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Karl Ropkins

Researcher at University of Leeds

Publications -  72
Citations -  2976

Karl Ropkins is an academic researcher from University of Leeds. The author has contributed to research in topics: Air quality index & Cold start (automotive). The author has an hindex of 23, co-authored 65 publications receiving 2401 citations. Previous affiliations of Karl Ropkins include University of Nottingham & Imperial College London.

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openair - An R package for air quality data analysis

TL;DR: It is demonstrated how air pollution data can be analysed quickly and efficiently and in an interactive way, freeing time to consider the problem at hand.
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Detecting and quantifying aircraft and other on-airport contributions to ambient nitrogen oxides in the vicinity of a large international airport

TL;DR: In this article, the authors present approaches that aim to detect and quantify the airport contribution to NO x concentrations for a network of seven measurement sites close to London Heathrow (LHR) airport.
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Evaluation of worldwide approaches to the use of HACCP to control food safety

TL;DR: Hazard Analysis by Critical Control Point (HACCP) is a management tool, developed in the late 1960s, to ensure the safety of foods for space flights as discussed by the authors, which was subsequently recognised as an effective alternative to conventional end-point testing by the World Health Organisation (WHO) and the United States Food and Agriculture Organisation (FAO), amongst others, and recommended for use in commercial food production.
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Real-World Vehicle Exhaust Emissions Monitoring. Review and Critical Discussion

TL;DR: In this paper, the use of vehicle emissions monitoring methods (e.g., in-situ methods such as tunnel, inverse dispersion, and remote sensing studies, and in-traffic measures such as probe vehicle and car chaser) to provide real-world emission estimates is reviewed and discussed in detail.
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Early Observations on the impact of the COVID-19 Lockdown on Air Quality Trends across the UK

TL;DR: In this article, breakpoint/segment methods are applied to air pollutant time-series from the first half of 2020 to provide an independent estimate of the timings of discrete changes in NO, NO2, NOx, O3, PM10 and PM2.5.