AIMC Topic: Environmental Monitoring

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From sample to sonde to Sentinel-2: insights from a multi-scale chlorophyll-a monitoring effort in the Hudson River, New York.

Environmental monitoring and assessment
Monitoring cyanobacteria and other nuisance phytoplankton in the Hudson River is of great interest given its societal and ecological importance. Satellite remote sensing provides a cost-effective method to monitor chlorophyll-a (chl-a), a common prox...

Multi-scale dynamic graph neural network for PM2.5 concentration prediction in regional station cluster.

PloS one
Accurate prediction of PM2.5 concentrations is crucial for public health and environmental management. However, effectively capturing complex spatiotemporal dependencies across multiple time scales remains a persistent challenge for existing methods,...

A new approach improving koala habitat prediction using hyperspectral airborne imagery.

The Science of the total environment
Koala populations are declining primarily due to habitat loss, making large-scale habitat quality prediction essential for conservation. A first approach to defining koala habitat quality involves identifying the number of different 'koala' trees spe...

Assessing climatic and non-climatic habitat suitability of Haloxylon salicornicum (Moq.) Bunge ex Boiss using ensemble species distribution modelling coupled with analytic hierarchy process.

Environmental monitoring and assessment
Haloxylon salicornicum is a keystone shrub of the Indian arid zone, valued for dune stabilization, fodder, and ecosystem restoration, yet its climatic resilience remains poorly understood. This study assessed the ecological thresholds, current habita...

Ecology-informed symbolic machine learning: a methodological framework for classification of forest succession.

Environmental monitoring and assessment
Accurately classifying forest successional stages remains a major challenge in applied ecology due to the continuum of succession, ecological heterogeneity, and limited interpretability of many machine learning (ML) approaches. Prevailing models typi...

Interpretable artificial intelligence modeling of pre-emergence herbicide bioactivity in weakly weathered soils for optimized dose recommendations, Part I: Diclosulam.

The Science of the total environment
Conventional herbicide recommendations seldom consider soil physicochemical attributes beyond texture, overlooking key factors that govern bioavailability and environmental fate. This study presents an integrated framework for optimizing the doses of...

Land use change and soil salinization in the Sundarbans: a machine-learning based analysis of long-term transformation and future projections.

Environmental monitoring and assessment
Quantitative data on coastal land use changes are essential for effective resource management and sustainable development. In this study, we examined land use and land cover (LULC) changes, along with erosion and accretion, in the climate-sensitive S...

High-resolution predictive mapping reveals novel benthic habitats in the southern Baltic Sea.

The Science of the total environment
Marine benthic habitats are essential for biodiversity and ecosystem functioning, yet their complexity and inaccessibility challenge effective mapping and management. This study presents the first high-resolution predictive benthic habitat maps at th...

Unveiling and interpreting the relationships among multi-pollutant emission factors in municipal solid waste incineration by machine learning.

Waste management (New York, N.Y.)
Effective control of key parameters is critical for regulating pollutant emissions in municipal solid waste incineration (MSWI), but existing research on these parameters remains limited and lacks comprehensiveness. This study used over 600,000 indus...

Regional Air Quality Management: A Data-Driven Airshed Approach in the Eastern IGP Regions.

Environmental science & technology
In India, ensuring clean air for all is vital and should not be limited to urbanites. However, the current air quality monitoring networks and management plans are limited to cities, air quality status across regions is yet to be measured, and compre...