Determining of risk areas due to exposure to heavy metals in the Toluca Valley using epiphytic mosses as a biomonitor

Ávila-Pérez, Pedro; Ortiz-Oliveros, Huemantzin Balan; Zarazúa-Ortega, Graciela; Tejeda-Vega, Samuel; Villalva, Alejandra S.; Sánchez-Muñoz, R.

doi:10.1016/j.jenvman.2019.04.018

Cited by 28 publications

(16 citation statements)

References 51 publications

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“…In SLP, the main source of Cu and Zn is the metallurgical complex and to a lesser extent the industrial park, Pb could have the same sources, in addition to vehicular traffic (Aguilera et al, 2019). In Toluca, the main sources of Pb can be the combustion processes of food waste, paper, plastics, textiles, rubber, wood, and metal smelting; other sources, other than combustion, from these industries; as well as the old Pb deposit from leaded gasoline (Ávila-Pérez et al, 2019).…”

Section: Most Polluted Citiesmentioning

confidence: 99%

“…Globally, Pb is also one of the metals of greatest concern, along with Cr (Aguilera, Bautista, Goguitchaichvili, et al, 2021). In the case of Mexico, practically in all cities, it is attributed the source of this metal in urban dust to vehicular traffic, however, it is also investigated how much the deposit of old Pb from leaded gasoline contributes (Ávila-Pérez et al, 2019). On the other hand, it is necessary to establish a monitoring system for heavy metals in the dust of the streets, soils, and plants of Mexican cities, as well as pollution indicators with proxy characteristics (easy analysis and low cost) that allow the analysis of thousands of dust samples to identify the sites of high concentration of heavy metals.…”

Section: Human Health Riskmentioning

confidence: 99%

See 1 more Smart Citation

Heavy Metal Contamination (Cu, Pb, Zn, Fe, and Mn) in Urban Dust and its Possible Ecological and Human Health Risk in Mexican Cities

Aguilera

Cortés

Delgado

et al. 2022

Front. Environ. Sci.

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Cities occupy a relatively small percentage of the Earth’s surface. However, they influence the entire biosphere, affect biodiversity and environmental conditions, which end up affecting human health and well-being. Therefore, it is necessary to evaluate the level of contamination by heavy metals in urban environments, as well as the possible ecological and human health risks. In this study, the urban dust of six Mexican cities was analyzed and it was found that all studied cities were contaminated, except for Mérida, when soil world background value was used as reference. In contrast, Mérida and Morelia were the most contaminated when a local background was used (decile 1). The concentrations in the cities for the metals Cu, Pb and Zn, decreased in the order CDMX > San Luis Potosí > Toluca > Morelia-Ensenada > Mérida. In the particular case of Cu and Pb, SLP accompanied CDMX as the most polluted city. For Mn and Fe concentrations, the order was CDMX > Toluca > Ensenada > SLP > Morelia-Mérida. No potential ecological risk was found due to contamination by Cu, Pb, and Zn, in the urban dust of the studied cities. However, the higher metal contribution to the potential ecological risk in all the cities was from Pb; and it represented a moderate ecological risk of more than 25% on CDMX, SLP, and Toluca sites. Pb can also be a potential risk for children’s health. In addition, chronic exposure to Fe and Mn could trigger many ailments. In the future, it is important to identify the main sources of Pb in cities and seek mitigation strategies to reduce the possible adverse effects that this metal may be causing.

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Section: Most Polluted Citiesmentioning

confidence: 99%

Section: Human Health Riskmentioning

confidence: 99%

Heavy Metal Contamination (Cu, Pb, Zn, Fe, and Mn) in Urban Dust and its Possible Ecological and Human Health Risk in Mexican Cities

Aguilera

Cortés

Delgado

et al. 2022

Front. Environ. Sci.

