FY-3E WindRAD (Fengyun-3E Wind Radar) is a dual-frequency rotating fan-beam scatterometer. Its data characteristics, NOC (NWP Ocean Calibration), and wind retrieval performance are investigated in this paper. The radar view geometry diversity varies across the swath, with maximum diversity in the sweet swaths and limited diversity in the outer and nadir swaths. When NOC backscatter calibration coefficients are computed as a function of incidence angle only (NOCint), a smooth correction is found. However, when relative antenna azimuth angle is included (NOCant), it appears that the corrections as a function of relative azimuth angle vary harmonically and substantially for a specific incidence angle. NOCant corrections yield a better fit of the measurements to the GMF (Geophysical Model Function). Hence NOCant is applied for the wind retrieval analysis from Ku-band, C-band. An extra engineering correction of 0.15dB and 0.2dB is applied on Ku-band and C-band backscatter values, respectively, to reduce the wind speed bias without increasing the standard deviation. Overall, NOCant is the best option for both channels. Also, the instrument backscatter data stability over time is good and the retrieved winds can fulfill operational requirements.

We consider a class of density-driven flow problems. We are particularly interested in the problem of the salinization of coastal aquifers. We consider the Henry saltwater intrusion problem with uncertain porosity, permeability, and recharge parameters as a test case. The reason for the presence of uncertainties is the lack of knowledge, inaccurate measurements, and inability to measure parameters at each spatial or time location. This problem is nonlinear and time-dependent. The solution is the salt mass fraction, which is uncertain and changes in time. Uncertainties in porosity, permeability, recharge, and mass fraction are modeled using random fields. This work investigates the applicability of the well-known multilevel Monte Carlo (MLMC) method for such problems. The MLMC method can reduce the total computational and storage costs. Moreover, the MLMC method runs multiple scenarios on different spatial and time meshes and then estimates the mean value of the mass fraction. The parallelization is performed in both the physical space and stochastic space. To solve every deterministic scenario, we run the parallel multigrid solver ug4 in a black-box fashion. We use the solution obtained from the quasi-Monte Carlo method as a reference solution.

Ecological niche models have become exceptionally important, since their results allow to un-derstand many aspects related to the biology of the species being studied, even understanding its evolutionary relationships or their response to past or future projections. In this research, models were prepared to create climatic surfaces so as to produce bioclimatic layers based on the mete-orological data of the south of Peru. Temperature and precipitation data from 119 stations were obtained and homogenized. Then, using geographic and orographic covariates, models were prepared so as to obtain climatic surfaces of maximum and minimum temperature, and precipi-tation. The produced layers were evaluated through root-mean-square deviation (RMSD), mean absolute deviation of error (MAD) and goodness of fit (R2), and they were compared to other models for the area. Finally, the 19 bioclimatic surfaces were created. The results show general patterns for temperature and precipitation, some of them being particular. The climatic layers produced showed acceptable values for RMSD, MAD and R2. Comparison with other models shows statistically significant differences. Both the climatic and the produced bioclimatic surfaces were entered into a database for free access. Finally, comments are made on the importance and application of the bioclimatic layers produced here.

This study aimed to investigate the possibility of using one-shot hyperspectral airborne images to recognize crops for an area with many small plots. The results showed that unsupervised clustering methods could classify crops with an accuracy of 80%, which improved to 90% when restricted to only grain crops, using a single airborne hyperspectral recording. However, additional layers such as NDVI, DTM, slope, and aspect did not improve classification accuracy. For comparison, the accuracy of clustering time series Sentinel-2 images with NDVI layers and DTM-derived data yielded an accuracy of: 74% ,Sentinel-2 time series 68% and single one registration before harvest - 39%. The results of the random forest classification were slightly less accurate due to a lack of sufficient reference data. However, it is challenging to verify the reported accuracy of crop recognition in the literature above 90% due to differences in analysis methodologies, reference data selection, pixel/object approaches, metric choice, and calculation formulas used.

The 2030 global agenda of the United Nations emphasizes peace, human rights, gender equality, partnership, and women's empowerment. It balances the social, economic, and environmental dimensions of sustainable development. The SDG framework was designed to be integrated and indivisible, making the interconnections of 17 Goals and 169 Sustainable Development Goals Targets (SDTs) more complex that need extensive and intensive investigative research. Recent studies have increased on the interrelationships of SDGs, but none of these have focused on integrating Climate Change (CC), Sustainable Forest Management (SFM), and Information and Communication Technology (ICT), also known as CSI Nexus. This study aims to investigate and identify the 169 SDTs linked to CC, SFM, and ICT and assesses the significance of the relationships between these variables. The alignment of SDTs to CSI Nexus was identified through clustering and mapping techniques. The result argued that 56 SDTs are directly connected within CC+SFM+ICT, 16 within CC+SFM, one within SFM+ICT, and 51 within ICT+CC. Our result suggests that CC is significantly associated with SFM; however, ICT has no significant association with CC and SFM. It further asserts that the ICT and SFM have minimal influence on the SDG 2030 framework. The proposed CSI Nexus and SDTs Integration Framework was a science-informed guide for priority-setting, policy coherence, and decision-making supporting the 2030 Sustainable Development Goals. This study does not include interactions, network analysis and visualization, simulations, and modeling between SDG targets.

The emission of black carbon (BC) particles, which cause atmospheric warming by affecting radiation budget in the atmosphere, is the result of an incomplete combustion process of organic materials. The recent wildfire event during the summer 2019-2020 in South-Eastern Australia was unprecedented in scale. The wildfires lasted for nearly 3 months over large areas of the two most populated states of New South Wales and Victoria. This study on the emission and dispersion of BC emitted from the biomass burnings of the wildfires using the Weather Research Forecast – Chemistry (WRF-Chem) model is aimed to determine the extent of the BC spatial dispersion and ground concentration distribution and the effect of BC on air quality and radiative transfer at the top of the atmosphere, the atmosphere and on the ground. The predicted aerosol concentration and AOD are compared with the observed data from the New South Wales Department of Planning and Environment (DPE) aethalometer and air quality network and from remote sensing data. The BC concentration as predicted from WRF-Chem model is in general less than the observed data as measured from the aethalometer monitoring network, but the spatial pattern corresponds well, and the correlation is relatively high. The total BC emission into the atmosphere during the event and the effect on radiation budget were also estimated. This study shows that the summer 2019-2020 wildfires affect not only the air quality and health impact on the east coast of Australia but also short-term weather in the region via aerosol interactions with radiation and cloud.

Since the early 1990s, the European (ESA) and Italian (ASI) space agencies have managed and distributed a huge amount of satellite-recorded SAR data to the research community and private industries. Moreover, the availability of advanced cloud computing services implementing different multi-temporal SAR interferometry techniques allows the generation of deformation time series from massive SAR images. We exploit the information provided by a large PS dataset to determine the temporal trend of ground deformation and the relative deformation rate with millimetric accuracy to analyse the spatial and temporal distribution of land subsidence induced by water pumping from a deep confined aquifer in the Northern Valle Umbra Basin (Central Italy), exploiting 24 years of Permanent Scatterers – Interferometric SAR data archives. The SAR images were acquired between 1992 and 2016 by satellite ERS1/2, ENVISAT and Sentinel 1 ESA missions, and the COSMO-SkyMed ASI mission). We observe ground velocities and deformation geometries between 1992 and 2016, with displacements of more than 70 cm and velocities of up to 55 mm/yr and the results suggest that the shape and position of the surface ground displacement are controlled by the fault activity hidden under the valley deposits.

Wetlands are complex ecosystems, considered among the most productive environments of the world. They contain major biodiversity hotspots, providing the resources upon which countless species of plants and animals depend and they perform important environmental and economic functions. Wetlands reduce flooding events, improve water quality and represent a valuable cultural and natural heritage. It is estimated that 2/3 of Europe wetlands have disappeared since the beginning of the 20th Century, mainly lost through development processes which did not take their functions and values adequately into account. The European Directive (2000/60/EC) re-quires to foster an integrated approach for wetland management through collaborative governance processes. The Wetland Contract is a tool that has been developed and implemented to set the ground for voluntary-based commitments for the sustainable governance of water systems. Among the Mediterranean countries, Italy and Croatia count on a rich variety of coastal wetlands that, together with the plants and animals inhabiting and crossing them, constitute an extremely precious natural heritage. The aim of this paper is to present and discuss, whit a critical approach, the output of the Interreg Project CREW that, between 2018-21, contributed to the drafting of seven new Wetland Contracts.

We developed and experimented at sea an innovative fiber optic hydrophone (FOH) capable of operating at very low frequency of the seismic spectrum and for detecting small magnitude earthquakes. The FOH exploits an optical fiber coil wrapped around a sensitive mandrel in a Michelson interferometric configuration. The FOH operated for about seven days at a water depth of 40 m, in the Campi Flegrei volcanic area (Southern Italy), a few meters from a well calibrated PZT hydrophone used as a reference. Thirty-three local earthquakes occurred during the simultaneous operation of the two hydrophones, allowing a straightforward comparison of the recordings. The local earthquakes occurred at an epicentral distance shorter than 2.5 km from the site of recording, and were estimated to be in the range of magnitude from -0.8 to 2.7. The analysis of the recorded earthquake waveforms in the frequency and time domain allowed retrieving the response function of the FOH in the frequency range from 5 to 70 Hz. The FOH responsivity in terms of acoustic pressure reaches about 230 nm/Pa and is flat in the studied frequency range. The high quality of the FOH recordings make this equipment suitable for applications addressing submarine volcanic activity and the background seismicity of active faults in the ocean.

This work compared the performance of three methods for constructing a regional ensemble prediction system (EPS) for wind speed forecasts: dynamical downscaling, breeding of growth modes (BGM), and blending method. The Weather Research and Forecasting (WRF) model was used to downscale the European Centre for Medium-range Weather Forecast (ECMWF) EPS. In addition, as the BGM method needs observation data for generating scaling factors, an alternative method for generating scaling factors was proposed to eliminate dependence on observation data. One-month tests between October 1st and October 30th, 2020, were implemented to evaluate the performance of three methods in the Gansu province of China. The results demonstrate that the blending method outperforms the other two methods. Furthermore, the difference in performance is evident mainly in early forecast lead time and becomes negligible as forecast time increases.

We describe an efficient approach to radiometrically flatten geocoded stacks of calibrated synthetic aperture radar (SAR) data for terrain-related effects. We use simulation to demonstrate that, for the Sentinel-1 mission, one static radiometric terrain flattening factor derived from actual SAR imaging metadata per imaging geometry is sufficient for flattening interferometrically compliant stacks of SAR data. We quantify the loss of precision due to application of static flattening factors, and show that these are well below stated requirements of change detection algorithms. Finally, we discuss the implications of applying radiometric terrain flattening to geocoded SAR data instead of the traditional approach of flattening data provided in the original SAR image geometry. The proposed approach allows for efficient and consistent generation of five different Committee of Earth Observation Satellites (CEOS) Analysis Ready Dataset (ARD) families - Geocoded Single Look Complex (GSLC), Interferometric Radar (InSAR), Normalized Radar Backscatter (NRB), Polarimetric Radar (POL) and Ocean Radar Backscatter (ORB) from SAR missions in a common framework.

Variations in geochemical signatures of fossil brachiopod shells may be due to diagenesis and/or biological processes (i.e. ‘vital effects’). Characterize them is crucial to identify reliable areas into the shell suitable to paleoclimatological studies. This investigation contributes to an in-depth understanding of geochemical variations of Gigantoproductus sp. shells (SW Spain, Serpukhovian age), which could affect to the Late Paleozoic Ice Age interpretation. Microstructural, crystallographic, cathodoluminescence and geochemical (minor and trace elements, δ18O, δ13C, and strontium isotopes) characterisation have been performed on the tertiary layer of the ventral valve, to assess the preservation state. Poorly-preserved areas exhibit microstructural and geochemical changes such as recrystallisation, fracturing and higher Mn and Fe enrichment. Moreover, these areas have higher dispersion of ⁸⁶Sr, ⁸⁷Sr, δ18O and δ13C than well-preserved areas. Three structural regions have been identified in well-preserved areas of ventral valve by differences in valve curvature and thickness, such as the umbonal, thick and thin regions. These regions have different proportions of Mg, S, Na, δ18O, and δ13C, whom are interpreted as ‘vital effects’ and probably are related with growth rate differences during shell growth. Gigantoproductus tertiary layer seems the best suitable to paleoclimatological studies because it retains the original microstructure and geochemistry.

Airborne virus, such as COVID-19, caused pandemics all over the world. Virus-containing particles produced by infected individuals are suspended in the air for extended periods of time, actually results in viral aerosols and the spread of infectious diseases. Aerosol collection and detection devices are essential for limiting the spread of airborne virus diseases. This review provides an overview of the primary mechanisms and enhancement techniques for collecting and detecting airborne viruses. Indoor virus detection strategies for scenarios with varying ventilations are also summarized based on the excellent performance of existing advanced comprehensive devices. This review provides guidance for the development of future aerosol detection devices and aids in the control of airborne transmission diseases, such as COVID-19, monkeypox, and other airborne transmission viruses.

To investigate the process and chemistry of reaction zone formation, we conducted detailed petrographic observations and major-trace element analysis of rocks and minerals of lherzolite xenoliths from basanites of Tumusun volcano (Baikal Rift Zone). The reaction zones gradually disappear from contact toward center of xenoliths. This indicates their formation as a result of lherzolite-basanite interaction. The influence of basanite melt on major-trace element composition of secondary minerals of reaction zones is notable only at distance up to 100-200 μm from the contact. The major-trace element composition of secondary clinopyroxenes from orthopyroxene reaction zone indicates their formation from a melt formed by dissolution of orthopyroxene and influenced by element diffusion from basanite melt. Inside xenoliths the secondary minerals have Mg# values equal to or higher than Mg# of primary minerals. The secondary clinopyroxenes inherit their depleted or enriched REE pattern from primary pyroxenes. The major-trace element variations in secondary clinopyroxenes testify melt heterogeneity. Secondary clinopyroxene has slightly higher LILE and similar abundances of other trace elements compared to unreacted part of clinopyroxene grain. This is consistent with model developed from experimental studies: due to the interaction with basanite, incongruent dissolution of orthopyroxene occurs to form a melt which circulates in lherzolite and leads to pyroxenes and spinel dissolution. Non-dissolved centers of minerals are homogenous and similar in major-trace element composition to primary minerals. The process of peridotite-basanite interaction is not isochemical: even the centers of large xenoliths become enriched in LILE.

