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Abstract. In January 2013, February 2014, December 2015 and December 2016 to 10 January 2017, 12 persistent heavy aerosol pollution episodes (HPEs) occurred in Beijing, which received special attention from the public. During the HPEs, the precise cause of PM2.5 explosive growth (mass concentration at least doubled in several hours to 10 h) is uncertain. Here, we analyzed and estimated relative contributions of boundary-layer meteorological factors to such growth, using ground and vertical meteorological data. Beijing HPEs are generally characterized by the transport stage (TS), whose aerosol pollution formation is primarily caused by pollutants transported from the south of Beijing, and the cumulative stage (CS), in which the cumulative explosive growth of PM2.5 mass is dominated by stable atmospheric stratification characteristics of southerly slight or calm winds, near-ground anomalous inversion, and moisture accumulation. During the CSs, observed southerly weak winds facilitate local pollutant accumulation by minimizing horizontal pollutant diffusion. Established by TSs, elevated PM2.5 levels scatter more solar radiation back to space to reduce near-ground temperature, which very likely causes anomalous inversion. This surface cooling by PM2.5 decreases near-ground saturation vapor pressure and increases relative humidity significantly; the inversion subsequently reduces vertical turbulent diffusion and boundary-layer height to trap pollutants and accumulate water vapor. Appreciable near-ground moisture accumulation (relative humidity> 80 %) would further enhance aerosol hygroscopic growth and accelerate liquid-phase and heterogeneous reactions, in which incompletely quantified chemical mechanisms need more investigation. The positive meteorological feedback noted on PM2.5 mass explains over 70 % of cumulative explosive growth.

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Pollutants during haze and Asian dust storms are transported out of the Asian continent, affecting the regional climate and the hydrological and biogeochemical cycles. Understanding the physicochemical properties of aerosol particles is essential to quantify their impacts. In order to determine physicochemical properties of aerosols before leaving the Asian continent, we carried out a field campaign from 14 April to 2 May 2011 at a background site in the path of Asian dust and haze outflows. We measured concentrations of gaseous pollutants (SO 2 , NO 2 , NO, O 3 , and CO), black carbon (BC), and particle number in situ and collected airborne particles for microscopic analysis. Pollutant concentrations (BC, 4 μg m À3; CO, 808 ppb; SO 2 , 24 ppb; NO 2 , 37 ppb) were highest during haze periods, except for PM 2.5 mass, which was highest (162 μg m À3) during a dust storm. Seventy-one percent of haze particles were coated with organic films and 32% were internal mixtures of sulfates and refractory particles (e.g., soot, metal/fly ash, and mineral). Seventy-nine percent of haze particles have deliquescence relative humidity at 68-70%. During a dust storm, soot particles were observed among dust particles. Most dust particles were hydrophobic, and no Ca(NO 3 ) 2 was observed in dust particles collected during the dust storms, but up to 32% of dust particles were found to be coated with Ca(NO 3 ) 2 after the main dust storm moved out of the sampling area. These results indicated that both natural and anthropogenic aerosol particles in Asian outflow can undergo significant physicochemical processes before leaving the Asian continent.

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The Beijing government implemented a number of clean air action plans to improve air quality in the last 10 years, which contributed to changes in the concentration of fine particles and their compositions. However, quantifying the impacts of these interventions is challenging as meteorology masks the real changes in observed concentrations. Here, we applied a machine learning technique to decouple the effect of meteorology and evaluate the changes in the chemistry of nonrefractory PM 1 (particulate matter less than 1 μm) in winter 2007, 2016, and 2017 as a result of the clean air actions. The observed mass concentrations of PM 1 were 74.6, 90.2, and 36.1 μg m −3 in the three winters, while the deweathered concentrations were 74.2, 78.7, and 46.3 μg m −3 , respectively. The deweathered concentrations of PM 1 , organics, sulfate, ammonium, chloride, SO 2 , NO 2 , and CO decreased by −38, −46, −59, −24, −51, −89, −16, and −52% in 2017 in comparison to 2007. On the contrary, the deweathered concentration of nitrates increased by 4%. Our results indicate that the clean air actions implemented in 2017 were highly effective in reducing ambient concentrations of SO 2 , CO, and PM 1 organics, sulfate, ammonium, and chloride, but the control of nitrate and PM 1 organics remains a major challenge.

Identifying gene mutations in individual tumors is critical to improve the efficacy of cancer therapy by matching targeted drugs to specific mutations. Gastrointestinal stromal tumors (GIST) are stromal or mesenchymal subepithelial neoplasms affecting the gastrointestinal tract and frequently contain activating gene mutations in either KIT or platelet-derived growth factor A (PDGFRA). Although GIST is highly responsive to several selective tyrosine kinase inhibitors, combined use of inhibitors targeting other mutations is needed to further prolong survival in patients with GIST. In this study, we aim to screen and identify genetic mutations in GIST for targeted therapy using the new Ion Torrent next-generation sequencing platform. Utilizing the Ion Ampliseq Cancer Panel, we sequenced 737 loci from 45 cancer-related genes using DNA extracted from formalin-fixed and paraffin-embedded (FFPE) samples of 121 human gastrointestinal stromal tumors, set up stringent parameters for reliable variant calling by filtering out potential raw base calling errors, and identified frequent mutations in the KIT gene. This study demonstrates the utility of using Ion Torrent sequencing to efficiently identify human cancer mutations. This may provide a molecular basis for clinically developing new drugs targeting these gene mutations for GIST therapy.