Sustainability transition theories analyse a systematic shift towards sustainability at micro (niche), meso (regime) and macro (landscape) level. The assessment of technological systems and structures at the firm level in sustainability transition literature is scant. The present study, taking the technological assessment perspective at the firm level, finds answers to questions like (a) how do established corporations move towards sustainable practices? (b) what role does technological innovation play in the firm’s transition towards sustainability? (c) what technological modes are adopted for sustainability transition? We find answers to these questions through an in-depth case analysis of two multi-national companies in the consumer goods industry. Internally developed and externally acquired technologies by firms in the last 15 years are plotted using qualitative and quantitative indicators on pre-designed templates. Technologies for all three sustainability dimensions, namely, environmental, social and economic, are mapped and the impact assessed. The analysis finds a sustainability transition landscape that shows the use of protected (patents, trademarks, designs) and unprotected technologies (open innovation) to generate impacts like production efficiency, consumption reduction, emission reduction, reduce-recycle-reuse among others. Companies implementing sustainable technologies do observe positive impacts. Implementation of reduce-reuse-recycle (3R)-based technologies enhance the achievement of sustainable development targets. Furthermore, the use of trademarks seems common in differentiating their technologies and identities. These and other results are detailed and used to comment on the role of managing intellectual property and harnessing the effect of technological innovations in sustainability transition.

What is the ideal soil-grass combination for maximum photosynthesis? In this study, we investigated how soil and grassland types affect photosynthesis in the grasslands of Gannan, China. We divided the grasslands of Gannan into 166 study sites, each with a unique soil-grass combination by intersecting the soil and ecoregion maps using ArcGIS. We obtained 19 years of data on the Net Photosynthesis (PsnNet) of grasslands in the area from 2000 to 2018 and then divided them into “growing season” (June to September) and “non-growing season” (October top May). Between 2000 and 2018, PsnNet of grasslands showed a gradually increasing trend. The effect of soil type on PsnNet was not significant during the growing season. However, it was highly significant during the non-growing season. Among the soil types, grasses that grew in Mollic, Gelic, and Haplic soils had the highest rate of photosynthesis. The difference in PsnNet among the various grass types was highly significant during both seasons. However, Tropical and Subtropical succulent evergreen broad-leaf shrubs and Temperate meadows had the highest rate of photosynthesis. Additionally, there was a highly significant difference in PsnNet among the various soil-grass interactions. In the growing season, TStEgBLS growing Eutric soils had the highest PsnNet. However, SaDBLS growing in Gelic soils had the the highest.

Understanding the processes that govern the transformation of the landscape through time is essential for exploring the evolution of a coastal area. Coastal landscapes are dynamic sites, with their evolution strongly linked with waves and sea-level variations. Geomorphological features in the coastal area, such as beachrock formations and dune fields, can function as indicators of the coastal landscape evolution through time. However, our knowledge of the chronological framework of coastal deposits on the Aegean coasts is limited. Optically stimulated luminescence dating techniques are deemed to be very promising indirect dating of the coastal sediments, especially when they are linked with archaeological evidence. The dating of the sediments from different sediment core depths, as they are determined by the method of luminosity, allows us to calculate the rate of sediment deposition over time. Additionally, the coastal evolution and stability were studied from 1945 until today, with the use of aerial photographs and satellite images. This paper presents the 6000 ka years evolution of a coastal landscape based on geomorphological, archaeological, and radio-chronological data. Based on the results, the early stages of the Ammoudara beach dune field appear to be formed ~9.0 – 9.6 ka BP, while the OSL ages from 6 m depth represented the timing of its stabilization (OSL ages ~5–6 ka). This indicates that the dune field appears to already have been formed long before the Bronze Age (5-10 ka BP) and became stabilized with only localized episodes of dune reactivation occurring, while high coastal erosion rates are found in modern times.

COVID-19 greatly challenges human beings in the health sector and leaves behind a large amount of medical waste that poses many potential threats to the environment. In this paper, we compiled relevant data released by official agencies and the media, and conducted data supplementation based on previous studies to calculate the net value of medical waste production in Hubei Province during COVID-19 with the help of a neural network model. Then, we reviewed the data related to the environmental impact of medical waste per unit and designed four scenarios to estimate the environmental impact of new medical waste generated during the epidemic. The results showed that at a medical waste generation rate of\ 0.\ 5\ kg/(\ bed\ \cdot\ d) COVID-19 resulted in a net increase in medical waste volume of about 3366.99 tons in Hubei Province. In the four scenario assumptions, if the medical waste brought by COVID-19 is completely incinerated, it will have a large impact on the air quality. If it is disposed by distillation sterilization, it will produce a large amount of wastewater and waste residue. Based on the results of the study, three policy recommendations are proposed in this paper: strict control of medical wastewater discharge, reduction and transformation of the emitted acidic gases, and attention to the emission of metallic nickel in exhaust gas and chloride in soil. These policy recommendations provide a scientific basis for controlling medical waste pollution.

Shear deformation of a solid-fluid, two-phase material induces a fluid segregation process that produces fluid-enriched bands and fluid-depleted regions, and crystallographic preferred orientation (CPO) characterized by girdles of [100] and [001] axes sub-parallel to the shear plane and a cluster of [010] axes sub-normal to the shear plane, namely the AG-type fabric. Based on experiments of two-phase aggregates of olivine + basalt, a two-phase flow theory and a CPO-formation model were established to explain these microstructures. Here, we investigate the microstructure in a two-phase aggregate with supercritical CO2 as the fluid phase and examine the theory and model, as CO2 is different from basaltic melt in rheological properties. We conducted high‐temperature and high-pressure shear deformed experiments at 1 GPa and 1100ºC in a Griggs-type apparatus on samples made of olivine + dolomite, which decomposed into carbonate melt and CO2 at experimental conditions. After deformation, CO2 segregation and an AG-type fabric occurred in these CO2-bearing samples, inconsistency with basaltic melt-bearing samples. The SPO-induce CPO model was used to explain the formation of the fabric. Our results suggest that the influences of CO2 as a fluid phase on the microstructure of a two-phase olivine aggregate is similar to that of basaltic melt and can be explained by the CPO-formation model for the solid-fluid system.

Low-cost miniature hyperspectral and thermal cameras onboard lightweight unmanned aerial vehicles (UAV) bring new opportunities for monitoring land surface variables at unprecedented fine spatial resolution with acceptable accuracy. This research applies hyperspectral and thermal imagery from a drone to quantify upland rice growth and water use efficiency (WUE) after biochar application in a Costa Rican dry region. The field flights were conducted over two experimental groups with bamboo biochar and sugarcane biochar amendments and one control group without biochar application. Rice canopy biophysical variables were estimated by inversion of a canopy radiative transfer model on hyperspectral reflectance. Variations in gross primary production (GPP) and WUE across treatments were estimated from the normalized difference vegetation index (NDVI), canopy chlorophyll content (CCC), and evapotranspiration. We found that GPP was increased by 41.9±3.4 % when using bamboo biochar and 17.5±3.4 % when using sugarcane biochar, which was probably due to higher soil moisture in the biochar-amended plots and led to significantly higher WUE by 40.8±3.5 % in bamboo biochar and 13.4±3.5 % in sugarcane biochar. This study demonstrated the use of hyperspectral and thermal imagery from drone to provide indicators for quantifying biochar effects on tropical dry cropland by integrating with ground point samples and physical models.

Deforestation and forest degradation mostly caused by human interventions affects the capacity of forest ecosystem to provide ecosystem services and livelihood benefits. Forest Land Restoration (FLR) is an emerging concept which focuses on the improvement of ecosystem as well as livelihood of the people at the landscape level. Nepal has successfully recovered degraded forest land mainly from the hilly region through forest restoration initiatives especially community based forestry. However, the Terai region is still experiencing deforestation and forest degradation. This study navigated the gaps related to forest restoration in the existing policies and practices and revealed that the persistence of deforestation and forest degradation in Terai is a result of a complex socio-economic structure, limitation of government to implement appropriate management modality, unplanned infrastructure, and urban development. We suggest that forest restoration should focus on ecological and social wellbeing pathways at the landscape level, to reverse the trend of deforestation and forest degradation in the Terai regions of Nepal. The study provides a critical insight to the policy makers and practitioners of Nepal and other countries (with similar context) who are engaged in forest/ecosystem restoration enterprise.

China is out of extreme poverty in 2020 on schedule and one decade in advance to fulfill the UN 2030 Agenda for Sustainable Development Goals (SDGs), and became the first developing country to alleviate poverty in half century. Therefore, a large numbers of effective approaches are emerging, and the intellectual’s technology-led poverty reduction, which locally known as the “Straw Hat University”initiated by “Farmer's professor”mode, is the most tried and tested approach motivated by the intellectual’s “Serve the people” tradition and supported by the all nation. This research conduct case analysis with three most remarkable organic intellectuals as Agronomist Yuan longping, Mycologist Lin Zhanxi and Plant pathologist Zhu Youyong with their bridging gaps in food security, regional imbalance and ethnic disparity respectively to sort out the sustainable modules and universal experiences. The conclusion indicates that“Farmer's Professor” Initiated “Straw Hat University” is an effective approach to solve human beings’ development problems and benefit the livelihoods, especially in the under development regions; and the authentic down to earth experiments into productivity as well as the Intellectual property transformation is the perfect path to deploy offline and online resources building the effective production and supply chain to integrate industries by intellectual’s critical innovation.

Based on the cloud platform of Google Earth Engine (GEE), this study selected Landsat 5/8 and Sentinel-2 remote sensing images and used Support Vector Machine (SVM) classification method to classify the 35 years of intertidal salt marshes in China, and verified the classification results in combination with field survey. Finally, combining with various driving factors, the reasons and laws affecting the changes of salt marshes species and area were discussed and analyzed. The main results of the study are as follows:The main types of salt marshes plants in China include Phragmites australis, Spartina alterniflora, Suaeda salsa, Scirpus mariquete, Tamarix chinensis, Cyperus malaccensis and Sesuvium portulacastrum. The results salt marshes classification indicated that 166999.32 ha in 1985, 172893.87 ha in 1990, 174952.29 ha in 1995, 125567.51 ha in 2000, 93257.97 ha in 2005, 102539.04 ha in 2010, 96302.92 ha in 2015, and 115722.75 ha in 2019. The main driving factors of salt marsh change from 1985 to 2015 are reclamation, mudflat aquaculture, climate change, coastal zone erosion, invasion of alien species, and natural competition and succession among salt marshes species. The results can be used to quantitatively analyze the salt marshes carbon storage in space and time, and provide data support for the protection of salt marsh wetlands, the restoration of ecological functions and the implementation of "carbon neutral".

Coronavirus disease is a newly identified infectious disease that began in Wuhan, China in 2019 and is recognized as COVID-19. This virus has a serious impact on the planet that causes the deaths of millions of people; it produces unemployment and poverty. However, it has constructive environmental effects that reduce the amount of pollution in the atmosphere. Pakistan is also a developing economy caused by this deadly virus. Before 03 April 2021, the percentage of reported cases ranges from 682,888, with about 14,697 casualties and a total of 609,691 cases retrieved. Pakistan’s war against coronavirus can differ from advanced technical countries due to lack of resources. In this article, we will address the relation between COVID-19 and the environment in the particular case of Pakistan. There is evidence that this pandemic has lowered concentrations of greenhouse gases, including CO2 and NO2, as a consequence of global lock-down strategies.

Forests constitute a key component of the Earth system but the sustainability of the forest reserves in the semi-arid zone is a real concern since its vegetation is very sensitive to the climate fluctuation. The understanding of the mechanisms for the interaction vegetation-climate is poorly studied in the context of African Sahel. In this study, the characteristics of the vegetation response to the fluctuations of precipitation and temperature is determined for the forest reserve of Fina. Rainfall estimates, air temperature and NDVI are used to establish the lag correlations between fluctuations of vegetation and climate variables at both seasonal and interannual bases. Results shows increasing tendency of NDVI started from the 1990s coinciding the recovery of the rainfall from the 1980s drought and the obtained correlation(r=0.66) is statistically significant (pvalue<0.01). The strongest responses of vegetation to rainfall and temperature fluctuations were found after 30 and 15 days, respectively. Moreover, at shorter time lag (e.g. 15 days) more pronounced vegetation responses to both rainfall and temperature were found in agricultural dominated land while at longer time lag (e.g. 30 days) stronger response was observed in Bare dominated land. The vegetation response to the climate fluctuation is modulated by the land use/cover dynamics. Keywords: NDVI, Rainfall, Air temperature, vegetation response, Fina Forest Reserve, Mali.

Study of gross alpha (GA) and gross beta (GB) activity in road and surface deposited sediments were performed in various geographical areas in three Russian cities. To perform radiation measurements, new methods were applied which allow dealing with low mass and low volume dust size (2-100μm) samples obtained after the size fractionation procedure. The 2-10 μm fraction size has the highest gross beta activity concentration (GB)– 1.32Bq/g in Nizhny Novgorod (NN) and Rostov-On-Don (RND) while the 50-100 μm fraction size in Ekaterinburg. This is maybe associated with the subsistence of radionuclides that conveyed through natural processes and anthropogenic applications. The highest gross alpha activity concentration (GA) in fraction sizes was found in Rostov-on-Don city within the 50-100 μm– 0.22Bq/g. The fraction size 50-100 μmhas a higher gross alpha activity concentration than 2-10 and 10-50 μmfraction sizes due to natural partitioning of the main minerals constituting the urban surface deposited sediment (USDS). The geochemical processes that occur during the formation and transportation of USDS are reflected in the observed dependencies.Developed experimental methods of radiation measurements allowed to the extent the methodological base of urban geochemical studies.

The prevalence of corona virus and the novel COVID-19 disease in the entire globe has exacerbated different impact on socioeconomic spectrum in the world, including water use pattern. Thus a research was conducted to examine the comparative use of water during pre- and post-COVID-19 lockdown pattern among post-primary schools in Iwo, Osun State, Nigeria. A survey was conducted among fifteen schools which were randomly selected, but with eight public and seven private schools for the investigation. Both descriptive and inferential statistical techniques were used in data analysis. The results revealed that the major source of water to the schools investigated is ground water which is obtained through hand-dug wells and boreholes. It was further discovered that there was increase in water use during post-COVID-19 lockdown era as a result of the directive by the government that clean water should be provided for hand-washing by all schools regardless of the owner to curtail the spread of COVID-19 disease in the country. One sample t-test also revealed that there was a significant difference in water use at (p<0.01) level. It is recommended that the government and other stakeholders in water sector to ensure that all-time and non-seasonal dependent source of water be provided rather than ground water source which is susceptible to variations in water yields from seasonal variations. This will enable continuous clean water supply, for all purposes, including COVID-19 protocols.

