Air pollution has become the world’s single biggest environmental health risk of the past decade, causing about 7 million yearly deaths worldwide. One of the dominant air pollutants is fine particulate matter (PM2.5), a product of combustion. Exposure to PM2.5 has been associated with decreased lung function, impaired immunity, and exacerbations of lung disease. Accumulating evidence suggests that many of the adverse health effects of PM2.5 exposure are associated with lung inflammation and oxidative stress. While the physical structure and surface chemistry of PM2.5 are surrogate measures of particle oxidative potential, little is known about their contributions to negative health effects. In this study, we used functionalized carbon black particles as surrogates for atmospherically aged combustion formed soot to assess the effects of PM2.5 surface chemistry in lung cells. We exposed the BEAS-2B lung epithelial cell line to different soot at a range of concentrations, and assessed cell viability, inflammation, and oxidative stress. Our results indicate that exposure to soot with varying particle surface composition results in differential cell viability rates, expression of pro-inflammatory and oxidative stress genes, and protein carbonylation. We conclude that particle surface chemistry, specifically oxygen content, in soot modulates lung cell inflammatory and oxidative stress responses.

Henry Vidal first introduced the concept of using strips, grids, and sheets for reinforcing soil masses. Since then, a large variety of materials such as steel bars, tire shreds, polypropylene, polyester, glass fibers, coir, jute fibers etc. have been widely added to the soil mass randomly or in a regular, oriented manner. In this investigation, a new concept of multi-oriented plastic reinforcement (hexa-pods), is discussed. A systematic and comprehensive laboratory tests were conducted on unreinforced and reinforced soil samples. Laboratory tests such as direct shear teat and California bearing ratio (CBR) test were analyzed on soil samples consisting of only soil samples, soil sample with random inclusion of hexapods and soil samples with layered inclusion of hexapods. From the results obtained through direct shear test it could be observed that cohesion value of both the soil sample has increased and the angle of internal friction has been decreased after reinforcing it with inclusions in both randomly and layered conditions. CBR test indicates that for same amount of compactive effort, both random and layered inclusions of hexapods show improvement in strength and stiffness. Random inclusions of hexapods give better resistance to penetration as compared to layered inclusions. The hexa-pods also changed the brittle behavior of unreinforced sand samples to ductile ones.

Failaka Island, located in the far east of Kuwait Bay, is about 20 km from the State of Kuwait’s coast, and represents a focal point for regional geography and history, with pristine beaches and archaeological sites dating to the Bronze, Iron, Hellenistic, Christian and Islamic periods. According to environmental data and in coordination with local authorities to develop an urban plan, the island is set to become the first tourist destination for the State of Kuwait. To achieve the Vision of Kuwait 2035, one of the planning objectives focuses around Urban Planning for the Establishment of Environmental Cities that Achieve (UPEECA) environmental sustainability criteria. The paper then, aims to propose the environmental urban plan for Failaka Island. Based around Environmental Analytical Hierarchical Processes (EAHP), and using the Field Calculator and ModelBuilder functions in ArcGIS, this research centres on the feasibility of carrying out an urban plan using suitability modelling that includes four factors and 13 criteria covering the island’s ecological and human composition. This study utilizes both remote sensing (UAVs for 3D imaging) and field study (ground truthing) to identify changes in land use and land cover – such as using sample analysis of the historical sites and soils for tracing evidence and creating/updating a soil map – and create the first GIS database for the island that can lead to generating a suitability model.

Landslides impact on human activities and socio-economic development especially in mountainous areas. This study focuses on the comparison of the prediction capability of advanced machine learning techniques for rainfall-induced shallow landslide susceptibility of Deokjeokri catchment and Karisanri catchment in South Korea. The influencing factors for landslides i.e. topographic, hydrologic, soil, forest, and geologic factors are prepared from various sources based on availability and a multicollinearity test is also performed to select relevant causative factors. The landslide inventory maps of both catchments are obtained from historical information, aerial photographs and performing field survey. In this study, Deokjeokri catchment is considered as a training area and Karisanri catchment as a testing area. The landslide inventories content 748 landslide points in training and 219 points in testing areas. Three landslide susceptibility maps using machine learning models i.e. Random Forest (RF), Extreme Gradient Boosting (XGBoost) and Deep Neural Network (DNN) are prepared and compared. The outcomes of the analyses are validated using the landslide inventory data. A receiver operating characteristic curve (ROC) method is used to verify the results of the models. The results of this study show that the training accuracy of RF is 0.757 and the testing accuracy is 0.74. Similarly, training accuracy of XGBoost is 0.756 and testing accuracy is 0.703. The prediction of DNN revealed acceptable agreement between susceptibility map and the existing landslides with training and testing accuracy of 0.855 and 0.802, respectively. The results showed that, the DNN model achieved lower prediction error and higher accuracy results than other models for shallow landslide modeling in the study area

Urbanization has impacted biodiversity and ecosystems at a global scale. At the same time, it has been recognized as a driver of the gap between humans and nature. The lack of direct contact with nature can deteriorate several aspects of human wellbeing, and change knowledge and attitudes of people towards the environment. However, this phenomenon is still poorly understood in Megacities outside developed countries. Here, we explore the relationship between ecological knowledge and self-reported wellbeing in an important urban park in Santiago, Chile. We conducted semi-structured surveys to park users to explore their notions, preferences, ecological knowledge of plants and birds and self-reported wellbeing. Citizens associated urban parks mainly with “nature”, and particularly with the presence of trees and plants. Trees were recognized as the most relevant elements of urban parks, in turn, birds were ranked as the less relevant. Regarding ecological knowledge, respondents correctly identified an average of 2.01 plants and 2.44 birds out of a total of 10 for each taxon, and exotic species were more likely to be recognized. Park users also reported high scores for self-reported wellbeing. Interestingly, variance of self-reported wellbeing scores tended to increase at low levels of ecological knowledge of trees, but no significant relationship was detected with knowledge of birds, nor native species. These results suggest that parks can positively contribute to bring people closer to nature. Ecological knowledge was related to self-reported wellbeing. Improving ecological knowledge can be critical to restore the relationship between humans and nature in megacities.

With the rising trends in elderly populations around the world, there is a growing interest in understanding how climate sensitivity is related to their thermal perception. Therefore, we analyzed the associations between mortality in the elderly due to cardiovascular (CVD) and respiratory diseases (RD) and meteorological variables, for three cities in the State of São Paulo, Brazil: Campos do Jordão, Ribeirão Preto and Santos, from 1996 to 2017. We applied the Autoregressive Model Integrated with Moving Average (ARIMA) and the Principal Component Analysis (PCA) in order to evaluate statistical associations. Results showed CVD as a major cause of mortality, particularly in the cold period, when a high mortality rate is also observed due to RD. The mortality rate was higher in Campos do Jordão and lower in Santos (and intermediate values in Ribeirão Preto). Campos do Jordão results indicate an increased probability of mortality from CVD and RD due to lower temperatures. In Ribeirão Preto, the lower relative humidity may be related to the increase in CVD and RD deaths. This study emphasizes that, even among subtropical climates, there are significant differences. Therefore, this can assist decision makers in the implementation of mitigating and adaptive measures.

House is the haven that keeps people from natural and human conditions, it gives them trust, safety, and steadiness. It is one of the most basic human needs this became a serious function which cities offer, and became one of the most important aspects which caught urban researchers interest, they take into consideration a wide range of architectural, social, and economic indicators. The study aims to provide an overall conception of Rwandz residential functions, using a collection of parameters and some GIS and statistical techniques, to help establish plans and future projects to improve the growth of this city and other towns and cities in that area. The study found that the old parts of Rwandz city which are located in the core, differ from the outer parts which are relatively newer in many properties, generally, the core is more densely populated than the outer, bigger family size, more illiteracy, and unemployment, few incomes, older houses, smaller houses, in the opposite of the outer parts. Besides, the study tested the correlation coefficient between the criteria; it found some strong statistical relationships between them, which reflected some real-life properties of the residential function. Lastly, the study designed a regression model to predict the main residential function criteria.

Water pollution in Iraq has been extended disaster edge; this pollution is caused by wastes and sewages into soil and rivers, pollutant water sources influence the outbreak and serious epidemic status among the population.. The aim of this study is to determine the bacteriological quality of drinking water sources and characteristics of water-borne diseases especially diarrheal cases in Darbandikhan city. 166 water samples, collected from the different sources and areas, were tested for the presences of coliform bacteria as an indicator for pathogen contamination. Most probable number index was used for coliform enumeration. 161 diarrheal cases were taken as a sample from the patients were admitted to the general hospital in Darbandikhan district.. The questionnaire form was planned to view characteristics of diarrheal cases and patients were interviewed directly, the data was analyzed by STATA software application. 46% of the diarrheal cases used tap water for drinking. On the other hand, the reminder (54%) used other sources for the same purpose. All the risk factor such as type of water source, sufficiency of the water, duration of water storage and chlorination were associated with diarrhea. Almost half of the cases were children and three quarters were single. The data was analyzed by STATA version 13.1.This study indicated that the majority of drinking water sources in Darbandikhan city are not suitable for drinking, although net pipe system supplied chlorine significantly it has been proven that the tap water from the homes is not suitable for consumption.

Rapidly and repeatedly ascertaining the health status of coral reefs is an ever more pressing issue as part of activities to understand and monitor the damaging impacts of climate change. A combination of increasing ocean temperatures, acidity and frequency of extreme storm events continues to alter the marine environment beyond what sensitive organisms, such as coral, can cope with. It is therefore vital to establish technologies and validated methods to provide a metric or indication into the health of these organisms. There are currently many surveys and techniques used by coral scientists to uncover insights into the status and assessment of coral reefs, from colour wheels to multispectral satellite surveys. Here we outline an array of current techniques and methods focused specifically on coral monitoring and health diagnosis, ranging across the length scales from simple diver-based surveyance to satellite remote sensing. The technique of using hyperspectral fluorescence imaging is also introduced as a viable novel addition to aid and extend the current toolbox of available technologies.

Sentinel-1 satellite system continuously observes European countries in a relatively high revisit frequency of 6 days per orbital track. Given the Sentinel-1 configuration, most areas in Czechia are observed every 1–2 days by different tracks in a moderate resolution. This is attractive for various types of analyses by various research groups. The starting point for processing is an original data provided by ESA, for interferometry (InSAR) this level is a Single Look Complex (SLC) data. This work represents advantages of storing data augmented to a specifically corrected level of data, SLC-C. The presented database contains Czech nation-wide Sentinel-1 data stored in burst units that have been preprocessed to the state of a consistent well-coregistered dataset of SLC-C. These are resampled SLC data with their phase values reduced by a topographic phase signature, ready for fast interferometric analyses (an interferogram is generated by a complex conjugate between two stored SLC-C files). The data can be used directly into multitemporal interferometry techniques, e.g. Persistent Scatterers (PS) or Small Baseline (SB) techniques applied here. A further development of the nation-wide system utilising SLC-C data would lead into a dynamic state where every new pre-processed burst triggers a processing update to detect unexpected changes from InSAR time series and therefore provide a signal for early warning against a potential dangerous displacement, e.g. a landslide, instability of an engineering structure or a formation of a sinkhole. An update of the processing chain would also allow use of cross-polarised Sentinel-1 data, needed for polarimetric analyses. The current system is running at a national supercomputing centre IT4Innovations in interconnection to the Czech Copernicus Collaborative Ground Segment (CESNET), providing fast on-demand InSAR results over Czech territories. A full nation-wide PS processing using data over Czechia has been performed in 2017, discovering several areas of land deformation. Its downsampled version and basic findings are demonstrated within the article.

The paper presents a modeling framework to analyze the effect of episodic rainfall supply on groundwater dynamics in the Ionian coastal plain multilayered aquifer. The focus is essentially on the short-term behavior of the shallowest layer, with a specific analysis on episodic rainfall events. In the studied aquifer, groundwater level responds sensitively to rainfall events, highlighting the presence of preferential recharge zones. The hydraulic head peak is a function of groundwater level antecedent to the rainfall event. A kinematic dispersion wave model was used to model infiltration processes via preferential pathways. A one-dimensional and non-linear particle based numerical model was developed. Particles with constant water volume travel according to celerity and hydraulic dispersion to simulate the infiltration rate wave through the vadose zone. The flow rate that reaches the water table represents the input function to determine groundwater level fluctuations along groundwater flow direction and according to the lithological features of the surficial levels of the multilayered aquifer, its storage capacity changes passing from unconfined to confined conditions. The model was validated with high time resolution time series of precipitation and groundwater level coming from Terra Montonata meteo-climatic and groundwater level monitoring station. The developed model represents a basis for evaluating and predicting groundwater discharge of the shallowest layers of the Ionian coastal multilayered aquifer under natural conditions including episodic rainfall.

The South African Weather Service (SAWS) manages an in-situ solar irradiance radiometric network of 13 stations and a very dense sunshine recording network; located in all six macro-climate zones of South Africa. A sparsely distributed radiometric network and over a landscape with dynamic climate and weather shifts is inadequate for solar energy studies and applications. Therefore, there is a need to develop mathematical models to estimate solar irradiation for a multitude of diverse climates. In this study, the annual regression coefficients, a and b, of the Ångström-Prescott (AP) model that can be used to estimate global horizontal irradiance from observed sunshine hours were calibrated and validated with observed station data. The AP regression coefficients were calibrated and validated for each of the six macro-climate zones of South Africa using the observation data that spans 2013 to 2019. The predictive effectiveness of the calibrated AP model coefficients was evaluated by comparing estimated and observed daily global horizontal irradiance. The maximum annual relative Mean Bias Error (rMBE) was 0.371 %, relative Mean Absolute Error (rMAE) was 0.745 %, relative Root Mean Square Error (rRMSE) was 0.910 % and the worst-case correlation coefficient (R2) was 0.910. The statistical validation metrics results show that there is a strong correlation and linear relation between observed and estimated solar radiation values. The AP model coefficients calculated in this study can be used with quantitative confidence in estimating daily GHI data at locations in South Africa where the daily observation sunshine duration data is available.

The coronavirus disease (COVID-19) pandemic caused by spreading rapidly a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has imposed a unique situation for the humanity. Sao Paulo has reported 124,105 confirmed cases of COVID-19 and 5,623 deaths up to June 14th, being considered the epicenter of the pandemic in Brazil and in South America. Due to the measures for social distancing, there was a drop in the air pollution concentration in Sao Paulo. Starting on March 16th, 2020, we broke 90 days of social distancing into 13 weeks and compared to an equivalent period in 2019. We investigated the air quality improvement during the quarantine period and compared the associated avoided deaths to COVID-19 burden deaths. Nitrogen dioxide (NO₂) was the best indicator of air quality in the analyzed weeks, since its reduction reached 58 %. Our study showed that the 5,623 deaths occurred during the analyzed weeks of quarantine represents an economic health loss of US$ 10.5 billion. In opposite, we observed a significant air quality improvement due to pollutants concentrations’ reductions during the analyzed weeks. Considering PM₁₀, PM_2.5 and NO₂, the decrease of concentration levels respectively avoided 78, 337 and 387 premature deaths and prevented up to US$ 1.5 billion on health costs. These results highlight the importance of continuing to enforce existing air pollution regulations and measures to protect human health both during and after COVID-19 pandemic.

