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Plantations of fast-growing Eucalyptus trees have become a common sight in the western Iberian peninsula where they are planted to exploit their economic potential. Negative side-effects of large scale plantations including the invasive behavior of Eucalyptus trees outside of regular plantations have become apparent. This study uses medium resolution, multi-spectral imagery of the Sentinel 2 satellites to map Eucalyptus across Portugal and parts of Spain with a focus on Natura 2000 areas inside Portugal, that are protected under the European birds and habitats directives. This method enables the detection of small incipient as well as mixed populations outside of regular plantations. Ground truth maps were compiled using field surveys as well as high resolution satellite imagery and were used to train Feedforward Neural Networks. These models predict Eucalyptus tree cover with a sensitivity of up to 75.7% as well as a specificity of up to 95.8%. The overall accuracy of the prediction is 92.5%. A qualitative assessment of Natura 2000 areas in Portugal has been performed and 15 areas have been found to be affected by Eucalyptus of which 9 are strongly affected. This study demonstrates the applicability of multi-spectral imagery for tree-species classification and invasive species control. It provides a probability-map of Eucalyptus tree cover for the western Iberian peninsula with 10 m spatial resolution and shows the need for monitoring of Eucalyptus in protected areas.

The large-scale Land-Uses and Land-Cover Changes (LULCC) in India in the past several decades is primarily driven by anthropogenic factors that influence the climate from regional to global scales. Therefore, to understand the LULCC over the Indian region from 2002 to 2015 and its implications on temperature and precipitation, we performed Weather Research Forecast (WRF) model simulation using the European Centre for Medium-Range Weather Forecast (ECMWF) reanalysis data for the period 2009 to 2015 as a boundary condition with 2009 as spin-up time. The results showed moderate forest cover loss in major parts of northeast India, and the Himalayan region during 2002–2015. Such large LULC changes, primarily significant alteration of grassland and agriculture from the forest, led to increased precipitation due to increasing evapotranspiration (ET) similar to the forest-dominated regions. An increase in the precipitation patterns (>300 mm) was observed in the parts of eastern and western Himalayas, western Ghats, and the northwestern part of central India, while most parts of northeast Himalayas have an exceptional increase in precipitation (∼100–150 mm), which shows similar agreement with an increase of leaf area index (LAI) by ∼15%. The overall phenomenon leads to a greening-induced ET enhancement that increases atmospheric water vapor content and promotes downwind precipitation. In the case of temperature, warming was observed in the central to eastern parts of India, while cooling was observed in the central and western parts. The increase in vegetated areas over northwest India led to an increase in ET, which ultimately resulted in decreased temperature and increased precipitation. The study highlights the changes in temperature and precipitation in recent decades because of large LULCC and necessitates the formulation of sustainable land use-based strategies to control meteorological variability and augment ecological sustainability.

To address ambitious goals of carbon neutrality set at national and city scales, a number of atmospheric networks have been deployed to monitor greenhouse gas (GHG) concentrations in and around cities. To convert these measurements into estimates of emissions from cities, atmospheric models are used to simulate the transport of various trace gases and help interpret these measurements. We set up a modelling framework using the Weather Research and Forecasting (WRF) model applied at a high spatial resolution (up to 400 m) to simulate the atmospheric transport of GHGs and attempt a preliminary interpretation of the observations provided by the Munich Urban Carbon Column Network (MUCCnet). Building on previous analyses using similar measurements performed within a campaign for the city of Berlin and its surroundings (Zhao et al., 2019), our modelling framework has been improved regarding the initialization of tagged tracers, model settings, and input data. To assess the model performance, we validate the modelled output against two local weather stations and two radiosonde observations, as well as observed column GHG concentrations. The measurements were provided by the measurement campaign that was carried out from 1 to 30 August 2018. The modelled wind matches well with the measurements from the weather stations, with wind speeds slightly overestimated. In general, the model is able to reproduce the measured slant column concentrations of CH4 and their variability, while for CO2, a difference in the slant column CO2 of around 3.7 ppm is found in the model. This can be attributed to the initial and lateral boundary conditions used for the background tracer. Additional mismatches in the diurnal cycle could be explained by an underestimation of nocturnal respiration in the modelled CO2 biogenic fluxes. The differential column method (DCM) has been applied to cancel out the influence from the background concentrations. We optimize its application by selecting suitable days on which the assumption of the DCM holds true: a relatively uniform air mass travels over the city, passing from an upwind site to a downwind site. In particular, the Stochastic Time-Inverted Lagrangian Transport (STILT) model is used here and driven by our WRF-modelled meteorological fields to obtain footprints (i.e. the potential areas of influence for signals observed at measurement stations), further used for interpreting measurement results. Combining these footprints with local knowledge of emission sources, we find evidence of CH4 sources near Munich that are missing or underestimated in the emission inventory used. This demonstrates the potential of this data–model framework to constrain local sources and improve emission inventories.

