Modeling of SO<Subscript>2</Subscript> distribution in Istanbul using artificial neural networks

Urban air pollution is a growing problem in developing countries. Some compounds especially sulphur dioxide (SO₂) is considered as typical indicators of the urban air quality. Air pollution modeling and prediction have great importance in preventing the occurrence of air pollution episodes and provide sufficient time to take the necessary precautions. Recently, various stochastic image-processing algorithms such as Artificial Neural Network (ANN) are applied to environmental engineering. ANN structure employs input, hidden and output layers. Due to the complexity of the problem, as the number of input–output parameters differs, ANN model settings such as the number of neurons of these layers changes. The ability of ANN models to learn, particularly capability of handling large amounts (or sets) of data simultaneously as well as their fast response time, are invariably the characteristics desired for predictive and forecasting purposes. In this paper, ANN models have been used to predict air pollutant parameter in meteorological considerations. We have especially focused on modeling of SO₂ distribution and predicting its future concentration in Istanbul, Turkey. We have obtained data sets including meteorological variables and SO₂ concentrations from Istanbul-Florya meteorological station and Istanbul-Yenibosna air pollution station. We have preferred three-layer perceptron type of ANN which consists of 10, 22 and 1 neurons for input, hidden and output layers, respectively. All considered parameters are measured as daily mean. The input parameters are: SO₂ concentration, pressure, temperature, humidity, wind direction, wind speed, strength of sunshine, sunshine, cloudy, rainfall and output parameter is the future prediction of SO₂. To evaluate the performance of ANN model, our results are compared to classical nonlinear regression methods. The over all system finds an optimum correlation between input–output variables. Here, the correlation parameter, r is 0.999 and 0.528 for training and test data. Thus in our model, the trend of SO₂ is well estimated and seasonal effects are well represented. As a result, we conclude that ANN is one of the compromising methods in estimation of environmental complex air pollution problems.     

An Oligopoly Game of CDR Strategy Deployment in a Steady-State Net-Zero Emission Climate Regime

In this paper, we propose a simple oligopoly game model to represent the interactions between coalitions of countries in deploying carbon dioxide removal (CDR) strategies in a steady-state net-zero emission climate regime that could take place by the end of the twenty-first century. The emission quotas and CDR activities obtained in the solution of this steady-state model could then be used as a target for end-of-period conditions in a dynamic integrated assessment analysis studying the transition to 2100. More precisely, we analyze a steady-state situation where m coalitions exist and behave as m players in a game of supplying emission rights on an international emission trading system. The quotas supplied by a coalition must correspond to the amount of CO₂ captured through CDR activities in the corresponding world region. We use an extension of the computable general equilibrium model GEMINI-E3 to calibrate the payoff functions and compute an equilibrium solution in the noncooperative game.

     

How Certain Are Salmon Recovery Forecasts? A Watershed-scale Sensitivity Analysis

Complex relationships between landscape and aquatic habitat conditions and salmon (Oncorhynchus spp.) populations make science-based management decisions both difficult and essential. Due to a paucity of empirical data, models characterizing these relationships are often used to forecast future conditions. We evaluated uncertainties in a suite of models that predict possible future habitat conditions and fish responses in the Lewis River Basin, Washington, USA. We evaluated sensitivities of predictions to uncertainty in model parameters. Results were sensitive to 60% of model parameters but substantially so (|partial regression coefficients| >0.5) to <10%. We also estimated accuracy of several predictions using field surveys. Observations mostly fell within predicted ranges for riparian shade and fine-sediment deposition, but large woody debris estimates matched only half the time. We provide suggestions to modelers for improving model accountability, and describe how managers can incorporate prediction uncertainty into decision-making, thereby improving the odds of successful salmon habitat recovery.     

