Image fusion is a useful tool for producing a high-resolution multispectral image to be used for land use and land cover mapping. In this study, we use nine pansharpening algorithms namely Color Normalized (CN), Gram-Schmidt (GS), Hyperspherical Color Space (HCS), High Pass Filter (HPF), Nearest-Neighbor Diffusion (NND), Principal Component Analysis (PCA), Resolution Merge (RM), Stationary Wavelet Transform (SWT), and Wavelet Resolution Merge (WRM) to fusion Worldview-3 multispectral Bands and panchromatic band. In spectral and spatial fidelity, several image quality metrics are used to evaluate the performance of pansharpening algorithms. The SWT and PCA algorithms showed better results compared to other pansharpening algorithms while GS and CN algorithms showed the worst results for the original image fusion. The effect of fusion on each band was separately investigated and according to the calculations, we found that the CoastalBlue band and the Blue band showed the best result and the NIR-1 band and NIR-2 band show the worst result for the original image fusion. In the end, we conclude that the choice of fusion method depends on the requirement of remote sensing application.

Biochar application to the soil can improve soil quality and nutrient leaching loss. Recent studies have reported that surficial application of biochar to stored swine manure can reduce emissions of odorous compounds and reduce the volatilization loss of ammonia. Our working hypothesis was that the biochar-treated manure application to the soil would decrease nutrient leaching from manure and increase plant-available nutrients. The study objectives were to evaluate the impact of biochar-treated swine manure on soil total C, N, and other major and minor nutrients. Three biochars (i) neutral pH red-oak (RO), (ii) highly alkaline autothermal corn (Zea mays) stover (HAP), and (iii) mild acidic Fe-treated autothermal corn stover (HAPE) were incubated with swine manure for a month. The biochar-manure mixture was applied in triplicate to soil columns with application rate determined by the P₂O₅-P content in manure or manure-biochar mixtures after the incubation. The ammonium (NH₄⁺), nitrate (NO₃^-), and reactive P concentrations in soil column leachates were recorded for eight leaching events. Soil properties and plant-available nutrients were compared between treatments and control manure & soil. Manure-(HAP&HAPE) biochar treatments significantly increased soil organic matter (OM) and increased soil total C, N, and improved soil bulk density. Concentrations of KCl-extractable NH₄⁺ and NO₃^- significantly increased in HAPE column leachates during this 4-week study and in the soil after the experiment. A significant reduction in soil Mehlich3 Cu was also observed for the manure-HAPE mixture compared with the control. Overall, the manure-biochar incubation enabled biochar to sorb nutrients from manure, and the subsequent manure-biochar mixture application to soil improved soil quality and plant nutrient availability in comparison to conventional manure application to soil. This proof-of-the-concept study suggests that biochars could be used to solve both environmental and agronomic challenges and further improve the sustainability of animal and crop production agriculture.

Despite continued efforts by educators, UN declarations and numerous international agreements, progress is still limited in handling major global challenges such as ecosystem collapse, accelerating climate change, poverty and inequity. The capacity to collaborate globally on addressing these issues remains weak. This systematic review of research on global learning for sustainable development (GLSD) aims to clarify the diverse directions research on GLSD has taken, to present the historical development of the research area, and highlight emerging research issues. The review summarises key findings of the English language literature in the period 1994-2020 identified with the search terms “global learning” and “sustainable development”, sustainability or GLSD, respectively. The review documented a gradually growing knowledge base, mostly authored by scholars located in the global North. Conclusions point to what we might achieve if we could learn from one another in new ways, moving beyond Northern-centric paradigms. It is also time to re-evaluate core assumptions that underlie education for sustainable development more generally, such as a narrow focus on formal learning institutions. The review provides a benchmark for future reviews of research on GLSD, reveals the emerging transformative structure of this transdisciplinary field, and offers reference points for further research

The retrieval of sun-induced chlorophyll fluorescence is greatly beneficial to studies of marine phytoplankton biomass, physiology, and composition and is required for user applications and services. Customarily phytoplankton chlorophyll fluorescence is determined from satellite measurements through a fluorescence line-height algorithm using three bands around 680 nm. We propose here a modified retrieval, making use of all available bands in the relevant wavelength range with the goal to improve the effectiveness of the algorithm in optically complex waters. For the Ocean and Land Colour Instrument (OLCI) we quantify a Fluorescence Peak Height from fitting a Gaussian function and related terms into the top-of-atmosphere reflectance bands between 650 and 750 nm. This algorithm retrieves, what we call Fluorescence Peak Height from fitting a Gaussian function upon other terms to top-of-atmosphere reflectance bands between 650 and 750 nm. This approach is applicable to Level-1 and Level-2 data. We find a good correlation of the retrieved fluorescence product to global in-situ chlorophyll measurements, as well as a consistent relation between chlorophyll concentration and fluorescence from radiative transfer modelling and OLCI/in-situ comparison. The algorithm is applicable to complex waters without needing an atmospheric correction and vicarious calibration and features an inherent correction of small spectral shifts, as required for OLCI measurements.

Biochar application to the soil can improve soil quality and nutrient leaching loss. Recent studies have reported that surficial application of biochar to stored swine manure can reduce emissions of odorous compounds and reduce the volatilization loss of ammonia. Our working hypothesis was that the biochar-treated manure application to the soil would decrease nutrient leaching from manure and increase plant-available nutrients. The study objectives were to evaluate the impact of biochar-treated swine manure on soil total C, N, and other major and minor nutrients. Three biochars (i) neutral pH red-oak (RO), (ii) highly alkaline autothermal corn (Zea mays) stover (HAP), and (iii) mild acidic Fe-treated autothermal corn stover (HAPE) were incubated with swine manure for a month. The biochar-manure mixture was applied in triplicate to soil columns with application rate determined by the P₂O₅-P content in manure or manure-biochar mixtures after the incubation. The ammonium (NH₄⁺), nitrate (NO₃^-), and reactive P concentrations in soil column leachates were recorded for eight leaching events. Soil properties and plant-available nutrients were compared between treatments and control manure & soil. Manure-(HAP&HAPE) biochar treatments significantly increased soil organic matter (OM) and increased soil total C, N, and improved soil bulk density. Concentrations of KCl-extractable NH₄⁺ and NO₃^- significantly increased in HAPE column leachates during this 4-week study and in the soil after the experiment. A significant reduction in soil Mehlich3 Cu was also observed for the manure-HAPE mixture compared with the control. Overall, the manure-biochar incubation enabled biochar to sorb nutrients from manure, and the subsequent manure-biochar mixture application to soil improved soil quality and plant nutrient availability in comparison to conventional manure application to soil. This proof-of-the-concept study suggests that biochars could be used to solve both environmental and agronomic challenges and further improve the sustainability of animal and crop production agriculture.

