Inverse Modeling Approach Research Articles

Evaluation of the benefits of improved permeability estimation on high-resolution characterization of DNAPL distribution in aquifers with low-permeability lenses

High-resolution characterization of dense non-aqueous phase liquid (DNAPL) source zone architecture (SZA) is crucial for developing remediation strategies. Inverse modeling approaches are widely used to characterize DNAPL SZA. However, unknown parameters (permeability and DNAPL saturation) are usually assumed as Gaussian distribution in traditional geostatistical inverse methods (e.g., ensemble smoother with multiple data assimilation, ESMDA). This assumption may not be true in channels of fluvial deposits. The presence of clay lenses will bring significant challenges in reconstructing the DNAPL SZA using geostatistical inverse methods based on the Gaussian assumption. Moreover, the DNAPL characterization also suffers from a limited number of borehole data in the field. In this study, we use ESMDA's variant, ensemble smoother with multiple data assimilation-direct sampling (ESMDA-DS), to improve the estimation of non-Gaussian permeability field and evaluate the benefits of improved permeability estimation on the reconstruction of DNAPL source zone. The inversion technique is based on the iterative inversion framework to incorporate the clay content information into the DNAPL characterization. The performance of our proposed framework is examined and discussed through a numerical synthetic case with challenging pool-dominated SZA. To overcome data scarcity, we integrated hydrogeological and geophysical (electrical resistance tomography) datasets. Results show that the permeability is characterized well using ESMDA-DS method compared with ESMDA method. With the improved permeability estimation, the DNAPL source zone can be characterized in higher resolution. Compared with ESMDA method, the root mean square error (RMSE) of permeability is reduced by 38.5% using the ESMDA-DS method. Then the estimation RMSE of DNAPL saturation is reduced by 24.9% by improved permeability estimation. We also demonstrated the significant benefits of improved permeability estimation on DNAPL depletion behavior and plume evolution. Numerical simulations demonstrate with the improved permeability estimation, the DNAPL depletion prediction is highly improved, the prediction RMSE of the DNAPL dissolved plume is reduced by 34.5% after 50 years.

Full-field inverse identification of elasto-plastic model parameters for ductile cast iron

A key challenge in micromechanical simulation of ductile cast iron is the identification of the constitutive properties of the microstructural constituents. In recent scientific literature, ductile cast iron is predominantly modeled isotropically. Applied methods for the determination of model parameters for ductile cast iron include characterization by nanoindentation as well as inverse modeling approaches. In the present work, effective elastic and plastic model parameters for ferrite were determined by full-field inverse calibration. For that purpose, a stochastic speckle pattern was introduced in tensile specimens by focused ion beam (FIB). In-situ experiments were carried out in a scanning electron microscope and the strain in ferrite was measured by digital image correlation (DIC). A micromechanical model was built from the microstructure of the in-situ experiment. Measured and simulated strains were used in an optimization procedure to identify reliable plasticity parameters of ferrite in ductile cast iron. The results reveal the limitations of modeling the former isotropically.

A new inverse modeling approach for emission sources based on the DDM-3D and 3DVAR techniques: an application to air quality forecasts in the Beijing–Tianjin–Hebei region

Abstract. We develop a new inversion method which is suitable for linear and nonlinear emission source (ES) modeling, based on the three-dimensional decoupled direct (DDM-3D) sensitivity analysis module in the Community Multiscale Air Quality (CMAQ) model and the three-dimensional variational (3DVAR) data assimilation technique. We established the explicit observation operator matrix between the ES and receptor concentrations and the background error covariance (BEC) matrix of the ES, which can reflect the impacts of uncertainties of the ES on assimilation. Then we constructed the inversion model of the ES by combining the sensitivity analysis with 3DVAR techniques. We performed the simulation experiment using the inversion model for a heavy haze case study in the Beijing–Tianjin–Hebei (BTH) region during 27–30 December 2016. Results show that the spatial distribution of sensitivities of SO2 and NOx ESs to their concentrations, as well as the BEC matrix of ES, is reasonable. Using an a posteriori inversed ES, underestimations of SO2 and NO2 during the heavy haze period are remarkably improved, especially for NO2. Spatial distributions of SO2 and NO2 concentrations simulated by the constrained ES were more accurate compared with an a priori ES in the BTH region. The temporal variations in regionally averaged SO2, NO2, and O3 modeled concentrations using an a posteriori inversed ES are consistent with in situ observations at 45 stations over the BTH region, and simulation errors decrease significantly. These results are of great significance for studies on the formation mechanism of heavy haze, the reduction of uncertainties of the ES and its dynamic updating, and the provision of accurate “virtual” emission inventories for air-quality forecasts and decision-making services for optimization control of air pollution.

