István Matyasovszky

Application of a space-time stochastic model for daily precipitation using atmospheric circulation patterns

Space-time series of daily precipitation amount conditioned on daily circulation pattern (CP) types are calculated. A stochastic hydroclimatological model is used to define daily precipitation under the climate of eastern Nebraska. Principal component analysis and k means method result in nine CP types in west central United States on the basis of 40 years of data. Both the probability and the amount of daily precipitation are strongly related to CP types. The approach can be used to predict the regional or local hydrological effect of climate change.

Effect of Climate Change on Regional Precipitation in Lake Balaton Watershed

SummaryA methodology is developed and applied to the area of Lake Balaton and its drainage basin, a region of Western Hungary, to estimate the space-time distribution of daily precipitation under climate change. Lake Balaton is the largest lake in Central and Western Europe; it has a central location in the country and its drainage basin covers about the 20% of Hungary (together with the Sió Canal). The methodology is based on an analysis of the semi-Markovian properties of atmospheric macrocirculation pattern types (MCP), and a stochastic linkage between daily (here 700 hPa) MCP types and daily precipitation events. Historical data and General Circulation Model (GCM) output of daily MCP corresponding to 1 · CO2 and 2 · CO2 scenarios are considered in this study. Time series of both local and areal precipitation corresponding for both scenarios are simulated and their statistical properties are compared. For the temperate continental climate of Western Hungary a slightly variable spatial response to climate change is obtained. Under 2 · CO2 conditions most of the local and the areal average precipitation suggests, a somewhat dryer precipitation regime in Western Hungary. The sensitivity of the results to the GCM utilized should be considered.

Association of allergic asthma emergency room visits with the main biological and chemical air pollutants

Joint effect of biological (pollen) and chemical air pollutants on asthma emergency room (ER) visits was analyzed for Szeged region of Southern Hungary. Our database of a nine-year period (1999-2007) includes daily number of asthma emergency room (ER) visits, and daily mean concentrations of CO, PM(10), NO, NO(2), O(3) and SO(2), furthermore two pollen variables (Ambrosia and total pollen excluding Ambrosia), as well. The analysis was performed for ER visits of asthma bronchiale using two age groups (adults and the elderly) of males and females for three seasons. Factor analysis was performed in order to clarify the relative importance of the pollutant variables affecting asthma ER visits. Asthma ER visits denote notably stronger associations with the pollutants in adult male than in adult female patients both for the pollen season of Ambrosia and the pollen-free season. Furthermore, adults are substantially more sensitive to severe asthma attack than the elderly for the season of total pollen excluding Ambrosia pollen. The joint effect of the chemical and pollen variables is the highest for the asthma ER cases in the pollen season of Ambrosia, basically due to the extra impact of the total pollen excluding Ambrosia pollen and partly due to Ambrosia pollen. A nonparametric regression technique was applied to discriminate between events of ER visit-no ER visit using pollen and chemical pollutants as explaining variables. Based on multiple correlations, the strongest relationships between ER visits and pollutants are observed during the pollen-free season. The elderly group with asthma bronchiale is characterized by weaker relationships between ER visits and pollutants compared to adults. Ratio of the number of correct decisions on the events of ER visit-no ER visit is lowest for the season of total pollen excluding Ambrosia pollen. Otherwise, similar conclusions hold as those received by multiple correlations.

The effect of different transport modes on urban PM10 levels in two European cities

The aim of the study is to identify transport patterns that may have an important influence on PM10 levels in two European cities, namely Szeged in East-Central Europe and Bucharest in Eastern Europe. 4-Day, 6-hourly three-dimensional (3D) backward trajectories arriving at these locations at 1200 GMT are computed using the HYSPLIT model over a 5-year period from 2004 to 2008. A k-means clustering algorithm using the Mahalanobis metric is applied in order to develop trajectory types. Two statistical indices are used to evaluate and compare exceedances of critical daily PM10 levels corresponding to the trajectory clusters. For Bucharest, the major PM10 transport can be clearly associated with air masses arriving from Central and Southern Europe, as well as the Western Mediterranean. Occasional North African dust intrusions over Romania are also found. For Szeged, Southern Europe with North Africa, Central Europe and Eastern Europe with regions over the West Siberian Plain are the most important sources of PM10. The occasional appearance of North-African-origin dust over Hungary is also detected. A statistical procedure is developed in order to separate medium- and long-range PM10 transport for both cities. Considering the 500 m arrival height, long-range transport plays a higher role in the measured PM10 concentration both for non-rainy and rainy days for Bucharest and Szeged, respectively.

