Application of statistical modeling to optimize a coastal water quality monitoring program

The long-term water quality monitoring program implemented by the Massachusetts Water Resources Authority in 1992 is extensive and has provide substantial understanding of the seasonality of the waters in both Boston Harbor and Massachusetts Bay and the response to improvements in effluent quality and offshore transfer of the effluent in September 2000. The monitoring program was designed with limited knowledge of spatial and temporal variability and long-term trends within the system. This led to an extensive spatial and temporal sampling program. The data through 2003 showed high correlation within physical parameters measured (e.g., salinity, dissolved oxygen) and in biological measures such as chlorophyll fluorescence. To address the potential sampling redundancies in the measurement program, an assessment of the impact of reduced levels of monitoring on the ability to make water quality decisions was completed. The optimization was conducted by applying statistical models that took into account whether there was evidence of a seasonal pattern in the data. The optimization used model survey average readings to identify temporal fixed effects, model survey-average-corrected individual station readings to identify spatial fixed effects, corrected the individual station readings for temporal and spatial fixed effects and derived a correlation model for the corrected data, and applied the correlation model to characterize the correlation of annual average readings from reduced monitoring programs with true parameter levels. Reductions in the number of sampling stations were found less detrimental to the quality of the data for annual decision-making than reductions in the number of surveys per year, although there is less of a difference in this regard for dissolved oxygen than there is for chlorophyll. The analysis led to recommendations for a substantially lower monitoring effort with minimal loss of information. The recommendation supported an annual budget savings of approximately $183,000. Most of the savings was from fewer surveys as approximately $21,000 came from the reduction in the number of stations monitored from 21 to 7 and associated laboratory analytical costs.     

Artificial neural network based carbon monoxide persistence models for episodic urban air quality management

This paper describes the development of artificial neural network (ANN) based carbon monoxide (CO) persistence (ANNCOP) models to forecast 8-h average CO concentration using 1-h maximum predicted CO data for the critical (winter) period (November–March). The models have been developed for three 8-h groupings of 10 p.m. to 6 a.m., 6 a.m. to 2 p.m. and 2–10 p.m., at two air quality control regions (AQCRs) in Delhi city, representing an urban intersection and an arterial road consisting heterogeneous traffic flows. The result indicates that time grouping of 2–10 pm is dominantly affected by inversion conditions and peak traffic flow. The ANNCOP model corresponding to this grouping predicts the 8-h average CO concentrations within the accuracy range of 68–71%. The CO persistence values derived from ANNCOP model are comparable with the persistence values as suggested by the Environmental Protection Agency (EPA), USA. This work demonstrates that ANN based model is capable of describing winter period CO persistence phenomena.     

Determination of metal accumulation in deposited street dusts in Amman, Jordan

Street dust samples (120 in total) were collected under stable weather conditions during the hot, dry season (August and September) of 2004 from six different localities (industrial, heavy traffic, medium traffic, light traffic, low traffic and rural) in greater Amman, the capital of Jordan. The concentrations of Fe, Cu, Cd, Pb, Zn and Ni in the dusts were determined by atomic absorption spectrophotometry. The high concentrations of Pb, Fe and Zn in the street dust samples were related to both anthropogenic (industrial sources combined with traffic sources) and natural sources. Surprisingly, the concentrations of Cd in the dusts were low. Correlation coefficient analysis and principle component analysis identified three main sources of these elements and the corresponding distributions. The elements Pb, Zn, Cd, Fe, Cu and Ni were mainly derived from industrial sources, with Pb and Zn additionally derived from traffic sources. The street dusts were found to have highly elevated levels of Zn, particularly along the main trunk roads, indicating that the Zn in the street dusts may be derived from traffic sources, especially vehicle tyres. The concentrations of metals in the different street dust samples were found to vary depending on the density of traffic.     

Application of Multiattribute Decision-Making Methods for the Determination of Relative Significance Factor of Impact Categories

A relative significance factor (f _i ) of an impact category is the external weight of the impact category. The objective of this study is to propose a systematic and easy-to-use method for the determination of f _i . Multiattribute decision-making (MADM) methods including the analytical hierarchy process (AHP), the rank-order centroid method, and the fuzzy method were evaluated for this purpose. The results and practical aspects of using the three methods are compared. Each method shows the same trend, with minor differences in the value of f _i . Thus, all three methods can be applied to the determination of f _i . The rank order centroid method reduces the number of pairwise comparisons by placing the alternatives in order, although it has inherent weakness over the fuzzy method in expressing the degree of vagueness associated with assigning weights to criteria and alternatives. The rank order centroid method is considered a practical method for the determination of f _i because it is easier and simpler to use compared to the AHP and the fuzzy method.     

ANALYZING CORRELATIONS BETWEEN STREAM AND WATERSHED ATTRIBUTES1

ABSTRACT: Bivariate correlation analysis has been widely used to explore relationships between stream and watershed attributes that have all been measured on the same set of watersheds or sampling locations. Researchers routinely test H₀: ρ= 0 for each correlation in a large table and then go on to discuss only those that are declared “significant.” Such test results are inaccurate because no allowance is made for multiple testing, and also because the tests are not mutually independent. This paper reviews the Bonferroni approach to controlling the overall error rate in multiple testing and shows how the approach becomes impractical for large correlation tables. The Hotelling/Williams test is introduced for comparing two dependent correlations that share a variable, and numerical constraints for two such correlations are illustrated. References are also given for testing other hypothesized patterns among dependent correlations, and links to dependent correlation software are provided. The methods are illustrated for watershed and stream variables sampled in 23 small agricultural watersheds of the Willamette Valley, Oregon.     

