Bioactive Compounds in Plasma as a Function of Sex and Sweetener Resulting from a Maqui-Lemon Beverage Consumption Using Statistical and Machine Learning Techniques.

The present study analyses the effect of a beverage composed of citrus and maqui (Aristotelia chilensis) with different sweeteners on male and female consumers. Beverages were designed and tested (140 volunteers) as a source of polyphenols, in a previous work. Plasma samples were taken before and after two months of daily intake. Samples were measured for bioactive-compound levels with metabolomics techniques, and the resulting data were analysed with advanced versions of ANOVA and clustering analysis, to describe the effects of sex and sweetener factors on bioactive compounds. To improve the results, machine learning techniques were applied to perform feature selection and data imputation. The results reflect a series of compounds which are more regulated for men, such as caffeic acid or 3,4-dihydroxyphenylacetic acid, and for women, trans ferulic acid (TFA) or naringenin glucuronide. Regulations are also observed with sweeteners, such as TFA with stevia in women, or vanillic acid with sucrose in men. These results show that there is a differential regulation of these two families of polyphenols by sex, and that this is influenced by sweeteners.

Monitoring Apricot (Prunus armeniaca L.) Ripening Progression through Candidate Gene Expression Analysis.

This study aimed at the monitoring of the apricot (Prunus armeniaca L.) ripening progression through the expression analysis of 25 genes related to fruit quality traits in nine cultivars with great differences in fruit color and ripening date. The level of pigment compounds, such as anthocyanins and carotenoids, is a key factor in food taste, and is responsible for the reddish blush color or orange skin and flesh color in apricot fruit, which are desirable quality traits in apricot breeding programs. The construction of multiple linear regression models to predict anthocyanins and carotenoids content from gene expression allows us to evaluate which genes have the strongest influence over fruit color, as these candidate genes are key during biosynthetic pathways or gene expression regulation, and are responsible for the final fruit phenotype. We propose the gene CHS as the main predictor for anthocyanins content, CCD4 and ZDS for carotenoids content, and LOX2 and MADS-box for the beginning and end of the ripening process in apricot fruit. All these genes could be applied as RNA markers to monitoring the ripening stage and estimate the anthocyanins and carotenoids content in apricot fruit during the ripening process.

Evaluation of Multicriteria Decision Analysis Algorithms in Food Safety: A Case Study on Emerging Zoonoses Prioritization.

Decision making in food safety is a complex process that involves several criteria of different nature like the expected reduction in the number of illnesses, the potential economic or health-related cost, or even the environmental impact of a given policy or intervention. Several multicriteria decision analysis (MCDA) algorithms are currently used, mostly individually, in food safety to rank different options in a multifactorial environment. However, the selection of the MCDA algorithm is a decision problem on its own because different methods calculate different rankings. The aim of this study was to compare the impact of different uncertainty sources on the rankings of MCDA problems in the context of food safety. For that purpose, a previously published data set on emerging zoonoses in the Netherlands was used to compare different MCDA algorithms: MMOORA, TOPSIS, VIKOR, WASPAS, and ELECTRE III. The rankings were calculated with and without considering uncertainty (using fuzzy sets), to assess the importance of this factor. The rankings obtained differed between algorithms, emphasizing that the selection of the MCDA method had a relevant impact in the rankings. Furthermore, considering uncertainty in the ranking had a high influence on the results. Both factors were more relevant than the weights associated with each criterion in this case study. A hierarchical clustering method was suggested to aggregate results obtained by the different algorithms. This complementary step seems to be a promising way to decrease extreme difference among algorithms and could provide a strong added value in the decision-making process.

Mathematical modelling of the stress resistance induced in Listeria monocytogenes during dynamic, mild heat treatments.

