北京市气温与黑碳和PM2.5对疾病死亡影响的交互效应

通过收集北京市2010~2016年逐日呼吸和心脑血管疾病死亡数据、污染物（BC、PM_2.5、SO₂、O₃和NO₂）日均浓度资料以及同期的气象资料,采用广义相加模型（GAMs）中的主效应模型、非参数二元响应模型和温度分层模型探讨了北京市气温、PM_2.5和BC单效应,以及气温与PM_2.5和BC交互作用分别对呼吸和心脑血管疾病死亡人数的影响.单效应分析结果表明,气温与两种疾病死亡人数的累计暴露-反应关系均呈“J”型分布特征,最适温度为24℃;累计滞后一天情况下PM_2.5和BC的健康效应均最显著,此时PM_2.5和BC浓度每升高四分位间距（IQR）,呼吸系统疾病死亡人数超额增加百分比（ER）分别为2.21%和1.80%,心脑血管疾病死亡人数ER分别为2.02%和1.48%.交互作用的研究结果表明,高温与高浓度的PM_2.5（或BC）对疾病死亡的影响存在协同效应,且高温条件下BC对疾病死亡影响的健康风险大于PM_2.5,当气温大于24℃时,BC和PM_2.5浓度每升高IQR对应的呼吸系统疾病死亡人数ER分别为6.22%和6.17%,心脑血管疾病死亡人数ER分别为5.01%和3.97%.虽然BC只占PM_2.5的一部分,但BC对人群健康的影响不容忽视,应该引起足够的重视.     

贵州农村污水典型抗生素污染水平及生态风险

抗生素残留物是一种新兴的微污染物,广泛存在于各种环境介质及生物样品中,长期暴露在抗生素环境会对人体健康、生态环境产生一定的潜在威胁。由于农村污水排放量逐渐增大、农村地区对抗生素的不合理使用以及受经济、地理地形的限制大部分农村地区都缺乏完整的污水收集管网和污水治理设施,进而导致抗生素通过多种途径进入农村环境,可能会对农村生态环境产生一定的环境风险。为了解贵州农村污水中抗生素的污染水平及处理后污水对环境的影响,作者采用固相萃取—液相色谱—串联质谱(SPE-LC-MS)联用技术,选取贵州省3处典型农村地区,对农村污水治理设施进出水中典型抗生素(包括6种磺胺类、3种四环素类、1种喹诺酮类抗生素)的浓度水平进行分析。结果表明,3处农村污水治理设施进水和出水中均检出不同程度的抗生素,浓度范围分别在ND～417.57 ng/L和ND～253.68 ng/L,其中土霉素浓度最高,进出水的浓度分别为417.57、253.68 ng/L;其次是氧氟沙星,在进出水中的最高浓度分别为278.75、183.73 ng/L。3处农村污水治理设施对抗生素的去除率在-58.32%～45.52%,对目标抗生素的去除率较低。与国内外其他地区农村污水相比,所选地区的进出水中抗生素的浓度较高。通过生态风险评估发现,经处理后的污水中均含有一种RQ≥1的抗生素物质,并且发现氧氟沙星为处理后污水中的高风险污染物,污水的排放会对环境造成一定的生态风险。     

矿山排水污染稻田土壤胶体对砷的吸附研究

为研究矿山排水污染土壤胶体对砷的吸附影响,文章以高砷污染区和背景区土壤的胶体在不同的pH、离子强度以及好氧/厌氧淹水条件下对As(Ⅴ)的吸附进行对比试验。结果表明:土壤胶体对As(Ⅴ)的吸附量随pH的增大而减少,且污染区胶体比背景区胶体对As(Ⅴ)的吸附量大;离子强度越大,胶体对As(Ⅴ)的吸附量越大,Ca(NO_3)_2浓度从0.001 mol/L增至0.1 mol/L时,污染区与背景区胶体对As(Ⅴ)的吸附量分别增加了30.57%、50.25%;厌氧淹水后的胶体对As(Ⅴ)的吸附量低于好氧条件下的胶体,解吸量较高,且厌氧淹水后污染区胶体与背景区胶体相比无明显吸附优势,而好氧条件下污染区胶体的解吸率低于背景区胶体,说明厌氧淹水后的胶体不易吸附且易释放As(Ⅴ)。因此,低p H和高离子强度以及好氧的条件有利于土壤胶体对砷的吸附。     

