Urban heat mitigation by green and blue infrastructure: Drivers, effectiveness, and future needs

The Innovation, Journal Year: 2024, Volume and Issue: 5(2), P. 100588 - 100588

Published: Feb. 7, 2024

The combination of urbanization and global warming leads to urban overheating compounds the frequency intensity extreme heat events due climate change. Yet, risk can be mitigated by green-blue-grey infrastructure (GBGI), such as parks, wetlands, engineered greening, which have potential effectively reduce summer air temperatures. Despite many reviews, evidence bases on quantified GBGI cooling benefits remains partial practical recommendations for implementation are unclear. This systematic literature review synthesizes base mitigation related co-benefits, identifies knowledge gaps, proposes their maximize benefits. After screening 27,486 papers, 202 were reviewed, based 51 types categorized under 10 main divisions. Certain (green walls, street trees) been well researched capabilities. However, several other received negligible (zoological garden, golf course, estuary) or minimal (private allotment) attention. most efficient was observed in botanical gardens (5.0 ± 3.5°C), wetlands (4.9 3.2°C), green walls (4.1 4.2°C), trees (3.8 3.1°C), vegetated balconies 2.7°C). Under changing conditions (2070-2100) with consideration RCP8.5, there is a shift subtypes, either within same zone (e.g., Dfa Dfb Cfb Cfa) across zones [continental warm-summer humid] BSk [dry, cold semi-arid] Cwa [temperate] Am [tropical]). These shifts may result lower efficiency current future. Given importance multiple services, it crucial balance functionality, performance, co-benefits when planning future GBGI. inventory assist policymakers planners prioritizing effective interventions overheating, filling research promoting community resilience.

Language: Английский

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Heavy metals pollution from smelting activities: A threat to soil and groundwater

Ecotoxicology and Environmental Safety, Journal Year: 2024, Volume and Issue: 274, P. 116189 - 116189

Published: March 9, 2024

Throughout the literature, word "heavy metal" (HM) has been utilized to describe soil contamination; in this context, we characterize it as those elements with a density greater than 5 g per cubic centimeter. Contamination is one of major global health concerns, especially China. China's rapid urbanization over past decades caused widespread urban water, air, and degradation. This study provides complete assessment contamination by heavy metals mining smelting regions. The (HMs) includes an examination their potential adverse impacts, origins, strategies for remediation contaminated metals. presence can be linked both natural anthropogenic processes. Studies have demonstrated that soils present risks individuals. Children are more vulnerable effects metal pollution adults. results highlight significance operations Soil poses significant carcinogenic non-carcinogenic, particularly children individuals living heavily polluted areas. Implementing physical, chemical, biological techniques most productive approach addressing metal-contaminated soil. Among these methods, phytoremediation emerged advantageous option due its cost-effectiveness environmentally favorable characteristics. Monitoring utmost importance facilitate implementation improved management soils.

Language: Английский

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Impacts of renewable energy, trade globalization, and technological innovation on environmental development in China: Evidence from various environmental indicators and novel quantile methods

Environmental Development, Journal Year: 2023, Volume and Issue: 48, P. 100923 - 100923

Published: Aug. 31, 2023

Language: Английский

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Artificial intelligence for geoscience: Progress, challenges and perspectives

The Innovation, Journal Year: 2024, Volume and Issue: 5(5), P. 100691 - 100691

Published: Aug. 23, 2024

Public summary•What does AI bring to geoscience? has been accelerating and deepening our understanding of Earth Systems in an unprecedented way, including the atmosphere, lithosphere, hydrosphere, cryosphere, biosphere, anthroposphere interactions between spheres.•What are noteworthy challenges As we embrace huge potential geoscience, several arise reliability interpretability, ethical issues, data security, high demand cost.•What is future The synergy traditional principles modern AI-driven techniques holds immense promise will shape trajectory geoscience upcoming years.AbstractThis paper explores evolution geoscientific inquiry, tracing progression from physics-based models data-driven approaches facilitated by significant advancements artificial intelligence (AI) collection techniques. Traditional models, which grounded physical numerical frameworks, provide robust explanations explicitly reconstructing underlying processes. However, their limitations comprehensively capturing Earth's complexities uncertainties pose optimization real-world applicability. In contrast, contemporary particularly those utilizing machine learning (ML) deep (DL), leverage extensive glean insights without requiring exhaustive theoretical knowledge. ML have shown addressing science-related questions. Nevertheless, such as scarcity, computational demands, privacy concerns, "black-box" nature hinder seamless integration into geoscience. methodologies hybrid presents alternative paradigm. These incorporate domain knowledge guide methodologies, demonstrate enhanced efficiency performance with reduced training requirements. This review provides a comprehensive overview research paradigms, emphasizing untapped opportunities at intersection advanced It examines major showcases advances large-scale discusses prospects that landscape outlines dynamic field ripe possibilities, poised unlock new understandings further advance exploration.Graphical abstract

