N,N—二甲基甲酰胺在血液中代谢物S—（N—甲基氨基甲酰）谷胱…

根据先将ＳＧＭ转化为乙基、Ｎ－甲基氨基酸盐（ＥＭＣ），然后以气相色谱－质谱联用手段测定所产生的ＥＭＣ的原理，发展出生种定量检测大鼠血清中ＳＭＧ及其他Ｎ－甲基氨基甲酸硫酯类物质的分析方法。方法采用人工合成的ＭＳＧ和ＥＭＣ进行标定，每次测定需要样品量为０．５ｍＬ，方法的合理检出极限为ｃ（ＳＭＧ）＝１０μｍｏｌ／Ｌ。     

Field studies on 32P movement and P leaching from flooded paddy soils in the region of Taihu Lake, China

Field experiments were done in two sites, Yixing and Changshu, Jiangsu province, China, to study P movement and leaching in flooded paddy soils. P movement in soil was investigated by using the KH2 32PO4 tracker method, and the amount of P leached from the soil layer in different depths was estimated by measuring P concentrations in the soil solution and saturated hydraulic conductivities in field. Determination was done about one month after P application. There was 46% and 42% of total 32P retained in the 0-5cm layer of soil in the Yixing site and in the Changshu site respectively. The 32P retained in the 25-30 cm layer was only about 1-2% of the total 32P added. Furthermore, 8.01% of 32P in the soil of Yixing site and 16.8% of 32P in the soil of Changshu site was lost from the layer 0-30 cm soil. The seasonal amounts of P leached from the top soil layer and from bottom layer are about 4.5-5.8% and 1.6-2.1% of the total P application, respectively. Changes of total P concentrations in soil solutions during rice growth showed that the fertilizer P applied before flooding of the paddy fields suffered a flash leaching loss and a slow leaching loss. We concluded that the fertilizer P could quickly move in the flooded paddy rice field and parts of it can enter into surface water and ground water. Unless the P application is well managed the risk of P loss and consequently environmental pollution exist.     

Boiler briquette coal versus raw coal: Part I--Stack gas emissions

Stack gas emissions were characterized for a steam-generating boiler commonly used in China. The boiler was tested when fired with a newly formulated boiler briquette coal (BB-coal) and when fired with conventional raw coal (R-coal). The stack gas emissions were analyzed to determine emission rates and emission factors and to develop chemical source profiles. A dilution source sampling system was used to collect PM on both Teflon membrane filters and quartz fiber filters. The Teflon filters were analyzed gravimetrically for PM10 and PM2.5 mass concentrations and by X-ray fluorescence (XRF) for trace elements. The quartz fiber filters were analyzed for organic carbon (OC) and elemental carbon (EC) using a thermal/optical reflectance technique. Sulfur dioxide was measured using the standard wet chemistry method. Carbon monoxide was measured using an Orsat combustion analyzer. The emission rates of the R-coal combustion (in kg/hr), determined using the measured stack gas concentrations and the stack gas emission rates, were 0.74 for PM10, 0.38 for PM2.5, 20.7 for SO2, and 6.8 for CO, while those of the BB-coal combustion were 0.95 for PM10, 0.30 for PM2.5, 7.5 for SO2, and 5.3 for CO. The fuel-mass-based emission factors (in g/kg) of the R-coal, determined using the emission rates and the fuel burn rates, were 1.68 for PM10, 0.87 for PM2.5, 46.7 for SO2, and 15 for CO, while those of the BB-coal were 2.51 for PM10, 0.79 for PM2.5, 19.9 for SO2, and 14 for CO. The task-based emission factors (in g/ton steam generated) of the R-coal, determined using the fuel-mass-based emission factors and the coal/steam conversion factors, were 0.23 for PM10, 0.12 for PM2.5, 6.4 for SO2, and 2.0 for CO, while those of the BB-coal were 0.30 for PM10, 0.094 for PM2.5, 2.4 for SO2, and 1.7 for CO. PM10 and PM2.5 elemental compositions are also presented for both types of coal tested in the study.     

Occurrence of organo-arsenicals in jellyfishes and their mucus

Water-soluble arsenic compound fractions were extracted from seven species of jellyfishes and subjected to analysis by high-performance liquid chromatography-inductively coupled plasma mass spectrometry (HPLC-ICP-MS) for arsenicals. A low content of arsenic was found to be the characteristic of jellyfish. Arsenobetaine (AB) was the major arsenic compound without exception in the tissues of the jellyfish species and mucus-blobs collected from some of them. Although the arsenic content in Beroe cucumis, which preys on Bolinopsis mikado, was more than 13 times that in B. mikado, the chromatograms of these two species were similar in the distribution pattern of arsenicals. The nine species of jellyfishes including two species treated in the previous paper can be classified into arsenocholine (AC)-rich and AC-poor species. Jellyfishes belonging to Semaostamae were classified as AC-rich species.     

