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The large number of products sold by the Bill Lights Store resulted in a stockpile of several product items due to the large supply of products that were less attractive to customers, resulting in many unsold and under-sold products. Bill Lights struggles with inventory levels of sold and unsold products, as well as shortages and overstocks. Bill Lights stores should rank each product so that they know which products are in the most demand. The purpose of this research is to solve the problem of using inventory information by grouping inventory products based on product characteristics using data mining techniques. The technique used is the K-Means algorithm method. K-Means algorithm clustering method and RapidMiner software processing. The data mining process starts with data processing (selection, cleaning, transformation, data mining and interpretation/evaluation). So if we start with a dataset of 160 products, we get cluster 0 with 88 products classified as sold, cluster 1 with 26 products classified as unsold, and cluster 2 with 46 fewer products classified as sold. The result of using the K-Means method is grouped into three clusters. To enable Bill Lights Store to implement sales and growth strategies based on products that are selling well.

Gully erosion is a worldwide threat with numerous environmental, social, and economic impacts. The purpose of this research is to evaluate the performance and robustness of six machine learning ensemble models based on the decision tree principle: Random Forest (RF), C5.0, XGBoost, treebag, Gradient Boosting Machines (GBMs) and Adaboost, in order to map and predict gully erosion-prone areas in a semi-arid mountain context. The first step was to prepare the inventory data, which consisted of 217 gully points. This database was then randomly subdivided into five percentages of Train/Test (50/50, 60/40, 70/30, 80/20, and 90/10) to assess the stability and robustness of the models. Furthermore, 17 geo-environmental variables were used as potential controlling factors, and several metrics were examined to evaluate the performance of the six models. The results revealed that all of the models used performed well in terms of predicting vulnerability to gully erosion. The C5.0 and RF models had the best prediction performance (AUC = 90.8 and AUC = 90.1, respectively). However, according to the random subdivisions of the database, these models exhibit small but noticeable instability, with high performance for the 80/20% and 70/30% subdivisions. This demonstrates the significance of database refining and the need to test various splitting data in order to ensure efficient and reliable output results. [ABSTRACT FROM AUTHOR]

Technological comparison and system modelling of sewage sludge treatment are important in terms of sustainable development and climate change mitigation. Dewatering and drying are important processes for reducing volume for transportation and often a requirement for further sludge treatment. Inventory data on mass transfers and material and energy consumptions are therefore crucial in improving and understanding sludge management systems. Reviewing the scientific literature (2003–2021) revealed 55 and 21 datasets on dewatering and drying of sewage sludge, respectively. The scarcity of data did not allow for identifying detailed relationships between inputs and outputs for the technologies, but the reviewed data can serve as the first port of call when planning sludge management. The average total solid (TS) content obtained was statistically different for mechanical dewatering (MDW), deep dewatering, bio-drying (BDR) and thermal drying (TDR). Loss of volatile solids (VS) during dewatering is barely described, but a substantial VS loss was observed for TDR (8%) and BDR (27%). The use of chemical agents in MDW showed typical values of 5–15 g kg−1 TS. The use of energy is low for MDW (average of 0.12 and 0.26 kWh kg−1 TS for raw and digested sludges, respectively) but substantially higher for TDR (average of 3.8 kWh kg−1 TS). The justified inventory data for sludge dewatering and drying provide essential support to system modelling and technological comparison in future studies, but additional data from full-scale plants on energy consumption and the composition of removed water are strongly requested to improve the inventory. [ABSTRACT FROM AUTHOR]

In recent years, the food industry has expended considerable effort to design novel products that replace animal proteins with legumes; however, the actual environmental benefits of such products are often not quantified. Here, we performed Life Cycle Assessments (LCA) to evaluate the environmental performance of four new fermented food products based on different mixtures of animal (cow milk) and plant (pea) protein sources (100% Pea, 75% Pea-25% Milk, 50% Pea-50% Milk, 25% Pea-75% Milk). The system perimeter encompassed all stages from agricultural production of the ingredients to the creation of the final ready-to-eat products. Impacts were calculated for all environmental indicators included in the EF 3.0 Method in SimaPro software based on a functional unit of 1 kg of ready-to-eat product. Life cycle inventories included all of the flows analyzed by the LCA (raw materials, energy, water, cleaning products, packaging, transport, waste). Foreground data were acquired directly on the manufacturing site; background data were taken from the Ecoinvent 3.6 database. The dataset contains details on the products, processes, equipment, and infrastructure considered; mass and energy flows; Life Cycle Inventories (LCI); and Life Cycle Impact Assessment (LCIA). These data improve our understanding of the environmental impact of plant-based alternatives to dairy products, which is currently poorly documented.

