Laboratory comparison of low-cost particulate matter sensors to measure transient events of pollution

Airborne particulate matter (PM) exposure has been identified as a key environmental risk factor, associated especially with diseases of the respiratory and cardiovascular system and with almost 9 million premature deaths per year. Low-cost optical sensors for PM measurement are desirable for monitoring exposure closer to the personal level and particularly suited for developing spatiotemporally dense city sensor networks. However, questions remain over the accuracy and reliability of the data they produce, particularly regarding the influence of environmental parameters such as humidity and temperature, and with varying PM sources and concentration profiles. In this study, eight units each of five different models of commercially available low-cost optical PM sensors (40 individual sensors in total) were tested under controlled laboratory conditions, against higher-grade instruments for: lower limit of detection, response time, responses to sharp pollution spikes lasting <1 min , and the impact of differing humidity and PM source. All sensors detected the spikes generated with a varied range of performances depending on the model and presenting different sensitivity mainly to sources of pollution and to size distributions with a lesser impact of humidity. The sensitivity to particle size distribution indicates that the sensors may provide additional information to PM mass concentrations. It is concluded that improved performance in field monitoring campaigns, including tracking sources of pollution, could be achieved by using a combination of some of the different models to take advantage of the additional information made available by their differential response

Laboratory comparison of low-cost particulate matter sensors to measure transient events of pollution—part B—particle number concentrations

Low-cost Particulate Matter (PM) sensors offer an excellent opportunity to improve our knowledge about this type of pollution. Their size and cost, which support multi-node network deployment, along with their temporal resolution, enable them to report fine spatio-temporal resolution for a given area. These sensors have known issues across performance metrics. Generally, the literature focuses on the PM mass concentration reported by these sensors, but some models of sensors also report Particle Number Concentrations (PNCs) segregated into different PM size ranges. In this study, eight units each of Alphasense OPC-R1, Plantower PMS5003 and Sensirion SPS30 have been exposed, under controlled conditions, to short-lived peaks of PM generated using two different combustion sources of PM, exposing the sensors’ to different particle size distributions to quantify and better understand the low-cost sensors performance across a range of relevant environmental ranges. The PNCs reported by the sensors were analysed to characterise sensor-reported particle size distribution, to determine whether sensor-reported PNCs can follow the transient variations of PM observed by the reference instruments and to determine the relative impact of different variables on the performances of the sensors. This study shows that the Alphasense OPC-R1 reported at least five size ranges independently from each other, that the Sensirion SPS30 reported two size ranges independently from each other and that all the size ranges reported by the Plantower PMS5003 were not independent of each other. It demonstrates that all sensors tested here could track the fine temporal variation of PNCs, that the Alphasense OPC-R1 could closely follow the variations of size distribution between the two sources of PM, and it shows that particle size distribution and composition are more impactful on sensor measurements than relative humidity

Using an energy aware adaptive scheduling algorithm to increase sensor network lifetime

Sensor network research is an area which has experienced rapid growth in the last decade. One area in which it is proving particularly popular is that of environmental monitoring. Areas which have benefited from environmental monitoring include; volcanoes, crops, wildlife and, the test bed used for this thesis: glaciers. One of the main challenges faced by these networks is that of power management. This becomes even more important when energy harvesting techniques are used, as the availability of energy cannot be reliably predicted.In order to address this issue, an algorithm has been developed which allows a sensor node to adapt its schedule based on the available energy. This is achieved by using the average battery voltage to approximate energy reserves, then scaling the scheduled sensing tasks accordingly. This algorithm has been designed to work with differential GPS sensors which require multiple nodes to record in synchronisation. This means that a co-ordination system has been implemented to allow synchronisation between multiplesystems with no direct communication methods.This thesis makes three main contributions to sensor network research: the development of a flexible platform for gateway nodes, the development and analysis of an energy aware adaptive scheduling algorithm, and algorithms for the use of alternate communication links to provide resilience in communications. Each of these contributions has been tested in Iceland as part of a real deployment to asses how they actually perform. During this deployment it has been possible to gather more data about the Skalafellsjokull glacier than has previously been achievable

Reflectance transformation imaging systems for ancient documentary artefacts

This paper discusses the interim results of the AHRC RTISAD project. The project has developed and tested a range of techniques for gathering and processing reflectance transformation imaging (RTI) data. It has also assembled a detailed understanding of the breadth of RTI practice. Over the past decade the range of applications and algorithms in the broad domain of RTI has increased markedly, with current working addressing issues such as large resolution capture, 3D RTI, annotation, enhancement amongst others. Capture of RTI datasets has begun to occur in all aspects of cultural heritage and elsewhere. This has in turn prompted the development of policies and methods for managing and integrating the large quantities of data produced. The paper describes these techniques and issues in the context of a range of artefacts, including painted Roman and Neolithic surfaces, examples of ancient documents in a variety of forms, and archaeological datasets from Herculaneum, Çatalhöyük, Abydos and elsewhere. The paper also identifies on-going software development work of value to the broad EVA community and proposes further enhancements