The European heatwave of 2018 led to record-breaking temperatures and extremely dry conditions in many parts of the continent, resulting in widespread decrease in agricultural yield, early tree-leaf senescence, and increase in forest fires in Northern Europe. Our study aims to capture the impact of the 2018 European heatwave on the terrestrial ecosystem through the lens of a high-resolution solar-induced fluorescence (SIF) data acquired from the Orbiting Carbon Observatory-2 (OCO-2) satellite. SIF is proposed to be a direct proxy for gross primary productivity (GPP) and thus can be used to draw inferences about changes in photosynthetic activity in vegetation due to extreme events. We explore spatial and temporal SIF variation and anomaly in the spring and summer months across different vegetation types (agriculture, broadleaved forest, coniferous forest, and mixed forest) during the European heatwave of 2018 and compare it to non-drought conditions (most of Southern Europe). About one-third of Europe’s land area experienced a consecutive spring and summer drought in 2018. Comparing 2018 to mean conditions (i.e., those in 2015–2017), we found a change in the intra-spring season SIF dynamics for all vegetation types, with lower SIF during the start of spring, followed by an increase in fluorescence from mid-April. Summer, however, showed a significant decrease in SIF. Our results show that particularly agricultural areas were severely affected by the hotter drought of 2018. Furthermore, the intense heat wave in Central Europe showed about a 31% decrease in SIF values during July and August as compared to the mean over the previous three years. Furthermore, our MODIS (Moderate Resolution Imaging Spectroradiometer) and OCO-2 comparative results indicate that especially for coniferous and mixed forests, OCO-2 SIF has a quicker response and a possible higher sensitivity to drought in comparison to MODIS’s fPAR (fraction of absorbed photosynthetically active radiation) and the Normalized Difference Vegetation Index (NDVI) when considering shorter reference periods, which highlights the added value of remotely sensed solar-induced fluorescence for studying the impact of drought on vegetation.

Climate change causes a global rise in mean air temperature and an increased frequency of temperature extremes. Recent studies link sharp temperature changes between consecutive days to increased mortality, reduced economic growth, and negative effects on ecosystems. This study assesses the variability of the daily maximum air temperature between two consecutive days (i.e., temperature volatility) across Germany from 1990 to 2022. Using observation-based raster data of maximum daily temperature at 0.1° × 0.1° spatial resolution, we assess temperature volatility regarding: (1) magnitude, (2) seasonality, (3) directionality (day-to-day warming vs. cooling), and (4) trends. Further, we analyse land cover changes during the period and examine their correlation to extreme temperature volatility (Tve). The results show that Tve mostly occurred during spring and summer. The magnitude of Tve increased with distance to the coast north-west to south during all seasons and was highest during spring and summer (>10 °C). Overall, Tve was particularly associated with day-to-day cooling (in spring, summer, autumn), while in winter, >60% of days were associated with warming in north and central Germany. Less than 12% of Germany showed significant trends in median Tve associated with warming over the period. Significant trends included increases (>0.26 °C/year) and decreases (−0.09 °C/year) in extreme day-to-day warming during autumn and winter in northeastern Germany. In spring, the majority (>60%) of southern areas showed significant positive trends (up to 0.16 °C/year) in Tve associated with day-to-day cooling. During winter, summer, and autumn, trends in extreme day-to-day cooling were insignificant in over 80% of Germany. Within all land cover types, Tve predominantly varied between 6 °C and 9 °C. Changes in land cover, especially transforming coniferous forests for agricultural purposes, were accompanied by increasing Tve up to 0.49 °C. Understanding rapid temperature changes is crucial for climate change mitigation strategies and limiting the impacts on human health and on the environment.

