ALBERT

Description: The Aral Sea desiccation is one of the worst aquatic ecological disasters of the last century, important for understanding the worldwide trends to degradation of arid lakes under water use and climate change. Formerly the fourth largest lake worldwide, the Aral Sea has lost ∼90% of its water since the early 1960s due to irrigation in its drainage basin. Our survey on the seasonal thermal and mixing regime in Chernyshev—a semi-isolated hypersaline part of the Aral Sea—revealed a newly formed two-layered structure with strong gradients of salinity and water transparency at mid-depths. As a result, the Chernyshev effectively accumulates solar energy, creating a temperature maximum at the water depth of ∼5 m with temperatures up to 37 °C. Herewith, this part of the Aral Sea has evolved to an unprecedently large (∼80 km2) heliothermal lake akin to artificial solar ponds used for ‘green energy’ production. The newly formed heliothermal lake, with transparent and freshened layer on top of the hypersaline and nutrient-rich deep water, acts as a solar energy trap and facilitates intense biogeochemical processes. The latter reveal themselves in practically 100% opacity of the deep layer to the solar light, permanent deep anoxia, and growing methane concentrations. The recent emergence of the Chernyshev as a heliothermal lake provides an opportunity for tracing the biogeochemical and ecological response of aquatic ecosystems to suddenly changed environmental conditions.

Description: Circular Economy (CE) has emerged as a potential strategy for developing business practices based on sustainability concerns, especially in the fashion industry, which presents high environmental and social impacts. Startups are responsible for introducing innovations in business conduction toward CE. As a current theme, research on Business Model Innovation for Circular Economy (BMI4CE) has increased. However, empirical research in the fashion industry and startups is still scarce. This paper aimed to identify the key elements of startups' BMI4CEs, using the fashion industry as the context of the study. We conducted an exploratory and descriptive multiple case study composed of ten early-stage fashion startups from Europe, North America, and Asia. The findings suggest that environmental and economic sustainability dimensions receive priority in the analyzed BMI4CEs. On business type, we found differences between product-based and service-based Business Models (BMs). The Business Models Innovations (BMIs) were based mainly on CE principles of closed-loop and reducing material use and consumption. BMs focus on CE strategies of product reuse and extend resource time by lowering consumption and material use. Findings also demonstrate the role of emerging and digital technologies (e.g., blockchain and artificial intelligence) for BMI4CEs effectiveness. We developed five propositions and a theoretical framework from a triple bottom line perspective. This research highlights new theoretical perspectives under an investigation area still little explored in the literature. Results enable fashion startup managers to understand better the functioning of BMI4CEs and the critical elements needed for their effectiveness.

Description: In the process of continuously advancing urbanization, the energy efficiency of the construction industry, as a pillar industry of the national economy, has attracted much attention. The imbalance of regional economic development, the difference of regional resource endowment, and the incomplete energy infrastructure in some areas lead to the prominent problem of energy poverty in China. Based on the panel data of 30 provinces and cities from 2008 to 2017, this paper calculates the energy poverty index and the energy efficiency of the construction industry. Using OLS, Tobit, Probit, and other estimation methods, this paper analyzes the internal relationship between energy poverty and the energy efficiency of the construction industry, the mediating role of energy consumption structure, and the moderating effect of technological level and marketization degree. The results show the following: There is a negative correlation between energy poverty and the energy efficiency of the construction industry (Husnain, ENERG POLICY, 2021, 157). The energy consumption structure plays a partial mediating role in the impact of energy poverty on the energy efficiency of the construction industry (Li, CHEM ENG J, 2021, 408). It means that energy poverty inhibits the optimization of the energy consumption structure, and through this process, it also inhibits the improvement of the energy efficiency of the construction industry. The technological level and the degree of marketization play a moderating role in the main effect, which can weaken and strengthen the negative impact of energy poverty on the energy efficiency of the construction industry (Gajdzik, ENERGIES, 2021, 14).

Description: Secondary organic aerosols (SOA) play an important role in global climate change and air quality, and SOA tracers can directly characterize the source and reaction mechanism of SOA. However, it is not well known that whether the tracers can be oxidized or how the instability of the tracers in the atmosphere. In this paper, in-situ FTIR was used to analyze the chemical structure changes of erythritol, analogue of 2-methyl erythritol (AME) that is, a tracer of isoprene SOA, and 2, 3-dihydroxy-4-oxopentanoic acid (DHOPA), a tracer of toluene SOA, when exposed to high concentration of ozone for short periods. Under the condition of 20 ppm ozone exposure for 30 min, the change rate of absorption area of AME at 3,480 and 1700 cm−1 was −0.0134 and 0.00117 int.abs/s, respectively, and the change rate of the absorption area of DHOPA at 1,640 and 3340cm−1 was −0.00191 and 0.00218 int.abs/s, respectively. The pseudo-first-order reaction rate constant kapp were 1.89 × 10−8 and 2.12 × 10−7 s−1, and the uptake coefficients of ozone on the surface of AME and DHOPA were (1.3 ± 0.8) × 10−8 and (4.5 ± 2.7) × 10−8, respectively. These results showed the oxidation processes of AME and DHOPA were slow in the presence of high concentrations of ozone, which implied that AME and DHOPA could be considered to be stable in the atmospheric environment with ozone as the main oxidant.

