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Leaching dataset of dissolved organic carbon (DOC) and nitrogen (DON), nitrate (NO3+) and ammonium (NH4+) were collected from 6 cropping treatments (corn, switchgrass, miscanthus, native grass mix, restored prairie and poplar) established in the Bioenergy Cropping System Experiment (BCSE) which is a part of Great Lakes Bioenergy Research Center (www.glbrc.org) and Long Termn Ecological Research (LTER) program (www.lter.kbs.msu.edu). The site is located at the W.K. Kellogg Biological Station (42.3956° N, 85.3749° W and 288 m above sea level), 25 km from Kalamazoo in southwestern Michigan, USA. Prenart soil water samplers made of Teflon and silica (http://www.prenart.dk/soil-water-samplers/) were installed in blocks 1 and 2 of the BCSE (Fig. S1), and Eijkelkamp soil water samplers made of ceramic (http://www.eijkelkamp.com) were installed in blocks 3 and 4 (there were no soil water samplers in block 5). All samplers were installed at 1.2 m depth at a 45° angle from the soil surface, approximately 20 cm into the unconsolidated sand of the 2Bt2 and 2E/Bt horizons. Beginning in 2009, soil water was sampled at weekly to biweekly intervals during non-frozen periods (April to November) by applying 50 kPa of vacuum for 24 hours, during which water was collected in glass bottles. During the 2009 and 2010 sampling periods we obtained fewer soil water samples from blocks 1 and 2 where Prenart lysimeters were installed. We observed no consistent differences between the two sampler types in concentrations of the analytes reported here. Depending on the volume of leachate collected, water samples were filtered using either 0.45 µm pore size, 33-mm-dia. cellulose acetate membrane filters when volumes were <50 ml, or 0.45 µm, 47-mm-dia. Supor 450 membrane filters for larger volumes. Samples were analyzed for NO3-, NH4+, total dissolved nitrogen (TDN), and DOC. The NO3- concentration was determined using a Dionex ICS1000 ion chromatograph system with membrane suppression and conductivity detection; the detection limit of the system was 0.006 mg NO3--N L-1. The NH4+ concentration in the samples was determined using a Thermo Scientific (formerly Dionex) ICS1100 ion chromatograph system with membrane suppression and conductivity detection; the detection limit of the system was similar. The DOC and TDN concentrations were determined using a Shimadzu TOC-Vcph carbon analyzer with a total nitrogen module (TNM-1); the detection limit of the system was ~0.08 mg C L-1 and ~0.04 mg N L-1. DON concentrations were estimated as the difference between TDN and dissolved inorganic N (NO3- + NH4+) concentrations. The NH4+ concentrations were only measured in the 2013-2015 crop-years, but they were always small relative to NO3- and thus their inclusion or lack of it was inconsequential to the DON estimation. Leaching rates were estimated on a crop-year basis, defined as the period from planting or emergence of the crop in the year indicated through the ensuing year until the next year’s planting or emergence. For each sampling point, the concentration was linearly interpolated between sampling dates during non-freezing periods (April through November). The concentrations in the unsampled winter period (December through March) were also linearly interpolated based on the preceding November and subsequent April samples. Solute leaching (kg ha-1) was calculated by multiplying the daily solute concentration in pore-water (mg L -1) by the modeled daily drainage rates (m3 ha-1) from the overlying soil. The drainage rates were obtained using the SALUS (Systems Approach for Land Use Sustainability) model (Basso and Ritchie, 2015). SALUS simulates yield and environmental outcomes in response to weather, soil, management (planting dates, plant population, irrigation, nitrogen fertilizer application, tillage), and crop genetics. The SALUS water balance sub-model simulates surface run-off, saturated and unsaturated water flow, drainage, root water uptake, and evapotranspiration during growing and non-growing seasons (Basso and Ritchie, 2015). Drainage amounts and rates simulated by SALUS have been validated with measurements using large monolith lysimeters at a nearby site at KBS (Basso and Ritchie, 2005). On days when SALUS predicted no drainage, the leaching was assumed to be zero. The volume-weighted mean concentration for an entire crop-year was calculated as the sum of daily leaching (kg ha-1) divided by the sum of daily drainage rates (m3 ha-1). Weather data for the model were collected at the nearby KBS LTER meteorological station (lter.kbs.msu.edu). Leaching losses of dissolved organic carbon (DOC) and nitrogen (DON) from agricultural systems are important to water quality and carbon and nutrient balances but are rarely reported; the few available studies suggest linkages to litter production (DOC) and nitrogen fertilization (DON). In this study we examine the leaching of DOC, DON, NO3-, and NH4+ from no-till corn (maize) and perennial bioenergy crops (switchgrass, miscanthus, native grasses, restored prairie, and poplar) grown between 2009 and 2016 in a replicated field experiment in the upper Midwest U.S. Leaching was estimated from concentrations in soil water and modeled drainage (percolation) rates. DOC leaching rates (kg ha-1 yr-1) and volume-weighted mean concentrations (mg L-1) among cropping systems averaged 15.4 and 4.6, respectively; N fertilization had no effect and poplar lost the most DOC (21.8 and 6.9, respectively). DON leaching rates (kg ha-1 yr-1) and volume-weighted mean concentrations (mg L-1) under corn (the most heavily N-fertilized crop) averaged 4.5 and 1.0, respectively, which was higher than perennial grasses (mean: 1.5 and 0.5, respectively) and poplar (1.6 and 0.5, respectively). NO3- comprised the majority of total N leaching in all systems (59-92%). Average NO3- leaching (kg N ha-1 yr-1) under corn (35.3) was higher than perennial grasses (5.9) and poplar (7.2). NH4+ concentrations in soil water from all cropping systems were relatively low (<0.07 mg N L-1). Perennial crops leached more NO3- in the first few years after planting, and markedly less after. Among the fertilized crops, the leached N represented 14-38% of the added N over the study period; poplar lost the greatest proportion (38%) and corn was intermediate (23%). Requiring only one third or less of the N fertilization compared to corn, perennial bioenergy crops can substantially reduce N leaching and consequent movement into aquifers and surface waters. readme files are given that describe the data table

La planta media europea de residuos a energía (WtE) se define sobre la base del tratamiento de los residuos sólidos urbanos (MSW) medios europeos. El tratamiento térmico de una sola fracción de residuos como el papel o el plástico o incluso residuos específicos como la poliamida 6 no se realiza en realidad en una planta WtE para MSW. Los residuos se homogeneizan siempre para obtener un valor calorífico relativo constante y cumplir con las normas de emisión. No obstante, el modelo utilizado y los ajustes utilizados para el RMS medio permiten atribuir la carga ambiental (emisiones y también el consumo de recursos de los auxiliares) a la producción de energía, así como los créditos (exportación de chatarra de metal) a una sola fracción o residuos específicos incinerados dentro de un RMS medio.Por lo tanto, los datos de LCI son válidos para el tratamiento de los residuos específicos dentro de un RMS promedio (la proporción de la fracción de residuos del RMS se muestra en el gráfico circular de abajo, la composición elemental en el primer cuadro a continuación). La siguiente descripción de la tecnología explica los ajustes y la tecnología de la planta de WtE promedio