Effect of Manufacturing on the Transverse Response of Polymer Matrix Composites.

The effect of residual stress build-up on the transverse properties of thermoset composites is studied through direct and inverse process modeling approaches. Progressive damage analysis is implemented to characterize composite stiffness and strength of cured composites microstructures. A size effect study is proposed to define the appropriate dimensions of Representative Volume Elements (RVEs). A comparison between periodic (PBCs) and flat (FBCs) boundary conditions during curing is performed on converged RVEs to establish computationally efficient methodologies. Transverse properties are analyzed as a function of the fiber packing through the nearest fiber distance statistical descriptor. A reasonable mechanical equivalence is achieved for RVEs consisting of 40 fibers. It has been found that process-induced residual stresses and fiber packing significantly contribute to the scatter in composites transverse strength. Variation of ±5% in average strength and 18% in standard deviation are observed with respect to ideally cured RVEs that neglect residual stresses. It is established that process modeling is needed to optimize the residual stress state and improve composite performance.

Informing the planning of rotating power outages in heat waves through data analytics of connected smart thermostats for residential buildings

With climate change, heat waves have become more frequent and intense. Rotating power outages happen when the power supply is unable to meet the cooling demand increase resulting from extreme high temperatures. Power outages during heat waves expose residents to high risks of overheating. In this study, we propose a novel data-driven inverse modelling approach to inform decision makers and grid operators on planning rotating power outages. We first infer the building thermal characteristics using the connected smart thermostat data, and used the estimated thermal dynamics to simulate the thermal resilience during a heat wave event. Our proposed method was tested for the California power outage in August 2020 by using the open source Ecobee Donate Your Data dataset. We found in California the power outage should not last more than two hours during heat waves to avoid overheating risks. Informing the residents in advance so they can prepare for it through pre-cooling is a simple but effective strategy to expand the acceptable power outage duration. In addition to assisting power outage planning, the proposed method can be used for other applications, such as to evaluate a building energy efficiency policy, to examine fuel poverty, and to estimate the load shifting potential of building stocks.

Simulations of ENSO Phase-Locking in CMIP5 and CMIP6

AbstractThe characteristics of El Niño–Southern Oscillation (ENSO) phase-locking in observations and CMIP5 and CMIP6 models are examined in this study. Two metrics based on the peaking month histogram for all El Niño and La Niña events are adopted to delineate the basic features of ENSO phase-locking in terms of the preferred calendar month and strength of this preference. It turns out that most models are poor at simulating the ENSO phase-locking, either showing little peak strength or peaking at the wrong seasons. By deriving ENSO’s linear dynamics based on the conceptual recharge oscillator (RO) framework through the seasonal linear inverse model (sLIM) approach, various simulated phase-locking behaviors of CMIP models are systematically investigated in comparison with observations. In observations, phase-locking is mainly attributed to the seasonal modulation of ENSO’s SST growth rate. In contrast, in a significant portion of CMIP models, phase-locking is codetermined by the seasonal modulations of both SST growth and phase transition rates. Further study of the joint effects of SST growth and phase transition rates suggests that for simulating realistic winter peak ENSO phase-locking with the right dynamics, climate models need to have four key factors in the right combination: 1) correct phase of SST growth rate modulation peaking at the fall, 2) large-enough amplitude for the annual cycle in growth rate, 3) small amplitude of semiannual cycle in growth rate, and 4) small amplitude of seasonal modulation in SST phase transition rate.