This document describes the theoretical basis of the algorithms used to determine properties of snow and ice from the measurements of the Ocean and Land Color Instrument (OLCI) onboard Sentinel-3 satellites within the Pre-operational Sentinel-3 snow and ice products (SICE) project: http://snow.geus.dk/. The code used for the SICE retrieval and its documentation can be found at https://github.com/GEUS-SICE/pySICE. The algorithms were developed after the work from Kokhanovsky et al. (2018, 2019, 2020).

The detection of hormones, pharmaceutical and personal care products (PPCPs), in aqueous complex media as water bodies is an urgent and mandatory matter because of its harmful impact on the environment. This work presents a sensor device which is able to detect trace of the synthetic hormone 17α-ethinylestradiol (EE2), in a range of concentration of 10-15 to 10-9 M in both mineral and tap waters. These types of water were studied due to their different degrees of complexity, which allow to attain a better understanding of EE2’s behavior and response in different media. The sensor body consists of solid supports having interdigitated electrodes without and with a thin film deposited on it, and device response is evaluated through impedance spectroscopy. The thin films were prepared by the layer-by-layer technique from aqueous solutions of polyethylenimine (PEI) and poly(sodium 4-styrenesulfonate) (PSS) polyelectrolytes. Results demonstrated that it is possible to discriminate clearly the different waters used and to detect EE2, obtained from the second principal component (PC2) as a result of the application of principal component analysis method, with a sensitivity of -0.072 ± 0.009 and -0.44 ± 0.03 per decade of concentration, for mineral and tap water, respectively. Detection limits values were found to be of 8.6 fM (2.6 pg/L) and of 7.5 fM (22.2 pg/L) for TW and MW, respectively and are lower than the ones found in literature. Moreover, the achieved constant proportionality between the PC2 values for each EE2 concentration reveals that one can use this methodology to quantify EE2 concentration in unknown aqueous complex matrices.

Water scarcity is a key challenge to global development. In Brazil, the Sao Francisco River Basin (SFB) has experienced water scarcity problems because of decreasing streamflow and increasing demands from multiple sectors. However, the drivers of decreased streamflow, particularly the potential role of surface-groundwater interaction, have not been yet investigated. Here, we assess long-term trends in baseflow, quickflow, and streamflow of the SFB during 1980–2015 and constrain the most likely drivers of observed decreases through trend analysis of precipitation (P), evapotranspiration (ET), and terrestrial water storage change (TWS). We found that over 82% of the observed decrease in streamflow can be attributed to a significant decreasing baseflow trend (< -20 m3 s-1 y-1) along the SFR with spatial agreement between decreased baseflow, increased ET, and irrigated agricultural land. We also noted a decrease in TWS across the SFB with trends exceeding -20 mm y-1. Overall, our findings indicate that decreasing groundwater contributions (i.e., baseflow) is providing the observed reduction in total SFR flow. A lack of significant P trends indicates that only P variability likely has not caused the observed baseflow reduction, mainly in the Middle and Sub-middle SFB. Therefore, groundwater and surface withdrawals may be likely a driver of water scarcity over the SFB.

Boreal forests are one of the largest stores of carbon on Earth, and two-thirds of them are located in Siberia. Despite the fact that these forests have a significant influence on the global climate, they continue to remain understudied. Chernevaya taiga is a unique example of a highly productive Siberian boreal ecosystem. This type of forest is characterized by a series of unique ecological traits, the most notable of which are the gigantism of the perennial herbaceous plants and bushes, complete lack of moss cover on soil surface, and the type of soil it grows on, notable for its particularly high rate of decomposition of vegetative remains and low humic acid content. Abundant rainfall actively washes out nutrients from the top layers of the soil, but its fertility level remains very high. In fact, based on the existing data, it is twice as high as that of fertilized agricultural lands. In some ways the conditions within this type of forest closely resemble those observed in tropical rainforests. Microbiota associated with soil and plants represent an integral part of an interconnected system and contribute significantly to productive processes in soils. Its impacts on the environment require further study, since it could lead to discoveries that will help improve soil fertility without harming the natural environment.

UN 2030 Sustainable Development Goal 6 presents difficulties for small island developing states such as the Kingdom of Tonga, which relies on rainwater and groundwater lenses for freshwater supply. Planning and managing water resources to supply demands in dispersed small islands under variable climate and frequent extreme events is challenging. Tensions between water planning using top-down versus bottom-up processes have long been recognized. Tonga’s overarching national planning instrument is the Tonga Strategic Development Framework, 2015-2025 (TSDFII). This identifies desired national outcomes and is used to direct and resource Ministries and address international and regional commitments. Water supply was a low priority in the three-month consultations that led to TSDFII. Community Development Plans (CDPs), developed by rural villages throughout Tonga’s five Island Groups over nine years, involved participation from 80% of each village population who ranked local priorities. Analysis of priorities in 117 available village CDPs reveals improvements to village water supply was the highest overall priority in all five Island Groups and ranked within the top three priorities by 76% of all villages, with women, youth and men returning figures of 83%, 66% and 80% respectively. The mismatch between top-down and bottom-up priorities appears to result from an urban/rural divide.