To remain relevant in the green transition, companies are beginning to voluntarily account for the exchange of emissions in their supply chain transactions and using the resulting greenhouse gas inventories for climate resilient decision support. Market advantages of sustainability and transparency see a shift from internal decision support tools to external communication tools which potentially expose companies to the risk of uncovering greenwashing if claims are not supported by transparent data, sound modelling, and a climate just emissions inventory, which considers external impacts connected to the production system. The different methods and standards in place for such greenhouse gas inventories, despite all referring to the ISO life cycle analysis standards and guidelines, present mixed signals and leave room for different interpretations, that may ultimately lead to cascading greenwashing, misleading results, and false successes. The new GHG Protocol Land Sector and Removals Guidance draft addresses this in part. With the GHG Protocol moving into revision periods, we identify gaps that present barriers to companies, or allow for interpretations that goes against the intentions of reporting GHG emissions related to an activity or organisation. The literature agrees that not rectifying these subtle-ties present counterproductive decision support for the green transition’s overall goal: to reduce global emissions.

Novel strategies in coastal protection are needed to cope with climate change-induced sea level rise. They aim at the sustainable development of coastal areas in light of an intensification and land use changes. A promising approach is the design of nature-based solutions (NbS), complementing the safety levels of technical infrastructures. However, NbS lack a widespread and large-scale implementation. To address this deficit, co-design concepts are needed that combine experiences from science and practice. This work presents and discusses the approach of a coast-specific real-world laboratory (RwL) addressing the inclusive design of ecosystem-based coastal protection. Strategies of RwLs are applied for the first time in a coastal context along the North Sea coastline in Germany. We found the concept of RwLs suitable for coastal transdisciplinary research, although adaptions in the spatial reference level or flexibility in location and time of experimentation are necessary. A profound actor analysis is indispensable to specify participatory processes and interaction levels. A criteria-based cooperative selection of RwL sites helps to reveal and solve conflicting interests to achieve trust between science and practice. Addressing site-specific characteristics and practitioners’ needs, our coastal RwL provides a mutual learning space to develop and test NbS to complement technical coastal protection.

“Sustainable development” (SD) is a term that has gained prominence in the international dia-logue around development. Many, however, continue to misunderstand the concept, its principles, its development, and its operationalization. The wide variety of definitions and interpretations of SD lean toward institutional and social prerogatives rather than unifying the essence of the con-cept, which is rooted in conventional ideas and values. By delving deeper into the paradigm, its history, and its potential, this research sought to contribute to ongoing discussions about sus-tainable development. The literature was thoroughly analyzed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020. This historical and conceptual analysis sought to update the body of knowledge on sustainability and sustainable development by examining the metaphorical and epistemological underpinnings of SD’s various definitions, evolutions, and principles. The research concludes and argues that intergenerational justice, based on the environment, economy, and society, is the central tenet of the entire sustainable develop-ment argument. The focus and consideration of interested parties are moving from the protection of the environment to social sustainability. The future of SD is likely to continue to focus much more on the social pillar and integrate digitalization in implementing the SD concept, especially in the fourth industrial revolution. To promote ethical human behavior and activities worldwide, at national and local levels, decision-makers must be continuously cognizant of the interdepend-encies, direct interactions, and balance between the three core constructs of SD.

This study numerically evaluated the long-term and stable trends of meandering channel planform at different mean annual subcritical flows based on a depth-averaged linearized meander evolution model. The calculation cases included an idealized sine-generated meander with moderate max-deflection angle and a typical natural meander - Jiyun River (in China). Within the increase of the selection of the mean annual width-depth ratio in a certain scope, the results showed some specific phase characteristics of channel centerline evolution: (1) the meander straightening trend became weaker in phase 1 (B/H ≤ 16.5) and gradually turned into the meander developing trend in phase 2 (B/H ≤ 27) with the overall immunity to flow magnitude; (2) the symmetric development form along the transverse direction perpendicular to the flow average direction was broken starting from the downstream tail in phase 3 (B/H ≤ 28.9), meantime accompanied by the highlight of flow effects when the mean annual B/H exceeded 28; (3) in phase 4 (B/H ≤ 29.5), there occurred obvious incipience and jump process of the channel sensitivity responding to flow when the selected B/H crossed 29.3, which were very similar to the transition process from the laminar flow to turbulent flow. Besides, the initial sinuosity and curvature-ratio indicator could contribute much to the channel evolutionary state, as demonstrated in the comparison between natural Jiyun River case and the correspondingly idealized sine-generated meander case with the same level of mean annual B/H. This research provides innovation spaces for deeper meander mechanism exploration and effective riparian management.

The long-term resilience of Pyrenean forests in the face of historical anthropogenic clearing remains largely unknown. The palynological study of the varved sediments from a mid-elevation (1027 m) karstic lake provides a high-resolution record of three major century-scale deforestation/recovery (DR) cycles that occurred in the last two millennia, during Roman, Medieval and Modern times. Each DR cycle is characterized considering three different levels: overall forest trends, by forest type and by individual taxa. Overall, the studied forests exhibited high resilience, as they recovered almost completely after each deforestation event (bulk resilience). The critical point of no return (tipping point) beyond which forests would have irreversibly disappeared from the region was never reached, even after deforestation magnitudes above 60%. The different forest types identified (conifer, sclerophyll and deciduous) persisted over time, showing similar heterogeneous patterns with minor spatial reorganizations (mosaic resilience). Individually, the main forest taxa underwent minor variations in their relative abundances, always within the same attraction domains (community resilience). The high levels of resilience documented in these Pyrenean forests are attributed to the action of metapopulation and metacommunity processes and mechanisms in a highly dynamic patchy environment. Conservation actions should be focused on the maintenance of these spatial patterns and the associated ecological dynamics.

Coastal marine ecosystems worldwide, like Florida Bay, are increasingly affected by tide alteration and anthropogenic disturbances which affect water quality imbalance leading to algal blooms. Increased bloom persistence is a serious threat due to the long-lasting impact on processes like carbon cycle and services like species presence. However, exploring eco-environmental feedback patterns of algal blooms remains challenging and poorly investigated, also due to the paucity of data. Florida Bay, taken as an epitome, has long experienced algal blooms in its central and western regions, and in 2006 an unprecedented bloom occurred in the eastern habitats. We analyzed the occurrence of blooms from three perspectives: (1) the spatial spreading networks of chlorophyll-a (CHLa) that pinpoints source and unbalanced habitats; (2) the fluctuations of water quality factors pre- and post-bloom outbreaks to in assess environmental impacts of ecological dysbiosis and target prevention and control of algal of blooms; and (3) the biogeochemical-spreading network topological co-evolution to quantify ecosystemic stability and ecological shift likelihood in long-term. Here, we propose Transfer Entropy (TE) to quantify dynamical interactions between spatial areas and biogeochemical factors (ecosystem connectome) underpinning bloom emergence and spreading, as well as environmental effects; the Pareto principle is defined for identifying the salient eco-environmental interactions of CHLa. We quantified the spatial dynamics of algal blooms, and thus obtained areas in need for ecological monitoring and potential bloom control. Results show that algal blooms are increasingly persistent over space with long-term negative effects on water quality factor. A dichotomy is reported between spatial ecological corridors of spreading and biogeochemical networks as well as divergence from the optimal eco-organization: randomization of the former due to nutrients' overload and temperature increase leads to scale-free CHLa spreading and extreme outbreaks a posteriori. Subsequently this increases bloom persistence, turbidity and salinity with potentially strong ecological effects on highly biodiverse and vulnerable areas such as tidal flats and marshes. Algal blooms are important ecosystem regulators of nutrient cycles; however, beyond limit chlorophyll-a outbreaks cause aquatic species mortality due to their effect on water turbidity, nutrient balance (nitrogen and phosphorus in particular), salinity and temperature. Beyond compromising local environmental quality, socio-ecological services are also compromised at large scales, yet ecological assessment models like the one presented are in need of application in subtropical and tropical bays worldwide.

Integrated assessment modelling (IAM) has been successfully used in the development of international agreements to reduce transboundary pollution in Europe, based on the GAINS model of IIASA. At a national level in the UK a similar approach has been taken with the UK Integrated Assessment Model, UKIAM, superimposing pollution abatement measures and behavioural change on energy projections designed to meet targets set for reduction of greenhouse gas emissions, and allowing for natural and imported contributions from other countries and shipping. This paper describes how UKIAM has been used in development of proposed targets for reduction of fine particulate PM2.5 in the Environment Act, exploring scenarios encompassing different levels of ambition up to 2050 with associated health and other environmental benefits. There are two PM2.5 targets, an annual mean concentration target setting a maximum concentration to be reached by a future year, and a population exposure reduction target with benefits for health across the whole population. The work goes further, to also demonstrate links to social deprivation. There is a strong connection between climate measures aimed at reducing net GHG emissions to zero by 2050 and future air quality, which may be positive or negative, as illustrated by sectoral studies for road transport where electrification of the fleet needs to match the evolution of energy production, and for domestic heating where use of wood for heating is an air quality issue. UKIAM has been validated against air pollution measurements and other modelling, but there are many uncertainties including future energy projections. New work is beginning to link UKIAM directly with the TIMES model addressing future energy projections, to explore different uptake scenarios for hydrogen production and use with respect to air quality.

Newton’s law of universal gravitation applies between two point-masses. True gravitation of solid Earth is volume integration of gravitation of all point-masses inside the solid Earth on a point-mass in atmosphere. However, in meteorology the Earth “shrinks” into a point-mass located at Earth center with entire Earth mass to identify the Earth gravitation (untrue). Combination of untrue gravitational and centrifugal accelerations gives effective gravity (geff). Combination of true gravitational and centrifugal accelerations leads to true gravity (g). The true gravity g minus the effective gravity geff is the gravity disturbance vector, δg = g – geff. With the true gravity g used in the basic equations, seven non-dimensional numbers are proposed to identify the importance of δg versus traditional forcing terms such as horizontal pressure gradient force and Coriolis force. These non-dimensional numbers are calculated from two publicly available datasets with the geoid undulation (N) from the static gravity field model EIGEN-6C4 and long-term mean geopotential height (Z), wind velocity (u, v), and temperature (Ta) at 12 pressure levels in troposphere from the NCEP/NCAR reanalyzed climatology. The results demonstrate δg nonnegligible in hydrostatic equilibrium, geostrophic wind, geostrophic vorticity, Ekman pumping, Q vector, and Omega equation, but negligible in thermal wind relation.

Soil erosion is one of the most important environmental problems which can have various negative consequences, such as land degradation affecting the sustainable development and the agricultural production, especially for developing countries like Tunisia. Moreover, soil erosion is a major problem around the world because of its effects on soil fertility by nutriment loss and siltation in water bodies. Apart from this, soil erosion by water is the most serious type of land loss in several regions both locally and globally. This study evaluated regional soil erosion risk through the derivation of appropriate factors, using the Revised Universal Soil Loss Equation (RUSLE), which was applied to establish a soil erosion risk map of the whole Tunisian territory and to identify the vulnerable areas of the country. RUSLE model take into account all the factors playing a major role in erosion processes, namely the erodibility of soils, topography, land use, rainfall erosivity and anti-erosion farming practices. The equation is thus implemented under Geographic Information System (GIS) “Arc GIS Desktop”. The results indicated that Tunisia has a serious risk of soil water erosion, showing that 6.43% of the total area of the country is affected by a very high soil loss rate estimated at more than 30 t/ha/year and 4.20% are affected by high mean annual soil loss ranging from 20 to 30 t/ha/year. The most eroded areas were identified in west southern, central and western parts of the country. The spatial erosion map can be used as a decision support document to guide decision-makers towards better land management and provide the opportunity to develop management strategies for soil erosion prevention and control in the global scale of Tunisia.

The paper portrays a linear model for synchronous prediction of the dissolved oxygen (DO) level in a protected urban lake by using independent variables in real-time monitoring of the water quality parameters. Multi-linear regression and machine learning techniques were applied to the Dissolved Oxygen (DO) using the basic four parameters of Water Quality Index (WQI). Three real-time industry standard sensors PHEHT, CTZN, OPTOD were used for sampling, data was then interpolated through ArcGIS kriging method. 25 correlations were checked through the ML algorithm, a correlation heat map was produced and the top five relations were taken under consideration and validated by r-score and root mean squared error (RMSE) to develop the linear regression model. The performance of the model was validated through the RMSE, mean squared error (MSE), and mean absolute error (MAE) the r2 accuracy came to 0.983 when it is checked against the sample data. The suggested model can be used as a technique for unknown or malfunctioned sensor’s null DO data prediction.

Cities exposed their vulnerabilities during COVID-19 pandemic. Unprecedented policies restricted human activities but left an unique opportunity to quantify anthropogenic effects on urban air pollution. This study aimed to address the key questions of urban development behind the restrictions with the goal of supporting sustainable transition. Data from ground stations and Sentinel-5P satellite were used to assess the temporal and spatial anomalies of NO2. Beijing China was selected for a case study because this mega city maintained a “dynamic zero-COVID” policy with adjusted restrictions, allowing us better to track the effects. The time-series decomposition and prediction regression model were employed to estimate the normal NO2 levels in 2020. The anomalies between the observations and predictions as the deviation were identified due to the policy interventions and quantified different effects using spatial stratified heterogeneity statistics. The top three restrictions showing dominant effects were workplace closures, restricted public transport usage, and school closures, accounting for 54.8%, 52.3%, and 46.4% of NO2 anomalies, respectively; and they are directly linked to the mismatch of employment and housing (deter-mining the commuting patterns), educational inequality and the long-term unsolved road con-gestion. Promoting the transformation of urban spatial structure will effectively alleviate air pollution.

