Evaluating response surface designs for uncertainty analysis and prescriptive applications of a large-scale water quality model

Environmental models are often too large and cumbersome for effective use in regulatory decision making or in the characterization of uncertainty. This paper describes and compares four response surfaces that could complement a large-scale water quality model, the U.S. National Water Pollution Control Assessment Model (NWPCAM), in simulation and regulatory decision support applications. Results show that a physically based reduced-form model that exploits the mathematical structure of the underlying water quality model is a better predictor of policy-relevant outputs than the polynomial expansions that are frequently used in response surface studies.     

Circular business models: Babbling initial exploratory

This research aims to map Brazilian companies that have business models aligned with the concept of the circular economy. Data were collected for this study through use of a survey and additional research based on the survey results. Within these businesses’ models, an emphasis on the second use of materials as well as the marketing of the product as a service has been highlighted.     

利用LUR模型模拟杭州市PM2.5质量浓度空间分布

模拟城市大气污染物浓度空间分布对研究城市空气质量及人体健康至关重要.本研究利用土地利用回归模型(Land Use Regression,LUR),提取包括污染点源因子、交通因子、人口因子、土地利用因子和气象因子等60个预测因子,基于地理加权算法(GWR)建立春、夏、秋、冬四个季节的模型,实现对杭州地区近地表PM_(2.5)质量浓度空间分布的预测.结果表明:基于地理加权回归算法时,检验模型的R2值分别达到0.76(春季)、0.70(夏季)、0.73(秋季)、0.76(冬季),模型能够解释PM_(2.5)浓度值80%以上的变异.每个季度杭州地区PM_(2.5)浓度变化不尽相同,但总体以杭州中部最高,西南部偏低.研究说明基于LUR模型模拟大尺度地区PM_(2.5)质量浓度空间分布是可行的.     

Assessment of Future Drought Conditions in the Chesapeake Bay Watershed

Impacts of climate change on the severity and intensity of future droughts can be evaluated based on precipitation and temperature projections, multiple hydrological models, simulated hydrometeorological variables, and various drought indices. The objective of this study was to assess climate change impacts on future drought conditions and water resources in the Chesapeake Bay (CB) watershed. In this study, the Soil and Water Assessment Tool (SWAT) and the Variable Infiltration Capacity model were used to simulate a Modified Palmer Drought Severity Index (MPDSI), a Standardized Soil Moisture index (SSI), a Multivariate Standardized Drought Index (MSDI), along with Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models for both historical and future periods (f1: 2020‐2049, f2: 2050‐2079). The results of the SSI suggested that there was a general increase in agricultural droughts in the entire CB watershed because of increases in surface and groundwater flow and evapotranspiration. However, MPDSI and MSDI showed an overall decrease in projected drought occurrences due to the increases in precipitation in the future. The results of this study suggest that it is crucial to use multiple modeling approaches with specific drought indices that combine the effects of both precipitation and temperature changes.     

Consequences of ignoring dispersal variation in network models for landscape connectivity

Habitat loss and fragmentation can negatively influence population persistence and biodiversity, but the effects can be mitigated if species successfully disperse between isolated habitat patches. Network models are the primary tool for quantifying landscape connectivity, yet in practice, an overly simplistic view of species dispersal is applied. These models often ignore individual variation in dispersal ability under the assumption that all individuals move the same fixed distance with equal probability. We developed a modeling approach to address this problem. We incorporated dispersal kernels into network models to determine how individual variation in dispersal alters understanding of landscape-level connectivity and implemented our approach on a fragmented grassland landscape in Minnesota. Ignoring dispersal variation consistently overestimated a population's robustness to local extinctions and underestimated its robustness to local habitat loss. Furthermore, a simplified view of dispersal underestimated the amount of habitat substructure for small populations but overestimated habitat substructure for large populations. Our results demonstrate that considering biologically realistic dispersal alters understanding of landscape connectivity in ecological theory and conservation practice.     

Strategic Rat Control for Restoring Populations of Native Species in Forest Fragments