utilizada para generar el conjunto de datos LCI. El valor calorífico neto y la composición elemental de la fracción de residuos o residuos específicos se muestran en los cuadros siguientes (véase la columna correspondiente en las tablas). El conjunto de datos cubre todos los pasos/tecnologías relevantes del proceso a lo largo de la cadena de suministro del inventario de la cuna a puerta representada con una buena calidad general de datos. El inventario se basa principalmente en datos de la industria y se completa, cuando sea necesario, con datos secundarios. Sinónimos: Residuos energéticos de plásticos (Nylon 6 GF 30, Nylon 66 GF 30) Finalidad técnica: Servicio estándar de tratamiento al final de su vida útil para una fracción de residuos específica mediante tratamiento térmico. Representación geográfica: UE-27 La planta media europea de residuos a energía (WtE) se define sobre la base del tratamiento de los residuos sólidos urbanos (MSW) medios europeos. El tratamiento térmico de una sola fracción de residuos como el papel o el plástico o incluso residuos específicos como la poliamida 6 no se realiza en realidad en una planta WtE para MSW. Los residuos se homogeneizan siempre para obtener un valor calorífico relativo constante y cumplir con las normas de emisión. No obstante, el modelo utilizado y los ajustes utilizados para el RMS medio permiten atribuir la carga ambiental (emisiones y también el consumo de recursos de los auxiliares) a la producción de energía, así como los créditos (exportación de chatarra de metal) a una sola fracción o residuos específicos incinerados dentro de un RMS medio. Por lo tanto, los datos de LCI son válidos para el tratamiento de los residuos específicos dentro de un RMS promedio (la proporción de la fracción de residuos del RMS se muestra en el gráfico circular de abajo, la composición elemental en el primer cuadro a continuación).La siguiente descripción de la tecnología explica los ajustes y la tecnología de la planta de WtE promedio utilizada para generar el conjunto de datos LCI. El valor calorífico neto y la composición elemental de la fracción de residuos o residuos específicos se muestran en los cuadros siguientes (véase la columna correspondiente en las tablas). El conjunto de datos cubre todos los pasos/tecnologías relevantes del proceso a lo largo de la cadena de suministro del inventario de la cuna a puerta representada con una buena calidad general de datos. El inventario se basa principalmente en datos de la industria y se completa, cuando sea necesario, con datos secundarios. Sinónimos: Residuos energéticos de plásticos (Nylon 6 GF 30, Nylon 66 GF 30) Finalidad técnica: Servicio estándar de tratamiento al final de su vida útil para una fracción de residuos específica mediante tratamiento térmico. Representación geográfica: UE-27 La planta media europea de residuos a energía (WtE) se define sobre la base del tratamiento de los residuos sólidos urbanos (MSW) medios europeos. El tratamiento térmico de una sola fracción de residuos como el papel o el plástico o incluso residuos específicos como la poliamida 6 no se realiza en realidad en una planta WtE para MSW. Los residuos se homogeneizan siempre para obtener un valor calorífico relativo constante y cumplir con las normas de emisión. No obstante, el modelo utilizado y los ajustes utilizados para el RMS medio permiten atribuir la carga ambiental (emisiones y también el consumo de recursos de los auxiliares) a la producción de energía, así como los créditos (exportación de chatarra de metal) a una sola fracción o residuos específicos incinerados dentro de un RMS medio.Por lo tanto, los datos de LCI son válidos para el tratamiento de los residuos específicos dentro de un RMS promedio (la proporción de la fracción de residuos del RMS se muestra en el gráfico circular de abajo, la composición elemental en el primer cuadro a continuación). La siguiente descripción de la tecnología explica los ajustes y la tecnología de la planta de WtE promedio utilizada para generar el conjunto de datos LCI. El valor calorífico neto y la composición elemental de la fracción de residuos o residuos específicos se muestran en los cuadros siguientes (véase la columna correspondiente en las tablas). El conjunto de datos cubre todos los pasos/tecnologías relevantes del proceso a lo largo de la cadena de suministro del inventario de la cuna a puerta representada con una buena calidad general de datos.El inventario se basa principalmente en datos de la industria y se completa, cuando sea necesario, con datos secundarios. Sinónimos: Residuos energéticos de plásticos (Nylon 6 GF 30, Nylon 66 GF 30) Finalidad técnica: Servicio estándar de tratamiento al final de su vida útil para una fracción de residuos específica mediante tratamiento térmico. Representación geográfica: UE-27 A central europeia média de resíduos para energia (WtE) é definida com base no tratamento da média europeia de resíduos sólidos urbanos (MSW). O tratamento térmico de uma única fração de resíduos, como papel ou plástico, ou mesmo resíduos específicos, como a poliamida 6, não é, na realidade, feito numa instalação WtE para RSU. Os resíduos são sempre homogeneizados para obter um poder calorífico constante relativo e para cumprir as normas de emissão. No entanto, o modelo utilizado e os parâmetros utilizados para os RSU médios permitem atribuir a carga ambiental (emissões e também o consumo de recursos dos auxiliares) à produção de energia, bem como os créditos (exportação de sucata metálica) a uma única fração ou a resíduos específicos incinerados dentro de um RSU médio. Por conseguinte, os dados do ICM são válidos para o tratamento dos resíduos específicos no âmbito de um RSU médio (a parte da fração de resíduos dos RSU é apresentada no gráfico de tartes abaixo, a composição elementar no primeiro quadro abaixo). A descrição tecnológica a seguir explica as configurações e a tecnologia da fábrica média de WtE utilizada para gerar o conjunto de dados do LCI. O poder calorífico inferior e a composição elementar da fração de resíduos ou dos resíduos específicos são apresentados nos quadros abaixo (ver coluna correspondente nos quadros). O conjunto de dados abrange todas as etapas/tecnologias relevantes do processo ao longo da cadeia de abastecimento do inventário do berço ao portão representado, com uma boa qualidade geral dos dados. O inventário baseia-se principalmente em dados da indústria e é completado, sempre que necessário, por dados secundários. Sinônimos: Resíduos energéticos de plásticos (Nylon 6 GF 30, Nylon 66 GF 30) Finalidade técnica: Serviço padrão de tratamento em fim de vida para uma fração de resíduos específica através de tratamento térmico. Representação geográfica:UE-27Os resíduos são sempre homogeneizados para obter um poder calorífico constante relativo e para cumprir as normas de emissão. No entanto, o modelo utilizado e os parâmetros utilizados para os RSU médios permitem atribuir a carga ambiental (emissões e também o consumo de recursos dos auxiliares) à produção de energia, bem como os créditos (exportação de sucata metálica) a uma única fração ou a resíduos específicos incinerados dentro de um RSU médio. Por conseguinte, os dados do ICM são válidos para o tratamento dos resíduos específicos no âmbito de um RSU médio (a parte da fração de resíduos dos RSU é apresentada no gráfico de tartes abaixo, a composição elementar no primeiro quadro abaixo). A descrição tecnológica a seguir explica as configurações e a tecnologia da fábrica média de WtE utilizada para gerar o conjunto de dados do LCI. O poder calorífico inferior e a composição elementar da fração de resíduos ou dos resíduos específicos são apresentados nos quadros abaixo (ver coluna correspondente nos quadros).O conjunto de dados abrange todas as