Search published articles


Showing 2 results for Pm10

Ms Atefeh Bosak, Dr Zahra Hejazizadeh, Dr Akbar Heydari Tashekaboud,
Volume 0, Issue 0 (3-1921)
Abstract

Air pollution has significant impacts on human health, environmental quality, and the sustainable development of cities. This study aimed to evaluate PM10 using meteorological data from the city of Ahvaz through statistical methods and artificial neural networks. Daily meteorological data and air quality control station data for 4485 days (from 2011 to 2023) were obtained from the National Meteorological Organization and the Khuzestan Department of Environment. Initially, the data were processed and refined, and their normality was assessed using the Kolmogorov-Smirnov test. Given the non-normality of the data, Spearman's and Kendall's Tau-b methods were employed to examine their correlations. The time series and statistical information of the data were obtained using Python programming language. Furthermore, to predict future PM10 levels, the Multilayer Perceptron (MLP) neural network method was utilized. The results of these analyses indicated a significant correlation between meteorological variables and PM10. The Spearman and Kendall Tau-b correlations showed that PM10 had a positive and significant correlation with wind speed (0.094 and 0.061) and temperature (0.284 and 0.187) at a 99% confidence level. Conversely, PM10 exhibited a negative and significant correlation with visibility (-0.408 and -0.300), wind direction (-0.048 and -0.034), precipitation (-0.159 and -0.125), and relative humidity (-0.259 and -0.173) at the 99% confidence level. For future PM10 predictions, the MLP neural network was used. The model was of the Sequential type with an input layer consisting of 6 neurons, three hidden layers of Dense type with 16, 32, and 64 neurons, and an output layer with a linear activation function. The mean squared error (MSE) for the training set was 0.0034, and for the validation data, it was 0.0012. For the test set, the obtained validation accuracy was mse_mlp=0.0048 and val_loss=0.0012. The results indicate a significant direct or inverse correlation between meteorological data and PM10. Additionally, the outcomes of the MLP neural network demonstrated that the network provided satisfactory performance and acceptable predictions for PM10 data in Ahvaz.

, ,
Volume 17, Issue 47 (12-2017)
Abstract

 
Suspended particles management is one of the important issues in controlling the air pollution of cities. These particles cause and develop heart and respiratory diseases in people. Mashhad is considered as one of the main and populous cities of Iran. Because of its climatic conditions and its tourism, the city is at the highest risk of this type of pollution. We attempted to use the multi-layer perceptron (MLP) artificial neural network and a Markov chain model to predict PM10 concentrations the city. We applied hourly data of CO, SO2, PM2.5 and temperature in late March and April 2015. Out of 1488 data series, 1300 data were used for network training and 188 data were used for validation. The results indicated the optimal performance of these methods in predicting of the amount of pollutants and also the probability of occurrence of hours with different quality of contamination. The best MLP artificial neural network model predicted the amount of pollutant particles with a coefficient of determination (R2) 0.88, index of agreement of  0.91 and a mean square error of  2.26. Also, the Markov model with average absolute error predicted about 0.1 percent of the probability of transferring the condition and the continuation of different states of air pollution caused by suspended particles.
 

Page 1 from 1     

© 2024 CC BY-NC 4.0 | Applied researches in Geographical Sciences

Designed & Developed by : Yektaweb