Investigation of relationships and interconnections between Pollen and Air Quality data with the aid of Computational Intelligence Methods

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1 EnviroInfo 2009 (Berlin) Environmental Informatics and Industrial Environmental Protection: Concepts, Methods and Tools Investigation of relationships and interconnections between Pollen and Air Quality data with the aid of Computational Intelligence Methods Dimitris Voukantsis 1, Kostas Karatzas 1, Auli Rantio-Lehtimaki 2 and Mikhail Sofiev 3 1 Aristotle University, Dept. of Mechanical Engineering, Informatics Applications and Systems Group, P.O. Box 483 GR Thessaloniki, voukas@isag.meng.auth.gr, kkara@eng.auth.gr 2 University of Turku, Aerobiology Unit, Finland, ahrantio@utu.fi 3 Finnish Meteorological Institute, mikhail.sofiev@fmi.fi Abstract The impact of airborne pollen to human health has been recognized as being important, while several studies are suggesting that the synergy between high pollen concentrations and air pollutants may induce and exacerbate allergic reactions, thus affecting overall quality of life of citizens. In this paper, the detailed pollen, air quality and meteorological data for the years 2006 and 2007, for the city of Kuopio (Finland) were analyzed using Self Organizing Maps (SOM) and k-means clustering, in order to identify complex, non-linear relations and interconnections within the data. The results obtained indicate that there are strong correlations between pollen concentrations of particular species (Birch, Alder and Spruce) with air pollutant concentration, such as Particulate Matter (PM 10 ) and Ozone (O 3 ). Furthermore, pollen concentrations were identified to be strongly dependent on meteorological parameters, indicating negative correlation with humidity and positive correlation with temperature, solar radiation, atmospheric pressure and wind speed for some pollen species. The methodology applied was capable of identifying and quantifying the interconnections between certain pollen and air quality parameters, resulting to crucial information that could be facilitated in the area of synergy investigations between pollen and air pollution. Keywords: pollen, air pollution, computational intelligence, knowledge extraction 1. Introduction The impact of airborne pollen to human health has been recognized since 1870 s (Blackley 1873) and it has been associated to allergic reactions and to associated health problems. Nevertheless, pollen related atmospheric contamination has not been regulated yet. This means that there is no European legal framework putting up pollen concentration thresholds and limit values and suggesting that pollen levels should be monitored and assessed and measures should be taken in order to prevent or manage pollen pollution, as done in the case of air pollution. On the other hand, it has already been recognized that there are specific levels of pollen airborne content (in terms of pollen grains per cubic meter) that may be associated with allergy symptoms in patients (Viander and Koivikko, 1978). Yet, the problem of a commonly accepted set of pollen related limit values is still unsolved, one of the main reasons being the fact that for different parts of the world, different types of flora are associated to pollen related allergies. Thus, it is not easy to have a commonly acceptable and applied list of important pollen types that are considered to be harmful for a considerable part of the population at a, let s say, European scale, as it has been achieved in the case of other atmospheric contaminants, i.e. air pollutants. One of the issues that has been put under the scope of scientific investigation of the possible relationships and interconnections between pollen and air pollution, as they both have a negative effect to human health. On this basis, it is of major importance to investigate, understand and analyse the behaviour of aeroallergens and their relationships with other parameters of the atmospheric environment, like meteorological parameters and air pollutants. 195

