Evaluating the Influence of Meteorological Parameters on Ozone Concentration Levels

Nazif, Amina; Mohammed, Nurul Izma; Malakahmad, Amirhossein; Abualqumboz, Motasem S.

doi:10.14419/ijet.v7i3.7.16211

Cited by 2 publications

(3 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Most of these correlations were weak, although they are significant except for O 3 with temperature (0.802) and with relative humidity (-0.756), which means that O 3 increases with temperature and decreases with relative humidity. These relationships of O 3 agreed with the results of Dovile [32], Nazif et al [33] and Ilic et al [34].…”

Section: Resultssupporting

confidence: 91%

See 1 more Smart Citation

Investigating the Relationship between Air Pollutants and Meteorology: A Canonical Correlation Analysis

Shihab¹

2022

Pol. J. Environ. Stud.

View full text Add to dashboard Cite

In order to characterize the most important meteorological parameters that determine air pollutants' behavior, canonical correlation analysis was conducted. Meteorological parameters include temperature, rainfall, relative humidity, wind speed and wind direction. On the other hand, air pollutants include O 3 , NO, NO 2 , SO 2 , CO, CH 4 , non-methane hydrocarbons (NMHC) and PM 10 . The study was conducted in Mosul city, northern Iraq. The data were collected using a fixed monitoring station and ultrasonic weather station placed on the side of a separate main heavy traffic road. The first two canonical functions extracted by the analysis explained more than 95% of the variance in air pollutants. The first canonical function shows high canonical correlation coefficient between its pair canonical variates (0.849, p<0.001), versus 0.445 (p<0.001) between the pair variates of the second function. The results of the canonical correlation analysis highlighted that temperature was the major contributor in the explanatory capacity of meteorological parameters compared with the other parameters. On the other hand, ozone had a major contribution to the explanatory capacity of air pollutants compared with the other pollutants. The major conclusion is the ability of canonical correlation analysis to reduce the input parameters mainly to temperature and ozone in this study.

show abstract

Section: Resultssupporting

confidence: 91%

“…Furthermore, wind path is determined by the topography of the area or the distribution of civil facilities in the urban area, therefore, wind direction is important. Many studies had emphasize wind direction effect on air quality [33,42,43].…”

Section: Author Copy • Author Copy • Author Copy • Author Copy • Auth...mentioning

confidence: 99%

Investigating the Relationship between Air Pollutants and Meteorology: A Canonical Correlation Analysis

Shihab¹

2022

Pol. J. Environ. Stud.

View full text Add to dashboard Cite

show abstract

“…A study on O 3 variability due to meteorological parameters was investigated in Selangor, Malaysia. The result shows that wind speed, temperature, relative humidity, and wind direction significantly impact O 3 concentrations [26]. [27] studied air pollution variation due to meteorological in four areas in Malaysia, namely, Petaling Jaya, Cheras, Shah Alam, and Klang.…”

Section: Introductionmentioning

confidence: 99%

An Optimized Artificial Neural Network Model using Genetic Algorithm for Prediction of Traffic Emission Concentrations

Abdullah¹,

Usmani²,

Pillai³

et al. 2021

IJACSA

View full text Add to dashboard Cite

Global warming and climate change have become universal issues recently. One of the leading sources of climate change is automobiles. Automobiles are the prime source of air pollution in urban areas globally. This has resulted in a problematic and chaotic state in the development of an automatic traffic management system for capturing and monitoring vehicles' hourly and daily passage. With the significant advancement of sensor technology, atmospheric information such as air pollution, meteorological, and motor vehicle data can be harvested and stored in databases. However, due to the complexity and non-linear associations between air quality, meteorological, and traffic variables, it is difficult for the traditional statistical and mathematical models to analyze them. Recently, machine learning algorithms in the field of traffic emissions prediction have become a popular tool. Meteorological and traffic variables influence the variation and the trend of the traffic pollutants. In this paper, an optimized artificial neural network (OANN) was developed to enhance the existing artificial neural network (ANN) model by updating the initial weights in the network using a Genetic Algorithm (GA). The OANN model was implemented to predict the concentration of CO, N O, N O2, and N Ox pollutants produced by motor vehicles in Kuala Lumpur, Malaysia. OANN was compared with Artificial Neural Network (ANN), Random Forest (RF), and Decision Tree (DT) models. The results show that the developed OANN model performed better than the ANN, RF, and DT models with the lowest MSE values of 0.0247 for CO, 0.0365 for N O, 0.0542 N O2, and 0.1128 for N Ox. It can be concluded that the developed OANN model is a better choice in predicting traffic emission concentrations. The developed OANN model can help environmental agencies monitor traffic-related air pollution levels efficiently and take necessary measures to ensure the effectiveness of traffic management policy. The OANN model can also help decision-makers mitigate traffic emissions to protect citizens living in the neighborhood of highways.

show abstract

Evaluating the Influence of Meteorological Parameters on Ozone Concentration Levels

Cited by 2 publications

References 11 publications

Investigating the Relationship between Air Pollutants and Meteorology: A Canonical Correlation Analysis

Investigating the Relationship between Air Pollutants and Meteorology: A Canonical Correlation Analysis

An Optimized Artificial Neural Network Model using Genetic Algorithm for Prediction of Traffic Emission Concentrations

Contact Info

Product

Resources

About