Improving the spatial resolution of air-quality modelling at a European scale – development and evaluation of the Air Quality Re-gridder Model (AQR v1.1)

Currently, atmospheric chemistry and transport models (ACTMs) used to assess impacts of air quality, applied at a European scale, lack the spatial resolution necessary to simulate fine-scale spatial variability. This spatial variability is especially important for assessing the impacts to human health or ecosystems of short-lived pollutants, such as nitrogen dioxide (NO2) or ammonia (NH3). In order to simulate this spatial variability, the Air Quality Re-gridder (AQR) model has been developed to estimate the spatial distributions (at a spatial resolution of 1  ×  1 km2) of annual mean atmospheric concentrations within the grid squares of an ACTM (in this case with a spatial resolution of 50  ×  50 km2). This is done as a post-processing step by combining the coarse-resolution ACTM concentrations with high-spatial-resolution emission data and simple parameterisations of atmospheric dispersion. The AQR model was tested for two European sub-domains (the Netherlands and central Scotland) and evaluated using NO2 and NH3 concentration data from monitoring networks within each domain. A statistical comparison of the performance of the two models shows that AQR gives a substantial improvement on the predictions of the ACTM, reducing both mean model error (from 61 to 41 % for NO2 and from 42 to 27 % for NH3) and increasing the spatial correlation (r) with the measured concentrations (from 0.0 to 0.39 for NO2 and from 0.74 to 0.84 for NH3). This improvement was greatest for monitoring locations close to pollutant sources. Although the model ideally requires high-spatial-resolution emission data, which are not available for the whole of Europe, the use of a Europe-wide emission dataset with a lower spatial resolution also gave an improvement on the ACTM predictions for the two test domains. The AQR model provides an easy-to-use and robust method to estimate sub-grid variability that can potentially be extended to different timescales and pollutants

Suitability and uncertainty of two models for the simulation of ammonia dispersion from a pig farm located in an area with frequent calm conditions

We used two atmospheric dispersion models (ADMS and AERMOD) to simulate the short-range dispersion of ammonia emitted by two pig farms to assess their suitability in situations with frequent calm meteorological conditions. Simulations were carried out both using constant and temporally-varying emission rates to evaluate the effect on the model predictions. Monthly and annual mean concentrations predicted by the models at locations within one kilometre of the farms were compared with measured values. AERMOD predicted higher concentrations than ADMS (by a factor of 6–7, on average) and predicted the atmospheric concentrations more accurately for both the monthly and annual simulations. The differences between the concentrations predicted by the two models were mainly the result of different calm wind speed thresholds used by the models. The use of temporally-varying emission rates improved the performance of both models for the monthly and annual simulations with respect to the constant emission simulations. A Monte Carlo uncertainty analysis based on the inputs judged to be the most uncertain for the selected case study estimated a prediction uncertainty of ± a factor of two for both models with most of this due to uncertainty in emission rates

Catchment land use effects on fluxes and concentrations of organic and inorganic nitrogen in streams

We present annual downstream fluxes and spatial variation in concentrations of dissolved inorganic nitrogen (NH4+ and NO3−) and dissolved organic nitrogen (DON) in two adjacent Scottish catchments with contrasting land use (agricultural grassland vs. semi-natural moorland). Inter- and intra-catchment variation in N species and the relation to spatial differences in agricultural land use were studied by determining catchment N input through agricultural activities at the field scale and atmospheric inputs at a 25 m grid resolution. The average agricultural N input of 52 kg N ha−1 yr−1 to the grassland catchment was more than 4 times higher than the input of 12 kg N ha−1 yr−1 to the moorland catchment, supplemented by 12.3 and 8.2 kg N ha−1 yr−1 through atmospheric deposition, respectively. The grassland catchment was associated with an annual downstream total dissolved nitrogen (TDN) flux of 14.4 kg N ha−1 yr−1, which was 66% higher than the flux of 8.7 kg ha−1 yr−1 from the moorland catchment. This difference was largely due to the NO3− flux being one order of magnitude higher in the grassland catchment. Dissolved organic N fluxes were similar for the two catchments (7.0 kg ha−1 yr−1) with DON contributing 49% to the TDN flux in the grassland compared with 81% in the moorland catchment. The results highlight the importance of diffuse agricultural N inputs to stream NO3− concentrations and the importance of quantifying all the major aquatic N species for developing a better understanding of N transformations and transport in the atmosphere-soil-water system

Heterogeneity of atmospheric ammonia at the landscape scale and consequences for environmental impact assessment