This work examines the effects of two problematic trends in diesel passenger car emissions – increasing NO2/NOx ratio by conversion of NO into NO2 in catalysts and a disparity between the emission limit and the actual emissions in everyday driving – on ambient air quality in Prague. NO2 concentrations were measured by 104 membrane-closed Palmes passive samplers at 65 locations in Prague in March-April and September-October of 2019. NO2 concentrations measured by city stations during those periods were comparable with the 2016-2019. The average measured NO2 concentrations at the selected locations, after correcting for the 18.5% positive bias of samplers co-located with a monitoring station, were 36 µg/m3 (range 16-69 µg/m3, median 35.3 µg/m3), with the EU annual limit of 40 µg/m3 exceeded at 32% of locations. The NO2 concentrations have correlated well (R2=0.76) with the 2019 average daily vehicle counts, corrected for additional emissions due to uphill travel and intersections. In addition to expected “hot-spots” at busy intersections in the city center, new ones were identified, i.e. along a six-lane road V Holešovičkách. Comparison of data from six monitoring stations during March 15-April 30, 2020 travel restrictions with the same period in 2016-2019 revealed an overall reduction of NO2 and even a larger reduction of NO. The spatial analysis of data from passive samplers and time analysis of data during the travel restrictions both demonstrate a consistent positive correlation between traffic intensity and NO2 concentrations along/near the travel path. The slow pace of NO2 reductions in Prague suggests that stricter vehicle NOx emission limits, introduced in the last decade or two, have so far failed to sufficiently reduce the ambient NO2 concentrations, and there is no clear sign of remedy of Dieselgate NOx excess emissions.

Sea surface partial pressure of CO2 (pCO2) is a critical parameter in the quantification of air-sea CO2 flux, which plays an important role in calculating the global carbon budget and ocean acidification. In this study, we use chlorophyll-a concentration (Chla), sea surface temperature (SST), absorption due to dissolved and particulate detrital matter (Adg), diffuse attenuation coefficient of downwelling irradiance at 490nm (Kd) and mixed layer depth (MLD) as input data for retrieving the sea surface pCO2 in the North Atlantic based on a remote sensing empirical approach with the Categorical Boosting (CatBoost) algorithm. The results show that the root mean square error (RMSE) is 8.25μatm, the mean bias error (MAE) is 4.92μatm and the coefficient of determination (R2) can reach 0.946 in the validation set, which mean that the CatBoost model makes an improvement compared to other models in the published studies. In the further analysis of the spatial and temporal distribution of the sea surface pCO2 in the North Atlantic, it can be found that the North Atlantic sea surface pCO2 has a clear trend with latitude variations and have strong seasonal changes. Furthermore, the sea surface pCO2 in this area is mainly affected by sea temperature and salinity, and influenced by biological activities in some sub-regions.

Arctic ozone amount in winter to spring shows large year-to-year variation. This study investigates Arctic spring ozone in relation to the phase of quasi-biennial oscillation (QBO)/the 11-year solar cycle, using satellite observations, reanalysis data, and outputs of a chemistry climate model (CCM) during the period of 1979–2011. For this duration, we found that the composite mean of the Northern Hemisphere high-latitude total ozone in the QBO-westerly (QBO-W)/solar minimum (Smin) phase is slightly smaller than those averaged for the QBO-W/Smax and QBO-E/Smax years in March. An analysis of a passive ozone tracer in the CCM simulation indicates that this negative anomaly is primarily caused by transport. The negative anomaly is consistent with a weakening of the residual mean downward motion in the polar lower stratosphere. The contribution of chemical processes estimated using the column amount difference between ozone and the passive ozone tracer is between 10–20% of the total anomaly in March. The lower ozone levels in the Arctic spring during the QBO-W/Smin years are associated with a stronger Arctic polar vortex from late winter to early spring, which is linked to the reduced occurrence of sudden stratospheric warming in the winter during the QBO-W/Smin years.

Minerals play many important functions in plant and animal metabolism. Therefore, we investigated the concentration of Se and other minerals and their relationships in soils and fodder plants in Kosovo. Seventy-three samples of each soil and fodder plants (grass, maize, and wheat) from 30 farms were collected. Both soil and plant samples, after processing and digestion, were analyzed for mineral concentration by ICP-MS. Mineral concentrations in soil and fodder crops, and the best predicting/explanatory models for micro minerals concentration, achieved by stepwise linear regression, are presented. Results showed very low concentration of Se in most of the soil and all fodder samples. In addition, the concentration of Co, Zn and Fe was not sufficient to satisfy requirements for all categories of farm animals. Plant Se concentration showed a positive relationship with Se concentration in soils. Plant Zn, Mo, Mn, Fe and Pb, in general, showed no significant relationship with their concentration in soil, while plant Co and Cd showed positive relationship only in maize, and Cu in wheat grain. Among the soil properties, pH had the highest effect on the concentrations of Co, Mo, Mn, Cd and Pb in fodder crops.

Automation of forest field reference data collection has been an intensive research objective for laser scanning scientists ever since the invention of terrestrial laser scanning more than two decades ago. Recently, it has been proposed that such automated data collection providing both the tree heights and stem curves would require a combination of above-canopy UAV point clouds and terrestrial point clouds. In this study, we demonstrate that an under-canopy UAV laser scanning system utilizing a rotating laser scanner can alone provide accurate estimates of the canopy height and the stem volume for the majority of the trees in a boreal forest. To this end, we mounted a rotating laser scanner based on a Velodyne VLP-16 sensor onboard a manually piloted UAV. The UAV was commanded with the help of a live video feed from the onboard camera of the UAV. Since the system was based on a rotating laser scanner providing varying view angles, all important elements such as treetops, branches, trunks, and ground could be recorded with laser hits. In an experiment including two different forest structures, namely sparse and obstructed canopy, we showed that our system can measure the heights of individual trees with a bias of -20 cm and a standard error of 40 cm in the sparse forest and with a bias of -65 cm and a standard error of 1 m in the obstructed forest. The accuracy of the obtained tree height estimates was equivalent to airborne above-canopy UAV surveys conducted in similar forest conditions. The higher underestimation and higher inaccuracy in the obstructed site can be attributed to three trees with a height exceeding 25 m and the applied laser scanning system VLP-16 that had a limited height measurement capacity when it comes to trees taller than 25 m. Additionally, we used our system to estimate the stem volumes of individual trees with a standard error at the level of 10%. This level of error is equivalent to the error obtained when merging above-canopy UAV laser scanner data with terrestrial point cloud data. Future research is needed for testing new sensors, for implementing autonomous operation inside canopies through collision avoidance and navigation through canopies, and for developing robust methods that work also with more complex forest structure. The results show that we do not necessarily need a combination of terrestrial point clouds and point clouds collected using above-canopy UAV systems in order to accurately estimate the heights and the volumes of individual trees.

The combined influence of the North Atlantic Oscillation (NAO) and the East Atlantic (EA) patterns on the covariability of temperatures and precipitation in 35 stations of the Iberian Peninsula during the period 1950-2019 is analysed in this work. Four EA-NAO composites were defined from teleconnection patterns positive and negative phases: EA+NAO+, EA+NAO-, EA-NAO+, and EA-NAO-. Daily data of maximum and minimum temperature were used to obtain seasonal means (TX, and TN, respectively), and the covariability of these variables with accumulated seasonal rainfall (R) was studied comparing results obtained for different NAO and EA composites. Main results indicate slight differences in the spatial coverage of correlation coefficients between R and temperature variables, except in spring when the generalized negative relationship between R and TX under EA+NAO+ and EA-NAO- disappears under EA-NAO+ and EA+NAO- composites. This result may be useful to interpret and discuss historical reconstructions of Iberian climate.

Groundwater vulnerability assessment has become a useful tool for groundwater pollution pre-vention. Groundwater vulnerability maps provide useful data to protect groundwater resources. The identification of agricultural patterns is an important issue for optimized land management. The Tagus river watershed is the backbone of this survey. Naturtejo UNESCO Global Geopark, in central inland Portugal, corresponds to a rural territory. Intensive agricultural practices showed a rising tendency in the last decades. The most internationally used method for vulnerability evaluation is the DRASTIC index. In this survey, the DRASTICAI index is introduced. A new at-tribute - Anthropogenic Influence - is here added. Five levels of growing vulnerability, from low to high, can be here acknowledged. Idanha-a-Nova municipality is the most affected by intensive farming activities. A robust assessment of groundwater quality has a key role. Climate change scenarios and water scarcity are important issues in years to come. Therefore, optimized groundwater management is essential to consider in policy-making strategies.

The aim of this work was the study and evaluation of winery by-products in the framework of circular bioeconomy. Grape seeds and grape skins from Greek traditional Ionian Islands varieties were analyzed in an attempt to provide the appropriate basis for model development of their sustainable exploitation at a local or regional level. The wastes collected directly from the wineries immediately after the vinification process and analyzed by chromatographic and spectroscopic techniques. Also, annual production and yields were estimated. Grape seed oil quality was evaluated based on fatty acid methyl esters (FAMEs) composition. Grape skins phenolic fraction was extracted by an eco-friendly, non-toxic water-glycerol solvent system and were detected qualitatively. Also, total phenolic content (TPC) and antioxidant activity were measured. Based on estimated yields, our results demonstrate that winery by-products have the potential to promote the cyclical bioeconomy in a modern economic growth model that will reduce waste, and environmental costs as they can be reused as whole material in foods, dietary supplements, cosmetic ingredients, food colorants and preservatives.

Climate analogues provide forestry practice empirical evidence of how forests are managed in “twin” regions, i.e. regions where the current climate is comparable to the expected future climate at a site of interest. But the uncertain future climate creates uncertainty in how to adapt the forests. We therefore investigate how the uncertainty in future climate affects tree species suitability and whether there is a common underlying pattern. Like most studies we employ different ensemble variants of RCP 4.5 and 8.5. But instead of focusing on a single point in future time, we resolve each variant in a climate trajectory from 2000 to 2100. We calculate climatic distances between the climate trajectories of our site of interest and the current climate in Europe, generating maps with twin regions from 2000 to 2100. Forest inventories from the twin regions allow us to trace the changes in the prevalence of 23 major tree species. We find that it is not the direction but rather the velocity of the change that differs between the scenarios. We use this pattern to propose a tree species suitability concept that integrates the uncertainty in future climate. Twin regions provide further information on silvicultural practices, pest management, product chains etc.

Recently, there has been renewed interest in the performance, functionality, and sustainability of traditional small-scale storage interventions (check dams, farm bunds and tanks) used across India for the improvement of local water security. The Central Groundwater Board of India is en-couraging the construction of such interventions for the alleviation of water scarcity. It is of critical importance to understand the hydrological effect of these interventions at basin scales to maximise their effectiveness. The quantification of small-scale interventions in hydrological modelling is often neglected, especially in large-scale modelling exercises. A bespoke version of the GWAVA model was developed to assess the impact of interventions on the water balance of the Cauvery Basin and two smaller sub-catchments. Model results demonstrate that farm bunds appear to have a negligible effect on the estimated average annual simulated streamflow at the outlets of the two sub-catchments and the basin whereas tanks and check dams have a more significant effect. In-terventions generally were found to increase evaporation losses across the catchment. The model adaption used in this study provides a step-change in the conceptualisation and quantification of the consequences of small-scale storage interventions in large- or basin-scale hydrological models.

An effective empirical statistical method is developed to improve the process of mineral resource estimation of seabed polymetallic nodules and is applied to analyse the abundance of seabed polymetallic nodules in the Clarion Clipperton Zone (CCZ). The newly proposed method is based on three hypotheses as the foundation for a model of “Idealized Nodules”, which was validated by analysing nodule samples collected from the seabed within the Tonga Offshore Mining Limited (TOML) exploration contract. Once validated, the “Idealized Nodule” model was used to deduce a set of empirical formulae for predicting the nodule resources, in terms of Percentage Coverage and Abundance. The formulae were then applied to analysing a total of 188 sets of nodule samples collected across the TOML areas, comprising box-core samples and towed camera images collected by one of the authors and detailed in [4]. The analysis also relies upon detailed box-core sample measurements from other areas reported by [7]. Numerical results for resource prediction were compared with field measurements, and reasonable agreement has been achieved. The new method has the potential to achieve more accurate mineral resource estimation with reduced sample numbers and sizes. They may also have application in improving the efficiency of design and configuration of mining equipment.

Soils provide important regulating ecosystem services and have crucial implications for human well-being and environmental conservation. However, soil degradation and particularly soil erosion jeopardize the maintenance and existence of these services. This study explores the spatio-temporal relationships of soil erosion to understand the distribution patterns of sediment retention services in mainland Portugal. Based on Corine Land Cover maps from 1990 to 2018, the InVEST Sediment Delivery Ratio (SDR) model was used to evaluate the influence of sediment dynamics for soil and water conservation. Spatial differences in the sediment retention levels were observed within the NUTS III boundaries, showing which areas are more vulnerable to soil erosion processes. Results indicated that the Region of Leiria, Douro and the coastal regions have decreased importantly sediment retention capacity over the years. However, in most of the territory (77.52%) changes in sediment retention were little or not important (i.e., less than 5%). The statistical validation of the model proved the consistency of the results, highlighting the usefulness of this methodology to analyse the state of soil erosion in the country. These findings can be relevant to support strategies for more efficient land use planning regarding soil erosion mitigation practices.

After the COVID-19 outbreak, more and more people are seeking physiological and psychological healing by visiting the forest as the time of stay-at-home became longer. NVOC, a major healing factor of forests, has several positive effects on human health, and this study researched about the NVOC characteristics of bamboo groves. The study revealed that α-pinene, 3-carene, and camphene were the most emitted, and the largest amount of NVOC was emitted in the early morning and late afternoon in bamboo groves. Furthermore, NVOC emission was found to have normal correlations with temperature and humidity, and inverse correlations with solar radiation, PAR and wind speed. A regression analysis conducted to predict the effect of microclimate factors on NVOC emissions resulted in a regression equation with 82.9% explanatory power and found that PAR, temperature, and humidity had a significant effect on NVOC emission prediction. In conclusion, this study investigated NVOC emission characteristics of bamboo groves, examined the relationship between NVOC emissions and microclimate factors and derived a prediction equation of NVOC emissions to figure out bamboo groves' forest healing effects. These results are expected to provide a basis for establishing more effective forest healing programs in bamboo groves.

