Papers

@article{aguado_molina_comparative_2025,

title = {A comparative sustainability assessment of several grid energy storage technologies},

author = {Roque Aguado Molina and Juan José Cartelle Barros and María Pilar De-La-Cruz-López and Manuel Lara Coira and Alfredo Del-Caño-Gochi},

url = {https://www.sciencedirect.com/science/article/pii/S030626192500978X},

doi = {10.1016/j.apenergy.2025.126248},

issn = {0306-2619},

year  = {2025},

date = {2025-10-01},

urldate = {2025-10-01},

journal = {Applied Energy},

volume = {396},

pages = {126248},

abstract = {The global energy transition toward a low-carbon economy is driving increasing penetration of variable energy sources into electricity markets. This unprecedented deployment of intermittent renewables confronts decision-makers in the electricity sector with the challenge of selecting among different energy storage technologies, a choice that must be made on the basis of sustainability criteria. Existing studies present shortcomings, including the absence of the social dimension, the use of weights against sustainable development, or the application of methodologies affected by the rank reversal issue, among others. To address gaps in current knowledge, this study presents a novel probabilistic model for assessing the global sustainability of grid energy storage technologies. The model is based on the MIVES (Modelo Integrado de Valor para una Evaluación Sostenible)–Monte Carlo method, which combines requirement trees, value functions, the analytic hierarchy process, and probabilistic simulations. It consists of 19 indicators and makes it possible to obtain a sustainability index (SI), as well as partial economic, social, environmental, and technical indices for each technology. Data from an extensive literature review were integrated with expert input and estimations based on linear correlations to address challenges in assessing social and environmental indicators. The model was applied to six technologies: pumped hydroelectric energy storage (PHES), compressed air energy storage (CAES), liquid air energy storage (LAES), vanadium redox flow batteries (VRFB), sodium-sulfur batteries (NaSB), and hydrogen energy storage (HES). A comprehensive sensitivity analysis is also included. To the best of the authors’ knowledge, no existing study has utilized the innovative methodology presented in this paper, nor has any related research achieved the scope and depth proposed here. The top-performing technologies identified for the economic, social, environmental, and technical dimensions of sustainability are CAES, VRFB, LAES, and PHES, respectively. In terms of global sustainability, VRFB, LAES and PHES are the best options, while HES consistently ranks last. NaSB and CAES occupy intermediate positions.},

keywords = {Electricity storage, Global sustainability, MIVES–Monte Carlo method, Monte Carlo simulation, Multi-criteria decision-making, Requirement trees, Value functions},

pubstate = {published},

tppubtype = {article}

}

@article{soage_statistical_2024,

title = {Statistical assessment of the financial performance of shale-gas wells coupling stochastic and numerical simulation},

author = {Andrés Soage-Quintáns and Luis Ramírez-Palacios and Rubén Juanes and Luis Cueto-Felgueroso and Ignasi Colominas-Ezponda},

url = {https://www.sciencedirect.com/science/article/pii/S1995822624002024},

doi = {10.1016/j.petsci.2024.07.018},

issn = {1995-8226},

year  = {2024},

date = {2024-12-01},

urldate = {2026-07-28},

journal = {Petroleum Science},

volume = {21},

number = {6},

pages = {4497–4511},

abstract = {We present a new methodology to statistically determine the net present value (NPV) and internal rate of return (IRR) as financial estimators of shale gas investments. Our method allows us to forecast, in a fully probabilistic setting, financial performance risk and to understand the importance of the different factors that impact investment. The methodology developed in this study combines, through Monte Carlo simulation, the computational modeling of gas production from shale gas wells with a stochastic simulation of gas price as a geometric Brownian motion (GMB). To illustrate the methodology's validity, we apply it to an analysis of investments in shale gas wells. Our results show that gas price volatility is a key variable in the performance of an investment of this type, in such a way that at high volatilities, the potential return on an investment in shale gas increases significantly, but so do the risks of economic loss. This finding is consistent with the history of shale gas operations in which huge investment successes coexist with unexpected investment failures.},

keywords = {Financial estimator, Gas volatility, Internal rate return, Kernel density function, Monte Carlo simulation, Net present value, Shale gas, Stochastic model},

pubstate = {published},

tppubtype = {article}

}

@article{tan_integrated_2023,

title = {An integrated approach for quantifying source apportionment and source-oriented health risk of heavy metals in soils near an old industrial area},

author = {Chang Tan and Hao Wang and Qingchun Yang and Liyuan Yuan and Yuling Zhang and Jordi Delgado-Martín},

