Reducing the environmental impacts of Brazilian chicken meat production using different waste recovery strategies.

Chicken meat has achieved significant index rates worldwide, with Brazil leading production and exports. The agribusiness significance has led to strengthening attention to the environmental burdens produced by the poultry industry. This research considered reducing the environmental impacts in the life cycle of Brazilian chicken meat regarding strategies for recycling waste from the production process. An attributional cradle-to-gate life cycle assessment was performed, with the functional unit of 1 kg of slaughtered and unpacked chicken meat. The two suggested scenarios used: i) chicken bedding for biogas production and ii) chicken carcass waste as meat meals in feed production. Handling poultry litter for biogas production avoided methane and ammonia emissions, reducing over 50% of the environmental indicators of Climate Change, Terrestrial Acidification, and Freshwater Eutrophication. Reuse poultry waste to produce meat meals reduced from 12% to 55% in all impact categories, decreasing emissions from carcasses destined for decomposition in landfills and using less raw materials from bovine sources. Investigating the environmental performance of the chicken meat production chain encouraged the circularity of natural resources and waste recovery strategies in the system boundary, thus helping to accomplish Sustainable Development Goals 7, 9, 12, and 13 of the UN Agenda 2030.

Life cycle assessment of cheese production process in a small-sized dairy industry in Brazil.

Current research identifies, analyzes, and suggests improvements for minimizing environmental impacts in the manufacture of cheese using the life cycle assessment. Data collection and development of the inventory were performed in a small-sized dairy industry in Brazil. A cradle-to-gate approach was conducted based on the primary data from cheese production and secondary data from databases. The ReCiPe method was used for the impact assessment, considering the categories climate change, ozone depletion, terrestrial acidification, freshwater eutrophication, photochemical oxidant formation, particulate matter formation, water depletion, and fossil depletion. A sensitivity analysis was performed including evaluations of different fuels for generating thermal energy, strategies for cleaning of dairy plant and utensils, variations in the way of cheese production based on the fat content, and production percentage changes. The results showed that the skimmed milk and thermal energy productions, electricity usage, and water consumptions were the main elementary flows. The pallet residues showed the best to be used as fuel for thermal energy. Detergent combinations did not influence the impact categories. There was a direct relationship between fat content range (20 to 30%) and the contribution in six impact categories. Changes from 20% in cheese allocation factor influenced the impact assessment results. LCA allowed identifying the main elementary flow of cheese production, providing valuable information with the potential to verify opportunities for on-site improvements.

Reducing the life cycle GHG emissions of microalgal biodiesel through integration with ethanol production system.

Despite environmental benefits of algal-biofuels, the energy-intensive systems for producing microalgae-feedstock may result in high GHG emissions. Trying to overcome energy-costs, this research analyzed the biodiesel production system via dry-route, based on Chlorella vulgaris cultivated in raceways, by comparing the GHG-footprints of diverse microalgae-biodiesel scenarios. These involved: the single system of biomass production (C0); the application of pyrolysis on the residual microalgal biomass (cake) from the oil extraction process (C1); the same as C0, with anaerobic cake co-digested with cattle manure (C2); the same conditions as in C1 and C2, by integrating in both cases (respectively C3 and C4), the microalgae cultivation with an autonomous ethanol distillery. The reduction of GHG emissions in scenarios with no such integration (C1 and C2), compared to CO, was insignificant (0.53% and 4.67%, respectively), whereas in the scenarios with integration with ethanol production system, the improvements were 53.57% for C3 and 63.84% for C4.