This communication reports an improvement of the quality of the electrical data obtained from the application of electrical resistivity tomography method on archaeological studies. The electrical contact between ground and electrode enhances significantly by using carbomer-based gel during the electrical resistivity tomography measurements. Not only does the gel promote the conservation of the building surface under investigation, but it also virtually eliminates the necessity of conventional spike electrodes, which in many archaeological studies are inadequate or not permitted. Results evidenced an enhancement in the quality of the electrical data obtained in the order of thousands of units compared with those without using the carbomer-based gel. The potential and capabilities of this affordable gel make it appropriate to be applied to other geoelectrical studies beyond archaeological investigations. Moreover, it might solve corrosion issues on conventional spike electrodes, and electrical multicore cables usually provoked for added saltwater attempting to improve the electrical contact.

In the last decades droughts, deforestation and wildfires have become recurring phenomena that have affected both human activities and natural ecosystems in Amazonia. The time an ecosystem requires to recover from carbon losses is a crucial metric to evaluate disturbance impacts on forests. However, the factors influencing and controlling the recovery time and its spatiotemporal patterns at the regional scale are still poorly understood. In this study, we combined forest growth model, remote sensing and field plots, to map Amazonia-wide (300-ha resolution) impact and recovery time of aboveground biomass (AGB) after drought, fire and a combination of logging and fire. Our simulated results indicate that AGB decreases by 4%, 19% and 46% in forests disturbed by drought, fire and logging + fire, respectively, with an average AGB recovery time of 27 years for drought, 44 years for burned and 63 years for logged + burned areas and with maximum values reaching 184 years in areas of high fire intensity. Our findings provide two major insights in the spatial and temporal patterns of drought and wildfire in the Amazon: 1) the recovery time of the forests takes longer in the southeastern part of the basin, and, 2) as droughts and wildfires become more frequent – since the intervals between the disturbances is getting shorter than forest regeneration – potentially causing a long-lasting damage in these fragile ecosystems and a permanent degradation.

An accurate radiative transfer model (RTM) is essential for the retrieval of soil moisture (SM) from microwave remote sensing data, such as the passive microwave measurements from the Soil Moisture Active Passive (SMAP) mission. This mission delivers soil moisture products based upon L-band brightness temperature data, via retrieval algorithms for surface and root-zone soil moisture, the latter is retrieved using data assimilation and model support. We found that the RTM based on the tau-omega (?-ω) model, can suffer from significant errors over croplands (in average between -9.4K and + 12.0K for Single Channel Algorithm SCA; -8K and + 9.7K for Dual-Channel Algorithm DCA) if the vegetation scattering albedo (omega) is treated as a constant and the temporal variations are not accounted. In order to reduce this uncertainty, we propose a time-varying parameterization of omega for the widely established zeroth order radiative transfer ?-ω model. The main assumption is that omega can be expressed by a functional relationship between vegetation optical depth (tau) and the Green Vegetation Fraction (GVF). The validation was performed from 14 May to 13 December 2015 over 61 Climate Reference Network sites (SCRN) classified as croplands. The application of the proposed time-varying vegetation scattering albedo results in a consistent improvement for the unbiased root mean square error of 16% for SCA and 15% for DCA. The reduction for positive and negative biases was 45% and 5% for SCA and 26% and 12% for DCA, respectively. This indicates that vegetation dynamics on croplands are better represented by a time-dynamic single scattering albedo.

The study of hydrothermal travertines contributes to the understanding of the interaction between physico-chemical processes and the role played by microbial mats and biofilms in influencing carbonate precipitation. Three active travertine sites were investigated in Central Italy to identify the types of carbonate precipitates and the associated microbial mats at varying physico-chemical parameters. Carbonate precipitated fabrics at the decimetre- to millimetre-scale and microbial mats vary with decreasing water temperature: a) at high temperature (55-44°C) calcite or aragonite crystals precipitate on microbial mats of sulphide oxidizing, sulphate reducing and anoxygenic phototrophic bacteria forming filamentous streamer fabrics, b) at intermediate temperature (44-40°C), rafts, coated gas bubbles and dendrites are associated with Spirulina cyanobacteria and other filamentous and rod-shaped cyanobacteria, c) low temperature (34-33°C) laminated crusts and oncoids in a terraced slope system are associated with diverse Oscillatoriales and Nostocales filamentous cyanobacteria, sparse Spirulina and diatoms. At the microscale, carbonate precipitates are similar in the three sites consisting of prismatic calcite (40-100 µm long, 20-40 µm wide) or acicular aragonite crystals organized in radial spherulites, overlying or embedded within biofilm EPS (Extracellular Polymeric Substances). Microsparite and sparite crystal size decreases with decreasing temperature and clotted peloidal micrite dominates at temperatures < 40°C, also encrusting filamentous microbes. Carbonates are associated with gypsum and Ca-phosphate crystals; EPS elemental composition is enriched in Si, Al, Mg, Ca, P, S and authigenic aluminium-silicates form aggregates on EPS. This study confirms that microbial communities in hydrothermal travertine settings vary as a function of temperature. Carbonate precipitate types at the microscale do not vary considerably, despite different microbial communities suggesting that travertine precipitation, driven by CO2 degassing, is influenced by biofilm EPS acting as template for crystal nucleation (EPS-mediated mineralization) and affecting the fabric types, independently from specific microbial metabolism.

The present study investigates the relationship between the transmission of COVID-19 infections and climate indicators in Dhaka City, Bangladesh, using coronavirus infections data available from the Institute of Epidemiology, Disease Control and Research (IEDCR), Bangladesh. The Spearman-ranked correlation test was carried out to study the association of seven climate indicators, including humidity, air quality, minimum temperature, precipitation, maximum temperature, mean temperature and wind speed with the COVID-19 outbreak in Dhaka City, Bangladesh. The study found that, among the seven indicators, only three indicators (air quality, minimum temperature and average temperature) have a significant relationship with new COVID-19 cases. The results of this paper will give health regulators and policymakers valuable information to lessen the COVID-19 infection in Dhaka and other countries around the world.

This article presents a field-performance investigation on an Integrated Solar Water Supply System (SWSS) at two isolated agricultural areas in Thailand. The two case-study villages (Pongluek and Bangkloy ) have experienced severe draughts in the last decades, and therefore water supply has become a major issue. A stand-alone 15.36 kW solar power and a 15 kW solar submersible pump were installed along with the input power generated by solar panels supported by four solar trackers. The aim is to lift water at the static head of 64 and 48 m via piping length of 400 metres for each village to be stored in 1,000 m3 and 1,800 m3 reservoirs at an average of 300 m3 and 400 m3 per day, respectively for Pongluek and Bangkloy villages. The case study results have shown that the real costs of electricity generated by SWSS using solar PV systems intergraded with the solar tracking system yield better performance and are more advantageous compared with the non-tracking system. This study illustrates how system integration has been employed. System design and commercially available simulation predictions are elaborated. Construction, installation, and field tests for SWSS are discussed and highlighted. Performances of the SWSS in different weather conditions such as sunny, cloudy, and rainy days were analysed to make valuable suggestions for higher efficiency of the integrated solar water supply systems.

The availability of high quality surface observations of precipitation and volume observations by polarimetric operational radars make it possible to constrain, evaluate and validate numerical models with a wide variety of microphysical schemes. In this article, a novel particle-based Monte-Carlo microphysical model (called “McSnow”) is used to simulate the outer rain bands of Hurricane Dorian which traversed the densely instrumented precipitation research facility operated by NASA at Wallops Island, Virginia. The rain bands showed steady stratiform vertical profiles with radar signature of dendritic growth layers near −15 °C and peak reflectivity in the bright band of 55 dBZ along with polarimetric signatures of wet snow with sizes inferred to exceed 15 mm. A 2D-video disdrometer measured frequent occurrences of large drops >5 mm and combined with an optical array probe the drop size distribution was well-documented in spite of uncertainty for drops <0.5 mm due to high wind gusts and turbulence. The 1D McSnow control run and four numerical “experiments” were conducted and compared with observations. One of the main findings is that even at the moderate rain rate of 10 mm/h collisional breakup is essential for the shape of the drop size distribution.

For the first time the distribution and modifications of living ostracod associations present in the Gulf of Trieste (GoT) in relation to the alterations caused by human activity in the last 20 years were investigated. The results were compared with the main chemico-physical parameters (especially nitrogen and phosphorus) measured over the same period, which can lead to a general decrease in environmental quality. For a more in-depth analysis of the changes recorded by ostracods in the last 50 years, a period in which eutrophication and anoxia increased, we revisited the study carried out by Masoli in the GoT in 1967. The results obtained made it possible to verify how over the last 20 years, ostracod assemblages have suffered a decrease both qualitatively and quantitatively. Most of the species recovered show characteristics of opportunism and tolerance to environmentally stressful conditions, high organic matter concentrations and oxygen deficiency. The ostracods analyzed in 1967 showed similar results with few dominant opportunistic species. We verified how ostracods recorded in GoT, similar to Mollusks and Foraminifera, the possible environmental crisis linked to the recurrence of mucilage and hypoxic events documented for the Gulf of Trieste in the last 50 years. Finally, a comparison with the best environmental conditions found in the Marine Nature Reserve of Miramare (MPA) allowed us to emphasize the important role of protected areas to avoid the loss of biodiversity due to urbanization.

Hydrogen gas is seeping from the sedimentary basin of São Franciso, Brazil. The seepages of H₂ are accompanied by helium whose isotopes reveal a strong crustal signature. Geophysical data indicates that this intra-cratonic basin is characterized by i) a relatively high geothermal gradient, ii) deep faults delineating a horst and graben structure and affecting the entire sedimentary sequence, iii) an archean to paleoproterozoïc basements enriched in radiogenic elements and displaying mafic and ultramafic units, and iv) a possible karstic reservoir located 400 m below the surface. The high geothermal gradient could be due to a thin lithosphere enriched in radiogenic elements, which can also contribute to a massive radiolysis process of water at depth, releasing an important amount of H₂. Alternatively, ultramafic rocks that may have generated H₂ during their serpentinization are also documented in the basement. The seismic profiles show that the faults seen at the surface are deeply rooted in the basement, and can drain deep fluids to shallow depths in a short time scale. The carbonate reservoirs within the Bambuí group which forms the main part of the sedimentary layers are crossed by the fault system and represent good candidates for temporary H₂ accumulation zones. The formation by chemical dissolution of sinkholes located at 400 m depth might explain the presence of sub-circular depressions seen at the surface. These sinkholes might control the migration of gas from temporary storage reservoirs in the upper layer of the Bambuí formation to the surface. The very high fluxes of H₂ escaping out of these structures which have been recently documented are, however, in disagreement with the newly developed H₂ production model in the Precambrian continental crust. They either question the validity of these models or the measurement methodology.

The most extensive urban growths in the next 30 years are expected to occur in developing countries. Lagos, Nigeria - Africa’s second most populous megacity- is a prime example. To achieve more sustainable and resilient cities, there is a need for modeling the urban growth patterns of major cities and analyzing their implications. In this study, the urban growth of Lagos state was modeled using the Multi-Layer Perceptron (MLP) neural network for the transition modeling and the Markov Chain analysis for the change prediction, achieving a model accuracy of 81.8%. An innovative visual validation of the model results using the ArcGIS was combined with kappa correlation statistics. The results show that by 2031, built-up areas will be the most spatially extensive LULC class in the study area with percentage coverage of 34.1% as opposed to 9% in 1986. The coverage of bare areas is also expected to increase by 53% between 2016 and 2031. Conversely, 24.9% and 68.3% loss of forestlands and wetlands respectively, are expected between 2016 and 2031. In view of the 11^th goal of SDGs which focuses on achieving sustainable cities and communities, the objectives of African Union’s Agenda 2063, and based on the urban growth trends observed, the study recommends a prioritization of vertical expansion as opposed to the current horizontal urban growth trends in the study area.

Solar3D is an open-source software application designed to interactively calculate solar irradiation at three-dimensional (3D) surfaces in a virtual environment constructed with combinations of 3D city models, digital elevation models (DEMs), digital surface models (DSMs) and feature layers. The GRASS GIS r.sun solar radiation model computes solar irradiation based on two-dimensional (2D) raster maps for given day, latitude, surface and atmospheric conditions. With the increasing availability of 3D city models and demand for solar energy, there is an urgent need for better tools to computes solar radiation directly with 3D city models. Solar3D extends GRASS GIS r.sun from 2D to 3D by feeding the model with input, including surface slope, aspect and time-resolved shading, that is derived directly from the 3D scene using computer graphics techniques. To summarize, Solar3D offers several new features which, as a whole, distinguish itself from existing 3D solar irradiation tools: (1) the ability to consume massive heterogeneous 3D city models, including massive 3D city models such as oblique airborne photogrammetry-based 3D city models (OAP3Ds or integrated meshes); (2) the ability to perform near real-time pointwise calculation for duration from daily to annual; (3) the ability to integrate and interactively explore large-scale heterogeneous geospatial data. (4) the ability to calculate solar irradiation at arbitrary surface positions including at rooftops, facades and the ground. Solar3D is publicly available at https://github.com/jian9695/Solar3D.

Debris-covered glaciers are a notable feature in the greater Himalaya, and their ongoing mass loss under changing climate will affect the water resources of over a billion people. The current knowledge of the mass balance of Himalayan glaciers is restricted by the paucity of in-situ measurements of glaciers in both space and time, as well as the resolution of satellite remote sensing imageries. Recently, the use of Unmanned Aerial System (UAS) imagery has shown the potential to bridge this gap by enabling very detailed monitoring of inaccessible glacial areas. UAS imagery-based monitoring of Himalayan glaciers has so far been limited to a single glacier in the entire Himalaya, providing a limited understanding of spatial variability in glacier mass balance and driving factors. In the first UAS based glacial mass change estimation in the trans-Himalaya, we conducted two Unmanned Aerial System (UAS) surveys (May and November 2019) over the debris-covered Annapurna III glacier in the Himalaya. We performed Structure-from-Motion (SfM) analysis and utilized differential GPS field observations to derive geometrically accurate point clouds, ortho-mosaics and digital surface models (DSMs). The glacial volumetric loss was estimated from DSM differencing, and the magnitude and spatial variability of glacier surface change was derived from 3-D differencing of point clouds. Results revealed a heterogeneous glacial melt pattern, with an average elevation loss of 0.89 m during the monitored time period. The majority of the glacial tongue exhibited surface lowering except the area above and around the glacial snout that surprisingly exhibited significant elevation gain. Both the highest magnitude of mass loss and the highest spatial variability in mass change was observed in areas with exposed ice-cliffs and supraglacial ponds. Glacial surface velocity derived from manual feature tracking showed velocity ranging from 0-4.1 m. A detailed evaluation of specific areas allowed an improved understanding of the complex interplay of factors leading to observed surface change. Our findings expand the extent of UAS based monitoring of debris-covered glaciers in the Himalaya and conclude that UAS derived 3D topographic products will become increasingly important for monitoring of thinning debris-covered glaciers.

