Profiling Temporal Pattern of Particulate Matter (PM10) and Meteorological Parameters in Jakarta Province during 2020-2021

Air quality Correlation Jakarta Particulate matters

Authors

  • Zida Husnina
    zida.husnina@fkm.unair.ac.id
    1. Department of Environmental Health, Faculty of Public Health, Universitas Airlangga, Surabaya 60115, Indonesia; 2. Research Centre for Tropical Infectious Diseases, and Herbal Medicine, Universitas Airlangga, Surabaya 60115, Indonesia; 3. Research Centre for Domestic Environmental Health, Universitas Airlangga, Surabaya 60115, Indonesia https://orcid.org/0000-0003-3703-870X
  • Kinley Wangdi Department of Global Health, National Centre for Epidemiology and Population Health, Australian National University, ACT Canberra 2601, Australia https://orcid.org/0000-0002-8857-2665
  • Tities Puspita Research Centre for Public Health and Nutrition, National Research and Innovation Agency, Bogor 16915, Indonesia
  • Sarva Mangala Praveena Department of Environmental and Occupational Health, Faculty of Medicine and Health Science, Universiti Putra Malaysia, Serdang 43400, Malaysia https://orcid.org/0000-0002-0159-4395
  • Zhao Ni 1. School of Nursing, Yale University, Orange, CT 06477, United States; 2. Yale Institute for Global Health, Yale University, Orange, CT 06477, United States
January 30, 2023

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Introduction: Jakarta has recorded heightened air pollution for years, and particulate matter (PM10) is one of the pollutants that could bring health burden in population. This study described the distribution of PM10 as well as analysed the correlation with meteorological parameters during 2020–2021 in Jakarta Province. Methods: Air quality standard index daily data from January 1st 2020 to March 31st 2021 was retrieved from the official data portal (https://data.jakarta.go.id/). The Spearman Rank correlation was employed to understand the correlation between PM10 Index with meteorological factors. Autoregressive Integrative Moving Average (ARIMA) model was constructed and Akaike Information Criterion (AIC) selected the model. Cross-correlation analysis explored the association between PM10 with meteorological parameters at multiple time lags. Results and Discussion: PM10 Index started to increase in April 2020 and reached its peak in August 2020. PM10 was positively correlated with temperature (p-value <0.05, R2: 0.134), but it was negatively correlated with humidity and wind speed (p-value <0.05, R2: -0.392 and -0.129). The high cross-correlation coefficients were found between PM10 and temperature at lag 0, humidity at lag 1 and wind speed at lag 1 (rho: 0.42, -0.38 and -0.24). The time series model ARIMA with parameter (p,d,q) (1,1,1) describes the fluctuation of PM10 index data with AIC 3552.75. Conclusion: PM10 concentration in Jakarta is significantly correlated with meteorological factors. The implementation of social restriction in Jakarta need to be supported by pollution control in the neighbouring areas in order to be able to reduce PM10 pollution level.