• Inventory data of biological treatment of sludge address all inputs and outputs. • Biogas production varies considerably in the 64 datasets on anaerobic digestion. • Very few data are available on digestate or energy in anaerobic digestion of sludge. • VS degradation in composting is hard to estimate due to the added bulking materials. • Data on emissions into the air are currently scarce for sludge composting processes. The biological treatment of municipal sewage sludge, including anaerobic digestion and composting, was reviewed with the purpose of establishing inventory data to address all the inputs and outputs related to sludge treatment. We identified 193 scientific papers, resulting in 64 datasets on anaerobic digestion and 35 datasets on composting. For anaerobic digestion, biogas production varied significantly (up to a factor of four) depending on the sludge. A useful correlation was identified between the amount of methane produced and the degradation of volatile solids. According to statistical tests, no significant differences were found in biogas production for mesophilic and thermophilic digesters. In addition, methane content varied significantly, and very few data were available for digestate composition or for energy consumption and recovery. For composting, accurate estimates relating to the degradation of sewage sludge could not be made, since organic bulking materials were part of the final composted product. Data on emissions to air are currently scarce, which points to the need for more published information. The inventory data evaluated herein are useful in the feasibility assessment of the biological treatment of sewage sludge, for comparing technologies, for example in LCA studies and as a basis for evaluating the performance of a specific biological sludge treatment plant. However, a great deal of the reviewed data originated from laboratory and pilot-scale studies, and so there is a need for more complete datasets on the performance of full-scale technologies, in order to establish full inventories and identify differences in technologies and operational conditions. [ABSTRACT FROM AUTHOR]

We evaluated three estimation methods for fitting the three-parameter Weibull distribution to even-aged European beech (Fagus sylvatica) forests by using experimental tree diameter data collected in 3709 sample plots (500 m² each). The maximum likelihood estimation method (MLE), the method of moments (MOM) and the method of modified moments type 1 (MM1 ) were applied for fitting the Weibull function. The goodness-of-fit of stand parameters (total tree number, stand basal area, dominant stand diameter and mean quadratic diameter) was tested by MAE and RMSE, and the probability and cumulative density functions of trees per 5 cm diameter classes were additionally tested by the Kolmogorov-Smirnov test and compared with Kolmogorov-Smirnov's D statistic. All three methods are suitable for estimating stand parameters based on the fitted Weibull function. Fitting the diameter distribution per 5 cm diameter classes at the plot level was less accurate due to the low number of trees or irregular diameter distribution of trees. The MM1 method was found to be the most suitable for fitting the three-parameter Weibull distribution to beech forests represented by data derived from small plots. [ABSTRACT FROM AUTHOR]

Copyright of VertigO is the property of La Revue Electronique en Sciences de l'Environnement VertigO and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Estimating the average environmental impacts of a representative crop in a specific region is a helpful starting point from which to propose improvements in the agricultural sector. However, data collection from official representative sources is complex, and often they require subsequent treatment to be transformed into meaningful inventory data. This article shows a comprehensive dataset for obtaining inventory data and developing an environmental life cycle impact assessment of representative agricultural production corresponding to reference holdings at a regional level (NUTS 2) in Spain. The dataset comprises Excel files with the data compiled from secondary sources to be used in the assessment and the R code scripts to transform the data into relevant inventory data to estimate the environmental impacts of the reference holdings. This dataset is a reliable tool for researchers and other potential users to be used as a secondary information source for further studies. It can also be used to estimate the environmental impacts of the farming activity of agri-food products in other regions or countries by collecting similar data for the specific region and adjusting the R code.

