Bioventing
Description Bioventing is an in situ remediation technology that uses microorganisms to biodegrade organic constituents adsorbed on soils in the unsaturated zone. Bioventing enhances the activity of indigenous bacteria and simulates the natural in situ biodegradation of hydrocarbons in soil by inducing air or oxygen flow into the unsaturated zone and, if necessary, by adding nutrients. During bioventing, oxygen may be supplied through direct air injection into residual contamination in soil. Bioventing primarily assists in the degradation of adsorbed fuel residuals, but also assists in the degradation of volatile organic compounds (VOCs) as vapors move slowly through biologically active soil. The rate of natural degradation is generally limited by the lack of oxygen and other electron acceptor (i.e., a compound that gains electrons during biodegradation) rather than by the lack of nutrients (i.e., electron donors). In conventional bioventing systems, oxygen is delivered by an electric blower to subsurface wells. In contrast to soil vapor vacuum extraction, bioventing uses low airflow rates to provide only enough oxygen to sustain microbial activity. Passive bioventing systems use natural air exchange to deliver oxygen to the subsurface via bioventing wells. A one-way valve, installed on a vent well, allows air to enter the well when the pressure inside the well is lower than atmospheric pressure. When atmospheric pressure drops (due to a change in barometric pressure) below the subsurface pressure, the valve closes, trapping the air in the well and increasing oxygen to the soil surrounding the well. Limitations and ConcernsHigh soil moisture or low permeability soils reduce bioventing performance. Low temperatures may slow remediation. Extremely low soil moisture content may limit biodegradation and the effectiveness of bioventing.
Soil Erosion and Land Degradation.
Soil erosion and land degradation are the greatest environmental issues facing most regions and societies at this time. Although irrevocably linked to deforestation and pollution problems, soil erosion is bigger than the sum of these two crises combined.
Agrivoltaics (agrophotovoltaics, agrisolar, or dual-use solar) is the dual use of land for solar energy production and agriculture.
Electrothermal mineralization of per- and polyfluoroalkyl substances for soil remediation.
Per- and polyfluoroalkyl substances (PFAS) are persistent and bioaccumulative pollutants that can easily accumulate in soil, posing a threat to environment and human health.
All agricultural practices depend on stable and productive soil for their long-term sustainability. Soil erosion leads to the gradual loss of fertility in the landscape. Soil is a living entity formed gradually through ecological and geological processes.
Soildegradation is the loss of production and in turn the loss of dependent plants and animals. Land degradation is not a new phenomenon, Human societies have been degrading environments through their cultural practices for thousands of years.
Perhaps the root cause of soil erosion is mankind's general lack of understanding of the complex natural systems (i.e.plant phrenology) that constantly interact to produce dynamic and stable global ecosystems.
Machine learning (ML) models have the potential to significantly enhance soil erosion prediction accuracy. Here’s how they can contribute:
Nonlinear Relationships:
Feature Selection and Extraction:ML algorithms can automatically identify relevant features (such as slope, land use, vegetation cover, rainfall intensity) that impact soil erosion. This helps improve model accuracy by focusing on essential factors.
Traditional soil erosion models often assume linear relationships between variables. ML models, such as neural networks and decision trees, can capture nonlinear patterns, leading to more accurate predictions.
ML models learn from historical data, adapting to changing conditions. As more data becomes available, the models can refine their predictions, improving accuracy over time.
Ensemble Techniques:
Ensemble methods (e.g., Random Forests, Gradient Boosting) combine multiple models to make collective predictions. They reduce bias and variance, resulting in better accuracy.
Cross-Validation:ML models use techniques like k-fold cross-validation to assess their performance. This ensures robustness and helps prevent overfitting, leading to more accurate predictions.
Hyperparameter Tuning:
ML models have hyperparameters (e.g., learning rate, regularization strength) that affect their performance. Optimizing these hyperparameters can enhance accuracy.
Spatial and Temporal Considerations:
Soil erosion varies across space and time. ML models can incorporate spatial data (e.g., geographic information systems) and temporal trends, improving accuracy for specific regions and time periods.
