Local Scale Research Articles

Life Cycle Assessment of Banned Single-Use Plastic Products and Their Alternatives

Plastic and microplastic contamination continue to be growing problems across the globe for both ecosystems and human health. Canada has banned single-use plastic products such as bags, cutlery, and foodservice ware (containers) to address and mitigate plastics and microplastic contamination. This study evaluates the life cycle of banned plastic products and their alternatives to determine whether environmental impacts can be mitigated. The environmental impacts of bags (plastic, paper, cotton), cutlery (plastic, wooden, biodegradable), and containers (plastic, styrofoam, biodegradable) were determined considering both domestic and imported products. The bag study saw paper bags having the highest environmental impacts and cotton bags with the lowest due to their reusability. For the cutlery study, plastic cutlery was the most impactful across all categories except for eutrophication and ozone depletion, where biodegradable cutlery was the most impactful by 25% and 35%, respectively. In the case of foodservice ware (containers), styrofoam was found to be the least impactful. Similar to cutlery, the plastic containers had the greatest impact except where the biodegradable container contributed more to ozone depletion and eutrophication by 25% and 45%, respectively. Local production reduced impacts across all categories. Furthermore, on a local scale, biodegradable cutlery had a greater impact on the smog and respiratory effects categories than plastic by 10% and 30%, respectively. The results of this study indicate that future regulations should focus on promoting and educating consumers on the use of reusable products over single-use products, funding research to mitigate challenges associated with waste management, and consider an informed ban on all single-use products and not just those made of plastic material to mitigate environmental impacts.

Cultivated Land Quality Assessment and Obstacle Factors Diagnosis in Changtu County, Northeast China

ABSTRACTEffective evaluation of cultivated land quality is crucial for sustainable agricultural management. Existing research often focuses on regional scales and lacks sufficient detailed analysis of spatial distribution and limiting factors at localized scales. This study aims to select the key indicators to evaluate cultivated land quality and analyze their influence on cultivated land quality at the county level. Taking Changtu County as the research area, principal component analysis (PCA) was employed to identify the most effective Minimum Data Set (MDS) for evaluation, including cultivated layer thickness, soil organic matter (SOM), pH, bulk density (BD), and cultivated layer texture. Additionally, an obstacle degree model was used to analyze restricting factors and their degrees of impact. Results showed that the constructed MDS could replace all indicators for cultivated land quality evaluation. Quality was higher in central areas and lower in eastern and western regions. SOM content decreases with the decrease of quality grade, significantly affecting the quality distribution, and pH in the medium category (Grades III and VI) cultivated lands were lower compared to other categories. Low organic matter content and low pH levels were the main obstacles affecting cultivated land quality, with average obstacle degrees of 43.5% and 29.3%, respectively. Low SOM content significantly affected land quality, particularly in the western region, whereas acidic soils in the eastern region influenced quality grade distribution. Thus, strategies for pH control and SOM enhancement are essential for improving cultivated land quality. This study provides valuable insights into sustainable agriculture.

Basque Pelota and Europeanization: The Actions and Representations of Actors Involved in Two Cross-Border Projects in a Euroregion

ABSTRACT This article investigates Europeanization from the perspective of actors involved in the organization of cross-border projects promoting an autochthonous sport. It studies two projects involving Basque pelota, a sport historically played on both sides of the France-Spain border. Both projects were launched by entrepreneurs, in the sociological sense, within the EGTC area of the Nouvelle-Aquitaine Euskadi Navarra Euroregion. The study identifies the characteristics of these local actors who have bet on their sport and on the cross-border concept. The intercultural experiences enabled by the projects and the increased frequency of consecutive sports exchanges have created a transactional space of Europeanization at the local scale desired by the EU but have not achieved the Europeanization expected from the Euroregion system. The transnational Basque dimension remains relevant, principally between France and Spain.

