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Industrial activities, through the human-induced release of Green House Gas (GHG) emissions, have been identified as the primary cause of global warming. Accurate and quantitative monitoring of these emissions is essential for a comprehensive understanding of their impact on the Earth's climate and for effectively enforcing emission regulations at a large scale. This work examines the feasibility of detecting and quantifying industrial smoke plumes using freely accessible geo-satellite imagery. The existing system has somany lagging factors such as limitations in accuracy, robustness, and efficiency and these factors hinder the effectiveness in supporting timely response to industrial fires. In this work, the utilization of grayscale images is done instead of traditional color images for smoke plume detection. The dataset was trained through a ResNet-50 model for classification and a U-Net model for segmentation. The dataset consists of images gathered by European Space Agency's Sentinel-2 satellite constellation from a selection of industrial sites. The acquired images predominantly capture scenes of industrial locations, some of which exhibit active smoke plume emissions. The performance of the abovementioned techniques and models is represented by their accuracy and IOU (Intersection-over-Union) metric. The images are first trained on the basic RGB images where their respective classification using the ResNet-50 model results in an accuracy of 94.4% and segmentation using the U-Net Model with an IOU metric of 0.5 and accuracy of 94% which leads to the detection of exact patches where the smoke plume has occurred. This work has trained the classification model on grayscale images achieving a good increase in accuracy of 96.4%. [ABSTRACT FROM AUTHOR]

Abstract Models derived from satellite image data are needed to monitor the status of terrestrial ecosystems across large spatial scales. However, a remote sensing‐based approach to quantify soil multifunctionality at the global scale is missing despite significant research efforts on this topic. A major constraint for doing so is the availability of suitable global‐scale field data to calibrate remote sensing indicators (RSI) and, to a lesser extent, the sensitivity of spectral data of available satellite sensors to soil background and atmospheric conditions. Here, we aimed to develop a soil multifunctionality model to monitor global drylands coupling ground data on 14 soil functions of 222 dryland areas from six continents to 18 RSI derived from a time series (2006–2013) Landsat dataset. Among the RSI evaluated, the chlorophyll absorption ratio index was the best predictor of soil multifunctionality in single‐variable‐based models (r = 0.66, P

Models derived from satellite image data are needed to monitor the status of terrestrial ecosystems across large spatial scales. However, a remote sensing‐based approach to quantify soil multifunctionality at the global scale is missing despite significant research efforts on this topic. A major constraint for doing so is the availability of suitable global‐scale field data to calibrate remote sensing indicators (RSI) and, to a lesser extent, the sensitivity of spectral data of available satellite sensors to soil background and atmospheric conditions. Here, we aimed to develop a soil multifunctionality model to monitor global drylands coupling ground data on 14 soil functions of 222 dryland areas from six continents to 18 RSI derived from a time series (2006–2013) Landsat dataset. Among the RSI evaluated, the chlorophyll absorption ratio index was the best predictor of soil multifunctionality in single‐variable‐based models (r = 0.66, P < 0.01, NMRSE = 0.17). However, a multi‐variable RSI model combining the chlorophyll absorption ratio index, the global environment monitoring index and the canopy‐air temperature difference improved the accuracy of quantifying soil multifunctionality (r = 0.73, P < 0.01, NMRSE = 0.15). Furthermore, the correlation between RSI and soil variables shows a wide range of accuracy with upper and lower values obtained for AMI (r = 0.889, NMRSE = 0.05) and BGL (r = 0.685, NMRSE = 0.18) respectively. Our results provide new insights on assessing soil multifunctionality using RSI that may help to monitor temporal changes in the functioning of global drylands effectively. [ABSTRACT FROM AUTHOR]