With a worldwide growing concern for greenhouse gas (GHG) emissions and their impact on human health and the environment, transportation has become a central theme in mitigation, responsible for 14% of human GHG emissions. To build endurance to climate change, transportation services must adapt to the current scenario and act quickly to avert future changes. Deeply rooted changes in socio-technical systems will be necessary to achieve significant CO2 reduction and secure the well-being of future generations. This study's objective is to comprehensively review the current state of carbon mitigation in the transportation sector. This is done through a systematic literature review engrained in the socio-technical transition theory and the structural theory of contingency. Twenty-six review papers covering 2,983 original articles are selected for full-text examination concerning carbon emissions in transportation. Enablers, barriers, benefits, disadvantages and metrics in carbon emissions reduction are identified, and a comprehensive framework is built. Results provide a view of the current sustainability scenario in transportation and allow a better understanding of the factors influencing carbon emission initiatives in transportation and its outcomes.

Sustainability is essential for every business and organization, but how can it be achieved? This work is a study of sustainability based on the sufficiency economy philosophy, a valuable concept introduced by the Thai people. We apply it to the notion of sustainability in 400 agricultural community enterprise owners in Thailand, who participated in the study. An oblique rotation component analysis was performed, finding that the variables on the same side are related, and a confirmative component analysis with structural equation models was conducted. The results showed that the approach to applying the sufficiency economy philosophy among community enterprises consists of 11 components, as follows: 1) financial control, 2) planning, 3) member management, 4) morals, 5) prevention plan, 6) moderation, 7) knowledge and expertise, 8) market development, 9) care, 10) quality control and 11) value of investment. The confirmative component analysis with structural equation modeling was consistent with the empirical data. Agricultural community enterprises and entrepreneurs focus on the moral component, conduct business with honesty and avoid causing problems for others, value justice without taking advantage with diligence and adhere to moral principles in life, because all of the above will lead to sustainability in the future.

Senegal central regions face rainfall deficit combined with scarcity of surface water and poor quality. Populations use groundwater for drinking and irrigation. A Groundwater assessment studiebased on 42 samples aims to determine their quality. Several parameters (TDS, TH, WQI, SAR, RSC, %N, PI, KR and MR) and ion chemistry determined . Samples exhibit an alcaline pH (7.6) and hydrochemical facies mainly Ca-Na-Mg-HCO3. The ionic dominance is Na+ ˃ Ca2+ ˃ Mg2+ ˃ K+ and HCO3- ˃ Cl- ˃ SO42- ˃ NO3- ˃ F-. From TDS and WQI index most of the groundwaters are suitable for drinking. Irrigation quality (based on SAR, %N, RSC, KR, MR) varie from excellent to good water type. Plotting chemical data (USSL salinity & Wilcox diagrams) reveals that the majority of the groundwater samples belong to the fields excellent to good and from good to permissible . Natural processes (rock weathering, mineral dissolution, evaporation and ion exchange) control groundwater quality. As the region faced a persistent rainfall deficit combine with fragile groundwater quality should alert the stakeholders. A sustainable development of the region can be faced if strong measures are planned to follow water quality evolution and quantity available for human purposes

The overcrowding of high-rise buildings in the city adversely affects the wind environment by changing the air currents in the surrounding areas. In particular, extreme climate phenomena caused by climate change are stronger and more frequent, causing social damages in cities. To comprehensively better understand the wind properties around high-rise buildings, actual filed measurement is necessary to determine the environment assessment of wind effect. We performed the on-site measurement on LCT residential complex regions (411.6m tall) with highly concentrated high-rise buildings in coastal city Busan, South Korea under extreme weather conditions such as typhoon invasions to determine wind fluid characteristics. In the field monitoring, five anemometers were installed to analyze the wind environment around high-rise buildings when typhoon 'Hinnamnor' invaded. Compared to the nearby weather station operated by KMA(Korea Meteorological Administration), the gust was 3.7 times stronger and the max 1 min-mean wind speed was 3.1 times stronger, and the characteristics of the downward wind and the canyon wind were shown depending on the location characteristics of the point. Turbulence intensity decreased as the wind speed increased and converged to a certain value. Likewise, the gust factor also decreased as the wind speed increased and converged to 2.0, which is considered to be the parameter that best represents the intensity of the instantaneous gust caused by the skyscraper wind effect.

Natural hazards threaten millions of people all over the world. To address the risk, exposure and vulnerability models with high resolution data are essential. However, in many areas of the world, exposure models are rather coarse and aggregated over large areas. Although OpenStreetMap (OSM) offers great potential to assess risk at a detailed building-by-building level, completeness of OSM building footprints is still heterogeneous. We present an approach to close this gap by means of crowdsourcing based on the mobile App MapSwipe, where volunteers swipe through satellite images of a region collecting user feedback on classification tasks. For our application, MapSwipe was extended by a completeness feature that allows to classify a tile as “no building”, “complete” or “incomplete”. To assess the quality of the produced data, the completeness feature was applied at four regions. Our results show that the crowdsourced approach yields a reasonable classification performance of the completeness of OSM building footprints. Nevertheless, this study also revealed that volunteers tend to classify nearly completely mapped tiles as “complete”, especially in areas with a high OSM building density. Another factor that influenced the classification performance was the level of alignment of the OSM layer with the satellite imagery.

Large amounts of pumice stone generated by the submarine volcanic eruption at Fukutoku Okanoba on August 13, 2021, drifted ashore, affecting ship navigation and fishery operations. Through laboratory experiments, we investigated the possibility of using pumice as a sand-capping material for eutrophic sediments. Crushed pumice as a sand cover material effectively reduced the sedimentary oxygen consumption rate. Nutrient release from sediment showed a similar trend, with ~25% and 82% reduction in NH4-N and PO4-P release rates, respectively. Furthermore, the bivalve exposure experiments using crushed pumice suspended in seawater showed no adverse effects specific to pumice and lower bivalve mortality than that using kaolin at the same concentration. This could be owing to differences in gill accumulation and blockage owing to the particle size variation of the suspended particles. These results suggest that crushed pumice is effective for sand covering and suppresses oxygen consumption and nutrient release from the sediments.

Observations of ocean surface winds from Indian scatterometer SCATSAT-1 have been combined with background wind field from a numerical weather prediction (NWP) model available at National Centre for Medium Range Weather Prediction (NCMRWF) to generate a 6-hourly gridded hybrid wind product. A distinctive feature of the study is to produce a global gridded wind field from SCATSAT-1 scatterometer passes with spatio-temporal data gaps at regular synoptic hours relevant for forcing models and other NWP studies. This is done by making use of concepts from the modern particle filter technique, which does not represent the model probability density function (PDF) following the Gaussian technique. The 6 hourly hybrid wind is generated for the entire year of 2018 and is validated using the wind speed from daily gridded level-4 SCATSAT-1 winds (L4AW), Cross Calibrated Multi-Platform dataset (CCMP) and global buoy data from National Data Buoy Centre (NDBC). The results indicate potential of the technique to produce scatterometer winds at the desired temporal frequency with significantly less noise and along swath biases. The study shows the generated hybrid winds have very high quality with respect to the already existing daily product available from ISRO.

Mangrove ecosystems play an important role in local livelihoods in coastal regions of tropical and subtropical countries. However, in recent years urbanisation has changed the income structure of residents near mangroves. Different landscapes provide different job opportunities; thus, analysis of regional landscape patterns is important for understanding income structures. In this study, surveys on the income structure and landscape patterns of the surrounding areas of three mangrove sites were conducted in the Hau and Hoang Mai River estuaries in Nghe An Province, North-Central Vietnam. The results revealed that both natural and socio-economic landscape components affected income structure. The major occupations in the study area were agriculture, including husbandry, sea fishing, and trading. Land morphology and river type were the major factors influencing the income from agriculture, while coastline morphology primarily affected income from sea fishing. Community-based trading was carried out in the study area; thus, the population inside each administrative unit was a significant factor increasing income, while the retail market size in an area had significant negative effects, potentially due to the increasing number of competitors. Our study aimed to evaluate mangrove ecosystem importance for local livelihoods in relation to landscape patterns, and the results contribute to urban planning based on the conservation and sustainable use of mangrove ecosystems.

The Roman villa of Pisões (Beja, Portugal), was part of the Lusitanian colony of Pax Iulia. This place stands out for the predominance of the water element in several structures of the villa, highlighting the balneum and the large natatio, one of the largest known in Roman Hispania. The records of the initial excavations that took place since 1967 do not allow the establishment of clear functionalities of the villa. The University of Évora, owner of the site, conceived an action plan for the requalification and enhancement of the archaeological site. One of the tasks aims to investigate using Applied Geophysics. This work analyses the landscape directly related to the villa, given that it is in the flooded area of a river, with a Roman containment dam. It is uncertain whether the water supply comes from this structure or other nearby springs. The use of ground-penetrating radar, combined with unnamed aerial vehicles, all integrated in a geographic information system, allows us to know the location of underground water connections and create a topographic model with high resolution. Considering all the information, we propose a model for the water transport inside the villa and estimate the location of the water supply.

Water quality is the measure of chemical, physical and biological suitability of water in relation to natural effects and intended purpose which may affect human health and aquatic life. Assessment of water quality is very essential for the management of water resources and human health. Traditionally, in-situ measurements have been used to obtain the water quality parameters of the water bodies. However, with the availability of satellite images, researchers have shown that satellite images are a reliable tool that can be used to estimate water quality. Satellite image-derived water quality parameters provide extensive spatial extent and large temporal variations when compared to traditional in situ sample collection and laboratory measurements. The present work estimated several parameters for quality of water in the Kamuzu reservoir of Lilongwe River for the 2013-2020 period using Sentinel-2 and Landsat-8 satellite images. The band ratio algorithms were used to retrieve Chlorophyll a (Chl-a), Turbidity, Total Suspended Matter (TSM), Secchi depth, Coloured Dissolved Organic Matter (CDOM), and Cyanobacteria from the reservoir. Turbidity and TSM were compared with the in-situ data collected over the same period. The comparison indicated R² of 0.9 and 0.69 for TSM and Turbidity respectively from Sentinel-2 images whereas R² of 0.56 and 0.61 was obtained using Landsat 8 images which are quite encouraging. The other set of results included the spatial distribution maps of water quality parameters using Landsat-8 and Sentinel-2 satellite data. It was observed that the spatial distribution of water quality parameters, except for CDOM and Cyanobacteria, showed very good distribution and matches with the theoretical results. However, for CDOM and Cyanobacteria, the distribution was almost similar for the entire study area and the band ratio algorithm may not be able to estimate them quite reasonably. This research reiterates the need for the use of remote sensing in estimating the water quality parameters and may be a substitute to the in-situ data, in terms of spread and frequency, which is very common to most of the water bodies, across the globe.

The term ‘biodynamic’ lighting is proposed as a term to capture the true aspiration of ‘life-centric’ lighting: good human and non-human outcomes for all life-sustaining behaviours on earth driven by good design for phototropism, phototaxis, photoperiodism and circadian entrainment. While future studies are necessary to consolidate this aspiration, it is clear that lighting has immediate potential to better support planetary health. As many aspects of animal, human and plantbehaviour and survival are being perturbed by widespread exposure to artificial light at night (ALAN), lighting professionals are in the middle, attempting to make design decisions that directly affect these life forms. This paper empowers readers with fundamental understandings of how light information is used by these life forms in a way crucial for development, growth and survival. The paper then provides a process for life-centric lighting design by drawing inspiration from an existing process for human-centric lighting design which can dovetail with decision-making processes that are within the workflow of lighting professionals. Holistic consideration of life-centric lighting design should look beyond altruism and romanticism by firmly rooting itself into pragmatism about long-term considerations for planetary health. Key references are consolidated and synthesized with goals of supporting knowledge translation into pragmatic lighting strategies in a manner useful for lighting professionals. While the scientific community remains sceptical about the term ‘biodynamic’ – which is argued to be developed through mysticism instead of scientific methodology – this paper counter argues that with appropriate use, the term has the potential to comprehensively capture this aspiration.

Mangrove forests, which are essential for the maintenance of terrestrial and marine biodiversity in tropical coasts and constitute the main blue-carbon ecosystems for the mitigation of global warming, are among the world’s most threatened ecosystems. Mangrove conservation can greatly benefit from paleoecological and evolutionary studies, as past analogs documenting the responses of these ecosystems to environmental drivers such as climate change, sea level shifts and anthropogenic pressure. A database (CARMA) encompassing nearly all studies on mangroves from the Caribbean region, one of the main mangrove hotspots, and their response to past environmental shifts has recently been assembled and analyzed. The dataset contains over 140 sites and ranges from the Late Cretaceous to the present. The Caribbean was the cradle of Neotropical mangroves, where they emerged in the Middle Eocene (~50 million years ago; Ma). A major evolutionary turnover occurred in the Eocene/Oligocene transition (34 Ma) that set the bases for the shaping of modern-like mangroves. However, the diversification of these communities leading to their extant composition did not occur until the Pliocene (~5 Ma). The Pleistocene (the last 2.6 Ma) glacial-interglacial cycles caused spatial and compositional reorganizations with no further evolution. Human pressure on Caribbean mangroves increased in the Middle Holocene (~6000 years ago), when pre-Columbian societies began to clear these forests for cultivation. In the last decades, deforestation has reduced the Caribbean mangrove cover by one third and it has been estimated that, if urgent and effective conservation actions are not undertaken, these 50 million-year-old ecosystems might disappear in little more than half a century. A number of specific conservation and restoration applications based on the results of paleoecological and evolutionary studies are suggested.

