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

Received: January 17, 2023
Revised: June 17, 2023
Accepted: June 19, 2023

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Cite this article:

Istiana, T., Kurniawan, B., Soekirno, S., Nahas, A., Wihono, A., Nuryanto, D.E., Adi, S.P., Hakim, M.L. (2023). Causality Analysis of Air Quality and Meteorological Parameters for PM_2.5 Characteristics Determination: Evidence from Jakarta. Aerosol Air Qual. Res. 23, 230014. https://doi.org/10.4209/aaqr.230014

HIGHLIGHTS

• Convergent Cross Mapping can detect mirages and extracts weak couplings better than correlation.
• The coupling pattern between PM_2.5 and air quality parameters increased in Dec.–Feb.
• The coupling pattern between PM_2.5 and meteorological parameters increased in Dec.–Feb. and Mar.–May.
• The highest causality value for air quality parameters is 0.74 (PM₁₀).
• The highest causality value for meteorological parameters is 0.52 (wind speed).

Keywords: PM2.5, Pearson correlation, CCM, Jakarta, nonlinear coupling

1 INTRODUCTION

Jakarta is the capital city and the economic center of Indonesia, which attracts people to move in. From 2015 to 2020, Jakarta experienced an annual population growth of 1.15%. The population density itself has reached 14,464 people per square kilometer, with an estimated population of more than 10 million in 2016 (Mahesa et al., 2019), making Jakarta one of Indonesia’s megacities (Molina et al., 2007). The increase in population causes various problems, including a decrease in air quality (Han et al., 2015; Zhu et al., 2021). According to Andrade-Flores et al. (2016) and Baklanov et al. (2016), over the past 100 years, the decline in air quality has been directly related to the increase in the population living in urban areas around the world. Hence, it is obvious that one of the main problems in urban areas is pollutant emission from vehicles, contributing to 25% of all PM_2.5 emitted into the atmosphere (Aguiar-Gil et al., 2020). PM_2.5 (particulate matter) itself can be defined as fine inhalable particles with a diameter of 2.5 µm or smaller (The Lancet, 2006). Exposure to PM_2.5 is very harmful since it can penetrate the human respiratory system, which increases the risk of lung cancer, cardiopulmonary disease, and premature death (Latha and Badarinath, 2005; Pope and Dockery, 2006). Urban areas in America, Europe, and Asia, especially in China, India, and Southeast Asian countries, have a high PM_2.5 emission rate that comes from construction and industry activity, urbanization, population growth, and the use of fossil fuels (Hu et al., 2014; Lin et al., 2014; Song et al., 2015; Liang et al., 2016; Farrow et al., 2020). Anthropogenic emissions are the main driver of PM_2.5 (Artı́ñano et al., 2003; David et al., 2019; Zhang et al., 2019; Kumar et al., 2021), while the meteorological conditions exert a strong long-term influence on PM_2.5 variations (Wang et al., 2013; Westervelt et al., 2016; He et al., 2017; Dahari et al., 2020). Anthropogenic emissions can be defined as human activities that contribute to the increasing number of particles, including transportation and energy production of fossil fuel power plants (Farrow et al., 2020). Zhang et al. (2015) explained the role of meteorological factors and critical air pollutants in the formation of air pollutants in Beijing, Shanghai, and Guangzhou regions that vary significantly in geological seasons and locations.

Analysis of the influence of meteorological parameters and air quality on the particulate matter distribution in urban areas has widely been carried out by researchers, some of whom used the Pearson correlation (Lee, 2014; Islam et al., 2015; Hien et al., 2019; Onuorah et al., 2019; Qin and Lou, 2019; Chu et al., 2020; Singh et al., 2021; Kliengchuay et al., 2022). Statistical characteristics and correlations in Seoul, Korea, show that air pollutants, particularly PM_2.5, PM₁₀, SO₂, O₃, and CO have a correlation except for O₃ (Lee, 2014). According to Islam et al. (2015), PM_2.5 and PM₁₀ in Bangladesh's Dhaka city were positively correlated with a particulate dilution due to a very low increase in wind speed. Furthermore, a positive correlation with NO_x suggests that a higher number of particulates tends to reduce atmospheric temperature and humidity. Meanwhile, there is a negative correlation between PM_2.5 and wind speed in Ho Chi Minh City, Vietnam (Hien et al., 2019). In the Ratchaburi region of Thailand, PM_2.5 concentrations are positively and strongly correlated with PM₁₀, CO, and NO₂ and are weakly positively correlated with temperature and humidity (Kliengchuay et al., 2022).