The Impact of Uncertainty in Climate Targets and CO2 Storage Availability on Long-Term Emissions Abatement

A major characteristic of our global interactive climate-energy system is the large uncertainty that exists with respect to both future environmental requirements and the means available for fulfilling these. Potentially, a key technology for leading the transition from the current fossil fuel-dominated energy system to a more sustainable one is carbon dioxide capture and storage. Uncertainties exist, however, concerning the large-scale implementability of this technology, such as related to the regional availability of storage sites for the captured CO₂. We analyze these uncertainties from an integrated assessment perspective by using the bottom-up model TIAM-ECN and by studying a set of scenarios that cover a range of different climate targets and technology futures. Our study consists of two main approaches: (1) a sensitivity analysis through the investigation of a number of scenarios under perfect foresight decision making and (2) a stochastic programming exercise that allows for simultaneously considering a set of potential future states-of-the-world. We find that, if a stringent climate (forcing) target is a possibility, it dominates the solution: if deep CO₂ emission reductions are not started as soon as possible, the target may become unreachable. Attaining a stringent climate target comes in any case at a disproportionally high price, which indicates that adaptation measures or climate damages might be preferable to the high mitigation costs such a target implies.     

Blockwise AICc for Model Selection in Generalized Linear Models

The corrected Akaike information criterion (AICc) is a widely used tool in analyzing environmental and ecological data, and it outperforms the Akaike information criterion (AIC), especially in small-size samples. To take advantage of this property, we propose a modified version of the AICc in a generalized linear model framework, referred to as the blockwise AICc (bAICc). Compared with some other information criteria, extensive simulation results show that the bAICc performs well. We also analyzed two environmental datasets, one for snail survival and the other for fish infection, to illustrate the usefulness of this new model selection criterion.     

Assessing uncertainty in annual nitrogen,phosphorus, and suspended sediment load estimates in three agricultural streams using a 21-year dataset

Accurate estimation of constituent loads is important for studies of ecosystem mass balance or total maximum daily loads. In response, there has been an effort to develop methods to increase both accuracy and precision of constituent load estimates. The relationship between constituent concentration and stream discharge is often complicated, potentially leading to high uncertainty in load estimates for certain constituents, especially at longer-term (annual) scales. We used the loadflex R package to compare uncertainty in annual load estimates from concentration vs. discharge relationships in constituents of interest in agricultural systems, including ammonium as nitrogen (NH₄-N), nitrate as nitrogen (NO₃-N), soluble reactive phosphorus (SRP), and suspended sediments (SS). We predicted that uncertainty would be greatest in NO₃-N and SS due to complex relationships between constituent concentration and discharge. We also predicted lower uncertainty with a composite method compared to regression or interpolation methods. Contrary to predictions, we observed the lowest uncertainty in annual NO₃-N load estimates (relative error 1.5–23%); however, uncertainty was greatest in SS load estimates, consistent with predictions (relative error 19–96%). For all constituents, we also generally observed reductions in uncertainty by up to 34% using the composite method compared to regression and interpolation approaches, as predicted. These results highlight differences in uncertainty among different constituents and will aid in model selection for future studies requiring accurate and precise estimates of constituent load.     

Global Warming, Uncertainty and Endogenous Technical Change

What impact does ecological uncertainty have on agents' decisions concerning domestic emissions abatement, physical investments, and R&;D expenditures? How sensitive are the answers to these questions when we move from exogenous to endogenous technical change? To investigate these issues we modify the ETC-RICE model described in Buonanno et al. (2001) by embedding in it a hazard rate function as in Bosello and Moretto (1999). With this model at hand we run numerical optimisations focusing our attention on the control variables of the representative agents, i.e., domestic abatement rate, investments in physical capital, and R&;D spending, as well as on the endogenous patterns of GDP level and CO₂ emissions. Our results show that uncertainty strongly influences agents behaviour; in particular, agents slow down their emissions in order to maintain a more sustainable growth path. In addition, R&;D expenditures trigger the “engine of growth” exclusively when environmental technical change is formalized in an endogenous fashion. However, even if environmental uncertainty may stimulate technical change, long-run growth it turns out to be negatively affected by the former, as also predicted by Clarke and Reed (1994) Tsur and Zemel (1996) and Bosello and Moretto (1999).     

