Spatio-temporal statistical analysis of PM1 and PM2.5 concentrations and their key influencing factors at Guayaquil city, Ecuador

Abstract

Guayaquil, Ecuador, is in a tropical area on the equatorial Pacific Ocean coast of South America. Since 2008 the city has been increasing its population, vehicle fleet and manufacturing industries. Within the city there are various industrial and urban land uses sharing the same space. With regard to air quality there is a lack of government information on it. Therefore, the research’s aim was to investigate the spatio-temporal characteristics of PM1 and PM2.5 concentrations and their main influencing factors. For this, both PM fractions were sampled and a bivariate analysis (cross-correlation and Pearson's correlation), multivariate linear and logistic regression analysis was applied. Hourly and daily PM1 and PM2.5 were the dependent variables, and meteorological variables, occurrence of events and characteristics of land use were the independent variables. We found 48% exceedances of the PM2.5-24 h World Health Organization 2021 threshold’s, which questions the city’s air quality. The cross-correlation function and Pearson’s correlation analysis indicate that hourly and daily temperature, relative humidity, and wind speed have a complex nonlinear relationship with PM concentrations. Multivariate linear and logistic regression models for PM1-24 h showed that rain and the flat orography of cement plant sector decrease concentrations; while unusual PM emission events (traffic jams and vegetation-fires) increase them. The same models for PM2.5-24 h show that the dry season and the industrial sector (strong activity) increase the concentration of PM2.5-24 h, and the cement plant decrease them. Public policies and interventions should aim to regulate land uses while continuously monitoring emission sources, both regular and unusual.

A Moderation effect of atmospheric conditions on PM2.5traffic related concentration

“…Contrasted results are also found for wind speed and PM 2.5 concentration. [36][37][38], concluding that the relation wind speed -PM 2.5 is nonlinear (polynomial) over time. A lower wind speed does not favor the pollutants dispersion, while a moderate increase in wind speed allows their dispersion.…”

Comparison of Different Impact Factors and Spatial Scales in PM2.5 Variation

“…Ordinary linear regression (OLS) was used to determine the linear relationship between influencing factors and PM 2.5 . Due to the fact that simple OLS cannot accurately explore the relationship between various factors and PM 2.5 , scholars have started to use methods such as logistic regression [12] and multiple linear regression [13,14]. With the deepening of research, it has been found that the spatial distribution of various factors can also affect the concentration of PM 2.5 , so Geographically Weighted Regression (GWR) has been introduced.…”