etapas/tecnologias relevantes do processo ao longo da cadeia de abastecimento do inventário do berço ao portão representado, com uma boa qualidade geral dos dados. O inventário baseia-se principalmente em dados da indústria e é completado, sempre que necessário, por dados secundários. Sinônimos: Resíduos energéticos de plásticos (Nylon 6 GF 30, Nylon 66 GF 30) Finalidade técnica: Serviço padrão de tratamento em fim de vida para uma fração de resíduos específica através de tratamento térmico. Representação geográfica: UE-27 L'impianto medio europeo di Waste-to-Energy (WtE) è definito in base al trattamento dei rifiuti solidi urbani medi europei (MSW). Il trattamento termico di una singola frazione di scarto come carta o plastica o anche rifiuti specifici come la poliammide 6 non viene fatto in realtà in un impianto WtE per MSW. I rifiuti vengono sempre omogeneizzati per ottenere un relativo potere calorifico costante e per rispettare gli standard di emissione. Tuttavia, il modello utilizzato e le impostazioni utilizzate per la RMS media consentono di attribuire l'onere ambientale (emissioni e anche il consumo di risorse di energia ausiliari) nonché i crediti (esportazione di rottami metallici) a una singola frazione o a rifiuti specifici inceneriti all'interno di una RMS media. Pertanto i dati LCI sono validi per il trattamento dei rifiuti specifici all'interno di una MSW media (la quota di frazione di rifiuti del MSW è mostrata nel grafico a torta sottostante, la composizione elementare nella prima tabella sottostante). La seguente descrizione della tecnologia spiega le impostazioni e la tecnologia dell'impianto WtE medio utilizzato per generare il set di dati LCI. Il potere calorifico netto e la composizione elementare della frazione di rifiuto o dei rifiuti specifici sono riportati nelle tabelle sottostanti (cfr. colonna corrispondente nelle tabelle). Il set di dati copre tutte le fasi/tecnologie di processo rilevanti lungo la catena di approvvigionamento dell'inventario della culla per gate rappresentata con una buona qualità complessiva dei dati. L'inventario si basa principalmente sui dati del settore ed è completato, ove necessario, da dati secondari. Sinonimi: Rifiuti di materie plastiche (Nylon 6 GF 30, Nylon 66 GF 30) Scopo tecnico: Servizio di trattamento standard di fine vita per una frazione specifica di rifiuti tramite trattamento termico. Rappresentanza geografica:UE-27I rifiuti vengono sempre omogeneizzati per ottenere un relativo potere calorifico costante e per rispettare gli standard di emissione. Tuttavia, il modello utilizzato e le impostazioni utilizzate per la RMS media consentono di attribuire l'onere ambientale (emissioni e anche il consumo di risorse di energia ausiliari) nonché i crediti (esportazione di rottami metallici) a una singola frazione o a rifiuti specifici inceneriti all'interno di una RMS media. Pertanto i dati LCI sono validi per il trattamento dei rifiuti specifici all'interno di una MSW media (la quota di frazione di rifiuti del MSW è mostrata nel grafico a torta sottostante, la composizione elementare nella prima tabella sottostante). La seguente descrizione della tecnologia spiega le impostazioni e la tecnologia dell'impianto WtE medio utilizzato per generare il set di dati LCI. Il potere calorifico netto e la composizione elementare della frazione di rifiuto o dei rifiuti specifici sono riportati nelle tabelle sottostanti (cfr. colonna corrispondente nelle tabelle).Il set di dati copre tutte le fasi/tecnologie di processo rilevanti lungo la catena di approvvigionamento dell'inventario della culla per gate rappresentata con una buona qualità complessiva dei dati. L'inventario si basa principalmente sui dati del settore ed è completato, ove necessario, da dati secondari. Sinonimi: Rifiuti di materie plastiche (Nylon 6 GF 30, Nylon 66 GF 30) Scopo tecnico: Servizio di trattamento standard di fine vita per una frazione specifica di rifiuti tramite trattamento termico. Rappresentanza geografica: UE-27 Media europeană a deșeurilor în energie (WtE) este definită pe baza tratării deșeurilor municipale solide medii europene (MSW). Tratamentul termic al unei singure fracții de deșeuri, cum ar fi hârtia sau plasticul sau chiar deșeurile specifice, cum ar fi Polyamide 6, nu se realizează în realitate într-o instalație WtE pentru MSW. Deșeurile sunt întotdeauna omogenizate pentru a obține o putere calorifică relativ constantă și pentru a respecta standardele de emisie. Cu toate acestea, modelul utilizat și setările utilizate pentru MSW medii permit atribuirea sarcinii de mediu (emisii și, de asemenea, consumul de resurse al agenților auxiliari), precum și a creditelor (exportul deșeurilor metalice) unei singure fracții sau deșeurilor specifice incinerate în cadrul unui MSW mediu. Prin urmare, datele LCI sunt valabile pentru tratarea deșeurilor specifice într-un mediu MSW (partea fracției de deșeuri din MSW este prezentată în diagrama plăcintă de mai jos, compoziția elementară din primul tabel de mai jos). Următoarea descriere a tehnologiei explică setările și tehnologia uzinei medii WtE utilizate pentru generarea setului de date LCI. Puterea calorifică netă și compoziția elementară a fracțiunii de deșeuri sau a deșeurilor specifice sunt prezentate în tabelele de mai jos (a se vedea coloana corespunzătoare din tabele). Setul de date acoperă toate etapele/tehnologiile relevante ale procesului de-a lungul lanțului de aprovizionare al inventarului leagan în poarta reprezentată, cu o bună calitate generală a datelor. Inventarul se bazează în principal pe date din industrie și este completat, dacă este necesar, de date secundare. Sinonime: Deșeuri în energie ale materialelor plastice (Nylon 6 GF 30, Nylon 66 GF 30) Scop tehnic: Serviciul standard de tratare la sfârșitul ciclului de viață pentru o fracțiune specifică de deșeuri prin tratare termică. Reprezentarea geografică:UE-27Deșeurile sunt întotdeauna omogenizate pentru a obține o putere calorifică relativ constantă și pentru a respecta standardele de emisie. Cu toate acestea, modelul utilizat și setările utilizate pentru MSW medii permit atribuirea sarcinii de mediu (emisii și, de asemenea, consumul de resurse al agenților auxiliari), precum și a creditelor (exportul deșeurilor metalice) unei singure fracții sau deșeurilor specifice incinerate în cadrul unui MSW mediu. Prin urmare, datele LCI sunt valabile pentru tratarea deșeurilor specifice într-un mediu MSW (partea fracției de deșeuri din MSW este prezentată în diagrama plăcintă de mai jos, compoziția elementară din primul tabel de mai jos). Următoarea descriere a tehnologiei explică setările și tehnologia uzinei medii WtE utilizate pentru generarea setului de date LCI. Puterea calorifică netă și compoziția elementară a fracțiunii de deșeuri sau a deșeurilor specifice sunt prezentate în tabelele de mai jos (a se vedea coloana corespunzătoare din tabele).Setul de date acoperă toate etapele/tehnologiile relevante ale procesului de-a lungul lanțului de aprovizionare al inventarului leagan în poarta reprezentată, cu o bună calitate generală a datelor. Inventarul se bazează în principal pe date din industrie și este completat, dacă este necesar, de date secundare. Sinonime: Deșeuri în energie ale materialelor plastice (Nylon 6 GF 30, Nylon 66 GF 30) Scop tehnic: Serviciul standard de tratare la sfârșitul ciclului de viață pentru o fracțiune specifică de deșeuri prin tratare termică. Reprezentarea geografică: UE-27 Європейський середній завод з виробництва відходів (WtE) визначається на основі поводження з середньоєвропейськими твердими побутовими відходами (ТПВ). Термічна обробка однієї фракції відходів, таких як папір, пластик або навіть специфічні відходи, такі як поліамід 6, насправді не проводиться на заводі WtE для ТПВ. Відходи завжди гомогенізуються, щоб отримати відносну постійну теплотворну цінність і відповідати стандартам викидів.