A New Mathematical Approach in Environmental and Life Sciences: Gene–Environment Networks and Their Dynamics

An important research area in life sciences is devoted to modeling, prediction, and dynamics of gene-expression patterns. As clearly understood in these days, this enterprise cannot become satisfactory without acknowledging the role of the environment. To a representation of past, present, and most likely future states, we also encounter measurement errors and uncertainties. This paper surveys and improves recent advances in understanding the foundations and interdisciplinary implications of the newly introduced gene–environment networks, and it integrates the important theme of carbon dioxide emission reduction into the networks and dynamics. We also introduce some operational and managerial issues of practical working and decision making, expressed in terms of sliding windows, quadrants (modules) of parametric effects, and navigating (controlling) between such effects and directing them. Given data from DNA microarray experiments and environmental records, we extract nonlinear ordinary differential equations that contain parameters that have to be determined. For this, we employ modern (Chebychevian) approximation and (generalized semi-infinite) optimization. After this is provided, time- discretized dynamical systems are studied. A combinatorial algorithm with polyhedra sequences allows to detect the region of parametric stability. Finally, we analyze the topological landscape of gene–environment networks with its structural (in)stability. By embedding as a module and investigating CO₂ emission control and figuring out game theoretical aspects, we conclude. This pioneering work is theoretically elaborated, practically devoted to health care, medicine, education, living conditions, and environmental protection, and it invites the readers to future research.      

A General Equilibrium Assessment of Climate Change Impacts on Swiss Winter Tourism with Adaptation

Winter alpine tourism has been repeatedly identified as one of the economic sectors most at risk from climate change in Switzerland. However, all of the costs that have been estimated so far for the Swiss tourism sector are, to some extent, misleading as they do not, or only partially, incorporate adaptation possibilities and general equilibrium effects. We attempt to fill this gap using a computable general equilibrium model that is specifically designed for the purposes of this research. Our modeling efforts first consist in creating a tourism sector with a part of it being dependent on snow. We also carefully model the snowmaking technology. Using climate change scenarios on future snow cover, we analyze their impacts on the Swiss ski industry. We find welfare effects for the Swiss economy ranging from − 23 to 113 million CHF in 2050. This range arises from the use of various assumptions concerning adaptation possibilities. We also show that geographical substitutions between international ski destinations have large positive effects for Switzerland. From a more general perspective, our results exemplify the risks of estimating the consequences of climate change based only on domestic impacts of climate change with no adaptation being implemented.

     

Accounting for Uncertainty in Value Judgements when Applying Multi-Attribute Value Theory

In environmental decisions, analysts commonly face substantial uncertainties around stakeholders’ values judgments. Multi-Attribute Value Theory (MAVT), a family of multi-criteria decision analysis techniques, is applied in participative settings to articulate stakeholders’ values in decision-making. In MAVT, value judgments represent the intensity of individuals’ preferences in a set of objectives, which are operationalized as scaling factors or weights. Different sets of weights may express variation in people’s preferences or value judgments. Unfortunately, there are still important methodological gaps regarding how to incorporate uncertainty and the substantial variation commonly encountered in stakeholders’ preferences. This article presents a model of uncertainty that encompasses the dispersion of value judgments in MAVT. To achieve this goal, we draw on info-gap theory, which provides a mathematically grounded method for exploring sensitivity to preference weights when there are relatively high levels of uncertainties. We experimentally tested the uncertainty model in an environmental decision problem. We found that MAVT can use info-gap analysis to deal with multiple value judgments, avoiding exclusive reliance on nominal expected values to inform decisions. We explored a mechanism to explicitly consider the trade-offs between the performance of alternatives and the level of uncertainty that in any specified context a decision maker is willing to accept. Findings emphasize the potential of MAVT to support environmental management decisions, particularly in situations where multiple stakeholders and their contested value judgments have to be considered simultaneously to explore uncertainties around value trade-offs.     

