Phytoprevention of Heavy Metal Contamination From Terrestrial Enhanced Weathering: Can Plants Save the Day?

Enhanced weathering is a promising approach to remove carbon dioxide from the atmosphere. However, it may also pose environmental risks through the release of heavy metals, in particular nickel and chromium. In this perspective article I explore the potential role of plants in modulating these heavy metal fluxes. Agricultural basaltic soils may be valuable study sites in this context. However, the effect of biomass harvesting on the accumulation of heavy metals is currently not well studied. Mostly caused by different parent rock concentrations, there is a large variability of heavy metal concentrations in basaltic and ultramafic soils. Hence, to minimize environmental risks of enhanced weathering, basalts with low heavy metal concentrations should be favored. Existing phytoremediation strategies may be used to “phytoprevent” the accumulation of nickel and chromium released from enhanced weathering in soils. As a result, elevated nickel and chromium concentrations in rocks must not preclude enhanced weathering in all settings. In particular, hyperaccumulating plants could be used as part of a crop rotation to periodically remove heavy metals from soils. Enhanced weathering could also be employed on fields or forests of (non-hyper) accumulating plants that have a high primary production of biomass. Both approaches may have additional synergies with phytomining or bioenergy carbon capture and storage, increasing the total amount of carbon dioxide drawdown and at the same time preventing heavy metal accumulation in soils.

Current Developments in Catalytic Methanation of Carbon Dioxide—A Review

The utilization of fossil fuel has increased atmospheric carbon dioxide (CO2) concentrations drastically over the last few decades. This leads to global warming and climate change, increasing the occurrence of more severe weather around the world. One promising solution to reduce anthropogenic CO2 emissions is methanation. Many researchers and industries are interested in CO2 methanation as a power-to-gas technology and carbon capture and storage (CCS) system. Producing an energy carrier, methane (CH4), via CO2 methanation and water electrolysis is an exceptionally effective method of capturing energy generated by renewables. To enhance methanation efficiency, numerous researches have been conducted to develop catalysts with high activity, CH4 selectivity, and stability against the reaction heat. Therefore, in this mini-review, the characteristics and recent advances of metal-based catalysts in methanation of CO2 is discussed.

Periodic CO2 Injection for Improved Storage Capacity and Pressure Management under Intermittent CO2 Supply

Storing CO2 in geological formations is an important component of reducing greenhouse gases emissions. The Carbon Capture and Storage (CCS) industry is now in its establishing phase, and if successful, massive storage volumes would be needed. It will hence be important to utilize each storage site to its maximum, without challenging the formation integrity. For different reasons, supply of CO2 to the injection sites may be periodical or unstable, often considered as a risk element reducing the overall efficiency and economics of CCS projects. In this paper we present outcomes of investigations focusing on a variety of positive aspects of periodic CO2 injection, including pressure management and storage capacity, also highlighting reservoir monitoring opportunities. A feasibility study of periodic injection into an infinite saline aquifer using a mechanistic reservoir model has indicated significant improvement in storage capacity compared to continuous injection. The reservoir pressure and CO2 plume behavior were further studied revealing a ‘CO2 expansion squeeze’ effect that governs the improved storage capacity observed in the feasibility study. Finally, the improved pressure measurement and storage capacity by periodic injection was confirmed by field-scale simulations based on a real geological set-up. The field-scale simulations have confirmed that ‘CO2 expansion squeeze’ governs the positive effect, which is also influenced by well location in the geological structure and aquifer size, while CO2 dissolution in water showed minor influence. Additional reservoir effects and risks not covered in this paper are then highlighted as a scope for further studies. The value of the periodic injection with intermittent CO2 supply is finally discussed in the context of deployment and integration of this technology in the establishing CCS industry.

Could China Declare a “Coal Phase-Out”? An Evolutionary Game and Empirical Analysis Involving the Government, Enterprises, and the Public

There is a global move toward being “carbon neutral”. Reducing the use of coal to generate power has become an inevitable choice for many countries when transforming their energy structures. Many countries have proposed phasing out coal. China is a major energy producing and consuming country and intends to reach a carbon peak by 2030 and become carbon neutral by 2060. China has repeatedly emphasized coal reduction, but has not explicitly proposed phasing out coal, due to the influence of local governments, coal-related enterprises, and the public. This paper explores whether China could declare a “coal phase-out”, and the possible reasons for doing so, by constructing an evolutionary game model with two correlations. MATLAB was used to simulate the model results to determine the effectiveness of the fractal results of the model, and the entropy method was used to calculate the development level of “coal phase-out” related indicators in China and Germany. The results show that: (1) The government can phase out coal only when coal-related enterprises and the public can benefit from reducing coal production and consumption. In addition, these benefits are needed to ensure stable economic and social development without affecting people’s daily lives; (2) The development level of relevant indicators of “coal retreat” in China is lower than that in Germany. Based on these results, it is concluded that it is difficult for China to announce a “coal phase-out” at present. Faced with this reality, China should improve the efficiency of coal use, install carbon capture and storage facilities, vigorously develop renewable energy and reduce the share of coal in the energy system.

Accurate and Rapid Forecasts for Geologic Carbon Storage via Learning-Based Inversion-Free Prediction

Carbon capture and storage (CCS) is one approach being studied by the U.S. Department of Energy to help mitigate global warming. The process involves capturing CO2 emissions from industrial sources and permanently storing them in deep geologic formations (storage reservoirs). However, CCS projects generally target “green field sites,” where there is often little characterization data and therefore large uncertainty about the petrophysical properties and other geologic attributes of the storage reservoir. Consequently, ensemble-based approaches are often used to forecast multiple realizations prior to CO2 injection to visualize a range of potential outcomes. In addition, monitoring data during injection operations are used to update the pre-injection forecasts and thereby improve agreement between forecasted and observed behavior. Thus, a system for generating accurate, timely forecasts of pressure buildup and CO2 movement and distribution within the storage reservoir and for updating those forecasts via monitoring measurements becomes crucial. This study proposes a learning-based prediction method that can accurately and rapidly forecast spatial distribution of CO2 concentration and pressure with uncertainty quantification without relying on traditional inverse modeling. The machine learning techniques include dimension reduction, multivariate data analysis, and Bayesian learning. The outcome is expected to provide CO2 storage site operators with an effective tool for timely and informative decision making based on limited simulation and monitoring data.

Combination of Gibbs and Helmholtz Energy Equations of State in a Multiparameter Mixture Model Using the IAPWS Seawater Model as an Example

For carbon capture and storage (CCS) applications different sets of equations of state are used to describe the whole CCS-chain. While for the transport and pipeline sections highly accurate equations of state (EOS) explicit in the Helmholtz energy are used, properties under typical geological storage conditions are described by more simple, mostly cubic EOS, and brines are described by Gibbs energy models. Combining the transport and storage sections leads to inconsistent calculations. Since the used models are formulated in different independent variables (temperature and density versus temperature and pressure), mass and energy balances are challenging and equilibria in the injection region are difficult to model. To overcome these limitations, a predictive combination of the Gibbs energy-based IAPWS seawater model (IAPWS R13-08, 2008) with Helmholtz energy-based multi-parameter EOS is presented within this work. The Helmholtz energy model used in this work is based on the EOS-CG-2016 of Gernert and Span (J Chem Thermodyn 93:274–293, 10.1016/j.jct.2015.05.015, 2016). The results prove that a consistent combination of the two different models is possible. Furthermore, it is shown, that a more complex brine model needs to be combined with Helmholtz energy EOS for calculations at storage conditions.