A recent survey that determined heavy metal concentrations in an abandoned Hg mine in Palawan, Philippines, found the occurrence of Hg with As, Ba, Cd, Co, Cr, Cu, Fe, Hg, Mn, Ni, Pb, Sb, Tl, V, and Zn. While the Hg originated from the mine waste calcines as supported by previous studies, the critical knowledge about the origin of the other heavy metals remains to be unknown. Our study investigated the sources of heavy metal pollution surrounding the abandoned Hg mine; and assessed the soil and sediment quality, ecological risks, and health risks associated with these toxic metals. Multivariate analyses, such as hierarchical cluster analysis (HCA), principal component analysis (PCA), and Pearson correlation analysis, were used to identify the heavy metal sources from the results of a previous paper. Our results showed that Fe, Ni, Cr, Co, and Mn are associated with the ultramafic geology of the study, whereas As, Ba, Cd, Cu, Pb, Sb, Tl, V, and Zn are likely due to historical mining and processing of cinnabar from 1953-1976. The mine waste calcines were used as construction material for the wharf and as land filler for the adjacent communities. The modified contamination factor (mCdeg) showed that the coast of Honda Bay is highly contaminated, while the inland areas, including the rivers, are very- to ultra-highly contaminated. There is a considerable ecological risk associated with the heavy metals, wherein Ni, Hg, Cr, and Mn contribute an average of 46.3 %, 26.3 %, 11.2 %, and 9.3 % to the potential ecological risk index (RI), respectively. The overall mean hazard index (HI) for both adults (1.4) and children (12.1) exceeded 1, implying the probability of non-carcinogenic adverse effects. The mean total cancer risk over a lifetime (LCR) for both adults (1.19×10-3) and children (2.89×10-3) exceeded the tolerable threshold of 10-4, suggesting a potentially high risk for developing cancer mainly by Ni, Co, and Cr exposure.

Fossil-based emissions can be avoided through using wood in place of non-renewable raw materials as energy and materials. However, increasing wood harvest influences forest carbon stocks. This effect may reduce the overall climate benefit of wood use significantly but is widely overlooked. We carried out a systematic review of simulation studies and compared differences in forest carbon and amount of wood harvested between more and less intensive wood harvest scenarios for three different time perspectives; short (1-30 years), mid (31-70 years) and long (71-100 years). Out of more than 450 reviewed studies 44 provided adequate data. Our results show that increased harvesting reduced carbon stocks over 100 years in temperate and boreal forests roughly 1.6 (stdev 0.9) t_C per t_C harvested. The value proved to be robust when outliers explicitly influenced by other factors than change in harvest rate, such as increase in fertilization or forest area, were removed. Interestingly, no significant difference in carbon impacts was found for average values of boreal and temperate forests or between short and long time-horizons. However, impacts tend to be greatest in the mid-term. This carbon balance indicator that we estimated can be interpreted as carbon debit of wood harvest in forests. It is significant compared with the typical GHG credits in technosphere generated by avoiding fossil emissions in substitution and increase in carbon storage in harvested wood products, and should not be ignored. Our estimates provide default values that can directly be included in GHG balances of products or assessment of mitigation policies and measures related to wood use. However, more systematic scenarios and transparent data in which different factors influencing forest carbon stocks are separately studied are clearly required to provide better constrained estimates for specific forest types.

Undisturbed ground temperature (UGT), thermal conductivity (TC) and heat capacity (HC) are essential parameters for the design of borehole heat exchanger (BHE) and borehole thermal energy storage systems. However, field methods to assess the thermal state and properties of the sub-surface are costly and time consuming. Moreover, HC is often not evaluated but arbitrarily selected from literature considering the geological materials intercepted by boreholes. Therefore, this work aims at proposing a field heat tracing method to infer the thermal diffusivity (TD) and HC with assumption of natural transient heat conduction in the subsurface. Empirical equations were developed to reproduce a UGT profile measured along a BHE. Experimental coefficients are found with a non-linear least square solver optimization and used to calculate the damping depth and TD. Subsequently, the TD is used to evaluate HC considering TC obtained from a thermal response test (TRT). Results from this proposed heat tracing method were verified and validated against a set of TRT results and oscillatory TRT analysis using a field dual probe concept to infer HC. The example here described highlights the advantages and novelty of this fast and simple field method relying only on a single UGT profile measured before a TRT.

A healthy park-people relation depends essentially on the fair and sustainable maintenance of rural livelihood. When protected area is designated, rural people may face restrictions of access to land and resource use for multiple ecosystem services. In Wuyishan of China, we analysed the role of traditional tea cultivation during consistent protected area management to find ways to maintain stability of this social-ecological system in the new national park era. We used an intensive social survey to investigate tea’s role, perception of ecosystem services and impacts on tea cultivation from consistent conservation policies. Results showed that tea cultivation brought major household income and associated with multiple culture services. Protected area management affected land use and conservation outcomes were more obvious to farmers than economic and social ones. From the perspective of a social-ecological system, tea cultivation in national should be conservation-compatible activities from which the potentially lost economic value is remedied by ecological and cultural valorisation. To sustain the resilience of the social-ecological system, we proposed a three-scale management framework to regulate biophysical elements at land plot scale, to link production and market at the mountain level, and to secure tenure and encourage community participation at the landscape level.

Insecticide testing facilities that evaluate a variety of vector control products may generate large amount of hazardous wastes from routine operations. These wastes originate from degraded technical grade materials, sprayed substrates, redundant stock or working insecticidal solutions. The washing of Long-Lasting Insecticidal Nets (LLINs) during preparation for laboratory and experimental hut trials also contribute to waste water with insecticide content. Human and environmental exposure to insecticidal waste can occur during transport, categorization, storage and disposal in resulting in environmental pollution and potential health effects. Various national and international guidelines have been devised for safe disposal and should be strictly followed to avoid adverse effects on humans or environment. The current paper describes a case study from insecticide test facility in north-eastern Tanzania in management of insecticidal waste.

The concentration of total suspended particles (TSP) and nanoparticles (PM0.1) over Hat Yai city, Songkhla province, southern Thailand was measured in 2019. Organic carbon (OC) and elemental carbon (EC) were evaluated by carbon aerosol analyzer (IMPROVE-TOR) method. Thirteen trace elements including Al, Ba, K, Cu, Cr, Fe, Mg, Mn, Na, Ni, Ti, Pb, and Zn were evaluated by ICP-OES. Annual average TSP and PM0.1 mass concentrations were determined to be 58.3 ± 7.8 and 10.4 ± 1.2 µg/m3, respectively. The highest levels of PM occurred in the wet season with the corresponding values for the dry seasons being lower. The annual average OC/EC ratio ranged from 3.8 - 4.2 (TSP) and 2.5 - 2.7 (PM0.1). The char to soot ratios were constantly less than 1.0 for both TSP and PM0.1, indicating that land transportation is the main emission source. A principal component analysis (PCA) revealed that road transportation, industry, and biomass burning are the key sources of these particles. However, PM arising from Indonesian peatland fires causes an increase in the carbon and trace element concentrations in southern Thailand. The findings make useful information for air quality management and strategies for controlling this problem, based on a source apportionment analysis.

Turkey is located in the heart of complex transition geography between Eurasia and the Middle East. In the grand scheme, the so-called Eastern Mediterranean Basin is almost amidst the dusty belt and a hot spot of climate change. The downstream location of dust carrying winds from the closer desert sources reveals Turkey as an open plane to particulate matter exposure throughout the year. In order to clarify this phenomenon, it is aimed to find out the desert dust climatology of Turkey via CALIPSO onboard Lidar. This prominent instrument enables us to understand clouds, aerosols and their types and relatedly climatic systems with its valuable products. In this study, 9-year CALIPSO derived pure dust product is formed to explain horizontal and vertical distributions, transport heights and case incidences. Results indicated mass and conditional abundancy are higher with the location shifts from west to east. In the same direction, dominant spring months change to summer and autumn. Mountain range systems surrounding Anatolia are the main obstacles against lofted and buoyant dust particles travelling to northern latitudes. Even if high ridges accumulate mass load on the southern slopes, it also enables elevated particles to reach the ground level of the inner cities.

We examined PT parameters, geochemical peculiarities, and fluid sources of the Ulug-Sair ore occurrence attributable to class of intrusion-related gold deposits and according to ore mineral assemblages corresponding to Au-Bi type with wide Bi minerals (AgBiTe, Bi2Te2Se, Cu3,07BiS3, Bi), tellurides (Au and Ag), Se-tellurides (Ag and Bi), and selenides (Au, Ag, and Hg). We identified that ‘pre-gold’ quartz-tourmaline veins were deposited using an aqueous Mg-Na-K-chloride fluid with a salinity of 8–10 wt % NaCl eq. At 325–370 °C; host breasts were formed due to a CO2-water fluid containing CH4 and N2, with a salinity of 0,18–6,1 wt % NaCl eq. at least 200–400 °C. Gold-bearing mineral assemblages were formed at P ~ 0,75–1,0 kbar (~ 2,3–3 km) due to CO2-water chloride (Na-K±Fe, Mg) fluid with CH4, Na2SO4, and Na2B2O5, and salinities 1,7–12,5 wt % NaCl eq. during the decreasing temperatures from 360 up to 115 °C (gold-sulfide-quartz veins – 360–130 °С, and gold-telluride-sulfide-quartz veins – 330–115 °C) and variations fO2, fS2, fSe2, and fTe2. The isotopic composition of δ34SH2S fluid (-0,7…+2,5 ‰) indicates the juvenile or magmatic origin of fluid and ore elements. The δ34OH2O fluid indicates that, at an early substage, the formation of ore occurrence involved a fluid of magmatic or metamorphic origin (+7,3…+11,4 ‰), and, in the later substage, it mixed with meteoric waters (-2,3…+9,1 ‰).

Shrimp industry in the Philippines plays a vital role in the economy by contributing a total production in the fisheries sector of 60,000 tons of production and 7,000 tons of shrimp exported in overseas markets such as South Korea, Japan, USA and others. This study aimed to describe the various cultural and operational characteristics of a shrimp farm (Penaeus vannamei) in Davao region, and assessed the current risk and challenges experienced by shrimp farmers during the time of pandemic. This study made use of semi-structured questionnaire and focused on shrimp farmers, operators in the provinces of Davao Oriental, Davao del Sur and Davao de Oro as respondents (N=41 farmers). Results showed that small scale shrimp farming averaged 10 tons/ha per cropping; the highly intensive farms yielded 24 tons/ha per cropping. Most operators used generator machines as source of electricity to facilitate aeration accompanied with paddle wheel and blower in some farms; about 2/3 of the commercial shrimp farms used electricity from electric line. In terms of their expenditures, feed inputs have the highest cost (Php 566,700.00 for small-scale farmers, Php 5,572,900.00 for commercial farms) followed by fry (Php 144,800.00 for small-scale farmers, Php 2,080,000.00 for commercial farms) and fuel/electric bill (Php 114,000.00 for small-scale farmers, Php 4,380,000.00 for commercial farms). The average profit per ha of small holder farmers was Php 1,040,000.00. Most farmers mentioned that their shrimps experienced diseases such as white spot syndrome (60%), black gills (35%) and red tail (5%). The farmers point to contamination from water or poor water quality management which caused this disease. The study recommends that farmers should follow good shrimp aquaculture practices including water quality management.

Surface velocity is traditionally measured with in situ techniques such as velocity probes (in shallow rivers) or Acoustic Doppler Current Profilers (in deeper water). In the last years, researchers have developed remote sensing techniques, both optical (e.g., image-based velocimetry techniques) and microwave (e.g., Doppler radar). These techniques can be deployed from Unmanned Aerial Systems (UAS), which ensure fast and low-cost surveys also in remotely-accessible locations. We compare the results obtained with a UAS-borne Doppler radar and UAS-borne Particle Image Velocimetry (PIV) in different rivers, which presented different hydraulic–morphological conditions (width, slope, surface roughness and sediment material). The Doppler radar was a commercial 24 GHz instrument, developed for static deployment, adapted for UAS integration. PIV was applied with natural seeding (e.g., foam, debris) when possible, or with artificial seeding (woodchips) in the stream where the density of natural particles was insufficient. PIV reconstructed the velocity profile with high accuracy typically in the order of a few cm s⁻¹ and a coefficient of determination (R²) typically larger than 0.7 (in half of the cases larger than 0.85), when compared with acoustic Doppler current profiler (ADCP) or velocity probe, in all investigated rivers. However, UAS-borne Doppler radar measurements show low reliability because of UAS vibrations, large instrument sampling footprint, large required sampling time and difficult-to-interpret quality indicators suggesting that additional research is needed to measure surface velocity from UAS-borne Doppler radar.

Natural cycles underpin the very stuff of life. In this commentary we consider unnatural cycles: that is, anthropogenic activities which have a circularity, but whose nature is to have a detrimental effect on human health, exacerbating existing problems. Natural cycles have feedback loops, some of which have recently come to light, with an understanding that everything is connected in some way. In health, feedback loops are imperative in homeostatic mechanisms. However, in the unnatural cycle the feedback loops serve to reinforce (and in some cases amplify) negative problems. We offer a commentary on an unnatural cycle moving from air quality to lung function and back to air quality; we call this the lung disease unnatural cycle. We suggest where links occur, and where wider consideration of interactions between various disciplines can lead to breaking this unnatural (or vicious) cycle, changing it to a healthy cycle where individual health can be improved, along with better global scale outcomes. We suggest that many activities within this unnatural cycle occur within silos. However, the improved cycle incorporates joint activities at geological, health, and financial levels, to the mutual benefit of all, breaking the unnatural cycle, and improving health, life and financial costs.

Extraordinary high aerosol contamination observed in the atmosphere over Kyiv city, Ukraine, during the March – April 2020 period. The source of contamination was the large grass and forest fires in the northern part of Ukraine and the Kyiv region. The level of PM2.5 load investigated using newly established AirVisual sensors mini-network in five areas of the city. The aerosol data from the Kyiv AERONET sun-photometer site analyzed for that period. Aerosol optical depth, Angstrom exponent, and the aerosol particles properties (particles size distribution, single-scattering albedo, and complex refractive index) were analyzed using AERONET sun-photometer observations. The smoke particles observed at Kyiv site during the fires in general correspond to aerosol with optical properties of biomass burning particles. The variability of the optical properties and chemical composition indicates that the aerosol particles in the smoke plumes over Kyiv were produced by different burning material and phases of vegetation fires at different time. The case of enormous PM2.5 aerosol contamination in Kyiv city reveals the need to accept strong measures for forest fire control and prevention in Kyiv region, especially in the north-west region where radioactive contamination from Chornobyl disaster is still significant.

