Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), known to cause 2019-coronavirus disease (COVID-19) pandemic is a zoonotic coronavirus and crosses species to infect human populations, where an efficient transmission of virus occurs human-to-human. Nationwide lockdown is being adopted to stop public transport, keep people at their homes and out of their work, and maintain social distancing. In turn, large geographic areas in the world (including China, Italy, Spain, and USA) has been almost halted. This temporary halt is significantly slashing down the air pollution (air pollutants and warming gases) in most cities across the world. This paper: (i) introduces both COVID-19 and air pollution; (ii) overviews the relation of air pollution with respiratory/lung diseases; (iii) compiles and highlights major data appeared in media and journals reporting lowering of air pollution in major cities those have been highly impacted by the COVID-19; and also (iv) lists the way forward in the present context. Because COVID-19 is an ongoing pandemic and currently far from over, strong conclusions could not be drawn with very limited data at present. The temporary slashed down global air pollution as a result of COVID-19 restrictions are expected to stimulate the researchers, policy makers and governments for the judicious use of resources; thereby minimise the global emissions, and maintain their economies once the pandemic eases. On the other, lifting of the nationwide lockdown and eventual normalisation of the temporarily halted sectors may also reverse the currently COVID-19 pandemic-led significantly slashed down global air pollution that could make the future respiratory health crisis grimmer.

As people grow a custom to effortless outdoor navigation there is a rising demand for similar possibility indoors as well. Unfortunately, indoor localization, being one of the necessary requirements for navigation, continues to be problem without a clear solution. In this article we are proposing a method for an indoor positioning system using a single image. This is made possible using small preprocessed database of images with known control points as the only preprocessing needed. Using feature detection with SIFT algorithm we can look through the database and find image which is the most similar to the image taken by user. Pair of images is then used to find coordinates of database image using PnP problem. Furthermore, projection and essential matrices are determined allowing for the user image localization ~ determining the position of the user in indoor environment. Benefits of this approach lies in the single image being the only input from user and no requirements for new onsite infrastructure and thus enables a simpler realization for the building management.

The current study sets out to develop an empirical line method (ELM) radiometric calibration framework for reducing atmospheric contributions in UAS imagery and for producing scaled remote sensing reflectance imagery. Using a MicaSense RedEdge camera flown on a custom-built octocopter, the research reported herein finds that atmospheric contributions have an important impact on UAS imagery. Data collected over the Lower Pearl River Estuary in Mississippi during five week-long missions covering a wide range of environmental conditions was used to develop and test a simplified ELM radiometric calibration framework designed specifically for the reduction of atmospheric contributions to UAS imagery in studies with limited site accessibility or data acquisition time constraints. The framework was effective in reducing atmospheric and other external contributions to UAS imagery. Unique to the proposed radiometric calibration framework is the radiance to reflectance conversion conducted externally from the calibration equations which allows for the normalization of illumination independent from the time of UAS image acquisition and from the time of calibration equations development. This paper presents the simplified ELM radiometric calibration framework that can be used as a time-effective calibration technique to reduce errors in the UAS imagery.

In this paper we evaluate the effects of hard and soft constraints on the Iterative Data Snooping (IDS), an iterative outlier elimination procedure. Here, the measurements of a levelling geodetic network were classified according to the local redundancy and maximum absolute correlation between the outlier test statistics, referred to as clusters. We highlight that the larger the relaxation of the constraints, the higher the sensitivity indicators MDB (Minimal Detectable Bias) and MIB (Minimal Identifiable Bias) for both the clustering of measurements and the clustering of constraints. There are circumstances that increase the family-wise error rate (FWE) of the test statistics, increase the performance of the IDS. Under a scenario of soft constraints, one should set out at least three soft constraints in order to identify an outlier in the constraints. In general, hard constraints should be used in the stage of pre-processing data for the purpose of identifying and removing possible outlying measurements. In that process, one should opt to set out the redundant hard constraints. After identifying and removing possible outliers, the soft constraints should be employed to propagate their uncertainties to the model parameters during the process of least-squares estimation.

In recent years, marine, freshwater and terrestrial pollution with microplastics has been discussed extensively, whereas atmospheric microplastic transport has been largely overlooked. Here, we present the first global simulation of atmospheric transport of microplastic particles produced by road traffic (TWPs – tire wear particles and BWPs – brake wear particles), a major source that can be quantified relatively well. We find a high transport efficiency of these particles to remote regions, such as the Arctic Ocean (14%). About 34% of the emitted coarse TWPs and 30% of the emitted coarse BWPs (100 kt yr^-1 and 40 kt yr^-1 respectively) were deposited in the World Ocean. These amounts are of similar magnitude as the total estimated terrestrial and riverine transport of TWPs and fibres to the ocean (64 kt yr^-1). Atmospheric transport of microplastics is thus an underestimated threat to global terrestrial and marine ecosystems and affects air quality on a global scale, especially considering that other large but highly uncertain emissions of microplastics to the atmosphere exist. High latitudes and the Arctic are highlighted as an important receptor of mid-latitude emissions of road microplastics, which may imply a future climatic risk, considering their affinity to absorb solar radiation and accelerate melting.

Work has begun in earnest to formulate a post-2020 Global Biodiversity Framework which will outline the vision and targets for the next decade of biodiversity conservation and beyond. However, the performance of the 2011-2020 Strategic Plan for Biodiversity suggests that even a meaningful target can fail to deliver if not accompanied by fit-for-purpose indicators. Here we provide a review of how ‘protected area’ effectiveness was addressed in the 2011-2020 plan and based on this, provide recommendations for fit-for-purpose indicators that will measure how such efforts contribute to the conservation of biodiversity. Indicators need to be built on quantitative data from site-level biodiversity monitoring of species and ecosystems combined with measurements of the state of nature in near-time, informed by remote-sensed products and other technologies. Additionally, indicators need to capture whether the essential elements of good management are in place including the identification of ecological values, threats, and objectives, equitable governance, and sufficient management resources and capacity. These fit-for-purpose indicators will require multilateral collaboration to galvanize support for, and resources to develop, the necessary infrastructure to collate and store information from countries.

Background: Covid-19 was first reported in Wuhan, China in Dec 2019. Since then, it has been transmitted rapidly in China and the rest of the world. While Covid-19 transmission rate has been declining in China, it is increasing exponentially in Europe and America. Although there are numerous studies examining Covid-19 infection, including an archived paper looking into the meteorological effect, the role of outdoor air pollution has yet to be explored rigorously. It has been shown that air pollution will weaken the immune system, and increase the rate of respiratory virus infection. We postulate that outdoor air pollution concentrations will have a negative effect on Covid-19 infections in China, whilst lockdowns, characterized by strong social distancing and home isolation measures, will help to moderate such negative effect. Methods: We will collect the number of daily confirmed Covid-19 cases in 31 provincial capital cities in China during the period of 1 Dec 2019 to 20 Mar 2020 (from a popular Chinese online platform which aggregates all cases reported by the Chinese national/provincial health authorities). We will also collect daily air pollution and meteorology data at the city-level (from the Chinese National Environmental Monitoring Center and the US National Climatic Data Center), daily inter-city migration flows and intra-city movements (from Baidu). City-level demographics including age distribution and gender, education, and median household income can be obtained from the statistical yearbooks. City-level co-morbidity indicators including rates of chronic disease and co-infection can be obtained from related research articles. A regression model is developed to model the relationship between the infection rate of Covid-19 (number of confirmed cases/population at the city level) and outdoor air pollution at the city level, after taking into account confounding factors such as meteorology, inter- and intra-city movements, demographics, and co-morbidity and co-infection rates. In particular, we shall study how air pollution affects infection rates across different cities, including Wuhan. Our model will also study air pollution would affect infection rates in Wuhan before and after the lockdown. Expected findings: We expect there be a correlation between Covid-19 infection rate and outdoor air pollution. We also expect that reduced intra-city movement after the lockdowns in Wuhan and the rest of China will play an important role in reducing the infection rate. Interpretation: Infection rate is growing exponentially in major cities worldwide. We expect Covid-19 infection rate is related to the air pollution concentration, and is strongly dependent on inter- and intra-city movements. To reduce the infection rate, the international community may deploy effective air pollution reduction plans and social distancing policies.

Structural diversity is a key feature of forest ecosystems that influences ecosystem functions from local to macroscales. The ability to measure structural diversity in forests with varying ecological composition and management history can improve the understanding of linkages between forest structure and ecosystem functioning. Terrestrial LiDAR has often been used to provide a detailed characterization of structural diversity at local scales, but it is largely unknown whether these same structural features are detectable using aerial LiDAR data that are available across larger spatial scales. We used univariate and multivariate analyses to quantify cross-compatibility of structural diversity metrics from terrestrial versus aerial LiDAR in seven National Ecological Observatory Network sites across the eastern USA. We found strong univariate agreement between terrestrial and aerial LiDAR metrics of canopy height, openness, internal heterogeneity, and leaf area, but found marginal agreement between metrics that describe heterogeneity of the outer most layer of the canopy. Terrestrial and aerial LiDAR both demonstrated the ability to distinguish forest sites from structural diversity metrics in multivariate space, but terrestrial LiDAR was able to resolve finer-scale detail within sites. Our findings indicate that aerial LiDAR can be of use in quantifying broad-scale variation in structural diversity across macroscales.

Corporate sustainability reporting, also known as Triple-bottom-line reporting, involves reporting nonfinancial and financial information to a broader set of stakeholders than just shareholders and seek to fortify an organization’s ability to manage key risks. The current case is that, the quality, rigor, and utility of sustainability reporting remains contentious with concerns about the suitability of the criteria or standards used to prepare the reports. Despite the rapid increase in the number of companies around the world adopting Global Reporting Initiative standards, little is known about the extent of practice of corporate sustainability reporting in public universities in Kenya. The study selected five universities that had their 2017-18 audited financial reports available online for the readers, which served as the main source of secondary data. The guidelines on corporate sustainability reporting was derived from literature review, which provided key indicators upon which the data from each university was evaluated. It was observed that almost all the institutions recognize the critical role of both internal and external independent audit of financial statements. In conclusion, financial reporting sustainability is guided by strict compliance to the factors of sustainability.

Wildland-Urban Interfaces (W-UI) are at high risk of wildfires. Defensible spaces and home ignition zones are the two main aspects to protect lives and livelihoods of W-UI in the United States, Canada and Australia. The different part of the world has different rules and regulations for W-UI land management. We have discussed the defensible spaces in fire-prone areas, current ignition zone distances from structures, building materials, architectural design, the fire resistance trees, ground cover, landscaping and some other precautions to save lives and assets in the prominent fire-prone zones for three different countries (United States, Canada and Australia) of the world.

Precipitation and temperature are significant inputs for hydrological models. Currently, many satellite and reanalysis precipitation and air temperature datasets exist at different spatio-temporal resolutions at a global and quasi-global scale. This study evaluated the performances of three open-access precipitation datasets (gauge-adjusted research-grade Global Satellite Mapping of Precipitation (GSMaP_Gauge), Climate Hazards Group Infrared Precipitation with Station data (CHIRPS), Climate Forecast System Reanalysis(CFSR)) and CFSR air temperature dataset in driving the Soil and Water Assessment Tool (SWAT) model required for the monthly simulation of streamflow in the upper Shiyang River Basin of northwest China. After a thorough comparison of six model scenarios with different combinations of precipitation and air temperature inputs, the following conclusions were drawn: (1) Although the precipitation products had similar spatial patterns, however, CFSR differs significantly by showing an overestimation; (2) CFSR air temperature yielded almost identical performance in the streamflow simulation than the measured air temperature from gauge stations; (3) among the three open-access precipitation datasets, CHIRPS produced the best performance. These results suggested that the CHIRPS precipitation and CFSR air temperature datasets which are available at high spatial resolution (0.05), could be a promising alternative open-access data source for streamflow simulation in the case of limited access to desirable gauge data in the data-scarce area.

Sharing the same raw material, recycling and composting are in direct conflict with incineration of municipal solid waste in combined heath and power plants. Indeed, waste-to-energy plants in regions with high recycling rates import urban waste from other countries to use otherwise unused capacity, and raise revenues. Using the case of Italy’s second largest and economically most developed region, I discuss the economic viability of municipal solid waste incineration to produce electricity and heath in the context of the increasing role of electricity production from renewable energy sources as well as of the emerging mass-scale uptake of bioplastics. Four lessons and three guidelines aimed to local authorities and policy makers emerge from the present study.

North and West Africa are the most vulnerable regions to drought, due to the high variation in monthly precipitation. An accurate and efficient monitoring of drought is essential. In this study, we use TRMM data with remote sensing tools for effective monitoring of drought. The Drought Severity Index (DSI), Temperature Vegetation Drought Index (TVDI), Normalized Difference Vegetation Index (NDVI), and Normalized Vegetation Supply Water Index (NVSWI) are more useful for monitoring the drought over North and West Africa. To classify the areas affected by drought, we used the TRMM spatial maps to verify the TVDI, DSI and NVSWI indexes derived from MODIS. The DSI, TVDI, NVSWI and Monthly Precipitation Anomaly (NPA) indexes with the employ of MODIS-derived ET/PET and NDVI were chosen for monitoring the drought in the study area. The seasonal spatial correlation between the DSI, NPA, NVWSI, NDVI, TVDI and TCI indicates that NVSWI, NDVI and DSI present an excellent monitor of drought indexes. The change trend of drought from 2002 to 2018 was also characterized. The frequency of drought showed a decrease during this period.

Available water resources in the Middle East, as one of the most water-scarce regions of the world, have undergone extra pressure due to climatic change, population growth, and economic development during the past decades. The objective of this study is to detect the trends and quantify the changes in aridity with respect to precipitation and potential evapotranspiration in 20 countries of the Middle East and the adjacent area. A Pixel-wised trend analysis was conducted on precipitation, potential evapotranspiration, and aridity index for 71 years from 1948 to 2018. A nonparametric Mann-Kendall test was used over 14106 points in the study area to detect the trends at monthly and annual time scales. Results showed statistically significant (|Z| >1.96) upward trends in aridity (a downward trend in aridity index) up to 96 percent from December through September in most parts of the region. Aridity in October and November had a downward tendency in most parts of the study area. At the annual time scale, 62.5 percent of the statistically significant trends in aridity were found to be upward (up to 96 percent increase in aridity) due to the combined effects of the decrease in precipitation and the increase in potential evapotranspiration and 37.5 percent of the detected trends were downward (up to 61 percent decrease in aridity). The highest and the lowest trends in aridity were found in the north of Sudan (96 percent increase in aridity) and Eastern Arabia (61 percent decrease in aridity), respectively.