In the future, climate change is expected to affect the spatial distribution of most tree species in Europe. The European beech (Fagus sylvatica), a drought-sensitive tree species, is currently distributed throughout Europe, where it is an ecologically and economically important species. In Slovenia, the European beech represents 33% of the growing stock, but such a proportion greatly varies across Europe. Whether such a variation is related to the climate environmental gradients or because of historical or management decisions is an as-yet unexplored question. For this study, we employed the Slovenian Forests Service inventory, where the proportion of beech in the forest stock has been monitored in 341,341 forest stands across the country. Modeled climate data from the SLOCLIM database, calculated for each of the stands, was also used to test the hypothesis that although beech forests have always been influenced by human activity, the dominance of beech trees in forest stands is at least partially dictated by the climate. The results showed the distribution of the main climate variables (annual precipitation, the share of summer and spring precipitation, and annual maximum and minimum temperatures) and how they affect the current dominance of beech trees at the stand level. Due to the large number and variability of forest stands studied, the results should be transferable to better understand and manage the climatic suitability and risks of Fagus sylvatica. The modeled data is publicly available in the web repository Zenodo. [ABSTRACT FROM AUTHOR]

• 140 datasets are identified for incineration, gasification and pyrolysis of sludge. • Energy in incineration is almost balanced for dried sludge but not for dewatered one. • The outputs from sludge gasification vary significantly as an experimental technology. • Pyrolysis provides different amounts of outputs for dewatered and digested sludge. • Energy consumption data for pyrolysis and gasification are lacking in literature. The thermal treatment of sewage sludge has gained much interest in recent years, as exemplified by the 269 papers found in the scientific literature for the period 2010–2021. We identified 140 datasets in 57 papers presenting inventory data related to mass flows, energy and emissions for the incineration, gasification and pyrolysis of sewage sludge. Sewage sludge incineration (excess oxygen, 850–950 ℃) is an established technology; however, data on flue gas cleaning and air emissions are scarce. The recovery of energy is close to the amount of energy used for incinerating dried sludge (0.2 kWh/kg TS), while dewatered sludge incineration uses more energy (1–2 kWh/kg TS) than what can be recovered. Sewage sludge gasification (limited oxygen, 650–950 ℃) is an experimental technology with four outputs (kg/kg sludge TS): char 0.43, tar 0.02, fly ash 0.06 and syngas 0.53. The data vary significantly in this regard, suggesting than many factors affect the performance of the gasification process. Sewage sludge pyrolysis (no oxygen, 400–800 ℃) is an experimental technology with five outputs (kg/kg sludge TS): char 0.53, tar 0.21, water < 0.05, fly ash set to zero and syngas 0.21. The values are somewhat different for digested sludge. Energy consumption for the pyrolysis of sewage sludge cannot be estimated from the literature. The current literature provides useful data on the main flows of thermal technologies, although large variations are in evidence. However, data are limited on energy consumption and recovery in general, and they are scarce on direct emissions to the air from incineration. [ABSTRACT FROM AUTHOR]

Gully erosion is a worldwide threat with numerous environmental, social, and economic impacts. The purpose of this research is to evaluate the performance and robustness of six machine learning ensemble models based on the decision tree principle: Random Forest (RF), C5.0, XGBoost, treebag, Gradient Boosting Machines (GBMs) and Adaboost, in order to map and predict gully erosion-prone areas in a semi-arid mountain context. The first step was to prepare the inventory data, which consisted of 217 gully points. This database was then randomly subdivided into five percentages of Train/Test (50/50, 60/40, 70/30, 80/20, and 90/10) to assess the stability and robustness of the models. Furthermore, 17 geo-environmental variables were used as potential controlling factors, and several metrics were examined to evaluate the performance of the six models. The results revealed that all of the models used performed well in terms of predicting vulnerability to gully erosion. The C5.0 and RF models had the best prediction performance (AUC = 90.8 and AUC = 90.1, respectively). However, according to the random subdivisions of the database, these models exhibit small but noticeable instability, with high performance for the 80/20% and 70/30% subdivisions. This demonstrates the significance of database refining and the need to test various splitting data in order to ensure efficient and reliable output results.

