Multi-pathway human exposure risk assessment using Bayesian modeling at the historically largest mercury mining district.

The largest mercury (Hg) mining district in the world is located in Almadén (Spain), with well-known environmental impacts in the surrounding ecosystem. However, the impact of mercury on the health of the inhabitants of this area has not been documented accordingly. This study aims to carry out a probabilistic human health risk assessment using Bayesian modeling to estimate the non-carcinogenic risk related to Hg through multiple exposure pathways. Samples of vegetables, wild mushrooms, fish, soil, water, and air were analyzed, and adult residents were randomly surveyed to adjust the risk models to the specific population data. On the one hand, the results for the non-carcinogenic risk based on Hazard Quotient (HQ) showed unacceptable risk levels through ingestion of Hg-contaminated vegetables and fish, with HQ values 20 and 3 times higher, respectively, than the safe exposure threshold of 1 for the 97.5th percentile. On the other hand, ingestion of mushrooms, dermal contact with soil, ingestion of water, dermal contact with water and inhalation of air, were below the safety limit for the 97.5th percentile, and did not represent a risk to the health of residents. In addition, the probabilistic approach was compared with the conservative deterministic approach, and similar results were obtained. This is the first study conducted in Almadén, which clearly reveals the high levels of human health risk to which the population is exposed due to the legacy of two millennia of Hg mining.

Material flow analysis and regional greenhouse gas emissions associated to permanent magnets and batteries used in electric vehicles.

Clean technologies are rapidly increasing in the last decade. In the transport sector, market share of global electric car sales has changed from 0.0 % in 2010 to 3.2 % (2.1million) in 2020, and predictions show that sales could reach near 30 % in 2030. This drastic change is mainly encouraged by environmental goals set to reduce greenhouse gas emissions (GHG) expressed in CO2-eq, not emitted by electric vehicles (EVs) during the use phase. However, clean technologies might cause other impacts during manufacture and, while clearly reduce the dependency on oil, can increase the dependency on other materials. In this context, the objectives of our work are quantifying the critical raw materials needed by permanents magnets and batteries of EVs (neodymium, lithium, and cobalt); their supply risk, performing a material flow analysis; and studying their environmental impacts using the methodology "Environmentally-Extended Multi-Regional Input-Output Analysis". This methodology is used to quantify the produced impacts and the country where the impacts are being produced, in contrast to conventional methodologies that only calculate global impacts. Therefore, environmental impacts are estimated considering different scenarios, based on environmental objectives of the European Union and China. In most scenarios China shows a key role in mining and processing of metals, being the country where major impacts are produced. Obtained results are useful to assess which environmental proposals are more effective to reduce the environmental impact of EVs and set the ground to understand the geostrategic importance of key metals used for EVs manufacture.

Weighted linear models for simulation and prediction of biodegradation in diesel polluted soils.

The purpose of this research is to find a mathematical model based on a statistical analysis to predict the evolution of the total petroleum hydrocarbons (TPH) concentrations with time in the bioremediation process of diesel contaminated soils. The analysis is useful to compare and ascertain the efficiency of different remediation treatments and the influence of both soil characteristics and initial concentration levels of hydrocarbons on the biodegradation process. An experimental design, considering two types of soil, two concentration levels of hydrocarbons and six different amendments was carried out. A total of 336 laboratory tests were conducted during a year in 48 land plots of 4×4m, spreading over eight field campaigns. The results show, for the first time to the best of our knowledge, that the bioremediation process can be adjusted quantitatively to an exponential model, following a first-order kinetic equation. The model explains correctly the higher efficiency of some treatments. In the case of hydrocarbon concentrations <16,000mg/kg, it is advisable to use slow-release fertilizer without the use of surfactant; whereas, for concentrations above 30,000mg/kg, the addition of surfactants improves the results considerably.

Polycyclic Aromatic Hydrocarbons (PAHs) produced in the combustion of fatty acid alkyl esters from different feedstocks: Quantification, statistical analysis and mechanisms of formation.

Polycyclic Aromatic Hydrocarbons (PAHs) are pollutants of concern due to their carcinogenic and mutagenic activity. Their emissions are mainly related with the combustion or pyrolysis of the organic matter, such as in fossil fuels combustion. It is important to characterize PAHs in the combustions of biofuels due to their increasing importance in the actual energetic setting. There is a lot of research focused in PAHs emission due to the combustion in diesel engines; but only few of them have analyzed the effect of raw material and type of alcohol used in the transesterification process. Different raw materials (i.e. animal fat, palm, rapeseed, linseed, peanut, coconut, and soybean oils) have been used for obtaining FAME and FAEE. A method for measuring PAHs generated during combustion in a bomb calorimeter has been developed. Combustion was made at different oxygen pressures and the samples were taken from the bomb after each combustion. Samples were extracted and the PAHs amounts formed during combustion were analyzed by GC-MS. This research shows the statistical relationships among the 16 PAHs of concern, biodiesel composition and oxygen pressure during combustion.

Biodegradation of oil tank bottom sludge using microbial consortia.

We present a rationale for the selection of a microbial consortia specifically adapted to degrade toxic components of oil refinery tank bottom sludge (OTBS). Sources such as polluted soils, petrochemical waste, sludge from refinery-wastewater plants, and others were used to obtain a collection of eight microorganisms, which were individually tested and characterized to analyze their degradative capabilities on different hydrocarbon families. After initial experiments using mixtures of these strains, we developed a consortium consisting of four microorganisms (three bacteria and one yeast) selected in the basis of their cometabolic effects, emulsification properties, colonization of oil components, and degradative capabilities. Although the specific contribution each of the former parameters makes is not clearly understood, the activity of the four-member consortium had a strong impact not only on linear alkane degradation (100%), but also on the degradation of cycloalkanes (85%), branched alkanes (44%), and aromatic and sulphur-aromatic compounds (31-55%). The effectiveness of this consortium was significantly superior to that obtained by individual strains, commercial inocula or an undefined mixture of culturable and non-culturable microorganisms obtained from OTBS-polluted soil. However, results were similar when another consortium of four microorganisms, previously isolated in the same OTBS-polluted soil, was assayed.