An Air Pollutants Prediction Method Integrating Numerical Models and Artificial Intelligence Models Targeting the Area around Busan Port in Korea

Abstract

Exposure to air pollutants, such as PM2.5 and ozone, has a serious adverse effect on health, with more than 4 million deaths, including early deaths. Air pollution in ports is caused by exhaust gases from various elements, including ships, and to reduce this, the International Maritime Organization (IMO) is also making efforts to reduce air pollution by regulating the sulfur content of fuel used by ships. Nevertheless, there is a lack of measures to identify and minimize the effects of air pollution. The Community Multiscale Air Quality (CMAQ) model is the most used to understand the effects of air pollution. In this paper, we propose a hybrid model combining the CMAQ model and RNN-LSTM, an artificial neural network model. Since the RNN-LSTM model has very good predictive performance, combining these two models can improve the spatial distribution prediction performance of a large area at a relatively low cost. In fact, as a result of prediction using the hybrid model, it was found that IOA improved by 0.235~0.317 and RMSE decreased by 4.82~8.50 μg/m3 compared to the case of using only CMAQ. This means that when PM2.5 is predicted using the hybrid model, the accuracy of the spatial distribution of PM2.5 can be improved. In the future, if real-time prediction is performed using the hybrid model, the accuracy of the calculation of exposure to air pollutants can be increased, which can help evaluate the impact on health. Ultimately, it is expected to help reduce the damage caused by air pollution through accurate predictions of air pollution.

The Development of a Low-Cost Particulate Matter 2.5 Sensor Calibration Model in Daycare Centers Using Long Short-Term Memory Algorithms

“…The correlation coefficient (R) and r 2 between the beta-ray absorption and light-scattering methods were 0.76 and 0.57, respectively. The comparison tests between the light-scattering method sensor used in this study and similar sensors tested using the FEM equipment in the field exhibited slightly higher performances, with PMS1003 at R 2 = 0.73-0.97 and PMS3003 (r 2 ) (Figure 5) [34]. Additionally, the comparison analyses between the PM 2.5 sensors located at the same position as the FEM equipment had r 2 values in the range of 0.33-45 [33].…”

“…The r 2 for each influencing factor is indicated in Table 3, and the regression equation is as follows. FEM = −7.783 + 0.564 × Sensor + 1.363 × Temperatuer + (−0.228× Humidity + 0.033×CO 2 + (−7.369) × weekday (11) in the field exhibited slightly higher performances, with PMS1003 at R 2 = 0.73-0.97 and PMS3003 (r 2 ) (Figure 5) [34]. Additionally, the comparison analyses between the PM2.5 sensors located at the same position as the FEM equipment had r 2 values in the range of 0.33-45 [33].…”

“…The input data, including the FEM and sensor measurements, temperature, humidity, and CO 2 , were diverse. Therefore, normalization was performed to convert all the training data into values between 0 and 1 and achieve accurate learning [34]. The values of the LSTM network parameters are crucial for obtaining a good calibration and prediction model.…”