Ana Russo

Air quality prediction using optimal neural networks with stochastic variables

We apply recent methods in stochastic data analysis for discovering a set of few stochastic variables that represent the relevant information on a multivariate stochastic system, used as input for artificial neural network models for air quality forecast. We show that using these derived variables as input variables for training the neural networks it is possible to significantly reduce the amount of input variables necessary for the neural network model, without considerably changing the predictive power of the model. The reduced set of variables including these derived variables is therefore proposed as an optimal variable set for training neural network models in forecasting geophysical and weather properties. Finally, we briefly discuss other possible applications of such optimized neural network models.

Neural network forecast of daily pollution concentration using optimal meteorological data at synoptic and local scales

We present a simple neural network and data pre–selection framework, discriminating the most essential input data for accurately forecasting the concentrations of PM10, based on observations for the years between 2002 and 2006 in the metropolitan region of Lisbon, Portugal. Starting from a broad panoply of different data sets collected at several air quality and meteorological stations, a forward stepwise regression procedure is applied enabling to automatically identify the most important variables for predicting the pollutant and also to rank them in order of importance. The importance of this variable ranking is discussed, showing that it is very sensitive to the urban location where measurements are obtained. Additionally, the importance of Circulation Weather Types is highlighted, characterizing synoptic scale circulation patterns and the concentration of pollutants. We then quantify the performance of linear and non–linear neural network models when applied to PM10 concentrations. In the light of contradictory results of previous studies, our results show no clear superiority for the case studied of non–linear models over linear models. While all models show similar predictive performances, we find important differences in false alarm rates and demonstrate the importance of removing weekly cycles from input variables.

The record precipitation and flood event in Iberia in December 1876: description and synoptic analysis

The first week of December 1876 was marked by extreme weather conditions that affected the south-western sector of the Iberian Peninsula, leading to an all-time record flow in two large international rivers. As a direct consequence, several Portuguese and Spanish towns and villages located in the banks of both rivers suffered serious flood damage on 7 December 1876. These unusual floods were amplified by the preceding particularly autumn wet months, with October 1876 presenting extremely high precipitation anomalies for all western Iberia stations. Two recently digitised stations in Portugal (Lisbon and Evora), present a peak value on 5 December 1876. Furthermore, the values of precipitation registered between 28 November and 7 December were so remarkable that, the episode of 1876 still corresponds to the maximum average daily precipitation values for temporal scales between 2 and 10 days. Using several different data sources, such as historical newspapers of that time, meteorological data recently digitised from several stations in Portugal and Spain and the recently available 20th Century Reanalysis, we provide a detailed analysis on the socio-economic impacts, precipitation values and the atmospheric circulation conditions associated with this event. The atmospheric circulation during these months was assessed at the monthly, daily and sub-daily scales. All months considered present an intense negative NAO index value, with November 1876 corresponding to the lowest NAO value on record since 1865. We have also computed a multivariable analysis of surface and upper air fields in order to provide some enlightening into the evolution of the synoptic conditions in the week prior to the floods. These events resulted from the continuous pouring of precipitation registered between 28 November and 7 December, due to the consecutive passage of Atlantic low-pressure systems fuelled by the presence of an atmospheric-river tropical moisture flow over central Atlantic Ocean.

Hybrid Model for Urban Air Pollution Forecasting: A Stochastic Spatio-Temporal Approach

Air pollution is usually driven by a complex combination of factors in which meteorology, physical obstacles, and interactions between pollutants play significant roles. Considering the characteristics of urban atmospheric pollution and its consequent impacts on human health and quality of life, forecasting models have emerged as an effective tool to identify and forecast air pollution episodes. The overall objective of the present work is to produce forecasts of pollutant concentrations with high spatio-temporal resolution and to quantify the uncertainty in those forecasts. Therefore, a new approach was developed based on a two-step methodology. Firstly, neural network models were used to generate short-term temporal forecasts based on air pollution and meteorology data. The accuracy of those forecasts was then evaluated against an independent set of historical data. Secondly, local conditional distributions of the observed values with respect to the predicted values were used to perform spatial stochastic simulations for the entire geographic area of interest. With this approach the spatio-temporal dispersion of a pollutant can be predicted, while accounting for both the temporal uncertainty in the forecast (reflecting the neural networks efficiency at each monitoring station) and the spatial uncertainty as revealed by the spatial variograms. Based on an analysis of the results, our proposed method offers a highly promising alternative for the characterization of urban air quality.