Choosing the optimal location for a city based on sound environmental geomorphology planning is of the utmost importance to achieving environmental sustainability, as it can spare the State and other decision-making entities a great deal of stress in the long run. GIS offers great potential for environmental planners to choose the most appropriate places for the cities of the future, especially when coupled with environmental geomorphological analyses. The State of Kuwait seeks sustainable development through the implementation of clear and specific urban plans, some of which suffer from a severe lack of geomorphological and spatially based environmental planning. This study aims to: 1) Conduct suitability modelling for establishing new cities in Kuwait, 2) Assess the current 2005-2030 urban plan, and 3) Propose possible recommendations and solutions for potential urban problems. The study relies on integrating several methods to devise a framework that will aid researchers and decision-makers in selecting optimal locations for built structures based on analysis and modelling (e.g., digital elevation model, geologic mapping, geomorphology, natural hazards, heritage/archaeological sites, military areas, oil fields, soils). Using this methodology in choosing city sites contributes to achieving sustainable development, reducing city problems, saving countries’ budgets, and saving lives. Results from this study enhance understanding of how environmental geomorphology, when combined with GIS, can be harnessed to achieve sustainable urban development in the Arabian Gulf countries and other desert countries.

Different machine learning models (multiple linear regression, vector support machines, artificial neural networks and random forests) are applied to predict the monthly global irradiation (MGI) from different input variables (latitude, longitude and altitude of meteorological station, month, average temperatures, among others) of different areas of Galicia (Spain). The models were trained, validated and queried using data from three stations, and each best machine model was checked in two independent stations. The results obtained confirmed that the best ML methodology is the ANN model which presents the lowest RMSE value in the validation and querying phases 122.6·10kJ/(m2∙day) and 113.6·10kJ/(m2∙day), respectively, and predict conveniently for independent stations, 201.3·10kJ/(m2∙day) and 209.4·10kJ/(m2∙day), respectively. Given the good results obtained, it is convenient to continue with the design of artificial neural networks applied to the analysis of monthly global irradiation.

Wave breaking is one of the most important yet poorly understood water wave phenomena. It is via wave breaking that waves dissipate most of their energy and the occurrence of wave breaking directly influences several environmental processes, from ocean-atmosphere gas exchanges to beach morphodynamics. Large breaking waves also represent a major threat for navigation and for the survivability of offshore structures. This paper provides a systematic search for intermediate to deep water breaking waves with particular focus on the elusive occurrence of plunging breakers. Using modern remote sensing and deep learning techniques, we identify and track the evolution of over four thousand unique wave breaking events using video data collected from La Jument lighthouse during ten North Atlantic winter storms. Out of all identified breaking waves (Nb=4683), ≈22% were dominant breaking waves, that is, waves that have speeds within [0.77cp, 1.43cp], where cp is the peak wave speed. Correlations between the occurrence rate of dominant breaking waves (that is, waves per area and time per peak wave period) and wave steepness and wave age were observed. As expected, the number of identified plunging waves was small and six waves of all detected breaking waves, or 0.13%, could undoubtedly be considered as plunging waves. Two waves were also identified as unusually large, or rogue waves. Although afflicted by several technical issues, the data presented here provides a good indication that the probability of occurrence of plunging waves should be better incorporated into the design of offshore structures, particularly the ones that aim to harvest energy in offshore environments.

United Nations Framework Conventions on Climate Change (UNFCC) conventions in their conference of parties (COPs) has continuously considered and agreed reducing emission level in order to minimize the impact of global climate change. Reducing emission due to deforestation and degradation (REDD) ,was considered as one of the major activities in this regard during Kyoto protocol in 2009 which laid foundation for the participating countries to be compensated financially for reduced carbon emission. Mexico convention -2012 required the countries to develop and implement a transparent and consistent monitoring, reporting and verification (MRV) process. Later in Paris agreement-2015, the parties agreed to limit the global warming to 2 degree centigrade and with further efforts to 1.5-degree centigrade furthering entailing the parties to prepare and communicate nationally determined contributions (NDCs) every five years. Nepal aimed to decrease the average annual deforestation rate by 0.05 percent from existing 0.44 percent in the terai region and 0.1 percent in the Chure. Nepal decided to develop its forest reference level (FRL) in national level for the historical period 2000-2010 considering Carbon dioxide and carbon pools above and below ground. As per the Forestry Sector Strategy, Nepal aims to increase carbon stock growth by at least 5% by 2025 as compared to 2015 and decrease mean annual deforestation rate to 0.05. After major change in administrative division in Nepal, forest management responsibility has shifted down to the Sub-national level. But forest resource studies have not been conducted yet in these levels. Despite a small country, Nepal has at least four clear physiological regions. The amount of carbon stock stored by different forest type are different depending upon species distribution, carbon volume and density for each species, and their distribution along ecological and physiological regions. Sal (shorea Robusta), for example, having one of the highest carbon densities, is a major forest types in Nepal. The purpose of this study was to generate forest map of the country, calculate carbon stock, gain and loss, and their rate in each province due to deforestation/afforestation using remote sensing data. Further Sal forest map was generated and its contribution in carbon stock was calculated using averaged national carbon density as well as using regional density method. According to the study, around 5.1 million hectares of Nepali land was forest in 2015 increasing from 4.2 million hectares in 2005. However, Sal forest has decreased during the same period. Province 1 contributed the maximum (130 Tg) and Province 2 the minimum (40Tg) of Carbon stock in 2015. Using the conventional method of calculation with national average density (108.08 t/ha), a total of 36.7T CO2 yr-1 carbon sink was observed in the Country. Whereas, with the new approach of calculation, a total of 44.7 T CO2 e of carbon sink per year was estimated during the same period. This approach holds potential for qualifying as an MRV process of Nepal. The subnational level forest and carbon statistics produced during this study can be important assets for the better forest governance. This can also pave way for policy formation and preparation of action plan for sustainable forest management and intervention strategy and obtaining better financial incentives participating in the reduction of emission due to deforestation and forest degradation (REDD) plus programs.

This study explores the changes in the landmass bounded by the coast of India during 1975-2005 by using on-screen visual interpretation technique (with 100m resolution and 1:50,000 scale) from NASA Landsat Imagery in three different time periods viz. 1975, 1990, and 2005. The result indicated an overall expansion of 130 sq. km area of the landmass that surrounded by the Indian coast during 1975-2005 (74 sq. km during 1975-1990 and 56 sq. km during 1991-2005). These estimations are based on the preliminary analysis and may be estimated more accurately by reducing the scale and using further higher resolution images.

Forest carbon sequestration is a widely accepted natural climate solution, however, methods to determine net carbon offsets are limited to commercial carbon proxies and CO2 eddy covariance research. Non-CO2 greenhouse gases (GHG) (e.g., CH4, N2O) receive less attention in the context of forests, in part, due to emphasis on CO2 and the operational requirements and cost for three-gas eddy covariance platforms. In this study, Howland forest flux tower (CO2, CH4) and soil flux data (CO2, CH4, N2O), representing net emission reductions, are linked to their respective social costs to estimate commercial revenue if sold as a GHG social cost forest offset product (GHG-SCF). Estimated annual revenue for GHG-SCF products, applicable to realization of a Green New Deal, range from 120,000 covering the site area of 557 acres in 2021, to 12,000,000 for extrapolation to 40,000 acres in 2040, assuming a 3% discount rate. The Howland Forest CO2 flux record for two adjacent towers is compared to California Air Resources Board forest carbon proxy data for compliance sequestration offsets, the only project site where these approaches overlap. Overcrediting, incomplete carbon accounting with annual errors of up to 2,256%, inadequate third-party verification, and limited application to non-CO2 GHG’s are established. In contrast, direct measurement of one or more GHG’s offers new forest products and revenue incentives to restore and conserve forests worldwide.

The Ituri-Epulu-Aru landscape (IEAL) is experiencing deforestation and forest degradation. This deforestation is at the root of many environmental disturbances in a region characterized by endemism in biodiversity. This article focuses on the triangulation of spatialized prospective scenarios in order to identify future trajectories based on the knowledge of historical dynamics through the diachronic analysis of three satellite images (2003-2010-2014-2016). The scenarios were de-signed in a supervised model implemented in the DINAMICA EGO platform. The three scenarios Business-As-Usual (BAU), Rapid Economic Growth (REG) and Sustainable Management of the Environment (SME), extrapolating current trends, show that by 2061 this landscape will always be dominated forests (+ 84%). Old-growth forests occupy 74.2% of the landscape area in the BAU scenario, 81.4% in the SEM scenario and 61.2% in the REG scenario. The SEM scenario gives hope that restoration and preservation of biodiversity priority habitats is still possible if policy makers become aware of it.

Sedimentary microfacies division is the basis of oil and gas exploration research. The traditional sedimentary microfacies division mainly depends on human experience, which is greatly influenced by human factor and is low in efficiency. Although deep learning has its advantage in solving complex nonlinear problems, there is no effective deep learning method to solve sedimentary microfacies division so far. Therefore, this paper proposes a deep learning method based on DMC-BiLSTM for intelligent division of well-logging—sedimentary microfacies. First, the original curve is reconstructed multi-dimensionally by trend decomposition and median filtering, and spatio-temporal correlation clustering features are extracted from the reconstructed matrix by Kmeans. Then, taking reconstructed features, original curve features and clustering features as input, the prediction types of sedimentary microfacies at current depth are obtained based on BiLSTM. Experimental results show that this method can effectively classify sedimentary microfacies with its recognition efficiency reaching 96.84%.

This article argues that we need to look at living examples provided by non-state communities in various regions of the world that are, perhaps unwittingly, contributing to the maintenance of the Earth's optimal thermal balance. These fully sustainable communities have been living outside the mainstream for centuries, even millennia, providing examples in the global struggle against the degradation of social–ecological systems. They have all, to varying degrees, embraced simple forms of living that make them ‘exemplary ethical communities’ (EECs) – human communities with a track record of sustainability related to forms of traditional knowledge and the capacity to survive outside the capitalist market and nation-state system. The article proceeds in three steps: First, it condenses a large body of research on the limits of the existing nation-state system and its accompanying ideology, nationalism, identifying this institutional–ideological complex as the major obstacle to tackling climate change. Second, alternative social formations that could offer viable micro-level and micro-scale alternatives are suggested. These are unlikely to identify with existing nation-states as they often form distinct types of social communities. Taking examples from hunter-gatherer societies and simple-living religious groups, it is shown how the protection and maintenance of these EECs could become the keystone in the struggle for survival of humankind and other forms of life. Finally, further investigation is called for, into how researchers can come forward with more examples of actually existing communities that might provide pathways to sustainability and resistance to the looming global environmental catastrophe.

Ponderosa pine is an integral part of the forested landscape in the western US; it is the dominant tree species on landscapes that provide critical ecosystem services. Moderate drought tolerance allows it to occupy the transition zone between forests and open woodlands and grasslands. Increases in stand density resulting from wildfire suppression, combined with lengthening, intensifying and more frequent droughts have resulted in reduced tree vigor and stand health in dry ponderosa pine throughout its range. To address a management need for efficient landscape-level surveys of forest health, we used Random Forests to develop an object-oriented classification of individual tree crowns (ITCs) into vigor classes using existing, agency acquired 4-band aerial imagery. Classes of tree vigor were based on quantitative physiological and morphological attributes established in a previous study. We applied our model across a landscape dominated by ponderosa pine with a variety of forest treatments to assess their impacts on tree vigor and stand health. We found that stands that were both thinned and burned had the lowest proportion of low vigor ITCs, and that stands treated before the 2014-2016 drought had lower proportions of low vigor ITCs than stands treated more recently (2016). Upland stands had significantly higher proportions of low vigor trees than lowland stands. Maps identifying the low vigor ITCs would assist managers in identifying priority stands for treatment and marking trees for harvest or retention. These maps can be created using already available imagery and GIS software.

Significant earthquakes frequently occur in Indonesia. Indonesia is situated over three active tectonic plates, resulting in the formation of faults and trenches on the land and ocean floor. For the last 120 years since 1900, there have been more than 1,250 significant earthquake events in Indonesia. In this study, we analyse Indonesia's significant earthquake events using geostatistical and geovisualisation methods to produce an appropriate geospatial analysis platform using the RShiny package to build the WebGIS application. The results show that the earthquake events were spatially distributed from the Sumatera fault in the western part of Indonesia to the southern part of Indonesia, where the Java trench was located and the eastern part of Indonesia. The WebGIS application received a positive evaluation by respondents, with a mean value of 1.617 for pragmatic quality, 1.808 for hedonic quality, and 1.713 for overall quality. This means that the WebGIS application is of good quality based on respondents' impressions. The users also more easily gained insight into information as a result of geostatistical methods. The information gained by the users during the user interaction with the WebGIS platform overlapped with the information that the researcher started with, that is, the spatial cluster of significant earthquakes in Indonesia.

As for most Mediterranean countries, groundwater is the main resource for irrigation and drinking supply in most parts of Syria, however this resource suffers from mismanagement. In the study area (Northeast of Mt. Hermon), the lack of information makes water management in this area extremely difficult. Assessing groundwater pollution risk is the most essential issue for water resources management, especially in the regions where complex interaction between climate, geology, geomorphology, hydrogeology, water scarcity and water resource mismanagement exist. This complexity leads to significant complication in determining pollution risk of studied system. In the present work, we adopted an Integrative Approach to assess groundwater pollution risk in the study area. This methodology is based on the analysis of hydrogeological characteristic of aquifer system and the available information about socio-economic context and physio-chemical groundwater condition that might affect this system. This approach allowed us to delineate the groundwater pollution risk map based on the analysis of concerning parameters/ indicators. The degree of risk was assessed based as the sum and average of rating of these parameters and indicators for each subarea. Typically, very high pollution risk index was identified over the Quaternary/Neogene horizon, i.e shallow and unconfined aquifer and in the lower part of Jurassic aquifer. In these two parts, the majority of anthropogenic activities are concentrated. Low pollution risk index was found for the outcropping of low permeable Quaternary basalt at the Southern part of the study area. A moderate pollution index was identified for the low/moderate permeability of silt, clay and marly limestone rich horizons of the major part of Neogene aquifer outside of the intersected zones with Quaternary aquifer and for the Paleogene formations. The spatial analysis shows that about 50% of the study area is characterized as being at very high and high pollution risk index. Hence, the overall natural protective capacity of this area is still poor. This study demonstrates the flexibility of the proposed approach to assess groundwater pollution risk in local complex aquifer system characterized by lack of information and data in order to reduce the risk of future groundwater pollution.

