Quantifying Contributions of Factors and Their Interactions to Aerosol Acidity with a Multiple-Linear-Regression-Based Framework: A Case Study in the Pearl River Delta, China

This study presents an approach using multiple linear regression to quantify the impact of meteorological parameters and chemical species on aerosol pH variance in an urban setting in the Pearl River Delta, China. Additionally, it assesses the contributions of interactions among these factors to the variance in pH. The analysis successfully explains over 96% of the pH variance, attributing 85.8% to the original variables and 6.7% to bivariate interactions, with further contributions of 2.3% and 1.0% from trivariate and quadrivariate interactions, respectively. Our results highlight that meteorological factors, particularly temperature and humidity, are more influential than chemical components in affecting aerosol pH variance. Temperature alone accounts for 37.3% of the variance, while humidity contributes approximately 20%. On the chemical front, sulfate and ammonium are the most significant contributors, adding 14.3% and 9.1% to the pH variance, respectively. In the realm of bivariate interactions, the interplay between meteorological parameters and chemical components, especially the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>TNO</mi></mrow><mrow><mn>3</mn></mrow></msub><mo>–</mo><mi>RH</mi></mrow></semantics></math></inline-formula> pair, is exceptionally impactful, constituting 58.1% of the total contribution from interactions. In summary, this study illuminates the factors affecting aerosol pH variance and their interplay, suggesting the integration of statistical methods with thermodynamic models for enhanced understanding of aerosol acidity dynamics in the future.