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Environmental Monitoring

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Co-exposure to microplastics and soil pollutants significantly exacerbates toxicity to crops: Insights from a global meta and machine-learning analysis.

The Science of the total environment
Environmental contamination of microplastics (MPs) is ubiquitous worldwide, and co-contamination of arable soils with MPs and other pollutants is of increasing concern, and may lead to unexpected consequences on crop production. However, the overall ...

Integrating multisource data and machine learning for supraglacial lake detection: Implications for environmental management and sustainable development goals in high mountainous regions.

Journal of environmental management
The accurate detection and monitoring of supraglacial lakes in high mountainous regions are crucial for understanding their dynamic nature and implications for environmental management and sustainable development goals. In this study, we propose a no...

Does COVID-19 lockdown matter for air pollution in the short and long run in China? A machine learning approach to policy evaluation.

Journal of environmental management
This paper leverages a data-driven two-step approach to effectively evaluate the effects of COVID-19 lockdown on air pollution in both the short and long-term in China. Using air pollution, meteorological conditions, and air mass clusters from 34 air...

Multiple remotely sensed datasets and machine learning models to predict chlorophyll-a concentration in the Nakdong River, South Korea.

Environmental science and pollution research international
The Nakdong River is a crucial water resource in South Korea, supplying water for various purposes such as potable water, irrigation, and recreation. However, the river is vulnerable to algal blooms due to the inflow of pollutants from multiple point...

Unravelling nitrate transformation mechanisms in karst catchments through the coupling of high-frequency sensor data and machine learning.

Water research
Nitrate dynamics within a catchment are critical to the earth's system process, yet the intricate details of its transport and transformation at high resolutions remain elusive. Hydrological effects on nitrate dynamics in particular have not been tho...

Explainable machine learning for predicting diarrhetic shellfish poisoning events in the Adriatic Sea using long-term monitoring data.

Harmful algae
In this study, explainable machine learning techniques are applied to predict the toxicity of mussels in the Gulf of Trieste (Adriatic Sea) caused by harmful algal blooms. By analysing a newly created 28-year dataset containing records of toxic phyto...

Evaluation of crop water stress index of wheat by using machine learning models.

Environmental monitoring and assessment
The Crop Water Stress Index (CWSI), a pivotal indicator derived from canopy temperature, plays a crucial role in irrigation scheduling for water conservation in agriculture. This study focuses on determining CWSI (by empirical method) for wheat crops...

Towards sustainable coastal management: aerial imagery and deep learning for high-resolution mapping.

PeerJ
The massive arrival of pelagic on the coasts of several countries of the Atlantic Ocean began in 2011 and to date continues to generate social and environmental challenges for the region. Therefore, knowing the distribution and quantity of in the o...

Spatiotemporal modelling of airborne birch and grass pollen concentration across Switzerland: A comparison of statistical, machine learning and ensemble methods.

Environmental research
BACKGROUND: Statistical and machine learning models are commonly used to estimate spatial and temporal variability in exposure to environmental stressors, supporting epidemiological studies. We aimed to compare the performances, strengths and limitat...

Integrating deep learning techniques for effective river water quality monitoring and management.

Journal of environmental management
Effective river water quality monitoring is essential for sustainable water resource management. In this study, we established a comprehensive monitoring system along the Kaveri River, capturing real-time data on multiple critical water quality param...