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The adverse effects of climate change calls for the rapid transformation of manufacturing processes to decrease the emissions of carbon dioxide. In particular, a lower carbon footprint can be achieved by capturing carbon dioxide at the site of emission. Here we review the use of industrial effluents, waste and residues to capture carbon dioxide. Waste include steelmaking slag, municipal solid waste incinerator ashes, combustion fly ash, black liquor, paper mill waste, mining waste, cement waste, construction and demolition waste, waste from the organic industry, and flue gas desulfurization gypsum waste. Capture capacities range from 2 to 800 kg of carbon dioxide per ton of waste, depending on processes, waste type and conditions. Cement waste and flue gas desulfurization gypsum waste show the highest capture capacity per ton of waste. [ABSTRACT FROM AUTHOR]

The global warming and climate change caused by the continuing rise in carbon dioxide (CO2) emissions have attracted the attention of the public, scientists, and governments in recent years. An effective approach to limit the impact of such emissions is the implementation of advanced carbon capture and storage systems. This paper provides an overview of technologies and recent advances in carbon capture and utilization. The main methods include postcombustion, precombustion, and oxyfuel-combustion, adsorption, and membrane capture. In addition, various problems associated with the storage, transportation, and utilization of CO2 are discussed. [ABSTRACT FROM AUTHOR]

Accurately locating and quantifying carbon dioxide (CO2) leakage to the atmosphere is important for diffuse degassing studies in volcanic / geothermal areas and for safety monitoring and/or carbon credit auditing of Carbon Capture and Storage (CCS) sites. This is typically conducted by measuring CO2 flux at numerous points over a large area and applying statistics or geostatistical interpolation. Accuracy of the results will depend on many factors related to survey/data-processing choices and site characteristics, and thus uncertainties can be difficult to quantify. To address this issue, we have developed a Monte Carlo-based program (MC-Flux) that repeatedly subsamples a high-resolution synthetic or real dataset using a choice of different sampling strategies (one random and four grid types) at multiple user-defined sample densities. The program keeps track of the anomalies found and estimates total flux using two statistical and two geostatistical approaches from the literature. This paper describes the use of MC-Flux to assess the potential impact of various sampling and interpretation decisions on the accuracy of the final results. Simulations show that an offset grid sample distribution yields the best results, however relatively dense sampling is required to obtain a high probability of an accurate flux estimate. For the test dataset used, ordinary kriging interpolation produces a range of flux estimates that are centered on the true value while sequential Gaussian simulation tends to slightly overestimate values at intermediate sample spacings and is sensitive to input parameters. These results point to the need for developing new approaches that decrease uncertainty, such as integration with high-resolution co-kriging datasets that complement the more accurate point flux measurements. [ABSTRACT FROM AUTHOR]