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Ranjeet S. Sokhi

Researcher at University of Hertfordshire

Publications -  120
Citations -  7351

Ranjeet S. Sokhi is an academic researcher from University of Hertfordshire. The author has contributed to research in topics: Air quality index & Air pollution. The author has an hindex of 40, co-authored 113 publications receiving 6094 citations. Previous affiliations of Ranjeet S. Sokhi include Aston University.

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Atmospheric polycyclic aromatic hydrocarbons: Source attribution, emission factors and regulation

TL;DR: In this paper, a review of the sources of polycyclic aromatic hydrocarbons (PAHs) in the environment is presented, where various approaches including diagnostic ratio (DR) and principal component analysis (PCA) are discussed in detail.
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A review of operational, regional-scale, chemical weather forecasting models in Europe

TL;DR: In this article, chemical weather forecasting models are described and compared on regional and continental scales in Europe, and the most prominent gaps of knowledge are highlighted for the following selected focus areas: emission inventories, the integration of numerical weather prediction and atmospheric chemical transport models, boundary conditions and nesting of models, data assimilation of the various chemical species, improved understanding and parameterization of physical processes, better evaluation of models against data and the construction of model ensembles.
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Online coupled regional meteorology chemistry models in Europe: current status and prospects

TL;DR: A comprehensive review of the current research status of online coupled meteorology and atmospheric chemistry modelling within Europe and highlights selected scientific issues and emerging challenges that require proper consideration to improve the reliability and usability of these models for the three scientific communities.
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Particulate matter air pollution components and risk for lung cancer

TL;DR: This study indicates that the association between PM in air pollution and lung cancer can be attributed to various PM components and sources, and PM containing S and Ni might be particularly important.
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Source apportionment of traffic emissions of particulate matter using tunnel measurements

TL;DR: In this paper, the authors quantify exhaust/non-exhaust emissions and the uncertainties associated with them by combining innovative motorway tunnel sampling and source apportionment modelling, and use principal component analysis and multiple linear regression analysis to identify the emission sources for 82% of the total PM 10 mass inside the tunnel.