We examined the consequences of the spatial heterogeneity of atmospheric ammonia (NH3) by measuring and modelling NH3 concentrations and deposition at 25 m grid resolution for a rural landscape containing intensive poultry farming, agricultural grassland, woodland and moorland. The emission pattern gave rise to a high spatial variability of modelled mean annual NH3 concentrations and dry deposition. Largest impacts were predicted for woodland patches located within the agricultural area, while larger moorland areas were at low risk, due to atmospheric dispersion, prevailing wind direction and low NH3 background. These high resolution spatial details are lost in national scale estimates at 1 km resolution due to less detailed emission input maps. The results demonstrate how the spatial arrangement of sources and sinks is critical to defining the NH3 risk to semi-natural ecosystems. These spatial relationships provide the foundation for local spatial planning approaches to reduce environmental impacts of atmospheric NH3

Accurate and Rapid Identification of the Burkholderia pseudomallei Near-Neighbour, Burkholderia ubonensis, Using Real-Time PCR

Burkholderia ubonensis is an environmental bacterium belonging to the Burkholderia cepacia complex (Bcc), a group of genetically related organisms that are associated with opportunistic but generally nonfatal infections in healthy individuals. In contrast, the near-neighbour species Burkholderia pseudomallei causes melioidosis, a disease that can be fatal in up to 95% of cases if left untreated. B. ubonensis is frequently misidentified as B. pseudomallei from soil samples using selective culturing on Ashdown’s medium, reflecting both the shared environmental niche and morphological similarities of these species. Additionally, B. ubonensis shows potential as an important biocontrol agent in B. pseudomallei-endemic regions as certain strains possess antagonistic properties towards B. pseudomallei. Current methods for characterising B. ubonensis are laborious, time-consuming and costly, and as such this bacterium remains poorly studied. The aim of our study was to develop a rapid and inexpensive real-time PCR-based assay specific for B. ubonensis. We demonstrate that a novel B. ubonensis-specific assay, Bu550, accurately differentiates B. ubonensis from B. pseudomallei and other species that grow on selective Ashdown’s agar. We anticipate that Bu550 will catalyse research on B. ubonensis by enabling rapid identification of this organism from Ashdown’s-positive colonies that are not B. pseudomallei

Eurodelta multi-model simulated and observed particulate matter trends in Europe in the period of 1990-2010

The Eurodelta-Trends (EDT) multi-model experiment, aimed at assessing the efficiency of emission mitigation measures in improving air quality in Europe during 1990-2010, was designed to answer a series of questions regarding European pollution trends; i.e. were there significant trends detected by observations? Do the models manage to reproduce observed trends? How close is the agreement between the models and how large are the deviations from observations? In this paper, we address these issues with respect to particulate matter (PM) pollution. An in-depth trend analysis has been performed for PM10 and PM2.5 for the period of 2000-2010, based on results from six chemical transport models and observational data from the EMEP (Cooperative Programme for Monitoring and Evaluation of the Long-range Transmission of Air Pollutants in Europe) monitoring network. Given harmonization of set-up and main input data, the differences in model results should mainly result from differences in the process formulations within the models themselves, and the spread in the model-simulated trends could be regarded as an indicator for modelling uncertainty.The model ensemble simulations indicate overall decreasing trends in PM10 and PM2.5 from 2000 to 2010, with the total reductions of annual mean concentrations by between 2 and 5 (7 for PM10) mu g m(-3) (or between 10 % and 30 %) across most of Europe (by 0.5-2 mu g m(-3) in Fennoscandia, the north-west of Russia and eastern Europe) during the studied period. Compared to PM2.5, relative PM10 trends are weaker due to large interannual variability of natural coarse PM within the former. The changes in the concentrations of PM individual components are in general consistent with emission reductions. There is reasonable agreement in PM trends estimated by the individual models, with the inter-model variability below 30 %-40 % over most of Europe, increasing to 50 %-60 % in the northern and eastern parts of the EDT domain.Averaged over measurement sites (26 for PM10 and 13 for PM2.5), the mean ensemble-simulated trends are - 0.24 and -0.22 mu g m(-3) yr(-1) for PM10 and PM2.5, which are somewhat weaker than the observed trends of - 0.35 and -0.40 mu g m(-3) yr(-1) respectively, partly due to model underestimation of PM concentrations. The correspondence is better in relative PM10 and PM2.5 trends, which are -1.7 % yr(-1) and -2.0 % yr(-1) from the model ensemble and -2.1 % yr(-1) and -2.9 % yr(-1) from the observations respectively. The observations identify significant trends (at the 95 % confidence level) for PM10 at 56 % of the sites and for PM2.5 at 36 % of the sites, which is somewhat less that the fractions of significant modelled trends. Further, we find somewhat smaller spatial variability of modelled PM trends with respect to the observed ones across Europe and also within individual countries.The strongest decreasing PM trends and the largest number of sites with significant trends are found for the summer season, according to both the model ensemble and observations. The winter PM trends are very weak and mostly insignificant. Important reasons for that are the very modest reductions and even increases in the emissions of primary PM from residential heating in winter. It should be kept in mind that all findings regarding modelled versus observed PM trends are limited to the regions where the sites are located.The analysis reveals considerable variability of the role of the individual aerosols in PM10 trends across European countries. The multi-model simulations, supported by available observations, point to decreases in SO42- concentrations playing an overall dominant role. Also, we see relatively large contributions of the trends of NH4+ and NO3- to PM10 decreasing trends in Germany, Denmark, Poland and the Po Valley, while the reductions of primary PM emissions appear to be a dominant factor in bringing down PM10 in France, Norway, Portugal, Greece and parts of the UK and Russia. Further discussions are given with respect to emission uncertainties (including the implications of not accounting for forest fires and natural mineral dust by some of the models) and the effect of inter-annual meteorological variability on the trend analysis.Peer reviewe