The correlation method can help us study meteorological interactions and PM_2.5 concentrations but cannot determine the causal relationship between the two variables and identify the direction of the cause (Zou et al., 2021). Therefore, causality analysis is needed to identify the causal relationship (Dab et al., 2011). Granger causality is one of the classic causality methods used to explore the relationship between health effects, mortality rates, and air pollution (Xiao et al., 2006; Wang et al., 2007). Sfetsos and Vlachogiannis (2010) conducted a causality relationship between meteorological patterns and PM₁₀ concentration in Athens, Greece using Granger causality. The results showed that PM₁₀ excess could be classified and identified according to spatial distribution and air pollution contributors. Xiao et al. (2006) identified causality relationships using Granger causality between PM₁₀ and NO₂ pollutants in the Hong Kong region and showed PM₁₀ and NO₂ causality in both directions. Granger causality was first introduced by Granger (1969). In addition to Granger causality, a new method of causality was introduced by Sugihara et al. (2012) called the convergent cross-mapping (CCM) method. This method has the advantage of being able to detect causal relationships and weak couplings in complex ecosystems and reconstruct nonlinear pullers to time series data, which perfects Granger’s causality method (Sugihara et al., 2012; Chen et al., 2016). CCM is widely used by researchers to determine meteorological interactions at local PM_2.5 concentrations in the area (Chen et al., 2017; Chelani, 2017; Chen et al., 2018; Wang et al., 2019; Yan et al., 2018; Chen et al., 2020). Zou et al. (2021) studied meteorological influences by measuring PM_2.5 concentrations at Jing-Jin-Ji, China in various seasons using the CCM method. The higher the concentration of PM_2.5, the stronger the influence of individual meteorological factors in the Jing-Jin-Ji region. The effect of pollutant causality in the Chinese region was measured by Wang et al. (2019) with the results of PM_2.5 sensitivity to NO₂ and SO₂ followed by CO, especially in the Hangzhou region. Zou et al. (2021) then identified a nonlinear coupling network of PM_2.5 and meteorological factors in different seasons based on CCM in Xining Tibet. The CCM method was able to show the causality influence of individual meteorological factors on PM_2.5 concentrations and was revealed to be better than correlation analysis (Chen et al., 2017). The influence of individual meteorological factors on PM_2.5 concentrations throughout China was studied by Chen et al. (2018) using the CCM method resulting in the influence of individual meteorological factors on local PM_2.5 concentrations having a strike in regional seasonal variations. More detailed information from previous reports can then be summarized through Table S1.

This study aims to analyze the influence of meteorological parameters and air quality that affect PM_2.5 concentrations in Jakarta_. With Jakarta’s background as a megacity with complex air quality problems, it is necessary to understand the characteristics of local PM_2.5 concentration and the influence of meteorological and air quality parameters on PM_2.5 concentration. It also aims to identify and analyze the correlation relationship of air quality parameters (PM₁₀, NO₂, SO₂, CO, and O₃) and the causality relationship of meteorological parameters and air quality with PM_2.5 concentrations based on seasonal variations. The main causes and local dominant factors that influence the concentration of PM_2.5 in the Jakarta area are also analyzed. We use the Pearson correlation to study subsequent interactions and the nonlinear state space method with CCM to analyze the causal relationship and local dominant factors that influence PM_2.5.

 
2 MATERIAL AND METHODS

 
2.1 Jakarta Meteorological and PM_2.5 Conditions

Based on its geographical position, Jakarta is located at a longitude 106°41′7′′ to 106°58′23′′ East and a latitude 6°10′15′′ to 6°22′11′′ South with the north coastal boundary stretching from west to east for ± 35 km. Jakarta is in a lowland area with an average altitude of 8 mdpl. Jakarta has hot and dry air temperatures with a tropical climate causing a longer duration of solar irradiation with different intensities. In the period between 2001 and 2014, there was an increase in average air temperature in Jakarta by 2°C–3°C (Ramdhoni et al., 2016). Furthermore, Jakarta's annual average temperature increases by 1.6°C per century which also exceeds the increase in the global average land temperature (Siswanto et al., 2016). Jakarta has two major seasons called the dry season and the rainy season which are influenced by two different wind patterns. During the dry season, southeast monsoon winds dominate and bring cold and dry air resulting in little rainfall. Meanwhile, in the rainy season, the northwest wind brings warm and wet air, so there is a high evaporation rate above the Java Sea (including Jakarta) resulting in more rainfall (Aldrian and Dwi Susanto, 2003).