The global lockdown to mitigate COVID-19 pandemic health risks has altered human interactions with nature. Here, we report immediate impacts of changes in human activities on wildlife and environmental threats during the early lockdown months of 2020, based on 877 qualitative reports and 332 quantitative assessments from 89 different studies. Hundreds of reports of unusual species observations from around the world suggest that animals quickly responded to the reductions in human presence. However, negative effects of lockdown on conservation also emerged, as confinement resulted in some park officials being unable to perform conservation, restoration and enforcement tasks, resulting in local increases in illegal activities such as hunting. Overall, there is a complex mixture of positive and negative effects of the pandemic lockdown on nature, all of which have the potential to lead to cascading responses which in turn impact wildlife and nature conservation. While the net effect of the lockdown will need to be assessed over years as data becomes available and persistent effects emerge, immediate responses were detected across the world. Thus, initial qualitative and quantitative data arising from this serendipitous global quasi-experimental perturbation highlights the dual role that humans play in threatening and protecting species and ecosystems. Pathways to favorably tilt this delicate balance include reducing impacts and increasing conservation effectiveness.

Objectives: We aimed to develop a Service Capacity Index for Substance Use Disorders (SCI‐SUD) that would reflect the capacity of national health systems to provide treatment for alcohol and drug use disorders, in terms of the proportion of available service elements in a given country from a theoretical maximum. Methods: Data were collected through the WHO Global Survey on Progress with Sustainable Development Goals (SDG) Health Target 3.5, conducted between December 2019 and July 2020 to produce the SCI‐SUD, based on 378 variables overall. Results: The SCI‐SUD was directly derived for 145 countries. We used multiple imputation to produce comparable SCI‐SUD estimates for countries that did not submit data (40 countries) or had very high level of missingness (9 countries). The final SCI‐SUD demonstrates considerable consistency and internal stability and is strongly associated with the macro‐level economic, healthcare‐related and epidemiologic (such as prevalence rates) variables. Conclusion: The presented methodology represents a step forward in monitoring the global situation in regard to the development of treatment systems for SU disorders, however, further work is warranted to improve the external validity of the measure (e.g., in‐depth data generation in countries) and ensure its feasibility for regular reporting (e.g., reducing the number of variables). [ABSTRACT FROM AUTHOR]

This paper is an attempt to explore the interplay of humanity and science to respond to novel globalization processes in the face of the 21st century. The human situation is presented through two schisms: the human-nature and the socio-cultural. Sustainability and ecumene studies are proposed as the new aggregated science fields aiming at solutions for the 21st century. Ecumene studies respond to the need to create a planetary intellectual infrastructure (a planetary brain) that “thinks” in an ecumenial way (beyond border conviviality) for a global transformation and reconciliation of human differences in/for a cosmopolitan perspective. Ecumenic, humanitarian and design movements/studies are considered the main areas of ecumene studies as an inter- and transdisciplinary field, with the city (in all its forms) as its core platform. The city as a human extension and dissipative system reveals a duality since its origin, coming to a peak of conviviality as it will comprise two third of humanity by 2050. This peak will either lead to higher forms of organization (anastrophe), or to collapse (catastrophe). The paper presents the “ecumenial city” or Ecumenopolis (city of Human Rights) as a anastrophic future projection and a call for everyone, emphasizing both a destiny and small actions for activating transformation and reconfiguration in times of approaching the peak.

Cloud-based platforms are changing the way of analyzing remotely sensed data by providing high computational power and rapid access to massive volumes of data. Several types of studies use cloud-based platforms for global-scale analyses, but the number of land-surface phenology (LSP) studies that use cloud-based platforms is low. We analyzed the performance of the state-of-the-art LSP algorithms and propose a new threshold-based method that we implemented in Google Earth Engine (GEE). This new LSP method, called maximum separation (MS) method, applies a moving window that estimates the ratio of observations that exceed a given threshold before and after the central day. The start and end of the growing season are the days of the year when the difference between the ratios before and after the central day are minimal and maximal. The MODIS phenology metrics estimated with the MS method showed similar performances as traditional threshold methods when compared with ground estimations derived from the PhenoCam dataset, a network of digital cameras that provides near-surface remotely sensed observations of vegetation phenology. The main advantage of the MS method is that it can be directly applied to daily nonsmoothed time series without any additional preprocessing steps. The implementation of the proposed method in GEE allowed the processing of global phenological maps derived from MODIS. The distribution of code in GEE allows the reproducibility of results and the rapid processing of LSP metrics by the scientific community.

