Prediction of the concentration of chlorophyll-a for Liuhai urban lakes in Beijing City

The weekly water quality monitor data of Liuhai lakes between April 2003 and November 2004 in Beijing City were used as an example to build an artificial neural networks （ANN） model and a multi-varieties regression model respectively for predicting the fresh water algae bloom. The different predicted abilities of the two methods in Liuhai lakes were compared. A principle analysis method was first used to select the input variables of the models to avoid the phenomenon of collinearity in the data. The results showed that the input variables for the artificial neural networks were T, TP, transparency（SD）, DO, chlorophyll-a （Chl-a）,pH and the output variable was Chl-a. A three layer Levenberg-Marguardt feed forward leaming algorithm in ANN was used to model the eutrophication process of Liuhai lakes. 20 nodes in hidden layer and 1 node of output for the ANN model had been optimized by trial and error method. A sensitivity analysis of the input variables was performed to evaluate their relative significance in determining the predicted values. The correlation coefficient between predicted value and observed value in all data and in test data were 0.717 and 0.816 respectively in the artificial neural networks. The stepwise regression method was used to simulate the linear relation between Chl-a and temperature, of which the correlation coefficient was 0.213. By comparing the results of the two models, it was found that neural network models were able to simulate non-linear behavior in the water eutrophication process of Liuhai lakes reasonably and could successfully estimate some extreme values from calibration and test data sets.     

Biomineralogy of human urinary calculi (kidney stones) from some geographic regions of Sri Lanka

Kidney stones (urinary calculi) have become a global scourge since it has been recognized as one of the most painful medical problems. Primary causative factors for the formation of these stones are not clearly understood, though they are suspected to have a direct relationship to the composition of urine, which is mainly governed by diet and drinking water. Sixty nine urinary calculi samples which were collected from stone removal surgeries were analyzed chemically for their Na, K, Ca, Mg, Cu, Zn, Pb, Fe and phosphate contents. Structural and mineralogical properties of stones were studied by XRD and FT-IR methods. The mean contents of trace elements were 1348 mg kg⁻¹ (Na); 294 mg kg⁻¹ (K); 32% (Ca); 1426 mg kg⁻¹ (Mg); 8.39 mg kg⁻¹ (Mn); 258 mg kg⁻¹ (Fe); 67 mg kg⁻¹ (Cu); 675 mg kg⁻¹ (Zn); 69 mg kg⁻¹ (Pb); and 1.93% (PO₄³⁻). The major crystalline constituent in the calculi of Sri Lanka is calcium oxalate monohydrate. Principal component analysis was used to identify the multi element relationships in kidney stones. Three components were extracted and the first component represents positively correlated Na-K-Mg-PO₄³⁻ whereas the␣second components represent the larger positively weighted Fe–Cu–Pb. Ca–Zn correlated positively in the third component in which Mn–Cu correlated negatively. This study indicates that during the crystallization of human urinary stones, Ca shows more affinity towards oxalates whereas other alkali and alkaline earths precipitate with phosphates.Contribution from the Environmental Geology Research Group (EGRG), Department of Geology, University of Peradeniya, Sri Lanka.     

Spatiotemporal Features of Severe Air Pollution in Northern Taiwan (8 pp)

Background, Aims and Scope This research attempted to identify the dominant factors simultaneously affecting the airborne concentrations of five air pollutants with principal component analysis and to determine the meteorologically related parameters that cause severe air-pollution events. According to the definition of subPSI and PSI values through the U.S. EPA, the historical raw data of five criteria air pollutants, SO2, CO, O3, PM10 and NO2, were calculated as daily subPSI values. In addition to the airborne concentrations, this study simultaneous collected the surface meteorological parameters of the Taipei meteorological station, established by the Central Weather Bureau. Methods Principal component analysis was conducted to screen severe air pollution scenarios for five air pollutants: SO2, CO, O3, PM10 and NO2. The concentrations of various air pollutants measured at 17 air-quality stations in northern Taiwan from 1995 to 2001 were transformed into daily subPSI values. The correlation analysis of the five air pollutants and four meteorological parameters (wind speed, temperature, mixing height and ventilation rate) were included in this research. After screening severe air pollution scenarios, this study recognized the synoptic patterns easily causing the severe air-pollution events. Results and Discussion Analytical results showed that the eigenvalues of the first two principal components for SO2, CO, O3, PM10 and NO2 were greater than 1. The first component of five air pollutants explained 64, 64, 67, 76 and 63% of subPSI variance for SO2, CO, O3, PM10 and NO2, respectively. Only the correlation coefficient of NO2 and CO had statistically significant positive values (0.82); other pollutant pairs presented medium (0.4 to 0.7) or low (0 to 0.4) positive values. The correlation coefficients for air pollutants and three meteorological parameters (wind speed, mixing height and ventilation index) were medium or low negative values. In northern Taiwan, spring was most likely induced high concentrations and the component scores of the first component for SO2, CO, PM10 and NO2; summer was the worst season that caused high O3 episodes. Consequently, the analytical results of factor loadings for the first principal component and emission inventory of various sources revealed that mobile sources were dominant factors affecting ambient air quality in northern Taiwan. Conclusion According to the results of principal component analysis for the five air pollutants, the first two of 17 components were cited as major factors and explained 71% of subPSI variance. Based on the inventory of NOx emissions and the isopleth diagram of factor loading for the first component, mobile sources in the southwest Taipei City accounted for the highest factor loading values and emission inventory values. Synoptic analysis and principal component analysis demonstrated that three types of weather patterns (high-pressure recirculation, prefrontal warm sector and the southwesterly wind system) easily caused the severe air-pollution scenarios. In summary, if severe air-pollution days occurred, the average meteorological parameters experienced adverse conditions for diffusing air pollutants; that is, the average values of wind speed, mixing height and ventilation index were lower than 2.1 ms-1, 360 m and 800 m2s-1, respectively. If one of the three synoptic patterns were to occur in combination with adverse meteorological conditions, severe air-pollution events would be developed. Recommendation and Outlook By utilizing synoptic patterns, this work found three weather systems easily caused severe air-pollution events over northern Taiwan. Analytical results showed, respectively, the wind speed and mixing height were less than 2.1 m/s and 360 m during severe air-pollution events.     