By using Central Jakarta and South Jakarta’s hourly data collected from https://www.airnow.gov, we managed to get the diurnal pattern of PM_2.5 concentration from January 2016 to December 2019. The data is important to have a grasp of Jakarta’s latest PM_2.5 condition before analyzing its correlation with air quality and meteorological parameters. As shown in Fig. 1, PM_2.5 concentration in Central Jakarta tends to rise significantly from 19.00 to 03.00 before levelling off until 11.00 and starts to decline. On the other hand, PM_2.5 concentration in South Jakarta tends to rise from 18.00 to 03.00. It then remains constant until 08.00 before declining again. The graph shows that the ranges of PM_2.5 concentration in 2016, 2018, and 2019 are similar and close to each other, while it is completely off for the one in 2017. The mean value of PM_2.5 concentration in 2016, 2018, and 2019 can then be determined with a peak of 50.96 µg m^–3 at 07.00 in Central Jakarta and a peak of 54.89 µg m^–3 at 03.00 in South Jakarta. However, the data from 2017 shows that the mean value of PM_2.5 concentrations has a peak of 33.48 µg m^–3 at 11.00 in Central Jakarta and a peak of 36.38 at 22.00 in South Jakarta. The large difference between them can be caused by the La Nina phenomenon.

Fig. 1. Mean PM_2.5 hourly concentration in Central Jakarta and South Jakarta from January 2016 to December 2019.

The patterns are quite similar in different places. This can be caused by other parameters, such as the weather in Jakarta. As shown in Fig. S1, Jakarta’s PM_2.5 concentration exhibits a correlation with both the temperature and wind speed in 2016. As the temperature reaches its minimum value during the morning, aerosol will not be able to rise into the atmosphere. On the contrary, aerosol tend to rise into the atmosphere during the afternoon accompanied by an increase of temperature. Moreover, the mean wind speed with a value less than 1 knot is not strong enough and causes pollutant to be trapped near the Earth surface (Vallero, 2014; Hutauruk et al., 2020). It aligns with our finding that there is a high concentration of PM_2.5 in the morning.

 
2.2 Data Collection

The data of PM_2.5 can be accessed through http://aqicn.org/ (last accessed: August 4^th, 2022) which contains official information about PM_2.5 AQI (air quality index) collected by the US Embassy in Indonesia (https://www.airnow.gov) using several sensors in Central Jakarta (–6.18098, 106.83021) and South Jakarta (–6.25521, 106.80699) since December 4^th, 2015. In addition, AQI PM_2.5 data were also collected from Indonesia’s Meteorological, Climatological, and Geophysical Agency (BMKG). Furthermore, information regarding the air quality index in Jakarta including PM₁₀, NO₂, SO₂, CO, and O₃ since 2010 can be accessed through https://data.jakarta.go.id/dataset/. As shown in Fig. 2, the data were collected from five different locations in Jakarta, such as Bunderan HI (HI) sensors which are located on the side of the roadway in Central Jakarta (–6.194699, 106.823028), Kelapa Gading (KG) sensors which are located near the industrial and residential areas in North Jakarta (–6.159690, 106.905541), Jagakarsa (JK) sensors which are located near residential areas with lots of vegetations in South Jakarta (–6.336216, 106.818082), Lubang Buaya (LB) sensors which are located near residential areas in East Jakarta (–6.290561, 106.906839), and Kebon Jeruk (KJ) sensors which are located near the residential areas in West Jakarta (–6.192061, 106.770606). Meteorological parameter data were obtained from the https://power.larc.nasa.gov/data-access-viewer/ website that contains data from the MERRA-2 satellite. The AQI data of PM_2.5, PM₁₀, NO₂, SO₂, CO, and O₃ that we collected ranges from December 25^th, 2015, to February 28^th, 2020.

