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Abstract
Background
In Europe, ammonia (NH3) emissions strongly contribute to fine particulate matter (PM2.5) pollution and associated premature human mortality. The National Emission Ceilings Directive 2016/2284/EU has set an obligation for all European Union countries to reduce the NH3 emissions by 6%, relative to 2005, by 2020. This study aims to assess the costs and benefits of four NH3 emission abatement options for the compliance of the agricultural sector with the commitments of the European air quality regulatory framework. A regional atmospheric model (WRF/Chem) was used to assess the effects of regulating NH3 emissions reductions on PM2.5 concentrations over Europe. Non-market valuation techniques (value of statistical life) were used to monetize the associated health outcomes.

Results
We calculated that 16 out of the 28 EU member states exceeded their 2020 NH3 emission ceilings in 2016. The highest exceedances from the 2020 emission commitment level occurred in Latvia (15%), Germany (12%) and the UK (12%). Simulation of the required NH3 emission reduction by WRF/Chem showed that relatively large reductions in PM2.5 concentrations occur over central-western Europe and the UK. The largest health benefits (> 5% reduction in premature mortality) were found for Scandinavia. The economic benefit from avoided premature deaths over Europe amounts to 14,837 M€/year. The costs of four NH3 emission abatement options, where each would fully achieve the required emission reduction, range from 80 M€/year for low nitrogen feed to 3738 M€/year for low-emission animal housing, with covered manure storage (236 M€/year) and urea fertilizer application (253 M€/year), in between.

Conclusion
Our analysis indicates that the costs of compliance by the agricultural sector with the commitments of the European air quality regulations are much lower than the economic benefit. Thus, much more ambitious reduction commitments for NH3 emissions could be applied by the EU-28. The monetization of the health benefits of NH3 emission abatement policies and the assessment of the implementation costs can help policy-makers devise effective air pollution control programmes.

AbstractPhysically based computational modeling is an effective tool for estimating and predicting the spatial distribution of pollutant concentrations in complex environments. A detailed and up-to-date emission inventory is one of the most important components of atmospheric modeling and a prerequisite for achieving high model performance. Lebanon lacks an accurate inventory of anthropogenic emission fluxes. In the absence of a clear emission standard and standardized activity datasets in Lebanon, this work serves to fill this gap by presenting the first national effort to develop a national emission inventory by exhaustively quantifying detailed multisector, multi-species pollutant emissions in Lebanon for atmospheric pollutants that are internationally monitored and regulated as relevant to air quality. Following the classification of the Emissions Database for Global Atmospheric Research (EDGAR), we present the methodology followed for each subsector based on its characteristics and types of fuels consumed. The estimated emissions encompass gaseous species (CO, NOx, SO2), and particulate matter (PM2.5 and PM10). We compare totals per sector obtained from the newly developed national inventory with the international EDGAR inventory and previously published emission inventories for the country for base year 2010 presenting current discrepancies and analyzing their causes. The observed discrepancies highlight the fact that emission inventories, especially for data-scarce settings, are highly sensitive to the activity data and their underlying assumptions, and to the methodology used to estimate the emissions.

Satellite observations of the Ozone Monitoring Instrument (OMI) for tropospheric sulfur dioxide (SO(2)) and formaldehyde (HCHO) column mass densities (CMD) are analyzed for the period 2005–2016 over the atmosphere of Kazakhstan. Regarding SO(2) the major hot spots relate to regions with high population and large industrial facilities. Such an example is the city of Ekibastuz that hosts the biggest thermal power plants in the country and exhibits the higher SO(2) CMD at national level. The annual average CMD in Ekibastuz reaches 2.5 × 10(−5) kg/m(2), whereas for the rest of the country respective values are 6 times lower. Other hotspots, mostly urban conglomerates such as Almaty and Nur-Sultan, experience high CMDs of SO(2) in particular years, such as 2008. One of the main reasons for this behavior is the financial crisis of 2008, forcing the application of alternate heating sources based on cheap low-quality coal. Regarding HCHO, an oxygenated Volatile Organic Compound (VOC), the main hot spot is noticed over the city Atyrau, the oil capital of the country where two massive oil fields are located. The highest HCHO CMD (9 × 10(15) molecules/cm(2)) appears in the summertime due to secondary production as a result of the photo-oxidation of VOCs emitted by industrial sectors, oil refinery plants and vehicles. Strongly elevated HCHO amounts are also observed in Nur-Sultan in 2012 that could be due to the residential coal combustion and vehicle exhaust under poor winter dispersion conditions. Significant reductions in HCHO observed between 2012 and 2015 can be attributed to two significant measures implemented in the country in 2013 that aimed at the improvement of air quality: the introduction of the emission trading system (ETS) for greenhouse gases and Euro-4 standards for new vehicles entering the national vehicle fleet.

AbstractAir pollution from fine particulate matter (PM2.5) has been associated with various health implications that can lead to increased morbidity and excess mortality. Epidemiological and toxicological studies have shown that carbonaceous particles (black carbon and organic aerosols) may be more hazardous to human health than inorganic ones. Health impact studies and emission reduction policies are based on total PM2.5 concentration without differentiating the more harmful components. In such assessments, PM2.5 and their sub-component concentrations are usually modeled with air quality models. Organic aerosols have been shown to be consistently underestimated, which may affect excess mortality estimates. Here, we use the WRF-Chem model to simulate PM2.5 (including carbonaceous particles) over the wider European domain and assess some of the main factors that contribute to uncertainty. In particular, we explore the impact of anthropogenic emissions and meteorological modeling on carbonaceous aerosol concentrations. We further assess their effects on excess mortality estimates by using the Global Exposure Mortality Model (GEMM). We find that meteorological grid nudging is essential for accurately representing both PM2.5 and carbonaceous aerosols and that, for this application, results improve more significantly compared to spectral nudging. Our results indicate that the explicit account of organic precursors (semi-volatile and intermediate-volatile organic carbons—SVOCs/IVOCs) in emission inventories would improve the accuracy of organic aerosols modeling. We conclude that uncertainties related to PM2.5 modeling in Europe lead to a ∼15% deviation in excess mortality, which is comparable to the risk model uncertainty. This estimate is relevant when all PM2.5 sub-components are assumed to be equally toxic but can be higher by considering their specific toxicity.

AbstractThe 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.