Robust clustering-based hybrid technique enabling reliable reservoir water quality prediction with uncertainty quantification and spatial analysis.

Machine learning methodology has recently been considered a smart and reliable way to monitor water quality parameters in aquatic environments like reservoirs and lakes. This study employs both individual and hybrid-based techniques to boost the accuracy of dissolved oxygen (DO) and chlorophyll-a (Chl-a) predictions in the Wadi Dayqah Dam located in Oman. At first, an AAQ-RINKO device (CTD sensor) was used to collect water quality parameters from different locations and varying depths in the reservoir.

Soil nutrients and plant uptake parameters as related to greenhouse gas emissions.

Fertilizer and water management practices have short- and long-term effects on soil chemical and physical properties and, in turn, greenhouse gas (GHG) emissions. The goal of this 4-yr field study was to establish the relationships between soil properties, agronomic practices, and GHG (CO and N O) emissions under different fertilizer and water table management practices. There were two fertilizer treatments: inorganic fertilizer (IF) and a mix of solid cattle manure and inorganic fertilizer (SCM), combined with tile drainage(DR) and controlled drainage with subirrigation(CDS).

Machine learning for predicting greenhouse gas emissions from agricultural soils.

Machine learning (ML) models are increasingly used to study complex environmental phenomena with high variability in time and space. In this study, the potential of exploiting three categories of ML regression models, including classical regression, shallow learning and deep learning for predicting soil greenhouse gas (GHG) emissions from an agricultural field was explored. Carbon dioxide (CO) and nitrous oxide (NO) fluxes, as well as various environmental, agronomic and soil data were measured at the site over a five-year period in Quebec, Canada.

Assessing climate change impacts on greenhouse gas emissions, N losses in drainage and crop production in a subsurface drained field.

Future climate change-driven alterations in precipitation patterns, increases in temperature, and rises in atmospheric carbon dioxide concentration ([CO]) are expected to alter agricultural productivity and environmental quality, while high latitude countries like Canada are likely to face more challenges from global climate change. However, potential climate change impact on GHG emissions from tile-drained fields is poorly documented. Accordingly, climate change impacts on GHG emissions, N losses to drainage and crop production in a subsurface-drained field in Southern Quebec, Canada were assessed using calibrated and validated RZWQM2 model.

Water table management and fertilizer application impacts on CO, NO and CH fluxes in a corn agro-ecosystem.

Water table management with controlled drainage and subsurface-irrigation (SI) has been identified as a Beneficial Management Practice (BMP) to reduce nitrate leaching in drainage water. It has also been shown to increase crop yields during dry periods of the growing season, by providing water to the crop root zone, via upward flux or capillary rise. However, by retaining nitrates in anoxic conditions within the soil profile, SI could potentially increase greenhouse gas (GHG) fluxes, particularly NO through denitrification.

Mitigating greenhouse gas emissions in subsurface-drained field using RZWQM2.

Greenhouse gas (GHG) emissions from agricultural soils are affected by various environmental factors and agronomic practices. The impact of inorganic nitrogen (N) fertilization rates and timing, and water table management practices on NO and CO emissions were investigated to propose mitigation and adaptation efforts based on simulated results founded on field data. Drawing on 2012-2015 data measured on a subsurface-drained corn (Zea mays L.

Comparison of cyanobacterial microcystin synthetase (mcy) E gene transcript levels, mcy E gene copies, and biomass as indicators of microcystin risk under laboratory and field conditions.

Increased incidences of mixed assemblages of microcystin-producing and nonproducing cyanobacterial strains in freshwater bodies necessitate development of reliable proxies for cyanotoxin risk assessment. Detection of microcystin biosynthetic genes in water blooms of cyanobacteria is generally indicative of the presence of potentially toxic cyanobacterial strains. Although much effort has been devoted toward elucidating the microcystin biosynthesis mechanisms in many cyanobacteria genera, little is known about the impacts of co-occurring cyanobacteria on cellular growth, mcy gene expression, or mcy gene copy distribution.

Modelling phosphate adsorption to the soil: application of the non-ideal competitive adsorption model.

Phosphorus (P) transport in subsurface runoff has increased despite the limited mobility of P in soils. This study investigated the ability of the non-ideal competitive adsorption (NICA) model to describe phosphate (PO(4)) adsorption for soils in southern Quebec (Canada). We measured the surface charge and PO(4) adsorption capacity for 11 agricultural soils.