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A chemical transport model (CTM) is an essential tool for air quality prediction and management, widely used in air pollution control and health risk assessment. However, the current models do not perform very well in reproducing the observations of some major chemical components, for example, sulfate, nitrate, ammonium and organic carbon. Studies have suggested that the uncertainties in the model chemical mechanism, source emission inventory and meteorological field can cause inaccurate simulation results. Still, the emission source profile (used to create speciated emission inventories for CTMs) of PM 2.5 has not been fully taken into account in current numerical simulation. Based on the characteristics and variation rules of chemical components in typical PM 2.5 sources, different simulation scenarios were designed and the sensitivity of simulated PM 2.5 components to the source chemical profile was explored. Our findings showed that the influence of source profile changes on simulated PM 2.5 components' concentrations cannot be ignored. Simulation results of some components were sensitive to the adopted source profile in CTMs. Moreover, there was a linkage effect: the variation in some components in the source profile would bring changes to the simulated results of other components. These influences are connected to chemical mechanisms of the model since the variation in species allocations in emission sources can affect the potential composition and phase state of aerosols, chemical reaction priority, and multicomponent chemical balance in thermodynamic equilibrium systems. We also found that the perturbation of the PM 2.5 source profile caused variation in simulated gaseous pollutants, which indirectly indicated that the perturbation of source profile would affect the simulation of secondary PM 2.5 components. Our paper highlights the necessity of paying enough attention to the representativeness and timeliness of the source profile when using CTMs for simulation. [ABSTRACT FROM AUTHOR]

Reducing ambient black carbon (BC) relies on the targeted control of anthropogenic emissions. Measuring emission changes in source‐specific BC aerosol is essential to assess the effectiveness of regulatory policies but is difficult due to the presence of meteorology and multiple co‐existing emissions. Herein, we propose a data‐driven approach, combining dispersion‐normalized factor analysis (DN‐PMF) with a machine learning weather adjustment (deweathering) technique, to decompose ambient BC into source emissions and meteorological drivers. Six refined BC sources were extracted from the factor analysis of aethalometer multi‐wavelength BC and concurrent observational datasets. In addition to the widely reported dominant sources, such as vehicular emissions (VE) and coal/biomass burning (BB), a discernible port and shipping emission source were identified with potential impacts on coastal air quality. The source‐specific BC showed abrupt changes in response to interventions (e.g., holidays) after separating weather‐related confounders. Significant reductions in deweathered coal and BB, VE, and local dust verified the effectiveness of policies, such as clean winter‐heating and support for the Clean Air Actions. As revealed by a post‐hoc model explanation technique, the evolution of the boundary layer was the predominant meteorological driver exerting the opposite impact on local sources with respect to distant regional‐wide sources, that is, the port and shipping emissions. Plain Language Summary: Throughout the course of human history, substantial efforts have been made worldwide to alleviate air pollution. Quantifying how a regulatory action (intervention) can impact air pollutant emissions and air quality is crucial, but complicated owing to multiple confounders (e.g., meteorology). Black carbon (BC) is a primary air pollutant that warms the atmosphere, worsens air quality, and impairs human health. Herein, we applied a data‐driven approach, combining factor analysis with machine learning techniques, to attribute ambient BC to its emission sources and meteorological‐related drivers. Using this coupled approach, the impacts of a series of human interventions (e.g., holiday effects, the COVID‐19 lockdown, clean‐heating policy, and the Chinese Clean Air Action) on source‐specific BC can be quantified. Our analyses highlight that this approach is also applicable to other regulated air pollutants, with great potential to facilitate air quality accountability studies. Key Points: Six refined black carbon (BC) sources were resolved from the factor analysis of aethalometer BC combined with concurrent measurementsPositive matrix factorization (PMF) coupled with the deweathering technique enables the detection of emission changes in source‐specific aerosol over timeImpacts of human interventions (holidays and the COVID lockdown, winter heating policy, Clean Air Actions) on BC aerosol were quantified [ABSTRACT FROM AUTHOR]

The chemical transport model (CTM) is an essential tool for air quality prediction and management, widely used in air pollution control and health risk assessment. However, the current models do not perform very well in simulating PM2.5 components. Studies suggested that the uncertainties of model chemical mechanism, source emission inventory and meteorological field can cause inaccurate simulation results. Still, the emission source profile of PM2.5 has not been fully taken into account in current numerical simulation. This study aims to answer (1) Whether the variation of source profile adopted in chemical transport models (CTMs) has an impact on the simulation of PM2.5 chemical components? (2) How much does it impact? (3) How does the impact work? Based on the characteristics and variation rules of chemical components in typical PM2.5 sources, different simulation scenarios were designed and the sensitivity of components simulation results to PM2.5 sources profile was explored. Our findings showed that the influence of source profile changes on simulated PM2.5 concentration was insignificant, but its impact on PM2.5 components could not be ignored. The variations of simulated components ranged from 8 % to 167 % under selected different source profiles, and simulation results of some components were sensitive to the adopted PM2.5 source profile in CTMs. These influences are connected to the chemical mechanisms of the model since the variation of species allocations in emission sources directly affected the thermodynamic equilibrium system. We also found that the perturbation of the PM2.5 source profile caused the variation of simulated gaseous pollutants, which indirectly indicated that the perturbation of the source profile affected the simulation of secondary PM2.5 components. Given the vital role of air quality simulation in environment management and health risk assessment, the representativeness and timeliness of source profile should be considered. [ABSTRACT FROM AUTHOR]

The nitrite removal pathway in Acinetobacter sp. TX5 was explored through the key gene identification and the corresponding enzyme purification, after which the capability to reduce nitrite by immobilized beads was investigated in a fixed-bed reactor. Results revealed that a nosZ gene encoding nitrous oxide reductase (N2OR) exists in TX5 cells, and a N2OR responsible for the reduction of N2O to N2 was purified successfully with a molecular weight of 70.05 kDa, a purification fold of 16.30 and a recovery rate of 5.17%. For TX5 immobilization, the optimal values of polyvinyl alcohol (PVA), spent mushroom substrate (SMS) and Aci (TX5) obtained by response surface methodology (RSM) were 6.32%, 2.92% and 4.57%, respectively. In a fixed-bed reactor packed with immobilized TX5, the removal efficiency (RE) achieved 90% (at 50 h) for NO 2 − -N and 85% (at 96 h) for total nitrogen (TN). On the basis of these results, a nitrite removal pathway in TX5 was proposed. Overall, Acinetobacter sp. TX5 might be a promising candidate for nitrite removal with an ability to suppress N2O accumulation. [ABSTRACT FROM AUTHOR]