RESUMEN
The high concentration of nitrogen dioxide (NO2) is to blame for West Java's poor Air Quality Index (AQI). So, this study aims to determine the influence of industrial activity as reflected by the value of its imports and exports, wind speed, and ozone (O3) on the high concentration of tropospheric NO2. The method used is the econometric Vector Error Correction Model (VECM) approach to capture the existence of a short-term and long-term relationship between tropospheric NO2 and its predictor variables. The data used in this study is in the form of monthly time series data for the 2018-2022 period sourced from satellite images (Sentinel-5P and ECMWF Climate Reanalysis) and publications of the Central Bureau of Statistics (BPS-Statistics Indonesia). The results explained that, in the short-term, tropospheric NO2 and O3 influence each other as they would in a photochemical reaction. In the long-term, exports from the industrial sector and wind speed have a significant effect on the concentration of tropospheric NO2. The short-term effect occurs directly in the first month after the shock, while the long-term effect occurs in the second month after the shock. Wind gusts originating from industrial areas cause air conditions to be even more alarming because tropospheric NO2 pollutants spread throughout the region in West Java. Based on the coefficient correlation result, the high number of pneumonia cases is one of the impacts caused by air pollution.
Asunto(s)
Contaminantes Atmosféricos , Contaminación del Aire , Ozono , Dióxido de Nitrógeno/análisis , Contaminantes Atmosféricos/análisis , Tecnología de Sensores Remotos , Contaminación del Aire/análisis , Ozono/análisis , Monitoreo del Ambiente/métodosRESUMEN
As an area experiencing air pollution, especially ozone concentrations that often exceed the threshold or are unhealthy, JABODETABEK (Jakarta, Bogor, Depok, Tangerang, and Bekasi) seeks to prevent and control pollution as well as restore air quality. Therefore, this study aims to build a predictive model of ozone concentration using Harris hawks optimization-support vector regression (HHO-SVR) in 14 sub-districts in JABODETABEK. This goal is achieved by collecting data on ozone concentration as a response variable and meteorological factors as predictor variables from the website that provides the data. Other predictor variables such as time and significant lag detected with partial autocorrelation function of ozone concentration were also used. Then the variables will be selected using the recursive feature elimination-support vector regression (RFE-SVR) to obtain a significant predictor variable that affects the ozone concentration. After that, the prediction model will be built using the HHO-SVR method, support vector regression (SVR) whose parameter values are optimized with the Harris hawks optimization (HHO) algorithm. When the model has been formed, several evaluation metrics used to determine the best model include mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), Coefficient of Determination (R2), Variance Ratio (VR), and Diebold-Mariano test. The results of this study indicate that lag 1, lag 2, air temperature, humidity, and UV index are significant predictor variables of the RFE-SVR results for most sub-districts. In general, the HHO process takes longer than other metaheuristic algorithms. On average, 7 of the 14 sub-districts using the HHO-SVR model yielded the best predictions with MAE below 10, RMSE and MAPE below 20, R2 around 0.97, and VR around 0.98. Then, the results of the Diebold-Mariano test also show that the accuracy of the prediction results and the stability of the performance of the HHO-SVR model is better, especially for the Ciputat and South Bekasi sub-districts. This shows that the two sub-districts are very suitable to use HHO-SVR in predicting ozone concentrations.
RESUMEN
This paper presents the dataset about the social vulnerability in Indonesia. This dataset contains several dimensions which rely on previous studies. The data was compiled mainly from the 2017 National Socioeconomic Survey (SUSENAS) done by BPS-Statistics Indonesia. We utilize the weight to obtain the estimation based on multistage sampling. We also received additional information on population, the number, and population growth from the BPS-Statistics Indonesia's 2017 Population projection. Furthermore, we provide the distance matrix as the supplementary information and the number of populations to do the Fuzzy Geographically Weighted Clustering (FGWC). This data can be utilized to do further analysis of social vulnerability to promote disaster management. The data can be accessed further at https://raw.githubusercontent.com/bmlmcmc/naspaclust/main/data/sovi_data.csv.