The Ptarmigan and Tom mesothermal gold deposits are located 10 km to the northeast of the city of Yellowknife, Northwest Territories in northern Canada. Both gold deposits comprise a series of en echelon veins that are hosted within upper greenschist to lower amphibolite facies ~2630 Ma (peak) rocks. Supracrustal units across the craton are intruded by the ca. 2610–2605-Ma granodiorite, tonalite, monzodiorite, quartz diorite, and affiliated rocks of the Concession Suite. Hydrothermal apatite is a common accessory mineral in both mineralized and non-mineralized quartz veins in the metasedimentary host rocks that constitute the Ptarmigan and Tom deposits. This study characterizes and compares turbidite-hosted hydrothermal apatite from the Ptarmigan and Tom deposits, non-mineralized veins adjacent to the ore body, and magmatic apatite from proximal LCT-pegmatites. Using electron probe microanalyses (EPMA), laser ablation inductively coupled plasma-mass spectrometry (LA-ICP-MS), micro-XRF, and cathodoluminescence (CL), the major, minor, and trace element abundances have been quantified and mapped. In addition to utilizing this data to determine if the chemistry of apatite can be used to constrain the source of hydrothermal fluids, the apparent age of the apatite is also evaluated utilizing in situ U-Pb dating. The distribution and abundance of major, minor, and trace elements from in situ recovered apatite were studied to characterize the nature of mineralizing fluids. Most apatite from mineralized and non-mineralized veins show different Mn, Sr, and Pb contents, as well as chondrite-normalized rare-earth element (REE) and Y abundance patterns. REEs display five unique chondrite-normalized patterns: (1) negative sloped pattern with slight negative Eu anomaly, (2) a flat pattern with a positive Eu anomaly, (3) a positive slope with a negative Eu anomaly, (iv) light rare earth element (LREE) depleted pattern with positive Eu anomaly, and (v) bell-shaped pattern with a negative Eu anomaly. The REE patterns reflect both the source of the auriferous hydrothermal fluids and, perhaps, co-precipitating mineral phases. Apatite from the Ptarmigan vein occurs with both: (1) a flat pattern with a positive Eu anomaly and (2) bell-shaped pattern with a negative Eu anomaly. The bell-shaped and flat patterns typify orogenic gold deposits. Vein-hosted apatite commonly displays compositional zoning with a characteristic yellow cathodoluminescence (CL) emission spectra with darker cores and brighter rims. The cores have lower REE, whereas the rims are notably higher in REE. It is thought that the darker cores in CL images reflect a transition from an early low REE hydrothermal fluid to one enriched in REE. The hydrothermal apatite age of 2585 ± 15 Ma is consistent with the intrusions of the 2605 and 2590 Ma two-mica granites of the Prosperous Suite and associated LCT pegmatites.

Spatiotemporal dynamics of the atmospheric kinetic energy and its components caused by the ordered and turbulent motions of air masses are estimated from minisodar measurements of three velocity vector components and their variances within the lowest 5–200 m layer of the atmosphere, with a particular emphasis on the turbulent kinetic energy. The layered structure of the total atmospheric kinetic energy has been established. From the diurnal hourly dynamics of the altitude profiles of the turbulent kinetic energy (TKE) retrieved from minisodar data, four layers are established by the character of the altitude TKE dependence, namely, the near-ground layer, the surface layer, the layer with a linear TKE increase, and the transitive layer above. In the first layer, the most significant changes of the ТКЕ were observed in the evening hours. In the second layer, no significant changes in the TKE values were observed. A linear increase in the TKE values with altitude was observed in the third layer. In the fourth layer, the TKE slightly increased with altitude and exhibited variations during the entire observation period. The altitudes of the upper boundaries of these layers depended on the time of day. The MKE values were much less than the corresponding TKE values, they did not exceed 50 m²/s². From two to four MKE layers were distinguished based on the character of its altitude dependence. The two-layer structures were observed in the evening and at night (under conditions of the stable atmospheric boundary layer). In the morning and daytime, the four-layer MKE structures with intermediate layers of linear increase and subsequent decrease in the MKE values were observed. Our estimates demonstrated that the ТКЕ contribution to the total atmospheric kinetic energy considerably (by a factor of 2.5–3) exceeded the corresponding МКЕ contribution.

Tropical island countries are often highly populated and deliver immense ecosystem service benefits. As human wellbeing depends on these ecosystems proper management is crucial in the resource-rich tropical lands where related research is less. Though the ecosystem service and biodiversity studies are a promising path to inform the ecosystem management for these mostly developing countries published evidence of using ecosystem service studies in decision-making is lacking. The purpose of this study is to provide an overview of ecosystem services and related research in Sri Lanka examining trends and gaps and how these studies are conceptualized. Out of considered 139 peer-reviewed articles majority of articles 42.4% were terrestrial and forest related while coastal ecosystems were considered in 34.5% of studies. In most studies, the ecosystem service category was provisioning (33.8%) followed by regulatory service (30.9%). Studies investigating and quantifying ecosystem services, pressures on ecosystems, and their management were fewer compared to studies related to biodiversity or species introduction. Moreover, studies investigating the value of ecosystem services and biodiversity to the communities or involvement of stakeholders in the development of management actions regarding the ecosystem services were rare in Sri Lanka and intense focus of future studies in these aspects are timely and necessary.

This study aims to determine the impact of climate change on market garden production in the extreme south of Mali through the perception and adaptation of market gardeners to climatic phenomena. The study used two models, namely the probit selection and Heckman results models and multinomial logistic regression, based on data collected from producers. A total of 194 producers were surveyed. The results of Heckman's probit model indicate that experience in agriculture and the educational level of the producers are the two main determinants of producers' perception and simultaneous adaptation to climate change. Among these variables agricultural experience is both positively and negatively correlated with perception.

Results of geological and geophysical investigations of the Siljan Ring impact structure (central Sweden) revealed complicated relationships between Paleozoic sedimentary succession and the Precambrian basement. Tectonic and depositional evolution caused complex geology. Studies of a new drill core from the C-C-1 well provide information necessary for the reconstruction of the geological setting in the southwestern part of Siljan Ring. The whole interval of the core section is from 32.60 to 634.90 m with almost no breaks. The sedimentary cover is 373.55 m thick in total. The sedimentary sequences are predominantly composed of wackestones, mudstones, and shales. In the lower part of the sedimentary section, limestone layers intercalate with black shales. In a result of the investigations, it has been suggested that sedimentary layers represent Late Ordovician and Silurian deposits and have disturbed stratigraphic relations. The basement section is composed of Precambrian meta-volcanic and meta-sedimentary rocks. The contact between the basement and the sedimentary cover is tectonic, not normal sedimentary, in origin. Tectonic processes caused intensive rock fracturing. Four generations of fractures were identified with analysis of fracture relations and mineralization sequence. Only two of them occur in sedimentary rocks that probably belong to the latest stages of tectonic activity. Highly fractured basement rocks in some cases contain open vugs developed along the fractures. Rock matrix is tight either in sedimentary and basement rocks and only micro-porosity space is recognized in cataclastic zones. Single evidence of bituminous filling of micro-porosity zone and partly cemented vug is established in limestone from the lower part of the sedimentary section. These findings are particularly valuable for stratigraphy refinement and tectonic setting reconstructions as well as oil and gas reservoir forecasts.

: During the period of COVID-19, the Chinese government implemented a series of actions to prevent the spread of the virus. It is noticed that these preventive actions have generated positive effects on air pollution in Wuhan and Beijing. And, due to the differences in city functions, geographic locations, meteorological conditions, and preventive action details between the two cities, noticeable difference is observed on how they reduced air pollution. This unfortunate incident has become a social sample for studying the industrial and livelihood activities impact on air pollution, otherwise would be highly impossible. This paper starts from observing data from NASA's real-time fine particulate matter (PM2.5) and NO2, and comparing the air pollution in this special time domain with previous years. It is discovered that both PM2.5 and NO2 have been reduced in Wuhan, while only NO2 has significant reduction in Beijing, during the COVID-19 responses. This suggests that the human activity suppression by the COVID-19 control yields different effects on different pollutants and in different cities. Possible reasons for such effects are analysed by considering the public health control levels as well as the pollutant migration, industrial activities, and the weather conditions.

Georeferencing using ground control points (GCPs) is the most common strategy in photogrammetry modeling using UAV-acquired imagery. However, with the increased availability of UAVs with onboard GNSS RTK, georeferencing without GCPs is a promising alternative. However, systematic elevation error remains a problem of this technique. We aimed to analyze the reasons for this systematic error and propose strategies for the elimination of this error. Multiple flights differing in the flight altitude and image acquisition axis were performed at two real-world sites. A flight height of 100m with vertical (nadiral) image acquisition axis was considered primary, supplemented with flight altitudes of 75 m and 125 m with vertical image acquisition axis and two flights at 100 m with oblique image acquisition axes (30° a 15°). Each of these flights was performed twice to produce a full double grid. Models were calculated from individual flights and their combinations. The elevation error from individual flights or even combinations yielded systematic elevation errors of up to several decimeters. This error was linearly dependent on the deviation of the focal length from the reference value. A combination of two flights from the same altitude (with nadiral and oblique image acquisition) was capable of reducing the systematic elevation error to less than 0.03 m. This study is the first to demonstrate the linear dependence between the systematic elevation error of the models based only on the onboard GNSS-RTK data and the deviation in the determined internal orientation parameters (focal length). Besides, we have shown that a combination of two flights with different image acquisition axis can eliminate this systematic error even in real-world conditions and that georeferencing without GCPs is, therefore, a feasible alternative to the use of GCPs.

Forest ecosystems are important providers of ecosystem functions and services belonging to four categories: supporting, provisioning, regulating, and cultural ecosystem services. Forest management, generally focused on timber production, has consequences on the ability of the system to keep providing services. Silviculture, in fact, may affect ecological structures and processes from which services arise. In particular, the removal of biomass causes a radical change in the stocks and flows of energy characterizing the system. Aiming at the assessment of differences in stored natural capital and ecosystem functions and services provision, three differently managed temperate forests of common beech (Fagus sylvatica) were considered: (1) a forest in semi-natural condition, (2) a forest carefully managed to get timber in a sustainable way and (3) a forest exploited without management. Natural capital and ecosystem functions and services are here accounted in biophysical terms. Specifically, all the resources used up to create the biomass (stock) and maintain the production (flow) of the different components of the forest system were calculated. Both stored emergy and empower decrease at increasing human pressure on the forest, resulting in a loss of natural capital and a diminished ability of the natural system to contribute to human well-being in terms of ecosystem services provision.

The Pannonian basin major heat system in Central Europe. Their peripheral basins like the East Slovakian basin is a example of a geothermal structure with a linear directed heat flow ranging from 90 to 100 mW /m2 from west to east. However, the use of the geothermal source is limited by several critical tectono-geologic factors: (a) tectonics, and the associated disintegration of the aquifer block by multiple deformations during the pre-Paleogene mainly Miocene period. The main discontinuities of NW-SE and N-S direction negatively affect the permeability of the environment. On the contrary, for utilization are important the secondary minor NE-SW dilatation open fractures which have developed by sinistral transtension on N–S faults and accelerated normal movements to the southeast in the present. (b) hydrogeological conditions, the geothermal structure accommodated three water types, namely Na-HCO3 with 10.9 g.l-1 mineralization (in the north), the Ca-Mg-HCO3 with 0.5 – 4.5 g.l-1 mineralization (in the west), and Na-Cl water type containing 26.8-33.4 g.l-1 (in the southwest) mineralization. The chemical composition is influenced by the Middle Triassic dolomites aquifer as well as by infiltration of saline solutions and meteoric waters along open fractures/faults. (c) geothermally anomalous heat 123 – 129 °C close to volcanic chain with 170 l/s total flow seems to be the perspective for heat production.

Great Malang region is developing rapidly with the population increase and inhabitant`s activity, like migration and urbanization. Other activities like agricultural expansion as well as an uncontrolled residential development need to be monitored to avoid any negative impact in the future. The availability of free and open-source software, spatial high-resolution satellite imagery datasets, and powerful algorithms open the possibilities to map, monitor, and predict the future trend of land use land cover (LULC) changes. However, the accuracy and precision of this model is still in doubt, especially in the Great Malang region. Research is needed to provide a foundational basis and documentation on how the changes occur, where did the changes occur, and the accuracy of the predicted model. This study tries to answer those questions using the high spatial resolution of Sentinel-2 imageries. Combination of the fuzzy algorithm, artificial neural network, and cellular automata was utilized to process the datasets. We analysed four different scenarios of simulation and the result then compared. The different number of hidden layers and iteration was used and evaluated to understand the effect of different parameters in the prediction result. The best scenario was then used to predict future land use changes. This study has successfully produced the future LULC model of Great Malang region with high accuracy level (87%). The study also found that the land use transformation from agriculture to urban built-up area is relatively low, where changes of the built-up area over three periods of analysis are below than 5%. This is due to the physical condition of Great Malang region where mountainous areas are dominated.

Analysis of the correlation between indices (Normalized Difference Vegetation Index, Normalized Difference Barren Index and Modified Normalized Difference Water Index) and land surface temperature is used to natural resources and environmental studies. This research aimed to analysis of Land Surface Temperature due to dynamics of Different Indices (NDVI, NDBaI and MNDWI) Using Remote Sensing Data in three selected districts (Gida Kiremu, Limu and Amuru), western Ethiopia. From thermal and multispectral bands of landsat imageries (Landsat TM of 1990, landsat ETM+ of 2003 and landsat OLI/TIRS of 2020) Land surface temperature and NDVI, NDBaI and MNDWI were calculated. Correlation analysis was used to indicate relationships between LST with NDVI, NDBaI and MNDWI. The study found that Land Surface Temperature was increased by 5⁰C from 1990 to 2020. Vegetation areas (NDVI) and Water bodies (MNDWI) have strong negative relationship with Land Surface Temperature (R²= 0.99, 0.95) whereas Barren land (NDBaI) has positive relationship with Land Surface Temperature (R²= 0.96). Finally, we recommend the decision makers and environmental analyst to emphasis the importance of vegetation cover and water body to minimize the potential impacts of land surface temperature.

