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Noise from industrial plants, wind farms, ports, and airports ranges long distances and merges with noise from linear transport infrastructures (i.e. roads and railways). A straightforward description of people’s long-term noise exposure is usually performed using simplified algorithms implemented in software packages. However, when focusing on the worst-case scenario in favourable sound propagation conditions, the prediction needs to be more accurate and requires the model’s calibration through actual measurements. In these cases, where the noise source under investigation, the concurrent sources, or all of them may be far away from the receivers, the correct choice of the temporal and meteorological windows to perform the measurements demands specific knowledge of weather-related phenomena. Such an approach is also essential in developing technically and economically sustainable mitigation strategies when required. This article first introduces some of the meteorological principles underpinning these phenomena. Within this framework, three of the most commonly used road-traffic noise calculation methods have been applied to identical scenarios, and their performances in accounting for weather-related effects on sound propagation have been analysed. The chosen methodological approach can be used for other environmental noise sources.

In 2021, the energy sector was put at risk by extreme weather in many different ways: North America and Spain suffered heavy winter storms that led to the collapse of the electricity network; California specifically experienced heavy droughts and heat-wave conditions, causing the operations of hydropower stations to halt; floods caused substantial damage to energy infrastructure in central Europe, Australia, and China throughout the year, and unusual wind drought conditions decreased wind power production in the United Kingdom by almost 40% during summer. The total economic impacts of these extreme weather events are estimated at billions of U.S. dollars. Here we review and assess in some detail the main extreme weather events that impacted the energy sector in 2021 worldwide, discussing some of the most relevant case studies and the meteorological conditions that led to them. We provide a perspective on their impacts on electricity generation, transmission, and consumption, and summarize estimations of economic losses. [ABSTRACT FROM AUTHOR]

Summary: The genus Oscheius includes species with different habits and some of them have been shown to kill some insect pests. The occurrence of Oscheius in hay meadow soils from the area recently invaded by the Japanese beetle Popillia japonica in Lombardy, Italy, has been surveyed. Oscheius myriophilus was detected at five out of 18 sites resulting positive to entomopathogenic nematodes (EPN), either alone or together with other genera of EPN, indicating a widespread occurrence. The morphological analysis of the strains retrieved showed that they are morphologically in agreement with those of the type population and with other populations recently described. The phylogenetic analysis supported the sub-grouping of all sequences of O. myriophilus from Lombardy with those present in the databases and a close relationship with O. safricana was observed. The capacity to kill the laboratory host Galleria mellonella was similar to that observed in a previous study related to isolates from P. japonica , although O. myriophilus did not cause significant mortality in laboratory trials with the invasive beetles. The isolates of O. myriophilus from Lombardy were characterised in relation to their infectivity to G. mellonella and response to environmental stress, such as heat tolerance and desiccation. Overall, O. myriophilus appears to be an important component of the EPN community of hay meadow soil in Lombardy and might play a role in the containment of the invasive P. japonica. [ABSTRACT FROM AUTHOR]

The importance of energy transition was underlined at COP28 in Dubai, where governments committed to tripling renewables capacities and doubling the rate of energy efficiency by 2030. However, the power generated by climate-dependent energy sources exhibits greater vulnerability to potential climate changes in the long term. Therefore, climate models play a pivotal role in estimating the effects of climate change on renewables in the context of strategic planning for the development and operation of new renewable power plants. In this context, the World Meteorological Organization (WMO) developed a Global Energy Resilience Atlas aimed at providing insights into the climate change risks for the hydropower sector, the largest renewable electricity source for most countries, generating over 4300 TWh globally. This study focuses on defining four Hydro Climate Risk indices ( H C R I s ) using historical and climate projection precipitation data for three climate scenarios. The final product is a freely available and interactive tool. The developed methodology and tool address how climate changes have historically affected hydropower generation and how they will impact the future at national scales. The final product also addresses the needs of policymakers at national, regional, and global levels in crafting long-term planning for a more secure energy sector, accelerating the energy transition to more sustainable and reliable energies. [ABSTRACT FROM AUTHOR]