Prediction of PM2.5 via precursor method using meteorological parameters
Air pollution is one of the major environmental concerns faced by many countries including Pakistan. Being the major component of the pollution, particulate matters 2.5µm diameter (PM2.5), are known to highly raise health risks to people in the country. The present study investigates the modelling a...
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University of Bologna
2025-02-01
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| Series: | EQA |
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| Online Access: | https://eqa.unibo.it/article/view/21009 |
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| author | Naeem Sadiq Zaheer Uddin |
| author_facet | Naeem Sadiq Zaheer Uddin |
| author_sort | Naeem Sadiq |
| collection | DOAJ |
| description | Air pollution is one of the major environmental concerns faced by many countries including Pakistan. Being the major component of the pollution, particulate matters 2.5µm diameter (PM2.5), are known to highly raise health risks to people in the country. The present study investigates the modelling and prediction of particulate matter 2.5µm using its precursor values and meteorological parameters; temperature, humidity levels, and wind speeds in Lahore and Karachi. The air quality of Lahore repeatedly plummets to hazardous levels in the winter season which is a severe threat to the public health and environment. An Artificial Neural Network (ANN) architecture was designed to predict PM2.5 by employing meteorological parameters (temperature, relative humidity & wind speed) and precursor values of PM2.5. The model consists of an input layer with four input variables, a hidden layer with 10 neurons, and an output layer consisting of PM2.5. The model was used for both the cities of Lahore and Karachi. The Root Mean Square Error (RMSE) value for Karachi was less than 18 and for Lahore, it was 39. The prediction of PM2.5 via ANN was good for Lahore and Karachi. However, the results of the modeling are better for Karachi. The accuracy of results were further verified by Mean Absolute Percentage Error (MAPE), Mean Absolute Bias Error (MABE), and Chi square statistics (Chi). |
| format | Article |
| id | doaj-art-d8a0a4005feb482dbbfd0413a16efb21 |
| institution | Kabale University |
| issn | 2039-9898 2281-4485 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | University of Bologna |
| record_format | Article |
| series | EQA |
| spelling | doaj-art-d8a0a4005feb482dbbfd0413a16efb212025-02-05T14:02:21ZengUniversity of BolognaEQA2039-98982281-44852025-02-0167455010.6092/issn.2281-4485/2100919381Prediction of PM2.5 via precursor method using meteorological parametersNaeem Sadiq0Zaheer Uddin1Institute of Space Science and Technology, University of Karachi, Karachi, PakistanDepartment of Physics, University of Karachi, Karachi, PakistanAir pollution is one of the major environmental concerns faced by many countries including Pakistan. Being the major component of the pollution, particulate matters 2.5µm diameter (PM2.5), are known to highly raise health risks to people in the country. The present study investigates the modelling and prediction of particulate matter 2.5µm using its precursor values and meteorological parameters; temperature, humidity levels, and wind speeds in Lahore and Karachi. The air quality of Lahore repeatedly plummets to hazardous levels in the winter season which is a severe threat to the public health and environment. An Artificial Neural Network (ANN) architecture was designed to predict PM2.5 by employing meteorological parameters (temperature, relative humidity & wind speed) and precursor values of PM2.5. The model consists of an input layer with four input variables, a hidden layer with 10 neurons, and an output layer consisting of PM2.5. The model was used for both the cities of Lahore and Karachi. The Root Mean Square Error (RMSE) value for Karachi was less than 18 and for Lahore, it was 39. The prediction of PM2.5 via ANN was good for Lahore and Karachi. However, the results of the modeling are better for Karachi. The accuracy of results were further verified by Mean Absolute Percentage Error (MAPE), Mean Absolute Bias Error (MABE), and Chi square statistics (Chi).https://eqa.unibo.it/article/view/21009pm2.5meteorological parametersartificial neural networkair quality indexlahorekarachi |
| spellingShingle | Naeem Sadiq Zaheer Uddin Prediction of PM2.5 via precursor method using meteorological parameters EQA pm2.5 meteorological parameters artificial neural network air quality index lahore karachi |
| title | Prediction of PM2.5 via precursor method using meteorological parameters |
| title_full | Prediction of PM2.5 via precursor method using meteorological parameters |
| title_fullStr | Prediction of PM2.5 via precursor method using meteorological parameters |
| title_full_unstemmed | Prediction of PM2.5 via precursor method using meteorological parameters |
| title_short | Prediction of PM2.5 via precursor method using meteorological parameters |
| title_sort | prediction of pm2 5 via precursor method using meteorological parameters |
| topic | pm2.5 meteorological parameters artificial neural network air quality index lahore karachi |
| url | https://eqa.unibo.it/article/view/21009 |
| work_keys_str_mv | AT naeemsadiq predictionofpm25viaprecursormethodusingmeteorologicalparameters AT zaheeruddin predictionofpm25viaprecursormethodusingmeteorologicalparameters |