The 2019-2020 summer wildfire event on the east coast of Australia was a series of major wildfires occurring from November 2019 to end of January 2020 across the states of Queensland, New South Wales (NSW), Victoria and South Australia. The wildfires were unprecedent in scope and the extensive extend of the wildfires has caused smoke pollutants transported not only to New Zealand but across the Pacific Ocean to South America. At the height of the wildfires, smoke plumes were injected into the stratosphere at height up to 25km and hence transported across the globe. Based on meteorological and air quality simulation using WRF-Chem model, air quality monitoring data collected during the bushfire period and remote sensing data from MODIS and CALIPSO satellites, the extend of the wildfires and the pollutant transport, and their impacts on air quality and health on exposed population in NSW can be analysed. The results showed that WRF-Chem model using Fire Emission Inventory from NCAR (FINN) predicts the dispersion and transport of pollutants and the predicted concentration of PM2.5 and other pollutants from wildfires reasonably well when compared with ground-based and satellite data. The impact on health endpoints such as mortality, respiratory and cardiovascular diseases hospitalisation across the modelling domain is then estimated. The estimated health impact is comparable with previous study based only on observation data, but the results in this study provide much more detailed spatially and temporally with regards to the health impact from the 2019-2020 wildfire.

The frequent incidents of oil spills and other forms of pollution arising from crude oil exploration and exploitation (OEE) in the Niger Delta have caused several investigations on Polycyclic Aromatic Hydrocarbons (PAHs) pollution. This study aimed at developing a comprehensive report on PAH pollution and its human health risks recorded in the Niger Delta. Studies were extracted from Google Scholar, PubMed, and ResearchGate using a defined selection criterion. The quality of each study was assessed using the Newcastle – Ottawa Scale. Thirty-eight studies were selected with the majority reporting on PAH pollution in aquatic environments. Across all the selected studies, the total number of PAHs recorded ranged from 7 to 28 PAH congeners. Also, PAH potential sources reported in the studies were of pyrogenic and petrogenic sources. PAH concentrations recorded in water, sediment, aquatic organisms (fish and shrimp), soil, dust, and crop samples ranged from below detection limit (BDL) to 450 ± 117.9 mg/L, BDL to 1821.5 mg/kg, 0.005 to 1.098 mg/kg, ND to 4154 ± 3461 mg/kg, 165.1 to 1012 mg/kg, and 0.020 to 3.37 mg/kg, respectively. Majority of the selected studies reported PAH levels which were higher than the permissible limits. Incremental Lifetime Cancer Risk (ILCR) assessment of PAHs in samples ranged from low to high via ingestion and dermal routes of exposure to humans. It is recommended that the Federal Government of Nigeria promotes environmentally friendly operations of OEE. Future studies should focus on PAH pollution in farmlands, ambient air and the associated human and ecological health risks.

This paper argues that, in 2020, the beneficial atmospheric effect from the reduction in aviation may have been at least 7-8 times greater than that occurring from the reduction in fossil carbon dioxide emissions from all sectors. Specifically, compared to potential atmospheric effects in 2020 without the pandemic, the decrease in effective radiative forcing from reduced contrail-cirrus formation may have been in the order of 35mWm^-2 in 2020, compared to a reduction of only 4-5mWm^-2 from the drop in fossil CO₂ emissions. Over time, pursuing a low carbon pathway generates benefits that mount up to be much more significant than 2020 effects might imply, and is essential to stabilise the climate. However, a twin-track policy focus may be needed, with more emphasis on reducing short-term climate forcing, to minimise the impacts of climate change now, and to avoid detrimental feedback events. Future policy decisions about aviation should be made in this context.

Accurately mapping individual tree species in densely forested environments is crucial to forest inventory. When considering only RGB images, this is a challenging task for many automatic photogrammetry processes. The main reason for that is the spectral similarity between species in RGB scenes, which can be a hindrance for most automatic methods. State-of-the-art deep learning methods could be capable of identifying tree species with an attractive cost, accuracy, and computational load in RGB images. This paper presents a deep learning-based approach to detect an important multi-use species of palm trees (Mauritia flexuosa; i.e., Buriti) on aerial RGB imagery. In South-America, this palm tree is essential for many indigenous and local communities because of its characteristics. The species is also a valuable indicator of water resources, which comes as a benefit for mapping its location. The method is based on a Convolutional Neural Network (CNN) to identify and geolocate singular tree species in a high-complexity forest environment, and considers the likelihood of every pixel in the image to be recognized as a possible tree by implementing a confidence map feature extraction. This study compares the performance of the proposed method against state-of-the-art object detection networks. For this, images from a dataset composed of 1,394 airborne scenes, where 5,334 palm-trees were manually labeled, were used. The results returned a mean absolute error (MAE) of 0.75 trees and an F1-measure of 86.9%. These results are better than both Faster R-CNN and RetinaNet considering equal experiment conditions. The proposed network provided fast solutions to detect the palm trees, with a delivered image detection of 0.073 seconds and a standard deviation of 0.002 using the GPU. In conclusion, the method presented is efficient to deal with a high-density forest scenario and can accurately map the location of single species like the M flexuosa palm tree and may be useful for future frameworks.

The electrokinetics methods have a great potential to characterize hydrogeological processes in geological media, especially in complex hydrosystems such as fractured formations. In this work, we conceptualize fractured media as a bunch of parallel capillary fractures following the fractal size distribution. This conceptualization permits to obtain analytical models for both the electrical conductivity and the electrokinetic coupling in water saturated fractured media. We explore two different approaches to express the electrokinetic coupling. First, we express the streaming potential coupling coefficient as a function of the zeta potential and then we obtain the effective charge density in terms of macroscopic hydraulic and electrokinetic parameters of porous media. We show that when the surface electrical conductivity is negligible, the proposed models reduces to the previously proposed one based on a bundle of cylindrical capillaries. This model opens up a wide range of applications to monitor the water flow in fractured media.

Convective rainfall can cause dangerous flash floods within less than six hours. Thus, simple approaches are required for issuing quick warnings. The Flash Flood Guidance (FFG) approach pre-calculates rainfall levels (thresholds) potentially causing critical water levels for a specific catchment. Afterwards, only rainfall and soil moisture information is required to issue warn-ings. This study applied the principle of FFG to the Wernersbach Catchment (Germany) with excellent data coverage using the BROOK90 water budget model. The rainfall thresholds were determined for durations of 1 to 24 hours, by running BROOK90 in “inverse” mode, identifying rainfall values for each duration that led to exceedance of critical discharge (fixed value). After calibrating the model based on its runoff, we ran it in hourly mode with four precipitation types and various levels of initial soil moisture for the period 1996 – 2010. The rainfall threshold curves showed a very high probability of detection (POD) of 91% for the 40 extracted flash flood events in the study period, however, the false alarm rate (FAR) of 56% and the critical success index (CSI) of 42% should be improved in further studies. The approach proved potential as an early flood indicator for head-catchments with limited available information.

Sea fog event over the Eastern Yellow Sea on 15–16 April 2012 was reproduced in the Weather Research and Forecasting (WRF) simulation with high-resolution to investigate the roles of phys-ical processes and synoptic-scale flows on advection fog with sea surface warming. Initially, longwave radiative cooling (LRC) with negative sensible heat flux (SHF) due to the turbulence af-ter sunset triggered a formation of cloud at the surface under the moist advection with a southerly wind. This is a conventional type of advection fog. At night, continuous cooling due to longwave radiation and SHF near the surface modulated the change of the SHF from negative to positive, resulting in a drastic increase in the latent heat flux (LHF) that provided sufficient moisture at lower atmosphere (self-moistening). This is a favorable condition for advection fog with sea sur-face heating (ssH), and this transition represents advection fog with ssH. Enhanced turbulent mixing driven by a buoyancy force increased the depth of the sea fog with a gradual rise in the marine atmospheric boundary layer (MABL) height, even at nighttime. In addition, cold advec-tion with a prevailing northerly wind at the top of the MABL led to a drastic increase in turbulent mixing and the MABL height, which resulted in rapid growth of the height of sea fog due to ver-tical diffusion. After sunrise, shortwave radiative warming in the fog layers offsetting the LRC near the surface weakened thermal instability, which contributed to the reduction in the MABL height, even during the daytime. In addition, dry advection of northerly wind induced dissipa-tion of the fog via evaporation. An additional sensitivity test of sea surface salinity showed weaker and shallower sea fog than the control due to the decrease in both the LHF and local self-moistening.

Fire risk can be defined as the probability that a fire will spread. Reliable monitoring of fire risk is essential for effective landscape management. Compilation of fire risk records enable identification of seasonal and inter-annual patterns and provide a baseline to evaluate the trajectories in response to climate change. Typically, fire risk is estimated from meteorological data. In regions with sparse meteorological station coverage environmental proxies provide important additional data stream for estimating past and current fire risk. Here we use a 60-year record of daily flows from two rivers (Franklin and Davey) in the remote Tasmanian Wilderness World Heritage Area (TWWHA) to characterize seasonal patterns in fire risk in temperate Eucalyptus and rainforests. We show that river flows are strongly related to landscape soil moisture estimates derived from down-scaled re-analysis of meteorological data available since 1990. To identify river flow thresholds where forests are likely to burn, we relate river flows to known forest fires that have occurred in the previously defined ecohydrological domains that surround the Franklin and Davey catchments. Our analysis shows that the fire season in the TWWHA is centered on February (70% of all years below the median threshold), with shoulders on December-January and March. Since 1954 forest fire can occur in at least one month for all but four summers in the ecohydrological domain that includes the Franklin catchment, and since 1964 fire fires could occur in at least one month in every summer in the ecohydrological domain that includes the Davey catchment. Our analysis shows that mangers can use river flows as a simple index that provide a landscape-scale forest fire risk in the TWWHA.

Remote sensing and Geographical Information System (GIS) have played an important role in exploration and management of groundwater resources. In this study, we present modeling of groundwater potential zone in Khoyrasol block in Birbhum district, West Bengal by using remote sensing and GIS techniques. The objective of the study is to explore groundwater as well as surface water availability in different geomorphic units. Different thematic maps of geology, hydro-geomorphology, lineament, slope, land use/land cover (LULC), depth to water level and soil maps are prepared and groundwater potential zones are obtained by overlaying all thematic maps in terms of Weightage Index Overlay (WIO) method. All the thematic map classes have been assigned weightage according to their role in groundwater occurrence. Finally, groundwater potential zones are classified into four categories viz., excellent, good to medium, medium to poor and poor. The outcome of the present research work will help the local farmers, decision-maker, researchers and planners for exploration, monitoring, and management of groundwater resources for this study area.

Water mist systems (WMS) are used for evaporative cooling in public areas. The health risks associated with their colonization by opportunistic premise plumbing pathogens (OPPPs) is not well understood. To advance the understanding of the potential health risk of OPPPs in WMS, biofilm, water and bioaerosol samples (n = 90) from ten (10) WMS in Australia were collected and analyzed by culture and polymerase chain reaction (PCR) methods to detect the occurrence of 5 representative OPPPs: Legionella pneumophila, Pseudomonas aeruginosa, Mycobacterium avium, Naegleria fowleri and Acanthamoeba. P. aeruginosa (44%, n = 90) occurred more frequently in samples, followed by L. pneumophila serogroup (Sg) 2 - 14 (18%, n = 90) and L. pneumophila Sg 1 (6%, n = 90). A negative correlation between OPPP occurrence and residual free chlorine was observed except with Acanthamoeba ,rs (30) = 0.067, p > 0.05. . All detected OPPPs were positively correlated with water temperature. Biofilms contained higher concentrations of L. pneumophila Sg 2 - 14 (1000 – 3000 CFU/ml) in comparison to water samples (0-100CFU/ml). This study suggests that WMS can be colonized by OPPPs and are a potential health risk if OPPP contaminated aerosols get released into ambient atmospheres.

Bacterial resistance to antibiotics is a growing global concern, threatening human and environ-mental health, particularly among urban populations. Wastewater treatment plants (WWTPs) are thought to be “hotspots” for antibiotic resistance dissemination. The conditions of WWTPs, in conjunction with the persistence of commonly used antibiotics, may favor the selection and trans-fer of resistance genes among bacterial populations. WWTPs provide an important ecological niche to examine the spread of antibiotic resistance. We used heterotrophic plate count methods to identify phenotypically resistant cultivable portions of these bacterial communities and charac-terized the composition of the culturable subset of these populations. Resistant taxa were more abundant in raw sewage and wastewater before the biological aeration treatment stage. While some antibiotic resistant bacteria (ARB) were detectable downstream of treated wastewater re-lease, these organisms are not enriched relative to effluent-free upstream water, indicating effi-cient removal during treatment. Combined culture-dependent and culture-independent analyses revealed a stark difference in community composition between culturable fractions and the envi-ronmental source material, irrespective of culturing conditions. Higher proportions of the envi-ronmental populations were recovered than predicted by the widely accepted 1% culturability paradigm. These results represent baseline abundance and compositional data for ARB commu-nities for reference in future studies addressing the dissemination of antibiotic resistance associ-ated with urban wastewater treatment ecosystems.

In early 2020 from April to early June, the metropolitan area of Sydney as well as the rest of New South Wales (NSW, Australia) experienced a period of lockdown to prevent the spread of Covid-19 virus in the community. The effect of reducing anthropogenic activities including transportation had an impact on the urban environment in term of air quality which is shown to have improved for a number of pollutants, such as nitrogen dioxides (NO2) and carbon monoxide (CO), based on monitoring data on ground and from satellite. Besides primary pollutants CO and NOx emitted from mobile sources, PM2.5 (primary and secondary) and secondary ozone (O3) during the lockdown period will also be analysed using both air quality data and modelling method. The results show that NO2, CO and PM2.5 levels decreased during the lockdown, but O3 instead increased. The change in the concentration levels however are small considering the large reduction in traffic volume of ~30%. By estimate the decrease in traffic volume in Sydney region, the corresponding decrease in emission input to the WRF-CMAQ (Weather Research and Forecasting - Community Multiscale Air Quality Modeling System) air quality model is then used to estimate the effect of lockdown on the air quality especially CO, NO2, O3 and PM2.5 in the Greater Metropolitan Region (GMR) of Sydney. COVID-19 lockdown period is an ideal case to study the effect of motor vehicle and mobile source contribution to air pollutants such as those listed above in the GMR.

We revisit the notion of climate, along with its historical evolution, tracing the origin of the modern concerns about climate. The notion (and the scientific term) of climate has been established during the Greek antiquity in a geographical context and it acquired its statistical content (average weather) in modern times, after meteorological measurements had become common. Yet the modern definitions of climate are seriously affected by the wrong perception of the previous two centuries that climate should regularly be constant, unless an external agent acted. Therefore, we attempt to give a more rigorous definition of climate, consistent with the modern body of stochastics. We illustrate the definition by real-world data, which also exemplify the large climatic variability. Given this variability, the term “climate change” turns out to be scientifically unjustified. Specifically, it is a pleonasm as climate, like weather, has been ever changing. Indeed, a historical investigation reveals that the aim in using that term is not scientific but political. Within the political aims, water issues have been greatly promoted by projecting future catastrophes while reversing the true roles and causality directions. For this reason, we provide arguments that water is the main element that drives climate and not the opposite.

