Comparing the Number of Ozone Exceedances in Different Seasons of the Year in Mexico City

Ozone air pollution is a serious problem in several cities of the world. Hence, to analyse the behaviour of this pollutant is a very important issue. One problem of interest is to study the behaviour of the inter-occurrences times between two ozone exceedances, i.e. between two days in which the pollutant’s measurement surpasses a given threshold. Another interest resides in comparing the behaviour of ozone measurements in different seasons of the year. In this paper we use some Poisson models to analyse this problem. The time interval at which the ozone measurements were taken is split into subintervals corresponding roughly to the seasons of the year. We consider three parametric forms for the mean of the Poisson model, and consequently for the mean of the inter-occurrences times. In each model, the parameters describing its mean are estimated using Bayesian inference via Markov chain Monte Carlo methods. The models are applied to the ozone measurements provided by the Mexico City monitoring network. Theoretical results suggest that an increase has occurred in the mean inter-exceedances times and this is corroborated by the observed data. Differences between the behaviour of the pollutant during different seasons of the year are also detected as well as similarities in the same season in different years. Besides estimating the mean of the Poisson models, inference for the possible presence and location of change-points indicating change of parameters of the model is also performed.     

Application of the benthic index of biotic integrity to environmental monitoring in Chesapeake Bay

The Chesapeake Bay benthic index of biotic integrity (B-IBI) was developed to assess benthic community health and environmental quality in Chesapeake Bay. The B-IBI provides Chesapeake Bay monitoring programs with a uniform tool with which to characterize bay-wide benthic community condition and assess the health of the Bay. A probability-based design permits unbiased annual estimates of areal degradation within the Chesapeake Bay and its tributaries with quantifiable precision. However, of greatest interest to managers is the identification of problem areas most in need of restoration. Here we apply the B-IBI to benthic data collected in the Bay since 1994 to assess benthic community degradation by Chesapeake Bay Program segment and water depth. We used a new B-IBI classification system that improves the reliability of the estimates of degradation. Estimates were produced for 67 Chesapeake Bay Program segments. Greatest degradation was found in areas that are known to experience hypoxia or show toxic contamination, such as the mesohaline portion of the Potomac River, the Patapsco River, and the Maryland mainstem. Logistic regression models revealed increased probability of degraded benthos with depth for the lower Potomac River, Patapsco River, Nanticoke River, lower York River, and the Maryland mainstem. Our assessment of degradation by segment and water depth provided greater resolution of relative condition than previously available, and helped define the extent of degradation in Chesapeake Bay.     

Syndromes of Global Change: a qualitative modelling approach to assist global environmental management

A novel transdisciplinary approach to investigate Global Change (GC) is presented. The approach rests on the decomposition of the intrigue dynamics of GC into patterns of civilization–nature interactions (syndromes) by an iterative scientific process of observations, data and system theoretical analyses, and modelling attempts. We illustrate the approach by a detailed analysis of the Sahel Syndrome, which describes the rural poverty driven overuse of natural resources. The investigation is performed by (i) identifying relevant symptoms and interlinkages which are characteristics for this pattern, and (ii) a qualitative model representing the internal dynamics of the essential flywheel. The geographical patchwork of the regions affected by the syndrome which is obtained by global data analysis, proves the high global relevance of this pattern. The qualitative model is employed for an evaluation of basic policy strategies debated in the context of rural poverty driven environmental degradation. It turns out that a mixed policy of combating poverty and introducing soil preserving agricultural techniques and practices is most promising to tackle the syndrome dynamics.     

Dietary equivalence analysis: Quantifying the loss of food resource to marine predators from the entrapment of fish in power station cooling water systems

As part of the Environmental Impact Assessment and Habitats Regulations Assessment process, developers are required to assess the indirect effects of fish entrapment however, presently there is little guidance to support quantitative assessments of food resource losses to higher trophic levels. To address this gap, a detailed and quantitative tool termed Dietary Equivalence Analysis is presented in this paper. Dietary Equivalence Analysis combines bioenergetic modelling with dietary and demographic information to express entrapment predictions in terms of the number of marine predators, or the proportion of a population, that would be sustained by the biomass of fish prey had it not been entrapped. The application of this tool is demonstrated by way of a worked example and includes a sensitivity analysis using Monte Carlo simulation to determine the influence of input parameter uncertainty on dietary equivalence estimates. It is shown that the Dietary Equivalence Analysis framework can be used to develop project- and regional-specific quantitative assessments for a number of marine predators. This is considered to provide competent authorities with a new and more realistic perspective from which the magnitude of predicted fish entrapment effects can be viewed and assessed.     

