Sampling strategies in the assessment of long‐term exposures to toxic substances in air

The decision to mitigate exposures from vapor intrusion (VI) is typically based on limited data from 24‐hour air samples. It is well documented that these data do not accurately represent long‐term average exposures linked to adverse health effects. Limited decision guidance is currently available to determine the most appropriate sampling strategy, considering the cost of sampling alternatives along with the economic consequences of exposure‐related health effects. We present a decision model that introduces economic and statistical considerations in evaluating alternative VI sampling methods. The model characterizes the best sampling method by factoring economic and health consequences of exposure, the variability of exposure, the cost of sampling and mitigation, and the likelihood of false‐negatives and false‐positives. Decision‐makers can use results to select the sample size that maximizes net benefit. Conceptual and mathematical models are presented linking biological, statistical, and economic considerations to assess the cost and effectiveness of different sampling strategies. The model relates an average exposure concentration, determined statistically, to abatement costs and to the monetary value of health deterioration. The value of the information provided by different strategies is calculated and used to select the optimum sampling method. Simulations show that longer‐term sampling methods tend to be more accurate and cost‐effective than short‐term samples. The ideal sampling strategy shows significant seasonal variation (it is typically optimal to use longer samples in the winter) and also varies significantly with the stringency of regulatory standards. Longer‐term sample collection provides a more accurate representation of average VI exposure and reduces the likelihood of type I and type II errors. This reduces expected costs of mitigation and exposure (e.g., health consequences, legal and regulatory penalties), which in some cases can be quite significant. The model herein shows how these savings are balanced against the additional costs of longer‐term sampling.     

南宁城市大气污染对人体健康的危害及治理对策

南宁市大气属煤烟型污染 ,大气的主要污染物为SO2 、NOx、TSP、降尘 ,虽然随着环境管理和污染治理工作的加强 ,污染物浓度逐年下降 ,但是工业区大气污染仍然较重。污染物流行病学调查显示 :工业区癌症和呼吸系统疾病死亡率均高于全市平均水平 2倍左右 ,城区又高于郊区 2倍 ,大气污染综合指数与呼吸内科门诊就诊人数呈正相关。用邓聚龙的灰色系统理论分析得知污染物对癌症和呼吸系统疾病死亡率有关联 ,关联度从大到小排序为 :TSP >降尘 >SO2 >NOx ,最后提出了大气污染治理的对策。     

转换电极的电动力强化植物修复高浓度砷污染土壤

电极转换下,以Capsicum annuum L.为对象探讨电动力强化的植物修复高浓度砷污染土壤效果。结果表明:对照组土壤pH降低,实验组升高。对照组土壤中砷浓度从（524.1±5.6） mg/kg降低为（500.5±8.3） mg/kg,实验组不同区域土壤砷浓度分别为（179.8±10.6）,（674.9±5.43）,（512.8±7.3） mg/kg。相较于对照组,实验组植物对砷的吸收量分别增加了-18.1%、282.8%和170.3%。对照组、实验组区域1、2、3的土壤和根际土壤砷浓度之比分别为2.62、0.64、1.48和1.26,电场增高了植物根系附近的砷浓度。对照组、实验组区域1、2、3土壤第5天的残渣态砷占比为55.64%、34.99%、39.06%和0%;对照组、实验组第5天阴极和第10天阴极附近土壤,结晶水铁铝氧化物结合态和残渣态砷占比为67.45%、0%和8.88%;体现了电场对土壤重金属形态的活化作用。实验组修复10 d的电耗成本为79.5元/m3,具备一定经济优势。     

组配改良剂联合硅肥对Cd污染稻田的修复效果

选取湖南省浏阳市某Cd污染稻田进行田间试验,研究组配改良剂石灰石+海泡石（LS）、基施硅肥及叶面喷施硅肥对Cd污染稻田修复效果,结果表明：（1）基施硅肥90kg/hm²和叶面喷施硅肥（0.2,0.4g/L）对土壤pH值无明显影响,添加LS（2250,4500kg/hm²）的各处理均显著提高土壤pH值（P < 0.05）.（2）基施硅肥90kg/hm²分别降低土壤交换态、毒性特征浸出（TCLP）提取态Cd含量20.0%和18.5%,叶面喷施硅肥对土壤Cd两种提取态含量无明显影响,添加LS的各处理（2250,4500kg/hm²）分别使土壤交换态、TCLP提取态Cd含量降低25.8%~49.9%、26.4%~44.5%.（3）3种单一技术措施均能明显降低水稻各部位Cd含量,但降低糙米中Cd含量的效果低于3种技术措施的组合处理;“组配改良剂LS+基施硅肥+叶面喷施硅肥”各处理（JL1F1、JL1F2、JL2F1、JL2F2）使水稻糙米中Cd含量降低25.6%~70.5%.（4）“组配改良剂LS+基施硅肥+叶面喷施硅肥”的组合技术处理能显著降低土壤中Cd的有效性,显著降低水稻各部位中Cd含量,其中JL2F2处理效果最佳,能使对照糙米Cd含量从0.66mg/kg降低到0.19mg/kg,实现中重度Cd污染稻田的水稻安全生产.     