We have conducted a comprehensive year-long sampling campaign for particulate matter (PM) pollutants at a site near a major highway, following standard protocols. Total mass, and elemental and chemical composition of the fine fractions (PM2.5) of traffic-related pollutants are determined utilizing several complementary techniques. These complementary techniques included gravimetric analysis, X-ray fluorescence (XRF), scanning electron microscopy and energy dispersive spectroscopy, X-ray diffraction (XRD) and black carbon multi-wavelength absorption. The results show that the PM2.5 mass concentrations are within or slightly above international standards and include natural dust and anthropogenic pollutants such as black carbon, sulfates and other traffic-related elements. Anthropogenic sources include the secondary pollutants Mascagnite and Koktaite, traffic emissions such as exhaust emissions and tires, brakes and road erosions. It was found that the sulfates are the highest contributors to PM2.5 (~40%), as evident from XRD results and the S content in the XRF analysis.

There are abundant coal, oil and natural gas resources in "Golden Triangle" energy region. Energy resources development, coal chemical industry, coal power and other industries play an important role in promoting regional economic growth, but facing the insufficient water supply. Under the condition of marginal equilibrium of water resources, the paper firstly analyzes the water utilization, and calculates the potential water-saving amount. Then, the paper constructs a cooperative game model and allocates the newly increased GDP by using the weight distribution method. Finally, the paper puts forward some policy suggestions to reduce the efficiency gap of water utilization among regions.

Land degradation is a critical issue globally putting our future generations at risk. The decrease in farm productivity over the years is evidence of land degradation severity in Nepal. Among the many strategies in place, agroforestry, which is an integrated tree-based farming, is widely recommended to address this productivity issue. This paper thoroughly examines what influences the choice of agroforestry adoption by farmers and what discourages the adoption. For this, a total of 288 households were surveyed using a structured questionnaire. Two agroforestry practices were compared with conventional agriculture with the help of the Multinomial Logistic Regression (MNL) model. The likelihood of adoption was found to be influenced by gender; the male-headed households were more likely to adopt the tree-based farming practice. Having a source of off-farm income was positively associated with the adoption decision of farmers. Area of farmland was found being the major constraint to agroforestry adoption for smallholder farmers. Some other variables that affected positively included livestock herd size, provision of extension service, home-to- forest distance, farmers’ group membership and awareness of farmers about environmental benefits of agroforestry. Irrigation was another adoption constraint that the study area farmers were faced with. The households with means of transport and with larger family (household) size were found to be reluctant towards agroforestry adoption. A collective farming practice could be a strategy to engage the smallholder farmers in agroforestry.

Research subjects of this study are four representative locations in the industrial complex, in the city of Banja Luka, Republic of Srpska, Bosnia and Herzegovina. 16 polycyclic aromatic hydrocarbons (PAHs) (∑16PAHs), humus and pH were determined. The main objective of the paper is to determine the concentration levels, to assess the probable sources of PAHs contamination in soil and groundwater and to determine the ecological risk. The ∑16PAHs in soil (at depths of 30 cm, 100 cm, 200 cm, 300 cm and 400 cm) ranged from 0.99 to 2.24 mg/kg, from 0.34 to 0.46, from 0.24 to 0.32, from 0.13 to 0.27 and from 0.13 to 0.47, with mean values of 1.70 mg/kg, 0.40 mg/kg, 0.28 mg/kg, 0.20 mg/kg and 0.26 mg/kg, respectively. The ∑16PAHs in groundwater ranged from 0.23 to 4.50 mg/m³, with a mean value of 1.42 mg/m³. Surface soil and groundwater are heavily contaminated. All values of ∑PAHs in soil layers were lower in the depths of the soil. Factor analysis indicates three sources of contamination, i.e. principal component (PC) PC1 (pyrogenic), PC2 (petrogenic) and PC3 (biomass), with 52.39%, 26.14% and 8.46% of the total variance, respectively. ∑PAH and PAHs indicate high ecological risk for most PAHs, which decreases with soil depth.

Sufficient evidence is currently available to demonstrate the reality of the warming of our planet's climate system. Global warming has different effects on climate at the regional and local levels. The detection of changes in extreme events using instrumental data provides further evidence of such warming and allows for the characterization of its local manifestations. The present study analyzes changes in temperature and precipitation extremes in the Mexican state of Zacatecas using climate change indices developed by the Expert Team on Climate Change Detection, Monitoring and Indices (ETCCDI). We studied a 40-year period (1976-2015) using annual and seasonal time scales. Maximum and minimum temperature data were used, as well as precipitation statistics from the Mexican climatology database (CLICOM) provided by the Mexican meteorological service. Weather stations with at least 80% of data availability for the selected study period were selected; these databases were subjected to quality control, homogenization, and data filling using Climatol, which runs in the R programming language. These homogenized series were used to obtain daily grides of the three variables at a resolution of 1.3 km. Results reveal important changes in temperature-related indices, such as the increase in maximum temperature and the decrease in minimum temperature. Irregular variability was observed in the case of precipitation, which could be associated with low-frequency oscillations such as the Pacific Decadal Oscillation and the El Niño–Southern Oscillation. The possible impact of these changes in temperature and the increased irregularity of precipitation could have a negative impact on the agricultural sector, especially given that the state of Zacatecas is the largest national bean producer. The most important problems in the short term will be related to the difficulty of adapting to these rapid changes and the new climate scenario, which will pose new challenges in the future.

Excessive nutrient discharge to tropical island coastlines drives eutrophication and algal blooms with significant implications for reef ecosystem condition and provision of ecosystem services. Management actions to address nutrient pollution in coastal ecosystems include setting water quality standards for discharging surface waters. However, these standards do not account for the effects of groundwater discharge, variability in flow, or dilution, all of which may influence assessment of true nutrient impacts on nearshore reef habitats. We developed a method to estimate dissolved inorganic nitrogen (DIN) loads to coastal zones by integrating commonly available datasets within a geospatial modeling framework for Tutuila, American Samoa. The DIN loading model integrated an open-source water budget model, water sampling results, and publically available streamflow data to predict watershed-scale DIN loading to the island’s entire coastline. When compared to surface water pathways, submarine groundwater discharge (SGD) was determined to be the most important coastal delivery mechanism of terrigenous DIN, which supports findings from other tropical islands. Onsite wastewater disposal systems were also found to be the primary anthropogenic sources DIN to coastal waters. Our island-wide DIN loading model provides a simple and robust metric to define spatially-explicit sources and delivery mechanisms of nutrient pollution to nearshore reef habitats. Understanding the sources and primary transport modes of inorganic nitrogen to nearshore reef ecosystems can have significant implications for place-based management interventions aimed at increasing the adaptive capacity of unique island ecosystems to environmental variation and disturbances.

Forests across the world stand at the crossroad with climate and land use changes shaping their future. Despite the demonstration of political will and global efforts, forest loss, fragmentation and land degradation continue unabated. No clear evidence exists that these initiatives are working. Why are policies designed to halt deforestation and increase restoration of forest landscapes failing? A key reason for this apparent ineffectiveness lies in the failure to recognize the agency of the stakeholders involved and the adaptive capacities of the systems we seek to steer. Landscapes do not happen. We make them. They are the result of the sum of individual actions and decisions made by all stakeholders, and the interactions between these and biophysical processes. Likewise, forest transitions are not ecological, but social and behavioral. They are a product of the way humans manage ecosystems. Decision-makers need to integrate better representations of people’s agency in their mental models. We suggest possible solution pathways to overcome this key current barrier. These involve eliciting mental models behind policy decision, changing perspectives to better understand divergent points of view and refining strategies through explicit theories of change. Games designed to represent the constraints and opportunities that exist in the landscapes can help decision makers in these task.

Recent advancements in cloud-computing and social-networking are influencing how we communicate professionally, work collaboratively, and approach data-science tasks. Here we show how the groundwater modeling field is well positioned to benefit from these advancements. We present a case study detailing a vertically-integrated, collaborative modeling framework jointly developed by participants at the American Samoa Power Authority and at the University of Hawaii Water Resources Research Center. The framework components include direct collection and analysis of climatic and streamflow data, development of a water budget model, and initiation of a dynamic groundwater modeling process. The framework is entirely open-source and applies newly available data-science infrastructure using Python-based tools compiled with Jupyter Notebooks and cloud computing services such as GitHub. These resources allow for seamless integration of multiple computational components into a dynamic cloud-based workflow that is immediately accessible to stakeholders, resource managers, or anyone with an internet connection

Accurate inundation maps for flooded wetlands and rivers are a critical resource for their management and conservation. In this paper we automate a method (thresholding of the short-wave infrared band) for classifying inundation, using Landsat imagery and Google Earth Engine. We demonstrate the method in the Okavango Delta, northern Botswana, a complex case study due to the spectral overlap between inundated areas covered with aquatic vegetation and dryland vegetation classes on satellite imagery. Inundation classifications in the Okavango Delta have predominately been implemented on broad spatial resolution images. We present the longest time series to date (1990-2019) of inundation maps at high spatial resolution (30m) for the Okavango Delta. We validated the maps using image-based and in situ data accuracy assessments, with accuracy ranging from 91.5 - 98.1%. Use of Landsat imagery resulted in consistently lower estimates of inundation extent than previous studies, likely due to the increased number of mixed pixels that occur when using broad spatial resolution imagery, which can lead to overestimations of the size of inundated areas. We provide the inundation maps and Google Earth Engine code for public use.

Mercury is a toxic metal, thus, it is an element which has more and more restrictions in its uses, but despite the above, the removal of this metal, from whatever the form in which it is encountered (zero valent metal, inorganic or organic compounds), and from different sources, is of a widespread interest. In the case of Hg(II), or Hg²⁺, the investigations about the treatment of Hg(II)-bearing liquid effluents (real or in most cases synthetic solutions) appear not to end, and from the various separation technologies, adsorption is the most popular among researchers. In this topic, and in the 2019 year, more than 100 publications had been devote to this field: Hg(II)-removal-adsorption. This work examined all of them.

The Philippines is both a biodiversity hotspot and a megadiverse country. The country also has experienced one of the highest rates of deforestation in Southeast Asia and is among the first countries to introduce a massive reforestation program to address the country’s rapid biodiversity and forest loss. Drawing upon an empirical study from the Leyte island and other relevant case studies from the Philippines, in this chapter, we demonstrate that recovering secondary forests following shifting cultivation, locally known as kaingin have the high potentials for biodiversity and carbon co-benefits. Based on our empirical study, we also found that secondary forest regrowing after kaingin use can potentially be used as a cost-effective reforestation measure with multiple benefits to people and the environment in upland areas of the Philippines. We also discuss measures that are essential for such programs to be successful.

The formation of sinkholes in Winkler County, Texas is concerning due to the amount of oil and gas infrastructure and the potential for catastrophic losses. Evidences of new and potential sinkholes have been documented, and determining the cause of these sinkholes is paramount to mitigate the devastating consequences thereof. Studies shown that the Wink sinkholes result from both natural and anthropogenic influences. Data depicting land-cover changes, alterations in the hydrologic systems, climate changes, and oil and gas activity were analyzed in an effort to better understand the link between these processes and sinkhole formation. Results indicate that the combination of these processes lead to the current state; Land cover changes were highest in shrub versus grasses, undeveloped to developed and croplands. Rises in temperature and a decrease in precipitation indicate a shift towards a more arid climate. Changes to the hydraulic system are a direct result of these land cover changes while the groundwater quality depict an environment prone to dissolution. Historical oil and gas activities have created pathways of meteoric water infiltration to the underlying limestone and evaporite formation. The combination of these processes create an environment that accelerates sinkhole formations. Understanding these processes allows for the development and implementation of better land practices, better groundwater protections, and the need for monitoring and maintaining aging oil and gas infrastructure.

River regulations ultimately degrade fluvial forms and morphodynamics and simplify riparian and aquatic habitats. For several decades, river restoration actions have been performed to recover geomorphic processes and diversify these habitats to enhance both river biodiversity and ecosystem services. The objective of this study is to provide quantitative feedback on the experimental restoration of a large regulated and by-passed river (the Upper Rhine downstream of the Kembs Dam, France/Germany). This restoration consisted of the construction of two transverse groynes and the removal of bank protection. A monitoring framework composed of topo-bathymetric surveys as well as flow velocity and grain size measurements was established to assess the channel morphodynamic responses and evaluate their effects on habitat suitability for five native fish species using habitat models. A riverscape approach was used to evaluate the landscape changes in terms of both the configuration and the composition, which cannot be considered with classic approaches (e.g., WUA). Our results show that the two transverse groynes and, to a lesser extent, bank erosion, which was locally enhanced by the two groynes, increased habitat diversity due to the creation of new macroforms (e.g., pools and mid-bars) and fining of the bed grain size. Using a riverscape approach, our findings highlight that the restoration improved lentic fish habitats (eel and juvenile nase species) due to slowing of the local current and the deposition of fine sediments downstream of both groynes. As a consequence, the restoration improved the habitat suitability of the studied reach for more fish species compared with the pre-restoration conditions. This study also demonstrates that the salmon habitats downstream of the restored reach were improved due to fining of the bed grain size. This finding highlights that for restorations aimed at fish habitats, the grain size conditions must be taken into consideration along with the flow conditions. Furthermore, the implementation of groynes, while not a panacea, can be a strategy for improving fish habitats on highly regulated rivers, but only when more functional and natural options are impossible due to major constraints.

Unmanned Aerial Vehicle (UAV) based remote sensing (RS) studies in glaciology are mainly focusing on obtaining accurate high-resolution data from UAV images. Studies for identifying and minimising the challenges faced during the UAV-based RS data acquisition survey on inaccessible and harsh terrains of mountain glaciers is limited. This study aims to examine the practical challenges faced during UAV surveys of glaciers and derive strategies to minimize them. To the authors' knowledge, this is the first study that addresses such problems over the Himalayan region. Here, the UAV surveys were conducted using a fixed-wing commercial-grade off-the-shelf UAV (eBee plus, SenseFly) on three glacier sites (East Rathong, Hamtah and Panchinala-A) located in different zones and climate regimes lying within the Indian part of Himalayas. From UAV collected photos, the study was able to generate ultra-high-resolution ortho-mosaicked images and Digital Elevation Models (DEMs) at 0.1m GSD. UAV-derived DEMs was able to achieve vertical (horizontal) accuracy of 0.45 and 0.21 m (0.15 and 0.1 m) with 3 and 6 ground control points (GCPs) for an area of 0.75 km2 and 1.38 km2. Accuracy assessment of UAV DEMs generated with and without GCPs indicate that GCPs are must to obtain decimetre level accurate DEM especially on glaciers with steep-valleyed terrains. The utility of the obtained ultra-high-resolution ortho-mosaicked images was demonstrated by generating glacier surface feature maps. Based on the challenges observed during UAV surveys, the study identifies and recommends best-suited locations on a glacier and its adjacent regions for conducting UAV surveys efficiently in the glaciated terrain of Himalayas and possibly beyond. Recommendations reported in this article shall minimise the challenges faced and involved risks for data acquisition and thus enable UAVs to cover more glaciated area successfully.

