RELATIONSHIP BETWEEN AIR POLLUTION STANDARD INDEX (APSI) IN NORTH JAKARTA AND WEST JAKARTA USING VECTOR AUTOREGRESSIVE (VAR) MODELING

Sarah Sholikhatun Risma

Abstract

The high level of APSI in Jakarta and surrounding regions has been categorized as an unhealthy level of air pollution, which can consequently affect the level of public health. This study aims to determine whether there is an influence of APSI pollution level from the previous period as well as the influence of APSI level on other regions from the previous period that are neighboring. VAR modeling approach used which had advantages in considering changes in data behavior over time and can also be used to explains the interaction between a number of variables. APSI level data in West Jakarta region and North Jakarta region have been stationary which are indicated by the significance of the ADF test. The analysis results found that the simultaneous testing of VAR model was significant with p-value <0.05 which can be concluded that the APSI level in West Jakarta was influenced by APSI level in the previous period and was also influenced by APSI level of North Jakarta in the previous period. Similarly, the level of APSI level of North Jakarta was influenced by APSI level in the previous period and was also influenced by APSI level of West Jakarta in the previous period.

Keywords

Air Pollution, APSI, Neighboring, VAR

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References

Černikovský, L., Krejčí, B., Blažek, Z., & Volná, V. (2016). Transboundary air-pollution transport in the Czech-Polish border region between the cities of Ostrava and Katowice. Central European Journal of Public Health, 24, S45–S50. https://doi.org/10.21101/cejph.a4532 Dandotiya, B. (2019). Health Effects of Air Pollution in Urban Environment. January, 96–115. https://doi.org/10.4018/978-1-5225-7387-6.ch006 Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, Vol. 37, N, 424–438. Greenpeace. (2019). Data Terkini Kualitas Udara Kota-kota di Seluruh Dunia. https://www.greenpeace.org/indonesia/publikasi/2217/data-terkini-kualitas-udara-kota-kota-di-seluruh-dunia/ Lütkepohl, H., & Krätzig, M. (2004). Applied time series econometrics. In Applied Time Series Econometrics. https://doi.org/10.1017/CBO9780511606885 Provinsi DKI Jakarta. (2017). Data Indeks Standar Pencemaran Udara DKI Jakarta Tahun 2017. https://data.jakarta.go.id/dataset/data-indeks-standar-pencemaran-udara-di-dki-jakarta Sims, C. A. (2014). Prior Restrictions The Role of Approximate Lag Estimation Distributed. 67(337), 169–175. WHO. (2018). WHO Global Ambient Air Quality Database (update 2018). https://www.who.int/airpollution/data/cities/en/ World Air Quality Index project. (2018). World’s Air Pollution: Real-time Air Quality Index. https://waqi.info/ Wu, D., Xu, Y., & Zhang, S. (2015). Will joint regional air pollution control be more cost-effective? Anempirical study of China’s Beijing-Tianjin-Hebei region. Journal of Environmental Management, 149, 27–36.https://doi.org/10.1016/j.jenvman.2014.09.032

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