Benchmark diets using the most affordable locally available items to meet health and nutrition needs have long been used to guide food choice and nutrition assistance. This paper describes the result of recent innovations scaling up the use of such least-cost diets by UN agencies, the World Bank, and national governments for a different purpose, which is monitoring food environments and targeting systemic interventions to improve a population's access to sufficient food for an active and healthy life. Measuring food access using least-cost diets allows a clearer understanding of where poor diets are caused by unavailability or high prices for even the lowest-cost healthy foods, insufficient income or other resources to acquire those foods, or the use of other foods instead due to reasons such as time use and meal preparation costs, or cultural factors such as taste and aspirations. This paper reviews the data, methods and results that have led to official FAO and the World Bank adoption of cost and affordability metrics for global monitoring, and the parallel use of similar methods to guide interventions in country studies led by the World Food Programme with partner agencies across Africa, Asia and Latin America. We conclude by summarizing how increasing availability of food price data, matched to food composition and dietary requirements, allows analysts to use recently developed software tools for least-cost diet assessment to improve food access in a wide range of settings. • Least-cost healthy diets are used to monitor food access and guide interventions. • Indicators of cost per day and affordability help reveal causes of poor nutrition. • Use for global monitoring provides international benchmarks. • Use within countries helps target actions to improve affordability. • Results show impacts of new diet cost indicators and the path to further improvements. [ABSTRACT FROM AUTHOR]

The article discusses the importance of establishing a global wastewater surveillance consortium for pandemic preparedness. Wastewater surveillance is a cost-effective method for assessing COVID-19 infection, as infected individuals shed the virus into sewer systems. The article highlights a study in Japan that found a strong correlation between the concentration of SARS-CoV-2 RNA in wastewater and the number of reported cases. Wastewater surveillance can also be used to identify specific variants of the virus. The article proposes incorporating wastewater surveillance into international and regulatory frameworks, such as the WHO, to create a global infectious disease surveillance system. The EU is already establishing an international wastewater monitoring consortium, but more countries need to participate for it to be effective. The consortium could share experiences and protocols and support monitoring in low-income countries. [Extracted from the article]

Plankton are the base of marine food webs, essential to sustaining fisheries and other marine life. Continuous Plankton Recorders (CPRs) have sampled plankton for decades in both hemispheres and several regional seas. CPR research has been integral to advancing understanding of plankton dynamics and informing policy and management decisions. We describe how the CPR can contribute to global plankton diversity monitoring, being cost-effective over large scales and providing taxonomically resolved data. At OceanObs09 an integrated network of regional CPR surveys was envisaged and in 2011 the existing surveys formed the Global Alliance of CPR Surveys (GACS). GACS first focused on strengthening the dataset by identifying and documenting CPR best practices, delivering training workshops, and developing an integrated database. This resulted in the initiation of new surveys and manuals that enable regional surveys to be standardized and integrated. GACS is not yet global, but it could be expanded into the remaining oceans; tropical and Arctic regions are a priority for survey expansion. The capacity building groundwork is done, but funding is required to implement the GACS vision of a global plankton sampling program that supports decision-making for the scientific and policy communities. A key step is an analysis to optimize the global sampling design. Further developments include expanding the CPR for multidisciplinary measurements via additional sensors, thus maximizing the ship-of-opportunity platform. For example, defining pelagic ecoregions based on plankton and ancillary data could support high seas Marine Protected Area design. Fulfillment of Aichi Target 15, the United Nation’s Sustainable Development Goals, and delivering the Essential Ocean Variables and Essential Biodiversity Variables that the Global Ocean Observing System and Group on Earth Observation’s Biodiversity Observation Network have, respectively, defined requires the taxonomic resolution, spatial scale and time-series data that the CPR approach provides. Synergies with global networks exploiting satellite data and other plankton sensors could be explored, realizing the Survey’s capacity to validate earth observation data and to ground-truth emerging plankton observing platforms. This is required for a fully integrated ocean observing system that can understand global ocean dynamics to inform sustainable marine decision-making.