The influence of circulation weather patterns at different spatial scales on drought variability in the Iberian Peninsula

Europe has suffered several extreme weather events which were responsible for considerable ecological and economic losses in the last few decades. In Southern Europe, droughts are one of the most frequent extreme weather events, causing severe damages and various fatalities. The main goal of this study is to determine the role of Circulation Weather Types (CWT) on spatial and temporal variability of droughts by means the new multi-scalar Standardized Precipitation Evapotranspiration Index (SPEI). This study aims also to identify the main CWTs associated with winter and summer droughts over different regions of Iberian Peninsula. During the period between 1950 and 2012, the most frequent CWTs were found to be the Anticyclonic (A), Cyclonic (C), North (N) and Northeast (NE) types. The trend analysis for winter season shows a clear increase of frequency CWTs associated to dry events (A, East and Southeast) and a decrease of frequency of C and northern types while in summer a clear decrease of NE is observed. The spatial patterns of correlation between SPEI and CWT show large patterns of negative correlations with winter frequencies of A and eastern weather types, while the reverse occurs with C and western types. This feature is highlighted on a regional approach. The NE type presents negative correlations Central, Northwestern and Southwestern regions during winter and positive correlations in Eastern region during summer. In opposition, the West type presents positive correlations in all regions (except Eastern region) during winter and does not present significant correlations during summer. In general, the predominant CWT associated to winter or summer drought conditions differs greatly between regions. The winter droughts are associated mainly with high frequency of E types and low frequency of W types for all areas, while the summer drought in eastern sectors are linked with low frequency of C type, as well as the western regions are related with the N type.

Saharan dust intrusions in Spain: Health impacts and associated synoptic conditions

Background: A lot of papers have been published about the impact on mortality of Sahara dust intrusions in individual cities. However, there is a lack of studies that analyse the impact on a country and scarcer if in addition the analysis takes into account the meteorological conditions that favour these intrusions. Objectives: The main aim is to examine the effect of Saharan dust intrusions on daily mortality in different Spanish regions and to characterize the large‐scale atmospheric circulation anomalies associated with such dust intrusions. Methods: For determination of days with Saharan dust intrusions, we used information supplied by the Ministry of Agriculture, Food & Environment, it divides Spain into 9 main areas. In each of these regions, a representative province was selected. A time series analysis has been performed to analyse the relationship between daily mortality and PM10 levels in the period from 01.01.04 to 31.12.09, using Poisson regression and stratifying the analysis by the presence or absence of Saharan dust advections. Results: The proportion of days on which there are Saharan dust intrusions rises to 30% of days. The synoptic pattern is characterised by an anticyclonic ridge extending from northern Africa to the Iberian Peninsula. Particulate matter (PM) on days with intrusions are associated with daily mortality, something that does not occur on days without intrusions, indicating that Saharan dust may be a risk factor for daily mortality. In other cases, what Saharan dust intrusions do is to change the PM‐related mortality behaviour pattern, going from PM2.5. Conclusions: A study such as the one conducted here, in which meteorological analysis of synoptic situations which favour Saharan dust intrusions, is combined with the effect on health at a city level, would seem to be crucial when it comes to analysing the differentiated mortality pattern in situations of Saharan dust intrusions. Graphical abstract Figure. No Caption available. HighlightsThe proportion of days on which there are Saharan dust intrusions rises to 30% of days.An intrusion is accompanied by an increase in particle levels in all regions.The synoptic pattern is characterised by an anticyclonic ridge.PM on days with intrusions are associated with daily mortality.Saharan dust may be a risk factor for daily mortality.