Some ML models provide uncertainty estimates (e.g., Bayesian methods). Understanding prediction uncertainty is crucial for decision-making and risk assessment.
Remember that accurate soil erosion prediction depends not only on the model but also on high-quality input data. Proper data collection, preprocessing, and domain knowledge play essential roles in achieving accurate predictions.
Today the scale of the impact is greater due to exploding human populations, unsustainable agricultural practices, and urban development.
Recognising and respecting the different ways nature is valued can enable better environmental decision-making, according to new research led by the University of East Anglia (UEA).
Grassland CRP, offered by USDA’s Farm Service Agency (FSA), is a voluntary working lands conservation program that enables participants to conserve grasslands and provide important conservation benefits for wildlife, soil health and carbon sequestration, all while continuing most grazing and haying practices.
Today the scale of the impact is greater due to exploding human populations, unsustainable agricultural practices, and urban development.
Scour Protection
Runoff and soil erosion are major environmental threats to European agricultural land use.
Large-scale land-use disasters, such as floods, droughts, vegetation removal, and extreme events, can lead to changes in the shape of rivers and catchments. This can cause sand and gravel to accumulate in former reservoirs, and instability can result in river erosion and changes in the angle of attack, contributing to bridge scour.
Debris flow can significantly impact bridge scours by reducing the waterway under a bridge and causing contraction scour in the channel.
Seven in 10 skip waste soils, Soil, and Landscape Consultant Tim O'Hare, tests contain the carcinogen Benzo(a)pyrene, and he estimates that around one-quarter could contain asbestos.
Impervious Pavements
Impervious pavements deprive tree roots of aeration, eliminating the "urban forest" and the canopy shade that would otherwise moderate the urban climate. Because impervious surfaces displace living vegetation, they reduce ecological productivity and interrupt atmospheric carbon cycling.
Controlling stormwater flow over impervious areas is a multidisciplinary eco script where;
- The pavement materials seal the soil surface, eliminating rainwater infiltration and natural groundwater recharge.
The warm runoff from impervious surfaces reduces dissolved oxygen in stream water, making life difficult in aquatic ecosystems.
Clean Water
Compliance with the Clean Water Act mandatory erosion and sediment control devices must be installed on construction sites to minimize soil release into runoff waters.
Many construction sites have relied on straw bale and silt fence barriers. Straw bales have been proven ineffective due to inappropriate placement, bad installation, and the nature of their structure.
Silt fences require expensive manpower for installation, inspection maintenance, and removal. Silt fences cannot be placed on a slope or across a contour line and are not effective unless trenched or keyed in.
If not installed at a consistent elevation, silt fences cause erosion.
Biodiversity Offsets
Biodiversity offsetting is a method intended to help compensate for the detrimental impacts of development on biodiversity.
Biodiversity offsettingis a system used predominantly by planning authorities and developers to fully compensate for biodiversity impacts associated with economic development, through the planning process.
Such an approach is designed to work by creating a credit-based market that developers could use to offset actions deemed harmful to the environment by investing in habitat restoration for biodiversity elsewhere.
The idea is that losses of biodiversity at an impact site are compensated for by the generation of ecologically equivalent gains elsewhere, resulting in ‘no net loss’ of biodiversity.
In some circumstances, biodiversity offsets are designed to result in an overall biodiversity gain.
Offsetting is generally considered the final stage in a mitigation hierarchy, whereby predicted Biodiversity impacts must first be avoided, minimized, and reversed by developers before any remaining impacts are offset.
The mitigation hierarchy is used to meet the environmental policy principle of "No Net Loss" of biodiversity alongside development.
The legal and institutional dimensions of biodiversity are a highly topical and increasingly popular approach used to compensate for impacts on species and ecosystems as a result of development and is the subject of a large and growing body of scientific research.
Acknowledging the limitations of what can be achieved through biodiversity offsetting is important if we are not to wake up one day and discover we have lost what we cannot replace.
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