Nonparametric functional data estimation applied to ozone data: prediction and extreme value analysis

The study of extreme values and prediction of ozone data is an important topic of research when dealing with environmental problems. Classical extreme value theory is usually used in air-pollution studies. It consists in fitting a parametric generalised extreme value (GEV) distribution to a data set of extreme values, and using the estimated distribution to compute return levels and other quantities of interest. Here, we propose to estimate these values using nonparametric functional data methods. Functional data analysis is a relatively new statistical methodology that generally deals with data consisting of curves or multi-dimensional variables. In this paper, we use this technique, jointly with nonparametric curve estimation, to provide alternatives to the usual parametric statistical tools. The nonparametric estimators are applied to real samples of maximum ozone values obtained from several monitoring stations belonging to the Automatic Urban and Rural Network (AURN) in the UK. The results show that nonparametric estimators work satisfactorily, outperforming the behaviour of classical parametric estimators. Functional data analysis is also used to predict stratospheric ozone concentrations. We show an application, using the data set of mean monthly ozone concentrations in Arosa, Switzerland, and the results are compared with those obtained by classical time series (ARIMA) analysis.     

Issues to consider in the derivation of water quality benchmarks for the protection of aquatic life

While water quality benchmarks for the protection of aquatic life have been in use in some jurisdictions for several decades (USA, Canada, several European countries), more and more countries are now setting up their own national water quality benchmark development programs. In doing so, they either adopt an existing method from another jurisdiction, update on an existing approach, or develop their own new derivation method. Each approach has its own advantages and disadvantages, and many issues have to be addressed when setting up a water quality benchmark development program or when deriving a water quality benchmark. Each of these tasks requires a special expertise. They may seem simple, but are complex in their details. The intention of this paper was to provide some guidance for this process of water quality benchmark development on the program level, for the derivation methodology development, and in the actual benchmark derivation step, as well as to point out some issues (notably the inclusion of adapted populations and cryptic species and points to consider in the use of the species sensitivity distribution approach) and future opportunities (an international data repository and international collaboration in water quality benchmark development).     

Setting the most robust effluent level under severe uncertainty: Application of information-gap decision theory to chemical management

Decisions in ecological risk management for chemical substances must be made based on incomplete information due to uncertainties. To protect the ecosystems from the adverse effect of chemicals, a precautionary approach is often taken. The precautionary approach, which is based on conservative assumptions about the risks of chemical substances, can be applied selecting management models and data. This approach can lead to an adequate margin of safety for ecosystems by reducing exposure to harmful substances, either by reducing the use of target chemicals or putting in place strict water quality criteria. However, the reduction of chemical use or effluent concentrations typically entails a financial burden. The cost effectiveness of the precautionary approach may be small. Hence, we need to develop a formulaic methodology in chemical risk management that can sufficiently protect ecosystems in a cost-effective way, even when we do not have sufficient information for chemical management. Information-gap decision theory can provide the formulaic methodology. Information-gap decision theory determines which action is the most robust to uncertainty by guaranteeing an acceptable outcome under the largest degree of uncertainty without requiring information about the extent of parameter uncertainty at the outset. In this paper, we illustrate the application of information-gap decision theory to derive a framework for setting effluent limits of pollutants for point sources under uncertainty. Our application incorporates a cost for reduction in pollutant emission and a cost to wildlife species affected by the pollutant. Our framework enables us to settle upon actions to deal with severe uncertainty in ecological risk management of chemicals.     

Assessment of metal pollution based on multivariate statistical modeling of 'hot spot' sediments from the Black Sea

The paper deals with application of different statistical methods like cluster and principal components analysis (PCA), partial least squares (PLSs) modeling. These approaches are an efficient tool in achieving better understanding about the contamination of two gulf regions in Black Sea. As objects of the study, a collection of marine sediment samples from Varna and Bourgas "hot spots" gulf areas are used. In the present case the use of cluster and PCA make it possible to separate three zones of the marine environment with different levels of pollution by interpretation of the sediment analysis (Bourgas gulf, Varna gulf and lake buffer zone). Further, the extraction of four latent factors offers a specific interpretation of the possible pollution sources and separates natural from anthropogenic factors, the latter originating from contamination by chemical, oil refinery and steel-work enterprises. Finally, the PLSs modeling gives a better opportunity in predicting contaminant concentration on tracer (or tracers) element as compared to the one-dimensional approach of the baseline models. The results of the study are important not only in local aspect as they allow quick response in finding solutions and decision making but also in broader sense as a useful environmetrical methodology.     