Identification of point source emission in river pollution incidents based on Bayesian inference and genetic algorithm: Inverse modeling, sensitivity, and uncertainty analysis

Identification of pollution point source in rivers is strenuous due to accidental chemical spills or unmanaged wastewater discharges. It is crucial to take physical characteristics into account in the estimation of pollution sources. In this study, an integrated inverse modeling framework is developed to identify a point source of accidental water pollution based on the contaminant concentrations observed at monitoring sites in time series. The modeling approach includes a Markov chain Monte Carlo method based on Bayesian inference (Bayesian-MCMC) inverse model and a genetic algorithm (GA) inverse model. Both inverse models can estimate the pollution sources, including the emission mass quantity, release time, and release position in an accidental river pollution event. The developed model is first tested for a hypothetical case with field river conditions. The results show that the source parameters identified by the Bayesian-MCMC inverse model are very close to the true values with relative errors of 0.02% or less; the GA inverse model also works with relative errors in the range of 2%–7%. Additionally, the uncertainties associated with model parameters are analyzed based on global sensitive analysis (GSA) in this study. It is also found that the emission mass of pollution source positively correlates with the dispersion coefficient and the river cross-sectional area, whereas the flow velocity significantly affects release position and release time. A real case study in the Fen River is further conducted to test the applicability of the developed inverse modeling approach. Results confirm that the Bayesian-MCMC model performs better than the GA model in terms of accuracy and stability for the field application. The findings of this study would support decision-making during emergency responses to river pollution incidents.

Sorption- and diffusion-induced isotopic fractionation in chloroethenes

Isotopic fractionation of groundwater contaminants can occur due to degradation, diffusion and sorption. Of these, only degradation has been extensively explored, yet diffusive isotopic fractionation (DIF) and sorptive isotopic fractionation (SIF) can have significant effects on the isotopic enrichment of groundwater contaminants. Understanding how to mathematically describe and model these processes is vital to the correct interpretation of compound-specific isotope analysis (CSIA) data in the field. Here, models for these physical fractionation processes are developed and described, including the definition of a sorption enrichment factor. These models are then implemented numerically using inverse and finite-element methods to investigate two scenarios, diffusion-sorption and diffusion-sorption-advection, that have been measured in the laboratory. Concentration, δ37Cl, and δ2H data from cis-dichloroethene (cDCE) and trichloroethene (TCE) are used as inputs to the models. Unknown transport parameters including diffusive fractionation exponents are determined from an inverse modelling approach. DIF is shown to have a stronger influence on chlorine isotopologues than on hydrogen isotopologues. For both cDCE and TCE, the sorption enrichment factor of chlorine is found to be negative while that of hydrogen is positive. The presented approach and results provide novel tools and insight into DIF and SIF and underline that these processes should be taken into account when using CSIA to assess contaminant fate.

Application of Biochar in Stormwater Treatment: Experimental and Modeling Investigation

This research investigated the removal of heavy metal ions (Cd, Cu, Pb, and Zn) and metalloid (As) common to stormwater runoff onto biochar-based media arranged in multiple configurations. Laboratory scale column experiments were conducted to quantify heavy metal removal efficiencies using sand, biochar, and nZVI-modified biochar (BC-nZVI) in four media configurations: a homogeneous mixture of sand and biochar (BCM); biochar layered in sand (BCL); BC-nZVI layered in sand (BCZ); and sand as a control. An inverse modeling approach was implemented to measured moisture and experimental data to determine media hydraulic parameters (θr, θs, α, n and Ks) and adsorption coefficients. The experiment was conducted using laboratory synthesized stormwater over 200 days at a rate of 5 cm/day. BCZ exhibited an excellent removal (99%) of As due to the high attachment to nZVI, via surface complexations. Biochar with abundant surface oxygen functional groups exhibited a great (99%) removal of Cd and Zn in both BCL and BCM columns. Water contents were observed 66.0, 44.3, 41.4, and 7.2% for BCL, BCM, BCZ, and sand, respectively. The attachment coefficients varied from 21.5 to 44.9, 16.1 to 19.3, 18.8 to 26.0, and 9.6 to 19.9 L/kg for BCL, BCM, BCZ, and sand, respectively. This study’s output provides useful information for stormwater management practices.