Forest fragments have biodiversity value that may be enhanced through management such as control of non‐native predators. However, such efforts may be ineffective, and research is needed to ensure that predator control is done strategically. We used Bayesian hierarchical modeling to estimate fragment‐specific effects of experimental rat control on a native species targeted for recovery in a New Zealand pastoral landscape. The experiment was a modified BACI (before‐after‐control‐impact) design conducted over 6 years in 19 forest fragments with low‐density subpopulations of North Island Robins (Petroica longipes). The aim was to identify individual fragments that not only showed clear benefits of rat control, but also would have a high probability of subpopulation growth even if they were the only fragment managed. We collected data on fecundity, adult and juvenile survival, and juvenile emigration, and modeled the data in an integrated framework to estimate the expected annual growth rate (λ) of each subpopulation with and without rat control. Without emigration, subpopulation growth was estimated as marginal (λ = 0.95–1.05) or negative (λ = 0.74–0.90) without rat control, but it was estimated as positive in all fragments (λ = 1.4–2.1) if rats were controlled. This reflected a 150% average increase in fecundity and 45% average increase in adult female survival. The probability of a juvenile remaining in its natal fragment was 0.37 on average, but varied with fragment connectivity. With juvenile emigration added, 6 fragments were estimated to have a high (>0.8) probability of being self‐sustaining (λ > 1) with rat control. The key factors affecting subpopulation growth rates under rat control were low connectivity and stock fencing because these factors were associated with lower juvenile emigration and higher fecundity, respectively. However, there was also substantial random variation in adult survival among fragments, illustrating the importance of hierarchical modeling for fragmentation studies. Control Estratégico de Ratas para Restaurar Poblaciones de Especies Nativas en Fragmentos de Bosque     

Probabilistic multiple model neural network based leak detection system: Experimental study

This paper presents an effective decision making system for leak detection based on multiple generalized linear models and clustering techniques. The training data for the proposed decision system is obtained by setting up an experimental pipeline fully operational distribution system. The system is also equipped with data logging for three variables; namely, inlet pressure, outlet pressure, and outlet flow. The experimental setup is designed such that multi-operational conditions of the distribution system, including multi pressure and multi flow can be obtained. We then statistically tested and showed that pressure and flow variables can be used as signature of leak under the designed multi-operational conditions. It is then shown that the detection of leakages based on the training and testing of the proposed multi model decision system with pre data clustering, under multi operational conditions produces better recognition rates in comparison to the training based on the single model approach. This decision system is then equipped with the estimation of confidence limits and a method is proposed for using these confidence limits for obtaining more robust leakage recognition results.     

Street canyon ventilation and atmospheric turbulence

Operational models for pollutant dispersion in urban areas require an estimate of the turbulent transfer between the street canyons and the overlying atmospheric flow. To date, the mechanisms that govern this process remain poorly understood. We have studied the mass exchange between a street canyon and the atmospheric flow above it by means of wind tunnel experiments. Fluid velocities were measured with a Particle Image Velocimetry system and passive scalar concentrations were measured using a Flame Ionisation Detector. The mass-transfer velocity between the canyon and the external flow has been estimated by measuring the cavity wash-out time. A two-box model, used to estimate the transfer velocity for varying dynamical conditions of the external flow, has been used to interpret the experimental data. This study sheds new light on the mechanisms which drive the ventilation of a street canyon and illustrates the influence of the external turbulence on the transfer process.     

Environment-specific elasticity and sensitivity analysis of the stochastic growth rate

For matrix population models, analyses of how sensitive the population growth rate is to changes in vital rates (i.e. perturbations) are important for studies of life history evolution as well as for management and conservation of threatened species. There are two types of sensitivity analyses corresponding to absolute (sensitivity) or relative (elasticity) changes in the vital rates and both types can be applied to both deterministic and stochastic matrix population models. To date, most empirical studies of elasticity and sensitivity of the stochastic growth rate have examined the response to perturbations in the vital rates in a complete set of possible environments. However, it is often of interest to examine the response to perturbations occurring in only a subset of the possible environments. This has been done for periodic time-varying models elsewhere, but here we describe a recently published method for calculating the environment-specific sensitivity and elasticity of the stochastic growth rate and apply this method to data. These environment-specific perturbation analyses provide a logical way of dividing the sensitivity and elasticity among the environments. They give important insight into the selection regime in different environments and can provide valuable information for making management decisions and management evaluations in stochastic environments.     

Uncertainty evaluation in air quality planning decisions: a case study for Northern Italy

In recent years, evaluating the robustness of environmental models results has become essential in order to effectively support decision makers to define suitable emission control strategies. This evaluation is performed in literature through uncertainty and sensitivity analyses. Therefore, the application of such methodologies to air quality Integrated Assessment Models (IAMs) is extremely challenging. In fact, in this case uncertainty and sensitivity analyses should be assessed not only for each single component of the system, but also for the overall IAM. In the paper, an attempt is made to extend and systematize the information available on uncertainty/sensitivity analysis, at first considering environmental models in general, and then focusing on air quality IAMs. The study aims to offer a tentative framework addressed to modelers and decision makers in the implementation of IAM and evaluation of its results. The framework has been tested on Lombardy region (Northern Italy). The results show how the uncertainty on Drivers of emissions propagates on the whole modelling chain characterizing an integrated assessment study.