Estimation of trace-mercury concentration in water using a smartphone

Hatiboruah, Diganta; Das, Trishna; Chamuah, Nabadweep; Rabha, Diganta; Talukdar, Bishal; Bora, Utpal; Ahamad, Kamal Uddin; Nath, Pabitra

doi:10.1016/j.measurement.2020.107507

Cited by 32 publications

(17 citation statements)

References 29 publications

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“…It should be highlighted that using mobile phones for measurements is not a new idea; however, we have not met such an approach in underground mining. Mobile phones have been used in tele-medicine [9], in condition monitoring [10,11], and environment monitoring [12,13]. See [14,15] for a review.…”

Section: Introductionmentioning

confidence: 99%

A Portable Environmental Data-Monitoring System for Air Hazard Evaluation in Deep Underground Mines

et al. 2020

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Air-quality measurements in a deep underground mine are a critical issue. The cost of ventilation, as well as the geometry of the considered mine, make this process very difficult, and local air quality may be a danger to miners. Thus, portable, personal devices are required to inform miners about gas hazards. There are available tools for that purpose; however, they do not allow the storage of data collected during a shift. Moreover, they do not allow the basic analysis of the acquired data cost-effectively. This paper aims to present a system using low-cost gas sensors and microcontrollers, and takes advantage of commonly used smartphones as a computing and visualization resource. Finally, we demonstrate monitoring system results from a test in an underground mine located in Poland.

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Section: Introductionmentioning

confidence: 99%

A Portable Environmental Data-Monitoring System for Air Hazard Evaluation in Deep Underground Mines

et al. 2020

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“…Distinguishing between chl-a and CDOM, which both absorb in the B and G bands, may require a three-band algorithm that also estimates the backscattering coefficient b b from the R-band (Hoge and Lyon, 1996). Alternative color spaces like relative RGB (Hoguane et al, 2012;Iwaki et al, 2021), hue-saturationintensity (Hatiboruah et al, 2020), and CIE L*a*b* (Watanabe et al, 2016) are also worth exploring. Potential algorithms may be identified through spectral convolution of archival R rs spectra (Burggraaff, 2020).…”

Section: Recommendationsmentioning

confidence: 99%

“…Smartphones are especially effective as low-cost sensing platforms thanks to their wide availability, cameras, and functionalities including accelerometers, GPS, and wireless communications. They are already commonly used in place of professional sensors in laboratories (Friedrichs et al, 2017;Hatiboruah et al, 2020). However, what smartphones truly excel at is providing a platform for citizen science in the field (Snik et al, 2014;Garcia-Soto et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Accuracy and Reproducibility of Above-Water Radiometry With Calibrated Smartphone Cameras Using RAW Data

Burggraaff

Werther²,

Boss³

et al. 2022

Front. Remote Sens.

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Consumer cameras, especially on smartphones, are popular and effective instruments for above-water radiometry. The remote sensing reflectance Rrs is measured above the water surface and used to estimate inherent optical properties and constituent concentrations. Two smartphone apps, HydroColor and EyeOnWater, are used worldwide by professional and citizen scientists alike. However, consumer camera data have problems with accuracy and reproducibility between cameras, with systematic differences of up to 40% in intercomparisons. These problems stem from the need, until recently, to use JPEG data. Lossless data, in the RAW format, and calibrations of the spectral and radiometric response of consumer cameras can now be used to significantly improve the data quality. Here, we apply these methods to above-water radiometry. The resulting accuracy in Rrs is around 10% in the red, green, and blue (RGB) bands and 2% in the RGB band ratios, similar to professional instruments and up to 9 times better than existing smartphone-based methods. Data from different smartphones are reproducible to within measurement uncertainties, which are on the percent level. The primary sources of uncertainty are environmental factors and sensor noise. We conclude that using RAW data, smartphones and other consumer cameras are complementary to professional instruments in terms of data quality. We offer practical recommendations for using consumer cameras in professional and citizen science.

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“…Despite being accurate, this procedure is inefficient, resource-consuming and offline because no real-time information is provided—which is essential to detect outbreaks of contaminated water (see Table 9 ). To evaluate the quality of water in real-time, electrochemical sensors can monitor changes in water parameters that become affected by chemical and biological pollutants, such as turbidity, free/total chlorine, oxidation-reduction potential, electrical conductivity, pH, nitrates level and temperature [ 223 , 224 , 225 ]. Furthermore, further approaches have proposed the detection of floating debris in contaminated water by means of aquatic sensors embedding a CMOS camera [ 226 ].…”

Section: Sensors: Definition and Taxonomymentioning

confidence: 99%

Sensors for Context-Aware Smart Healthcare: A Security Perspective

Batista

Moncusi

López-Aguilar³

et al. 2021

Sensors

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The advances in the miniaturisation of electronic devices and the deployment of cheaper and faster data networks have propelled environments augmented with contextual and real-time information, such as smart homes and smart cities. These context-aware environments have opened the door to numerous opportunities for providing added-value, accurate and personalised services to citizens. In particular, smart healthcare, regarded as the natural evolution of electronic health and mobile health, contributes to enhance medical services and people’s welfare, while shortening waiting times and decreasing healthcare expenditure. However, the large number, variety and complexity of devices and systems involved in smart health systems involve a number of challenging considerations to be considered, particularly from security and privacy perspectives. To this aim, this article provides a thorough technical review on the deployment of secure smart health services, ranging from the very collection of sensors data (either related to the medical conditions of individuals or to their immediate context), the transmission of these data through wireless communication networks, to the final storage and analysis of such information in the appropriate health information systems. As a result, we provide practitioners with a comprehensive overview of the existing vulnerabilities and solutions in the technical side of smart healthcare.

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Estimation of trace-mercury concentration in water using a smartphone

Cited by 32 publications

References 29 publications

A Portable Environmental Data-Monitoring System for Air Hazard Evaluation in Deep Underground Mines

A Portable Environmental Data-Monitoring System for Air Hazard Evaluation in Deep Underground Mines

Accuracy and Reproducibility of Above-Water Radiometry With Calibrated Smartphone Cameras Using RAW Data

Sensors for Context-Aware Smart Healthcare: A Security Perspective

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