Modelling of stress acclimation induced by thermal inactivation of Listeria monocytogenes under dynamic conditions is analyzed in this work. A mathematical model that separates the effect of the instantaneous temperature from the one of stress acclimation, was used. The model was trained using isothermal inactivation experiments, and one biphasic dynamic treatment with a heating rate of 1 °C/min and a holding phase of 60 °C. These experiments were performed in laboratory media (Tryptic Soy Broth; TSB). The model parameters estimated through these experiments (D55=12.87±0.82min, z=4.58±0.04°C, a=0.11±0.01min-1, E=0.50±0.01°C and c=1.23±0.03) were successfully used to predict the microbial inactivation for another seven inactivation profiles, with and without a holding phase. Moreover, similar experiments were performed using milk as heating media, obtaining a good agreement between the model predictions and the empirical observations. The results of this study are compatible with the hypothesis that L. monocytogenes is able to develop a physiological response during dynamic treatments that increases its thermal resistance. Also, that the model used can be used to predict microbial inactivation of this microorganism taking into consideration stress acclimation.

Parameter estimation in large-scale systems biology models: a parallel and self-adaptive cooperative strategy.

BACKGROUND: The development of large-scale kinetic models is one of the current key issues in computational systems biology and bioinformatics. Here we consider the problem of parameter estimation in nonlinear dynamic models. Global optimization methods can be used to solve this type of problems but the associated computational cost is very large. Moreover, many of these methods need the tuning of a number of adjustable search parameters, requiring a number of initial exploratory runs and therefore further increasing the computation times. Here we present a novel parallel method, self-adaptive cooperative enhanced scatter search (saCeSS), to accelerate the solution of this class of problems. The method is based on the scatter search optimization metaheuristic and incorporates several key new mechanisms: (i) asynchronous cooperation between parallel processes, (ii) coarse and fine-grained parallelism, and (iii) self-tuning strategies. RESULTS: The performance and robustness of saCeSS is illustrated by solving a set of challenging parameter estimation problems, including medium and large-scale kinetic models of the bacterium E. coli, bakerés yeast S. cerevisiae, the vinegar fly D. melanogaster, Chinese Hamster Ovary cells, and a generic signal transduction network. The results consistently show that saCeSS is a robust and efficient method, allowing very significant reduction of computation times with respect to several previous state of the art methods (from days to minutes, in several cases) even when only a small number of processors is used. CONCLUSIONS: The new parallel cooperative method presented here allows the solution of medium and large scale parameter estimation problems in reasonable computation times and with small hardware requirements. Further, the method includes self-tuning mechanisms which facilitate its use by non-experts. We believe that this new method can play a key role in the development of large-scale and even whole-cell dynamic models.

MEIGO: an open-source software suite based on metaheuristics for global optimization in systems biology and bioinformatics.

BACKGROUND: Optimization is the key to solving many problems in computational biology. Global optimization methods, which provide a robust methodology, and metaheuristics in particular have proven to be the most efficient methods for many applications. Despite their utility, there is a limited availability of metaheuristic tools. RESULTS: We present MEIGO, an R and Matlab optimization toolbox (also available in Python via a wrapper of the R version), that implements metaheuristics capable of solving diverse problems arising in systems biology and bioinformatics. The toolbox includes the enhanced scatter search method (eSS) for continuous nonlinear programming (cNLP) and mixed-integer programming (MINLP) problems, and variable neighborhood search (VNS) for Integer Programming (IP) problems. Additionally, the R version includes BayesFit for parameter estimation by Bayesian inference. The eSS and VNS methods can be run on a single-thread or in parallel using a cooperative strategy. The code is supplied under GPLv3 and is available at http://www.iim.csic.es/~gingproc/meigo.html. Documentation and examples are included. The R package has been submitted to BioConductor. We evaluate MEIGO against optimization benchmarks, and illustrate its applicability to a series of case studies in bioinformatics and systems biology where it outperforms other state-of-the-art methods. CONCLUSIONS: MEIGO provides a free, open-source platform for optimization that can be applied to multiple domains of systems biology and bioinformatics. It includes efficient state of the art metaheuristics, and its open and modular structure allows the addition of further methods.

