Integrating modelling and smart sensors for environmental and human health

Reis, Stefan; Seto, Edmund; Northcross, Amanda; Quinn, Nigel W.T.; Convertino, Matteo; Jones, R. L.; Maier, Holger R.; Schlink, Uwe; Steinle, Susanne; Vieno, Massimo; Wimberly, Michael C.

doi:10.1016/j.envsoft.2015.06.003

Cited by 87 publications

(58 citation statements)

References 68 publications

(76 reference statements)

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“…The analysis involved a three-way coupling of data [(i) spatial plant species distributions, (ii) land-use data, and (iii) physical geography-related data, such as climatic, altitudinal, and soil data]. Technological developments in smart sensors, social networks, and digital maps, spatio-temporal data are more available than ever before (Reis et al, 2015;Miyazaki et al, 2016;Niphadkar and Nagendra, 2016) and ecology in the big data era needs to integrate novel methods for their analysis (Moustakas, 2017). The availability of large datasets poses great challenges in data analytics (Moustakas and Evans, 2017) but also increased availability of computing power facilitates the use of computationally-intensive methods for the analysis of such data in ecology (Moustakas and Evans, 2015).…”

Section: Discussionmentioning

confidence: 99%

Evaluating Hypotheses of Plant Species Invasions on Mediterranean Islands: Inverse Patterns between Alien and Endemic Species

et al. 2017

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Invasive alien species cause major changes to ecosystem functioning and patterns of biodiversity, and the main factors involved in invasion success remain contested. Using the Mediterranean island of Crete, Greece as a case study, we suggest a framework for analyzing spatial data of alien species distributions, based on environmental predictors, aiming to gain an understanding of their spatial patterns and spread. Mediterranean islands are under strong ecological pressure from invading species due to their restricted size and increased human impact. Four hypotheses of invasibility, the "propagule pressure hypothesis" (H1), "biotic resistance hypothesis vs. acceptance hypothesis" (H2), "disturbance-mediated hypothesis" (H3), and "environmental heterogeneity hypothesis" (H4) were tested. Using data from alien, native, and endemic vascular plant species, the propagule pressure, biotic resistance vs. acceptance, disturbance-mediated, and environmental heterogeneity hypotheses were tested with Generalized Additive Modeling (GAM) of 39 models. Based on model selection, the optimal model includes the positive covariates of native species richness, the negative covariates of endemic species richness, and land area. Variance partitioning between the four hypotheses indicated that the biotic resistance vs. acceptance hypothesis explained the vast majority of the total variance. These results show that areas of high species richness have greater invasibility and support the acceptance hypothesis and "rich-get-richer" distribution of alien species. The negative correlation between alien and endemic species appears to be predominantly driven by altitude, with fewer alien and more endemic species at greater altitudes, and habitat richness. The negative relationship between alien and endemic species richness provides potential for understanding patterns of endemic and alien species on islands, contributing to more effective conservation strategies.

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Section: Discussionmentioning

confidence: 99%

Evaluating Hypotheses of Plant Species Invasions on Mediterranean Islands: Inverse Patterns between Alien and Endemic Species

et al. 2017

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“…The development of a new generation of sensors, more accurate, smaller, cheaper to manufacture, and able to transmit the information in almost real time, is a contributing factor to the ubiquity of devices generating data of use for the water industry [59][60][61][62][63]. However, despite having access to a broad range of data sources and technical resources, the water utility sector appears to make very limited use of it for the improvement of water quality and source apportionment.…”

Section: Key Opportunities and Challengesmentioning

confidence: 99%

Leveraging Big Data Tools and Technologies: Addressing the Challenges of the Water Quality Sector