Description: This study attempts to analyze the time-varying pattern between the exchange rates, stock market return, temperature, and number of confirmed COVID-19 cases in G7 countries caused by the COVID-19 pandemic. We have implemented our analysis using wavelet coherence and partial wavelet coherence (PWC) on independent variables from January 4, 2021 to July 31, 2021. This paper contributes to the earlier work on the same subject by employing wavelet coherence to analyze the effect of the sudden upsurge of the COVID-19 pandemic on exchange rates, stock market returns, and temperature to sustain and improve previous results regarding correlation analysis between the above-mentioned variables. We arrived at the following results: 1) temperature levels and confirmed COVID-19 cases are cyclical indicating daily temperatures have a material bearing on propagating the novel coronavirus in G7 nations; 2) noteworthy correlations at truncated frequencies show that a material long-term impact has been observed on exchange rates and stock market returns of G7 and confirmed COVID-19 cases; 3) accounting for impact of temperature and equity market returns, a more robust co-movement is observed between the exchange rate returns of the respective nations and the surge in COVID-19 cases; and 4) accounting for the influence of temperature and exchange rate returns and the increase in the confirmed number of coronavirus-infected cases and equity returns, co-movements are more pronounced. Besides academic contributions, this paper offers insight for policymakers and investment managers alike in their attempt to navigate the impediments created by the coronavirus in their already arduous task of shaping the economy and predicting stock market patterns.

Description: Sustainable development remains unattainable unless we move to reduce the negative impact of economic factors on environmental quality. It is noteworthy to provide new evidence on whether and how the empirical association between export diversification, agricultural value-addition, renewable energy, and regulatory quality with greenhouse gas (GHG) emissions evolved in Asian countries from 1996 to 2014. The study examines the relationships between these variables using current panel data techniques. The econometric procedure includes second-generation cointegration and unit root tests together with a novel Method of Movements Quantile Regression (MMQR). This approach offers an asymmetric relationship between the variables and is very robust to outliers compared to traditional quantile regression. The empirical outcomes show that export diversification, renewable energy, and regulatory quality are significantly and negatively associated with GHG emissions. In contrast, agricultural value-added in Asia has become a source of increased GHG emissions. Our findings are also robust with alternate specifications, including fully modified, dynamic and fixed effect regressions. This study will help policymakers for diversifying their export portfolio while ensuring a sustainable environment in Asia.

Description: Urbanization and seasonality strongly influence the bacterial composition of the soil. However, aquatic environments such as rivers are understudied owing to their high dynamics and therefore rules relating to more static habitats such as lentic or terrestrial environments may be limited. Here, we compared the spatiotemporal patterns of bacterioplankton communities in the Zhangxi river along a gradient of urbanization using 16S ribosomal DNA identification. The alpha and beta diversity of bacterioplankton showed no significant response to watershed urbanization. A significant difference in predicted functional profiles of the bacterioplankton community was also revealed between the wet and dry seasons. The bacterioplankton community assembly was driven by both deterministic and stochastic processes. Stochasticity was one of the most vital processes affecting the bacterioplankton communities in both wet and dry seasons, explaining over 50% variation in the community by the null model analysis. Bacterioplankton co-occurrence patterns in the river changed with the seasons. More notably, the composition of bacterioplankton communities was inconsistent with alternations of the spatial distance offering meaningful implications for interactions between zero-radius operational taxonomic units and the dynamics of the bacterioplankton communities in surface water. In summary, we found clear patterns of seasonal variations in the bacterioplankton community structures.

Description: Climate change and human activities are having increasing impacts on the global water cycle, particularly on streamflow. Current methods for quantifying these impacts are numerous and have their merits and limitations. There is a lack of a guide to help researchers select one or more appropriate methods for attribution analysis. In this study, hydrological modeling, statistical analysis, and conceptual approaches were used jointly to develop a methodological options framework consisting of three modules, to guide researchers in selecting appropriate methods and assessing climatic and anthropogenic contributions to streamflow changes. To evaluate its effectiveness, a case study in the Upper Yangtze River Basin (UYRB) of China was conducted. The results suggest that the SWAT-based method is the best approach to quantify the influences of climate change and human activities on streamflow in the UYRB. The comprehensive assessment indicates that climate change is the dominant cause of streamflow changes in the UYRB, and the contribution of climate change, indirect human activities, and direct human activities to streamflow changes is about 7:1:2. The proposed framework is efficient and valuable in assisting researchers to find appropriate methods for attribution analysis of streamflow changes, which can help to understand the water cycle in changing environments.

Description: The time and annealing temperature for the film crystallization in perovskite solar cells (PSCs) is critical and is at the stake of device optimization. It governs the crystallization process, the film’s morphorlogy and texture and the level of non-radiative defects, which in whole control the power conversion efficiency (PCE). However, deciphering each of these parameters in the device cell characteristics remains not totally clear. In this work, we led a holistic study considering temperature and time for the MAPbI3 crystallization as a free parameter to study how the latter is impacting on the film’s characteristics and how the device figure of merit is affected. The results suggest that the crystallinity level of the grains plays an important role in the photo-current value whereas the morphology and PbI2 impurities resulting from the onset of thermal decomposition of MAPbI3 penalizes the cell photovoltage and the fill factor values. Based on this study, it is highlighted that flash high temperature annealing is beneficial to limit out-of-plane substrate grain boundaries, resulting in a device exhibiting 18.8% power conversion efficiency compared to 18.0% when more standard post-annealing procedure is employed.

