The Science of the total environment
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Sci. Total Environ. · Apr 2014
Impact of meteorological parameters on the development of fine and coarse particles over Delhi.
Measurements of ambient particulate matters (viz., PM10 and PM2.5) were made with an hourly sampling frequency at Indian Institute of Tropical Meteorology (IITM), New Delhi Branch (a residential area) during a period from December 2010 to November 2011. The data so generated were analyzed to understand frequency distribution of their concentrations and the impact of meteorological parameters on the distribution of particulate matters on different time scales. It is found that the particulate matters with cut off aerodynamic diameter of 10 μm (PM10) preferentially occurred in the concentration range of 301-350 μg/m(3) during winter and post-monsoon, 251-300 μg/m(3) during summer and 51-100 μg/m(3) during monsoon season. ⋯ Annual distribution of the concentration of particulate matters showed seasonality with maximum in winter and minimum in monsoon season. Diurnal variation of PM10 and PM2.5 showed bimodal distribution with one maximum in the forenoon and the other at around mid-night. The observed seasonality and diurnal variability in the distribution are attributed mainly to the meteorology.
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Sci. Total Environ. · Apr 2014
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. ⋯ 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.