Environmental change and connectivity drive coral reef fish abundance in the Western Indian Ocean

Abstract Worldwide, coral reefs are facing risk from climate change. The Western Indian Ocean (WIO) harbours about 16% of global coral reefs with highly reef-dependent local communities. Coastal protection and food security depend on effective conservation management, which requires understanding species abundances. Here, we explore how fish group distribution and abundance across the WIO, categorized by their trophic function, are explained by oceanographic connectivity, sea surface temperature (SST), and chlorophyll a. We designed a proportional oceanographic connectivity metric describing the relative strength of connectivity between all WIO coral reefs and each survey site. We created statistical models for four trophic groups: grazers and detritivores, herbivorous excavators, corallivores, and primary piscivores across 51 sites in the WIO. We show that SST and chlorophyll a are strong predictors of all trophic fish groups and that the proportional oceanographic connectivity metric improved the model predictions significantly for grazers and detritivores and excavators. For excavators, peak abundances were predicted at medium connectivity, and for grazers and detritivores, at low and medium connectivity, suggesting that larvae dispersal predominates at a local scale. Decision making should include connectivity for efficient conservation area prioritization, for which our proportional oceanographic connectivity metric is a valid and useful parameter.

Examining Complexities of Artisanal and Small-Scale Gold Mining on Buru Island, Maluku Province, Indonesia

The Artisanal and Small-scale Gold Mining (ASGM) sector is presently one of the largest global sources of anthropogenic mercury emissions. The risk of mercury pollution from ASGM in Indonesia challenges other important industries including fisheries and tourism. Environmental degradation and risks to food safety and human health are major concerns that have been realised at local scales, including on Buru Island, Indonesia. Since the discovery of gold on Buru Island in 2011 the community has undergone dramatic changes. Some of these, such as rapid wealth accumulation, have been of benefit to people involved in the industry, however, they have been marred by negative social and economic consequences including rapid inflation, reduced rice production, and changes to the social fabric. We explore the Buru Island example through over 12 years of research interest and using empirical material through a socio-legal lens. Various legislative changes and government interventions have occurred since 2011 and there are complex interactions between industry players. Currently, the mining is low key with ore being transported to ‘back yard’ processing operations while a permitting system is anticipated. There is a legacy of land degradation and contamination as a result of the mining and ore processing. Alternatives to mercury are being considered but are challenged by uncertainty about product effectiveness, potential toxicity, and a lack of processing knowledge.

Conductivity scaling and absence of localization in disordered nodal-line semimetals

Conductivity scaling and absence of localization in disordered nodal-line semimetals

Global change drives potential niche contraction and range shift of globally threatened African vulture

Human-induced global change poses an increasingly severe threat to biodiversity, with species having limited population sizes being particularly vulnerable. Mapping and modeling the distribution ranges of such species, along with detecting potential range shifts and contractions at both local and regional scales, are essential for developing effective conservation plans. Ruppell's vulture Gyps rueppelli, an ecologically important bird species native to Africa, is experiencing a rapid decline in its range. The purpose of this study is to map and model potential regional spatio-temporal distribution of Ruppell's vulture in Africa, alongside detecting the possibility of the species' range shifts and contractions. A total of 804 rarefied localities were identified where the Ruppell's vulture was the dominant bird species. This study employed the Maximum Entropy (MaxEnt) algorithm to perform species distribution modeling for the Ruppell's vulture. The modeling considered current climate conditions (1970s-2000s) as a baseline, along with two future climate change scenarios (Shared Socioeconomic Pathways: SSPs 245 and 585) for two future time periods (2050s and 2070s). The model's performance was evaluated by optimizing settings and examining the Area under the Receiver Operating Characteristic Curve (AUC-ROC). Among the 13 bioclimatic and anthropogenic variables included in the model, four (isothermality, cropland expansion, anthropogenic biomes, and urban expansion (in order of importance)) emerged as the most influential drivers of Ruppell's vulture regional distribution. All considered Species distribution models (SDMs) achieved high predictive performance, with AUC-ROC values exceeding 0.9.The model predicted a total of approximately 19,453 ha of suitable habitat for Ruppell's vultures in Africa, with East Africa identified as the most prominent region under the current climate scenario. Isothermality (38.8%) was the primary factor influencing Ruppell's vulture distribution, followed by agricultural expansion (29.9%) and anthropogenic biomes (7.2%) in the face of global change. The results reveal considerable future habitat loss (up to 61%) for Ruppell's vultures in the study area, alongside an eastward range shift (longitudinal axis) by the 2050s under projected climate change scenarios. These imply that Ruppell's vultures face imminent population decline and range shift due to significant habitat loss and climate change. Hence, prioritizing the development and implementation of a coordinated conservation program that incorporates captive breeding and assisted migration is critical to save this vulture species in its native African range.

