A novel, scenario-based approach to comparing non-pharmaceutical intervention strategies across nations.

Comparing COVID-19 response strategies across nations is a key step in preparing for future pandemics. Conventional comparisons, which rank individual non-pharmaceutical intervention (NPI) effects, are limited by: (i) a focus on epidemiological outcomes; (ii) NPIs typically being applied as packages of interventions; and (iii) different political, economic and social conditions among nations. Here, we develop a coupled epidemiological-behavioural-macroeconomic model that can transfer NPI effects from a reference nation to a focal nation. This approach quantifies epidemiological, behavioural and economic outcomes while accounting for both packaged NPIs and differing conditions among nations. As a first proof of concept, we take Germany as our focal nation during Spring 2020, and New Zealand and Switzerland as reference nations with contrasting NPI strategies. Our results suggest that, while New Zealand's more aggressive strategy would have yielded modest epidemiological gains in Germany, it would have resulted in substantially higher economic costs while dramatically reducing social contacts. In contrast, Switzerland's more lenient strategy would have prolonged the first wave in Germany, but would also have increased relative costs. More generally, these findings indicate that our approach can provide novel, multifaceted insights on the efficacy of pandemic response strategies, and therefore merits further exploration and development.

Monitoring vegetation- and geodiversity with remote sensing and traits.

Geodiversity has shaped and structured the Earth's surface at all spatio-temporal scales, not only through long-term processes but also through medium- and short-term processes. Geodiversity is, therefore, a key control and regulating variable in the overall development of landscapes and biodiversity. However, climate change and land use intensity are leading to major changes and disturbances in bio- and geodiversity. For sustainable ecosystem management, temporal, economically viable and standardized monitoring is needed to monitor and model the effects and changes in vegetation- and geodiversity. RS approaches have been used for this purpose for decades. However, to understand in detail how RS approaches capture vegetation- and geodiversity, the aim of this paper is to describe how five features of vegetation- and geodiversity are captured using RS technologies, namely: (i) trait diversity, (ii) phylogenetic/genese diversity, (iii) structural diversity, (iv) taxonomic diversity and (v) functional diversity. Trait diversity is essential for establishing the other four. Traits provide a crucial interface between in situ, close-range, aerial and space-based RS monitoring approaches. The trait approach allows complex data of different types and formats to be linked using the latest semantic data integration techniques, which will enable ecosystem integrity monitoring and modelling in the future. This article is part of the Theo Murphy meeting issue 'Geodiversity for science and society'.

Invasive Growth of Ailanthus altissima Trees is Associated with a High Rate of Sensitization in Atopic Patients.

Purpose: Ailanthus altissima is one of the world's most invasive species with a globally problematic spread. Pollen is dispersed locally and partially airborne. We aimed at investigating if (i) A. altissima pollen can be detected in relevant quantity in the air and if (ii) sensitization to A. altissima can be detected in patients with seasonal exacerbation of atopic diseases. Patients and Methods: We recorded distribution of A. altissima in Leipzig, Germany. In 2019 and 2020, pollen was collected with a Hirst-type pollen trap placed on the roof of the University Hospital. Specific IgE investigations were performed in children and adults with history of atopic diseases with deterioration between May and July. We analysed specific IgE for A. altissima, Alternaria sp., birch, grasses, profilins, polcalcins and crossreacting carbohydrates. Results: We found abundant growth of A. altissima and pollen was detected from early June to mid-July with a maximum pollen concentration of 31 pollen/m3. Out of 138 patients (63 female, 69 children/adolescents), 95 (69%) had seasonal allergic rhinitis, 84 (61%) asthma, and 43 (31%) atopic dermatitis. Sensitization to A. altissima was shown in 59 (42%). There were no significant differences between age groups. In 59% of patients sensitized (35/59), there was no sensitization to possibly cross-reacting structures. Conclusion: Sensitization to A. altissima pollen could be detected in 42% of our patients with atopic diseases, suggesting allergenic potential of this neophyte. In the context of further spread with climate change, eradication strategies and population-based sensitization studies are needed.

Running a dense air temperature measurement field campaign at the urban neighbourhood level: Protocol and lessons learned.

The cooling capacity of urban green spaces constitutes a key measure for cities to mitigate heat events, which is gaining importance in climate change adaptation and mitigation. In this protocol article, we present details on two field campaigns aiming at collecting dense air temperature data in two urban inner city parks in Leipzig, Germany, under unprecedented heat and drought conditions. We introduce all the steps required to plan and conduct qualified fieldwork in environmental research, including study design, technical and logistical preparations, on-site work and data management steps from data acquisition, transfer into research outcomes to dissemination. We further share valuable lessons learned before, during and after fieldwork that helped us improve our work and that could support and improve similar future project campaigns.

Prediction of soil organic carbon and the C:N ratio on a national scale using machine learning and satellite data: A comparison between Sentinel-2, Sentinel-3 and Landsat-8 images.