 Fig. 2. Study area map of PM_2.5 sensor and air quality sensor locations.
 

2.3 Method

As shown in Fig. 3, the Pearson correlation analysis examined the relationship and influence of meteorological and air quality parameters based on seasonal variations in PM_2.5 concentrations. The correlation coefficient (r) was used to determine whether the relationship between the free variables X and the bound variable Y is strongly correlated or not (Pearson, 1895). The causality was then analyzed using the CCM method.

Fig. 3. Study flowchart.

 
2.3.1 Convergent cross mapping (CCM) analysis

As shown in Fig. S2, in the Y = f(X, Y) system, cross mapping means that the point on the manifold is a variable My, so the corresponding point Mx can be searched for the same time  According to the Takens theorem, we can calculate the Mx and My shadow manifolds that cross the map to the actual manifolds of the system (Sauer, 2006; Huke, 2006). Manifold is a summary of the system with Mx and My as the summary of X and Y. Hence, Mx can be used to predict Y, namely Ŷ|Mx, and vice versa (Joseph and Javier, 2012; Yan et al., 2018).

The CCM method was first proposed by Sugihara et al. (2012). CCM can detect weak to moderate couplings and deliver bidirectional causality through convergent mapping. For example, consider X and Y as two variables and ρ as a skill prediction. ρ values range from 0 to 1, indicating the influence of the variable X on Y. If X causes Y then Y can be used to predict X  If ρ(X|Y) < 1 and is high enough, X causes Y and leaves information on Y that can be used to recover X from Y (Yan et al., 2018), and vice versa (Joseph and Javier, 2012). X → Y means unidirectional causality. If the value of ρ(X|Y) is high, prediction from X to Y is good. In contrast, if ρ(Y|X) is low, prediction from Y to X is bad. X ↔ Y means bidirectional causality. If the value of ρ(X|Y) and ρ(Y|X) are high, predictions from X to Y and Y to X are good.

When compared with Granger causality which states that X causes Y, we will be able to predict Y if the X is given (Joseph and Javier, 2012; Sugihara et al., 2012). Granger causality is based on linear and multivariate processes in nature involving statistical regression so the methods are derived from causality that requires extensive data (Sugihara et al., 2012; Zhao et al., 2021). CCM works based on nonlinear time series analysis and was developed to overcome this so that the CCM method can detect and measure the causes between two dynamic variables based on time series (Huke, 2006; Sugihara et al., 2012; Ye et al., 2015; Tsonis et al., 2018).

 
3 RESULTS AND DISCUSSION

3.1 PM_2.5 Characteristics in Jakarta

We used PM_2.5 concentration data from 2015 to 2020 in two different locations, namely Central Jakarta and South Jakarta. The data were divided based on the variation of the seasons, namely the wet season in December–February (DJF), the March–May transition (MAM), the dry season in June–August (JJA), and the September–November transition (SON). According to (Aldrian and Dwi Susanto, 2003), seasonal analyses in Indonesia are carried out based on the monsoon activity. We then analyzed the highest value, the average from each season, and the annual average of PM_2.5 concentrations for each location.

We then calculated the mean monthly, seasonal, and annual AQI of PM_2.5 for both locations as shown in Fig. S3. We found that the daily average of PM_2.5 has a value of 98.17 µg m^–3 in Central Jakarta and 109.72 µg m^–3 in South Jakarta. The monthly averages of PM_2.5 concentration seems to fluctuate throughout the year with a peak in July. There is also an increase in annual average of PM_2.5 AQI with a peak of 98.75 µg m^–3 in Central Jakarta and 112.23 µg m^–3 in South Jakarta during 2018. Many factors caused the decrease of PM_2.5 concentrations in the Jakarta area during 2017, such as the global factor that occurred, namely the La Nina phenomenon. According to Boer and Suharnoto (2012), Sumatera, Java, Eastern Sulawesi, Central Papua, and Kalimantan are affected by this La Nina phenomenon. The La Nina phenomenon occurs in some areas with an increase in rainfall, which results in the atmosphere being “cleaned” and causes a decrease in PM_2.5 concentrations. During the wet season in December–February (DJF), PM_2.5 concentration in Central Jakarta and South Jakarta increases and peaks during the JJA season where the temperature and low wind speed cause the pollutants to be trapped and particles to be built up (Yu et al., 2013).