Soil hydraulic properties are often derived indirectly, i.e. computed from easily available soil properties with pedotransfer functions (PTFs), when those are needed for catchment, regional or continental scale applications. When predicted soil hydraulic parameters are used for the modelling of the state and flux of water in soils, uncertainty of the computed values can provide more detailed information when drawing conclusions. The aim of this study was to update the previously published European PTFs (Tóth et al., 2015, euptf v1.4.0) by providing prediction uncertainty calculation built into the transfer functions. The new set of algorithms was derived for point predictions of soil water content at saturation (0 cm matric potential head), field capacity (both -100 and -330 cm matric potential head), wilting point (-15.000 cm matric potential head), plant available water, and saturated hydraulic conductivity, as well as the Mualem-van Genuchten model parameters of the moisture retention and hydraulic conductivity curve. The minimum set of input properties for the prediction is soil depth and sand, silt and clay content. The effect of including additional information like soil organic carbon content, bulk density, calcium carbonate content, pH and cation exchange capacity were extensively analysed. The PTFs were derived adopting the random forest method. The advantage of the new PTFs is that they i) provide information about prediction uncertainty, ii) are significantly more accurate than the euptfv1, iii) can be applied for more predictor variable combinations than the euptfv1, 32 instead of 5, and iv) are now also derived for the prediction of water content at -100 cm matric potential head and plant available water content.

In the Lower Yarmouk Gorge the chemical composition of regional, fresh to brackish, mostly thermal groundwater reveal a zonation in respect to salinization and geochemical evolution, which is seemingly controlled by the Lower Yarmouk fault (LYF) but does not strictly follow the morphological Yarmouk Gorge. South of LYF the artesian Mukeihbeh well field produces in its central segment groundwaters of almost pure basaltic-rock type with low contribution (<0.3 vol-%) of Tertiary brine, hosted in deep Cretaceous and Jurassic formations. Further distal, the contribution of limestone water increases originating from the Ajloun Mts. North of the LYF, the Mezar wells, the springs of Hammat Gader and Ain Himma produce dominantly limestone water, which contains 0.14-3 vol-% of the Tertiary brine and possess hence variable salinity. The total dissolved equivalents of solutes gained by water/rock interaction (WRI) and mixing with brine, TDE(WRI+brine), amounts to 10-70 % in the region comprising the Mukheibeh field, Ain Himma and Mezar 3 well, to 55-70 % in the springs of Hammat Gader, and to 80-90 % in wells Mezar 1 and 2. The type of salinization indicates that the Lower Yarmouk fault seemingly acts as the divide between the Ajloun and the Golan Heigths dominated groundwater.

Landslides are a frequent and recurrent problem in hilly regions of India and predicting them is always a challenging task. In this paper, an attempt was made to deal with this problem using advanced physical and numerical modeling methods. Detailed understanding of the initial slope failures is very interesting, and challenging at the same time, in the design and development of wireless sensor network based on early warning of landslide monitoring. A small scale physical model was developed to assess the instability through a sensor network with variable rain fall intensity. This was achieved by increasing the simulated rain water flow intensity in different time spans (dry condition, at t=0 to t= 30 min, 0.5 mm/min at t=30 to t= 60 min, 0.75 mm/min at t=60 to t=91 min and 1 mm/min at t=91 to t= 120 min). The water level and movement in the slope was recorded by rainfall sensor, vibration sensor, soil moisture sensor and a digital camera. The following changes were observed during the slope failure: a) movement of small particles at top of the slope; b) initial failure of medium size soil particle; c) scouring of soil mass; d) whole slope collapse. The obtained results clearly indicated the superiority and effectiveness of the proposed system in providing a factor of safety for the progressive slope.

In the last few decades, a lot has been written on the use of sustainable agriculture to improve ecosystem services for resilience to climate change. However, no tangible and systematic evidence exists on how this would participate in alleviating impacts on vulnerable rural communities. This paper provides a narrative systematic review (SR) integrated with a bibliometric analysis and a concept network analysis to understand how, in a changing climate, sustainable agriculture builds the resilience of agro-systems. The search was set from the date of the first relevant article until the end of 2018. Results generated have demonstrated that: a. Only single practices and methods have been studied to assess impacts on single ecosystem services. b. Soil quality and health are considered a key indicator of sustainable agriculture. c. Albeit the assessed practices and methods have shown to improve the biodiversity of agro-systems, which makes them more resilient to extreme climate events, we are still far from reaching interdisciplinary and multi-dimensional agriculture which integrates all management aspects and generates a full range of ecosystem services. In conclusion, the study addressed the following recommendations for the scientific community and for decision-makers to orient future research strategies and efforts: a. Integration of all agro-systems services into sustainable management using an ecosystem-based approach on a life-cycle basis using Life Cycle Assessment (LCA) method; b. Improving the scientific understanding of traditional knowledge for higher synergies and for further integration; c. Unification of assessment methods and indicators for the quantification of impacts; d. Creation of a platform to share, monitor, screen, and approve assessments and evaluations of sustainable agriculture by region.

The ionospheric scintillation is a rapid phase and amplitude fluctuation of satellite signals due to the small-scale irregularity of electron density in the ionosphere. The characterization of the scintillation index in a proper way is a crucial aspect of the Global Positioning System (GPS) satellite signals for the purpose of space-based navigation, satellite communication, space weather as well as earth observation applications. In the current study, we analyzed the ionospheric scintillation index during the year of 2018 to 2019 over the Nepal region which locates itself almost being sandwiched between India and China and in the vicinity of low latitudes. The characteristic variations of scintillation occurrence are studied during the several geomagnetic storm and quiet days. The results show that the S4 indexes are varying from the 0.05 to 0.45 during the whole year. The S4 indexes behave higher variations during the whole day in the starting of the year and start to decrease at end of the day as well as at the ending months of the year 2019. The S4 values become completely less during the sunset time, while they have higher values during the sunrise. Especially, the S4 index during the storm days are larger than during the quiet days. It is worthy to note that the variations of S4 index studied in this current study do not follow the sunset property during the year of 2019. Consequently, the causes should be discovered and discussed additionally in the next research paper.

We analyzed the peak spring tidal current speeds, annual mean tidal power densities (TPD) and annual energy production (AEP) obtained from experiment 06.1, referred as the "HYCOM model" throughout, of the three dimensional (3D), global model HYCOM in an area covering the Baja California Pacific and the Gulf of California. The HYCOM model is forced with astronomical tides and surface winds alone, and therefore is particularly suitable to assess the tidal current and wind-driven current contribution to in-stream energy resources. We find two areas within the Gulf of California, one in the Great Island Region and one in the Upper Gulf of California, where peak spring tidal flows reach speeds of 1.1 meters per second. Second to fifth-generation tidal stream devices would be suitable for deployment in these two areas, which are very similar in terms of tidal in-stream energy resources. However, they are also very different in terms of sediment type and range in water depth, posing different challenges for in-stream technologies. The highest mean TPD value when excluding TPDs equal or less than 50 W/m² (corresponding to the minimum velocity threshold for energy production) is of 172.8 W/m², and is found near the town of San Felipe, at (lat lon) = (31.006 -114.64); here energy would be produced during 39.00% of the time. Finally, wind-driven currents contribute very little to the mean TPD and the total AEP. Therefore, the device, the grid, and any energy storage plans need to take into account the periodic tidal current fluctuations, for optimal exploitation of the resources.

Soil temperature reflects the impact of local factors，such as vegetation，soil and the atmosphere of a region. Therefore, it is important to understand the regional variation of soil temperature. However, lack of observations with adequate spatial and/or temporal coverage, it would be difficult to use the observation data to study the regional variation. Based on the observation data from Nanchang and Ganzhou stations and ERA-Interim/Land reanalysis data, this study analyzed the temporal-spatial distribution characteristics of soil temperature over Poyang Lake Basin. The results showed close correlations between observation data and reanalysis data at different depths. Reanalysis data could mainly reproduce the temporal-spatial distributions of soil temperature over the Poyang Lake Basin, but generally underestimate their magnitudes. Temporally, there is an obvious warming trend in the basin. Seasonally, the temperature raised fastest in spring and slowest in summer, except for the ST4, which rising fastest in spring and slowest in winter. In terms of depths, the temperature of ST1 rises fastest. For the other layers, the warming trend is almost similar. An abrupt change of annual soil temperature at all depths occurred in 1997, and annual soil temperatures at all depths were abnormally low in 1984. Spatially, annual soil temperature decreased with latitude, except for the summer ST1. Because of the high temperature and precipitation in summer, the ST1 are higher around the lake and the river. The climatic trend of soil temperature presents the general increase trend from south to north, opposite to the distribution of soil temperature. The findings provide a basis for understanding and assessing the variation of the soil temperature over the Poyang Lake Basin.

Interest in drone solutions in forestry applications is growing. Using drones, datasets can be captured flexibly and at high spatial and temporal resolutions when needed. In forestry applications, fundamental tasks include the detection of individual trees, tree species classification, bio-mass estimation, etc. Deep Neural Networks (DNN) have shown superior results when comparing with conventional machine learning methods such as Multi-Layer Perceptron (MLP) in cases of huge input data. The objective of this research was to investigate 3D convolutional neural networks (3D-CNN) to classify three major tree species in a boreal forest: pine, spruce, and birch. The proposed 3D-CNN models were employed to classify tree species in a test site in Finland. The classifiers were trained with a dataset of 3039 manually labelled trees. Then the accuracies were assessed by employing independent datasets of 803 records. To find the most efficient set of feature combination, we compare the performances of 3D-CNN models trained with hyperspectral (HS) channels, RGB channels, and canopy height model (CHM), separately and combined. It is demonstrated that the proposed 3D-CNN model with RGB and HS layers produces the highest classification accuracy. The producer accuracy of the best 3D-CNN classifier on the test dataset were 99.6%, 94.8%, and 97.4% for pines, spruces, and birches, respectively. The best 3D-CNN classifier produced ~5% better classification accuracy than the MLP with all layers. Our results suggest that the proposed method provides excellent classification results with acceptable performance metrics for HS datasets. Our results show that pine class was detectable in most layers. Spruce was most detectable in RGB data, while birch was most detectable in the HS layers. Furthermore, the RGB datasets provide acceptable results for many low-accuracy applications.

This paper examines the diurnal cycle of convection (DCC) over Lake Titicaca Basin (LTb) during summertime months based on the high spatial resolution (8 x 8 km2) and hourly temporal resolution, estimates of Climate Prediction Center Morphing technique (CMORPH). Analysis was performed using observed data from rain gauges (Rg-SENAMHI) for the period 2002 to 2013. Graphical comparisons and several statistical metrics such as correlation coefficient, bias, and root mean square error were used to evaluate CMORPH product. Spatial maps and graphic metrics of diurnal cycle were developed to assess CMORPH data, spatial dependency an accuracy over the LTb. Approximately, 43% of the total Rg-SENAMHI variation is explained CMORPH data. The correlation between Rg-SENAMHI and CMORPH is positively over southeast and northern LTb, and negatively in the central and southern LTb. A underestimation bias is observed over most the LTb areas and overestimation bias (e.g., Lagunillas, Isla Suana and Desaguadero stations). In general, spatial patterns of rainfall over the LTb were captured through CMORPH data. Over the surrounding lake area, high mountain, and plateau area, maximum peaks of precipitation occur in the early evening, neverhtheless over low areas such as the lake, surrounding and valleys, maximum precipitation values occur early morning. The results show that DCC its very related by surface exchange processes and local circulation resulting from solar radiation and heterogeneous topography.

Spaceborne altimeters are an important data source for obtaining global sea surface wind speeds (U10). Although many altimeter U10 algorithms have been proposed and they perform well, there is still room for improvement. In this study, the data from ten altimeters were collocated with buoys to investigate the error of the altimeter U10 retrievals. The U10 residuals were found to be significantly dependent on many oceanic and atmospheric parameters. Because these oceanic and atmospheric parameters are inter-correlated, an asymptotic strategy was used to isolate the impact of different parameters and establish a neural-network-based correction model of altimeter U10. The results indicated that significant wave heights and mean wave periods are effective in correcting U10 retrievals, probably due to the tilting modulation of long-waves on the sea surface. After the wave correction, the root-mean-square error of the retrieved U10 was reduced from 1.42 m/s to 1.24 m/s and the impacts of thermodynamic parameters, such as sea surface (air) temperate, became negligible. The U10 residuals after correction showed that the atmospheric instability can lead to errors on extrapolated buoy U10. The buoy measurements with large air-sea temperature differences need to be excluded in the Cal/Val of remotely sensed U10.

In this work, a semi-empirical relationship of carbon dioxide emissions with atmospheric CO₂ concentrations has been developed that is capable of closely replicating observations from 1751 to 2018. The correlation consists of a superposition of a linear component that may be attributed to the net emission flux from land use changes coupled with a rapidly varying component of the terrestrial sink combined with a fossil-fuel combustion/cement production emissions-based calculation with a single, fixed, scaling parameter determined by the ocean sink coupled with the remaining slowly varying component of the land sink (the fossil-fuel combustion airborne fraction).

The growing interest related to the proposal of formal definition of a new geological period that has being called “Anthropocene” has introduced a buzzing dynamics in the scientific community, but its conduct is perhaps due to various interests involved in a discussion that has long surpassed the contours of the so-called “Geosciences”. Themes such as “Climate Change or Drift”, “Global Warming”, “Massive Extinction of Species” and “Loss of bio and geodiversity”, among others, are the wheel of a geomorphogenetic dynamics of anthropocentric origin, which leads the debate. But are the conditions for the formal establishment of a new morphosedimentary cycle following the Meghalayanian (Upper Holocene) Age? This work pursues a line of thought that seeks to answer these and other questions, based on the official position of the institutes that have the scientific competence for the formalization under consideration, and the formal criteria that should be considered for this purpose.