2 There are various suggestions concerning interrelationships and cross influences between pollen and air pollution levels. Thus, for example, Ranzi et. al. (2003) suggested that Rising sensitivity of the population to pollen and to other airborne allergenics seems correlated to the air quality, mainly influenced by traffic pollution. Chehregani et. al., (2004), on the other hand, claimed that air pollutants can cause allergic symptoms, but when associated with allergen pollen grains, their allergenicity power is increased. These types of statements were repeated, as in the case of Cariñanos et. al., 2007, who have made a study in Córdoba, suggesting that the occurrence of simultaneous peaks in airborne pollen-grain and PM 10 counts suggests potential co-adjuvant activity, which may lead to high-risk situations for people with respiratory disease. On the other hand, both air pollutants and aeroallergens were perceived as asthma triggers in recent studies (Ritz et. al., 2008). The application of computational intelligence (CI) methods for analyzing and modelling pollen concentration data has increased in the recent years, since it was identified that CI methods, such as Artificial Neural Networks and Neuro-Fuzzy models, clearly outperform traditional linear methods in forecasting tasks (Sanchez-Mesa, 2002; Ranzi et al, 2003; Aznarte et al, 2007). Most of these applications have taken into account daily average pollen concentrations and meteorological parameters, aiming at forecasting pollen concentration of certain species 1 to 5 days ahead. There are no previous reports, to the knowledge of the authors, involving CI methods in order to analyze such detailed (2 hourly resolution) information of pollen, air quality and meteorological data, although there are some papers published already in the use of CI methods for analysing and forecasting pollen data (Degaudenzi and Arizmendi, 1999; Aznarte et al., 2007). Thus, the objectives of the present paper are i) to identify patterns of pollen and compare them with air quality and meteorological ones, with the purpose of identifying interrelationships and similarities and ii) to quantify the recognized interconnections. 2. Material and Methods 2.1 Area of Interest and Data Presentation The city of Kuopio is located in a fairly flat area in the region of northern Savonia in the eastern part of Finland. The city accounts for approximately 92,000 inhabitants and it covers an area of approximately 1,730km 2, of which 600km 2 is water and half forest. Although the overall population density is not high (82 inhabitants/km 2 ), it is almost 20 times higher at the urban area (1,615 inhabitants/km 2 ). Figure 1 provides an overview of the area of interest. The climate in the Kuopio region can be characterized as typically Finnish, i.e., cold winters with regular rain or snow falls and summers with daily average temperatures constantly above 10 C. Although the air quality of the city of Kuopio can be characterized as good, during certain hours of the day it is heavily influenced by traffic. Furthermore, during early spring the melting of snow and ice combined with car traffic results to high concentrations of particulate matter. Thus, it is likely that high pollen concentrations during the early spring time might coincide with high concentrations of particulate matter and possibly other air pollutants. Since the co-existence of high concentrations of air pollutants and pollen grains has been suspected to induce and exacerbate allergic reactions, it is of great interest to identify and quantify the potential interconnections. 196

3 Figure 1: The area of study, Kuopio (Finland) The analysis performed in this paper included three types of data, i.e. pollen, air quality and meteorological data, from the city of Kuopio. The pollen data were provided by the Aerobiology Unit (University of Turku), while the air quality and meteorological data were provided by the Finnish Meteorological Institute (FMI). The data corresponded to the time period and the time resolution was 2 hours. Although the initial pollen data set included information for 30 different pollen species, only 5 of them indicated significant concentrations (over 20counts/m 3 for at least one day during the years 2006 and 2007) and were considered for further analysis. However, two of them, i.e. Betula (Birch) and Alnus (Alder), are considered highly allergenic pollen, affecting a significant fraction of the population. Table 1 presents in more detail the variables taken into account, as well as the sampling and monitoring locations (one for pollen and two for air quality). The pollen concentration data were combined with the air quality and meteorological data separately, resulting into two different datasets (Pollen-AQ and Pollen-MET). Type of Data Pollen Air Quality Meteorology Concentrations of the Concentrations of air species: pollutants: Variable s Description Units and Alnus, Betula, Poaceae, Pinus, Salix (counts/m 3 ) PM 10, CO*, NO, O 3 (µg/m 3, *mg/m 3 ) Atm. Pressure (mb) Temperature (ºC) Rel. Humidity (%) Wind Speed (m/s) Sol. Radiation (W/m 2 ) Monitoring/Sampling Station University of Kuopio (62 o 53 N, 27 o 38 E ca. 100 m above sea level) Maaherrankatu (urban/traffic) Kasarmipuisto (urban/background) FMI MPI preprocessed, representative for the city of Kuopio Table 1: Description of the data considered at the analysis. Pollen concentrations are characterized by strong seasonality, indicating their maximum values during spring (birch family, conifers), and summer (grasses, mugwort) and zero values during winter. Thus, the analysis was applied to that fraction of the data that indicated non-zero pollen concentration, i.e. during late spring and early summer. Figure 2 presents typical pollen concentrations behaviour. 197

