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Introduction & BackgroundMethane (CH4) is a powerful greenhouse gas, leaving both a physical and digital footprint from natural (40%) and human (60%) sources. Its atmospheric concentration has increased from 722 ppb before the industrial age to ~1,922 ppb in recent times. Because of its global warming potential, measuring and monitoring CH4 is crucial to mitigating the impacts of climate change. However, large uncertainties exist in "bottom-up" inventories (a product of activity data based on counts of components, equipment or throughput, and estimates of gas-loss rates per unit of activity for different land uses) reported to the United Nations Framework Convention on Climate Change, making it difficult for policymakers to set emission reduction targets.
To address this, we employ causality-constrained machine learning (ML) to combine different gas observations from satellite sensors onboard the TROPOspheric Monitoring Instrument (which measure a digital footprint of human methane-generating behaviour) with outputs from chemical modelling. These are linked with datasets from the national statistics office, meteorology office and a comprehensive survey on quality of life in the emission field, to improve bottom-up estimates of CH4 emissions at the Earth's surface.
Objectives & ApproachThe research uses mixed methods for collecting and analysing both qualitative and quantitative data for multidisciplinary processing strategies for monitoring CH4 emissions locally and regionally. It also assesses whether additional "digital footprint" variables besides the well-known chemical sources and sinks can be studied to improve our understanding of the CH4 budget.
We have conducted an "analytical inversion" of satellite observations of CH4 to obtain emission fluxes. These represent the dependent variable for our ML model, in combination with 22 independent variables (co-occurring trace gases, meteorological fields, land use, land cover, population, livestock, and data from a survey of quality of life from the Gauteng City-Region Observatory, covering a broad range of socio-economic, personal and political issues) with near-real-time Earth observation data, to aid the development of a causality-constrained ML model for the prediction of CH4 fluxes.
Relevance to Digital FootprintsWe make use of not only satellite imagery, but socio-economic, demographic, and environmental data, and repurpose it for environmental sustainability in the context of mitigating climate change. We are creating unique resources in documenting rapid changes in emissions.
Conclusions & ImplicationsThis research will make important contributions to developing countries with limited resources, enabling them to contribute to the global stocktake towards net-zero by helping policymakers identify geographic regions that are major emitters, enabling them to put measures into place to mitigate emissions.

This work assesses the feasibility of national ground deformation monitoring of Great Britain using synthetic aperture radar (SAR) imagery acquired by Copernicus' Sentinel-1 constellation and interferometric SAR (InSAR) analyses. As of December 2016, the assessment reveals that, since May 2015, more than 250 interferometric wide (IW) swath products have been acquired on average every month by the constellation at regular revisit cycles for the entirety of Great Britain. A simulation of radar distortions (layover, foreshortening, and shadow) confirms that topographic constraints have a limited effect on SAR visibility of the landmass and, despite the predominance of rural land cover types, there is potential for over 22,000,000 intermittent small baseline subset (ISBAS) monitoring targets for each acquisition geometry (ascending and descending) using a set of IW image frames covering the entire landmass. Finally, InSAR results derived through ISBAS processing of the Doncaster area with an increasing amount of Sentinel-1 IW scenes reveal a consistent decrease of standard deviation of InSAR velocities from 6 mm/year to ≤2 mm/year. Such results can be integrated with geological and geohazard susceptibility data and provide key information to inform the government, other institutions and the public on the stability of the landmass.

This work assesses the feasibility of national ground deformation monitoring of Great Britain using synthetic aperture radar (SAR) imagery acquired by Copernicus' Sentinel-1 constellation and interferometric SAR (InSAR) analyses. As of December 2016, the assessment reveals that, since May 2015, more than 250 interferometric wide (IW) swath products have been acquired on average every month by the constellation at regular revisit cycles for the entirety of Great Britain. A simulation of radar distortions (layover, foreshortening, and shadow) confirms that topographic constraints have a limited effect on SAR visibility of the landmass and, despite the predominance of rural land cover types, there is potential for over 22,000,000 intermittent small baseline subset (ISBAS) monitoring targets for each acquisition geometry (ascending and descending) using a set of IW image frames covering the entire landmass. Finally, InSAR results derived through ISBAS processing of the Doncaster area with an increasing amount of Sentinel-1 IW scenes reveal a consistent decrease of standard deviation of InSAR velocities from 6 mm/year to ≤2 mm/year. Such results can be integrated with geological and geohazard susceptibility data and provide key information to inform the government, other institutions and the public on the stability of the landmass

Neighborhood design affects lifestyle physical activity, and ultimately human wellbeing. There are, however, a limited number of studies that examine neighborhood design types. In this research, we examine four types of neighborhood designs: traditional development, suburban development, enclosed community, and cluster housing development, and assess their level of walkability and their effects on physical activity and wellbeing. We examine significant associations through a questionnaire (n = 486) distributed in Tucson, Arizona using the Walkability Model. Among the tested neighborhood design types, traditional development showed significant associations and the highest value for walkability, as well as for each of the two types of walking (recreation and transportation) representing physical activity. Suburban development showed significant associations and the highest mean values for mental health and wellbeing. Cluster housing showed significant associations and the highest mean value for social interactions with neighbors and for perceived safety from crime. Enclosed community did not obtain the highest means for any wellbeing benefit. The Walkability Model proved useful in identifying the walkability categories associated with physical activity and perceived crime. For example, the experience category was strongly and inversely associated with perceived crime. This study provides empirical evidence of the importance of including vegetation, particularly trees, throughout neighborhoods in order to increase physical activity and wellbeing. Likewise, the results suggest that regular maintenance is an important strategy to improve mental health and overall wellbeing in cities. ; Mexican government (CONACYT); Wilderness Society; University of Arizona ; Open access journal. ; This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.

The most recent Global Slavery Index estimates that there are 40.3 million people enslaved globally. The UN's Agenda 2030 for Sustainable Development Goal number 8, section 8.7 specifically refers to the issue of forced labour: ending modern slavery and human trafficking, including child labour, in all forms by 2025. Although there is a global political commitment to ending slavery, one of the biggest barriers to doing so is having reliable and timely, spatially explicit and scalable data on slavery activity. The lack of these data compromises evidence-based action and policy formulation. Thus, to meet the challenge of ending modern slavery new and innovative approaches, with an emphasis on efficient use of resources (including financial) are needed. This paper demonstrates the fundamental role of remote sensing as a source of evidence. We provide an estimate of the number of brick kilns across the 'Brick Belt' that runs across south Asia. This is important because these brick kilns are known sites of modern-day slavery. This paper reports the first rigorous estimate of the number of brick kilns present and does so using a robust method that can be easily adopted by key agencies for evidence-based action (i.e. NGOs etc) and is based on freely available and accessible remotely sensed data. From this estimate we can not only calculate the scale of the slavery problem in the Brick Belt, but also calculate the impact of slavery beyond that of the enslaved people themselves, on, for example, environmental change and impacts on ecosystem services – this links to other Sustainable Development Goals. As the process of achieving key Sustainable Development Goal targets will show, there are global benefits to ending slavery - this will mean a better world for everyone: safer, greener, more prosperous, and more equal. This is termed here a Freedom Dividend.