The use of swine manure as a source of plant nutrients is one alternative to synthetic fertilizers. However, conventional manure application with >90% water and a low C:N ratio results in soil C loss to the atmosphere. Our hypothesis was to use biochar as a manure nutrient stabilizer that would slowly release nutrients to plants upon biochar-swine manure mixture application to soil. The objectives were to evaluate the impact of biochar-treated swine manure on soil total C, N, and plant-available macro and micronutrients in greenhouse-cultivated corn (Zea mays L.) and soybean (Glycine max (L.) Merr.). Neutral pH red oak (RO), highly alkaline autothermal corn stover (HAP), and mild acidic Fe-treated autothermal corn stover (HAPE) biomass were pyrolyzed to prepare biochars. Each biochar was surface-applied to swine manure at a 1:4 (biochar wt/manure wt) ratio to generate mixtures of manure and respective biochars (MRO, MHAP, and MHAPE). Conventional manure (M) control and manure-biochar mixtures were then applied to the soil at a recommended rate. Corn and soybean were grown under these controls and treatments (S, M, MRO, MHAP, and MHAPE) to evaluate the manure-biochar impact on soil quality, plant biomass yield, and nutrient uptake. Soil OM significantly (<0.05) increased in all manure-biochar treatments; however, no change in soil pH or N was observed under any treatment. No difference in soil ammonium between treatments was identified. There was a significant decrease in soil M3-P and soil NO₃^- for all manure-biochar treatments compared to the conventional M. However, the plant biomass nutrient concentrations were not significantly different from control manure. Moreover, an increasing trend of N and decreasing trend of P in the plant under all biochar-manure treatments than the controls were noted. This observation suggests that the presence of biochar is capable of influencing the soil N and P in such a way as not to lose those nutrients at the early growth stages of the plant. In general, no statistical difference in corn or soybean biomass yield and plant nutrient uptake for N, P, and K was observed. Interestingly, manure-biochar application to soil significantly diluted the M3-extractable soil Cu and Zn concentrations. The results attribute that manure-biochar has the potential to be a better soil amendment than conventional manure application to the soil.

One of society’s greatest challenges is sequestering vast amounts of carbon to avoid dangerous climate change without driving competition for land and resources. Here we assess the potential of an integrated approach based on enhancement of natural biogeochemical cycles in agro-ecosystems that stimulate carbon capture and storage while increasing resilience and long-term productivity. The method integrates plant photosynthesis in the form of (cover) crops and agroforestry which drives carbon capture. Belowground plant-carbon is efficiently stored as stable soil organic carbon (SOC). Aboveground crop and tree residues are pyrolyzed into biochar, which is applied to the soil reducing carbon release through decomposition. Enhanced weathering of basalt powder worked into the soil further captures and stores carbon, while releasing nutrients and alkalinity. The integrated system is regenerative, through enhanced virtuous cycles that lead to improved plant capture, biomass storage and crop yield, the prerequisites for large-scale carbon sequestration along with food security.

The drought is one of all phenomena at local to a global scale that caused climate change impacts, alongside it also the human activities related, which were deforested and land use changed that caused to ecological disturbance, which one is hydrological changes. Hence, it’s a lot of lost productivity, chiefly to farming land aspects and the other, so the land restoration by reforestation is needed in framework to water cycle (hydrology) process kindly. The aim of this research has identified and analyzed ecological restoration to ecosystem services, chiefly in wastewater management (treatment) to conservation, especially in Al Zaytun areas (Pesantren), so observing and in-depth individual interview (with some of personage and pesantren of boards) is one method that used to data collected, alongside land survey management to classified type development in Al Zaytun areas. The result of this research revealed that Al Zaytun successfully in water management to conservation by wastewater treated management by Eichhornia crassipes (Enceng gondok), afterward to the reservoir as water saved development. Those conservation types considered capability in reviving to ecological systems to ecosystem services increased kindly, alongside to land restoration by some of the plant species or trees to grow and developed in which Tectona grandis L. f is more dominantly and it's favorite because of investment economic that advantage based on ecological perspective. These were type management that did by Al Zaytun, alongside its able in coping to both drought and climate change impacts kindly and adaptively, and of course, it’s part of an obligation as the follower’s Islam religion in preserving and maintaining natural resources, chiefly to water resources.

The disastrous 2017 fire season in Portugal lead to widespread recognition of the need for a paradigm shift in forest and fire management. We focused our study on Alvares, a parish in central Portugal which had 60% of its area burned in 2017, with a large record of historical. We evaluated how different fuel treatment strategies can reduce wildfire hazard in Alvares, through i) a fuel break network with different priorities and ii) random fuel treatments resulting from stand-level management intensification. To assess this, we developed a stochastic fire simulation system (FUNC-SIM) that integrates uncertainties in fuel distribution over the landscape. If the landscape remains unchanged, Alvares will have large burn probabilities in the north, northeast, and center-east areas of the parish that are very often associated with high fire line intensities. The different fuel treatment scenarios decreased burned area between 12.1-31.2%, resulting from 1%-4.6% increases in annual treatment area, and reduced 10%-40% the likelihood of wildfires larger than 5000 ha. On average, simulated burned area decreased 0.22% per each ha treated, and effectiveness decreased with increasing area treated. Overall, both fuel treatment strategies effectively reduced wildfire hazard and should be part of a larger, holistic and integrated plan to reduce the vulnerability of the Alvares parish to wildfires.

Miombo woodlands are extensive dry forest ecosystems in central and southern Africa covering ≈2.7 million km2. Despite their vast expanse and global importance for carbon storage, the long-term carbon stocks and dynamics have been poorly researched. The objective of this paper is to present and summarize the evidence gathered on above- and belowground (root and soil) carbon stocks of miombo woodlands from the 1960s to mid-2018 through a review. We analyzed data to answer: (1) What is the range of aboveground and belowground carbon stocks found in miombo woodlands over the last six decades? (2) Are there differences in carbon stocks based on land-management categories? (3) Does precipitation influence aboveground carbon stocks in old-growth miombo? (4) Do differences in cover type, age and region influence carbon stocks? (5) How does previous land-use affect carbon stocks in re-growth miombo? A literature review protocol was used to identify 56 publications from which quantitative data on aboveground and soil carbon pools were extracted. We found that the mean aboveground carbon stock in old-growth miombo was 30.83±16.76 Mg C ha-1 (range 1.48—107.24 Mg ha-1). Old-growth miombo had an average calculated root carbon stock of 16.49±9.18 Mg C ha-1 (range 0.8—57.81 Mg ha-1). Soil carbon stocks in old-growth miombo varied widely, between 8.75 and 134.6 Mg C ha-1 while in re-growth miombo they varied between 10.73 and 52.2 Mg C ha-1. It must be noted these soil data are given only for information; they inconsistently refer to varying soil depths and are thus difficult to interpret. The wide range reported suggests a need for further studies, much more systematic in methods and reporting. Other limitations of the dataset include the lack of systematic sampling and lack of data in some countries, viz. Angola and Democratic Republic of the Congo.

River valley bottoms have hydrological, geomorphological, and ecological importance and are buffers for protecting the river from upland nutrient loading coming from agriculture and other sources. They are relatively flat, low-lying areas of the terrain that are adjacent to the river and bound by increasing slopes at the transition to the uplands. These areas have under natural conditions, a groundwater table close to the soil surface. The objective of this paper is to present a stepwise GIS approach for the delineation of river valley bottom within drainage basins and use it to perform a national delineation. We developed a tool that applies a concept called cost distance accumulation with spatial data inputs consisting a river network and slope derived from a digital elevation model. We then used wetlands adjacent to rivers as a guide finding the river valley bottom boundary from the cost distance accumulation. We present results from our tool for the whole country of Denmark carrying out a validation within three selected areas. The results reveal that the tool visually performs well and delineates both confined and unconfined river valleys within the same drainage basin. We use the most common forms of wetlands (meadow and marsh) in Denmark's river valleys known as Groundwater Dependent Ecosystems (GDE) to validate our river valley bottom delineated areas. Our delineation picks about half to two-thirds of these GDE. However, we expected this since farmers have reclaimed Denmark's low-lying areas during the last 200 years before the first map of GDE was created. Our tool can be used as a management tool, since it can delineate an area that has been the focus of management actions to protect waterways from upland nutrient pollution.

For the Altai mountainous region, especially the arid south-eastern part, the history of glacier fluctuations in Pleistocene and Holocene is still poorly known. The key plots were located in the Kargy valley (2288-2387 m a. s. l.) that is not currently affected by glaciations. The relative dating method was applied to define Pleistocene moraine chronology and configuration in the Kargy valley. Taking into account that relative dating methods are primarily based on weathering pat-terns, the mineralogy, porosity, and specificity of biological colonization as an agent of weath-ering were obtained for the moraine samples. Three moraine groups of different age (presumably MIS 6, MIS 4, and MIS 2) were identifies based on detailed investigation of morphological features. The moraine age was indirectly confirmed by the mesostructure of the moraine samples repre-sented by fine-grained shale: the older sample is characterized by a more developed fractal sur-face than the younger one. The growth of biota (crustose lichen and micromycetes) leads to initial biomass accumulation and subsequent rock disintegration. The accumulation of autochthonous fine earth on the rock surface was considered the initial stage of fine earth formation affected by biota.

Hydraulic conductivity is the key and one of the most uncertain parameters in groundwater modeling. The grid based numerical simulation require spatial distribution of sampled hydraulic conductivity at un-sampled locations in the study area. This spatial interpolation has been routinely performed using variogram based models (two-point geostatistics methods). These traditional techniques fail to capture the complex geological structures, provides smoothing effects and ignore the higher order moments of subsurface heterogeneities. In this work, a multiple-point geostatistics (MPS) method is applied to interpolate hydraulic conductivity data which will be further used in WASH123D numerical groundwater simulation model for regional smart groundwater management. To do this, MPS need ‘training images (TIs) as a key input. TI is a conceptual model of subsurface geological heterogeneity which was developed by using concept of ages, topographic slope as an index criteria and knowledge of geologist. After considerations of full physics of study area, an example shows the advantages of using multiple-point geostatistics compared with the traditional two-point geostatistics methods (such as Kriging) for the interpolation of hydraulic conductivity data in a complex geological formation.

Wind is one of the factors that has a great influence on suspended matter in lakes, especially in shallow lagoons. In order to know how wind affects the water in Albufera of Valencia, a shallow coastal lagoon, the measured variables of turbidity and transparency have been correlated with the estimates by processing Sentinel-2 satellite images with the Sen2Cor processor. Data from four years of study show that most of them are light to gentle easterly breezes and moderate to fresh westerly breezes. The results obtained show significant correlations between the measured variables and those obtained from the satellite images for total suspended matter and water transparency and with the average daily wind speed. There is no significant correlation between wind and chlorophyll a. Moderate to fresh breezes resuspend the fine sediment reaching concentration values from 100 to 300 mg L-1 according to satellite data. However, it is necessary to obtain field data for the values of moderate and fresh winds, as for now there are no experimental data to verify the validity of the satellite estimates.

With the Paris Agreement, countries are obliged to report greenhouse gas (GHG) emission reduc-tions, which will ensure that the global temperature increase is maintained well below 2C. The Parties will report their Nationally Determined Contributions in terms of plans and progress to-wards these targets during the postponed COP26 in Glasgow in November 2021. These commit-ments however do not take significant portions of the consumption related emissions related to countries imports in to account. Similarly, the majority of companies that report their emissions to CDP also do not account for their embodied value-chain related emissions. Municipalities, on the path towards carbon neutrality in accordance with the methods outlined by C40, also do not in-clude imported and embodied CO2e in their total emission tallies. So, who is responsible for these emissions - the producer or the consumer? How can we ensure that the NDC's, municipalities and companies reduction targets share the responsibility of the emissions in the value-chain thus en-suring that targets and plans become, sustainable, climate fair, and just in global value chains? Today the responsibility lays with the producer, which is not sustainable. We have the outline for the tools needed to quantify and transparently share the responsibility between producers and consumers at corporate, municipal and national level based on an improved understanding of the attendant sources, causes, flows and risks og GHG emissions globally. Hybrid LCA/EEIO models can for example be further developed. This will, in the end, enable everyday consumption to support a more sustainable, green and low carbon transition of our economy.

Remote sensing (RS) has been widely adopted as a tool to investigate several biotic and abiotic factors, directly and indirectly, related to biodiversity conservation. European grasslands are one of the most biodiverse habitats in Europe. Most of these habitats are subject to priority conservation measure, and they are threatened by several human induced process. The broad expansions of few dominant species are widely reported as drivers of biodiversity loss. In this context, using Sentinel-2 (S2) images, we investigate the distribution of one of the most spreading species: <i>Brachypodium genuense</i>. We performed a binary Random Forest (RF) classification of <i>B. genuense</i> using a RS image and field sampled presence/absence points. Then, we integrate the occurrences obtained from RS classification into niche models to identify the topographic drivers of <i>B. genuense</i> distribution. Lastly, the impact of <i>B. genuense</i> distribution in the N2k habitats was assessed by overlay analysis. The RF classification process detected <i>B. genuense</i>'s cover with an overall accuracy of 91.18%. The integration of RS and topographic niche models shows that the most relevant topographic variables that influence the distribution of <i>B. genuense</i> are slope, elevation, solar radiation and Topographic Wet Index (TWI) in order of importance. The overlay analysis shows that 74.04% of the <i>B. genuense</i> identified in the study area falls on the semi-natural dry grasslands. The study highlights the importance of the RS classification and the topographic niche models as an integrated approach for mapping a broad-expansion species such as <i>B. genuense</i>. The coupled techniques presented in this work should be applicable to other plant communities with remotely recognizable characteristics for more effective management of N2k habitats.

How much is religion quantitatively involved in global climate politics? After assessing the role of the Conference of Parties to the United Nations Framework Convention on Climate Change from a normative perspective, this descriptive, transdisciplinary and unconventional study offers the first comprehensive quantitative examination of religious nongovernmental organizations that formally participate in its annual meetings, the largest attempts to solve the climate crisis through global governance. This study finds that although their numbers are growing, only about 3 percent of registered nongovernmental organizations accredited to participate in the conference are overtly religious in nature — and that more than 80 percent of those faith-based groups are Christian. Additionally, this study finds that religious nongovernmental organizations that participate in the conference are mostly from the Global North. The results call for greater participation of religious institutions in the international climate negotiations in order for society to address the planetary emergency of climate change.

