Search

This study focuses on filter-based aerosol light absorption measurement biases and their correction algorithms in the coastal urban-industrial area of Houston-Galveston region. Known as a major petrochemical hub in the United States, this area is dominated by industrial flaring emissions. The aerosols in this region are mainly composed of organic carbon and sulfates. We observed that conventional filter-based instruments, despite their cost-effectiveness and simplicity of use, overestimate aerosol light absorption by approximately four times in comparison to reference particle phase instruments, such as photoacoustic spectrometers. To mitigate these unquantifiable measurement biases, we applied and compared different analytical correction algorithms including the widely used Bond-Ogren (2010) and Virkkula (2010), as well as a customized Random Forest Regression (RFR) machine learning algorithm. Our analysis revealed that RFR significantly improved correction efficacy, reducing the wavelength-averaged Root Mean Square Error (RMSE) by approximately 50% compared to traditional analytical methods. We performed SHapley Additive exPlanations (SHAP) analysis to identify the key parameters that influence the accuracy of our RFR correction algorithm. We find that at longer visible wavelengths, dark-brown carbon from flaring emissions in the sampling region exacerbates biases in filter-based measurements. This study underscores the importance of employing advanced correction algorithms for correcting filter-based aerosol light absorption measurements, especially in complex urban settings influenced by industrial emissions. Copyright © 2024 American Association for Aerosol Research [ABSTRACT FROM AUTHOR]

Light‐absorbing carbonaceous aerosols that dominate atmospheric aerosol warming over India remain poorly characterized. Here, we delve into UV‐visible‐IR spectral aerosol absorption properties at nine PAN‐India COALESCE network sites (Venkataraman et al., 2020, https://doi.org/10.1175/bams‐d‐19‐0030.1). Absorption properties were estimated from aerosol‐laden polytetrafluoroethylene filters using a well‐constrained technique incorporating filter‐to‐particle correction factors. The measurements revealed spatiotemporal heterogeneity in spectral intrinsic and extrinsic absorption properties. Absorption analysis at near‐UV wavelengths from carbonaceous aerosols at these regional sites revealed large near‐ultraviolet brown carbon absorption contributions from 21% to 68%—emphasizing the need to include these particles in climate models. Further, satellite‐retrieved column‐integrated absorption was dominated by surface absorption, which opens possibilities of using satellite measurements to model surface‐layer optical properties (limited to specific sites) at a higher spatial resolution. Both the satellite‐modeled and direct in‐situ absorption measurements can aid in validating and constraining climate modeling efforts that suffer from absorption underestimations and high uncertainties in radiative forcing estimates. Plain Language Summary: Particulate pollution in the atmosphere scatter and absorb incoming solar energy, thus cooling or warming Earth's atmosphere. In developing countries and especially in India, one of the most polluted regions of the world, the extent to which particles can absorb solar energy and warm the atmosphere is not well understood. Here, for the first time, we measure particle absorption simultaneously at nine ground sites across India, in diverse geographical regions with different levels and types of particulate pollution. We find that organic carbon particles exert large absorption at near‐ultraviolet wavelengths, which contain significant solar energy. These light absorbing organic carbon particles, called brown carbon, are emitted in large quantities from biomass burning (e.g., burning crop residue and cooking on wood‐fired stoves). Comparing ground measurements of absorption with satellite‐retrieved measurements that are representative of the entire atmospheric column, we find that near‐surface atmospheric particles can exert significant warming. This study highlights the need to improve climate model simulations of particulate pollution's impact on the climate by incorporating spatiotemporal surface‐level absorption measurements, including absorption by brown carbon particles. Key Points: Measurements at nine regional PAN‐India sites reveal several regions with large aerosol absorption strengthBrown carbon contributes significantly (21%–68%) to near‐ultraviolet absorption, indicating its importance in shortwave light absorptionStrong correlations observed between satellite data and surface absorption indicate future potential in modeling surface absorption [ABSTRACT FROM AUTHOR]