Climate sensitivity of allergenic taxa in Central Europe associated with new climate change related forces

The aim of the study was to analyse trends of the pollen season with its duration, start and end dates, as well as trends of the annual total pollen count and annual peak pollen concentration for the Szeged agglomeration in Southern Hungary. The data set covered an 11-year period (1997-2007) that included eight taxa and seven daily climate variables. Trend analysis was performed on both annual and daily bases. Trend analysis on a daily basis is a new approach that provides information on the annual cycles of the trends. To quantify the strength of the relationship between the annual cycle of the slope of a pollen concentration trend and the annual cycles of the slopes of the climate variable trends, an association measure and a multiple association measure are introduced. Individual taxa were sorted into three categories according to their climate sensitivities. These were compared with two novel climate change-related forces, namely risk potential and expansion potential due to the climate change. The total annual pollen counts indicated significant trends for 4 taxa and 3 of these 4 trends increased on a daily basis. At the same time, significant changes were detected for the pollen season characteristics of three taxa. The association measures performed well when compared to the climate change-related forces. Significant changes in pollen season characteristics were also in accordance with the risk potential and expansion potential due to the climate change. A novel procedure was applied to separate the effects of the past and current weather conditions that influence the current Ambrosia pollen concentrations. The potential effect of land use changes on pollen release of the given taxa was also discussed using the CORINE Land Cover Database.

Predicting daily ragweed pollen concentrations using Computational Intelligence techniques over two heavily polluted areas in Europe

Forecasting ragweed pollen concentration is a useful tool for sensitive people in order to prepare in time for high pollen episodes. The aim of the study is to use methods of Computational Intelligence (CI) (Multi-Layer Perceptron, M5P, REPTree, DecisionStump and MLPRegressor) for predicting daily values of Ambrosia pollen concentrations and alarm levels for 1-7 days ahead for Szeged (Hungary) and Lyon (France), respectively. Ten-year daily mean ragweed pollen data (within 1997-2006) are considered for both cities. 10 input variables are used in the models including pollen level or alarm level on the given day, furthermore the serial number of the given day of the year within the pollen season and altogether 8 meteorological variables. The study has novelties as (1) daily alarm thresholds are firstly predicted in the aerobiological literature; (2) data-driven modelling methods including neural networks have never been used in forecasting daily Ambrosia pollen concentration; (3) algorithm J48 has never been used in palynological forecasts; (4) we apply a rarely used technique, namely factor analysis with special transformation, to detect the importance of the influencing variables in defining the pollen levels for 1-7 days ahead. When predicting pollen concentrations, for Szeged Multi-Layer Perceptron models deliver similar results with tree-based models 1 and 2 days ahead; while for Lyon only Multi-Layer Perceptron provides acceptable result. When predicting alarm levels, the performance of Multi-Layer Perceptron is the best for both cities. It is presented that the selection of the optimal method depends on climate, as a function of geographical location and relief. The results show that the more complex CI methods perform well, and their performance is case-specific for ≥2 days forecasting horizon. A determination coefficient of 0.98 (Ambrosia, Szeged, one day and two days ahead) using Multi-Layer Perceptron ranks this model the best one in the literature.

