Analysing PM2.5 and its Association with PM10 and Meteorology in the Arid Climate of Makkah, Saudi Arabia

Munir, Shahid; Habeebullah, Turki M.; Mohammed, Atef M. F.; Morsy, Essam A.; Rehan, Mohammad; Ali, Kawsar

doi:10.4209/aaqr.2016.03.0117

Cited by 83 publications

(68 citation statements)

References 14 publications

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“…e lifetime of atmospheric particles, spanned from few seconds to several months, is another aspect of such particles which determines their harmfulness [4]. Beside emission sources, levels of PM 2.5 and PM 10.0 depend on the geographic characteristics and meteorological parameters including wind, relative humidity temperature, atmospheric pressure, and boundary layer height [6,7].…”

Section: Introductionmentioning

confidence: 99%

“…In this study, we propose a novel PSR-based scientific solution that accounts for the information contained at multiple time scales to predict mass concentrations of atmospheric particulates in air. e data used in this study are collected from the Masfalah air quality monitoring station, Makkah, Saudi Arabia [6]. Previously Munir et al [6] used these data to analyze the mass concentrations of PM 2.5 and its association with PM 10.0 and meteorology.…”

Section: Introductionmentioning

confidence: 99%

“…e data used in this study are collected from the Masfalah air quality monitoring station, Makkah, Saudi Arabia [6]. Previously Munir et al [6] used these data to analyze the mass concentrations of PM 2.5 and its association with PM 10.0 and meteorology.…”

Section: Introductionmentioning

confidence: 99%

“…is site is important because throughout the year, huge number of pilgrims visit Saudi Arabia to perform religious obligations using this road. Makkah is surrounded by large sandy deserts, receives little rain, and experiences high temperature throughout the year [6]. e expansion of Holy mosque, construction of railway train stations, mountain digging and construction of multistoried buildings, frequent sand and dust storms, frequent traffic jams, and congestions during the busy hours constitute the atmospheric pollution in the city [6,7].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Shah

Aziz

Nadeem

et al. 2019

Scientific Programming

Self Cite

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“…A comprehensive approach is therefore needed to understand the levels and potential impacts of particulate air pollution under varied climatic conditions. Munir et al (2017) have analyzed various aspects of PM 2.5 including PM 2.5 /PM 10 ratios and association with meteorological parameters using data collected in Makkah, Saudi Arabia which is influenced by arid climatic conditions throughout a year. The outcome of the study may be helpful for developing an effective air quality management plan in arid regions.…”

mentioning

confidence: 99%

Overview of the Special Issue "PM2.5 in Asia" for 2015 Asian Aerosol Conference

Balasubramanian

Gao

Hatakeyama

et al. 2017

Aerosol Air Qual. Res.

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Machine learning and deep learning‐driven methods for predicting ambient particulate matters levels: A case study

Harrou

Dairi

et al. 2022

Concurrency and Computation

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Summary Dust, or particulate matter (PM2.5), is among the most harmful pollutants negatively affecting human health. Predicting indoor PM2.5 concentrations is essential to achieve acceptable indoor air quality. This study aims to investigate data‐driven models to accurately predict PM 2.5 pollution. Notably, a comparative study has been conducted between twenty‐one machine learning and deep learning models to predict PM2.5 levels. Specifically, we investigate the performance of machine learning and deep learning models to predict ambient PM2.5 concentrations based on other ambient pollutants, including SO2$$ {}_2 $$, NO2$$ {}_2 $$, O3$$ {}_3 $$, CO, and PM10. Here, we applied Bayesian optimization to optimally tune hyperparameters of the Gaussian process regression with different kernels and ensemble learning models (i.e., boosted trees and bagged trees) and investigated their prediction performance. Furthermore, to further enhance the forecasting performance of the investigated models, dynamic information has been incorporated by introducing lagged measurements in the construction of the considered models. Results show a significant improvement in the prediction performance when considering dynamic information from past data. Moreover, three methods, namely, random forest (RF), decision tree, and extreme gradient boosting, are applied to assess variables contribution and revealed that lagged PM2.5 data contribute significantly to the prediction performance and enables the construction of parsimonious models. Hourly concentration levels of ambient air pollution from the air quality monitoring network located in Seoul are employed to verify the prediction effectiveness of the studied models. Six measurements of effectiveness are used for assessing the prediction quality. Results showed that deep learning models are more efficient than the other investigated machine learning models (i.e., SVR, GPR, bagged and boosted trees, RF, and XGBoost). Also, the results showed that the bidirectional long short term memory (BiLSTM) and bidirectional gated recurrent units (BiGRU) networks produce higher performance than the investigated machine learning models (i.e., SVR, GPR, bagged and boosted trees, RF, and XGBoost) and deep learning models (i.e., LSTM, GRU, and convolutional neural network).

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Analysing PM2.5 and its Association with PM10 and Meteorology in the Arid Climate of Makkah, Saudi Arabia

Cited by 83 publications

References 14 publications

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

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

Overview of the Special Issue "PM2.5 in Asia" for 2015 Asian Aerosol Conference

Machine learning and deep learning‐driven methods for predicting ambient particulate matters levels: A case study

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