Wetlands are carriers of metals and organic pollutants from natural and anthropogenic sources. Studies have shown that sediments are sinks for these pollutants as they carry more than 99% of the total amount of metals which are released as soluble metals into the Lagoon. The University of Lagos, located at Akoka North eastern part of Yaba in Lagos Mainland runs along a highly industrialized and residential area. This paper reports the total metal concentrations in wetland and sediment and their seasonal variations. Wetland and sediment samples were collected for two seasons in 8 sampling sites. Results showed wetland data having lower concentrations of metals as compared to sediments. Wet season had higher concentrations of metals as compared to other season. Total concentrations in wetlands were Cd (1.0-6.0), Cu (1.0-11.0), Cr (0.3-82.7), Zn (2.7-65.0) and Pb (0.3-16.0) all in μg L-1 respectively. Statistical analysis showed that there was a significant difference between the wet and dry seasons for sediments. Cd was the only metal at acceptable levels while Cr, Cu, Pb and Zn concentrations all exceeded levels deemed acceptable by the SABS and the EPA. Therefore, the wetland is in poor health.

Heavy metals have been implicated as Lagoon pollutants from sources such as agriculture, mining and manufacturing industries and waste water treatment works. A study was carried out in the University of Lagos lagoon to investigate the seasonal bioavailability of the heavy metal contaminants Cd, Cr, Cu, Pb and Zn. The physical parameters pH, redox potential, temperature, TDS and conductivity were measured on site. Dried sediment samples were extracted using the Community Bureau of Reference (BCR) sequential procedure and analysed by ICP-OES. A certified reference material (CRM), BCR 701 (lake sediment) was used for quality assurance with recoveries ranging between 80-120%. Statistical analysis (ANOVA) showed that there was a significant difference between metal distribution in the dry and wet seasons. Cu, Pb and Zn were in the available fractions (carbonate, Fe/Mn oxide and organic). Cu was highest in the Fe/Mn oxide and organic fractions. This indicated that an increase in organic matter and reducing agents will avail the Cu. Zn was distributed in all fractions while Pb was found in the Fe/Mn oxide fraction (3.93- 21.3 %). Results showed that the bioavailability of Cu, Pb and Zn was high. Metal bioavailability by BCR indicates a potential risk of pollution in lagoon sediments as the available metals exceeded the permissible Sediment Quality Assessment Guidelines (SQAG’s) from Environmental Protection Agency (EPA).

This study investigated the spatial variability of soil organic carbon (SOC), total nitrogen (TN), soil microbial biomass carbon (SMBC) and soil microbial biomass nitrogen (SMBN) in Hongqipao reservoir dominated by different vegetation types and the possible relationships with other soil properties. Top 0–50cm soil samples were collected in sites dominated by different vegetation types within the reservoir littoral zone. There was high spatial variability for SOC, TN, SMBC and SMBN in the Hongqipao reservoir. In addition, the SOC, TN, SMBC and SMBN contents decreased with increasing soil depth. This could be attributed by the fact that when plants detritus decompose, most of their organic matter is mineralized and a new soil layer which contains a greater amount of organic carbon is formed at the top. According to Pearson's correlation values and redundancy analysis (RDA) results, SOC was significantly and positively correlated with TN likely because the vegetation organic matter and liter could be the main nitrogen sources. Similarly, soil moisture content (MC) was significant positive correlated with SOC and TN. Conversely, BD was significant negative correlated with SOC and TN contents in the 0-50 cm soil profiles. However, no significant correlations were observed between SOC, TN, SMBC and SMBN contents and soil pH values. SMBN was significantly and positive correlated with C:N ratio and BD and negative related with MC. Multiple linear regression model revealed that all measures soil properties in this study could explain higher significant variability of the response variables (SOC, TN, SMBC and SMBN contents). This implies that all the measured soil variables within the different vegetation types in the reservoir played a crucial role in determining the contents of SOC, TN, SMBC and SMBN. This study further suggests that vegetation types play a major role in determining the spatial characteristics of SOC and TN. Any changes in the vegetation types in the reservoir may influence the distribution of SOC and TN. This may affect the global carbon budget and the atmospheric greenhouse gas concentration significantly.

Mangrove forests store and sequester large area-specific quantities of blue carbon (C_org). Except for tundra and peatlands, mangroves store more C_org per unit area than any other ecosystem. Mean mangrove C_org stock is 738.9 Mg C_org ha⁻¹ and mean global stock is 6.17 Pg C_org, which equates to only 0.4–7% of terrestrial ecosystem C_org stocks but 17% of total tropical marine C_org stocks. Seagrasses sequester more C_org per unit area than mangroves (179.6 g C_org m⁻²·a⁻¹) but twice the C_org sequestered by mangroves globally (15 Tg C_org a⁻¹). Mangroves sequester only 4% (range 1.3–8%) of C_org sequestered by terrestrial ecosystems, indicating that mangroves are a minor contributor to global C storage and sequestration. CO₂ emissions from mangrove losses equate to 0.036 Pg CO₂-equivalents a⁻¹ based on rates of C sequestration but 0.088 Pg CO₂-equivalents a⁻¹ based on complete destruction for conversion to aquaculture and agriculture. Mangrove CO₂ emissions account for only 0.2% of total global CO₂ emissions but 18% of CO₂ emissions from the tropical coastal ocean. Despite significant data limitations, the role of mangrove ecosystems in climate change mitigation is globally insignificant but may be more significant and effective at the national and regional scale.

The most massive and fast-eroding thaw slump of the Northern Hemisphere located in the Yana uplands of northern Yakutia was investigated to assess in detail the cryogenic inventory and carbon pools of two distinctive Ice Complex stratigraphic units and the uppermost cover deposits. Differentiating into modern and Holocene near-surface layers (active layer and shielding layer), highest total carbon contents were found in the active layer (18.7 kg m-2), while the shielding layer yielded much lower carbon content of 1.8 kg m-2. The late Pleistocene upper Ice Complex contained 10.4 kg m-2 total carbon, and the mid-Pleistocene lower Ice Complex 17.7 kg m-2. The proportion of organic carbon from total carbon content is well above 70% in all studied units with 94 % in the active layer, 73% in the shielding layer, 83% in the upper Ice Complex and 79% in the lower Ice Complex. Inorganic carbon is low in the overall structure of the deposits.

Remote sensing has been used as an important tool for disaster monitoring and disaster scope extraction, especially for the analysis of spatial and temporal disaster patterns of large-scale and long-duration series. Based on the Google Earth Engine cloud platform, this study used MODIS vegetation index products with 250-m spatial resolution synthesized over 16 days from the period 2005–2019 to develop a rapid and effective method for monitoring disasters across a wide spatiotemporal range. Three types of disaster monitoring and scope extraction models are proposed: the normalized difference vegetation index (NDVI) median time standardization model (R_{NDVI_TM(i)}), the NDVI median phenology standardization model (R_{NDVI_AM(i)(j)}), and the NDVI median spatiotemporal standardization model (R_{NDVI_ZM(i)(j)}). The optimal disaster extraction threshold for each model in different time phases was determined using Otsu’s method, and the extraction results were verified by medium-resolution images and ground-measured data of the same or quasi-same period. Finally, the disaster scope of cultivated land in Heilongjiang Province from 2010–2019 was extracted, and the spatial and temporal patterns of the disasters were analyzed based on meteorological data. This analysis revealed that the three aforementioned models exhibited high disaster monitoring and range extraction capabilities, with verification accuracies of 97.46%, 96.90%, and 96.67% for R_{NDVI_TM(i)}, R_{NDVI_AM(i)}, and _(j)R_{NDVI_ZM(i)(j)}, respectively. The spatial and temporal disaster distributions were found to be consistent with the disasters of the insured plots and the meteorological data across the entire province. Moreover, different monitoring and extraction methods were used for different disasters, among which wind hazard and insect disasters often required a delay of 16 days prior to observation. Each model also displayed various sensitivities and were applicable to different disasters. Compared with other techniques, the proposed method is fast and easy to implement. This new approach can be applied to numerous types of disaster monitoring as well as large-scale agricultural disaster monitoring and can easily be applied to other research areas. This study presents a novel method for large-scale agricultural disaster monitoring.

Carbonaceous material (CM) is common in meta-sediments and is generally interpreted to be intimately associated with gold mineralization. For the Bumo deposit in Hainan Province, South China, CM is mainly hosted by greenschist facies- to amphibolite-facies metamophic rocks of the Paleo- to Mesoproterozoic Baoban Group and by auriferous veins and is used as an important gold prospecting indicator. However, the genesis of CM and the relationship with gold mineralization are still not clear. Field work and thin section observation indicates that two types of CM occur, i.e., layered and veinlet. Layered CM, up to meters thick, prevails in the deposit. More importantly, Au-bearing sulfides are commonly distributed along the boundary between the quartz veins and layered CM. In contrast, veinlet CM, co-precipitated with gold and sulfides, has the thickness of micro- to centi- meters, and these thin veins occur in quartz veins and hydrothermally altered rocks. Scanning Electron Microscope (SEM) analysis indicates that layered CM has a stringy shape and laminate structure, while veinlet CM occurs as isometric particles. Raman carbonaceous material geothermometer indicates that layered CM with high maturity is formed at elevated temperatures of 400 – 550°C, consistent with X-ray diffraction (XRD) analysis. In contrast, veinlet CM with low maturity is formed at 200 – 350°C, generally consistent with gold mineralization. In addition, layered CM has δ13C values ranging from -30 to -20%, demonstrating a biogenic origin. Consequently, it is interpreted that layered CM is formed by a pre-ore metamorphic event during Caledonian, and its reducing nature promotes gold precipitation via destabilization of aqueous Au bisulfide complexes or facilitating sulfidation. Veinlet CM is hydrothermal origin, and its precipitation modified the chemical conditions of ore fluids, leading to the destabilization of Au complexes and thus favorable for mineralization.

The Geostationary Environment Monitoring Spectrometer (GEMS) onboard the Geostationary Korean Multi-Purpose Satellite 2B was successfully launched in February 2020. GEMS is a hyperspectral spectrometer measuring solar irradiance and Earth radiance in the range of 300 to 500 nm. This paper introduces the spectral calibration algorithm for GEMS, which uses a nonlinear least-squares approach. To assess the performance of the algorithm, sensitivity tests for a series of spectral parameters such as shift, spectral range for fitting, signal-to-noise ratio, spectral response function (SRF), and reference spectrum have been conducted. To improve the assessment, a synthetic GEMS spectrum using the prelaunch GEMS SRF is adopted here. The test results show that the required accuracy (0.002 nm) is achievable for the expected uncertainties of the parameters except for the SRF and the choice of high-resolution reference spectrum, which degrade the algorithm performance by an order magnitude. To mitigate the sensitivity to SRF, retrieval of in-orbit SRF using an analytic function is suggested. Finally, a few candidates for the high-resolution solar reference spectrum are prepared for testing by the instrument during in-orbit tests.

Sentinel-2 provides the opportunity to map the snow cover at unprecedented spatial and temporal resolution at global scale. Here we calibrate and evaluate a simple empirical function to estimate the fractional snow cover (FSC) in open terrain using the normalized difference snow index (NDSI) from 20 m resolution Sentinel-2 images. The NDSI is computed from flat surface reflectances after masking cloud and snow-free areas. The NDSI-FSC function is calibrated using Pléiades very high resolution images and evaluated using independent datasets including SPOT 6/7 satellite images, time lapse camera photographs, terrestrial lidar scans and crowd-sourced in situ measurements. The calibration results show that the FSC can be represented with a sigmoid-shaped function 0.5×tanh(a×NDSI+b)+0.5 where a = 2.65 and b = -1.42 yielding a root mean square error of 25%. Similar RMSE are obtained with different evaluation datasets with a high topographic variability. With this function, we estimate that the confidence interval on the FSC retrievals is 38% at the 95% confidence level.

Corundum-rich (up to 55 vol.%) felsic dykes formed by albite, ±K-feldspar, ±hercynite and ±biotite-siderophyllite cut the lower crustal rocks exposed in the Ivrea-Verbano Zone (NW Italy). Zircon is an abundant accessory mineral and its investigation through LA-ICP-(MC)MS has allowed to directly constrain the timing of emplacement, as well as petrology and geochemistry of parental melts. Zircons are characterised by very large concentration in REE, Th, U, Nb and Ta, and negative Eu anomaly. U–Pb analysis points to Norian emplacement ages (223±7 Ma and 224±6 Ma), whereas large positive εHf(t) values (+13 on average) indicate a derivation from depleted to mildly enriched mantle source. The mantle signature and the corundum oversaturation were preserved thanks to limited crustal contamination of the host, HT refractory granulites and mafic intrusives. According to the geochemical data and to the evidence of the development of violent explosions into the conduits, it is proposed that dykes segregated from peraluminous melts produced by exsolution processes affecting volatile-rich differentiates during alkaline magmatism. This work provides robust evidence about the transition of the geochemical affinity of Southern Alps magmatism from orogenic-like to anorogenic during Norian, linked to a regional uprising of the asthenosphere and change of tectonic regime.

The presence of heavy metals in cocoa crops is currently a serious problem for farmers and producers in various regions of Peru. Exports of cocoa and its derivatives to European markets are threatened by possible indications of contamination of heavy metals such as Cadmium (Cd) in cocoa beans for export, some at levels higher than those allowed by food regulations established by European Community, which are oriented to consumer protection. The possible sources of cadmium contamination in cocoa crops can be due to natural activities, such as volcanic activities, erosion processes and sedimentation in rocks that contain minerals with a high content of heavy metals (Greenockite, Burnsite, Hawleite, among others), and by anthropogenic activities such as: mining of associated deposits, agriculture (use of fertilizers), smelting of minerals and burning of fossil fuels. Currently, there is no information about contents of heavy metals in areas located in The Campo Verde-Honoria-Tournavista corridor (Ucayali Region and Huanuco Region), due to this, in this work we make a study of this region. A methodology will be used to take cocoa samples from the seed and soil in these areas, and then through chemical analysis, determine the concentrations of heavy metals, using an atomic absorption spectrophotometer. As a consequence of this study, we determined and concluded that the concentrations of cadmium obtained in soils are slightly higher than European standards allowed. Therefore, with this information we prepare a geochemical cadmium map in soils for the study area, which will help cocoa producers to identify areas that exceed the allowed cadmium values, in order to comply with standards that European Community demands in relation of concentration levels of heavy metals (Cadmium) in chocolate. In this way, we can carry out in the future a mitigation plan for areas with cadmium problems, which allows to reduce their content in order to export the product without any problem. .

The geometry of mesoscopic inhomogeneities plays an important role in determining the macroscopic propagation behaviors of elastic waves in a heterogeneous medium. Non-equiaxed inhomogeneities can lead to anisotropic wave velocity and attenuation. Developing an accurate scattering theory to describe the quantitative relation between the microstructure features and wave propagation parameters is of fundamental importance for seismology and ultrasonic nondestructive characterization. This work presents a multiple scattering theory for strongly scattering elastic media with general tri-axial heterogeneities. A closed analytical expression of the shape-dependent singularity of the anisotropic Green’s tensor for the homogeneous reference medium is derived by introducing a proper non-orthogonal ellipsoidal coordinate. Renormalized Dyson’s equation for the coherent wave field is then derived with the help of Feynman’s diagram technique and the first-order-smoothing approximation. The exact dispersion curves and the inverse Q-factors of coherent waves in several representative medium models for the heterogeneous lithosphere are calculated numerically. Numerical results for small-scale heterogeneities with the aspect ratio varying from 1 to 7 show satisfactory agreement with those obtained from real earthquakes. The results for velocity dispersion give rise to a novel explanation to the formation mechanism of different seismic phases. The new model has potential applications in seismology and ultrasonic microstructure characterization.