Much of the current understanding of pharmaceutical pollution in the aquatic environment is based on research conducted in Europe, North America and other select high-income nations. One reason for this geographic disparity of data globally is the high cost and analytical intensity of the research, limiting accessibility to necessary equipment. To reduce the impact of such disparities, we present a novel method to support large-scale monitoring campaigns of pharmaceuticals at different geographical scales. The approach employs the use of a miniaturised sampling and shipping approach with a high throughput and fully validated direct-injection High-Performance Liquid Chromatography-Tandem Mass Spectrometry method for the quantification of 61 active pharmaceutical ingredients (APIs) and their metabolites in tap, surface, wastewater treatment plant (WWTP) influent and WWTP effluent water collected globally. A 7-day simulated shipping and sample stability assessment was undertaken demonstrating no significant degradation over the 1–3 days which is typical for global express shipping. Linearity (r2) was consistently ≥0.93 (median = 0.99 ± 0.02), relative standard deviation of intra- and inter-day repeatability and precision was <20% for 75% and 68% of the determinations made at three concentrations, respectively, and recovery from Liquid Chromatography Mass Spectrometry grade water, tap water, surface water and WWTP effluent were within an acceptable range of 60–130% for 87%, 76%, 77% and 63% of determination made at three concentrations respectively. Limits of detection and quantification were determined in all validated matrices and were consistently in the ng/L level needed for environmentally relevant API research. Independent validation of method results was obtained via an interlaboratory comparison of three surface-water samples and one WWTP effluent sample collected in North Liberty, Iowa (USA). Samples used for the interlaboratory validation were analysed at the University of York Centre of Excellence in Mass Spectrometry (York, UK) and the U.S. Geological Survey National Water Quality Laboratory in Denver (Colorado, USA). These results document the robustness of using this method on a global scale. Such application of this method would essentially eliminate the interlaboratory analytical variability typical of such large-scale datasets where multiple methods were used. [ABSTRACT FROM AUTHOR]

Gathering data and monitoring performance are at the heart of energy efficiency and comfort securing strategies in smart buildings. Therefore, it is crucial to present the obtained data to the user or administrator of such a building in an appropriate form. Moreover, evaluating the data in real time not only helps to maintain comfort, but also allows for a timely response from the user or operator to a possible fault. Continuous online monitoring and analysis of process behaviour, which is referred to as advanced supervision, is addressed in this paper by developing a procedure that will form an artificial operator autonomously supervising process. After introducing several techniques that are used for signal analysis, we propose an approach to advanced supervision of processes in smart buildings or other industrial control systems. The developed procedure is implemented on a control system platform that is particularly suitable for this purpose. Moreover, this platform includes a framework that provides support for the implementation of advanced control techniques and it is based on open-source tools, which is rarely seen in industrial applications. The developed advanced supervision procedure has been tested in simulation as well as in a practical case study using a real two-storey family house.

Disturbances in space environment around the Earth (Geospace disturbances), which are driven by the solar activities, are causes of spacecraft anomalies, radiation hazards of astronauts and aircrews on polar route, problem of navigations and HF communications, and induction current in long-line power cables. To mitigate the risk of geospace disturbances, improvement of operational space weather forecast with high precision based on numerical forecast scheme would be important. However, the poor observation points in geospace prevent us to introduce numerical forecast scheme with data assimilation technique which was common in terrestrial weather forecast. To breakthrough this situation, we are planning to develop a space environment sensor package, which can contribute to safety operation of the micro satellite itself, and to realize low-cost global monitoring of space environment based on constellation of micro satellites. Space weather observations by several tens of micro satellite constellation enables us to narrow down the sensor performance of single satellite. The optimization strategy of sensor performance for further miniaturization and power saving can accelerate mountability of the package. Based on this approach, it is expected that numerical forecast scheme with data assimilation technique will be introduced for operational space weather forecast in the future. [ABSTRACT FROM AUTHOR]

