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Mapping urban air quality in near real-time using observations from low-cost sensors and model information.

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TLDR
The results indicate that the data fusion method provides a robust way of extracting useful information from uncertain sensor data using only a time-invariant model dataset and the knowledge contained within an entire sensor network.
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This article is published in Environment International.The article was published on 2017-09-01 and is currently open access. It has received 229 citations till now. The article focuses on the topics: Air quality index & Sensor fusion.

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Performance assessment of a low-cost PM2.5 Sensor for a near four-month period in Oslo, Norway

TL;DR: In this paper, three SDS011 sensors were evaluated by co-locating them at an official, air quality monitoring station equipped with reference-equivalent instrumentation in Oslo, Norway.

Applications of low-cost sensing technologies for air quality monitoring and exposure assessment: How far have they gone?

TL;DR: In this article, the authors conducted a comprehensive literature search including both the scientific and grey literature, and concluded that there is no clear answer to the question, due to a lack of: sensor/monitor manufacturers' quantitative specifications of performance, consensus regarding recommended end-use and associated minimal performance targets of these technologies, and the ability of the prospective users to formulate the requirements for their applications, or conditions of the intended use.
References
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Journal ArticleDOI

Robust Locally Weighted Regression and Smoothing Scatterplots

TL;DR: Robust locally weighted regression as discussed by the authors is a method for smoothing a scatterplot, in which the fitted value at z k is the value of a polynomial fit to the data using weighted least squares, where the weight for (x i, y i ) is large if x i is close to x k and small if it is not.
Journal ArticleDOI

5. Statistics for Spatial Data

TL;DR: Cressie et al. as discussed by the authors presented the Statistics for Spatial Data (SDS) for the first time in 1991, and used it for the purpose of statistical analysis of spatial data.
Book

Geostatistics for natural resources evaluation

TL;DR: In this article, an advanced-level introduction to geostatistics and Geostatistical methodology is provided, including tools for description, quantitative modeling of spatial continuity, spatial prediction, and assessment of local uncertainty and stochastic simulation.
Journal ArticleDOI

Geostatistics for Natural Resources Evaluation

TL;DR: This paper presents a meta-modelling framework that automates the very labor-intensive and therefore time-heavy and expensive process of manually cataloging and estimating the effects of noise in a discrete-time model.
Book

Geostatistics: Modeling Spatial Uncertainty

TL;DR: In this article, the Intrinsic Model of Order (IMO) is used for structural analysis and nonlinear methods are used for nonlinear models of scale effects and inverse problems.
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