Carbon emissions and low-carbon development in Olefin industry.

Olefin industry as a vital part in economic development is facing a problem of high CO2 emission. In this work, for the global and China's olefin industry under different development scenario, the carbon emission is predicted after the revealing of carbon footprint in different olefin routes. The results show that the carbon footprint of the natural gas liquids (NGLs)-derived route is highly lower than that of the oil- and coal-derived routes. The carbon emission from the global olefin industry in 2015 is 553 million ton CO2 (MtCO2). In 2030, it will be ranged between 739 and 924 MtCO2 under different scenarios. Under sustainable development scenario, 15% reduction space is existed, whereas 6% growth is observed under the hybrid-development scenario compared to the business-as-usual situation. In the case of China, its carbon emission is 120 MtCO2 in 2015. Its potential carbon emission in 2030 will increase to 264-925 MtCO2, depending on the rest new capacity from low-carbon or high-carbon routes. The large gap implies the significant influence of the development route choice. However, if most new capacity is from the existed planned olefin projects, the carbon emission will be ranged between 390 and 594 MtCO2. Finally, the low-carbon roadmaps as well as polices are proposed for sustainable development of olefin industry.

Vertical Hydrologic Exchange Flows Control Methane Emissions from Riverbed Sediments.

CH4 emissions from inland waters are highly uncertain in the current global CH4 budget, especially for streams, rivers, and other lotic systems. Previous studies have attributed the strong spatiotemporal heterogeneity of riverine CH4 to environmental factors such as sediment type, water level, temperature, or particulate organic carbon abundance through correlation analysis. However, a mechanistic understanding of the basis for such heterogeneity is lacking. Here, we combine sediment CH4 data from the Hanford reach of the Columbia River with a biogeochemical-transport model to show that vertical hydrologic exchange flows (VHEFs), driven by the difference between river stage and groundwater level, determine CH4 flux at the sediment-water interface. CH4 fluxes show a nonlinear relationship with the magnitude of VHEFs, where high VHEFs introduce O2 into riverbed sediments, which inhibit CH4 production and induce CH4 oxidation, and low VHEFs cause transient reduction in CH4 flux (relative to production) due to reduced advective CH4 transport. In addition, VHEFs lead to the hysteresis of temperature rise and CH4 emissions because high river discharge caused by snowmelt in spring leads to strong downwelling flow that offsets increasing CH4 production with temperature rise. Our findings reveal how the interplay between in-stream hydrologic flux besides fluvial-wetland connectivity and microbial metabolic pathways that compete with methanogenic pathways can produce complex patterns in CH4 production and emission in riverbed alluvial sediments.

Machine Learning Analysis of Hydrologic Exchange Flows and Transit Time Distributions in a Large Regulated River.

Hydrologic exchange between river channels and adjacent subsurface environments is a key process that influences water quality and ecosystem function in river corridors. High-resolution numerical models were often used to resolve the spatial and temporal variations of exchange flows, which are computationally expensive. In this study, we adopt Random Forest (RF) and Extreme Gradient Boosting (XGB) approaches for deriving reduced order models of hydrologic exchange flows and associated transit time distributions, with integrated field observations (e.g., bathymetry) and hydrodynamic simulation data (e.g., river velocity, depth). The setup allows an improved understanding of the influences of various physical, spatial, and temporal factors on the hydrologic exchange flows and transit times. The predictors also contain those derived using hybrid clustering, leveraging our previous work on river corridor system hydromorphic classification. The machine learning-based predictive models are developed and validated along the Columbia River Corridor, and the results show that the top parameters are the thickness of the top geological formation layer, the flow regime, river velocity, and river depth; the RF and XGB models can achieve 70% to 80% accuracy and therefore are effective alternatives to the computational demanding numerical models of exchange flows and transit time distributions. Each machine learning model with its favorable configuration and setup have been evaluated. The transferability of the models to other river reaches and larger scales, which mostly depends on data availability, is also discussed.

Temporal flow variations interact with spatial physical heterogeneity to impact solute transport in managed river corridors.

