Elemental carbon (EC) has been widely used as a tracer to track the portion of co-emitted primary organic carbon (OC) and, by extension, to estimate secondary OC (SOC) from ambient observations of EC and OC. Key to this EC tracer method is to determine an appropriate OC / EC ratio that represents primary combustion emission sources (i.e., (OC / EC)_pri) at the observation site. The conventional approaches include regressing OC against EC within a fixed percentile of the lowest (OC / EC) ratio data (usually 5–20 %) or relying on a subset of sampling days with low photochemical activity and dominated by local emissions. The drawback of these approaches is rooted in its empirical nature, i.e., a lack of clear quantitative criteria in the selection of data subsets for the (OC / EC)_pri determination. We examine here a method that derives (OC / EC)_pri through calculating a hypothetical set of (OC / EC)_pri and SOC followed by seeking the minimum of the coefficient of correlation (R²) between SOC and EC. The hypothetical (OC / EC)_pri that generates the minimum R²(SOC,EC) then represents the actual (OC / EC)_pri ratio if variations of EC and SOC are independent and (OC / EC)_pri is relatively constant in the study period. This Minimum R Squared (MRS) method has a clear quantitative criterion for the (OC / EC)_pri calculation. This work uses numerically simulated data to evaluate the accuracy of SOC estimation by the MRS method and to compare with two commonly used methods: minimum OC / EC (OC / EC_min) and OC / EC percentile (OC / EC_10 %). Log-normally distributed EC and OC concentrations with known proportion of SOC are numerically produced through a pseudorandom number generator. Three scenarios are considered, including a single primary source, two independent primary sources, and two correlated primary sources. The MRS method consistently yields the most accurate SOC estimation. Unbiased SOC estimation by OC / EC_min and OC / EC_10 % only occurs when the left tail of OC / EC distribution is aligned with the peak of the (OC / EC)_pri distribution, which is fortuitous rather than norm. In contrast, MRS provides an unbiased SOC estimation when measurement uncertainty is small. MRS results are sensitive to the magnitude of measurement uncertainty but the bias would not exceed 23 % if the uncertainty is within 20 %.

Secondary organic aerosol (SOA) produced by atmospheric oxidation of primary emitted precursors is a major contributor to fine particulate matter (PM_2.5) air pollution worldwide. Observations during winter haze pollution episodes in urban China show that most of this SOA originates from fossil-fuel combustion but the chemical mechanisms involved are unclear. Here we report field observations in a Beijing winter haze event that reveal fast aqueous-phase conversion of fossil-fuel primary organic aerosol (POA) to SOA at high relative humidity. Analyses of aerosol mass spectra and elemental ratios indicate that ring-breaking oxidation of POA aromatic species, leading to functionalization as carbonyls and carboxylic acids, may serve as the dominant mechanism for this SOA formation. A POA origin for SOA could explain why SOA has been decreasing over the 2013–2018 period in response to POA emission controls even as emissions of volatile organic compounds (VOCs) have remained flat. Significance Secondary organic aerosol (SOA) from fossil-fuel combustion is a major component of Beijing winter haze pollution but its formation mechanisms are unclear. It is generally assumed to originate from atmospheric oxidation of volatile organic compounds (VOCs). However, we show here that fast aqueous-phase oxidation of directly emitted primary organic aerosol (POA) takes place at high relative humidity in Beijing winter haze and can explain the observed SOA. This could explain why SOA has decreased in response to POA emission controls even as VOC emissions have stayed flat. The mechanism may involve ring-breaking oxidation and functionalization of polycyclic aromatic hydrocarbons.