The Enactment of Sustainable Development Goals at a Local Scale Leads to Various Implications

The hallmark of global sustainable development efforts is the Millennium Development Goals (MDGs) initiative, so the SDGs were proposed after this. Early initiatives highlighted the need for a more comprehensive and globally applicable set of goals, thus laying the groundwork for the SDGs. Among them are sustainability, ecological environment protection and so on. This article explores the local implementation and impact of the United Nations Sustainable Development Goals (SDGs). The main questions are how to effectively implement SDGs policies in different social cultures, and the role of local government and community organizations is carefully analyzed. These are also areas where SDGs can be effectively promoted at the local level through muti-stakeholder collaboration, inclusive policies and innovative approaches. And there are three field examples to prove its effectiveness. For example, sustainable transport and energy management in Barcelona, participatory planning in Kerala and community conservation in Kenya. The significance of the study highlights the key role of local action in achieving the global Sustainable Development Goals, and provides concrete cases for analysis.

Explosion Detection Using Smartphones: Ensemble Learning with the Smartphone High-Explosive Audio Recordings Dataset and the ESC-50 Dataset.

Explosion monitoring is performed by infrasound and seismoacoustic sensor networks that are distributed globally, regionally, and locally. However, these networks are unevenly and sparsely distributed, especially at the local scale, as maintaining and deploying networks is costly. With increasing interest in smaller-yield explosions, the need for more dense networks has increased. To address this issue, we propose using smartphone sensors for explosion detection as they are cost-effective and easy to deploy. Although there are studies using smartphone sensors for explosion detection, the field is still in its infancy and new technologies need to be developed. We applied a machine learning model for explosion detection using smartphone microphones. The data used were from the Smartphone High-explosive Audio Recordings Dataset (SHAReD), a collection of 326 waveforms from 70 high-explosive (HE) events recorded on smartphones, and the ESC-50 dataset, a benchmarking dataset commonly used for environmental sound classification. Two machine learning models were trained and combined into an ensemble model for explosion detection. The resulting ensemble model classified audio signals as either "explosion", "ambient", or "other" with true positive rates (recall) greater than 96% for all three categories.

Unifying Coral Reef States Through Space and Time Reveals a Changing Ecosystem

ABSTRACTAimEcological state shifts that alter the structure and function of entire ecosystems are a concerning consequence of human impact. Yet, when, where and why discrete ecological states emerge remains difficult to predict and monitor, especially in high‐diversity systems. We sought to quantify state shifts and their drivers through space and time in the most ecologically complex marine ecosystem: tropical coral reefs.LocationWorldwide.Time Period1987–2019.Major Taxa StudiedCoral reef communities.MethodsUsing a global dataset of 3375 coral reef surveys, along with 13 time series datasets ranging between 1987 and 2019, we applied a novel double‐dichotomy approach to classify coral reefs into four simplified and discrete states based on the relative contributions of corals versus algae to benthic cover and small‐bodied versus large‐bodied fishes to fish standing stock. We then examined state shifts considering a range of spatial predictors and tested whether states have shifted directionally over time, and the nature of the most common transitions.ResultsWe show that geographic, environmental and anthropogenic context fundamentally shapes coral reef states at the local scale, which explains disparities among case studies, and stakes out critical baseline expectations for regional management efforts. We also reveal clear multi‐decadal state shifts on coral reefs: over time, systems dominated by reef‐building corals and small‐bodied, planktivorous fishes tend to have been replaced with reefs characterised by algae and larger‐bodied fishes.Main ConclusionsOur results suggest a previously unrecognised transition from systems that harness external subsidies through small‐bodied consumers associated with structurally complex live corals, to herbivore‐dominated systems with stronger bottom‐up dynamics. Overall, the partitioning of complex reef ecosystems into a small suite of discrete ecological states suggests that spatial context‐dependency, shifting baselines and changes in reef functioning are crucial considerations for coral reef management in the 21st century.