The interactions between surface water and groundwater in river corridors lead to temporal fluctuations in subsurface water fluxes which have a critical role on solute transport dynamics. In this work, we develop a framework to analyze the relative impacts of different temporal frequencies of the flow field in a spatially heterogeneous aquifer on solute transport. Our analysis indicates that the advection-dispersion equation behaves as a low-pass filter by wiping out the effect of high-frequency velocity fluctuations on the first two spatial moments of the solute plume, namely its center of mass and spreading. The concepts discussed in the theoretical analysis are then applied to understand solute transport dynamics at the 300 Area of the Hanford site (USA) adjacent to the Columbia River. We examine the temporal behavior of the solute plume's spatial moments for different temporal frequencies utilizing geostatistical parameters estimated in the 300 Area. Due to the proximity to the Columbia river, groundwater fluxes at the Hanford site are highly dynamic resulting in a large range of characteristic temporal frequencies. Nonetheless, similar to the theoretical analysis, our results show that the effect of high-frequency fluctuations is filtered, with most of the solute transport dynamics being controlled by fluctuations characterized by a large characteristic period.

Integrated hydrogeophysical modelling and data assimilation for geoelectrical leak detection.

Time-lapse electrical resistivity tomography (ERT) measurements provide indirectobservations of hydrological processes in the Earth's shallow subsurface at high spatial and temporal resolution. ERT has been used in the past decades to detect leaks and monitor the evolution of associated contaminant plumes. Specifically, inverted resistivity images allow visualization of the dynamic changes in the structure of the plume. However, existing methods do not allow the direct estimation of leak parameters (e.g. leak rate, location, etc.) and their uncertainties. We propose an ensemble-based data assimilation framework that evaluates proposed hydrological models against observed time-lapse ERT measurements without directly inverting for the resistivities. Each proposed hydrological model is run through the parallel coupled hydro-geophysical simulation code PFLOTRAN-E4D to obtain simulated ERT measurements. The ensemble of model proposals is then updated using an iterative ensemble smoother. We demonstrate the proposed framework on synthetic and field ERT data from controlled tracer injection experiments. Our results show that the approach allows joint identification of contaminant source location, initial release time, and solute loading from the cross-borehole time-lapse ERT data, alongside with an assessment of uncertainties in these estimates. We demonstrate a reduction in site-wide uncertainty by comparing the prior and posterior plume mass discharges at a selected image plane. This framework is particularly attractive to sites that have previously undergone extensive geological investigation (e.g., nuclear sites). It is well suited to complement ERT imaging and we discuss practical issues in its application to field problems.

Carbon reduction potential and cost evaluation of different mitigation approaches in China's coal to olefin Industry.

Coal-based olefin (CTO) industry as a complement of traditional petrochemical industry plays vital role in China's national economic development. However, high CO2 emission in CTO industry is one of the fatal problems to hinder its development. In this work, the carbon emission and mitigation potentials by different reduction pathways are evaluated. The economic cost is analyzed and compared as well. According to the industry development plan, the carbon emissions from China's CTO industry will attain 189.43 million ton CO2 (MtCO2) and 314.11 MtCO2 in 2020 and 2030, respectively. With the advanced technology level, the maximal carbon mitigation potential could be attained to 15.3% and 21.9% in 2020 and 2030. If the other optional mitigation ways are combined together, the carbon emission could further reduce to some extent. In general, the order of mitigation potential is followed as: feedstock alteration by natural gas > CO2 hydrogenation with renewable electricity applied > CCS technology. The mitigation cost analysis indicates that on the basis of 2015 situation, the economic penalty for feedstock alteration is the lowest, ranged between 186 and 451 CNY/tCO2, and the cost from CCS technology is ranged between 404 and 562 CNY/tCO2, which is acceptable if the CO2 enhanced oil recovery and carbon tax are considered. However, for the CO2 hydrogenation technology, the cost is extremely high and there is almost no application possibility at present.

Regulation-Structured Dynamic Metabolic Model Provides a Potential Mechanism for Delayed Enzyme Response in Denitrification Process.

In a recent study of denitrification dynamics in hyporheic zone sediments, we observed a significant time lag (up to several days) in enzymatic response to the changes in substrate concentration. To explore an underlying mechanism and understand the interactive dynamics between enzymes and nutrients, we developed a trait-based model that associates a community's traits with functional enzymes, instead of typically used species guilds (or functional guilds). This enzyme-based formulation allows to collectively describe biogeochemical functions of microbial communities without directly parameterizing the dynamics of species guilds, therefore being scalable to complex communities. As a key component of modeling, we accounted for microbial regulation occurring through transcriptional and translational processes, the dynamics of which was parameterized based on the temporal profiles of enzyme concentrations measured using a new signature peptide-based method. The simulation results using the resulting model showed several days of a time lag in enzymatic responses as observed in experiments. Further, the model showed that the delayed enzymatic reactions could be primarily controlled by transcriptional responses and that the dynamics of transcripts and enzymes are closely correlated. The developed model can serve as a useful tool for predicting biogeochemical processes in natural environments, either independently or through integration with hydrologic flow simulators.