Globally mangrove forests are substantially declining and a globally synthesized database of the drivers of deforestation and drivers’ interaction is scarce. Here we synthesized the key social-ecological drivers of global mangrove deforestation by reviewing about two hundred published scientific studies over the last four decades (from 1980 to 2021). Our focus was on both natural and anthropogenic drivers with gradual and abrupt impacts and their geographic ranges of effects and how these drivers interact. We also summarized the patterns of global mangrove coverage decline between 1990 and 2020 and identified the threatened mangrove species and their geographic ranges. Our consolidated studies reported a 8,600 km2 decline in the global mangrove coverage between 1990 and 2020 with the highest decline occurring in South and Southeast Asia (3870 km2). We could identify 11 threatened mangrove species, two of which are critically endangered (Sonneratia griffithii and Bruguiera hainseii). Our reviewed studies pointed to aquaculture and agriculture as the predominant driver of global mangrove deforestation though the spatial distribution of their impacts varied. Gradual climate variations, i.e. seal-level rise, long-term precipitation and temperature changes and driven coastline erosion, constitute the second major group of drivers. Our findings underline a strong interaction across natural and anthropogenic drivers with the strongest interaction between the driver groups aquaculture and agriculture and industrialization and pollution. Our results suggest prioritizing globally coordinated empirical studies linking drivers and mangrove changes and a global development of policies for mangrove conservation.

The article reports morphotectonic and petrological characteristics of magmatic systems of Permo-Triassic traps in the sedimentary cover of the Siberian Craton, Taimyr, as well as in the crystalline basement of the Mesozoic cover of the West Siberian Plate and the Kara sedimentary basin based on the relief analysis, seismotomography data, magnetic and gravitational anomalies. Four development sectors of magma-permeable zones were distinguished along the perimeter of the craton of the Anabar Shield. The western sector is characterized by an extensive stretching area, on which the lava region of volcanic ridges junction of the Tunguska syncline formed. A striking feature of subaerial volcanism is the meridional petrochemical trend of increasing the silica of basic magmas in intrusive rocks of the Siberian Craton while the volumes of embedded melts are reduced.

The 26th June, 1917 tsunamigenic earthquake in Samoa is considered the largest historical event on record to have impacted this region in terms of earthquake magnitude and intensity. Yet, very little is known about the scale and distribution of tsunami impacts for this event compared with the recent 2009 tsunamigenic earthquake which originated in a proximal source region at the Northern Tonga Trench. In this paper, we reconstruct the 1917 tsunami from source to inundation using the BG-Flood numerical modelling suite to understand the magnitude of inundation for this event. Model outputs representing inundation extent and hazard depth intensities at spatially flexible grid resolution (10 m, 20 m and 40 m), are validated using available tide gauge records in Apia harbour and limited observations of runup that were derived from historical records. Results indicate variable modelled-to-observed consistency using available source models, wave and runup validation data. Significant discrepancies in recorded vs modelled wave arrival time at Apia of between 30—40 mins are observed, with modelled runup underestimated in southeast Upolu Island compared with the rest of the country where runup observations are available (e.g., west Savai’i Island). We combine the inundation model with available digital distributions of buildings and roads in the RiskScape multi-hazard risk analysis software, to produce exposure metrics for understanding the likely impacts on present-day coastal asset and population distributions if a similar tsunami were to occur. A comparison between the distribution of hazard risk exposure for the 1917 and 2009 events is discussed along with the uncertainties in our results, with suggestions for future work offered.

The ridge line of the western Pacific subtropical high (WPSHRL) plays an important role in determining the shift of the summer rain belt in eastern China. In this study, we developed a forecast system for the June WPSHRL index based on the latest autumn and winter sea surface temperature (SST). Considering the adverse condition of the small observed sample size, a very simple neural network (NN) model was selected to extract the non-linear relationship between input predictors (SST) and target predictands (WPSHRL) in the forecast system. In addition, some techniques are used to deal with the adverse condition, enhance the stabilization of forecast skills, and analyze the interpretability of the forecast system. The forecast experiments show that the linear correlation coefficient between the predictions from the forecast system and their corresponding observations is around 0.6, and about three-fifths of the observed abnormal years (the years with an obviously high or low WPSHRL index) are successfully predicted. Furthermore, sensitivity experiments show that the forecast system is relatively stable in terms of forecast skill. The above evaluations suggest that the forecast system is valuable in a real application sense.

The forming of large rivers are the integral consequences of the deep earth process and the surface. In contrast to the hot topics for rivers related to orogenic domains, rift-related large rivers are largely ignored especially in deep time studies. The Cenozoic East Asia margin provides very good opportunity to observe this kind of rivers. It has been believed that basin-and-swell physiography dominated the East Asia margin and impeded the forming of large rives in the early Cenozoic. In this paper, we combined provenance analysis of East China Sea Basin, where is a crucial place to trace the river evolution in East Asia margin, and regional geologic constraints to reveal drainage reorganizations. Detrital zircon U-Pb ages from the Early Eocene sediments of the East China Sea Basin are firstly reported. Our results together with literature data demonstrate that regional provenance changes occurred at the middle Eocene from one singe age peak at ~110 Ma of proximal sources to multiple age spectrum derived from far inland. Source to sink analysis indicated that the North China Block and Korea Peninsular provided the most detritus. Sedimentation and tectonic features of rift basins in the potential source areas indicated that rivers flowed into Bohai Basin and Jianghan Basin cannot provide terrigenous clasts for the lower reaches in the Eocene. Contrastingly, the dominantly fluvial sediments across the Subei-South Yellow Sea Basin suggested external river system and a bypassing region since the middle Eocene, coinciding with provenance change in ECSB. All these demonstrated that a large river (East Asia River) established in east Asia margin in the middle Eocene and flowed southwestward approximately 1500km to the sea in southern ECSB. This river might last to the middle Miocene. The deep earth processes driven by Izanagi-Pacific ridge subduction resulted in the overfilled stage of Subei-South Yellow Sea Basin and the post-rift subsidence in west depression of ECSB, and thus facilitated the initiation of the EAR. Our finding shed new light on the evolving landscape in East Asia and showed how subduction of deep earth process controlled the initiation of rift-related large rivers.

Urban flood risk mapping is an important tool for the mitigation of flooding in view of human activities and climate change. Many developing countries, however, lack sufficiently detailed data to produce reliable risk maps with existing methods. Thus, improved methods are needed that can improve urban flood risk management in regions with scarce hydrological data. Given this, we estimated the flood risk map for Rasht City (Iran), applying a composition of decision-making and machine learning methods. Flood hazard maps were produced applying six state-of-the-art machine learning methods such as classification and regression trees (CART), random forest (RF), boosted regression trees (BRT), multivariate adaptive regression splines (MARS), multivariate discriminant analysis (MDA), and support vector machine (SVM). Flood conditioning parameters applied in modeling were elevation, slope angle, aspect, rainfall, distance to river (DTR), distance to streets (DTS), soil hydrological group (SHG), curve number (CN), distance to urban drainage (DTUD), urban drainage density (UDD), and land use. In total, 93 flood location points were collected from the regional water company of Gilan province combined with field surveys. We used the Analytic Hierarchy Process (AHP) decision-making tool for creating an urban flood vulnerability map, which is according to population density (PD), dwelling quality (DQ), household income (HI), distance to cultural heritage (DTCH), distance to medical centers and hospitals (DTMCH), and land use. Then, the urban flood risk map was derived according to flood vulnerability and flood hazard maps. Evaluation of models was performed using receiver-operator characteristic curve (ROC), accuracy, probability of detection (POD), false alarm ratio (FAR), and precision. The findings showed that the CART method is most accurate method (AUC = 0.947, accuracy = 0.892, POD = 0.867, FAR = 0.071, and precision = 0.929). The results also demonstrated that DTR, UDD, and DTUD played important roles in flood hazard modeling; whereas, the population density was the most significant parameter in vulnerability mapping. These findings indicated that machine learning methods can improve urban flood risk management significantly in regions with limited hydrological data.

Arctic sea ice characteristics have been changing rapidly and significantly in the last few decades. Using a long-term time series of sea ice products from satellite observations - the extended AVHRR Polar Pathfinder (APP-x), trends in sea ice concentration, ice extent, ice thickness, and ice volume in the Arctic from 1982 to 2020 are investigated. Results show that the Arctic has warmed and become less ice covered in all seasons, especially in summer and autumn. Arctic sea ice thickness has been decreasing at the rate of -3.24 cm per year, resulting in about a 52% reduction in thickness from 1982 to 2020. Arctic sea ice volume has been decreasing at the rate of -467.7 km3 per year, resulting in a volume of 10305.5 km3 in 2020 compared to 27590.4 km3 in 1982. These trends are further examined from a new perspective. The Arctic Ocean is classified into open water, and perennial and seasonal sea ice-covered areas based on the sea ice persistence. The loss of the perennial sea ice covered area is the major factor in the total sea ice loss in all seasons. If the current rates of sea ice changes continue, the Arctic is expected to have ice-free summers by the mid-2060s.

Dams have made great contributions to human society, facilitating flood control, power generation, shipping, agriculture and industry. However, the construction of dams greatly impacts downstream ecological environments and nearby marine areas. To summarize these impacts, this review used recent research to comprehensively analyze how dam construction has affected river hydrology, geomorphology, and ecosystem of downstream reaches globally. Effects of dams on ecosystems occur through reduced river flow, reduced sediment flux, altered water temperature, changed estuary delta, altered composition and distribution of nutrients, altered structure and distribution of phytoplankton populations, caused habitat fragmentation, and blocked migration routes in river sections and adjacent seas. Besides, exploring new reservoir management strategies (including targeted control of dam storage and flushing sediment operations), banning fishing activities, and removing unnecessary dams (obsolete or small dams) are becoming crucial tools for ecosystem restoration.

This article focuses on the comparison and perception of the potential of water’s surface in selected parts of the waterfront in two contrasting cities. The aim is to define the potential of the water’s surface in terms of psychology and its effects on climate. The analysis was performed on the example of Prague with its waterfront on the river Vltava, and Santa Cruz de Tenerife with its Atlantic coastline. Data was sourced from maps, questionnaire surveys and interviews with waterfront users during October 2020. The research showed a higher importance of the water’s surface for users in Prague, than in Santa Cruz. The main finding is that the psychological perception of the waterfront by its the users is affected by the possibility of a visual contact with the water’s surface, the design and composition of the space, and the climate situation of a site affected by the presence of an urban heat island (UHI). The results promote an adherence to ecosystem-based approaches in future design projects and modifications of existing public spaces. The purpose of the work is to open a broader debate on the sustainability of the potential of waterfronts and their positive impact on human health.

The transmission and analysis of data is one of the challenges of the 21st century. In the field of environmental measurement technology, existing broadband and wireless technologies have not been able to transmit data reliably and cost-effectively over long distances and in hard-to-reach places. LoRaWAN, an IoT technology, could be an energy-efficient, cost-effective and secure alternative as a narrowband technology in combination with battery-powered sensors and thus make an important contribution to the intelligent, largely wireless networking of objects, plants and machines (IoT), for example in the municipal sector. In addition to ecological and economic benefits, the quality of life in modern, intelligently networked cities can be enhanced by real time data acquisition. However, the prerequisite is that the quality of the data acquired via this method is sufficiently good. This paper therefore addresses the question of the quality of particulate matter data collected by low-cost sensors. To determine this, an SDS011 particulate matter sensor from Nova Fitness was ported to LoRaWAN. The sensor was installed next to a governmental measurement station. In a test that lasted five weeks, data from the SDS011 sensor were compared with those from the governmental station. Differences were identified and a correction approach was developed and applied. The efficiency of the approach was verified. Based on the results, it can be seen that the use of the low cost sensors has weaknesses. Problems can only be partially reduced. Nevertheless, the use of the low-cost sensors can be helpful for a flexible and cost effective collection of environmental data.

The quantification of sea surface temperature (SST) through space platforms has revolutionized how we obtain information at a global level. However, the main disadvantage of obtaining SST with satellite images consists of its inherent coarse spatial resolution. One solution could be the use of downscaling algorithms to create sequences of matrices at a higher resolution. We used the same SST data source from the MODIS-Aqua sensor at three spatial resolutions of 9 km, 4.5 km, and 1 km in the Gulf of California. Based on an open-source algorithm, the original SST images were downscaled to 4.5 km, 1 km, 500 m, 250 m, and 125 m per pixel scales. Results indicate a strong linear relationship between the original SST-MODIS data and the modeled data for all spatial resolutions. This study demonstrates the feasibility of an open-source downscaling algorithm to enhance the spatial resolution of SST images in a marginal sea.

Until recently, the classic approach to mineral exploration studies is to bring the field samples/drill cores collected during field studies to the laboratory followed by laborious analysis procedures to generate the analytical data. This is very expensive, time consuming and difficult for exploring vast areas. But rapid technological advances in field portable analytical instruments such as portable ultraviolet–visible and near-infrared spectrophotometers, gamma ray spectrometer, pXRF, pXRD, pLIBS, and µRaman spectrometer have changed this scenario completely and increased their on-site applications in mineral exploration studies. These instruments are currently providing direct, rapid, on-site, real-time, non-destructive, cost-effective identification, and determination of target elements, indicator minerals and pathfinder elements in rock, soil, and sediment samples. These portable analytical instruments are currently helping to obtain accurate chemical and mineralogical information directly in field with minimal or no sample preparation, and providing decision-making support during field work as well as during drilling operations in several successful mineral exploration programs. In this article, the developments in these portable devices, and their contributions in the platinum group elements (PGE), rare earth elements (REE), gold, base metals, and lithium exploration studies both on land and on ocean bed have been summarized with examples.

The aim of this work is to study the zonally asymmetric stratopause that occurred in the Arctic winter of 2019/2020, when the polar vortex was particularly strong and there was no sudden stratospheric warming. Aura Microwave Limb Sounder temperature data were used to analyze the evolution of the stratopause with a particular focus on its zonally asymmetric wave 1 pattern. There was a rapid descent of the stratopause height below 50 km in the anticyclone region in mid-December 2019. The descended stratopause persisted until mid-January 2020 and was accompanied by a slow descent of the higher stratopause in the vortex region. The results show that the stratopause in this event was inclined and lowered from the mesosphere in the polar vortex to the stratosphere in the anticyclone. It was found that the vertical amplification of wave 1 between 50 km and 60 km closely coincides in time with the rapid stratopause descent in the anticyclone. Overall, the behavior contrasts with the situation during sudden stratospheric warmings when the stratopause reforms at higher altitudes following wave amplification events. We link the mechanism responsible for coupling between the vertical wave 1 amplification and this form of zonally asymmetric stratopause descent to the unusual disruption of the quasi-biennial oscillation that occurred in late 2019.

