On the 23 March 2020, a country-wide COVID-19 lockdown was imposed on the UK. The following 100 days saw anthropogenic movements quickly halt, before slowly easing back to a “new” normality. In this short communication, we use data from official UK air-quality sensors (DEFRA AURN) and the UK Met Office stations to show how lockdown measures affected air quality in the UK. We compare the 100 days post-lockdown (23 March to 30 June 2020) with the same period from the previous 7 years. We find, as shown in numerous studies of other countries, the nitrogen oxides levels across the country dropped substantially (∼ 50%). However, we also find the ozone levels increased (∼ 10%), and the levels of sulphur dioxide more than doubled across the country. These changes, driven by a complex balance in the air chemistry near the surface, may reflect the influence of low humidity as suggested by Met Office data, and potentially, the reduction of nitrogen oxides and their interactions with multiple pollutants.

The present work proposes a novel method of detection and estimation of outliers in particle image velocimetry measurements by the modification of the temporal coefficients associated with a proper orthogonal decomposition of an experimental time series. Using synthetic outliers applied to two sequences of vector fields, the method is benchmarked against state-of-the-art approaches recently proposed to remove the influence of outliers. Compared with these methods, the proposed approach offers an increase in accuracy and robustness for the detection of outliers and comparable accuracy for their estimation.

As a result of the outbreak and diffusion of SARS-CoV-2, there has been a directive to advance medical working conditions. In dentistry, airborne particles are produced through aerosolization facilitated by dental instruments. To develop methods for reducing the risks of infection in a confined environment, understanding the nature and dynamics of these droplets is imperative and timely. This study provides the first evidence of aerosol droplet formation from an ultrasonic scalar under simulated oral conditions. State-of-the-art optical flow tracking velocimetry and shadowgraphy measurements are employed to quantitatively measure the flow velocity, trajectories and size distribution of droplets produced during a dental scaling process. The droplet sizes are found to vary from 5 µm to 300 µm; these correspond to droplet nuclei that could carry viruses. The droplet velocities also vary between 1.3 m s −1 and 2.6 m s −1 . These observations confirm the critical role of aerosols in the transmission of disease during dental procedures, and provide invaluable knowledge for developing protocols and procedures to ensure the safety of both dentists and patients.

This work deals with the capabilities of two synoptic modal decomposition techniques for the identification of the spatial patterns and temporal dynamics of coherent structures in shallow flows. Using two different experimental datasets it is shown that due to the linear behaviour of large-scale, quasi-two-dimensional flow structures, there are almost no differences in the identification of dominant modes between the results obtained from a traditional proper orthogonal decomposition and the more recently developed dynamic mode decomposition. However, it is also shown that nonlinear dynamics can arise in the transition of these structures to a quasi-two-dimensional behaviour, which can result in the proper orthogonal decomposition identifying structures composed of multi-frequencies, a sign of a convoluted dynamics. Thus dynamic mode decomposition is recommended instead for the analysis of such phenomena. In addition, this paper introduces a simple ranking methodology for the use of the dynamic mode decomposition technique in shallow flows, which is based on the results of the proper orthogonal decomposition.

Abstract. The increasing volume and spatio-temporal resolution of satellite-derived ice velocity data have created new exploratory opportunities for the quantitative analysis of glacier dynamics. One potential technique, proper orthogonal decomposition (POD), also known as empirical orthogonal functions, has proven to be a powerful and flexible technique for revealing coherent structures in a wide variety of environmental flows. In this study we investigate the applicability of POD to an openly available TanDEM-X/TerraSAR-X-derived ice velocity dataset from Sermeq Kujalleq (Jakobshavn Isbræ), Greenland. We find three dominant modes with annual periodicity that we argue are explained by glaciological processes. The primary dominant mode is interpreted as relating to the stress reconfiguration at the glacier terminus, known to be an important control on the glacier's dynamics. The second and third largest modes together relate to the development of the spatially heterogenous glacier hydrological system and are primarily driven by the pressurisation and efficiency of the subglacial hydrological system. During the melt season, variations in the velocity shown in these two subsidiary modes are explained by the drainage of nearby supraglacial melt ponds, as identified with a Google Earth Engine Moderate Resolution Imaging Spectroradiometer (MODIS) dynamic thresholding technique. By isolating statistical structures within velocity datasets and through their comparison to glaciological theory and complementary datasets, POD indicates which glaciological processes are responsible for the changing bulk velocity signal, as observed from space. With the proliferation of optical- and radar-derived velocity products (e.g. MEaSUREs, ESA CCI, PROMICE), we suggest POD, and potentially other modal decomposition techniques, will become increasingly useful in future studies of ice dynamics.

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High-resolution solar observations show the complex structure of the magnetohydrodynamic (MHD) wave motion. We apply the techniques of proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD) to identify the dominant MHD wave modes in a sunspot using the intensity time series. The POD technique was used to find modes that are spatially orthogonal, whereas the DMD technique identifies temporal orthogonality. Here, we show that the combined POD and DMD approaches can successfully identify both sausage and kink modes in a sunspot umbra with an approximately circular cross-sectional shape. This article is part of the Theo Murphy meeting issue ‘High-resolution wave dynamics in the lower solar atmosphere’.

In this paper, we provide clear direct evidence of multiple concurrent higher-order magnetohydrodynamic (MHD) modes in circular and elliptical sunspots by applying both proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD) techniques on solar observational data. These techniques are well documented and validated in the areas of fluid mechanics, hydraulics, and granular flows but are relatively new to the field of solar physics. While POD identifies modes based on orthogonality in space and provides a clear ranking of modes in terms of their contribution to the variance of the signal, DMD resolves modes that are orthogonal in time. The clear presence of the fundamental slow sausage and kink body modes, as well as higher-order slow sausage and kink body modes, have been identified using POD and DMD analysis of the chromospheric Hα line at 6562.808 Å for both the circular and elliptical sunspots. Additionally, for the various slow body modes, evidence for the presence of the fast surface kink mode was found in the circular sunspot. All of the MHD mode patterns were cross-correlated with their theoretically predicted counterparts, and we demonstrated that ellipticity cannot be neglected when interpreting MHD wave modes. The higher-order MHD wave modes are even more sensitive to irregularities in umbral cross-sectional shapes; hence, this must be taken into account for more accurate modeling of the modes in sunspots and pores.