The paper presents a conceptual model of the route of macroplastic debris (5 > mm) through a fluvial system, which can support future works on the overlooked processes of macroplastic storage and remobilization in rivers. We divided the macroplastic route into (1) input, (2) transport, (3) storage, (4) remobilization and (5) output phases. Phase 1 is mainly controlled by humans, phases 2–4 by fluvial processes, and phase 5 by both types of controls. We hypothesize that natural characteristics of fluvial systems and their modification by dam reservoirs and flood embankments construction are key controls on macroplastic storage and remobilization in rivers. The zone of macroplastic storage can be defined as a river floodplain inundated since the beginning of widespread disposal of plastic waste to the environment in the 1960s and remobilization zone as a part of the storage zone influenced by floodwaters and bank erosion. The amount of macroplastic in both zones can be estimated using data on the abundance of surface- and subsurface-stored macroplastic and the lateral and vertical extent of the zones. Our model creates the framework for estimation of how much plastic has accumulated in rivers and will be present in future riverscapes.

Phenology is one of the ubiquitous fingerprints of climate change on our ecosystems. Monitoring the spatiotemporal patterns of vegetation phenology is thus critical. A wide range of sensors have been used to monitor vegetation phenology. Sensor point of view and resolution can potentially impact estimates of phenology. We compared three different sensors from three different remote sensing platforms—a UAV mounted RGB camera, an under canopy, upward facing hemispherical camera with R, G and NIR capabilities, and a tower mounted RGB PhenoCam—to estimate spring phenological transition in a mixed-species temperate forest in central Virginia, USA. Our study had two objectives: 1) to compare the above- and below- canopy inference of canopy greenness (green chromatic coordinate and normalized difference vegetation index) and canopy structural attributes (leaf area and gap fraction) by matching under-canopy hemispherical photos with high spatial resolution (0.03 m) drone imagery to find the appropriate spatial coverage and resolution for comparison; 2) to compare how each sensor performed in estimating the temporality of the spring phenological transition. We find that a spatial buffer of 20 m radius for UAV imagery is most closely comparable to under-canopy imagery in this system. Sensors and platforms agree within +/- 5 days of when canopy greenness stabilizes from the spring phenophase into the growing season. This work has implications for paring UAV imagery with both tower-based observation platforms, as well as plot-based studies (e.g. long-term monitoring, existing research networks, permanent plots).

Monitoring lakes in high-latitude areas can provide a better understanding of freshwater systems sensitivity and accrete knowledge on climate change impacts. Phytoplankton are sensitive to various conditions: warmer temperatures, earlier ice-melt and changing nutrient sources. Satellite imagery can monitor algae biomass over large areas. The detection of chlorophyll a (chl-a) concentrations in small lakes is hindered by the low spatial resolution of conventional ocean colour satellites. The short time-series of the newest generation of space-borne sensors (e.g. Sentinel-2) is a bottleneck for assessing long-term trends. Although previous studies have evaluated the use of high-resolution sensors for assessing lakes' chl-a, it is still unclear how the spatial and temporal variability of chl-a concentration affect the performance of satellite estimates. We discuss the suitability of Landsat (LT) 30-m resolution imagery to assess lakes' chl-a concentrations under varying trophic conditions, across extensive high-latitude areas in Finland. We use in situ data obtained from field campaigns in 19 lakes and generate remote sensing estimates of chl-a, taking advantage of the long-time span of the LT 5 and 7 archives, from 1984 to 2017. Our results show that linear models based on LT data can explain approximately 50 % of the chl-a interannual variability. However, we demonstrate that the accuracy of the estimates is dependent on the lake's trophic state, with models performing in average twice as better in lakes with higher chl-a concentration (> 20 µg/l) in comparison with less eutrophic lakes. Finally, we demonstrate that linear models based on LT data can achieve high accuracy (R2 = 0.9; p-value < 0.05) in determining lakes' annual mean chl-a concentration, allowing the mapping of the trophic state of lakes across large regions. Given the long time-series and high spatial resolution, LT-based estimates of chl-a provide a tool for assessing the impacts of environmental change.

The river Teign in Devon has come under scrutiny for failing to meet Environmental Quality Standards for ecotoxic metals due to past mining operations. A disused mine known as Bridford Barytes mine, has been found to contribute a significant source of Zn, Cd and Pb to the river. Recently, studies have been focused on the remediation of such mine sites using low-cost treatment methods to help reduce metal loads to the river downstream. Red mud is a waste product from the aluminium industry, the utilization of this resource has proven an attractive low-cost treatment method for adsorbing toxic metals. Adsorption kinetics and capacity experiments reveal metal removal efficiencies of up to 70% within the first 2 hours when red mud is applied in pelletized form. Biochar is another effective adsorbent with the potential to remove >90% Zn using agricultural feedstock. Compliance of the Teign has been investigated by analysing dissolved metal concentrations and bioavailable fractions of Zn to assess if levels are of environmental concern. By applying a Real-World Application Model, this study reveals that compressed pellets and agricultural biochar offer an effective, low-cost option to reducing metal concentrations and thus improving the quality of the river Teign.

Consistent data is seldom available for whole-catchment flood modelling in many developing regions, thus this study demonstrates how the complementary strengths of open and readily available geospatial datasets and tools can be leverage to map flood risk within acceptable levels of uncertainty for flood risk management. Available fragmented remotely-sensed and in situ datasets (including hydrological data, altimetry, digital elevation model, bathymetry, aerial photos, optical and radar imageries) are systematically integrated using 2-dimensional CAESAR-LISFLOOD model to quantify and recreate the extent and impact of the historic 2012 flood in Nigeria. Experimental modelling, calibration and validation is undertaken for the whole Niger-South hydrological catchment area of Nigeria, then segmented into sub-domains for re-validation to understand how data variability and uncertainties impact on the accuracy of model outcomes. Furthermore, aerial photos are applied for the first time in the study area for flood model validation and to understand how different physio-environmental properties influence synthetic aperture radar flood delineation capacity in the Niger Delta region of Nigeria.

The mass concentration of particulate matter (PM) has been systematically used in epidemiological studies as exposure indicator, to relate airborne concentrations with a wide variety of human health effects, which can be hardly explained by using this single parameter. In fact, PM is a “particle cocktail” that includes a complex mixture of compounds with a wide range of sizes, chemical compositions and emission sources. Current research hypothesizes that many of the adverse health effects are derived from oxidative stress in biological systems caused by the deposition of PM into the lungs. This emerging hypothesis is called the oxidative stress paradigm. In this commentary article we analize how this new paradigm could help to answer the as-of-yet unanswered questions related to the mechanism of action of PM pollution on human health. Acellular oxidative potential (OP) assays have been emerged as a promising approach to quantify the PM potential to induce oxidative stress and to relate it with the chemical composition and size distribution of PM. Recent researches have shown that the OP is related to the presence of metals, organic carbon, polyaromatic hydrocarbons and quinones. However, the association between PM and particle-induced toxicity is still largely unknown. Therefore, additional research is needed to identify the specific PM characteristic(s), such as its specific size, emission source or chemical content, which contribute the most to its redox activity. Thus, the OP measurements provide information that allows us to evaluate and integrate the toxic potential of PM in a unique parameter, whose relationships with emission sources, size distribution and/or chemical composition should be faced in the near future.

Lake Urmia in northwestern Iran is the largest lake in Iran and the second largest saltwater lake in the world. The water level in Lake Urmia has decreased dramatically in recent years, due to drought, climate change, and overuse of water resources for irrigation. This shrinking of the lake may affect local climate conditions, assuming that the lake itself affects the local climate. In this study, we quantified the lake’s impact on the local climate by analyzing hourly time series of data on climate variables (temperature, vapor pressure, relative humidity, evaporation, and dewpoint temperature for all seasons, and local lake/land breezes in summer) for the period 1961-2016. For this, we compared high quality, long-term climate data obtained from Urmia and Saqez meteorological stations, located 30 km and 185 km from the lake center, respectively. We then investigated the effect of lake level decrease on the climate variables by dividing the data into 1961-1995 (normal lake level) and 1996-2016 (low lake level). The results showed that at Urmia station (close to the lake), climate parameters displayed fewer fluctuations and were evidently affected by Lake Urmia compared with those at Saqez station. The effects of the lake on the local climate increased with increasing temperature, with the most significant impact in summer and the least in winter. The results also indicated that, despite decreasing lake level, local climate conditions are still influenced by Lake Urmia, but to a lesser extent.

The boundary layer height (BLH) distinguishes the interface between the lower and free atmosphere, which is a key variable in numerical simulation, aerosol, and environmental pollution studies. Thus, the goal of this work is to propose a novel method conjuncting with numerical regularization to analyze climate characteristics of the marine boundary layer height (MBLH) using 2007-2011 GPS-RO data from the COSMIC mission. While traditionally, the difference method has been used to achieve this aim, herein, we propose an innovative method, in which the bending angle profile gradient was calculated using the numerical regulation method where the regulation parameters are determined by the double-parameter model function method. Then, the MBLH was determined by employing the maximum gradient method to ascertain the height corresponding to the smallest gradient. The results show a correlation between currents and the MBLH—a relationship that has not been previously demonstrated. A low MBLH is associated with seasons and regions where cold ocean currents are prevailing; whereas a high MBLH is observed in the seasons and places where warm currents are prevailing. This correlation was validated by comparing the obtained results with different occultation data—i.e., atmprf and echprf—which also established that atmprf is more sensitive to convective cloud top capture. In seas with active convection, the armprf calculated bending angle is higher than that from echprf. Subsequently, the standard deviation was used to express the MBLH confidence level. The results show that the MBLH standard deviation is highest in low latitudes and lowest in the middle and high latitudes. Furthermore, we analyzed the interannual MBLH variation trend, which displayed a seasonal variation and spatial distribution corresponding with the current and subsolar point. Finally, we conducted a case study in the South China Sea, and identified a distinctive seasonal change and downward trend.

In-cloud ice mass accretion on wind turbines is a common challenge faced by energy companies operating in cold climates. On-shore wind farms in Scandinavia are often located in regions near patches of forest, the heterogeneity length scales of which are often less than the resolution of many numerical weather prediction (NWP) models. The representation of these forests--including the cloud water response to surface roughness and albedo effects related to them--must therefore be parameterized in NWP models used as meteorological input in ice prediction systems, resulting in an uncertainty that is poorly understood and to present date not quantified. The sensitivity of ice accretion forecasts to the subgrid representation of forests is examined in this study. A single column version of the HARMONIE-AROME 3D NWP model is used to determine the sensitivity of the forecast of ice accretion on wind turbines to the subgrid forest fraction. Single column simulations of a variety of icing cases at a location in northern Sweden were examined in order to investigate the impact of vegetation cover on ice accretion in varying levels of solar insulation and wind magnitudes. In mid-winter cases, the wind speed response to surface roughness was the primary driver of the vegetation effect on ice accretion. In early season cases, the cloud water response to surface albedo effects plays a secondary role in the impact of in-cloud ice accretion, with the wind response to surface roughness remaining the primary driver for the surface vegetation impact on icing. Two different surface boundary layer (SBL) forest canopy subgrid parameterizations were tested in this study that feature different methods for calculating near-surface profiles of wind, temperature, and moisture, with the ice mass accretion again following the wind response to surface vegetation between both of these schemes.

Increased construction activities in Australia have led to the generation of a massive volume of construction and demolition (C&D) waste annually. Management of this volume of waste requires an effective waste framework. The literature reported that the current Australian waste framework does not meet expectations and needs immediate improvements. This study was therefore conducted to seek the key C&D stakeholders' opinion about various issues identified in this waste stream. The specific objectives of this study are identification of the main barriers to effective C&D waste management, the impact of current regulations and opportunities to enhance the current C&D waste management system. A survey was conducted in 2019 to capture the stakeholders' perception. 132 participants from various industries and government agencies based in various Australian jurisdictions took part in the survey. The key results showed that the main barriers towards an effective management system are "overregulation, tough acceptance criteria, and increased testing requirements", "lack of local market" and "culture, poor education and low acceptance". Furthermore, a low number of participants indicated that the current legislation encourages waste recovery activities. The main areas of improvements included "providing a guideline that determines the accepted level of contamination for reusing C&D waste”, "preparation of guidelines on requirements of using recycled C&D materials in different industries" and "setting target for reduction, reusing, and recycling C&D waste". The results can inform policy development and be used as evidence for a broader discussion to solve lasting issues in C&D waste management.

In this paper, 6063 aluminum alloy for common building profiles is used as the research object. By adding a new Al-Ti-C-La composite alloy, the effect of 6063 aluminum alloy on the microstructure and properties of 6063 aluminum alloy is studied. The results show that Al-Ti-C-La composite alloy has obvious effect on grain refinement of 6063 aluminum alloy. With the addition of Al-Ti-C-La composite alloy, the grain size decreased significantly. When the addition amount of Al-Ti-C-La composite alloy is 1%, the grain size is reduced from 482 μm to 121 μm. Rare earth La is mainly distributed near the Mg2Si phase and β-AlFeSi, and complex compounds such as AlFeSiMgLa are formed. After aging for 270 days on the basis of T6 heat treatment, the tensile strength and elongation of 6063 aluminum alloy augmented with the addition of Al-Ti-C-La composite alloy, and the Vickers hardness gradually diminished. When the addition amount of Al-Ti-C-La composite alloy is 1%, the tensile strength, elongation and Vickers hardness of 6063 aluminum alloy reach 177.2 MPa, 17.8% and 60.9 HV, respectively, and the tensile strength is increased by 16.3%. The elongation is increased by 50.8%, the Vickers hardness is reduced by 15.4%, and the fracture of the alloy is mainly ductile fracture.

Remote sensing has been used as an important tool for disaster monitoring and disaster scope extraction, especially for the analysis of temporal and spatial disasters patterns of large-scale long time series. In order to find out a rapid and effective method to monitor disaster in a wide range, based on the Google Earth Engine cloud platform, this study used MODIS vegetation index products of 250 meter spatial resolution synthesized in 16 days during the year 2005-2019 and three kinds of disaster monitoring and scope extraction models are proposed: normalized vegetation index median time standardization (RNDVI_TM(i)) model, the normalized vegetation index median phenology Standardization(RNDVI_AM(i)(j)) model, normalized vegetation index median spatiotemporal Standardization (RNDVI_ZM(i)(j)) model. The optimal threshold of disaster extraction for each model in different time phases was determined by Otsu method, and the extraction results were verified by Medium resolution image and ground measured data of the same or quasi-same period. Finally, the disaster scope of cultivated land in Heilongjiang province from 2010 to 2019 was extracted and the temporal and spatial pattern of disasters was analyzed based on the meteorological data. It shows that the three above-mentioned models have high disaster monitoring and range extraction capabilities with the verification accuracy of RNDVI_TM(i) 97.46%, RNDVI_AM(i)(j) 96.90%, and RNDVI_ZM(i)(j) 96.67% respectively. The spatial and temporal distribution of disasters is consistent with the disaster of the insured plots and meteorological data in the whole province. Meanwhile, it turns out that different monitoring and extraction methods are used in different disasters, among which wind hazard and insect disasters often need to be delayed for 16 days to observe. Each model also has various sensitivity and applicability to different disasters. Compared with other methods, this method is fast, and convenient, which allows it to be used for large-scale agricultural disaster monitoring and is easy to be applied into other research areas. The research provides a new idea for large-scale agricultural disaster monitoring.