As an addition, Fig. S4 shows the hourly behavior of PM_2.5 concentration based on seasonal variation. It is worth to note that the highest PM_2.5 concentration can be found during the JJA season, more specifically in July, with a value of 60.74 µg m^–3 (07.00) in Central Jakarta and 66.26 µg m^–3 in South Jakarta (03.00). This finding is supported by the fact that PM_2.5 concentration usually reaches its peak in the morning or in the afternoon (Gusnita and Cholianawati, 2019). In Central Jakarta, the high concentration of PM_2.5 at 07.00 is a result of a high number of human activities during the rush hour, especially due to traffic and vehicle pollution. In South Jakarta, the high concentration of PM_2.5 at 03.00 is caused by a pollution accumulation due to thermal inversion. The temperature of Earth surface is usually relatively low at dawn. The lack of sunshine contributed to the formation of cold layer of air near the Earth surface. If there is a warmer layer of air above it, air pollution cannot be dispersed vertically and started to accumulate (Vallero, 2014; Tiwari et al., 2013). In conclusion, the diurnal pattern of PM_2.5 concentration varies with the season and the location as we discussed before in sub-chapter 2.1.

 
3.2 Causality of Local PM_2.5 Concentrations on Individual Meteorological and Air Quality Parameters

The causal relationship and quantitative influence between local PM_2.5 concentrations with individual air quality and meteorological parameters were studied using CCM analysis. The Pearson correlation results are shown in Figs. 4(a–b) while the correlation and causality values between local PM_2.5 in Central Jakarta and South Jakarta regions with the air quality parameters at five different sensor locations and individual meteorological parameters are shown in Tables 1(a–b). The display of the first number in front of the comma is the result of the correlation. The number after the comma is the result of the CCM that describes the air quality parameters related to PM_2.5 with the value of ρ. This can explain the ability of air quality parameters to predict PM_2.5. The author uses a causal-ccm package with pip install causal-ccm (Joseph and Javier, 2012).

Fig. 4(a). Pearson correlation between PM_2.5 and air quality parameter.

Fig. 4(b). Pearson correlation between PM_2.5 and meteorological parameters.

By taking environmental factors into account, it is worth noting that the environment where the sensors are in Central Jakarta is dominated with office towers and skyscrapers with a high number of vehicles surrounding them which contributed to the emission. In South Jakarta, however, it is mixed with residential areas. In more detail, the sensors located in Bunderan HI are placed on the side of the roadway making vehicles the highest contributor to air pollution. The sensors located in Kelapa Gading are located near the industrial and residential areas which contributes to the air pollution. The sensors located in Jagakarsa, Lubang Buaya, and Kebon Jeruk are placed near the residential area making the domestic activities the main contributor to air pollution. In addition, the sensors located in Jagakarsa are surrounded by city parks with lots of vegetations that might reduce the air pollution.

 
3.2.1 Causality of PM_2.5 concentrations on individual air quality parameters

As shown in Table 1(a), there is almost no difference between CCM values and correlation values between PM_2.5 with PM₁₀ and O₃. In the country, the parameters of SO₂ pollutants in almost all seasons and locations vary significantly. It also applies to NO₂ gas pollutants with the KG sensor in South Jakarta during all seasons where the CCM result is higher than the resulting correlation value. In KG area, the pollutant mainly comes from the traffic and industrial activities. According to Chen et al. (2017) and Sugihara et al. (2012), CCM can detect mirages and extract weak couplings better than the correlation values. Note that SO₂ and NO₂ are the main precursors of PM_2.5 (Wang et al., 2015; Shimadera et al., 2016; Lee et al., 2018; Chu et al., 2020; Uhm et al., 2021). In addition, the contribution of SO₂ (fossil burning) and NO₂ (vehicle emissions) from the total amount of PM_2.5 is 48.6% and 33.9% respectively (Wang et al., 2015). CO values by CCM were in the range of 0.17–0.59 and were the highest during the DJF season period in South Jakarta and the JK sensor region. However, the increase of rainfall during DJF season does not necessarily lower the concentration since there is high number of CO pollutant near the JK sensors and a high concentration of PM_2.5 in South Jakarta area (0.59). This finding contradicts the fact that the increase of rainfall usually plays a role in cleaning the air by dragging the air pollutant along with the raindrops (Cai et al., 2009; Mukhtar et al., 2013). Meanwhile, its values were significantly different from the correlation results in the JJA season period at the KJ sensor location in Central Jakarta (–0.017, 0.52) and South Jakarta (–0.23, 0.58). In JK, LB, and KJ area, however, a high concentration of CO particles is mainly caused by domestic activities.