The nationwide lockdown imposed to control the spread of novel coronavirus induced dramatic alterations in different sectors of the Nepalese governance including Solid Waste Management (SWM) practices. This study identifies SW collection gaps in nine major cities of Nepal, and highlights the municipal and public hearings on SW management practices before and during the lockdown with a view to emphasize the linkage between Covid-19 and SWM. It includes information on solid waste workers safety practices, collection coverage dynamics, types of vehicles operated for collection and alternative methods adopted by households to manage SW during the collection gaps. It was found that although the coverage of the collection service was similar during the pandemic, there was drastic decrease in the collection frequency leading to a collection gap of 560.58 tons/day and no proper measures were adopted in more than 50% of the households surveyed for SWM during the collection gap. The study reveals poor occupational health and safety practices among the solid waste workers due to unavailability of safety gears and equipment, in spite of being aware of the modes of transmission of the virus. The pandemic exacerbated the challenges of smooth SWM making its significance as an essential service more apparent. This highlights the need of a timely strategic emergency management framework to be developed by the government to continue the invaluable public service without any hindrance.

This work aimed to assess and characterize the air quality in what concerns particulate pollution in urban-industrial environments. The main objectives were to study the spatial variability of the deposition flux of particulate pollution identifying areas with higher deposition, associate the variability with climatological variables and with possible surrounding emitting sources. The method for collecting the deposited particles was based on the standard NF X 43-007. Sampling for particulate pollution took place between April 2015 and February 2016, through seven sampling campaigns. Maps of the spatial dispersion for the particulate pollution were obtained through statistics and geostatistics techniques. Elemental identification by scanning electron microscopy (SEM) was also used but only in two sampling campaigns. The results show that the sampling campaigns that took place during hot and dry periods, 2nd and 3rd, present higher deposition flux: 2.04 g/(m2 x month) and 1.72 g/(m2 x month), respectively. Lower deposition fluxes were registered in the 6th and 7th campaigns: 0.23 g/(m2 x month) and 0.24 g/(m2 x month), respectively. It was also observed a recurrent high deposition at specific sampling points which may be due to both the nearby road traffic and the presence of chimneys. SEM analysis allowed to associate repetitive element deposition, at the same sampling point, to the same emitting source.

Mangroves and salt marshes are among the most productive ecosystems in the global coastal ocean. Mangroves store more carbon (739 Mg CORG ha-1) than salt marshes (334 Mg CORG ha-1), but the latter sequester proportionally more (24%) net primary production (NPP) than mangroves (12%). Mangroves exhibit greater rates of gross primary production (GPP), above-ground net primary production (NPP) and plant respiration (RC) with higher PGPP/RC ratios, but salt marshes exhibit greater rates of below-ground NPP. Mangroves have greater rates of subsurface DIC production and, unlike salt marshes, exhibit significant microbial decomposition to a soil depth of 1 m. Salt marshes release more soil CH4 and export more dissolved CH4 , but mangroves release more CO2 from tidal waters and export greater amounts of POC, DOC and DIC to adjacent waters. Both ecosystems contribute only a small proportion of GPP, RE (ecosystem respiration) and NEP (net ecosystem production) to the global coastal ocean due to their small global area, but contribute 72% of air-sea CO2 exchange from the world’s wetlands and estuaries and contribute 34% of DIC export and 17% of DOC + POC export to the world’s coastal ocean. Thus, both wetland ecosystems contribute disproportionately to carbon flow of the global coastal ocean.

Stormwater management is a key urban issue in the world, in line with the global issues of urban sprawl and climate change. It is urgent to investigate the effectiveness in managing stormwater with different strategies for maintain urban resilience. A method based on a storm water management model (SWMM) was developed for assessing the control of stormwater runoff volume and the percentage removal of suspended solids by implementing a Sponge City strategy. An interdisciplinary approach was adopted incorporating Low Impact Development (LID) with urban Green Infrastructure and Gray Infrastructure paradigms in a typical old residential community in Suzhou, China. Four types of sponge facilities for reducing stormwater runoff were bio-retention cells, permeable pavements, grassed pitches, and stormwater gardens. The simulation results indicate that the stormwater pipe system can meet the management standard for storms with a five-year recurrence interval. The volume capture ratio of annual runoff is 91% and the reduction rate of suspended solids is 56%. This study demonstrates that Sponge City strategy is an effective approach for managing stormwater, particularly in old and densely populated urban areas. Implementing spongy facilities with a LID strategy for stormwater management can significantly enhance urban water resilience and increase ecosystem services.

The COVID-19 pandemic is adversely impacting food and nutrition security and requires urgent attention from policymakers. Sustainable intensification of agriculture is one strategy that attempts to increase food production without adversely impacting the environment, by shifting from water-intensive crops to other climate-resistant and nutritious crops. This paper focuses on the Indian state of Andhra Pradesh by studying the impact of shifting 20% of the area under paddy and cotton cultivation to other crops like millets and pulses. Using FAO’s CROPWAT model, along with monsoon forecasts and detailed agricultural data, we simulate the crop water requirements across the study area. We simulate a business-as-usual base case and compare it to multiple crop diversification strategies using various parameters – food, calories, protein production, as well as groundwater and energy consumption. Results from this study indicate that reduced paddy cultivation decreases groundwater and energy consumption by around 9-10%., and a calorie deficit between 4-8% - making up this calorie deficit requires a 20-30% improvement in the yields of millets and pulses. We also propose policy interventions to incentivize the cultivation of nutritious and climate-resistant crops as a sustainable strategy towards strengthening food and nutrition security while lowering the environmental footprint of food production.

Green Infrastructures (GI) are considered key to reconcile ecological and social benefits by providing multiple functions. The concept is increasingly promoted and guidelines for its implementation have been developed in many countries and regions of the Western Hemisphere. However, for other parts of the world, especially for countries with less developed infrastructures, promotion, guidance for decision-making and manuals for GI are often lacking. But the state of infrastructure development and often unplanned character of settlements in the Global South differ and result in specific constraints as well as demands to GI that need to be addressed explicitly. This study presents a methodological approach to explicitly address the specific conditions and physical limitations to GI development in urban areas of the Global South. A four step methodology was developed to assess the implementation potential for retrofitted and multifunctional urban green infrastructure in public areas. An initial site analysis (1) and the definition of design criteria as well as general strategies (2) to achieve the different dimensions of multi-functionality are the basis to derive spatial typologies (3) for GI elements and finally the spatial suitability assessment for potential placements (4). An application of the methodology to a study area in the metropolitan region of San José, Costa Rica, shows exemplarily that the potential to improve the hydrological conditions (up to 34% of surface runoff reduction), ecological conditions (increase of green space by 2,2 %, creation of 1500 m length of roadside greenery and two new habitat types), and social conditions (2.200 m of road type upgrading) of multi-functionality of the site through Green Infrastructures. These assessment results of different multi-functionality dimension can serve as a guidance for GI promotion and implementation in urban areas of the Global South.

