Estimating Spectral Information of Complex Fenestration Systems in a Video-Goniophotometer

The effective use of complex fenestration systems in buildings requires knowledge of their optical spectral and directional properties. While the directional properties are commonly assessed by the measurement of bidirectional transmission or reflection distribution functions, the addition of spectral information would significantly aid in the design and analysis of such systems. This paper describes the development of a spectral estimation method that reconstructs reflectance and transmittance spectra of unknown complex fenestration samples in the Heliodome, an innovative video-goniophotometer. The estimation method relies on the digital output of a tri-chromatic charge-coupled device camera in eight filterbands to reconstruct a sample's spectrum using the truncated generalised singular value decomposition. This method is validated by comparing estimated spectra with documented reflectance and transmittance spectra of reference samples. In most spectrally selective materials, the method achieved average improvements of 50% over the Heliodome's previous quasi-spectral assessment method

Community prevalence of SARS-CoV-2 in England from April to November, 2020: results from the ONS Coronavirus Infection Survey

Background: Decisions about the continued need for control measures to contain the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) rely on accurate and up-to-date information about the number of people testing positive for SARS-CoV-2 and risk factors for testing positive. Existing surveillance systems are generally not based on population samples and are not longitudinal in design. Methods: Samples were collected from individuals aged 2 years and older living in private households in England that were randomly selected from address lists and previous Office for National Statistics surveys in repeated crosssectional household surveys with additional serial sampling and longitudinal follow-up. Participants completed a questionnaire and did nose and throat self-swabs. The percentage of individuals testing positive for SARS-CoV-2 RNA was estimated over time by use of dynamic multilevel regression and poststratification, to account for potential residual non-representativeness. Potential changes in risk factors for testing positive over time were also assessed. The study is registered with the ISRCTN Registry, ISRCTN21086382. Findings: Between April 26 and Nov 1, 2020, results were available from 1 191 170 samples from 280327 individuals; 5231 samples were positive overall, from 3923 individuals. The percentage of people testing positive for SARS-CoV-2 changed substantially over time, with an initial decrease between April 26 and June 28, 2020, from 0·40% (95% credible interval 0·29–0·54) to 0·06% (0·04–0·07), followed by low levels during July and August, 2020, before substantial increases at the end of August, 2020, with percentages testing positive above 1% from the end of October, 2020. Having a patient facing role and working outside your home were important risk factors for testing positive for SARS-CoV-2 at the end of the first wave (April 26 to June 28, 2020), but not in the second wave (from the end of August to Nov 1, 2020). Age (young adults, particularly those aged 17–24 years) was an important initial driver of increased positivity rates in the second wave. For example, the estimated percentage of individuals testing positive was more than six times higher in those aged 17–24 years than in those aged 70 years or older at the end of September, 2020. A substantial proportion of infections were in individuals not reporting symptoms around their positive test (45–68%, dependent on calendar time. Interpretation: Important risk factors for testing positive for SARS-CoV-2 varied substantially between the part of the first wave that was captured by the study (April to June, 2020) and the first part of the second wave of increased positivity rates (end of August to Nov 1, 2020), and a substantial proportion of infections were in individuals not reporting symptoms, indicating that continued monitoring for SARS-CoV-2 in the community will be important for managing the COVID-19 pandemic moving forwards

Characterizing and measuring urban landscapes for sustainability

Urban areas are key to sustainability, and understanding heterogeneity in urban landscapes is important for linking development patterns to ecological, economic, and social health. Here, we characterize the urban landscape for the purpose of revealing structural variations that affect sustainability. We develop a new language and classification schema for breaking down urban areas into sub-metropolitan land units that, unlike administrative boundaries, are based on objective measures of the built and natural environment and are comparable across and within urban areas. These units capture structural differences that population density does not. The classification schema offers a process-based characterization of urban landscapes—one where ‘urban’ is defined by the human and biophysical interactions mediated by the urban environment and complements existing land classification systems, like those based on land use and land cover. As an example, the schema is applied here to understand transportation behaviors—a particular urban process with wide-ranging implications for urban sustainability. Using GIS, satellite, and census spatial data, we apply the classification schema in 909 US urban areas, systematically clustering development with similar structural attributes linked to transportation behaviors. In this way, an urban area is divided into a collection of smaller landscapes, larger than individual households and smaller than census tracts, that are distinct in how they function. The study shows that characterizing the urban landscape in this way can distinguish between neighborhoods with different travel behaviors. Extensions of the schema can be used to monitor and manage urban systems towards sustainability, targeting spatial planning strategies to the micro-geographies where they would be most relevant