 
3.2.2 Causality of PM_2.5 concentrations on meteorological parameters

As shown in Table 1(b), it appears that CCM can identify mirage correlations with the individual meteorological factors with the result of the correlation of precipitation in the MAM season period (–0.35, 0.11), pressure (PS) in the JJA season period (0.07, 0.23), WD10M in the SON season period (–0.43, 0.23). This proves that CCM is better than correlation analysis at showing the influence of causality between individual meteorological parameters and local PM_2.5 concentrations (Chen et al., 2017).

 
3.3 PM_2.5 Nonlinear Coupling with Air Quality and Meteorological Parameters

3.3.1 PM_2.5 nonlinear coupling with air quality parameters

Table 2(a) shows the results of the CCM nonlinear coupling relationship between PM_2.5 and air quality parameters using HI and JK sensors. This is following the explanation in Section 2.3.1 where Mx can be used to predict Y or Ŷ|Mx. During DJF season, the individual CO at Bunderan HI was found with an influence of ρ = 0.43 on local PM_2.5 concentration in Central Jakarta and had a feedback effect of ρ = 0.34. Chen et al. (2020) explained that the CCM method did not show two positive or negative causality variables directly. It was found that the CO from Bunderan HI had a positive effect on PM_2.5 and had positive feedback in Central Jakarta. This feedback relationship between CO and PM_2.5 was bidirectional. Based on the data collected from HI sensors, a high concentration of CO and PM_2.5 particles are caused by the vehicles on the roadway. Unidirectional nonlinear coupling occurred in the Central Jakarta area between PM_2.5 and NO₂ at the JK location where NO₂ had the influence of ρ = 0.42 on local PM_2.5 concentrations and feedback of ρ = 0 (no causality relationship). In addition, the results from KG, LB, and KJ sensors are shown in Table S2.

Fig. 5(a) describes charts of CCM results of air quality parameters based on the DJF, MAM, JJA, and SON season variations. During the DJF season, with Y as CO (HI) and X as PM_2.5 (CENTRAL), it can be shown that the value of My could predict the value of X and vice versa where the CCM value, X̂|My, was obtained with the value of 0.43. The same results can also be obtained from other season variations. Similar approach has been done in 93 cities in China (Wang, 2019, 2021). The research shows that the air quality parameters, such as NO₂, has a significant causality effect and correlates with PM_2.5 concentration in 93 cities in China. Other research shows that there is a complex unidirectional nonlinear coupling pattern between PM_2.5 concentration and its air quality parameters, such as NO₂ and CO, in Shenyang, China (Yang et al., 2020). Fig. 6 is then made to illustrate the nonlinear coupling patterns of air quality parameters with PM_2.5 concentration based on seasonal variation.

Fig. 5(a). Example of CCM causality results between air quality, and PM2.5.

Fig. 5(b). Example of CCM causality results between meteorological parameters, and PM_2.5.

Fig. 6. Nonlinear coupling pattern of air quality parameters and PM_2.5 concentrations based on seasonal variations.

3.3.2 PM_2.5 nonlinear coupling with meteorological parameters

As shown in Table 2(b), the meteorological nonlinear coupling relationship and the amount of PM_2.5 varies based on the season. In Central Jakarta, PM_2.5 and T2M had significant positive bidirectional effects during the DJF and MAM seasons. A significant relationship can be interpreted as a convergence relationship (Joseph and Javier, 2012). In the JJA season, PM_2.5 and T2M have a negative unidirectional effect that was not significant. The SON season did not have a coupling pattern at all. Unlike the PM_2.5 coupling pattern, the dew point (DEW) was not significant in all seasons. Precipitations in almost all seasons had negative bidirectional effects and were not significant, except in the SON season, which had precipitation with an insignificant unidirectional negative effect. RH2M in the SON season had a significant negative bidirectional effect. Meanwhile, the DJF, MAM, and JJA seasons had significant bidirectional negative effects. Humidity influenced the formation and conversion of aerosol particles (Li et al., 2015). In the JJA and SON seasons, PM_2.5 and air pressure had significant unidirectional positive effects while the wind direction in DJF and MAM seasons had a significant bidirectional positive pattern.