活性污泥数学模型(ASM1)入流组分测定及应用

阐述了活性污泥 1号模型 (ASM1)入流中 7个含碳有机物、4个含氮物质、碱度等四大类 13种组分的分析方法 ,并用这些方法测定了西安市北石桥、电子村两个排放口的城市污水水质。实验结果表明快速可生物降解有机物 (SS)采用间歇实验法、慢速可生物降解有机物 (XS)采用测定BOD5间接计算法既操作简单又准确可靠 ,能为利用活性污泥 1号模型进行污水处理厂的设计、模拟及管理提供入流组分分析依据     

Degradation–environment–society spiral: A spatial auto‐logistic model in Thailand

In this contemporary interpretation of the widespread land degradation problem in Southeast Asia, it is hypothesized that spatial interplay of environmental and socioeconomic predictors determines the occurrence of land degradation. Village surveys, remote sensing and spatial auto‐logistic modelling of the relationship between degradation and land use dynamics in Lam Phra Phloeng watershed of Thailand enabled 80.2% of land to be classified correctly in terms of the presence or absence of erosion and explained 53.2% of the total variation. Cultivation and dependence on agriculture for livelihood positively and significantly affect degradation. Lack of access to institutional credit and land titles significantly increased the probability of occurrence of degradation. On the other hand, education and social cohesion are negatively associated with the occurrence of degradation. The Relative Operating Characteristic (ROC) curve was used to measures the performance of the model. The calculated area under the curve (0.879) suggests that significant predictor variables in the model can be confidently used to forecast the likelihood of occurrence of degradation and thus to identify priority areas for intervention. Policies to reduce land degradation should include measures to reduce pressure on the land, including alternative income sources. Policies could mobilize capital to invest in encouraging nature‐based tourism and other off‐farm income options.     

ARSENIC IN THE SHALLOW GROUND WATERS OF CONTERMINOUS UNITED STATES: ASSESSMENT,HEALTH RISKS,AND COSTS FOR MCL COMPLIANCE1

A methodology consisting of ordinal logistic regression (OLR) is used to predict the probability of occurrence of arsenic concentrations in different threshold limits in shallow ground waters of the conterminous United States (CONUS) subject to a set of influencing variables. The analysis considered a number of maximum contaminant level (MCL) options as threshold values to estimate the probabilities of occurrence of arsenic in ranges defined by a given MCL of 3, 5, 10, 20, and 50 μg/l and a detection limit of 1 μg/l. The fit between the observed and predicted probability of occurrence was around 83 percent for all MCL options. The estimated probabilities were used to estimate the median background concentration of arsenic in the CONUS. The shallow ground water of the western United States is more vulnerable than the eastern United States. Arizona, Utah, Nevada, and California in particular are hotspots for arsenic contamination. The risk assessment showed that counties in southern California, Arizona, Florida, and Washington and a few others scattered throughout the CONUS face a high risk from arsenic exposure through untreated ground water consumption. A simple cost effectiveness analysis was performed to understand the household costs for MCL compliance in using arsenic contaminated ground water. The results showed that the current MCL of 10 μg/l is a good compromise based on existing treatment technologies.     