Due to increase demand of food grain in the world, assessment of yield before actual production is important in making policies and decisions in agricultural production system. For a large area, forecast models developed from vegetation indices derived from remote sensing satellite data possesses the potential to give quantitative and timely information on crops over large areas. Different vegetation indices are being made used for this purpose, however, their efficiency in estimating crop yield is needed to be certainly tested. In this study, wheat yield forecast was derived by regressing ground truthing yield data against time series of spatial vegetation indices for the 2013 to 2019 growing seasons. These spatial vegetation indices derived from Landsat 8 image data: Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) were compared to evaluate the most appropriate index that performs better in forecasting wheat production at Karcag, Kunhegyes and Ecsegfalva settlements in Jász-Nagykun-Szolnok county, in the Northern Great Plain region of central Hungary. The best time for making wheat yield prediction with Landsat 8- SAVI and NDVI was found to be the beginning of ripening period (160th day of the year) with higher correlation between the vegetation indices and the wheat yield. The validation results revealed that the model from SAVI provides more consistent and accurate forecasts yield compared to NDVI. The SAVI model forecast yield for the validation years, 2018 and 2019 were within 6.00% and 4.41% of the final reported values while that of NDVI model were within 8.31% and 6.27%. Nash-Sutcliffe efficiency index is positive with E1= 0.99 for the model from SAVI and for NDVI, E1=0.57, which connote that the forecasting method developed and evaluated performs acceptable forecast efficiency.

The conservation of coastal wetland ecosystems, like mangrove forests and salt marshes, represents a critical strategy for mitigating atmospheric emissions and climate change in the 21st century. Yet the existence of these environments is threatened by human-induced disturbances, namely deforestation and accelerated sea-level rise. Coastal systems maintain surface elevation in response to sea-level rise through a combination of physical and biological processes both above and below the ground surface. The quantification and relative contribution of belowground process controls (e.g., seasonal water content, organic matter decomposition) on surface elevation change is largely unexplored but crucial for informing coastal ecosystem sustainability. To address this knowledge deficit, we integrated measurements of surface elevation change of the live root zone (0.5 to 1 m depth) with geotechnical data from co-located sediment cores in the Sundarbans mangrove forest (SMF) of southwest Bangladesh. Core data reveal that the primary belowground controls on surface elevation change include seasonal fluctuations in pore-water content and the relative abundance of fine-grained sediments capable of volumetric expansion and contraction, supporting an elevation gain of ~2.42 ± 0.26 cm yr⁻¹. In contrast to many mangrove environments, the soils of the SMF contain little organic matter and are dominantly composed (>90%) of inorganic clastic sediments. The mineral-rich soil texture likely leads to less compaction-induced subsidence as compared to organic-rich substrates and facilitates surface equilibrium in response to sea level rise. Despite a relatively high soil bulk density, soil carbon (C) density of the SMF is very low owing to the dearth of preserved organic content. However, rates of C accumulation are balanced out by locally high accretion rates, rendering the SMF a greater sink of terrestrial C than the worldwide mangrove average. The findings of this study demonstrate that C accumulation in the SMF, and possibly other alluvial mangrove forests, is highly dependent on the continued delivery of sediment to the mangrove platform and associated settings.

The world witnessed one of the largest lockdowns in the history of mankind ever, spread over months in an attempt to contain the contact spreading of the novel coronavirus induced COVID-19. As billions around the world stood witness to the staggered lockdown measures, a storm brewed up in the urns of the rather hot Bay of Bengal (BoB) in the Indian Ocean realm. When Thailand proposed the name “Amphan” (pronounced as “Um-pun” meaning ‘the sky’), way back in 2004, little did they realize that it was the christening of the 1st super cyclone (Category-5 hurricane) of the century in this region and the strongest on the globe this year. At the peak, Amphan clocked wind speeds of 168 mph (Joint Typhoon Warning Center) with the pressure drop to 925 h.Pa. What started as a depression in the southeast BoB at 00 UTC on 16th May 2020 developed into a Super Cyclone in less than 48 hours and finally made landfall in the evening hours of 20th May 2020 through the Sundarbans between West Bengal and Bangladesh. Did the impact of the COVID-19 induced lockdown drive an otherwise typical pre-monsoon tropical depression into a super cyclone?

Substantial resources are invested in conservation of marine biodiversity globally. Fishing is the primary threat to many marine species and is one we can manage. However, threatened marine species are legally caught in industrial fisheries. To determine the magnitude and extent of this problem, we analysed global fisheries catch and import data and found reported catch records of 91 globally threatened species, thirteen of which are traded internationally. Seventy-three species targeted in industrial fisheries account for 99% of threatened species catch volume and value. Our results are a conservative estimate of threatened species catch and trade because we only consider species-level data, excluding group records; for example, we omit ‘sharks and rays,’ which represents over 200 threatened species. Although most fishing countries are involved in catch or trade of threatened species, it is driven largely by European nations. On land and for charismatic marine animals (e.g., whales), industrial-scale harvest of species at risk of extinction is controversial and usually highly regulated. In contrast, fishing for endangered fish and invertebrates is widespread but poorly documented. Given the development of new fisheries monitoring technologies and the current push for stronger international mechanisms for biodiversity management, industrial fishing of threatened fish and invertebrates should no longer be neglected in conservation and sustainability commitments.

As the amount of sensor data made available online increases, it becomes more difficult for users to identify useful datasets. Semantic web technologies improve discovery with meaningful ontologies, but the decision of suitability remains with the users. The GEO label provides a visual summary of the standardised metadata to aid users in this process. This work presents novel rules for deriving the information for the GEO label's multiple facets, such as user feedback or quality information, based on the Semantic Sensor Network Ontology and related ontologies. It enhances an existing implementation of the GEO label API to generate labels for resources of the Semantic Sensor Web. The prototype is deployed to serverless cloud infrastructures. We find that serverless GEO label generation is capable of handling two evaluation scenarios for concurrent users and burst generation. More real-world semantic sensor descriptions and an integration into large scale discovery platforms are needed to develop the presented solutions further.

Eichornia crassipes is one of the most dangerous aquatic weeds for Lake Tana and other water Bodies of Ethiopia .Use of natural enemies of the weed to discourage its propagation and impact is one of the best recommended options by scientific society.Among them, there are more fungi naturally a pathogen for water hyacinth and other plants. To use those pathogens to manage the highly water hyacinth infestation area of Lake Tana specfically Gondar Zuria woreda , Lemeba Kebela , infected water hyacinth plant material by fungi were collected from Goregora side of Lake Tana. The collection was done from infected water hyacinth leaves by fungi.All isolated fungi(Zygomycota .,Candidaalbicanaa,Trichoderma ,Aspergillus flavus, Fusarium s,Aspergillus niger,Pencillium and Rhizocotosolani spp) were attempted to infect the healthy water hyacinth in Lemeba Kebela by spraying 2ooo ml cultured mixed fungi in 20 metre square areas and after three weeks of exprement the fungi were severely affected the weeds and expanded in all wetland areas of lemba kebala that infested by water hyacinth but in some exprement areas the leaf of water hyacinth show disease symptoms after 7 days of fungi releasd on targeted area.This observational research shows that the fungi were high potential to attack healthy water hyacinth at high enviroment temprature and at less humidity

COVID-19, caused by SARS-CoV-2, is responsible for widespread mortality and economic loss across almost 212 countries. The distribution of disease prevalence is, however, uneven and likely dependent on the range of human behavior and/or the pathogen and environmental variables. Here in this research we examined the correlation between daily and total COVID-19 case reports from Bangladesh and meteorological variables (average temperature, average humidity, and average wind speed) against one another using Spearman rank and Kendall correlation tests to highlight significant meteorological variables in relation to COVID-19. The tests revealed that both temperature and humidity had a non-significant correlation with COVID-19 transmission amid epicenter (EC) and non-epicenter (NEC) areas. However, a weak (KCC: 0.168, p < 0.05; SCC: 0.271, p < 0.05) correlation was found for temperature in ECs. Wind speed showed moderate to strong correlation in ECs with a very high statistical significance to both daily new cases [(KCC: 0.494, p < 0.01); (SCC: 0.689, p < 0.01)] and total cases [(KCC: 0.426, p < 0.01); (SCC: 0.617, p < 0.01)]. The COVID-19 transmission was found to have a weak (KCC: 0.354, p < 0.01) to moderate (SCC: 0.465, p < 0.01) correlation with daily new cases; however, a moderate (KCC: 0.497, p < 0.01) to strong (SCC: 0.712, p < 0.01) correlation with total cases was found in NECs. The results suggest that temperature and humidity have little influence on COVID-19 transmission, but wind speed may have some influence. Countries should prioritize social engineering to modify human behavior in order to combat the spread of COVID-19 and future pandemics.

Solar Insolation is the major contributor of earth’s radiation and energy budget. The insolation reaching the surface is a prime input for eco-physiological processes such as evapotranspiration and photosynthesis. Therefore, it is as critical component to assess bio-energy and bio-fuel resources. It is also a crucial input to crop simulation model for yield forecasting and its further applications in solar energy solutions. Although ground observations are better for accuracy purpose, they have challenges of maintenance, regular calibration and upkeeping etc. This call for the continuous spatio-temporal satellite based observations barring the acceptable accuracy. In case of INSAT3D/3DR, Bhattacharya et al. (2015) have derived the surface insolation product which is being used widely. We propose a method of improvement in this product. It is envisaged that a correction applied with the help of ground truth estimates may enhance the utilization of insolation products derived from INSAT3D/3DR datasets. In the present study, surface insolation product derived from INSAT 3D/3DR data at an interval of 30 minutes each (collectively 15 minutes interval) with 4 km spatial resolution was used for duration from May-2017 to Apr-2019 over Nagpur. Ground truth observations for same duration were carried out with CNR4, which were used to correct the INSAT 3D/3DR surface insolation product using the statistical best-fit method. Corrected INSAT 3D/3DR products are found correlating with the ground values well with differences of approximately < 1 W/m2. Best-fit parameters evolved in the present study uses only 2 years of simultaneous ground and satellite data which can be further improved by multi-year data base. We propose better utilization of INSAT 3D/3DR based surface insolation products in the assessment of solar resource mapping over Nagpur (and possibly other regions, such as Bhandara) with the help of best-fit parameters as assessed in the present study.

The non-secular anomalies in carbon dioxide measurements at Mauna Loa are compared to the non-secular anomalies in the HadCRUT4 median global temperature from 1991 to 2020. A strong and significant Pearson's product-moment correlation is found between carbon dioxide anomalies and global temperature anomaly, indicating that the correlation is an actual characteristic of the atmosphere. But more important from a numerical weather prediction perspective, this result indicates that carbon dioxide anomalies can have no skills as temperature precursors on short time scales.

This article investigated whether the atmospheric temperature had any role in the spread and vulnerability to COVID-19 worldwide and how that knowledge can be utilized to contain the fast-spreading disease. It highlighted that temperature was an important factor in transmitting the virus, and a moderately cool environment was the most favourable state for its susceptibility. In fact, the risk from the virus is reduced significantly in high temperature environment. Warm countries and places were likely to be less vulnerable. We identified various degrees of vulnerability based on temperature and specified countries for March and April. The maximum reported case, as well as death, was noted when the temperature was in the range of around 275°K (2°C) to 290°K (17°C). Countries like the USA, UK, Italy and Spain belonged to this category. The vulnerability was moderate when the temperature was less than around 275°K (2°C) and countries in that category were Russia, parts of Canada and few Scandinavian countries. For temperature 300°K (27°C) and above, a significantly lesser degree of vulnerability was noted. Countries from SAARC, South East Asia, the African continent and Australia fell in that category. In fact, when the temperature was more than 305°K (32°C), there was a unusually low number of reported cases and deaths. For warm countries, further analyses on the degree of vulnerability were conducted for the group of countries from SAARC and South East Asia and individual countries were compared. We also showed countries can switch from one vulnerability state to another based on the variability of temperature. We provided maps of temperature to identify countries of different vulnerability states in different months of the year. That influence of temperature on the virus and previous results of clinical trials with similar viruses gave us a useful insight that regulating the level of temperature can provide remarkable results to arrest and stop the outbreak. Based on that knowledge, some urgent solutions are proposed, which are practically without side effects and very cost-effective too.

The COVID-19 pandemic has obliged Governments all around the world to take confinement and social 13 distancing measures. The reduction of leisure and production activities on beaches and ports have 14 disappeared direct and indirect contamination such as plastics, hydrocarbon spill, microbiological loads, 15 noise level, etc. leading to temporary improved environmental conditions, converting the beaches 16 similar to Marine Protected Areas. Some conditions are briefly analyzed through local surveys and in situ 17 observations in the popular beaches and ports of Salinas, Manta and Galápagos. 97-99 % of surveyed 18 people agreed that beaches have notoriously improved during confinement at least from visual 19 observation. On a scale from 1 (worst) to 5 (best), the beaches were rated 2.23 and 2.83 (less than 20 acceptable) before quarantine, and 4.48 and 4.33 after it for Salinas and Manta respectively. The 21 beaches have less garbage in general and plastic, even though there has been an increase in plastic and 22 face mask production around the world. In Salinas, 72%, and 23 % of surveyed people have seen small 23 pelagic fish, whilst in Manta 75%, and 41% of people saw the same, but also 17% of people have seen 24 whales (humpback and shark-whales) and dolphins practically swimming on the beach. Manta rays, 25 turtles, and other types of species were also observed. In Galapagos beaches, turtles have been 26 observed many more times than usual. The main plausible reason is the decrease in noise level. It is 27 recommended to take this unique opportunity, to construct a baseline data and information on physical, 28 chemical, biological, microbiological coastal oceanographic science, and from them to establish a proper 29 Coastal Zone Management based on beach description, water, and beach quality, human dimension, 30 and economic value indexes. This data and information construction should ideally be done before the 31 beaches are open.

COVID 19 has caused social distancing and lead to the reductions of various anthropogenic activities. Correspondingly this study has two fold objectives. First, aims to provide quantification measurement of social distancing impacts on air quality. Second, to forecast the air quality if social distancing is continued. The measured air quality parameters consist of NO₂, SO₂, and O₃. According to the results, the order of air quality parameters was NO₂<SO₂<O₃. The NO₂, SO₂, and O₃ levels were observed lower after social distancing than before social distancing was implemented. The reductions of NO₂, SO₂, and O₃ levels were 5%, 3%, and 5% respectively. Likewise, 65% of study periods (30 days) after implementation of social distancing have lower NO₂ than before social distancing. The exponential smoothing forecasts show the decreasing trends for NO₂ and SO₂. While O₃ levels are estimated will remain stable after social distancing. This study has shown that the social distancing has an impact on the NO₂, SO₂, and O₃. Correspondingly, if the social distancing is continued, then it is estimated can provide a positive impact on urban quality. Keywords: COVID 19, forecast, NO₂, O₃, SO₂.