Socioeconomic Risks of Extreme El Niño Event-Related Road Damages in Peru

This study evaluates the socioeconomic risk that extreme El Niño event-related road damages present to Peru by combining an environmental modelling of events’ occurrences in the country with a quantitative modelling of their effects on its economy. The dynamic of occurrence of events is modelled as a stochastic process with a vector autoregressive representation based on historical climatic data, and simulated over a 10-year period with a non-parametric bootstrap procedure. The indirect consequences of events’ related road damages are addressed with a multiregional dynamic computable general equilibrium model through an increase in interregional transportation costs and, more originally, a negative externality effect on activities’ output, which is estimated beforehand using a firm database. We find that extreme El Niño events constitute a significant one-off disaster risk for the country, threatening shifts of???2.8% in GDP and?+?1.9% in poverty rates with an annual probability p?=?1.4%. We further show that they also present a longer-term risk, leading to average annual deviations from normal trend by???0.8% in GDP and?+?0.4% in poverty rate with a probability p?=?12.6% over a 10-year period. However, we finally show that Peru might reduce these socioeconomic risks associated with these non-frequent but recurrent climatic shocks in constructing more disaster-resilient road infrastructure.

     

The precautionary principle stimulus for solutions- and alternatives-based environmental policy

The precautionary principle is increasingly discussed in debates over appropriate measures to address complex and uncertain risks. The principle has generally been defined as having two main components: preventive action in the face of uncertainty and reversing the burden of proof. In isolation, these two components would suggest that precaution is primarily reactive to potential problems rather than proactive. More recent statements have suggested that thorough assessment of alternatives is critical to the effective implementation of the precautionary principle. Alternatives assessment—also referred to as options analysis and facility planning—redirects environmental science and policy debates from characterizing problems to identifying solutions. In this commentary, we examine the rationale for a focus on alternatives assessment in implementing the precautionary principle. We examine methods and examples of alternatives assessment, as well as opportunities for the principle's integration in environmental policy. We argue that a greater focus on alternatives assessment can enhance the ability of decision makers to make truly precautionary decisions, stimulate innovation toward sustainable production, and more effectively place burdens on to those creating risks. Such a focus forms an essential component of a shift from “problem-based” environmental policy to “solutions-based” policy. This shift requires adequate research resources, tools, and a government commitment to a new paradigm of environmental protection. We conclude that we will only reach the goal of sustainable production if we change our environmental protection focus from figuring out how bad the situation will be to seeking alternatives to problematic activities and designing the conditions for a more sustainable future.     

Sensitivity analysis of water quality for Delhi stretch of the River Yamuna,India

Simulation models are used to aid the decision makers about water pollution control and management in river systems. However, uncertainty of model parameters affects the model predictions and hence the pollution control decision. Therefore, it often is necessary to identify the model parameters that significantly affect the model output uncertainty prior to or as a supplement to model application to water pollution control and planning problems. In this study, sensitivity analysis, as a tool for uncertainty analysis was carried out to assess the sensitivity of water quality to (a) model parameters (b) pollution abatement measures such as wastewater treatment, waste discharge and flow augmentation from upstream reservoir. In addition, sensitivity analysis for the “best practical solution” was carried out to help the decision makers in choosing an appropriate option. The Delhi stretch of the river Yamuna was considered as a case study. The QUAL2E model is used for water quality simulation. The results obtained indicate that parameters K ₁ (deoxygenation constant) and K ₃ (settling oxygen demand), which is the rate of biochemical decomposition of organic matter and rate of BOD removal by settling, respectively, are the most sensitive parameters for the considered river stretch. Different combinations of variations in K ₁ and K ₂ also revealed similar results for better understanding of inter-dependability of K ₁ and K ₂. Also, among the pollution abatement methods, the change (perturbation) in wastewater treatment level at primary, secondary, tertiary, and advanced has the greatest effect on the uncertainty of the simulated dissolved oxygen and biochemical oxygen demand concentrations.     

