In this study aerosol samples of PM10 and PM2.5 collected from 18 February 2001 to 1 May 2001 in Nanjing, China were analyzed for their water-soluble organic compounds. A series of homologous dicarboxylic acids (C2-10) and two kinds of aldehydes (methylglyoxal and 2-oxo-malonaldehyde) were detected by GC and GC/MS. Among the identified compounds, the concentration of oxalic acid was the highest at all the five sites, which ranged from 178 to 1423 ng/m3. The second highest concentration of dicarboxylic acids were malonic and succinic acids, which ranged from 26.9 to 243 ng/m3. Higher level of azelaic acid was also observed, of which the maximum was 301 ng/m3. As the highest fraction of dicarboxylic acids, oxalic acid comprised from 28% to 86% of total dicarboxylic acids in PM10 and from 41% to 65% of total dicarboxylic acids in PM2.5. The dicarboxylic acids (C2, C3, C4) together accounted for 38-95% of total dicarboxylic acids in PM10 and 59-87% of dicarboxylic acids in PM2.5. In this study, the total dicarboxylic acids accounted for 2.8-7.9% of total organic carbon (TOC) of water-soluble matters for PM10 and 3.4-11.8% of TOC for PM2.5. All dicarboxylic acids detected in this study together accounted for about+N35 1% of particle mass. The concentration of azelaic acid was higher at one site than others, which may be resulted from higher level of volatile fat used for cooking. The amounts of dicarboxyic acids (C2,3,4,9) and 2-oxo-malonaldehyde of PM2.5 were higher in winter and lower in spring. Compared with other major metropolitans in the world, the level of oxalic acid concentration of Nanjing is much higher, which may be contributed to higher level of particle loadings, especially for fine particles.

Batch data for hydrolysis of phenylsulfonyl-eycloalkane carboxylates have been measured at a specific pH at various temperatures. A set of regression equations between hydrolysis rate constants and temperatures was established. Effects of various chemical groups of the tested compounds on hydrolytic reactions were compared.

Quantitative structure-activity relationship (QSAR) approaches are proposed in this study to predict the joint effects of mixture toxicity. The initial investigation studies the joint effects between cyanogenic toxicants and aldehydes to Photobacterium phosphoreum. Joint effects are found to result from the formation of a carbanion intermediate produced through the chemical interactions between cyanogenic toxicants and aldehydes. Further research indicates that the formation of carbanion intermediate is highly correlated with not only the charge of the carbon atom in the-CHO of aldehydes but also the charge of the carbon atom (C*) in the carbochain of cyanogenic toxicants. The charge of the carbon atom in the-CHO of aldehydes is quantified by using the Hammett constant (

The MEDV-13, molecular electronegativity distance vector based on 13 atomic types, has at best 91 descriptors. It is impossible to indirectly use multiple linear regression (MLR) to derive a quantitative structure-activity relationship (QSAR) model. Although principal component regression (PCR) or partial least-squares regression (PLSR) can be employed to develop a latent QSAR model, it is still difficult how to determine the principal components (PCs) and depict the physical meaning of the PCs. So, a genetic algorithm (GA) is first employed to select an optimal subset of the descriptors from original MEDV-13 descriptor set. Then MLR is utilized to build a QSAR model between the optimal subset and the biological activities of three sets of compounds. For 31 benchmark steroids, a 5-descriptor QSAR model (M1) between the corticosteroid-binding globulin (CBG) binding affinity of the steroids and 5-descriptor subset is developed. The root-mean-square error of estimations (RMSEE) and the correlation coefficient of estimations (r) between the CBG binding affinity (BA) observed and the BA estimated by M1 are 0.422 and 0.9182, respectively. The root-mean-square error of predictions (RMSEP) and the correlation coefficient of predictions (q) between the BA observed and the BA predicted by leave-one-out cross validations are 0.504 and 0.8818, respectively. For 58 dipeptides inhibiting angiotensin-converting enzyme (ACE), a 5-variable QSAR model (M2) between the pIC50 of peptides and 5-descriptor subset is derived. The M2 has a high quality with RMSEE) 0.339 and r) 0.9398 and RMSEP) 0.370 and q) 0.9280. For 16 indomethacin amides and esters (ImAE) inhibiting cyclooxygenase-2 (COX-2), a 6-variable QSAR model (M3) with RMSEE) 0.079 and r) 0.9839 and RMSEP) 0.151 and q) 0.9413 is built.

Acute 12h and 24h lethal toxicity (12h-LC50 and 24h-LC50) of 31 substituted phenols to Rana japonica tadpoles was determined. Results indicate that toxicity of phenols to tadpoles varied only slightly with length of exposure and the 12-h test could serve as surrogate of the 24-h test. A mechanism-based quantitative structure-activity relationship (QSAR) method was employed and 1-octanol/water partition coefficient (log Kow)-dependent models were developed to study different modes of toxic action. Most phenols elicited their response via a polar narcotic mechanism and an excellent log Kow-dependent model was obtained. Soft electrophilicity and pro-electrophilicity were observed for some phenols and a good log Kow-dependent model was also achieved. Additionally, the significant dissociation of carboxyl on benzoic acid derivatives sharply reduced their toxicity. A statistically robust QSAR model was developed for all studied compounds with the combined application of log Kow, energy of lowest unoccupied orbital (Elumo), heat of formation (HOF) and the first-order path molecular connectivity dices (1Xp).