Water and Soil Pollution: Ecological Environmental Study Methodologies Useful for Public Health Projects. A Literature Review

Lillini, Roberto; Tittarelli, Andrea; Bertoldi, Martina; Ritchie, David; Katalinic, Alexander; Pritzkuleit, Ron; Launoy, Guy; Launay, Ludivine; Guillaume, Élodie; Žagar, Tina; Modonesi, Carlo; Meneghini, Elisabetta; Amati, C.; Salvo, Francesca Di; Contiero, Paolo; Borgini, Alessandro; Baili, Paolo

doi:10.1007/398_2020_58

Cited by 6 publications

(5 citation statements)

References 51 publications

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“…We simulate data for n students responding to 20 items. For each simulation, the model parameters were drawn randomly as β i ∼ Gamma (20,20), α i ∼ N (0, 1), κ j ∼ N (0, 1). We consider various values for n and present the results in Table 3.…”

Section: Simulated Examplementioning

confidence: 99%

“…Public health studies on HIV and associated risk factors were performed using INLA by [11,33,2,45]. In environmental statistics, air pollution and its health effects were modeled by [41,40] while models for water and soil pollution were considered by [20]. In ecology, [36,35,21] analyzed data on forest fires and species distribution modeling was performed by [15,27,8,31].…”

Section: Introductionmentioning

confidence: 99%

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A new avenue for Bayesian inference with INLA

Niekerk¹,

Krainski²,

Rustand³

et al. 2022

Preprint

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Integrated Nested Laplace Approximations (INLA) has been a successful approximate Bayesian inference framework since its proposal by [38]. The increased computational efficiency and accuracy when compared with samplingbased methods for Bayesian inference like MCMC methods, are some contributors to its success. Ongoing research in the INLA methodology and implementation thereof in the R package R-INLA, ensures continued relevance for practitioners and improved performance and applicability of INLA. The era of big data and some recent research developments, presents an opportunity to reformulate some aspects of the classic INLA formulation, to achieve even faster inference, improved numerical stability and scalability. The improvement is especially noticeable for data-rich models. We demonstrate the efficiency gains with various examples of data-rich models, like Cox's proportional hazards model, an item-response theory model, a spatial model including prediction, and a 3-dimensional model for fMRI data.

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Section: Simulated Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A new avenue for Bayesian inference with INLA

Niekerk¹,

Krainski²,

Rustand³

et al. 2022

Preprint

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show abstract

“…The impacts of building construction processes on the environment have become a hotspot of research. Initial studies focused only on the impact of construction activities on groundwater , and the neighboring soil environment. , However, with the increasing awareness of environmental protection, the dust pollution caused by construction is gradually being recognized by more and more people . A complete construction process generates a large amount of dust, mainly fine particles, with particles less than 10 μm in diameter accounting for about 90% of the total emissions .…”

Section: Introductionmentioning

confidence: 99%

Preparation and Characteristics of Building Dust Suppressants with Strong Resistance to Harsh Environments

Ding,

Huang,

Peng

et al. 2024

ACS Omega

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Most dust suppressants used for buildings currently lack sufficient resistance to harsh conditions, such as high temperatures and wind erosion. To solve this problem, it is necessary to develop a new type of dust suppressant. In this study, the guar gum molecule was chemically modified to remove the active hydroxyl group in order to significantly improve the stability and adhesion of guar gum. Eventually, a composite dust suppressant was synthesized by incorporating a surfactant and an absorbent agent into modified guar gum. The functional groups of the reaction products were analyzed via infrared experiments, thus confirming the success of the modification. Wind erosion resistance and scanning electron microscopy experiments confirmed the improved bonding capabilities of the composite dust suppressant with dust particles. In experiments on wind erosion resistance, the dust fixation rate exceeded 50% after the application of the composite dust suppressant. The results of the thermogravimetric tests showed that the maximum mass loss rate of the samples with modified guar gum dust suppressants was 6.0% and 28% lower than those of the samples with unmodified guar gum dust suppressants and water, respectively. Furthermore, the tests conducted on pH value and corrosion resistance indicated that the pH value of this dust suppressant was comparable to that of tap water and demonstrated a similar rate of metal corrosion. The practical significance of this study is to improve the dust suppressant used in buildings, to improve the performance of dust suppressant and resistance to harsh environment, and to help to continuously improve the health of personnel and environmental protection during construction. The practical significance of this study is to improve the dust suppressant used in buildings, to improve the performance of dust suppressant and resistance to harsh environments, and to help to continuously improve the health of personnel and environmental protection during construction, which has positive practical significance for the building industry and related fields.

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“…[31,34], including a wide variety of applications, see e.g. [1,5,7,10,13,21,23,24,28,29,32,33,36,41,42,50,51]. Algorithmic concepts and improvements concerning performance matters of the continuously growing software library have been discussed much less, even though they are a key component to INLA's success.…”

Section: Introductionmentioning

confidence: 99%

Parallelized integrated nested Laplace approximations for fast Bayesian inference

Gaedke-Merzhäuser

Niekerk²,

Schenk³

et al. 2022

Stat Comput

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There is a growing demand for performing larger-scale Bayesian inference tasks, arising from greater data availability and higher-dimensional model parameter spaces. In this work we present parallelization strategies for the methodology of integrated nested Laplace approximations (INLA), a popular framework for performing approximate Bayesian inference on the class of Latent Gaussian models. Our approach makes use of nested thread-level parallelism, a parallel line search procedure using robust regression in INLA's optimization phase and the state-of-the-art sparse linear solver PARDISO. We leverage mutually independent function evaluations in the algorithm as well as advanced sparse linear algebra techniques. This way we can flexibly utilize the power of today's multi-core architectures. We demonstrate the performance of our new parallelization scheme on a number of different real-world applications. The introduction of parallelism leads to speedups of a factor 10 and more for all larger models. Our work is already integrated in the current version of the open-source R-INLA package, making its improved performance conveniently available to all users.

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Water and Soil Pollution: Ecological Environmental Study Methodologies Useful for Public Health Projects. A Literature Review

Cited by 6 publications

References 51 publications

A new avenue for Bayesian inference with INLA

A new avenue for Bayesian inference with INLA

Preparation and Characteristics of Building Dust Suppressants with Strong Resistance to Harsh Environments

Parallelized integrated nested Laplace approximations for fast Bayesian inference

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