url = {https://www.sciencedirect.com/science/article/pii/S0269749123002737},

doi = {10.1016/j.envpol.2023.121271},

issn = {0269-7491},

year  = {2023},

date = {2023-04-01},

urldate = {2026-08-13},

journal = {Environmental Pollution},

volume = {323},

pages = {121271},

abstract = {Soil contamination of heavy metals (HMs) caused by the long-term industrial activities has become a major environmental issue due to its adverse effects on human health and ecosystem. In this paper, 50 soil samples were analyzed to evaluate the contamination characteristics, source apportionment and source-oriented health risk of HMs in soils near an old industrial area in NE China by applying an integrated approach of Pearson correlation analysis, Positive matrix factorization (PMF) model and Monte Carlo simulation. The results showed that the mean concentrations of all HMs greatly exceeded the soil background values (SBV), and the surface soils in the study area were heavily polluted with HMs, displaying a very high ecological risk. The toxic HMs emitted from the bullet production were identified as the primary source of HMs contamination in soils, with a contribution rate of 33.3%. The human health risk assessment (HHRA) suggested that the Hazard quotient (HQ) values of all HMs for children and adults are within the acceptable risk level (HQ < 1). The carcinogenic risk (CR) values of HMs for children and adults significantly exceeded the acceptable threshold of 1E-6 with a basic trend: As > Pb > Cr > Co > Ni, indicating a high cancer risk. For source-oriented health risk, the CR of four pollution sources for children and adults shows a same trend: Factor 4 > Factor 3 > Factor 2 > Factor 1. Among those, the source of HMs pollution from bullet production is the largest contributor to cancer risk, and As and Pb are the most important HMs pollutants that cause cancer risk to humans. The present study sheds some light on the contamination characteristics, source apportionment and source-health risk assessment of HMs in industrially contaminated soils, which helps improve the management of environmental risk control, prevention and remediation.},

keywords = {Heavy metals, Monte Carlo simulation, Positive matrix factorization model, Probabilistic health risk assessment, Soil contamination, Source apportionment},

pubstate = {published},

tppubtype = {article}

}

@article{gui_spatial_2023,

title = {Spatial distribution, contamination characteristics and ecological-health risk assessment of toxic heavy metals in soils near a smelting area},

author = {Han Gui and Qingchun Yang and Xingyu Lu and Hualin Wang and Qingbao Gu and Jordi Delgado-Martín},

url = {https://www.sciencedirect.com/science/article/pii/S0013935123001202},

doi = {10.1016/j.envres.2023.115328},

issn = {0013-9351},

year  = {2023},

date = {2023-04-01},

urldate = {2026-08-13},

journal = {Environmental Research},

volume = {222},

pages = {115328},

abstract = {Soil heavy metals (HMs) contamination stemming from smelting and mining activities is becoming a global concern due to its devastating impacts on the environment and human health. In this study, 128 soil samples were investigated to assess the spatial distribution, contamination characteristics, ecological and human health risk of HMs in soils near a smelting area by using BP artificial neural network (BP-ANN) and Monte Carlo simulation. The results showed that the concentrations of all five HMs in the soil greatly exceeded the background value of study area with a basic trend: Pb > As > Cr > Cd > Hg, indicating a high pollution level. Arsenic and lead were the major pollutants in the study area with an exceedance rate of 78.95% and 28.95%, respectively. The toxic fume and dust emitted during the smelting process were identified as the major sources of HMs pollution in soil, while Cd pollution was mainly caused by agricultural activities near the study area. The probabilistic risk assessment suggested that the average HQ values of five HMs for children and adults exceeded the acceptable threshold with a trend: As > Pb > Cr > Cd > Hg. The average CR values of As, Cr and Pb for all population were greatly larger than the acceptable threshold (CR ≥ 1), indicating a high cancer risk. However, the CR values of Cd for adults and children were within the acceptable threshold (CR < 1), implying no cancer risk. The results of the present study can provide some insight into the contamination characteristics, ecological and human health risk of HMs in contaminated soils by mining and smelting activities, which can help prevent and control soil pollution and environmental risk.},

keywords = {BP artificial Neural network, Ecological risk, Health risk assessment, Heavy metals, Monte Carlo simulation, Soil contamination},

pubstate = {published},

tppubtype = {article}

}

@article{yang_bioavailability_2022,

title = {Bioavailability and health risk of toxic heavy metals (As, Hg, Pb and Cd) in urban soils: A Monte Carlo simulation approach},

author = {Qingchun Yang and Liangmiao Zhang and Hualin Wang and Jordi Delgado-Martín},

url = {https://www.sciencedirect.com/science/article/pii/S0013935122010994},

doi = {10.1016/j.envres.2022.113772},

issn = {0013-9351},

year  = {2022},

date = {2022-11-01},

urldate = {2026-08-10},

journal = {Environmental Research},

volume = {214},

pages = {113772},

abstract = {Toxic heavy metals pollution in urban soil has become a major global issue due to its adverse effects on the environment and human health. In this paper, 26 soil samples were analyzed to assess the speciation, bioavailability and human health risk of Arsenic (As), Mercury (Hg), Lead (Pb) and Cadmium (Cd) in urban soils of a heavy industrial city in NE China by using a Monte Carlo simulation approach. The results showed that As, Hg, Pb and Cd concentrations in the soil all exceed the corresponding background value of study area. Mercury displays the highest value of geo-accumulation index (Igeo), followed by Cd, Pb and As. The pollution load index (PLI) value (>2) indicates a moderate pollution level in the study area. The chemical speciation of HMs mainly exists in residual fraction except Cd. The probabilistic health risk assessment demonstrated that the mean values of Total Carcinogenic Risk (TCR) and Hazard Index (HI) calculated with total concentration are at the unacceptable level, with a higher risk to children than adults. However, the mean values calculated with bioavailable fraction are all within the acceptable level. The mean value of TCR and HI obtained by bioavailable fraction is about 96% and 95% lower than that obtained by total concentration, respectively. Thus, this study suggested that the bioavailable fraction of HMs is a more reliable parameter for health risk assessment, while the total concentration of HMs can overestimate the true risk. The results of this study provide some insight into the speciation, bioavailability and health risks of toxic heavy metals in urban soils in those heavy industrial cities.},

keywords = {Bioavailable fraction, Monte Carlo simulation, Probabilistic health risk assessment, Toxic heavy metals, Urban soil},

pubstate = {published},

tppubtype = {article}

}