Weather elements, chemical air pollutants and airborne pollen influencing asthma emergency room visits in Szeged, Hungary: performance of two objective weather classifications

Weather classification approaches may be useful tools in modelling the occurrence of respiratory diseases. The aim of the study is to compare the performance of an objectively defined weather classification and the Spatial Synoptic Classification (SSC) in classifying emergency department (ED) visits for acute asthma depending from weather, air pollutants, and airborne pollen variables for Szeged, Hungary, for the 9-year period 1999–2007. The research is performed for three different pollen-related periods of the year and the annual data set. According to age and gender, nine patient categories, eight meteorological variables, seven chemical air pollutants, and two pollen categories were used. In general, partly dry and cold air and partly warm and humid air aggravate substantially the symptoms of asthmatics. Our major findings are consistent with this establishment. Namely, for the objectively defined weather types favourable conditions for asthma ER visits occur when an anticyclonic ridge weather situation happens with near extreme temperature and humidity parameters. Accordingly, the SSC weather types facilitate aggravating asthmatic conditions if warm or cool weather occur with high humidity in both cases. Favourable conditions for asthma attacks are confirmed in the extreme seasons when atmospheric stability contributes to enrichment of air pollutants. The total efficiency of the two classification approaches is similar in spite of the fact that the methodology for derivation of the individual types within the two classification approaches is completely different.

Impact of global climate change on temperature and precipitation in Greece

Abstract A stochastic space-time model is used at four locations in Greece for estimating the effect of global climate change on daily temperature and precipitation. The approach is based on a semi-empirical downscaling of simulated daily atmospheric Circulation Patterns (CP) of General Circulation Models (GCM). Historical data and a 10-year outputs of the Max Planck Institute GCM for the 1 × CO 2 and 2 × CO 2 cases are used. Nine CP types for the winter and summer half years are obtained to characterize large-scale climatic forcing in Greece, Local temperature and precipitation appear to be highly dependent on CP types. The space-time response of daily temperature to global climate change is slightly variable in Greece. In general, a warmer climate will imply nearly 3°C increase in fall and in winter. The variability within the month will not change considerably. A slight but statistically significant increase of precipitation is obtained at one location and an insignificant increase is found at the other three locations.

Estimating the daily Poaceae pollen concentration in Hungary by linear regression conditioning on weather types

Abstract Objectively defined clusters of meteorological elements and weather types described by weather fronts and precipitation occurrences are produced in order to classify Poaceae pollen levels. The Poaceae pollen concentration was then estimated one day ahead for each of the above categories of days at the villages of Szeged and Győr in Hungary. The database describes an 11-year period from 1997 to 2007. For weather-front recognition purposes, the ECMWF ERA-INTERIM database was used. In order to estimate the actual daily Poaceae pollen concentrations, previous-day values of five meteorological variables and previous-day Poaceae pollen concentrations were applied. We find that both for Szeged and Győr, as well as both for the subjective and objective classifications, high daily mean Poaceae pollen levels are favoured by anticyclone ridge weather situations, as we might expect. When estimating the Poaceae pollen level, the previous-day pollen concentration, previous-day mean temperature, and previous-day mean global solar flux for Győr were statistically significant, but for Szeged it was only the previous-day pollen concentration. Taking into account the clusters, the objective classification based on original data proved the most effective. For the subjective classification, the best estimates were obtained for days with a warm front and precipitation.

The influence of extreme high and low temperatures and precipitation totals on pollen seasons of Ambrosia, Poaceae and Populus in Szeged, southern Hungary

Abstract Extreme high and low temperatures and precipitation totals may have important effect on daily and annual pollen concentrations. The aim of this study is to analyse the associations between pollen characteristics and meteorological variables, furthermore between the rank of pollen characteristics and the rank of annual values of meteorological variables for Szeged, southern Hungary. Pollen characteristics include pollen count parameters (TPA, total annual pollen amount; APC, annual peak pollen concentration) and pollen season parameters (start, end and duration of the pollen season). Meteorological variables are temperature and precipitation. The data set used covers a 14-year period (1997–2010) and contains daily values of Ambrosia (ragweed), Poaceae (grasses) and Populus (poplar) pollen concentrations, as well as those of temperature and precipitation. Both Pearson and Spearman rank correlations were calculated, because the rank correlation is less sensitive than the correlation to outliers that are in the tails of the sample. Our results suggest that Ambrosia and Populus are reversely related to temperature (negative correlations), while Poaceae exhibit a parallel relationship with precipitation (positive correlations). On the whole, pollen count characteristics (TPA and APC) indicate a decrease for Ambrosia and Poaceae, while for Populus an increase is expected.