Mass Balance Models to Derive Critical Loads of Nitrogen and Acidity for Terrestrial and Aquatic Ecosystems

2015 ◽  
pp. 171-205 ◽  
Author(s):  
Maximilian Posch ◽  
Wim de Vries ◽  
Harald U. Sverdrup
Keyword(s):  
Mass Balance ◽  
Critical Loads ◽  
Aquatic Ecosystems ◽  
Mass Balance Models

Comparison between energy balance and mass balance models for actual evapotranspiration assessment

2009 ◽  
Author(s):  
A. Gentile ◽  
L. Pierce ◽  
G. Ciraolo ◽  
G. Zhang ◽  
G. La Loggia ◽  
...  
Keyword(s):  
Energy Balance ◽  
Mass Balance ◽  
Actual Evapotranspiration ◽  
Mass Balance Models

Investigating Fast- and Slow-settling Phosphorus Fractions in Lakes using Steady-state Modeling

2021 ◽  
Author(s):  
Hamed Khorasani ◽  
Zhenduo Zhu
Keyword(s):  
Steady State ◽  
Mass Balance ◽  
Large Scale ◽  
Hydraulic Residence Time ◽  
Simple Method ◽  
Retention Models ◽  
Mass Balance Models ◽  
Limiting Nutrient ◽  
Fast Settling ◽  
P Retention

<p>Phosphorus (P) is the key and limiting nutrient in the eutrophication of freshwater resources. Modeling P retention in lakes using steady-state mass balance models (i.e. Vollenweider-type models) provides insights into the lake P management and a simple method for large-scale assessments of P in lakes. One of the basic problems in the mass balance modeling of P in lakes is the removal of P from the lake water column by settling. A fraction of the incoming P into the lake from the watershed is associated with fast-settling particles (e.g. sediment particles) that result in the removal of that fraction of P quickly at the lake entrance. However, existing models considering a constant fraction of fast-settling TP for all lakes are shown to result in overestimation of the retention of P in lakes with short hydraulic residence time. In this study, we combine a hypothesis of the fast- and slow-settling P fractions into the steady-state mass balance models of P retention in lakes. We use a large database of lakes to calibrate the model and evaluate the hypothesis. The results of this work can be used for the improvement of the prediction power of P retention models in lakes and help to better understand the processes of P cycling in lakes.</p>

scholarly journals Estimates of Exceedances of Critical Loads for Acidifying Deposition in Alberta and Saskatchewan

2018 ◽  
Author(s):  
Paul A. Makar ◽  
Ayodeji Akingunola ◽  
Julian Aherne ◽  
Amanda S. Cole ◽  
Yayne-abeba Aklilu ◽  
...  
Keyword(s):  
Critical Load ◽  
Critical Loads ◽  
Oil Sands ◽  
Aquatic Ecosystems ◽  
Reasonable Agreement ◽  
Base Cation ◽  
Spatial Extent ◽  
Fugitive Dust ◽  
Sulphur Deposition ◽  
Total Deposition

Abstract. Estimates of potential harmful effects to ecosystems in the Canadian provinces of Alberta and Saskatchewan due to acidifying deposition were calculated, using a one year simulation of a high resolution implementation of the Global Environmental Multiscale – Modelling Air-quality and Chemistry (GEM-MACH) model, and estimates of aquatic and terrestrial ecosystem critical loads. The model simulation was evaluated against two different sources of deposition data; total deposition in precipitation and total deposition to snowpack in the vicinity of the Athabasca oil sands. The model captured much of the variability of observed ions in wet deposition in precipitation (observed versus model sulphur, nitrogen and base cation R2 values of 0.90, 0.76 and 0.72, respectively), while being biased high for sulphur deposition, and low for nitrogen and base cations (slopes 2.2, 0.89 and 0.40, respectively). Aircraft-observation-based estimates of fugitive dust emissions, shown to be a factor of ten higher than reported values (Zhang et al., 2017), were used to estimate the impact of increased levels of fugitive dust on model results. Model comparisons to open snowpack observations were shown to be biased high, but in reasonable agreement for sulphur deposition when observations were corrected to account for throughfall in needleleaf forests. The model-observation relationships for precipitation deposition data, along with the expected effects of increased (unreported) base cation emissions, were used to provide a simple observation-based correction to model deposition fields. Base cation deposition was estimated using published observations of base cation fractions in surface collected particles (Wang et al., 2015). Both original and observation-corrected model estimates of sulphur, nitrogen and base cation deposition were used in conjunction with critical load data created using the NEG-ECP (2001) and CLRTAP (2004, 2016, 2017) protocols for critical loads, using variations on the Simple Mass Balance model for forest and terrestrial ecosystems, and the Steady State Water Chemistry and the First-order Acidity Balance models for aquatic ecosystems. Potential ecosystem damage at 2013/14 emissions and deposition levels was predicted for regions within each of the ecosystem critical load datasets examined here. The spatial extent of the regions in exceedance of critical loads varied between 1 × 104 and 3.3 × 105 km2, for the more conservative observation-corrected estimates of deposition, with the variation dependant on the ecosystem and critical load protocol. The larger estimates (for aquatic ecosystems) represent a substantial fraction of the area of the provinces examined. Base cation deposition was shown to have a neutralizing effect on acidifying deposition, and the use of the aircraft and precipitation observation-based corrections to base cation deposition resulted in reasonable agreement with snowpack data collected in the oil sands area. However, critical load exceedances calculated using both observations and observation-corrected deposition suggest that the neutralization effect is limited in spatial extent, decreasing rapidly with distance from emissions sources, due to the rapid deposition of emitted primary particles dust particles as a function of their size.

