A comparison of XRFS and ICP-OES methods for soil trace metal analyses in a mining impacted agricultural watershed

Sikora, Amy; Maguire, Lane W.; Nairn, Robert W.; Knox, Robert C.

doi:10.1007/s10661-021-09275-9

“…The big data thinking and technology are applied in environmental governance to provide data support for environmental public governance. Through data collection, realtime monitoring, the citizen participation in the form of management, and environmental governance, it can provide scientific and accurate thinking for the government decision-making in public environment monitoring and early warning [3].…”

Section: Introductionmentioning

confidence: 99%

A Monitoring System for Air Quality and Soil Environment in Mining Areas Based on the Internet of Things

Dai

¹

,

Huang

²

,

Mao

³

2022

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In order to solve the intensifying problem of heavy metal pollution of soil in mining areas, a method for monitoring air quality and soil environment in mining areas based on the Internet of Things is proposed. Using meta-analysis method and health risk assessment method, the impact of mining on soil heavy metal content in Southwest China was quantitatively analyzed, and the relationship between soil heavy metal value and its potential influencing factors was discussed, as well as the heavy metal pollution, ecological risk, and health caused by soil mining activities. Risks were assessed. The results showed that artificial and oral intake were the main modes of soil heavy metal exposure, with the highest daily intakes for noncarcinogenic risk children and the highest daily intakes for carcinogenic risk adult females. The noncarcinogenic risk (HQ>1) of soil As and Pb exposure to children was 3.74 and 1.44, respectively. The carcinogenic risk values of As, Cd, Cr, and Ni in soil were all higher than 10-6, indicating that the carcinogenic risk was within the tolerance range of human body. Children were exposed to the combined noncarcinogenic risk ( HI = 3.83 ), and the risk values of the three types of recipients were 1.19 × 10 − 4 , 1.21 × 10 − 4 , and 1.06 × 10 − 4 , respectively. The correlation between heavy metal content and environmental factors was obtained. It is verified that the system in this paper can effectively monitor the meteorological environment and soil environment, and at the same time, it reveals the pollution law of heavy metals in the soil of the mining area, which provides supporting conditions for future mining and heavy metal pollution management.

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“…Para la medición de la concentración de elementos menores y traza se analizaron 324 muestras de roca caliza y lutita, libres de superficie de meteorización y sin presencia de cementos secundarios o macrofósiles, las cuales, fueron pulverizados en un mortero de ágata hasta obtener polvo fino (<75 µm). Los contenidos de Al, K, Zr, Ca, Fe, V, Mo, Ni, Zn y S de las muestras pulverizadas se determinaron usando un analizador portátil de fluorescencia de rayos-X (Thermo Scientific Niton XL3t) en modo suelo, usando un tiempo de medición de 120 s. Este equipo ha demostrado tener una buena correlación con los resultados de laboratorio, además de tener resultados rápidos y de bajo costo (Lin, 2009;Somarin et al, 2012;Sikora et al, 2021). El equipo se localiza en el Laboratorio de Yacimientos Minerales del Centro de Geociencias de la UNAM.…”

Section: Métodos Analíticosunclassified

Microfacies y edades U–Pb en lutitas gasíferas del Cretácico Superior de las cuencas de Sabinas y Parras, NE de México

