56.1 Introduction

The city of Jakarta, as the capital city of Indonesia, has various kinds of progress in various sectors, both industry, tourism, education and the environment. In a higher population rate, which of course also increases the mobility and activities of people living in the city of Jakarta and surrounding cities such as Depok, Bekasi, Bogor. The rapid progress of the city of Jakarta has an impact on the environment, especially the air quality in DKI Jakarta. Increasing industrial and transportation activity was associated with air pollution, especially in the urban and industrial areas. Biomass burning and urban air pollution that occur, consecutively, in several wildfire-prone provinces and big cities in Indonesia are likely worsened in the future [1].

The Air Pollutant Standard Index is a number that does not have units that describe the condition of ambient air quality at specific location and time based on the impact on human health, aesthetic value, and other living things. The air quality standard index officially used in Indonesia is the Air Pollutant Standard Index (ISPU); this is following the Ministerial Decree State Environment: KEP 45/MENLH/1997 concerning Standard Index Air Pollutants [2] (Table 56.1).

Table 56.1 Air pollutant standard index range
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Air pollution in an urban area is a dynamic mixture of pollutants emitted from numerous sources, including motor vehicles and other anthropogenic activities. The impact of air pollution can be one of the reasons for civilization diseases [3].

Besides emission factors, local meteorological conditions, especially wind, air temperature, rainfall, and radiation factors, also affect the concentration and distribution of pollutants in the air [4]. The concentration of pollutants in the atmosphere is not only influenced by the number of pollutant sources, but also by meteorological parameters, namely air temperature and wind speed [5]. In addition, the increase in temperature and rainfall also affects the increase in CO2 from the surface [6]. From several locations, temperature and RH are also significant contributors to the variability of the concentration of CO2. Mahesh et al., research the correlation of meteorological factors with CO2, statistical analysis of the data shows that precipitation and relative humidity independently correlated 55% (r =  − 0.55) and 32% (r =  − 0.32), respectively [7]. Influences of prevailing meteorology (air temperature, wind speed, wind direction, and relative humidity) on GHGs have also been investigated. CO2 and CH4 show a strong positive correlation during winter, pre-monsoon, monsoon, and post-monsoon with correlation coefficients (Rs) equal to 0.80, 0.80, 0.61, and 0.72 respectively [8].

Radiation also affects the concentration of NOx in the atmosphere. In summer, NO converted to NOx increases with the increase in solar radiation. For example, in some large cities in Japan, due to the increase in urban population, the atmospheric temperature tends to be increased, which is strongly correlated with high NO2 concentration It is also supported by research in Bahrain, which showed the highest NOx concentration was in the urban areas with high traffic density [9].

Wind will affect the dispersion of pollutants (transport process) and determine which direction and how high the pollutant concentration is. SO2, aerosols, nitrogen oxides, and hydrocarbons in the atmosphere will form a photochemical haze with the help of solar energy (radiation). SO2 also, if it reacts with rainwater, will increase the acidity of rain water which can cause acidification water sources and reduction of soil nutrients; also cause corrosion metal and other building materials [10].

56.2 Data and Methods

56.2.1 Data

This study uses secondary data consisting of 2 primary data, namely meteorological data and air pollution data for time January 1, 2017; Dec 31, 2020 time period. Meteorological data comes from data released by the central BMKG consisting of relative humidity (RH), temperature, and duration of solar radiation, while air pollution data obtained from Provincial Environment Service DKI Jakarta in the form of air pollutant standard index (APSI) data of PM10, O3, CO, NO2, and SO2. These two types of meteorology and pollutant data use data for the 2017–2020 period due to the completeness of the available data.

56.2.2 Methods

To analyze the correlation between meteorological factors and air pollutant standard index, used Heil-Sen Siegel regression method [11].