Тим не менш, використовувана модель і використовувані параметри для середньої ТПВ дозволяють віднести екологічне навантаження (викиди, а також споживання ресурсів допоміжних засобів) виробництва енергії, а також кредити (експорт металобрухту) до однієї фракції або конкретних відходів, спалених в межах середньої ТПВ. Тому дані LCI дійсні для обробки конкретних відходів в межах середнього ТПВ (частка відходів ТПВ показана в круговій діаграмі нижче, елементарної композиції в першій таблиці нижче). Наступний опис технології пояснює налаштування та технологію середнього заводу WtE, який використовується для створення набору даних LCI. Чиста теплотворна цінність і елементарний склад фракції відходів або конкретних відходів показані в таблицях нижче (див. відповідну колонку в таблицях). Набір даних охоплює всі відповідні етапи процесу / технології в ланцюжку поставок представленої колиски до інвентаризації воріт з хорошою загальною якістю даних. Інвентаризація в основному базується на галузевих даних і завершується, коли це необхідно, вторинними даними. Синоніми: Відходи до енергії пластмас (Нейлон 6 GF 30, нейлон 66 GF 30) Технічне призначення: Стандартний термін служби обробки для конкретної фракції відходів шляхом термічної обробки. Географічне представництво: ЄС-27 Європейський середній завод з виробництва відходів (WtE) визначається на основі поводження з середньоєвропейськими твердими побутовими відходами (ТПВ). Термічна обробка однієї фракції відходів, таких як папір, пластик або навіть специфічні відходи, такі як поліамід 6, насправді не проводиться на заводі WtE для ТПВ.Відходи завжди гомогенізуються, щоб отримати відносну постійну теплотворну цінність і відповідати стандартам викидів. Тим не менш, використовувана модель і використовувані параметри для середньої ТПВ дозволяють віднести екологічне навантаження (викиди, а також споживання ресурсів допоміжних засобів) виробництва енергії, а також кредити (експорт металобрухту) до однієї фракції або конкретних відходів, спалених в межах середньої ТПВ. Тому дані LCI дійсні для обробки конкретних відходів в межах середнього ТПВ (частка відходів ТПВ показана в круговій діаграмі нижче, елементарної композиції в першій таблиці нижче). Наступний опис технології пояснює налаштування та технологію середнього заводу WtE, який використовується для створення набору даних LCI. Чиста теплотворна цінність і елементарний склад фракції відходів або конкретних відходів показані в таблицях нижче (див. відповідну колонку в таблицях). Набір даних охоплює всі відповідні етапи процесу / технології в ланцюжку поставок представленої колиски до інвентаризації воріт з хорошою загальною якістю даних.Інвентаризація в основному базується на галузевих даних і завершується, коли це необхідно, вторинними даними. Синоніми: Відходи до енергії пластмас (Нейлон 6 GF 30, нейлон 66 GF 30) Технічне призначення: Стандартний термін служби обробки для конкретної фракції відходів шляхом термічної обробки. Географічне представництво: ЄС-27 Den genomsnittliga europeiska avfalls-till-energianläggningen (WtE) definieras på grundval av behandlingen av genomsnittligt kommunalt fast avfall i Europa. Termisk behandling av en enda avfallsfraktion som papper eller plast eller till och med specifikt avfall som Polyamid 6 sker inte i verkligheten i en WtE-anläggning för hushållsavfall. Avfallet är alltid homogeniserat för att uppnå ett relativt konstant värmevärde och uppfylla utsläppsnormerna. Den använda modellen och de använda inställningarna för det genomsnittliga maskinavfallet gör det dock möjligt att tillskriva energiproduktionen (utsläpp och resursförbrukning) energiproduktion samt krediter (export av metallskrot) till en enda fraktion eller specifikt avfall som förbränns inom ett genomsnittligt kommunalt avfall. LCI-uppgifterna är därför giltiga för behandling av det specifika avfallet inom ett genomsnittligt kommunalt avfall (avfallsfraktionens andel av kommunalt avfall visas i cirkeldiagrammet nedan, den elementära sammansättningen i den första tabellen nedan). Följande teknikbeskrivning förklarar inställningarna och tekniken för den genomsnittliga WtE-anläggningen som används för att generera LCI-datauppsättningen. Nettovärmevärdet och den elementära sammansättningen av avfallsfraktionen eller det specifika avfallet visas i tabellerna nedan (se motsvarande kolumn i tabellerna). Datauppsättningen omfattar alla relevanta processsteg/tekniker över leveranskedjan för den representerade vaggan till grindinventering med en god övergripande datakvalitet. Inventeringen baseras huvudsakligen på branschdata och kompletteras vid behov med sekundärdata. Synonymer: Avfall till energi från plast (Nylon 6 GF 30, Nylon 66 GF 30) Tekniskt syfte: Standardbehandlingstjänst för uttjänta produkter för en specifik avfallsfraktion genom termisk behandling. Geografisk representation:EU-27Avfallet är alltid homogeniserat för att uppnå ett relativt konstant värmevärde och uppfylla utsläppsnormerna. Den använda modellen och de använda inställningarna för det genomsnittliga maskinavfallet gör det dock möjligt att tillskriva energiproduktionen (utsläpp och resursförbrukning) energiproduktion samt krediter (export av metallskrot) till en enda fraktion eller specifikt avfall som förbränns inom ett genomsnittligt kommunalt avfall. LCI-uppgifterna är därför giltiga för behandling av det specifika avfallet inom ett genomsnittligt kommunalt avfall (avfallsfraktionens andel av kommunalt avfall visas i cirkeldiagrammet nedan, den elementära sammansättningen i den första tabellen nedan). Följande teknikbeskrivning förklarar inställningarna och tekniken för den genomsnittliga WtE-anläggningen som används för att generera LCI-datauppsättningen. Nettovärmevärdet och den elementära sammansättningen av avfallsfraktionen eller det specifika avfallet visas i tabellerna nedan (se motsvarande kolumn i tabellerna).Datauppsättningen omfattar alla relevanta processsteg/tekniker över leveranskedjan för den representerade vaggan till grindinventering med en god övergripande datakvalitet. Inventeringen baseras huvudsakligen på branschdata och kompletteras vid behov med sekundärdata. Synonymer: Avfall till energi från plast (Nylon 6 GF 30, Nylon 66 GF 30) Tekniskt syfte: Standardbehandlingstjänst för uttjänta produkter för en specifik avfallsfraktion genom termisk behandling. Geografisk representation: EU-27 Eiropas vidējo atkritumu pārvēršanas elektrostaciju (WtE) nosaka, pamatojoties uz Eiropas vidējo cieto sadzīves atkritumu (MSW) apstrādi. Vienas atkritumu frakcijas, piemēram, papīra vai plastmasas, vai pat specifisku atkritumu, piemēram, poliamīda 6, termiskā apstrāde faktiski netiek veikta WtE rūpnīcā CSA vajadzībām. Atkritumus vienmēr homogenizē, lai iegūtu relatīvi nemainīgu siltumspēju un atbilstu emisiju standartiem.Tomēr vidējais CSA izmantotais modelis un izmantotie iestatījumi ļauj attiecināt vides slogu (palīgierīču emisijas un arī resursu patēriņu) enerģijas ražošanu, kā arī kredītus (metālu lūžņu eksportu) vienai frakcijai vai konkrētiem atkritumiem, kas sadedzināti vidējos CSA. Tāpēc DII dati ir derīgi, lai apstrādātu konkrētos atkritumus vidējā CSA (atkritumu frakcijas daļa ir norādīta zem pīrāga diagrammas, pirmās tabulas elementārais sastāvs). Tālāk sniegtais tehnoloģiju apraksts izskaidro vidējās WtE rūpnīcas iestatījumus un tehnoloģiju, ko izmanto DII datu kopas ģenerēšanai. Atkritumu frakcijas vai īpašo atkritumu zemākā siltumspēja un elementārais sastāvs ir parādīts tabulās zem (sk. attiecīgo sleju tabulās). Datu kopa aptver visus attiecīgos procesa posmus/tehnoloģijas visā pārstāvētā “no šūpuļa līdz vārtiem” krājumu piegādes ķēdē ar labu vispārējo datu kvalitāti. Inventarizācija