Description: Antibiotics resistance genes (ARGs) are mainly caused by the extensive use and abuse of antibiotics and have become a global public health concern. Owing to the development of high-throughput sequencing, metagenomic sequencing has been widely applied to profile the composition of ARGs, investigate their distribution pattern, and track their sources in diverse environments. However, the lack of a detailed transmission mechanism of ARGs limits the management of its pollution. Hence, it’s essential to introduce how to utilize the metagenomic data to obtain an in-depth understanding of the distribution pattern and transmission of ARGs. This review provides an assessment of metagenomic data utilization in ARG studies and summarizes current bioinformatic tools and databases, including ARGs-OAP, ARG analyzer, DeepARG, CARD, and SARG, for profiling the composition of ARGs and tracking the source of ARGs. Several bioinformatic tools and databases were then benchmarked. Our results showed that although SARG is a good database, the application of two or more bioinformatic tools and databases could provide a comprehensive view of ARG profiles in diverse environmental samples. Finally, several perspectives were proposed for future studies to obtain an in-depth understanding of ARGs based on metagenomic data. Our review of the utilization of metagenomic data together with bioinformatic tools and databases in ARG studies could provide insights on exploring the profiles and transmission mechanism of ARG in different environments that mitigate the spread of ARGs and manage the ARGs pollution.

Description: The present paper discusses families of Interior Penalty Discontinuous Galerkin (IP) methods for solving heterogeneous and anisotropic diffusion problems. Specifically, we focus on distinctive schemes, namely the Hybridized-, Embedded-, and Weighted-IP schemes, leading to final matrixes of different sizes and sparsities. Both the Hybridized- and Embedded-IP schemes are eligible for static condensation, and their degrees of freedom are distributed on the mesh skeleton. In contrast, the unknowns are located inside the mesh elements for the Weighted-IP variant. For a given mesh, it is well-known that the number of degrees of freedom related to the standard Discontinuous Galerkin methods increases more rapidly than those of the skeletal approaches (Hybridized- and Embedded-IP). We then quantify the impact of the static condensation procedure on the computational performances of the different IP classes in terms of robustness, accuracy, and CPU time. To this aim, numerical experiments are investigated by considering strong heterogeneities and anisotropies. We analyze the fixed error tolerance versus the run time and mesh size to guide our performance criterion. We also outlined some relationships between these Interior Penalty schemes. Eventually, we confirm the superiority of the Hybridized- and Embedded-IP schemes, regardless of the mesh, the polynomial degree, and the physical properties (homogeneous, heterogeneous, and/or anisotropic).

Description: Small-scale natural gas liquefaction processes have several clear advantages, particularly in the exploitation of ‘unconventional’ natural gas (NG) from sources such as difficult-to-access and offshore gas fields. Moreover, conventional liquefaction processes have a number of disadvantages such as high energy consumption, large cooling loads required in the refrigeration cycle, and non-uniform matching of cold and hot flows in liquified natural gas (LNG) heat exchanger (HE). The main objective of this study was to optimize the most commonly used mixed refrigerant process. The liquefaction performance of the optimized process was analyzed and the influence of gas parameters on the power consumption, exergy loss, freezing mixture circulation, and cooling water load were investigated. The results show that compressor power consumption can be reduced by 29.8%, the cooling water load can be reduced by 21.3%, and the system exergy efficiency can be increased by 41% with the optimized process. Furthermore, throttling and compression of the freezing mixture were increased during the refrigeration stage. It can be concluded that reducing the feed gas temperature and increasing the feed gas pressure can reduce the total power consumption, exergy loss, freezing mixture circulation, and cooling water load, which can significantly improve liquefaction performance.

Description: In recent decades, degradation and loss of the world’s forest ecosystems have been key contributors to biodiversity loss and future climate change. This article analyzes plant diversity, biomass, carbon sequestration potential (CSP), and the net primary productivity (NPP) of four vegetation types viz., Dense mixed forest (DMF); Open mixed forest (OMF); Teak plantation (TP), and Sal mixed forest (SMF) in the dry tropical forest ecosystem of central India through remote sensing techniques together with physical ground observations during 2013–2018. The total C storage in trees varied from 16.02 to 47.15 Mg ha−1 in studied vegetation types with the highest in DMF and lowest in OMF. The total C storage in stem wood, branches, and foliage falls in the range of 52.93–78.30%, 9.49–22.99%, and 3.31–12.89% respectively. The total standing biomass varied from 83.77 to 111.21 Mg ha−1 and these variations are due to different vegetation types, with the highest in DMF followed by TP, SMF while the lowest was estimated in OMF. The net primary productivity (NPP) [aboveground (AG) + belowground (BG)] varied from 7.61 to 9.94 Mg ha−1 yr−1 with mean values of 8.74 Mg ha−1 yr−1 where AG shares a maximum contribution of 77.66%. The total biomass production was distributed from 64.09 to 82.91% in AG and 17.08–35.91% in BG components. The present study outlines that the studied forest ecosystem has the substantial potential of carbon sequestration and a great possibility of mitigating local and global climate change.

