The Association Between Nitrous Oxide (NO2) Levels and Seasonal Variations of Weather Conditions Using Artificial Intelligence

It is well known that degraded air quality affects human health and the surrounding environment. Air quality is related to emissions from various sources, meteorological (weather) conditions, topography, and vegetation. Weather conditions such as temperature, humidity, dew point, wind speed and precipitation vary significantly over the year and have the potential to affect the formation, transport, and dispersion of air pollutants. Therefore, it is important to understand the correlation between the seasonal variations of weather conditions on the air quality and be able to forecast it with reasonable accuracy. The aim of this study is to apply Artificial intelligence (AI) technique to investigate such correlation. For this purpose, four (4) AI algorithms namely: neural networks (NN), Decision tree (DT), Random forest (RF) and Gradient boosting (GB) have been applied to assess the correlation between Nitrous Oxide (NO₂) and seasonal variations of weather conditions (i.e. temperature, humidity, wind speed, wind direction, and pressure). NO₂ was selected as a target air pollutant which considered a serious air quality parameter and one of the greenhouse gases. The effect of seasonal variations of weather conditions on the air quality parameters was presented by the date of measurement as a parameter or feature. Air quality data were collected for the period between 2017 and 2021 from a local air monitoring station, while weather conditions were obtained from the weather station at the airport located the Eastern region of Saudi Arabia. The accuracy of the correlation between was tested using mean square error (MSE), mean root square error (MRSE), mean absolute error (MAE) and correlation coefficient (R²). Results of the study revealed a strong association between NO₂ levels and seasonal variations of weather conditions. The MEA ranges between 1.765 to 1.439 using NN, DT, RF and GB respectively.  The correlation coefficient (R²) ranges between 0.564 to 0.826 using NN, DT, RF and GB respectively. The results showed that GB algorithm generated better correlation for NO₂ compared to other algorithms. The study results can be used for better predicting air quality for NO₂ that can be used for the assessment of potential global warming and climate change phenomena