PM_2.5 pollution and visibility impairment has attracted wild public attention in urban China during the past decade. Field observation was carried out in Tianjin, China, during heating (HP) and non-heating periods (NHP). The IMPROVE (Interagency Monitoring of Protected Visual Environments) method was localized to better understand the quantitative impact of PM_2.5 composition on extinction coefficient (B_ext). B_ext of organic mass (OM) was classified into that of primary organic aerosol (POA) and secondary organic aerosol (SOA). B_ext of Rayleigh scattering was calculated based on observation data directly, instead of a fixed value in the original IMPROVE method. The mass extinction efficiency (MEE) of EC was also updated based on literature research. The estimation bias of reconstructed B_ext was reduced from 4.8 Mm⁻¹ to 0.8 Mm⁻¹ with the localized algorithm. It was found that the secondary components contribute more than 40% of B_ext, and ammonium sulfate (AS) and ammonium nitrate (AN) are the dominant components. The different formation pathway leads to significant difference in secondary inorganic components during HP and NHP. Based on the positive matrix factorization (PMF) model and localized IMPROVE method, secondary aerosols were identified as the main source contributing to both PM_2.5 concentration (48.2%) and B_ext (44.3%) during HP, followed by vehicular emission and coal combustion, biomass burning with municipal incinerators, fugitive dust, and steel processing.

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 ${\mathrm{TNO}}_3–\mathrm{RH}$ 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.