The 1884 Andalusia Earthquake, with an estimated Magnitude between 6.2 and 6.7, is one of the most destructive events that shook the Iberian Peninsula, causing around 1200 casualties. Ac-cording to paleoseismology studies and intensity maps, the earthquake source relates to the nor-mal Ventas de Zafarraya Fault (Granada, Spain). Diverse studies registered and later analyzed hydrological effects, such as landslides, rockfalls, soil liquefaction, all-around surge and loss of springs, alterations in the phreatic level, discharge in springs and brooks, and well levels, along with changes in water properties. Further insight into these phenomena found an interplay be-tween hydro-geomechanical processes and crust surface deformations, conditions, and properties. This study focuses on analyzing and simulating the features involved in the major 1884 event and aims at elucidating the mechanisms concerning the mentioned effects. This ex-post analysis builds on the qualitative effects and visible alterations registered by historical studies. It encompasses conceptual geological and kinematic models, and a 2D finite element simulation to account for the processes undergone by the Zafarraya Fault. The study focuses on the variability of hy-dro-geomechanical features and the time evolution of the ground pore-pressure distribution in both the preseismic and coseismic stages, matching some of the shreds of evidence found by field studies. This methodology can be applied to other events registered in the National Catalogues of Earth-quakes to achieve a deeper insight, further knowledge, and a better understanding of past earth-quakes.

We studied the link between carbon and water fluxes to understand the response of net ecosystem carbon exchange (NEE) to water availability conditions of three different potato water regimes cropping systems [full irrigation (FI), deficit irrigation (DI) and rainfed (RF)]. Through the eddy covariance technique, we measured CO₂ and water vapor exchanges and determined surface resistances, omega factor, and inherent water use efficiency (IWUE). Additionally, continuous plant growth determinations of leaf area index (LAI) and specific leaf area (SLA) were made over the three cropping systems. The RF potato was a net carbon source (NEE = 187.21 ± 3.84 g C m⁻²), while both, FI (NEE= −311.96 ± 12.82 g C m⁻²) and DI (−17.3 ± 4.6 g C m⁻²) were a net carbon sink. Greater sink activity is due to high fluxes of gross primary productivity (GPP) [where the GPP > ecosystem respiration (R_eco)] and evapotranspiration (ET), and the high efficiency in the exchange of carbon and water. Without water limitations, the larger canopy, with greater photosynthetic activity (GPP/R_eco > 2) as well as with low internal resistance offers a greater area for water and carbon exchange, and the highly coupled and synchronized ET – GPP fluxes are primarily controlled by the radiative environment. The lower sink capacity of the DI potato crop and the carbon source activity from the RF, are consequences of a smaller area for water and carbon exchange due to the smaller canopy, and a low IWUE from decoupled and desynchronized carbon and water exchange caused by unbalanced restrictions on ET and GPP fluxes. Specifically, at DI potato, ET remained at a high rate, while GPP was reduced by means of non-stomatal limitations. In the rainfed potato, vapor pressure deficit (VPD) played a significant role increasing midday canopy resistance (Rc) up to 13 times compared to irrigated sites, when VPD was around 0.8 kPa. In consequence, ET and GPP fluxes decreased together, but GPP decreased more than ET because of stomatal and non-stomatal limitations.

Digital Elevation Model (DEM) is a representation of elevation data that represent terrain with or without overlaying objects of the Earth. It is the ideal and most widely used method for determining topography. DEMs are generated from various techniques such as traditional Surveying, Photogrammetry, InSAR, LiDAR, Clinometry and radargrammetry. It has been observed that mostly LiDAR-generated DEMs provide the best accuracy. The unavailability of LiDAR data in most of the region restricts global researchers from high-resolution and accurate DEMs. The recent launch of ICESat-2 with a 13m beam footprint and 0.7m pulse interval, promises elevations at high orbital precision. Its accuracy is of the order of few centimeters in complex topography, because of this ICESat-2 proves to be a good source to generate high-accuracy DEMs. ICESat-2 provides discrete photon data with elevations of points on the Earth’s surface. Traditional interpolation techniques tend to over-smooth the estimated space and still are unable to justify the complicated continuity in the topographical data. Machine learning algorithms are widely being used to extract patterns and spatial extent in geographic data. To estimate a DEM from ICESat-2 LiDAR point data, machine learning regression algorithms are implemented in this study. The present study has been performed for a plain region of Ghaziabad, Uttar Pradesh, India. Studies have shown that Cartosat-1 DEM V3 R1 product provides an accuracy of the order of 2m in predominantly plain regions, hence taken for this region. Current work focuses on comparing various regression-based machine-learning techniques to interpolate DEM from ICESat-2 data The RMSE of the interpolated DEM resulted from the Gradient Boosting Regressor, Random Forest Regressor, Decision Tree Regressor, and Multi-Layer Perceptron Regressor was 7.13m, 7.01m, 7.15m, and 3.76m, respectively when evaluated against the TANDEM-X DEM of the same region. The MLP Regressor is found to perform the best among the four algorithms tested.

Tropical mangroves, one of the most important blue-carbon ecosystems, are among the world’s most threatened ecosystems and are in danger of disappearing within the next century, which may have a significant impact on coastal ecology and biodiversity, and also on the global capacity of climate change mitigation. The Caribbean region is one of the main mangrove hotspots on Earth. This paper is a thorough updated synthesis on the origin, evolutionary turnover, diversification and historical decline of the Caribbean mangroves, in relation to tectonic, climatic, eustatic and anthropogenic shifts. Neotropical mangroves originated in the Caribbean during the Middle Eocene, after the emergence of the first known mangrove-forming tree (Pelliciera). The abrupt cooling and sea-level fall of the Eocene-Oligocene transition (EOT) would have facilitated the replacement of the primeval mangroves by others dominated by Rhizophora. This was a major evolutionary disruption and gave rise to the modern Neotropical mangrove forests, still dominated by Rhizophora, with Pelliciera restricted to a relictual equatorial patch. The other extant mangrove-forming trees (Avicennia, Pelliciera) appeared in the Mio-Pliocene and the main diversification burst occurred in the Plio-Pleistocene, when 80% of extant genera emerged, with no extinctions documented. Pleistocene climatic and eustatic glacial cycles promoted recurrent spatial and compositional reorganizations. Natural drivers were the most influential until the Middle Holocene, when Amerindian cultures settled the whole Caribbean region and began to modify mangrove communities for agriculture and other activities using fire. During the last decades, deforestation has accelerated and the Caribbean mangrove cover has been reduced by >30%. A variety of conservation actions have been undertaken, but paleoecological and evolutionary knowledge remains to be fully incorporated into mangrove preservation. It is hoped that contributions like the present will help raising awareness on the importance of past records for conservation purposes.

In January and February 2001, El Salvador was hit by two strong earthquakes that triggered thousands of landslides, causing 1,259 fatalities and extensive damage. The analysis of aerial and SPOT-4 satellite images taken a few days after the events allowed us to map 6,491 coesismic landslides, which occurred in 14 study areas extending for about 400 km2. Four different Multivariate Adaptive Regression Splines (MARS) models were produced by using different covariate sets and landslide inventories, the latter containing the slope failures triggered by an extreme rainfall event of November 2009 and those induced by the earthquakes of 2001. Moreover, two validation scenarios were employed, including the 25% and 95% of the mapped landslides, respectively. The results of our experiment revealed that: (i) MARS algorithm provides reliable predictions of coesismic landslides; (ii) models calibrated with rainfall-induced landslides predict with acceptable accuracy landslides caused by deep earthquakes and distributed over vast areas; (iii) the best accuracy is achieved by models trained with both preparatory and trigger variables; (iv) a small portion of the landslides produced by an earthquake can be used to calibrate MARS predictive models that help to identify slopes where yet unreported landslides may have occurred.

InSAR and associated analytic methods enable relative surface deformation measurements from low Earth orbit with a potential accuracy of centimeters to millimeters. However, assessing the actual accuracy can be quite difficult. The analytic methods are complicated enough that naive analytic error propagation is infeasible, and, in many settings, InSAR practitioners lack sufficient ground truth to assess results. Phase noise due to partial decorrelation from changes in the scattering properties of the ground is a prominent source of accuracy loss. In this paper we present a method to assess the loss of precision due to this component of phase noise. The proposed method consists of generating synthetic data stacks whose statistical properties match those of the actual input SAR data stacks, and then using the synthetic data for an ensemble calculation. The spread of the results of the ensemble calculation indicates the loss of precision. We show examples of the ensemble analysis at a mining operation in South Africa, and demonstrate the ability to estimate the precision of two InSAR deformation retrieval methods on a point-by-point and epoch-by-epoch basis.

Plastic contamination is a burning issue costing the global cotton and textile industries billions of dollars annually. Any time plastics from different sources end up in a cotton lint bale, the value to the textile mills plummets significantly. Various industry players have therefore made a concerted effort to find lasting solutions to the menace posed by plastic to cotton profitability and sustainability. Nevertheless, until now, there have been no up-to-date comprehensive documents detailing the numerous and ever-growing efforts committed to solving this challenge. Therefore, this article provides a detailed yet compact review of this highly dynamic subject matter. First, it puts into perspective plastic contamination in the cotton and textile industries. Then, the cotton value chain is subdivided into phases from pre-cultivation to textile mills. The root causes of plastic contamination are discussed in each stage, followed by discussions of some already developed and emerging solutions in response to the challenge by the affected industries and researchers. Concluding from the author’s perspective, the paper makes projections for the future directions of plastic mitigation efforts within the cotton and textile industries. This article also infers from the reviewed literature that research on finding alternative materials to plastic as module wrap, the development of new, effective, and all-condition plastic sensing techniques for ginning and spinning equipment, and standardized protocols for UAV in-field surveys of plastic trash are some of the areas that will be beneficial to finding a permanent solution to the challenge.

The Lokpaukwu Uru Quarry was examined geologically, geophysically, and core-wise. The location is between 5056.149'N and 5056.193'N and 7028.312'E and 7028.356'E. The study location may include the Asu River Group and the Eze-Aku Formation. This area has five rock units. In the eastern research region, siltstone forms a "CAP" on the shale. Shale underlies half of the study area. The west has calcareous sandstone. The eastern part of the area is dolerite, the main rock that spans siltstone and shale. The region's geological matter contains iron. Two geological sections were analysed and interpreted to identify the five rock units and their outcrops in the study area. electroresistivity in geophysical research Schlumberger found that the western, northwesterly, and central sections of the research region had a thick sedimentary sequence, whereas the eastern half has an igneous body, the project's main component. Sandstone, siltstone, and shale follow the high-resistivity rock in this location. The rock unit in the region was found in eleven core samples from the east half of the study area. Nine rock-unit core samples were found near Obichioke. The Lokpaukwu area's core data shows the rocks' positions, kinds, minerals, and strengths. Geologic mapping shows that a major fault separates the viable Uru end from the unviable Obichioke lot. Recrystallization dominates the fault track (alcitic matter). Thus, prior to quarrying igneous (basic) units, comparable investigations are advised.

Although total nett CO₂ atmospheric flow can be estimated with reasonable accuracy, the contributing gross fluxes between the atmosphere and the earth's surface are poorly understood. This paper presents a method, driven by the objective of simplicity, by which the global outflow and inflow of CO₂ between atmosphere and a globally equivalent "mixing reservoir" can be estimated, using the isotopes ¹⁴C and ¹³C as tracers. It has been asserted that the isotopic carbon in CO₂ cannot be directly used as a tracer in flow studies because it is not subject to the Revelle factor. Evidence is provided showing that this view is mistaken. The model contains 7 key parameters which are used to create synthetic records of Δ¹⁴C and d¹³C spanning 200 years or more, including during the period of atmospheric weapons testing and its decay known as the "bomb pulse". By optimising the fit between these computed values and the historical records of d¹³C and Δ¹⁴C, all seven key parameters are determined. The effective "mixing reservoir" is thereby determined to have a size around six times that of the atmosphere, with global outflux rising from 39.7 GTC yr-1 in 1750 to 58.9 GTC/yr in 2020, this figure probably not including annually cycled carbon.

Clew Bay is an important aquaculture production area in Ireland. In this study, we focused on a high-resolution simulation of the Clew Bay region based on a Regional Ocean Modelling System (ROMS). Freshwater discharges from eight rivers are included in the model and a wetting-drying scheme has been implemented. The Clew Bay model simulation was validated and calibrated with available observations (e.g., Acoustic Doppler Current Profilers (ADCP), vertical salinity and temperature profiles and tide gauge) in the geographic area of the model domain. High correlations were found between the model outputs and observed temperature, salinity and tide gauge water levels along with small Root Mean Square Errors. This indicates that the model was able to reproduce the oceanographic phenomena in the study area. The Taylor diagram analysis showed a high correlation coefficient (R=0.99) between the ADCP bottom temperature in the Inner Bay and the Clew Bay model, along with a small Centered Root Mean Square Error (RMSD =0.5 ◦ C). High correlation coefficients (R > 0.80) were found between the model and the two ADCPs for the zonal current component. There was a resemblance in structure between the model and the observed salinity profiles, indicating that freshwater was correctly implemented in the model. Moreover, the correlation coefficient between the model and the tidal sea surface height (SSH) was 0.99, with an RMSD of 0.09 m. We discovered that wind direction and speed have a significant impact on the bay's water inflow rate. The model outputs can be used to provide scientists, fishermen and decision-makers with hydrodynamic information on ocean conditions in the bay.

Maize (Zea mays) is one of the most important staple food and primary source of livestock feed in the world. As the consumption of maize grown on the selenium-enriched soils of Naore Valley is one of the apparent causes of selenosis in the area, this work collected and analyzed total Se, Se fractions, and Se species distributions in maize plant samples, including grains, leaves, stalks, roots, rhizosphere soils, and the most representative parent rock materials from Naore Valley, Ziyang County, China. The Se distribution in soils markedly correlated with the weathered Se-enriched bedrocks, but most of the Se in the analyzed soils is enclosed as recalcitrant residual Se and organic-sulfide bound Se. In contrast, Se in rocks had a comparatively higher bioavailability and is bounded mainly to organic matter and sulfides minerals, with very few of the Se enclosed in the residual fraction. Maize plants might take a large amount of Se from the organic-sulfide bound Se fraction in the Se-rich soils, the weathered products from bedrocks or plant litters. Total Se concentrations in the collected samples were observed in descending order soil>leaf>root>grain>stalk. The predominant Se species detected in maize plants was SeMet. Se inorganic forms, mainly Se(VI), decreased from root to grain and were possibly assimilated into organic forms. Se (IV) was barely present. The natural increases in Se concentration affected mainly leaf and root dry-weight biomass as they are the organs that coped with the highest Se accumulation. This paper offers an insight into the uptake, accumulation, and distribution of Se forms in natural Se-rich maize crops and an opportunity for shifting Se-rich soils from menaces to valuable resources for growing Se-rich agricultural products.