Comparison of various models for multi-scenario simulation of land use/land cover to predict ecosystem service value: A case study of Harbin-Changchun Urban Agglomeration, China

Understanding the intricate changes in land use and land cover (LULC) transformations, as well as accurately quantifying the ecosystem services value (ESV), holds paramount importance in achieving sustainable development goals. However, previous studies have notably neglected conducting empirical evaluations across various LULC prediction models under identical conditions within the same geographical region. Additionally, the majority of relevant studies primarily concentrate on local scales. In this study, we used CA-Markov, Future Land Use Simulation (FLUS) and Patch-generating Land Use Simulation (PLUS) models to simulate the dynamics of LULC, respectively. Subsequently, we successfully projected the future LULC patterns and corresponding ESV within the Harbin-Changchun Urban Agglomeration (HCUA), China. During the period spanning from 2000 to 2020, a persistent reduction of 2067 km2 in farmland was witnessed within the HCUA, while an accompanying expansion of 4081 km2 in built-up land occurred concurrently. The ESV in HCUA experienced a fluctuating trend. There was an initial decline of 7.443 × 109 yuan during the first decade, which was subsequently followed by an increase of 4.615 × 109 yuan during the latter decade. PLUS, FLUS, and CA-Markov models can all simulate the LULC of HCUA, but in decreasing order of accuracy and ease of use, with OA values of 0.8987, 0.8944 and 0.8651, and Kappa values of 0.8217, 0.8150 and 0.7860, respectively. The PLUS model was chosen to predict the future LULC. Then four development scenarios were set. Our optimization results indicate that it is advisable to restrict the area of built-up land within 17000 km2 in 2030. Moreover, under the economic and ecological balance (EEB) scenario, both ESV and economic benefits are projected to increase compared to the business as usual (BAU) scenario. This study offers valuable insights and serves as a significant reference for future research endeavors focused on optimizing land use.

Investigating the Spatial Pattern of White Oak (Quercus alba L.) Mortality Using Ripley’s K Function across the Ten States of the Eastern US

White oak mortality is a significant concern in forest ecosystems due to its impact on biodiversity and ecosystem functions. Understanding the factors influencing white oak mortality is crucial for effective forest management and conservation efforts. In this study, we aimed to investigate the spatial pattern of WOM rates across the eastern US and explore the underlying processes behind the observed spatial patterns. Multicycle forest inventory and analysis data were compiled to capture all white oak plots. WOM data were selected across plot systems that utilized declining basal areas between two periods. Ripley’s K function was used to study the spatial pattern of WOM rates. Results showed clustered patterns of WOM rates at local and broad scales that may indicate stand-level competition and regional variables affecting white oaks’ dynamics across southern and northern regions. Results also indicated random patterns at broad scales, suggesting variations in topographic and hydrological conditions across the south and northern regions. However, the central region indicated both clustered and random patterns at the local scale that might be associated with inter-species competition and the possibility of environmental heterogeneity, respectively. Furthermore, uniform patterns of WOM rate at a broad scale across all regions might suggest regions with spatially homogeneous environmental factors acting on the dynamics of white oaks. This research might be helpful in identifying impacted areas of white oaks at varying scales. Future research is needed to comprehensively assess biotic and abiotic factors at various spatial scales aimed at mitigating WOM.