Predicting particulate (PM10) personal exposure distributions using a random component superposition statistical model

This paper presents a new statistical model designed to extend our understanding from prior personal exposure field measurements of urban populations to other cities where ambient monitoring data, but no personal exposure measurements, exist. The model partitions personal exposure into two distinct components: ambient concentration and nonambient concentration. It is assumed the ambient and nonambient concentration components are uncorrelated and add together; therefore, the model is called a random component superposition (RCS) model. The 24-hr ambient outdoor concentration is multiplied by a dimensionless "attenuation factor" between 0 and 1 to account for deposition of particles as the ambient air infiltrates indoors. The RCS model is applied to field PM10 measurement data from three large-scale personal exposure field studies: THEES (Total Human Environmental Exposure Study) in Phillipsburg, NJ; PTEAM (Particle Total Exposure Assessment Methodology) in Riverside, CA; and the Ethyl Corporation study in Toronto, Canada. Because indoor sources and activities (smoking, cooking, cleaning, the personal cloud, etc.) may be similar in similar populations, it was hypothesized that the statistical distribution of nonambient personal exposure is invariant across cities. Using a fixed 24-hr attenuation factor as a first approximation derived from regression analysis for the respondents, the distributions of nonambient PM10 personal exposures were obtained for each city. Although the mean ambient PM10 concentrations in the three cities varied from 27.9 micrograms/m3 in Toronto to 60.9 micrograms/m3 in Phillipsburg to 94.1 micrograms/m3 in Riverside, the mean nonambient components of personal exposures were found to be closer: 52.6 micrograms/m3 in Toronto; 52.4 micrograms/m3 in Phillipsburg; and 59.2 micrograms/m3 in Riverside. The three frequency distributions of the nonambient components of exposure also were similar in shape, giving support to the hypothesis that nonambient concentrations are similar across different cities and populations. These results indicate that, if the ambient concentrations were completely controlled and set to zero in all three cities, the median of the remaining personal exposures to PM10 would range from 32.0 micrograms/m3 (Toronto) to 34.4 micrograms/m3 (Phillipsburg) to 48.8 micrograms/m3 (Riverside). The highest-exposed 30% of the population in the three cities would still be exposed to 24-hr average PM10 concentrations of 47-74 micrograms/m3; the highest 20% would be exposed to concentrations of 56-92 micrograms/m3; the highest 10% to concentrations of 88-131 micrograms/m3; and the highest 5% to 133-175 micrograms/m3, due only to indoor sources and activities. The distribution for the difference between personal exposures and indoor concentrations, or the "personal cloud," also was similar in the three cities, with a mean of 30-35 micrograms/m3, suggesting that the personal cloud accounts for more than half of the nonambient component of PM10 personal exposure in the three cities. Using only the ambient measurements in Toronto, the nonambient data from THEES in Phillipsburg was used to predict the entire personal exposure distribution in Toronto. The PM10 exposure distribution predicted by the model showed reasonable agreement with the PM10 personal exposure distribution measured in Toronto. These initial results suggest that the RCS model may be a powerful tool for predicting personal exposure distributions and statistics in other cities where only ambient particle data are available.     

Mobility of antimony in soil and its availability to plants

In a historical mining area residual material has been filled on land and these locations are used today as agricultural soils or house gardens. The antimony concentrations in these soils are up to 500 mg/kg. Antimony transfer into 19 vegetable and crop species was investigated. In grain and other storage organs up to 0.09 mg Sb/kg were found, whereas maximum antimony concentrations in shoots and leaves were determined to be 0.34 mg Sb/kg and 2.2 mg Sb/kg, respectively. Despite the high antimony contamination of the soils, concentrations in the investigated plants in general corresponded to concentrations only reported for uncontaminated soils. NH4NO3 extraction of some of the soils indicated that the mobile fraction of antimony present was only 0.06-0.59%. In contrast, in leaves of spinach grown under controlled conditions in soils with a high mobile antimony content an accumulation of the element could be observed: a maximum value of 399 mg Sb/kg was detected, and a correlation between the mobile fraction in the soils and antimony in leaves was found.     