In 2010, Parties to the Convention on Biological Diversity (CBD) adopted the Strategic Plan for Biodiversity 2011–2020 to address the loss and degradation of nature. Subsequently, most biodiversity indicators continued to decline. Nevertheless, conservation actions can make a positive difference for biodiversity. The emerging Post-2020 Global Biodiversity Framework has potential to catalyze efforts to ‘bend the curve’ of biodiversity loss. Thus, the inclusion of a goal on species, articulated as Goal B in the Zero Draft of the Post-2020 Framework, is essential. However, as currently formulated, this goal is inadequate for preventing extinctions, and reversing population declines; both of which are required to achieve the CBD’s 2030 mission. We contend it is unacceptable that Goal B could be met while most threatened species deteriorated in status and many avoidable species extinctions occurred. We examine the limitations of the current wording and propose an articulation with robust scientific basis. A goal for species that strives to end extinctions and recover populations of all species that have experienced population declines, and especially those at risk of extinction, would help to align actors towards the transformative actions and interventions needed for humans to live in harmony with nature.

Forest plantations have a large potential for carbon sequestration, playing an important role in the global carbon cycle. However, despite the huge amount of research carried out worldwide, the absolute contribution of industrial forest plantations is still incomplete for some parts of the world. To contribute to bridge this gap, we calculated the amount of C stock in three fast growing forest species in Chile. Relevant C pools (above-ground and below-ground biomass, forest floor, and soil) were considered for this analysis. Across the industrial plantation forests of Chile, carbon accumulated in the above-ground biomass was 181–212 Mg · ha−1 for Pinus radiata, 147–180 Mg · ha−1 for Eucalyptus nitens, and 95–117 Mg · ha−1 for Eucalyptus globulus (age 20–24 years for P.radiata and 10–14 years for Eucalyptus). Our results agree with other studies showing that 30%–50% of the total C stock is stored in the soil. Total C stocks were for 343 Mg · ha−1 for P.radiata, 352 Mg · ha−1 for E.nitens, and 254 Mg · ha−1 for E. gloubulus, also at the end of a typical rotation. The carbon pool in the forest floor was found to be significantly lower (less than 4% of the total) when compared to the other pools and showed large spatial variability. We conclude that industrial forest plantations are a valuable tool to reduce atmospheric CO2 and mitigate climate change. Given the contribution of soils to total carbon stocks, special attention should be paid to forest management activities that affect the soil organic carbon pool.

This study has been monitored for five years by Sentinel-2 satellite images, at different seasons of the year, of the fluctuations in the water level of the Gallocanta Lake (between the provinces of Teruel and Zaragoza, Aragón, Spain) considered a hypersaline and endorheic wetland, which has characteristics that make it unique in the geographical area in which it is located, as well as for the operation of the system. Rainfall in the area has a wide variation giving the maximums in the months of May and June and the minimums in January and February. There are considerable fluctuations in the water level from the almost total drying of the lagoon to the filling with a depth of approximately 3 meters.

Impact of land use and land cover (LULC) change on soil erosion is still imperfectly understood, especially in northeastern China (NEC). Based on the Revised Universal Loss Equation (RUSLE), the variability of soil erosion at different spatial scales following land use changes in1980, 1990, 2000, 2010, and 2017 was analyzed. The regionally spatial patterns of soil loss coincided with the topography, rainfall erosivity, soil erodibility, and use patterns, and around 45% soil loss came from arable land. Regionally, soil erosion rates increased from 1980 to 2010 and decreased from 2010 to 2017, ranging from 3.91 to 4.45 t ha-1 yr-1 with an average of 4.22 t ha-1 yr-1 in 1980-2017. The rates of soil erosion less than 1.41 t ha-1 yr-1 decreased from 1980 to 2010, and increased from 2010 to 2017, and opposite changing patterns occurred in higher erosion classes (i.e., above 5 t ha-1 yr-1). At a provincial scale, Liaoning Province experienced the highest soil erosion rate of 9.43 t ha-1 yr-1, followed by Jilin Province, the east Inner Mongolia, and Heilongjing Province. Arable land continuously increased at the expense of forest in the high-elevation and steep-slope areas from 1980 to 2010, and decreased from 2010 to 2017, resulting in increased areas with erosion rates higher than 7.05 t ha-1 yr-1. At a county scale, around 75% of the countries had soil erosion rate higher than its tolerance level. The county numbers with higher erosion rate increased in 1980-2010 and decreased in 2010- 2017, resulting from the sprawl and withdrawal of arable land. The results indicate that appropriate policies can control soil loss through limiting arable land sprawl in areas of unfavorable regions in the NEC.

This research was conducted to analyze the content of Fe, Cu, Cd, Cr, and Pb in several species of fish taken from three lakes that is closely to disposal of industrial waste in Indonesia. The fish samples were taken from three lakes, namely, Muara Angke, Weda, and Morowali. The samples from Morowali were analyzed in April 2019, those from Weda from November to December 2019, and those from Muara Angke in June 2018. All the samples were then analyzed at the chemistry laboratory of the Department of Chemistry, Faculty of Mathematics and Natural Science, University of Indonesia, and the Integrated Laboratory of IPB. The results showed that all types of fish from Morowali and Weda were no longer safe to consume because they contained Fe, Cu, Cd, and Cr exceeding the threshold of metal contamination. Meanwhile, all types of fish from Muara Angke, except for ayam-ayam, are still safe for consumption. The results of this study can be a source of information regarding metal content in fish and fish feed for safe consumption. Several studies have been done to determine the metal pollutants contained by fish. Given the high consumption rate of fish and the hazards of heavy metals on humans’ health, such research must be furthered

Circular Economy (CE) is a growing topic among scholars, industry and government aiming at decoupling economic growth and development from the consumption of finite resources. CE incorporates different meanings, from reduce, reuse and recycle activities to environmental degradation or resource scarcity, and supported by specific indicators to attain sustainable development. However, so far, there has been no agreement to measure how effective an industry/product is in making the transition from linear to circular approaches, particularly those affected the society. This research work aims to perform a systematic literature review (n=60) to analyze and discuss how social aspects have been considered and integrated in the CE research so far. Moreover, this review provides and overview of the literature on social impact within CE, that resulted in three main outputs: a knowledge map of the CE, an analysis of social aspects within CE, and the theories/frameworks used to evaluate social impact of CE. Finally, this study brings to light how CE implementation can affect society and highlights the importance of social dimension in the domains of CE and policy-making community, which could help moving CE towards a sustainable development.