Description: The United Nations' Development Goals (SDGs) have been criticized but are nonetheless seen by many as an important, if imperfect, international effort to address climate and environmental change, resource depletion and the unsustainability of contemporary life. Many of the Goals need to be implemented at the local level, yet sub-national governments have not been granted any enhanced status at the UN to facilitate this process. As a result, the role and effectiveness of local governments in localizing the SDGs is dependent on multi-level arrangements within respective national contexts. In this paper we present findings on the challenges facing local authorities in England, namely co-dependent ambivalence, partial holism and narrow practices of knowledge governance. We draw on work carried out collaboratively with local authorities and other stakeholders in Greater Manchester and Sheffield, and a UK-wide national workshop. These challenges explain the relatively low uptake and engagement with the SDGs in the context of wider political and economic concerns compared with international comparator cities. Against this background our research found that making the Goals real, relevant, relatable and relational offered a tactical route to localization for English local government.

Description: Fossil fuel and aerosol emissions have played important roles on climate over the Indian subcontinent over the last century. As the world transitions toward decarbonization in the next few decades, emissions pathways could have major impacts on India’s climate and people. Pathways for future emissions are highly uncertain, particularly at present as countries recover from COVID-19. This paper explores a multimodel ensemble of Earth system models leveraging potential global emissions pathways following COVID-19 and the consequences for India’s summertime (June–July–August–September) climate in the near- and long-term. We investigate specifically scenarios which envisage a fossil-based recovery, a strong renewable-based recovery and a moderate scenario in between the two. We find that near-term climate changes are dominated by natural climate variability, and thus likely independent of the emissions pathway. By 2050, pathway-induced spatial patterns in the seasonally-aggregated precipitation become clearer with a slight drying in the fossil-based scenario and wetting in the strong renewable scenario. Additionally, extreme temperature and precipitation events in India are expected to increase in magnitude and frequency regardless of the emissions scenario, though the spatial patterns of these changes as well as the extent of the change are pathway dependent. This study provides an important discussion on the impacts of emissions recover pathways following COVID-19 on India, a nation which is likely to be particularly susceptible to climate change over the coming decades.

Description: Plant pest invasions cost billions of Euros each year in Europe. Prediction of likely places of pest introduction could greatly help focus efforts on prevention and control and thus reduce societal costs of pest invasions. Here, we test whether generic data-driven risk maps of pest introduction, valid for multiple species and produced by machine learning methods, could supplement the costly species-specific risk analyses currently conducted by governmental agencies. An elastic-net algorithm was trained on a dataset covering 243 invasive species to map risk of new introductions in Europe as a function of climate, soils, water, and anthropogenic factors. Results revealed that the BeNeLux states, Northern Italy, the Northern Balkans, and the United Kingdom, and areas around container ports such as Antwerp, London, Rijeka, and Saint Petersburg were at higher risk of introductions. Our analysis shows that machine learning can produce hotspot maps for pest introductions with a high predictive accuracy, but that systematically collected data on species’ presences and absences are required to further validate and improve these maps.

Description: Currently available water-energy-food (WEF) modelling frameworks to analyse cross-sectoral interactions often share one or more of the following gaps: (a) lack of integration between sectors, (b) coarse spatial representation, and (c) lack of reproducible methods of nexus assessment. In this paper, we present a novel clustering tool as an expansion to the Climate-Land-Energy-Water-Systems modelling framework used to quantify inter-sectoral linkages between water, energy, and food systems. The clustering tool uses Agglomerative Hierarchical clustering to aggregate spatial data related to the land and water sectors. Using clusters of aggregated data reconciles the need for a spatially resolved representation of the land-use and water sectors with the computational and data requirements to efficiently solve such a model. The aggregated clusters, combined together with energy system components, form an integrated resource planning structure. The modelling framework is underpinned by an open-source energy system modelling tool—OSeMOSYS—and uses publicly available data with global coverage. By doing so, the modelling framework allows for reproducible WEF nexus assessments. The approach is used to explore the inter-sectoral linkages between the energy, land-use, and water sectors of Viet Nam out to 2030. A validation of the clustering approach confirms that underlying trends actual crop yield data are preserved in the resultant clusters. Finally, changes in cultivated area of selected crops are observed and differences in levels of crop migration are identified.

Description: Agro-food systems require nutrient input from several sources to provide food products and food-related services. Many of the nutrients are lost to the environment during supply chains, potentially threatening human and ecosystem health. Countries therefore need to reduce their nutrient/nitrogen footprints. These footprints are importantly affected by links between sectors. However, existing assessments omit the links between sectors, especially between the agriculture, manufacturing, and energy sectors. We propose a novel approach called the nutrient-extended input–output (NutrIO) method to determine the nutrient footprint as a sum of direct and indirect inputs throughout the supply chains from different sources of nutrients. The NutrIO method is based on a nutrient-based material flow analysis linked to economic transactions. Applying this method, we estimated the nitrogen footprint of Japan in 2011 at 21.8 kg-N capita−1yr−1: 9.7 kg-N capita−1 yr−1 sourced from new nitrogen for agriculture and fisheries, 7.0 kg-N capita−1 yr−1 from recycled nitrogen as organic fertilizers, and 5.1 kg-N capita−1 yr−1 from industrial nitrogen for chemical industries other than fertilizers. A further annexed 55.4 kg-N capita−1 yr−1 of unintended nitrogen input was sourced from fossil fuels for energy production. The nitrogen intensity of the wheat and barley cultivation sector, at 1.50 kg-N per thousand Japanese yen (JPY) production, was much higher than that of the 0.12 kg-N per thousand JPY production for the rice cultivation sector. Industrial nitrogen accounted for 2%–7% of the nitrogen footprint of each major food-related sector. The NutrIO nitrogen footprint sourced from new nitrogen for agriculture and fisheries, at 8.6 kg-N capita−1 yr−1 for domestic final products, is comparable to the food nitrogen footprint calculated by other methods, at 8.5–10.5 kg-N capita−1 yr−1. The NutrIO method provides quantitative insights for all stakeholders of food consumption and production to improve the nutrient use efficiencies of agro-food supply chains.