Light-absorbing organic carbon (or brown carbon, BrC) in atmospheric particles has received much attention for its potential role in global radiative forcing. While a number of field measurement campaigns have differentiated light absorption by black carbon (BC) and BrC, the chemical characteristics of BrC are not well understood. In this study, we present co-located real-time light absorption and chemical composition measurements of atmospheric particles to explore the relationship between the chemical and optical characteristics of BrC at a suburban site downwind of Guangzhou, China, from November to December 2014. BrC and BC contributions to light absorption were estimated using measurements from a seven-wavelength aethalometer, while the chemical composition of non-refractory PM₁ was measured with a high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS). Using the absorption Ångström exponent (AAE) method, we estimated that BrC contributed 23.6 % to the total aerosol absorption at 370 nm, 18.1 % at 470 nm, 10.7 % at 520 nm, 10.7 % at 590 nm, and 10.5 % at 660 nm. Biomass burning organic aerosol (BBOA) has the highest mass absorption coefficient among sources of organic aerosols. Its contribution to total brown carbon absorption coefficient decreased but that of low-volatility oxygenated organic aerosol (LVOOA) increased with increasing wavelength, suggesting the need for wavelength-dependent light absorption analysis for BrC in association with its chemical makeup. Clear correlations of N-containing ion fragments with absorption coefficient were observed. These correlations also depended on their degrees of unsaturation/cyclization and oxygenation. While the current study relates light absorption by BrC to ion fragments, more detailed chemical characterization is warranted to constrain this relationship.

Black carbon (BC) particles in the atmosphere can absorb more light when coated by non-absorbing or weakly absorbing materials during atmospheric aging, due to the lensing effect. In this study, the light absorption enhancement factor, E_abs, was quantified using a 1-year measurement of mass absorption efficiency (MAE) in the Pearl River Delta region (PRD). A new approach for calculating primary MAE (MAE_p), the key for E_abs estimation, is demonstrated using the minimum R squared (MRS) method, exploring the inherent source independency between BC and its coating materials. A unique feature of E_abs estimation with the MRS approach is its insensitivity to systematic biases in elemental carbon (EC) and σ_abs measurements. The annual average E_abs550 is found to be 1.50 ± 0.48 (±1 SD) in the PRD region, exhibiting a clear seasonal pattern with higher values in summer and lower in winter. Elevated E_abs in the summertime is likely associated with aged air masses, predominantly of marine origin, along with long-range transport of biomass-burning-influenced air masses from Southeast Asia. Core–shell Mie simulations along with measured E_abs and absorption Ångström exponent (AAE) constraints suggest that in the PRD, the coating materials are unlikely to be dominated by brown carbon and the coating thickness is higher in the rainy season than in the dry season.

Linear regression techniques are widely used in atmospheric science, but they are often improperly applied due to lack of consideration or inappropriate handling of measurement uncertainty. In this work, numerical experiments are performed to evaluate the performance of five linear regression techniques, significantly extending previous works by Chu and Saylor. The five techniques are ordinary least squares (OLS), Deming regression (DR), orthogonal distance regression (ODR), weighted ODR (WODR), and York regression (YR). We first introduce a new data generation scheme that employs the Mersenne twister (MT) pseudorandom number generator. The numerical simulations are also improved by (a) refining the parameterization of nonlinear measurement uncertainties, (b) inclusion of a linear measurement uncertainty, and (c) inclusion of WODR for comparison. Results show that DR, WODR and YR produce an accurate slope, but the intercept by WODR and YR is overestimated and the degree of bias is more pronounced with a low R ² X Y dataset. The importance of a properly weighting parameter λ in DR is investigated by sensitivity tests, and it is found that an improper λ in DR can lead to a bias in both the slope and intercept estimation. Because the λ calculation depends on the actual form of the measurement error, it is essential to determine the exact form of measurement error in the X Y data during the measurement stage. If a priori error in one of the variables is unknown, or the measurement error described cannot be trusted, DR, WODR and YR can provide the least biases in slope and intercept among all tested regression techniques. For these reasons, DR, WODR and YR are recommended for atmospheric studies when both X and Y data have measurement errors. An Igor Pro-based program (Scatter Plot) was developed to facilitate the implementation of error-in-variables regressions.