galvenokārt balstās uz nozares datiem, un vajadzības gadījumā to papildina ar sekundāriem datiem. Sinonīmi: Plastmasas atkritumi enerģijā (Nylon 6 GF 30, Nylon 66 GF 30) Tehniskais mērķis: Standarta poligona beigu apstrādes pakalpojums konkrētai atkritumu frakcijai, izmantojot termisko apstrādi. Ģeogrāfiskā pārstāvība: ES-27 Eiropas vidējo atkritumu pārvēršanas elektrostaciju (WtE) nosaka, pamatojoties uz Eiropas vidējo cieto sadzīves atkritumu (MSW) apstrādi. Vienas atkritumu frakcijas, piemēram, papīra vai plastmasas, vai pat specifisku atkritumu, piemēram, poliamīda 6, termiskā apstrāde faktiski netiek veikta WtE rūpnīcā CSA vajadzībām.Atkritumus vienmēr homogenizē, lai iegūtu relatīvi nemainīgu siltumspēju un atbilstu emisiju standartiem. Tomēr vidējais CSA izmantotais modelis un izmantotie iestatījumi ļauj attiecināt vides slogu (palīgierīču emisijas un arī resursu patēriņu) enerģijas ražošanu, kā arī kredītus (metālu lūžņu eksportu) vienai frakcijai vai konkrētiem atkritumiem, kas sadedzināti vidējos CSA. Tāpēc DII dati ir derīgi, lai apstrādātu konkrētos atkritumus vidējā CSA (atkritumu frakcijas daļa ir norādīta zem pīrāga diagrammas, pirmās tabulas elementārais sastāvs). Tālāk sniegtais tehnoloģiju apraksts izskaidro vidējās WtE rūpnīcas iestatījumus un tehnoloģiju, ko izmanto DII datu kopas ģenerēšanai. Atkritumu frakcijas vai īpašo atkritumu zemākā siltumspēja un elementārais sastāvs ir parādīts tabulās zem (sk. attiecīgo sleju tabulās). Datu kopa aptver visus attiecīgos procesa posmus/tehnoloģijas visā pārstāvētā “no šūpuļa līdz vārtiem” krājumu piegādes ķēdē ar labu vispārējo datu kvalitāti.Inventarizācija galvenokārt balstās uz nozares datiem, un vajadzības gadījumā to papildina ar sekundāriem datiem. Sinonīmi: Plastmasas atkritumi enerģijā (Nylon 6 GF 30, Nylon 66 GF 30) Tehniskais mērķis: Standarta poligona beigu apstrādes pakalpojums konkrētai atkritumu frakcijai, izmantojot termisko apstrādi. Ģeogrāfiskā pārstāvība: ES-27 Evropski povprečni obrat za odpadno energijo (WtE) je opredeljen na podlagi obdelave povprečnih evropskih komunalnih trdnih odpadkov. Toplotna obdelava posamezne frakcije odpadkov, kot sta papir ali plastika ali celo posebni odpadki, kot je poliamid 6, se v obratu za komunalne odpadke dejansko ne izvaja. Odpadki se vedno homogenizirajo, da se dobi relativna konstantna kalorična vrednost in da se upoštevajo emisijski standardi. Kljub temu uporabljeni model in uporabljene nastavitve za povprečne komunalne odpadke omogočajo, da se okoljska obremenitev (emisije in tudi poraba virov pomožnih pripomočkov) kot tudi dobropisi (izvoz odpadnih kovin) pripišejo eni sami frakciji ali posebnim odpadkom, ki se sežigajo v povprečnih komunalnih odpadkih. Zato podatki ISD veljajo za obdelavo določenih odpadkov v okviru povprečnega komunalnega komunalnega odpadkov (delež frakcij odpadkov v komunalnih odpadkih je prikazan v spodnjem tortnem diagramu, osnovna sestava v prvi tabeli spodaj). V naslednjem opisu tehnologije so pojasnjene nastavitve in tehnologija povprečne tovarne WtE, ki se uporablja za ustvarjanje nabora podatkov ISD. Neto kalorična vrednost in osnovna sestava frakcije odpadkov ali posebnih odpadkov sta prikazani v spodnjih tabelah (glej ustrezni stolpec v tabelah). Podatkovni niz zajema vse ustrezne korake/tehnologije postopka v dobavni verigi zastopane zibelke do inventarja z dobro splošno kakovostjo podatkov. Popis temelji predvsem na podatkih industrije in je po potrebi dopolnjen s sekundarnimi podatki. Sopomenke: Pridobivanje energije iz plastike (Nylon 6 GF 30, najlon 66 GF 30) Tehnični namen: Standardna storitev obdelave ob koncu življenjske dobe za določeno frakcijo odpadkov s toplotno obdelavo. Geografska zastopanost:EU-27Odpadki se vedno homogenizirajo, da se dobi relativna konstantna kalorična vrednost in da se upoštevajo emisijski standardi. Kljub temu uporabljeni model in uporabljene nastavitve za povprečne komunalne odpadke omogočajo, da se okoljska obremenitev (emisije in tudi poraba virov pomožnih pripomočkov) kot tudi dobropisi (izvoz odpadnih kovin) pripišejo eni sami frakciji ali posebnim odpadkom, ki se sežigajo v povprečnih komunalnih odpadkih. Zato podatki ISD veljajo za obdelavo določenih odpadkov v okviru povprečnega komunalnega komunalnega odpadkov (delež frakcij odpadkov v komunalnih odpadkih je prikazan v spodnjem tortnem diagramu, osnovna sestava v prvi tabeli spodaj). V naslednjem opisu tehnologije so pojasnjene nastavitve in tehnologija povprečne tovarne WtE, ki se uporablja za ustvarjanje nabora podatkov ISD. Neto kalorična vrednost in osnovna sestava frakcije odpadkov ali posebnih odpadkov sta prikazani v spodnjih tabelah (glej ustrezni stolpec v tabelah).Podatkovni niz zajema vse ustrezne korake/tehnologije postopka v dobavni verigi zastopane zibelke do inventarja z dobro splošno kakovostjo podatkov. Popis temelji predvsem na podatkih industrije in je po potrebi dopolnjen s sekundarnimi podatki. Sopomenke: Pridobivanje energije iz plastike (Nylon 6 GF 30, najlon 66 GF 30) Tehnični namen: Standardna storitev obdelave ob koncu življenjske dobe za določeno frakcijo odpadkov s toplotno obdelavo. Geografska zastopanost: EU-27 Ο ευρωπαϊκός μέσος ευρωπαϊκός σταθμός παραγωγής ενέργειας (WtE) ορίζεται με βάση την επεξεργασία των μέσων ευρωπαϊκών αστικών στερεών αποβλήτων (MSW). Η θερμική επεξεργασία ενός μόνο κλάσματος αποβλήτων όπως το χαρτί ή το πλαστικό ή ακόμη και συγκεκριμένα απόβλητα όπως το Polyamide 6 δεν γίνεται στην πραγματικότητα σε μονάδα WtE για MSW. Τα απόβλητα είναι πάντοτε ομογενοποιημένα ώστε να επιτυγχάνεται σχετική σταθερή θερμογόνος δύναμη και να συμμορφώνονται με τα πρότυπα εκπομπών. Ωστόσο, το χρησιμοποιούμενο μοντέλο και οι χρησιμοποιούμενες ρυθμίσεις για το μέσο MSW επιτρέπουν την απόδοση της περιβαλλοντικής επιβάρυνσης (εκπομπές και κατανάλωση πόρων από βοηθητικούς φορείς) της παραγωγής ενέργειας, καθώς και των πιστώσεων (εξαγωγή μεταλλικών απορριμμάτων) σε ένα μόνο κλάσμα ή σε συγκεκριμένα απόβλητα που αποτεφρώνονται μέσα σε ένα μέσο MSW. Ως εκ τούτου, τα δεδομένα LCI είναι έγκυρα για την επεξεργασία των συγκεκριμένων αποβλήτων στο πλαίσιο ενός μέσου MSW (το μερίδιο του κλάσματος αποβλήτων του MSW εμφανίζεται στο διάγραμμα πίτας κάτω, η στοιχειώδης σύνθεση στον πρώτο πίνακα κατωτέρω). Η ακόλουθη περιγραφή της τεχνολογίας εξηγεί τις ρυθμίσεις και την τεχνολογία του μέσου εργοστασίου WtE που χρησιμοποιείται για τη δημιουργία του συνόλου δεδομένων LCI. Η κατώτερη θερμογόνος δύναμη και η στοιχειώδης σύνθεση του κλάσματος αποβλήτων ή των ειδικών αποβλήτων παρουσιάζονται στους παρακάτω πίνακες (βλ. αντίστοιχη στήλη στους πίνακες). Το σύνολο δεδομένων καλύπτει όλα τα σχετικά στάδια/τεχνολογίες της διαδικασίας σε όλη την αλυσίδα εφοδιασμού του αντιπροσωπευόμενου λίκνου έως την πύλη απογραφής με καλή συνολική ποιότητα δεδομένων. Η απογραφή βασίζεται κυρίως σε δεδομένα του κλάδου και συμπληρώνεται, όπου είναι απαραίτητο, με δευτερεύοντα στοιχεία. Συνώνυμα: Απόβλητα σε ενέργεια από πλαστικά (νάυλον 6 GF 30, νάυλον 66 GF 30) ΤΕΧΝΙΚΟΣ ΣΚΟΠΟΣ: Τυποποιημένη υπηρεσία επεξεργασίας στο τέλος του κύκλου ζωής ενός συγκεκριμένου κλάσματος αποβλήτων μέσω θερμικής επεξεργασίας. Γεωγραφική Αντιπροσώπευση: ΕΕ-27 La moyenne européenne des déchets à l’énergie (WtE) est définie sur la base du traitement des déchets solides municipaux (MSW) européens moyens. Le traitement thermique d’une fraction de déchets unique comme le papier ou le plastique ou même des déchets spécifiques comme Polyamide 6 ne se fait pas en réalité dans une usine WtE pour MSW. Les déchets sont toujours homogénéisés pour obtenir un pouvoir calorifique relativement constant et pour se conformer aux normes d’émission. Néanmoins, le modèle utilisé et les paramètres utilisés pour le MSW moyen permettent d’attribuer la charge environnementale (émissions