Validation of satellite sea surface salinity (SSS) products is typically based on comparisons with in-situ measurements at a few meters depth, that are mostly done at a single location and time. The difference in term of spatio-temporal resolution between the in-situ near-surface salinity and the two-dimensional satellite SSS results in a sampling mismatch uncertainty. The Climate Change Initiative (CCI) project has merged SSS from three satellite missions. Using an optimal interpolation, weekly and monthly SSS and their uncertainties are estimated at a 50 km spatial resolution over the global ocean. Over the 2016-2018 period the mean uncertainty on weekly CCI SSS is 0.13, whereas the standard deviation of weekly CCI minus in-situ Argo salinities is 0.24. Using high resolution SSS simulations, we estimate the expected uncertainty due to the CCI versus Argo sampling mismatch. Most of the largest spatial variability of the satellite minus Argo salinity are observed in regions with large mismatch. A quantitative validation is performed by considering the statistical distribution of the CCI minus Argo salinity normalized by the sampling and retrieval uncertainties. This quantity should follow a Gaussian distribution with a standard deviation of 1, if all uncertainty contributions are properly considered. We find that 1) the sampling mismatch can explain most of the observed differences between Argo and CCI data, especially for monthly products and in dynamical regions (river plumes, fronts), 2) overall, the uncertainties are well estimated in CCI version 3, much better compared to CCI version 2. There are a few dynamical regions where discrepancies remain, and where the satellite SSS, their associated uncertainties and the sampling mismatch estimates should be further validated.

Aquaculture production is under pressure to increase its production to meet the growing demand for food from a growing population. In the Philippines, aquaculture has experienced the shift from milkfish to prawn with its attractive marketable price. Its intensification has led to negative and positive impacts making its collapse inevitable in the mid-1990s and raised a range of environmental and socioeconomic problems. This paper reviewed the environmental impacts, challenges, and disease outbreaks that overtook the industry using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method. We examined gaps and changes required to revitalize the industry to properly take-off. It considers and gives details on the impacts of shrimp culture on the environment e.g. shrimp farm management, marine pollution, disease outbreaks, climate change impacts, and socioeconomic impacts. Moreover, the presence of viral diseases such as White Spot Syndrome Virus (WSSV), Monodon Baculovirus (MBV), Infectious Hypodermal and Hematopoietic Necrosis Virus (IHHNV), Hepatopancreatic Parvovirus (HPV), and Yellow Head Virus (YHV), have caused socioeconomic impacts with approximate losses of 40,080 mt in 1997 to 51,000 mt in 2014 in the shrimp industry. Recommended strategies were considered to improve the environmental management of shrimp aquaculture, including disease management, and priorities that were highlighted for future research. This management relates to adopting good aquaculture practices on shrimp culture, proper environmental monitoring and sustainable practices at the farm level.

Remote sensing technology, especially using satellite images, has become essential support in many aspects of decision-making, particularly in disaster risk management. It requires a shorter period of data updates and less cost compared to conventional field observations and surveys. Yet, the intensive processing and high-powered computing resources are necessary to analyze satellite imagery data through Geographic Information System (GIS). In this paper, we introduce the identification and mapping of natural disaster impact in Indonesia using the open-source collaborative tool of Google Earth Engine (GEE) application which analyzes the relative temporal difference of Earth surface from three major satellite images: Sentinel-1, Sentinel-2, and Landsat-8. Taking the advantage of the geographical, geological, and demographic conditions of Indonesia's disaster-prone areas, we analyze relative difference from normalized difference vegetation index (NDVI) out of months before and after natural disaster occurrence to measure the impact of natural disaster in focus study areas. Given the high-vegetation nature of three main natural disaster impacted areas in Indonesia: Aceh, Palu, and Yogyakarta, we are able to simplify the analysis by highlighting areas with vegetative loss or gain after the event. Using an open-source GEE application, namely HazMapper, we identify and visualize the aftermath of the tsunami disaster in Aceh and Palu as well as the earthquake in Yogyakarta. Our study is potentially beneficial for government and decision-makers to utilize publicly available satellite images for disaster recovery and mitigation policy.

A 3-month (June-August) regime of the year 2002 summer rainfall (JJA2002) was simulated with 30 physics combinations using the Weather Research and Forecasting (WRF) model at 12-km horizontal grid resolution. The objective is to examine summer rainfall sensitivity to parameterization of microphysical, convective, and boundary layer processes and identify the best possible combination of parameterization options that perform relatively better in simulating the spatial and temporal distribution of summer rains over Ethiopia. The WRF simulated rainfall was evaluated against station data and satellite rainfall products (CHIRPS and ENACTS) using mean absolute error, Pearson and Pattern correlation coefficients (PCC), pattern correlation, and error in a number of rainy days as evaluation metrics. Summer rainfall is found to be most sensitive to the choice of cumulus parameterization and least sensitive to cloud microphysics. All simulations captured the spatial distribution of mean seasonal precipitation with PCC ranging from 0.89-0.94. However, all simulations overestimated precipitation amount and number of rainy days. Out of the 30, the simulations that use a combination of Grell-3D cumulus scheme, ACM2 boundary layer, Lin Microphysics, Dudhai shortwave radiation, and RRTM longwave radiation scheme ranked the top and provided the most realistic simulation in terms of amount and spatio-temporal distribution of summer rainfall.

Research have been conducted in many countries around the world to assess air quality during COVID-19 pandemic, especially during lockdown period, some of these studies found an increase or decrease in some pollutants. This paper investigates the impact of COVID-19 on seven air pollutants (i.e., PM_2.5, PM₁₀, NO₂, O₃, SO₂, H₂S, CO) from the period January 2020 to December 2020 in the State of Kuwait. Kuwait is a desert country located in the north-eastern part of the Arabian Peninsula, and the northeast of the Arabian Gulf (Persian as it is sometimes called). Several analytical methods were conducted, such as spatial analysis (spatial interpolation) to study the distribution of the studied variables. The data was also statistically analysed (time series analysis - Kernel density) to study the temporal changes. The analysis also included applying air quality index to the data. We found that concentrations for the pollutants decreased during the pandemic due to the decrease of anthropogenic sources including such as traffic and petroleum activities, but the concentration for PM_2.5 increased, mostly because of the transported dust coming with the northwest winds prevailing in Kuwait from the Arabian deserts and Iraq.

In this study, we analyzed the feasibility of various carbon neutrality methods based on the first principles of carbon sequestration, namely energy use efficiency and elemental stoichiometry. We believe that wood burial is the only currently feasible carbon neutrality method because this method has no theoretical uncertainties, can be implemented immediately on a large scale, has a long sequestration time, low cost, low technical requirements, and relatively little impact on agriculture.

The Philippine shrimp industry has been affected by the impacts of marine pollution, diseases and climate change variabilities. This study assessed the vulnerability to climate change of selected small-scale shrimp farms of Penaeus vannamei and market vendors in Davao, Philippines using a modified Fisheries Vulnerability Assessment Tool (FishVool). Shrimp farmers and vendors were interviewed using two separate semi-structured questionnaires. A total of thirty nine (39) shrimp farmers and forty eight (48) market vendors from various market areas within the region were interviewed. Data regarding exposure (E), sensitivity (S), and adaptive capacity (AC) were collected following the FishVool parameters with modifications. Results revealed that overall climate change vulnerability of the shrimp farmers was medium (M), where both exposure and adaptive capacity were low (L) while sensitivity was medium (M). In addition, the shrimp market vulnerability of the various sites examined revealed medium (M) scores for markets in Pantukan, Mabini, Tagum, Maco, Lupon, Davao City, and Digos. But high (H) vulnerability scores for the markets in Panabo and Sta Cruz. Overall, the study provided a better understanding about shrimp farming in relation to climate change impacts and vulnerability and provided information for future shrimp farm management, marketing and conservation in the region.

Management approaches inspired by the variability of natural disturbances are expected to produce forests in the future that will be significantly more resilient and better adapted to local environmental conditions. Due to climate change, windstorms are becoming increasingly common resulting in the destruction not only of extensive forest areas but, quite often, of small-sized and scattered forest lands that can ultimately become home to insects and disease dissemination sites. In the present study, an attempt is made to identify and record areas in the northeastern forests of Greece covered by mixed stands of conifers and broadleaves that experienced massive windthrow following local storms. Based on tree-level data, local topographic features, forest characteristics and the mechanical properties of green wood, a reliable model, to be used for the prediction of similar disturbances in the future, has been created after a thorough comparative study of the most well-known intelligent machine learning algorithms.

Estimating evapotranspiration at field scale is a major component of sustainable water management. Due to the difficulty to assess some major unknowns of the water cycle at that scale, including irrigation amounts, evapotranspiration is often computed as the residual of the instantaneous surface energy budget. One of the Surface Energy Bal-ance components with the largest uncertainties in their quantification over bare soils and sparse vegetation areas is the ground heat flux (G). Over the last decades, the es-timation of G with RS data has been mainly achieved with empirical equations, on the basis of the G and net radiation (Rn) ratio, G/Rn. G/Rn empirical equations generally require vegetation data (Type I empirical equations), in combination with surface tem-perature (Ts) and albedo (Type II empirical equations). In this article we aim to evalu-ate the estimation of G with RS. For the first time, we compare eight G/Rn empirical equations against two types of machine learning (ML) methods: an ensemble ML type, the Random Forest (RF), and the Neural Networks (NN). The comparison of each method is evaluated over dense dataset, including a wide range of climate and land covers, with data of Eddy-Covariance towers extended along the mid-latitude area that encompass the European and African continent. Our results have shown evidence that the driver of G in bare soils and sparse vegetation areas (Fraction of Vegetation, Fv <= 0.25) is Ts, instead of vegetation greenness indexes. On the other hand, the estimation of G with Rn, Ts or Fv decreases at dense vegetation areas (Fv >= 0.50). There are not significant differences between the most accurate type I and II empirical equations. For bare soils and sparse vegetation areas the empirical equation that better estimates G is E8, which combines the Leaf Area Index (LAI) and Ts. In dense vegetation areas (Fv >= 0.25), an exponential empirical equation based on Fv (E4), shows the best performance. However, ML better estimates G than the empirical equations, independently of the Fv ranges. A RF model with Rn, LAI and Ts as predictor variables shows the best accuracy and performance metrics, outperforming the NN model.

An increase in air temperature leads to a significant transformation of the relief and landscapes of the Arctic. The rate of permafrost degradation, posing a profound change in the Arctic landscape, depends on air temperature, vegetation cover, type of soils, surface and ground waters. The existing international circumpolar programs dedicated to monitoring the temperature state of permafrost TSP (Thermal State Permafrost) and active layer thickness CALM (Circumpolar Active Layer Monitoring) are not sufficient for a comprehensive characterization of geocryological conditions. Yet, no standardized protocol exists for permafrost monitoring and related processes. Here, we propose a novel multi-parameter monitoring protocol and implement for two sites in the European part of the Russian Arctic: the Yary site along the coast of the Baydaratskaya Bay in the Kara Sea (68.9°N) within the continuous permafrost area and the Hanovey site in the Komi Re-public (67.3°N) within the discontinuous permafrost one. The protocol includes drilling boreholes, determining the composition and properties (vegetation cover and soils), snow cover measure-ment, geophysical imaging, active layer estimation and continuous ground temperature meas-urements. Ground temperature measured in 2014 – 2020 revealed that amplitudes of surface tem-perature fluctuation had no significant difference between the Yary and Hanovey sites, while that the mean annual temperatures between the areas had a considerable difference of greater than 3.0 °C. The period of the presence of the active layer changed with the year (e.g., ranging between 135 and 174 days in the Yary site), showing longer when the air temperatures in summer and the preceding winter were higher. Electrical resistivity tomography (ERT) allowed determining the permafrost distribution and active layer thicknesses. Thermometry results were consistent with our geophysical data. Analyzing the composition and properties of frozen soils helped better interpret the data of geophysical and temperature measurements. Our work by integrating the study of the soil properties, ground temperatures, and ERT allowed fully characterize these sites, suggesting that it helps better understand the thermal state in any other research sites in the European north of Russia. Our suggested monitoring protocol enables calibrating and verifying the numerical and analytical models of the heat transfer through the earth’s surface.

This article focuses on the comparison and perception of the potential of water’s surface in selected parts of the waterfront in two contrasting cities. The aim is to define the potential of the water’s surface in terms of psychology and its effects on climate. The analysis was performed on the example of Prague with its waterfront on the river Vltava, and Santa Cruz de Tenerife with its Atlantic coastline. Data was sourced from maps, questionnaire surveys and interviews with waterfront users during October 2020. The research showed a higher importance of the water’s surface for users in Prague, than in Santa Cruz. The main finding is that the psychological perception of the waterfront by its the users is affected by the possibility of a visual contact with the water’s surface, the design and composition of the space, and the climate situation of a site affected by the presence of an urban heat island (UHI). The results promote an adherence to ecosystem-based approaches in future design projects and modifications of existing public spaces. The purpose of the work is to open a broader debate on the sustainability of the potential of waterfronts and their positive impact on human health.

The purpose of this work is to study quasi-stationary wave structure in the mid-latitude stratosphere and mesosphere (40–50°N) and its role in the formation of the annual ozone cycle. Geopotential height and ozone from Aura MLS data are used and winter climatology for January–February 2011–2020 is considered. More closely examined is the 10-degree longitude segment centered on Longfengshan Brewer station, China, and located in the region of the Aleutian Low influence associated with the quasi-stationary zonal maximum of total ozone. Annual and semi-annual oscillations in ozone were compared using units of ozone volume mixing ratio and concentration, as well as changes in ozone peak altitude and in time series of ozone at individual pressure levels between 316 hPa (9 km) and 0.001 hPa (96 km). The ozone maximum in the vertical profile is higher in volume mixing ratio (VMR) values than in concentration by about 15 km (5 km) in the stratosphere (mesosphere), in consistency with some previous studies. We found that the properties of the annual cycle are better resolved in the altitude range of the main ozone maximum: middle–upper stratosphere in VMR and lower stratosphere in concentration. Both approaches reveal SAO/AO-related changes in the of ozone peak altitudes in a range of 4–6 km during the year. In the lower-stratospheric ozone of the Longfengshan domain, an earlier development of the annual cycle takes place with a maximum in February and a minimum in August compared to spring and autumn, respectively, in zonal means. This is presumably due to the higher rate of dynamical ozone accumulation in the region of the quasi-stationary zonal ozone maximum. The “no-annual-cycle” transition layers are found in the stratosphere and mesosphere. These layers with undisturbed ozone volume mixing ratio throughout the year are of interest for more detailed future study.