Solid waste management is a global issue that affects every individual living in the world directly or indirectly. Failing to manage the waste properly will cause harm on public health, our natural environment, and even our happiness and prosperity. When asked about the reason behind the failure of third time plastic ban in Bhutan, one of the Bhutanese said it’s because of the lack of cooperation from public to support the move of government. Clean Bhutan, a non-governmental organization (NGO) that aims Zero Waste Bhutan by 2030, has collected around 5,900 metric tons of waste from December 2014 to February 2018. This project will create some awareness regarding the management of waste at individual level when people come in contact with the family of the researcher. It also talks abot how waste management and biodiversity conservation can go side by side.

Long-term dewatering of groundwater is a necessary operation for mining safety in open-pit coal mines, while extensive dewatering might cause ecological problems due to dramatical changes of moisture movement in the soil, especially in ecological-fragile areas. This paper presents a quantitative methodology to evaluate the impact of the coal mining operation on moisture movement in the vadose zone by taking the Baorixile open-pit coal mine as an example. A long-term in-situ experiments（from 2004 to 2018), laboratory analysis and numerical modelling were conducted to analyse the mechanisms and relationship among the dropping groundwater level, the vadose-zone moistures, and the ecological responses in the grassland area. The experiment data and modelling results suggest that groundwater level dropping during open-pit mining operation has limited influence on the vadose zone, exhibiting a variation of capillary water zone within a depth of 3 m while the vadose zone and soil water zone were at least 16 m deep. The critical evaporation depth of ground water is 8 m. The long-term influence radius of groundwater dewatering is about 2.72 km during the Baorixile mining operation, and the groundwater level change mainly influences the lower part of the intermediate vadose zone and the capillary water zone below 16 m, with little influence on the moisture contents in the soil water zone where the roots of shallow vegetation grow. The results from this study provide useful insight for sustainable development of coal mining in ecological-fragile areas.

The quantity of soil loss as a result of soil erosion is dramatically increasing in catchment where land resources management is very weak. In this paper, a RUSLE model-based soil loss quanti-fication technique is presented to estimate the annual soil loss and identify the severity of the erosion in the catchment. This study uses Fincha catchment in Abay river basin as the study area to quantify the annual soil loss by implementing Revised Universal Soil Loss Equation (RUSLE) model developed in ArcGIS version 10.4. Digital Elevation Model (12.5 x 12.5), LANDSAT 8 of Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS), Annual Rainfall of 10 stations and soil maps of the catchment were used as input parameters to generate the significant factors. Rainfall erosivity factor (R), soil erodibility factor (K), cover and management factor (C), slope length and steepness factor (LS) and support practice factor (P) were used as soil loss quantification significant factors. A model builder for the RUSLE model was developed and raster map calcula-tion algebra was applied in ArcGIS version 10.4 to quantify the total annual soil loss. It was found that the quantified average annual soil loss ranges from 0.0 to 76.5 t ha-1 yr-1 was obtained in the catchment. The area coverage of soil erosion severity with 55%, 35% and 10% as low to moderate, high and very high respectively were identified. The information about the spatial variation of soil loss severity map generated in RUSLE model has a paramount role to alert land resources man-agers and all stakeholders in controlling the effects via implementation of both structural and non-structural mitigations. The results of the RUSLE model can also be further considered along with the catchment for practical soil loss quantification that can help for protection practices.

There is an increasing need for quantitative rockfall hazard and risk assessment that requires a precise definition of the terms and concepts used for this particular type of landslide. This paper suggests to use terms that appear to be the more logic and explicit as possible, and describes methods to derive some of the main hazard and risk descriptors. The terms and concepts presented concern the rockfall process (failure, propagation, fragmentation, modelling) and the hazard and risk descriptors, distinguishing the cases of localized hazards and diffused hazards. For a localized hazard, the failure probability of the considered rock compartment in a given period of time has to be assessed and the probability for a given element at risk to be impacted with a given energy must be derived combining the failure probability, the propagation probability and the exposure of the element. For a diffuse hazard that is characterized by a failure frequency, the number of rockfalls reaching the element at risk per unit of time and with a given energy (reach frequency) can be derived. However, when the element at risk is not replaced or repaired, the probability that it is impacted by at least one rockfall must be considered.

In cotton cultivars, an insect that causes irreversible damage is the Spodoptera frugiperda, known as the fall armyworm. Since the visual detection of plants is a burdensome task for human inspection, the spectral information related to plant damage, registered on a spectral scale, can be useful. These measurements, associated with machine learning techniques, produce useful information for a rapid and non-invasive inspection method development. To contribute to this gap fulfillment, this paper proposes a machine learning framework to model the spectral response of cotton plants under the attack of the fall armyworm. Additionally, a theoretical model is presented, built from the results of the machine learning analysis, to infer this damage with up-to-date orbital sensors. The data was composed of the reflectance measurements collected at a cotton field with control plants and plants submitted to Spodoptera frugiperda damage. Their spectral response was recorded with a hand-held spectroradiometer ranging from 350 to 2,500 nm, for eight consecutive days. Different machine learning models were evaluated and the overall best model was defined by accuracies comparisons on a testing-set. A ranking approach was adopted based on the model accuracy, returning the most contributive wavelengths for the classification. Sequentially, an unsupervised neural network (Self-Organizing Map - SOM) was implemented to reduce data-dimensionality and assist in the definition of important spectral regions. The regions were associated with the spectral bands of the two sensors (OLI and MSI) and a theoretical model using a band simulation process with the overall best machine learning model was proposed. The results indicated that the Random Forest (RF) algorithm is the most suitable to predict cotton-plants damaged by insects and that the last day of analysis (8th day) was better to separate it, with F-measure equals 0.912. The ranking approach combined with the SOM method indicated the spectral regions at the red to near-infrared (650 to 1,350 nm) and shortwave infrared (1,570 to 1,640 nm) as the most important regions to the analysis. The proposed theoretical model simulated with the OLI and MSI sensor-bands returned an F-Measure of 0.865 and 0.886, respectively. In conclusion, this framework can be used to map cotton-plants under insect-attack. The theoretical model presents high accuracy to infer the insect-damaged on cotton plants based on multispectral bands from other sensors, being a useful tool for future research that intends to evaluate it in other areas and at different field scales.

Sea-level rise is a problem increasingly affecting coastal areas worldwide. The existence of Free and Open-Source Models to estimate the sea-level impact can contribute to better coastal man-agement. This study aims to develop and to validate two different models to predict the sea-level rise impact supported by Google Earth Engine (GEE) – a cloud-based platform for planetary-scale environmental data analysis. The first model is a Bathtub Model based on the uncertainty of projections of the Sea-level Rise Impact Module of TerrSet - Geospatial Monitoring and Modeling System software. The validation process performed in the Rio Grande do Sul coastal plain (S Brazil) resulted in correlations from 0.75 to 1.00. The second model uses Bruun Rule formula implemented in GEE and is capable to determine the coastline retreat of a profile through the creation of a simple vector line from topo-bathymetric data. The model shows a very high cor-relation (0.97) with a classical Bruun Rule study performed in Aveiro coast (NW Portugal). The GEE platform seems to be an important tool for coastal management. The models developed have been openly shared, enabling the continuous improvement of the code by the scientific commu-nity.

A strategy to reduce qualitative and quantitative losses in crop-yields refers to early and accurate detection of insect-damage caused in plants. Remote sensing systems like hyperspectral proximal sensors are a promising strategy for managing crops. In this aspect, machine learning predictions associated with clustering techniques may be an interesting approach mainly because of its robustness to evaluate high dimensional data. In this paper, we model the spectral response of insect-herbivory-damage in maize plants and propose an approach based on machine learning and a clustering method to predict whether the plant is herbivore-attacked or not using leaf reflectance measurements. We differentiate insect-type damage based on the spectral response and indicate the most contributive wavelengths to perform it. For this, we used a maize experiment in semi-field conditions. The maize plants were submitted to three different treatments: control (health plants); plants submitted to Spodoptera frugiperda herbivory-damage, and; plants submitted to Dichelops melacanthus herbivory-damage. The leaf spectral response of all plants (controlled and submitted to herbivory) was measured with a FieldSpec 3.0 Spectroradiometer from 350 to 2500 nm for eight consecutive days. We evaluated the performance of different learners like random forest (RF), support vector machine (SVM), extreme gradient boost (XGB), neural networks (MLP), and measured the impact of a day-by-day analysis into the prediction. We proposed a novel framework with a ranking strategy, based on the accuracy returned by predictions, and a clusterization method based on a self-organizing map (SOM) to identify important regions in the reflectance measurement. Our results indicated that the RF-based framework algorithm is the overall best learner to deal with this type of data. After the 5th day of analysis, the accuracy of the algorithm improved substantially. It separated the three treatments into different groups with an F-measure equal to 0.967, 0.917, and 0.881, respectively. We also verified that the most contributive spectral regions are situated in the near-infrared domain. We conclude that the proposed approach with machine learning methods is adequate to monitor herbivory-damage of S. frugiperda and stink bugs like Dichelops melacanthus in maize, differentiating the types of insect-attack early on. We also demonstrate that the framework proposed for the analysis of the most contributive wavelengths is suitable to highlight spectral regions of interest.

Sediment accumulation in a dam reservoir is a common happening environmental problem throughout the world. Topographic conditions, land use land cover change, the intensity of rainfall, and the soil characteristics are the major driving factors for sedimentation to occur. The effect of sedimentation in a dam reservoir is very visible in the watershed as a result of hilly topographic conditions, high rainfall intensity, thin land cover, and less soil infiltration capacity. In this paper, an integrated RUSLE and GIS technique was implemented to estimate a mean annual sediment yield based on spatial and temporal variations in Nashe dam reservoir situated in Fincha catchment, Abaya River basin, Ethiopia. Spatial and temporal estimation of mean annual sediment yield was estimated using the Revised Universal Soil Loss Equation (RUSLE) model and GIS. Historical 6-year (2014-2019) rainfall for the temporal variations and other physical factors such as soil erodibility, slope and length steepness, management and land used land cover, and support practice for spatial variations were used as sediment driving factors. The mean annual sediment yield ranges from 0 to 2712.65 t ha-1 year-1 was seen. Spatially, Very high, high, moderate, low, and very low sediment yield severity with total area coverage with 25%, 10%, 30%, 15%, and 20% in 2017, 2015, 2019, 2014, and 2018 respectively. The information about the spatial and temporal variations of the severity of sediment yield in RUSLE model has a paramount role to control the entry of sediment into the dam reservoir in this watershed. The results of the RUSLE model can also be further considered along with the watershed for planning strategies for dam reservoirs in the catchment.

Sherubtse College under Royal University of Bhutan has a total of 1582 students, where 787 0f them are female. They suffer from menstrual and menstrual cramps every month and they use napkins to stay clean and healthy. As all the college girls are educated on the norms and use of sanitary napkins, the proper management of the waste generated is lacking. The disposal of used sanitary napkins and management is a challenge to the college management. This small research aims to investigate on how girls in college campus manage the menstural waste and the amount of sanitary napkins used. The lack of proper dumping areas leads to the improper sanitary waste management and sometimes the waste generated from the sanitary napkins are toxic and unhealthy. It is very important to initate some ways to dump the pads and its related problem should be seriously addressed by the college management. It is also important to come up with different soultion and management of these wastes for sustainable environment. Therefore, this research will be mainly mentioning about the suggestions and how the sanitary waste is being managed in Sherubtse College campus and the amount of sanitary pads used in a month or an year.

Utilization of Bidirectional Reflectance Distribution Function (BRDF) model parameters obtained from the multi-angular remote sensing is one of the approaches for the retrieval of vegetation structural information. In this research, the potential of multi-angular vegetation indices, formulated by the combination of multi-spectral reflectance from different view angles, for the retrieval of forest above ground biomass was assessed. This research was implemented in the New England region with the availability of a high quality forest inventory database. The multi-angular vegetation indices were generated by the simulation of the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo Model Parameters Product (MCD43A1 Version 6) based BRDF parameters. The effects of seasonal (spring, summer, autumn, and winter) composites of the multi-angular vegetation indices on above ground biomass, angular relationship of the spectral reflectance with above ground biomass, and the interrelationships between the multi-angular vegetation indices were analyzed. Among the existing multi-angular vegetation indices, only the Nadir BRDF-adjusted NDVI ( and Hot-spot incorporated NDVI ( showed significant relationship (more than 50%) with the above ground biomass. This research proposed two more sensitive vegetation structural indices, Fore-scattering Back-scattering NDVI and Vegetation Structure Index (VSI). The Fore-scattering Back-scattering NDVI showed higher sensitivity (R² = 0.62, RMSE = 52.46) towards the above ground biomass than existing multi-angular vegetation indices. Furthermore, the VSI performed in the most efficient way explaining 64% variation of the above ground biomass, suggesting that the right choice of the spectral channel and observation geometry should be considered for improving the estimates of the above ground biomass. In addition, the right choice of seasonal data (summer) was found to be important for estimating the forest biomass while other seasonal data were either insensitive or pointless. The promising results shown by the VSI suggest that it could be an appropriate candidate for monitoring vegetation structure from the multi-angular satellite remote sensing.

In this work, we propose simple and robust technique for the retrieval of underlying surface spectral albedo using spaceborne observations. It can be used to process both multispectral moderate resolution satellite data and also multi - zone high spatial resolution data. The technique can work automatically for different types of land surfaces without using huge databases accumulated in advance. The new cloud discrimination and retrieval of the water vapor content in atmosphere procedures are presented. The key point of the proposed atmospheric correction technique is the suggested single-wavelength method for determining the atmospheric aerosol optical thickness without reference to a specific type of underlying surface spectrum. The retrievals of spectral albedo for various land surfaces with developed technique, performed using computer simulation and experimental data, have demonstrated a high retrieval accuracy.

This paper describes and demonstrates an autonomous robotic team that can rapidly learn the characteristics of environments that it has never seen before. The flexible paradigm is easily scalable to multi-robot, multi-sensor autonomous teams, and is relevant to satellite calibration/validation and the creation of new remote sensing data products. A case study is described for the rapid characterisation of the aquatic environment, over a period of just a few minutes we acquired thousands of training data points. This training data allowed our machine learning algorithms to rapidly learn by example and provide wide area maps of the composition of the environment. Along side these larger autonomous robots two smaller robots that can be deployed by a single individual were also deployed (a walking robot and a robotic hover-board), observing significant small scale spatial variability.