Description: Urban water utilities are increasing their use of energy-intensive technologies such as desalination and long-distanced pumped transfers. Under pressure to reduce their energy-related carbon emissions to zero, water utilities have devised a variety of energy management strategies, including the purchase of renewable energy and self-generation of electricity using locally installed renewables. These strategies will incur different costs for the utility, whilst some may have implications for the reliability of water supply systems. Yet the trade-offs between costs, water security and energy sustainability remain unexplored. Here, we present a regional scale analysis to test competing energy strategies, mapping pathways to zero carbon energy and water security. Results from a case-study of the London water system show a balanced approach that allows for some energy self-generation, using biogas, solar and wind, while also purchasing green energy credits from the electricity supply grid can best navigate this trade-off. Balanced investment plans can accommodate energy-intensive water supply techniques such as long-distance transfers, desalination and effluent reuse while meeting energy targets. By becoming energy generators and also adopting more flexible arrangements for energy use, water utilities could become significant players in energy markets.

Description: Decades of agricultural intensification have led to elevated stream nitrogen (N) concentrations and eutrophication of inland and coastal waters. Despite widespread implementation of a range of strategies to reduce N export, expected improvements in water quality have not been observed. This lack of success has often been attributed to the existence of legacy N stores within the landscape. Here, we use the ELEMeNT-N model to quantify legacy accumulation and depletion dynamics over the last century (1930–2016) across 14 nested basins within the Grand River Watershed, a 6800 km2 agricultural watershed in the Lake Erie Basin. Model results reveal significant legacy N accumulation across the basin, ranging from 705 to 1071 kg ha−1, creating a checkered landscape of N legacies. The largest proportion (82%–96%) of this accumulated N is stored in soil organic N reservoirs, as biogeochemical legacy, and the remaining in groundwater, as hydrologic legacy. The fraction of N surplus accumulated in soil and groundwater is most strongly correlated with the calibrated watershed mean travel time µ, with the accumulation increasing with increases in µ. The mean travel time ranges from 5 to 34 years across the watersheds studied, and increases with increase in tile drainage, highlighting the strong control of anthropogenic management on legacy accumulation. Water quality improvement timescales were found to be heterogeneous across the watersheds, with greater legacies contributing to slower recovery.

Description: Amino-thiol bifunctional polysilsesquioxane/carbon nanotubes (PSQ/CNTs) magnetic composites were prepared by sol-gel method with two types of functional siloxanes coating on carboxyl CNTs simultaneously. The composites were served as efficient adsorbents for removing Hg(II) in aqueous solution and the adsorption properties were investigated systematically. The optimal pH of bifunctional composites for Hg(II) removal is at pH 4.5. The thermodynamic fitting curves are more consistent with the Langmuir model and the adsorption capacities of the bifunctional composites for Hg(II) varied from 1.63 to 1.94 mmol g−1 at 25°C according to the Langmuir model. The kinetics curves are more fitted to the pseudo-second-order model and the composites could selectively adsorb Hg(II) in a series of binary metal ions solution. The elution regeneration tests showed that the adsorption rate could still reach 78% after repeat cycle three times. It is expected that the bifunctional PSQ/CNTs magnetic composites can be potentially applied to remove low concentration Hg(II) from waste water.

Description: Citizen science and informed citizens have become fundamental in providing the first records and accounts about the expansion of numerous non-indigenous species. However, implementing a successful citizen science campaign can be expensive and particularly difficult for aquatic species. Here, we demonstrate how a low-cost citizen science campaign and its outreach plan in social and traditional media enabled to track the expansion of the Atlantic blue crab Callinectes sapidus Rathbun, 1896 along the coast of Algarve (southern Portugal, Europe). We describe the outreach strategy and a cost-benefit analysis of the first year of the citizen science campaign. Social media platforms allowed us to reach a significant number of citizens (over 31,500 clicks in Facebook publications), while traditional media gave national visibility to the citizen science campaign and biological invasions. In only 1 year, we documented the spread of the invasive Atlantic blue crab across the entire 140 km of the Algarve coast with 166 valid observations referring to 1747 specimens, submitted by 62 citizen scientists. We spent 0 € on the citizen science campaign, but considering the time invested in the campaign the cost would have summed up to 3,751 €, while the total minimum cost for one scientist to go to the field and retrieve the equivalent information would have exceeded 11,000 €. We used free online tools of communication to obtain the records about the Atlantic blue crab, instead of a dedicated web platform or mobile app, and handled social media accounts ourselves, which saved us at least 18,815 €. The citizen science campaign revealed that the Atlantic blue crab is unequivocally established in southern Portugal and that females appear to exhibit summer migrations to coastal areas to spawn as in the native area. Overall, our low-cost citizen science campaign effectively documented the rapid spread of a marine invasive species while providing some insights into its ecology. Our strategy can be easily replicated and implemented elsewhere in the world to tackle the ever-growing problem of biological invasions while increasing the scientific literacy of local populations.

