SEASONALITY DEDUCTION PLATFORM: FOR PM₁₀, PM_2.5, NO, NO₂ AND O₃ IN RELATIONSHIP WITH WIND SPEED AND HUMIDITY

Abstract

Human and ecosystem health is affected by the risk of air pollution. A comprehensive understanding of the parameters generating pollution and governing their nature in time is essential to devise functional policies focusing on minimising the concentration of the pollutants. The effect of pollution parameters on meteorological data and existing in between relationships, have been the focus of the researcher’s planning of better city future. Thorough study of resources utilisation is required for contributing to framing effective, sustainable development, government policies management, and advance public services convenience. For protecting the environmental quality, renewable resources like solar and wind are more incorporated in techniques supporting better city planning. This paper considers the hourly time series Particular Matter (PM) PM2.5 and PM10, Nitrogen Oxide (NO), and Nitrogen Dioxide (NO2), and Ozone (O3) along with measured wind flow and humidity. This study’s objective is to assess the temporal seasonality patterns of these parameters in Stuttgart, Germany. The temporal variations over the city center in Stuttgart are analysed using unsupervised approach to perform seasonal hierarchical clustering on a series of parameters NO, NO2, O3, PM10, and PM2.5, wind speed and humidity. Furthermore, the correlations between meteorological and pollution parameters are analysed using the Spearman rank correlation method. Moreover, a dashboard is developed to provide the user desired time frame visualisation of these parameters. Proposed work would provide empirical meaning and seasonality comparison among the above mentioned parameters combined with interactive dashboard support. The analyses of the presented results clearly demonstrates the relationship between air pollutants, wind, humidity together in combine temporal activities frame. Thus, it would help city planner and policies maker with advanced knowledge of seasonality for meteorological and pollution parameters conditions.