The effect of oxygenate fuels on PN emissions from a highly boosted GDI engine

Gasoline Direct Injection (GDI) engines are increasingly available in the market. Such engines are known to emit more Particulate Matter (PM) than their port-fuel injected predecessors. There is also a widespread use of oxygenate fuels in the market, up to blends of E85, and their impact on PN emissions is widely studied. However the impact of oxygenate fuels on PN emissions from downsized, and hence highly-boosted engines is not known. In this work, PN emissions from a highly boosted engine capable of running at up to 35 bar Brake Mean Effective Pressure (BMEP) have been measured from a baseline gasoline and three different oxygenate fuels (E20, E85, and GEM – a blend of gasoline, ethanol, and methanol) using a DMS500. The engine has been run at four different operating points, and a number of engine parameters relevant to highly-boosted engines (such as EGR, exhaust back pressure, and lambda) have been tested – the PN emissions and size distributions have been measured from all of these. The results show that the oxygenate content of the fuel has a very large impact on its PN emissions, with E85 giving low levels of PN emissions across the operating range, and GEM giving very low and extremely high levels of PN emissions depending on operating point. These results have been analysed and related back to key fuel properties

The impact of COVID-19 public health restrictions on particulate matter pollution measured by a validated low-cost sensor network in Oxford, UK

Emergency responses to the COVID-19 pandemic led to major changes in travel behaviours and economic activities with arising impacts upon urban air quality. To date, these air quality changes associated with lockdown measures have typically been assessed using limited city-level regulatory monitoring data, however, low-cost air quality sensors provide capabilities to assess changes across multiple locations at higher spatial-temporal resolution, thereby generating insights relevant for future air quality interventions. The aim of this study was to utilise high-spatial resolution air quality information utilising data arising from a validated (using a random forest field calibration) network of 15 low-cost air quality sensors within Oxford, UK to monitor the impacts of multiple COVID-19 public heath restrictions upon particulate matter concentrations (PM10, PM2.5) from January 2020 to September 2021. Measurements of PM10 and PM2.5 particle size fractions both within and between site locations are compared to a pre-pandemic related public health restrictions baseline. While average peak concentrations of PM10 and PM2.5 were reduced by 9–10 μg/m3 below typical peak levels experienced in recent years, mean daily PM10 and PM2.5 concentrations were only ∼1 μg/m3 lower and there was marked temporal (as restrictions were added and removed) and spatial variability (across the 15-sensor network) in these observations. Across the 15-sensor network we observed a small local impact from traffic related emission sources upon particle concentrations near traffic-oriented sensors with higher average and peak concentrations as well as greater dynamic range, compared to more intermediate and background orientated sensor locations. The greater dynamic range in concentrations is indicative of exposure to more variable emission sources, such as road transport emissions. Our findings highlight the great potential for low-cost sensor technology to identify highly localised changes in pollutant concentrations as a consequence of changes in behaviour (in this case influenced by COVID-19 restrictions), generating insights into non-traffic contributions to PM emissions in this setting. It is evident that additional non-traffic related measures would be required in Oxford to reduce the PM10 and levels to within WHO health-based guidelines and to achieve compliance with PM2.5 targets developed under the Environment Act 2021

Silica and other materials as supports in liquid chromatography. Chromatographic tests and their importance for evaluating these supports. Part I

Reversed-phase liquid chromatography (RP-HPLC) has become a powerful and widely employed technique in the separation and analysis of a great variety of compounds with different functionalities. The most common type of stationary phase for RP-HPLC consists of nonpolar, hydrophobic organic species (e.g., octyl, octadecyl) attached by siloxane bonds to the surface of a silica support. In the first part of this article, a description of the many beneficial properties that make porous silica the most employed support in RP-HPLC will be presented, starting from the synthesis of silica. It is noteworthy that the chromatographic properties of the final column are strictly correlated to the preparation type. A silica surface possesses a number of attractive properties, but also some drawbacks. Unreacted or residual silanols interact with basic compounds and can induced peak tailing, which means a loss in chromatographic performance. This problem has lead many manufactures to produce stationary phases with reduced silanol activity which improve dramatically the peak shape of basic compounds. In the second part of this review, different approaches are proposed to obtain less reactive stationary phases