Parameterization of nearshore wave breaker index

The performances of phase-averaged parametric nearshore wave transformation models depend significantly on a reliable estimate of the wave breaker index γ (the breaker height-to-depth ratio), a free model parameter that essentially controls the amount of breaking energy dissipation. While the previous studies have suggested separate relationships between γ and the offshore wave steepness (s0) or the normalized local water depth (kh), the parameterization of γ still requires further investigation considering a wider variety of conditions and a sounder physical basis. In this study, we use the field datasets of wave height and the inverse modelling approach to reveal a composite dependence of γ on both s0 and kh. Specifically, the results show a positive dependence of γ on kh for larger s0, and a negative dependence of γ on kh for smaller s0. Based on such composite relationships, a new γ formula is proposed, and its performance is verified against the available datasets of wave height in three coasts and 14 laboratory tests. Implementation of this new formula in a parametric wave model leads to the error reduction of wave height prediction by 10–24% (mean = 19%) relative to seven widely used models in literatures. In particular, a remarkably higher model accuracy is obtained under wave conditions with small offshore wave steepness, which is important for studying onshore sediment transport and beach recovery. Two counteractive physical mechanisms for wave nonlinearity effects, namely the breaking intensification mechanism and the breaking resistance mechanism, are suggested to explain the opposite γ-kh relationships within different ranges of s0.

Mapping available soil water capacity in New South Wales, Australia using sparse data - An inverse Bayesian approach

Data sparsity is a major limitation in modelling soil hydraulic properties across large spatial extents, such as the continent of Australia, and can lead to erroneous models being developed. Inverse modelling can be used to make inferences about underlying environmental processes, even when the data is subject to error. Bayesian inverse modelling is one method to quantify uncertainties from multiple sources, including data, model structure, and model parameters. Although, Bayesian inverse modelling approaches have been proven useful in many applications, they are still not commonplace in digital soil mapping (DSM). In this work, we present a Bayesian inverse model for predicting available water capacity (AWC) of soils using sparse data. Specifically, we estimate upper and lower drainage limits (DUL and LL15) using a Bayesian geostatistical model. DUL and LL15 are then differenced to estimate the AWC. This study was carried out using measurements of upper and lower drained limits (DUL & LL15) across NSW, Australia. Two separate Bayesian inverse models were calibrated and validated for the DUL and LL15 soil hydraulic parameters. The calibrated models were then used to spatially predict AWC of soils across a cropping region in New South Wales (NSW), Australia on a 90-m grid. We also present the uncertainty of the parameters and output maps. Our results gave similar estimates of AWC values when compared to those predicted using the Cubist machine learning model. Although the predictions are similar, the Bayesian method allows for the direct quantification of the prediction uncertainties, while the Cubist method cannot.

The Environmental Effects of the April 2020 Wildfires and the Cs-137 Re-Suspension in the Chernobyl Exclusion Zone: A Multi-Hazard Threat

