Here, we report, for what we believe to be the first time, on the modification of a low cost sensor, designed for the smartphone camera market, to develop an ultraviolet (UV) camera system. This was achieved via adaptation of Raspberry Pi cameras, which are based on back-illuminated complementary metal-oxide semiconductor (CMOS) sensors, and we demonstrated the utility of these devices for applications at wavelengths as low as 310 nm, by remotely sensing power station smokestack emissions in this spectral region. Given the very low cost of these units, ≈ USD 25, they are suitable for widespread proliferation in a variety of UV imaging applications, e.g., in atmospheric science, volcanology, forensics and surface smoothness measurements.

Smartphones are playing an increasing role in the sciences, owing to the ubiquitous proliferation of these devices, their relatively low cost, increasing processing power and their suitability for integrated data acquisition and processing in a ‘lab in a phone’ capacity. There is furthermore the potential to deploy these units as nodes within Internet of Things architectures, enabling massive networked data capture. Hitherto, considerable attention has been focused on imaging applications of these devices. However, within just the last few years, another possibility has emerged: to use smartphones as a means of capturing spectra, mostly by coupling various classes of fore-optics to these units with data capture achieved using the smartphone camera. These highly novel approaches have the potential to become widely adopted across a broad range of scientific e.g., biomedical, chemical and agricultural application areas. In this review, we detail the exciting recent development of smartphone spectrometer hardware, in addition to covering applications to which these units have been deployed, hitherto. The paper also points forward to the potentially highly influential impacts that such units could have on the sciences in the coming decades.

[1] In contrast to the seismic and infrasonic energy released from quiescent and erupting volcanoes, which have long been known to manifest episodes of highly periodic behavior, the spectral properties of volcanic gas flux time series remain poorly constrained, due to a previous lack of hightemporal resolution gas-sensing techniques. Here we report on SO 2 flux measurements, performed on Mount Etna with a novel UV imaging technique of unprecedented sampling frequency (0.5 Hz), which reveal, for the first time, a rapid periodic structure in degassing from this target. These gas flux modulations have considerable temporal variability in their characteristics and involve two period bands: 40-250 and 500-1200 s. A notable correlation between gas flux fluctuations in the latter band and contemporaneous seismic root-mean-square values suggests that this degassing behavior may be generated by periodic bursting of rising gas bubble trains at the magma-air interface.

We report on the development of a low-cost spectrometer, based on off-the-shelf optical components, a 3D printed housing, and a modified Raspberry Pi camera module. With a bandwidth and spectral resolution of ≈60  nm and 1 nm, respectively, this device was designed for ultraviolet (UV) remote sensing of atmospheric sulphur dioxide (SO), ≈310  nm. To the best of our knowledge, this is the first report of both a UV spectrometer and a nanometer resolution spectrometer based on smartphone sensor technology. The device performance was assessed and validated by measuring column amounts of SO within quartz cells with a differential optical absorption spectroscopy processing routine. This system could easily be reconfigured to cover other UV-visible-near-infrared spectral regions, as well as alternate spectral ranges and/or linewidths. Hence, our intention is also to highlight how this framework could be applied to build bespoke, low-cost, spectrometers for a range of scientific applications.

Recently, we reported on the development of low-cost ultraviolet (UV) cameras, based on the modification of sensors designed for the smartphone market. These units are built around modified Raspberry Pi cameras (PiCams; ≈USD 25), and usable system sensitivity was demonstrated in the UVA and UVB spectral regions, of relevance to a number of application areas. Here, we report on the first deployment of PiCam devices in one such field: UV remote sensing of sulphur dioxide emissions from volcanoes; such data provide important insights into magmatic processes and are applied in hazard assessments. In particular, we report on field trials on Mt. Etna, where the utility of these devices in quantifying volcanic sulphur dioxide (SO 2 ) emissions was validated. We furthermore performed side-by-side trials of these units against scientific grade cameras, which are currently used in this application, finding that the two systems gave virtually identical flux time series outputs, and that signal-to-noise characteristics of the PiCam units appeared to be more than adequate for volcanological applications. Given the low cost of these sensors, allowing two-filter SO 2 camera systems to be assembled for ≈USD 500, they could be suitable for widespread dissemination in volcanic SO 2 monitoring internationally.