PM10 and their Bio-Contamination in Makkah Saudi Arabia- Case Studys

Mohammed, Atef M. F.; Attala, Ossama A; Habeebullah, Turki M.

doi:10.15406/ijbsbe.2017.02.00007

Cited by 1 publication

(2 citation statements)

References 34 publications

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“…For PM 2 Due to the geographical characteristics and climatic conditions, PM 2.5 and PM 10.0 pollutants frequently exceed the national and international air quality standards in Makkah region [5,32]. ese particles are very tiny, and their exposure is associated with adverse health effects.…”

Section: Resultsmentioning

confidence: 99%

“…Air pollution is one of the emerging environmental issues in the developing as well as developed countries across the globe [1]. A large amount of gaseous pollutants and other atmospheric particulate matter (APM) are being produced through immense pollution generating activities including vehicles emitting smoke and fossil fuels used for energy requirements, cooking, and different anthropogenic activities [2]. APM is reportedly one of the major causes of adverse health issues particularly which are related to human respiratory and cardiovascular systems [3].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Phase Space Reconstruction- (PSR-) Based Predictive Algorithm to Forecast Atmospheric Particulate Matter Concentration

Shah

Aziz

Nadeem

et al. 2019

Scientific Programming

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e prediction of atmospheric particulate matter (APM) concentration is essential to reduce adverse effects on human health and to enforce emission restrictions. e dynamics of APM are inherently nonlinear and chaotic. Phase space reconstruction (PSR) is one of the widely used methods for chaotic time series analysis. e APM mass concentrations are an outcome of complex anthropogenic contributors evolving with time, which may operate on multiple time scales. us, the traditional single-variable PSR-based prediction algorithm in which data points of last embedding dimension are used as a target set may fail to account for multiple time scales inherent in APM concentrations. To address this issue, we propose a novel PSR-based scientific solution that accounts for the information contained at multiple time scales. Different machine learning algorithms are used to evaluate the performance of the proposed and traditional PSR techniques for predicting mass concentrations of particulate matter up to 2.5 micron (PM 2.5 ), up to 10 micron (PM 10.0 ), and ratio of PM 2.5 /PM 10.0 . Hourly time series data of PM 2.5 and PM 10.0 mass concentrations are collected from January 2014 to September 2015 at the Masfalah air quality monitoring station (couple of kilometers from the Holy Mosque in Makkah, Saudi Arabia). e performances of various learning algorithms are evaluated using RMSE and MAE. e results demonstrated that prediction error of all the machine learning techniques is smaller for the proposed PSR approach compared to traditional approach. For PM 2.5 , FFNN leads to best results (both RMSE and MAE 0.04 μgm −3 ), followed by SVR-L (RMSE 0.01 μgm −3 and MAE 0.09 μgm −3 ) and RF (RMSE 1.27 μgm −3 and MAE 0.86 μgm −3 ). For PM 10.0 , SVR-L leads to best results (both RMSE and MAE 0.06 μgm −3 ), followed by FFNN (RMSE 0.13 μgm −3 and MAE 0.09 μgm −3 ) and RF (RMSE 1.60 μgm −3 and MAE 1.16 μgm −3 ). For PM 2.5 /PM 10.0 , FFNN is the best and accurate method for prediction (0.001 for both RMSE and MAE), followed by RF (0.02 for both RMSE and MAE) and SVR-L (RMSE 0.05 μgm −3 and MAE 0.04).

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%