磁化水抑垢的研究

通过静态试验研究了Ｃａ＾２＋、Ｍｇ＾２＋的总浓度、碱度、ＰＨ、ＳＯ＾２－４对磁水抑垢效果的影响,得出了应用磁化水抑垢的适宜条件,为磁水器的实际应用提供了依据。     

Geospatial Analysis of Climatic and Geomorphic Interactions Influencing Stream Discharge, Appalachian Mountains, USA

Streamflow values are commonly synthesized for locations where flow measurement stations are lacking or where only intermittent measurements are available. In an Appalachian Mountains dataset comprised of 29 watersheds, the most appropriate among geomorphic, geologic, and hydrogeologic datasets were selected for use in prediction of streamflow at watershed scale. A statistical model was developed using principal components analysis (PCA) and cluster analysis (CA) for. Using CA on variables derived from the PCA, an optimum set of variables was derived for predicting streamflow. Results indicate there are two categories of watersheds in the study area. The first is strongly correlated with climatic variables (precipitation, temperature, elevation, and groundwater recharge). The second is strongly correlated with two geomorphic variables (watershed slope and percentage of forested area). The spatial distribution of cluster classifications shows that watersheds dominated by the climatic component are located along the Allegheny Front while watersheds dominated by the geomorphic component are located in the Allegheny Plateau and Valley and Ridge physiographic provinces. These variations between the Allegheny Plateau and Valley and Ridge physiographic provinces suggest that, to accurately model streamflow, modeling needs be done based on natural physiographic boundaries rather than political boundaries. In this physiographic setting, elevation seems to be a major control.     

西安市采暖季过渡期高时间分辨率细颗粒物组分特征及来源解析

受到供暖影响,北方城市秋冬季的大气细颗粒物(PM_2.5)浓度升高,空气污染加剧.利用气溶胶化学组分监测仪、七波段黑碳仪以及大气多金属元素在线监测仪于2019年10月25日至11月17日在西安市开展高时间分辨率PM_2.5化学组分在线监测,分析采暖季过渡期PM_2.5污染特征,同时结合正定矩阵因子分解模型解析PM_2.5来源.结果表明,观测期间ρ(PM_2.5)平均值为(78.3±38.5)μg·m^-3,主要化学组分为有机物(OA)、二次无机离子(SIA)和粉尘,其占比分别为38.7%、31.6%和21.2%,其中ρ(SO₄^2-)、ρ(NO₃^-)和ρ(NH₄⁺)平均值分别为(4.0±3.1)、(14.9±13.7)和(5.8±4.8)μg·m^-3,主要金属ρ(K)、ρ(Ca)和ρ(Fe)平均值分别为(1.0±0.4...     

Agricultural causes of desertification risk in Minqin, China

This study uses statistical modeling techniques to develop a desertification risk index (RI) for Minqin County, Gansu Province, China. Twenty socio-economic factors were selected and compared with the RI results to explore the spatial and temporal variability of desertification risk in the study area and to identify possible local driving forces behind desertification risk. The explanatory factors were different in 1988, 1992 and 1997, possibly reflecting the role of temporal variation as a contributor to desertification. The average number of sheep per-household was found to be an important indicator of change in desertification risk, while changes in ridge crop planting area explained the distribution of the rate of change in desertification risk in 1988-1992. The results suggests that the RI was useful in expanding the understanding of spatial temporal desertification issues in Minqin County, as well as identifying a current set of agricultural activities related to desertification risk. Further, given the limited nature of consistent data and observations for the area, development of the RI also served to establish a baseline for future investigations into desertification change and the risks such change might pose for the region.     

Atmospheric transport pathways from the Bilibino nuclear power plant to Alaska

The Bilibino nuclear power plant (68°03′N, 166°20′E, 340 m asl) in northeastern Siberia is the closest Russian nuclear power plant to the USA. We used an isentropic trajectory model to estimate the probability that air in the Bilibino region would be transported to Alaska following a hypothetical accident. This estimate is based on the meteorological data from 1991 to 1995. Our calculations indicate that the probability that air in the Bilibino region will be transported to Alaska is approximately 6–16%, averaged over the entire year. This probability doubles in the summer and early fall with a maximum in August of 12–33%. For the entire year the mean, median, and minimum transport times from the plant to Alaska are 4, 3.5 and 1 d, respectively. Since rapid transport (1–2 d) could bring air parcels containing short-lived radionuclides, these events potentially represent the greatest risk to inhabitants of Alaska.     

