RESUMEN
Amid the growing concerns about air toxics from pollution sources, much emphasis has been placed on their impacts on human health. However, there has been limited research conducted to assess the cumulative country-wide impact of air toxics on both terrestrial and aquatic ecosystems, as well as the complex interactions within food webs. Traditional approaches, including those of the United States Environmental Protection Agency (US EPA), lack versatility in addressing diverse emission sources and their distinct ecological repercussions. This study addresses these gaps by introducing the Ecological Health Assessment Methodology (EHAM), a novel approach that transcends traditional methods by enabling both comprehensive country-wide and detailed regional ecological risk assessments across terrestrial and aquatic ecosystems. EHAM also advances the field by developing new food-chain multipliers (magnification factors) for localized ecosystem food web models. Employing traditional ecological multimedia risk assessment of toxics' fate and transport techniques as its foundation, this study extends US EPA methodologies to a broader range of emission sources. The quantification of risk estimation employs the quotient method, which yields an ecological screening quotient (ESQ). Utilizing Kuwait as a case study for the application of this methodology, this study's findings for data from 2017 indicate a substantial ecological risk in Kuwait's coastal zone, with cumulative ESQ values reaching as high as 3.12 × 103 for carnivorous shorebirds, contrasted by negligible risks in the inland and production zones, where ESQ values for all groups are consistently below 1.0. By analyzing the toxicity reference value (TRV) against the expected daily exposure of receptors to air toxics, the proposed methodology provides valuable insights into the potential ecological risks and their subsequent impacts on ecological populations. The present contribution aims to deepen the understanding of the ecological health implications of air toxics and lay the foundation for informed, ecology-driven policymaking, underscoring the need for measures to mitigate these impacts.
RESUMEN
Energy storage plays an important role in the adoption of renewable energy to help solve climate change problems. Lithium-ion batteries (LIBs) are an excellent solution for energy storage due to their properties. In order to ensure the safety and efficient operation of LIB systems, battery management systems (BMSs) are required. The current design and functionality of BMSs suffer from a few critical drawbacks including low computational capability and limited data storage. Recently, there has been some effort in researching and developing smart BMSs utilizing the cloud platform. A cloud-based BMS would be able to solve the problems of computational capability and data storage in the current BMSs. It would also lead to more accurate and reliable battery algorithms and allow the development of other complex BMS functions. This study reviews the concept and design of cloud-based smart BMSs and provides some perspectives on their functionality and usability as well as their benefits for future battery applications. The potential division between the local and cloud functions of smart BMSs is also discussed. Cloud-based smart BMSs are expected to improve the reliability and overall performance of LIB systems, contributing to the mass adoption of renewable energy.
