北京夏季高温高湿和降水过程对大气颗粒物谱分布的影响

2004-07-13～2004-08-23使用TDMPS-APS系统在线测量颗粒物的数浓度谱分布,并于07-16～07-18选取了高温闷热夜晚、日间高温高湿和雨后晴朗干洁3种天气条件,使用多级串联撞击式采样器(MOUDI)测量颗粒物的质量浓度谱分布,结果表明,高温高湿天气条件下颗粒物的污染、尤其细粒子污染严重,导致很低的能见度(2.5km);PM_1.8和PM₁₀的质量浓度分别为170.68μg/m³和249.35μg/m³,细粒子质量浓度占PM10的68%;粒径为50～100nm颗粒物的数浓度最高,为2×10⁴～3×10⁴个/cm³;降雨过程对粗粒子和细粒子均有去除作用,对细粒子的去除作用尤为明显;降雨后PM₁₀和PM_1.8浓度分别比降雨前降低3倍和6倍;降雨过后的晴朗干洁天气有利于新粒子(3～20nm)的生成,生成的新粒子快速长大到50～100nm;随着污染物的累积,以后几天内又变为污染天气.     

Atmospheric Transfer and Deposition Mechanisms of 210Pb Aerosols onto Forest Soils

Activities of ²¹⁰Pb carrier aerosols in an age-graded Sitka spruce conifer, three deciduous (oak, lime and sycamore) foliage and in rain and throughfall samples have been measured during the period of 2001–2002. The ²¹⁰Pb concentrations in the age-graded Sitka leaf needles have shown to accumulate until a steady state between accretion and loss of particulate matter is maintained with time. Similarly, the concentrations of ²¹⁰Pb on deciduous tree leaves increased with time until the leaves began to senesce. The ²¹⁰Pb inventory in bulk precipitation was significantly (r ²?=?0.99; P?<?0.001) large compared with that in throughfall samples, as indicated by a ratio of 1 to 0.1 of ²¹⁰Pb deposition in bulk precipitation to throughfall. This suggests that ²¹⁰Pb is retained in the Sitka spruce foliage during deposition until transfer to the ground mainly through litterfall. These findings suggest that the presence of woodland is responsible for enhanced ²¹⁰Pb deposition fluxes beneath wooded areas relative to open grassland soils.     

Regional analysis of global change impacts: Concepts, tools and first results

The paper introduces an approach for the analysis of global change impacts on river basins or regions. This approach is quite general and can be transferred to any region or river basin of interest on earth. The first application of the approach was in the Elbe river basin, with primary focus on the hydrologic model part and on the integration of crop growth and nitrogen dynamics. Finally, concepts for the integration of socio-economic aspects in the analysis are introduced.     

Incentives to diffuse advanced abatement technology under environmental liability law

Polluting firms with advanced abatement technology at their disposal have incentives or disincentives to share this technology with other polluting firms. The ‘direction’ and extent of those incentives depends on the liability rule applicable and the way technical change impacts marginal abatement costs. We establish that incentives for diffusion are socially optimal under strict liability and socially suboptimal under negligence if technical change lowers marginal abatement costs for all levels of abatement. Negligence may, however, induce better diffusion incentives than strict liability if technical change decreases (increases) marginal abatement costs for low (high) levels of abatement.     

Stochastic landslide vulnerability modeling in space and time in a part of the northern Himalayas, India

Little is known about the quantitative vulnerability analysis to landslides as not many attempts have been made to assess it comprehensively. This study assesses the spatio-temporal vulnerability of elements at risk to landslides in a stochastic framework. The study includes buildings, persons inside buildings, and traffic as elements at risk to landslides. Building vulnerability is the expected damage and depends on the position of a building with respect to the landslide hazard at a given time. Population and vehicle vulnerability are the expected death toll in a building and vehicle damage in space and time respectively. The study was carried out in a road corridor in the Indian Himalayas that is highly susceptible to landslides. Results showed that 26% of the buildings fall in the high and very high vulnerability categories. Population vulnerability inside buildings showed a value >0.75 during 0800 to 1000 hours and 1600 to 1800 hours in more buildings that other times of the day. It was also observed in the study region that the vulnerability of vehicle is above 0.6 in half of the road stretches during 0800 hours to 1000 hours and 1600 to 1800 hours due to high traffic density on the road section. From this study, we conclude that the vulnerability of an element at risk to landslide is a space and time event, and can be quantified using stochastic modeling. Therefore, the stochastic vulnerability modeling forms the basis for a quantitative landslide risk analysis and assessment.     

Bodenkontaminationen in Rüstungsaltlasten aus dem Ersten Weltkrieg

The results of the chemical analysis of soil samples from WWI munition plants showed that the most important compounds are dinitrobenzenes, trinitrotoluene and metabolites, and dinitronaphthalenes. The presence of 1,2-dinitrobenzene and 1,3-dinitrobenzene in soil samples shows that, during the production of dinitrobenzenes, in WWI, the raw dinitrobenzene was not washed with sulphite. The results show that the parameter list must be different from the parameter list for the analysis of soil samples from munition plants of WW II.     

Artificial intelligence in obstetric ultrasound: A scoping review

The objective is to summarize the current use of artificial intelligence (AI) in obstetric ultrasound. PubMed, Cochrane Library, and ClinicalTrials.gov databases were searched using the following keywords “neural networks”, OR “artificial intelligence”, OR “machine learning”, OR “deep learning”, AND “obstetrics”, OR “obstetrical”, OR “fetus”, OR “foetus”, OR “fetal”, OR “foetal”, OR “pregnancy”, or “pregnant”, AND “ultrasound” from inception through May 2022. The search was limited to the English language. Studies were eligible for inclusion if they described the use of AI in obstetric ultrasound. Obstetric ultrasound was defined as the process of obtaining ultrasound images of a fetus, amniotic fluid, or placenta. AI was defined as the use of neural networks, machine learning, or deep learning methods. The authors’ search identified a total of 127 papers that fulfilled our inclusion criteria. The current uses of AI in obstetric ultrasound include first trimester pregnancy ultrasound, assessment of placenta, fetal biometry, fetal echocardiography, fetal neurosonography, assessment of fetal anatomy, and other uses including assessment of fetal lung maturity and screening for risk of adverse pregnancy outcomes. AI holds the potential to improve the ultrasound efficiency, pregnancy outcomes in low resource settings, detection of congenital malformations and prediction of adverse pregnancy outcomes.