Heavy metals pollution in the Straits of Malacca warrants the development of rapid, simple and sensitive assays. Enzyme-based assays are excellent preliminary screening tool with near real-time potential. The heavy-metal assay based on the protease ficin was optimized for mercury detection using Response Surface Methodology. The inhibitive assay is based on ficin action on the substrate casein and residual casein is determined using the Coomassie dye-binding assay. Heavy metals strongly inhibit the hydrolysis. A Central Composite Design (CCD) was utilized to optimize detection. The results show a marked improvement for the concentration causing 50% inhibition (IC50) for mercury, silver and copper. Compared to One-factor-at-a-time (OFAT) optimization, RSM gave an improvement of IC50 from 0.060 (95% CI, 0.0300.080) to 0.017 (95% CI, 0.0160.019), from 0.098 (95% CI, 0.0770.127) to 0.028 (95% CI, 0.0220.037) and from 0.040 (95% CI, 0.035.045) to 0.023 (95% CI, 0.0200.027), for mercury, silver and copper, respectively. A near real-time monitoring of mercury concentration in the Straits of Malacca at one location in Port Klang was carried out over a 4-h interval for a total of 24 h and validated by instrumental analysis with the result revealing an absence of mercury pollution in the sampling site.

Among others, road traffic, industrial emissions, commercial activities, smoking and cooking are considered as major contributing factors for the increasing levels of pollutants in atmosphere. High levels of potentially toxic metals and microbes in atmosphere, especially in indoor air, may pose serious threat to human health. Therefore, concentration and associated health risks of potentially toxic trace metals (Cd, Cr, Cu, Fe, Mn, Pb, and Zn) and their risk to human health, and microbial load in indoor air was assessed using air condition (AC) filter dust samples collected from 5 locations representing residential, agricultural and industrial settings of Eastern Province, Kingdom of Saudi Arabia. The levels of trace metals varied considerably among sampling areas, with the highest levels of Cr and Cd recorded in the Industrial-area sites followed by the Agricultural and Urban-Residential sites. The highest levels of Pb and Fe were found in the Agricultural area sites followed by the Industrial and Urban-Residential area sites. The metals in dust sample, especially Cd, Cr and Pb, showed a considerable health risk through dermal pathway. Among the sites, the highest hazard quotient for these metals was found for Al-Qatif-Industrial areas sites and among the metals it was the highest for Cd. The cancer risk from the metals contained in AC filter dust was negligible. Samples collected from Agricultural and Industrial area sites were substantially contaminated with bacteria and fungi, respectively. Bacterial contaminants were mostly Gram Negative, with considerable antibiotic resistance and haemolytic activity. Thus, indoor air quality as assessed by AC filter dust depicted that a considerable health risk could be posed by the trace heavy metals and microorganisms for a long-term exposure. Furthermore, this study demonstrated that AC filters dust could be a unique and reliable test sample for the assessment of indoor environment.

In the present work, we evaluate the prediction capability of six Pedotransfer functions (PTFs), reported in the literature, for the saturated hydraulic conductivity estimations (Ks). We used a database with 900 measured samples obtained from the Irrigation District 023, in San Juan del Rio, Queretaro, Mexico. Additionally, six new PTFs were construct for Ks from clay percentage, bulk density and saturation water content data. The results show, for the evaluated models, that one model present an overestimation for Ks>0.5 cm h-1 values, three models have a underestimation for Ks>1.0 cm h-1 and two models have a good correlation (R2>0.98) but are necessary more than three parameters. Nevertheless, the last two models requires from three to four parameters in order to get the optimization. By other hand, the models proposed in this work have a similar correlation with a less number of parameters: the fit is seen to be much better than using the existing ones, achieving a correlation of R2 = 0.9822 with only one variable and a R2 = 0.9901 when we use two.

Water resources are threatened nowadays by pollution that comes from domestic, industrial and agricultural discharges without prior treatment. This pollution causes the degradation of water quality. Surface pollutants can seep through the soil into water tables. The objective of our work is to assess and control the physicochemical quality of the Chari Baguirmi groundwater, to protect human health. The quality of 83 boreholes was assessed, while performing analysis for 12 physicochemical parameters at the National Water Laboratory and at the Center for Quality Control of Foodstuffs in Chad. These parameters are: pH, EC, Ca2+, Mg2+, Na+, K+, Cl-, SO42-, NH4+ and NO3- , Fe2+ and HCO3-. Also, the results obtained were compared with WHO standards. The geochemical statistical approach has made it possible to characterize the hydro geochemical properties and to understand the major processes of the mineralization of groundwater resources in Chari Baguirmi region in Chad. Some of its waters are acidic and weakly mineralized, rich in Fe2+ and NH4+. The origin of the mineralization is due to the alteration of the host rocks and to the hydrolysis of silicate and ferromagnesian minerals as well as anthropogenic pollution. The ammonium concentrations in N’Djamena boreholes are higher than WHO standards, indicating the presence of pollution which may come from organic waste. These results constitute a preliminary step in understanding hydro geochemical functioning and a basis for monitoring the physicochemical quality of the water in the study area.