Description: An estimated 90% of the Swan Coastal Plain (SCP) wetlands, located in southwestern Australia, have been lost because of infilling or drainage. This loss continues despite the well-known causes, which include nutrient enrichment; the invasion of exotic flora and fauna; loss of fringing vegetation; and altered hydrological regimes caused by groundwater abstraction; urbanization; and a drying climate. Further loss is expected with climate change exacerbating the undesirable ecosystem changes of remaining wetlands. In this study, we consider these wetlands as examples of social-ecological systems (SES) which are characterized by a close interaction of the ecosystem with the social system. We take the theory of resilient SES as a starting point to identify the adaptive capacity and resilience of the wetlands. We argue that resilience provides a useful framework to analyze adaptation processes and to identify appropriate policy responses. We explore incremental adjustments and transformative action and demonstrate that policy responses arise across multiple scales and levels of jurisdiction and institution. By applying the theoretical framework of resilience to the SCP wetlands, we identified (un)desired ecosystem states of wetlands (hydrology and ecology) through different set of policy actions. Our results show that current wetland management is inadequate to maintain the ecosystem's functioning. We recommend cross-jurisdictional collaboration and the use of conceptual eco-hydrological models to depict gradual ecological change and types of regime shifts (thresholds, hysteresis, and irreversible changes). The different adaption options inform decision-makers to adequately adapt wetland management practices when uncertainty in ecosystem responses exist. Empirical data on how multiple jurisdictions operate and decide could help to further support decision-making. With this research we aim to narrow the science-policy interface which depends on corresponding cross-jurisdictional and institutional responses to coordinate wetland management policies and actions.

Description: In the domain of globalization, the global value chain (GVC) is formed across diverse economies. Owing to the expansion of economic globalization and the resultant global trade liberalization, the geographical isolation of production and consumption has resulted in the transfer of polluting emissions from advanced economies to developing states. Developing economies, falling in the middle and low end of the GVC, have to strive hard to expand their production in the GVC, while simultaneously addressing the consequent environmental damage and attending to the core environmental concerns of sustainable growth, energy preservation and drastic cuts in CO2. This paper addresses how manufacturing subsidiaries can improve their low-carbon innovation ability and help to achieve a sustainable competitive advantage through the embedded GVC. Data were gathered employing interview and survey methods, 31 manufacturing companies and 56 enterprise groups across 16 Chinese provinces were selected as the case study. A partial least square structural equation modelling was adopted to analyse the responses. The results indicate that the embedded GVC significantly enhances low-carbon innovation capability and promotes a sustainable competitive advantage. This study proposes that companies should continuously identify high-quality resources from the GVC and discover ways of integrating internal and external carbon innovation resources to form innovation capabilities.

Description: Phytoplankton functional groups and their influence on water quality have been studied in various types of water bodies but have yet to be studied in agricultural irrigation ponds. Freshwater sources (e.g., lakes, rivers, and reservoirs) have been previously shown to exhibit high spatial and temporal variability in phytoplankton populations. Improvements in the monitoring of phytoplankton populations may be achieved if patterns of stable spatial variability can be found in the phytoplankton populations through time. The objective of this work was to determine if temporally stable spatial patterns in phytoplankton communities could be detected in agricultural irrigation ponds using a functional group approach. The study was performed at two working agricultural irrigation ponds located in Maryland, USA over two summer sampling campaigns in 2017 and 2018. Concentrations of four phytoplankton groups, along with sensor-based and fluorometer based water quality parameters were measured. Temporal stability was assessed using mean relative differences between measurements in each location and averaged measurements across ponds on each sampling date. Temporally stable spatial patterns of three phytoplankton functional groups were found for both ponds over the two sampling seasons. Both ponds had locations where specific phytoplankton functional group concentrations were consistently higher or lower than the pond's average concentration for each sampling date. Zones of consistently higher or lower than average concentrations were associated with flow conditions, pond morphology, and human activities. The existence of temporally stable patterns of phytoplankton functional group concentrations can affect the outcome of a water quality assessment and should be considered in water quality monitoring designs.

Description: The suitability of remediation systems using metallic iron (Fe0) has been extensively discussed during the past 3 decades. It has been established that aqueous Fe0 oxidative dissolution is not caused by the presence of any contaminant. Instead, the reductive transformation of contaminants is a consequence of Fe0 oxidation. Yet researchers are still maintaining that electrons from the metal body are involved in the process of contaminant reduction. According to the electron efficiency concept, electrons from Fe0 should be redistributed to: i) contaminants of concern (COCs), ii) natural reducing agents (e.g., H2O, O2), and/or iii) reducible co-contaminants (e.g. NO3-). The electron efficiency is defined as the fraction of electrons from Fe0 oxidation which is utilized for the reductive transformations of COCs. This concept is in frontal contradiction with the view that Fe0 is not directly involved in the process of contaminant reduction. This communication recalls the universality of the concept that reductive processes observed in remediation Fe0/H2O systems are mediated by primary (e.g., FeII, H/H2) and secondary (e.g., Fe3O4, green rusts) products of aqueous iron corrosion. The critical evaluation of the electron efficiency concept suggests that it should be abandoned. Instead, research efforts should be directed towards tackling the real challenges for the design of sustainable Fe0-based water treatment systems based on fundamental mechanisms of iron corrosion.

