Institute for Global Change Studies
Institute for Global Change Studies
Philip S.,Dalhousie University |
Martin R.V.,Dalhousie University |
Martin R.V.,Harvard - Smithsonian Center for Astrophysics |
Van Donkelaar A.,Dalhousie University |
And 11 more authors.
Environmental Science and Technology | Year: 2014
Epidemiologic and health impact studies are inhibited by the paucity of global, long-term measurements of the chemical composition of fine particulate matter. We inferred PM2.5 chemical composition at 0.1 × 0.1 spatial resolution for 2004-2008 by combining aerosol optical depth retrieved from the MODIS and MISR satellite instruments, with coincident profile and composition information from the GEOS-Chem global chemical transport model. Evaluation of the satellite-model PM2.5 composition data set with North American in situ measurements indicated significant spatial agreement for secondary inorganic aerosol, particulate organic mass, black carbon, mineral dust, and sea salt. We found that global population-weighted PM2.5 concentrations were dominated by particulate organic mass (11.9 ± 7.3 μg/m3), secondary inorganic aerosol (11.1 ± 5.0 μg/m3), and mineral dust (11.1 ± 7.9 μg/m3). Secondary inorganic PM2.5 concentrations exceeded 30 μg/m3 over East China. Sensitivity simulations suggested that population-weighted ambient PM2.5 from biofuel burning (11 μg/m3) could be almost as large as from fossil fuel combustion sources (17 μg/m3). These estimates offer information about global population exposure to the chemical components and sources of PM2.5. © 2014 American Chemical Society.
Wang Y.,Institute for Global Change Studies |
Zhang Q.Q.,Institute for Global Change Studies |
He K.,Tsinghua University |
Zhang Q.,Institute for Global Change Studies |
Chai L.,Institute for Global Change Studies
Atmospheric Chemistry and Physics | Year: 2013
We use a chemical transport model to examine the change of sulfate-nitrate-ammonium (SNA) aerosols over China due to anthropogenic emission changes of their precursors (SO2, NOx and NH3) from 2000 to 2015. From 2000 to 2006, annual mean SNA concentrations increased by about 60% over China as a result of the 60% and 80% increases in SO 2 and NOx emissions. During this period, sulfate is the dominant component of SNA over South China (SC) and Sichuan Basin (SCB), while nitrate and sulfate contribute equally over North China (NC). Based on emission reduction targets in the 12th (2011-2015) Five-Year Plan (FYP), China's total SO2 and NOx emissions are projected to change by -16% and +16% from 2006 to 2015, respectively. The amount of NH3 emissions in 2015 is uncertain, given the lack of sufficient information on the past and present levels of NH3 emissions in China. With no change in NH3 emissions, SNA mass concentrations in 2015 will decrease over SCB and SC compared to their 2006 levels, but increase over NC where the magnitude of nitrate increase exceeds that of sulfate reduction. This suggests that the SO2 emission reduction target set by the 12th FYP, although effective in reducing SNA over SC and SCB, will not be successful over NC, for which NOx emission control needs to be strengthened. If NH3 emissions are allowed to keep their recent growth rate and increase by +16% from 2006 to 2015, the benefit of SO2 reduction will be completely offset over all of China due to the significant increase of nitrate, demonstrating the critical role of NH3 in regulating nitrate. The effective strategy to control SNA and hence PM2.5 pollution over China should thus be based on improving understanding of current NH3 emissions and putting more emphasis on controlling NH3 emissions in the future. © Author(s) 2013.
Ji Y.,Institute for Global Change Studies |
Ji Y.,Tsinghua University |
Liu L.,Institute for Global Change Studies |
Yang G.,Institute for Global Change Studies |
Yang G.,Tsinghua University
Proceedings of the ACM SIGPLAN 2012 X10 Workshop, X10 2012 | Year: 2012
Productivity and performance are always viewed as two sides of parallel programming languages. X10 is a new object-oriented parallel language for both high-productivity and high-performance. To help the development of X10, we characterize the performance of X10 in bioinformatics using the fundamental application Smith-Waterman (SW) sequence database search. We implement the SW application in X10 on multi-core shared-memory architecture. Through comparing with three SW implementations in C++, we make following suggestions for X10 as well as its compiler. (1) X10 compiler should improve its array access implementation in kernel loop to avoid redundant check and inefficient offset computation. The array access of the latest version X10 is much slower than that of C++, which results in poor single-core performance of SW in X10. (2) X10 should support the utilization of SIMD instructions. With 128-bit SSE instructions, SW in X10 can achieve 8.7-17.7 fold speedup. Note that there are many applications in the world which can dramatically benefit from SIMD architectures on modern processors. Copyright © 2012 ACM.