The Functional Resonance Analysis Method for a systemic risk based environmental auditing in a sinter plant: A semi-quantitative approach

Environmental auditing is a main issue for any production plant and assessing environmental performance is crucial to identify risks factors. The complexity of current plants arises from interactions among technological, human and organizational system components, which are often transient and not easily detectable. The auditing thus requires a systemic perspective, rather than focusing on individual behaviors, as emerged in recent research in the safety domain for socio-technical systems. We explore the significance of modeling the interactions of system components in everyday work, by the application of a recent systemic method, i.e. the Functional Resonance Analysis Method (FRAM), in order to define dynamically the system structure. We present also an innovative evolution of traditional FRAM following a semi-quantitative approach based on Monte Carlo simulation. This paper represents the first contribution related to the application of FRAM in the environmental context, moreover considering a consistent evolution based on Monte Carlo simulation. The case study of an environmental risk auditing in a sinter plant validates the research, showing the benefits in terms of identifying potential critical activities, related mitigating actions and comprehensive environmental monitoring indicators.     

Uncertainty Assessment of Voc Emissions from Paint in the Netherlands Using the Nusap System

We demonstrate an innovative approach to uncertainty assessment known as the NUSAP system, to assess qualitative and quantitative uncertainty for the case of emissions of volatile organic compounds (VOC) from paint in The Netherlands. Using expert elicitation, we identified key sources of error, critical assumptions, and bias in the monitoring process. We assessed pedigree and probabilistic uncertainty of all input quantities. We used four pedigree criteria to assess the strength of the knowledge base: proxy representation, empirical basis, methodological rigour and degree of validation. Using Monte Carlo analysis, we assessed sensitivity and propagation of uncertainty. Results for sensitivity and pedigree were combined in a NUSAP Diagnostic Diagram, which effectively highlighted the assumption for VOC percentage of imported paint as the weakest spot in the monitoring of VOC emissions. We conclude that NUSAP facilitates systematic scrutinization of method and underlying assumptions and structures creative thinking on sources of error and bias. It provides a means to prioritise uncertainties and focus research efforts on the potentially most problematic parameters and assumptions, at the same time identifying specific weaknesses in the knowledge base. With NUSAP, nuances of meaning about quantities can be conveyed concisely and clearly, to a degree that is not possible with statistic methods only.     

Severe Wind Hazard Assessment using Monte Carlo Simulation

A Monte Carlo-based model to assess severe wind hazard is presented. Synthetic wind datasets for hazard analysis have been generated using Monte Carlo simulation of the physics of severe wind gust generation, to overcome the limitations of data-based statistical models. These statistical models consider extreme wind gust speeds to calculate the average probability of exceedance of a given wind speed in a single year (return period), and hence the return period is calculated using extreme value distributions. Monte Carlo wind hazard results are shown to be comparable to those produced by data-based statistical methods. They are similar to the results reported by Holmes (Australian Journal of Structural Engineering 4(1):29–40, 2002) and also to the prescribed wind gust speeds of the Australian/NZ standards for wind loading of structures in Region A (non-cyclonic regions) of Australia (AS/NZS 1170.2 2002).     

Combining Stochastic Optimization and Monte Carlo Simulation to Deal with Uncertainties in Climate Policy Assessment

In this paper, we explore the impact of several sources of uncertainties on the assessment of energy and climate policies when one uses in a harmonized way stochastic programming in a large-scale bottom-up (BU) model and Monte Carlo simulation in a large-scale top-down (TD) model. The BU model we use is the TIMES Integrated Assessment Model, which is run in a stochastic programming version to provide a hedging emission policy to cope with the uncertainty characterizing climate sensitivity. The TD model we use is the computable general equilibrium model GEMINI-E3. Through Monte Carlo simulations of randomly generated uncertain parameter values, one provides a stochastic micro- and macro-economic analysis. Through statistical analysis of the simulation results, we analyse the impact of the uncertainties on the policy assessment.     

