Current status and perspectives of accelerated carbonation processes on municipal waste combustion residues

The increasing volumes of municipal solid waste produced worldwide are encouraging the development of processes to reduce the environmental impact of this waste stream. Combustion technology can facilitate volume reduction of up to 90%, with the inorganic contaminants being captured in furnace bottom ash, and fly ash/APC residues. The disposal or reuse of these residues is however governed by the potential release of constituent contaminants into the environment. Accelerated carbonation has been shown to have a potential for improving the chemical stability and leaching behaviour of both bottom ash and fly ash/APC residues. However, the efficacy of carbonation depends on whether the method of gas application is direct or indirect. Also important are the mineralogy, chemistry and physical properties of the fresh ash, the carbonation reaction conditions such as temperature, contact time, CO₂ partial pressure and relative humidity. This paper reviews the main issues pertaining to the application of accelerated carbonation to municipal waste combustion residues to elucidate the potential benefits on the stabilization of such residues and for reducing CO₂ emissions. In particular, the modification of ash properties that occur upon carbonation and the CO₂ sequestration potential possible under different conditions are discussed. Although accelerated carbonation is a developing technology, it could be introduced in new incinerator facilities as a “finishing step” for both ash treatment and reduction of CO₂ emissions.     

Correction to: Briquettes of acai seeds: characterization of the biomass and influence of the parameters of production temperature and pressure in the physical-mechanical and energy quality

Environmental Science and Pollution Research - A Correction to this paper has been published: 10.1007/s11356-021-16529-z     

Briquettes of acai seeds: characterization of the biomass and influence of the parameters of production temperature and pressure in the physical-mechanical and energy quality

Environmental Science and Pollution Research - Agro-industries, leveraged by the high demand of acai products, promote environmental impact through the generation of wastes in several locals in...     

Predicting landscape-scale biodiversity recovery by natural tropical forest regrowth

Natural forest regrowth is a cost-effective, nature-based solution for biodiversity recovery, yet different socioenvironmental factors can lead to variable outcomes. A critical knowledge gap in forest restoration planning is how to predict where natural forest regrowth is likely to lead to high levels of biodiversity recovery, which is an indicator of conservation value and the potential provisioning of diverse ecosystem services. We sought to predict and map landscape-scale recovery of species richness and total abundance of vertebrates, invertebrates, and plants in tropical and subtropical second-growth forests to inform spatial restoration planning. First, we conducted a global meta-analysis to quantify the extent to which recovery of species richness and total abundance in second-growth forests deviated from biodiversity values in reference old-growth forests in the same landscape. Second, we employed a machine-learning algorithm and a comprehensive set of socioenvironmental factors to spatially predict landscape-scale deviation and map it. Models explained on average 34% of observed variance in recovery (range 9–51%). Landscape-scale biodiversity recovery in second-growth forests was spatially predicted based on socioenvironmental landscape factors (human demography, land use and cover, anthropogenic and natural disturbance, ecosystem productivity, and topography and soil chemistry); was significantly higher for species richness than for total abundance for vertebrates (median range-adjusted predicted deviation 0.09 vs. 0.34) and invertebrates (0.2 vs. 0.35) but not for plants (which showed a similar recovery for both metrics [0.24 vs. 0.25]); and was positively correlated for total abundance of plant and vertebrate species (Pearson r = 0.45, p = 0.001). Our approach can help identify tropical and subtropical forest landscapes with high potential for biodiversity recovery through natural forest regrowth.