Bioenergy with carbon capture and sequestration (BECCS) is one strategy to remove CO2 from the atmosphere. To assess the potential scale and cost of CO2 sequestration from BECCS in the US, this analysis models carbon efficiencies and costs of biomass production, delivery, power generation, and CO2 capture and sequestration in saline formations. The analysis includes two biomass supply scenarios (near-term and long-term), two biomass logistics scenarios (conventional and pelletized), two generation technologies (pulverized combustion and integrated gasification combined cycle), and three cost accounting scenarios (gross cost, net cost after electricity revenues, and net cost after electricity revenues with avoided emissions from conventional power generation). Results show cost Mg-1 CO2 as a function of CO2 sequestered (simulating capture up to 90% of total CO2 sequestration potential) and associated spatial distribution of resources and generation locations for the array of scenario options. Under a near-term scenario using 222 million Mg yr-1 of biomass, up to 196 million Mg CO2 can be sequestered at scenario-average costs ranging from $60 to $158 Mg 1 CO2; under a long-term scenario using 823 million Mg yr-1 of biomass, up to 727 million Mg CO2 yr 1 can be sequestered at scenario-average costs ranging from $32 to $242 Mg-1 CO2. These costs are largely influenced by cost accounting scenario, and the CO2 sequestration potential may be reduced if future competing demand reduces resource availability. Results suggest there are multiple feedstock-logistics-generation pathways toward CO2 drawdown that could be incrementally trialed and monitored for environmental sustainability effects. Interactive visualization of results is available at [final link to be determined].

Abstract The COVID-19 pandemic has tremendously affected the African continent and the rest of the world. Most businesses have closed and a lot of people have lost their jobs. The aviation industry has been shaken to the core with airlines losing millions of dollars and flights being cancelled. The tourism industry has consequently been affected due to restricted travel of tourists, impacting wildlife conservation and livelihoods. Schools, colleges and universities have been closed. The virus has infected millions of people and hundreds of thousands of people have died globally putting strain on health systems especially those of hard hit countries. Various countries all over the world have put measures to control the spread of the virus through lockdowns and social distancing policies. The reduced economic activities and mobility of people has resulted in improved air quality, cleaner water and beaches in some countries. However there are also negative impacts such as challenges in waste management, increased pharmaceutical and household waste and discovery of the corona virus in wastewater, a potential threat to public health. A considerable amount of research has been done on the socio-economic impacts of COVID-19 in Africa but there is still limited research on its impact on the environment. This paper serves to highlight the observed and potential environmental impacts of COVID-19 in Africa.

Bacteria and archaea participate in and are influenced by processes of substance circulation and energy exchanges in natural environment. Generally, the community changes of bacteria and archaea in sediment are mainly driven by seasonality in mid-latitude regions. But in our study, water diversion to Fen river played a more important role on OTU number, diversity and community structure of bacteria and archaea in sediment than seasonal variation, which was found by 16S rRNA high-throughput sequencing technology. This phenomenon might be caused by external transferred water on the physicochemical water environment and accelerated release of positive nitrogen from sediment caused by rise of water level. Changes of carbon-nitrogen cycle and increase of electrical conductivity (EC) value induced more diversion-responders than season-responders both for bacteria and archaea. Seasonal changes have been influencing bacteria and archaea mildly throughout the whole study reach. After water diversion, the environment indicators relating to bacteria community obviously changed from nutrients to salinity while that for archaea almost disappeared. Our research showed the effects of human activities on the communities of bacteria and archaea outweigh the forcing from natural seasonal changes in mid-latitude regions and revealed the mechanism, highlighting different responses of bacteria and archaea to environmental changes.

In this study a method for predicting the preferred pleasantness induced by different forest environments, represented by virtual photographs, was proposed and evaluated using a novel Anti-Environmental Forest Experience Scale psychometric test. The evaluation questionnaire contained twenty-one items divided into four different subscales. The factor structure was assessed in two separate samples collected online (sample 1: N = 254, sample 2: N = 280). The internal validity of the four subscales was confirmed using an exploratory factor analysis. Discriminant validity was tested and confirmed using the Amoebic Self Scale (Spatial-Symbolic domain). Concurrent validity was confirmed using the Connectedness to Nature Scale. Predictive validity was based on assessment of pleasantness induced by nine different photographs (control – urban landscapes, forest landscapes, dense forest landscapes), with subscales differently correlated with the level of pleasantness assessed for each photograph. This evaluation instrument is appropriate for predicting preferred pleasantness induced by different forest environments.

Wildfires generate large amounts of atmospheric pollutants yearly. The development of an emissions inventory for this activity is a challenge today, mainly to perform modeling of air quality. There are free available databases with historical information about this source. The main goal of this study was to process the results of biomass burning emissions for the year 2014 from the Global Fire Assimilation System (GFAS). The pollutants studied were the black carbon, the organic carbon, fine and coarse particulate matter, respectively. The inputs were pre-formatted to enter to the simulation software of the emission inventory. In this case, the Sparse Matrix Operator Kernel Emissions (SMOKE) was used and the values obtained in various cities were analyzed. As a result, the spatial distribution of the forest fire emissions in the Southern Hemisphere was achieved, with the polar stereographic projection. The highest emissions were located in the African continent, followed by the northern region of Australia. Future air quality modeling at a local level could apply the results and the methodology of this study. The biomass burning emissions could add a better performance of the results and more knowledge on the effect of this source.

The European heatwave of 2018 led to record-breaking temperatures and extremely dry conditions in many parts of the continent resulting in widespread decrease in agricultural yield, early tree-leaf senescence, and increase in forest fires in Northern Europe. Our study aims to capture the impact of the 2018 European heatwave on terrestrial ecosystem through the lens of a high-resolution solar-induced fluorescence (SIF) data acquired from the Orbiting Carbon Observatory (OCO-2) satellite. SIF is proposed to be a direct proxy for gross primary productivity (GPP) and thus can be used to draw inferences about changes in photosynthetic activity in vegetation due to extreme events. We explore spatial and temporal SIF variation and anomaly during spring and summer months across different vegetation types (agriculture, broadleaved forest, coniferous forest, and mixed forest) during the European heatwave of 2018 and compare it to non-drought conditions (most of Southern Europe). About one-third of Europe’s land area experienced a consecutive spring and summer drought in 2018. Comparing 2018 to mean (2015-2017) conditions, we found a change in intra-spring season SIF dynamics for all vegetation types, with lower SIF during the start of spring followed by an increase in fluorescence from mid-April. Summer, however, showed a significant decrease in SIF. Our results show that particularly agricultural areas were severely affected by the hotter drought of 2018. Furthermore, the intense heat wave in Central Europe showed about 31% decrease in SIF values during July and August as compared to the mean over three previous years. Furthermore, our MODIS and OCO-2 comparative results indicate that especially for forests, OCO-2 SIF has a quicker response and possible higher sensitivity to drought in comparison to MODIS’s fPAR and NDVI when considering shorter reference periods, which highlights the added value of remotely sensed solar-induced fluorescence for studying the impact of drought on vegetation.