Fig. 5(b) describes the charts of CCM results of meteorological parameters based on DJF, MAM, JJA, and SON season variations. During the DJF season, with Y as WS10M and X as PM_2.5 (CENTRAL), it can be shown that the value of My could predict the value of X and vice versa where the CCM value, X̂|My, was obtained with the value of 0.46. The same results can also be obtained from other season variations. We found that during the JJA and SON seasons, there were insignificant unidirectional and bidirectional patterns. The wind speed parameter in the DJF season had a significant negative pattern. In Central Jakarta, the coupling pattern improved during the DJF and MAM seasons while most of the meteorological parameters had no effect during the JJA season. Moreover, the increase in humidity could also increase the amount of PM_2.5 during the SON season. The same results can also be obtained from other season variations. Similar approach has been done in Beijing, China (Chen et al., 2017, 2018). The research shows a negative bidirectional coupling pattern between the wind speed and PM_2.5 concentration and a positive bidirectional coupling pattern between the relative humidity and PM_2.5 concentration. Other research was held in a high-altitude area shows that the meteorological parameters, such as relative humidity, precipitation, air pressure, and sunshine duration, have a significant causality effect and correlates with PM_2.5 concentration in Xining, China (Zou et al., 2021). Fig. 7 is then made to illustrate the nonlinear coupling patterns of meteorological parameters with PM_2.5 concentration based on seasonal variation.

Fig. 7. Nonlinear coupling pattern of meteorological parameters and PM_2.5 concentrations based on seasonal variations.

4 CONCLUSIONS

In this paper, we analyzed the causal relationship and the quantitative influence of meteorological and individual air quality parameters affecting local PM_2.5 concentrations in Jakarta based on seasonal variations. Causality analysis with the CCM method showed that PM_2.5 coupling patterns differed every season. PM_2.5 showed a positive bidirectional relationship with PM₁₀ in all seasonal variations. The DJF and MAM season periods in South Jakarta showed that the PM_2.5 concentrations had a bidirectional relationship with SO₂ at the JK, KJ, and KG locations. In addition, during the DJF season, PM_2.5 concentrations in Central and South Jakarta had bidirectional positive relationships with CO gas pollutants from HI, JK, and KJ locations. During the DJF and MAM seasons, PM_2.5 concentration had a bidirectional relationship with O₃ from the JK, LB, and KJ locations. In Central Jakarta and South Jakarta, the coupling pattern of PM_2.5 concentration and air quality parameters improved during the DJF season. The coupling pattern of PM_2.5 concentration and meteorological parameters in Central Jakarta improved during the DJF and MAM seasons. In the JJA season, most meteorological parameters had no effect while in the SON season, the increased humidity could also increase the amount of PM_2.5. The highest causality (r) values for air quality parameters were 0.74 (PM₁₀), 0.68 (SO₂), 0.61 (CO), 0.55 (NO₂), and 0.54 (O₃) while those for meteorological parameters were 0.52 (wind speed), 0.51 (temperature), 0.47 (relative humidity), 0.38 (wind direction), 0.38 (dew point), 0.31 (pressure), and 0.26 (precipitation). In this study, CCM was able to analyze mirage correlation better than the Pearson correlation method. Research showed that individual meteorological and air quality parameters could influence local PM_2.5 concentrations in the Jakarta region.

 
ACKNOWLEDGMENTS

This research was carried out with the support of Universitas Indonesia (PUTI Research Grant 2022 NKB-274/UN2.RST/HKP.05.00/2022) by Mr. B. Kurniawan and National Research and Innovation Agency. The authors would like to extend gratitude to agencies and persons who supported the authors work in providing data such as http://aqicn.org/, Meteorological, Climatological, and Geophysical Agency of Indonesia (BMKG), US Embassy in Jakarta through AirNow Program for monitoring of PM_2.5 concentration in Jakarta, https://data.jakarta.go.id/dataset/ and https://power.​larc.nasa.gov/data-access-viewer/.