Suppose the equation for the linear relationship of the independent variable (X) to the dependent variable (Y) is

$$Y = Q_{o} + Q_{1} X + s$$

where βo is the intercept, β1 is the regression coefficient, and ε is residual; the value of β1 is estimated by calculating the gradient of n(n − 1)/2 datum pairs, finding the median of the resulting gradient for each gradient between the datum and other n − 1 datums, then calculating the median of the n medians that have been obtained. The estimated value of a robust regression coefficient can be obtained by calculating the median of the following least-squares estimate

$$\begin{aligned} \tilde{\beta }_{n} & = Median_{i} \left\{ {\tilde{\beta }_{1} = \frac{{\left( {Y_{i} - Y_{j} } \right)}}{{\left( {X_{j} - X_{i} } \right)}}:X_{i} \ne X_{j} ,\;1 \le i < j \le n} \right\} \\ \tilde{\alpha }_{n} & = Median_{i} \left\{ {\tilde{\beta }_{0} = \frac{{\left( {Y_{j} X_{i} - Y_{i} X_{j} } \right)}}{{\left( {X_{j} - X_{i} } \right)}}} :X_{i} \ne X_{j} ,\;1 \le i < j \le n \right\} \\ \end{aligned}$$

Thus, the Theil-Sen Siegel regression equation is written as follows:

$$YTSS = \tilde{\alpha }n + \tilde{\beta }X$$

56.3 Result and Discussion

The meteorological parameters discussed in this study are sunshine duration, temperature, and relative humidity (RH). These three parameters will be associated with several pollutants, namely particulate matter (PM10), ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO), to find out how the three meteorological parameters relate to the five pollutants. Thus, there are eight research variables. Each variable was observed from January 1, 2017; December 31, 2020. To obtain a clear pattern, the daily data were averaged per month (Table 56.2).

Table 56.2 Meteorological data and air pollutant standard index in Jakarta in 2017–2020
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Figure 56.1 presents average seasonal data for relative humidity, temperature, and sunshine duration for 2017–2020. For seasonal data, the length of the sun’s duration shows a similar pattern during 2017–2020. This pattern repeatedly happened for four years of observation. It’s just that in 2020 there will be a slight decrease in the length of the sun’s duration. The peak duration of solar radiation occurs in September–October November (purple) in transition season. In contrast the minimum value of solar radiation occurs in the wet season, namely Dec–Jan–Feb (the red one).

Fig. 56.1
Three graphs illustrate meteorological data for the seasons D J F, M A M, J J A, and S O N from January 2017 to January 2021 on the horizontal axis. Non-linear curves are plotted for the seasons with respect to sunshine duration, temperature, and relative humidity on the vertical axis.

Seasonal meteorological data

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Temperature meteorological analysis data shows a pattern/trend of temperature increases, especially in 2019 during the observation period. The maximum temperature generally occurs in the dry season of June–July–August, with the highest temperature 29.64 °C (in 2019). Meanwhile, minimum temperature occurred during December–February (rainy season) with the lowest temp at 27.2 °C.

The relative humidity pattern in Jakarta from 2017 to 2020 shows a relatively constant. The results of the RH data analysis showed that the exact peak of RH occurred in February from 2017 to 2020. However, in February 2019, the peak RH value fell to 80%, lower than in 2017–2018, 83%. And peak RH increased in February 2020 by 85%. The increase in RH is thought to be due to the decrease in the standard index of air pollutants in the city of Jakarta during the Covid 19 pandemic.

Data for Air Pollutant Standard index PM10, O3, CO, NO2, and SO2 are presented in Fig. 56.2. PM10 shows the highest concentration in the dry season of JJA, this is caused by the accumulation of particulate dust. The increasing the PM10 pollutant index during this season. O3 shows the highest pollutant index in July. This is to be linear with the high sunshine duration in the transition season. Increased surface ozone concentration (O3) in July 2018 in line with the decrease in CO gas concentration in July 2018 related to the photo chemical reaction of CO oxidation [12].

Fig. 56.2
Five graphs for the seasonal air pollutant standard index for the seasons D J F, M A M, J J A, and S O N from January 2017 to January 2021 are given on the horizontal axis. Non-linear curves are plotted for the seasons in relation to the following pollutants in the vertical axis: C O, S O subscript 2, N O subscript 2, O subscript 3, P M subscript 10.

Seasonal air pollutant standard index

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In NO2 and SO2, there was a significant increase in the pollutant index in October 2020; this related to the resumption of community activities after the Covid19 (New Normal policy) pandemic. Ozone concentrations at the ground level, depend on the formation and dispersion processes. Formation process, mainly depends on the precursor sources, whereas, the dispersion of ozone depends on meteorological factors. In addition, the level of ozone concentration at the surface can be estimated by the result of source and sink mechanism, which predominately rely on the meteorological conditions of the environment [13]. The high pollutant index of NO2 and SO2 is thought to come from transportation activities.