Compared to drought and heat waves, the impact of winter warming on forest CO2 fluxes has been less studied, despite its significant relevance in colder regions with higher soil carbon content. Our objective was to test the effect of the exceptionally warm winter of 2020 on the winter CO2 budget of cold-adapted evergreen needleleaf forests across Europe and identify the contribution of climate factors to changes in winter CO2 fluxes. Our hypothesis was that warming in winter leads to higher emissions across colder sites due to increased ecosystem respiration. To test this hypothesis, we used 98 site-year eddy covariance measurements across 14 evergreen needleleaf forests (ENFs) distributed from the north to the south of Europe (from Sweden to Italy). We used a data-driven approach to quantify the effect of radiation, air temperature, and soil temperature on changes in CO2 fluxes during the warm winter of 2020. Our results showed that warming in winter decreased forest net ecosystem productivity (NEP) significantly across most sites. The contribution of climate variables to CO2 fluxes varied across the sites: in southern regions with warmer mean temperatures, radiation had a greater influence on NEP. Conversely, at colder sites, air temperature played a more critical role in affecting NEP. During the warm winter of 2020, colder regions experienced larger air temperature anomalies compared to the other sites; however we did not observe a significantly larger increase at colder sites due to winter warming. The varying responses of NEP across different sites highlight the complex interactions between climate variables such as air temperature, soil temperature, and radiation. These findings underscore the importance of integrating winter warming effects to more accurately predict the impacts of climate change on forest carbon dynamics.

Wound dressings were prepared by using polyethylene terephthalate (PET) fabric as the base material. The functionality of the fabric was enhanced by alkaline hydrolysis. The hydrolysis of the fabric led to the formation of carboxyl groups on its surface. FTIR and contact angle measurements were carried out to monitor the chemical and physical changes on the fabric surface. Silver was immobilized on the fabric as the bioactive component. The fabric was characterized by EDX in mapping mode to observe distribution of silver on the fabric. PEG 600 used as carrier for silver ions to be loaded of the material surface. Antimicrobial studies of the fabric was carried out by zone of inhibition and colony reduction methods against E. coli and S. aureus . Bacterial adhesion was completely inhibited in silver immobilized samples. On the basis of observations from mechanical analysis, EDX, carboxyl content estimation, and antibacterial resistance we concluded that material was the most suitable sample to develop wound dressing.

Amazonian ecosystems are major biodiversity hotspots and carbon sinks that may lose species to extinction and become carbon sources due to extreme dry or warm conditions. We investigated the seasonal patterns of high-resolution solar-induced chlorophyll fluorescence (SIF) measured by the satellite Orbiting Carbon Observatory-2 (OCO-2) across the Amazonian ecoregions to assess the area´s phenology and extreme drought vulnerability. SIF is an indicator of the photosynthetic activity of chlorophyll molecules and is assumed to be directly related to gross primary production (GPP). We analyzed SIF variability in the Amazon basin during the period between September 2014 and December 2018. In particular, we focused on the SIF drought response under the extreme drought period during the strong El Niño in 2015–2016, as well as the 6-month drought peak period. During the drought´s peak months, the SIF decreased and increased with different intensities across the ecoregions of the Amazonian moist broadleaf forest (MBF) biome. Under a high temperature, a high vapor pressure deficit, and extreme drought conditions, the SIF presented differences from −31.1% to +17.6%. Such chlorophyll activity variations have been observed in plant-level measurements of active fluorescence in plants undergoing physiological responses to water or heat stress. Thus, it is plausible that the SIF variations in the ecoregions’ ecosystems occurred as a result of water and heat stress, and arguably because of drought-driven vegetation mortality and collateral effects in their species composition and community structures. The SIF responses to drought at the ecoregional scale indicate that there are different levels of resilience to drought across MBF ecosystems that the currently used climate- and biome-region scales do not capture. Finally, we identified monthly SIF values of 32 ecoregions, including non-MBF biomes, which may give the first insights into the photosynthetic activity dynamics of Amazonian ecoregions.