Abstract Leather industry is facing new trends on production and consumption patterns due to society concerns. Circular economy is proposing a transition from the current economic model to a more sustainable one, in which waste is designed out and resources will be reused and recycled as long as possible. In this transition, Life Cycle Assessment (LCA) is an important tool to help decision-making. In the present review, 39 English-written peer-reviewed papers related to LCA and leather have been found, 30 of which were published in the last 6 years, meaning LCA is nowadays an important subject. Papers are presented within 4 types, focused on: 1) the whole leather production process, 2) a single step in the production process (e.g, new technologies for unhairing), 3) waste treatment and recycling, and 4) life cycle thinking with ideas on long-term strategies for leather industries. As discussed in the literature review, leather industry has important challenges to address: increasing sustainability and transparency on the supply chain, and strengthening the beauty of leather. Taking up these challenges from a life cycle perspective will help leather industry flourish in the coming future. Graphical abstract

Extreme droughts are expected to increase in frequency and severity in many regions of the world, threatening multiple ecosystem services provided by forests. Effective strategies to adapt forests to such droughts require comprehensive information on the effects and importance of the factors influencing forest resistance and resilience. We used a unique combination of inventory and dendrochronological data from a long‐term (>30 years) silvicultural experiment in mixed silver fir and Norway spruce mountain forests along a temperature and precipitation gradient in southwestern Germany. We aimed at examining the mechanisms and forest stand characteristics underpinning the resistance and resilience to past mild and severe droughts. We found that (i) fir benefited from mild droughts and showed higher resistance (i.e., lower growth loss during drought) and resilience (i.e., faster return to pre‐drought growth levels) than spruce to all droughts; (ii) species identity determined mild drought responses while species interactions and management‐related factors strongly influenced the responses to severe droughts; (iii) intraspecific and interspecific interactions had contrasting effects on the two species, with spruce being less resistant to severe droughts when exposed to interaction with fir and beech; (iv) higher values of residual stand basal area following thinning were associated with lower resistance and resilience to severe droughts; and (v) larger trees were resilient to mild drought events but highly vulnerable to severe droughts. Our study provides an analytical approach for examining the effects of different factors on individual tree‐ and stand‐level drought response. The forests investigated here were to a certain extent resilient to mild droughts, and even benefited from such conditions, but were strongly affected by severe droughts. Lastly, negative effects of severe droughts can be reduced through modifying species composition, tree size distribution and stand density in mixed silver fir‐Norway spruce forests. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Dalian University of Technology / Dalian Ligong Daxue Xuebao is the property of Journal of Dalian University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the future, climate change is expected to affect the spatial distribution of most tree species in Europe. The European beech (Fagus sylvatica), a drought-sensitive tree species, is currently distributed throughout Europe, where it is an ecologically and economically important species. In Slovenia, the European beech represents 33% of the growing stock, but such a proportion greatly varies across Europe. Whether such a variation is related to the climate environmental gradients or because of historical or management decisions is an as-yet unexplored question. For this study, we employed the Slovenian Forests Service inventory, where the proportion of beech in the forest stock has been monitored in 341,341 forest stands across the country. Modeled climate data from the SLOCLIM database, calculated for each of the stands, was also used to test the hypothesis that although beech forests have always been influenced by human activity, the dominance of beech trees in forest stands is at least partially dictated by the climate. The results showed the distribution of the main climate variables (annual precipitation, the share of summer and spring precipitation, and annual maximum and minimum temperatures) and how they affect the current dominance of beech trees at the stand level. Due to the large number and variability of forest stands studied, the results should be transferable to better understand and manage the climatic suitability and risks of Fagus sylvatica. The modeled data is publicly available in the web repository Zenodo.

