To control the spread of the 2019 novel coronavirus (COVID-19), China imposed nationwide restrictions on the movement of its population (lockdown) after the Chinese New Year of 2020, leading to large reductions in economic activities and associated emissions. Despite such large decreases in primary pollution, there were nonetheless several periods of heavy haze pollution in East China, raising questions about the well-established relationship between human activities and air quality. Here, using comprehensive measurements and modeling, we show the haze during the COVID lockdown were driven by enhancements of secondary pollution. In particular, large decreases in NOx emissions from transportation increased ozone and nighttime NO3 radical formation, and these increases in atmospheric oxidizing capacity in turn facilitated the formation of secondary particulate matter. Our results, afforded by the tragic natural experiment of the COVID-19 pandemic, indicate that haze mitigation depends upon a coordinated and balanced strategy for controlling multiple pollutants.

Abstract. Haze pollution caused by PM2.5 is the largest air quality concern in China in recent years. Long-term measurements of PM2.5 and the precursors and chemical speciation are crucially important for evaluating the efficiency of emission control, understanding formation and transport of PM2.5 associated with the change of meteorology, and accessing the impact of human activities on regional climate change. Here we reported long-term continuous measurements of PM2.5, chemical components, and their precursors at a regional background station, the Station for Observing Regional Processes of the Earth System (SORPES), in Nanjing, eastern China, since 2011. We found that PM2.5 at the station has experienced a substantial decrease (−9.1 % yr−1), accompanied by even a very significant reduction of SO2 (−16.7 % yr−1), since the national “Ten Measures of Air” took action in 2013. Control of open biomass burning and fossil-fuel combustion are the two dominant factors that influence the PM2.5 reduction in early summer and winter, respectively. In the cold season (November–January), the nitrate fraction was significantly increased, especially when air masses were transported from the north. More NH3 available from a substantial reduction of SO2 and increased oxidization capacity are the main factors for the enhanced nitrate formation. The changes of year-to-year meteorology have contributed to 24 % of the PM2.5 decrease since 2013. This study highlights several important implications on air pollution control policy in China.

Four-dimensional variational data assimilation (4DVar) is one of the most promising methods to provide optimal analysis for numerical weather prediction (NWP). Five national NWP centers in the world have successfully applied 4DVar methods in their global NWPs, thanks to the increment method and adjoint technique. However, the application of 4DVar is still limited by the computer resources available at many NWP centers and research institutes. It is essential, therefore, to further reduce the computational cost of 4DVar. Here, an economical approach to implement 4DVar is proposed, using the technique of dimensionreduced projection (DRP), which is called "DRP-4DVar." The proposed approach is based on dimension reduction using an ensemble of historical samples to define a subspace. It directly obtains an optimal solution in the reduced space by fitting observations with historical time series generated by the model to form consistent forecast states, and therefore does not require implementation of the adjoint of tangent linear approximation.To evaluate the performance of the DRP-4DVar on assimilating different types of mesoscale observations, some observing system simulation experiments are conducted using MM5 and a comparison is made between adjoint-based 4DVar and DRP-4DVar using a 6-hour assimilation window.

To subvert recent advances in perimeter and host security, the attacker community has developed and employed various attack vectors to make a malware much stealthier than before to penetrate the target system and prolong its presence. Such advanced malware or "stealthy malware" makes use of various techniques to impersonate or abuse benign applications and legitimate system tools to minimize its footprints in the target system. It is thus difficult for traditional detection tools, such as malware scanners, to detect it, as the malware normally does not expose its malicious payload in a file and hides its malicious behaviors among the benign behaviors of the processes. In this paper, we present PROVDETECTOR, a provenancebased approach for detecting stealthy malware. Our insight behind the PROVDETECTOR approach is that although a stealthy malware attempts to blend into benign processes, its malicious behaviors inevitably interact with the underlying operating system (OS), which will be exposed to and captured by provenance monitoring. Based on this intuition, PROVDETECTOR first employs a novel selection algorithm to identify possibly malicious parts in the OS-level provenance data of a process. It then applies a neural embedding and machine learning pipeline to automatically detect any behavior that deviates significantly from normal behaviors. We evaluate our approach on a large provenance dataset from an enterprise network and demonstrate that it achieves very high detection performance of stealthy malware (an average F1 score of 0.974). Further, we conduct thorough interpretability studies to understand the internals of the learned machine learning models.