4 Concentration [counts/m 3 ] Concentration [counts/m 3 ] Birch Pollen, Year Birch Pollen, Year Figure 2: Concentrations of the Birch Pollen for the years 2006 and 2007 (April, May and June) in the City of Kuopio. The time resolution is 2 hours. Concentration is provided in pollen grain counts per m Self Organizing Maps The Self Organizing Map (SOM) is a CI method that is applied for the visualization and modelling of high-dimensional data, while it is also one of the best known unsupervised neural learning algorithms (Kohonen, 1997). The objectives of the SOM algorithm are to calculate a set of prototype vectors that is representative for the data being investigated, and meanwhile achieve a continuous mapping from the multidimensional input space of the data into a low dimensional lattice map (usually two dimensional). Thus, the SOM algorithm is capable of identifying complex non-linear relations within the data and represent them with an information revealing way. Furthermore, SOMs are computationally efficient and robust against missing values and outliers. On this basis, SOM was considered a suitable tool for identifying relations and interconnections within the data under consideration. The map size of the SOMs applied was 7x10, thus consisting of 70 neurons, while the lattice shape applied was hexagonal. 2.3 K Means Clustering Although the SOM is a clustering algorithm by itself, it has been successfully combined with other clustering algorithms, in order to facilitate quantitative analysis. In this approach, the prototype vectors produced by SOM are used as input in clustering instead of the actual data. Since each data point has been assigned to a specific prototype vector during the learning process of the SOM algorithm, the clustering of the prototype vectors can be perceived also as clustering the actual data points. In this way the clustering algorithm is modified to use a two-step approach that has already been outlined by Vesanto and Alhoniemi, (2000), improving its performance in terms of computational efficiency and noise reduction. Especially in this case, where pollen concentrations indicate many peak values, the use of a robust method, such as SOM, that is capable of handling these values is of particular importance. SOM has been successfully combined with several clustering algorithms, such as k-means, hierarchical clustering and fuzzy c-means, in several application domains (Lu et al, 2006; Räsänen et al, 2008). In the 198

5 current paper, SOM was combined with k-means clustering, a well known non-hierarchical algorithm (MacQueen, 1967), that has been successfully applied in many case studies. However, there are certain issues that need to be considered when applying k-means clustering. In particular, the random initialisation of the cluster centres, results to different partitioning of data space every time the algorithm is applied. For this reason it was decided to apply another computational approach, where the k-means algorithm was repeated 20 times for each dataset and the best clustering was determined by the smallest sum-of-squared error. Furthermore, the k-means algorithm requires that the number of clusters is known in advance. Since in this application there was no a priori knowledge concerning the optimum number of clusters, the process was repeated for different cluster numbers, ranging from 2 to 20. The optimum number of clusters was determined by the Davies-Bouldin Index (Davies and Bouldin, 1979), i.e. by its minimum value. 3. Results and Discussion The methodology presented in section 2 was applied at both data sets (Pollen-MET and Pollen-AQ) separately. The objectives were to identify relations between pollen concentrations, air pollutant concentrations and meteorological parameters using the SOMs and provide a quantitative description of the findings by clustering the SOM prototype vectors, using k-means clustering. 3.1 Pollen Meteorology Data The interconnections between pollen concentrations and meteorological parameters have been studied in detail in the past (Cariñanos et al., 2000; Rodríguez-Rajo et al., 2006 and references therein). However, the identification of known correlations between pollen concentrations and meteorological parameters serves as a proof of effectiveness for the chosen methodology. Figure 3 presents the map resulted using SOM on the Pollen-MET data set. Each one of the hexagonal cells represents a prototype vector and the corresponding values of the variables in each one of them are represented in a colour coded map. Of particular interest is the bottom- right corner of the map, where the prototype vectors with high concentrations of the pollen species Alnus, Betula and Salix are accumulated. By inspecting the values of the other variables for the same area of the map it is possible to draw conclusions on the interrelationships between these parameters. On this basis, it becomes evident that high concentrations of the pollen species Alnus, Betula and Salix occur during high Atmospheric Pressure situations, that are accompanied by moderate Temperature, low Relative Humidity, moderate Wind Speed and high Solar Radiation. Similarly, the bottom-left corner of the map, that indicates high concentrations of the pollen species Picea and Pinus, is associated with moderate Atmospheric Pressure, high Temperature, low Relative Humidity, high Wind Speed and high Solar Radiation. These findings suggest that there is a strong negative relationship with Relative Humidity, expressing low concentration of pollen during rainy days, while there is a strong positive association mostly with Solar Radiation and then with Temperature and Atmospheric Pressure, expressing the optimum meteorological condition for pollination. Furthermore, Wind Speed indicates strong correlation with Picea and moderate correlation with Pinus, indicating that concentrations of these pollen species are favoured during windy days, a fact that can be attributed to the similarity of the pollen grains of the particular pollen species, which are big in size (Picea: longest axis μm; Pinus: longest axis μm), and carry air sacks that support their dispersion. 199