High rates of phosphorus (P) currently being applied to soils for the production of vegetables in the Mekong Delta, Vietnam has led to the concern of its negative effect on the economics and the environment. This research presents a comprehensive study on the determination of P supplying capacity in this region of Vietnam to examine the possibility of reducing P fertilizer input. One hundred twenty (120) soil samples were collected to evaluate total P and Bray 1 available P in the soils. Phosphorus maximum sorption, degree of P saturation, P release, and the effect of P fertilizer on corn (Zea mays L.) yield in greenhouse and fields were also determined. Total P concentrations of 56.7% soil samples evaluated yielded high P concentrations (>560 mg P/kg), while 74.2% of the samples had high Bray 1 available P concentrations (>20 mg P/kg soil). Maximum P sorption ranged from 149 to 555 mg P/kg soil, respectively and has negative correlation to available P (r = - 0.63*), degrees of P saturation ranged from 0.63 to 5.46% correlated to available P (r = 0.98**) and maximum P release ranged from 1.2 to 61.9 mg P/kg soil, respectively correlated to available P (r = 0.96**). Corn grown in soils with available P concentrations >15 mg P/kg did not respond to P fertilizer in greenhouse or field experiments. We conclude that many farmers in this region can reduce P fertilizer input, thus increasing their profits and reducing negative environmental impacts associated with excess soil P for sustainable agriculture.

This study aims to investigate the Stratosphere-Troposphere Exchange (STE) events and ozone trends over Irene (25.5°S, 28.1°E). Twelve years of ozonesondes data (2000–2007, 2012–2015) from Irene station operating in the framework of the Southern Hemisphere Additional Ozonesodes (SHADOZ) was used to study the troposphere (0–16 km) and stratosphere (17– 28 km) ozone (O₃) vertical profiles. Ozone profiles were grouped into three categories (2000–2003, 2004–2007 and 2012–2015) and average composites were calculated for each category. Fifteen O₃ enhancement events were identified over the study period. These events were observed in all seasons (one event in summer, four events in autumn, five events in winter and five events in spring), however, they predominantly occur in winter and spring. The STE events presented here are observed to be influenced by the Southern Hemisphere polar vortex. During the STE events, the advected potential vorticity maps assimilated using Modélisation Isentrope du transport Méso–échelle de l’Ozone Stratosphérique par Advection (MIMOSA) model for the 350 K (~12–13 km) isentropic level indicated a transport of high latitude air masses which seems to be responsible for the reduction of the O₃ mole fractions at the lower stratosphere over Irene which takes place at the same time with the enhancement of ozone in the upper troposphere. In general, the stratosphere is dominated by higher Modern Retrospective Analysis for Research Application (MERRA-2) potential vorticity (PV) values compared to the troposphere. However, during the STE events, higher PV values from the stratosphere were observed to intrude the troposphere. Ozone decline was observed from 12 km to 24 km with highest decline occurring from 14 km to 18 km. An average decrease of 6.0 and 9.1% was calculated from 12 to 24 km in 2004–2007 and 2012–2015 respectively. The observed decline occurred in the upper troposphere and lower stratosphere with winter and spring showing more decline compared with summer and autumn.

Studies on the detection of layers with elevated black carbon aerosol (BC) concentrations and the formation conditions of these layers help understand the vertical distribution of BC concentrations, which will provide a basis for the assessment of climate effects and early BC pollution warnings. By using the Weather Research and Forecasting with Chemistry (WRF-Chem) numerical model, we performed a numerical simulation analysis on the authenticity of strong elevated BC concentration layers that were detected by an aircraft in the mixing layer over Harbin, China, which is a high-emission area, on a clear sunny afternoon in the early heating period of 2016. We then discuss possible problems and solutions when non-vertical paths are used to detect the vertical distribution of BC concentrations. Finally, we discuss the favorable conditions for the formation of elevated BC concentration layers by weak vertical flow. The results show that the horizontal variability of BC concentration in the mixing layer in the observation area in Harbin was sufficiently large during the measurement. This produced a false elevated layer, as detected by the aircraft during one round of spiral flight in the mixing layer. The root mean square of the horizontal distribution of BC concentration did not change with height in the mixing layer during the daytime, but it decreased with the thickness of the mixing layer and was higher in the mixing layer than in the free atmosphere. Therefore, the thinner the mixing layer, in which the vertical distribution of the BC concentration is detected in an inclined path, the stronger interference of the horizontal variability on the detected results. When a spiral flight detection path is used, the aircraft should fly at least two rounds in the mixing layer. In the daytime, due to strong turbulence in the mixing layer, weak vertical uplift is not favorable for the occurrence of elevated BC concentration layers in the mixing layer. In the nighttime, if weak vertical uplift is well matched with the BC concentration or its vertical gradient, elevated BC concentration layers can be formed in the atmosphere. Compared with upper layers far from the ground, nighttime elevated layers are easier to form in lower layers near the ground because high BC concentrations or large vertical gradients are more likely to occur in the lower layers. Both cases facilitate the occurrence of large vertical upward transport rates of BC.

The emergence of proteins in the prebiotic world was a watershed event at the origin of life. With their astonishing versatility, the protein enzymes catalyzed crucial biochemical reactions within protocells into more complex biomolecules in diverse metabolic pathways, whereas structural proteins provided strength and permeability in the cell membrane. Five major biochemical innovations followed in succession after availability of various kinds of protein molecules during decoding and translation of mRNAs. These are: (1) the modification of the phospholipid membrane into the plasma membrane; (2) the origin of primitive cytoplasm; (3) primitive gene regulation; (4) the beginnings of the virus world; and (5) the advent of DNA. The creative role of viruses during prebiotic synthesis led to the origin of the DNA world, when DNA replaced mRNA as the major genome of the protocells. With the advent of DNA, replication of information was entirely dissociated from its expression. Because DNA is much more stable than mRNA with more storage capacity, it is a superb archive for information systems in the form of base sequences. DNA progressively took over the replicative storage function of mRNA, leaving the latter for protein synthesis. Genetic information began to flow from DNA to mRNA to protein in a two-step process involving transcription and translation. In the biological stage, DNA replication was central to the binary fission of the first cell, orchestrated by the duplication of genomes and then the division of the parent cell into two identical daughter cells. With the onset of binary fission, the population of primitive cells grew rapidly in the hydrothermal vent environment, undergoing Darwinian evolution and diversification by mutation. The habitat of the earliest fossil record (≥ 3.5 Ga) from the Archean sedimentary rocks of Canada, Greenland, Australia, South Africa, and India offers a new window on the early radiation of microbial life. The development of anoxygenic and then oxygenic photosynthesis from early hyperthermophiles would have allowed life to escape the hydrothermal setting to the mesophilic global ocean.

Floods and droughts are driven, in part, by spatial patterns of extreme rainfall. Heat waves are driven by spatial patterns of extreme temperature. It is therefore of interest to design statistical methodologies that allow the identification of likely patterns of extreme rain or temperature from observed historical data. The standard work-horse for identifying patterns of climate variability in historical data is Principal Component Analysis (PCA) and its variants. But PCA optimizes for variance not spatial extremes, and so there is no particular reason why the first PCA spatial pattern should identify, or even approximate, the types of patterns that may drive these phenomena, even if the linear assumptions underlying PCA are correct. We present an alternative pattern identification algorithm that makes the same linear assumptions as PCA, but which can be used to explicitly optimize for spatial extremes. We call the method Directional Component Analysis (DCA), since it involves introducing a preferred direction, or metric, such as `sum of all points in the spatial field'. We compare the first PCA and DCA spatial patterns for US rainfall anomalies on a 6 month timescale, using the sum metric for the definition of DCA in order to focus on total rainfall anomaly over the domain, and find that they are somewhat different. The definitions of PCA and DCA result in the first PCA spatial pattern having the larger explained variance of the two patterns, while the first DCA spatial pattern, when scaled appropriately, has a higher likelihood and greater total rainfall anomaly, and indeed is the pattern with the highest total rainfall anomaly for any given likelihood. In combination these two patterns yield more insight into rainfall variability and extremes than either pattern on its own.

Global Positioning System (GPS) stations located along coastal areas have the ability to measure tide gauge (TG) records by reflected signal reception from the sea water surface. In this study we used the GPS signal-to-noise ratio (SNR) data from the SEPT station (44.63 ⁰N, 124.05 ⁰W) located at South Beach, Oregon, USA, to estimate the TG records using only a few measurements. First, we derived the TG record from a GPS station (GPS-TG) and used traditional TG data from the National Water Level Observation Network (NWLON) sentinel station (Station ID: 9435380) located in Oregon for validation purposes because it was closest to the SEPT station. Our results show that the GPS-TG and NWLON-TG correlate well with the correlation coefficient (CC) of 0.942 and the root mean square (RMS) of their residuals was about 12.90 cm. The corresponding TG prediction by autoregressive moving average (ARMA-TG) and singular spectrum analysis (SSA-TG) models are evaluated for their effectiveness over the station. The comparative analysis demonstrates that the GPS-TG has improved correlation with ARMA-TG (CC of ~0.981 CC, RMS of ~4.80 cm), and SSA-TG (CC of ~0.998 CC, RMS of ~ 0.88 cm) compared to the NWLON-TG (CC of ~0.942 CC, RMS of ~12.90 cm) values. We believe the outcomes from this study contribute to a better understanding of the numerical modeling of TG records as well as other measurements based on reflectometry techniques.

The pile foundation in the permafrost region is in a negative temperature environment, so the concrete is affected by the negative temperature of the surrounding soil.It not only affects the formation of concrete strength, but also leads to engineering quality accidents in serious cases.Based on the actual measurement of temperature at different strata depths and the comprehensive consideration of surface temperature, terrestrial heat flux and other parameters, the law curve of temperature change along depth in Greater Khingan is established.The calculated results of the curve are consistent with the measured results of ground temperature.The results show that the variation trend of ground temperature along the strata depth at different monitoring sites is basically the same. From June to November, the ground temperature at different depths tends to be constant.From December to May, the ground temperature at any depth within the depth range of 0 to 5.5m follows the law of the cosine function.Below 5.5m, the earth temperature no longer varies with depth.The research results can be used as reference for pile foundation construction under negative temperature environment.

Archaeological topography identification from high-resolution DEMs is a current method that is used with high success in archaeological prospecting of wide areas. I present a methodology trough which burial mounds (tumuli) from LiDAR DEMS can be identified. This methodology uses geomorphometric and statistical methods to identify with high accuracy burial mound candidates. Peaks, defined as local elevation maxima are found as a first step. In the second step, local convexity watershed segments and their seeds are compared with positions of local peaks and the peaks that correspond or have in vicinity local convexity segments seeds are selected. The local convexity segments that correspond to these selected peaks are further feed to a Random Forest algorithm together with shape descriptors and descriptive statistics of geomorphometric variables in order to build a model for the classification. Multiple approaches to tune and selected the proper training dataset, settings and variables were tested. The validation of the model was performed on the full dataset where the training was performed and on an external dataset in order to test the usability of the method for other areas in a similar geomorphological and archaeological setting. The validation was performed against manually mapped and field checked burial mounds from two neighbor study areas of 100 km² each. The results show that by training the Random Forest on a dataset composed of between 75% to 100% of the segments corresponding to burial mounds and ten times more non-burial mounds segments selected using latin hypercube sampling, 93% of the burial mound segments from the external dataset are identified. There are 42 false positive cases that need to be checked, and there are two burial mound segments missed. The method shows great promise to be used for burial mound detection on wider areas by delineating a certain number of tumuli mounds for model training.

New data on the climate in southern Spain from 1792 to 1808 are analyzed in this work. The data source is the newspaper Correo Mercantil de España y sus Indias, where summaries of the weather conditions in Spain were published at weekly resolution. The study is focused on southern provinces, providing 2788 new records, some of them corresponding to areas without data previously recorded. The analysis indicates the predominance of cold-dry winters, cold-wet springs, warm-dry summers, and variable conditions in autumn, from west (cold-wet) to east (warm-dry). Some examples of these situations are presented.

Growing potato demands considerable external inputs of pesticides due to its susceptibility to various pests and pathogens. Here we present an attempt to differentiate the Slovak rural landscape with respect to the possibility of effective potato cultivation and to characterise soil parameters of current potato cultivation areas with the aim to increase the sustainability of the potato production. The selection was based on soil climatic, production and economic parameters. By using the GIS tools and existing databases on soil characteristics in Slovakia, maps of soil suitability categories for potato cultivation were generated. In Slovakia, it was found that 12.3% of farmland is very suitable for potato cultivation and that as much as 43.1% is not suitable. Later the specified categories were characterised in detail and specified with respect to geographic, soil, climatic, production and economic parameters. Currently, most potato crops are cultivated on Eutric Cambisols (27%), Chernozems (20%) and Mollic Fluvisols (18%). Loamy soils (65%), soils without gravel (62%), deep soils (74%) and soil situated on plains (55%) are dominant in these regions. We suggest that potato cultivation should be concentrated on the most suitable areas, thereby increasing the economic profitability, improving the ecological stability of the country and supporting the sustainability of the agriculture.

This work examines drought and wet events based on Standardized Precipitation-Evapotranspiration Index (SPEI) over Kenya from 1981 to 2016. Spatiotemporal analysis of dry and wet events is conducted for 3 and 12-month SPEI. The drought incidences were observed during the period 1984, 1987, 2000, 2006, 2009, 2015, and 2016 for SPEI-3 whilst the SPEI-12 demonstrated the manifestation of drought during the year 2000 and 2006. SPEI clearly shows that the wettest period, 1997 and 1998 that coincide with the El Nino event in both time steps. SPEI -3 shows a reduction in moderate drought events while severe and extreme cases were on increase towards the end of the twentieth century. Conversely, SPEI-12 depicts an overall increase in severe drought occurrence over the study location with observed intensity of -1.54 and cumulative frequency of 64 months during the study period. The trend of wet events is upwards in the western and central highlands while the rest of the regions show increase in dry events during the study period. Moreover, moderate dry/wet events predominate whilst extreme events occur least frequent across all grid cells. It is apparent that the study area experiences mild extreme dry events in both categories although moderately severe dry events dominate most parts of the study area. High intensity and frequency of drought is noted in SPEI-3 while least occurrences of extreme events are recorded in SPEI-12. Although drought event prevails across the study area, there is evidence of extreme flood conditions over the recent decades. These findings form a good basis for next step of research that will look at projection of droughts over the study area based on regional climate models.