Description: This study develops an implementation framework for asset management strategic planning of water distribution networks to meet sustainable infrastructure, socio-political, and financial targets over the life cycle of the infrastructure. The proposed framework is comprised of three decision-making layers: (1) Visions and Values, (2) Function, and (3) Performance. The asset management strategy framework is implemented and validated by demonstrating functionality and value by using data from three water utilities in Canada. The Visions and Values layer is set to meet the needs of the water utilities' stakeholders. The Function layer uses an advanced system dynamics model to simulate and forecast the system's future behavior. The Performance layer benchmarks, compares, and graphically illustrates the situation and performance of water utilities against each other regardless of their size. Benchmarking results indicate that all three water utilities can sustainably meet the strategic targets established in the Visions and Values layer of the asset management strategy over the benchmarking period. The impact of the desired cash reserve on infrastructure and financial benchmarking performance indicators is also investigated to explore the “optimal” combination of allowable fee-hike and rehabilitation rates using the contour plots developed over the benchmarking period. The results indicate that the optimal combinations of allowable fee-hike of ~8% per year and rehabilitation rate of 1.3% per year along with a 1–4% cash reserve, depends on the network condition, will allow water utilities to have sufficient funds to meet their strategic targets. The performance modeling and simulation approach presented in this study represents a powerful tool for other utilities to develop optimal strategic and operational plans for their networks and thus better service to their stakeholders.

Description: Spent carbon anode (SCA) is a dangerous solid waste that is continuously discharged from the aluminum electrolysis industry and has a large number of valuable resources and a high risk of environmental pollution. Its safe disposal and resource utilization have become a resource and environmental problem that must be solved urgently. Current methods for SCA disposal include flotation, vacuum metallurgy, physical activation, roasting, bubbling fluidized bed combustion, alkali fusion, alkali leaching, and chemical leaching combined with high temperature graphitization. In this paper, the material composition, resource properties, and environmental risks of SCA are discussed. Working principle, treatment process, advantages and disadvantages of the above methods are also briefly described and compared. Results showed that flotation is the safest disposal and comprehensive utilization technology that is suitable for characteristics of SCA raw materials and has the most large-scale application potential. In addition, characteristics of SCA recovery products are correlated to the recycling of aluminum reduction cells. This technology can alleviate the shortage of high-quality petroleum coke resources in China’s carbon material industry and the high cost of raw materials in aluminum electrolysis industry.

Description: Choosing appropriate spatial priorities for protected areas (PAs) to conserve ecosystem services (ESs) and biodiversity is a challenge for decision-makers under limited land resources, especially when facing uncertain protection consequences or conflicting protection objectives. Attitudes toward risk will influence actions, which will, in turn, impact consequences. To understand how theoretical decision-maker attitudes towards risk impact protection effectiveness for biodiversity and ESs (e.g., water retention, soil retention, flood mitigation, water purification and carbon sequestration) and how this information can be integrated into effective PAs management, we examined Hainan Island as a case study. We used the ordered weighted averaging algorithm to assess the impact of attitude towards risk in PA management. Decision-makers’ attitude towards risk scenarios (from risk-averse and risk-taking) showed higher mean protection effectiveness (2.41–2.85) than existing PAs (2.37), indicating that there is still room for improvement in biodiversity and ESs conservation in existing PAs. In addition, among the seven examined risk scenarios, the higher risk aversion scenario showed the best outcome. In comparison to existing PAs, this scenario improved mean protection effectiveness (20.13%) as well as the protection effectiveness of water retention (24.84%), water purification (11.46%), flood mitigation (8.84%), soil retention (16.63%), carbon sequestration (5.31%), and biodiversity (12.84%). Thus, our research shows that the influence of theoretical decision-makers’ attitudes towards risk could be considered by OWA method which could provide a normative model of what the right choice given theoretical risk attitudes is while selecting priority area for biodiversity and ESs.

Description: Achieving an economy-wide net-zero greenhouse gas emissions goal by mid-century in the United States entails transforming the energy workforce. In this study, we focus on the influence of increased labor compensation and domestic manufacturing shares on (1) renewable energy technology costs, (2) the costs of transitioning the U.S. economy to net-zero emissions, and (3) labor outcomes, including total employment and wage benefits, associated with the deployment of utility-scale solar photovoltaics (PV) and land based and offshore wind power. We find that manufacturing and installation labor cost premiums as well as increases in domestic content shares across wind and utility-scale solar photovoltatic supply chains result in relatively modest increases in total capital and operating costs. These small increases in technology costs may be partially or fully offset by increases in labor productivity. We also show that solar and wind technology cost premiums associated with high road labor policies have a minimal effect on the pace and scale of renewable energy deployment and the total cost of transitioning to a net-zero emissions economy. Public policies such as tax credits, workforce development support, and other instruments can redistribute technology cost premiums associated with high road labor policies to support both firms and workers.

Description: Accurate prediction of the East Asian summer monsoon (EASM) is beneficial to billions of people’s production and lives. Here convolutional neural networks (CNN) and transfer learning are used for predicting the EASM. The results of the constructed CNN regression model show that the prediction of the CNN regression model is highly consistent with the reanalysis dataset, with correlation coefficient of 0.78, which is higher than that of each of the current state-of-the-art dynamic models. The heat map method indicates that the robust precursor signals in the CNN regression model agree well with previous theoretical studies, and can provide the quantitative contribution of different signals for EASM prediction. The CNN regression model can predict the EASM one year ahead with a confidence level above 95%. The above method can not only improve the prediction of the EASM but also help to identify the involved physical predictors.