González--Betancourt¹,

et al. 2022

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En este trabajo se pretende observar los cambios en los patrones de sedimentación y fauna de las Formaciones Eagle Ford e Indidura que afloran en el estado de Coahuila. Estas sucesiones sedimentarias se depositaron dentro del rango Cenomaniano-Turoniano en el interior de la cuenca de Sabinas y se componen de facies arcillosas, carbonatadas, bioclásticas y arenosas. Fueron descritas nueve microfacies (Mf), la Mf-1 mudstone; la Mf-2 lutita y wackestone con foraminíferos oportunistas; la Mf-3 wackestone de foraminíferos especialistas, la Mf-4 wackestone y packstone de radiolarios; la Mf-5 wackestone y packstone con filamentos; la Mf-6 grainstone peloidal; la Mf-7 grainstone bioclástico; la Mf-8 calcarenitas; y la Mf-9 tobas volcánicas intercaladas en la sucesión. La ocurrencia del evento oceánico anóxico OAE-2 (OAE del inglés: Oceanic Anoxic Event) está presente dentro de la sucesión sedimentaria, indicado por los depósitos de lutitas negras ricas en materia orgánica, la muerte masiva de foraminíferos planctónicos, abundantes filamentos, cristales de pirita y las concentraciones elevadas de elementos redox como el V, Mo y Mn. Sin embargo, las condiciones de depósito presentan una variación en la concentración de oxígeno, micronutrientes y materia orgánica a lo largo de la paleogeografía. Elementos como Ni y Zn en las microfacies 1, 2, 3 y 4 evidencian una alta productividad, la cual está asociada a la presencia de organismos planctónicos y/o radiolarios. Otros elementos químicos como el Al, K y Zr muestran un incremento en el aporte de material detrítico dentro de la cuenca asociado a las microfacies arcillosas y arenosas. El contenido de materia orgánica es más elevado de la microfacies 2 a la 5 y además se considera de origen autóctono. La variación en las condiciones óxico-anóxicas es notoria gracias a la relación de foraminíferos planctónicos-bentónicos y las relaciones geoquímicas entre los elementos de Mo, Va, Ni y S. En la Formación Indidura las edades de U–Pb en granos de circón procedentes de capas de tobas restringen el depósito entre 98.2 ± 0.9 Ma a los 91.7 ± 0.8 Ma, es decir Cenomaniano- Turoniano, aunque la presencia de Praeglobotruncana a la base restringe el depósito al Cenomaniano tardío- Turoniano. Mientras que la edad de la Formación Eagle Ford está indicada por los géneros Rotalipora cushmani, Hedbergella y Helveglobotruncana helvética del Cenomaniano y Turoniano.

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“…Soils in different regions have unique compositions and properties, so rapid and accurate identification and classification of soils in different regions is of great significance for the rational use of land resources, optimization of agricultural production and protection of the ecological environment. In recent years, atomic absorption spectrometry (AAS), [1] X-ray fluorescence spectrometry (XRF), [2] X-ray diffraction (XRD), [3] plasma emission spectrometry (ICP-OES), [4] infrared spectrometry (IR), [5] ion chromatography (IC), [6] gas chromatographymass spectrometry (GC-MS) [7] and high performance liquid chromatography (HPLC). [8] Various analytical techniques have been used for qualitative and quantitative analysis of soil, but all of them have some shortcomings.…”

Section: Introductionmentioning

confidence: 99%

Laser induced breakdown spectroscopy (LIBS) combined with adaptive particle swarm optimization-based radial basis neural network (APSO-RBF) for rapid identification of soils from different regions

Chen,

Hao,

Li

et al. 2024

Preprint

0

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Traditional soil identification methods require a lot of time, while this paper proposes a combination of laser-induced breakdown spectroscopy (LIBS) and adaptive particle swarm optimization-based radial basis neural network (APSO-RBF), which is able to quickly and accurately classify soils from different regions, and the experimental results show that the classification accuracy in the test set reaches 99.8148%, which is verified by comparing with the backpropagation ( BP) algorithm, Adaptive Particle Swarm Optimization-Based Backpropagation (APSO-BP) algorithm and Radial Basis Neural Network (RBF) algorithm, which verifies the powerful classification performance of the model.

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A comparison of XRFS and ICP-OES methods for soil trace metal analyses in a mining impacted agricultural watershed

Cited by 8 publications

References 32 publications

A Monitoring System for Air Quality and Soil Environment in Mining Areas Based on the Internet of Things

A Monitoring System for Air Quality and Soil Environment in Mining Areas Based on the Internet of Things

Microfacies y edades U–Pb en lutitas gasíferas del Cretácico Superior de las cuencas de Sabinas y Parras, NE de México

Laser induced breakdown spectroscopy (LIBS) combined with adaptive particle swarm optimization-based radial basis neural network (APSO-RBF) for rapid identification of soils from different regions

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