This paper examines the influence of the petrographic characteristics of sandstones from Klepa Nafpaktias (Greece) on their suitability in construction (concrete) and energy storage applications. For this scope, ten sandstones were collected in order to study their petrographic characteristics using petrographic microscope and a GIS software as well as their basic physical, mechanical and physicochemical properties. Concrete specimens (C25/30) were produced with constant volume proportions, workability, mixing and curing conditions using different sizes of each aggregate type. Aggregates were mixed both in dry and water saturated states in concrete. Three different types of sandstone aggregates were examined and classified in three district groups according to their physicomechanical properties, petrographic characteristics and surface texture. The classification in groups after the concrete compressive strength test (UCS) verified the initial classification in the same three groups relative to their grain size from coarse to fine grained. As the grain size decreases their physicomechanical and physicochemical properties are getting better resulting in higher concrete strength values (25 to 32 MPa). Furthermore, the proposed ratio C/A (crystals/ mm2) seems to influence the aggregate properties which constitute critical factors for the final concrete strength, presenting the more fine grained sandstones as the most suitable for concrete aggregates. Concerning the use of Klepa Nafpaktias sandstones as potential energy reservoirs, the studied sandstones have the appropriate physicochemical properties for the implementation of a financially feasible CO₂ capture and storage scenario.

Precipitation is a crucial driver for many environmental processes and weather radars are capable of providing precipitation information with high spatial and temporal resolution. However, radar-based quantitative precipitation estimates (QPE) are also subject to various potential uncertainties. This study explores the development, uncertainties and potentials of the hourly operational German radar-based and gauge-adjusted QPE called RADOLAN and its reanalysed radar climatology dataset named RADKLIM in comparison to ground-truth rain gauge data. The precipitation datasets are statistically analysed across various time scales ranging from annual and seasonal aggregations to hourly rainfall intensities in regard to their capability to map long-term precipitation distribution, to detect low intensity rainfall and to capture heavy rainfall. Moreover, the impacts of season, orography and distance from the radar on long-term precipitation sums are examined in order to evaluate dataset performance and to describe inherent biases. Results revealed that both radar products tend to underestimate total precipitation sums and particularly high intensity rainfall. But our analyses also showed significant improvements throughout the RADOLAN time series as well as major advances through the climatologic reanalysis regarding the correction of typical radar artefacts, orographic and winter precipitation as well as range-dependent attenuation.

Easy to use satellite-based water quality visualizations are needed for monitoring and understanding coastal and inland waters, but to date, no publicly accessible real-time global visualization system was in place. Here we introduce the Ulyssys Water Quality Viewer (UWQV), a Sentinel Hub EO Browser Custom script designed for qualitative views of aquatic chlorophyll and suspended sediment concentrations. The viewer avoids unmixing of the chlorophyll and suspended sediment spectral signal by visualizing these parameters together, with high concentrations of suspended sediment obscuring chlorophyll if present. Cloud masking uses the Hollstein and Braaten algorithms (existing EO Browser custom script code), additionally water surfaces are masked using the Normalized Differential Water Index. Chlorophyll is estimated using reflectance line height-based indicators such as fluorescence line height and maximum chlorophyll index. Suspended sediment is visualized based on single-band reflectances at 620 or 700 nm. Data sources are Sentinel-2 and Sentinel-3 images, allowing either 20 m spatial resolution or up to daily imaging. This visualization system is easy to operate and interpret, and combined with the data service capacity of the Sentinel Hub, it is expected that UWQV will contribute to monitoring of remote water bodies and to our overall understanding of physical limnology and aquatic ecology.

Wildfire susceptibility maps display the wildfires occurrence probability, ranked from low to high, under a given environmental context. Current studies in this field often rely on expert knowledge, including or not statistical models allowing to assess the cause-effect correlation. Machine learning (ML) algorithms can perform very well and be more generalizable thanks to their capability of learning from and make predictions on data. Italy is highly affected by wildfires due to the high heterogeneity of the territory and to the predisposing meteorological conditions. The main objective of the present study is to elaborate a wildfire susceptibility map for Liguria region (Italy) by applying Random Forest, an ensemble ML algorithm based on decision trees. Susceptibility was assessed by evaluating the probability for an area to burn in the future considering where wildfires occurred in the past and which are the geo-environmental factors that favor their spread. Different models were compared, including or not the neighboring vegetation and using an increasing number of folds for the spatial-cross validation. Susceptibility maps for the two fire seasons were finally elaborated and validated and results critically discussed highlighting the capacity of the proposed approach to identify the efficiency of fire fighting activities.

Adding a submerged zone (SZ) is deemed to promote denitrification during dry periods and thus improve NO3--N removal efficiency of a bioretention system. However, few studies had investigated the variation of nitrogen concentration in the SZ during dry periods and evaluated the effect of the variation on nitrogen removal of the bioretention system. Based on the experiment in a mesocosm bioretetion system with SZ, this study investigated the variation of nitrogen concentration of the system under 17 consecutive cycles of wet and dry alternation with varied rainfall amount, influent nitrogen concentration and antecedent dry periods (ADP). The results indicated that (1) during the dry periods, NH4+-N concentrations in SZ showed an exponential decline trend, decreasing by 50% in 12.9 ± 7.3 hours; while NO3--N concentrations showed an inverse S-shape decline trend, decreasing by 50% in 18.8 ± 6.4 hours; (2) during the wet periods, NO3--N concentration in the effluent showed an S-shape upward trend; and at the early stage of the wet periods, the concentration was relatively low and significantly correlated with ADP, while the corresponding volume of the effluent was significantly correlated with the SZ depth; (3) in the whole experiment, the contribution of nitrogen decrease in SZ during dry periods to NH4+-N and NO3--N removal accounted for 12% and 92%, respectively; and the decrease of NO3--N in SZ during the dry period was correlated with the influent concentration in the wet period and the length of the dry period.

Demographic and socio-economic developments couple with other requirements to satisfy human needs result in rapid urban expansion sometimes with increasing rate of surface extent greater than the rate of growth of population, that result in continuous sealing of ground surface thereby affecting ecosystem services. This study applied remote sensing toward achieving the progress of SDGs and stage to determine the ratio of the rate of land consumption to the rate of population growth of Gombe metropolis. QGIS 2.18 was used for the image processing and classification analysis for the key Landsat ETM+ (Enhanced Thematic Mapper), Impervious Surface Indices and population data to inform on the urban trend and LCR/PGR for the periods 2000-2005, 2005-2010, and 2010-2015. The result appears that the LCR/PGR for the periods of study show split trends. During 2000-2005 the result shows that the study area expanded outward with LCR/PGR of 1.2. The result also indicate that during 2005-2010, the study area densified with little expansion with the LCR/PGR of 0.8. The result further reveals that during 2010-2015 the LCR/PGR reached 1.8. Pointing that the study area expanded outward with the rate of ground sealing getting high.

Legacy mining industry has left a large number of tailings ponds exposed to water and wind erosion that causes serious environmental and health problems. Prior to rehabilitation actions a deep sampling of the materials infilling the pond used to be necessary. Thus, the primary objective of this study is to demonstrate the usefulness of the Electrical Resistivity Tomography (ERT) method as a non-invasive tool to determine the physicochemical composition of mine tailings ponds, enabling more efficient and low-cost surveys. To achieve this objective, three ERT profiles and three boreholes in each profile were carried out, from each borehole three waste samples from differents depths were collected and a geochemical characterization of the samples was carried. In order to estimate the composition of the infilling wastes in tailing ponds from electrical resistivity measures, several regression models were calculated for different physicochemical properties and metal concentrations. As a result, a high resistivity area was depicted in profiles G2 and G3 while a non-resistive area (profile G1) was also found. Relationships among low resistivity values and high salinity, clay content and high metal concentrations and mobility were established. Specifically, calibrated models were obtained for electrical conductivity, particles sizes of 0.02-50 µm and 50-2000 µm, total Zn and Cd concentration, and bioavailable Ni, Cd and Fe. Therefore, the ERT technique could be considered as a useful tool for mine tailings ponds characterization, and it can be used to estmate some physicochemical properties and metal concentrations of this mine waste.

Morphometric analysis of Holocene pebbles from the Sava River gravels, in Zagreb alluvial aquifer system (NW Croatia), revealed distribution of their shapes along 30 km long observed watercourse. Limestones, dolomites and sandstones are determined as major (> 4%), and effusive magmatics, cherts and tuffs as minor lithotypes of the pebbles (up to 4%). Their distributions indicate mainly distant Alpine provenance for carbonate (limestones and dolomites) pebbles and local input for sandstones and minor lithotypes, laterally from the Samoborska gora and Medvednica Mts. Carbonates have predominately disc and sphere shapes, implying also their mainly distant sources. Scattered distributions of pebble shapes (sphere, disc, blade and rod) for sandstones and minor lithotypes indicate multiple sources, some of them probably local. Original sedimentary environments for main pebble lithotypes are tentatively interpreted from their flatness ratios, indicating predominant lake shore environments, followed by moraine and riverbed.

Land use/land cover (LULC) and climate changes are two main factors directly affecting hydrologic conditions. However, very few studies in Vietnam have investigated changes in hydrological process under the impact of climate and land use changes on a basin scale. The objective of this study is to assess the individual and combined impacts of land use and climate changes on hydrological processes for the Nam Rom river basin, Northwestern Viet Nam using Remote Sensing (RS) and Soil and Water Assessment Tools (SWAT) model. SWAT model was used for hydrological process simulation. Results indicated that SWAT proved to be a powerful tool in simulating the impacts of land use and climate change on catchment hydrology. The change in historical land use between 1992 and 2015 strongly contributed to increasing hydrological processes (ET, percolation, ground water, and water yield), whereas, climate change led to significant decrease of all hydrological components. The combination of land use and climate changes significantly reduced surface runoff (-16.9%), ground water (-5.7%), water yield (-9.2%), and sediment load (-4.9%). Overall climatic changes had more significant effect on hydrological components than land use changes in the Nam Rom river basin during the 1992–2015. Under impacts of projected land use and climate change scenarios in 2030 on hydrological process of the upper Nam Rom river basin indicate that ET and surface flow are more sensitive to the changes in land use and climate in the future. In conclusion, the findings of this study will basic knowledge of the effects of climate and land-use changes on the hydrology for future development of integrated land use and water management practices in Nam Rom river basin.

Tengchongite is a uranyl molybdate uranium mineral and it was found and named by Chen 1985.No more scholars studied on tengchongite after Chen’s work, The identification of this mineral has only been confirmed by single crystal X-ray diffraction. In the paper, micro laser Raman spectroscopy and infrared spectroscopy are used to identify the spectroscopy properties of tengchongite. The studies fill in the gaps of more than 30 years in terms of the molecular spectroscopy research of tengchongite. The mineral has an ideal model of Ca(UO₂)₆(MoO₄)₂O₅•12H₂O its bands attributed to the vibrating units are clearly identified in the Raman spectrum. Symmetric stretching modes at 812 cm^-1 and 839 cm^-1 are assigned to ν1 (UO₂)²⁺ ,The ν3 antisymmetric stretching modes of (UO₂)²⁺ are observed at 896 cm^-1, Symmetric stretching modes at 419^-1 and 440 cm^-1 are assigned to ν2 (UO₂)²⁺ .Symmetric stretching modes at 919cm-1 are assigned to ν1(MoO₄)^2-,The ν3 antisymmetric stretching modes of (MoO₄)^2- are observed at 752 cm^-1, the in-plane ν2(MoO₄)^2-and the out-of-plane ν4(MoO₄)^2- bending modes are at 169 cm^-1 and 254 cm^-1. IR spectrum of tengchongite shows the major uranyl band at 858 cm^-1 and 693 cm^-1, Mo-O bonds are observed at about 985 cm^-1 and 780 cm^-1, and H₂O groups are present with a wide range peaks from 3100 cm^-1 to 3500 cm^-1 and 1647 cm^-1, and the band at 1432.4 cm^-1 is probably due to the stretching vibration hydroxyl (OH^-1), therefore, tengchongite contains may include hydroxyl and its chemical formula needs to be modified .

This study investigates the spatial signatures of seasonal snow in Synthetic Aperture Radar (SAR) observations at different spatial scales and for different physiographic regions. Sentinel-1 C-band (SAR) backscattering coefficients (BSC) were analyzed in the Swiss Alps (SA), in high elevation forest and grasslands in Grand Mesa (GM), Colorado, and in North Dakota (ND) croplands. GM BSC exhibit 10dB sensitivity to wetness at small scales (~100 m) over homogeneous grassland. Sensitivity decreases to 5 dB in the presence of trees, and it is demonstrated that VH BSC sensitivity enables wet snow mapping below the tree-line. Area-variance scaling relationships show minima at ~100 m and 150-250 m respectively in barren and grasslands in SA and GM, increasing up to 1 km and longer in GM forests and ND agricultural fields. The spatial organization of BSC (as described by 1D-directional BSC wavelength spectra) exhibits multi-scaling behavior in the 100 -1,000 m range with a break at (180-360 m) that is also present in UAVSAR L-band measurements in GM. Spectral slopes in GM forested areas steepen during accumulation and flatten in the melting season with mirror behavior for grasslands reflecting changes in scattering mechanisms with snow depth and wetness, and vegetation mass and structure. Overall, this study reveals persistent patterns of SAR scattering variability spatially organized by land-cover, topography and regional winds with large inter-annual variability tied to precipitation. This dynamic scaling behavior emerges as an integral physical expression of snowpack variability that can be used to model sub-km scales and for downscaling applications.

We studied the influence of lava meal, zeolite and top layer of sandy soil as bedding additives on gaseous C and N losses from a sloping-floor barn of naturally ventilated animal housing. We selected four barn units where eight young bulls’ group was reared in each barn. Chopped straw of wheat and barley applied daily at the rate of 5 kg per livestock unit (LU) in bedding areas where one LU consisted of 500 kg body mass of live bulls. Zeolite, lava meal and sandy soil (18% clay + silt) applied in barn at the rate of 10, 20 and 30% of straw dose, respectively. Static flux chamber was used to measure gases emissions from the barn unit and mass balance calculation was used to calculate straw manure total N (TN) losses during housing phase. On an average, all bedding additives decreased 85% of the NH₃ emission compared to control; however, they did not influence CH₄ emission. Zeolite decreased CO₂ (35%) and N₂O (37%) emission rates. Subsequently, lava meal, sandy soil and zeolite decreased 23, 37, and 50% of TN losses from barn manure, respectively. Overall, measured N emissions through NH₃-N and N₂O-N from the barns was 11% of calculated TN losses while remainder 89% was most probably attributed to di-nitrogen (N₂), a harmless gas. Hence, in straw-based cattle housings, zeolite could be a promising additive for reduction of CO₂, N₂O and NH₃ emissions and sandy soil can be considered as cheap and readily available resource for reducing NH₃ emission.