Description: In recent years, Chinese cities have begun to pay attention to their rivers, and a large number of waterfront linear parks have been built in the riverside areas, so that the public can easily enjoy their landscape and entertainment functions. In this study, the visual quality of the waterfront trails and the greenbelt trails in the Waterfront linear Park around the Hunhe river in Shenyang was evaluated the basis of the Scenic Beauty Estimation Method and Semantic Differential Method, and the principal components of the landscape characteristics were extracted and a regression model of the visual quality and the landscape characteristics was established. Results show that the natural feature and the formal feature have a positive influence on the visual quality in waterfront linear parks, and the man-made feature has a negative impact on the visual quality. The six landscape characteristics are Sense of seclusion, ecology, intactness, uniqueness, unity and vitality, which are the main factors which affect the visual quality. This study puts forward improvement measures for the waterfront trails and the greenbelt trails, and the results can be applied to the planning, construction, and management of waterfront linear parks.

Description: This study conducted a systematic literature review of the technical aspects and methodological choices in life cycle assessment (LCA) studies of using hydrogen for road transport. More than 70 scientific papers published during 2000–2021 were reviewed, in which more than 350 case studies of use of hydrogen in the automotive sector were found. Only some studies used hybrid LCA and energetic input-output LCA, whereas most studies addressed attributional process-based LCA. A categorization based on the life cycle scope distinguished case studies that addressed the well-to-tank (WTT), well-to-wheel (WTW), and complete life cycle approaches. Furthermore, based on the hydrogen production process, these case studies were classified into four categories: thermochemical, electrochemical, thermal-electrochemical, and biochemical. Moreover, based on the hydrogen production site, the case studies were classified as centralized, on-site, and on-board. The fuel cell vehicle passenger car was the most commonly used vehicle. The functional unit for the WTT studies was mostly mass or energy, and vehicle distance for the WTW and complete life cycle studies. Global warming potential (GWP) and energy consumption were the most influential categories. Apart from the GREET (Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation) model and the Intergovernmental Panel on Climate Change for assessing the GWP, the Centrum voor Milieukunde Leiden method was most widely used in other impact categories. Most of the articles under review were comparative LCA studies on different hydrogen pathways and powertrains. The findings provide baseline data not only for large-scale applications, but also for improving the efficiency of hydrogen use in road transport.

Description: Cities are at the front line of combating environmental pollution and climate change, thus support from cities is crucial for successful enforcement of environmental policy. To mitigate environmental problems, China introduced at provincial level the Environmental Protection Tax Law in 2018. Yet the resulting economic burden on households in different cities with significantly different affluence levels remains unknown. The extent of the economic impacts is likely to affect cities’ support and public acceptability. This study quantifies the economic burden of urban households from taxation of fine particle pollution (PM2.5) for 200 cities nationwide from a “consumer” perspective, accounting for PM2.5 and precursor emissions along the national supply chain. Calculations are based on a Multi-Regional Input-Output (MRIO) analysis, the official tax calculation method and urban household consumption data from China’s statistical yearbooks. We find that the current taxation method intensifies economic inequality between cities nationally and within each province, with some of the richest cities having lower tax intensities than some of the poorest. This is due to the fact that taxes are collected based on tax rates of producing regions rather than consuming regions, that cities with very different affluence levels within a province bear the same tax rate, and that emission intensities in several less affluent cities are relatively high. If the tax could be levied based on tax rates of each city where the consumer lives, with tax rates determined based on cities’ affluence levels and with tax revenues used to support emission control, inter-city economic inequality could be reduced. Our work provides quantitative evidence to improve the environmental tax and can serve as the knowledge base for coordinated inter-city policy.

Description: While drinking water is known to create significant health risk in arsenic hazard areas, the role of exposure to arsenic through food intake is less well understood, including the impact of food trade. Using the best available datasets on crop production, irrigation, groundwater arsenic hazard, and international crop trade flows, we estimate that globally 17.2% of irrigated harvested area (or 45.2 million hectares) of 42 main crops are grown in arsenic hazard areas, contributing 19.7% of total irrigated crop production, or 418 million metric tons (MMT) per year of these crops by mass. Two-thirds of this area is dedicated to the major staple crops of rice, wheat, and maize (RWM) and produces 158 MMT per year of RWM, which is 8.0% of the total RWM production and 18% of irrigated production. More than 25% of RWM consumed in the South Asian countries of India, Pakistan, and Bangladesh, where both arsenic hazard and degree of groundwater irrigation are high, originate from arsenic hazard areas. Exposure to arsenic risk from crops also comes from international trade, with 10.6% of rice, 2.4% of wheat, and 4.1% of maize trade flows coming from production in hazard areas. Trade plays a critical role in redistributing risk, with the greatest exposure risk borne by countries with a high dependence on food imports, particularly in the Middle East and small island nations for which all arsenic risk in crops is imported. Intensifying climate variability and population growth may increase reliance on groundwater irrigation, including in arsenic hazard areas. Results show that RWM harvested area could increase by 54.1 million hectares (179% increase over current risk area), predominantly in South and Southeast Asia. This calls for the need to better understand the relative risk of arsenic exposure through food intake, considering the influence of growing trade and increased groundwater reliance for crop production.