Effects of Recent Minimum Temperature and Water Deficit Increases on Pinus pinaster Radial Growth and Wood Density in Southern Portugal

Western Iberia has recently shown increasing frequency of drought conditions coupled with heatwave events, leading to exacerbated limiting climatic conditions for plant growth. It is not clear to what extent wood growth and density of agroforestry species have suffered from such changes or recent extreme climate events. To address this question, tree-ring width and density chronologies were built for a Pinus pinaster stand in southern Portugal and correlated with climate variables, including the minimum, mean and maximum temperatures and the number of cold days. Monthly and maximum daily precipitations were also analyzed as well as dry spells. The drought effect was assessed using the standardized precipitation-evapotranspiration (SPEI) multi-scalar drought index, between 1 to 24-months. The climate-growth/density relationships were evaluated for the period 1958-2011. We show that both wood radial growth and density highly benefit from the strong decay of cold days and the increase of minimum temperature. Yet the benefits are hindered by long-term water deficit, which results in different levels of impact on wood radial growth and density. Despite of the intensification of long-term water deficit, tree-ring width appears to benefit from the minimum temperature increase, whereas the effects of long-term droughts significantly prevail on tree-ring density. Our results further highlight the dependency of the species on deep water sources after the juvenile stage. The impact of climate changes on long-term droughts and their repercussion on the shallow groundwater table and P. pinaster’s vulnerability are also discussed. This work provides relevant information for forest management in the semi-arid area of the Alentejo region of Portugal. It should ease the elaboration of mitigation strategies to assure P. pinaster’s production capacity and quality in response to more arid conditions in the near future in the region.

Stochastic Modelling Applied to Air Quality Space-Time Characterization

Atmospheric pollution directly affects the respiratory system, aggravating several chronicle illnesses (e.g. bronchitis, pulmonary infections, cardiac illnesses and cancer). This pertinent issue concerns mainly highly populated urban areas, in particular when meteorological conditions (e.g. high temperature in summer) emphasise its effects on human health.

Searching for optimal variables in real multivariate stochastic data

Article history: By implementing a recent technique for the determination of stochastic eigendirections of two coupled stochastic variables, we investigate the evolution of fluctuations of NO2 concentrations at two monitoring stations in the city of Lisbon, Portugal. We analyze the stochastic part of the measurements recorded at the monitoring stations by means of a method where the two concentrations are considered as stochas- tic variables evolving according to a system of coupled stochastic differential equations. Analysis of their structure allows for transforming the set of measured variables to a set of derived variables, one of them with reduced stochasticity. For the specific case of NO2 concentration measures, the set of derived vari- ables are well approximated by a global rotation of the original set of measured variables. We conclude that the stochastic sources at each station are independent from each other and typically have ampli- tudes of the order of the deterministic contributions. Such findings show significant limitations when predicting such quantities. Still, we briefly discuss how predictive power can be increased in general in the light of our methods.

Drought Trends in the Iberian Peninsula over the Last 112 Years

The Iberian Peninsula (IP) is a drought-prone area located in the Mediterranean which presents a significant tendency towards dryness during the last decades, reinforcing the need for a continuous monitoring of drought. The long-term evolution of drought in the IP is analyzed, using the Standardized Precipitation Evaporation Index (SPEI) and the Standardized Precipitation Index (SPI), for the period of 1901–2012 and for three subperiods: 1901–1937, 1938–1974, and 1975–2012. SPI and SPEI were calculated with a 12-month time scale, using data from the Climatic Research Unit (CRU) database. Trends in the drought indices, precipitation, and reference evapotranspiration (ET0) were analysed and series of drought duration, drought magnitude, time between drought events, and mean intensity of the events were computed. SPI and SPEI significant trends show areas with opposite signals in the period 1901–2012, mainly associated with precipitation trends, which are significant and positive in the northwestern region and significant and negative in the southern areas. Additionally, SPEI identified dryer conditions and an increase in the area affected by droughts, which agrees with the increase in ET0. The same spatial differences were identified in the drought duration, magnitude, mean intensity, and time between drought events.