This paper demonstrates the environmental impacts of the wildfires occurring at the beginning of April 2020 in and around the highly contaminated Chernobyl Exclusion Zone (CEZ). Due to the critical fire location, concerns arose about secondary radioactive contamination potentially spreading over Europe. The impact of the fire was assessed through the evaluation of fire plume dispersion and re-suspension of the radionuclide Cs-137, whereas, to assess the smoke plume effect, a WRF-Chem simulation was performed and compared to Tropospheric Monitoring Instrument (TROPOMI) satellite columns. The results show agreement of the simulated black carbon and carbon monoxide plumes with the plumes as observed by TROPOMI, where pollutants were also transported to Belarus. From an air quality and health perspective, the wildfires caused extremely bad air quality over Kiev, where the WRF-Chem model simulated mean values of PM2.5 up to 300 µg/m3 (during the first fire outbreak) over CEZ. The re-suspension of Cs-137 was assessed by a Bayesian inverse modelling approach using FLEXPART as the atmospheric transport model and Ukraine observations, yielding a total release of 600 ± 200 GBq. The increase in both smoke and Cs-137 emissions was only well correlated on the 9 April, likely related to a shift of the focus area of the fires. From a radiological point of view even the highest Cs-137 values (average measured or modelled air concentrations and modelled deposition) at the measurement site closest to the Chernobyl Nuclear Power Plant, i.e., Kiev, posed no health risk.

Reservoir Computing as a Tool for Climate Predictability Studies

AbstractReduced‐order dynamical models play a central role in developing our understanding of predictability of climate irrespective of whether we are dealing with the actual climate system or surrogate climate models. In this context, the linear inverse modeling (LIM) approach, by capturing a few essential interactions between dynamical components of the full system, has proven valuable in providing insights into predictability of the full system. We demonstrate that reservoir computing (RC), a form of learning suitable for systems with chaotic dynamics, provides an alternative nonlinear approach that improves on the predictive skill of the LIM approach. We do this in the example setting of predicting sea surface temperature in the North Atlantic in the preindustrial control simulation of a popular earth system model, the Community Earth System Model so that we can compare the performance of the new RC‐based approach with the traditional LIM approach both when learning data are plentiful and when such data are more limited. The improved predictive skill of the RC approach over a wide range of conditions—larger number of retained EOF coefficients, extending well into the limited data regime, etc.—suggests that this machine‐learning technique may have a use in climate predictability studies. While the possibility of developing a climate emulator—the ability to continue the evolution of the system on the attractor long after failing to be able to track the reference trajectory—is demonstrated in the Lorenz‐63 system, it is suggested that further development of the RC approach may permit such uses of the new approach in more realistic predictability studies.

Improving the MJO Forecast of S2S Operation Models by Correcting Their Biases in Linear Dynamics

AbstractThe operational dynamic subseasonal to seasonal (S2S) models for Madden‐Julian oscillation (MJO) forecasting mostly still suffer from systematic errors in capturing the MJO's key dynamic features, such as its growth rate and propagation speed. By deriving the linear dynamic operators using the linear inverse modeling (LIM) approach, we propose a method to partly correct the errors in MJO linear dynamic operators to improve the MJO predictions of three operational dynamic S2S models. Correcting the deficiencies of the too‐fast decay rates and the unrealistic propagating phase speeds lead to MJO prediction skills being extended by approximately 2–4 days. The improvements are more significant for the models with larger biases in MJO amplitude and propagation. This approach in principle may be extendable to predictions of other types of climate variability such as ENSO on one hand, and possible inclusions of nonlinear dynamics effects on the other hand.

Virtual metering of heat supplied by zone-level perimeter heaters: An investigation with three inverse modelling approaches

Virtual metering at the equipment level provides a non-intrusive tool to understand energy flows in commercial buildings and monitor building energy performance. The accuracy of virtual meters depends on the modelling approach and its ability to capture the underlying physical processes. This paper presents a novel comparison and provides guidance for the selection of inverse modelling approaches used to create virtual meters to estimate the heat supplied by zone-level hydronic perimeter heaters. The approaches investigated are: steady-state inverse greybox modelling, transient inverse greybox modelling, and water-side load disaggregation. Models that represent these three approaches are trained using data from a highly instrumented academic building in Ottawa, Canada. Model parameters are identified using the genetic algorithm and used in creating virtual meters that can estimate the heat added by perimeter heaters into building variable air volume zones. The accuracy of these virtual meters is assessed by comparing them to physical meters installed in these zones. The results indicate that the three modelling approaches can estimate the daily heating energy supplied by perimeter heaters at a normalized root-mean-square error between 16% and 18%. However, the accuracy of the virtual meters declines as the number of zones increases. The performance of the three modelling approaches is evaluated through illustrative examples that compare modelling assumptions, data requirements, data processing, and model prediction accuracy.