海水入侵指标对比分析与评价—以珠江口地下水含水层为例

合理评价海水入侵对于地下水含水层管理和居民生活健康有着重要的意义。当前海水入侵评价应用较广泛的为包含单一因子和基于统计分析的多种水化学方法。本文综合阐述了海水入侵评价中所用的水化学指标、原理与方法,包括在国内评价体系中少见的一些方法,并以珠江口地下水含水层为例,对比了各种指标的有效性,评价了各类方法的优缺点以及在实际应用中可能产生的问题。结果显示,以Cl-和TDS作为简单直接的单因子可以快速评价海水入侵,尤其对于大范围的海水入侵评价十分有效。而和Ca2+,HCO₃-,SO₄2-等变化有关的指标具有地域性,在研究区对海水入侵的指示并不是十分敏感。在复杂评价因子中,与离子交换有关SAR,BEX以及GQI_SWI与Cl-相关性较高,可以较好的指示海水入侵及水岩相互作用。经过分析,受到地下含水层和地表水道双重介质入侵的影响,研究区海水入侵范围在过去几个年代有向北推进的趋势。本研究对于高速经济发展下的沿海地下水含水层管理和评价有重要的指导意义。     

Effect of TiO2 nanoparticle aggregation on marine microalgae Isochrysis galbana

TiO_2 nanoparticles(NPs) could adversely impact aquatic ecosystems. However, the aggregation of these NPs could attenuate this effect. In this work, the biological effects of TiO_2 NPs on a marine microalgae Isochrysis galbana were investigated. The aggregation kinetics of TiO_2 NPs under different conditions was also investigated to determine and understand these effects. Results showed that, though TiO_2 NPs had no obvious impact on the size and reproducibility of algal cells under testing conditions, they caused a negative effect on algal chlorophyll, which led to a reduction in photosynthesis. Furthermore, fast aggregation of TiO_2 NPs occurred under all conditions, especially at the pH close to the p Hzpc. Increasing ionic strength and NP concentration also enhanced the aggregation rate.The aggregation and the following sedimentation of TiO_2 NPs reduced their adverse effects on I. galbana.     

Analysing the sensitivity behaviour of two hydrology models

Recently, the New Morris Method has been presented as an effective sensitivity analysis tool for mathematical models. The New Morris Method estimates the sensitivity of an output parameter to a given set of input parameters (first-order effects) and the extent these parameters interact with each other (second-order effects). This method requires the specification of two parameters (runs and resolution) that control the sampling of the output parameter to determine its sensitivity to various inputs. The criteria for these parameters have been set on the analysis of a well-behaved analytical function (see Cropp and Braddock, Reliab. Eng. Syst. Saf. 78:77–83, 2002), which may not be applicable to other physical models that describe complex processes. This paper will investigate the appropriateness of the criteria from (Cropp and Braddock, 2002) and hence the effectiveness of the New Morris Method to determine the sensitivity behaviour of two hydrologic models: the Soil Erosion and Deposition System and Griffith University Representation of Urban Hydrology. In the first case, this paper will separately analyse the sensitivity of an output parameter on a set of input parameters (first- and second-order effects) for each model and discuss the physical meaning of these sensitivities. This will be followed by an investigation into the sampling criteria by exploring the convergence of the sensitivity behaviour for each model as the sampling of the parameter space is increased. By comparing these trends to the convergence behaviour from Cropp and Braddock (2002), we will determine how well the New Morris Method estimates the sensitivity for each model and whether the sampling criteria are appropriate for these models. It will be shown that the New Morris Method can provide additional insight into the functioning of these models, and that, under a different metric, the sensitivity behaviour of these models does converge confirming the sampling criteria set by Cropp and Braddock.     

Application of cryofocusing hydride generation and atomic fluorescence detection for dissolved mercury species determination in natural water samples

The concentration levels of mercury (Hg) species in natural water samples are usually low. Consequently, accurate analysis with low detection limits is still a major problem. In this work, a method was applied for the simultaneous direct determination of dissolved mercury species in water samples by on-line hydride generation (HG), cryogenic trapping (CT), gas chromatography (GC) and detection by atomic fluorescence spectrometry (AFS). The suitability of the method for real samples with different organic matter and chloride contents was evaluated by recovery experiments in synthetic and natural spiked water samples. The HG method was compared with other current available methods for mercury analysis with respect to the different fraction of mercury analysed, i.e. 'reactive', 'reducible' or total. HG derivatization and SnCl2 reduction (with and without previous oxidation with BrCl) were applied to synthetic and natural (spiked and non-spiked) water samples. The influence of chloride and dissolved organic matter concentrations was studied. The results suggest that the HG procedure is suitable for the simultaneous determination of Hg2+ and MeHg+ in surface water samples. Inorganic mercury analysed by HG (i.e. reducible) is close to the total inorganic mercury.     