Soil organic carbon (SOC) and soil carbon-to-nitrogen ratio (C:N) are the main indicators of soil quality and health and play an important role in maintaining soil quality. Together with Landsat, the improved spatial and temporal resolution Sentinel sensors provide the potential to investigate soil information on various scales. We analyzed and compared the potential of satellite sensors (Landsat-8, Sentinel-2 and Sentinel-3) with various spatial and temporal resolutions to predict SOC content and C:N ratio in Switzerland. Modeling was carried out at four spatial resolutions (800 m, 400 m, 100 m and 20 m) using three machine learning techniques: support vector machine (SVM), boosted regression tree (BRT) and random forest (RF). Soil prediction models were generated in these three machine learners in which 150 soil samples and different combinations of environmental data (topography, climate and satellite imagery) were used as inputs. The prediction results were evaluated by cross-validation. Our results revealed that the model type, modeling resolution and sensor selection greatly influenced outputs. By comparing satellite-based SOC models, the models built by Landsat-8 and Sentinel-2 performed the best and the worst, respectively. C:N ratio prediction models based on Landsat-8 and Sentinel-2 showed better results than Sentinel-3. However, the prediction models built by Sentinel-3 had competitive or better accuracy at coarse resolutions. The BRT models constructed by all available predictors at a resolution of 100 m obtained the best prediction accuracy of SOC content and C:N ratio; their relative improvements (in terms of R2) compared to models without remote sensing data input were 29.1% and 58.4%, respectively. The results of variable importance revealed that remote sensing variables were the best predictors for our soil prediction models. The predicted maps indicated that the higher SOC content was mainly distributed in the Alps, while the C:N ratio shared a similar distribution pattern with land use and had higher values in forest areas. This study provides useful indicators for a more effective modeling of soil properties on various scales based on satellite imagery.

No SARS-CoV-2 detected in air samples (pollen and particulate matter) in Leipzig during the first spread.

The SARS-CoV-2 pandemic co-occurred with pollen season in Europe 2020 and recent studies suggest a potential link between both. Air samples collected at our measuring station in Leipzig and purified pollen were analyzed for SARS-CoV-2 typical signals or for virus-induced cytopathic effects, to test if the virus could bind to bioaerosols and if so, whether these complexes are infectious. The results show that neither our air samples nor purified pollen were infectious or could act as carrier for virus particles.

Spatial Retrieval of Broadband Dielectric Spectra.

A broadband soil dielectric spectra retrieval approach ( 1 MHz⁻ 2 GHz) has been implemented for a layered half space. The inversion kernel consists of a two-port transmission line forward model in the frequency domain and a constitutive material equation based on a power law soil mixture rule (Complex Refractive Index Model - CRIM). The spatially-distributed retrieval of broadband dielectric spectra was achieved with a global optimization approach based on a Shuffled Complex Evolution (SCE) algorithm using the full set of the scattering parameters. For each layer, the broadband dielectric spectra were retrieved with the corresponding parameters thickness, porosity, water saturation and electrical conductivity of the aqueous pore solution. For the validation of the approach, a coaxial transmission line cell measured with a network analyzer was used. The possibilities and limitations of the inverse parameter estimation were numerically analyzed in four scenarios. Expected and retrieved layer thicknesses, soil properties and broadband dielectric spectra in each scenario were in reasonable agreement. Hence, the model is suitable for an estimation of in-homogeneous material parameter distributions. Moreover, the proposed frequency domain approach allows an automatic adaptation of layer number and thickness or regular grids in time and/or space.

Long-term environmental monitoring infrastructures in Europe: observations, measurements, scales, and socio-ecological representativeness.

The challenges posed by climate and land use change are increasingly complex, with ever-increasing and accelerating impacts on the global environmental system. The establishment of an internationally harmonized, integrated, and long-term operated environmental monitoring infrastructure is one of the major challenges of modern environmental research. Increased efforts are currently being made in Europe to establish such a harmonized pan-European observation infrastructure, and the European network of Long-Term Ecological Research sites - LTER-Europe - is of particular importance. By evaluating 477 formally accredited LTER-Europe sites, this study gives an overview of the current distribution of these infrastructures and the present condition of long-term environmental research in Europe. We compiled information on long-term biotic and abiotic observations and measurements and examined the representativeness in terms of continental biogeographical and socio-ecological gradients. The results were used to identify gaps in both measurements and coverage of the aforementioned gradients. Furthermore, an overview of the current state of the LTER-Europe observation strategies is given. The latter forms the basis for investigating the comparability of existing LTER-Europe monitoring concepts both in terms of observational design as well as in terms of the scope of the environmental compartments, variables and properties covered.

Carry-over effects of the membrane interface probe.

The membrane interface probe (MIP) is widely used to characterize the subsurface distribution of volatile organic compounds (VOCs). One problem that arises during MIP application is that disproportionately high MIP signals are obtained after passing source zones which contain mobile or residual phases. This serious problem occurs because of a carry-over effect, in particular caused by compound-specific retention times in the conventional unheated transfer line, commonly used during such an investigation. The objective of this study was to perform a qualitative methodical field evaluation of the carry-over effect of a conventional MIP system with a conventional unheated transfer line. This was achieved by coupling a mobile mass spectrometer to the MIP device. Results obtained were then further compared with those achieved using a laser induced fluorescence (LIF) system. Because of this coupling, time- and depth-dependent signals for different substances became known. Field evaluation data obtained showed complex superpositions of compounds with MIP system results. As a result of this superposition, MIP signals from the saturated zone beneath the source zone (zone with free and/or residual phase) are blurred and are therefore not representative of particular depths. However, utilizing multidirectional probing alongside conventional MIP probing (forwards and backwards), it was possible to detect the upper and lower phase boundary of the source zone. These MIP results correlated excellently with the LIF results. An important conclusion that can be drawn from the field investigation is that coupling a mobile mass spectrometer to the MIP system enables advanced MIP signal interpretation to be successfully achieved.