Public opinion and siting solid waste landfills in Kuwait

Siting municipal solid waste landfills in Kuwait had not considered public concerns about the location of such facilities. Kuwait Municipality has disposed urban waste in abandoned sand quarries for the past 20 years in an unplanned fashion. Due to this improper siting criteria, environmental and health problems have risen from old landfills that are located in residential areas. In an effort to help responsible decision-makers in assessing public support for siting landfills a structured, yet simple, questionnaire was designed and distributed to a random sample of heads of households. Statistical techniques were employed to examine trends and relationships between the sample public responses concerning landfill siting criteria. The findings of the research indicated that a significant percentage of the responding sample did not know about the various landfill impacts and less than 50% were aware of the negative impacts of landfills on the public health and the environment. The visual appearance of the landfill scored the lowest impact in the opinion of the sample public while the most important aspect in siting a landfill appeared to be its economic consideration. The research findings also directed attention to the important role that the media could play in increasing public awareness. Results from the cross-classification analysis demonstrated the existence of differences in awareness levels according to age, nationality, martial status, education, and occupation of the responding sample. Statistically, these differences were significant at the 95% significance level, as supported by the test of the χ-square.     

清洁生产纳入环境影响评价的意义

当前的环境影响评价在某种程度上更侧重于环境的末端治理,无法将污染控制与生产过程控制密切结合,资源和能源不能在生产过程中得到充分利用.而清洁生产在这方面却有着独特的特点和优势.根据清洁生产与环境影响评价的内在联系,尽快地将清洁生产纳入环境影响评价是具有重要意义的,清洁生产应该成为环境影响评价报告撰写中的重要内容.     

华南地区酸沉降的硫同位素组成及其环境意义

为探讨华南珠江三角洲和湘桂走廊及其周边地区的酸沉降硫源,分别对大气降水、大气SO₂、气溶胶和工业用煤、重油及其燃烧产物进行了硫同位素组成测定.研究结果表明,珠江三角洲和湘桂走廊地区的大气降水硫同位素组成有明显的差异,其大气降水δ³⁴S值的变化范围分别为1.9‰～10.3‰和-4.8‰～-0.1‰.湘桂走廊地区大气降水明显的富集轻硫同位素(32S),而珠江三角洲地区则富集重硫同位素(34S).在煤、重油燃烧过程中硫同位素分馏效应研究基础上,将该地区酸雨硫源区分为4种类型,即人为成因硫、天然生物硫、海雾硫和远距离传输硫.在定量计算不同硫源对酸雨贡献的基础上.提出人为成因硫是珠江三角洲和湘桂走廊地区最强的污染硫源,而生物硫在夏季贡献突出,其贡献率分别为47%和52%.传输硫在冬季贡献率可达49%.上述结果,对研究治理华南地区的酸雨具有重要意义.     

达里诺尔湖近2400年以来Hg元素含量变化及环境意义

汞（Hg）元素作为一种全球性循环元素,对环境的影响正逐渐成为人们关注的焦点之一.为揭示寒旱区达里诺尔湖沉积物的Hg污染历史及人类活动对湖泊沉积物中Hg污染的影响,通过采集湖泊沉积物岩芯（编号DL1）,在建立DL1岩芯沉积地层年代框架的基础上,结合沉积物粒度特征、总有机质（TOC）含量等指标参数的变化,对DL1岩芯记录的地质历史上的Hg含量变化特征及环境意义进行了综合分析.结果显示,沉积物记录的Hg元素污染具有明显的阶段性变化特征：2400~1400 cal.a BP,Hg含量呈逐渐降低趋势,期间降水量呈逐步减少趋势,区域气候显著干旱;1400~200 cal.a BP,Hg含量相对稳定,呈小幅度波动变化,对应了区域气候的干-湿交替;近200 cal.a BP以来,Hg含量明显增加,这可能是受人类活动影响的结果.整体上,地质历史上DL1沉积物中Hg含量变化受气候干冷-暖湿交替周期性变化影响较大.而近200 cal.a BP来Hg与TOC等指标间显著正相关,则显示了沉积物中有机质可能吸附了大量的Hg元素,使其呈顶端富集趋势,进而对湖泊水生态环境造成影响.     

When leaders are forced to stay: The indirect effects of leaders' reluctant staying on subordinates' performance

Leaders who desire to leave the current organization are sometimes forced to stay. The leadership behaviors of these leaders are underexplored in the current literature. Building on proximal withdrawal states theory, this study examines two pathways through which leaders' reluctant staying mindset (i.e., desire but are unable to quit) relates to their subordinates' task performance and organizational citizenship behaviors (OCB). One pathway proposes increased laissez-faire leadership behaviors due to leaders' lower intrinsic motivation; the second pathway proposes increased delegation behaviors due to leaders' higher extrinsic motivation. Using three-wave data collected from 100 leaders and 313 subordinates, we found that leaders' reluctant staying was indirectly and negatively associated with subordinates' task performance and OCB through leaders' lower perceptions of task significance and higher laissez-faire leadership behaviors. At the same time, leaders' reluctant staying increased their bottom-line mentality and delegation behaviors, but the indirect effects on subordinates' performance outcomes were not significant. We discuss the implications of our findings for theory, practices, and future research regarding how to manage leaders who stay reluctantly in the organization.