There has been a large volume of research on structural health monitoring since the 1970s but this research effort has yielded relatively few routine industrial applications. Structural health monitoring can include applications on very different structures with very different requirements; this article splits the subject into four broad categories: rotating machine condition monitoring, global monitoring of large structures (structural identification), large area monitoring where the area covered is part of a larger structure, and local monitoring. The capabilities and potential applications of techniques in each category are discussed. Condition monitoring of rotating machine components is very different to the other categories since it is not strictly concerned with structural health. However, it is often linked with structural health monitoring and is a relatively mature field with many routine applications, so useful lessons can be read across to mainstream structural health monitoring where there are many fewer industrial applications. Reasons for the slow transfer from research to practical application of structural health monitoring include lack of attention to the business case for monitoring, insufficient attention to how the large data flows will be handled and the lack of performance validation on real structures in industrial environments. These issues are discussed and ways forward proposed; it is concluded that given better focused research and development considering the key factors identified here, structural health monitoring has the potential to follow the path of rotating machine condition monitoring and become a widely deployed technology. [ABSTRACT FROM AUTHOR]

Whilst international policymakers have reached consensus on the importance of investing in early childhood development and increasingly monitor that investment using standardized measurement, the nature and rationale of early childhood education and care (ECEC) provision remain diverse. In the context of that disparity, this article explores an aspect of ECEC provision that is commonly recognized for its potential to enhance young children’s development and learning, yet for which characteristics remain variable: partnerships between ECEC practitioners and parents. The article reports and discusses results from a cross-cultural narrative study that investigated the nature of such partnerships in three different countries: England, Hungary and Kazakhstan. During focus group interviews, ECEC academics (n = 16) discussed five themes that emerged from literature reviews. Findings indicate more differences than similarities between the countries’ narratives concerning ECEC parent-practitioner partnerships, suggesting such partnerships may be an aspect of ECEC provision for which a homogeneous approach and quality measure across countries are not feasible. [ABSTRACT FROM AUTHOR]

Much of the current understanding of pharmaceutical pollution in the aquatic environment is based on research conducted in Europe, North America and other select high-income nations. One reason for this geographic disparity of data globally is the high cost and analytical intensity of the research, limiting accessibility to necessary equipment. To reduce the impact of such disparities, we present a novel method to support large-scale monitoring campaigns of pharmaceuticals at different geographical scales. The approach employs the use of a miniaturised sampling and shipping approach with a high throughput and fully validated direct-injection High-Performance Liquid Chromatography-Tandem Mass Spectrometry method for the quantification of 61 active pharmaceutical ingredients (APIs) and their metabolites in tap, surface, wastewater treatment plant (WWTP) influent and WWTP effluent water collected globally. A 7-day simulated shipping and sample stability assessment was undertaken demonstrating no significant degradation over the 1–3 days which is typical for global express shipping. Linearity (r2) was consistently ≥0.93 (median = 0.99 ± 0.02), relative standard deviation of intra- and inter-day repeatability and precision was

Sentinel-2 is a wide-swath and fine spatial resolution satellite imaging mission designed for data continuity and enhancement of the Landsat and other missions. The Sentinel-2 data are freely available at the global scale, and have similar wavelengths and the same geographic coordinate system as the Landsat data, which provides an excellent opportunity to fuse these two types of satellite sensor data together. In this paper, a new approach is presented for the fusion of Landsat 8 Operational Land Imager and Sentinel-2 Multispectral Imager data to coordinate their spatial resolutions for continuous global monitoring. The 30 m spatial resolution Landsat 8 bands are downscaled to 10 m using available 10 m Sentinel-2 bands. To account for the land-cover/land-use (LCLU) changes that may have occurred between the Landsat 8 and Sentinel-2 images, the Landsat 8 panchromatic (PAN) band was also incorporated in the fusion process. The experimental results showed that the proposed approach is effective for fusing Landsat 8 with Sentinel-2 data, and the use of the PAN band can decrease the errors introduced by LCLU changes. By fusion of Landsat 8 and Sentinel-2 data, more frequent observations can be produced for continuous monitoring (this is particularly valuable for areas that can be covered easily by clouds, thereby, contaminating some Landsat or Sentinel-2 observations), and the observations are at a consistent fine spatial resolution of 10 m. The products have great potential for timely monitoring of rapid changes. [ABSTRACT FROM PUBLISHER]