Gold nanoparticles, were deposited in titanium oxide (TiO2) Degussa-P25 with the Photodeposition method in the presence of UV light at different concentrations. It was determined by diffuse reflectance (DF), Scanning electron microscopy (SEM), that the Photodeposition method is effective for the inclusion of gold particles on the surface. The catalyst band gap showed a reduction to 2.9 e.V, as well as it was observed that the gold-doped catalyst shows absorption in the visible light range around 500 to 600 nm. The percentage of deposited gold nanoparticles was determined by energy dispersive spectroscopy (EDS). The experimental data were analyzed using different analytical techniques (UV-Vis spectrophotometry, TOC total organic carbon), with these results a carbon-based mass balance and reaction kinetics were generated using the Langmuir-Hinshelwood (LH-HW) heterogeneous catalysis model. For the estimation of the kinetic constants, the non-linear regression of the Levengerd Marquad algorithm was used, with these results, kinetic models of the degradation of the molecule and the generation and consumption of Organic Intermediate Products (OIP) were generated.

In the present study, a five-year follow-up was performed by remote sensing of the calcium carbonate precipitation in La Gitana karstic lake (located on the province of Cuenca, Spain). The important role that calcium carbonate precipitation plays in the ecology of the lake is well known for its influence on the vertical migrations of phytoplankton, the concentration of bioavailable phosphorus and, therefore, the eutrophication and quality of the waters. Whiting take place between the months of July and August, and it can be studied at this time through its optical properties, with the main objective of offering updated data on a phenomenon traditionally studied and establishing possible relationships between abiotic factors such as temperature and/or rainfall. The atmospheric temperature data collected by the meteorological station suggest a possible relationship between the appearance of the white phenomenon and a pulse of previous maximum temperatures. On the other hand, no apparent relationship was found between rainfall and water bleaching.

The Sustainable Development Goals (SDGs) purport to report holistically on progress towards sustainability and do so using more than 231 discrete indicators with a primary objective to achieve a balance between the environment, social and economic aspects of development. The research question underpinning the analyses presented in this paper is: are the indicators in the SDGs sufficient and fit-for-purpose to assess the trajectory of natural resources towards sustainability? We extracted the SDG indicators that monitor the state of natural resources, or alternately support policy or governance for their protection, and determined if these are adequate to provide the essential data on natural resources to achieve the aims of the SDGs. The indicators are clustered into four natural resource categories; land, water (both marine and freshwater), air and biodiversity. Indicators for monitoring land resources show that the most comprehensive land resource indicator, for degraded land, is not fully implemented and that missing from land monitoring is an evaluation of vegetation health outside of forests and mountains, the condition of soils, and most importantly the overall health of terrestrial ecosystems. Indicators for monitoring water resources have substantial gaps, unable to properly monitor water quality, water stress, many aspects of marine resources and most significantly, the health of fresh and salt water ecosystems. Indicators for monitoring of air have recently become more comprehensive, but linkage to IPCC results would benefit both programmes. Monitoring of biodiversity is perhaps the greatest weakness of the SDG Agenda, having no comprehensive assessment even though narrow aspects are monitored. Again, deliberate linkages to other global biodiversity programmes (e.g. CBD and the Post 2020 Biodiversity Framework; IPBES; Living Planet, etc.) are recommended on condition that data can be defined at a country level. While the SDG list of indicators in support of natural resource is moderately comprehensive, it lacks holistic monitoring in relation to evaluation of ecosystems and biodiversity to the extent that these missing but vital measures of sustainability threaten the entire SDG Agenda. In addition, an emerging issue is that even where there are appropriate indicators, the amount of country-level data remains inadequate to fully evaluate sustainability. This signals the delicate balance between the extent and complexity of the SDG Agenda and uptake at a country level

This work aimed to study the spatial variability of particulate matter deposition flux in urban-industrial environments. The main objective was to identify areas with higher deposition flux and associate the variability with climatological variables and with possible surrounding emitting sources. The method for collecting the deposited particles was based on the standard NF X 43-007. Sampling for particulate matter took place between April 2015 and February 2016, through seven sampling campaigns. Maps of the spatial dispersion for the particulate matter were obtained through statistics and geostatistics techniques. Elemental identification by scanning electron microscopy (SEM) was also used in two sampling campaigns. The results show that the sampling campaigns that took place during hot and dry periods, 2nd and 3rd, present higher deposition flux: 2.04 g/(m2 x month) and 1.72 g/(m2 x month), respectively. Lower deposition fluxes were registered in the 6th and 7th campaigns: 0.23 g/(m2 x month) and 0.24 g/(m2 x month), respectively. It was also observed a recurrent high deposition at specific sampling points which may be due to both the nearby road traffic and the presence of chimneys. SEM analysis allowed to associate repetitive element deposition, at the same sampling point, to the same emitting source.

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.

Mitigation of global warming requires an understanding of where energy is produced and consumed, the magnitude of carbon dioxide generation, and proper understanding of the Carbon Cycle. The latter leads to the distinction between and need for both CO2 and biomass CARBON sequestration. Short reviews are provided for prior technologies proposed for reducing CO2 emissions from fossil fuels or substituting renewable energy, focusing on their limitations. None offer a complete solution. Of these, CO2 sequestration is poised to have the largest impact. We know how to do it. It will just cost money, and scale-up is a huge challenge. Few projects have been brought forward to semi-commercial scale. Transportation accounts for only about 30% of U.S. overall energy demand. Biofuels penetration remains small, and thus, they contribute a trivial amount of overall CO2 reduction, even though 40% of U.S. corn and 30% of soybeans are devoted to their production. Bioethanol is traced through its Carbon Cycle and shown to be both energy inefficient, and an inefficient use of biomass carbon. Both biofuels and CO2 sequestration reduce FUTURE CO2 emissions from continued use of fossil fuels. They will not remove CO2 ALREADY in the atmosphere. The only way to do that is to break the Carbon Cycle by growing biomass from atmospheric CO2 and sequestering biomass CARBON. Theoretically, sequestration of only a fraction of the world’s tree leaves, which are renewed every year, can get the world to Net Zero CO2 without disturbing the underlying forests. Thoughts are put forth on how to achieve secure permanent biomass sequestration.

