Author: Latest Results

Convection Permitting Regional Climate Modelling Over the Carpathian Region

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Abstract

A preliminary analysis of the performance of the latest version of the RegCM regional modelling system, RegCM5, run at a convection permitting resolution (2 km) over the Carpathian Basin is presented for the following years: 1980, 2006, 2008 and 2010. The performance of the model is assessed using various statistics of surface air temperature and precipitation against the CARPATCLIM high-resolution observational dataset and the ERA5 reanalysis, which also provides the driving field for the simulations. While the model performs generally well, it exhibits a warm bias over the Hungarian lowlands during the warm season and a wet (dry) bias over the mountain chains (flat regions) within the basin. The model also shows a strong orographic forcing of precipitation. In general, RegCM5 has a systematic positive precipitation bias over mountainous regions, which can also be attributed to the relatively low station density of the observation network. The high-resolution model adds value especially for simulating medium to high-intensity precipitation events. Our preliminary experiments provide encouraging indications towards the applicability of RegCM5 to the Carpathian region. Future work will include testing the model with different physics configurations and longer simulations and applying the model to climate change studies over the Carpathian Basin.

Digital Transformations Through the Lens of the Collaborative, Co-Generative and Domesticative

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Abstract

We explore digital transformation and socio-technical systems through the perspectives of collaboration, co-generation, and domestication. Building on Morten Levin’s influential work, we discuss how digital technologies are integrated into work environments, emphasising the necessity for democratic, participatory approaches. We discuss how his ideas of collaborative, democratic practice originating in the 1990s hold up to today’s organisational challenges like digital and sustainability transitions. Are the ideas and practices still valid, or do we need to update them? We identify the grand challenges that must be met: Ensuring sustainable digital transitions and developing educated, skilled workers in digitally transformed organisations.

Monitoring Terrestrial Water Storage Using GRACE/GRACE-FO Data over India: A Review

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Abstract

The gravity recovery and climate experiment (GRACE) satellite mission, which was active between March 2002 and June 2017 and its successor, the GRACE follow-on (GRACE-FO), which has been in operation since May 2018, marked the pioneering remote sensing missions to track changes in terrestrial water storage (TWS) across time. TWS encompasses the cumulative water masses found in the Earth’s soil column, including elements like surface water, soil moisture, snow water equivalent and groundwater (GW). Over the course of the last 20 years, there has been extensive research conducted on fluctuations in the mass of different Elements of the Earth's system, such as the hydrosphere, seas, cryosphere, and solid Earth, utilizing time-varying gravity measurements from the GRACE/GRACE-FO missions. This technology can be utilised to improve monitoring results of large-scale spatial and temporal variations in the water cycle patterns. A review of recent GRACE data used for monitoring terrestrial hydrology over India is provided in this work. The primary applications of GRACE data in the context of large-scale terrestrial hydrological monitoring, such as assessing alterations in terrestrial water storage, involve: retrieving the hydrological components of GW, analysing droughts, floods, land subsidence and determining how glaciers are responding to climate change, have recently been described. India has the tenth position globally in the utilization of GRACE data. Therefore, more investigation is required to completely understand the potential of GRACE data. It was found through a review of the literature that several hydrological models have not yet been thoroughly examined with GRACE data. Furthermore, small river basins can be analysed at a fine scale with downscale GRACE data using machine learning/artificial intelligence. In the Indian context, no research has been conducted to estimate river discharge by using GRACE data.

Global patterns and drivers of tropical aboveground carbon changes

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Abstract

Tropical terrestrial ecosystems play an important role in modulating the global carbon balance. However, the complex dynamics and factors controlling tropical aboveground live biomass carbon (AGC) are not fully understood. Here, using remotely sensed observations, we find a moderate net AGC sink of 0.21 ± 0.06 PgC yr−1 throughout the global tropics from 2010 to 2020. This arises from a gross loss of −1.79 PgC yr−1 offset by a marked gain of 2.01 ± 0.06 PgC yr−1. Fire emissions in non-forested African shrubland/savanna biomes, coupled with post-fire carbon recovery, substantially dominated the interannual variability of tropical AGC. Fire radiative power was identified as the primary determinant of the spatial variability in AGC gains, with soil moisture also playing a crucial role in shaping trends. We highlight the dominant roles of anthropogenic and hydroclimatic determinants in orchestrating tropical land carbon dynamics and advocate for land management to conserve indispensable ecosystem services worldwide.