Dynamic Modelling and Simulation of Food Systems: Recent Trends and Applications.

Several factors influence consumers' choices of food products [...].

Application of High Hydrostatic Pressure in fresh purple smoothie: Microbial inactivation kinetic modelling and qualitative studies.

The inactivation kinetics of Listeria monocytogenes during High Hydrostatic Pressure (HHP) treatments was studied in a purple smoothie based of fresh fruit and vegetables. Pressure intensity studied was 300, 350, 400 and 450 MPa. Untreated samples were used as control. Furthermore, the effects on quality attributes (sensory, total soluble solids content, colour, titratable acidity, pH, vitamin C and total phenolics content) were also monitored. Microbial inactivation was modelled as a function of the HHP intensity using the Geeraerd model. Shoulder and tail effects were observed only for the 300 MPa pressure assayed, supporting a multiple hit kinetic inactivation of critical factors. Increasing the HHP intensity resulted in a faster inactivation with tailing. A strong positive correlation was observed between the pressure level and the inactivation rate (k). Hence, a linear model was used to describe the relationship between both variables. Nevertheless, further data are required to confirm this secondary model. Quality was mostly unaffected by the HHP treatments, except for the vitamin C content, which reported reductions of 26 and 21% after 300 and 350 MPa, respectively. In conclusion, HHP can be a viable technology for processing fruit and vegetable-based smoothies to preserve quality and safety. A pressure of 400 MPa is advisable to ensure an efficient microbial inactivation with the best sensory and nutritional quality retention.

Differences Due to Sex and Sweetener on the Bioavailability of (Poly)phenols in Urine Samples: A Machine Learning Approach.

Metabolic diseases have been related to the overdrinking of high-sugar content beverages. As a result, the demand for alternative formulations based on plant-based ingredients with health-promoting properties has increased during the last few years. Nonetheless, the design and production of effective formulations requires understanding the bioavailability of these compounds. For this purpose, a two-month longitudinal trial with 140 volunteers was conducted to measure the beneficial effects of a maqui-citrus beverage, rich in (poly)phenols. From data obtained by quantifying metabolites present in urine samples, biostatistical and machine learning (data imputation, feature selection, and clustering) methods were applied to assess whether a volunteer's sex and the sweetener added to the beverage (sucrose, sucralose, or stevia) affected the bioavailability of (poly)phenol metabolites. Several metabolites have been described as being differentially influenced: 3,4-dihydroxyphenylacetic acid and naringenin with its derivatives were positively influenced by stevia and men, while eriodictyol sulfate and homoeridictyol glucunoride concentrations were enhanced with stevia and women. By examining groups of volunteers created by clustering analysis, patterns in metabolites' bioavailability distribution as a function of sex and/or sweeteners (or even due to an uncontrolled factor) were also discovered. These results underline the potential of stevia as a (poly)phenol bioavailability enhancer. Furthermore, they also evidence sex affects the bioavailability of (poly)phenols, pointing at a sex-dependent metabolic pathway regulation.

The Effect of Silver Nanoparticle Addition on Micropropagation of Apricot Cultivars (Prunus armeniaca L.) in Semisolid and Liquid Media.

