Detection, provenance and associated environmental risks of water quality pollutants during anomaly events in River Atoyac, Central Mexico: A real-time monitoring approach

Hernández‐Ramírez, Aracely; Martínez-Tavera, E.; Rodríguez-Espinosa, P.F.; Mendoza-Pérez, Jorge A.; Tabla-Hernandez, J.; Escobedo-Urías, Diana Cecilia; Jonathan, M.P.; Sujitha, S.B.

doi:10.1016/j.scitotenv.2019.03.138

Cited by 35 publications

(7 citation statements)

References 73 publications

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“…Los metales y metaloides son elementos con propiedades metálicas que incluyen a los que presentan una densidad mayor a 5 g/cm 3 como el mercurio (Hg), el arsénico (As), el plomo (Pb) o el cromo (Cr), el zinc (Zn), el cadmio (Cd) y el níquel (Ni), por mencionar algunos (Azizullah et al, 2011;Covarrubias & Peña-Cabriales, 2017). Varios autores asocian la presencia de los metales pesados y los metaloides a la aplicación de pesticidas y fertilizantes (Kong et al, 2014), a las descargas de plantas termoeléctricas (Villalba-Atondo et al, 2013), a las industrias automotrices (Hernández-Ramírez et al, 2019), a los residuos de baterías, pigmentos, recubrimientos, estabilizadores para plásticos, desechos hospitalarios e industriales, que después de ciertos procesos son transportados a los cultivos, animales, aguas residuales y al hombre (Abdel-Satar et al, 2017;Yuan et al, 2019;Wijesiri et al, 2019). Un caso particular es el estudio de Yang et al (2013) donde reportan que las concentraciones de Cu, Zn, Cd y Pb en suelos agrícolas han superado las de los suelos no agrícolas, posiblemente por el riego con aguas residuales o bien, por el uso de fertilizantes y pesticidas.…”

Section: Contaminación Por Metales Y Metaloidesunclassified

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Influencia de la contaminación del agua y el suelo en el desarrollo agrícola nacional e internacional

Velázquez-Chávez

Ortiz-Sánchez²,

Chávez-Simental³

et al. 2022

TIP RECQB

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Los contaminantes del agua y el suelo son un tema polémico por los problemas que ocasionan a la agricultura moderna. El crecimiento de la población ha provocado la expansión de las áreas de cultivo, sobreexplotación de los recursos naturales, así como problemas de disponibilidad y degradación de los recursos. Esta situación se agrava debido al manejo inapropiado de los residuos industriales, de los pesticidas y los fertilizantes que contaminan con elementos orgánicos e inorgánicos persistiendo en el ambiente y dispersándose por medio de los procesos de bioacumulación en especies animales y vegetales, lo que contribuye al desequilibrio de los ecosistemas. Este artículo de revisión presenta un panorama general de los principales factores de contaminación que afectan los recursos naturales y en específico, la situación que enfrenta la agricultura en México y otros países por causa del transporte de compuestos químicos a los cultivos.

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Section: Contaminación Por Metales Y Metaloidesunclassified

“…n los últimos años, se han realizado diversos estudios relacionados con la contaminación de los recursos naturales a escala nacional (Hernández-Ramírez et al, 2019) e internacional (Ahmed, Matsumoto,…”

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Influencia de la contaminación del agua y el suelo en el desarrollo agrícola nacional e internacional

Velázquez-Chávez

Ortiz-Sánchez²,

Chávez-Simental³

et al. 2022

TIP RECQB

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“…Perubahan data yang tidak wajar ini dapat terjadi karena disebabkan kerusakan sensor maupun adanya upaya sabotase pada kolam. Penelitian terkait deteksi anomali pada air telah dilakukan pada [10][11][12][13][14] untuk keperluan air minum maupun air sungai.…”

