A 3D aerosol and visibility information system for urban areas using remote sensing and GIS

Currently, the depiction of urban air quality at boundary layer scale uses modelled climatic and land cover data. However, such models are difficult to verify, and only low to moderate accuracy may be achieved due to the complexity of the input data required and the reliance on assumptions about dispersion patterns. The provision of comprehensive air quality data to urban residents in city districts, at a level of detail commensurate with other Location-Based Services (LBS) which are time- and place-sensitive, has therefore not been possible. A method for urban air quality monitoring over cities at boundary layer scale, other than by the use of air quality models is presented here. The system presented uses empirical Aerosol Optical Thickness (AOT) data in near-real time, combining AOT data from AERONET with aerosol vertical profiles computed from twice-daily MODIS satellite images at 500 m resolution, to give three dimensional (3D) air quality data over the urban landscape. There has been no previous attempt to project the horizontal spatial distribution of aerosols from satellite image pixels into a vertical dimension to give a spatially comprehensive three dimensional record of air quality. The paper describes the sources and accuracy of the AOT data input to the system as well as its storage and retrieval on a Geographic Information System (GIS) platform, to provide air quality and visibility information according to user query at any 3D geographical location, including individual buildings or building floor.     

母亲,在太阳里

我最早的一张照片是站在母亲身旁照的。母亲抱着四弟,我穿着有褶皱的棉袍,一只袖口露着镯子,一只手搭在母亲的腿上,面部表情凝重而又满足,显得对母亲十分依恋。母亲穿着带有暗花的夹袍,留着披肩长发,面带微笑。幼年的我,脸庞十分像母亲。这张照片应拍摄于1944年早春,当时我三岁多。     

Accumulation of Fluoride and Aluminium Related to Different Varieties of Tea Plant

Samples of young shoot, mature leaf, twig, root, and litter were taken from four recently bred varieties Zhe-nong 113, Zhe-nong 121, Zi-sun, and Bi-feng grown at two tea plantations located at Zhejiang and Jiangsu Provinces. The results showed that the concentrations of F and Al in tea plants were significantly different among the four varieties. It is therefore possible that F and Al concentrations in tea products can be reduced through variety selection. The results also revealed that old leaves would be the major contribution to the high levels of F and Al in brick tea since it is produced mainly for old leaves. Therefore, in order to eliminate the hazard of over-exposure to F and Al derived from tea, younger shoot should be used for making tea products while mature leaves should be avoided.     

Percentile estimation using variable censored data

Much progress has been made in recent years to address the estimation of summary statistics, using data that are subject to censoring of results that fall below the limit of detection (LOD) for the measuring instrument. Truncated data methods (e.g., Tobit regression) and multiple-imputation are two approaches for analyzing data results that are below the LOD. To apply these methods requires an assumption about the underlying distribution of the data. Because the log-normal distribution has been shown to fit many data sets obtained from environmental measurements, the common practice is to assume that measurements of environmental factors can be described by log-normal distributions. This article describes methods for obtaining estimates of percentiles and their associated confidence intervals when the results are log-normal and a fraction of the results are below the LOD. We present limited simulations to demonstrate the bias of the proposed estimates and the coverage probability of their associated confidence intervals. Estimation methods are used to generate summary statistics for 2,3,7,8-tetrachloro dibenzo-p-dioxin (2,3,7,8-TCDD) using data from a 2001 background exposure study in which PCDDs/PCDFs/cPCBs in human blood serum were measured in a Louisiana population. Because the congener measurements used in this study were subject to variable LODs, we also present simulation results to demonstrate the effect of variable LODs on the multiple-imputation process.     

Effects of pig manure compost and nonionic-surfactant Tween 80 on phenanthrene and pyrene removal from soil vegetated with Agropyron elongatum

