Compositional time series analysis for Air Pollution Index data

AL-Dhurafi, Nasr Ahmed; Masseran, Nurulkamal; Zamzuri, Zamira Hasanah

doi:10.1007/s00477-018-1542-0

Cited by 32 publications

(10 citation statements)

References 22 publications

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“…VAR model is a widely used model to study correlation. 31 Based on VAR model, autoregression (AR) root test and cointegration test were carried out. Impulse response function analysis was used to determine the relationship between the quarterly emission of PAHs in China and macroeconomic variables such as GDP.…”

Section: Methodsmentioning

confidence: 99%

A prediction model of polycyclic aromatic hydrocarbon quarterly emissions based on GDP from a government policy perspective

Jing

Bai

Chen

et al. 2021

Indoor and Built Environment

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Polycyclic aromatic hydrocarbons (PAHs) are a kind of carcinogenic, teratogenic and mutagenic pollutants that exist widely. In this study, a quarterly emission inventory of 16 PAHs listed as the US EPA priority pollutants was established by using the emission factor (EF) method. The results showed that the distribution of PAHs in different industries varied greatly with different year and quarter. The main characteristics of PAHs seasonal emission are that Q1 emission is the most and Q4 emission is the least. Among them, naphthalene and phenanthrene are the most important compounds. There are significant differences in PAHs emissions from different sources. Traffic and civil use emissions are the main sources. For example, in 2019, traffic emissions accounted for 20.9% and civil use emissions accounted for 78.3% of the total emissions. Then, a linear multiple regression model was established to predict the quarterly emission of PAHs. A vector autoregressive (VAR) approach was applied to analyse the correlation between the gross domestic product (GDP) and other macroeconomic parameters. This study could be used as a guide to provide recommendations for government policy and macro-control efforts.

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Section: Methodsmentioning

confidence: 99%

A prediction model of polycyclic aromatic hydrocarbon quarterly emissions based on GDP from a government policy perspective

Jing

Bai

Chen

et al. 2021

Indoor and Built Environment

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“…Based on these individual indices, the API value at a particular time can be determined based on the highest value among these sub-indices [48,49]. Figure 2 shows a schematic illustration of the process of determining the API value.…”

Section: O I F Omentioning

confidence: 99%

Mixed POT-BM Approach for Modeling Unhealthy Air Pollution Events

Masseran

Safari

2021

IJERPH

Self Cite

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This article proposes a novel data selection technique called the mixed peak-over-threshold–block-maxima (POT-BM) approach for modeling unhealthy air pollution events. The POT technique is employed to obtain a group of blocks containing data points satisfying extreme-event criteria that are greater than a particular threshold u. The selected groups are defined as POT blocks. In parallel with that, a declustering technique is used to overcome the problem of dependency behaviors that occurs among adjacent POT blocks. Finally, the BM concept is integrated to determine the maximum data points for each POT block. Results show that the extreme data points determined by the mixed POT-BM approach satisfy the independent properties of extreme events, with satisfactory fitted model precision results. Overall, this study concludes that the mixed POT-BM approach provides a balanced tradeoff between bias and variance in the statistical modeling of extreme-value events. A case study was conducted by modeling an extreme event based on unhealthy air pollution events with a threshold u > 100 in Klang, Malaysia.

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“…Therefore, it would be inappropriate to use the linear time series in this scenario. However, in the recent years, the emphasis has been on the nonlinear time series forecasting, which is a deep challenge of the researchers and industry practitioners (De Gooijer & Hyndman, 2006;AL-Dhurafi et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Hybrid Vector Autoregression Feedforward Neural Network with Genetic Algorithm Model for Forecasting Space-Time Pollution Data

Caraka

Chen

Yasin

et al. 2021

Indonesian J. Sci. Technol

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The exposure rate to air pollution in most urban cities is really a major concern because it results to a life-threatening consequence for human health and wellbeing. Furthermore, the accurate estimation and continuous forecasting of pollution levels is a very complicated task. In this paper, one of the space-temporal models, a vector autoregressive (VAR) with neural network (NN) and genetic algorithm (GA) was proposed and enhanced. The VAR could tackle the issue of multivariate time series, NN for nonlinearity, and GA for parameter estimation determination. Therefore, the model could be used to make predictions, such as the information of series and location data. The applied methods were on the pollution data, including NOX, PM2.5, PM10, and SO2 in Taipei, Hsinchu, Taichung, and Kaohsiung. The metaheuristics genetic algorithm was used to enhance the proposed methods during the experiments. In conclusion, the VAR-NN-GA gives a good accuracy when metric evaluation is used. Furthermore, the methods can be used to determine the phenomena of 10 years air pollution in Taiwan.

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Compositional time series analysis for Air Pollution Index data

Cited by 32 publications

References 22 publications

A prediction model of polycyclic aromatic hydrocarbon quarterly emissions based on GDP from a government policy perspective

A prediction model of polycyclic aromatic hydrocarbon quarterly emissions based on GDP from a government policy perspective

Mixed POT-BM Approach for Modeling Unhealthy Air Pollution Events

Hybrid Vector Autoregression Feedforward Neural Network with Genetic Algorithm Model for Forecasting Space-Time Pollution Data

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