Characterising the hydrological regime of an ungauged temporary river system: a case study

Temporary streams are characterised by specific hydrological regimes, which influence ecosystem processes, groundwater and surface water interactions, sediment regime, nutrient delivery, water quality and ecological status. This paper presents a methodology to characterise and classify the regime of a temporary river in Southern Italy based on hydrological indicators (HIs) computed with long-term daily flow records. By using a principal component analysis (PCA), a set of non-redundant indices were identified describing the main characteristics of the hydrological regime in the study area. The indicators identified were the annual maximum 30- and 90-day mean (DH4 and DH5), the number of zero flow days (DL6), flow permanence (MF) and the 6-month seasonal predictability of dry periods (SD6). A methodology was also tested to estimate selected HIs in ungauged river reaches. Watershed characteristics such as catchment area, gauging station elevation, mean watershed slope, mean annual rainfall, land use, soil hydraulic conductivity and available water content were derived for each site. Selected indicators were then linked to the catchment characteristics using a regression analysis. Finally, MF and SD6 were used to classify the river reaches on the basis of their degree of intermittency. The methodology presented in this paper constitutes a useful tool for ecologists and water resource managers in the Water Framework Directive implementation process, which requires a characterisation of the hydrological regime and a ‘river type’ classification for all water bodies.     

A methodology for the estimation of road transport air emissions in urban areas of Italy

This paper focuses on the top–down approach for estimating road transport emissions at a local level. A bottom–up approach is preferable where the data and information required by estimation methodologies are available at regional territorial level. In the absence of this regional data, emissions are adapted from national to smaller levels by means of proxy variables. This study highlights the importance of an improvement of the top–down methodology and identifies a corrective index to better characterise road transport emissions at a local level. A set of indicators related to transport activities is selected in order to identify homogeneous areas in the Italian territory. For each area, COPERT (Computer Programme to estimate Emissions from Road Traffic) methodology is applied to estimate the atmospheric emissions of different pollutants; the same methodology is used to calculate road transport emissions at a national level. The results, calculated according to vehicle category and driving mode, are compared with those deriving from a spatial disaggregation of national data by means of simple surrogate variables.     

水环境数学模型的研究进展

在作者多年研究工作和查阅大量文献资料的基础上,本文综述了环境数学模型国内外的研究现状、发展历史及未来趋势。重点介绍了这方面研究的最新进展。该文还介绍了本领域所涉及的理论和实际应用问题,提出了今后值得进行研究的若干前沿课题,给出了60余篇关于水环境数学模型研究的主要参考文献目录。     

Multidimensional modeling of aerosol monitoring data

The present study deals with the application of N-way factor analysis for modeling and interpretation of a three-dimensional environmental data set acquired from monitoring of particulate matter (PM) collected at four different sampling locations in Lower Austria region (Central Europe). In the study the Tucker3 algorithm for N-way modeling was used. It was statistically validated that the Tucker3 model offered having the dimensionality [222] is appropriate for correct interpretation of the relationships between chemical parameters, sampling locations and sampling period. The Tucker3 model allowed to distinguish three major sources of pollution in the region of interest conditionally named “soil dust”, “combustion” and “street dust” latent factors as responsible for chemical profile of PM and to identify seasonal variability. Additionally, some specificity of the sampling locations was also pointed out.     

