This study presents a fine-scale simulation approach to assess the representativity of ammonia (NH
Excess atmospheric nitrogen leads to an increased public health risk through the formation of particulate matter and causes environmental damage; nitrogen deposition leads to eutrophication, ecosystem acidification and shifts in climate change
It is therefore important to have a network of NH
The emitted NH
Furthermore, plume dispersion studies generally focus on chemically inert gases, e.g., methane
In this study, we investigate the impact of a typical ammonia emission source on the regional representativeness of NH We use a fine-scale large-eddy simulation (LES) model with explicitly resolved turbulence at a very high spatio-temporal resolution (10–100 m and 10 s–1 min). We include realistic representations of surface–atmosphere exchanges, chemical gas–aerosol transformations and a background ammonia concentration.
Following this approach, we combine fine-scale simulations, where turbulence is explicitly resolved, with theoretical concepts on turbulent emission plume dispersion. We then translate this knowledge to practical applications for the measurement community. The aim is to carry out a systematic analysis of how meteorological factors, including boundary-layer dynamics, deposition, chemical transformation and model resolution influence the relationships between emission and receptor. To this end, we introduce and analyze the concept of a blending distance (BD), i.e., the horizontal distance at which the emission plume can be considered well-mixed with respect to the background NH
To understand the variations of the NH
The atmospheric ammonia budget is further governed by surface–atmosphere exchanges and chemical gas–particle transformations
The representation of the chemical gas–aerosol transformations follows the approach of the OPS model, applying a percentage per hour change in the molar fraction of gaseous NH
Special attention is placed on the representation of one NH
We identify the individual contributions of ammonia sources to the NH
Further modifications to DALES v4.2 are made to include the remaining processes governing the variability of the atmospheric ammonia budget. The scalar surface flux (
The final modification adds an additional term to the change in the scalar molar fraction
We simulate the meteorological conditions observed on 8 May 2008 at the Ruisdael CESAR Observatory (
In the morning, a 1500 m residual layer leads to an overshooting of the boundary layer height around 10:30 CEST, up to roughly 1800 m. In the afternoon (12:30–17:00 CEST), CBL growth is weak and the thermodynamic conditions remain relatively constant
The numerical experiments are split into three phases: the meteorological spin-up phase, the buffer phase and the analysis phase. During the meteorological spin-up, 08:00–12:30 CEST, the ammonia surface–atmosphere exchange and chemical transformations are not active. These processes are activated at the start of the buffer phase, from 12:30–14:00 CEST. Entrainment is still an important factor until around 13:00 CEST, causing large fluctuations of the NH
Inspired by the plume observation study by
We first introduce the intermittency factor (
Panel
The second variable, fluctuation intensity (fI), determines the magnitude of the NH
The fluctuation intensity quantifies the level of turbulent mixing. High fI indicates that there are large fluctuations in the measured NH
Figure
Finally, we introduce the 30 min NH
We use the fluctuation intensity and flux to quantify the impact of the emission plume on the simulated NH
Based on this percentage change, we define a threshold for which we assume that the impact of the emission plume is negligible. The blending distance (BD
The concept of the blending distance is applied to the fluctuation intensity (BD
A key aspect of the study is to determine the sensitivity of the concept of the blending distance to variations in meteorological and NH
Parameter names, symbols, reference values and their respective variations for the sensitivity study of the blending distance, with the reference settings highlighted in bold.
The sensitivity study is structured from large-scale processes to small-scale processes and modeling numerics. Starting with mesoscale processes, we vary the geostrophic wind speed to study the impact of the atmospheric stability on blending distance, i.e., a shear or convection-dominated CBL. Atmospheric stability plays a key role in the turbulent mixing of local sources (emission) and sinks (entrainment and deposition), affecting both the fluctuations in the background molar fraction and the mixing of the emission plume
Furthermore, we study the sensitivity of both BD
Finally, we study the sensitivity of BD to choices made in the numerical setup of the experiments. We vary the height of the simulated measurements. The numerical experiments are generally taken at a simulated height of 37.5 m. This is a trade-off between simulating measurements close to the surface to mimic in-field observations and the resolved turbulent kinetic energy (TKE
The concept of blending distance is based on fluctuations in the NH
10 s time series
As discussed in Sect.
The
Now that we understand the source of the NH
The intermittency cross-section in Fig.
The cross-section of fI changes dramatically when analyzing NH
Finally, Fig.
We apply the concept of blending distance in Fig.
The percentage change of NH
We interpret the calculation of the blending distance based on four arbitrary threshold levels (5 %, 10 %, 25 % and 50 %) for fI and
Starting with the fluctuation intensity (Fig.
