzy decision space is delimited into a more robust one through dimensional enlargement of the original fuzzy constraints; moreover, an interval-element cost matrix can be transformed into an interval-element regret matrix, such that the decision makers can identify desired alternatives based on the inexact minimax regret criterion. The developed method has been applied to a case study of energy and environmental systems planning under uncertainty. The results indicate that reasonable solutions have been generated

Proteomic responses reveal the differential effects induced by cadmium in mussels Mytilus galloprovincialis at early life stages

Cadmium (Cd) has become an important metal contaminant and posed severe risk on the organisms in the coastal environments of the Bohai Sea. Marine mussel Mytilus galloprovincialis is widely distributed along the Bohai coast and consumed as seafood by local residents. Evidences indicate that the early stages of marine organisms are more sensitive to metal contaminants. In this study, we applied two-dimensional electrophoresis-based proteomics to characterize the biological effects of Cd (50 mu g L-1) in the early life stages (D-shape larval and juvenile) of mussels. The different proteomic responses demonstrated the differential responsive mechanisms to Cd exposure in these two early life stages of mussels. In details, results indicated that Cd mainly induced immune and oxidative stresses in both D-shape larval and juvenile mussels via different pathways. In addition, the significant up-regulation of triosephosphate isomerase and metallothionein confirmed the enhanced energy demand and mobilized detoxification mechanism in D-shape larval mussels exposed to Cd. In juvenile mussels, Cd exposure also induced clear apoptosis. Overall, this work suggests that Cd is a potential immune toxicant to mussel M. galloprovincialis at early life stages. (C) 2016 Elsevier Ltd. All rights reserved

Energy and environmental systems planning under uncertainty-An inexact fuzzy-stochastic programming approach

In this study, an inexact fuzzy-stochastic energy model (IFS-EM) is developed for planning energy and environmental systems (EES) management under multiple uncertainties. In the IFS-EM, methods of interval parameter fuzzy linear programming (IFLP) and multistage stochastic programming with recourse (MSP) are introduced into a mixed-integer linear programming (MILP) framework, such that the developed model can tackle uncertainties described in terms of interval values, fuzzy sets and probability distributions. Moreover, it can reflect dynamic decisions for facility-capacity expansion and energy supply over a multistage context. The developed model is applied to a case of planning regional-scale energy and environmental systems to demonstrate its applicability, where three cases are considered based on different energy and environmental management policies. The results indicate that reasonable solutions have been generated. They are helpful for supporting: (a) adjustment or justification of allocation patterns of regional energy resources and services, (b) formulation of local policies regarding energy consumption, economic development and environmental protection, and (c) in-depth analysis of tradeoffs among system cost, satisfaction degree and environmental requirement under multiple uncertainties. (C) 2010 Elsevier Ltd. All rights reserved

Comparative analyses of the scaling diversity index and its applicability

As well as the newly developed scaling diversity index, there are also eleven traditional diversity indices to be found in the literature. Analyses show that these eleven traditional indices are unable to formulate the richness component of diversity. In particular, the most widely used index, the Shannon-Weiner index, cannot express the evenness component. On the contrary, the scaling diversity index is able to formulate both the richness aspect and the evenness aspect of diversity. The scaling diversity index has been applied to developing scenarios of ecological diversity at different spatial resolutions and spatial scales. A case study in Fukang in the Xinjiang Uygur Autonomous Region in China shows that the scaling diversity index is sensitive to spatial resolution and is easy to understand. It is scientifically sound and could be operated at affordable cost

A two-stage inexact-stochastic programming model for planning carbon dioxide emission trading under uncertainty

In this study, a two-stage inexact-stochastic programming (TISP) method is developed for planning carbon dioxide (CO2) emission trading under uncertainty. The developed TISP incorporates techniques of interval-parameter programming (IPP) and two-stage stochastic programming (TSP) within a general optimization framework. The TISP can not only tackle uncertainties expressed as probabilistic distributions and discrete intervals, but also provide an effective linkage between the pre-regulated greenhouse gas (GHG) management policies and the associated economic implications. The developed method is applied to a case study of energy systems and CO2 emission trading planning under uncertainty. The results indicate that reasonable solutions have been generated. They can be used for generating decision alternatives and thus help decision makers identify desired GHG abatement policies under various economic and system-reliability constraints. (C) 2009 Elsevier Ltd. All rights reserved

Groundwater fluctuations induced by ecological water conveyance in the lower Tarim River, Xinjiang, China