The implementation impact of the Working from home policy regarding the Covid-19 pandemic on air quality conditions in Jakarta can be seen qualitatively and quantitatively, especially decrease in PM10 concentration levels [14]. Based on air pollutant standard index data, it is known after the implementation of the new normal policy, there was a significant increase in pollutant in Jakarta, especially the SO2 and NO2 parameters (Fig. 56.2). CO concentration was increased significantly with the implementation of the new normal policy during the period of covid-19 pandemic.

56.3.1 The Correlation Between the Meteorology with Pollutants

The correlation between the independent variables and the dependent variables can be analyzed through regression analysis. In this case, each meteorological parameters act as an independent variable and pollutant as the dependent variable, with the correlation pattern assumed to be linear. From the way of correlation that are built, the correlation value between variables can be received.

The air pollutant standard index data for NO2 and SO2 in October 2020 experienced a significant increase, in contrast to other data. The solution to this problem is to use the Thiel -Sen-Siegel method [11]. This method is also relatively better for data that is not normally distributed and data with non-homogeneous variance (heteroscedasticity).

The results of the analysis in the form of correlation values obtain based on the Theil-Sen-Siegel method regression model are presented in Table 56.3 with a visualization of the correlation pattern in Figs. 56.3, 56.4 and 56.5 (Table 56.4).

Table 56.3 Summary statistics of research variables
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Fig. 56.3
Five graphs depict the correlation between pollutant standard index and sunshine duration for the seasons D J F, M A M, J J A, and S O N. The duration of sunlight increases linearly with respect to the following pollutants: particle matter, ozone, and Sulphur dioxide; and it is at zero with respect to nitrogen dioxide and decreases linearly with respect to C O.

Correlation between sunshine duration and air pollutant standard index PM10, O3, NO2, SO2, CO

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Fig. 56.4
Five graphs depict the correlation between pollutant standard index and temperature for the seasons D J F, M A M, J J A, and S O N. The temperature increases linearly with respect to the following pollutants: particle matter, ozone, and Sulphur dioxide; and it is at zero with respect to nitrogen dioxide and decreases linearly with respect to C O.

Correlation between temperature and PM10, O3, NO2, SO2, CO

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Fig. 56.5
Five graphs depict the correlation between pollutant standard index and relative humidity for the seasons D J F, M A M, J J A, and S O N. The relative humidity decreases linearly with respect to the following pollutants: particle matter, ozone, and Sulphur dioxide; and it is at zero with respect to nitrogen dioxide and increases linearly with respect to C O.

Correlation between RH and PM10, O3, NO2, SO2, CO

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Table 56.4 Correlation values based on Theil-Sen-Siegel method
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For the sun’s duration with a significance level of 5%, it was found that there was a significant linear relationship between the length of sun’s duration and PM10, O3, NO2, CO, while the relationship with SO2 is not significant. The correlation value between sun’s duration and air pollution standard index is presented in Table 56.5.

Table 56.5 Correlation between sun’s duration and air pollutant standard index
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For the temperature with a significance level of 5%, it was found that there was a significant linear relationship between the temperature on PM10, O3, and CO, while the relationship with SO2 and NO2 is not significant. The correlation value between T and air pollution standard index is presented in Table 56.6.

Table 56.6 Correlation between temperature and air pollutant standard index
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Relative humidity (RH), with a significance level of 5%, found a significant linear correlation between temperature and PM10, O3, and CO, while correlation with NO2 and SO2 is not significant. The correlation value between RH and air pollution standard index is presented in Table 56.7.

Table 56.7 Correlation between RH and air pollutant standard index
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56.4 Summary and Conclusion

The results of the correlation analysis between meteorological parameters data and air pollutant standard index parameters show the effect of pollutants are positively correlated with the sun’s durations, except parameter pollutant CO that has negative correlation. The correlation between temperature and air pollutants is positive, meaning that the higher temperature, the higher the concentration. RH is negatively correlated with the air pollutant standard index for all parameters, except for CO.