Among the impacts of climate change in West Africa, coastal erosion is the most threatening disaster apart from floods and the increase in temperature. The southern coast of the Dakar region, as part of the most threatened coastal zones in West Africa, records the most current coastal damages in Dakar due to its coastline dynamics and low-lying area. This study was conducted in three littoral cells (Mbao, Bargny and Toubab Dialaw) and aims to investigate if there is a relationship between the coastline dynamic, topographic and slope and the contribution of the geomorphology along the southern coast of Dakar. It helps to understand better the role which plays the topography, Slope and geomorphology in coastal dynamics using remote sensing, cartographic tools and statistical methods. The Modified Normalized Different Water index (MNDWI) was employed to delineate the coastlines before computing the dynamic rate of the coastline using the Digital Shoreline Analysis System (DSAS) software. After that, Topography and Slope were determined using Digital Elevation Model (DEM). Then, the correlation between the coastline dynamic, topography and slope was analyzed through the coefficient of correlation and multiple linear regression model. High dynamic rates were noted in the littoral cells, where the topography and slope are low. In Mbao, there is a significant relationship between the coastline dynamic, topography and slope with a coefficient of correlation of about 0.63. In Bargny, the relationship is also significant, with a coefficient of 0.87. The relationship is not significant in Toubab Dialaw, where topography and Slope are high, with a coefficient of 0.15. We conclude that topography, slope and soil properties play an important role in the study area.

The assessment of an organization/product's environmental footprint is based on the protocols developed by the Joint Research Centre of the European Union, which take into account 16 impact categories. Among the categories covered are toxicity to freshwater ecosystems and to humans. Standard protocols use only chemical parameters as input data, preventing the true impact of entire complex mixtures, such as emissions discharged into the environment, from being determined. Biological assays allow us to bridge this gap: in the current study, assays were used to determine baseline toxicity towards aquatic organisms (green algae, luminescent bacteria, and crustaceans) as well as specific toxicity (mutagenicity and carcinogenicity). Expected impacts were compared using two approaches (standard and bioassay-centered results obtained are not always in line and, in general, the traditional method underestimates impacts). This demonstrates the importance of these assays and suggests that they be used in LCA (Life Cycle Assessment) protocols as well.

Landslide risk mitigation is limited by data scarcity. This could be improved using continuous landslide detection systems. In order to investigate which image types and machine learning (ML) models are most useful for landslide detection in a Norwegian setting, we compared the performance of five different ML models, for the Jølster case study (30-July-2019), in Western Norway. These included three globally pre-trained models; i) the Continuous Change Detection and Classification (CCDC) algorithm, ii) a combined k-means clustering and Random Forest classification model, and iii) a convolutional neural network (CNN), and two locally-trained models, including; iv) Classification and Regression Trees and v) a U-net CNN model. Images used included Sentinel-1, Sentinel-2, digital elevation model (DEM) and slope. The globally-trained models performed poorly in shadowed areas, and were all outperformed by the locally-trained models. A maximum Matthew’s correlation coefficient (MCC) score of 89% was achieved with model v, using combined Sentinel-1 and -2 images as input. This is one of the first attempts to apply deep-learning to detect landslides with both Sentinel-1 and -2 images. Using Sentinel-1 images only, the locally-trained deep-learning model significantly outperformed the conventional ML model. These findings contribute towards developing a national continuous monitoring system for landslides.

The paper presents the open source tool climdex-kit which includes utilities to compute, analyze and visualize climate indices based on the input data, target domain and temporal extent defined by the user. It is intended to support researchers as well as practitioners and decision makers to derive, handle and interpret meaningful information for climate change studies and sectoral applications. It currently includes the computation of 30 indices based on temperature and precipitation, describing both mean and extreme climate conditions, and it is designed to work with climate model projections. The tool is written in Python and integrates utilities from the well-established Climate Data Operators (CDO) and NetCDF Operators (NCO) libraries. The specific utilities for selecting, aggregating and visualizing data are thought to help users to produce tailored results improving understanding and communication of future climate change. In order to show the functionalities of the package as well as its potential integration in regional climate services, climdex-kit was applied to an ensemble of downscaled climate model projections up to 2100 for the region Trentino – South Tyrol (north-eastern Italian Alps). The projections for a selection of indices accounting for extreme temperature and precipitation conditions were derived and different visualization choices discussed. The package climdex-kit is developed in a way that allows users to implement additional routines for calculating other indices as well as for easily adapting the routines to handle with different data types and spatio-temporal targets defined by the specific application. Effective climate services can in fact be developed only if flexible tools and customizable climate information are integrated with a clear understanding of data features and limitations.

Biodiversity loss has been identified as the environmental impact where humankind has been trespassing planetary boundaries most ruthlessly. Going beyond the pressures causing damages and analysing their un-derlying driving forces, ipbes identified a series of drivers. The Montreal-Kunming Global Biodiversity Framework GBF is intended to and claims to be a policy response to such analyses. To enhance the resilience of ecological systems, to allow for their recovery and enable the restoration efforts foreseen in the GBF to be successful, the pressures/direct drivers have to be reduced and the drivers/indirect drivers of biodiversity loss have to be redirected. However, often the necessary (semi-)quantitative infor-mation needed to politically address the drivers is absent or patchy. The data collected under the United Nations System of Environmental-Economic Accounting—Ecosystem Accounting, to which the GBF is affiliated, monitors the state of ecosystems, with no priority for pressure/direct driver analysis. Hence we suggest LUI, a deliberately simple index designed for two purposes, as a tool for communicating where sophisticated statistics are available, and as an information collection tool elsewhere Its simple and intuitively understandable structure makes it suitable for citizens’ science applications, and thus for partici-pative monitoring when extensive statistical data gathering is not feasible.

The modification of land use is a crucial factor in the dysfunction of ecosystems. It considerably influences the hydrological cycle of basins. This study focuses on the Oueme basin, which represents almost half of the total area of Benin, and describes the current and future evolution of its land cover using the maps of the Land Use Land Cover Dynamics project (1975, 2000 and 2013). A temporal analysis of the surface states was performed with QGIS and the potential land cover in 2025, 2050 and 2085 was estimated using the Markov chain algorithm in the IDRISI software. The results show that the Oueme basin is predominantly savanna (77.70% in 1975, 66.29% in 2000 and 57.10% in 2013). Forest areas, which represented the second class in 1975 with a total proportion of 13.34%, have gradually decreased to 8.66% and 6.89% respectively in 2000 and 2013. Conversely, cultivated areas more than tripled in 2000 and quadrupled in 2013. Residential areas are increasing very strongly in the southern part of the basin with an amplification in the recent period (6% between 2000-2013 against 2% between 1975-2000). The recent period is more exposed to changes in surface condition and these changes are likely to be amplified in the future (probable total disappearance of forested areas in 2085). We recommend clarifying the impacts that each land use category generates/will generate on the hydrological cycle of this basin.

This note makes available five years of data gathered in a measurement site equipped with a micrometeorological station and two monitoring wells. Series of data of hydrological and atmospheric variables allow to estimate the flux of water across the atmosphere-land interface and to calculate the water budget, that are crucial topics in climate and environmental sciences. The measures of water-table began during 2017, one of the driest year of the whole instrumental period of climate history for the Central Mediterranean. Data from the micrometeorological station have been used to construct two more datasets of daily and monthly cumulative values of different terms of the surface water budget, from which the groundwater infiltration has been estimated. A significant decreasing trend characterizes both the data time series of piezometric heights and estimated infiltration in the considered period. All the data are collected in 2017-2022 datasets that are available in the supplementary material.

In the Czech Republic, the Universal Soil Loss Equation provides the basis for defining the soil protection strategy. Field rainfall simulators were used to define the actual cover-management factor values of the most extensively seeded crops in the Czech Republic. More than 380 simulations between 2016 and 2021 provided data. The methodology focused on multi-seasonal measurements to cover the most important phenological phases. A comparison with the original USDA values for maize showed that it is desirable to redefine the C-factor. 71 fallow plot experiments showed that the rainfall-runoff relation is much easier to replicate than the actual sediment transport. For 30-minute intensive rainfall, the runoff ratio reached 62%, and the coefficient of variation was 25%. On saturated soil, the runoff ratio reached 81% and the coefficient of variation dropped to 12%. Soil protection techniques have a significant effect on runoff reduction. Maize seeded after cover crops and combined with reduced tillage or direct seeding can reduce the runoff ratio to 10-20% for ‘dry’ conditions and to 12-40% for ‘saturated’ conditions. Concerning soil loss, the variations are greater, with the coefficient of variation reaching 42% during fallow plot experiments. The reader should consider associated uncertainties.

Riverine particulates dominate the transport of vital nutrients like Si, Fe or P to the ocean mar-gins, where they may increase primary production by acting as slow release fertilizer. Further-more, the supply of particulate surface area to the ocean is considered to be a major control or-ganic carbon burial. Taken together, these observations suggest a close link between the supply of riverine particulate material and the organic carbon cycle. To explore this link, we conducted microcosm experiments to measure the growth of the marine diatom Thalassiosira weissflogii in the presence and absence of different types and concentrations of riverine particulate material. Results demonstrate a strong positive effect of riverine particulate material on diatom growth with increased total diatom concentrations and slowed post-exponential death rates with in-creasing particulate concentration. Moreover, SEM and optical microscope investigations con-firm that riverine particulates facilitates organic carbon burial through their role in the aggrega-tion and sedimentation of phytoplankton. The supply of riverine particulate material has been shown to be markedly climate sensitive with their fluxes increasing dramatically with increas-ing global temperature and runoff. This pronounced climate sensitivity implies that riverine particulates contribute substantially in regulating atmospheric CO2 concentrations through their role in the organic carbon cycle.

High-resolution (2 km) high-frequency (hourly) SST data from 2015-2020 provided by the Advanced Himawari Imager (AHI) onboard the Japanese Himawari-8 geostationary satellite positioned over 140.7°E were used to study spatial and temporal variability of the China Coastal Front (CCF) in the East China Sea. The hourly SST data were processed with the Belkin and O’Reilly (2009) algorithm to generate long-term mean monthly maps of SST gradient magnitude (GM) and frontal frequency (FM) using a GM threshold of 0.1°C/km. Horizontal structure of SST field in the vicinity of the CCF was investigated from cross-frontal distributions of SST along 8 parallels from 31°N to 24°N. The high resolution (2 km) monthly distributions of SST along these 8 parallels were used to determine inshore and offshore boundaries of the CCF and calculate the CCF strength defined as the total cross-frontal SST step dSST=Offshore SST – Inshore SST. The CCF emerges in November, fully develops in December and peaks in strength in January-February. The front’s fragmentation and shrinking/weakening begins in February and March, respectively. In winter (December-February), the front’s strength dSST exceeds 5°C offshore the Zhejiang-Fujian coast and could be as high as 7.5°C when nearshore waters cool down to 7°C. In winter, the front’s strength decreases downstream from 31°N to 24°N. The CCF changes its physical nature as the seasons progress. In winter, the CCF is a water mass front between cold and fresh water coming from the north and warm and salty water coming from the south. In summer, the CCF becomes a coastal upwelling front maintained largely by southerly winds. In winter, the CCF’s cross-frontal structure in the SST field is ramp-shaped, with SST increasing monotonously in the offshore direction. In summer, the CCF’s cross-frontal structure in the SST field is V-shaped or U-shaped, featuring a minimum SST formed by cold upwelled water at some distance from the shore. Thus, the summer SST structure effectively consists of two fronts, a nearshore and offshore, with a minimum SST in-between. Across the inshore/offshore front, the SST decreases/increases in the offshore direction. The local bathymetry, especially the relatively steep shelf slope between 20-m and 50-m isobath, steers the front, which does not meander in winter offshore the Zhejiang-Fujian coast. As the cold season progresses, the front’s axis gradually shifts into deeper waters, from ~20 m to ~50 m.

This paper presents a simple drone survey and image processing approach to identifying St. John's wort (Hypericum perforatum) on grazing land in the Hunter Valley NSW, Australia. St John’s wort is an invasive species (weed) that competes with pasture, poisons livestock, can downgrade wool with ‘vegetable fault’, and decreases property values. Identifying the locations of St John wort from the ground can be difficult due to topography, limited access, and/or larger land areas of mixed vegetation. In this study, a drone was used to survey a 174-ha grazing property in the Hunter Valley NSW (Australia). The images were stitched together using commercially available software. A unique Visual Atmospheric Resistance Index (VARI) attribute was identified and used to highlight the presence of St John’s wort in the survey area. These sub-areas were then digitised onto high resolution maps for future planning use by land managers.

Although several carbon sequestration methods have been proposed according to theories from various disciplines, it is not known which method is the best. This study evaluated and summarized the advantage and disadvantages of several major carbon sequestration methods, including carbon capture and sequestration, ocean alkalinization, algal cultivation, and wood burial, based on the first principles, namely, sequestration scale and time, elemental stoichiometry, and energy use efficiency. This study suggested that, at present, wood burial may be a leading candidate for carbon sequestration because it can be implemented immediately on a large scale, is cost-effective and efficient, has a long sequestration time, has low technical requirements, and has a relatively low impact on agriculture. This study also discussed the implementation of wood burial from the perspective of nutrient requirements. To achieve carbon neutrality by 2050, the present study proposed a 25-year project starting in 2025 with an average sequestration rate of 3 Pg C per year, which includes burying 1.5 Pg of dead wood carbon and the creation of 9.7 × 10⁶ ha woodland.