6 Figure 3: Visualization of the SOM prototype vectors (pollen meteorological data) arranged in twodimensional, hexagonal lattice. The values of the variables within each prototype vector are presented as separate colour coded maps. These findings were expected to some extent since the effects of meteorological parameters on pollen concentrations are well studied (Gioulekas et al., 2004; Moreno-Grau et al., 2000). The SOM results, presented in Figure 3, provide with insight of the data and highlight the relations and interconnections between the investigated parameters. In order to quantify these results, the k-means algorithm was applied to the SOM prototype vectors. During the clustering process it was identified that the optimum number of clusters is 9, however high pollen concentrations were identified only in 3 of these clusters (C 3, C 6 and C 7 ). Table 1 presents the cluster centres (mean values) identified by the k-means clustering algorithm, while Figure 4 presents clusters of particular interest, due to the high pollen concentrations identified in those clusters. Variable C 1 C 2 C 3 C 4 C 5 C 6 C 7 C 8 C 9 MeanValues Atm. Pr Temp Hum WS Sol. Rad Alnus Betula Picea Pinus Salix Table 2: Cluster centres of the Pollen-MET data set. Indicated with bold letters are the highest values of the pollen concentrations 200

7 Figure 4: Clusters of particular interest, due to the high pollen concentrations. The values of the variables are normalized using the variance scaling for better demonstration. 3.2 Pollen Air Quality Data The application of SOMs on the Pollen-AQ dataset resulted to the map that is presented in Figure 5. The inspection of the map reveals a strong pattern within the data that can be associated with traffic and it is located on the upper-left corner of the map. High concentrations of NO, CO and low concentrations of O 3 are its characteristics and none of the pollen species indicates significant association to this pattern. However, the upper-right corner of the map indicates high concentrations of PM 10 in both monitoring stations (that cannot be associated with traffic, due to low concentrations of NO and CO), and high concentrations of O 3. There is a strong relationship of this pattern to Alnus concentrations and to Picea to some extent. Furthermore, high concentrations of Betula and Salix are identified to coexist with rather high concentrations of O 3. Finally, Pinus indicates a significantly different behaviour, with corresponding prototype vectors of high concentrations accumulating in the central part of the map and correlating to moderate concentrations of O 3. Figure 5: Visualization of the SOM prototype vectors (pollen air quality data) arranged in two-dimensional, hexagonal lattice. (Urb/Traf: Urban/Traffic monitoring station, Urb/Back: Urban/Background monitoring station). 201

8 The results obtained by the visual inspection of Figure 5, were quantified by the use of k-means clustering. The optimum number of clusters was identified to be 9 and the corresponding cluster centres (mean values) are presented in Table 3. Four of these clusters (C 1, C 3, C 7 and C 9 ) are of particular interest due to high pollen concentrations and are presented in Figure 6. Variable C 1 C 2 C 3 C 4 C 5 C 6 C 7 C 8 C 9 MeanValues NO (U/B) O 3 (U/B) PM 10 (U/B) CO (U/T) NO (U/T) PM 10 (U/T) Alnus Betula Picea Pinus Salix Table 3: Cluster centres of the Pollen-AQ data set. Indicated with bold letters are the highest values of the pollen concentrations (U/T: Urban/Traffic monitoring station, U/B: Urban/Background monitoring station). Figure 6: Clusters of particular interest, due to the high pollen concentrations. The values of the variables are normalized using the variance scaling for better demonstration (U/T: Urban/Traffic monitoring station, U/B: Urban/Background monitoring station). The results obtained indicated that high concentrations of particular pollen species are correlated with high air pollution concentration, in a rather specific and concrete way. For the city of Kuopio it was identified that high concentrations of Betula and Salix pollen are usually accompanied with high concentrations of 202