Low-cost particle sensors are now used worldwide to monitor outdoor air quality. However, they have only been in wide use for a few years. Are they reliable? Does their performance deteriorate over time? Are the algorithms for calculating PM2.5 provided by the Plantower company for PurpleAir monitors accurate? We investigate these questions using continuous measurements of four monitors (8 sensors) under normal conditions inside and outside a home for 1.5-3 years. A recently-developed algorithm (called ALT-CF3) is compared to the two existing Plantower algorithms. The Plantower CF1 algorithm was shown to lose 25-50% of all indoor data due to the questionable practice of assigning zero to all concentrations below a threshold. None of these data were lost using the ALT-CF3 algorithm. About 92% of all data showed precision better than 20% using the ALT-CF3 algorithm, but only about 45-75% of data achieved that level using the Plantower CF1 algorithm. The limits of detection (LODs) using the ALT-CF3 algorithm were mostly under 1 ug/m3, compared to about 3-10 ug/m3 using the Plantower CF1 algorithm. The percent of observations exceeding the LOD was 53-92% for the ALT-CF3 algorithm, but only 16-44% for the Plantower CF1 algorithm. For indoor air in homes, the Plantower algorithms are inappropriate.

Forest degradation is known to be widespread in the tropics, but is currently very poorly mapped, in part because there is little quantitative data on which satellite sensor characteristics and analysis methods are best at detecting it. To improve this, we used data from the Tropical Forest Degradation Experiment (FODEX) plots in the southern Peruvian Amazon, where different numbers of trees had been removed from four 1 ha forest plots, carefully inventoried by hand and Terrestrial Laser Scanning before and after the logging to give a range of biomass change (ΔAGB) values. We conducted a comparative study of six multispectral optical satellite sensors (WorldView-3, SkySat, SPOT-7, PlanetScope, Sentinel-2 and Landsat 8) at 0.3 – 30 m spatial resolution, to find the best combination of sensor and remote sensing indicator for change detection. Spectral reflectance, the Normalized Difference Vegetation Index (NDVI) and texture parameters were extracted after radiometric calibration and image preprocessing. The strength of the relationships between the change in these values and field-measured ΔAGB (computed in % ha−1) was analysed. The results demonstrate that: (a) texture measures correlates more with ΔAGB than simple spectral parameters; (b) the strongest correlations are achieved for those sensors with spatial resolutions in the intermediate range (1.5 - 10 m), with finer or coarser resolutions producing worse results, and (c) when texture is computed using a moving square window ranging between 9 - 14 m in length. Maps predicting ΔAGB showed very promising results using a NIR-derived texture parameter for 3 m resolution PlanetScope (R2 = 0.97 and RMSE = 1.80 % ha−1), followed by 1.5 m SPOT-7 (R2 = 0.74 and RMSE = 5.25 % ha−1) and 10 m Sentinel-2 (R2 = 0.71 and RMSE = 5.55 % ha−1). Texture models derived from 0.3 m WorldView-3 improved with increasing window size, with highest R2 of 0.62 and RMSE = 6.35 % ha−1 for a window of 14 m in length. The degradation in our field plots is invisible to the 30 m resolution Landsat data. Our findings imply that, at least for lowland Peru, low-medium intensity disturbance can be detected best in optical wavelengths using a texture measure derived from 3 m PlanetScope data. That such data are being collected daily, and currently released free as monthly mosaics over tropical forests as part of the Norway’s International Climate and Forest Initiative (NICFI), is excellent news for monitoring such degradation.

To become mainstream, Nature Positive development needs positive messaging, measures and metrics to guide, plan and assess urban outcomes. With accelerating climate crisis and negative messages getting the upper-hand, it is important to avoid paralysis by bad news. Whilst striving for a nature positive world, more effort should be on moving beyond zero to qualify and quantify benefits, gains and regenerative outcomes instead of around damage and loss sticking points. Life Cycle Benefit Assessment (LCBA) methods measure gains in accelerating regeneration and climate security that enables a good news focus. Its reach beyond negative quantifies and shows positive gain beyond zero loss outcomes. The aims are to clarify concepts, challenges and quantitative methods then review real-world 3rd party Certified nature positive case studies. Climate security, human wellness and resource viability gains inside safe operating space within planetary boundaries are quantified as positive benefits. contrary to conventional Life Impact Cycle Impact Assessment LCBA assigns damage losses as negatives debts and benefit gains as positive savings. It concludes that LCBA remains under development with more research needed to model economic outcomes.

This paper focuses on the application of principal component analysis (PCA) to conduct a source apportionment of atmospheric aerosols from 8 sampling locations along the Marilao-Meycauayan-Obando River System (MMORS). Aerosols were collected on May 2016 during the same time that water samples were collected. Elemental analysis was conducted using a scanning electron microscope coupled with energy dispersive x-ray (SEM-EDX). Carbon (C), nitrogen (N), oxygen (O), sodium (Na), magnesium (Mg), aluminum (Al), silicon (Si), sulfur (S), chlorine (Cl), potassium (K), calcium (Ca), titanium (Ti), manganese (Mn), iron (Fe), copper (Cu), zinc (Zn), bromine (Br), niobium (Nb), barium (Ba), mercury (Hg), and lead (Pb) concentrations were measured and used as inputs in Principal Component Analysis (PCA). The aerosol samples showed the presence of heavy metals Pb and Hg, elements that were also detected in trace amounts in the water measurements. Concentrations of heavy metals Fe, Pb, Hg in the aerosols were attributed to industrial sources. However, it was determined that the primary source of aerosols in the area were traffic and crustal emissions (C, N, O, Si, Al, Ca). Thus, control of traffic emissions would be more beneficial in reducing aerosol emissions in Meycauayan.

The research presents the inter-comparison of atmospheric variables measured by 9 automatic meteorological stations. This set of data was compared with the measurements of other meteorological stations in order to standardize the values that must be adjusted when taken to different areas. The data of a set of a total of 9 GMX500, which measures conventional meteorological variables, and 10 WS100 sensors, which measures precipitation parameters. The automatic stations were set up at the Huancayo Observatory (Geophysical Institute of Peru) for a period of 5 months. The data set of GMX500 were evaluated comparing with the average of the 9 sensors and the WS100 was compared with a optical disdrometer Parsivel2. The temperature, pressure, relative humidity, wind speed, rainfall rate, and drop size distribution was evaluated. A pair of GMX500 sensors presented high data dispersion, it was found found that the errors came from a bad configuration; once this problem was solved, good agreement was archived, with low RMSE and high correlation. I was found that the WS100 sensors overestimate the precipitation with a percent bias close to 100% and the differences increase with the greater intensity of rain. The DSD retrieved by WS100 have unrealistic behavior with higher concentrations in diameters of 1 mm and 5 mm, in addition to a flattened curve.

Generative adversarial networks (GAN) opened new possibilities for image processing and analysis. Inpainting, dataset augmentation using artificial samples or increasing resolution of aerial imagery are only a few notable examples of utilizing GANs in remote sensing. This is due to a unique construction and training process expressed as a duel between GAN components. In the following research, GAN has been applied to enhance panchromatic images with Normalized Difference Vegetation Index (NDVI). Panchromatic orthoimagery dataset with NDVI ground-truth labels was prepared by combining RGB and NIR orthophoto. The dataset was then utilized as input for a conditional generative adversarial network (cGAN) to perform an image-to-image translation. The main goal of the neural network was to generate an artificial NDVI channel for each processed 256px × 256px patch using only information available in the panchromatic imagery. The network achieved 0.9869 ± 0.0099 SSIM, 47.1635643 ± 4.0527963 PNSR and 0.0048023 ± 0.0018756 RSME on the test set. Perceptual evaluation was performed to verify the usability of the method when working with a real-life scenario. The research confirms that the structure and texture of the panchromatic aerial remote sensing image contains sufficient information for NDVI estimation for various objects of urban space. Even though these results can be used to highlight areas rich in vegetation and distinguish them from urban background, there is still room for improvement in terms of accuracy of estimated values.

The direct radiative effects of atmospheric aerosols are essential for climate, as well as for other societal areas, like the energy sector. The goal of the present study is to exploit the newly devel-oped ModIs Dust AeroSol (MIDAS) dataset for quantifying the direct effects on the downwelling surface solar irradiance (DSSI), induced by the total and dust aerosols amount, under clear-sky conditions and the associated impacts on solar energy for the broader Mediterranean basin, over the period 2003 – 2017. Aerosol optical depth (AOD) and dust optical depth (DOD) derived by the MIDAS dataset, along with additional aerosol and dust optical properties and atmospheric variables were used as inputs to radiative transfer modeling to simulate DSSI components. A 15-year climatology of AOD, DOD and of clear-sky Global Horizontal Irradiation (GHI) and Direct Normal Irradiation (DNI) was derived. The spatial and temporal variability of the aerosol and dust effects on the different DSSI components was assessed. Aerosol attenuation of annual GHI and DNI range from 1-13% and 5-47%, respectively. Significant attenuation by dust 2-10% and 9-37% was found over North Africa and the Middle East, contributing to 45-90% of the total aero-sol effects. The mean GHI and DNI attenuation during extreme dust episodes reached values up to 12% and 44%, respectively, for different areas. After 2008 a decline of aerosol effects on DSSI was found, attributed mainly to the dust component. Sensitivity analysis using different AOD/DOD inputs from Copernicus Atmosphere Monitoring Service (CAMS) reanalysis dataset, revealed CAMS underestimation of aerosol and dust radiative effects compared to MIDAS, due to slight AOD and stronger DOD underestimation, respectively.

Rice production in the Tonle Sap basin is one of the main drivers for economic and social development in Cambodia. The Tonle Sap basin has experienced many different forms of disasters while more attention has been drawn to drought disaster. The objective of this study is to assess the impacts of drought on agriculture and food security through a case study of the Baribo basin, a sub-basin of Tonle Sap basin, Cambodia. Ground observations and satellite-based products were used for drought assessment from 1985 to 2008 which was the period with relatively good data quality. The Standardized Precipitation Index (SPI) and Standard Vegetation Index (SVI) were selected for meteorological and agricultural droughts assessment, correspondingly. Both SPI and SVI consistently suggested that drought is a major natural hazard causing food insecurity in the target basin. The highest drought intensity (DI) and severity (DS) occurred between 1993-1994 and the longest drought duration (DD) occurred between 2002 and 2006. The most severe damage to rice production was in 2004, affecting about 46% of the total cultivated area. The analysis showed that drought duration had a strong relationship with the affected area growing rice as well as food insecurity in the Tonle Sap basin.

With the development of the city, a large number of water networks in the built-up areas of Bangkok have been filled and hardened, resulting in poor urban flooding and aggravating flooding, causing loss of life and property of citizens. In this paper, on the basis of combing the current water networks and open space potential flood storage points in the built-up areas of Bangkok, the complex network diagram of the water system in the built-up areas of Bangkok is constructed by combining the theory of complex networks and analyzing the attribute parameters of the network and the characteristic parameters of the open space storage nodes and water system paths, and finding that the water system network in the built-up area of Bangkok has complex network characteristics such as robustness, clustering and hierarchy. By exploring the key storage points and water system connection paths, the researchers initially constructed a flood safety pattern in the built-up area of Bangkok with 145 key nodes and 127 river paths as the backbone, and conceptualized the development study of the flood safety pattern in both horizontal and vertical directions. The urban flood safety pattern based on complex network theory proposed in this paper provides a case reference and methodological ideas to scientifically solve the game conflict between the demand for construction land for urban development and the construction area of urban open space storage points and water storage network under the increasingly severe flooding situation.

In the past years, our knowledge of coastal environments has been enriched by remotely sensed data. However, to successfully extract information from a combination of different sensors systems, it should be understood how these interact with the common coastal environment. In this research we co-analyze two sensor systems: Terrestrial Laser Scanning (TLS) and satellite based Synthetic Aperture Radar (SAR). TLS shows large potential for examining coastal processes thanks to the possibility to retrieve repeated, accurate and dense topographic information in a rapid and non-invasive manner. However, TLS presents some limits due to its high economic costs and limited field of view. SAR systems are among the most used active remote sensor system for Earth Observation. Despite their relatively low resolution, SAR systems provide the ability to monitor and map coastal areas with complete, repeated and frequent coverage, penetrating through clouds and providing all weather monitoring. Moreover, Sentinel-1 SAR images are freely available. The availability of a permanently installed TLS system (PLS, Permanent Laser Scanner) allows us, to extensively compare Sentinel-1 SAR data and topographic laser scans during different conditions on a sandy beach. PLS data are compared with simultaneous Sentinel-1 SAR images in order to investigate the combined use of PLS and SAR in coastal environments. The purpose of this comparison is the investigation of a possible relation between PLS and SAR data: knowing their relation, SAR dataset could be correlated to beaches characteristics. Meteorological and surface roughness have also been taken into consideration in the evaluation of the correlation between PLS and SAR data. The permanently installed laser scanner for the present study is located in Noordwijk (the Netherlands). A generally positive but low correlation exists between the two variables. When considering weather phenomena, their correlation increases and shows a dependence on wind directions and speed. The correlation with the surface roughness, evaluated in terms of root-mean squared height, also depends on specific wind speed and directions.

At present, indoor localization is one of the core technologies of location-based services (LBS), and there exist numerous scenario-oriented application solutions. Visual features, as the main semantic information to help people understand the environment and thus occupy the dominant part, many techniques about indoor scene recognition are widely adopted. However, the engineering application problem of cell phone indoor scene recognition and localization has not been well solved due to insufficient semantic constraint information of building map and the immaturity of building map location anchors (MLA) matching positioning technology. To address the above problems, this paper proposes a cell phone indoor scene recognition and localization method with building map semantic constraints. Firstly, we build a library of geocoded entities for building map location anchors (MLA), which can provide users with "immersive" real-world building maps on the one hand and semantic anchor point constraints for cell phone positioning on the other. Secondly, using the improved YOLOv5s deep learning model carried on the mobile terminal, we recognize the universal map location anchors (MLA) elements in building scenes by cell phone camera video in real-time. Lastly, the spatial location of the scene elements obtained from the cell phone video recognition is matched with the building MLA to achieve real-time positioning and navigation. The experimental results show that the model recognition accuracy of this method is above 97.2%, and the maximum localization error is within the range of 0.775 m, and minimized to 0.5 m after applying the BIMPN road network walking node constraint, which can effectively achieve high positioning accuracy in the building scenes with rich MLA element information. In addition, the building map location anchors (MLA) has universal characteristics, and the positioning algorithm based on scene element recognition is compatible with the extension of indoor map data types, so this method has good prospects for engineering applications.