Silver nanoparticles (AgNPs) are novel compounds used as antimicrobial and antiviral agents. In addition, AgNPs have been used to improve the growth of different plants, as well as the in vitro multiplication of plant material. In this work the effect of AgNPs on in vitro growth of 'Canino' and 'Mirlo Rojo' cultivars, as well as the leaf ion composition, are studied. Different concentrations of AgNPs (0, 25, 50, 75 and 100 mg L-1) were added to two culture systems: semisolid medium with agar (SSM) in jars and liquid medium in temporary immersion system (TIS). Proliferation (number of shoots), shoot length, productivity (number of shoot × average length), leaf surface, fresh and dry weight were measured. Additionally, the silver and other ion accumulation in the leaves were evaluated by inductively coupled plasma optical emission spectroscopy (ICP-OES) analysis. The productivity of 'Canino' and 'Mirlo Rojo' decreased when increasing the concentration of AgNPs in the semisolid medium. However, the use of AgNPs in the TIS improved the proliferation and productivity of 'Canino' and Mirlo Rojo', increasing biomass production, and the concentration of nutrients in the plants, although these effects are genotype-dependent. TISs are the best system for introducing silver into shoots, the optimum concentration being 50 mg L-1 for 'Canino' and 75 mg L-1 for 'Mirlo Rojo'. Principal component analysis, considering all the analyzed ions along the treatments, separates samples in two clear groups related to the culture system used. The use of bioreactors with a liquid medium has improved the productivity of 'Canino' and 'Mirlo Rojo' in the proliferation stage, avoiding hyperhydration and other disorders. The amount of metallic silver that penetrates apricot plant tissues depends on the culture system, cultivar and concentration of AgNPs added to the culture medium. Silver ion accumulation measured in the shoots grown in the TIS was higher than in shoots micropropagated in a semisolid medium, where it is barely detectable. Furthermore, AgNPs had a beneficial effect on plants grown in TIS. However, AgNPs had a detrimental effect when added to a semisolid medium.

Effect of Storage Conditions on the Stability of Polyphenols of Apple and Strawberry Purees Produced at Industrial Scale by Different Processing Techniques.

During a food product's life, storage conditions affect its composition of nutrients, bioactive compounds, and sensory attributes. In this research, strawberry and apple purees were selected as a model to examine how the storage of various purees industrially produced with different technologies affect the bioactive phenolic compounds, color, and sensory attributes. Specifically, fruit products processed on an industrial scale by different technologies including freezing, thermal treatment (mild and standard), and high-pressure processing were studied, as well as storage for up to 12 months at -20, 4, and 24 °C. In strawberry puree, storage conditions had a stronger impact on phenolic compound levels, particularly on anthocyanins, whereas in apple puree, the initial processing techniques exerted a greater influence than storage conditions, mainly caused by the hot or cold crushing processes. In general, proanthocyanidins were the major phenolic group and the most stable during storage, while anthocyanins were the group most affected by both processing and storage. Apple flavonols and dihydrochalcones were quite stable, while strawberry ellagitannins suffered higher degradations during storage. Through our analysis, it is found that during storage, the stability of polyphenols in each fruit is different, and processing and storage can be either detrimental or even beneficial. The selection of the ideal storage conditions (time and temperature) is a key factor to maintaining the polyphenol content in sensitive fruits such as strawberries. However, storage conditions are in some cases more important to minimizing the polyphenol losses than how the product is processed.

Deterministic global optimization algorithm based on outer approximation for the parameter estimation of nonlinear dynamic biological systems.

BACKGROUND: The estimation of parameter values for mathematical models of biological systems is an optimization problem that is particularly challenging due to the nonlinearities involved. One major difficulty is the existence of multiple minima in which standard optimization methods may fall during the search. Deterministic global optimization methods overcome this limitation, ensuring convergence to the global optimum within a desired tolerance. Global optimization techniques are usually classified into stochastic and deterministic. The former typically lead to lower CPU times but offer no guarantee of convergence to the global minimum in a finite number of iterations. In contrast, deterministic methods provide solutions of a given quality (i.e., optimality gap), but tend to lead to large computational burdens. RESULTS: This work presents a deterministic outer approximation-based algorithm for the global optimization of dynamic problems arising in the parameter estimation of models of biological systems. Our approach, which offers a theoretical guarantee of convergence to global minimum, is based on reformulating the set of ordinary differential equations into an equivalent set of algebraic equations through the use of orthogonal collocation methods, giving rise to a nonconvex nonlinear programming (NLP) problem. This nonconvex NLP is decomposed into two hierarchical levels: a master mixed-integer linear programming problem (MILP) that provides a rigorous lower bound on the optimal solution, and a reduced-space slave NLP that yields an upper bound. The algorithm iterates between these two levels until a termination criterion is satisfied. CONCLUSION: The capabilities of our approach were tested in two benchmark problems, in which the performance of our algorithm was compared with that of the commercial global optimization package BARON. The proposed strategy produced near optimal solutions (i.e., within a desired tolerance) in a fraction of the CPU time required by BARON.