2017

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Abstract:The water utility sector is subject to stringent legislation, seeking to address both the evolution of practices within the chemical/pharmaceutical industry, and the safeguarding of environmental protection, and which is informed by stakeholder views. Growing public environmental awareness is balanced by fair apportionment of liability within-sector. This highly complex and dynamic context poses challenges for water utilities seeking to manage the diverse chemicals arising from disparate sources reaching Wastewater Treatment Plants, including residential, commercial, and industrial points of origin, and diffuse sources including agricultural and hard surface water run-off. Effluents contain broad ranges of organic and inorganic compounds, herbicides, pesticides, phosphorus, pharmaceuticals, and chemicals of emerging concern. These potential pollutants can be in dissolved form, or arise in association with organic matter, the associated risks posing significant environmental challenges. This paper examines how the adoption of new Big Data tools and computational technologies can offer great advantage to the water utility sector in addressing this challenge. Big Data approaches facilitate improved understanding and insight of these challenges, by industry, regulator, and public alike. We discuss how Big Data approaches can be used to improve the outputs of tools currently in use by the water industry, such as SAGIS (Source Apportionment GIS system), helping to reveal new relationships between chemicals, the environment, and human health, and in turn provide better understanding of contaminants in wastewater (origin, pathways, and persistence). We highlight how the sector can draw upon Big Data tools to add value to legacy datasets, such as the Chemicals Investigation Programme in the UK, combined with contemporary data sources, extending the lifespan of data, focusing monitoring strategies, and helping users adapt and plan more efficiently. Despite the relative maturity of the Big Data technology and adoption in many wider sectors, uptake within the water utility sector remains limited to date. By contrast with the extensive range of applications of Big Data in in other sectors, highlight is drawn to how improvements are required to achieve the full potential of this technology in the water utility industry.

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“…Questions have arisen regarding the accuracy of such methods compared to traditional standard methods (Nieuwenhuijsen et al, 2015;Reis et al, 2015). We present here results from using such method for monitoring plume opacity.…”

Section: Introductionmentioning

confidence: 99%

Performance and Uncertainty in Measuring Atmospheric Plume Opacity Using Compact and Smartphone Digital Still Cameras

Yuen

Mao

et al. 2017

Aerosol Air Qual. Res.

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Quantification of visible ambient plume opacity measurements using compact and smartphone digital still cameras (DSCs), and Digital Optical Method (DOM) are evaluated here. A new camera calibration method that employs exposure value compensation in place of exposure time or radiance of a surface is described and evaluated. This new and simpler method allows an automatic exposure controlled DSC to be calibrated using its own DSC settings. We also test the use of color in place of grayscale pixel values (PVs) to measure opacity. Finally, we determine the uncertainty of the opacity measurements. Two compact DSCs and two smartphone DSCs are tested to measure plume opacity values of smoke generated with an outdoor smoke generator, in comparison to the plume opacity values measured with an in-stack transmissometer. Results show that: 1) smartphone DSCs, like compact DSCs, can pass opacity measurement requirements set by USEPA; 2) the new simpler calibration method generates values within 5% in opacity on average compared to opacity values from the reference transmissometer; 3) non-uniform background color dominates the uncertainty of opacity measurements, and such uncertainty is wavelength dependent; and 4) the diffusive scattering parameter, used in DOM's transmission model, is lower for black plumes than white plumes, and is wavelength dependent. These results improve our understanding of using DSCs and the parameters that introduce uncertainty to DOM to improve measurements of plume opacity that can improve protection of human health.

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Integrating modelling and smart sensors for environmental and human health

Cited by 87 publications

References 68 publications

Evaluating Hypotheses of Plant Species Invasions on Mediterranean Islands: Inverse Patterns between Alien and Endemic Species

Evaluating Hypotheses of Plant Species Invasions on Mediterranean Islands: Inverse Patterns between Alien and Endemic Species

Leveraging Big Data Tools and Technologies: Addressing the Challenges of the Water Quality Sector

Performance and Uncertainty in Measuring Atmospheric Plume Opacity Using Compact and Smartphone Digital Still Cameras

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