Evaluation of GOCE/GRACE and combined global geopotential models using GNSS/levelling data over Nigeria

AbstractGlobal Geopotential Models (GGMs) provide valuable information about Earth’s gravity field functionals, such as geoid heights and gravity anomalies. However, ground-based datasets are required to validate these GGMs at the regional and local scales. In this study, we validated the accuracy of GGMs by comparing them with ground-based Global Navigation Satellite System (GNSS)/levelling data for the first time in Nigeria. We employed two validation scenarios: with and without considering spectral consistency using the spectral enhancement method (SEM) to incorporate high and very high frequencies of the gravity field spectrum from the combined global gravity field model (XGM2019e_2159) and the residual terrain model (RTM) derived from the Shuttle Radar Topography Mission (SRTM) data, respectively. The results of this evaluation confirmed that the application of SEM improved the assessment of the GGM solutions in an unbiased manner. Integrating XGM2019e_2159 and SRTM data to constrain the high-frequency component of geoid heights in Gravity Field and Steady-State Ocean Circulation Explorer (GOCE)-based GGMs led to an improvement of approximately 10% in reducing the standard deviation (STD) relative to when SEM was not applied. GO_CONS_GCF_2_TIM_R6 at spherical harmonics (SH) of up to degree and order 260 demonstrated the lowest STD when compared to GO_CONS_GCF_2_DIR_R6 and GO_CONS_GCF_2_SPW_R5, with a reduction from 0.380 m without SEM application to 0.342 m with SEM implementation. In addition, four transformation models, namely, linear, four-parameter, five-parameter, and seven-parameter models, were evaluated. The objective is to mitigate the reference system offsets between the GNSS/levelling data and the GGMs and to identify the particular parametric model with the smallest STD across all GGMs. This effort reduced the GGMs misfits to GNSS/levelling to 0.30 m, representing a 15.3% decrease in STD. Notably, the XGM2019e_2159 model provides this improvement.

The Single-Scattering Albedo of Black Carbon Aerosols in China

Black carbon (BC) aerosols have attracted wide attention over the world due to their significant climate effects on local and global scales. BC extinction aerosol optical thickness (AOT), scattering AOT, and single scattering albedo (SSA) over China are systematically studied based on the MERRA-2 satellite reanalysis data from 1983 to 2022 in terms of the spatial, yearly, seasonal, and monthly variations. The extinction and scattering AOTs of BC show similar spatial distribution, with high values in eastern and southern China, generally as opposed to BC SSA. A decrease in BC extinction and scattering AOTs has been documented over the last decade. The mean BC extinction AOT, scattering AOT, and SSA over China are 0.0054, 0.0014, and 0.26, respectively. The BC SSA showed small variations during 1983–2022, although a high BC extinction AOT and scattering AOT have been seen in the last two decades. During different decades, the seasonal patterns of BC extinction and scattering AOTs may differ, whereas the BC SSA shows seasonal consistency. Significant monthly variations in the BC SSA are seen over four decades, which are in agreement with their seasonal patterns. The mean BC extinction AOTs are 0.037, 0.033, 0.023, and 0.0054, whereas the average BC scattering AOTs are 0.0088, 0.0082, 0.0060, and 0.0014 in the Pearl River Delta (PRD), Yangtze River Delta (YRD), Beijing–Tianjin–Hebei (BTH) region, and Tarim Basin (TB), respectively. It is interesting to see that BC SSA values in the TB region are generally higher than those over the PRD, YRD and BTH areas, whereas the reverse is true for BC extinction and scattering AOTs. This study provides references for further research on black carbon aerosols and air pollution in China.