Modeling and Statistical Approaches for Air Pollution Analysis

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Abstract

This chapter thoroughly explores modeling and statistical approaches for addressing the persistent global challenge of air pollution, emphasizing its significant impact on public health and environmental sustainability. From a historical perspective, the study traces the evolution from localized industrial impacts to current transboundary and global considerations, with examples from Africa highlighting the historical context and environmental awareness. Modeling and statistical methodologies, including dispersion models, time series analysis, source apportionment techniques, and machine learning applications, and this chapter highlights their historical significance as well as current and future relevance for air pollution mitigation. Furthermore, contemporary modeling approaches, categorized into atmospheric dispersion modeling, chemical transport models, and hybrid models, provide unique insights into pollutant behavior. Statistical techniques involving time series analysis, source apportionment, and machine learning applications showcase their adaptability in diverse air pollution contexts. Case studies involving urban air quality modeling, industrial emissions, and regional/global perspectives, highlighting challenges and effective mitigation strategies. The analysis of challenges and limitations emphasizes issues such as data quality, model validation, uncertainty, and computational complexity, crucial for refining methodologies. Future directions outline emerging technologies, remote sensing, IoT integration, and the role of air pollution analysis in policy formulation, signaling a new era of proactive air quality management informed by cutting-edge technologies. By addressing challenges and embracing emerging technologies, the scientific community can contribute to effective air quality management and sustainable environmental practices.

Advertisements for prescription-free drugs and dietary supplements in the Deutsche Apotheker Zeitung (German Pharmacist Journal)

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Abstract

The Deutsche Apotheker Zeitung (DAZ, German Pharmacist Journal) is an independent pharmaceutical newspaper focusing on science and practice, mainly for the profession of pharmacist. In this study, drug advertising in the DAZ was analysed. To our knowledge, there is little scientific data available on drug advertising in professional journals. We assumed that professional journals provide particularly good background information on the advertised drugs because they are targeted to specialists. All non-prescription medicines and preparations that fall under the Medicines Advertising Law (Heilmittelwerbegesetz, HWG) were studied. The Medicines Advertising Law regulates the legal procedure for advertising medicinal products in Germany. The 167 product advertisements from the 52 issues of 2021 were analysed and checked for compliance with the Medicines Advertising Law. We identified significant deficiencies in compliance with the legislation. These included the lack of mandatory information required by the Medicines Advertising Law, for example the indication of adverse drug reactions and the listing of contraindications. There are very few peer-reviewed references on the efficacy of the advertised preparations. A scientific validation was carried out using the PubMed database, with the result that scientific information was available only for 1/3 of the advertisements. In addition, the appearance and target groups as well as social structures, images and feelings conveyed by the advertising were analysed. This study provides insights into the mechanisms of drug advertising in professional journals, which have not yet been researched to any great extent. Even in professional journals, pharmacological evidence plays a much smaller role than marketing, psychology and traditional social values. It seems that drug manufacturers deliberately ignore the German Medicines Advertising Law to advertise their products in the best possible way. Stricter legal controls should be put in place to prevent this practice and protect consumers from misinformation. This will increase drug safety.