This paper evaluates the effects of pig manure compost (PMC) and Tween 80 on the removal of phenanthrene (PHE) and pyrene (PYR) from soil cultivated with Agropyron elongatum. Soils spiked with about 300mgkg(-1) of PHE and PYR were individually amended with 0%, 2.5%, 5% and 7.5% (dry wt) of PMC or 0, 20 and 100mgkg(-1) of Tween 80. Unplanted and sterile microcosms were prepared as the controls. PAH concentration, total organic matter (TOM), dissolved organic carbon (DOC), total heterotrophic and PAH degrading microbial populations in soil were quantified before and after 60d period. The results indicated that A. elongatum could significantly enhance PYR removal (from 46% to 61%) but had less impact on PHE removal (from 96% to 97%). Plant uptake of the PAHs was insignificant. Biodegradation was the key mechanism of PAH removals (<3% losses in the sterile control). Increase in PMC or Tween 80 levels increased the removal of PYR but not of PHE. Maximal PYR removal of 79% and 92% were observed in vegetated soil receiving 100mgkg(-1) Tween 80 and 7.5% PMC, respectively. Enhanced PYR removal in soil receiving PMC could be explained by the elevated levels of DOC, TOM and microbial populations as suggested by Pearson correlation test. While the positive effect of Tween 80 on PYR removal could probably due to its capacities to enhance PYR bioavailability in soil. This paper suggests that the addition of either PMC or nonionic-surfactant Tween 80 could facilitate phytoremediation of PAH contaminated soil.     

Sources and seasonal variation of PAHs in the sediments of drinking water reservoirs in Hong Kong and the Dongjiang River (China)

The main objective of this study was to investigate occurrence of polycyclic aromatic hydrocarbons (PAHs) in the sources of the drinking water supply of Hong Kong. The main emphasis was on the Dongjiang River in mainland China which is the major source, supplying 80% of the total consumption in Hong Kong (the remaining 20% is obtained from rain water). Sediments were collected from four sites along the Dongjiang River and four reservoirs in Hong Kong during both the dry and wet weather seasons. The concentrations of total PAHs in the sediments ranged between 36 and 539 microg/kg dry wt. The lower levels were detected at the upstream site on the Dongjiang River and at the reservoirs in Hong Kong (44-85 microg/kg dry wt), while the mid- and downstream sites on the Dongjiang River were more polluted (588-658 microg/kg dry wt). Examination of the PAH profiles revealed that the mid- and downstream sections of the Dongjiang River contained high percentages of 4,5,6-ring PAHs, similar to the amounts of atmospheric particulate matter and road dust collected during the dry weather season from the Pearl River Delta region as reported in the literature. Seasonal changes were revealed in the reservoirs of Hong Kong, with higher PAH levels in the wet weather season than in the dry weather season. For those reservoirs in Hong Kong that store water from the Dongjiang River, a distinct seasonal pattern was also observed, namely, that under dry weather season conditions the PAHs found in the sediments were primarily from petrogenic source, while under wet weather season conditions they were from pyrolytic sources. No such pattern was detected in the reservoirs which stored only rain water.     

Modelling and Prediction of Diesel Engine Performance using Relevance Vector Machine

Diesel engines are being increasingly adopted by many car manufacturers today, yet no exact mathematical diesel engine model exists due to its highly nonlinear nature. In the current literature, black-box identification has been widely used for diesel engine modelling and many artificial neural network (ANN) based models have been developed. However, ANN has many drawbacks such as multiple local minima, user burden on selection of optimal network structure, large training data size, and over-fitting risk. To overcome these drawbacks, this article proposes to apply an emerging machine learning technique, relevance vector machine (RVM), to model and predict the diesel engine performance. The property of global optimal solution of RVM allows the model to be trained using only a few experimental data sets. In this study, the inputs of the model are engine speed, load, and cooling water temperature, while the output parameters are the brake-specific fuel consumption and the amount of exhaust emissions like nitrogen oxides and carbon dioxide. Experimental results show that the model accuracy is satisfactory even the training data is scarce. Moreover, the model accuracy is compared with that using typical ANN. Evaluation results also show that RVM is superior to typical ANN approach.     

基于流域视角的水环境综合治理规划研究——以江苏省淀山湖为例

以流域为单元进行水资源综合规划和管理是实现水环境改善的重要途径。本文以太湖流域第二大省界湖泊—淀山湖为例,在综合分析流域水环境质量基础上,利用GIS 分析工具划分流域治理片区并制定分区管控策略。根据流域所含骨干河流流向、骨干河流与淀山湖交汇特点、上中下游不同河段及镇域行政边界,将淀山湖流域分为吴淞江流域、千灯浦- 淀山湖流域、昆南湖荡流域、元荡湖荡流域、太浦河流域五大片区138 个子评价单元。通过水环境容量与压力两类空间叠加分析,构建形成污染重点减排区、污染综合治理区、产业绿色化提升区、生态环境保育区等四个类型区域,并提出差异化的产业准入和环境治理措施。本研究不仅为以流域为治理单元的水环境治理规划提供了较为可行的技术体系,而且为太湖流域水环境综合整治思路创新提供了可借鉴的案例。