New Biorefinery Approach for the Valorization of Fruit Processing Waste at a Local Scale: Pomegranate Pomace as Case Study

AbstractThe processing of fruit and vegetables generates globally high amount of organic waste, which is suitable to be valorized because of the chemistry it encloses. Conventional treatment methods for waste biomass generate low value products and cause climate altering emissions. Small biorefineries are valid alternatives for the sustainable waste biomass conversion, but their feasibility is strictly related to the use of low-energy process, and the market positioning of the final product. The present work provides an innovative approach for the green conversion of vegetable waste into high value product, with the aim to encourage the deployment of biorefinery at a local scale. It involves the enzymatic disassimilation of plant cell wall into chemicals with specific functions, and their recombination in form of emulsion, as a product prototype for food and cosmetic sector. To explain the biorefinery model, we applied it to pomegranate pomace, the residue from juicing, for the recovery of oil, pectin and antioxidant molecules via enzyme assisted extraction. The process left behind 30% of the initial solid waste. Finally, the dispersion of pomegranate oil into pomegranate pectin solution as emulsifier, brought to a novel emulsion, 98.9% waste-derived, further functionalizable with pomegranate exocarp hydrolysate with high antioxidant capacity. Graphical Abstract

Enhancing local-scale groundwater quality predictions using advanced machine learning approaches

Assessing groundwater quality typically involves labor-intensive, time-consuming, and costly laboratory tests, making real-time monitoring impractical, especially at the local level. Groundwater quality projections at the local scale using broad spatial datasets have been inaccurate due to variations in hydrogeology, human activities, industrial operations, groundwater extraction, and waste disposal. This study aims to identify the most dependable and resilient machine learning algorithms for forecasting groundwater quality at nearby monitoring locations by utilizing simple water quality metrics that can be quickly assessed without extensive sampling and laboratory testing. The Entropy-weighted Water Quality Index (EWQI) was calculated using a large spatial and temporal dataset (2014–2021) of 977 wells with parameters including pH, total hardness (TH), calcium (Ca2⁺), magnesium (Mg2⁺), sodium (Na⁺), potassium (K⁺), sulfate (SO₄2⁻), chloride (Cl⁻), nitrate (NO₃⁻), total dissolved solids (TDS), and fluoride (F⁻). Further, similar parameters were also observed in 33 open wells at the three local monitoring sites from December 2022 to March 2023. The EWQI was predicted using a Random Forest (RF), eXtreme Gradient Boosting (XGB), and Deep Neural Network (DNN). The pH, TH, and TDS were used as input variables for EWQI predictions, as they can be easily measured using handheld probes or multi-parameters. The model performance was evaluated using R2, MAE, and RMSE. During the training stage, all three models predicted the EWQI with an R2 greater than 90%, with minimal errors when pH, TH, and TDS were input variables. To validate the models at a local scale, the EWQI was predicted at the village level (e.g., Antoli, Balapura, and Lapodiaya) using pH, TH, and TDS as input variables. The machine learning models were able to predict the EWQI very closely to the actual EWQI, with an R2 greater than 90%. It is also evident that the models could predict the EWQI using basic parameters that are easily measured, providing an overall idea of the water quality for a small area. Hence, these machine learning models could be useful for accurately representing groundwater quality, thereby avoiding the use of time-consuming and costly laboratory techniques.

A Multi-Scale Species Distribution Model for Migrating and Overwintering Western Monarch Butterflies: Climate Is the Best Predictor

Western Monarch butterflies (Danaus plexippus) migrate from inland breeding ranges to coastal overwintering grounds in California. Given that migratory individuals may make multi-scale habitat selection decisions, we considered a multi-scale species distribution model (SDM) using range-wide climatic and local landscape-level predictors of migratory and overwintering habitat and community-science presence data. The range-wide model output was included as a predictor in the local-scale model, generating multi-scale habitat suitability. The top range-wide predictor was the minimum temperature in December, contributing 83.7% to the model, and was positively associated with presence. At the local scale, the strongest predictors of presence were the range-wide output and percent coverage of low and medium levels of development, contributing > 95%, with 61–63% from the range-wide output, with local-scale suitability coinciding with the California coastal zones. Development’s positive association with overwintering monarch presence was counterintuitive. It is likely that our local-scale model is overfit to these development zones, but it is unclear whether this overfitting resulted from modeler choices, monarchs overwintering close to human development, biased detection near human development, or a combination of these factors. Therefore, alternative approaches to collecting local-scale attribute data are suggested while recognizing the primacy of climate in restricting overwinter sites.

