Abstract—The present study analyses the statistical relationships between atmospheric pollutants and meteorological variables in the municipality of Barreiro (Portugal), based on daily mean data from the local air quality monitoring network. The concentrations of CO, NO₂, SO₂, PM₁₀, and O₃ were considered, as well as meteorological variables including air temperature, relative humidity, wind, and solar radiation. The analysis of linear associations was carried out using the Pearson correlation coefficient. The results show a consistent positive relationship between solar radiation and air temperature (ρ = 0.46 for mean temperature; ρ = 0.55 for maximum temperature), confirming the radiative control of local thermal conditions. PM₁₀ particles showed a moderate positive association with temperature (ρ up to 0.30), with a non-linear behavior observed, with higher concentrations both under elevated temperature conditions and in situations of greater atmospheric stability associated with lower temperatures. Ozone showed the most robust meteorological dependence, with a positive correlation with mean temperature (ρ = 0.53) and negative correlations with NO and NOx (ρ up to −0.58), behavior consistent with photochemical mechanisms and the titration effect in urban environments. Strong correlations were also observed between primary pollutants, namely between CO and NOx (ρ up to 0.84), reflecting common emission sources associated with road traffic. In contrast, SO₂ showed generally weaker associations with the remaining pollutants. Overall, the identified correlation structure highlights the combined influence of urban emission sources and meteorological conditions on air quality dynamics, contributing to a better understanding of interactions between pollutants in the urban-metropolitan context.
Keywords—air quality, atmospheric pollutants, statistical correlation, meteorological variables, urban environment
Cite: João Garcia, "Statistical Correlation Analysis Between Atmospheric Pollutants Based on Air Quality Monitoring Station Data in Barreiro (Portugal)," International Journal of Environmental Science and Development vol. 17, no. 4, pp. 301-311, 2026.
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