Background and Purpose: Coppice forests have a particular socio-economic and ecological role in forestry and environmental management. Their production sustainability and spatial stability become imperative for forestry sector as well as for local and global communities. Recently, integrated forest inventory and remotely sensed data analysed with non-parametrical statistical methods have enabled more detailed insight into forest structural characteristics. The aim of this research was to estimate forest attributes of beech coppice forest stands in the Sarajevo Canton through the integration of inventory and Sentinel S2A satellite data using machine learning methods. Materials and Methods: Basal area, mean stand diameter, growing stock and total volume data were determined from the forest inventory designed for represented stands of coppice forests. Spectral data were collected from bands of Sentinel S2A satellite image, vegetation indices (difference, normalized difference and ratio vegetation index) and biophysical variables (fraction of absorbed photosynthetically active radiation, leaf area index, fraction of vegetation cover, chlorophyll content in the leaf and canopy water content). Machine learning rule-based M5 model tree (M5P) and random forest (RF) methods were used for forest attribute estimation. Predictor subset selection was based on wrapping assuming M5P and RF learning schemes. Models were developed on training data subsets (402 sample plots) and evaluations were performed on validation data subsets (207 sample plots). Performance of the models was evaluated by the percentage of the root mean squared error over the mean value (rRMSE) and the square of the correlation coefficient between the observed and estimated stand variables. Results and Conclusions: Predictor subset selection resulted in a varied number of predictors for forest attributes and methods with their larger contribution in RF (between 8 and 11). Spectral biophysical variables dominated in subsets. The RF resulted in smaller errors for training sets for all attributes than M5P, while both methods delivered very high errors for validation sets (rRMSE above 50%). The lowest rRMSE of 50% was obtained for stand basal area. The observed variability explained by the M5P and RF models in training subsets was about 30% and 95% respectively, but those values were lower in test subsets (below 12%) but still significant. Differences of the sample and modelled forest attribute means were not significant, while modelled variability for all forest attributes was significantly lower (p

Copyright of Geomatik is the property of Murat Yakar and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Leather industry is facing new trends on production and consumption patterns due to society concerns. Circular economy is proposing a transition from the current economic model to a more sustainable one, in which waste is designed out and resources will be reused and recycled as long as possible. In this transition, Life Cycle Assessment (LCA) is an important tool to help decision-making. In the present review, 39 English-written peer-reviewed papers related to LCA and leather have been found, 30 of which were published in the last 6 years, meaning LCA is nowadays an important subject. Papers are presented within 4 types, focused on: 1) the whole leather production process, 2) a single step in the production process (e.g, new technologies for unhairing), 3) waste treatment and recycling, and 4) life cycle thinking with ideas on long-term strategies for leather industries. As discussed in the literature review, leather industry has important challenges to address: increasing sustainability and transparency on the supply chain, and strengthening the beauty of leather. Taking up these challenges from a life cycle perspective will help leather industry flourish in the coming future. [ABSTRACT FROM AUTHOR]

Copyright of Tunnel Construction / Suidao Jianshe (Zhong-Yingwen Ban) is the property of Tunnel Construction Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Forestry Chronicle is the property of Canadian Institute of Forestry and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Forestry Chronicle is the property of Canadian Institute of Forestry and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Forestry Chronicle is the property of Canadian Institute of Forestry and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

It is challenging to measure the environmental impact of concrete with the absence of a consensus on a standardized methodology for life cycle assessment (LCA). Consequently, the values communicated in the literature for "green" concrete alternatives vary widely between 84 and 612 kg eq CO2/m3. This does not provide enough evidence regarding the acclaimed environmental benefits compared to ordinary Portland cement concrete knowing that the average for the latter was concluded in this study to be around 370 kg eq CO2/m3. Thus, the purpose of this study was to survey the literature on concrete LCAs in an attempt to identify the potential sources of discrepancies and propose a potential solution. This was done through examining 146 papers systematically and attributing the sources of error to the four stages of an LCA: scope definition, inventory data, impact assessment and results interpretations. The main findings showed that there are 13 main sources of discrepancies in a concrete LCA that contribute to the incompatibility between the results. These sources varied between (i) user-based choices such as depending on a cradle-to-gate scope, selecting a basic volume-based functional unit and ignoring the impact allocation and (ii) intrinsic uncertainty in some of the elements, such as the means of transportation, the expected service life and fluctuations in market prices. The former affects the reliability of a study, and hence, a concrete LCA methodology should not allow for any of the uncertainties. On the other hand, the latter affects the degree of uncertainty of the final outcome, and hence, we recommended conducting scenario analyses and communicating the aggregated uncertainty through the selected indicators. [ABSTRACT FROM AUTHOR]