Vertical concentration gradients of volatile organic compounds in two NS-oriented street canyons.

Diffusive samplers were used to measure the vertical concentrations of benzene, toluene, n-hexane, cyclohexane, ethylbenzene and o-, m- and p-xylenes on both sides of two NS-oriented street canyons in Murcia (Spain) during a 5-day period. Non-dimensional relationships of concentration and height were calculated in order to study the behaviour of their concentration vertical profiles. The results show that the vertical profiles of benzene, toluene, n-hexane and cyclohexane concentrations were similar in both streets and on both sides of each street. Some differences were found in vertical profiles between streets and sides for ethylbenzene and xylenes, probably due to their higher affinity for adsorption into building materials. The similarities found for the first set of VOCs suggest that the dynamics of the dispersion was the same for both streets and was mainly influenced by microscale thermal effects. Finally, the concentration measurements of benzene, toluene, n-hexane, cyclohexane, and ethylbenzene were adjusted to expressions in the form c = c 0(h/h 0) A , and a regression coefficient R 2 = 0.962 (p = 0.0000) was obtained. The decreasing concentration of these compounds with height should be taken into account when assessing population exposure to these pollutants.

Training in tools to develop quantitative risk assessment of fresh produce using water reuse systems in Mediterranean production.

Water resources are increasingly coming under pressure specially around the Mediterranean area, leading to water scarcity and a deterioration in water quality. The use of treated wastewater represents an alternative source to enhance the demand for irrigation water. Water reuse in combination with the promotion of the use of water-efficient technologies in industry and water-saving irrigation techniques could lead to good qualitative and quantitative water status for surface and ground water bodies. Nevertheless, food-borne outbreaks linked to fresh produce irrigated with partially or untreated wastewater caused by bacteria, parasites and enteropathogenic viruses have been widely reported. In the absence of solid scientific understanding of the actual risks involved, consumers are likely less receptive to buy leafy greens irrigated with treated wastewater, also known as reclaimed water. In this study, we aimed to assess the microbiological risks of leafy green vegetables irrigated with treated wastewater in Spain using Norovirus as a model organism to facilitate the development of risk management strategies. A conceptual exposure model was designed to describe the virus fate and transport from the Wastewater treatment plant (WWTP) secondary effluent to the consumers' fork. This study is an example of the use of reclaimed water for irrigation of commercial fields producing leafy greens in the south-east of Spain and tries to assess potential microbiological risks to the consumers by establishing their safety.

Agroclimatic Metrics for the Main Stone Fruit Producing Areas in Spain in Current and Future Climate Change Scenarios: Implications From an Adaptive Point of View.

Stone fruit production has enormous economic importance in Spain. Cultivation locations for these fruit species (i.e., peach, apricot, plum, and sweet cherry) cover wide and climatically diverse geographical areas within the country. Climate change is already producing an increase in average temperatures with special intensity in certain areas like the Mediterranean ones. These changes lead to a decrease in the accumulated chill, which can have a profound impact on the phenology of Prunus species like stone fruits due to, e.g., difficulties to cover the chilling requirements to break endodormancy, the occurrence of late frost events, or abnormal early high temperatures. All these factors can severely affect fruit production and quality and therefore provoke very negative consequences from the socio-economic point of view in the incumbent regions. Thus, characterization of current cultivation areas in terms of agroclimatic variables (e.g., chill and heat accumulation and probabilities of frost and early abnormal heat events), based on data from 270 weather stations for the past 20 years, is carried out in this work to produce an informative picture of the current situation. Besides, future climatic projections from different global climate models (data retrieved from the Meteorological State Agency of Spain-AEMET) up to 2065 for two Representative Concentration Pathway scenarios (i.e., RCP4.5 and RCP8.5) are also analyzed. Using the current situation as a baseline and considering the future scenarios, information on the current and future adaptive suitability of the different species/cultivars to the different growing areas can be inferred. This information could be the basis of a decision support tool to help the different stakeholders to take optimal decisions regarding current and future stone fruit or other temperate species cultivation in Spain.