The access road to the powerhouse's hydraulic system's facilities in Guarumales (Azuay, Ecuador) presents a medium-sized landslide. Geophysical tests were conducted in the initial research stage, combining electrical and seismic methods. A SEV and HVSR surveys campaign have been taken as a reference for the analysis of the landslide area. The distribution of these test points has been at three different levels along the landslide where the access road crosses it and trying to cover the area's most extensive possible length and width. In the area, we find the geology dominated by the presence of schists, altered to different degrees and presenting blocks of material with a lower degree of alteration within colluvium formed by a clayey matrix and coarse material of the exact nature. There is also evidence of surface and groundwater in the area. The results obtained through SEV tests have allowed for defining the separation zone of the mobilized or sliding materials compared to the fixed or immobile ones (potentially, the sliding surface was marked). Using the HVSR technique, the natural vibration frequencies of the ground associated with the sliding mass (separation of seismic impedances between a two-layer model: mobile and fixed) have been determined. Previous authors proposed an empirical relationship establishing the exponential relationship, already proposed by previous authors, between sediment thickness and natural frequencies. It has been possible to determine the depth of the position of the loosely compacted sediment zone (and probably moving or mobilized) compared to that of compact materials (immobile) and thereby define the potential rupture surface.

Keywords: Utilizing; HOMER; techno-economic; sustainable; urban (List three to ten pertinent keywords specific to the article yet reasonably common within the subject discipline.)

Downward short-wave (SW) solar radiation is the only essential energy source powering the atmospheric dynamics, ocean dynamics, biochemical processes, etc. on our planet. Clouds are the main factor limiting the SW flux over the land and the Ocean. For the accurate meteorological measurements of the SW flux one needs expensive equipment - pyranometers. For some cases where one does not need golden-standard quality of measurements, we propose estimating incoming SW radiation flux using all-sky optical RGB imagery which is assumed to incapsulate the whole information about the SW flux. We used DASIO all-sky imagery dataset with corresponding SW downward radiation flux measurements registered by an accurate pyranometer. The dataset has been collected in various regions of the World Ocean during several marine campaigns from 2014 to 2022, and it will be updated. We demonstrate the capabilities of several machine learning models in this problem, which are multilinear regression, Random Forests, Gradient Boosting and convolutional neural networks (CNN). We also applied the inverse target frequency (ITF) re-weighting of the training subset and the approach of pre-classification in order to raise the SW flux approximation quality. We found that the CNN is capable of approximating downward SW solar radiation with higher accuracy compared to existing empiric parameterizations. We also found that the ITF reweighting of the training subset increases the approximation quality substantially. The estimates of the SW radiation flux using all-sky imagery may be of particular use in case of the need for the fast radiative budgets assessment of a site.

Rescaling of rainfall requires measurements of rainfall rates over many dimensions. This paper develops one approach using 10 m vertical spatial observations of the Doppler spectra of falling rain every 10 seconds over intervals varying from 15 up to 41minutes in two different locations and in two different years using two different Micro-Rain Radars (MRR). The transformation of the temporal domain into spatial observations uses the Taylor ‘frozen’ turbulence hypothesis to estimate an average advection speed over an entire observation interval. Thus, when no other advection estimates are possible, this paper offers a new approach for estimating the appropriate frozen turbulence advection speed by minimizing power spectral differences between the ensemble of purely spatial radial power spectra observed at all times in the vertical and those using the ensemble of temporal spectra at all heights to yield statistically reliable scaling relations. Thus, it is likely that, MRR and other vertically pointing Doppler radars may often help to obviate the need for expensive and immobile large networks of instruments in order to determine such scaling relations, but not the need of those radars for surveillance.

Circular Economy (CE) is noted as an emerging tool or framework to support sustainable production and consumption agenda. In addition, CE is aiming to be a trigger for redefining economic growth pathways to be sustainable, inclusive, and sensitive to ecological and environmental agendas and to focus its operational standards on co-creating societal benefits. Concerning the guiding principles and the standards of practice applied to implement and scale circular economy, this study will provide an overview of the water sector-specific circularity roadmaps and strategies in the Latin American Region. By using a semi-systematic review, document analysis, and qualitative assessment approach, we highlight framings and operational pathways, gaps, and needs within existing practices of circularity in the water sector. The results provide an overview of CE pathways at the national level of selected countries in LAR iterating those nations reflect various levels of advancement (low to high) with CE-focused innovations and policy support structures specific to the water and wastewater sectors. Towards the closing, the study is pointing to the ‘call for action’ to integrate outstanding advances and innovations in the circular economy within sectoral mandates for water and wastewater management, making an argument that circularity in the water sector could serve as an accelerator toward implementing the agenda outlined in Sustainable Development Goals (SDGs) and in particular for SDG 6 (water security for all).

This communication investigates possible anomalies in the lithosphere atmosphere and ionosphere on the occasion of the ML=3.3 earthquake that occurred on 1st January 2023 close to Guidonia Montecelio (Rome, Italy). This earthquake followed another event on 23 December 2022 of magnitude ML=3.1 with a very close epicentre (distance less than 1km). Seismological investigations clearly show an acceleration of seismicity in the last six months in the area. Two solutions of fitting time to failure power law on the Cumulative Benioff strain curve are the more likelihood: the ML3.3 of 1 January is the mainshock of seismic sequence or incoming earthquakes of a magnitude of about 4.1 provides a slightly better fit of the seismic data. Further investigation are necessary to assess if the accumulated stress have been totally released or not. No atmospheric anomalies related to this seismic activity have been identified even if some SO2 emissions could come from tectonic or volcanic sources in the South-Tyrrhenian Sea. Swarm satellites' magnetic data shows an anomalous track on 16 December 2022, which is temporally compatible with the seismic acceleration but other sources for the anomalous signal are also possible.

Drone and aerial remote sensing images are widely used, but their imaging environment is complex and prone to image blurring. Existing CNN deblurring algorithms usually use multi-scale fusion to extract features in order to make full use of aerial remote sensing blurred image information, but images with different degrees of blurring use the same weights, leading to increasing errors in the feature fusion process layer by layer. Based on the physical properties of image blurring, this paper proposes an adaptive multi-scale fusion blind deblurred generative adversarial network (AMD-GAN), which innovatively applies the degree of image blurring to guide the adjustment of the weights of multi-scale fusion, effectively suppressing the errors in the multi-scale fusion process and enhancing the interpretability of the feature layer. The research work in this paper reveals the necessity and effectiveness of a priori information on image blurring levels in image deblurring tasks. By studying and exploring the image blurring levels, the network model focuses more on the basic physical features of image blurring. Meanwhile, this paper proposes an image blurring degree description model, which can effectively represent the blurring degree of aerial remote sensing images. The comparison experiments show that the algorithm in this paper can effectively recover images with different degrees of blur, obtain high-quality images with clear texture details, outperform the comparison algorithm in both qualitative and quantitative evaluation, and can effectively improve the object detection performance of aerial remote sensing blurred images. Moreover, the average PSNR of this paper's algorithm tested on the publicly available dataset RealBlur-R reached 41.02dB, surpassing the latest SOTA algorithm.

Currently, an interactive environmental planning analysis system platform based on " Three Lines One Permit " (TLOP) is being developed to support environmental planning, construction project approval, and the application of TLOP outcome data in Guangzhou. The main objective is to provide governments, businesses and the public with environmental planning analysis tools to determine the site of construction projects. The platform is using the system architecture of the browser and server. Its core functions are interactive environmental planning analysis tool for construction project and the results display tool supporting map viewing. It provides users with a large number of detailed geospatial data and TLOP results data access and environmental planning analysis functions. This article describes the system architecture and implementation of the system platform and has a case study illustrating the system functionality. At present, the platform has been deployed and trial-operated. The content of the analysis framework is constantly expanding. This promotes the matching of environmental planning and analysis with local conditions. This will implement the application of TLOP and improve the efficiency of project construction and the level of ecological environment planning and management.

The spatial heterogeneity in hydrologic simulations is a key difference between lumped and distributed models. Not all distributed models benefit from pedo-transfer functions based on soil properties and crop-vegetation dynamics. Mostly coarse scale meteorological forcing is used to estimate water balance at the catchment outlet only. Mesoscale hydrologic model (mHM) is one of the rare models that incorporates remote sensing data i.e. leaf area index (LAI) and aspect to improve actual evapotranspiration (AET) simulations and water balance together. The user can select either LAI or aspect to scale PET. However, herein we introduced a new weighting parameter “alphax” that allows user to incorporate both LAI and aspect together for PET scaling. With this mHM code enhancement, the modeler has an also option of using raw PET with no scaling. In this study, streamflow, and AET are simulated using the mesoscale Hydrological Model (mHM) in Main (Germany) basin for the period of 2002-2014. The additional value of PET scaling with LAI and aspect for model performance is investigated using Moderate Resolution Imaging Spectroradiometer (MODIS) AET and LAI products. From 69 mHM parameters, 26 parameters are selected for calibration using Optimization Software Toolkit (OSTRICH). For calibration and evaluation, KGE metric is used for water balance and SPAEF metric is used for evaluating spatial patterns of AET. Our results show that AET performance of the mHM is highest when using both LAI and aspect indicating that LAI and aspect contain valuable spatial heterogeneity information from topography and canopy (e.g., forests, grasslands, and croplands) that should be preserved during modeling. The additional “alphax” parameter makes the model physically more flexible and robust as the model can decide the weights according to the study domain.

The current COVID19 pandemic has raised huge concerns for outdoor air quality due to expected lungs deterioration. These concerns include the challenges in the scalable prediction of harmful gases like carbon dioxide, iterative/repetitive inhaling due to mask and environmental temperature harshness. Even in the presence of air quality sensing devices, these challenges lead to failed planning and strategy against respiratory diseases, epidemics, and pandemics in severe cases. In this work, a dual time-series with bi-cluster sensor data-stream-based novel optimized regression algorithm was proposed with optimization predictors and optimization responses that use automated iterative optimization of the model based on the similarity coefficient index. The algorithm was implemented over SeReNoV2 sensor nodes data, i.e. multi-variate dual time-series of environmental and US Environmental Protection Agency standard sensor variables for air quality index measured from air quality sensors with geospatial profiling. The SeReNoV2 systems were placed at four locations that were 3 km apart to monitor air quality and their data was collected at Ubidots IoT platform over GSM. Results have shown that the proposed technique achieved a root mean square error (RMSE) of 1.0042 with a training time of 469.28 seconds for normal and RMSE of 1.646 in the training time of 28.53 seconds for optimization. The estimated R-Squared error of 0.03 with Mean-Square Error for temperature 1.0084 ᵒC and 293.98 ppm for CO2 was observed. Furthermore, the Mean-Absolute Error (MAE) for temperature 0.66226 ᵒC and 10.252 ppm for CO2 at a prediction speed of ~5100 observations/second for temperature 45000 observations/second for CO2 due to iterative optimization of the training time 469.28 seconds for temperature and 28.53 seconds for CO2 was very promising in forecasting COVID19 countermeasures before time.

Sea surface wind forecasts in the Adriatic Sea often suffer for unadequate modelling, especially for the wind speed. This has detrimental effects on the accuracy of sea level and storm surge predictions. We present a numerical method to reduce the bias between the sea surface wind observed by the scatterometers and that supplied by the European Centre for Medium-Range Weather Forecasts (ECMWF) global atmospheric model, for storm surge forecasting applications. The method, called “wind bias mitigation”, relies on scatterometer observations to determine a multiplicative factor ∆ws which modulates the standard model wind in order to decrease the bias between scatterometer and model. We compare four different mathematical approaches to this method, for a total of eight different formulations of the multiplicative factor ∆ws. Four datasets are used for the assessment of the eight different bias mitigation methods: a collection of 29 Storm Surge Events (SEVs) cases in the years 2004-2014, a collection of 48 SEVs in the years 2013-2016, a collection of 364 cases of random sea level conditions in the same period, and a collection of the seven SEVs in 2012-2016 that were worst predicted by the Centro Previsioni e Segnalazioni Maree, Comune di Venezia (Tide Forecast and Early Warning Centre of the Venice Municipality - CPSM). The statistical analysis shows that the bias mitigation procedures supplies a mean wind speed more accurate than the standard forecast, when compared with scatterometer observations, in more than 70% of the analyzed cases.

The management of urban water has evolved from single-function systems to more sustainable designs promoting society and nature as inputs to engineer novel infrastructure. In transdisciplinary research, co-design refers to a design thinking strategy in which people jointly frame a problem-solution. This article presents a conceptual framework to assess a case study focusing on the process of co-design and implementation of green infrastructure as a prototype for stormwater management. The evaluation is carried out from a self-reflective post-implementation perspective. Research activities are translated into the framework to evaluate conditions shaping the trajectory of the prototype. As a result, key aspects driving the research regarding levels of stakeholder participation and dimensions of power are identified. Planning resilient co-design strategies to retrofit urban spaces is necessary to avoid unintended consequences, especially at the initial experimental stages. This study aims to contribute to the continuous improvement of pilot strategies in urban spaces by providing a framework for a structured evaluation of research experiences.

In the process of image feature extraction, noise in the image will greatly affect the accuracy of edge detection. In this paper, the image is filtered to remove noise before edge detection by using the algorithm of wavelet transformation. Different wavelet functions are used to decompose image. Based on the experimental results, the best denoising wavelet function is selected. Canny algorithm is used to detect the edge of the denoised image, and the result of edge detection is evaluated according to the Pratt quality factor. It is proved that wavelet transform can improve the edge detection results.

Drainage divide is a dynamic feature that migrates in response to tectonic activity. The asymmetric uplift between two adjacent basins causes the divide migration from a slower to faster uplift area. Sado Island, Japan, has been affected by southeastward tilting uplift since ca. 300k years. Despite the faster uplift on the northwest, the main divides have existed on the southeast side of the geometric center of the island, with no other feature suggesting tectonic inversion of the tilting direction. In this study, we conducted a DEM-based investigation that focused on divide migration. A spectrum from very inactive to active divide migration in the northwest. Regardless of their position, actively migrating divides are comprehensible, but inactive divides located in a relatively slow uplift area remain unclear. We concluded that some divides slowed down owing to the local balance of erosion rates across the divides, not implying the balance between uplift and river erosion at the basin scale, reflecting disequilibrium in river longitudinal profiles. The main divides of Sado have presumably continued to slowly migrate toward the faster uplift area; however, they are most likely to have never overcome the moving geometric center owing to land expansion at the seacoast due to asymmetric uplift.