Integration of FM‐process data in a virtual building model Building Information Modeling (BIM) is currently one of the most discussed terms in the construction and real estate industry. In Facility Management (FM), the use of BIM is also gaining in importance. The aim of an integrated BIM process is to create a digital building model where all necessary data of the entire life cycle are integrated and accessible for users. The Industry Foundation Classes (IFC), an open standard for improving the interoperability of planning processes in the construction and real estate industry, is the basis for data exchange. Other standards such as COBie and CAFM‐Connect are based on IFC. IFC, COBie as well as CAFM‐Connect enable the transfer of inventory data from the planning and construction phase to facility management systems. The process data and the historicization of the process data that arise in the context of facility management are currently of no importance in the prementioned standards. In order to obtain a complete digital virtual building model over the entire life cycle, it is therefore necessary to extend the IFC standard to include further property sets that contain process data. These property sets contain order data defined by the customer as well as data on status and consumption generated by FM. This historicization data can then not only be evaluated after it has been recorded, but it can also be transferred to other service providers within the framework of the digital virtual building model without media discontinuity. [ABSTRACT FROM AUTHOR]

• About 88% of the MI in Finnish wooden houses comes from concrete. • Construction method, GFA and footprint shape significantly affect MI. • The number of storeys in 1–1.5 storeys buildings does not significantly impact MI. • The variability in MI within each building typology should be accounted for. • Comparing of MI faces challenges due to a lack of harmonized data and methods. Improving resource efficiency in the building sector is a significant challenge, largely due to a lack of knowledge about material usage in buildings. Material intensity (MI) quantifies materials used in buildings, normalized by floor area or volume. MIs serve as indices for material stock and flow models and as an inventory approach for assessing the environmental impact of the built environment. Therefore, this study aimed to determine MIs of Finnish wooden residential houses built between 1940 and 2010 due to the dominance of them in residential building stock and their demolition rates. Factors influencing MI and cross-country comparisons were also explored because they had not been explored enough in the literature. Results showed construction method, time cohort, floor area, design choices and footprint shape impacted MI. Accounting for variability of MI was recommended, particularly when using it for material stock and flow analysis. Data and method disparities restrict cross-country comparison of MI. [Display omitted] [ABSTRACT FROM AUTHOR]

Background During an inventory of insect diversity on Anticosti Island in 1993, we caught unprecedented numbers of Neospondylis upiformis (Mannerheim), a longhorned beetle rarely observed in eastern North America. All specimens were caught using 12-funnel Lindgren traps baited with 95% ethanol and α-pinene. This longhorned beetle was captured again in 2007 on Anticosti with the same traps. Other than that, seven specimens of N. upiformis were caught elsewhere in Quebec between 1993 and 2015. Only 14 specimens were found in the 45 most important insect collections of the province, the most recent specimen dating back to 1964. New information At least 90% of the captures came from old-growth balsam fir stands of the south-central part of the island. Seasonal flight activity ranged from early June to late July, but adult captures peaked in early July. Results suggest that Anticosti Island might be a hot spot for N. upiformis in eastern North America, particularly in its south-central part where oldgrowth balsam fir forests still exist. [ABSTRACT FROM AUTHOR]

Numerous studies exist on the environmental impact of Li-Ion battery (LIB) production. Nevertheless, these studies use different impact assessment methods and different approaches for modelling key aspects like energy demand for cell manufacturing or the composition of the cell package. Since the outcomes of the studies are highly sensitive on these aspects, a direct comparison of the corresponding results is not possible. However, a robust comparative analysis would be of high interest for evaluating the actual environmental performance of different alternative battery chemistries. Based on a review of existing LCA studies on LIB production, the corresponding discrepancies in the modelling of these key aspects are pointed out and their impact on the outcomes of the underlying studies is highlighted. The existing primary life cycle inventory data (LCI) for the principle LIB chemistries are then recompiled and common average values implemented for the identified key parameters. In this way, the environmental impacts associated with the production of different battery chemistries are assessed on a common base. This provides an improved comparability between studies and allows for a tentative technology benchmarking of different Li-Ion battery chemistries. It can be observed that the different assumptions and modelling approaches for the mentioned key aspects can have a stronger impact on the final results than the battery chemistry itself. Especially the approach for modelling the cell manufacturing energy demand, but also for the electrode binder and the battery management system influence the results significantly. Thus, putting existing LCA studies on a common base is essential for battery technology benchmarking and avoids erroneous conclusions when comparing the environmental impacts associated with the production of different Li-Ion battery chemistries. [ABSTRACT FROM AUTHOR]