Assessment of the sensitivity of model responses to urban emission changes in support of emission reduction strategies

© 2023 The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/The sensitivity of air quality model responses to modifications in input data (e.g. emissions, meteorology and boundary conditions) or model configurations is recognized as an important issue for air quality modelling applications in support of air quality plans. In the framework of FAIRMODE (Forum of Air Quality Modelling in Europe, https://fairmode.jrc.ec.europa.eu/) a dedicated air quality modelling exercise has been designed to address this issue. The main goal was to evaluate the magnitude and variability of air quality model responses when studying emission scenarios/projections by assessing the changes of model output in response to emission changes. This work is based on several air quality models that are used to support model users and developers, and, consequently, policy makers. We present the FAIRMODE exercise and the participating models, and provide an analysis of the variability of O3 and PM concentrations due to emission reduction scenarios. The key novel feature, in comparison with other exercises, is that emission reduction strategies in the present work are applied and evaluated at urban scale over a large number of cities using new indicators such as the absolute potential, the relative potential and the absolute potency. The results show that there is a larger variability of concentration changes between models, when the emission reduction scenarios are applied, than for their respective baseline absolute concentrations. For ozone, the variability between models of absolute baseline concentrations is below 10%, while the variability of concentration changes (when emissions are similarly perturbed) exceeds, in some instances 100% or higher during episodes. Combined emission reductions are usually more efficient than the sum of single precursor emission reductions both for O3 and PM. In particular for ozone, model responses, in terms of linearity and additivity, show a clear impact of non-linear chemistry processes. This analysis gives an insight into the impact of model’ sensitivity to emission reductions that may be considered when designing air quality plans and paves the way of more in-depth analysis to disentangle the role of emissions from model formulation for present and future air quality assessments.Peer reviewe

Dysmorphometrics: the modelling of morphological abnormalities

<p>Abstract</p> <p>Background</p> <p>The study of typical morphological variations using quantitative, morphometric descriptors has always interested biologists in general. However, unusual examples of form, such as abnormalities are often encountered in biomedical sciences. Despite the long history of morphometrics, the means to identify and quantify such unusual form differences remains limited.</p> <p>Methods</p> <p>A theoretical concept, called dysmorphometrics, is introduced augmenting current geometric morphometrics with a focus on identifying and modelling form abnormalities. Dysmorphometrics applies the paradigm of detecting form differences as outliers compared to an appropriate norm. To achieve this, the likelihood formulation of landmark superimpositions is extended with outlier processes explicitly introducing a latent variable coding for abnormalities. A tractable solution to this augmented superimposition problem is obtained using Expectation-Maximization. The topography of detected abnormalities is encoded in a dysmorphogram.</p> <p>Results</p> <p>We demonstrate the use of dysmorphometrics to measure abrupt changes in time, asymmetry and discordancy in a set of human faces presenting with facial abnormalities.</p> <p>Conclusion</p> <p>The results clearly illustrate the unique power to reveal unusual form differences given only normative data with clear applications in both biomedical practice & research.</p