While maintenance dredging of port access channels is often required to maintain navigability, it can result in increased turbidity, sediment plumes, and associated reductions in water quality. Unoccupied aircraft systems (UAS, or drones) are increasingly applied to study water quality due to their high spatial and temporal resolutions. In this study, we investigated the use of drone imagery to monitor turbidity in the Morehead City Harbor, North Carolina, USA, during channel maintenance by hopper dredge. Drone flights were conducted concurrently with in-situ sampling during active dredging and post-dredging. Multispectral drone images were radiometrically calibrated, converted to reflectance and then turbidity using two separate processing methods and a single-band (red; 620nm-700nm) generic turbidity retrieval algorithm, and then compared to in-situ measurements. The method of using average reflectance to retrieve a single turbidity measurement per drone image produced agreeable results when compared to the in-situ measurements (R² = 0.84). This method was then used to generate turbidity maps and extract surface plumes. While this could be considered a limited validation, the results indicate that realistic values can be obtained from drone imagery for low and high turbidity concentrations (1-72 FNU), making drones a viable option for monitoring surface turbidity associated with dredging.

While there are extensive studies on the mineralogy and geochemistry of laterites worldwide, the temporal and spatial mineralogical development of a typical nickel laterite profile is still poorly constrained. In this study, we present a detailed mineralogical and geochemical characterization of samples systematically collected from a nickel laterite profile at the Sta. Cruz nickel laterite deposit, Zambales, Philippines, to describe the temporal and spatial development of the laterite profile. Wavelength-dispersive X-ray fluorescence spectroscopy (WDSXRF), mass balance-element mobility calculations, transmitted and reflected light microscopy, and previously reported results from coupled X-ray diffraction (XRD) and Rietveld refinement analyses, reveal that the laterite profile investigated is composed of two main horizons: the limonite and saprolite zones, separated by a thin transitional zone. The main zones are further subdivided into subzones based primarily on the mineral assemblage and major element chemistry: upper limonite, lower limonite, transitional zone, upper saprolite, and lower saprolite. Late-stage Ni-rich serpentine veins were observed cutting the upper and lower saprolite subzones. Investigation of the structure of goethite within the limonite zone via Rietveld refinement show that the crystallinity of goethite decreases with increasing Ni content and increasing crystallite size. This suggests that upwards through the limonite zone, as goethite ages, its crystallinity increases which possibly results in the removal of Ni from its crystal structure and eventual remobilization to the lower laterite zones. We propose a genetic model of the formation of the Sta. Cruz laterite consisting of four stages: (1) early-stage alteration, (2) continued serpentinization and volume expansion, (3) late stage serpentinization, dissolution, and incipient oxide formation, and (4) goethite ageing and Ni-rich serpentine vein formation. Overall, our results suggest that serpentinization is an important aspect of Ni laterite formation, both before and after the emplacement of the peridotite protolith.

Natural cycles underpin the very stuff of life. In this commentary we consider unnatural cycles: that is, anthropogenic activities which have a circularity, but whose nature is to have a detrimental effect on human health, exacerbating existing problems. Natural cycles have feedback loops, some of which have recently come to light, with an understanding that everything is connected in some way. In health, feedback loops are imperative in homeostatic mechanisms. However, in the unnatural cycle the feedback loops serve to reinforce (and in some cases amplify) negative problems. We offer a commentary on an unnatural cycle moving from air quality to lung function and back to air quality; we call this the lung disease unnatural cycle. We suggest where links occur, and where wider consideration of interactions between various disciplines can lead to breaking this unnatural (or vicious) cycle, changing it to a healthy cycle where individual health can be improved, along with better global scale outcomes. We suggest that many activities within this unnatural cycle occur within silos. However, the improved cycle incorporates joint activities at geological, health, and financial levels, to the mutual benefit of all, breaking the unnatural cycle, and improving health, life and financial costs.

Abstract: In the last five years, the urban development of Limassol City has rapidly increased in the sectors of industry, trade, real estate, and many others. This exponentially increased urban development introduces several concerns about the aggravation of the land subsidence in the Limassol coastal front. Fifty Copernicus Sentinel-1 data from 2017-2021 have been processed and analyzed using the Sentinel Application Platform (SNAP) and the Stanford Method for Persistent Scatters (StaMPS). A case study for the identification and analysis of the elements (PS) in pixels in a series of interferograms, and then, the quantity of the land displacements in the Line of Sight, in the Limassol coastal front, is presented in this research, with the subsidence rates up to about (-5 to 4 mm / year). For the validation of the detected deformation, accurate ground-based geodetic measurements along the coastal area were used. Concordantly, taking into account that there are a significant number of skyscrapers planned to be built, this study attempts a preliminary assessment of the impact these structures will pose on the coastal front of the area of Limassol.

Default aerosol extinction coefficient profiles are commonly used instead of measured profiles in radiative transfer modelling, increasing the uncertainties in the simulations. The present study aims to determine the magnitude of these uncertainties and contribute towards the understanding of the complex interactions between aerosols and solar radiation. Default, artificial and measured profiles of the aerosol extinction coefficient are used to simulate the profiles of different radiometric quantities in the atmosphere for different surface, atmospheric, and aerosol properties and for four spectral bands: ultraviolet-B, ultraviolet-A, visible, and near infrared. Case studies are performed over different areas in Europe and North Africa. Analysis of the results shows that under cloudless-skies, changing the altitude of an artificial aerosol layer has minor impact on the levels of shortwave radiation at the top and the bottom of the atmosphere, even for high aerosol loads. Differences up to 30% were however detected for individual spectral bands. Using measured instead of default profiles for the simulations leads to more significant differences in the atmosphere, which become very large during dust episodes (10 – 60% for actinic flux at altitudes between 1 and 2 km, and up to 15 K/day for heating rates depending on site and solar elevation).

A very strong earthquake of magnitude Mw8.2 struck the coastal zone of Alaska (USA), on July 29, 2021. This earthquake was felt around the Gulf of Alaska, on a wide offshore area belonging to USA and Canada. In order to identify an anomalous geomagnetic signal before the onset of this earthquake, we retrospectively analyzed the data collected on the interval June 17 - July 31, 2021, via internet (www.intermagnet.org), at the two geomagnetic observatories, College (CMO) - Alaska and Newport (NEW)-USA, by using the polarization parameter (BPOL) and the strain effect–related to geomagnetic signal identification. Thus, for the both observation sites (CMO and NEW), the daily mean distribution of the BPOL and its standard deviation (STDEV) are carried out using an FFT band-pass filtering in the ULF range (0.001-0.0083Hz). Further on, a statistical analysis based on a standardized random variable equation was applied to emphasize the following: a) the anomalous signature related to Mw8.2 earthquake on the both time series BPOL*(CMO) and BPOL*(NEW); b) the differentiation of the transient local anomalies associated with Mw8.2 earthquake from the internal and external parts of the geomagnetic field, taking the NEW observatory as reference. Finally, on the BPOL*(NEW-CMO) time series, carried out on the interval 07-31 July, 2021, a very clear anomaly of maximum, greater than 1.2 STDEV, was detected on July 22, with 7 days before the onset of Mw8.2 earthquake

Obtaining classification confidence at the pixel level is a challenging task for accuracy assessment in remote sensing image classification. Among the various methods for estimating classification confidence at the pixel level, interpolation-based methods have drawn special attention in the literature. Even though they have been widely recognized in the literature, their usefulness has not been rigorously evaluated. This paper conducts a comprehensive evaluation of three interpolation-based methods: local error matrix method, bootstrap method, and geostatistical method. We applied each of the three methods to three representative datasets with different spatial resolutions, spectral bands, and the number of classes. We then derive the estimated classification confidence and true classification confidence and compared the results with each other using both exploratory data analysis (bi-histogram) and statistical analysis (Willmott's d and Binned classification quality). The results indicate that the three interpolation methods provide some interesting insights on various aspects of estimating per-pixel classification confidence. Unfortunately, the interpolation assumes that classification confidence is smooth across the space, which is usually not true in practice. In other words, interpolation-based methods have limited practical use.

The spatial distribution of elements can be regarded as a numerical field of concentration values with a continuous spatial coverage. An active area of research is to discover geologically meaningful relationships among elements from their spatial distribution. To solve this problem, we propose an association rule mining method based on clustered events of spatial auto-correlation and applied it to the polymetallic deposits of the Chahanwusu River area, Qinghai Province, China. The elemental data for stream sediments were first clustered into HH (high-high), LL (low-low), HL (high-low), and LH (low-high) groups by using local Moran’s I clustering map (LMIC). Then the Apriori algorithm was used to mine the association rules among different elements in these clusters. More than 86% of the mined rule points are located within 1000 m of faults and near known ore occurrences, and occur in the upper reaches of the stream and catchment areas. In addition, we found that the Indosinian granodiorite is enriched in sulfophile elements, e.g., Zn, Ag and Cd, and the Variscan granite quartz diorite (P1γδο) coexists with Cu and associated elements. Therefore, the proposed algorithm is an effective method for mining co-existence patterns of elements and provides an insight into their enrichment mechanisms.

The latest World Atlas of Desertification represents a turning point in the diagnosis of desertification. While it forgoes desertification mapping due to the intrinsic complexity of the phenomenon and the impossibility of measuring it using a single indicator, it introduces the Convergence of Evidence paradigm, which identifies socioeconomic and biophysical variables whose behavior allows pointing out those areas prone to desertification. The Spanish National Action Program Against Desertification (PAND), back to 2008, already implemented a similar approach to identify five “desertification landscapes” within Spain using both socio-economic and climatic information. The PAND was not only pioneering but also, unfortunately, accurate. Desertification in Spain has continued to worsen and the first two decades of the 21st century have consolidated an agri-food model whose dynamics have exacerbated the desertification processes identified in the PAND. Despite its scientific value, the PAND lacked a proper action plan and was completely detached from the diagnosis. As a result the diagnosis it provided was not followed by effective actions to halt desertification in Spain. The Spanish Government's recent declaration of climate and environmental emergency requires a new strategy to combat desertification. This commitment is an excellent opportunity to update the diagnosis of the situation and, more crucially, to unify the different desertification sectoral policies and actions under a single front. We provide here elements for a roadmap to be designed around the pressures, impacts, and drivers that define today's Spanish desertification landscapes to manage them effectively and avoid their further degradation.

The Compact Midwave Imaging System (CMIS) is a wide field of view, multi-angle, multi-spectral pushframe imager that relies on the forward motion of the satellite to create a two-dimensional (2D) image swath. An airborne demonstration of CMIS was successfully completed in January-February 2021 on the NASA Langley Research Center Gulfstream III. The primary objective of the four-flight campaign was to demonstrate the capability of this unique instrument to perform stereo observations of clouds and other particulates (e.g. smoke) in the atmosphere. It is shown that the midwave infrared (MWIR) spectral bands of CMIS provide a unique 24/7 capability with high resolution for accurate stereo sensing. The instrument relies on new focal plane array (FPA) technology, which provides excellent sensitivity at much warmer detector temperatures than traditional technologies. This capability enabled a compact, low-cost design that can provide atmospheric motion vectors and cloud heights to support requirements for atmospheric winds in the 2017-2027 Earth Science Decadal Survey. Applications include day/night observations of the planetary boundary layer, severe weather, and wildfires. A comparison with current space-based earth science instruments demonstrates that the SWIR/MWIR multi-spectral capability of CMIS is competitive with larger, more expensive instrumentation. Imagery obtained over a controlled burn and operating nuclear power plant demonstrated the sensitivity of the instrument to temperature variations. The system relies on a mature stereoscopic imaging technique applied to the same scene from two independent platforms to unambiguously retrieve atmospheric motion vectors (AMVs) with accurate height assignment. This capability has been successfully applied to geostationary and low-earth orbit satellites to achieve excellent accuracy. When applied to a ground-point validation case, the accuracy for the CMIS aircraft observations was 20 m and 0.3 m/s for cloud heights and motion vectors, respectively. This result was confirmed by a detailed error analysis with analytical and covariance models. The results for CMIS cases with underflights of Aeolus, CALIPSO and Aqua provided a good validation of expected accuracies. The paper also showed the feasibility of accommodating CMIS on CubeSats to enable multiple instruments to be flown in a leader-follower mode.

Vegetation status assessment is crucial for agricultural monitoring and management. Vegetation indices derived from high resolution image time series can be used to derive key phenological parameters for annual crops. In this work, we propose a procedure for the estimation of these parameters and their associated uncertainties. The approach uses Bayesian inference through Markov Chain Monte Carlo in order to obtain the full joint posterior distribution of the phenological parameters given the satellite observations. The proposed algorithm is quantitatively validated on synthetic data. Its use on real data is presented together with an application to real-time within season estimation allowing for phenology forecasting.

Bulukumba Regency, South Sulawesi, Indonesia is one of the centres for whiteleg shrimp, Litopenaeus vannamei production that applies intensive technology. However, no information has been obtained regarding the characteristics and status of water quality in coastal areas with environmentally friendly concepts and the potential to receive a waste load. The study aimed to determine the performance of whiteleg shrimp culture in relation to temporal and spatial aspects and characteristic and water quality status. Measurement and sampling of water were carried out before stocking/initial of culture whiteleg shrimp (rainy season) and end of culture/after harvesting of whiteleg shrimp (dry season) at two locations in the coastal area of ​​Bulukumba Regency, namely Bonto Bahari Subdistrict (BB) and Gantarang Subdistrict (GT). Variables measured and analyzed included temperature, salinity, pH, dissolved oxygen, nitrate, nitrite, ammonia, phosphate, total suspended solids, and total organic matter. Data analysis with descriptive statistics, multivariate statistics, and non-parametric statistics. The Storet (Storage and Retrieval) method was used to determine the water quality status. The results showed that the culture of whiteleg shrimp was technology-intensive with a stocking density of 110 - 220 ind/m² with productivity between 13.9 - 44.4 tons/ha/cycle. The predicted waste load of N is 28.00 tons/cycle and P reaches 6.61 tons/cycle. Another result was that changes in water quality status during the rainy season were classified as moderately polluted at the BB location and complying quality standards at the GT location, while in the dry season, both locations were categorized as heavily polluted. Variables of water quality that caused the decrease in water quality status in both locations (BB and GT) were observed to increase salinity, nitrate concentration, and ammonia concentration. However, there was a decrease in dissolved oxygen concentration in the dry season.