Data from 40 monitoring wells across 9 sections of the lower Tarim River from 2000 to 2006 were analyzed to investigate the relationship between the transmission loss per unit river length and the change in groundwater depth. The relationship between the rise of the groundwater table (y) and the distance from the main river reach (x) was then assessed through regression analysis. We concluded that the maximum affected area was 1933 m away from the main river reach in the Alagan section, and the minimum affected area was 576 m away in the Kaogan section. In addition, after 8 water deliveries, the volume for recharging the groundwater was 78 248.7 x 10(4) m(3). Using the Yingsu section as an example, we found that the volume for recharging the groundwater decreased with additional periods of delivery except after the second and sixth water delivery The results revealed that the beneficial effect of an ecological water conveyance project on the ecosystem in the lower Tarim River is a long-term process. These findings may be useful for guiding studies on instream flow requirements and provide a scientific basis for implementing similar ecological projects in other areas. (C) 2009 Elsevier Ltd. All rights reserved

Optimizing medium for producing ethanol from industrial crop Jerusalem artichoke by one- step fermentation and recombinant Saccharomyces cerevisiae

In order to obtain a high ethanol yield from the Jerusalem artichoke raw extract and reduce the fermentation cost, we have engineered a new recombinant Saccharomyces cerevisiae strain that could produce ex-inulinase. The response surface methodology based on Plackett-Burman and Box-Behnken design was used to optimize the medium for the ethanol production from the Jerusalem artichoke raw extracts by the recombinant strain. In the first optimization step, Plackett-Burman design was employed to select significant factors, including concentrations of yeast extract, inoculum, and MgSO(4)7H(2)O. In the second step, the steepest ascent experiment was carried out to determine the center point with the three significant factors; the selected combinations were further optimized using the Box-Behnken design. The maximum ethanol production rate was predicted at 91.1g/l, which was based on a medium consisting of yeast extract 9.24g/l, inoculum 39.8ml/l, and MgSO(4)7H(2)O 0.45g/l. In the validating experiment, the ethanol fermentation rate reached 102.1g/l, closely matching the predicted rate.In order to obtain a high ethanol yield from the Jerusalem artichoke raw extract and reduce the fermentation cost, we have engineered a new recombinant Saccharomyces cerevisiae strain that could produce ex-inulinase. The response surface methodology based on Plackett-Burman and Box-Behnken design was used to optimize the medium for the ethanol production from the Jerusalem artichoke raw extracts by the recombinant strain. In the first optimization step, Plackett-Burman design was employed to select significant factors, including concentrations of yeast extract, inoculum, and MgSO(4)7H(2)O. In the second step, the steepest ascent experiment was carried out to determine the center point with the three significant factors; the selected combinations were further optimized using the Box-Behnken design. The maximum ethanol production rate was predicted at 91.1g/l, which was based on a medium consisting of yeast extract 9.24g/l, inoculum 39.8ml/l, and MgSO(4)7H(2)O 0.45g/l. In the validating experiment, the ethanol fermentation rate reached 102.1g/l, closely matching the predicted rate

Global Carbon Budget 2022

Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate is critical to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize data sets and methodologies to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFOS) are based on energy statistics and cement production data, while emissions from land-use change (ELUC), mainly deforestation, are based on land use and land-use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly, and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) is estimated with global ocean biogeochemistry models and observation-based data products. The terrestrial CO2 sink (SLAND) is estimated with dynamic global vegetation models. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the year 2021, EFOS increased by 5.1 % relative to 2020, with fossil emissions at 10.1 ± 0.5 GtC yr−1 (9.9 ± 0.5 GtC yr−1 when the cement carbonation sink is included), and ELUC was 1.1 ± 0.7 GtC yr−1, for a total anthropogenic CO2 emission (including the cement carbonation sink) of 10.9 ± 0.8 GtC yr−1 (40.0 ± 2.9 GtCO2). Also, for 2021, GATM was 5.2 ± 0.2 GtC yr−1 (2.5 ± 0.1 ppm yr−1), SOCEAN was 2.9  ± 0.4 GtC yr−1, and SLAND was 3.5 ± 0.9 GtC yr−1, with a BIM of −0.6 GtC yr−1 (i.e. the total estimated sources were too low or sinks were too high). The global atmospheric CO2 concentration averaged over 2021 reached 414.71 ± 0.1 ppm. Preliminary data for 2022 suggest an increase in EFOS relative to 2021 of +1.0 % (0.1 % to 1.9 %) globally and atmospheric CO2 concentration reaching 417.2 ppm, more than 50 % above pre-industrial levels (around 278 ppm). Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959–2021, but discrepancies of up to 1 GtC yr−1 persist for the representation of annual to semi-decadal variability in CO2 fluxes. Comparison of estimates from multiple approaches and observations shows (1) a persistent large uncertainty in the estimate of land-use change emissions, (2) a low agreement between the different methods on the magnitude of the land CO2 flux in the northern extratropics, and (3) a discrepancy between the different methods on the strength of the ocean sink over the last decade. This living data update documents changes in the methods and data sets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this data set. The data presented in this work are available at https://doi.org/10.18160/GCP-2022 (Friedlingstein et al., 2022b)