The environmental disaster in Kamchatka in the autumn of 2020 was caused by an extensive bloom of harmful microalgae of the genus Karenia. A spectral shape algorithm was used to detect algae. The algorithm calibration of in situ species composition data made it possible to identify areas where harmful algae dominated in biomass. Satellite images of chlorophyll-a concentra-tion, turbidity, specific fluorescence, and spectral shape parameter were computed. The images were used to recognize the stages of algal bloom: intensive growth, blooming, and change in the dominant algal species. Cases of an increase in the concentration of harmful substances in the coastal zone due to wind impact were analyzed. The following explanation of events has been offered. After the stage of intensive growth of microalgae, nutrient deficiency stimulated the production of metabolites that have a harmful effect on the environment. The change of the dominant alga species in the second half of September and the past storm contributed to a sharp increase in the concentration of metabolites and dead organic matter in the coastal zone, which caused an ecological disaster. The subsequent mass bloom of alga species of the same genus, and the regular wind impact leading to the concentration of harmful substances in the coastal zone, contributed to the development of this catastrophic phenomena.

Satellite measurements are one of the main sources of data on the state of the marine environment. To obtain information about the sea brightness coefficient , it is needed to correctly carry out atmospheric correction. In the presence of dust aerosol over the Black Sea, physically incorrect values of the spectral brightness coefficient often occur, and specifically negative values in the IR region of the spectrum. The main objective of the study is to evaluate the influence of dust aerosol on the spectral dependence of sea brightness, based on analytical calculations from the transfer theory using the principle of plane-parallel layers and results of validation of AERONET-OC field and remote sensing data. The work analyzes spectral dependence of the first error eigenvector of the standard atmospheric correction in the presence of dust aerosol. As result it is given that with an absorbing aerosol, the atmospheric correction error is described by the spectral course of molecular scattering, i.e. close to

Unusual outbreak of Covid 19 has put the whole new challenges around the globe. In a short span of time, the world has been changed, lifestyles, social events, cultural, political events everything has been put on hold as number of countries has enforced partial or complete lockdown that resulted positive in context of controlling spread and contamination of COVID-19. Such activities have also put some effects on environment in both positive and negative way. In this study only positive impacts of COVID-19 on Environment have been highlighted that played role in regeneration of natural systems i.e., reduced air emissions, cleaner air, beaches &water bodies, reduced noise levels, biodiversity rehabilitation etc.

Plankton monitoring by microscopy offers long-term ecological perspective of plankton com-munities but different detection approaches are biased uniquely. Genetic identification of marine plankton has become standard but is still not used in routine monitoring. This study assessed the diversity of plankton taxa using 18S high throughput sequencing from 2011-2012 from small-volume (~200ml) samples from the Water and Microplankton Sampler (WaMS) deployed on the Continuous Plankton Recorder platform (CPR). The 18S-HTS survey revealed a bias towards heterotrophic taxa, and phototrophs under 10µm within the photosynthetic community. In comparison with phytoplankton microscopic counts from the CPR survey and Western Channel Observatory station L4, only 8-12 taxonomic families were common to all three surveys, with a bias towards larger diatoms and dinoflagellate taxa in microscopy surveys. The WaMS survey detected a contrasting but complementary taxa set to that of microscopic surveys. Additional Quantitative PCR was carried out on the picoeukaryotic pelagophyte, Aureococcus anophagefferens, and the nanoeukaryotic potential harmful algae, Pseudo-nitzschia delicatissima, from 2011-2013. This confirmed the persistence presence of A. anophagefferens in the Western Channel and an elevated abundance of both species in 2011. Species specific seasonality were distinct from those of aggregrate phytoplankton groups.

Although nett CO₂ flows can be estimated with reasonable accuracy, the contributing gross fluxes between the atmosphere and the earth's surface are poorly understood. This paper presents a means by which the global outflow and inflow of CO₂ between atmosphere and "mixing reservoirs" can be calculated, using the radiocarbon isotopes ¹⁴CO₂ and ¹³CO₂ as a tracer. It has been asserted that isotopic CO₂ cannot be directly used as a tracer in flow studies because Δ¹⁴C is not subject to the Revelle factor; evidence is provided showing that this view is mistaken. The model contains 7 key parameters which are used to provide outputs of Δ¹⁴C and d¹³C as a function of time. By optimising the fit of these two outputs with historical records spanning 200 years or more, including during the bomb pulse, the key parameters are determined. The quality of fit of Δ¹⁴C and d¹³C is excellent and the internal parameters optimise at reasonable values. The global flux to and from the effective mixing reservoir, whose size is six times that of the atmosphere, is currently 58 GTC/yr in 2020, not including annually cycled carbon.

Hydraulic relationships are important for water resource management, hazard prediction, and modelling. Since Leopold first identified power law expressions that could relate streamflow to top-width, depth, and velocity, hydrologists have been estimating ‘At-a-station Hydraulic Geometries’ (AHG) to describe average flow hydraulics. As the amount of data, data sources, and application needs increase, the ability to apply, integrate and compare disparate and often noisy data is critical for applications ranging from reach to continental scales. However, even with quality data, the standard practice of solving each AHG relationship independently can lead to solutions that fail to conserve mass. The challenge addressed here is how to extend the physical properties of the AHG relations, while improving the way they are hydrologically addressed and fit. We present a framework for minimizing error while ensuring mass conservation at reach - or hydrologic Feature - scale geometries’(FHG) that complies with current state-of-the-practice conceptual and logical models. Through this framework, FHG relations are fit for the United States Geological Survey’s (USGS) Rating Curve database, the USGS HYDRoacoustic dataset in support of the Surface Water Oceanographic Topography satellite mission (HYDRoSWOT), and the hydraulic property tables produced as part of the NOAA/Oakridge Continental Flood Inundation Mapping framework. The paper describes and demonstrates the accuracy, interoperability, and application of these relationships to flood modelling and presents this framework in an R package.

Environmental physical and chemical factors controlling the abundance and biodiversity of zooplankton in permafrost-affected lakes are poorly known yet they determine the response of the ecosystems to on-going climate change and water warming. Here we assessed the current status of zooplankton communities of lake ecosystems in the North-West of the Bolshezemelskaya tundra (Nenets Autonomous district), and provide new information about the composition and structure of zooplankton. Results demonstrate that the structure of zooplankton communities is influenced by morphometric features of lakes and the degree of their overgrowth by macrophytes. According to the level of zooplankton development, most tundra lakes were of the oligotrophic type with an average biomass of up to 1 g/m3. The largest number of species was observed in zooplankton communities of small lakes with an area of up to 0.02 km2 and overgrown with macrophytes. The analysis of factors that influence the formation of the lakes zoocenosis demonstrated that the species composition and quantitative characteristics of zooplankton are chiefly controlled by pH and water mineralization. A comparison of the results obtained with the literature data on the lakes of this region collected 60 years ago confirms that the ecosystems of the studied lakes are in a stable state. Overall, these new insights will improve our knowledge of factors controlling the zooplankton spatial dynamics in unique but quite abundant thermokarst lakes of NE European Tundra, subjected to on-going climate warming.

Rangelands are a key resource present all over the world as it covers half of emerged lands. They are even more important in drylands, where they cover 48% of the total area. Intensification and the additional pressure added by climate change push these systems towards desertification. Over the last two decades, we have developed and applied System Dynamics (SD) models for the study of Mediterranean grasslands. In addition, we have developed procedures and analysis tools, such as global sensitivity analysis, stability analysis condition, or risk analysis, in order to detect the main drivers of these socio-ecological systems and provide indicators about their long-term sustainability. This paper reviews these works, their scientific background, and the most relevant conclusions, including purely technical and rangeland-related ones as well as our experience as systemic modelers in a world driven by field specialists.

Nanoparticles (NP), Nanoaerosols (NA), ultrafine particles (UFP), and PM0.1 (particles with a diameter ≤ 0.1 µm or 100 nm) are interchangeably used in the field of atmospheric studies. PM0.1 are emitted from both natural and anthropogenic sources. The main emission sources of PM0.1 are combustion processes, including biomass and fossil fuel. Moreover, secondary formation via atmospheric photochemical reactions can also occur depending on meteorological conditions and the suspended pollutant species. Identifying the physical and chemical characteristics and spatial and temporal variations is vital in terms of understanding the effect of NPs on the environment, the global climate, and human health risks. This review article summarizes recent research on PM0.1 in Thailand. The review involves peer-reviewed papers from Scopus and the Web of Science databases, and includes the most recently published articles in the past ten years (2013-2022). UFPs mainly come from the combustion processes such as motor vehicles. The high mass concentration of PM0.1 that occurs during the dry season in which open fires depend on the specific region of Thailand. Particulate pollution from local and cross-border countries also needs to be considered in terms of the concentrations of ambient nanoparticles. The overall conclusions reached will likely have a beneficial long-term impact on achieving a blue sky over Thailand through the development of coherent policies and managing new air pollution challenges and sharing knowledge with a broader audience.

A silicified, thick-shelled, smooth-surfaced nuculanoid bivalve has been recovered by acid maceration of the Late Triassic (Carnian-Norian) strata of the Luning Formation, Nevada. Comparable modern nuculanoid clams inhabit water depths from 525-2,562 meters, and the living clam (an undescribed species of Pseudoneilonella from Caleta Sierra, Coquimbo, Chile) most similar to the fossil lives at 878-933 m. The Triassic nuculanoid clam (possibly a neilonellid) is inferred here to have inhabited marine waters at approximately 1000 m deep during deposition of the Shaly Limestone Member of the Luning Formation. This new fossil discovery falsifies hypotheses that the ichthyosaurs (Shonisaurus popularis) of Berlin-Ichthyosaur State Park, Nevada, USA, were deposited, respectively, in either shoreline deposits or in strata that accumulated above storm wave base.

Rain occurs over a wide range of spatial scales. The challenge is to connect certain fine scale needs to the available large scale observations from radars, satellites or coarse grid numerical weather predictions. This is the problem of rescaling of the rainfall. Whatever approach is used, it requires a knowledge of rainfall scales over a wide range of possible dimensions from tens of meters to kilometers. It is also desirable to have measurements at different locations and under different meteorological settings. Such measurements are not necessarily readily obtainable, requiring extensive and usually fixed and expensive networks of multiple instruments over large areas. A mobile and less expensive alternative is the, Micro-Rain Radars (MRR). We illustrate this using observations of the Doppler spectra of falling rain every 10 m vertically and 10 second temporally over intervals varying from 15 up to 41 minutes collected at Wallop’s Island Virginia and Charleston South Carolina using two different MRR. An objective method to estimate advection velocity was developed so that the time-height profiles could be transformed into height-horizontal distance profiles in order to calculate scaling relations. Thus, MRR and other Doppler radars may obviate the need for networks of instruments.

A silicified, thick-shelled, smooth-surfaced nuculanoid bivalve has been recovered by acid maceration of the Late Triassic (Carnian-Norian) strata of the Luning Formation, Nevada. Comparable modern nuculanoid clams inhabit water depths from 525-2,562 meters, and the living clam (an undescribed species of Pseudoneilonella from Caleta Sierra, Coquimbo, Chile) most similar to the fossil lives at 878-933 m. The Triassic nucuanoid clam (possibly a neilonellid) is inferred here to have inhabited marine waters at approximately 1000 m deep during deposition of the Shaly Limestone Member of the Luning Formation.

Sea surface salinity (SSS) is one of the Essential Climate Variables (ECVs) as defined by the Global Climate Observing System (GCOS). Acquiring high-quality SSS datasets with high spatial-temporal resolution is cruicial for research on the hydrological cycle and earth climate. This study assessed the quality of SSS data provided by four high-resolution ocean reanalysis products, including the Hybrid Coordinate Ocean Model (HYCOM) 1/12° global reanalysis, the The Copernicus Global 1/12° Oceanic and Sea Ice GLORYS12 Reanal-ysis, the Simple Ocean Data Assimilation (SODA) reanalysis, the ECMWF Oceanic Reanalysis System 5 (ORAS5) product and the Estimating the Circulation and Climate of the Ocean Phase II (ECCO2) reanalysis. Regional comparison in the Mediterranean Sea shows that reanalysis largely depicts the accurate spatial SSS structure away from river mouths and coastal areas but slightly underestimates the mean SSS values. Better SSS reanalysis performance is found in the Levantine Sea while larger SSS uncertainties are found in the Adriatic Sea and the Aegean Sea. The global comparison with CMEMS level-4 (L4) SSS show generally con-sistent large-scale structures. The mean ΔSSS between monthly gridded reanalysis data and in situ analyzed data is -0.1 PSU in the open seas between 40°S and 40°N with the mean Root Mean Square Deviation (RMSD) generally smaller than 0.3 PSU and the majority of correlation coefficients higher than 0.5. Comparison with collocated buoy salinity shows that reanalysis products well captures the SSS variations at the locations of tropical moored buoy arrays at weekly scale. Among all the four products, the data quality of HYCOM re-analysis SSS is highest in marginal sea, GLORYS12 has the best performance in the global ocean especially in tropical regions. Comparatively, ECCO2 has the overall worst performance to reproduce SSS states and variations by showing the largest discrepancies with CMEMS L4 SSS.

Heritage monuments are religious, historical, strategic or civil edifices and could be deteriorated, even damaged due to their exposure to natural and human induced processes. The Roman Edifice with Mosaic (II-IV A.D.), the largest one in Eastern Europe is an ancient civil edifice built on the steep cliff in the western part of Black Sea, Constanța, Romania and being exposed to geomorphological and hydrogeological processes is affected by degradation. The main objective of this paper is to assess the current state of this ancient historical site in relation with the environmental instability in order to offer scientific support for rehabilitation process through interdisciplinary and non-destructive methods. Geophysical methods had been applied to analyze comparatively the spatial variations and flows of groundwater around Roman Mosaic over 10 years. Geomorphological hazards had been inventoried and mapped. The results emphasize the state of degradation of the roman mosaic pavement and ancient walls mainly through high variations of the deposits moisture due to poor maintenance which caused suffusion, slip processes, bring the mosaic into highly vulnerability range. Zoning vulnerability map of the Roman Mosaic environment is a necessary tool for continuously improvement of risk management because emphasize clearly the sectors which still confronting with hazards.