Impact of the choice of surface mass balance models and their calibration on large-scale glacier change projections

2021 ◽  
Author(s):  
Lilian Schuster ◽  
David Rounce ◽  
Fabien Maussion
Keyword(s):  
Mass Balance ◽  
Large Scale ◽  
Model Complexity ◽  
Model Parameters ◽  
Glacier Change ◽  
Surface Mass Balance ◽  
Ice Dynamics ◽  
Surface Mass ◽  
Mass Balance Models ◽  
Intercomparison Study

<p>A recent large model intercomparison study (GlacierMIP) showed that differences between the glacier models is a dominant source of uncertainty for future glacier change projections, in particular in the first half of the century.  Each glacier model has their own unique set of process representations and climate forcing methodology, which makes it impossible to determine the model components that contribute most to the projection uncertainty. This study aims to improve our understanding of the sources of large scale glacier model uncertainty using the Open Global Glacier Model (OGGM), focussing on the surface mass balance (SMB) in a first step. We calibrate and run a set of interchangeable SMB model parameterizations (e.g. monthly vs. daily, constant vs. variable lapse rates, albedo, snowpack evolution and refreezing) under controlled boundary conditions. Based on ensemble approaches, we explore the influence of (i) the parameter calibration strategy and (ii) SMB model complexity on regional to global glacier change. These uncertainties are then put in relation to a qualitative selection of other model design choices, such as the forcing climate dataset and ice dynamics model parameters. </p>

Computational Techniques Application in Environmental Exposure Assessment

2015 ◽  
pp. 471-505
Author(s):  
Karolina Jagiello ◽  
Tomasz Puzyn
Keyword(s):  
Mass Balance ◽  
Exposure Assessment ◽  
Environmental Exposure ◽  
Chemical Properties ◽  
Computational Techniques ◽  
Structure Property ◽  
Mass Balance Models ◽  
Modelling Methodology ◽  
Environmental Exposure Assessment ◽  
Input Variables

In this chapter, the application of computational techniques in environmental exposure assessment was described. The most important groups of these techniques are Multimedia Mass-balance (MM) modelling and Quantitative Structure-Activity/Structure-Property Relationships (QSAR/QSPR) modelling. Multimedia Mass-balance models have been widely utilized for studying Long-Range Transport Potential (LRTP) and overall persistence (POV) of Persistent Organic Pollutants (POPs), regulated by many national and international acts, including the Stockholm Convention on POPs. Recently, a novel modelling methodology that links QSPR and MM has been implemented. According to this approach, the physical/chemical properties required as the input variables for multimedia modelling can be calculated directly from appropriate QSPR models. QSPR models must be previously developed based on the relationships between the chemical structure and the modelled properties (QSPR).

scholarly journals Mathematical Modeling of a Domestic Wastewater Treatment System Combining a Septic Tank, an Up Flow Anaerobic Filter, and a Constructed Wetland

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3019
Author(s):  
Alberto Fernández del Castillo ◽  
Marycarmen Verduzco Garibay ◽  
Carolina Senés-Guerrero ◽  
Carlos Yebra-Montes ◽  
José de Anda ◽  
...  
Keyword(s):  
Mathematical Modeling ◽  
Wastewater Treatment ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Mass Balance ◽  
Constructed Wetland ◽  
Oxygen Demand ◽  
Septic Tank ◽  
First Order ◽  
Mass Balance Models

Systems combining anaerobic bioreactors with constructed wetlands (CW) have proven to be adequate and efficient for wastewater treatment. Detailed knowledge of removal dynamics of contaminants can ensure positive results for engineering and design. Mathematical modeling is a useful approach to studying the dynamics of contaminant removal in wastewater. In this study, water quality monitoring was performed in a system composed of a septic tank (ST), an up flow anaerobic filter (UAF), and a horizontal flow constructed wetland (HFCW). Biological oxygen demand (BOD5), chemical oxygen demand (COD), total Kjeldahl nitrogen (TKN), NH3, organic nitrogen (ON), total suspended solids (TSS), NO2−, and NO3− were measured biweekly during a 3-month period. First-order kinetics, multiple linear regression, and mass balance models were applied for data adjustment. First-order models were useful to predict the outlet concentration of pollutants (R2 > 0.87). Relevant multiple linear regression models were found, which could be applied to facilitate the system’s monitoring and provide valuable information to control and improve biological and physical processes necessary for wastewater treatment. Finally, the values of important parameters (μmax, Ks,  and Yx/s) in mass-balance models were determined with the aid of a differential neural network (DNN) and an optimization algorithm. The estimated parameters indicated the high robustness of the treatment system since performance stability was found despite variations in wastewater composition.

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