This analysis covers the period from 1925 to 2016 in respect of constructing national and global goals and targets in education and training. Tensions between global and national approaches to target-setting are identified. Equally, the ownership of the global target discourse is discussed along with its contested relevance to both developed and developing economies. The process of constructing Sustainable Development Goal 4 (on Education) is laid out against a background of this essential history. The milestones in this process include national initiatives on education for all in India and China, the Universal Declaration of Human Rights, the UNESCO regional conferences on education, the world conferences on education in Jomtien, Dakar and Incheon, and the UN’s Open Working Group on Sustainable Development Goals. Across these and several other staging posts, the actual texts and framing of the goals and targets are subjected to critical review. [ABSTRACT FROM PUBLISHER]

Recent guidance on environmental modeling and global land-cover validation stresses the need for a probability-based design. Additionally, spatial balance has also been recommended as it ensures more efficient sampling, which is particularly relevant for understanding land use change. In this paper I describe a global sample design and database called the Global Grid (GG) that has both of these statistical characteristics, as well as being flexible, multi-scale, and globally comprehensive. The GG is intended to facilitate collaborative science and monitoring of land changes among local, regional, and national groups of scientists and citizens, and it is provided in a variety of open source formats to promote collaborative and citizen science. Since the GG sample grid is provided at multiple scales and is globally comprehensive, it provides a universal, readily-available sample. It also supports uneven probability sample designs through filtering sample locations by user-defined strata. The GG is not appropriate for use at locations above ±85° because the shape and topological distortion of quadrants becomes extreme near the poles. Additionally, the file sizes of the GG datasets are very large at fine scale (resolution ~600 m × 600 m) and require a 64-bit integer representation. [ABSTRACT FROM AUTHOR]

The article presents a time series (2009–2013) analysis for a new version of the “Digital Divide” concept that developed in the 1990s. Digital information technologies, such as the Internet, mobile phones and social media, provide vast amounts of data for decision-making and resource management. The Data Poverty Index (DPI) provides an open-source means of annually evaluating global access to data and information. The DPI can be used to monitor aspects of data and information availability at global and national levels, with potential application at local (district) levels. Access to data and information is a major factor in disaster risk reduction, increased resilience to disaster and improved adaptation to climate change. In that context, the DPI could be a useful tool for monitoring the Sustainable Development Goals of the Sendai Framework for Disaster Risk Reduction (2015–2030). The effects of severe data poverty, particularly limited access to geoinformatic data, free software and online training materials, are discussed in the context of sustainable development and disaster risk reduction. Unlike many other indices, the DPI is underpinned by datasets that are consistently provided annually for almost all the countries of the world and can be downloaded without restriction or cost. [ABSTRACT FROM AUTHOR]

Introduction Global access to safe drinking water and sanitation has improved dramatically during the Millennium Development Goal (MDG) period. However, there is substantial heterogeneity in progress between countries and inequality within countries. Methods We assessed countries' temporal patterns in access to drinking water and sanitation using publicly available data. We then classified countries using non-linear modeling techniques as having one of the following trajectories: 100% coverage, linear growth, linear decline, no change, saturation, acceleration, deceleration, negative acceleration, or negative deceleration. We further assessed the degree to which temporal profiles follow a sigmoidal pattern and how these patterns might vary within a given country between rural and urban settings. Results Among countries with more than 10 data points, between 15% and 38% showed a non-linear trajectory, depending on the indicator. Overall, countries' progress followed a sigmoidal trend, but some countries are making better progress and some worse progress than would be expected. We highlight several countries that are not on track to meet the MDG for water or sanitation, but whose access is accelerating, suggesting better performance during the coming years. Conversely, we also highlight several countries that have made sufficient progress to meet the MDG target, but in which access is decelerating. Discussion Patterns were heterogeneous and non-linearity was common. Characterization of these heterogeneous patterns will help policy makers allocate resources more effectively. For example, policy makers can identify countries that could make use of additional resources or might be in need of additional institutional capacity development to properly manage resources; this will be essential to meet the forthcoming Sustainable Development Goals. [ABSTRACT FROM AUTHOR]