Evaluation of global EMEP MSC-W (rv4.34) WRF (v3.9.1.1)  model surface concentrations and wet deposition of reactive N and S with measurements

Ge, Yao; Heal, Mathew R.; Stevenson, David S.; Wind, Peter; Vieno, Massimo

doi:10.5194/gmd-14-7021-2021

Cited by 27 publications

(29 citation statements)

References 68 publications

(77 reference statements)

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“…For the trend studies presented in Theobald et al (2019), the fractional bias for the years 1990 to 2010 was −0.18, −0.02, and 0.22 for the wet deposition of reduced nitrogen, oxidised nitrogen, and SO 2− 4 respectively, but the overall overestimation of SO 2− 4 was mainly caused by an overestimation in the first years of the period. Running the EMEP model in global mode, Ge et al (2021) showed that the model captures the overall spatial and seasonal variations well for the major inorganic pollutants NH 3 , NO 2 , SO 2 , HNO 3 , NH + 4 , NO − 3 , and SO 2− 4 and wet depositions in East Asia, Southeast Asia, Europe, and North America.…”

Section: Model Descriptionmentioning

confidence: 99%

Modelling changes in secondary inorganic aerosol formation and nitrogen deposition in Europe from 2005 to 2030

et al. 2022

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Abstract. Secondary inorganic PM2.5 particles are formed from SOx (SO2+SO42-), NOx (NO+NO2), and NH3 emissions, through the formation of either ammonium sulfate ((NH4)2SO4) or ammonium nitrate (NH4NO3). EU limits and WHO guidelines for PM2.5 levels are frequently exceeded in Europe, in particular in the winter months. In addition the critical loads for eutrophication are exceeded in most of the European continent. Further reductions in NH3 emissions and other PM precursors beyond the 2030 requirements could alleviate some of the health burden from fine particles and also reduce the deposition of nitrogen to vulnerable ecosystems. Using the regional-scale EMEP/MSC-W model, we have studied the effects of year 2030 NH3 emissions on PM2.5 concentrations and depositions of nitrogen in Europe in light of present (2017), past (2005), and future (2030) conditions. Our calculations show that in Europe the formation of PM2.5 from NH3 to a large extent is limited by the ratio between the emissions of NH3 on one hand and SOx plus NOx on the other hand. As the ratio of NH3 to SOx and NOx is increasing, the potential to further curb PM2.5 levels through reductions in NH3 emissions is decreasing. Here we show that per gram of NH3 emissions mitigated, the resulting reductions in PM2.5 levels simulated using 2030 emissions are about a factor of 2.6 lower than when 2005 emissions are used. However, this ratio is lower in winter. Thus further reductions in the NH3 emissions in winter may have similar potential to SOx and NOx in curbing PM2.5 levels in this season. Following the expected reductions of NH3 emission, depositions of reduced nitrogen (NH3+NH4+) should also decrease in Europe. However, as the reductions in NOx emission are larger than for NH3, the fraction of total nitrogen (reduced plus oxidised nitrogen) deposited as reduced nitrogen is increasing and may exceed 60 % in most of Europe by 2030. Thus the potential for future reductions in the exceedances of critical loads for eutrophication in Europe will mainly rely on the ability to reduce NH3 emissions.

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Section: Model Descriptionmentioning

confidence: 99%

Modelling changes in secondary inorganic aerosol formation and nitrogen deposition in Europe from 2005 to 2030

et al. 2022

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“…The WRF 110 simulation in this work assimilates data of the numerical weather prediction model meteorological reanalysis from the US National Center for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) Global Forecast System (GFS) (Saha et al, 2010). The global EMEP-WRF configurations are detailed in Ge et al (2021).…”

Section: Model Descriptionmentioning

confidence: 99%

“…The lowest model layer has height ~50 m and surface concentrations of most species were adjusted to correspond to 3 m above the surface as described in Simpson et al (2012;. Ge et al (2021) document a comprehensive evaluation of surface concentrations and wet deposition of Nr and Sr species from this model configuration against global measurements.…”

Section: Model Descriptionmentioning

confidence: 99%

“…All emissions were aggregated to 1° resolution internally in the model. T he ECLIPSE emission sector-layers were reassigned to 11 Selected Nomenclature for sources of Air Pollution (SNAP) sectors (Ge et al, 2021). Monthly emission profiles by country/region and by emission sector based on EDGAR (Emission Database for Global Atmospheric Research, v4.3.2 datasets) time series (Crippa et al, 2020) (https://edgar.jrc.ec.europa.eu/dataset_temp_profile) were then applied to the ECLIPSE annual emissions for each pollutant.…”

Section: Emissionsmentioning

confidence: 99%

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A new assessment of global and regional budgets, fluxes and lifetimes of atmospheric reactive N and S gases and aerosols