et consommation de ressources des auxiliaires) ainsi que les crédits (exportation de déchets métalliques) à une seule fraction ou à des déchets spécifiques incinérés dans un MSW moyen. Par conséquent, les données de l’ICL sont valables pour le traitement des déchets spécifiques à l’intérieur d’un MSW moyen (la part de la fraction de déchets du MSW est indiquée dans le tableau ci-dessous, la composition élémentaire dans le premier tableau ci-dessous). La description de la technologie suivante explique les paramètres et la technologie de l’usine WtE moyenne utilisée pour générer l’ensemble de données LCI. Le pouvoir calorifique net et la composition élémentaire de la fraction de déchets ou des déchets spécifiques sont indiqués dans les tableaux ci-dessous (voir la colonne correspondante dans les tableaux). L’ensemble de données couvre toutes les étapes/technologies pertinentes du processus sur la chaîne d’approvisionnement de l’inventaire de berceau à porte représenté avec une bonne qualité globale des données. L’inventaire est principalement basé sur les données de l’industrie et est complété, le cas échéant, par des données secondaires. Synonymes: Déchets énergétiques des matières plastiques (Nylon 6 GF 30, Nylon 66 GF 30) Objet technique: Service standard de traitement en fin de vie d’une fraction de déchets spécifique par traitement thermique. Représentation géographique: EU-27

Tree cores were sampled using increment borers. At each site three trees were chosen for coring, with two or three cores taken per tree. Cores were sanded and ring widths measured based on high-resolution images of the sanded cores. Cores were cross-dated and summary statistics used to compare cross-dating accuracy. The dataset contains the resulting dated ring width series. This dataset includes tree ring width data, derived from tree cores, that were sampled from sites across the Rhön Biosphere Reserve (Germany). At each chosen site three trees were cored, with two or three cores taken per cored tree. Data was collected in August 2021.

This work models a last-mile network design problem for an e-retailer with a capacitated two-echelon distribution structure - typical in e-retail last-mile distribution, catering to a market with a stochastic and dynamic daily customer demand requesting delivery within time-windows. Considering the distribution evnironment, this work formulates last-mile network design problem for this e-retailer as a dynamic-stochastic two capacitated location routing problem with time-windows. In doing so, this work splits the last-mile network design problem into its constituent strategic, tactical, and operational decisions. Here, the strategic decisions undertake long-term planning to develop a distribution structure with appropriate distribution facilities and a suitable delivery fleet to service the expected customer demand in the planning horizon. The tactical decisions pertain to medium-term day-to-day planning of last-mile delivery operations to establish efficient goods flow in this distribution structure to service the daily stochastic customer demand. And finally, operational decisions involve immediate short-term planning to fine-tune this last-mile delivery to service the requests arriving dynamically through the day. Note, the last-mile network design problem formulated as a location routing problem constitutes three subproblems encompassing facility location problem, customer allocation problem, and vehicle routing problem, each of which are NP-hard combinatorial optimization problems. To this end, this work develops an adaptive large neighborhood search meta-heuristic algorithm that searches through the neighborhood by destroying and consequently repairing the solution thereby reconfiguring large portions of the solution with specific operators that are chosen adaptively in each iteration of the algorithm, hence the name adaptive large neighborhood search. Further, considering the stochastic and dynamic nature of the delivery environment, this work develops a Monte-Carlo framework simulating each day in the planning horizon, with each day divided into 1-hr timeslots, and with each time-slot accepting customer requests for service by the end of the day. In particular, the framework assumes the e-retailer will delay route commitments until the last-feasible time-slot to accumulate customer requests and consequently assign them to an uncommitted delivery route. Note, a delivery route is committed once the e-retailer starts loading packages assigned to this delivery route onto the delivery vehicle assigned for this delivery route. At the end of every time-slot then, this framework assumes the e-retailer integrates the new customer requests by inserting these customer nodes into such uncommitted delivery routes in a manner that results in the least increase in distribution cost keeping the customer-distribution facility allocation fixed. Thus, the framework iterates through the time-slots with the e-retailer processing route commitments, accumulating customer requests, and subsequently integrating them into the delivery operations for the day. E-commerce has the potential to make urban goods flow economically viable, environmentally efficient, and socially equitable. However, as e-retailers compete with increasingly consumer-focused services, urban freight witnesses a significant increase in associated distribution costs and negative externalities particularly affecting those living close to logistics clusters. Hence, to remain competitive, e-retailers deploy alternate last-mile distribution strategies. These alternate strategies, such as those that include use of electric delivery trucks for last-mile operations, a fleet of crowdsourced drivers for last-mile delivery, consolidation facilities coupled with light-duty delivery vehicles for a multi-echelon distribution, or collection points for customer pickup, can restore sustainable urban goods flow. Thus, in this study, the authors investigate the opportunities and challenges associated with such alternate last-mile distribution strategies for an e-retailer offering expedited service with rush delivery within strict timeframes. To this end, the authors formulate a last-mile network design (LMND) problem as a dynamic-stochastic two-echelon capacitated location routing problem with time-windows (DS-2E-C-LRP-TW) addressed with an adaptive large neighborhood search (ALNS) metaheuristic.

Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.NOAA-GFDL.GFDL-ESM4.ssp245' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The GFDL-ESM4 climate model, released in 2018, includes the following components: aerosol: interactive, atmos: GFDL-AM4.1 (Cubed-sphere (c96) - 1 degree nominal horizontal resolution; 360 x 180 longitude/latitude; 49 levels; top level 1 Pa), atmosChem: GFDL-ATMCHEM4.1 (full atmospheric chemistry), land: GFDL-LM4.1, landIce: GFDL-LM4.1, ocean: GFDL-OM4p5 (GFDL-MOM6, tripolar - nominal 0.5 deg; 720 x 576 longitude/latitude; 75 levels; top grid cell 0-2 m), ocnBgchem: GFDL-COBALTv2, seaIce: GFDL-SIM4p5 (GFDL-SIS2.0, tripolar - nominal 0.5 deg; 720 x 576 longitude/latitude; 5 layers; 5 thickness categories). The model was run by the National Oceanic and Atmospheric Administration, Geophysical Fluid Dynamics Laboratory, Princeton, NJ 08540, USA (NOAA-GFDL) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, atmosChem: 100 km, land: 100 km, landIce: 100 km, ocean: 50 km, ocnBgchem: 50 km, seaIce: 50 km.

Data type: Experimental measurements, correlations and Van't Hoff plot. Date format: .opj. Origin of the data: Experimental pressure composition isotherm measurements. Data generated by a home-made Sieverts’ type apparatus from CNRS, ICMPE, Thiais, France. Software needed to plot the data: Origin.