Eco-Friendly Methods for Combating Air Pollution

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Abstract

Air pollution, the presence of harmful or excessive concentrations of pollutants in the Earth’s atmosphere, results in severe health impacts, including respiratory and cardiovascular diseases, lung cancer, asthma, and premature death especially among vulnerable populations such as children, the elderly, and individuals with preexisting health conditions. It also affects ecosystems, leading to biodiversity loss, damage to vegetation, soil degradation, and contamination of water bodies. Given the significant health and environmental impacts of air pollution, the implementation of effective mitigation strategies is crucial. Despite growing awareness of the detrimental effects of air pollution, several challenges hinder efforts to achieve sustainable clean air. The chapter aims to explore sustainable strategies for mitigating air pollution and promoting cleaner, healthier environments. It examines various aspects of air pollution, including its sources, impacts, and mitigation measures, with a focus on sustainability and long-term solutions. The chapter highlights the roles of policy and governance frameworks, technological innovations, behavioral changes, sustainable urban planning, industrial and agricultural practices, climate change mitigation, and economic and social considerations to inform and inspire action toward achieving cleaner air and healthier communities. Efforts to address air pollution require both local and regional interventions and global cooperation. Promoting the use of renewable energy sources, such as solar, wind, and hydroelectric power, will reduce the use and reliance on fossil fuels and decrease emissions from power generation. In the same way, the implementation of pollution prevention and control measures in industries will help minimize emissions of pollutants such as sulfur dioxide, nitrogen oxides, particulate matter, and volatile organic compounds. More investment in public transportation infrastructure, the use of electric and hybrid vehicles, and clear emission standards will reduce emissions from transportation sources. National governments also need to establish monitoring networks, enforcement mechanisms, and reporting requirements to track air quality levels, assess compliance with regulatory standards, and take corrective actions to address noncompliance. Education and outreach play a pivotal role in raising awareness about air pollution issues and empowering individuals to act.

A deep learning approach for wind downscaling using spatially correlated global wind data

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Abstract

Wind forecasting is an integral part of wind energy management as a crucial instrument for predicting wind patterns in coastal areas. One common technique to predict the wind field in a specific area is the dynamical downscaling method, which is based on a physical model and requires a substantial computational cost. Instead, this study proposes a novel approach for wind downscaling based on deep learning techniques as a substitution for a dynamical downscaling method. Our methodology starts with generating a high-resolution wind dataset by dynamically downscaling global climate data using RegCM4.7. Then, we employ a feature selection technique to identify the optimal global wind data points that exhibit a strong spatial correlation with the local wind data of interest. The selected features from global climate data and the target from the high-resolution wind data are used to develop a machine learning-based model to predict wind variability in a specific location. We consider various models, namely multilayer perceptron (MLP), AdaBoost, XGBoost, long short-term memory (LSTM), and bidirectional LSTM (BiLSTM), and conduct performance analysis to find an optimum model. The BiLSTM model has been shown to be the most optimal algorithm for wind downscaling among various machine learning models. We also evaluated the model’s performance by conducting a comparative analysis between its predictions and the observed wind data gathered from Jakarta and Meulaboh. This analysis yields significant insights into the accuracy and applicability of our methodology. Our approach reveals a strong correlation coefficient of 0.963 and a low root mean square error (RMSE) of 0.476. These results highlight the efficacy of our method in correctly downscaling wind data.

Evaluation of Wind Speed Accuracy Enhancement in South Asia Through Terrain-Modified Wind Speed (Wt) Adjustments of High-Resolution Regional Climate Modeling

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Abstract

Due to the complex interactions between terrain and atmospheric processes, climate modeling can be challenging in regions with complex topography, such as South Asia. This research examines a critical gap in the added value of high-resolution climate simulations for daily wind speed in South Asia, focusing on the innovative terrain-modified wind speed (Wt) adjustment method. By applying distribution added value (DAV) and upper-tail PDF (95th percentile) analyses, we systematically evaluate the performance of regional climate models (RCMs) and global climate models (GCMs) pre- and post-Wt. The comparison of DAV results before and after applying the Wt method revealed the impact of the Wt adjustment on the performance of the regional climate models. In several models, such as IPSL-RCA, NorESM1-RCA, and CanESM2-RCA, the incorporation of Wt led to substantial improvement, as shown by positive DAV values. In the Upper-tail PDF analysis, improvements were more consistent, indicating that Wt adjustment generally enhanced the representation of extreme wind events. However, some models such as NorESM1-RCA and CanESM2-RCA consistently perform well before and after Wt adjustment by depicting positive DAV values. Overall, the results show that the Wt is effective in improving the representation of wind speed for most climate models. According to the DAV analysis, high-resolution models have a positive added value of 15% on average over lower-resolution models. The contribution of this study is bridging the gap between the observed wind speed patterns in South Asia and climate model outputs. As a result of this research, tailored approaches to evaluating and adjusting models are revealed, emphasizing the complexity of model behavior. In the subcontinent research domain, the results of this study provide crucial insights for climate-related decision-making, risk assessment, and infrastructure development.