Holiday in Lights: Tracking Cultural Patterns in Demand for Energy Services

Successful climate change mitigation will involve not only technological innovation, but also innovation in how we understand the societal and individual behaviors that shape the demand for energy services. Traditionally, individual energy behaviors have been described as a function of utility optimization and behavioral economics, with price restructuring as the dominant policy lever. Previous research at the macro-level has identified economic activity, power generation and technology, and economic role as significant factors that shape energy use. However, most demand models lack basic contextual information on how dominant social phenomenon, the changing demographics of cities, and the sociocultural setting within which people operate, affect energy decisions and use patterns. Here we use high-quality Suomi-NPP VIIRS nighttime environmental products to: (1) observe aggregate human behavior through variations in energy service demand patterns during the Christmas and New Year's season and the Holy Month of Ramadan and (2) demonstrate that patterns in energy behaviors closely track sociocultural boundaries at the country, city, and district level. These findings indicate that energy decision making and demand is a sociocultural process as well as an economic process, often involving a combination of individual price-based incentives and societal-level factors. While nighttime satellite imagery has been used to map regional energy infrastructure distribution, tracking daily dynamic lighting demand at three major scales of urbanization is novel. This methodology can enrich research on the relative importance of drivers of energy demand and conservation behaviors at fine scales. Our initial results demonstrate the importance of seating energy demand frameworks in a social context

Mapping Access to Electricity and Urban Night Lights: Leveraging the Massive Repository of Astronaut Photography of the Earth

The challenge Nighttime light (NTL) images from the International Space Station (ISS) have been used by very few researchers without exploring their full potential. Potential application fields have a large range, including the monitoring of energy consumption, light pollution, urban extent, socio-economic and population modelling, and electrification. The low spatial resolution of satellite-based observations, e.g., 750m for Suomi-NPP VIIRS-DNB has restricted researchers from exploring the full potential of NTL images for within-urban scales. In addition to the coarse spatial resolution, NPP was designed primarily for meteorological, and the recent shift to LED lights is not well captured because VIIRS-DNB is blind to blue wavelengths. Since the 1990s, NASA astronauts and later also ESA, JAXA, ROSCOSMOS and CSA-ASC, have been acquiring NTL images with DSLR cameras. However, images are not systematically calibrated, georeferenced and accessible. Methodology Urban monitoring with nighttime remote sensing is very different with daytime remote sensing. Commonly built-up areas have traces of NTL, however, many informal areas in the Global South are not (well) connected to electricity (Figure 1b). In ISS images, NTLs are often linear features along the main transport axes (Figure 1a). Acquired ISS NTL images come with various geometric distortions and require georeferencing (high order transformation) and radiometric correction for quantitative, temporal and comparative applications. The Citizen Science program Cities at Night tagged, located and georeferenced them to make NTL images accessible. Leveraging this repository of ISS NTL, we show the potential of ISS to support diverse EO application fields. For several application fields, we explore the required spatial resolution (defined by the focal length of the DSLR camera and the position of the ISS). For example, for monitoring the energy transition and the reduction of light pollution (e.g., for European cities), high spatial resolution is required to identify individual pollution sources. High spatial resolution nightlights are also needed for mapping access to the electricity grid (e.g., in African cities), to map the heterogenous intra urban patterns of NTL. We also explore whether high radiometric resolution and RGB images allow determining sources of lighting (e.g., the shift to LED lights), comparing images of different years for the same city. Results Related to lighting sources, since the LED lights have started to replace the old discharge lights, the radiometry values that VIIRS-DNB provides (DMSP-OLS did not have any radiometric calibration) can be misleading as they largely exclude light emitted in the blue spectrum. Therefore, VIIRS-DNB images might indicate a light reduction, which is not the case when compared to ISS images. Related to electrification, high spatial resolution (e.g., ISS images acquired with a focal length of 400 mm), is required to detect small neighbourhoods not connected with the electricity grid, while the presence or absence of street lights can be concluded with a focal length of 180 mm (slightly lower spatial resolution). Such information is essential for infrastructure development and the upgrading of informal settlements or planning to connect services (e.g., health, education) to the electricity grid. Related to light pollution, very high-resolution images allow the mapping of sources of light pollution and the modelling of dark corridors to support applications in the field of biodiversity. For example, the massive reduction of insects is related to the increase in light pollution in urban, suburban and peri-urban areas. Outlook for the future For the development of a high-resolution NTL satellite-based sensor system characteristics are not well understood and are challenging to define. Generally, there is limited scientific knowledge across application fields of suitable sensor requirements. The repository of ISS NTL images, which is extremely rich but very under-utilised by researchers, is an optimal testbed to develop applications and an understanding of requirements. Such requirements include defining spatial, spectral, radiometric and temporal base requirements for the application fields. We are presently exploring, collecting and expanding different applications and documenting their requirements toward defining sensor requirements for a high-resolution nighttime light mission