Language: Английский

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CAS Landslide Dataset: A Large-Scale and Multisensor Dataset for Deep Learning-Based Landslide Detection

Scientific Data, Journal Year: 2024, Volume and Issue: 11(1)

Published: Jan. 2, 2024

Abstract In this work, we present the CAS Landslide Dataset, a large-scale and multisensor dataset for deep learning-based landslide detection, developed by Artificial Intelligence Group at Institute of Mountain Hazards Environment, Chinese Academy Sciences (CAS). The aims to address challenges encountered in recognition. With increase occurrences due climate change earthquakes, there is growing need precise comprehensive support fast efficient contrast existing datasets with size, coverage, sensor type resolution limitations, Dataset comprises 20,865 images, integrating satellite unmanned aerial vehicle data from nine regions. To ensure reliability applicability, establish robust methodology evaluate quality. We propose use as benchmark construction identification models facilitate development learning techniques. Researchers can leverage obtain enhanced prediction, monitoring, analysis capabilities, thereby advancing automated detection.

Language: Английский

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Deep learning for cross-region streamflow and flood forecasting at a global scale

The Innovation, Journal Year: 2024, Volume and Issue: 5(3), P. 100617 - 100617

Published: March 26, 2024

Language: Английский

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Identifying regional eco-environment quality and its influencing factors: A case study of an ecological civilization pilot zone in China

Journal of Cleaner Production, Journal Year: 2023, Volume and Issue: 435, P. 140308 - 140308

Published: Dec. 19, 2023

Language: Английский

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Global carbon balance of the forest: satellite-based L-VOD results over the last decade

Frontiers in Remote Sensing, Journal Year: 2024, Volume and Issue: 5

Published: May 10, 2024

Monitoring forest carbon (C) stocks is essential to better assess their role in the global balance, and model predict long-term trends inter-annual variability atmospheric CO2 concentrations. On a national scale, inventories (NFIs) can provide estimates of stocks, but these are only available certain countries, limited by time lags due periodic revisits, cannot spatially continuous mapping forests. In this context, remote sensing offers many advantages for monitoring above-ground biomass (AGB) on scale with good spatial (50–100 m) temporal (annual) resolutions. Remote has been used several decades monitor vegetation. However, traditional methods AGB using optical or microwave sensors affected saturation effects moderately densely vegetated canopies, limiting performance. Low-frequency passive less effects: occurs at levels around 400 t/ha L-band (frequency 1.4 GHz). Despite its coarse resolution order 25 km × km, method based L-VOD (vegetation depth L-band) index recently established itself as an approach annual variations continental scale. Thus, applied continents biomes: tropics (especially Amazon Congo basins), boreal regions (Siberia, Canada), Europe, China, Australia, etc. no reference study yet published analyze detail terms capabilities, validation results. This paper fills gap presenting physical principles calculation, analyzing performance reviewing main applications tracking balance vegetation over last decade (2010–2019).

Language: Английский

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Surface soil moisture from combined active and passive microwave observations: Integrating ASCAT and SMAP observations based on machine learning approaches

Remote Sensing of Environment, Journal Year: 2024, Volume and Issue: 308, P. 114197 - 114197

Published: May 11, 2024

Language: Английский

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Hysteresis analysis reveals how phytoplankton assemblage shifts with the nutrient dynamics during and between precipitation patterns

Water Research, Journal Year: 2024, Volume and Issue: 251, P. 121099 - 121099

Published: Jan. 1, 2024

Language: Английский

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