Source apportionment of atmospheric particle number concentrations with wide size range by nonnegative matrix factorization (NMF).

Quantifying the sources of atmospheric particles is essential to air quality control but remains challenging, especially for the source apportionment of particles based on number concentration with wide size range. Here, particle number concentrations (PNC) with size range 19-20,000 nm involving four modes Nucleation, Aitken, Accumulation, and Coarse are used to do source apportionment of PNC at the Guangdong Atmospheric Supersite (Heshan) during July-October 2015 by nonnegative matrix factorization (NMF) with 6 factors. For July 2015, separated source apportionments for three different size ranges from collocated instruments nano scanning mobility particle sizer (NSMPS), SMPS, and aerodynamic particle sizer (APS) and for two different size ranges (below and above 100 nm) show similar quantitative source information with that for the one whole size range. The mean absolute difference of contribution percentages of total particle number concentrations (TPNC) based on 5 unique apportioned sources is 5.6 % (4.3-7.6 %) for the instrument segregated apportionment and 4.2 % (0-5.3 %) for the size range segregated apportionment respectively, relative to the one whole apportionment. Moreover, the contribution percentages of TPNC are close to the weighted sum of contribution percentages of all size bins, with a mean absolute difference of 1.1 % (0-3.4 %). In both these two aspects, the consistency among different technical paths proves the matrix factorization by NMF is practically desirable and the simplicity of reducing some steps or calculations saves time. Besides, dust can be identified with the wide size range including larger than 3000 nm. Six apportioned sources in the 4 months are Accumulation (32.4 %), Nucleation (20.0 %), Aitken (15.2 %), traffic (14.6 %), dust (10.6 %), and Coarse (7.1 %). Therefore, NMF would serve as a promising tool for PNC source apportionment with wide size range and conducting the apportionment with the whole size range in one matrix factorization procedure and using the single TPNC contribution percentage are feasible.

Multivariate statistical analysis of clinicopathologic factors influencing survival of patients with bile duct carcinoma.

AIM: To evaluate the influence of various clinicopathologic factors on survival of patients with bile duct carcinoma after curative resection. METHODS: A retrospective analysis was made for 86 cases of bile duct carcinoma treated from January 1981 to September 1995. Fifteen clinicopathologic factors possibly influencing survival were selected. Independent variables were first analyzed by univariate methods. Survival for variable was estimated by the method of Kaplan and Meier. The variables that were statistically significant by univariate analysis were included in a multivariate analysis, which were confirmed using the Cox stepwise proportion hazard model with the help of SPSS 10.0 for Windows software. RESULTS: The overall cumulative survival rate was 72.6 % at 1 year, 32.4 % at 3 years, and 18.7 % at 5 years. The results of univariate analysis showed that the major significant prognostic factors influencing survival of these patients were histological type of lesion, lymph node metastasis, pancreatic invasion, duodenal invasion, perineural invasion, macroscopic vessel involvement, resected surgical margin and depth of cancer invasion (P=0.02, 0.02, 0.004, 0.005, 0.01, 0.43, 0.03 and 0.04). Age, sex, location of tumor, size of tumor, macroscopic type of lesions, hepatic metastasis, and hepatic invasion were not significantly associated with prognosis (P>0.05). Pancreatic invasion, perineural invasion and lymph node metastases were the three most important prognostic factors by multivariate analysis using the Cox proportional hazards model. CONCLUSION: Pancreatic invasion, perineural invasion and lymph node metastases are the most important prognostic factors for bile duct carcinoma after curative resection.