The emergence of the Covid-19 pandemic has created both negative and positive changes, including implementing the circular economy across the globe. This Systematic Review follows the PRISMA statement and employs the Text Mining (Voyant Tools) technique to visualize and analyze the impacts of the Covid-19 on three aspects of the circular economy: economic, social, and environmental. The research employs Latent Dirichlet Allocation (LDA) to identify five major topics: (1) Shortage of medical equipment but high medical waste during Covid-19 due to the high demand in healthcare; (2) The long term negative impacts of lockdown on economic and social activities because of Covid-19 pandemic; (3) The reports on impacts of Covid-19 pandemic on the manufacturing globally, and their coping strategies and new opportunities; (4) The impacts of international restriction on the tourism, trade, shipping, and aviation industry, causing billion-dollar losses; (5) The reduction of pollution with health environment improvements with example cases from China and EU. The research identifies current literature gaps in the circular economy and Covid- 19 topics and encourages the application of text mining tools into researching to stimulate the research process and assist in communicating with the public.

During composting process, soils undergo many changes in their physical, chemical and biological properties. Composting has been widely known as an aerobic process during which organic matter is decomposed to humus like substances broken into many organic materials or compounds. The project aims to compare and analyze different combination of composting that yield different properties and nature of soils, and testing their soil physical properties. Dug three compost pits each 1.5 m in depth, labeled compost pit A consisting of potato peels – the only kitchen waste most common around the college hostels, compost pit B consisting of leaf litter and other garden trimmings and compost pit C consisting of cow dung and the other as a controlled experiment with no composting practices.

This paper presents the validation of the End Point Rate (EPR) tool for QGIS (EPR4Q), a tool built-in QGIS Graphical Modeler to calculate the shoreline change by End Point Rate method. The EPR4Q tries to fill the gap of user-friendly and free open-source tool for shoreline analysis in Geographic Information System environment, since the most used software - Digital Shoreline Analysis System (DSAS) - although is a free extension, is suited for commercial software. Besides, the best free open-source option to calculate EPR called Analyzing Moving Boundaries Using R (AMBUR), since it is a robust and powerful tool, the complexity and heavy processes can restrict the accessibility and simple usage. The validation methodology consists of applying the EPR4Q, DSAS, and AMBUR on different examples of shorelines found in nature, extracted from the U.S. Geological Survey Open-File. The obtained results of each tool were compared with Pearson correlation coefficient. The validation results indicate that the EPR4Q tool created acquired high correlation values with DSAS and AMBUR, reaching a coefficient of 0.98 to 1.00 on linear, extensive, and non-extensive shorelines, guarantying that the EPR4Q tool is ready to be freely used by the academic, scientific, engineering, and coastal managers communities worldwide.

Karst wetlands are being seriously damaged, and protecting it has become an important matter. Karst vegetation is the essential component of wetland and plays an important role in in the ecological functions of wetland ecosystems. Classifying karst vegetation is important for karst wetlands protection and management. This paper addressed to classify karst vegetation in Huixian National Wetland Park, located in China using the improved SegNet Deep-Learning Algorithm and UAV images. This study proposed a method to fuse single-class SegNet models using the maximum probability algorithm for karst vegetation classification, and compared with object-based RF classification and multi-class SegNet classification, respectively. This paper evaluated the performance of multi-class SegNet model and fusion of single-class SegNet model with different EPOCH values for mapping karst vegetation. A new optimized post-classification algorithm was proposed to eliminate the stitching traces caused by SegNet model prediction. The specific conclusions of this paper include the followings:(1) fusion of four single-class SegNet models produced better classification for karst wetland vegetation than multi-class SegNet model, and achieved the highest overall classification accuracy (87.34%); (2) The optimized post-classification algorithm was able to improve prediction accuracy of SegNet model, and it could eliminate splicing traces; (3) The karst wetland vegetation classifications produced by single-class SegNet model outperformed multi-class SegNet model, and improved classification accuracy(F1-Score) between 10%~25%;(4)The EPCOH values and textural feature important impact on karst wetland vegetation classifications. The SegNet model with EPCOH 15 achieved greater classification accuracy(F1-Score) than the model with EPOCH 5 or 10. The textural feature improved improves the capability of the SegNet model for mapping karst vegetation;(5) Fusion of single-class SegNet models and object-based RF model could provide high classifications results for karst wetland vegetation, and both achieved greater 87% overall accuracy.

Sea-level rise is a problem increasingly affecting coastal areas worldwide. The existence 15 of Free and Open-Source Models to estimate the sea-level impact can contribute to better coastal 16 management. This study aims to develop and to validate two different models to predict the 17 sea-level rise impact supported by Google Earth Engine (GEE) – a cloud-based platform for plan-18 etary-scale environmental data analysis. The first model is a Bathtub Model based on the uncer-19 tainty of projections of the Sea-level Rise Impact Module of TerrSet - Geospatial Monitoring and 20 Modeling System software. The validation process performed in the Rio Grande do Sul coastal 21 plain (S Brazil) resulted in correlations from 0.75 to 1.00. The second model uses Bruun Rule for-22 mula implemented in GEE and is capable to determine the coastline retreat of a profile through the 23 creation of a simple vector line from topo-bathymetric data. The model shows a very high correla-24 tion (0.97) with a classical Bruun Rule study performed in Aveiro coast (NW Portugal). The GEE 25 platform seems to be an important tool for coastal management. The models developed have been 26 openly shared, enabling the continuous improvement of the code by the scientific community.

Sentinel-1A/B radar remote sensing data were applied for the first time to determine the sixth nuclear test, its underground explosion h-bomb location and affected zone in North Korea, on September 3, 2017. Location of epicenters nuclear test were found according to line-of-sight displacement images via its maximum value. Line-of-sight displacement images were obtained by processing in the GMTSAR package in the VirtualBox virtual machine of the Linux Ubuntu 16.04 operation system. In this research, three scenes Sentinel-B data with descending orbits were considered, one after and two before the event (the nuclear test date) scene were used.

This paper presents ensemble learning of multi-source satellite sensors dataset to obtain better predictive performance of the forest biomass. Spectral, spectral-indices, and spectral-textural features were generated from two optical satellite sensors, Landsat 8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI). In addition, two radar satellite sensors, Sentinel-1 C-band Synthetic Aperture Radar (CSAR), and Advanced Land Observing Satellite (ALOS-2) Phased Array type L-band Synthetic Aperture Radar (PALSAR-2) were utilized to generate backscattering and backscattering-textural features. The plot-wise above ground biomass data available from five forests in New England region were utilized. Ensemble learning of multi-source satellite sensors dataset was carried out by employing four machine learning regressors namely, Support Vector Machines (SVM), Random Forests (RF), Gradient Boosting (GB), and Multilayer Perceptron (MLP). A five-fold cross-validation method was used to evaluate predictive performance of the multi-source satellite sensors. The integration of multi-source satellite features, comprising of spectral, spectral-indices, backscattering, spectral-textural, and backscattering-textural information, through ensemble learning and cross-validation approach implemented in the research showed promising results (R² = 0.81, RMSE = 46.2 Mg/ha) for the estimation of plots-level forest biomass in New England region.

This work assesses sea-level rise using three different models created on Free and Open-Source Software for Geographic Information System (FOSS4GIS). Based on regional projections of Special Report on Climate Change and Oceans and Cryosphere (SROCC) of the Intergovernmental Panel on Climate Change (IPCC), the models were applied to a case of study on Rio Grande do Sul coast – Brazil under different sea-level rise scenarios by the end of this century. The End Point Rate for QGIS (EPR4Q), calculates a shoreline projection using End Point Rate method. The Uncertainty Bathtub Model (uBTM), analyses the sea-level rise impact by the uncertainty of sea-level projec-tions and vertical error of the Digital Elevation/Terrain Model (DEM/DTM). The Bruun Rule for Google Earth Engine Model (BRGM) predicts the shoreline position with sea-level rise, using topographic and bathymetric data from Unmanned Aerial Vehicles (UAV) and Coastal Modelling System (SMC – Brazil), respectively. The results indicated a maximum shoreline retreat for 2100 of -502 m and -1727 m using EPR4Q and BRGM, correspondingly. The uBTM using the land-use of Mapbiomas showed a maximum of 44.57 km2 of urban area impacted by the sea-level flood. This research highlights the possibility of performing coastal management analysis in GIS environ-ment using non-commercial software.

The current research investigated the environmental risk of the polycyclic musk compounds, Galaxolide® (HHCB) and Tonalide® (AHTN), in the marine environments. These substances are lipophilic, bioaccumulated and potentially biomagnified in aquatic organisms. To understand the toxicity of HHCB and AHTN, we performed acute toxicity tests by exposing marine microalgae (Phaeodactylum tricornutum, Tretraselmis chuii and Isochrysis galbana), crustaceans (Artemia franciscana), echinoderms (Paracentrotus lividus), bivalves (Mytilus galloprovincialis), fish (Sparus aurata) and a candidate freshwater microalga (Raphidocelis subcapitata) to environmentally relevant concentrations (0.005 - 5 µg/L) following standardized protocols. The effects of both substances on microalgae growth were incipient and only I. galbana was sensitive to HHCB and AHTN, with IC10 values of 5.22 µg/L and 0.328 µg/L, respectively. Significant (p < 0.01) concentration dependent responses were measured in P. lividus and M. galloprovincialis larvae developments as well as S. aurata mortality tested with HHCB. The effect of HHCB on P. lividus larvae development was the most sensitive endpoint recorded, producing an EC50 value of 4.07 µg/L. Our results show that HHCB represents a high risk to P. lividus larvae development for early life stages in marine environments.

This paper proposed a new evaluation model based on analytic hierarchy process to quantitatively evaluate the capability of multi-satellite cooperative remote sensing observation. The analytic hierarchical process model is a combination of qualitative and quantitative analysis of systematic decision analysis method. According to the objective of the remote sensing cooperative observation mission, we decompose the complex problem into several levels and a number of factors, compare and calculate various factors in pairs, and obtain the combination weights of different schemes. The model can be used to evaluate the observation capability of resource satellites. Taking the optical remote sensing satellites such as China’s resource satellite series and GF-4 as examples, this paper verifies and evaluates the model for three typical tasks: point target observation, regional target observation and moving target continuous observation. The results show that the model can provide quantitative reference and model support for comprehensive evaluation of the collaborative observation capability of remote sensing satellites.

The goods and services produced by forest and vegetation ecology system is the power by which human society can be promoted fast in high-quality and sustainable way. With the increase of population and economic development in water-limited regions, there is an increasing demand for the quantity and variety of forest vegetation ecosystem products and services. To meet the demands of this situation, most of the original forest has changed into farmland, non-native forest and grass land. As a result, water-plant relationship changed from equilibrium to non-equilibrium, which led to soil drought, soil degradation and vegetation decline in dry years or waste of soil water in rainy years in most of water-limited regions. In order to solve the questions and realize the sustainable utilization of soil water resources and the high quality and sustainable development of social economy, it is necessary to apply the utilization limit theory of soil water resources by plants and the theory of soil water carrying capacity for vegetation to adjust the relationship between plant growth and soil moisture to obtain the maximum yield and benefit of vegetation and serve high-quality and sustainable development.

In 2004, first absolute gravity (AG) measurements were performed on the mountain tops of Mt. Zugspitze (2 sites) and Mt. Wank (1 site), and at the Wank foot (1 site). Wank (summit height 1780 m) and Zugspitze (2960 m) are about 20 km apart from each other and belong geologically to different parts of the Northern Limestone Alps. Bridging a time span of 15 years, the deduced gravity variations for Zugspitze are in the order of 0.30 μm/s² with a standard uncertainty of 0.04 μm/s². The Wank stations (foot and top) show no significant gravity variation. The vertical stability of Wank summit is also confirmed by results of continuous GNSS recordings. Because an Alpine mountain uplift of 1 or 2 mm/yr cannot explain the obtained gravity decline at Zugspitze, the dominating geophysical contributions are assumed to be due to the diminishing glaciers in the vicinity. The modelled gravity trend caused by glacier retreat between epochs 1999 and 2018 amounts to -0.012 μm/s²/yr at both Zugspitze AG sites. This explains more than half of the observed gravity decrease. Long-term variations on inter-annual and climate-relevant decadal scale will be investigated in the future using as a supplement superconducting gravimetry (installed in 2019) and GNSS equipment (since 2018).

Maghemite (γ-Fe2O3) is a mineral formed from magnetite oxidation at low temperatures, an intermediate metastable term of the magnetite to hematite oxidation and could be mixed with both. It has magnetic susceptibility similar to magnetite, crystal structure close to magnetite with which it forms a solid solution, while compositionally it equals hematite. Maghemite is thus easily misidentified as magnetite by X-ray diffraction and/or as hematite by spot chemical analysis in iron ore characterization routines. Nonstoichiometric magnetite could be quantified in samples of Brazilian soils and iron ores by the Rietveld method using a constrained refinement of the X-ray patterns. The results were confirmed by reflected light microscopy and Raman spectroscopy, thus qualitatively validating the method. X-ray diffraction with the refinement of the isomorphic substitution of Fe2+ by Fe3+ along the magnetite-maghemite solid solution could help to suitably characterize maghemite in iron ores, allowing for the evaluation of its ultimate influence on mineral processing, by affecting its surface and breakage properties.

The quantitative and qualitative assessment of gold grains from samples of glacial till is a well-established method for exploring gold deposits hidden under glaciated cover. This method, widely used by the industry and which produced numerous successes in locating gold deposits in glaciated terrain, is still based on artisanal gravity separation techniques and visual identification. However, being artisanal, it is limited by inconsistent recoveries and the difficulty to visually identify the predominantly occurring small gold grains. These limitations hinder its capability to deceipher subtle or complex signal. To improve detection limits through recovery of small gold grains, a new approach has recently been introduced in the industry (commercially referred as “ARTGold” procedure) using an optimized miniature sluice box coupled with an automated scanning electron microscopy routine. The capabilities of this improved method are highlighted by comparing till surveys conducted around the Borden gold deposit (Ontario, Canada) using the conventional and improved methods at both local and regional scales. Relative to the conventional approach, the improved method recovered almost one order of magnitude more gold grains from samples (regional and down-ice mineralization), dominantly in small size fractions. Increasing the counts in low-abundance regional samples enables better discrimination between background signals and significant dispersions. The method offers an alternative to improve characterization of gold dispersal in glaciated terrain and the related gold deposit footprints.