Machine Learning for Determining Interactions between Air Pollutants and Environmental Parameters in Three Cities of Iran

Abstract

Air pollution, as one of the most significant environmental challenges, has adversely affected the global economy, human health, and ecosystems. Consequently, comprehensive research is being conducted to provide solutions to air quality management. Recently, it has been demonstrated that environmental parameters, including temperature, relative humidity, wind speed, air pressure, and vegetation, interact with air pollutants, such as particulate matter (PM), NO2, SO2, O3, and CO, contributing to frameworks for forecasting air quality. The objective of the present study is to explore these interactions in three Iranian metropolises of Tehran, Tabriz, and Shiraz from 2015 to 2019 and develop a machine learning-based model to predict daily air pollution. Three distinct assessment criteria were used to assess the proposed XGBoost model, including R squared (R2), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). Preliminary results showed that although air pollutants were significantly associated with meteorological factors and vegetation, the formulated model had low accuracy in predicting (R2PM2.5 = 0.36, R2PM10 = 0.27, R2NO2 = 0.46, R2SO2 = 0.41, R2O3 = 0.52, and R2CO = 0.38). Accordingly, future studies should consider more variables, including emission data from manufactories and traffic, as well as sunlight and wind direction. It is also suggested that strategies be applied to minimize the lack of observational data by considering second-and third-order interactions between parameters, increasing the number of simultaneous air pollution and meteorological monitoring stations, as well as hybrid machine learning models based on proximal and satellite data.

Covid-19 Pandemic and Its Environmental Implications: An Investigation of Forest Fires in Northern Iran

“…Edgeley and Burnett [43] demonstrated that household responses to Covid-19 as a crisis promoted their comprehension of fire threats and preventive measures in Arizona (USA). Aside from enhancing the public's consciousness, policies and measures to minimize wildfires should be adopted to mitigate deforestation and air pollution [44,45]. Burning biomass and destroying vegetation causes various pollutants to be released into the atmosphere, which has a negative impact on air quality, the climate, and human health.…”

The Relationships between Air Pollution, Warming, and Health in Tehran Metropolis, Iran, during 2015–2019

“…With the predicted rising temperature owing to climatic change, the necessity to explore the implications of thermal stress has already been emphasized 3 , and a more profound understanding of the causes of climate change may assist in the creation of mitigation plans 39 . To further describe interactions from a statistical perspective, it is proposed that practices be appointed to reduce the problem of scarcity of observations by addressing second-and thirdorder interactions between variables 45 . Moreover, it is imperative to adopt more effective strategies for controlling air pollution as well as the mitigation of greenhouse gas (GHG) emissions in projects intended to prevent rising temperatures and following global warming 25,7 .…”

Klasifikasi Pencemaran Udara Di DKI Jakarta Menggunakan Metode Naïve Bayes

“…Data Inventarisasi Polutan Nasional Australia (NPI) selama tahun 2008-2018 dianalisa untuk memperkirakan polutan udara (Hendryx et al, 2020). Prediksi polutan udara juga dilakukan melalui eksplorasi interaksi polutan pada 3 kota di Iran dengan menggunakan tiga kriteria penilaian yang berbeda untuk menilai model XGBoost sebagai bentuk pengembangan model berbasis pembelajaran mesin (Rad et al, 2022).…”