Effects of changes in climate on landscape and regional processes, and feedbacks to the climate system

Biological and physical processes in the Arctic system operate at various temporal and spatial scales to impact large-scale feedbacks and interactions with the earth system. There are four main potential feedback mechanisms between the impacts of climate change on the Arctic and the global climate system: albedo, greenhouse gas emissions or uptake by ecosystems, greenhouse gas emissions from methane hydrates, and increased freshwater fluxes that could affect the thermohaline circulation. All these feedbacks are controlled to some extent by changes in ecosystem distribution and character and particularly by large-scale movement of vegetation zones. Indications from a few, full annual measurements of CO2 fluxes are that currently the source areas exceed sink areas in geographical distribution. The little available information on CH4 sources indicates that emissions at the landscape level are of great importance for the total greenhouse balance of the circumpolar North. Energy and water balances of Arctic landscapes are also important feedback mechanisms in a changing climate. Increasing density and spatial expansion of vegetation will cause a lowering of the albedo and more energy to be absorbed on the ground. This effect is likely to exceed the negative feedback of increased C sequestration in greater primary productivity resulting from the displacements of areas of polar desert by tundra, and areas of tundra by forest. The degradation of permafrost has complex consequences for trace gas dynamics. In areas of discontinuous permafrost, warming, will lead to a complete loss of the permafrost. Depending on local hydrological conditions this may in turn lead to a wetting or drying of the environment with subsequent implications for greenhouse gas fluxes. Overall, the complex interactions between processes contributing to feedbacks, variability over time and space in these processes, and insufficient data have generated considerable uncertainties in estimating the net effects of climate change on terrestrial feedbacks to the climate system. This uncertainty applies to magnitude, and even direction of some of the feedbacks.     

A disappearance model for the prediction of trichlorophenol ozonation

The disappearance and modeling of the ozonation of 2,4,6-trichlorophenol (TCP) was studied under different initial TCP concentrations and initial pH levels. The ozonation of TCP was found to follow a pseudo-first-order reaction. The degradation rates increased with the initial pH, and decreased with initial TCP concentration. 2,6-Dichlorohydroquinone was identified as the major intermediate, indicating that dechlorination and hydroxylation co-occurred during TCP ozonation. A model was proposed to quantitatively predict the pseudo-first-order rate constants under different initial TCP concentration and different initial pH levels. The proposed model can successfully describe the reaction; therefore another practical equation was proposed to predict the TCP removal rate at any detention time, which has high potential for practical applications and reactor design.     

Contamination by selected chlorinated pesticides in surface waters in Hanoi, Vietnam

Fifteen insecticides, which were banned in Vietnam in the period from 1990 to 1998, were chosen for the investigation of surface water samples in Hanoi and its surroundings. The investigation was focused on an area of approximately 30 by 20 km. Thirty water samples, in total were analysed: 11 samples from the Red river, seven from the Duong river, four from various lakes (West lake, Thuyen Quang, Bay Mau, Ba Mau), six from irrigation canals and two samples from wells. The procedure was repeated in November 1998 and in August 1999. The results showed that the contamination of the banned pesticides was highest in the rivers and then in the irrigation canals, followed by the lakes and wells. These pesticides could hardly be determined in just two drinking water samples (wells) and their concentrations rarely exceeded detection limits (0.05-0.25 ng l(-1)). The mean concentrations of sigmaHCHs (alpha, beta, gamma, delta-HCH) and sigmaDDTs (2,4'-, 4,4'-DDE; 2,4'-, 4,4'-DDD; 2,4'-, 4,4'-DDT) in the rivers were 17.2 +/- 71.8 and 43.7 +/- 79.9 ng l(-1) in the dry season (DS, November 1998), 29.3 +/- 117 and 56.1 +/- 65.6 ng l(-1) in the rainy season (RS, August 1999), respectively. However, the highest concentration of DDTs detected in a river sample (DS): 0.324 microg l(-1) was much lower than their allowable limit of concentration in surface waters, which is accorded with Criteria of Vietnam (1995) (DDTs < 10 microg l(-1)). Moreover, endrin, heptachlor, aldrin were also detected in most of water samples with considerable mean concentrations in rivers: 25.3 +/- 40.5, 17.4 +/- 23.8, 11.0 +/- 9.02 ng l(-1) in the DS and 18.5 +/- 23.2, 19.3 +/- 29.0, 12.8 +/- 8.44 ng l(-1) in the RS, respectively. Heptachlor epoxide (isomer A) and dieldrin were detected in some water samples with lowest concentrations.