Point Centred Variography to Assess the Spatial Representativeness of Air Quality Monitoring Sites: Application to the Datasets of the FAIRMODE Intercomparison Exercise of Spatial Representativeness

Common definitions for the spatial representativeness of air quality monitoring sites are based on the evaluation of the similarity of pollutant concentrations, in which the representativeness area of a monitoring site is basically described by the set of all locations where the concentration of a pollutant does not differ from the measurements at the central point by more than a certain threshold. Classical geostatistical analysis describes the spatial correlation structure of a concentration field in terms of the variogram. In contrary, the point centred variography is based on the average of squared concentration differences observed in pairs formed between a particular central point and the set of all other points in the domain. It thereby places a monitoring station in the context of the local or regional air quality pattern. In this report we demonstrate how a mathematical inversion of the point centred variogram can provide information about the extent of the spatial representativeness area of a monitoring site. The application of this approach is tested on a set of modelling data from the city of Antwerp. This dataset contains information at a very high spatial (street level) and temporal resolution for three main pollutants (PM10, NO2 and Ozone), over the whole city. Furthermore, FAIRMODE (Forum for Air Quality Modeling in Europe) is currently concluding an intercomparison exercise on spatial representativeness methods, which is also based on sharing this same dataset.JRC.C.5-Air and Climat

Model evaluation and ensemble modelling of surface-level ozone in Europe and North America in the context of AQMEII

More than ten state-of-the-art regional air quality models have been applied as part of the Air Quality Model Evaluation International Initiative (AQMEII). These models were run by twenty independent groups in Europe and North America. Standardised modelling outputs over a full year (2006) from each group have been shared on the web-distributed ENSEMBLE system, which allows for statistical and ensemble analyses to be performed by each group. The estimated ground-level ozone mixing ratios from the models are collectively examined in an ensemble fashion and evaluated against a large set of observations from both continents. The scale of the exercise is unprecedented and offers a unique opportunity to investigate methodologies for generating skilful ensembles of regional air quality models outputs. Despite the remarkable progress of ensemble air quality modelling over the past decade, there are still outstanding questions regarding this technique. Among them, what is the best and most beneficial way to build an ensemble of members? And how should the optimum size of the ensemble be determined in order to capture data variability as well as keeping the error low? These questions are addressed here by looking at optimal ensemble size and quality of the members. The analysis carried out is based on systematic minimization of the model error and is important for performing diagnostic/probabilistic model evaluation. It is shown that the most commonly used multi-model approach, namely the average over all available members, can be outperformed by subsets of members optimally selected in terms of bias, error, and correlation. More importantly, this result does not strictly depend on the skill of the individual members, but may require the inclusion of low-ranking skill-score members. A clustering methodology is applied to discern among members and to build a skilful ensemble based on model association and data clustering, which makes no use of priori knowledge of model skill. Results show that, while the methodology needs further refinement, by optimally selecting the cluster distance and association criteria, this approach can be useful for model applications beyond those strictly related to model evaluation, such as air quality forecasting. (C) 2012 Elsevier Ltd. All rights reserved.Peer reviewe

Fossil CO2 emissions of all world countries - 2020 Report

The Emissions Database for Global Atmospheric Research provides emission time series from 1970 until 2019 for fossil CO2 for all countries. This report is contributing to the Paris Agreement process with an independent and quantitative view of global fossil CO2 emissions.JRC.C.5 - Air and Climat

Fossil CO2 emissions of all world countries - 2018 Report

The Emissions Database for Global Atmospheric Research provides time series of CO2 emissions for all world countries from 1970 until 2017. In this report, fossil CO2 emissions are presented for the period 1990-2017 as well as the per capita and per GDP trends.JRC.C.5-Air and Climat

GHG emissions of all world countries

The Emissions Database for Global Atmospheric Research provides emission time series from 1970 until 2020 for fossil CO2 and until 2018 for non-CO2 GHGs for all countries, and covers the emissions and removals from land use and forestry for the years 2000 to 2015. This report is contributing to the Paris Agreement process with an independent and quantitative view of global GHG emissions.JRC.C.5 - Air and Climat

CO2 emissions of all world countries

This report presents the fossil CO2 emission time series of the Emissions Database for Global Atmospheric Research (EDGAR) from 1970 until 2021, together with CO2 emissions and removals from land use and forestry for period 1990 to 2021. For the first time, the report uses IEA calculated fossil-fuel CO2 emissions directly where appropriate, rather than calculating them from the underlying energy use statistics, to ensure greater coherence with the IEA data. The report contributes to the Paris Agreement process with an independent and quantitative view of global emissions.JRC.C.5 - Air and Climat

European anthropogenic AFOLU emissions and their uncertainties: a review and benchmark data

Emission of greenhouse gases (GHG) and removals from land, including both anthropogenic and natural fluxes, require reliable quantification, along with estimates of their inherent uncertainties, in order to support credible mitigation action under the Paris Agreement. This study provides a state-of-the-art scientific overview of bottom-up anthropogenic emissions data from agriculture, forestry and other land use (AFOLU) in Europe. The data integrates recent AFOLU emission inventories with ecosystem data and land carbon models, covering the European Union (EU28) and summarizes GHG emissions and removals over the period 1990–2016, of relevance for UNFCCC. This compilation of bottom-up estimates of the AFOLU GHG emissions of European national greenhouse gas inventories (NGHGI) with those of land carbon models and observation-based estimates of large-scale GHG fluxes, aims at improving the overall estimates of the GHG balance in Europe with respect to land GHG emissions and removals. Particular effort is devoted to the estimation of uncertainty, its propagation and role in the comparison of different estimates. While NGHGI data for EU28 provides consistent quantification of uncertainty following the established IPCC guidelines, uncertainty in the estimates produced with other methods will need to account for both within model uncertainty and the spread from different model results. At EU28 level, the largest inconsistencies between estimates are mainly due to different sources of data related to human activity which result in emissions or removals taking place during a given period of time (IPCC 2006) referred here as activity data (AD) and methodologies (Tiers) used for calculating emissions/removals from AFOLU sectors. The referenced datasets related to figures are visualised at https://doi.org/10.5281/zenodo.3460311, Petrescu et al., 2019

Improving the deterministic skill of air quality ensembles

<p><strong>Abstract.</strong> Forecasts from chemical weather models are subject to uncertainties in the input data (e.g. emission inventory, initial and boundary conditions) as well as the model itself (e.g. physical parameterization, chemical mechanism). Multi-model ensemble forecasts can improve the forecast skill provided that certain mathematical conditions are fulfilled. We demonstrate through an intercomparison of two dissimilar air quality ensembles that unconditional raw forecast averaging, although generally successful, is far from optimum. One way to achieve an optimum ensemble is also presented. The basic idea is to either add optimum weights to members or constrain the ensemble to those members that meet certain conditions in time or frequency domain. The methods are evaluated against ground level observations collected from the EMEP and Airbase databases.<br><br> The two ensembles were created for the first and second phase of the Air Quality Model Evaluation International Initiative (AQMEII). Verification statistics shows that the deterministic models simulate better O<sub>3</sub> than NO<sub>2</sub> and PM<sub>10</sub>, linked to different levels of complexity in the represented processes. The ensemble mean achieves higher skill compared to each station's best deterministic model at 39 %–63 % of the sites. The skill gained from the favourable ensemble averaging has at least double the forecast skill compared to using the full ensemble. The method proved robust for the 3-monthly examined time-series if the training phase comprises 60 days. Further development of the method is discussed in the conclusion.</p&gt