Neural Network Based Inverse Modelling for Pneumatic Artificial Muscles

Pneumatic Artificial Muscles (PAM) are soft actuators with advantages such as high force to weight ratio, flexible structure and low cost. Pneumatic Artificial Muscles have inherent compliance that makes them feasible for exoskeletons and rehabilitation robots. However, their inherent nonlinear characteristics yield difficulties in modelling and control actions, which is an important factor restricting use of PAM. The compliance of PAM is associated with nonlinearity, hysteresis, and time varying characteristics, which makes it more difficult to model the dynamics and operation with model based high-performance controllers. Although there are many studies to overcome the modelling issue such as virtual work , empirical and phenomenological models, they are either much complicated or very approximate ones as a variable stiffness spring for model with nonlinear input-output relationship. Based on the analysis of well known previous modeling works in our PAM test bed, it has been observed that efficacy of the those methods are limited for representing the physical behaviour of PAM and thus there is still requirement for simple and effective models . In this work, apart from previous modeling approaches, the behaviour of PAM is foreseen as an integrated response to pressure input, which results in simultaneous force and muscle length change. Therefore, standard direct input-output identification methods are not suitable for modelling that behaviour. An inverse modeling approach is proposed in order to utilize it in control applications. The black box model is implemented by an Artificial Neural Network (ANN) structure using the experimental data collected from the PAM test bed. According to implementation results, an ANN based inverse model has yielded satisfactory performance deducing that it could be a simple and effective solution for PAM modelling and control .

Regional Hydrogeochemical Evolution of Groundwater in the Ring of Cenotes, Yucatán (Mexico): An Inverse Modelling Approach

The Ring of Cenotes (RC) extends along the edge of the Chicxulub crater, in the limestone platform of the Yucatan Peninsula (YP), where groundwater shows two preferential flow paths toward the coast near Celestun and Dzilam Bravo towns. The objectives of this study were to describe the regional hydrogeochemical evolution of the groundwater in the RC, and its association with the dissolution/precipitation of the minerals present along its pathway to the ocean. These objectives results were obtained by: (a) characterizing groundwater hydrogeochemistry; (b) calculating calcite, dolomite, and gypsum saturation indexes in the study area; and (c) developing a hydrogeochemical model using PHREEQC (U. S. Geological Survey) inverse modelling approach. The model predictions confirmed that there are two evolution pathways of the groundwater consistent with the preferential flow paths suggested in a previous regionalization of the RC. On the western path, where groundwater flows towards Celestun, marine intrusion influences the hydrogeochemical processes and represents a risk for the freshwater. On the eastern path, where groundwater flows toward Dzilam Bravo, rainfall has an important effect on the hydrogeochemical processes, evidenced by a higher concentration in sulfates during droughts than during rainy periods. Then, monitoring of marine intrusion and phases dissolution in the RC is highly recommended.

Sensitivity of ice sheet surface velocity and elevation to variations in basal friction and topography in the full Stokes and shallow-shelf approximation frameworks using adjoint equations

Abstract. Predictions of future mass loss from ice sheets are afflicted with uncertainty, caused, among others, by insufficient understanding of spatiotemporally variable processes at the inaccessible base of ice sheets for which few direct observations exist and of which basal friction is a prime example. Here, we present a general numerical framework for studying the relationship between bed and surface properties of ice sheets and glaciers. Specifically, we use an inverse modeling approach and the associated time-dependent adjoint equations, derived in the framework of a full Stokes model and a shallow-shelf/shelfy-stream approximation model, respectively, to determine the sensitivity of grounded ice sheet surface velocities and elevation to time-dependent perturbations in basal friction and basal topography. Analytical and numerical examples are presented showing the importance of including the time-dependent kinematic free surface equation for the elevation and its adjoint, in particular for observations of the elevation. A closed form of the analytical solutions to the adjoint equations is given for a two-dimensional vertical ice in steady state under the shallow-shelf approximation. There is a delay in time between a seasonal perturbation at the ice base and the observation of the change in elevation. A perturbation at the base in the topography has a direct effect in space at the surface above the perturbation, and a perturbation in the friction is propagated directly to the surface in time.

