Causality Analysis of Air Quality and Meteorological Parameters for PM2.5 Characteristics Determination: Evidence from Jakarta

Tri Istiana, Budhy Kurniawan, Santoso Soekirno, Alberth Nahas, Alvin Wihono, Danang Eko Nuryanto, Suko Prayitno Adi, Muhammad Lukman Hakim

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

The development of Jakarta as a metropolitan city worsens the PM2.5 concentration in the area, causes health problems for the citizens, and becomes a major public concern. In this study, we use Pearson correlation and convergent cross mapping (CCM) to analyze any correlation between air quality and individual meteorological parameters, as well as the local PM2.5 nonlinear coupling pattern at two different locations in Jakarta. The influence of meteorological parameters and other pollutants in various seasons can be used to determine the variability of PM2.5. We found that the PM2.5 concentration is affected by PM10, SO2, and NO2 pollutant and is negatively correlated with precipitation, relative humidity, and the wind speed in all variations of the season. Causality analysis using CCM showed that PM2.5 coupling patterns differ for every season. The highest causality values (ρ) for air quality parameters are 0.74 (PM10), 0.68 (SO2), 0.52 (wind speed), and 0.51 (temperature). In Central Jakarta and South Jakarta, the coupling pattern of PM2.5 concentration and air quality parameters increased during the DJF (December–February) season, while the coupling pattern of PM2.5 concentration and meteorological parameters increased during the DJF and MAM (March–May) seasons. During the JJA (June–August) season, most of the meteorological parameters did not have any impact, whereas the increased humidity during the SON (September– November) season also increased the PM2.5 concentration. In conclusion, the significant outcome of our research is to show that individual air quality and meteorological parameters had an influence on local PM2.5 concentrations in the Jakarta region. In addition, it has been proved that CCM can analyze mirage correlation better than other correlation methods.

Original languageEnglish
Article number230014
JournalAerosol and Air Quality Research
Volume23
Issue number9
DOIs
Publication statusPublished - Sept 2023

Keywords

  • CCM
  • Jakarta
  • nonlinear coupling
  • Pearson correlation
  • PM

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