Geospatial Analysis of Zinc Contamination in Lake Ontario Sediments

Forsythe, K. Wayne; Paudel, Kamal; Märvin, Chris H.

doi:10.3808/jei.201000172

Cited by 17 publications

(28 citation statements)

References 17 publications

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“…air, temperature and precipitation) were used for climatological modelling [29], while radar data was also used for recording precipitation in flood forecasting [30]. In studies relevant to analysis of surfaces and geo-statistics, groundwater sample sensors and spectrophotometers for detection of heavy metals [31], surface soil sampling [32], sampling for zinc contamination [33], acoustic data, conductivity, temperature, and depth sensors [34] have been recorded.…”

Section: Iot Devices and Sensorsmentioning

confidence: 99%

“…Satellite communications were involved in deep-ocean tsunami measuring [18]. Some papers did not involve any wired/wireless transmissions, recording the sensory measurements at the measuring spot, and this was the case in the majority of the papers dealing with surface analysis and geo-statistics, such as [29], [31], [32], [33].…”

Section: Iot Data Transmission Standardsmentioning

confidence: 99%

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Geospatial Analysis and the Internet of Things

Kamilaris

Ostermann

2018

IJGI

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Geospatial analysis offers large potential for better understanding, modelling and visualizing our natural and artificial ecosystems, using Internet of Things as a pervasive sensing infrastructure. This paper performs a review of research work based on the IoT, in which geospatial analysis has been employed in environmental informatics. Six different geospatial analysis methods have been identified, presented together with 26 relevant IoT initiatives adopting some of these techniques. Analysis is performed in relation to the type of IoT devices used, their deployment status and data transmission standards, data types employed, and reliability of measurements. This paper scratches the surface of this combination of technologies and techniques, providing indications of how IoT, together with geospatial analysis, are currently being used in the domain of environmental research. IntroductionThe Internet of Things (IoT) includes technologies and research disciplines that enable the Internet to reach out into the real world of physical objects [1]. The vision of IoT involves seamless integration of physical devices to the internet/web, by means of well-understood, accepted and used protocols, technologies and programming/description languages, for efficient human-to-machine or machine-to-machine communication [2]. Internet-enabled things are equipped with sensors, measuring the physical world and its phenomena such as temperature, humidity, radiation, electromagnetism, noise, chemicals etc. Moreover, the rise of multi-sensory mobile phones offers advanced sensing capabilities, such as measuring proximity, acceleration and location, recording audio/noise, sensing electromagnetism or capturing images and videos [3]. An important characteristic of the measurements performed by Internet-enabled things (i.e. sensors, mobile phones, cameras etc.) is the geo-location where each measurement was done. As the physical or artificial environments are generally quite complex, being characterized by a wide variety of parameters, a rich collection of measurements in space and time is necessary in order to model and understand these ecosystems [4]. Hence, sensory-based IoT information needs to be aggregated, stored and analyzed for more elaborate and holistic reasoning and inference. To exploit this variety of IoT measurements in type, space and time, geospatial analysis is used in order to provide high-quality analytics and insights [5].The contribution of this paper is to survey IoT applications that employ analytic operations that focus on location, examining the various geospatial analysis techniques employed. It is noted that we refer to the IoT as used in environmental informatics, i.e. sensing equipment and measurement systems recording

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Section: Iot Devices and Sensorsmentioning

confidence: 99%

Section: Iot Data Transmission Standardsmentioning

confidence: 99%

Geospatial Analysis and the Internet of Things

Kamilaris

Ostermann

2018

IJGI

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“…There are many derivations of the kriging model. Ordinary kriging was chosen for these analyses based on successful applications of this technique in the Great Lakes by Forsythe and Marvin (2005), Forsythe et al (2010), Gawedzki and Forsythe (2012), and Forsythe et al (2015).…”

Section: Methodsmentioning

confidence: 99%

“…Ordinary kriging techniques were utilized based on the results of Forsythe et al (2004), Forsythe et al (2010), Jakubek and Forsythe (2004), and Forsythe and Watt (2006). The kriging technique includes cross validation procedures that provide measures of accuracy for the predictions that are made.…”

Section: Lead Kriging Mapsmentioning

confidence: 99%

Using geovisualization to assess lead sediment contamination in Lake St. Clair

Forsythe¹,

Märvin

Valancius³

et al. 2016

Canadian Geographies / Géographies canadiennes

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“…The analysis was run independently on each of the two contaminants and their associated subsurface depths. The kriging geospatial technique was originally developed for the mining industry to estimate ore reserves, but has been effectively been used to estimate sediment contamination [16][17][18].…”

Section: Methodsmentioning

confidence: 99%

Assessing Anthracene and Arsenic Contamination within Buffalo River Sediments

Gawedzki

Forsythe

2012

International Journal of Ecology

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Anthracene and arsenic contamination concentrations at various depths in the Buffalo River were analyzed in this study. Anthracene is known to cause damage to human skin and arsenic has been linked to lung and liver cancer. The Buffalo River is labelled as an Area of Concern defined by the Great Lakes Water Quality Agreement between Canada and the United States. It has a long history of industrial activity located in its near vicinity that has contributed to its pollution. An ordinary kriging spatial interpolation technique was used to calculate estimates between sample locations for anthracene and arsenic at various depths. The results show that both anthracene and arsenic surface sediment (0–30 cm) is less contaminated than all subsurface depths. There is variability of pollution within the different subsurface levels (30–60 cm, 60–90 cm, 90–120 cm, 120–150 cm) and along the river course, but major clusters are identified throughout all depths for both anthracene and arsenic.

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Geospatial Analysis of Zinc Contamination in Lake Ontario Sediments

Cited by 17 publications

References 17 publications

Geospatial Analysis and the Internet of Things

Geospatial Analysis and the Internet of Things

Using geovisualization to assess lead sediment contamination in Lake St. Clair

Assessing Anthracene and Arsenic Contamination within Buffalo River Sediments

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