High-resolution hybrid inversion of IASI ammonia columns to constrain US ammonia emissions using the CMAQ adjoint model

Abstract. Ammonia (NH3) emissions have large impacts on air quality and nitrogen deposition, influencing human health and the well-being of sensitive ecosystems. Large uncertainties exist in the “bottom-up” NH3 emission inventories due to limited source information and a historical lack of measurements, hindering the assessment of NH3-related environmental impacts. The increasing capability of satellites to measure NH3 abundance and the development of modeling tools enable us to better constrain NH3 emission estimates at high spatial resolution. In this study, we constrain the NH3 emission estimates from the widely used 2011 National Emissions Inventory (2011 NEI) in the US using Infrared Atmospheric Sounding Interferometer NH3 column density measurements (IASI-NH3) gridded at a 36 km by 36 km horizontal resolution. With a hybrid inverse modeling approach, we use the Community Multiscale Air Quality Modeling System (CMAQ) and its multiphase adjoint model to optimize NH3 emission estimates in April, July, and October. Our optimized emission estimates suggest that the total NH3 emissions are biased low by 26 % in 2011 NEI in April with overestimation in the Midwest and underestimation in the Southern States. In July and October, the estimates from NEI agree well with the optimized emission estimates, despite a low bias in hotspot regions. Evaluation of the inversion performance using independent observations shows reduced underestimation in simulated ambient NH3 concentration in all 3 months and reduced underestimation in NH4+ wet deposition in April. Implementing the optimized NH3 emission estimates improves the model performance in simulating PM2.5 concentration in the Midwest in April. The model results suggest that the estimated contribution of ammonium nitrate would be biased high in a priori NEI-based assessments. The higher emission estimates in this study also imply a higher ecological impact of nitrogen deposition originating from NH3 emissions.

Disaggregating the effects of nitrogen addition on gross primary production in a boreal Scots pine forest

Adding nitrogen to boreal forest ecosystems commonly increases gross primary production (GPP). The effect of nitrogen addition on ecosystem GPP is convoluted due to the impacts of and interactions among leaf scale photosynthetic productivity, canopy structure, site fertility, and environmental constraints. We used a unique controlled nitrogen fertilisation experiment combined with eddy covariance measurements and the calibration of a LUE-based (light use efficiency) photosynthetic production model in order to reveal differences in photosynthetic capacity due to nitrogen addition.A systematically designed soil moisture survey was conducted to characterise the within-site spatial heterogeneity and validate the difference of water stress between fertilised and control sites. The canopy photosynthetic light responses and environmental constraints were evaluated using an inverse modelling approach. We found that nitrogen fertilisation elevated ecosystem GPP by 24% according to model simulations. This was caused by increases in ecosystem light interception (through an increase in leaf area index (LAI)) and LUE by 7% and 17%, respectively. Nitrogen addition increased canopy potential LUE for both low and high photosynthetic photon flux density (PPFD) conditions. The calculations of leaf area and light interception indicated that the understorey vegetation contributed 9% of ecosystem GPP in the fertilised site and 7% in the control site when assuming a same LUE for trees and shrubs. The constraint arising from atmospheric water demand, rather than soil water stress, was the dominating control of the intra- and inter-annual GPP variations. The uncertainty propagated from soil moisture data is negligible for GPP predictions, but influential in the inference on the severity of the drought. This study demonstrates the combination of the controlled field experiment with the inverse modelling approach provides a powerful tool to quantitatively describe and disaggregate N addition effects on forest ecosystem GPP.