Crop-type mapping is an important intermediate step for cost-effective crop management at the field level, as an overview of all fields with a particular crop type can be used for monitoring or yield forecasting, for instance. Our study used a data set with 2400 fields and corresponding satellite observations from the federal state of Bavaria, Germany. The study classified corn, winter wheat, winter barley, sugar beet, potato, and winter rapeseed as the main crops grown in Upper Bavaria. We additionally experimented with a rejection class "Other", which summarised further crop types. Corresponding Sentinel-2 data included the normalised difference vegetation index (NDVI) and raw bands from 2016 to 2018 for each selected field. The influence of raw bands compared to NDVI was analysed and the classification algorithms, i.e. support vector machine (SVM) and random forest (RF), were compared. The study showed that the use of an index should be critically questioned and that raw bands provided a wider spectral bandwidth, which significantly improved the mapping of crop types. The results underline the use of RF with raw bands and achieved overall accuracies (OA) of up to 92%. We also predicted crop types in an unknown year with significantly different weather conditions and several months before the end of the growing season. Thus, the influence of climate anomalies and the accuracy depending on the time of prediction were assessed. The crop types of a test site and year without labels could be determined with an OA of up to 86%. The results demonstrate the usefulness of the proof-of-concept and its readiness for use in real applications.

Aquaculture production is under pressure to increase its production to meet the growing demand for food from a growing population. In the Philippines, aquaculture has experienced the shift from milkfish to prawn with its attractive marketable price. Its intensification has led to negative and positive impacts making its collapse inevitable in the mid-1990s and raised a range of environmental and socioeconomic problems. This paper reviewed the environmental impacts, challenges, and disease outbreaks that overtook the industry using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method. We examined gaps and changes required to revitalize the industry to properly take-off. It considers and gives details on the impacts of shrimp culture on the environment e.g. shrimp farm management, marine pollution, disease outbreaks, climate change impacts, and socioeconomic impacts. Moreover, the presence of viral diseases such as White Spot Syndrome Virus (WSSV), Monodon Baculovirus (MBV), Infectious Hypodermal and Hematopoietic Necrosis Virus (IHHNV), Hepatopancreatic Parvovirus (HPV), and Yellow Head Virus (YHV), have caused socioeconomic impacts with approximate losses of 40,080 mt in 1997 to 51,000 mt in 2014 in the shrimp industry. Recommended strategies were considered to improve the environmental management of shrimp aquaculture, including disease management, and priorities that were highlighted for future research. This management relates to adopting good aquaculture practices on shrimp culture, proper environmental monitoring and sustainable practices at the farm level.

Walker Glacier near the northern coast of Ellesmere Island in the High Canadian Arctic (terrestrial margin of the ‘Last Ice Area’) is undergoing rapid ice attrition in response to climate change. We applied culture and molecular methods to investigate fungal diversity in sediments at the terminus of this glacier. Analysis of the mycoflora composition showed that the Walker Glacier isolates separated into two clusters: the surface of the glacier ice and the glacier foreland. The recently exposed soils of the foreland had a lower fungal diversity and different species from those on the ice, with the exception of five species that occurred in both habitats. This loss of glacial ice in the Arctic is therefore resulting in the loss of habitats for cold-dwelling fungal species. Fungal diversity is a potentially rich biological resource of glacial ecosystems, with unique taxa. The rapid loss of these glacial habitats underscores the urgency for more detailed surveys of fungal diversity in the High Arctic, and the need for further isolation of strains as well as cryopreservation of environmental microbiome samples for future research and conservation.

Satellite Crop Detection technologies are focused on detection of different types of crops on the field in the early stage before harvesting. Crop detection is usually done on a time series of satellite data by classification of the desired fields. Currently, data obtained from Remote Sensing (RS) are used to solve tasks related to the identification of the type of agricultural crops, also modern technologies using AI methods are desired in the postprocessing part. In this challenge Sentinel-1 and Sentinel-2 time series data were used due to their periodic availability. Our focus was to develop methodology for classification of time series of Sentinel 2 and Sentinel 1 data and compare how accuracy of classification can be increased, but also how to guarantee availability of data. We analyse phenology of single crops and on the basis of this analysis we started to provide crop classification. Original crop classifications were made from Enhanced Vegetation Index (EVI) layers made from Sentinel-2 time-series data and then we added also . To increase accuracy we also integrate into the process parcel borders and provide classification of fields..

The introduction of organic and inorganic substances to the environment is a result of human activities such as agriculture, domestic and industrial wastewater which leads to pollution. Treatment processes of these wastewaters are being conducted globally to eliminate easily settled materials and recover nutrients in an attempt to release clear and apparently clean effluent into natural waters. Lack of removing inorganic nitrogen and phosphorus nutrients is the greatest cause of eutrophication in water bodies which inhibits the life of other organisms as well as pose a threat to human life and loss of the economy. Different technologies have been applied and are being developed to recover nutrients as well as heavy metals from wastewater to meet the permissible limits before discharging effluents. Wastewater treatment using microalgae offers an opportunity to provide tertiary bio-treatment and production of valuable biomass. Microalgae use the available inorganic nitrogen and phosphorus for their growth which are then harvested for various uses. Additionally, they have the ability to remove heavy metals and some toxic compounds. The main specific microalgae species in this study is the Chlorella sorokiniana with the Aequorea victoria jellyfish This paper reviews some of the wastewater treatment processes and focus on the use of microalgae and some of the shortcomings of the technologies and how they can be improved to achieve maximum nutrient recovery economically with low energy demand.

Every day around 93% of children under the age of 15 (1.8 billion children) breathe outdoor air that is so polluted it puts their health and development at serious risk. Due to the pandemic, however, ventilation of buildings using outdoor air has become an important safety technique to prevent the spread of COVID-19. With the mounting ev-idence suggesting that air pollution is impactful to human health and educational out-comes, this contradictory guidance may be problematic in schools with higher air pol-lution levels, but keeping kids COVID-19 free and in school to receive their education is now more pressing than ever. To understand if all schools in an urban area are ex-posed to similar outdoor air quality and if school infrastructure protects children equally indoors, we installed research grade sensors to observe PM2.5 concentrations in indoor and outdoor settings to understand how unequal exposure to indoor and out-door air pollution impacts indoor air quality among high- and low-income schools in Salt Lake City, Utah. Based on this approach, we found that during atmospheric inver-sions and dust events, there was a lag ranging between 35 to 73 minutes for the out-door PM2.5 concentrations to follow a similar temporal pattern as the indoor PM2.5. This lag has policy and health implications and may help to explain the rising concerns re-garding reduced educational outcomes related to air pollution in urban areas. These data and resulting analysis show that poor air quality may impact school settings, and the potential implications with respect to environmental inequality.

Wind energy in Europe is aimed to grow at a steady, high pace. Wind turbine noise is an important issue for residents. Environmental noise management aims to reduce the exposure of the population, usually based on acoustics and restricted to a limited number of sources (such as transportation or industry) and sound descriptors (such as L_den). Individual perceptions are taken into account only at an aggregate, statistical level (such as percentage of exposed, annoyed or sleep-disturbed persons in the population). Individual perceptions and reactions to sound vary in intensity and over different dimensions (such as pleasure/fear or distraction). Sound level is in fact a weak predictor of the perceived health effects of sound. The positive or negative perception of the sound (source) is a better predictor of its effects. This article aims to show how the two perspectives (based on acoustics and on perception) can lead to a combined approach in the management of environmental sound. In this approach the reduction of annoyance, not primarily of level, is the main aim. An important aspect in this approach is what a sound means to people: does it lead to anxiety or worry, is it appropriate? The available knowledge will be applied to wind farm management: planning as well as operation.

The capabilities of a quadcopter in the hover mode for low-altitude sensing of atmospheric turbulence with high spatial resolution in urban areas characterized by complex orography are investigated. The studies were carried out in different seasons (winter, spring, summer, and fall), and the quadcopter hovered in the immediate vicinity of ultrasonic weather stations. The DJI Phantom 4 Pro quadcopter and AMK-03 ultrasonic weather stations installed in different places of the studied territory were used in the experiment. The smoothing procedure was used to main regularities in the behavior of the longitudinal and lateral spectra of turbulence in the inertial and energy production ranges. The longitudinal and lateral turbulence scales were estimated by the least-square fit method with the von Karman model as a regression curve. It is shown that the turbulence spectra obtained with DJI Phantom 4 Pro and AMK-03 generally coincide with minor differences observed in the high-frequency region of the spectrum. In the inertial range, the behavior of the turbulence spectra shows that they obey the Kolmogorov-Obukhov “5/3” law. In the energy production range, the longitudinal and lateral turbulence scales and their ratio measured by DJI Phantom 4 Pro and AMK-03 agree to a good accuracy. Discrepancies in the data obtained with the quadcopter and the ultrasonic weather stations at the territory with complex orography are explained by the partial correlation of the wind velocity series at different measurement points and the influence of the inhomogeneous surface.

Lead-210 from natural atmospheric fallout is widely used in multidisciplinary studies to date recent sediments. Some of the ²¹⁰Pb-based dating models can produce historical records of sediment accumulation rates (SAR) and initial activity concentrations ( ). The former have been profusely used to track past changes in the sedimentary conditions. Both physical magnitudes are differently affected by model errors (those arising for the partial or null accomplishment of some model assumptions). This work is aimed at assessing the effects on SAR and of model errors in the CRS, CS, PLUM and TERESA dating models, due to random variability in ²¹⁰Pb fluxes, which is a usual sedimentary condition. Synthetic cores are used as virtual laboratories for this goal. Independently of the model choice, SARs are largely affected by model errors, resulting in some large and spurious deviations from the true values. This questions their general use for tracking past environmental changes. are less sensitive to model errors and their trends of change with time may reflect real changes in sedimentary conditions, as it is shown with some real cores from varved sediments.

Regional Climatic Comfort Index (CCI) deteriorated significantly due to the climate change and anthropogenic interference. Knowledge regarding the long-term temporal dynamics of CCI in typical regions should be strengthened. In this study, we analyze the temporal and spatial evolution of CCI from 1969 to 2018 in Guangdong Province, based on meteorological indicators, including heat, humidity, wind and cloth loading etc.. Additionally, the population exposure to climate unconformity was examined since 1990 with the help of population data. Our study found that: (1) the warming and humidifying of the summer climate served as the main driving force for the continuous deterioration of CCI, with the comfortable days decreased by 1.06d/10a and the extremely muggy days increased by 2.83d/10a; (2) spatially, the lowest climate comfortability concentrated in southwestern Guangdong with more than 50 uncomfortable days each year, while the climate comfortability in northeastern Guangdong tends to deteriorated whit higher rate, which can reach as high as 6d/10a; (3) in summer, the population exposure to uncomfortable climate highly centralized in the Pearl River Delta, Shantou, Jieyang, and the surrounding areas, and both area and population exposure showed increasing trends. Particularly, Shenzhen held the highest growth rate of population exposure with an increase rate of 2.94 million/10a; (4) although the discomfort distribution and deterioration rate vary across the province, the spatial heterogeneity of comfortability is diminishing in Guangdong Province. This study will provide scientific reference for regional urban planning, thermal environment improvement, local resident health risk analysis, and key strategy implementation, etc.

Urban flood risk mapping is an important tool for the mitigation of flooding in view of human activities and climate change. Many developing countries, however, lack sufficiently detailed data to produce reliable risk maps with existing methods. Thus, improved methods are needed that can improve urban flood risk management in regions with scarce hydrological data. Given this, we estimated the flood risk map for Rasht City (Iran), applying a composition of decision-making and machine learning methods. Flood hazard maps were produced applying six state-of-the-art machine learning algorithms such as: classification and regression trees (CART), random forest (RF), boosted regression trees (BRT), multivariate adaptive regression splines (MARS), multivariate discriminant analysis (MDA), and support vector machine (SVM). Flood conditioning parameters applied in modeling were elevation, slope angle, aspect, rainfall, distance to river (DTR), distance to streets (DTS), soil hydrological group (SHG), curve number (CN), distance to urban drainage (DTUD), urban drainage density (UDD), and land use. In total, 93 flood location points were collected from the regional water company of Gilan province combined with field surveys. We used the Analytic Hierarchy Process (AHP) decision-making tool for creating an urban flood vulnerability map, which is according to population density (PD), dwelling quality (DQ), household income (HI), distance to cultural heritage (DTCH), distance to medical centers and hospitals (DTMCH), and land use. Then, the urban flood risk map was derived according to flood vulnerability and flood hazard maps. Evaluation of models was performed using receiver-operator characteristic curve (ROC), accuracy, probability of detection (POD), false alarm ratio (FAR), and precision. The results indicated that the CART model is most accurate model (AUC = 0.947, accuracy = 0.892, POD = 0.867, FAR = 0.071, and precision = 0.929). The results also demonstrated that DTR, UDD, and DTUD played important roles in flood hazard modeling; whereas, the population density was the most significant parameter in vulnerability mapping. These findings indicated that machine learning methods can improve urban flood risk management significantly in regions with limited hydrological data.

Volcanic fumaroles are openings in the earth's surface, where volcanic gases discharge to the atmosphere. Metallic and non-metallic elements contained in gases form specific mineral precipitates upon cooling. Although the presence of metals in fumarolic gases has long been known, their concentrations are generally low and difficult to measure directly. A laboratory model of a fumarole may resolve the situation if the complex gas composition could be accurately reproduced. Here we describe a new experimental approach that allows accurately simulating fumarolic gases in terms of their main components (H₂O, CO₂, S, HCl), as well as adding volatile metal compounds. Gas is generated inside a special flow-through reactor, at the outlet of which the elements contained in the gas form temperature-dependent mineral sequence inside the attached silica-glass tube. Using this installation, we obtained laboratory sublimates from reducing (H₂S-rich) gases similar to natural ones in terms of mineral composition and mineral habits. Twenty-one phases have been identified in sublimates, among which are simple and complex chlorides, simple sulfides and six sulfosalts. Comparison of the sublimate deposition from H₂O-rich gas at 1 bar with similar works performed in evacuated ampoules at low pressure showed that fumarolic gases behave like an ideal gas, in which molecules do not interact with each other, and reactive compounds in the gas serve in fact as an inert carrier of volatile metals species. Changing the composition of the gas at the outlet of the installation, its flow rate and temperature, we can observe the corresponding changes in mineral precipitates and in such a way study the factors affecting mineral formation on natural fumarolic fields.