两种四环素类抗生素厌氧生物毒性试验研究

在35℃中温厌氧条件下,采用间歇试验法,通过测定甲烷累积产量,研究脱水土霉素和强力霉素两种四环素类物质的厌氧生物降解性,利用相对活性来判断两类物质对产甲烷菌的抑制程度。试验结果表明：脱水土霉素浓度低于200mg／L时产生轻度抑制,高于200mg/L产生中度抑制;强力霉素低于200mg／L时产生中度抑制,高于200mg／L时对产甲烷菌产生重度抑制,厌氧污泥活性很难恢复。     

Prevalence of Conservation Design in an Agriculture-Dominated Landscape: The Case of Northern Indiana

We examined the prevalence of residential development that occurs with consideration of the natural features of the site, known as conservation design, within county-level planning jurisdictions across Northern Indiana. Using data from telephone interviews with representatives of planning departments, jurisdictions were ranked based on reported use of conservation design. Three categories of use emerged from the data: no use, use of individual practices associated with conservation design, and integration of multiple conservation design practices. Qualitative data analysis revealed that conservation design practices were not being used widely and, when used, were often used to fulfill stormwater requirements. Statistical analysis, using data from interviews, spatial data sets, and the U.S. Census Bureau, identified several significant positive predictors of the levels of conservation design use including conversion of forest or agricultural land cover to urban uses and education levels in the jurisdiction. Many of the interviewees noted that agricultural land is perceived to meet open space needs within their counties. Given that agricultural land does not fully meet all ecosystem needs, education about the benefits of other types of open space is suggested.

History Matters: Relating Land-Use Change to Butterfly Species Occurrence

Western European landscapes have drastically changed since the 1950s, with agricultural intensifications and the spread of urban settlements considered the most important drivers of this land-use/land-cover change. Losses of habitat for fauna and flora have been a direct consequence of this development. In the present study, we relate butterfly occurrence to land-use/land-cover changes over five decades between 1951 and 2000. The study area covers the entire Swiss territory. The 10 explanatory variables originate from agricultural statistics and censuses. Both state as well as rate was used as explanatory variables. Species distribution data were obtained from natural history collections. We selected eight butterfly species: four species occur on wetlands and four occur on dry grasslands. We used cluster analysis to track land-use/land-cover changes and to group communes based on similar trajectories of change. Generalized linear models were applied to identify factors that were significantly correlated with the persistence or disappearance of butterfly species. Results showed that decreasing agricultural areas and densities of farms with more than 10 ha of cultivated land are significantly related with wetland species decline, and increasing densities of livestock seem to have favored disappearance of dry grassland species. Moreover, we show that species declines are not only dependent on land-use/land-cover states but also on the rates of change; that is, the higher the transformation rate from small to large farms, the higher the loss of dry grassland species. We suggest that more attention should be paid to the rates of landscape change as feasible drivers of species change and derive some management suggestions.     

Assessment of surface water quality of selected estuaries of Malaysia: multivariate statistical techniques

Multivariate statistical techniques such as cluster analysis (CA), factor analysis (FA) were used for the evaluation of spatial variations and the interpretation of a large complex water quality data set of two selected estuaries of Malaysia. The two locations of interest with 10 sites in each location were Kuala Juru (Juru estuary) and Bukit Tambun (Jejawi estuary). Cluster analysis showed that some sites in both locations have similar sources of pollution from point or non-point sources whereas FA yielded four factors which are responsible for water quality variations explaining more than 80% of the total variance of the data set and allowed to group the selected water quality. Correlation analysis of the data showed that some parameters have strong association with other parameters and they share a common origin source. This study illustrates the usefulness of multivariate statistical analysis for evaluation and interpretation of complex data sets to get better information about the pollution sources/factors and understanding the behavior of the parameters in water quality for effective river water quality management.     

Exploring the severity of bicycle–vehicle crashes using latent class clustering approach in India

IntroductionBicyclists are vulnerable users in the shared asset like roadways. However, people still prefer to use bicycles for environmental, societal, and health benefits. In India, the bicycle plays a role in supporting the mobility to more people at lower cost and are often associated with the urban poor. Bicyclists represents one of the road user categories with highest risk of injuries and fatalities. According to the report by the Ministry of Road Transport and Highways (Accidents, 2017) in India, there is a sharp increase in the number of fatal victims for bicyclists in 2017 over 2016. The number of cyclists killed jumped from 2,585 in 2016 to 3,559 in 2017, a 37.7% increase. Method: Few studies have only investigated the crash risk perceived by the bicyclists while interacting with other road users. The present paper investigates the injury severity of bicyclists in bicycle-vehicle crashes that occurred in the state of Tamilnadu, India during the nine year period (2009–2017). The analyses demonstrate that dividing bicycle-vehicle collision data into five clusters helps in reducing the systematic heterogeneity present in the data and identify the hidden relationship between the injury severity levels of bicyclists and cyclists demographics, vehicle, environmental, temporal cause for the crashes. Results: Latent Class Clustering (LCC) approach was used in the present study as a preliminary tool for the segmentation of 9,978 crashes. Later, logistic regression analysis was used to identify the factors that influence bicycle crash severity for the whole dataset as well as for the clusters that were obtained from the LCC model. Results of this study show that combined use of both techniques reveals further information that wouldn’t be obtained without prior segmentation of the data. Few variables such as season, weather conditions, and light conditions were significant for certain clusters that were hidden in the whole dataset. This study can help domain experts or traffic safety researchers to segment traffic crashes and develop targeted countermeasures to mitigate injury severity.