Investigating the effects of criteria air pollutants and meteorological parameters on the change of black carbon concentration in Tehran and Tabriz

<p>Black carbon (BC) is a primary component of fine particulate matter which has a significant effect on climate and human health, and anthropogenic activity along with weather conditions affects its long-term variability. This study aimed to investigate the statistical relationships between meteorological elements (temperature, rainfall, wind speed, relative humidity, air pressure, sunshine hours, solar radiation, and cloudiness), criteria air pollutants (CO, NO<sub>2</sub>, SO<sub>2</sub>, O<sub>3</sub>, PM<sub>10</sub>, and PM<sub>2.5</sub>) and black carbon particles (BC), as well as assess and compare the efficacy of five different machine learning algorithms (multiple linear regression (MLR), generalized additive model (GAM), classification and regression trees (CART), random forest (RF) and gradient boosting machine (GBM)) in modeling pollutants and climatic factors responsible for variations in black carbon concentration levels in Tabriz and Tehran from 2004 to 2021 using R 4.3.2 software. The results of the present study showed a significant variation in the influence of meteorological parameters and criteria air pollutants on the level of black carbon pollutant concentration in Tabriz and Tehran depending on the different geographical locations, weather conditions, and regional structure. Black carbon particles have experienced a significant upward trend with a relatively equal speed during the statistical period studied in the cities of Tabriz and Tehran. Based on the results of Spearman's correlation analysis, black carbon particles have a positive correlation with PM<sub>2.5</sub>, NO<sub>2</sub>, CO, and SO<sub>2</sub> and a negative correlation with O<sub>3</sub>. Black carbon was highly correlated with parameters of wind speed (negatively) and relative humidity (positively) in Tabriz and temperature (negatively) and air pressure (positively) in Tehran. Based on the performance evaluation of predictive models and concerning the parsimony principle, the GAM model in Tabriz and the MLR model in Tehran had better performance in predicting black carbon values than other models.</p>