Figure
The left panels show the spatial structure of the percentage change in grayscale for the fluctuation intensity (PC
We study the sensitivities of BD
The sensitivity of BD
The sensitivity of BD
Starting with BD
The geostrophic wind speed (
One panel below, Fig.
At the local scale, Fig.
We only briefly touch upon the chemical conversion rate (
Next, we vary the model resolution (
Finally, Fig.
Figure
When analyzing Fig.
One of main differences between BD
Significant differences between BD
Figure
There are also strong similarities between the sensitivity of BD
The turbulent dispersion of the emission plume is chaotic by nature and driven by a wide range of factors. We therefore carry out a systematic analysis on how these factors and the model resolution influence the relationships between emissions and simulated in-field measurements. The chaotic nature of turbulence results in random variations in both the emitted NH
Increasing the length analysis window, however, means that the blending distance is calculated using a wider range of boundary layer dynamics and variations in the thermodynamic variables. Boundary layer dynamics are especially relevant in the morning and early afternoon when the boundary layer grows and air from the residual layer and free troposphere is entrained, or in the afternoon when turbulence decays
Finally, there is a downside to our simplified representation of chemical transformations in that it is applied uniformly to the 3D domain. In reality, the equilibrium molar fractions for these chemical transformations are related to temperature and humidity, and results in a near-surface NH
The blending distance cannot be captured by a single number. This is partly due to the uncertainty involved in calculating the blending distance, but the blending distances is, most of all, an integrated variable. Several processes are captured by the blending distance in one single variable, including the chaotic nature of turbulent plume dispersion, convective and shear-induced turbulence, atmospheric pollution levels and surface heterogeneity. As shown in Sect.
The applicability of the results presented here depends not only on the meteorological and NH
Evaluating the blending distance results against typical literature on plume dispersion is a difficult exercise. The topic is generally not mentioned as these studies focus on the release of passive scalars in an unpolluted environment, and only few studies even research (near) surface releases
We therefore try to estimate the order of magnitude of the blending distance based on the in-plume molar fraction and fluctuation intensity of plume dispersion modeling studies. Following figures by
These rough estimates of the 6 to 15 km distance are significantly larger than the blending distances presented in this study. Such long distances between source and measurement site would not make feasible requirements in densely agricultural regions, but are likely an overestimation of the blending distance. These estimates are based on the molar fraction and fI of the emission plume, with no representation of background ammonia levels. The latter is especially important, as we show in Sect.
Articles on ammonia measurements in close proximity to an emission source implicitly include all relevant processes. These studies could also provide a qualitative, perhaps more realistic, evaluation of the NH
Finally, we can evaluate our findings against measurement site requirements of air quality networks. The Dutch air quality network and the EMEP (European Monitoring and Evaluation Programme) network do set requirements for the minimum distance from emission sources and no references to scientific studies are provided. Back in 1990, the Dutch network required a minimum distance for NH
This study is the first that specifically addresses the regional representativity of ammonia measurements in proximity to an emission source. The systematic analysis presented in Figs.
The DALES model proved to be flexible, allowing for simulations of a convective, sheared convective, stable and cloud-topped boundary layer
We recommend expanding the simulation framework to create a testbed to study NH
This paper presents a fine-scale simulation framework with which we assess the regional representativity of NH
By means of fine-scale simulation of atmospheric NH
The modified version of the Dutch Atmospheric Large-Eddy Simulation (DALES) version 4.2, including data processing scripts and documentation, is available at
RBS and JVGdA worked on the conceptualization and developed the methodology of the numerical experiments. RBS and BJHvS made modifications to the software, i.e., the DALES model. The numerical experiments were performed by RBS, who also analyzed and visualized resulting data. The manuscript draft was written by RBS and reviewed by both JVGdA and MCvZ. Finally, the project was supervised by JVGdA and MCvZ.
The contact author has declared that neither they nor their co-authors have any competing interests.
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This project is part of the Monitoring of dry ammonia deposition (project no. 36.7) project of the Dutch National Institute of Public Health and the Environment (RIVM). The numerical simulations were performed with the supercomputer facilities at SURFsara and financially sponsored by the Netherlands Organization for Scientific Research (NWO) Physical Science Division (project no. 2021/ENW/01081379).
This research has been supported by the Rijksinstituut voor Volksgezondheid en Milieu (project no. 36.7: Monitoring of dry ammonia deposition) and the Netherlands Organization for Scientific Research (NWO) Physical Science Division (project no. 2021/ENW/01081379).
This paper was edited by Stefano Galmarini and reviewed by two anonymous referees.