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.

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.

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.

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.

Mitigation of global warming requires an understanding of where energy is produced and consumed, the magnitude of carbon dioxide generation, and proper understanding of the Carbon Cycle. The latter leads to the distinction between and need for both CO2 and biomass CARBON sequestration. Short reviews are provided for prior technologies proposed for reducing CO2 emissions from fossil fuels or substituting renewable energy, focusing on their limitations. None offer a complete solution. Of these, CO2 sequestration is poised to have the largest impact. We know how to do it. It will just cost money, and scale-up is a huge challenge. Few projects have been brought forward to semi-commercial scale. Transportation accounts for only about 30% of U.S. overall energy demand. Biofuels penetration remains small, and thus, they contribute a trivial amount of overall CO2 reduction, even though 40% of U.S. corn and 30% of soybeans are devoted to their production. Bioethanol is traced through its Carbon Cycle and shown to be both energy inefficient, and an inefficient use of biomass carbon. Both biofuels and CO2 sequestration reduce FUTURE CO2 emissions from continued use of fossil fuels. They will not remove CO2 ALREADY in the atmosphere. The only way to do that is to break the Carbon Cycle by growing biomass from atmospheric CO2 and sequestering biomass CARBON. Theoretically, sequestration of only a fraction of the world’s tree leaves, which are renewed every year, can get the world to Net Zero CO2 without disturbing the underlying forests.

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.

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.

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.

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.

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 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 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.

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.

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.

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.

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.

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.

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.

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.

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%.

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.

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.

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.

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.

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.

Crop type and field boundary mapping enable cost-efficient crop management on the field scale and serve as the basis for yield forecasts. Our study uses a data set with crop types and corresponding field borders from the federal state of Bavaria, Germany, as documented by farmers from 2016 to 2018. The study classified corn, winter wheat, barley, sugar beet, potato, and rapeseed as the main crops grown in Upper Bavaria. Corresponding Sentinel-2 data sets include the normalised difference vegetation index (NDVI) and raw band data from 2016 to 2018 for each selected field. The influences of clouds, raw bands, and NDVI on crop type classification are analysed, and the classification algorithms, i.e., support vector machine (SVM) and random forest (RF), are compared. Field boundary detection and extraction are based on non-iterative clustering and a newly developed procedure based on Canny edge detection. The results emphasise the application of Sentinel’s raw bands (B1–B12) and RF, which outperforms SVM with an accuracy of up to 94%. Furthermore, we forecast data for an unknown year, which slightly reduces the classification accuracy. The results demonstrate the usefulness of the proof-of-concept and its readiness for use in real applications.

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.

In 2010, Parties to the Convention on Biological Diversity (CBD) adopted the Strategic Plan for Biodiversity 2011–2020 to address the loss and degradation of nature. Subsequently, almost all biodiversity indicators have continued to decline. Nevertheless, it is well established that conservation actions can make significant positive differences for species and ecosystems. Therefore, the Post-2020 Global Biodiversity Framework, which is currently being developed, has immense potential to motivate efforts to ‘bend the curve’ of biodiversity loss. Here we contend that Goal B on species, as articulated in the Zero Draft of the Post-2020 Framework, is inadequate for preventing extinctions, and ambiguous regarding reversing population declines, both of which are required to achieve the CBD’s proposed 2030 mission “to put biodiversity on a path to recovery for the benefit of planet and people”. We examine the limitations of the current wording of the goal and propose an articulation with a robust scientific basis. A global goal for species that strives to end extinctions and recover populations of all threatened and depleted species can help align actors towards the transformative actions and interventions needed to allow humans to live in harmony with nature.

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.

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

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).

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Over the past 150 million years, the hyperarid core of the Atacama Desert has been transformed by geologic and atmospheric conditions into one of the most unique and inhospitable landscapes on the planet. This makes it an ideal Mars analog that has been explored for decades as preliminary studies on the space life discovery. However, two heavy rainfalls that occurred in the Atacama in 2015 and 2017 provide a unique opportunity to study the response of resident extremophiles to rapid environmental change associated with excessive water and salt shock. Here we combine geochemical analyses with molecular biology to study the variations in salts and microbial communities along an aridity gradient, and to examine the reshuffling of hyperarid microbiomes before and after the two rainfall events. Analysis of microbial community composition revealed that soils within the southern desert were consistently dominated by Actinobacteria, Proteobacteria, Acidobacteria, Planctomycetes, Chloroflexi, Bacteroidetes, Gemmatimonadetes, and Verrucomicrobia; soils within the hyperarid sites were dominated by Aquificae and Deinococcus-Thermus before heavy rainfalls, while these organisms almost totally diminished after rainfall, and the hyperarid microbial consortia and metabolisms transformed to a more southern desert pattern along with increased biodiversity. Salts at the shallow subsurface were dissolved and leached down to a deeper layer, both benefitting and challenging indigenous microorganisms with the excessive input of water and ions. Microbial viability was found to change with aridity and rainfall events but correlated with elevation, pH, conductivity, chloride, nitrate, sulfate, and soil organic matters (SOM). Metagenomic functional pathways related to stressor responses also increased in post-rainfall hyperarid soils. Our findings contribute to the primary goal of Atacama Mars analog research for understanding the microbial community structure and adaptations: this study sheds light on the structure of xerophilic, halophilic, and radioresistant microbiomes in hyperarid environments, and their response to changes in water availability.

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

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

Urgent action is required to ‘bend the curve’ on biodiversity loss. However, the ‘species goal’ (Goal B) unveiled in the recently released Zero Draft of the Post-2020 Global Biodiversity Framework (GBF) is inadequate for mobilizing conservation actions to achieve the outcomes required to halt and reverse species declines. Here we examine the limitations of the goal as presented in the Zero Draft and propose a more ambitious goal for species. The conservation community must ensure that the species goal of the GBF captures what is actually needed from a species perspective for the Post-2020 Framework to achieve its 2030 mission “to put biodiversity on a path to recovery for the benefit of planet and people”, lest the mission be doomed from the start.