An integrated analysis of air pollution and meteorological conditions in Jakarta

Abstract

Air pollution and climate change are general problems for society. This paper proposes an integrated analysis of the Air Quality Index (AQI) and meteorological conditions in Jakarta. The column-based data integration model is applied to create integrated data of the Air Quality Index and meteorological conditions. The integrated data is then used to generate a causal graph using the PC algorithm. The causal graph reveals that there exist causal relationships between pollutants and meteorological conditions, e.g, humidity, rainfall, wind speed, and duration of sunshine affect particulate matter 10 (PM[Formula: see text]); wind speed affects sulfur dioxide (SO[Formula: see text]); temperature affects ozone (O[Formula: see text]). The historical data records that the average wind speed is decreased and the number of unhealthy days has risen. Ozone and particulate matter are two pollutants that mainly influence poor air quality in Jakarta. The integrated data is also used to train Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) for forecasting. Experimental results show that LSTM using integrated data produces smaller errors for forecasting AQI and meteorological conditions.

Figure 1

The proposed method of an integration analysis of air pollution and meteorological conditions.

Figure 2

A causal graph is generated from AQI and meteorological conditions.

Figure 3

The (A) annual and (B) monthly average AQI in Jakarta from 2010 to 2021.

Figure 4

The actual data (A) and forecasting (B) of AQI in Jakarta.

Figure 5

The actual data (A) and forecasting (B) of meteorological condition in Jakarta.

Figure 6

A comparison performance using integrated data (i) and not integrated data (p).