Bayesian autoregressive spatiotemporal model of PM10 concentrations across Peninsular Malaysia

Manga, Edna; Awang, Norhashidah

doi:10.1007/s00477-018-1574-5

Cited by 8 publications

(2 citation statements)

References 30 publications

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“…The adverse effects of particulate matter (PM10 and PM2.5) atmospheric and microscopic air pollutants have since been established and identified as the most prevalent amongst other criteria pollutants in Malaysia affecting human health, animal, ecosystem, and environment (Fong et al 2018;Manga & Awang 2018;Masseran & Mohd Safari 2020;Tajudin et al 2019;Usmani et al 2020). The sources of these particles are both from natural and human made.…”

Section: Introductionmentioning

confidence: 99%

Systematic Approach to Determining True Long Memory on PM10 Data

Isma’il

Awang

Kane

2023

Preprint

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Particulate matter pollutants are common atmospheric air pollutants in Malaysia which have numerous harmful effects on the environment, the flora and fauna, as well as human health. Long memory process may spuriously be detected due to presence of break in the time series data since a short memory process with occasional structural break can show a slower rate of decay in the autocorrelation function and other properties of fractionally integrated \(I\left(d\right)\) process. Numerous research around the globe confirmed evidence of long memory on particulate matter pollutant, but few or none in Malaysia have tested for it and investigated whether the persistence (if evident) is truly detected or merely spurious due to presence of neglected structural breaks. In this paper, we examined the statistical properties of daily PM10 emission in fourteen Malaysian air quality stations over the period 1 January 2011 through 31 December 2020 by applying a fractional integration framework on both the original and partitioned series that encountered structural break as confirmed by the OLS-based CUSUM test. Both the original and sub-series (before and after the break regime) are found to be characterized by long memory with orders of integration within the range \(\left(0, 1\right)\) implying the evidence of mean reversion form of long memory. Thus, this evidence confirms the presence of true long memory not due to structural break. We also obtained higher values for Kurtosis statistic implying that the emission fluctuates significantly.

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Section: Introductionmentioning

confidence: 99%

Systematic Approach to Determining True Long Memory on PM10 Data

Isma’il

Awang

Kane

2023

Preprint

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“…In this paper, we attempt to build a NANI prediction HSVC model based on as few predictor variables as possible for the Yangtze River Basin, China.The HSVC model is a generalization of the variable coefficient model, aiming to determine the spatial variations of data by allowing the coefficients to be functions of location. Since the model has obvious indigenous effects on analyzing spatial heterogeneity, and the coefficient function has a strong flexibility, it has been widely used in environmental science, ecology, and epidemiology [23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Spatially Varying Coefficient Process Regression for Modeling Net Anthropogenic Nitrogen Inputs (NANI) from the Watershed of the Yangtze River, China

Liu,

Huang,

Lian

et al. 2023

Sustainability

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The increasing discharge of nitrogen nutrients into watersheds calls for assessing and predicting nitrogen inputs, as an important basis for formulating management strategies. The traditional net anthropogenic nitrogen inputs (NANI) budgeting model relies on 45 predictor variables, for which data are sourced from local or national statistical yearbooks. The large number of predictor variables involved makes NANI accounting difficult, and the missingness of data reduces its accuracy. This study aimed to build a prediction model for NANI based on as few predictor variables as possible. We built a prediction model based on the last 30 years of NANI data from the watershed of the Yangtze River in China, with readily available and complete socio-economic predictor variables (per gross domestic product, population density) through a hierarchical spatially varying coefficient process model (HSVC), which exploits underlying spatial associations within 11 sub-basins and the spatially varying impacts of predictor variables to improve the accuracy of NANI prediction. The results showed that the hierarchical spatially varying coefficient model performed better than the Gaussian process model (GP) and the spatio-temporal dynamic linear model (DLM). The predicted NANIs within the entire catchment of the Yangtze River in 2025 and in 2030 were 11,522.87 kg N km−2 to 12,760.65 kg N km−2, respectively, showing an obvious increasing trend. Nitrogen fertilizer application was predicted to be 5755.1 kg N km−2 in 2025, which was the most significant source of NANI. In addition, the point prediction and 95% interval prediction of NANI in the watershed of the Yangtze River for 2025 and 2030 were also provided. Our approach provides a simple and easy-to-use method for NANI prediction.

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