1,721,064 research outputs found
The role of multi-model ensembles in assessing the air quality impact on crop yields and mortality
No full textThis work promotes a critical use of modelling information on air-pollution health and agriculture impacts, with the primary goal of providing more reliable estimates to decision makers and stakeholders. To date, the accuracy of air quality (AQ) models and the quantification of the uncertainty of their results have rarely been quantified explicitly in impact assessment studies, therefore without giving information on the robustness of the information used in the decision making process and undermining the confidence in the results obtained. A suite of twelve regional-scale chemistry transport AQ models produced in the third phase of the Air Quality Model Evaluation International Initiative (AQMEII) is used here to calculate the impact of PM2.5and ozone on human health and crop yields and the associated uncertainties over Europe. A novel methodology is developed and applied to remove the offsetting bias from the models, which are then combined in multi-model (MM) ensembles. The application of unbiased MM ensembles offers an unprecedented attempt to i) establish and ii) mitigate the uncertainty due to AQ modelling on impact calculations. We use the FASST (FAst Scenario Screening Tool) impact assessment tool to demonstrate that the accuracy of assessment of ozone-induced crop loss of wheat and maize and impact on human health (mortality) can improve dramatically when using accurate MM ensembles in place of single model realizations, as it is commonly assumed
Evaluation and uncertainty estimation of the impact of air quality modelling on crop yields and premature deaths using a multi-model ensemble
No full textThis study promotes the critical use of air pollution modelling results for health and agriculture impacts, with the primary goal of providing more reliable estimates to decision makers. To date, the accuracy of air quality (AQ) models and the effects of model-to-model result variability (which we will refer to as model uncertainty) on impact assessment studies have been often ignored, thus undermining the robustness of the information used in the decision making process and the confidence in the results obtained. A suite of twelve PM2.5and ozone concentration fields produced by regional-scale chemistry transport Air Quality (AQ) models during the third phase of the Air Quality Model Evaluation International Initiative (AQMEII) has been used to calculate the impact of air pollution on premature deaths and crop yields. An innovative technique is applied to bias-adjust the models to available observations. The model results for ozone and PM2.5are combined in a multi-model (MM) ensemble, which is used to estimate the damage and economic cost to human health and crop yields, as well as the associated uncertainties. The MM ensemble quantifies directly the uncertainty introduced by AQ models into the air pollution impact assessment chain, while the indirect use of experimental information through a bias adjustment, reduces the uncertainty in the ozone and PM2.5fields and subsequently the uncertainty of the final impact assessment and cost valuation. The analysis over the European countries analysed in this study shows a mean number of premature deaths due to exposure to PM2.5and ozone of approximately 370,000 (inter-quantile range between 260,000 and 415,000) and a relative yield loss of approximately 7% to 9% (depending on the exposure metrics used, for wheat and maize together). Furthermore, the results indicate that a reduction in the uncertainty of the modelled ozone by 61% and by 80% (depending on the aggregation metric used) and by 46% for PM2.5, produces a reduction in the uncertainty in premature mortality and crop loss of >60%, and of an equivalent percentage in the final uncertainty of cost valuation, providing decision makers with more accurate estimations for more targeted interventions
On the Spatial Support of Time Series of Monitoring Data for Model Evaluation
No full textWe use time series of hourly records of ozone for a whole year (2006) collected by the European AirBase network to analyse the area of representativeness of monitoring stations and find, for similar class of stations (urban, suburban, rural), large heterogeneity and high sensitivity to the density of the network and to the noise of the signal. This suggests the mere station classification to be not a suitable method to help select the pool of stations used in model evaluation. Therefore a novel, more robust technique is developed consisting in studying the spatial properties of the associativity of the spectral component of the ozone time series, in an attempt to determine the level of homogeneity.JRC.C.5 - Air and Climat
Error apportionment for atmospheric chemistry-transport models. A new approach to model evaluation
No full textIn this study, methods are proposed to diagnose the causes of errors in air quality (AQ) modelling systems. We investigate the deviation between modelled and observed time series of surface ozone through a revised formulation for breaking down the mean square error (MSE) into bias, variance, and the minimum achievable MSE (mMSE). The bias measures the accuracy and implies the existence of systematic errors and poor representation of data complexity, the variance measures the precision and provides an estimate of the variability of the modelling results in relation to the observed data, and the mMSE reflects unsystematic errors and provides a measure of the associativity between the modelled and the observed fields through the correlation coefficient. Each of the error components is analysed independently and apportioned to resolved process based on the corresponding timescale (long scale, synoptic, diurnal, and intra-day) and as a function of model complexity.
The apportionment of the error is applied to the AQMEII (Air Quality Model Evaluation International Initiative) group of models, which embrace the majority of regional AQ modelling systems currently used in Europe and North America.
The proposed technique has proven to be a compact estimator of the operational metrics commonly used for model evaluation (bias, variance, and correlation coefficient), and has the further benefit of apportioning the error to the originating timescale, thus allowing for a clearer diagnosis of the process that caused the error.JRC.H.2 - Air and Climat
A Science-Based Use of Ensembles of Opportunities for Assessment and Scenario studies
No full textThe multi-model ensemble exercise performed within the HTAP project context [Fiore et al., 2009] is used here as an example of how a pre-inspection, diagnosis and selection of an ensemble, can produce much better and more reliable results. This procedure is contrasted with the often-used practice of simply averaging model simulations, assuming model difference as equivalent to independence, and using the diversity of simulation as an illusory estimate of model uncertainty. It is further and more importantly demonstrated how conclusions can drastically change when future emission scenarios are analysed using and un-inspected ensemble. The HTAP multi-model ensemble analysis is only taken as an example of a wide spread and common practice in air quality modelling.JRC.H.2 - Air and Climat
Comparing apples with apples: Using spatially distributed time series of monitoring data for model evaluation
No full textA more sensible use of monitoring data for the evaluation and development of regional-scale atmospheric
models is proposed. The motivation stems from observing current practices in this realm where
the quality of monitoring data is seldom questioned and model-to-data deviation is uniquely attributed
to model deficiency. Efforts are spent to quantify the uncertainty intrinsic to the measurement process,
but aspects connected to model evaluation and development have recently emerged that remain
obscure, such as the spatial representativeness and the homogeneity of signals subjects of our investigation.
By using time series of hourly records of ozone for a whole year (2006) collected by the European
AirBase network the area of representativeness is firstly analysed showing, for similar class of stations
(urban, suburban, rural), large heterogeneity and high sensitivity to the density of the network and to the
noise of the signal, suggesting the mere station classification to be not a suitable candidate to help select
the pool of stations used in model evaluation. Therefore a novel, more robust technique is developed
based on the spatial properties of the associativity of the spectral components of the ozone time series, in
an attempt to determine the level of homogeneity. The spatial structure of the associativity among
stations is informative of the spatial representativeness of that specific component and automatically
tells about spatial anisotropy. Time series of ozone data from North American networks have also been
analysed to support the methodology. We find that the low energy components (especially the intra-day
signal) suffer from a too strong influence of country-level network set-up in Europe, and different
networks in North America, showing spatial heterogeneity exactly at the administrative border that
separates countries in Europe and at areas separating different networks in North America. For model
evaluation purposes these elements should be treated as purely stochastic and discarded, while retaining
the portion of the signal useful to the evaluation process. Trans-boundary discontinuity of the intra-day
signal along with cross-network grouping has been found to be predominant. Skills of fifteen regional
chemical-transport modelling systems have been assessed in light of this result, finding an improved
accuracy of up to 5% when the intra-day signal is removed with respect to the case where all components
are analysed.JRC.H.2 - Air and Climat
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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