A regional flux-based risk assessment approach for multiple contaminated sites on groundwater bodies

In the context of the Water Framework Directive (EP and CEU, 2000), management plans have to be set up to monitor and to maintain water quality in groundwater bodies in the EU. In heavily industrialized and urbanized areas, the cumulative effect of multiple contaminant sources is likely and has to be evaluated. In order to propose adequate measures, the calculated risk should be based on criteria reflecting the risk of groundwater quality deterioration, in a cumulative manner and at the scale of the entire groundwater body. An integrated GIS- and flux-based risk assessment approach for groundwater bodies is described, with a regional scale indicator for evaluating the quality status of the groundwater body. It is based on the SEQ-ESO currently used in the Walloon Region of Belgium which defines, for different water uses and for a detailed list of groundwater contaminants, a set of threshold values reflecting the levels of water quality and degradation with respect to each contaminant. The methodology is illustrated with first results at a regional scale on a groundwater body-scale application to a contaminated alluvial aquifer which has been classified to be at risk of not reaching a good quality status by 2015. These first results show that contaminants resulting from old industrial activities in that area are likely to contribute significantly to the degradation of groundwater quality. However, further investigations are required on the evaluation of the actual polluting pressures before any definitive conclusion be established.     

Statistical Study of Air Pollutant Concentrations via Generalized Gamma Distributions

This paper deals with modeling observed frequency distributions of air quality data measured in the area of Venice, Italy. The paper discusses the application of the generalized gamma distribution (ggd) which has not been commonly applied to air quality data notwithstanding the fact that it embodies most distribution models used for air quality analyses. The approach yields important simplifications for statistical analyses. A comparison among the ggd and other relevant models (standard gamma, Weibull, lognormal), carried out on daily sulphur dioxide concentrations in the area of Venice underlines the efficiency of ggd models in portraying experimental data.     

A comparison of statistical methods for deriving freshwater quality criteria for the protection of aquatic organisms

Species sensitivity distributions (SSDs) are increasingly used in both ecological risk assessment and derivation of water quality criteria. However, there has been debate about the choice of an appropriate approach for derivation of water quality criteria based on SSDs because the various methods can generate different values. The objective of this study was to compare the differences among various methods. Data sets of acute toxicities of 12 substances to aquatic organisms, representing a range of classes with different modes of action, were studied. Nine typical statistical approaches, including parametric and nonparametric methods, were used to construct SSDs for 12 chemicals. Water quality criteria, expressed as hazardous concentration for 5 % of species (HC₅), were derived by use of several approaches. All approaches produced comparable results, and the data generated by the different approaches were significantly correlated. Variability among estimates of HC₅ of all inclusive species decreased with increasing sample size, and variability was similar among the statistical methods applied. Of the statistical methods selected, the bootstrap method represented the best-fitting model for all chemicals, while log-triangle and Weibull were the best models among the parametric methods evaluated. The bootstrap method was the primary choice to derive water quality criteria when data points are sufficient (more than 20). If the available data are few, all other methods should be constructed, and that which best describes the distribution of the data was selected.     

Air distribution in the Borden aquifer during in situ air sparging

A field experiment was conducted at Canadian Forces Base Borden (CFB Borden) to assess the air distribution from a single in situ air sparging injection point. This aquifer consists of fine to medium sand deposited in horizontal layers. The permeability at the study location varied from 10(-10) to 10(-14) m2 and distinct low permeability horizons were present at approximately 1.2, 2.0, and 2.9 m below the water table. Prior to air injection, a 15x15-m portion of the vadose zone was excavated to the water table (approximately 1 m below ground surface) in order to visually observe air release distribution at the water table. The water table was actively maintained 5 cm above the excavated surface. The sparging system operated for a period of 7 days with an injection flow rate of 200 m3/days (5 scfm). The resulting subsurface air distribution was assessed using a variety of techniques including neutron logging, borehole and surface ground penetrating radar, piezometric head measurements, surface visualization, and hydraulic testing. Through this combination of tests, it was demonstrated that variations in permeability and, hence, capillary pressure at the site were sufficient to cause the injected air to spread laterally, forming stratigraphically trapped air pockets beneath the low permeability horizons. The formation of these air pockets eventually resulted in a buildup of capillary pressure that exceeded the air entry pressure and allowed some air to migrate up through the lower permeability layers. Each of the assessment techniques employed generated information at different spatial scales that prevented a direct comparison of the results from the various techniques; however, the results from all techniques proved to be critical in the interpretation of the experimental data. As a consequence, the different assessment techniques should not be viewed as alternatives, but rather as complimentary techniques.     