Vieno

Stevenson

et al. 2022

Preprint

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Abstract. We used the EMEP MSC-W model version 4.34 coupled with WRF model version 4.2.2 meteorology to undertake a present-day (2015) global and regional quantification of the concentrations, deposition, budgets, and lifetimes of atmospheric reactive N (Nr) and S (Sr) species. These are quantities that cannot be derived from measurements alone. In areas with high levels of reduced Nr (RDN = NH3 + NH4+), oxidised Nr (OXN = NOx + HNO3 + HONO + N2O5 + NO3- + Other OXN species), and oxidised Sr (OXS = SO2 + SO42-), RDN is predominantly in the form of NH3 (NH4+ typically < 20 %), OXN has majority gaseous species composition, and OXS predominantly comprises SO42- except near major SO2 sources. Most continental regions are now ‘ammonia rich’, and more so than previously, which indicates that whilst reducing NH3 emissions will decrease RDN concentration it will have small effect on mitigating SIA. South Asia is the most ammonia-rich region. Coastal areas around East Asia, northern Europe, and north-eastern United States are ‘nitrate rich’ where NH4NO3 formation is limited by NH3. These locations experience transport of OXN from the adjacent continent and/or direct shipping emissions of NOx but NH3 concentrations are lower. The least populated continental areas and most marine areas are ‘sulfate rich.’ Deposition of OXN (57.9 TgN yr-1, 51 %) and RDN (55.5 TgN yr-1, 49 %) contribute almost equally to total nitrogen deposition. OXS deposition is 50.5 TgS yr-1. Globally, wet and dry deposition contribute similarly to RDN deposition; for OXN and OXS, wet deposition contributes slightly more. Dry deposition of NH3 is the largest contributor to RDN deposition in most regions except for Rest of Asia and marine areas where NH3 emissions are small and RDN deposition is mainly determined by transport and rainout of NH4+ (rather than rainout of gaseous NH3). Reductions in NH3 would thus efficiently reduce the deposition of RDN in most continental regions. The two largest contributors to OXN deposition in all regions are HNO3 and coarse NO3- (via both wet and dry deposition). The deposition of fine NO3- is only important over East Asia. The tropospheric burden of RDN is 0.75 TgN of which NH3 and NH4+ comprise 32 % (0.24 TgN; lifetime = 1.6 days) and 68 % (0.51 TgN; lifetime = 8.9 days), respectively. The lifetime of RDN (4.9–5.2 days) is shorter than that of OXN (7.6–7.7 days), consistent with a total OXN burden (1.20 TgN) almost double that of RDN. The tropospheric burden of OXS is 0.78 TgS with a lifetime of 5.6–5.9 days. Total nitrate burden is 0.58 TgN with fine NO3- only constituting 10 % of this total, although fine NO3- dominates in eastern China, Europe, and eastern North America. It is important to account for contributions of coarse nitrate to global nitrate budgets. Lifetimes of RDN, OXN, and OXS species vary by a factor of 4 across different continental regions. In East Asia, lifetimes for RDN (2.9–3.0 days), OXN (3.9–4.5 days), and OXS (3.4–3.7 days) are short, whereas lifetimes in Rest of Asia and Africa are about twice as long. South Asia is the largest net exporter of RDN (2.21 TgN yr-1, 29 % of its annual emission), followed by Euro_Medi region. Despite having the largest RDN emissions and deposition, East Asia has only small net export and is therefore largely responsible for its own RDN pollution. Africa is the largest net exporter of OXN (1.92 TgN yr-1, 22 %), followed by Euro_Medi (1.61 TgN yr-1, 26 %). Considerable marine anthropogenic Nr and Sr pollution is revealed by the large net import of RDN, OXN and OXS to these areas. Our work demonstrates the substantial regional variation in Nr and Sr budgets and the need for modelling to simulate the chemical and meteorological linkages underpinning atmospheric responses to precursor emissions.

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“…It is well known that pollution processes in the atmospheric environment are very complex, including air-pollutant generation, transport, chemical transformation, decomposition, and deposition. However, most studies focus on common atmospheric pollutants, such as PM, O 3 , NO 2 , SO 2 , and CO. As an important sink of atmospheric pollutants, deposition pollution has seldom been predicted or estimated by applying ML models, and the common simulation method for deposition has been numerical models such as the global 3-D model from the Goddard Earth Observing System (GEOS), GEOS-Chem [113], and the chemical transport model developed at Meteorological Synthesizing Centre-West (MSC-W) from the European Monitoring and Evaluation Programme (EMEP), the EMEP MSC-W chemical transport model [114].…”

Section: Case Study: ML Application To Nitrate Wet Deposition Estimationmentioning

confidence: 99%

The Development and Application of Machine Learning in Atmospheric Environment Studies

2021

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Machine learning (ML) plays an important role in atmospheric environment prediction, having been widely applied in atmospheric science with significant progress in algorithms and hardware. In this paper, we present a brief overview of the development of ML models as well as their application to atmospheric environment studies. ML model performance is then compared based on the main air pollutants (i.e., PM2.5, O3, and NO2) and model type. Moreover, we identify the key driving variables for ML models in predicting particulate matter (PM) pollutants by quantitative statistics. Additionally, a case study for wet nitrogen deposition estimation is carried out based on ML models. Finally, the prospects of ML for atmospheric prediction are discussed.

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Evaluation of global EMEP MSC-W (rv4.34) WRF (v3.9.1.1) model surface concentrations and wet deposition of reactive N and S with measurements

Cited by 27 publications

References 68 publications

Modelling changes in secondary inorganic aerosol formation and nitrogen deposition in Europe from 2005 to 2030

Modelling changes in secondary inorganic aerosol formation and nitrogen deposition in Europe from 2005 to 2030

A new assessment of global and regional budgets, fluxes and lifetimes of atmospheric reactive N and S gases and aerosols

The Development and Application of Machine Learning in Atmospheric Environment Studies

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