Absorption Koeffizient der farbigen detritalen Substanz bei 443nm (adg in m^-1 bei 4 km Auflösung): Der Absorptionskoeffizient adg stellt den Anteil des einfallenden Lichts dar, das sowohl von detritalen Partikeln als auch von farbiger gelöster organischer Substanz (CDOM) absorbiert wird. Gelöste organische Substanz ist ein wichtiger Bestandteil des ozeanischen Kohlenstoffkreislaufs. Es wird auch als Proxy verwendet, um die Auswirkungen von Terrigenous Inputs in Küstengewässern zu bewerten. Συντελεστής απορρόφησης της χρωματισμένης αποτριχωτικής ύλης στα 443nm (adg σε m^-1 σε ανάλυση 4 km): Ο συντελεστής απορρόφησης adg αντιπροσωπεύει το κλάσμα του προσπίπτοντος φωτός που απορροφάται τόσο από τα διακριτικά σωματίδια όσο και από τη χρωματισμένη διαλυμένη οργανική ύλη (CDOM). Η διαλυμένη οργανική ύλη είναι ένα σημαντικό συστατικό του ωκεάνιου κύκλου του άνθρακα. Χρησιμοποιείται επίσης ως υποκατάστατο για την εκτίμηση των επιπτώσεων των εδαφικών εισροών στα παράκτια ύδατα. Współczynnik absorpcji barwnej substancji detrytalnej przy 443 nm (adg w m^-1 przy rozdzielczości 4 km): Współczynnik absorpcji adg reprezentuje ułamek padającego światła pochłanianego zarówno przez cząstki detrytalne, jak i przez kolorowe rozpuszczone substancje organiczne (CDOM). Rozpuszczone materia organiczna jest ważnym składnikiem oceanicznego cyklu węgla. Jest on również wykorzystywany jako wskaźnik zastępczy do oceny wpływu czynników atmosferycznych w wodach przybrzeżnych. Coeficientul de absorbție al materiei detritale colorate la 443nm (adg în m^-1 la o rezoluție de 4 km): Coeficientul de absorbție adg reprezintă fracțiunea de lumină incidentă absorbită atât de particulele detritale, cât și de materia organică colorată dizolvată (CDOM). Materia organică dizolvată este o componentă importantă a ciclului carbonului oceanic. Acesta este, de asemenea, utilizat ca indicator pentru a evalua impactul factorilor de producție terrigeni în apele costiere. Assorbiment Koeffiċjent tal-materja detritali kkulurita f’443nm (adg f’m^-1 b’riżoluzzjoni ta’ 4 km): Il-koeffiċjent ta’ assorbiment adg jirrappreżenta l-frazzjoni ta’ dawl inċidentali assorbit kemm minn partiċelli detritali kif ukoll minn materja organika maħlula kkulurita (CDOM). Il-materja organika maħlula hija komponent importanti taċ-ċiklu tal-karbonju oċeaniku. Tintuża wkoll bħala indikatur biex jiġi vvalutat l-impatt tal-inputs terriġenużi fl-ilmijiet kostali. Coefficiente di assorbimento della materia detritale colorata a 443nm (adg in m^-1 a risoluzione di 4 km): Il coefficiente di assorbimento adg rappresenta la frazione di luce incidente assorbita sia dalle particelle detritali che dalla materia organica disciolta colorata (CDOM). La materia organica disciolta è una componente importante del ciclo del carbonio oceanico. Viene anche utilizzato come proxy per valutare l'impatto degli input terrigeni nelle acque costiere. Coeficiente de absorción de materia detrital de color a 443 nm (adg en m^-1 a 4 km de resolución): El coeficiente de absorción adg representa la fracción de luz incidente absorbida tanto por partículas detritales como por materia orgánica disuelta coloreada (CDOM). La materia orgánica disuelta es un componente importante del ciclo del carbono oceánico. También se utiliza como representante para evaluar el impacto de los insumos territoriales en las aguas costeras. Коефициент на абсорбция на цветна детритална материя при 443nm (adg в m^-1 при разделителна способност 4 km): Коефициентът на поглъщане adg представлява частта от падащата светлина, абсорбирана както от детритните частици, така и от оцветената разтворена органична материя (CDOM). Разтворената органична материя е важен компонент на океанския въглероден цикъл. Той се използва и като заместител за оценка на въздействието на теригенните суровини в крайбрежните води. Coefficient d’absorption de la matière détritique colorée à 443nm (adg en m^-1 à 4 km de résolution): Le coefficient d’absorption adg représente la fraction de lumière incidente absorbée à la fois par les particules détritales et par la matière organique dissoute colorée (CDOM). La matière organique dissoute est une composante importante du cycle du carbone océanique. Il sert également de proxy pour évaluer l’impact des apports terriens dans les eaux côtières. Absorptiecoëfficiënt van gekleurde detritale materie bij 443nm (adg in m^-1 bij 4 km resolutie): De absorptiecoëfficiënt adg vertegenwoordigt de fractie van invallend licht geabsorbeerd door zowel detritale deeltjes als gekleurd opgelost organisch materiaal (CDOM). Opgelost organisch materiaal is een belangrijk onderdeel van de oceanische koolstofcyclus. Het wordt ook gebruikt als volmacht om de impact van terrigeneuze inputs in kustwateren te beoordelen.

For this manuscript, there were three types of methods performed to make our main conclusions: genetic diversity and structure analyses, downloading and mapping butternut fossil pollen during the last 20,000 years, and modeling and hindcasting butternut's (Juglans cinerea) distribution 20,000 years ago to present. Genetic analyses and species distribution modeling were performed in Emily Schumacher’s Github repository (https://github.com/ekschumacher/butternut) and pollen analyses and mapping were performed in Alissa Brown’s repository (https://github.com/alissab/juglans). Here is information detailing the Genetic data Data collection description: To perform genetic diversity and structure analyses on butternut, we used genetic data from the publication Hoban et al. (2010) and genetic data from newer sampling efforts on butternut from 2011 - 2015. Individuals were collected by Jeanne Romero-Severson, Sean Hoban, and Martin Williams over the course of ~ten years with a major sampling effort closer to 2009 followed up by another round of sampling 2012 - 2015. The initial 1,004 butternut individuals that were collected were genotyped by Sean Hoban and then the subsequent 757 individuals were genotyped in the Romero-Severson lab at Notre Dame non-consecutively. Genotyping was performed according to Hoban et al. (2008); DNA was extracted from fresh cut twigs using DNeasy Plant Mini kits (QIAGEN). PCR was performed by using 1.5 mM MgCl2, 1x PCR buffer [50 mm KCl, 10 mm Tris-HCl (pH 9.0), 0.1% Triton-X-100 (Fisher BioTech)], 0.2 mm dNTPs, 4 pm each forward and reverse primer, 4% Bovine Serum Albumin, 0.25 U TaKaRa Ex Taq Polymerase (Panvera), and 20 ng DNA template (10 μL total volume). The PCR temperature profile was as follows: 2 min at 94 °C; 30 cycles of 94 °C for 30 s, Ta for 30 s, and 72 °C for 30 s; 45 min at 60 °C; and 10 min at 72 °C on a PTC-225 Peltier Thermal Cycler (MJ Research). The process of assessing loci and rebinning for differences in years is detailed in the Schumacher et al. (2022) manuscript. Data files butternut_44pop.gen: Genepop file of original 1,761 butternut individuals, sampling described above, separated into original 44 sampling populations. butternut_24pop_nomd.gen: Genepop file of 1,635 butternut individuals, following rebinning based on researcher binning, reduced based on geographic isolation and missing data, organized into 24 populations. Used to generate all genetic diversity results. butternut_24pop_relate_red.gen: Genepop file of 993 butternut individuals, reduced for 25% relatedness, used to generate all clustering analyses. butternut_26pop_nomd.gen: Genepop file of 1,662 butternut individuals, reduced based on geographic isolation and missing data, including Quebec individuals, organized into 26 populations. Used to generate genetic diversity results with Quebec individuals. butternut_26pop_relate_red.gen: Genepop file of 1,015 butternut individuals, including Quebec individuals, reduced for 25% relatedness, used to generate clustering analyses with Quebec individuals. Fossil Pollen Data collection description: Pollen records for butternut were downloaded from Neotoma Paleoecology Database in 500-year time increments and visualized in 1,000 year-time increments 20,000 years ago to present. Data files butternut_pollen_data.csv: CSV of pollen records used