New indicator approaches for effective urban air quality management

Measurements of urban air quality at monitoring stations in developed countries have frequently involved the criteria gaseous pollutants, particulates, hazardous air pollutants, perceived air quality and relevant meteorological conditions. Large numbers of indicators have therefore been established to quantify emissions, concentrations and environmental and human health impacts of each of these groups of substances. To simplify the data for management, several indicators have been grouped together to form urban air quality indices but the weightings of individual variables is contentious. In industrialising and developing countries, data may be limited and traditional air pollutant indicators cannot often be constructed. The emphasis therefore has to be placed on the development of policy-relevant indicators, such as Response Indicators that reflect different policy principles for regulating air pollutant emissions. Indices that quantify the air quality management capabilities and capacities at the city level provide further useful decision-relevant tools. Four sets of indices, namely, 1. air quality measurement capacity, 2. data assessment and availability, 3. emissions estimates, and 4. management enabling capabilities, and a composite index to evaluate air quality management capability, were constructed and applied to 80 cities. The indices revealed that management capability varied widely between the cities. In some of the cities, existing national knowledge on urban air quality could have been more effectively used for management. It was concluded that for effective urban air quality management, a greater emphasis should be given, not just to monitoring and data capture programmes, but to the development of indicators and indices that empower decision-makers to initiate management response strategies. Over-reliance on restricted, predetermined sets of traditional air quality indicators should be avoided.     

Monitoring atmospheric pollutants in the biosphere reserve Wienerwald by a combined approach of biomonitoring methods and technical measurements

In this study a combined approach of bioindication results correlated with an extensive set of data on air pollution and climate was used to assess the pollution status of the Man and Biosphere Reserve Wienerwald (Austria). Bryophytes served as impact indicators (via the Index of Atmospheric Purity-method IAP) at 30 sites as well as accumulation monitors for airborne trace elements (Al, Pb, V, S, Zn, Fe, Cu, Cr, Ni, Co, Mo, Cd, As, Sb and 16 EPA-PAHs) at 10 sites within the reserve. The results of these bioindication methods were subsequently correlated with further pollution (NO(2), SO(2) and dust) and climate data (precipitation, temperature and humidity). The findings obtained clearly indicate the following: Bryophyte distribution is solely influenced by the status of air quality, without interference by climatic or site-related factors, which is in contrast to several previous investigations. IAP-values correlated significantly with NO(2) (0.553; P=0.004), SO(2) winter values (0.511; P=0.013) and PM10 (dust) (0.561; P=0.013). The results obtained via chemical analyses revealed a strong correlation with data derived from the IAP methodology. In terms of the overall air quality within the biosphere reserve Wienerwald, the north-eastern part appears to be the most affected one with a most likely pollution contribution emitted by the capital city Vienna, agriculture and neighbouring countries.     

Forewarning model for water pollution risk based on Bayes theory

In order to reduce the losses by water pollution, forewarning model for water pollution risk based on Bayes theory was studied. This model is built upon risk indexes in complex systems, proceeding from the whole structure and its components. In this study, the principal components analysis is used to screen out index systems. Hydrological model is employed to simulate index value according to the prediction principle. Bayes theory is adopted to obtain posterior distribution by prior distribution with sample information which can make samples’ features preferably reflect and represent the totals to some extent. Forewarning level is judged on the maximum probability rule, and then local conditions for proposing management strategies that will have the effect of transforming heavy warnings to a lesser degree. This study takes Taihu Basin as an example. After forewarning model application and vertification for water pollution risk from 2000 to 2009 between the actual and simulated data, forewarning level in 2010 is given as a severe warning, which is well coincide with logistic curve. It is shown that the model is rigorous in theory with flexible method, reasonable in result with simple structure, and it has strong logic superiority and regional adaptability, providing a new way for warning water pollution risk.     

A holistic approach for optimal design of air quality monitoring network expansion in an urban area

This paper presents an objective methodology for determining the optimum number of ambient air quality stations in a monitoring network. The methodology integrates the multiple-criteria method with the spatial correlation technique. The pollutant concentration and population exposure data are used in this methodology in different ways. In the first stage, the Fuzzy Analytic Hierarchy Process (FAHP) with triangular fuzzy numbers (TFNs) is used to identify the most desirable monitoring locations. The network configuration is then determined on the basis of the concept of sphere of influences (SOIs). The SOIs are dictated by a predetermined cutoff value (r_c) in the spatial correlation coefficients (r) between the pollutant concentrations at the monitoring stations identified from first step and the corresponding concentrations at neighboring locations in the region. Finally, the optimal station locations are ranked by using combined utility scores gained from the first and second steps. The expansion of air quality monitoring network of Riyadh city in Saudi Arabia is used as a case study to demonstrate the proposed methodology.     