Background: There are major air pollution monitoring gaps in sub-Saharan Africa. Developing capacity in the region to conduct air monitoring in the region can help estimate exposure to air pollution for epidemiology research. The purpose of our study is to develop a land use regression (LUR) model using low-cost air quality sensors developed by a research group in Uganda (AirQo). Methods: Using these low-cost sensors, we collected continuous measurements of fine particulate matter (PM2.5) between May 1, 2019 and February 29, 2020 at 22 monitoring sites across urban municipalities of Uganda. We compared average monthly PM2.5 concentrations from the AirQo sensors with measurements from a BAM-1020 reference monitor operated at the US Embassy in Kampala. Monthly PM2.5 concentrations were used for LUR modeling. We used eight Machine Learning (ML) algorithms and ensemble modeling; using 10-fold cross validation and root mean squared error (RMSE) to evaluate model performance. Results: Monthly PM2.5 concentration was 60.2 µg/m³ (IQR: 45.4-73.0 µg/m³; median= 57.5 µg/m³). For the ML LUR models, RMSE values ranged between 5.43 µg/m³ - 15.43 µg/m³ and explained between 28% and 92% of monthly PM2.5 variability. Generalized additive models explained the largest amount of PM2.5 variability (R²=0.92) and produced the lowest RMSE (5.43 µg/m³) in the held-out test set. The most important predictors of monthly PM2.5 concentrations included monthly precipitation, major roadway density, population density, latitude, greenness, and percentage of households using solid fuels. Conclusion: To our knowledge, ours is the first study to model the spatial distribution of urban air pollution in sub-Saharan Africa using air monitors developed from the region itself. Non-parametric ML for LUR modeling performed with high accuracy for prediction of monthly PM2.5 levels. Our analysis suggests that locally produced low-cost air quality sensors can help build capacity to conduct air pollution epidemiology research in the region.

Sustainable urban drainage systems (SuDS) such as swales are designed to collect, store and infiltrate a large amount of surface runoff water during heavy rainfall. Stormwater is known to transport pollutants, such as particle-bound Potential Toxic Elements (PTE), which are known to often accumulate in the topsoil. A portable XRF instrument (pXRF) is used to provide in situ spatial characterization of soil pollutants, specifically lead (Pb), zink (Zn) and copper (Cu). The method uses pXRF measurements of PTE along profiles with set intervals (1 meter) to cover the swale with cross-sections, across the inlet, the deepest point and the outlet. Soil samples are collected, and the In-Situ measurements are verified by the results from laboratory analyses. Stormwater is here shown to be the transporting media for the pollutants, so it is of importance to investigate areas most prone to flooding and infiltration. This quick scan method is time and cost-efficient, easy to execute and the results are comparable to any known (inter)national threshold criteria for polluted soils. The results are of great importance for all stakeholders in cities that are involved in climate adaptation and implementing green infrastructure in urban areas. However, too little is still known about the long-term functioning of the soil-based SuDS facilities.

This new study revised interlinked issues of global soil organic carbon (SOC), annual net primary productivity (NPP) and atmospheric CO₂ turnover time (τ). Soil is confirmed as both the greatest sink and source for excess atmospheric CO₂. Most terrestrial NPP (~218 Gt C/yr) is ultimately processed in topsoil and SOC stocks now total >10,000–12,000 Gt. More excess carbon is released into the air and water from SOC loss (>20 Gt C/yr) due to land clearance for pasture/crops, fires, agrichemical poisoning and erosion, than from fossil fuels (~10 Gt C/yr). NOAA’s Barrow bounce and isotopic analyses support high terrestrial flux up to ~800 Gt C/yr and CO₂ turnover time of ~1–4 years. Earth’s re-humification via compost offers the best and only practical/time-critical fix for climate, strategy for species extinction plus remedy for human health.

Subsurface drainage systems remove excess water from the soil profile thereby improving crop yields in poorly drained farmland. Knowledge of the position of the buried drain lines is important: 1) to improve understanding of leaching and offsite release of nutrients and pesticides, and 2) for the installation of a new set of drain lines between the old ones for enhanced soil water removal efficiency. Traditional methods of drainage mapping involve the use of tile probes and trenching equipment. While these can be effective, they are also time-consuming, labor-intensive, and invasive, thereby entailing an inherent risk of damaging the drainpipes. Non-invasive geophysical soil sensors provide a potential alternative solution. Previous research has focused on the use of time-domain ground penetrating radar (GPR), with variable success depending on local soil and hydrological conditions and the central frequency of the specific equipment employed. The objectives of this study were 1) to test the use of a stepped-frequency continuous wave (SFCW) 3D-GPR (GeoScope Mk IV 3D-Radar with DXG1820 antenna array) for subsurface drainage mapping, and 2) to evaluate the performance of a 3D-GPR with the use of a single-frequency multi-receiver electromagnetic induction (EMI) sensor (DUALEM) in-combination. The 3D-GPR system offers more flexibility for application to different (sub)surface conditions due to the coverage of wide frequency bandwidth. The EMI sensor simultaneously provides information about the apparent electrical conductivity (EC_a) for different soil volumes, corresponding to different depths. This sensor combination was evaluated on twelve different study sites with various soil types with textures ranging from sand to clay till. While the 3-D GPR showed a high success rate in finding the drainpipes at five sites (sandy, sandy loam, loamy sand, and organic topsoils), the results at the other seven sites were less successful due to limited penetration depth (PD) of the 3D-GPR signal. The results suggest that the electrical conductivity estimates produced by the inversion of EC_a data measured by the DUALEM sensor could be a useful proxy to explain the success achieved by the 3D-GPR in finding the drain lines. The high attenuation of electromagnetic waves in highly conductive media limiting the PD of the 3D-GPR can explain the findings obtained in this research.

Hydrogen sulfide and ammonia are always a concern in the livestock industries, especially when farmers try to clear their manure storage pits. Agitation of manure can cause dangerously high concentrations of harmful agents such as H₂S and NH₃ to be emitted into the air. Biochar has the ability to sorb these gases. We hypothesized that applying biochar on top of manure can create an effective barrier to protect farmers and animals from exposure to NH₃ and H₂S. In this study, two kinds of biochar were tested, highly alkaline, and porous (HAP, pH 9.2) biochar made from corn stover and red oak biochar (RO, pH 7.5). Two scenarios of (6 mm) 0.25” and (12 mm) 0.5” thick layers of biochar treatments were topically applied to the manure and tested on a pilot-scale setup, simulating a deep pit storage. Each setup experienced 3-min of agitation using a transfer pump, and measurements of the concentrations of NH₃ and H₂S were taken in real-time and measured until the concentration stabilized after the sharp increase in concentration due to agitation. The results were compared with the control in the following 3 situations: 1. The maximum (peak) flux 2. Total emission from the start of agitation until the concentration stabilized, and 3. The total emission during the 3 min of agitation. For NH₃, 0.5” HAP biochar treatment significantly (p<0.05) reduced maximum flux by 63.3%, overall total emission by 70%, and total emissions during the 3-min agitation by 85.2%; 0.25” HAP biochar treatment significantly (p<0.05) reduced maximum flux by 75.7%, overall, total emission by 74.5%, and total emissions during the 3-min agitation by 77.8%. 0.5” RO biochar treatment significantly reduced max by 8.8%, overall total emission by 52.9%, and total emission during 3-min agitation by 56.8%; 0.25” RO biochar treatment significantly reduced max by 61.3%, overall total emission by 86.1%, and total emission during 3-min agitation by 62.7%. For H₂S, 0.5” HAP biochar treatment reduced the max by 42.5% (p=0.125), overall total emission by 17.9% (p=0.290), and significantly reduced the total emission during 3-min agitation by 70.4%; 0.25” HAP treatment reduced max by 60.6% (p=0.058), and significantly reduced overall and 3-min agitation’s total emission by 64.4% and 66.6%, respectively. 0.5” RO biochar treatment reduce the max flux by 23.6% (p=0.145), and significantly reduced overall and 3-min total emission by 39.3% and 62.4%, respectively; 0.25” RO treatment significantly reduced the max flux by 63%, overall total emission by 84.7%, and total emission during 3-min agitation by 67.4%.

Sentinel-1 (S1) extra-wide (EW) swath data in cross-polarization (horizontal-vertical, HV or vertical-horizontal, VH) are strongly affected by the scalloping effect and thermal noise, particularly over areas with weak backscattered signals, such as sea surfaces. Although noise vectors in both the azimuth and range directions are provided in the standard S1 EW data for subtraction, the residual thermal noise still significantly affects sea ice detection by the EW data. In this paper, we improve the denoising method developed in previous studies to remove the additive noise for the S1 EW data in cross-polarization. Furthermore, we propose a new method for eliminating the residual noise (i.e. multiplicative noise) at the sub-swath boundaries of the EW data, which cannot be well processed by simply subtracting the reconstructed 2-D noise field. The proposed method of removing both the additive and multiplicative noise was applied to EW HV-polarized images processed using different Instrument Processing Facility (IPF) versions. The results suggest that the proposed algorithm significantly improves the quality of EW HV-polarized images under various sea ice conditions and sea states in marginal ice zone (MIZ) of the Arctic. This is of great support for the utilization of cross-polarization SAR images in wide swaths for intensive sea ice monitoring in polar regions.

Space-borne Synthetic Aperture Radar (SAR) Interferometry (InSAR) is now a key geophysical tool for surface deformation studies. The European Commission’s Sentinel-1 Constellation began acquiring data systematically in late 2014. The data, which are free and open access, have global coverage at moderate resolution with a 6 or 12-day revisit, enabling researchers to investigate large-scale surface deformation systematically through time. However, full exploitation of the potential of Sentinel-1 requires specific processing approaches as well as the efficient use of modern computing and data storage facilities. Here we present LiCSAR, an operational system built for large-scale interferometric processing of Sentinel-1 data. LiCSAR is designed to automatically produce geocoded wrapped and unwrapped interferograms and coherence estimates, for large regions, at 0.001° resolution (WGS-84 system). The products are continuously updated in a frequency depending on prioritised regions (monthly, weekly or live update strategy). The products are open and freely accessible and downloadable through an online portal. We describe the algorithms, processing, and storage solutions implemented in LiCSAR, and show several case studies that use LiCSAR products to measure tectonic and volcanic deformation. We aim to accelerate the uptake of InSAR data by researchers as well as non-expert users by mass producing interferograms and derived products.

COVID-19 appeared as an infectious disease of global health emergency and highest public health concern of 21st century for this world due to its high speed of spread across the globe. The disease started as one single case to a cluster of cases in Wuhan, China (Dec 2019) and within few months with its continuous upsurge of cases spreading globally which has created enormous threat and tension across the global Public health care field. Most significantly to share the fact that, this disease caused high level of Risk Group mortality, high morbidity, health care services burden, panic anxiety, mental trauma and tension, social and economic insecurity, which also collectively surfaced by diverse range of social reaction and political pressure across the world. Also with the appearance of new and unknown pathogenesis of the disease has created the most and significant attention and concern for the scientific community and political leaders as well. The disease also varies significantly with its pattern of virus, sign, symptoms, characteristics including its epidemiological and public health response (like prevention strategy, diagnoses, case management, treatment pattern) across the countries. However, this variance is comparatively less rather even more in commonality while it comes to basic public health prevention interventions like frequent hand washing, wearing face mask, maintaining social/physical distancing (2 - 6 meter in between), individual isolation and community quarantine for suspected exposure and lockdown in community areas where cases are identified. These are basically the most commonly practiced public health interventions to deal with this disease in majority of the epicenters across countries. Also with the progression of the disease with its diverse categorical appearance of sign symptoms which are uniquely portraying this disease is more of a kind of COVID-19 syndrome rather than as COVID-19. Moreover, various factors including socio economic status, health status, population dynamics, health system and infrastructure, health behavioral pattern, nutrition and food habit and also access to information and knowledge made this viral disease one of the historically counted on expensive disease of the modern world to fight for. Specially to mention here, the case fatality rate distinctively vary with the population dynamics and the health system infrastructure and ability factors which have already created clear evidence as explained in this paper how these factors are uniquely distinguished and different from country to country. Apart from the contextual differences, the various kind of ongoing preventive measures (like prevention, diagnosis and treatment) learnt every day from the emerging characteristics and pattern of this viral disease which are most commonly practiced across countries with some kind of variance. According to the Public health prevention practice and interventions, the total world is quite connected and learning from each other’s public health experiences to fight for the virus. However, the whole world community are eagerly waiting to see the result and outcome of the ongoing therapeutics and vaccine trial initiated in several countries, the people of the world are quite optimistic and hopeful that, global scientific community will be able to invent some miraculous and magic solution in upcoming months which ultimately will free the world from this most terrifying COVID-19 pandemic of 21st century

It has been hypothesized that social distancing as the prevention measures for COVID 19 can affect the air quality including PM_2.5 and PM₁₀ in urban areas. According to this situation, this study aims to compare the PM_2.5 and PM₁₀ before and after the implementation of social distancing. Likewise, this study also forecasts the benefits of social distancing on PM_2.5 and PM₁₀ if social distancing period is continued and extended. To achieve these objectives, an Auto Regressive Integrated Moving Average (ARIMA) model to investigate the daily PM_2.5 and PM₁₀ trends has been developed for social distancing periods (March– May 2020) and after May as well. The model confirms that if social distancing period is extended after May 2020 then the PM_2.5 and PM₁₀ are estimated will be 4% and 9% lower. To confirm that the PM_2.5 and PM₁₀ reductions are only due to social distancing effect, the study has investigated the possible effects of wind speed and rainfall on PM_2.5 and PM₁₀. Nonetheless, the reductions do not correlate with those factors. To conclude social distancing should be considered as an option to control PM_2.5 and PM₁₀ in urban areas.

Sustainable development goals (SDGs) are a global agenda consisting of 17 goals which are to be achieved in 2030 by all member states. SDGs are more holistic goals i.e. these goals are closely interrelated and they affect the progress of one another. Sub-Saharan Africa countries are, once more lagging behind in the implementations of SDGs despite the efforts by governments, non-government organisations and international agencies. Rwanda, South Africa and Zambia where the three Sub-Saharan Africa countries on which the study focused. The three countries in this study were chosen on the basis that they cater to the general overview of African countries performance on SDGs. To conduct this study, a desk research method was adopted and secondary data was utilised. An in-depth analysis was done on the on three subs Saharan African countries i.e. Rwanda, South Africa, Zambia. Those goals where serious attention is needed are goals 1-9, 16 and 17. Most Sub-Saharan African countries performed better on goals 11, 12 and 15. It was concluded that the achievement of Sustainable development goals remains a mere dream for Sub Saharan Africa unless serious interventions are made.

The basic principles of geological risk calculation through Probability of Success (PoS) are mostly applied for numerical estimation of additional hydrocarbon existence in proven reservoirs or potential hydrocarbon discoveries in selected geological regional subsurface volume. It can be tailored and validated for a comprehensive input dataset collected in the selected petroleum province, adapted by dividing up geological events into several probability categories and classes. The most applied categories are (existence of) reservoir rocks, traps and isolators, source rocks, migration pathways and preservation conditions for hydrocarbons The methodology results in unique probability values as multiplication of independent statistical events, which can also be applied in the assessment of a potential hydrocarbon discovery of desired minimal volume and its value in any virtual currency like risk-neutral dollars. Such methodology has been extensively developed in the last decades in the Croatian subsurface, mostly in the Croatian part of the Pannonian Basin System (CPBS). Through the adaptation of geological categories, it was also applied in hybrid, i.e., stochastical, models developed in the CPBS (Drava Depression). Stochastically estimation of porosity was already applied. As the robustness of this methodology is very high, it was also modified to estimate the influence of water-flooding in increasing oil recovery in some proven Neogene sandstone reservoirs in the CPBS (Sava Depression). This new modification is being presented to be applied to geological risk calculation, intending to assess the safety of geological environment in deep wells, where depleted radioactive fuel would be disposed, a subject of great importance. The case study encompassed the magmatic and metamorphic rocks in the pre-Neogene basement of the CPBS. For disposal purpose, these are regionally lithologies considered as to be the safest ones considering petrophysical values, water saturation, recent weathering and tectonic activity.