Natural gas is considered a bridging technology in the energy transition because it produces fewer carbon emissions than coal, for example. However, when leaks exist, methane is released into the atmosphere, leading to a dramatic increase in the carbon footprint of natural gas, as methane is a much stronger greenhouse gas than carbon dioxide. Therefore, we conducted a detailed study of methane emissions from gas‐powered end‐use appliances and then compared their climate impacts with those of electricity‐powered appliances. We used the Munich Oktoberfest as a case study and then extended the study to 25 major natural gas consuming countries. This showed that electricity has been the more climate‐friendly energy source at Oktoberfest since 2005, due to the extensive use of renewable electricity at the festival and the presence of methane emissions, particularly caused by the incomplete combustion and leakages of natural gas in cooking and heating appliances. By contrast, at the global level, our study shows that natural gas still produces lower carbon emissions for end‐user appliances than electricity in 18 of the 25 countries studied. However, as the share of renewable energy in the electricity mix steadily increases in most countries, the carbon footprint of electricity will be lower than that of natural gas in these countries in the near future. These findings from our comparison of the total carbon emissions of electric and gas‐powered end‐use appliances can help inform the debate on how to effectively address climate change. Plain Language Summary Although natural gas is considered a relatively climate‐friendly energy source compared to coal, leakage of methane, the main component of natural gas, can significantly increase the climate impact of natural gas. This is because methane is a very strong greenhouse gas. In this study, we focused on methane leakage from end‐use appliances used for cooking and heating. Using the Munich Oktoberfest as a case study, we found that these end‐use appliances produce significant methane emissions. Therefore, we investigated at which leakage rates and which electricity mixes it would be better to use electric appliances for cooking and heating instead to reduce overall carbon emissions. We found that despite leakage rates, natural gas is still more climate‐friendly than electricity in most countries around the world. However, as the share of renewable energy in the electricity mix increases in most countries, electricity is becoming a more climate‐friendly energy source every year. With this study, we want to make people aware of how the climate friendliness of electricity compares to natural gas over time. Key Points Methane emissions at Oktoberfest are measured and classified as natural gas‐based using isotopic analysis and the ratio of ethane to methane Oktoberfest could save 87% of total carbon emissions from energy consumption if all gas‐powered appliances were replaced with electric ones We aim to make people aware how the carbon footprint of electric and natural gas‐driven end‐user appliances compares and evolves over time

We estimate CO2 emissions from the Nile Delta region of Egypt, using over five years of column-averaged CO2 dry air mole fraction (XCO2) data from the NASA's OCO-2 satellite. The Nile Delta has significant anthropogenic emissions of CO2 from urban areas and irrigated farming. It is surrounded by the Sahara desert and the Mediterranean Sea, minimizing the confounding influence of CO2 sources in surrounding areas. We compiled the observed spatial and temporal variations of XCO2 in the Nile Delta region (XCO2,del), and found that values for XCO2,del were on average 1.1 ppm higher than XCO2,des (mean XCO2 in desert area). We modelled the expected enhancements of XCO2 over the Nile Delta based on two global CO2 emission inventories, EDGAR and ODIAC. Modelled XCO2 enhancements were much lower, indicating underestimation of CO2 emissions in the Nile Delta region by mean factors of 4.5 and 3.4 for EDGAR and ODIAC, respectively. Furthermore, we captured a seasonal pattern of XCO2 enhancement (ΔXCO2), with significantly lower ΔXCO2 during the summer agriculture season in comparison to other seasons. Additionally, we used solar-induced fluorescence (SIF) measurement from OCO-2 to understand how the CO2 emissions are related to agricultural activities. Finally, we estimated an average emission of CO2 from the Nile Delta from 2014-2019 of 470 Mt CO2/year, about 1% of global anthropogenic emissions, which is significantly more than estimated hitherto.