9 O 3, while high concentrations of Alnus, Picea, O 3 and PM 10, usually appear together. The coexistence of high concentrations of allergenic pollen species and air pollutants is a curtail piece of information, since health impacts of pollen and air pollutant synergies seem to play an important role in the overall quality of life of citizens (Carracedo-Martinez et al, 2008; Epstein, 2008). 4. Conclusions The current paper has presented a methodology for analyzing pollen, meteorological and air quality data using a two step clustering process consisting of the application of SOM and K-means algorithms, and was demonstrated to 2-hourly data sampled at the city of Kuopio, Finland. Meteorological correlations could be confirmed, while pollen and air quality parameters indicated specific interrelations, thus providing input to the area of synergy investigations between pollen and air pollution. The applied methodology was capable of providing with a clear overview of the identified interrelations and quantify the results. Furthermore, its robustness makes it a suitable tool for analyzing and modelling pollen concentration data. It would be of great interest to repeat the presented analysis by including symptom or hospital admission data, in order to investigate possible correlations between the identified pollen-air pollutants patterns with effects on human s health. Finally, the results presented in this paper could be facilitated in combined (pollen air quality) forecasting models. Acknowledgement The authors wish to thank the Aerobiology Unit (University of Turku) for providing the pollen data, as well as the Finnish Meteorology Institute (FMI) for providing the air quality and meteorology data. This project has been supported by the COST Action ES0603 Assessment of production, release, distribution and health impact of allergenic pollen in Europe and COST Action ES0602 Towards a European Network on Chemical Weather Forecasting and Information Systems. References Aznarte J.L., Nieto Lugilde D., Benítez J.M., Alba Sánchez F. and de Linares Fernández C. (2007): Forecasting airborne pollen concentration time series with neural and neuro-fuzzy models. Expert Systems with Applications 32, Blackley, C.H (1873): Experimental researches on the causes and nature of Catarrhus Aestivus (Hay fever or hay asthma). Ballière, Tindall & Cox, London, 202 pp. Cariñanos P., Galán C., Alcázar P. and Domínguez E. (2000), Meteorological phenomena affecting the presence of solid particles suspended in the air during winter, Int. J. Biometeolrology 44, 6-10 Cariñanos P., Galán C., Alcázar P. and Domínguez E. (2007): An Analysis of solid particulate matter suspended in the air of Córdoba, southweastern Spain, Ann Agric Environ Med 14, Carracedo-Martinez E., Sanchez C., Taracido M., Saez M., Jato V. and Figueiras A. (2008): Effect of short-term exposure to air pollution and pollen on medical emergency calls: a case-crossover study in Spain, Allergy, 63(3), Chehregani A, Majde A, Moin M, Gholami M, Ali Shariatzadeh M. and Nassiri H. (2004): Increasing allergy potency of Zinnia pollen grains in polluted areas, Ecotoxicology and Environmental Safety 58, Degaudenzi M.E. and Arizmendi C.M. (1998): Wavelet Based Fractal Analysis of Airborne Pollen, Phys. Rev. E 59,

10 Davies D., Bouldin D. (1979): A Cluster Separation Measure. IEEE Transactions on Pattern Analysis and Machine Intelligence 2, Epstein P. R. (2008): Fossil fuels, allergies and a host of other ills. Journal of Allergy and Clinical Immunology, 122(3), Gioulekas D., Balafoutis C., Damialis A., Papakosta D., Gioulekas G. And Patakas D. (2004), Ffiften years record of airborne allergenic pollen and meteorological parameters in Thessaloniki, Greece, Int. J. Biometeorol.48, pp Kohonen T. (1997): Self-organizing maps. 2nd ed., Springer, Berlin Lu, H.-C., Chang, C.-L. and Hsieh, J.-C. (2006): Classification of PM10 distributions in Taiwan, Atmospheric Environment 40(8), MacQueen J. (1967): Some Methods for Classification and Analysis of Multivariate Observations. In: The Fifth Berkeley Symposium on Mathematical Statistics and Probability, Vol. 1, University of California Press Moreno-Grau S., Angosto J.M., Elvira-Rendueles B.,Bayo J., Moreno J. and Moreno-Clavel J. (2000), Effects of meteorological parameters and plant distribution on Chenopodiaceae-Amaranthaceae, Quercus and Olea airborne pollen concentrations in the atmosphere of Cartagena (Spain), Aerobiologia 16(1) pp Ranzi A., Lauriola P., Marletto V. and Zinoni F. (2003): Forecasting airborne pollen concentrations: Development of local models, Aerobiologia 19, Räsänen T., Ruuskanen J. and Kolehmainen M. (2008): Reducing energy consumption by using selforganizing maps to create more personalized electricity use information Applied Energy, 85, Ritz T, Kullowatz A, Kanniess F, Dahme B and Magnussen H. (2008): Perceived triggers of asthma: Evaluation of a German version of the Asthma Trigger Inventory, Respiratory Medicine 102, Rodríguez-Rajo F.J., Jato V. And Aira, M.J. (2006), Relationship between meteorology and Castanea airborne pollen, Belgian Journal of Botany 138(2), Sanchez-Mesa J. A., Galan C., Martinez-Heras J. A. and Hervas-Martinez C. (2002): The use of a neural network to forecast daily grass pollen concentration in a Mediterranean region: the southern part of the Iberian Peninsula, Clin Exp Allergy 32, Vesanto J. and Alhoniemi E. (2000): Clustering of the Self-Organizing Map. IEEE Transactions on Neural Networks, Vol. 11, 3, Viander, M. & Koivikko, A. (1978): The seasonal symptoms of hyposensitized and untreated hay fever patients in relation to birch pollen counts: correlations with nasal sensitivity prick tests and RAST. Clinical Allergy 8,