A quite energetic seismic excitation consisting of one main and additional three distinctive earthquake clusters that occurred in the transition area between the Kefalonia Transform Fault Zone (KTFZ) and the continental collision between Adriatic and Aegean microplates, is thoroughly studied after high–precision aftershocks’ relocation. The activated fault segments are in an area where historical and instrumental data have never claimed the occurrence of a catastrophic (M>6.0) earthquake. The relocated seismicity initially defines an activated structure extending from the northern segment of the Lefkada branch of KTFZ with the same NNE–SSW orientation and dextral strike-slip faulting and then keeping the same sense of motion its strike becomes NE–SW and its dip direction NW. This provides unprecedented information on the link between the KTFZ and the Collision front and sheds more light on the regional geodynamics. The earthquake catalog, which is specially compiled for this study, starts one year before the occurrence of the Mw5.4 mainshock and adequately provides the proper data source for investigating the temporal variation of the b–value, which might be used for discriminating foreshock and aftershock behavior.

Global emerge of zoonotic novel corona virus (COVID-19) became a pandemic and its effect to mankind is talk of the town now a days. This tiny, invisible enemy has affected every country in the world and almost every living directly or indirectly and nationwide complete lockdown has triggered a short-term environmental impact. Since 2003, corona virus came into existence in the form of Severe Acute Respiratory Syndrome (SARS) and more evolved Middle East Respiratory Syndrome (MERS) in 2012. This time, at the end of December 2019, outbreak of novel corona virus COVID-19 (also known as SARS-CoV₂, nCoV-2019) draw attention as global health emergency. World Health Organization (WHO) report says that the outbreak of this virus is so immense, it has already affected 35,57,235 people and caused death to 2,45,150 people worldwide and 46,433 Indians got affected with 1568 death as on 5^th May 2020 (2:00 am) and these numbers are increasing exponentially day by day. Virologist, micro-biologist and science community are hammering their head very hard to find out cure and vaccine against this powerful virus and to prevent mass demise of mankind. In order to curb the spread of COVID-19, Janta curfew on 22.03.2020 and nationwide complete lockdown was implemented in India for 21 days (phase-I, from 25.03.2020 to 14.04.2020) to stop community transmission of third stage, for 19 days (phase-II, 15.04.2020 to 03.05.2020) and 14 days (phase-III, 04.05.2020 to 17.05.2020) complete lockdown to minimize the community transmission effect. During complete lockdown and quarantine period a drastic change in Earth’s atmosphere, including reduction in emission of greenhouse gases, air pollution (~50% fall in air quality index), noise pollution, water pollution and solid waste pollution, have been recorded by government agencies as well as private agencies. In this paper we considered data of Janta curfew, phase-I and phase-II lockdown to link between geological and environmental aspect related to environmental impact due to emerge of COVID-19 and massive reduction in pollution level during complete lockdown in India. We propose future lockdown strategies to minimize the emission of greenhouse gas by ~100 Mt to ~200Mt (3.33% to 6.66%) of GHG_total per year by 2-4 days per month nationwide lockdown or ~70 Mt to ~140 Mt (2.33% to 4.66%) of GHG_total per year by 2-4 days per month complete lockdown of energy sectors only.

In this paper, we focus on developing a novel method to extract sea ice cover (i.e., discrimination/classification of sea ice and open water) using Sentinel-1 (S1) cross-polarization (vertical-horizontal, VH or horizontal-vertical, HV) data in extra wide (EW) swath mode based on the machine learning algorithm support vector machine (SVM). The classification basis includes the S1 radar backscatter coefficients and texture features that are calculated from S1 data using the gray level co-occurrence matrix (GLCM). Different from previous methods where appropriate samples are manually selected to train the SVM to classify sea ice and open water, we proposed a method of unsupervised generation of the training samples based on two GLCM texture features, i.e. entropy and homogeneity, that have contrasting characteristics on sea ice and open water. We eliminate the most uncertainty of selecting training samples in machine learning and achieve automatic classification of sea ice and open water by using S1 EW data. The comparison shows good agreement between the SAR-derived sea ice cover using the proposed method and a visual inspection, of which the accuracy reaches approximately 90% - 95% based on a few cases. Besides this, compared with the analyzed sea ice cover data Ice Mapping System (IMS) based on 728 S1 EW images, the accuracy of extracted sea ice cover by using S1 data is more than 80%.

In this paper, we presented a method of retrieving sea surface wind speed from Sentinel-1 synthetic aperture radar (SAR) horizontal-horizontal (HH) polarization data in extra-wide mode, which have been extensively acquired over the Arctic for sea ice monitoring. In contrast to the conventional algorithm, i.e., using a geophysical model function (GMF) to retrieve sea surface wind by spaceborne SAR, we introduced an alternative method based on physical model guided neural network. Parameters of SAR normalized radar cross section, incidence angle, and wind direction are used as the inputs of the backward propagation (BP) neural network, and the output is the sea surface wind speed. The network is developed based on more than 11,000 HH-polarized EW images acquired in the marginal ice zone (MIZ) of the Arctic and their collocations with scatterometer measurements. Verification of the neural network based on the testing dataset yields a bias of 0.23 m/s and a root mean square error (RMSE) of 1.25 m/s compared to the scatterometer wind speed. Further comparison of the SAR retrieved sea surface wind speed with independent buoy measurements shows a bias and RMSE of 0.12 m/s and 1.42 m/s, respectively. We also analyzed the uncertainty of retrieval when the wind direction data of a reanalysis model are used as inputs to the neural network. By combining the detected sea ice cover information based on the EW data, one can expect to derive simultaneously sea ice and marine-meteorological parameters by spaceborne SAR in a high spatial resolution in the Arctic.

This dataset consists of integral sea state parameters of significant wave height (SWH) and mean wave period (zero-upcrossing mean wave period, MWP) data derived from the advanced synthetic aperture radar (ASAR) onboard the ENVISAT satellite over its full life cycle (2002-2012) covering the global ocean. Both parameters are calibrated and validated against buoy data. A cross-validation between the ASAR SWH and radar altimeter (RA) data is also performed to ensure that the SAR-derived wave height data are of the same quality as the RA data. These data are stored in the standard NetCDF format, which are produced for each ASAR wave mode Level1B data provided by the European Space Agency. This is the first time that a full sea state product in terms of both the SWH and MWP has been derived from spaceborne SAR data over the global ocean for a decadal temporal scale.

The article describes the results of comparison of occurence of buildings (and address points) in Poland with delimitations of land use belonging to particular classes in the CORINE Land Cover (CLC) 2018 dataset. Large discrepancies have been identified, which reach on average approx. 34% of addresses and 35% of buildings located outside class 1 (artificial surfaces), mainly on terrains of class 2 (agricultural areas). Among single-family buildings it was 37% and among new addresses (forecasted or ‘under construction’ buildings) – as much as 50%. This puts a question mark over the possibility of using CLC data with resolution of 25 ha for monitoring of spatial planning and development in Poland for purposes of the diagnosis and assessment of the scale of dispersion of built-up areas. It is worth carrying out similar analyses in other countries, known for the deconcentration processes and a relatively large share of dispersed settlement e.g. other CEE countries, Spain, Portugal, Italy.

Users of meteorological forecasts are often faced with the question of whether to make a decision now based on the current forecast or whether to wait for the next and hopefully more accurate forecast before making the decision. One would imagine that the answer to this question should depend on the extent to which there is a benefit in making the decision now rather than later, combined with an understanding of how the skill of the forecast improves, and information about the possible size and nature of forecast changes. We extend the well-known cost-loss model for forecast-based decision making to capture an idealized version of this situation. We find that within this extended cost-loss model, the question of whether to decide now or wait depends on two specific aspects of the forecast, both of which involve probabilities of probabilities. For the special case of weather and climate forecasts in the form of normal distributions we derive a simulation algorithm, and equivalent analytical expressions, for calculating these two probabilities. We apply the algorithm to forecasts of temperature and find that the algorithm leads to better decisions relative to three simpler alternative decision-making schemes. Similar problems have been studied in many other fields, and we explore some of the connections.

The COVID 19 related social distancing is hypothesized can affect the environmental quality including the air and water quality. Correspondingly, this study aims to study how the reduction of activities of people living near the rivers and the coastal areas due to social distancing may decrease the discharges of materials and nutrients to the water body. The chlorophyll-a was used as bio indicators of nutrient contents related to the anthropogenic activities in the coast. The study was conducted in the Jakarta coast considering that this coast was surrounded by populated cities with total population equal to 16 million people. The chlorophyll-a was measured in mg/m³ and monitored using remote sensing data from January to April 2020 representing the period before and after the implementation of social distancing. The determinant environmental factor measured was sea surface temperature (⁰C). The study considered that there were reductions of levels and areas of chlorophyll-a in the coast. The chlorophyll-a levels were reduced from January to April (p<0.05). The chlorophyll-a levels for January, February, March, and April were 7.36 mg/m³ (95%CI: 6.34-8.37), 7.90 mg/m³ (95%CI: 7.32-8.47), 6.52 mg/m³ (95%CI: 5.37-7.66), and 4.21 mg/m³ (95%CI: 3.34-5.07) respectively. However, the differences of chlorophyll-a were not influenced by the sea surface temperature factor (p>0.05). Based on remote sensing data in January and February, the sizes of coastal areas with chlorophyll-a levels >7.00 mg/m³ were larger than areas observed in March and April. Contrarily, the coastal area sizes with low chlorophyll-a levels <5.00 mg/m³ were increasing in April. To conclude the dynamic of anthropogenic activities in coastal setting is responsible and associated with the water quality and nutrient contents as indicated by chlorophyll-a levels.

Population concentration in cities brings new risks as an increase in pollution, which causes urban health problems. In order to address this problem, traffic reduction measures are being implemented, as pedestrianization areas and the definition of Low Emissions Zones (LEZ). When the effectiveness of these types of measures is in doubt, smart city tools provide data that can be used to scientifically asses their impact. This article analyzes the situation of Madrid Central (Spain), a LEZ subject to controversy. We apply statistical and regression analyses to evaluate the effectiveness of this measure to reduce air pollution and outdoor noise. According to the results, this LEZ was able to significantly reduce NO$_2$, PM$_{2.5}$, and PM$_{10}$ concentration locally, having the same positive impact in the rest of the city. In terms of noise, this measure is able to mitigate background noise levels generated by the road traffic.

There is a lot of discussion underway with conflicting opinions examining the airborne nature of the SARS-CoV2 virus. Surprisingly, important phenomena prevalent with respect to aerosols (suspended droplets) have not been considered. In this Technical Note, we propose a methodology for the coupling of aerosol phenomena (such as evaporation, particle transport accounting for drag) to accurately establish the lifetimes of the droplets. A characteristic time analysis illustrates the time scales for evaporation and settling: for example, the characteristic time for evaporation of a 10 µm droplet is 0.036 s at a relative humidity of 25%; compared to a settling time of about 500 s. For any particle smaller than ~ 100 µm, the evaporation of the emitted or exhaled droplet has to be considered. Coupling evaporation of the droplet as it settles, we estimate the horizontal distance traversed. Trajectories of a 10 µm and 100 µm particle emitted with a typical initial velocity of release associated with coughing and sneezing indicates the greater spread in the horizontal direction when evaporation is accounted for. The life time of the 10 µm particle increases from 8.3 min to 12 hours (will be intercepted prior and the actual airborne time will then be shorter); and for a 100 µm particle from 4.9 s to 39.4 s.

Social distancing policies put in place during COVID-19 epidemic in addition to helping to limit the spread of the disease also contributed to improving urban air quality. Here we show a decrease in air pollutant concentration as a consequence of mobility reduction in São Paulo during the containment measure which began on 22^nd March 2020. When comparing to foregoing weeks to equivalent periods of 2019, the concentration of most air pollutants sharply decreased in the first days of mobility restriction, to then increase again after government officials downplayed the threat of the disease. This trend is also followed by a decrease in hospital admissions by SARS-influenza. Therefore, despite the great economic and social unrest caused by the pandemic, this unique situation shows that large-scale mobility reduction policy had a significant impact on air quality, benefiting, directly and indirectly, the public health system.

The severity of the ongoing COVID-19 outbreak demand from countries to adopt extreme measurements of social isolation to stop the spread and flatten the curve of contamination. Although social isolation measures may have negative impacts on economy, historically it has been showed to be more effective in saving lives and less damaging to economy than not adopting these measurements during a viral pandemic. In Brazil, despite the positioning of the president against social isolation due to the consequent economic recession, the rapid spread of the virus has worried the governors of the Brazilian states, which are thus managing stringent social isolation measurements to avoid the advance of the virus. Since one of the main strategies to guarantee progress and economic growth in Brazil has been the exploitation of natural resources from the Amazonia biome, here we discuss the importance of this biome to Brazilian economy during the post-pandemic recession and highlights potential strategies to burst the economy without promoting Amazonia destruction. We show that, together with the REDD+ and the Amazon Fund, the Forest bonds represents good strategies to burst Brazilian economy in a sustainable way, showing that it is possible to improve the commodities without increasing Amazonia deforestation or the greenhouse gases emissions. Amazonia is a biome of global importance for the avoidance of another global crisis, which will occur if we reach the climatic tipping point of 1.5°C. Thus, governmental actions should go towards its preservation, not exploitation and depletion. The commitment of the government with environmental conservation is paramount so that these economic strategies have positive results, especially in a post-pandemic scenario, where the economy will be extremely weakened. The COVID-19 brings us a lesson regarding how our attitudes can impact the world, and what we can expect from a global crisis. Perhaps we can apply these lessons and focus on change our economy towards a sustainable direction to avoid another global crisis in the years to come.

The social distancing as a response to COVID 19 pandemic has led to the exceptional reductions of daily routine people activities and vehicle uses mainly in city. This same situation was also experienced by several busy, large, and populous cities in Southeast Asia (SA) countries. Correspondingly, this study aimed to test the hypothesis that the social distancing implementation period has increased the air quality in the term of carbon monoxide (CO) emission reduction as drawn from Jakarta city as an example of the one of populated cities in SA region. The CO was measured in parts per billions (ppb) and monitored on the daily basis employing remote sensor platform. The monitor periods were started from January, February, March, and April 2020 with 10 measurement days for each month. The social distancing was implemented from mid of March to the recent April. The CO measurement data were statistically tested to justify the significant effects of social distancing on the CO levels. Based on the CO data analysis, the order of CO mean by months is February > January > March > April. The CO levels for January, February, March, and April were 87.46 ppb (95%CI: 83.54-91.37), 88.20 ppb (95%CI: 81.65-94.74), 86.38 (95%CI: 81.06-91.69), and 78.68 (95%CI: 74.03-83.32) respectively. This study also find significant difference (p<0.05) of CO levels especially in April when social distancing has been implemented. Hence, these findings illustrate the potential air pollutant reduction gained from implementing social distancing as can be seen in April.