Significant amounts of anthropogenic radionuclides were introduced in ocean waters following nuclear atmospheric tests and development of the nuclear industry. Dispersion of artificial dissolved radionuclides has been extensively measured for decades over the European continental shelf. The radionuclide measurement and release fluxes databases provided here represent an exceptional opportunity to validate dispersion hydrodynamic models. MARS hydrodynamic model have been applied at different scales to reproduce in realistic conditions the measured dispersion. Specific methods have been developed to obtain qualitative and quantitative results and perform model/measurement comparisons. Model validation concerns short to large scales with dedicated surveys following the dispersion: it was performed within two and three dimensions framework and from minutes and hours following a release up to several years. Results are presented concerning the dispersion of radionuclides in marine systems deduced from standalone measurements, or according to model comparisons. It allows characterising dispersion over the continental shelf, pathways, transit times, budgets and source terms. This review exhibits the main features retained from the point of view of radiotracers, hydrodynamic models and model/measurement methods with perspectives of applications in other areas or oceanographic domains.

This study was conducted to investigate the impact of El Niño Southern Oscillation on rainfall distribution and productivity of major Agricultural crops in the Kembta Tembaro Zone of Southern Ethiopia over the past 30 years. Precipitation and temperature data were obtained from the National Meteorology Agency, crop data from the Central Statistical Agency of Ethiopia, and the Sea Surface Temperature data from the NOAA website. The rainfall trend had shown decreasing trend with high variability at all the stations (p<0.05). Over the same period, El Niño and La Niña event were observed and highly affected rainfall distribution. It was found that Coefficient Variation was greater than 30%, which indicates the area was prone to drought episodes. The impacts of the ENSO events on the yield of Maize, Wheat, Barely, Sorghum and Enset were assessed. Wheat and Maize were highly affected by the ENSO events. Enset was found to be more resistant crop to the influence of ENSO. Barely and Sorghum were affected at varying magnitude. Among the five chosen crop for this investigation two of the crops were seriously affected during the two extremes, i.e. El Niño and La Niña. From this investigation it is conclude that the overall cereal crop productivity was decreased and precipitation variability was noticed. So, having the information about ENSO phase in advance can be used to forecast ENSO and select crop types and varieties to maximize agricultural rain fed cereal crop productivity while minimizing the crop risk associated with seasonal rainfall and ENSO phases.

Road dust resuspension, especially the particulate matter fraction below 10 µm (PM10), is one of the main air quality management challenges in Europe. Road dust samples were collected from representative streets (suburban and urban) of the city of Viana do Castelo, Portugal. PM10 emission factors (mg veh-1 km-1) ranging from 49 (asphalt) to 330 (cobble stone) were estimated by means of the United Stated Environmental Protection Agency method. Two road dust fractions (< 0.074 mm and from 0.0074 to 1 mm) were characterised for their geochemical, mineralogical and morphological properties. In urban streets, road dusts reveal the contribution from traffic emissions, with higher concentrations of e.g. Cu, Zn, and Pb. In the suburban area, agriculture practices likely contributed to As concentrations of 180 mg kg-1 in the finest road dust fraction. Samples are primarily composed of quartz, but also of muscovite, albite, kaolinite, microcline, Fe-enstatite, graphite and amorphous content. Particle morphology clearly shows the link with natural and traffic related materials, with well-formed minerals and irregular aggregates. The hazard quotient suggests a probability to induce non-carcinogenic adverse health effects in children by ingestion of Zr. Arsenic in the suburban street represents a human health risk of 1.58 x 10-4.

In this research paper, change detection based methods were considered to find collapsed and intact buildings using radar remote sensing data or radar imageries. Main task of this research paper is collection of most relevant scientific research in field of building damage assessment using radar remote sensing data. Several methods are selected and presented as best methods in present time, there are methods with using interferometric coherence, backscattering coefficients in different spatial resolution. In conclusion, methods are given in end, which show, which methods and radar remote sensing data give more accuracy and more available for building damage assessment. Low resolution Sentinel-1A/B radar remote sensing data are recomended as free available for monitoring of destruction degree in microdistrict level. Change detection and texture based method are used together to increase overall accuracy. Homogeneity and Dissimilarity GLCM texture parameters found as better for separation of a collapsed and intact buildings. Dual polarization (VV,VH) backscattering coefficients and coherence coefficients (before earthquake and coseismic) were fully utilized for this study. There were defined the better multi variable for supervised classification of none building, damaged and intact buildings features in urban areas. In this work, we were achieved overall accuracy 0.77, producer’s accuracy for none building is 0.84, for damaged building case 0.85, for intact building 0.64. Amatrice town was chosen as most damaged from 2016 Central Italy Earthquake.

The drafting of a new Global Biodiversity Framework for the Convention on Biological Diversity (CBD) and Biodiversity Strategy for the European Union (EU) render 2020 a critical crossroad for biodiversity conservation. Freshwater biodiversity is disproportionately threatened and poorly studied relative to marine and terrestrial biota, despite providing numerous essential ecosystem services. The urgency of the mounting freshwater biodiversity crisis necessitates approaches catered to the unique ecology and threats of freshwater life, which are not adequately addressed by current strategies. We present a set of 15 special recommendations for freshwater biodiversity to guide the CBD’s post-2020 framework and the 2020 EU strategy based on European case studies, both challenges and successes. Our recommendations cover key outcomes and guiding concepts, enabling conditions and methods of implementation, planning and accountability modalities, and cross-cutting issues. They address topics including invasive species, integrated water resources management, strategic conservation planning, data management, and emerging technologies for freshwater monitoring, among others. These recommendations will enhance the ability of global and European post-2020 biodiversity agreements to halt and reverse the rapid global decline of freshwater biodiversity.

The megacities´ sewage creates socioeconomic dependence related to water availability in the nearby zones, especially in countries with hydric stress. The present paper studies the water balance progression of realistic scenarios from 2005 to 2050 in the Mezquital Valley, the receptor of Mexico City untreated sewage since 1886, allowing agriculture irrigation in unsustainable conditions. WEAP model calculated the water demand and supply. Validation was performed with outflows data of the Tula River and simulated three scenarios: 1st) Steady-state based on inertial growth rates, 2nd) Transient scenario concerned climate change outcomes, with minor influence in surface water and hydric stress in 2050; 3rd) Transient scenario perturbed with a planned reduction of 36% in the imported wastewater and the start-up of a massive Water Treatment Plant, allowing drip and sprinkler irrigation since 2030. In the 2005-2017 period, 59% of the agriculture depended on the flood irrigation with megacity sewage. The water balance scenarios evaluated the sectorial supply of the ground and superficial water. Drip irrigation would reduce 42% of agriculture demands, but still does not grant the downflow hydroelectric requirements, aggravated by the lack of wastewater supply since 2030. This research alerts about how present policies compromise future Valley demands.

Pore-fractures network play a key role in coalbed methane (CBM) accumulation and production, while the impacts of coal facies on the pore-fractures network performance are still poorly understood. In this work, the research on the pore-fracture occurrence of 38 collected coals from Sangjiang-Muling coal-bearing basins with multiple techniques including mercury intrusion porosimetry (MIP), micro-organic quantitative analysis, and optic microscopy, and its variation controlling of coal face were studied. The MIP curves of 38 selected coals indicating pore structures were subdivided into three typical types including type I of predominant micropores, type Ⅱ of predominant micropores and macropores with good connectivity and type Ⅲ of predominant micropores and macropores with poor connectivity. For coal facies, there are three various coal facies were distinguished, which include lake shore coastal wet forest swamp, the upper delta plain wet forest swamp, tidal flat wet forest swamp with Q-cluster analysis and tissue preservation index - gelification index (TPI-GI) and Wood index - groundwater influence index (WI -GWI). The results show there is positive relationship between tissue preservation index (TPI), wood index (WI) and mesopores (102nm-103nm), while a negative relationship between TPI, WI and macropores/fractures. In addition, groundwater level fluctuations can control the development of type C and D fractures, and the frequency of type C and D fractures shows an ascending trend with increasing GWI, which may be caused by the mineral hydration of the coal. Finally, from the perspective of the pore-fractures occurrence in CBM reservoirs, the wet forest swamp of upper delta plain is considered to be the optimization areas for Sanjiang-Mulinghe coal-bearing basins by a comparative study of various coal facies.

The direct radiative effect of aerosols is taken into account in many limited area numerical weather prediction models using wavelength-dependent aerosol optical depths of a range of aerosol species. We study the impact of aerosol distribution and optical properties on radiative transfer, based on climatological and more realistic near real-time aerosol data. Sensitivity tests were carried out using the single column version of the ALADIN-HIRLAM numerical weather prediction system, set up to use the HLRADIA broadband radiation scheme. The tests were restricted to clear-sky cases to avoid the complication of cloud-radiation-aerosol interactions. The largest differences in radiative fluxes and heating rates were found to be due to different aerosol loads. When the loads are large, the radiative fluxes and heating rates are sensitive to the aerosol inherent optical properties and vertical distribution of the aerosol species. Impacts of aerosols on shortwave radiation dominate longwave impacts. Sensitivity experiments indicated the important effects of highly absorbing black carbon aerosols and strongly scattering desert dust.

Fertility management techniques being promoted in sub-Saharan Africa (SSA) seek to grow indigenous vegetables economically and sustainably. This study was conducted in a phytotron chamber and compared yield, soil carbon (C) speciation and greenhouse gas (nitrous oxide (N₂O) and carbon dioxide (CO₂)) emissions from SSA soils of two ecoregions; the dry savanna (lna, Republic of Benin) and rainforest (Ife, Nigeria) cultivated with local amaranth (Amaranthus cruentus) under manure (5 t/ha) and/or urea (80 kg N/ha) fertilization. Vegetable yield ranged from 1753 kg/ac to 3198kg/ac in the rainforest, RF, soils and 1281 kg/ac to 1951 kg/ac in the dry savanna, DS, soils. Yield in the urea treatment was slightly higher compared to the manure+urea treatment, but the difference was not statistically significant. Cumulative CO₂ emissions over 21 days ranged from 497.06 to 579.47 g CO₂ in the RF, and 322.96 to 624.97 g CO₂ in the DS, while cumulative N₂O emissions ranged from 60.53 to 220.86 mg N₂O in the RF, and 24.78 to 99.08 mg N₂O in the DS. In the RF samples, the combined use of manure and urea reduced CO₂ and N₂O emissions but led to an increase in the DS samples. ATR-FTIR analysis showed that the combined use of manure and urea increased the rate of microbial degradation in the soils of the DS, but no such effect was observed in soils of the RF. We conclude that combining manure and urea fertilization has different effects on soils of the two ecoregions, and that RF farmers can reduce agricultural emissions without compromising soil productivity and yield potential.

The outcomes of the 2015 Paris Agreement triggered a number of climate impact assessments, such as for floods and droughts, to focus on future time frames corresponding to the years of reaching specific levels of global warming. Yet, the links between the timing of the warming levels and the corresponding greenhouse gas concentration pathways to reach them, remain poorly understood. To address this gap, we compare projected changes of annual mean, extreme high and extreme low river discharges in Europe at 1.5°C and 2°C under scenarios RCP8.5 and RCP4.5 from an ensemble of Regional Climate Model (RCM) simulations. The statistical significance of the difference between the two scenarios for both warming levels is then evaluated. Results show that in the majority of Europe (>95% of the surface area for the annual mean discharge, >98% for high and low extremes), the changes projected in the two pathways are statistically indistinguishable. These results suggest that in studies of changes at specific warming levels the projections of the two pathways can be merged into a single ensemble without major loss of information. With regard to the uncertainty of the unified ensemble, findings show that the projected changes of annual mean, extreme high and extreme low river discharge are statistically significant in large portions of Europe.

The mass composition of Particulate Matter (PM) with an aerodynamic diameter of 2.5 microns (PM2.5) in San Joaquin Valley (SJV) is dominated by ammonium nitrate (NH4NO3) which is a secondary pollutant. The goal of this research was the investigation of the relationship between emissions, meteorology and PM2.5 concentrations in Fresno for the winter season. It was found that the location of sites near emission sources such as freeways compared with residential sites strongly affected measured PM2.5 concentrations. It was found that although long-term trends showed declines in both emissions and PM2.5 concentrations there was substantial variability between the years in the PM2.5/emissions relationship. Much of the yearly variation in the relationship between emissions and PM2.5 concentrations can be attributed to yearly variations in weather, such as atmospheric stability, precipitation frequency and average wind speed. There are moderate correlations between PM2.5 concentrations and temperature differences between nearby surface stations at varying elevations which explains some of the daily and seasonal variation in PM2.5. Occurrence of precipitation was related to low PM¬2.5 although the higher wind speeds and lower atmospheric stability associated with precipitation likely explain some of the low PM2.5 as well as washout of PM.

Accurate assessment and projections of extreme climate events requires the use of climate datasets with no or minimal error. This study uses quantile mapping bias correction (QMBC) method to correct the bias of five Regional Climate Models (RCMs) from the latest output of Rossby Climate Model Center (RCA4) over Kenya, East Africa. The outputs were validated using various scalar metrics such as Root Mean Square Difference (RMSD), Mean Absolute Error (MAE) and mean Bias. The study found that the QMBC algorithm demonstrate varying performance among the models in the study domain. The results show that most of the models exhibit significant improvement after corrections at seasonal and annual timescales. Specifically, the European community Earth-System (EC-EARTH) and Commonwealth Scientific and Industrial Research Organization (CSIRO) models depict exemplary improvement as compared to other models. On the contrary, the Institute Pierre Simon Laplace Model CM5A-MR (IPSL-CM5A-MR) model show little improvement across various timescales (i.e. March-April-May (MAM) and October-November-December (OND)). The projections forced with bias corrected historical simulations tallied observed values demonstrate satisfactory simulations as compared to the uncorrected RCMs output models. This study has demonstrated that using QMBC on outputs from RCA4 is an important intermediate step to improve climate data prior to performing any regional impact analysis. The corrected models can be used for projections of drought and flood extreme events over the study area. This study analysis is crucial from the sustainable planning for adaptation and mitigation of climate change and disaster risk reduction perspective.