Multiobjective Optimization of a Frying Process Balancing Acrylamide Formation and Quality: Solution Analysis and Uncertainty Propagation.

In this study, we performed multi-objective model-based optimization of a potato-frying process balancing between acrylamide production and a quality parameter (yellowness). Solution analysis revealed that, for most of the Pareto solutions, acrylamide levels exceeded the EFSA recommendation. Almost equivalent optimal solutions were found for moderate processing conditions (low temperatures and/or processing times) and the propagation of the uncertainty of the acrylamide production model parameters led to Pareto fronts with notable differences from the one obtained using the nominal parameters, especially in the ranges of high values of acrylamide production and yellowness. These results can help to identify processing conditions to achieve the desired acrylamide/yellowness balance and design more robust processes allowing for the enhancement of flexibility when equivalent optimal solutions can be retrieved.

Reducing the uncertainty on chilling requirements for endodormancy breaking of temperate fruits by data-based parameter estimation of the dynamic model: a test case in apricot.

The Dynamic model has been described as one of the most accurate models to quantify chill accumulation based on hourly temperatures in nuts and temperate fruits. This model considers that a dynamic process occurs at a biochemical level that determines the endodormancy breaking through the accumulation of the so-called portions. The kinetic parameters present in the model should reflect how the fruit trees integrate chilling exposure and thus they should be characteristic for each species. However, the original parameter values, reported in the late 1980s, are still being used. Even if the use of such parameter values is useful to compare among chilling requirements (CRs) for different species or cultivars, it is not the optimal choice when one intends to explain the CR variations in different years for a given cultivar. In this work we propose a data-based model calibration that makes use of phenological data for different apricot cultivars within different years to obtain model parameters, which minimize the variations among years and that have, at the same time, physical meaning to characterize the incumbent species. Results reveal that the estimation not only reduces the accumulated portion dispersion within the considered time periods but also allows to improve the CR predictions for subsequent years. We propose a set of model parameter values to predict endodormancy breaking dates in the apricot cultivars studied here.

Dynamics of Microbial Inactivation and Acrylamide Production in High-Temperature Heat Treatments.

In food processes, optimizing processing parameters is crucial to ensure food safety, maximize food quality, and minimize the formation of potentially toxigenic compounds. This research focuses on the simultaneous impacts that severe heat treatments applied to food may have on the formation of harmful chemicals and on microbiological safety. The case studies analysed consider the appearance/synthesis of acrylamide after a sterilization heat treatment for two different foods: pureed potato and prune juice, using Geobacillus stearothermophilus as an indicator. It presents two contradictory situations: on the one hand, the application of a high-temperature treatment to a low acid food with G. stearothermophilus spores causes their inactivation, reaching food safety and stability from a microbiological point of view. On the other hand, high temperatures favour the appearance of acrylamide. In this way, the two objectives (microbiological safety and acrylamide production) are opposed. In this work, we analyse the effects of high-temperature thermal treatments (isothermal conditions between 120 and 135 °C) in food from two perspectives: microbiological safety/stability and acrylamide production. After analysing both objectives simultaneously, it is concluded that, contrary to what is expected, heat treatments at higher temperatures result in lower acrylamide production for the same level of microbial inactivation. This is due to the different dynamics and sensitivities of the processes at high temperatures. These results, as well as the presented methodology, can be a basis of analysis for decision makers to design heat treatments that ensure food safety while minimizing the amount of acrylamide (or other harmful substances) produced.