This research introduces Genus-Physiognomy-Ecosystem (GPE) mapping at a prefecture level through machine learning of multi-spectral and multi-temporal satellite images at 10m spatial resolution, and later integration of prefecture wise maps into country scale for dealing with 88 GPE types to be classified from a large size of training data involved in the research effectively. This research was made possible by harnessing entire archives of Level-2A product, Bottom of Atmosphere reflectance images collected by MultiSpectral Instruments onboard a constellation of two polar-orbiting Sentinel-2 mission satellites. The satellite images were pre-processed for cloud masking and monthly median composite images consisting of 10 multi-spectral bands and 7 spectral indexes were generated. The ground truth labels were extracted from extant vegetation survey maps by implementing systematic stratified sampling approach and noisy labels were dropped out for preparing a reliable ground truth database. Graphics Processing Unit (GPU) implementation of Gradient Boosting Decision Trees (GBDT) classifier was employed for classification of 88 GPE types from 204 satellite features. The classification accuracy computed with 25% test data varied from 65-81% in terms of F1-score across 48 prefectural regions. This research produced seamless maps of 88 GPE types first time at a country scale with an average 72% F1-score.

Particulate matter (PM) represents an air quality management challenge for confined swine production systems. Because of the limited space and ventilation rate, PM can reach relatively high concentrations in swine barns. PM in swine barns possesses different physical, chemical, and biological characteristics than that in the atmosphere and other indoor environments. As a result, it exerts different environmental and health effects and creates some unique challenges regarding PM measurement and mitigation. Numerous research efforts have been made, generating massive data and information. However, relevant review reports are sporadic. This study aims to provide an updated comprehensive review of swine barn PM, focusing on publications since 1990. It covers various topics, including PM characteristics, sources, measurement methods, and in-barn mitigation technologies. Since PM in swine barns is of primarily biological origins, bioaerosols are reviewed in great detail. Relevant topics include bacterial/fungal counts, viruses, microbial community composition, antibiotic-resistant bacteria, antibiotic resistance genes, endotoxins, and (1→3)-β-D-glucans. For each topic, existing knowledge is summarized and discussed and knowledge gaps are identified. Overall, PM in swine barns is complicated in chemical and biological composition and highly variable in mass concentrations, size, and microbial abundance. Feed, feces, and skins constitute the major PM sources. Regarding in-barn PM mitigation, four technologies (oil/water sprinkling, ionization, alternation of feed and feeders, and recirculating air filtration) are dominant. However, none of them have been widely used in commercial barns. A collective discussion of major knowledge gaps and future research needs is offered at the end of the report.

Optimal use of aerosol optical depth (AOD)-PM2.5 fused surfaces in epidemiologic studies requires homogeneous temporal and spatial fused surfaces. No analytic method is currently available to evaluate the spatial dimension. The temporal case-crossover design was modified to assess the association between Community Multiscale Air Quality (CMAQ) lag grids and four respiratory-cardiovascular hospital events. The maximum number of adjacent lag grids with the expo-sure-health outcome association determined the size of the homogeneous spatial area. The largest homogeneous spatial area included 5 grids (720 km2) and the smallest 2 grids (288 km2). PMC and PMCK analyses of ED asthma, IP asthma, IP MI, and IP HF were significantly higher in rural grids without air monitors than in urban with air monitors at lag grids 0, 1, and 01. Grids without air monitors had higher AOD-PM2.5 concentration levels, poverty percent, population density, and environmental hazards than grids with air monitors. ED asthma, IP MI, and HF PMCK ORs were significantly higher during the warm season than during the cold season at lag grids 0, 1, 01, and 04. The possibility of elevated fine PM and other demographic and environmental risk factors contributing to elevated respiratory-cardiovascular diseases in persons residing in rural areas was discussed.

In Nigeria, desertification has become one of the most pronounced ecological disasters, with the impacts mostly affecting eleven frontline States. This has been attributed to a range of both nat-ural and man-made factors. This study applied a remote sensing-based change detection and indicator analysis to explore land use/land cover changes and detect major conversions from ecologically active land covers to sand dunes. Results indicate that areas covered by sand dunes (a major indicator of desertification) have doubled over the 25 years under consideration (1990 to 2015). Although about 0.71 km2 of dunes have been converted to vegetation, indicative of the success of various international, national, local, and individual afforestation efforts, conversely about 10.1 km2 of vegetation were converted to sand dunes, implying around 14 times more de-forestation compared to afforestation. Juxtaposing the progression of sand dune with climate records of the study area and examining the relationship between indicators of climate change and desertification suggested a mismatch between both processes as increasing rainfall and lower temperatures observed in 1994, 2005, 2012, and 2014 did not translated into positive feedbacks for desertification in the study area. On average, our results reveal that sand dune is progressing at a mean annual rate of about 15.2 km2 in the study area. Based on this study’s land cover change, trend and conversion assessment, visual reconciliation of climate records with land cover data, statistical analysis, observations from ground-truthing, as well as previous literature, it can be inferred that desertification in Nigeria is less a function of climate change, but more a product of human activities driven by poverty, population growth and failed government policies. Further projections by this study also reveal a high probability of more farmlands being converted to sand dunes by the year 2030 and 2045 if current practices prevail.

Shallow bathymetry inversion algorithms have long been applied in various types of remote sensing imagery with relative success. However, this approach requires that imagery with increased radiometric resolution in the visible spectrum is available. The recent developments in drones and camera sensors allow for testing current inversion techniques on new types of datasets. This study explores the bathymetric mapping capabilities of fused RGB and multispectral imagery, as an alternative to costly hyperspectral sensors. Combining drone-based RGB and multispectral imagery into a single cube dataset, provides the necessary radiometric detail for shallow bathymetry inversion applications. This technique is based on commercial and open-source software and does not require input of reference depth measurements in contrast to other approaches. The robustness of this method was tested on three different coastal sites with contrasting seafloor types. The use of suitable end-member spectra which are representative of the seafloor types of the study area and the sun zenith angle are important parameters in model tuning. The results of this study show good correlation (R2>0.7) and less than half a meter error when they are compared with sonar depth data. Consequently, integration of various drone-based imagery may be applied for producing centimetre resolution bathymetry maps at low cost for small-scale shallow areas.

This paper presents the results of an experimental study of the patterns of steel and lead shot transformation under the impact of environmental factors (two types of shot exposed alone and in combination with each other). The analyzed environmental factors include atmospheric precipitation of various acidity and soil solutions with a higher content of organic acids. This research demonstrated that steel shot is characterized by a high transformation rate that is an order of magnitude higher on average than the transformation rate of lead under the same conditions. The prevalence of the suspended iron form (excluding the interaction with organic acids) presents risks for such environmental components as soils and sediments and may be hazardous to the ambient air and natural waters in case of wind erosion and surface runoff from the catchment area. Furthermore, the joint presence of steel and lead shot mutually accelerates the corrosion of both metals, thus increasing the environmental risks.

Improved oil recovery from tight carbonate formations may provide the world with a major source of lower-rate power over several decades. Here we provide an overview of the Arab D formation in the largest oil field on earth, the Ghawar. We investigate the occurrence of microporosity of different origins and sizes using scanning electron microscopy (SEM) and pore casting techniques. Then, we present a robust calculation of the probability of invasion and oil saturation distribution in the nested micropores using mercury injection capillary pressure data available in the literature. We show that large portions of the micropores in Arab D formation would have been bypassed during primary drainage unless the invading crude oil ganglia were sufficiently long. Considering the asphaltenic nature of oil in the Ghawar, we expect the invaded portions of the pores to turn mixed-wet, thus becoming inaccessible to waterflooding until further measures are taken to modify the system’s chemistry.

According to this study, approximately half of Africa's forests are utilized primarily or partially for the production of wood and non-wood commodities. Aims to evaluate Africa's forestry and forest products, namely Wood Forest Products (WFPs) and Non-wood Forest Products (NWFPs) in the sixteen (16) West African countries. While adhering to the following guidelines: wood extraction and preparation, analyzing wood primarily used as an energy source in Africa, identifying non-wood forest products in Africa, the state of export, trade, and customs procedures in West Africa, and examining the role of forests and forest stakeholders in Africa's low-carbon economy transition. An exploratory literature review of selected wood forest products and non-wood forest products (plants and animals) in West Africa identifying the country, the natural land area with the natural habitat issues of the forest, the species most harvested and traded in the West African sub-region. The study reemphasized some government legislation, policies, and market trade failures and limitations while also stating that trees may help in the low-carbon revolution through interventions aimed at maintaining, improving, and restoring natural capital have demonstrated that high environmental requirements of sustainable forest management (SFM) may be met in both natural and planted forests. The study identified a systematic assessment of the most common forest products (wood and non-wood forest products) considering the available data on the national forest reserves of the selected countries in West Africa. The study also revealed the need for biodiversity conservation of the available forest reserves to help mitigate the impact of global warming targeting the United Nation’s Sustainable Development Goal 13- Climate Action. Which is focused on integrating climate change mitigation, adaptation, impact reduction, and early warning signs into the national policies, improving forest planning and management education, awareness-raising, and institutional capacity within the sub-region.

The recent glaciation of the southern part of the Altai is estimated (1256 glaciers with the total area of 559.15±31.13 km2), the area of the glaciers of the whole Altai mountains is evaluated by 1096.55 km2. In the southern part of Altai 2276 glaciers with the total area of 1348.43±56.16 km2 were reconstructed, the first estimate of the LIA glacial area for the whole Altai mountain system is given (2288.04 km2). Since the LIA the glaciers decrease by 59% in the southern part of Altai and by 47.9% for the whole Altai. The ELA in the southern part of Altai increased averagely by 106 m. The higher changes of ELA in relatively humid areas is probably caused by decrease of precipitation. Glaciers of Tavan Bogd glacial center degraded with higher rates after 1968 relative to the interval between 1850-1968. One of the intervals of the fastest shrinkage of the glaciers in 2000-2010 was caused by unfavorable for the glaciers dry and warm interval 1989-2004. However, the fast decrease of the glaciers in 2000-2010 was mainly caused by the shrinkage or disappearance of the smaller glaciers, large valley glaciers started fast retreat after 2010.

To become mainstream, Nature Positive development needs positive messaging, measures and metrics to guide, plan and assess urban outcomes. With accelerating climate crisis and negative messages getting the upper-hand, it is important to avoid paralysis by bad news. Whilst striving for a nature positive world, more effort should be on moving beyond zero to qualify and quantify benefits, gains and regenerative outcomes instead of around damage and loss sticking points. Life Cycle Benefit Assessment (LCBA) methods measure gains in accelerating regeneration and climate security that enables a good news focus. Its reach beyond negative quantifies and shows positive gain beyond zero loss outcomes. The aims are to clarify concepts, challenges and quantitative methods then review real-world 3rd party Certified nature positive case studies. Climate security, human wellness and resource viability gains inside safe operating space within planetary boundaries are quantified as positive benefits. contrary to conventional Life Impact Cycle Impact Assessment LCBA assigns damage losses as negatives debts and benefit gains as positive savings. It concludes that LCBA remains under development with more research needed to model economic outcomes.

Sustainability is pivotal in the urban transformation strategy in order to reach more resource-efficient, resilient and smarter cities. The goal of being a sustainable city should drive the decisions for city interventions. Nonetheless, impacts need to be quantified, lacking of standard and/or common methodologies that could be replicable across multiple cities. There exist many initiatives aiming at defining indicators and assessment procedures, but without convergence in the definition of terms and application methodologies, making complex its real implementation. Within mySMARTLife project (GA#731297), a KPI-driven evaluation framework is defined with the aim of covering the multiple pillars of a city (i.e. energy, mobility, citizens, economy) in a holistic way. This methodology also defines the concepts and terms to guide urban planners and/or experts at time of implementing the framework in a specific city. The evaluation framework has been deployed in the three cities of Nantes, Hamburg and Helsinki and some lessons learnt have been extracted, such as the necessity of providing a definition of measurement boundary to avoid interpretations. Thanks to a co-creation strategy, the main difficulties and issues from the cities have been taken into consideration for increasing the replicability.

This study examines the effect of surface currents on the bulk algorithm calculation of wind stress estimated using the scatterometer data during 2007-2020 in the Indian Ocean. In the study region as a whole the wind stress decreased by 5.4% by including currents into the wind stress equation. The most significant reduction in the wind stress is found along the most energetic regions with strong currents such as Somali Current, Equatorial Jets and Aghulhas retroflection. A highest reduction of 11.5% is observed along the equator where the Equatorial Jets prevail. A sensitivity analysis has been carried out for the study region and for different seasons to assess the relative impact of winds and currents in the estimation of wind stress by changing the winds while keeping the currents constants and vice versa. The inclusion of currents decreased the wind stress and this decrease is prominent when the currents are stronger. This study showed that equatorial Indian Ocean is the most sensitive region where the current can impact on wind stress estimation. The results showed that uncertainties in the wind stress estimations are quite large at regional levels and hence better representation of wind stress incorporating ocean currents should be considered in the ocean/climatic models for accurate air-sea interaction studies.

This review asks some hard questions about what the enigmatic graphoglyptid trace fossils are, documents some of their early fossil record from the Ediacaran-Cambrian transition and explores the idea that they may not have been fossils at all. Most researchers have considered the Graphoglyptida to have had a microbial-farming mode of life similar to that proposed for the fractal Ediacaran Rangeomorpha. This begs the question “What are the Graphoglyptida if not the Rangeomorpha persevering” and if so then “What if…?”. This provocative idea has at its roots some fundamental questions about how to distinguish burrows sensu-stricto from the external molds of endobenthic sediment displacive organisms.

The article analyzes two existing social, technical and economic problems which the world community shall focus on and pay special attention to.

The global carbon neutrality challenge places a spotlight on forests as carbon sinks. However, greenhouse gas (GHG) balances of wood for material and energy use often reveal GHG emission savings in comparison with a non-wood reference. Is it thus better to increase wood production and use, or to conserve and expand the carbon stock in forests? GHG balances of wood products mostly ignore the dynamics of carbon storage in forests, which can be expressed as the carbon storage balance in forests (CSBF). For Germany, a CSBF of 0.25 to 1.15 t CO2/m³ wood can be assumed. When the CSBF is integrated into the GHG balance, GHG mitigation substantially deteriorates and wood products may even turn into a GHG source, e.g. in the case of energy wood. Here, building up the forest carbon sink would be the better option. We conclude that it is vital to include the CSBF in GHG balances of wood products if the wood is extracted from forests. Only then can GHG balances provide political decision-makers and stakeholders in the wood sector with a complete picture of GHG emissions.