Section: Pendahuluanunclassified

Deteksi Anomali Konduktivitas Air Menggunakan Kalman Filter

Putra

Koprawi

Ashari

et al. 2022

Buletin Ilmiah Sarjana Teknik Elektro

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Water quality is an essential part of shrimp farming. Data integrity is one of the challenges in building a water conductivity monitoring system. Data read by the sensor should represent the physical conditions that occur. However, some factors can cause abnormal data changes. This abnormal data change can occur due to sensor damage or an attempt to sabotage the pool. In this study, a data anomaly detection algorithm was built using the Kalman filter and standard deviation to solve the problem of determining the normal range of data. The designed algorithm was then tested and evaluated using Arduino nano, Arduino mega, and Wemos D1 Microcontrollers to determine the algorithm's performance on limited computing devices. Based on the data analysis that has been carried out, it is found that the anomaly detection algorithm based on the Kalman filter has an accuracy of 92.5% and can detect anomaly data that occurs with TPF = 1 and FNR = 0 values. The implementation of the detection algorithm on the microcontroller shows that WEMOS D1 (ESP8266) has an excellent average computational speed of 27.99 us. As for the stability of the Arduino Nano (ATMEGA328) and Arduino Mega 2560 (ATMEGA 2560) microcontrollers, the computation time deviation is about 2.8 us. Kualitas air merupakan bagian penting pada budidaya udang. Salah satu tantangan dalam membangun sebuah sistem monitoring konduktivitas air adalah Keutuhan data. Suatu data yang terbaca oleh sensor seharusnya mewakili kondisi fisik yang terjadi. Akan tetapi ada faktor-faktor dapat menyebabkan perubahan data yang tidak wajar. Perubahan data yang tidak wajar ini dapat terjadi karena disebabkan kerusakan sensor maupun adanya upaya sabotase pada kolam. Pada penelitian ini dibangun sebuah algoritma deteksi anomali data menggunakan Kalman filter dan standar deviasi untuk mengatasi masalah penentuan rentang data normal. Algoritma yang dirancang kemudian diuji dan dievaluasi dengan menggunakan Mikrokontroller Arduino nano, Arduino mega dan Wemos D1 untuk mengetahui performa algoritma yang dirancang pada perangkat komputasi terbatas. Berdasarkan analisis data yang telah dilakukan didapatkan hasil bahwa algoritma deteksi anomali berbasis kalman filter memiliki akurasi 92,5% dan dapat mendeteksi data anomali yang terjadi dengan nilai TPF =1 dan FNR=0. Implementasi algoritma deteksi pada mikrokontroller menunjukkan bahwa WEMOS D1 (ESP8266) memiliki rata-rata kecepatan komputasi yang baik yaitu 27,99 us. Sedangkan untuk kestabilan mikrokontroller Arduino Nano (ATMEGA328) dan Arduino Mega 2560 (ATMEGA 2560) memiliki deviasi waktu komputasi sekitar 2,8 us.

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“…It is challenging to identify multiple contamination sources in polluted surface water simultaneously because each pollution source contains a mixture of chemical pollutants. The commonly used method for identifying pollutants is based on water quality parameters such as pH, COD, and NH 3 -N. , However, different pollution sources may influence the same water quality parameter, indicating that the water quality parameter and the pollution source do not have one-to-one correspondence. This lack of correspondence can affect the accuracy of the pollution source identification.…”

Section: Introductionmentioning

confidence: 99%

Identification of Multiple Surface Water Contamination Sources Based on UV–Vis Spectral Unmixing with Turbidity Adaptiveness

Li,

Shao,

Wei

et al. 2024

ACS EST Water

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Surface water contamination incidents are typically caused by an enterprise's excessive or clandestine illegal discharge. Accurate and rapid identification of pollution sources is crucial for timely pollution control and the enforcement of penalties for the responsible enterprises. At present, the identification of water pollution sources based on UV−Vis spectroscopy mostly relies on pattern recognition. However, this method is suitable only for identifying a single pollution source and cannot differentiate mixtures of multiple pollution sources. To solve this problem, an enhanced spectral unmixing method is proposed in this paper to identify multiple sources of contamination in surface water. In this method, the spectra of multiple pollution sources are used as endmembers to unmix the spectrum of the polluted surface water. The proportions (abundances) of each pollution source are then calculated to identify the pollution sources and determine the types of pollution discharge enterprises. Generally, the linear unmixing model is suitable for low-turbidity surface water, while the nonlinear unmixing model is suitable for high-turbidity surface water. A single linear or nonlinear unmixing model is not adaptive to turbidity variations in polluted surface water. Therefore, an unsupervised threshold SK-Hype hybrid unmixing algorithm is proposed in this paper. A weighted nonlinear fluctuation of endmembers is introduced to the linear function to characterize the turbidity change of surface water, and its weight is adaptively determined by the mixing mechanism of the target mixture. In this paper, experiments were conducted to unmix turbid polluted water and polluted surface water. The experimental results demonstrate that the algorithm presented in this paper exhibits higher accuracy in identifying sources of surface water pollution with turbidity adaptiveness.

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Detection, provenance and associated environmental risks of water quality pollutants during anomaly events in River Atoyac, Central Mexico: A real-time monitoring approach

Cited by 35 publications

References 73 publications

Influencia de la contaminación del agua y el suelo en el desarrollo agrícola nacional e internacional

Influencia de la contaminación del agua y el suelo en el desarrollo agrícola nacional e internacional

Deteksi Anomali Konduktivitas Air Menggunakan Kalman Filter

Identification of Multiple Surface Water Contamination Sources Based on UV–Vis Spectral Unmixing with Turbidity Adaptiveness

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