for analyses and mapping. Includes original coordinates for each record (“og_long”, “og_lat”), the count of Juglans cinerea pollen at each site (“Juglans_cinerea_count”), and the age of the record (“Age”). To create the final maps, the coordinates were projected into Albers for each record (“Proj_Long,” “Proj_Lat”). Species Distribution Modeling and Hindcast Modeling Data collection description: We wanted to identify butternut's ecological preferences using boosted regression trees (BRT) and then hindcast distribution models into the past to identify migration pathways and locations of glacial refugia. Species distribution modeling was performed using boosted regression trees according to Elith et al. (2008). To run BRT, we needed to: 1. Reduce occurrence records to account for spatial autocorrelation, 2. Generate pseudo-absence points to identify the habitat where butternut is not found, 3. Obtain and extract the 19 bioclimatic variables at all points, 4. Select ecological variables least correlated with each other and most correlated with butternut presence. The BRT model that predicted butternut's ecological niche was then used to hypothesize butternut's suitable habitat and range shifts in the past. We downloaded occurrence records according to Beckman et al. (2019) as described here: https://github.com/MortonArb-ForestEcology/IMLS_CollectionsValue. The habitat suitability map generated from the BRT were projected into the past 20,000 years using Paleoclim variables (Brown et al., 2018). Data files butternut_BRT_var.csv: A CSV of the butternut presence and pseudoabsence points and extracted Bioclim variables (Fick & Hijman, 2017) used to run BRT in the final manuscript. Longitude and latitude coordinates are projected into Albers Equal Area Conic project, same with all of the ecological variables. Presence points are indicated with a 1 in the “PA” column and pseudo-absence points are indicated with a “0.” The variables most correlated with presence and least correlated with each other in this analysis were precipitation of the wettest month (“PwetM”), mean diurnal range (“MDR”), mean temperature of the driest quarter (“MTDQ”), mean temperature of the wettest quarter (“MTwetQ”), and seasonal precipitation (“precip_season”). References Brown, J. L., Hill, D. J., Dolan, A. M., Carnaval, A. C., & Haywood, A. M. (2018). PaleoClim, high spatial resolution paleoclimate surfaces for global land areas. Scientific Data, 5, 1-9 Elith, J., Leathwick, J. R., & Hastie, T. (2008). A working guide to boosted regression trees. Journal of Animal Ecology, 77, 802-813. Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37, 4302-4315. Hoban, S., Anderson, R., McCleary, T., Schlarbaum, S., and Romero-Severson, J. (2008). Thirteen nuclear microsatellite loci for butternut (Juglans cinerea L.). Molecular Ecology Resources, 8, 643-646. Hoban, S. M., Borkowski, D. S., Brosi, S. L., McCleary, T. S., Thompson, L. M., McLachlan, J. S., ... Romero-Severson, J. (2010). Range‐wide distribution of genetic diversity in the North American tree Juglans cinerea: A product of range shifts, not ecological marginality or recent population decline. Molecular Ecology, 19, 4876-4891. Aim: Range shifts are a key process that determine species distributions and genetic patterns. A previous investigation reported that Juglans cinerea (butternut) has lower genetic diversity at higher latitudes, hypothesized to be the result of range shifts following the last glacial period. However, genetic patterns can also be impacted by modern ecogeographic conditions. Therefore, we re-investigate genetic patterns of butternut with additional northern population sampling, hindcasted species distribution models, and fossil pollen records to clarify the impact of glaciation on butternut. Location: Eastern North America Taxon: Juglans cinerea (L., Juglandaceae) (butternut) Methods: Using 11 microsatellites, we examined range-wide spatial patterns of genetic diversity metrics (allelic richness, heterozygosity, FST) for previously studied butternut individuals and an additional 757 samples. We constructed hindcast species distribution models and mapped fossil pollen records to evaluate habitat suitability and evidence of species’ presence throughout space and time. Results: Contrary to previous work on butternut, we found that genetic diversity increased with distance to range edge, and previous latitudinal clines in diversity were likely due to a few outlier populations. Populations in New Brunswick, Canada were genetically distinct from other populations. At the Last Glacial Maximum, pollen records demonstrate butternut likely persisted near the glacial margin, and hindcast species distribution models identified suitable habitat in the southern United States and near Nova Scotia. Main conclusions: Genetic patterns in butternut may be shaped by both glaciation and modern environmental conditions. Pollen records and hindcast species distribution models combined with genetic distinctiveness in New Brunswick suggest that butternut may have persisted in cryptic northern refugia. We suggest that thorough sampling across a species range and evaluating multiple lines of evidence are essential to understanding past species movements. Data was cleaned and processed in R - genetic data cleaning and analyses and species distribution modeling methods were performed in Emily Schumacher's butternut repository and fossil pollen data cleaning and modeling was performed in Alissa Brown's juglans repository. Steps for performing data cleanining, analyses, and generating figures for the manuscript are described within each repo.

Origin of the data: Experimental spectroscopic measurements Data Type: experimental measurements, open access supporting information The data are in CSV, DSW and FBSW format. Supporting information are supplied in PDF format. Data generated by instruments: Varian Cary 5E-UV-Vis-NIR spectrophotometer for UV-Vis measurements, Varian Cary Eclipse fluorescence spectrophotomer for fluorescence quenching measurements. Analytical and procedural information: Stern-Volmer fluorescence quenching experiments, UV-Vis measurements and Fluorescent Quantum Yield determination via ferrioxalate actinometry. Definition of variables: Wavelength, Absorbance, Concentration Units of measurement: nanometers (nm), moles-per-litre (mol/l) Abbreviations: File names and data headers use the following abbreviations: FQY refers to Fluorescence Quantum Yield determination experiments Light refers to irradiated samples in the actinometry experiment, as detailed in the procedure in the supporting information. Dark refers to non-irradiated samples in the actinometry experiment, as detailed in the procedure in the supporting information. SVQuench refers to Stern-Volmer quenching experiments RAxx refer to measurements related to allylbenzene. Xx is the amount of quencher in mol/l (05 should be intended as 0.5 mol/l and so on). RTxx refer to measurements related to S-(4-methylphenyl) 4-methylbenzenethiosulfonate. Xx is the amount of quencher in mol/l as above. RExx refer to measurements related to 1,2-dimethoxy-4-(prop-2-en-1-yl)benzene. Xx is the amount of quencher in mol/l as above. RSxx refer to measurements related to styrene. Xx is the amount of quencher in mol/l. RTFxx refer to measurements related to S-(4-fluorophenyl) 4-fluorobenzenethiosulfonate. Xx is the amount of quencher in mol/l as above. MesAcrMe Xx refers to data related to catalyst 9-mesityl-10-methylacridinium. Xx is the amount of catalyst in mol/l as above. DMC for measurements employing dimethylcarbonate as solvent. ACN for measurements employing acetonitrile as solvent. FBSW and DSW data are used by the proprietary software of the Varian spectrometers (CARY WinUV and Cary Eclipse). Information can be found at https://www.agilent.com/en/product/molecular-spectroscopy/uv-vis-uv-vis-nir-spectroscopy/uv-vis-uv-vis-nir-software/cary-winuv-software and https://www.agilent.com/en/product/molecular-spectroscopy/fluorescence-spectroscopy/fluorescence-software/cary-eclipse-software