Multi-criteria analysis for PM10 planning

To implement sound air quality policies, Regulatory Agencies require tools to evaluate outcomes and costs associated to different emission reduction strategies. These tools are even more useful when considering atmospheric PM10 concentrations due to the complex nonlinear processes that affect production and accumulation of the secondary fraction of this pollutant. The approaches presented in the literature (Integrated Assessment Modeling) are mainly cost-benefit and cost-effective analysis. In this work, the formulation of a multi-objective problem to control particulate matter is proposed. The methodology defines: (a) the control objectives (the air quality indicator and the emission reduction cost functions); (b) the decision variables (precursor emission reductions); (c) the problem constraints (maximum feasible technology reductions). The cause-effect relations between air quality indicators and decision variables are identified tuning nonlinear source–receptor models. The multi-objective problem solution provides to the decision maker a set of not-dominated scenarios representing the efficient trade-off between the air quality benefit and the internal costs (emission reduction technology costs). The methodology has been implemented for Northern Italy, often affected by high long-term exposure to PM10. The source–receptor models used in the multi-objective analysis are identified processing long-term simulations of GAMES multiphase modeling system, performed in the framework of CAFE-Citydelta project.     

Improved spatial allocation methodology for on-road mobile emissions

Emissions from automobiles and trucks operating on public roads represent a major portion of the air pollutants included in emission inventories. When emission data are prepared for air quality modeling studies, such as those supporting development of a State Implementation Plan, an emission processor matches the spatial and temporal resolution of the emissions to the requirements of the modeling study. However, the spatial location of vehicular emissions is not known and must be estimated. This paper presents a methodology for determining the spatial distribution of the roads belonging to a road class using geospatial data functions, such as those commonly provided by a geographic information system. Vehicle-miles traveled (VMT) are then allocated to medium-resolution (12 x 12-km) and fine-resolution (4 x 4-km) modeling grids using both this methodology and the existing top-down methodology, which uses population density. The results show a significant difference in the spatial distribution of VMT between these two methodologies. Based upon these results, we recommend using the road class-specific methodology in lieu of the population methodology for spatially allocating vehicular emissions for medium- and finer-resolution modeling grids.     

Assessment and rationalization of water quality monitoring network: a multivariate statistical approach to the Kabbini River (India)

The establishment of an efficient surface water quality monitoring (WQM) network is a critical component in the assessment, restoration and protection of river water quality. A periodic evaluation of monitoring network is mandatory to ensure effective data collection and possible redesigning of existing network in a river catchment. In this study, the efficacy and appropriateness of existing water quality monitoring network in the Kabbini River basin of Kerala, India is presented. Significant multivariate statistical techniques like principal component analysis (PCA) and principal factor analysis (PFA) have been employed to evaluate the efficiency of the surface water quality monitoring network with monitoring stations as the evaluated variables for the interpretation of complex data matrix of the river basin. The main objective is to identify significant monitoring stations that must essentially be included in assessing annual and seasonal variations of river water quality. Moreover, the significance of seasonal redesign of the monitoring network was also investigated to capture valuable information on water quality from the network. Results identified few monitoring stations as insignificant in explaining the annual variance of the dataset. Moreover, the seasonal redesign of the monitoring network through a multivariate statistical framework was found to capture valuable information from the system, thus making the network more efficient. Cluster analysis (CA) classified the sampling sites into different groups based on similarity in water quality characteristics. The PCA/PFA identified significant latent factors standing for different pollution sources such as organic pollution, industrial pollution, diffuse pollution and faecal contamination. Thus, the present study illustrates that various multivariate statistical techniques can be effectively employed in sustainable management of water resources. Highlights ? The effectiveness of existing river water quality monitoring network is assessed ? Significance of seasonal redesign of the monitoring network is demonstrated ? Rationalization of water quality parameters is performed in a statistical framework