Assessment of groundwater quality: a fusion of geochemical and geophysical information via Bayesian neural networks

Deplorable quality of groundwater arising from saltwater intrusion, natural leaching and anthropogenic activities is one of the major concerns for the society. Assessment of groundwater quality is, therefore, a primary objective of scientific research. Here, we propose an artificial neural network-based method set in a Bayesian neural network (BNN) framework and employ it to assess groundwater quality. The approach is based on analyzing 36 water samples and inverting up to 85 Schlumberger vertical electrical sounding data. We constructed a priori model by suitably parameterizing geochemical and geophysical data collected from the western part of India. The posterior model (post-inversion) was estimated using the BNN learning procedure and global hybrid Monte Carlo/Markov Chain Monte Carlo optimization scheme. By suitable parameterization of geochemical and geophysical parameters, we simulated 1,500 training samples, out of which 50 % samples were used for training and remaining 50 % were used for validation and testing. We show that the trained model is able to classify validation and test samples with 85 % and 80 % accuracy respectively. Based on cross-correlation analysis and Gibb’s diagram of geochemical attributes, the groundwater qualities of the study area were classified into following three categories: “Very good”, “Good”, and “Unsuitable”. The BNN model-based results suggest that groundwater quality falls mostly in the range of “Good” to “Very good” except for some places near the Arabian Sea. The new modeling results powered by uncertainty and statistical analyses would provide useful constrain, which could be utilized in monitoring and assessment of the groundwater quality.     

Improving the precision of sample-based forest damage inventories through two-phase sampling and post-stratification using remotely sensed auxiliary information

Many countries have a national forest inventory (NFI) designed to produce statistically sound estimates of forest parameters. However, this type of inventory may not provide reliable results for forest damage which usually affects only small parts of the forest in a country. For this reason, specially designed forest damage inventories are performed in many countries, sometimes in coordination with the NFIs. In this study, we evaluated a new approach for damage inventory where existing NFI data form the basis for two-phase sampling for stratification and remotely sensed auxiliary data are applied for further improvement of precision through post-stratification. We applied Monte Carlo sampling simulation to evaluate different sampling strategies linked to different damage scenarios. The use of existing NFI data in a two-phase sampling for stratification design resulted in a relative efficiency of 50 % or lower, i.e., the variance was at least halved compared to a simple random sample of the same size. With post-stratification based on simulated remotely sensed auxiliary data, there was additional improvement, which depended on the accuracy of the auxiliary data and the properties of the forest damage. In many cases, the relative efficiency was further reduced by as much as one-half. In conclusion, the results show that substantial gains in precision can be obtained by utilizing auxiliary information in forest damage surveys, through two-phase sampling, through post-stratification, and through the combination of these two approaches, i.e., post-stratified two-phase sampling for stratification.     

Mercury Air Emission from Coal-Fired Public Power Sector: Uncertainty and Its Monthly Distribution. Case Study from Poland

The paper presents two aspects concerned with the mercury air emission inventory from coal-fired public power and energy plants: an uncertainty analysis, using Monte Carlo simulation (Journal of the American Statistical Association, 44(247), 335–341 1949) and the monthly distributions applying the Denton-Cholette approach (Dagum & Cholette 2006). The analysis determines uncertainty about the estimates mercury air emission from 1990 to 2012 including the development of air pollution control (APC) technologies in the Polish public power and energy sector, also the monthly distributions in comparison with previously obtained results (H?awiczka 2008). The uncertainty of mercury (Hg) content in fuel is 31.6% for hard coal and 42.4% for brown coal. The confidence interval for the estimated emission changed from [kg] (16,082.2; 16,242.2) in 1990 to (10,525.9;10,671.1) in 2012. However, the Denton-Cholette approach overestimates the emissions for the warmer periods of the year, but it could, however, in our view, be applied to attain the monthly distributions.     