The influence of natural vs anthropogenic factors on trace metal(loid) levels in the Mussel Watch programme: Two decades of monitoring in the Spanish Mediterranean sea.

The spatial distribution and temporal trends of trace metals (i.e. Cd, Cu, Hg, Pb and Zn) and a metalloid (i.e. As) along the Spanish Mediterranean coast from 1993 to 2013 are presented with a new estimation of their background levels monitored using wild mussels. Over a 20 years period, yearly mussel monitoring was undertaken with a rigorous field sampling protocol using 3 pooled samples strategy (3 x n = 80, with 8 mussels in the 3.0 to 3.9 size categories at each site), obtained in the pre-spawning period (May-June) to minimize biological factors and seasonal variability, which is a fundamental element of the international programme. Spatial distribution was characterized every 5 years and temporal trends were determined in 11 locations. The main aims of the present long term study are to evaluate the environmental status of different coastal locations regarding trace metal levels and follow the evolution of these levels over time after the implementation of regulatory measures. Regarding spatial distribution, the highest values for Cd, Cu, Hg and Pb were found close to known highly anthopogenic cities or shipyard areas. However, As and Zn did not strictly follow this pattern, showing natural increased concentrations in the Levantine Balearic and Strait of Gibraltar-Alboran Sea demarcations respectively. These distributions are associated with the conjunction of two geological formation inputs (Massif Central in France and Iberian Pyritic Belt in Spain) and the oceanographic conditions in the adjacent coastal area. In the case of temporal trends, metal concentrations decreased significantly over time in most stations, confirming the effectiveness of the regulatory measures and prohibitions established under European legislation. Concentrations of Pb were above the established thresholds for human consumption in only 12-14% of the sampling areas. With the information obtained for this study, we estimate background concentrations and propose new Background Assesment Criteria (BAC) for the Spanish Mediterranean coast as a threshold criterion: 1.62 mg/kg d.w. for Cd, 8.75 mg/kg d.w. for Cu, 0.202 mg/kg d.w. for Hg and 2.83 mg/kg d.w. for Pb. Exceptions should exist for As and Zn, for which there should be different levels in each demarcation, due to the geological, hydrological and oceanographic peculiarities of the Spanish coast. For the Levantine-Balearic demarcation, the proposed background concentrations are 117 mg/kg d.w. for As and 200 mg/kg d.w. for Zn., whereas in the Strait of Gibraltar-Alboran Sea demarcation, they are 27.5 mg/kg d.w. for As, and 471 mg/kg d.w. for Zn. This work demonstrates the vital importance of defining the background levels of metal(loid)s at a regional or subregional level because, for areas not affected by anthropogenic causes which have high values as the result of natural processes, this would avoid the risk of constantly surpassing the levels proposed in directives.

A multi-pollutant methodology to locate a single air quality monitoring station in small and medium-size urban areas.

Air quality management is underpinned by continuous measurements of concentrations of target air pollutants in monitoring stations. Although many approaches for optimizing the number and location of air quality monitoring stations are described in the literature, these are usually focused on dense networks. However, there are small and medium-size urban areas that only require one monitoring station but also suffer from severe air pollution. Given that target pollutants are usually measured at the same sampling points; it is necessary to develop a methodology to determine the optimal location of the single station. In this paper, such a methodology is proposed based on maximizing an objective function, that balances between different pollutants measured in the network. The methodology is applied to a set of data available for the city of Cartagena, in southeast Spain. A sensitivity analysis reveals that 2 small areas of the studied city account for 80% of the optimal potential locations, which makes them ideal candidates for setting up the monitoring station. The methodology is easy to implement, robust and supports the decision-making process regarding the siting of fixed sampling sites.