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To investigate the composition and possible sources of aerosol particles in Beijing urban area, a single particle aerosol mass spectrometer (SPAMS) was deployed from April 22 to May 4, 2011. 510,341 particles out of 2,953,200 sized particles were characterized by SPAMS in combination with the ART-2a neural network algorithm. The particles were classified as rich-K (39.79%), carbonaceous species (32.7%), industry metal (19.2%), dust (5.7%), and rich-Na (1.76%). Industrial emissions related particles, rich-Fe, rich-Pb, and K-nitrate, were the major components of aerosol particles during haze periods, which were mainly from the steel plants and metal smelting processes around Beijing. Under stagnant meterological conditions, these regional emissions have a vital effect on haze formation. Organic carbon (OC) particles were attributed to biomass burning. NaK-EC was likely to come from local traffic emissions. Internally mixed organic and elemental carbon (OCEC) was found to be from possible sources of local traffic emission and biomass burning. It was found that coarse dust particles were mainly composed of four different types of dust particles, dust-Si, dust-Ca, dust-Al, and dust-Ti. It is the first time that SPAMS was used to study a dust storm in Beijing. Our results showed that SPAMS could be a powerful tool in the identification and apportionment of aerosol sources in Beijing, providing useful reference information for environmental control and management. [ABSTRACT FROM AUTHOR]

Background: Rice paddies have been identified as major methane (CH4) source induced by human activities. As a major rice production region in Northern China, the rice paddies in the Three-Rivers Plain (TRP) have experienced large changes in spatial distribution over the recent 20 years (from 1990 to 2010). Consequently, accurate estimation and characterization of spatiotemporal patterns of CH4 emissions from rice paddies has become an pressing issue for assessing the environmental impacts of agroecosystems, and further making GHG mitigation strategies at regional or global levels. Methodology/Principal Findings: Integrating remote sensing mapping with a process-based biogeochemistry model, Denitrification and Decomposition (DNDC), was utilized to quantify the regional CH4 emissions from the entire rice paddies in study region. Based on site validation and sensitivity tests, geographic information system (GIS) databases with the spatially differentiated input information were constructed to drive DNDC upscaling for its regional simulations. Results showed that (1) The large change in total methane emission that occurred in 2000 and 2010 compared to 1990 is distributed to the explosive growth in amounts of rice planted; (2) the spatial variations in CH4 fluxes in this study are mainly attributed to the most sensitive factor soil properties, i.e., soil clay fraction and soil organic carbon (SOC) content, and (3) the warming climate could enhance CH4 emission in the cool paddies. Conclusions/Significance: The study concluded that the introduction of remote sensing analysis into the DNDC upscaling has a great capability in timely quantifying the methane emissions from cool paddies with fast land use and cover changes. And also, it confirmed that the northern wetland agroecosystems made great contributions to global greenhouse gas inventory. [ABSTRACT FROM AUTHOR]