The global distribution and drivers of wood density and their impact on forest carbon stocks.

The density of wood is a key indicator of the carbon investment strategies of trees, impacting productivity and carbon storage. Despite its importance, the global variation in wood density and its environmental controls remain poorly understood, preventing accurate predictions of global forest carbon stocks. Here we analyse information from 1.1 million forest inventory plots alongside wood density data from 10,703 tree species to create a spatially explicit understanding of the global wood density distribution and its drivers. Our findings reveal a pronounced latitudinal gradient, with wood in tropical forests being up to 30% denser than that in boreal forests. In both angiosperms and gymnosperms, hydrothermal conditions represented by annual mean temperature and soil moisture emerged as the primary factors influencing the variation in wood density globally. This indicates similar environmental filters and evolutionary adaptations among distinct plant groups, underscoring the essential role of abiotic factors in determining wood density in forest ecosystems. Additionally, our study highlights the prominent role of disturbance, such as human modification and fire risk, in influencing wood density at more local scales. Factoring in the spatial variation of wood density notably changes the estimates of forest carbon stocks, leading to differences of up to 21% within biomes. Therefore, our research contributes to a deeper understanding of terrestrial biomass distribution and how environmental changes and disturbances impact forest ecosystems.

Implementation of frozen density embedding in CP2K and OpenMolcas: CASSCF wavefunctions embedded in a Gaussian and plane wave DFT environment.

Most chemical processes happen at a local scale where only a subset of molecular orbitals is directly involved and only a subset of covalent bonds may be rearranged. To model such reactions, Density Functional Theory (DFT) is often inadequate, and the use of computationally more expensive correlated wavefunction (WF) methods is required for accurate results. Mixed-resolution approaches backed by quantum embedding theory have been used extensively to approach this imbalance. Based on the frozen density embedding freeze-and-thaw algorithm, we describe an approach to embed complete active space self-consistent field simulations run in the OpenMolcas code in a DFT environment calculated in CP2K without requiring any external tools. This makes it possible to study a local, active part of a chemical system in a larger and relatively static environment with a computational cost balanced between the accuracy of a WF method and the efficiency of DFT, which we test on environment-subsystem pairs. Finally, we apply the implementation to an oxygen molecule leaving an aluminum (111) surface and a ruthenium(IV) oxide (110) surface.

The first global catalogue of gas flaring sources derived from a multi-temporal time series of OLI and MSI daytime data: the DAFI v2 algorithm

Abstract Gas flaring (GF) is an industrial phenomenon requiring a special attention for the serious impacts on environment, climate and human health. To analyses and map GF sites (GFs), the Daytime Approach for GF Investigation (DAFI), which is based on daytime data from the Operational Land Imager (OLI) aboard the Landsat-8 satellite, has recently been developed. The GFs catalogue from the DAFI system, spanning over the years 2013–2021, represents the first worldwide GF product generated from mid-high spatial resolution data (30 m). The DAFI version 2 (v2), also including information from the Multispectral Instrument (MSI) aboard Sentinel-2 satellites, already showed an improved capacity in the mapping of GFs over Iran and Iraq. In this work, the new catalogue arising from the porting of DAFI v2 at global scale is presented and discussed. By using a parallel workflow, based on a loop tiling scheme running in Google Earth Engine, DAFI v2 allowed us to extend the GF inventory globally up to 2023, by analyzing ∼4 million of OLI/OLI-2 (Collection 2) and MSI imagery. Results of this study show: (i) an increased DAFI v2 capacity, of about 41%, in detecting GFs compared to the previous DAFI version; (ii) a downward trend (∼4.6%) in terms of GF sites detected from 2021 to 2023. These findings demonstrated that DAFI v2, which will also include the estimates of radiative power at different spatial scales, may contribute in assessing and monitoring the GFs activities and in evaluating the effectiveness of the greenhouse gas-emission reduction strategies at global, continental, national and local scales.