Monte Carlo assessment of uncertainty in the simulated hydrological response to land use change

We quantified the uncertainty in hydrological response for a set of land use change scenarios by varying plant parameters within realistic uncertainty bounds in a Monte Carlo analysis. The results show that simulated hydrological fluxes significantly change after the introduction of outwintering suckler cow management, despite the presence of a significant amount of output uncertainty due to uncertainty in the plant parameterisation. The key to a proper uncertainty assessment was to consider the uncertainty in the difference between the scenarios instead of the absolute uncertainty of each single scenario. Additionally, a sensitivity analysis showed that changing soil properties in response to land use change does not result in significantly different results in the scenario analysis.     

Quantitative health impact assessment methodology for societal initiatives: A scoping review

Health initiatives are increasingly situated outside the institutionalised public health sector. The intersectoral character of societal initiatives, along with indirect relationships between initiatives and health, makes making projections of reach, impact and goal achievement complex. This scoping review of the peer-reviewed literature searches for appropriate methods to conduct quantitative health impact assessment for such initiatives. Database searches were done in PubMed and Web of Science, as well as a reference list search. Studies were then selected in a systematic manner. The review includes 64 studies. Most studies made estimates using simulation methods, notably with Monte Carlo, Markov and system dynamics modelling. Inputs for the models such as transition probabilities and price elasticities were taken from census, register and survey data, evidence from previous (scientific) studies and sometimes outcomes from stakeholder participation. Of different health outcome measures, the number of deaths was most frequently used, followed by QALYs and DALYs and life years. Health effect distribution is frequently mentioned, but not often estimated. Scientific methodological publications on HIAs focusing on civil society initiatives are relatively sparse, indicating possibilities for further methodological advancement. Estimating health effect distributions and incorporating stakeholder participation could make meaningful additions to standard practice.     

RCRA ground-water monitoring decision procedures viewed as quality control schemes

The problems of developing and comparing statistical procedures appropriate to the monitoring of ground water at hazardous waste sites are discussed. It is suggested that these decision procedures should be viewed as quality control schemes and compared in the same way that industrial quality control schemes are compared. The results of a Monte Carlo simulation study of run-length distribution of a combined Shewhart-CUSUM quality control scheme are reported.     

Polynomial Surrogates for Open-Channel Flows in Random Steady State

Assessing epistemic uncertainties is considered as a milestone for improving numerical predictions of a dynamical system. In hydrodynamics, uncertainties in input parameters translate into uncertainties in simulated water levels through the shallow water equations. We investigate the ability of generalized polynomial chaos (gPC) surrogate to evaluate the probabilistic features of water level simulated by a 1-D hydraulic model (MASCARET) with the same accuracy as a classical Monte Carlo method but at a reduced computational cost. This study highlights that the water level probability density function and covariance matrix are better estimated with the polynomial surrogate model than with a Monte Carlo approach on the forward model given a limited budget of MASCARET evaluations. The gPC-surrogate performance is first assessed on an idealized channel with uniform geometry and then applied on the more realistic case of the Garonne River (France) for which a global sensitivity analysis using sparse least-angle regression was performed to reduce the size of the stochastic problem. For both cases, Galerkin projection approximation coupled to Gaussian quadrature that involves a limited number of forward model evaluations is compared with least-square regression for computing the coefficients when the surrogate is parameterized with respect to the local friction coefficient and the upstream discharge. The results showed that a gPC-surrogate with total polynomial degree equal to 6 requiring 49 forward model evaluations is sufficient to represent the water level distribution (in the sense of the \(\ell _2\) norm), the probability density function and the water level covariance matrix for further use in the framework of data assimilation. In locations where the flow dynamics is more complex due to bathymetry, a higher polynomial degree is needed to retrieve the water level distribution. The use of a surrogate is thus a promising strategy for uncertainty quantification studies in open-channel flows and should be extended to unsteady flows. It also paves the way toward cost-effective ensemble-based data assimilation for flood forecasting and water resource management.     