View full text Add to dashboard Cite

show abstract

“…When coupled with geographic information system (GIS) (Schroder and Nickel, 2019 ; Nickel & Schroder, 2017 ), moss records can help the authorities understand spatial structure (and variability) of pollutant species, which in turn can be used to establish vulnerability maps and develop spatially optimize regulatory interventions. For example, in an urban risk analysis study, in and around the Metropolitan Zone, Toluca Valley, Mexico, Avlia-Perez et al ( 2016 ) integrated moss-based results ( Fabriona cilaris and Leskea angustata ) with geostatistical tools. The study identified regions with low urban vegetation coverage and closer to highways as high-risk areas.…”

Section: Resultsmentioning

confidence: 99%

“…Industrial and densely packed urban areas bore distinct signatures of heavy metals (Cr, Cu, Zn, Pb) owing mainly to intense vehicular emissions and fossil fuel combustion. In the same area, Avlia-Perez et al ( 2016 ) used Fabriona cilaris and Leskea angustata , to find that a confluence of anthropogenic activities including combustion, vehicular emissions, biomass burning, brick kiln emissions, agricultural and livestock activities, manufacturing industry and re-deposition by the action of the wind, impacted heavy metals (Cr, Cu, Pb, Zn) occurrence in ambient air.…”

Section: Resultsmentioning

confidence: 99%

Global ambient air quality monitoring: Can mosses help? A systematic meta-analysis of literature about passive moss biomonitoring

Chaudhuri

Roy

2023

Environ Dev Sustain

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“…Avila‐Perez et al. (2019) identified risk areas due to exposure to heavy metals in the Toluca Valley using epiphytic mosses as a biomonitor. However, to our best knowledge, there are still no studies considering the risk diffusion areas in MWDC location‐allocation problems.…”

Section: Model Formulationmentioning

confidence: 99%

A Soft‐Path Solution to Risk Reduction by Modeling Medical Waste Disposal Center Location‐Allocation Optimization

Yao

Zeng

2020

Risk Analysis

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The risk of medical waste pollution and huge demand of daily medical waste disposal pose great difficulties to medical waste management. Establishing medical waste disposal centers (MWDCs) is considered one of the ways to reduce the environmental and public risk of medical waste pollution. However, how to serve the medical waste disposal demand in optimal MWDCs' locations is a key challenge due to the complexity of the whole system and relationships among stakeholders. This article develops a soft-path solution for reducing risks as well as mitigating the related costs by optimizing the MWDC location-allocation problem. A risk mitigation-oriented bilevel equilibrium optimization model is developed for modeling the Stackelberg game behavior between the local government and the medical institutions. The objectives of the local government are minimizing the total risk of loss, the subsidy costs, and the investment cost of building the MWDCs, while minimizing the disposal and transportation costs are the objectives at the medical institution level. Fuzzy random variables are introduced by combining insufficient historical data with expert knowledge via consulting surveys to describe the coexisting uncertainties in the data. To solve the model, a hybrid approach combined with the interactive fuzzy programming technique and an Entropy-Boltzmann selection-based genetic algorithm are designed and tested. The Chengdu Medical Waste Disposal Centers Planning Project is used as a practical application. The results show that it is possible to achieve a balanced market with higher economic efficiency and significantly reduced risk through an appropriate principle of interactive actions between the bilevel stakeholders.

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Determining of risk areas due to exposure to heavy metals in the Toluca Valley using epiphytic mosses as a biomonitor

Cited by 28 publications

References 51 publications

Heavy Metal Contamination (Cu, Pb, Zn, Fe, and Mn) in Urban Dust and its Possible Ecological and Human Health Risk in Mexican Cities

Heavy Metal Contamination (Cu, Pb, Zn, Fe, and Mn) in Urban Dust and its Possible Ecological and Human Health Risk in Mexican Cities

Global ambient air quality monitoring: Can mosses help? A systematic meta-analysis of literature about passive moss biomonitoring

A Soft‐Path Solution to Risk Reduction by Modeling Medical Waste Disposal Center Location‐Allocation Optimization

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