A Multiscale Landscape Approach to Predicting Bird and Moth Rarity Hotspots in a Threatened Pitch Pine–Scrub Oak Community

Abstract:  In the northeastern United States, pitch pine (  Pinus rigida Mill.)–scrub oak ( Quercus ilicifolia Wang.) communities are increasingly threatened by development and fire suppression, and prioritization of these habitats for conservation is of critical importance. As a basis for local conservation planning in a pitch pine–scrub oak community in southeastern Massachusetts, we developed logistic-regression models based on multiscale landscape and patch variables to predict hotspots of rare and declining bird and moth species. We compared predicted moth distributions with observed species-occurrence records to validate the models. We then quantified the amount of overlap between hotspots to assess the utility of rare birds and moths as indicator taxa. Species representation in hotspots and the current level of hotspot protection were also assessed. Predictive models included variables at all measured scales and resulted in average correct classification rates (optimal cut point) of 85.6% and 89.2% for bird and moth models, respectively. The majority of moth occurrence records were within 100 m of predicted habitat. Only 13% of all bird hotspots and 10% of all moth hotspots overlapped, and only a few small patches in and around Myles Standish State Forest were predicted to be hotspots for both taxa. There was no correlation between the bird and moth species-richness maps across all levels of richness ( r =−0.03, p = 0.62). Species representation in hotspots was high, but most hotspots had limited or no protection. Given the lack of correspondence between bird and moth hotspots, our results suggest that use of species-richness indicators for conservation planning may be ineffective at local scales. Based on these results, we suggest that local-level conservation planning in pitch pine–scrub oak communities be based on multitaxa, multiscale approaches.     

基于灰色关联和主成分分析的农村建设用地集约利用评价——以江苏省为例

基于农村集体建设用地集约利用的内涵,构建了一套评价农村集体建设用地利用集约度的指标体系,并以江苏省苏南、苏中、苏北的12个行政村为例,采用灰色关联与主成分分析方法对各行政村集体建设用地利用的集约度进行了对比分析。分析结果表明:苏南地区的农村集体建设用地利用的集约度较高,而位于苏北地区的农村集体建设用地利用的集约度则相对较低,苏中地区在两者之间,整体上与江苏省区域经济的发展水平相符;从近郊与远郊的农村集体建设用地集约利用水平来看,两种评价方法测算的集约度虽有所差异,但两种方法对于近远郊的排序是基本一致的,呈现的趋势是近郊农村集体建设用地利用集约度高于远郊的。研究表明,两种方法所得的农村集体建设用地利用的集约度大致相似,在近郊远郊空间分布以及苏南、苏中、苏北的区域差异所呈现出的规律性,可为制定促进区域农村经济发展和提高农村建设用地的集约利用水平等相关的宏观政策提供参考依据。     

重庆市PM2.5浓度空间分异模拟及影响因子

基于Arcgis平台,利用土地利用回归模型模拟重庆市PM2.5浓度分布,获取了高分辨率结果图.从重庆市环保局网上获取了17个空气质量监测站点的PM2.5数据,利用16个监测点数据,结合土地利用数据、路网数据、DEM数据和人口数据建立土地利用回归模型,利用剩余的1个监测点数据来对回归映射结果进行检验.按照模型设置的变量生成方法,对监测点建立多种尺度的缓冲区,提取变量数据,最终生成了56个变量.按照土地利用回归模型的设置,56个自变量最终有3个变量进入PM2.5的回归方程,模型的R2逐步增大,且最终R2为0.84,模型拟合程度非常好.回归方程中,与研究区PM2.5浓度空间分布相关性最大的因素是空气质量监测站点500 m范围内的农用地面积,然后依次是DEM和1 000 m范围内一级公路总长度,它们与PM2.5的皮尔森相关系数依次是:0.695、-0.599和0.394.回归映射检验结果显示,检验点的误差率为2.7%,误差可以接受.回归映射结果显示,PM2.5浓度以高值分布于主城区,沿一级公路分布趋势明显,与高层紧密相关,模拟结果与实际情况相符.     

鄱阳湖-乐安河湿地水土环境中重金属污染的时空分布特征

选取流域两岸富含有色金属矿产资源的乐安河及至鄱阳湖段的典型湿地区域,分别于2012年4月(平水期)、8月(丰水期)、11月(枯水期)等不同时段采集不同样点底泥、表土、上覆水等环境样品,监测分析重金属Cu、Pb、Cd的含量,并借助统计分析方法识别乐安河湿地重金属污染的时空分布特征及其来源.结果表明,乐安河流域各样点的重金属Cu含量最高,且各样点重金属的含量值均表现为Cu>Cd>Pb.以丰水期的重金属污染最严重,平水期次之,枯水期的重金属污染最轻.重金属Cu含量的高值区出现在乐安河上游;而重金属Pb含量的高值区出现在乐安河下游及入湖区域;重金属Cd的高值区出现在乐安河中游.表征重金属Cu污染的主成分贡献率为36.99%,表征重金属Cd的主成分贡献率为30.12%.底泥Cu和上覆水Cu、河滩表土Cu含量具有较强的相关性;底泥Cd和表土Cd的含量也表现出强相关性.以上结果反映出水体、底泥和土壤中的Cu污染或Cd污染的来源具有一致性,主要来源于矿山开采排放的重金属酸性污废水;而其余组分间的相关性则表现不甚明显,反映出不同污染物的来源存在一定的差异性.