Analysis of the Threshold and Expected Coverage Approaches to the Probabilistic Reserve Site Selection Problem

Two approaches to formulating the reserve site selection problem when species occurrence data is probabilistic were solved for terrestrial vertebrates in a small set of potential reserve sites in Oregon. The expected coverage approach, which maximizes the sum of the occurrence probabilities, yielded solutions that covered more species on average in Monte Carlo simulations than the threshold approach, which maximizes the number of species for which the occurrence probability exceeds some threshold.     

Evaluating cumulative effects of small scale hydropower development using GIS modelling and representativeness assessments

Assessing cumulative effects are a vital task for strategic environmental assessments (SEA) but lack of consistent methodology has hampered the development and implementation of useful tools. We present a model for GIS and multivariate analysis to assess the effects on a valued ecosystem type at a regional scale based on the sum of impacts of local projects. We demonstrate application of the model by assessing how hydropower developments would generate cumulative impacts on river gorges for a county in northern Norway. We use principal component analyses (PCA) of spatially-explicit variables from the region to describe the diversity of river gorge ecology with a mathematical low-dimensional bioclimatic space. We then calculate cumulative effects of hydropower development as the proportions of subspaces of the multidimensional bioclimatic PCA that are affected by either existing infrastructure or planned and possible hydropower developments. The results showed that adding development of all potential sites for small-scale hydropower would have substantial impacts on over half of all bioclimatic segments where gorges were registered and more than 70% of all segments with forested river gorges. By demonstrating these possible cumulative effects we can illustrate the need for caution in hydropower planning to avoid reducing river gorge representativeness and diversity. The method can be applied for other types of development projects and other valued ecosystems, provided the assessed ecosystems and development installations can be mapped or modelled over a sufficiently large area.     

Signal-transfer modeling for regional assessment of forest responses to environmental changes in the southeastern United States

Stochastic transfer of information in a hierarchy of simulators is offered as a conceptual approach for assessing forest responses to changing climate and air quality across 13 southeastern states of the USA. This assessment approach combines geographic information system and Monte Carlo capabilities with several scales of computer modeling for southern pine species and eastern deciduous forests. Outputs, such as forest production, evapotranspiration and carbon pools, may be compared statistically for alternative equilibrium or transient scenarios providing a statistical basis for decision making in regional assessments. This revised version was published online in July 2006 with corrections to the Cover Date.     

Factor analysis of trace-element data from tree-bark samples in The Netherlands

Monte Carlo-assisted factor analysis has been applied to a data set of 20 trace-element concentrations in tree-bark samples obtained from 123 locations in The Netherlands, with the aim to investigate the suitability of bark as a biomonitor for air pollution. A Monte Carlo approach was used to give more insight to the uncertainties and significance levels of the factor analysis results. Notwith-standing a rather strong influence of soil material on the concentration levels, factor analysis enabled the identification of five significant pollution source types, all of which corresponded with source types found in an earlier biomonitoring study in The Netherlands using epiphytic lichens. A more detailed comparison with the lichen results showed a remarkable difference in lead concentrations between bark and lichen. It was concluded that bark can be successfully employed as a biomonitor for air pollution. The power of factor analysis to adequately determine the soil contribution may render extensive sample washing procedures superfluous.     

Climatic Implications of Revised IPCC Emissions Scenarios, the Kyoto Protocol and Quantification of Uncertainties

In December 1997, the United Nations Framework Convention on Climate Change (FCCC) adopted the Kyoto Protocol. This paper describes a framework that models the climatic implications of this international agreement, using Monte Carlo simulations and the preliminary Intergovernmental Panel on Climate Change emissions scenarios (SRES). Emissions scenarios (including intervention scenarios), climate sensitivity, and terrestrial carbon sink are the key sampled model parameters. This framework gives prior probability distributions to these parameters and, using a simple climate model, posterior distributions of global temperature change are determined for the future. Our exercise showed that the Kyoto Protocol's effectiveness will be mostly dependent upon which SRES world evolves. In some worlds the Protocol decreases the warming considerably but in others it is almost irrelevant. We exemplified this approach with a current FCCC issue, namely “hot air”. This modelling framework provides a probabilistic assessment of climate policies, which can be useful for decision-makers involved in global climate change management. This revised version was published online in July 2006 with corrections to the Cover Date.