Predictive modeling of fractional plankton-assisted cholera propagation dynamics using Bayesian regularized deep cascaded exogenous neural networks

Process Safety and Environmental Protection, Год журнала: 2025, Номер unknown, С. 106819 - 106819

Опубликована: Фев. 1, 2025

Язык: Английский

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Exploring the triple burden of social disadvantage, mobility poverty, and exposure to traffic-related air pollution

The Science of The Total Environment, Год журнала: 2024, Номер 920, С. 170947 - 170947

Опубликована: Фев. 15, 2024

Understanding the relationships between ultrafine particle (UFP) exposure, socioeconomic status (SES), and sustainable transportation accessibility in Toronto, Canada is crucial for promoting public health, addressing environmental justice, ensuring equity. We conducted a large-scale mobile measurement campaign employed gradient boost model to generate exposure surfaces using land use, built environment, meteorological conditions. The Ontario Marginalization Index was used quantify various indicators of social disadvantage Toronto's neighborhoods. Our findings reveal that people socioeconomically disadvantaged areas experience elevated UFP exposures. highlight significant disparities accessing transportation, particularly with higher ethnic concentrations. When factoring daily mobility, populations are further exacerbated. Furthermore, individuals who do not emissions themselves consistently exposed UFPs, active users experiencing highest exposures both at home activity locations. Finally, we proposed novel index, Community Prioritization (CPI), incorporating three indicators, including air quality, disadvantage, transportation. This index identifies neighborhoods triple burden, often situated near major infrastructure hubs high diesel truck lacking greenspace, marking them as high-priority policy action targeted interventions.

Язык: Английский

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Spatial prediction of on-road air pollution using long-term mobile monitoring: Insights from Delhi

Urban Climate, Год журнала: 2025, Номер 60, С. 102347 - 102347

Опубликована: Март 1, 2025

Язык: Английский

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Unveiling the Impact of Wildfires on Nanoparticle Characteristics and Exposure Disparities through Mobile and Fixed-Site Monitoring in Toronto, Canada

Environmental Science & Technology, Год журнала: 2025, Номер unknown

Опубликована: Март 12, 2025

This study investigates the impacts of wildfires on nanoparticle characteristics and exposure disparities in Toronto, integrating data from a large-scale mobile monitoring campaign fixed-site measurements during unprecedented 2023 wildfire season. Our results reveal changes particle days, with number concentrations decreasing by 60% diameter increasing 30% compared to nonwildfire days. Moreover, median lung deposited surface area (LDSA) levels rose 31% events. We employed gradient boosting models estimate near-road LDSA both The ratio (wildfire/nonwildfire) exceeded 2.0 certain areas along highways downtown Toronto. Furthermore, our findings show that marginalized communities faced greater increases than less ones. Under conditions, difference between most least groups was 16% for recent immigrants visible minorities 7% seniors children, statistically significant. delivers critical insights into spatiotemporal variations periods, demonstrating substantial health risks posed increased inequitable distribution these among Toronto's diverse population.

Язык: Английский

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Traffic-related air pollution backcasting using convolutional neural network and long short-term memory approach

The Science of The Total Environment, Год журнала: 2025, Номер 976, С. 179286 - 179286

Опубликована: Апрель 7, 2025

Язык: Английский

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Embedding AI-Enabled Data Infrastructures for Sustainability in Agri-Food: Soft-Fruit and Brewery Use Case Perspectives

Sensors, Год журнала: 2024, Номер 24(22), С. 7327 - 7327

Опубликована: Ноя. 16, 2024

The agri-food sector is undergoing a comprehensive transformation as it transitions towards net zero. To achieve this, fundamental changes and innovations are required, including in how food produced delivered to customers, new technologies, data physical infrastructures, algorithmic advancements. In this paper, we explore the opportunities challenges of deploying AI-based infrastructures for sustainability by focusing on two case studies: soft-fruit production brewery operations. We investigate potential benefits incorporating Internet Things (IoT) sensors AI technologies improving use resources, reducing carbon footprints, enhancing decision-making. identify user engagement with key challenge, together issues quality arising from environmental volatility, difficulties generalising models, those designed calculators, socio-technical barriers adoption. highlight advocate engagement, more granular availability sensor, production, emissions data, transparent footprint calculations. Our proposed future directions include semantic integration enhance interoperability, generation synthetic overcome lack real-world farm multi-objective optimisation systems model competing interests between yield goals. general, argue that not silver bullet zero industry, but at same time, solutions, when appropriately deployed, can be useful tool operating synergy other approaches.

Язык: Английский

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Hyper-local Black Carbon Prediction by Integrating Land Use Variables with Explainable Machine Learning Model

Atmospheric Environment, Год журнала: 2024, Номер 336, С. 120733 - 120733

Опубликована: Авг. 3, 2024

Язык: Английский

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