Bird skins in museum collections: are they suitable as indicators of environmental metal load after conservation procedures?

To find out whether modern conservation treatments alter thelevel of metals in feathers, we analysed the content of 10metals in feathers before and after skins were washed withdetergent and treated with Eulan U-33 (a commonly usedpreservative at museums). Feathers of 31 birds of Goshawk Accipiter gentilis, Eagle Owl Bubo bubo, Feral PigeonColumba livia domest. and Common Eider Somateriamollissima were analysed. We found that in most cases metalswere partly washed out of the feathers, but the effects wererelated to species and type of feather. The value of bird skinsas indicators of environmental metal load is therefore affectedby this treatment. It is recommended that the conservationtechniques used at museums should be reconsidered if skins areintended for specimen banking for future reference inenvironmental monitoring schemes and research.     

我国生态环境监测网络建设成效与展望

2015年中央全面深化改革领导小组审议通过《生态环境监测网络建设方案》,推动全国生态环境监测网络建设取得重大成就。对照《生态环境监测网络建设方案》提出的"全面设点,完善生态环境监测网络"方面的任务与要求,对生态环境监测网络建设成效进行客观评估,并对生态环境监测面临的形势与不足进行了深入剖析。针对加强生态环境监测网络建设、探索生态环境监测多手段融合应用模式、强化生态环境监测数据智慧应用等方面提出了当前及今后中长期生态环境监测发展的相关建议。     

Modelling the Distribution and Quality of Sand and Gravel Resources in 3D: a Case Study in the Thames Basin,UK

Three-dimensional (3D) models are often utilised to assess the presence of sand and gravel deposits. Expanding these models to provide a better indication of the suitability of the deposit as aggregate for use in construction would be advantageous. This, however, leads to statistical challenges. To be effective, models must be able to reflect the interdependencies between different criteria (e.g. depth to deposit, thickness of deposit, ratio of mineral to waste, proportion of ‘fines’) as well as the inherent uncertainty introduced because models are derived from a limited set of boreholes in a study region. Using legacy borehole data collected during a systematic survey of sand and gravel deposits in the UK, we have developed a 3D model for a 2400 km² region close to Reading, southern England. In developing the model, we have reassessed the borehole grading data to reflect modern extraction criteria and explored the most suitable statistical modelling technique. The additive log-ratio transform and the linear model of coregionalization have been applied, techniques that have been previously used to map soil texture classes in two dimensions, to assess the quality of sand and gravel deposits in the area. The application of these statistical techniques leads to a model which can be used to generate thousands of plausible realisations of the deposit which fully reflect the extent of model uncertainty. The approach offers potential to improve regional-scale mineral planning by providing an enhanced understanding of sand and gravel deposits and the extent to which they meet current extraction criteria.

     

Assessment of Spatiotemporal Varying Relationships Between Rainfall,Land Cover and Surface Water Area Using Geographically Weighted Regression

Traditional regression techniques such as ordinary least squares (OLS) are often unable to accurately model spatially varying data and may ignore or hide local variations in model coefficients. A relatively new technique, geographically weighted regression (GWR) has been shown to greatly improve model performance compared to OLS in terms of higher R ² and lower corrected Akaike information criterion (AIC_C). GWR models have the potential to improve reliabilities of the identified relationships by reducing spatial autocorrelations and by accounting for local variations and spatial non-stationarity between dependent and independent variables. In this study, GWR was used to examine the relationship between land cover, rainfall and surface water habitat in 149 sub-catchments in a predominately agricultural region covering 2.6 million ha in southeast Australia. The application of the GWR models revealed that the relationships between land cover, rainfall and surface water habitat display significant spatial non-stationarity. GWR showed improvements over analogous OLS models in terms of higher R ² and lower AIC_C. The increased explanatory power of GWR was confirmed by the results of an approximate likelihood ratio test, which showed statistically significant improvements over analogous OLS models. The models suggest that the amount of surface water area in the landscape is related to anthropogenic drainage practices enhancing runoff to facilitate intensive agriculture and increased plantation forestry. However, with some key variables not present in our analysis, the strength of this relationship could not be qualified. GWR techniques have the potential to serve as a useful tool for environmental research and management across a broad range of scales for the investigation of spatially varying relationships.     