Abiogenic hydrocarbons are fundamentally important for understanding the deep microbial communities and the origin of life. The generation of abiogenic hydrocarbons was proposed to be limited to ultramafic-hosted hydrothermal systems, fueled by the serpentinization product H₂. Here, we present the discharge of short-chain alkanes from an andesitic rock-hosted Lutao geothermal field in the north Luzon arc, carrying abiotic chemical and isotopic signals. These abiogenic hydrocarbons were generated from CO₂-H₂O-rich fluid inclusions, where the long-term storage since Lutao volcanism (~ 1.3 Ma) allowed overcoming the sluggish kinetics of CO₂ to CH₄ reduction at temperatures of 174 - 206 ^oC. Natural abiogenic production of hydrocarbons, therefore, can be more ubiquitous than previously thought. The hypothesis regarding the origin of methane in Earth’s early atmosphere and its implication to the origin of life may require reconsideration.

Geomathematics is extremely important in geosciences, particularly in the geology. The key for any geomathematical analysis is the definition of a typical model to be applied for further prognosis, either through deterministical or stochastical approaches. The selection of the appropriate procedure is presented in this paper. Two different geomathematical subfield datasets were used in subsurface geological mapping and palaeontology and different biostatistics applications, representing important geomathematical subfields in the Croatian geology. The different subsurface interpolation methods, tested, validated and recommended for application, were used to obtain the best possible outcome in reservoir modelling, in the cases with small datasets. Cross-validation may be selected as the main selection criteria, applied to the Croatian part of the Pannonian Basin System (abbr. CPBS). Recent advances in biostatistics applied in palaeontology and case studies from Croatia are also presented, where biometric studies are of significant importance in fossil biota. Data, methods and problems in geosciences is a vast subject, and address a wide spectrum of fundamental science. Because geology includes subsurface and surface geology, and very different datasets regarding variable and number of data, here are chosen two representative case study groups with original samples from Northern Croatia. Subsurface mapping has been presented on limited petrophysical datasets from the Northern Croatian, Miocene, hydrocarbon reservoirs. Biostatistics has been presented on very different samples, allowing to achieve paleoenvironmental reconstructions of size of relevant fossils, as dinosaurs or other species and their paleoenvironments. All examples highlight examples of the valuable application of geomathematical tools in geology. The results, cautiously validated and correlated with other, non-numerical (indicator, categorical) geological knowledge, are of enormous assistance in creating better geological models.

This review chronicles the indirect transmission method which seems to be overlooked by most people and makes attempts to clearly document the various transmission ways with a hope that such information may strengthen the knowledge base of researchers towards methods of eradicating the pandemic. Current knowledge of transmission and exposure of SARS-CoV-2 has been explained. Various researchers have put forward different ways of exposure and transmission. Literature does not reveal whether the indirect transmission route is the dominant one. However total lockdown could be a veritable means to reduce both direct and indirect transmission routes. In many countries where the indirect transmission has been reduced, the scourge of the virus is less. The work creates awareness on the need to watch out for those routes of transmissions that may not be popular and suggested key knowledge gaps that needs to filled.

The world tsunami hazard map is developed to understand the pattern of occurrence of tsunami across the globe and for using this database for preparing effective evacuation plan and rescue operations. It has been developed by using Web GIS tools. HTML, CSS, Java Script, JQuery, IIS and Maps API are utilized to develop hazard map. These data can be even used by government authorities as well as by other nodal agencies.

Reliable estimates of poplar plantations area are not available at the French national scale due to the unsuitability and low update rate of existing forest databases for this short-rotation species. While supervised classification methods have been shown to be highly accurate in mapping forest cover from remotely sensed images, their performance depends to a great extent on the labelled samples used to build the models. In addition to their high acquisition cost, such samples are often scarce and not fully representative of the variability in class distributions. Consequently, when classification models are applied to large areas with high intra-class variance, they generally yield poor accuracies. In this paper, we propose the use of active learning (AL) to efficiently adapt a classifier trained on a source image to spatially distinct target images with minimal labelling effort and without sacrificing classification performance. The adaptation consists in actively adding to the initial local model, new relevant training samples from other areas, in a cascade that iteratively improves the generalisation capabilities of the classifier, leading to a global model tailored to different areas. This active selection relies on uncertainty sampling to directly focus on the most informative pixels for which the algorithm is the least certain of their class labels. Experiments conducted on Sentinel-2 time series showed that when the same number of training samples was used, active learning outperformed passive learning (random sampling) by up to 5% of overall accuracy and up to 12% of class F-score. In addition, and depending on the class considered, the random sampling required up to 50% more samples to achieve the same performance of an active learning-based model. Moreover, the results demonstrate the suitability of the derived global model to accurately map poplar plantations among other tree species with overall accuracy values up to 14% higher than those obtained with local models. The proposed approach paves the way for national-scale mapping in an operational context.

Environmental conditions, such as temperature, carbon dioxide, and nutrient availability, are altered by urban conditions at regional scales with potential for impact on tree leaf structure. Our goal was to compare leaf morphological characteristics driven by physiological acclimation in red maple (Acer rubrum L.) trees in deciduous forests embedded in a small (Newark, DE) and a large (Philadelphia, PA) city. The study was conducted in six urban forests on eighteen mature red maple trees in a long-term urban forest network. We hypothesized that red maples in Philadelphia forests compared to Newark forests will have a thicker upper epidermal layer, spongy palisade and mesophyll layer, longer and wider stomates, and lower stomate density. Additionally, we hypothesized that red maples in Philadelphia forests compared to Newark forests will have lower leaf water content and specific leaf area, and greater leaf thickness, fresh leaf weight, dry leaf weight, and leaf dry matter content. Our results for stomate length and stomate width supported our predictions; red maple leaves had longer and wider stomates in Philadelphia forests than in Newark forests. The increased stomate size in red maple trees suggests potential altered gas exchange behavior and mutual abiotic stress mitigation responses in red maple to greater urbanization impacts in Philadelphia forests. This supports previous findings of possible physiological and biochemical acclimation of red maple trees to urban conditions. Furthermore, the findings from this study suggest red maple trees may be a good biomonitor of regional scale impacts in urban environments.

Phthalates are esters of Pthalic acid, compounds mainly used as plasticisers. Phthalates are widely used chemicals which are of significant research interest as their exposure causes various consequences for human health. There are several categories of phthalates extensively used in many commercial productions with diverse uses, physicochemical properties and toxicological effects. This chapter discusses the toxicity of Phthalates and its potential risks to human health.

India is one of the large sources of the anthropogenic pollutants and their increasing emission due to the recent economic growth in India. In this study we analyzed the annual and seasonal behaviors of ozone (O₃) gas using satellite remote sensing dataset from the sources Ozone Monitoring Instrument (OMI) over India region from 2006-2015. The study focuses on the seasonal behaviors of O₃ gas i.e., monthly, seasonal, annual mean variations of trace gas and also trend analysis of O₃ gas and comparison of the seasonal behavior of the ozone gas by trend analysis were assessed. In this study we also taken eleven cities to show the increment and decrement in four seasons of O₃ gas by taking 2006 as a base year and investigate the behaviors of gases during (2007-2015) years. Higher concentrations of O₃ south-to-north gradient, indicating the variations due to the impact of emissions and local meteorology. Ozone concentrations were higher during the warmer months. However, in winter season lowest concentration of O₃ seen due to the less amount of heat and due to cold days and ozone holes in the stratosphere. Instead, total O₃ concentrations rises over Delhi, Lucknow and Kolkata due to large population density, high traffic emission, highly polluted air and larger industrial activities.

The present study analyzed the efficiency of a naturally derived fenugreek powder for removal of Congo red dye from the aqueous solution. The flocculation Studies on Congo Red (CR) a hazardous, textile dye onto Fenugreek Powder and its adsorption was analyzed. Fenugreek Powder is Eco-friendly, biodegradable and locally available in the market. The dye adsorption process was performed in different batches at varying pH, dye concentration, adsorbent concentration and contact time to get the best results. The result showed that the maximum removal of dye was 42.4% with 10mg/l of Fenugreek powder at pH 4.

As the haze engulfed the Delhi NCR after Diwali there is a panic in public about its causes and consequences. Here are some facts and figures to know the truth behind the scene. Temperature fell down from 24’C on October 31 to 21’C on November 2 as recorded at UPPCB station at sector-125, Noida which is adjacent to Delhi a very strategic location and can be considered as reference point for Delhi-NCR. The details of the study conducted is given below.

The use of Hyperspectral (HS) and LiDAR acquisitions has a great potential to enhance mapping and monitoring practices of endangered grasslands habitats, beyond conventional botanical field surveys. In this study we assess the potentiality of Recursive Feature Elimination (RFE) in combination with Random Forest (RF) classification in extracting the main HS and LiDAR features needed to map selected Natura 2000 grasslands along Polish lowland river valleys, in particular alluvial meadows 6440, lowland hay meadows 6510 and xeric and calcareous grasslands 6120. We developed an automated RFE-RF system capable to combine the potentials of both techniques and applied it to multiple acquisitions. Several LiDAR-based products and different Spectral Indexes (SI) were computed and used as input in the system, with the aim of shedding light on the best-to-use features. Results showed a remarkable increase in classification accuracy when LiDAR and SI products are added to the HS dataset, strengthening in particular the importance of employing LiDAR in combination with HS. Using only the 24 optimal features selection generalized over the three study areas, strongly linked to the highly heterogeneous characteristics of the habitats and landscapes investigated, it was possible to achieve rather high classification results (K around 0.7-0.77 and habitats F1 accuracy around 0.8-0.85), indicating that the selected Natura 2000 meadows and dry grasslands habitats can be automatically mapped by airborne HS and LiDAR data. Similar approaches might be considered for future monitoring activities in the context of habitats protection and conservation

This study evaluates spatial analyses of groundwater quality and environmental changes to obtain information on the groundwater contamination in the Permian Basin, Texas. Coupled with the U.S. government’s open data, these analyses can identify regions where environmental change could have potentially effected groundwater quality. A total of thirty-six wells were selected within the six counties: Andrews, Martin, Ector, Midland, Crane, and Upton. Spatial distribution maps were created for six different parameters: pH, total dissolved solids (TDS), chloride, fluoride, nitrate, and arsenic. Total groundwater quality maps incorporate all the contaminants and denote regions of poor, medium, and optimum conditions. To identify spatial changes in groundwater quality, maps were separated into two different time intervals, 1992-2005 and 2006-2019. We found that groundwater contamination resulted primarily from the mobilization of the contaminant from natural sources or anthropogenic activities such as chemical fertilizers. Overall, groundwater quality decreased during the study period from 1992 to 2019 as population and urban growth began to develop in the Permian Basin. This study contributes on understanding of the response of groundwater quality associated with environmental change in the Permian Basin. Therefore, this research provides important information for groundwater managements in developing plans for the use of water resource in the future for Texas.

In this study, a new approach for estimating volume variations of lakes and reservoirs using water levels from satellite altimetry and surface areas from optical imagery is presented. Both input data sets, namely water level time series and surface area time series, are provided by DGFI-TUM‘s Database of Hydrological Time Series of Inland Waters (DAHITI). The approach is divided into three parts. In the first part, a hypsometry model based on the new modified Strahler approach is computed by combining water levels and surface areas. The hypsometry model describes the dependency between water levels and surface areas of lakes and reservoirs. In the second part, a bathymetry between minimum and maximum surface area is computed. For this purpose, DAHITI land-water masks are stacked using water levels derived from the hypsometry model. Finally, water levels and surface areas are intersected with the bathymetry to estimate a time series of volume variations in relation to the minimum observed surface area. The results are validated with volume time series derived from in-situ water levels in combination with bathymetric surveys. In this study, 28 lakes and reservoirs located in Texas are investigated. The absolute volumes of the investigated lakes and reservoirs vary between 0.099 km³ and 6.448 km³. The correlation coefficients of the resulting volume variation time series with validation data vary between 0.80 and 0.99. Overall, the relative errors with respect to volume variations vary between 2.8% and 14.9% with an average of 8.3% for all 28 investigated lakes and reservoirs. When comparing the resulting RMSE with absolute volumes, the absolute errors vary between 1.5% and 6.4% with an average of 3.1%. This study shows that volume variations can be calculated with a high accuracy which depends essentially on the quality of the used water levels and surface areas. In addition, this study provides a hypsometry model, high-resolution bathymetry and water level time series derived from surface areas based on the hypsometry model. All data sets are publicly available on the Database of Hydrological Time Series of Inland Waters (DAHITI, https://dahiti.dgfi.tum.de).

The reliability analysis allows to estimate the system's probability of detecting and identifying outlier. Failure to identify an outlier can jeopardise the reliability level of a system. Due to its importance, outliers must be appropriately treated to ensure the normal operation of a system. The system models are usually developed from certain constraints. Constraints play a central role in model precision and validity. In this work, we present a detailed optical investigation of the effects of the hard and soft constraints on the reliability of a measurement system model. Hard constraints represent a case in which there exist known functional relations between the unknown model parameters, whereas the soft constraints are employed for the case where such functional relations can slightly be violated depending on their uncertainty. The results highlighted that the success rate of identifying an outlier for the case of hard constraints is larger than soft constraints. This suggested that hard constraints should be used in the stage of pre-processing data for the purpose of identifying and removing possible outlying measurements. After identifying and removing possible outliers, one should set up the soft constraints to propagate the uncertainties of the constraints during the data processing. This recommendation is valid for outlier detection and identification purpose.

Water quality retrieval for small urban waterbodies by remote sensing get used to be difficult due to coarse spatial resolution of the remote sensing imagery. The recently launched Sentinel-2 produces imagery with a spatial resolution of 10 m. It provides an opportunity to solve the problem of retrieving water quality for small waterbodies. Additionally, many water management issues also require fine resolution of imagery, e.g. illegal discharge to an urban waterbody. Since illegal discharges are an important issue for urban water management, chemical oxygen demand (COD), total phosphorous (TP), and total nitrogen (TN) were chosen as the target parameters for water quality retrieval in this study. COD, TP and TN, however, are non-optically active parameters. There were limited studies in the past to retrieve these parameters in comparison with optically active parameters, e.g. Chlorophyll-A etc. This study compared three machine learning models, namely Random Forest (RF), Support Vector Regression (SVR), and Neural Networks (NN), to investigate the opportunity to retrieve the above non-optically active parameters. Results showed that R² of TP, TN, and COD by NN, RF and SVR were 0.94, 0.88, and 0.86, respectively. The performances of water quality retrieval for these non-optically active parameters were significantly improved by the optimized machine learning models. These models hence solved the problem to use remote sensing data to retrieve these non-optically active water quality parameters and provided a new monitoring strategy for small waterbodies. Water quality mapping obtained by Sentinel-2 imagery provided a full spatial coverage of the water quality characterization for the entire water surface. Compared with water samples collecting and testing, it greatly reduced labor cost, reagents cost, and waste treatment cost. It also may help identify illegal discharges to urban waterbodies. The method developed in this research provides a new practical and efficient water quality monitoring strategy in managing water with consideration of environmental sustainability.