Oiola archeological site, located in the mining complex of La Arboleda (Biscay, North Spain) was an important iron smelting center from the Roman Period to the Early Middle Ages and even in more current times (19th-20th centuries). Tap-slags and some plano-convex slags were identified as smelting slags. Samples were analyzed by optical microscopy, X-ray powder diffraction, scanning electron microscopy coupled with electron-dispersive spectroscopy and Raman microspectroscopy to perform a mineralogical and textural characterization. Additionally, thermogravimetric and thermodiffraction analyses were carried out to determine furnace operating temperatures. The mineral assemblage reflects furnace cooling rates and temperatures and the addition of quartz as the main flux to decrease the melting temperature of the iron ore. The comparison of slags from the Roman Period and the Early Middle Age allows to observe changes in the pyrometallurgical process through time.

Humanity will soon define a new era for nature – one that seeks to correct decades of underwhelming responses to the global biodiversity crisis. Area-based conservation efforts, which include both protected areas and other effective area-based conservation measures, are likely to extend and diversify. But persistent shortfalls in ecological representation, management effectiveness and measurable biodiversity outcomes diminish the potential role of area-based conservation in stemming biodiversity loss. Here we show how protected area expansion by governments since 2010 has had limited success in increasing biodiversity coverage, and identify four emergent issues that –if addressed – will enhance the performance of area-based conservation post-2020. We close with recommendations for a broad biodiversity agenda that maximises the potential of area-based conservation. Parties to the Convention on Biological Diversity must recognise that area-based conservation primarily focuses on local threats to species and ecosystems, and needs enhanced emphasis on biodiversity outcomes to better track and fund its contribution to global conservation efforts.

Whistler mode waves play a major role in regulating the lifetime of trapped electrons in the Earth's radiation belts. Specifically, whistler mode hiss waves are one of the mechanisms that maintains the slot region between the inner and outer radiation belts. The generation mechanism of hiss is a topic still under debate with at least three prominent theories present in the literature. Lightning generated whistlers in their ducted or non-ducted modes, are considered to be one of the possible sources of hiss. We present a study of new observations from the Radio Receiver Instrument (RRI) on the Enhanced Polar Outflow Probe (ePOP: currently known as SWARM-E). RRI consists of two orthogonal dipole antennas, which enables polarization measurements, when the satellite boresight is parallel to the geomagnetic field. Here we present 75 passes of ePOP - RRI from 2014 - 2018, in which lightning whistlers and hiss waves were observed. In more than 50% of those passes hiss is found to co-exist with the lightning whistlers. Moreover, the whistler observations are correlated with observations of wave power at the lower-hybrid resonance. The observations and a whistler mode ray-tracing study suggest that multiple-hop lightning induced whistlers can be a source of hiss and plasma instabilities in the magnetosphere.

Climate change impacts and adaptations is subject to ongoing issues that attract the attention of many researchers. Insight into the wind power potential in an area and its probable variation due to climate change impacts can provide useful information for energy policymakers and strategists for sustainable development and management of the energy. In this study, spatial variation of wind power density at the turbine hub-height and its variability under future climatic scenarios are taken under consideration. An ANFIS based post-processing technique was employed to match the power outputs of the regional climate model with those obtained from the reference data. The near-surface wind data obtained from a regional climate model are employed to investigate climate change impacts on the wind power resources in the Caspian Sea. Subsequent to converting near-surface wind speed to turbine hub-height speed and computation of wind power density, the results have been investigated to reveal mean annual power, seasonal, and monthly variability for a 20-year period in the present (1981-2000) and in the future (2081-2100). The results of this study revealed that climate change does not affect the wind climate over the study area, remarkably. However, a small decrease was projected for future simulation revealing a slightly decrease in mean annual wind power in the future compared to historical simulations. Moreover, the results demonstrated strong variation in wind power in terms of temporal and spatial distribution when winter and summer have the highest values of power. The findings of this study indicated that the middle and northern parts of the Caspian Sea are placed with the highest values of wind power. However, the results of the post-processing technique using adaptive neuro-fuzzy inference system (ANFIS) model showed that the real potential of the wind power in the area is lower than those of projected from the regional climate model.

Water resource is considered as a significant factor in development of regional environment and society. Water consumption prediction can provide important decision basis for the regional water supply scheduling optimisations. According to the periodicity and randomness nature of the daily water consumption data, a Markov modified autoregressive moving average (ARIMA) model is proposed in this study. The proposed model, combined with the Markov chain, can correct the prediction error, reduce the continuous superposition of prediction error, and improve the prediction accuracy of future daily water consumption data. The daily water consumption data of different monitoring points are used to verify the effectiveness of the model, and the future water consumption is predicted, in the study area. The results show that the proposed algorithm can effectively reduce the prediction error compared to the ARIMA.

Visualization of the seabed terrain is one of the key functions of marine geographic information system. The major challenge here is that the obtained data for seabed terrain usually consists of elevation and sediment only but does not include remote sensing images, which is important for ground terrain visualization, as they can be used as textures to reveal detailed information and achieve realistic visual results. Existing seabed terrain visualizing methods (including annotations, color-mapping, and texture-mapping) have limitations in reality and intuition, as they are inadequate to express the physical characteristics of sediment accurately. This paper presents a novel and advanced 3D visualization method of seabed terrain, which introduces the Physically Based Rendering (PBR) theory into the field of geographic visualization. We analyze the main categories of seabed sediments and their optical features respectively, then develop a procedural method to generate physically based rendering materials for different sediments. Then an enhanced bidirectional reflectance distribution function is employed to achieve accurate and intuitive terrain rendering. We also refine the texture sampling method and propose a procedural seabed objects generation method to construct a more natural and realistic undersea environment. Experimental results reveal our method can make good use of the limited seabed terrain data and get significant improvements in visual effect, which can help users to cognize and analyze the seabed geographic environment more accurately and intuitively.

Mining is one of the main economic activities of several developing countries as Chile. Due to the progressive fall of the ore grades and the increasingly refractory composition of minerals, concentrating plants have increased that has led to an increase in the generation of tailings. Tailings, especially those obtained in the past, have remaining copper and other valuable species in quantities that can potentially be recovered, such as gold, silver, vanadium and rare earth elements. This transforms this abundant waste into a potential source of precious or strategic metals for secondary mining. One of the techniques of solid-liquid separation that allows the processing of solutions with low concentrations of metals corresponds to the adsorption, and more recently the biosorption, which is based on the use of biological matrices that do not constitute an environmental liability after application. Biosorption occurs as a consequence of the wide variety of active functional groups present in the different types of biomass. Bacterial, fungal, plant and algal biomasses have been described as biosorbents, mainly for the treatment of diluted and simple solutions. This work aims to recover copper from leached tailings using biomass of the red algae Gracilaria chilensis as a biosorbent. The tailing samples were taken from an abandoned deposit in the north of Chile and after an acid leaching copper was biosorbed, for which the kinetics of adsorption and the equilibrium isotherms were studied, applying the Freundlich and Langmuir models. Operational parameters such as adsorbent dose, pH and initial metal concentration were studied.

Recently, both global and domestic environmental events have been occurring more frequently, bringing catastrophic consequences to humans and the environment. These adverse events have caused widespread concern among the general public. In positive terms, these devastating events could potentially enhance people’s environmental awareness, which, in turn, could instill a greater sense of environmental responsibility. This study aims to investigate how university students concern themselves with global and domestic catastrophic environmental events and to examine how global and domestic environmental concerns mediate the effect of environmental knowledge and attitudes on university students’ environmental responsibility. Students of King Mongkut’s University of Technology Thonburi in Bangkok, Thailand were selected as the participants. A simple random technique was applied to select the research participants. Questionnaire surveys with 863 students were carried out during September–October 2019. A path analysis was performed to test how global and local environmental concerns mediate the effect of environmental knowledge and attitudes on university students’ environmental responsibility. The results demonstrated that domestic environmental concerns, taken alone, contributed less to the students’ sense of environmental responsibility. Domestic environmental concerns had a stronger effect on environmental responsibility when taken together with global environmental concerns. In addition, both domestic and global environmental concerns could help transform environmental knowledge and attitudes into environmental responsibility. Only environmental attitudes had no direct effect on responsibility. These results show that domestic and global catastrophic environmental events could raise students’ levels of concern for the environment, and, ultimately, enhance their sense of responsibility to protect the environment.

In this study a new approach for planning Micro-Catchment Water Harvesting (M.C.W.H.) systems for irrigation in semi-arid regions such as the Aegean islands, is presented. M.C.W.H. is a cheap solution for constructing irrigation infrastructure with zero energy cost in regions where water is scarce. The proposed approach introduces simple linear relationships for estimating the annual volume of water V_s collected mainly from the CA (Contributing Area), stored in the root zone (Infiltration Basin, IB), according to the annual rainfall and runoff depths, after having determined the ratio of areas of micro-catchment (MC) components i.e. λ= Α_CA/Α_ΙΒ and its whole area A_MC This procedure was applied in Paros island of the Cyclades complex in the middle of the Aegean sea in east Mediterranean. Besides, income-cost analysis was performed via NPV method for almond, peach and apricot trees. The new approach was proved versatile and easy to use. Besides, the investment turned out to be advantageous two years after the MCs construction.

An improved FSM method is used in geophysical and environmental corrections to enhance the final product of the along track Jason-2 SLA data and extend it near the Red Sea borders. In this study the ionospheric correction range, wet tropospheric correction range, sea state bias correction range and dry tropospheric correction range are enhanced and improved using FSM01, which helped to retrieve three more tracks (106, 170 and 234), earlier neglected by the distribution centers, and extend the tracks towards the coast. The FSM01 SLA is compared with Jason-2 SLA and AVISO SLA for the available 5 tracks, in which the FSM01 SLA show a good agreement and higher correlation with the Jason-2 SLA compared with that of AVISO, in addition to that it fills the gaps in the times series of all tracks. The new retrieved tracks also compared with those retrieved by AVISO, both data show similar variability, with FSM01 SLA show no gaps in the time series. The FSM01 SLA also extended towards the coast and show high correlation with the coastal tide measurements.

Land Surface Temperature (LST) and Land Use Land Cover (LULC) are the principal aspects of climate and environment studies. The object of the study is to assess spatial relationship between LST and remote sensing LULC indices at the global and continental scale. Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua daytime LST and eight LULC MODIS indices of 2018 prepared and processed using Earth Engine Code Editor. R squared and significance of the relationship values of randomly selected points computed in R program. The research observed the relationship between examined indices and LST is significant at the 0.001 level. Normalized Difference Water Index (NDWI) and Normalized Difference Snow Index (DSI) are the dominant drivers of LST in the world, Asia and North America. In Australia and Africa, Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) are the dominant drivers of LST. Albedo and Normalized Difference Soil Index (NDSI) have superior in Central America. In South America and Europe, the dominant driver of LST is NDWI. Relationship between albedo and LST is moderate inverse on a global scale. Observed relationship between LST and examined vegetation indices is positive in Europe and North America while inverse in Australia and Africa. All observed relationship between Normalized Difference Built-up Index (NDBI) and LST are positive. Association observed between NDSI and LST is positive in Australia, Africa and Central America.

A debris flows generation related to a poorly sorted mixture of soil, catchment topography and rainfall characteristic. Runoff of some depth on valley resulting from intensive rainfall can incur the sediments movement of beds or adjacent banks. The fluid flow in channel affected by rainfall parameters combinations, such as duration, intensity, cumulative rainfall, etc., is the key factor for debris movement. In this paper, the rainfall characteristics and occurrence conditions of debris flow in Xiongmao gully on July, 26th, 2016, have been explored, combined with field survey and indoor simulation experiment on the collected critical discharge parameters of debris movement. Further, debris distribution and the critical discharge characteristics have been analysed, by means of investigation on the catchment topography and occurrence cause of the debris flow, analysis of the critical discharge parameters on which the channel debris began to move, and K value clustering analysis method to characterize the rainfall pattern of the studied area, the discharge calculation of debris flow occurring in different rainfall patterns. The results have shown that, for the debris flow occurrence in Xiongmao gully, the debris initiation on the middle reaches of the gully provide the majority of solid particles for the disaster on July, 26th, 2016, and the upstream confluent provided catchment. Based on the relationship obtained from laboratory test, in which the calculated critical discharge was 43.8m3/s, less than the peak discharge (Qc =66.7m3/s), calculated by morphological method. In addition, it has been indicated that the dominated rainfall patterns of the studied area are first-quartile and second-quartile, that is, the rainfall is primarily at earlier or middle to preliminary stage of this time rainfall event. The critical discharge for the occurrence of debris flow on July, 26th was achieved 20a rainfall frequency, the larger runoff volume generated on shorten heavily rainfall. Based on individuality characteristics, such as distributed hydrological analysis, critical discharge and rainfall pattern of debris flow, the forewarning could be more efficient.

Weather surveillance radars routinely detect smoke of various origin. Of particular significance to the meteorological community are wildfires in forests and/or prairies. For example, one responsibility of the National Weather Service in the USA is to forecast fire outlooks as well as to monitor wild fire evolution. Polarimetric variables have enabled relatively easy recognitions of smoke plumes in data fields of weather radars. Presented here are the fields of these variables from smoke plumes caused by grass fire, brush fire, and forest fire. Histograms of polarimetric data from plumes contrast these three cases. Most of the data are from the polarimetric Weather Surveillance Radar 1988 Doppler (WSR-88D aka Nexrad, 10 cm wavelength) hence the wavelength does not influence these comparisons. Nevertheless, in one case simultaneous observations of a plume by the operational Terminal Doppler Weather Radar (TDWR, 5 cm wavelength) and a WSR-88D is used to infer backscattering characteristic and hence sizes of dominant contributors to the returns. In addition, comparisons with observations by other investigators of plumes from urban area but at a 5 cm wavelength are made. To interpret some measurements Computational Electromagnetics (CEM) tools are applied.

Assessment of meadow restoration benefits from understanding the connection between geomorphology, hydrology and vegetation, and multispectral imagery captured from unpiloted aerial vehicles (UAV) may provide the method most suitable in terms of cost, spatial resolution, support for vegetation indices, and frequency of acquisition. Our field studies of northern Sierra Nevada montane meadows (with <40 km2 watersheds) in various stages of restoration includes GPS + laser-leveling channel survey, laser-leveled cross section, aerial LiDAR, vegetation sampling, soil measurements, and UAV imaging using a sensor capturing calibrated blue (465-485 nm), green (550-570 nm), red (663-673 nm), near infrared (820-860 nm), and red edge (712-722 nm) bands at 5.5 cm resolution, providing multispectral false-color images and vegetation indices such as normalized difference vegetation index (NDVI) and red-edge Chlorophyll index (Clre). This fine-scale imagery extends our morphometric assessment of post-restoration channel scouring exhibiting bedform patterns and sinuosity related to Carex-influenced soil properties and Salix copses, but also provided a view of groundwater-related drainage effects including sinuous Carex-vegetated zones evident from spring snowmelt images as well as NDVI and Clre in growing to senescent phenological stages. Carex in particular was significantly associated with low bulk density and high soil moisture, NDVI, and Clre at lower elevations.

