Prediction of the SO2 Hourly Concentration for Sea Breeze and Land Breeze in an Urban Area of Split Using Multiple Linear Regression

Abstract

The main goal of this paper is to study pollution during sea breeze days in the Split town center, which is placed near the industrial area with three cement plants and one asbestos cement plant, as well as a harbor with high traffic, and investigate the sources of pollution with SO2 and its relation to atmospheric parameters using stepwise multiple linear regression (MLR). The hourly temperature difference from the time of the sea breeze lull (dT) was considered in evaluating the influence of meteorological parameters on hourly pollutant concentrations. It was found that the wind direction index (WDI) is a significant predictor for the sea breeze, and wind speed, relative humidity, and dT are significant for the land breeze. A very high index of agreement of 0.9 was obtained by the MLR model for the land breeze, and 0.8 for the sea breeze. Low SO2 concentrations are observed at night, and increased values are found between 0800 and 1800 UTC. With WDI being the only predictor during sea breeze, local traffic is found to be the main anthropogenic source of SO2 pollution.

An ensemble deep learning approach for air quality estimation in Delhi, India

“…To improve the estimation accuracy of the 1D-CNN model, supplementary features are created based on the meteorological features. Specifically, WD and WS features are utilized to derive the x and y components of wind (Wx and Wy) and wind direction index (wdi) (Mohan and Abraham 2022;Troši´c et al 2023), as given in equations ( 3), (4), and (5).…”