等离子体处理的时效性对芳Ⅲ/双马复合材料耐湿热性能的影响

目的考察等离子体处理前后及其时效性对芳Ⅲ/双马复合材料弯曲强度、界面性能及耐湿热性能的影响。方法用低温射频耦合等离子体处理芳纶纤维表面,分别将未处理的、等离子体刚处理过的和等离子体处理后在空气中放置三天后的纤维通过溶液预浸渍工艺制备芳Ⅲ/双马复合材料预浸料,再经过高温模压成形技术制备芳Ⅲ/双马复合材料单向板。采用万能材料试验机对水煮前后复合材料的弯曲强度和层间剪切强度进行测试,利用扫描电子显微镜对复合材料层间剪切破坏形貌进行观察。结果等离子体处理后,复合材料的吸水率由未处理时的0.51%下降到0.33%,复合材料层间剪切强度由49.4 MPa提高到62.9 MPa。等离子体处理时效后复合材料的吸水率增加到0.69%,复合材料的层间剪切强度下降到56.0 MPa。结论等离子体处理提高了复合材料界面性能,但等离子体处理的时效后,复合材料的界面性能及其耐湿热性能有所下降。等离子体处理及时效性对复合材料的弯曲强度和耐湿热性能变化不大。     

Analysis of pollutant levels in central Hong Kong applying neural network method with particle swarm optimization

Air pollution has emerged as an imminent issue in modernsociety. Prediction of pollutant levels is an importantresearch topic in atmospheric environment today. For fulfillingsuch prediction, the use of neural network (NN), and inparticular the multi-layer perceptrons, has presented to be acost-effective technique superior to traditional statisticalmethods. But their training, usually with back-propagation (BP)algorithm or other gradient algorithms, is often with certaindrawbacks, such as: 1) very slow convergence, and 2) easilygetting stuck in a local minimum. In this paper, a newlydeveloped method, particle swarm optimization (PSO) model, isadopted to train perceptrons, to predict pollutant levels, andas a result, a PSO-based neural network approach is presented. The approach is demonstrated to be feasible and effective bypredicting some real air-quality problems.     

Response of the Ascorbate-Peroxidase of Selenastrum capricornutum to Copper and Lead in Stormwaters

The green alga Selenastrum capricornutum expresses a uniqueascorbate peroxidase, that responds to copper and lead. Attemptswere made to test if this peroxidase could be used to monitor thelevels of copper and lead in natural waters. When S.capricornutum was exposed to a stormwater sample, the specificactivity of the peroxidase in the cell extract was commensuratewith the combined copper and lead contents in the sample. Theperoxidase responses were also correlated with the 96 hr biomasstoxicity assay of S. capricornutum. However, unlike thebiomass toxicity assay, the peroxidase activity was not affectedby the anions in the samples. The use of this peroxidase can beused as a marker for testing heavy metal toxicity in the water.     

Microwave-Assisted Solvent Extraction of Air Particulates for the Determination of PAHs

A method for the determination of polycyclic aromatic hydrocarbons (PAHs) in air particulates using microwave-assisted solvent extraction (MASE) coupled to a microwave extraction system (MES) is proposed. During the period of August to October, 1994, Singapore was enveloped by haze caused by forest fires in Sumatra, Indonesia. Air sampling were carried out during September and October; the determination of ambient air PAH levels in Singapore was undertaken. PAH analysis by GC-MS provided excellent sensitivity, linearity of quantitation, peak identification. This technique was evaluated using certified reference materials (CRMs) HS-4 and HS-6. Good recoveries of PAHs (>73.3%) were obtained for both CRMs. The MASE technique using MES is suitable for the determination of PAH levels in ambient air with no clean-up step required.     

Soil Lead and Other Metal Levels in Industrial, Residential and Nature Reserve Areas in Singapore

The use of pressure feedback microwave digestion technique has permitted rapid and efficient digestion of soil and sediment samples. The evaluation of different acid mixtures to digest soil samples were studied by mixed-level orthogonal array design. The selected acid mixture of HCl-HNO3-HF was employed in the survey of Pb, Zn, Cu, Cr and Mn in soil samples. Surface soil samples were collected from industrial, residential and nature reserve areas in Singapore. The five metal concentrations were determined by flame atomic absorption spectrometry and graphite furnace atomic absorption spectrometry. The lead and other metal contents in NBS SRM 1645, NIES CRM No. 2 and NRCC BCSS-1 sediment standard references were determined concurrently with the survey samples. Five measured metal loading on the surface soils was in the order: industrial area > residential area > nature reserve area. The trace metal concentrations in surface soil from areas of heavy traffic are higher than those from residential areas. The main sources of trace metal pollution are vehicular exhaust and industrial activities.