Underwater reflectance transformation imaging: a technology for in situ underwater cultural heritage object-level recording

There is an increasing demand for high-resolution recording of in situ underwater cultural heritage. Reflectance transformation imaging (RTI) has a proven track record in terrestrial contexts for acquiring high-resolution diagnostic data at small scales. The research presented here documents the first adaptation of RTI protocols to the subaquatic environment, with a scuba-deployable method designed around affordable off-the-shelf technologies. Underwater RTI (URTI) was used to capture detail from historic shipwrecks in both the Solent and the western Mediterranean. Results show that URTI can capture submillimeter levels of qualitative diagnostic detail from in situ archaeological material. In addition, this paper presents the results of experiments to explore the impact of turbidity on URTI. For this purpose, a prototype fixed-lighting semisubmersible RTI photography dome was constructed to allow collection of data under controlled conditions. The signal-to-noise data generated reveals that the RGB channels of underwater digital images captured in progressive turbidity degraded faster than URTI object geometry calculated from them. URTI is shown to be capable of providing analytically useful object-level detail in conditions that would render ordinary underwater photography of limited use

Multi-modal research imaging data management at University Hospital Southampton

At University Hospital Southampton we have a large, varied patient cohort with high enrolment in clinical research projects and trials, many of which include acquisitions using various modalities such as SPECT, PET, CT, and MRI. Clinical imaging data is well accommodated on PACS, but research imaging data management requires a different approach to provide greater flexibility, including robust anonymisation and access to large volumes of raw data. This has historically been performed on an ad-hoc basis, burning studies to removable media (time-consuming) or storing images on a small network-attached storage server which supports a proprietary image database. Data re-use has been limited in scope, whilst approaches to anonymisation have been difficult to implement and often utilised different tools for different projects, leading to duplication of effort. Occasionally, issues with retrieving large and complex archived datasets from PACS have led to significant project delays. Further, as collaborators from the µ-VIS X-ray Imaging Centre and Biomedical Imaging Unit develop 3D X-ray histology (XRH) and explore the potential to integrate this new technique into clinical workflows, the demand for flexible multi-modality research data management is ever increasing.Here we report on a project deploying the eXtensible Neuroimaging Archive Toolkit (XNAT) to improve the storage and management of research imaging data and metadata to better support our research projects; this talk will provide an outline of our progress, some of the challenges we faced, and our eventual goals.We have established and tested DICOM connectivity between XNAT and Nuclear Medicine, MRI and XRH scanners. The system is accessible to Imaging Physics staff and potentially across the Trust (and in future externally, for anonymised data), and provides reliable storage, searching, customisable anonymisation and access to research imaging data. Future directions to explore include integration with sample management systems and linkage to other clinical data repositories

Convergently evolved placental villi show multiscale structural adaptations to differential placental invasiveness

Despite having a single evolutionary origin and conserved function, the mammalian placenta exhibits radical structural diversity. The evolutionary drivers and functional consequences of placental structural diversity are poorly understood. Humans and equids both display treelike placental villi, however these villi evolved independently and exhibit starkly different levels of invasiveness into maternal tissue (i.e. the number of maternal tissue layers between placental tissue and maternal blood). The villi in these species therefore serve as a compelling evolutionary case study to explore whether placentas have developed structural adaptations to respond to the challenge of reduced nutrient availability in less invasive placentas. Here, we use three-dimensional X-ray microfocus computed tomography and electron microscopy to quantitatively evaluate key structures involved in exchange in human and equid placental villi. We find that equid villi have a higher surface area to volume ratio and deeper trophoblastic vessel indentation than human villi. Using illustrative computational models, we propose that these structural adaptations have evolved in equids to boost nutrient transfer to compensate for reduced invasiveness into maternal tissue. We discuss these findings in relation to the 'maternal-fetal conflict hypothesis' of placental evolution.</p

Lessons learned from the eMERGE Network: balancing genomics in discovery and practice

The Electronic Medical Records and Genomics (eMERGE) Network, established in 2007, is a consortium of academic and integrated health systems conducting discovery and implementation research in translational genomics. Here, we outline the history of the network, highlight major impacts and lessons learned, and present the tools and resources developed for large-scale genomic analyses and translation into a clinical setting. The network developed methods to extract phenotypes from the electronic medical record to perform genome-wide and phenome-wide association studies. Recruited cohorts were clinically sequenced off a custom panel for targeted sequencing of variants and monogenic disease risks and returned to participants to investigate the impact of return of genomic results. After generating a 105,000 participant-imputed genome-wide association study (GWAS) dataset for discovery, the network enrolled and sequenced 24,998 participants. Integration of these results into the medical record and the effects of results on participants provided key lessons to the field. These learned lessons inform genetic research in diverse populations and provide insights into the clinical impact of return and implementation of genomic medicine using the electronic medical record. The lessons produced by the eMERGE Network can be utilized by other consortia as translational genomic medicine research evolves