No abstract

Many places on earth still suffer from a high level of atmospheric fine particulate matter (PM2.5) pollution. Formation of a particulate pollution event or haze episode (HE) involves many factors, including meteorology, emissions, and chemistry. Understanding the direct causes of and key drivers behind the HE is thus essential. Traditionally, this is done via chemical transport models. However, substantial uncertainties are introduced into the model estimation when there are significant changes in the emissions inventory due to interventions (e.g., the COVID-19 lockdown). Here we applied a Random Forest model coupled with a Shapley additive explanation algorithm, a post hoc explanation technique, to investigate the roles of major meteorological factors, primary emissions, and chemistry in five severe HEs that occurred before or during the COVID-19 lockdown in China. We discovered that, in addition to the high level of primary emissions, PM2.5 in these haze episodes was largely driven by meteorological effects (with average contributions of 30–65 μg m–3 for the five HEs), followed by chemistry (∼15–30 μg m–3). Photochemistry was likely the major pathway of formation of nitrate, while air humidity was the predominant factor in forming sulfate. Our results highlight that the machine learning driven by data has the potential to be a complementary tool in predicting and interpreting air pollution.

Meteorological and aerosol data were measured at the atmospheric boundary layer observation station in Tianjin, China, and were analyzed to study the effects of aerosol mass, composition, and size distributions on visibility and short-wave radiation flux. The results show that fine particles played important roles in controlling visibility in Tianjin. The major contributors to light extinction coefficients included sulfate (28.7%), particulate organic matter (27.6%), elemental carbon (19.2%), and nitrate (6.1%). In addition to the measurement of aerosol composition, the size distribution of aerosol number concentrations were also measured and classified between haze days and non-haze days during spring. The extinction characteristics of ambient aerosol in haze days and non-haze days were calculated using Mie theory model. The average extinction coefficient and scattering coefficient of atmospheric aerosols were 0.253 1/km and 0.213 1/km in non-haze days, while 0.767 1/km and 0.665 1/km in haze days. A radiation transmission model LOWTRAN7 is also applied in this study. The model calculated radiant flux densities in haze days and non-haze days, which showed a fairly agreement with the observation results, showing that the heavy aerosol loadings in Tianjin had significantly impact on atmospheric visibility and radiation fluxes.

Wuhan was the first pandemic epicenter and imposed an unprecedented lockdown beginning January 23, 2020. China was subsequently placed into a lockdown. These restrictions interrupted a wide array of economic activities thereby reducing primary air pollutant emissions. The unexpected pollutant emission reductions created a unique opportunity to assess the responses of air quality to rapid temporary reductions in anthropogenic emissions, and inform future air quality abatement strategies.Many early studies showed substantial declines in observed concentrations of nitrogen dioxide (NO 2 ) during lockdown periods globally (

In this study, a nonnegative constrained principal component regression chemical mass balance (NCPCRCMB) model was used to solve the near collinearity problem among source profiles for source apportionment. The NCPCRCMB model added the principle component regression route into the CMB model iteration. The model was tested with the synthetic data sets, which involved contributions from eleven actual sources, with a serious near collinearity problem among them. The actual source profiles were randomly perturbed and then applied to create the synthetic receptor. The resulting synthetic receptor concentrations were also randomly perturbed to simulate measurement errors. The synthetic receptors were separately apportioned by CMB and NCPCRCMB model. The result showed that source contributions estimated by the NCPCRCMB model were much closer to the true values than those estimated by the CMB model. Next, five real ambient data sets from five cities in China were analyzed using the NCPCRCMB model to test the model practicability. Reasonable results were obtained in all cases. It is shown that the NCPCRCMB model has an advantage over the traditional CMB model when dealing with near collinearity problems in source apportionment studies.