6项环境监测分析方法标准中钴、铬、钼、钛的不确定度分析及其在达标判定中的应用

研究依据测定不确定度的基本理论和ISO 21748：2017《采用重复性、再现性和正确度评估测量不确定度的导则》，提出了基于中国环境监测分析方法标准多家实验室验证中已获得的数据计算合成标准不确定度的方法，将方法标准中规定的重复性、再现性等指标与合成标准不确定度进行了衔接。分析了近年发布的6项水质监测分析方法标准中钴、铬、钼、钛等4种金属元素的相对合成标准不确定度，结果表明：被测量的浓度是影响方法标准测量不确定度的重要因素。对于火焰原子吸收分光光度法（FAAS）和石墨炉原子吸收分光光度法（GAAS），样品浓度为方法标准测定下限3倍左右时，测定结果的相对标准不确定度可保持在15%以下；对于电感耦合等离子体发射光谱法（ICP-AES），样品浓度为方法标准测定下限3~5倍时，测定结果的相对标准不确定度为12%~17%；对于电感耦合等离子体质谱法（ICP-MS），钛元素浓度为测定下限3倍左右时，相对标准不确定度在15%以下，而钴、铬、钼的浓度在测定下限40~100倍以上时，相对标准不确定度在15%以下。6项方法标准可分别用于《地表水环境质量标准》（GB 3838—2002）以及22项水污染物排放标准钴、铬、钼、钛的达标监测。     

An inquiry into the potential of scenario analysis for dealing with uncertainty in strategic environmental assessment in China

Strategic environmental assessment (SEA) inherently needs to address greater levels of uncertainty in the formulation and implementation processes of strategic decisions, compared with project environmental impact assessment. The range of uncertainties includes internal and external factors of the complex system that is concerned in the strategy. Scenario analysis is increasingly being used to cope with uncertainty in SEA. Following a brief introduction of scenarios and scenario analysis, this paper examines the rationale for scenario analysis in SEA in the context of China. The state of the art associated with scenario analysis applied to SEA in China was reviewed through four SEA case analyses. Lessons learned from these cases indicated the word “scenario” appears to be abused and the scenario-based methods appear to be misused due to the lack of understanding of an uncertain future and scenario analysis. However, good experiences were also drawn on, regarding how to integrate scenario analysis into the SEA process in China, how to cope with driving forces including uncertainties, how to combine qualitative scenario storylines with quantitative impact predictions, and how to conduct assessments and propose recommendations based on scenarios. Additionally, the ways to improve the application of this tool in SEA were suggested. We concluded by calling for further methodological research on this issue and more practices.     

A Scenario Optimization Model for Dynamic Reserve Site Selection

Conservation planners are called upon to make choices and trade-offs about the preservation of natural areas for the protection of species in the face of development pressures. We addressed the problem of selecting sites for protection over time with the objective of maximizing species representation, with uncertainty about future site development, and with periodic constraints on the number of sites that can be selected. We developed a 0–1, linear optimization model with 2 periods to select the sites that maximize expected species coverage subject to budget constraints. The model is based on the idea that development uncertainty can be characterized by a set of scenarios, each of which is a possible second-period development outcome for the set of sites. We also suggest that our 2-period model can be used in a sequential fashion that is consistent with adaptive planning. Results are presented for the Fox River watershed in Chicago.