Due to rising global warming and climate change, biodiversity protection has become a critical ecological concern. The rich biodiversity zones are under threat and are deteriorating, necessitating national, regional, and provincial efforts to safeguard these natural areas. The effective conservation of National Parks and Nature-protected Areas helps to improve biodiversity conservations, forest, and urban air quality. The continuous encroachment and abuse of these protected areas have degraded the ecosystem over time. While exploring the geophysical ecology and biodiversity conservation of these areas in West Africa, Kainji National Park was selected for this study because of its notable location, naturalness, rich habitat diversity, topographic uniqueness, and landmass. The conservation of national parks and nature-protected areas is a cornerstone of biodiversity conservation globally. This study is aimed at the target of United Nations Sustainable Development Goal 13, 2030- Climate Action, targeted at taking urgent action towards combating climate change and its impacts. The study captures both flora and fauna that are dominant in the study area. The 15 identified trees were randomly sampled within a stratum of 10x10km shared into 24 plots for proper analyses using i-Tree Eco v6.0.23 software. The following data were captured and analyzed; Photosynthetically Active Radiation, Rain/Precipitation, Temperature, Transpiration, Evaporation, Water Intercepted by trees, Avoided Runoff by trees, Potential Evaporation by trees, Isoprene and Monoterpene by trees. This study also further discusses the tree benefits of green, low carbon, and sustainable environment within the context of biodiversity conservation considering carbon storage, carbon sequestration, hydrology effects, pollution removal, oxygen production, and volatile organic compounds (VOCs). There is a quick need for remotely-sensed information of the protected areas at regular intervals and government policies must be strict against illegal poaching and logging activities.

Chilean geography is highly variable, not only from a climatic and hydrological point of view, but also a morphological one, showing unpredictable natural patterns with marked contrasts throughout the country, for which sometimes it is considered as a "crazy" geography. In this paper we have investigated this apparent disorganized character by exploring the fractal properties of fluvial networks extracted from basins distributed across the continental territory. Analytical and semi-empirical methods were applied, finding striking patterns of organization in the distributions of Horton parameters and the fractal dimension of the drainage networks. Fractal dimension reveals to be quite dependent on the drainage area of each unit, showing clear groupings by tectonic and climatological factors. Such dimension reveals to be an important geomorphic parameter, if not the only one able to capture the real morphology of a fluvial network. From our results and despite the diversity of landforms, hydrological, climatic and tectonic conditions, Chilean’s geography is perhaps not as crazy and disorganized as believed.

Three significant changes have occurred in the winter climate in Europe recently: increased UK flooding; Iberian drought; and warmer temperatures north of the Alps. The literature links all three to a persistent, significant increase in sea level pressure over Southern Europe, the Mediterranean, Iberia and the Eastern Atlantic (SEMIEA) which changes the atmospheric circulation system: forcing cold fronts to the north away from Iberia; and creating a south westerly flow around the northern perimeter of the high-pressure region bringing warmer, moist air from the subtropical Atlantic to the UK and Europe which increases precipitation in the UK and raises the temperature in Europe. I use the Last Millennium Ensemble, MERRA-2 and Terra-NCEP data to demonstrate that the extreme, anthropogenic, West African aerosol Plume (WAP) which only exists from December to April perturbs the northern, regional Hadley Circulation creating the high pressure in the SEMIEA. I also show that the anthropogenic WAP has only existed in its extreme form in recent decades as the two major sources of the WAP aerosols: biomass burning; and gas flaring have both increased significantly since 1950 due to: a four-fold increase in population; and gas flaring rising from zero to 7.4 billion m3/annum and note that this time span coincides with the changes in the three elements of the winter climate of Europe. I also suggest that it may be possible to eliminate the WAP and return the winter climate of Europe to its natural state after the crucial first step of recognising the cause of the changes is taken.

The Brazilian livestock is predominantly extensive, with approximately 90% of the production being sustained on pasture, which occupies around 20% of the territory. In the current climate change scenario and where cropland is becoming a limited resource, there is a growing need for a more efficient land use and occupation. It is estimated that more than half of the Brazilian pastures have some level of degradation; however there is still no mapping of the quality of pastures on a national scale. In this study, we mapped and evaluated the spatio-temporal dynamics of pasture quality in Brazil, between 2010 and 2018, considering three classes of degradation: Absent (D0), Intermediate (D1), and Severe (D2). There was no variation in the total area occupied by pastures in the evaluated period, in spite of the accentuated spatial dynamics, with a retraction in the center-south and expansion to the north, over areas of ​​native vegetation. The percentage of non-degraded pastures increased ~12%, due to the recovery of degraded areas and the emergence of new pasture areas as a result of the prevailing spatial dynamics. However, about 44 Mha of the pasture area is currently severely degraded. The dynamics in pasture quality were not homogeneous in property size classes. We observed that in the approximately 2.68 million properties with livestock activity, the proportion with quality gains was twice as low in small properties compared to large ones, and the proportion with losses was three times greater, showing an increase in inequality between properties with more and less resources (large and small, respectively). The areas occupied by pastures in Brazil present an unique opportunity to increase livestock production and make available areas for agriculture, without the need for new deforestation in the coming decades.

The distributed acoustic sensing (DAS) has great potential for monitoring natural-resource reservoirs and borehole conditions. However, the large volume of data and complicated wavefield add challenges to processing and interpretation. In this study, we demonstrate that seismic interferometry based on deconvolution is a convenient tool for analyzing this complicated wavefield. We extract coherent wave from the observation of a borehole DAS system at the Brady geothermal field in Nevada. Then, we analyze the coherent reverberating waves, which are used for monitoring temporal changes of the system. These reverberations are tirelessly observed in the vertical borehole DAS data due to cable or casing ringing. The deconvolution method allows us to examine the wavefield at different boundary conditions. We interpret the deconvolved wavefields using a simple 1D string model. The velocity of this wave varies with depth, observation time, temperature, and pressure. We find the velocity is sensitive to disturbances in the borehole related to increasing operation intensity. The velocity decreases with rising temperature, which potentially suggests that the DAS cable or the casing are subjected to high temperature. This reverberation can be decomposed into distinct vibration modes in the spectrum. We find that the wave is dispersive, and the the fundamental mode propagate with a large velocity. The method can be useful for monitoring borehole conditions or reservoir property changes. For the later, we need better coupling than through only friction in the vertical borehole to obtain coherent energy from the formation.

Economic risks for livestock production are caused by volatile commodities and market conditions, but also by environmental drivers like increasing uncertainties due to weather anomalies and global warming. These risks impact the gross margin of farmers and can stimulated investment decisions. For confined pig and poultry production, farmers can reduce the environmental impact by implementing specific adaptation measures to reduce heat stress. A simulation model driven by meteorological data was used to calculate heat stress impact as a projection for 2030. For a business-as-usual livestock building, the indoor climate for several adaptation measures was calculated. The weather-related value-at risk quantified the economic risks caused by global warming and the stochastic component of the weather. The results show that only energy-saving adaptation measures to reduce the inlet air temperature are appropriate to reduce the economic risk to the level of the year 1980. The efficiency of other adaptation measures to reduce heat stress is distinctly lower. The results in this study can support the decision making of farmers concerning adaptation management and investments. It can inform agricultural policy design as well as technological development.

Sinkhole flooding is an essential hydrological process to recharge karst aquifer in arid to dry sub-humid regions. On the other hand, the increase of rain extremes is one of the major consequences of the global warming, together with the expansion of drylands. Thus, appropriate runoff regulation in endorheic karst basins in order to reduce the risk of flooding and improve the quantity and quality of the water drained by sinkholes will be more and more crucial. With these premises, a systematic review was performed by using WoS engine to infer the best practices for the karst water management in regions actually or potentially affected by water scarcity. Hydrological models are essential to manage the consequences of climate change on karst water resource, however the review shows that providing the tools necessary for reliable modeling is still challenging. Finally, due to the intrinsic vulnerability of the karst aquifers, pollution reduction and wastewater recycling policy will play key role in the next decades.

In the subcratonic mantle beneath Leningrad pipe, West Ukukit field, Yakutia garnet thermoba-rometry give division to 7 horizons (paleosubduction slabs). Cr-bearing amphiboles >500 reveal a broad range changing from Cr- pargasitic hornblendes to pargasites, edinites, kataforites, К-richterites with increasing pressure determined with new amphibole thermobarometer. Cr-hornblendes compiles the high-temperature branch from 3.5 GPa to Moho for basaltic melt. Amphiboles in the middle eddinites and high-pressure interval reveal different PT ranges from 35 to 40 mw/m2. Richterites near the lithosphere base both trace low –T and convective branches. The amphiboles reveal the 9 geochemical groups. The low-temperature varieties reveal Eu minima and U, Ba, Sr peaks high LILE, Sr, Rb and troughs in Nb, Pb. While high –T varieties have no Eu dips and reveal higher HFSE. Clinopyroxenes and garnets show variable trace ele-ment patterns and divisions in groups eth the plume and subduction signs. The contrasting be-haviour of Ta and Nb is regulated by the rutile partition coefficients likely for primary eclogites. Subduction and Na and K (siliceous) types of fluids percolated through the mantle with abun-dant eclogites causing amphibolization at the different levels of the mantle column. The plume melts produced hybridism and more smooth trace element patterns in reacted minerals, clino-pyroxene. monomineral thermobarometry.

Classification and mapping of plant communities is an essential step for conservation and management of ecosystems and biodiversity. We adopt the Genus-Physiognomy-Ecosystem (GPE) system developed in previous study for satellite-based classification of plant communities. This paper assesses the potential of multi-spectral and multi-temporal images collected by Sentinel-2 satellites. This research was conducted in five representative study sites in a temperate region. It consists of 44 types of plant communities including a few land cover types as well. The plant community types were enumerated in the study sites and ground truth data were prepared with reference to extant vegetation surveys, visual interpretation of high-resolution images, and onsite field observations. We acquired all Sentinel-2 Level-1C product images available for the study sites between 2017-2019 and generated monthly median composite images consisting of ten spectral and twelve spectral-indices. Gradient Boosting Decision Trees (GBDT) classifier was employed as an efficient and distributed gradient boosting technique for the supervised classification of big datasets involved in the research. The cross-validation accuracy in terms of kappa coefficient varied from 87% in Oze site with 41 land cover and plant community types to 95% in Hakkoda site with 19 land cover and plant community types; with average performance of 91% across all sites. In addition, the resulting maps demonstrated a clear distribution of plant community types involved in all sites, highlighting the potential of Sentinel-2 multi-spectral and multi-temporal images with GPE classification system for operational and broad-scale mapping of land cover and plant communities.

Advanced Land Observing Satellite 3 (ALOS-3) is capable of observing global land areas with wide swath (4000 km along-track direction and 70 km cross-track direction) at high spatial resolution (panchromatic: 0.8m, multispectral: 3.2m). Maintenance and updating of Land Cover and Vegetation (LCV) information at national level is one of the major goals of the ALOS-3 mission. This paper presents the potential of simulated ALOS-3 images for the classification and mapping of LCV types. We simulated WorldView-3 images according to the configuration of the ALOS-3 satellite sensor and the ALOS-3 simulated (ALOS-3S) images were utilized for the classification and mapping of LCV types in two cool temperate ecosystems. This research dealt with classification and mapping of 17 classes in the Hakkoda site and 25 classes in the Zao site. We employed a Gradient Boosted Decision Tree (GBDT) classifier with 10-fold cross-validation method for assessing the potential of ALOS-3S images. In the Hakkoda site, we obtained overall accuracy, 0.811 and kappa coefficient, 0.798. In the Zao site, overall accuracy and kappa coefficient were 0.725 and 0.711 respectively. Regardless of limited temporal scenes available in the research, ALOS-3S images showed high potential (at least 0.711 kappa-coefficient) for the LCV classification. The availability of more temporal scenes from ALOS-3 satellite is expected for improved classification and mapping of LCV types in the future.

Cultural ecosystem services has been increasingly influential in both environmental research and policy decision-making, such as for urban green spaces However, its popular definition conflates the concepts of ‘services’ and ‘benefits’ which made it challenging for planners to employ it directly for urban green space management. One the most widely used definition of this non-tangible ecosystem services are “functions of environmental spaces and cultural activities which may then result in the enjoyment of cultural ecosystem benefits”; yet the latter itself have never found its way into official laws and regulations. In this study, via a case study in Singapore, we propose new evidence to re-evaluate and re-position the two of the most important emerging concepts in managing the green spaces in urban areas. Using the transdisciplinary mixed methods of public participation GIS and social media text mining analysis, a wealth of cultural ecosystem services and their associated benefits were reported. This was especially so with regards to recreational and aesthetic services and experiential benefits. Recommendations to improve the park were also suggested, alongside sharing of methodological considerations for future research. Overall, this paper recommends the employment of the redefined cultural ecosystem services conceptual framework to generate relational, data-driven and actionable insights to better support urban green space management, which is not only useful to Singapore governments but also world-wide relevant.

Shallow bathymetry inversion algorithms have long been applied in various types of remote sensing imagery with relative success. However, this approach requires that imagery with increased radiometric resolution in the visible spectrum is available. The recent developments in drones and camera sensors allow for testing current inversion techniques on new types of datasets. This study explores the bathymetric mapping capabilities of fused RGB and multispectral imagery, as an alternative to costly hyperspectral sensors. Combining drone-based RGB and multispectral imagery into a single cube dataset, provides the necessary radiometric detail for shallow bathymetry inversion applications. This technique is based on commercial and open-source software and does not require input of reference depth measurements in contrast to other approaches. The robustness of this method was tested on three different coastal sites with contrasting seafloor types. The use of suitable end-member spectra which are representative of the seafloor types of the study area and the sun zenith angle are important parameters in model tuning. The results of this study show good correlation (R2>0.7) and less than half a meter error when they are compared with sonar depth data. Consequently, integration of various drone-based imagery may be applied for producing centimetre resolution bathymetry maps at low cost for small-scale shallow areas.

We developed a novel approach in the field of spatiotemporal modelling, based on the spatialisation of time: the Timescape algorithm. It is especially aimed at sparsely distributed datasets in ecological research, whose spatial and temporal variability is strongly entangled. The algorithm is based on the definition of a spatiotemporal distance that incorporates a causality constraint and that is capable of accommodating the seasonal behaviour of the modelled variable as well. The actual modelling is conducted exploiting any established spatial interpolation technique, substituting the ordinary spatial distance with our Timescape distance, thus sorting, from the same input set of observations, those causally related to each estimated value at a given site and time. The notion of causality is expressed topologically and it has to be tuned for each particular case. The Timescape algorithm originates from the field of stable isotopes spatial modelling (isoscapes), but in principle it can be used to model any real scalar random field distribution.