In this study, we present more than 8 years of observations of the quasi-16-day wave (Q16DW) in the mesosphere and lower thermosphere (MLT) wind at middle latitudes observed by the Mengcheng (33.4°N, 116.5°E) meteor radar. The long-term variation in amplitudes calculated from the data between April 2014 and December 2022 shows enhanced wave activity during winter and early spring (near equinox) and suppressed wave activity during the summer. The Q16DWs are relatively weak in the meridional wind. During the winter months, the Q16DWs in the zonal component exhibit a burst below 85 km, and their amplitudes reach up to 10 m/s. In the early spring, the Q16DWs strengthen above 90 km with amplitudes in excess of 12 m/s. The phase differences between the zonal and meridional components of the Q16DW are, on average, slightly smaller than 90°, suggesting the existence of orthogonal relationships between them. During strong bursts, the periods of the Q16DW in winter range between 15 and 18 d, whereas in winter, the periods tend to be more diffuse. The wintertime Q16DW is amplified, on average, when the zonal wind shear peaks, suggesting that barotropic instability may be one source of Q16DW. Q16DW amplitudes exhibit considerable interannual variability; however, a relationship between the 11-year solar cycle and the Q16DW is not found.

Low-cost monitors make it possible now for the first time to collect long-term (months to years) measurements of potential indoor exposure to fine particles. Indoor exposure is due to two sources: particles infiltrating from outdoors and those generated by indoor activities. Calculating the relative contribution of each source requires identifying an infiltration factor. We develop a method of identifying periods when the infiltration factor is not constant, and searching for periods when it is relatively constant. From an initial regression of indoor on outdoor particle concentrations, a Forbidden Zone can be defined with an upper boundary below which no observations should appear. If many observations appear in the Forbidden Zone, they falsify the assumption of a single constant infiltration factor. This is a useful quality assurance feature, since investigators may then search for subsets of the data in which few observations appear in the Forbidden Zone. The usefulness of this approach is illustrated using examples drawn from the PurpleAir network of optical particle monitors. An improved algorithm is applied with reduced bias, improved precision, and a lower limit of detection than either of the two proprietary algorithms offered by the manufacturer of the sensors used in PurpleAir monitors.

In the Yucatán Peninsula, anthropogenic activities such as urbanization and final dispose of solid and wastewaters critically impact aquatic systems. Here, we evaluated the anthropogenic-related environmental alteration of Lake La Sabana, located in the northern limit of one of the main cities of the Mexican Caribbean. We evaluate lake water quality using physical, chemical, and microbiological indicators, and heavy metals in surficial sediments and fish tissue to evaluate the potential environmental risk. Multivariate analyses reveal that environmental conditions in La Sabana are spatial and temporal heterogeneous. Medium to bad water quality was determined within basins by the NSFWQI, related with the degree of anthropogenic influence. The center-south zones display critical microbiological values largely exceeding national standards. Heavy metals in sediments (Zn>Hg) and fish tissue (Hg) were relatively low, but Hg concentration threat the ecological environment. Incipient wastewater treatment and final dispose in La Sabana is the main responsible of changes in the trophic status and nutrients availability, which in turn may have promoted changes in the biological structure and aquatic plant invasions. Lake La Sabana can be considered a model of the potential sequential effects of the anthropogenic alterations in oligotrophic karts tropical aquatic systems in Yucatán Peninsula.

The multiple regression method is still an important tool for establishing precipitation forecast models with a lead time of one season. This study developed a flexible statistical forecast model for July precipitation over the middle-lower reaches of the Yangtze River (MLYR) based on the prophase winter sea surface temperature (SST). According to the characteristics of observed samples and related theoretical knowledge, some special treatments (i.e., more flexible and better–targeted methods) were introduced in the forecast model. These special treatments include a flexible MLYR domain definition, the extraction of indicative signals from the SST field, artificial samples, and the amplification of abnormal precipitation. Rolling forecast experiments show that the linear correlation between prediction and observation is around 0.5, more than half of the abnormal precipitation years can be successfully predicted, and there is no contradictory prediction of the abnormal years. These results indicate that the flexible statistical forecast model is valuable in real-life applications. Furthermore, sensitivity experiments show that forecast skills without these special treatments are obviously decreased. This suggests that forecast models can benefit from using statistical methods in a more flexible and better-targeted way.

The multiple crises that the world is facing – climate change, COVID-19 and war have halted or reversed the progress of the world towards the achievement of Sustainable Development Goals. Using a case study of Dayalbagh, a locality in metropolitan Agra, India, and headquarters of the Radhasoami faith, we examine the potential benefits of employing agroecology to achieve the United Nations Sustainable Development Goals (SDGs). The active, disciplined and cooperative community-based lifestyle followed in Dayalbagh with a strong focus on agriculture and service demonstrates how most of the SDGs can be achieved. It offers lessons for policy makers in terms of focus areas for policy support and reaching the last, lowest, least and the lost.

The Hunga Tonga-Hunga Ha’apai underwater volcano (20.57°S, 175.38°W) violently erupted on 15 January 2022. The volcanic plume evolution during its initial stages is delineated by using Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC)-2 radio occultation (RO) measurements. The bending angle (BA) anomaly over the Tonga volcanic plume (within 200 km from the eruption center) at 5:17 UTC on 15 January shows a prominent peak at higher stratospheric heights. The top of the BA anomaly revealed that negative to positive change occurred at ~38 km indicating the first height where the RO line-of-sight encounters the volcanic plume. The BA anomaly further revealed an increase of ~50% at ~36 km and confirms the volcanic plume reached above ~36 km. Further, the evolution of BA perturbations within 24 hours after the initial explosion is also discussed. From collocated RO profiles with the volcanic plume, we find a clear descending of the peak altitude of the BA perturbation from ~36 km to ~30 km within 24 hours after the initial eruption. The results from the study will provide some insights into advancing our understanding of volcanic cloud dynamics and their implementation in volcanic plume modeling.

As an effective active remote sensing (ARS) technology for shallow underground targets, ground penetrating radar (GPR) is a detection method to obtain the characteristic information of underground targets by transmitting electromagnetic wave from the antenna and analyzing the propagation law of the electromagnetic wave underground. Due to the high frequency(1MHz-3GHz) of GPR, the depth of geological exploration is shallow(0.1m-30m). In order to remote sensing the deep earth, it is necessary to increase the size of the radiation source in order to reduce the radiation frequency. At the same time, for most separated GPRs, a single dipole antenna (SD) is used as the radiation source and another antenna device placed along the electromagnetic wave propagation direction in the far field as a remote sensing sensor (RSS), both of which are horizontally linearly polarized (LP) antennas. In some cases, such a design is apt to cause problems such as multipath effect (ME) and polarization mismatch (PM). When GPR in ARS of deep earth is performed, it often results in increased errors, signal attenuation during data reception and processing. In contrast, at the radiation source, with the use of large aperture multiple-dipole antennas (MD) and multi-channel sequential rotational excitation, the electromagnetic wave can radiate outward in the form of circular polarization at a low frequency. At the RSS, the trouble caused by ME and PM can be reduced even if the LP antennas are used. A novel sequential rotationally excited (SRE) circularly polarized (CP) MD array for separated GPR in ARS of deep earth is proposed in this paper, which uses a large aperture CP MD array instead of a small size LP SD. The analysis and simulation results demonstrate that under the premise of the same transmitting power, comparing circular polarization and linear polarization, by using SRE CP MD antennas array radiation source, a significant enhancement (about 7dB) of the Signal to Noise Ratio (SNR) will occur by collecting the radiant energy at the RSS. More importantly, by reducing the exploration frequency to 10KHz, the exploration depth will also be greatly increased by about 10 times.

The Land Surface Temperature (LST) is an essential indicator for analyzing the Surface Urban Heat Island (SUHI). A factor contributing to its occurrence is the reflections of the different materials in urban and rural areas, which significantly affect the energy balance near the surface. Therefore, recent studies have increasingly used the Local Climate Zones (LCZ) classification system to discriminate those urban areas. Therefore, our study aims to do a systematic review using the PRISMA method of LCZ classification applied to understand the LST and the SUHI phenomenon. At first, it was found in the scientific literature 10,403 articles which, after passing through filtering stages, resulted in 51 that were further analyzed. Our results showed that these articles were very recent, beginning in 2016. However, presenting an increasing trend. China was the country with more studies. Landsat and TERRA/AQUA sensors appeared in 82% of the studies. The method that appears the most to LCZ definitions is from the World Urban Database. Finally, considering the current climatic changes, this systematic review can help new studies on SUHI identification through LCZ in different world areas using remote sensing data to estimate the LST.

Land’s basic biota metric is soil organic carbon (SOC) yet global estimates range 1,417–15,000 Gt C. Erosion of ancient topsoil and loss of vital soil taxa are most urgent – and most ignored – of all context-triaged concerns. Albeit topographical terrain increases most soil calculations and inventories on a non-flat Earth, re-evaluation shows the major contribution to unrelenting atmospheric CO₂ increase more from topsoil loss than fossil fuels. Although increased CO₂ has resulted in a global greening effect with NPP productivity now ~220 Gt C/yr, this is arguably outweighed by more rapid erosion of topsoil and expansion of desertification at 2-15 Gt SOC/yr volatilized due to excessive meat eating with unsupportable soil extractive, toxic farm management. In particular excess synthetic Nitrogen acidifies topsoil and depletes the organic SOC biotic-dynamics. Carbon credits of our disappearing soil biotic stocks are enumerated for plant roots (916 Gt C), litter (600 Gt C), microbes (200 Gt C), fungi (30 Gt C), biocrust (10-20 Gt C), earthworms (2.3-3.6 Gt C), termites (0.15 Gt C), nematodes (0.06 Gt C), ants (0.024 Gt C), and soil viruses (0.02–4.0 Gt C). Consideration of soil microbes and review of global SOC inventories reveal critical topsoil loss up to 20,000 tonnes per second with species extinction as high as 23 taxa each second. Sustainable Development Goals (SDGs) fail without a solid soil foundation. However, heritage soil data points to remedy in modern organic farm restoration. Safe solution is via natural vermi-composting, 100% organic farming, and Permaculture under a simple logical premise that the Problem (i.e., SOC loss) is the Solution (viz., SOC restoration).

Valorization of excess sludge through the recovery of high-value products, such as biopolymers, could be a crucial step to implement circular economy principles in wastewater treatment plant (WWTP). In this frame, the present study was aimed at evaluating the simultaneous production of polyhydroxyalkanoates (PHA) and extracellular polymeric substances (EPS), obtainable from the treatment of an agro-industrial wastewater. Two biological systems, one implementing aerobic granular sludge (AGS) and the other a conventional activated sludge operating as a sequencing batch reactor (SBR) were operated for 204 and 186 days, respectively. Both the systems involved a three-stage process for mixed microbial culture enrichment and biopolymers accumulation. The maximum biopolymers accumulation capacity was close to 0.60 mg gVSS^-1 in the AGS when the enrichment reactor was operated at 3 kgCODm^-3d^-1, whereas in the SBR it was slightly more than half (0.35 mg gVSS^-1). Biopolymers extracted from the AGS were mainly constituted by EPS (>70%), which percentage increased with the organic loading rate applied in the enrichment reactor up to 95%. In contrast, SBR enabled to obtain a higher PHA production (50% of the biopolymers). Results suggested that organic carbon was mainly channeled toward metabolic pathways for extracellular storing in AGS, likely as a consequence of metabolic stressors (e.g., hydraulic selection pressure, shear forces) applied for promoting aerobic granulation.

In the context of the Atmospheric Observing System (AOS) international program, a new generation spaceborne lidar is expected to be in polar orbit for deriving new observations of aerosol and clouds. In this work, we analyze the added values of these new observations for characterizing aerosol vertical distribution. For this, synthetic observations are simulated using the BLISS lidar simulator in terms of backscatter coefficient at 532 nm. We consider two types of lidar instruments, an elastic backscatter lidar instrument and a high spectral resolution lidar (HSRL). These simulations are performed with atmospheric profiles from a Nature Run (NR) modeled by the MOCAGE Chemical Transport Model. In three case studies involving large events of different aerosol species, the added value of the HSRL channel for measuring aerosol backscatter profiles with respect to simple backscatter measurements is shown. Observations independent from an a-priori lidar Ratio assumption, as done typically for simple backscattering instruments, allows probing the vertical structure of aerosol layers without divergence, even in case of intense episodes. Relative error in the backscatter coefficient profiles are observed to lay between +40% and -40% for low abudancies, with mean biases between +5% and -5%. A 5-day study in the case of desert dust completes the study of the added value of the HSRL channel with relative mean bias from the NR of the order of 1.5%.

The characteristics of the eddy mass transport are estimated depending on the values of the parameters of a large-scale flow that forms under the conditions of the shelf seas in the Arctic. For this, the results of numerical simulation of the Kara Sea with a horizontal resolution permitting the development of mesoscale eddies are used. The parameters resulting from numerical experiment are considered as a statistical sample and are analyzed using methods of sensitivity study and clustering of sample elements. Functional dependencies are obtained that are closest to the simulated distributions of quantities. These expressions make it possible, within the framework of large-scale models, to evaluate the characteristics of the cross-isobatic eddy mass transport in the diffusion approximation with a counter-gradient flux. Numerical experiments using the SibCIOM model showed that areas along the Fram branch of the Atlantic waters trajectory in the Arctic as well as the shelf of the East Siberian and Laptev seas with adjacent deep water areas are most sensitive to proposed parameterization of eddy exchanges. Accounting for counter-gradient eddy fluxes turned out to be less important.