Bayesian Inference Approach to Quantify Primary and Secondary Organic Carbon in Fine Particulate Matter Using Major Species Measurements.

The determination of primary organic carbon (POC) and secondary organic carbon (SOC) in fine particulate matter using ambient measurements is essential in atmospheric chemistry. A novel Bayesian inference (BI) approach is proposed to achieve such quantification using only major component measurement data and tested in two case studies. One case study composes of filter-based daily compositional data made in the Pearl River Delta region, China, during 2012, while the other uses online measurement data recorded at the Dianshan Lake monitoring site in Shanghai in wintertime 2019. Source-specific organic trace measurement data are available in both the cases so that positive matrix factorization (PMF) analysis is performed, where PMF-resolved POC and SOC are used as the best available reference values for model evaluation. Meanwhile, traditional techniques, i.e., minimum ratio value, minimum R squared, and multiple linear regression, are also employed and evaluated. For both the cases, the BI models have shown significant advantages in accurately estimating POC and SOC amounts over conventional methods. Further analysis suggests that using sulfate as the SOC tracer in BI model gives the best model performance. This methodological advance provides an improved and practical tool to derive POC and SOC levels for addressing PM-related environmental impacts.

Simultaneous Determination of Aerosol Inorganic and Organic Nitrogen by Thermal Evolution and Chemiluminescence Detection.

Inorganic nitrogen (IN) and organic nitrogen (ON) molecules constitute a significant part of atmospheric aerosol. Unlike IN, the total ON quantity remains largely unquantified due to a lack of a simple and direct measurement method. This analytical deficiency hinders the quantitative assessment of the various environmental and health effect impacts by aerosol ON. In this work, we developed an analyzer system that utilizes programmed thermal evolution of carbonaceous and nitrogenous aerosols and chemiluminescence detection coupled with the multivariate curve resolution data treatment to achieve simultaneous quantification of IN and ON. The system is capable of detecting IN and ON as low as 96 ng N per sample on a small filter aliquot (1 cm2) without any pretreatment. This method breakthrough opens the door to quantifying an important pool of aerosol N that was analytically inaccessible in the past and holds the promise to quantifying IN and ON in other environmental samples. As a demonstration, quantification of aerosol ON at an urban site in Hong Kong, China, in samples spanning over a year reveals ON constituting a significant fraction (9-52%) of the total aerosol nitrogen and having major source origins in both secondary formation and primary emissions.

A multiple linear regression model with multiplicative log-normal error term for atmospheric concentration data.

The homoscedasticity assumption (the variance of the error term is the same across all the observations) is a key assumption in the ordinary linear squares (OLS) solution of a linear regression model. The validity of this assumption is examined for a multiple linear regression model used to determine the source contributions to the observed black carbon concentrations at 12 background monitoring sites across China using a hybrid modeling approach. Residual analysis from the traditional OLS method, which assumes that the error term is additive and normally distributed with a mean of zero, shows pronounced heteroscedasticity based on the Breusch-Pagan test for 11 datasets. Noticing that the atmospheric black carbon data are log-normally distributed, we make a new assumption that the error terms are multiplicative and log-normally distributed. When the coefficients of the multilinear regression model are determined using the maximum likelihood estimation (MLE), the distribution of the residuals in 8 out of the 12 datasets is in good accordance with the revised assumption. Furthermore, the MLE computation under this novel assumption could be proved mathematically identical to minimizing a log-scale objective function, which considerably reduces the complexity in the MLE calculation. The new method is further demonstrated to have clear advantages in numerical simulation experiments of a 5-variable multiple linear regression model using synthesized data with prescribed coefficients and lognormally distributed multiplicative errors. Under all 9 simulation scenarios, the new method yields the most accurate estimations of the regression coefficients and has significantly higher coverage probability (on average, 95% for all five coefficients) than OLS (79%) and weighted least squares (WLS, 72%) methods.

Abundance and sources of benzo[a]pyrene and other PAHs in ambient air in Hong Kong: A review of 20-year measurements (1997-2016).

The polycyclic aromatic hydrocarbon (PAH) family is of environmental concern due to its toxicity, prompting the need of monitoring their long-term trends. Three monitoring programs in Hong Kong report concentrations of ambient PAHs, namely (1) respirable suspending particle (RSP) speciation program that monitored benzo[a]pyrene (BaP) (1997 to March 2000), (2) total suspended particle speciation program that monitored BaP (1997-1999), and (3) toxic air pollutant monitoring program that monitors BaP and 16 other PAHs in the combined gas and particulate phases at two general urban stations once or twice a month since January 1998. In this work, we review all the available PAH measurements in Hong Kong during 1997-2016, with emphasis on the temporal trends of BaP and the other 16 PAHs. PAHs of 5-6 rings exhibit an ambiguous decline trend since 1998, with a negative Sen's slope that is statistically significant. Specifically, BaP was reduced by 78% from 1998 to 2016, with a Sen's slope of -0.013 ng m-3 year-1. Correlations of BaP with RSP major species of high source specificity and PAH diagnostic ratios are employed to explore the source origins of PAHs. Our analysis reveals that PAHs mainly come from a combination of vehicular emissions and biomass/coal combustion. The decline trend of PAHs is further found in consistence with the declined particulate matter emissions from vehicular exhaust and biomass/coal combustion. This study fills the data vacancy in the long-term trends of ambient PAHs for the Pearl River Delta region, one of the economically more advanced regions in China.