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The reduction of palladium, rhodium and neodymium ions at concentrations of 0.94, 0.97 and 0.69 mol dm -3 , respectively was studied in 1 mol dm -3 HNO 3 or 1 mol dm -3 HCl, at a stainless steel and a vitreous carbon electrode, at 25°C. At a vitreous carbon electrode in a solution containing rhodium and palladium ions in 1 mol dm -3 HCl electrolyte, the reduction of metal ions occurred at a similar potential to the formation of hydrogen gas, which impeded the selective separation of the two metals. At a stainless steel cathode in 1 mol dm -3 HNO 3 , palladium deposition occurred at a potential &0.35 V less negative than that of rhodium allowing the selective recovery of palladium. Neodymium ions were not electroactive in acidic chloride or nitrate media at pH 0. Using a solution obtained from a catalytic converter manufacturer containing palladium, rhodium and neodymium ions in 1 mol dm -3 HNO 3 , palladium ions were preferentially removed at 0.15 V versus SHE at an average cumulative current efficiency of 57%.

Land use and land cover (LULC) change has become an important research topic for global environmental change and sustainable development. As an important part of worldwide land conservation, sustainable development and management of water resources, developing countries must ensure the use of innovative technology and tools that support their various decision making systems. This study provides the most recent LULC change analysis for the last six years (2015–2021) of Coatzacoalcos, Veracruz, Mexico, one of the most important petrochemical cities in the world and host of the ongoing Interoceanic Corridor project. The analysis was carried out using Landsat 8 Operational Land Imager (OLI) satellite images, ancillary data and ground-based surveys and the Normalized Difference Vegetation Index (NDVI) to identify and to ameliorate the discrimination between four main macro-classes and fourteen classes. The LULC classification was performed using the maximum likelihood classifier (MLC) to produce maps for each year, as it was found to be the best approach when compared to minimum distance (MDM) and spectral angle mapping (SAM) methods. The macro-classes were water, built-up, vegetation and bare soil, whereas the classes were an improved classification within those. Our study achieved both user accuracy (UA) and producer accuracy (PA) above 90% for the proposed macro-classes and classes. The average Kappa coefficient for macro-classes was 0.93, while for classes it was 0.96, both comparable to previous studies. The results from the LULC analysis show that residential, industry and commercial areas slowed down their growth throughout the study period. These changes were associated with socio-economical drivers such as insecurity and lack of economic investments. Groves and trees presented steady behaviors, with small increments during the five-year period. Swamps, on the other hand, significantly degraded, being about 2% of the study area in 2015 and 0.93% in 2021. Dunes and medium and high vegetation densities (∼80%) transitioned mostly to low vegetation densities. This behavior is associated with rainfall below the annual reference and increments of surface runoff due to the loss of vegetation cover. Lastly, the present study seeks to highlight the importance of remote sensing for a better understanding of the dynamics between human–nature interactions and to provide information to assist planners and decision-makers for more sustainable land development.