Speciation of arsenic using solid phase extraction cartridges

Various solid phase extraction (SPE) cartridges were investigated for speciation of arsenite [As(III)], arsenate [As(v)], monomethylarsonic acid (MMA) and dimethylarsinic acid (DMA). Cartridges containing different types of sorbent materials were tested for arsenic retention and elution characteristics. Alumina cartridges were found to completely retain all the four target arsenic species, and are suitable for removal and preconcentration purposes. For speciation analysis, different arsenic species were separated on the basis of their selective retention on and elution from specific cartridges. DMA was retained on a resin-based strong cation exchange cartridge and eluted with 1.0 M HCl. MMA and As(v) were both retained on a silica-based strong anion exchange cartridge and sequentially eluted with 60 mM acetic acid (for MMA) and 1.0 M HCl [for As(v)]. As(III) was not retained on either cartridge and remained in solution. Arsenic species in solution and those eluted from the cartridges were subsequently quantified by using flow injection with hydride generation atomic fluorescence spectrometry (FI-HGAFS) and hydride generation atomic absorption spectrometry (FI-HGAAS). A detection limit of 0.05 microg L(-1) arsenic in water sample was achieved using HGAFS. An application of the method was demonstrated at a drinking water treatment facility. As(III) and As(v) species were determined in water at various stages of treatment. The method is suitable for routine determination of trace levels of arsenic in drinking water to comply with more stringent environmental regulations.     

Development of an ombrotrophic peat bog (low ash) reference material for the determination of elemental concentrations

Given the increasing interest in using peat bogs as archives of atmospheric metal deposition, the lack of validated sample preparation methods and suitable certified reference materials has hindered not only the quality assurance of the generated analytical data but also the interpretation and comparison of peat core metal profiles from different laboratories in the international community. Reference materials play an important role in the evaluation of the accuracy of analytical results and are essential parts of good laboratory practice. An ombrotrophic peat bog reference material has been developed by 14 laboratories from nine countries in an inter-laboratory comparison between February and October 2002. The material has been characterised for both acid-extractable and total concentrations of a range of elements, including Al, As, Ca, Cd, Cr, Cu, Fe, Hg, Mg, Mn, Na, Ni, P, Pb, Ti, V and Zn. The steps involved in the production of the reference material (i.e. collection and preparation, homogeneity and stability studies, and certification) are described in detail.     

Using improved neural network model to analyze RSP,NOx and NO2 levels in urban air in Mong Kok,Hong Kong

As the health impact of air pollutants existing in ambient addresses much attention in recent years, forecasting of airpollutant parameters becomes an important and popular topic inenvironmental science. Airborne pollution is a serious, and willbe a major problem in Hong Kong within the next few years. InHong Kong, Respirable Suspended Particulate (RSP) and NitrogenOxides NO_x and NO₂ are major air pollutants due to thedominant diesel fuel usage by public transportation and heavyvehicles. Hence, the investigation and prediction of the influence and the tendency of these pollutants are ofsignificance to public and the city image. The multi-layerperceptron (MLP) neural network is regarded as a reliable andcost-effective method to achieve such tasks. The works presentedhere involve developing an improved neural network model, whichcombines the principal component analysis (PCA) technique and theradial basis function (RBF) network, and forecasting thepollutant levels and tendencies based in the recorded data. Inthe study, the PCA is firstly used to reduce and orthogonalizethe original input variables (data), these treated variables arethen used as new input vectors in RBF neural network modelestablished for forecasting the pollutant tendencies. Comparingwith the general neural network models, the proposed modelpossesses simpler network architecture, faster training speed,and more satisfactory predicting performance. This improvedmodel is evaluated by using hourly time series of RSP, NO_x and NO₂ concentrations collected at Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000. By comparing the predicted RSP, NO_x and NO₂ concentrationswith the actual data of these pollutants recorded at the monitorystation, the effectiveness of the proposed model has been proven.Therefore, in authors' opinion, the model presented in the paper is a potential tool in forecasting air quality parameters and hasadvantages over the traditional neural network methods.