The object of this research is designing of new robot-to-robot communication system working in the middle of highway/roads to support mobile safety for approaching vehicles. The result of research project directs to a group of safety robot devices which induce a vehicle on a bypass route, as a vehicle guidance method using the same, and a vehicle safety guidance system. According to an embodiment of the present invention, a safety device includes a detector configured to detect a vehicle approaching the safety device, and an image projector configured to project an image onto a road surface approaching the vehicle when the detector recognizes an approach of the vehicle. It can include. According to the present invention, when it is necessary to guide the vehicle to the bypass route, the driver of the vehicle can grasp the detour route in time and move the vehicle to the next lane.

Mapping vegetation species is critical to facilitate related quantitative assessment, and for invasive plants mapping their distribution is important to enhance monitoring and controlling activities. Integrating high resolution multispectral remote sensing (RS) image and lidar (light detection and ranging) point clouds can provide robust features for vegetation mapping. However, using multiple source of high-resolution RS data for vegetation mapping at large spatial scale can be both computationally and sampling intensive. Here we designed a two-step classification workflow to decrease computational cost and sampling effort, and to increase classification accuracy by integrating multispectral and lidar data to derive spectral, textural, and structural features for mapping target vegetation species. We used this workflow to classify kudzu, an aggressive invasive vine, in the entire Knox County (1,362 km2) of Tennessee, the United States. Object-based image analysis was conducted in the workflow. The first-step classification used 320 kudzu samples and extensive coarsely labeled samples (based on national land cover) to generate an overprediction map of kudzu using random forest (RF). For the second step, 350 samples were randomly extracted from the overpredicted kudzu and labeled manually for the final prediction using RF and support vector machine (SVM). Computationally intensive features were only used for the second-step classification. SVM had constantly better accuracy than RF, and the Producer’s Accuracy, User’s Accuracy, and Kappa for the SVM model on kudzu was 0.94, 0.96, and 0.90, respectively. SVM predicted 1010 kudzu patches covering 1.29 km2 in Knox County. We found the sample size of kudzu used for algorithm training impacted the accuracy and number of kudzu predicted. The proposed workflow could also improve sampling efficiency and specificity. Our workflow had much higher accuracy than the traditional method conducted in this research, and could be easily implemented to map kudzu in other regions or other vegetation species.

Inland lake variations are considered sensitive indicators of global climate change. However, human activity is playing as a more and more important role in inland lake area variations. Therefore, it is critical to identify whether anthropogenic activity or natural event is playing as the dominant factor in inland lake surface area change. In this study, we proposed a Douglas-Peucker simplification algorithm and bend simplification algorithm combined method to locate major lake surface area disturbances; these disturbances were then characterized to extract the time series change features according to documented records; and the disturbances were finally classified into anthropogenic or natural. We took the nine lakes in Yunnan Province as test sites, a 31 years long (from 1987 to 2017) time series Landsat TM/OLI images and HJ-1A/1B used as data sources, the official records was used as references to aid the feature extraction and disturbance identification accuracy. Results of our method for both disturbance location and the disturbance identification could be concluded as follows: 1) The method can accurately locate the main lake changing events based on the time series lake surface area curve. The accuracy of this model for segmenting the lake area time series curves in our study area was 95.24%. 2) Our proposed method achieved an overall accuracy of 91.67%, with F-score of 94.67 for anthropogenic disturbances and F-score of 85.71 for natural disturbances. 3) According to our results, lakes in Yunnan Provence, China, have undergone extensive disturbances, and the human-induced disturbances occurred almost twice as often as natural disturbances, indicating intensified disturbances caused by human activities. This inland lake area disturbance identification method is expected to uncover whether a disturbance to inland lake area is human activity-induced or natural event.

The sea level rise, a consequence of climate change, is one of the biggest challenges that countries and regions with coastal lowland areas will face in the medium term. This study proposes a methodology for assessing the vulnerability to sea level rise (SLR) on the Atlantic coast of Portugal mainland. Some scenarios of extreme sea level for different return periods and extreme flooding events were estimated for 2050 and 2100, as proposed by the European Union Directive 2007/60/EC. A set of physical parameters are considered for the multi-attribute analysis technique implemented by the Analytic Hierarchy Process, in order to define a Physical Vulnerability Index fundamental to assess coastal vulnerability. For each SLR scenario, coastal vulnerability maps, with spatial resolution of 20 m, are produced at national scale to identify areas most at risk of SLR, constituting key documents for triggering adaptation plans for such vulnerable regions. For 2050 and 2100, it is estimated 903 km² and 1146 km² of vulnerable area, respectively, being the district of Lisbon the most vulnerable district in both scenarios. Results are available through a Web Map Service, for Portuguese public entities, and through a web map viewer for public and communities in general.

Safety of source water streams is an urgent environmental issue, while protections in monsoon controlled subtropical regions face difficulties because of the lack of small scaled observation and analysis in small source water catchments. Basing on continuous weekly water quality data (2014-2017) in Pingqiao River Catchment, the annual average values of TN, NO₃, NH₄ and TP are 3.36, 1.64, 0.28 and 0.02 (mg/L) respectively. During dry, normal and wet seasons, the variability of parameters is over 35%, which indicates an obvious seasonality. Multiple methods are combined in order to assess the water quality and find the driving factors during dry, normal and wet seasons. This study suggests precipitation and fertilization are the mainly seasonal factors, which can make water quality better in wet season than dry season due to the dilution effect. The mechanism between seasonality and compound of nutrients can also be traced by log(TN:TP), and log(NO₃:NH₄). Among six main land use types (forest, tea plantation, cropland (paddy), urban, bare soil and water), the former three ones are influential mostly during dry and wet season. Tea plantation has the largest nutrients discharge amount per area, which is similar to cropland in dry season. By contrary, forest has the double power in reducing nitrogen release in wet and normal seasons. When transformed into paddies, croplands can lower the phosphorus concentration. Conclusions of this study can be used in southeastern China and similar regions on source water protection and agricultural plans.

This study presents an exploration into identifying the interactions between ocean waves and the continental margin in the origination of double-frequency (DF, 0.1-0.5 Hz) microseisms recorded at 33 stations across East Coast of USA (ECUSA) during a ten-day period of ordinary ocean wave climate. Daily primary vibration directions are calculated in three frequency bands and projected as great circles passing through each station. In each band, the great circles from all stations exhibit largest spatial density primarily near the continental slope in the western North Atlantic Ocean. Generation mechanisms of three DF microseism events are explored by comparing temporal and spatial variations of the DF microseisms with the migration patterns of ocean wave fronts in Wavewatch III hindcasts. Correlation analyses are conducted by comparing the frequency compositions of and calculating the correlation coefficients between the DF microseisms and the ocean waves recorded at selected buoys. The observations and analyses lead to a hypothesis that the continental slope causes wave reflection, generating low frequency DF energy and that the continental shelf is where high frequency DF energy is mainly generated in ECUSA. The hypothesis is supported by the primary vibration directions being mainly perpendicular to the strike of the continental slope.

Here is analysed the process of water re-injection in the two hydrocarbon reservoirs/fields in the Upper Miocene sandstone reservoirs, located in the western part of the Sava Depression (Croatia). Namely, those are the "A" field with "L" reservoir and the "B" field with "K" reservoir. Both currently produce hydrocarbons using a secondary recovery method, i.e. water injection (in fact re-injection of the field waters). Three regional reservoir variables had been analysed, namely porosity, permeability and injected water volumes. The number of data was small in all three cases. For porosity: reservoir “L” included 25 data, reservoir “K” 19 data; for permeability: reservoir “L” 10 data, reservoir “K” 18 data; for injected volumes of water: reservoir “L” 10 data; reservoir “K” 3 data. It defined selection of mapping algorithms mostly designed for small datasets (less than 20 points), i.e. Inverse Distance Weighting, Nearest and Natural Neighbourhood. Additionally, the Ordinary Kriging was used, but only with jack-knifed variograms, producing many “artificial points”. Results are extensively tested, using cross-validation and shape recognitions, and the Inverse Distance Weighting method is described as the most appropriate approach for mapping permeability and injected volumes in both reservoirs (“K” and “L”). The Kriging could be slightly outlined as the best approach for porosity. Obtained maps made possible application of the modified geological probability calculation as tools for prediction of successfulness of future injection (probability of 0.56). Consequently, results made possible to plan future injection more efficiently, with smaller injected volumes and same of higher hydrocarbon recovery. That could prevent useless injection, decrease number of injection wells, and save energy and funds invested in such processes.

Water quality forecasting is increasingly significant for agricultural management and environmental protection. Enormous amounts of water quality data are increasingly being collected by advanced sensors, which leads to an interest in using data-driven models for predicting trends in water quality. However, the unpredictable background noises introduced during water quality monitoring seriously degrade the performance of those models. Meanwhile, artificial neural networks (ANN) with feed-forward architecture lack the capability of maintaining and utilizing the accumulated temporal information, which leads to biased predictions in processing time series data. Hence, we propose a water quality predictive model based on a combination of Kernal Principal Component Analysis (kPCA) and Recurrent Neural Network (RNN) to forecast the trend of dissolved oxygen. Water quality variables are reconstructed based on kPCA method, which aims to reduce the noise from the raw sensory data and preserve actionable information. With the RNN's recurrent connections, our model can make use of the previous information in predicting the trend in the future. Data collected from Burnett River, Australia was applied to evaluate our kPCA-RNN model. The kPCA-RNN model achieved R2 scores up to 0.908, 0.823 and 0.671 for predicting the concentration of dissolved oxygen in the upcoming 1, 2 and 3 hours, respectively. Compared to current data-driven methods like ANN and SVR, the predictive accuracy of the kPCA-RNN model was at least 8 %, 17 % and 21 % better than the comparative models in these 3 cases. The study demonstrates the effectiveness of the kPAC-RNN modeling technique in predicting water quality variables with noisy sensory data.

A recent study by the Wall Street Journal reveals that the hydrofractured horizontal wells in shales have been producing less than forecasted by the industry with the empirical hyperbolic decline curve analysis (DCA). As an alternative to DCA, we introduce a simple, fast and accurate method of estimating ultimate recovery (EUR) in oil shales. We adopt a physics-based scaling approach to analyze oil rates and ultimate recovery from 14,888 active horizontal oil wells in the Bakken shale. To predict EUR, we collapse production records from individual horizontal shale oil wells onto two segments of a master curve: (1) We find that cumulative oil production from 4,845 wells is still growing linearly with the square root of time; and (2) 6,401 wells are already in exponential decline after approximately seven years on production. In addition, 2,363 wells have discontinuous production records, because of refracturing or changes in downhole flowing pressure, and are matched with a linear combination of scaling curves superposed in time. The remaining 1,279 new wells with less than 12 months on production have too few production records to allow for robust matches. These wells are scaled with the slopes of other comparable wells in the square-root-of-time flow regime. In the end, we predict that total ultimate recovery from all existing horizontal wells in Bakken will be some 4.5 billion barrels of oil. We also find that wells completed in the Middle Bakken formation, in general, produce more oil than those completed in the Upper Three Forks formation. The newly completed longer wells with larger hydrofractures have higher initial production rates, but they decline faster and have EURs similar to the cheaper old wells. There is little correlation among EUR, lateral length, and the number and size of hydrofractures. Therefore, technology may not help much in boosting production of new wells completed in the poor immature areas along the edges of the Williston Basin. Operators and policy-makers may use our findings to optimize the possible futures of the Bakken shale and other plays. More importantly, petroleum industry may adopt our physics-based method as an alternative to the overly-optimistic hyperbolic DCA that yields an "illusory picture" of shale oil resources.

This study evaluated the impact of climate change on water resources in a large semi-arid urban watershed located in Niamey Republic of Niger, West Africa. The watershed was modeled using the fully integrated surface-subsurface HydroGeoSpheremodel at a high spatial resolution. Historical (1980-2005) and projected (2020-2050) climate scenario derived from the outputs of three Regional Climate Models (RCM) under the RCP 4.5 scenario were statistically downscaled using the multiscale quantile mapping bias correction method. Results show that the bias correction method is optimum at daily and monthly scales, and increased RCM resolution does not improve the performance of the model. The three RCMs predict increases of up to 1.6% in annual rainfall and of 1.58°C for mean annual temperatures between the historical and projected periods. The durations of the Minimum Environmental Flow (MEF) conditions, required to supply drinking and agriculture water, were found to be sensitive to changes in runoff resulting from climate change. MEF occurrences and durations are likely to be greater for (2020-2030), and then they will be reduced for (2030-2050). All three RCMs consistently project a rise in groundwater table of more than 10 meters in topographically high zones where the groundwater table is deep and an increase of 2 meters in the shallow groundwater table.

Eight samples of limestones and marbles were studied by neutron diffraction to collect 2 Quantitative Texture (i.e., Crystallographic Preferred Orientations or CPO) of calcite deforming at 3 different depths in the crust. We studied the different Texture patterns developed in shear zones at 4 different depth and their influence on seismic anisotropies. Samples were collected in the French and 5 Italian Alps, Apennines, and Paleozoic Sardinian basement. They are characterized by isotropic to 6 highly anisotropic (e.g., mylonite shear zone) fabrics. Mylonite limestones occur as shear zone horizons 7 within the Cenozoic Southern Domain in Alpine thrust-and-fold belts (Italy), the Briançonnais domain 8 of the Western Alps (Italy-France border), the Sardinian Paleozoic back-thrusts or in the Austroalpine 9 intermediate units. The analyzed marbles were collected in the Carrara Marble, in the Austroalpine Units 10 in the Central (Mortirolo) and Western Alps (Valpelline). The temperature and depth of development of fabrics vary from < 100◦C, to 800◦C and depth from <10 km to about 30 km, corresponding from upper 12 to lower crust conditions. Quantitative Texture Analysis shows different types of patterns for calcite: 13 random to strongly textured. Textured types may be further separated in orthorhombic and monoclinic 14 (Types A and B), based on the angle defined with the mesoscopic fabrics. Seismic anisotropies were 15 calculated by homogenizing the single crystal elastic tensor, using the Orientation Distribution Function 16 calculated by the Quantitative Texture Analysis. The resulting P- and S-waves anisotropies show a wide 17 variability due to the textural types, temperature and pressure conditions, and dip of the shear planes.