An environmental indicator: particulate characteristics on pedestrian pathway along integrated urban thoroughfare in Metropolis

Particulate matter (PM) originated by road transport constitutes an urgent task for megacities and pedestrians are supposed to be the first batch of innocent victims that exposed to and inhaled the polluted air. Footbridges have become a promising resolution to land tension, the location and design of them should be more considered in order to provide a more desirable walking system to pedestrians. In this study, three groups of PM [i.e., 0.3–0.9 μm (sub-fine), 0.9–2.5 μm (fine) and 2.5–10 μm (coarse)] were measured at different traffic scenario related footbridges (i.e., upstream of the on-ramp, downstream of the on-ramp, and signalized intersection) along an urban artery in Hong Kong, and their traffic volume composition, multifractality and cross-correlation behavior were investigated thereafter. Multifractal detrended fluctuation analysis and multifractal detrended fluctuation cross-correlation analysis were used simultaneously to quantify the persistency of different PM groups and interaction between them. The results indicate that although the particle concentration at intersection above footbridges presents the lowest, it has the highest emission rate and the strongest multifractality and cross-correlation behavior, especially the finer ones. Hence, it is suggest that the nature ventilation style of footbridges should avoid to be built above the signalized intersection due to the long persistency of particles and active interaction between different particle groups.     

Bayesian maximum entropy approach and its applications: a review

The present paper reviews the conceptual framework and development of the Bayesian Maximum Entropy (BME) approach. BME has been considered as a significant breakthrough and contribution to applied stochastics by introducing an improved, knowledge-based modeling framework for spatial and spatiotemporal information. In this work, one objective is the overview of distinct BME features. By offering a foundation free of restrictive assumptions that limit comparable techniques, an ability to integrate a variety of prior knowledge bases, and rigorous accounting for both exact and uncertain data, the BME approach was coined as introducing modern spatiotemporal geostatistics. A second objective is to illustrate BME applications and adoption within numerous different scientific disciplines. We summarize examples and real-world studies that encompass the perspective of science of the total environment, including atmosphere, lithosphere, hydrosphere, and ecosphere, while also noting applications that extend beyond these fields. The broad-ranging application track suggests BME as an established, valuable tool for predictive spatial and space–time analysis and mapping. This review concludes with the present status of BME, and tentative paths for future methodological research, enhancements, and extensions.     

Reducing uncertainty in stochastic streamflow generation and reservoir sizing by combining observed,reconstructed and projected streamflow

Reservoir sizing is one of the most important aspects of water resources engineering as the storage in a reservoir must be sufficient to supply water during extended droughts. Typically, observed streamflow is used to stochastically generate multiple realizations of streamflow to estimate the required storage based on the Sequent Peak Algorithm (SQP). The main limitation in this approach is that the parameters of the stochastic model are purely derived from the observed record (limited to less than 80 years of data) which does not have information related to prehistoric droughts. Further, reservoir sizing is typically estimated to meet future increase in water demand, and there is no guarantee that future streamflow over the planning period will be representative of past streamflow records. In this context, reconstructed streamflow records, usually estimated based on tree ring chronologies, provide better estimates of prehistoric droughts, and future streamflow records over the planning period could be obtained from general circulation models (GCMs) which provide 30 year near-term climate change projections. In this study, we developed paleo streamflow records and future streamflow records for 30 years are obtained by forcing the projected precipitation and temperature from the GCMs over a lumped watershed model. We propose combining observed, reconstructed and projected streamflows to generate synthetic streamflow records using a Bayesian framework that provides the posterior distribution of reservoir storage estimates. The performance of the Bayesian framework is compared to a traditional stochastic streamflow generation approach. Findings based on the split-sample validation show that the Bayesian approach yielded generated streamflow traces more representative of future streamflow conditions than the traditional stochastic approach thereby, reducing uncertainty on storage estimates corresponding to higher reliabilities. Potential strategies for improving future streamflow projections and its utility in reservoir sizing and capacity expansion projects are also discussed.     

A fully non-stationary linear coregionalization model for multivariate random fields

This paper introduces an extension of the traditional stationary linear coregionalization model to handle the lack of stationarity. Under the proposed model, coregionalization matrices are spatially dependent, and basic univariate spatial dependence structures are non-stationary. A parameter estimation procedure of the proposed non-stationary linear coregionalization model is developed under the local stationarity framework. The proposed estimation procedure is based on the method of moments and involves a matrix-valued local stationary variogram kernel estimator, a weighted local least squares method in combination with a kernel smoothing technique. Local parameter estimates are knitted together for prediction and simulation purposes. The proposed non-stationary multivariate spatial modeling approach is illustrated using two real bivariate data examples. Prediction performance comparison is carried out with the classical stationary multivariate spatial modeling approach. According to several criteria, the prediction performance of the proposed non-stationary multivariate spatial modeling approach appears to be significantly better.     

Stochastic synthesis approximating any process dependence and distribution

An extension of the symmetric-moving-average (SMA) scheme is presented for stochastic synthesis of a stationary process for approximating any dependence structure and marginal distribution. The extended SMA model can exactly preserve an arbitrary second-order structure as well as the high order moments of a process, thus enabling a better approximation of any type of dependence (through the second-order statistics) and marginal distribution function (through statistical moments), respectively. Interestingly, by explicitly preserving the coefficient of kurtosis, it can also simulate certain aspects of intermittency, often characterizing the geophysical processes. Several applications with alternative hypothetical marginal distributions, as well as with real world processes, such as precipitation, wind speed and grid-turbulence, highlight the scheme’s wide range of applicability in stochastic generation and Monte-Carlo analysis. Particular emphasis is given on turbulence, in an attempt to simulate in a simple way several of its characteristics regarded as puzzles.     

Predicting long-term urban growth in Beijing (China) with new factors and constraints of environmental change under integrated stochastic and fuzzy uncertainties

Numerous studies related to the simulation and prediction of urban growth to address land-use and land-cover (LULC) changes have been conducted in recent years, but very few have considered the impact of climate change, flooding impact, government relocation, corridor cities, and long-term rainfall variations simultaneously. To bridge the gap, this study predicts possible future LULC changes for 2030 and 2050 in Beijing (China), since Beijing is one of the fastest-growing megacities in the world. The proposed integrated modeling analysis covers four key scenarios to reflect the influences of different factors and constraints on LULC changes, in which cellular automata, Markov chain, and multi-criteria evaluation are fully coupled. While fuzzy membership function was used to address the uncertainty associated with the decision analysis, Markov chain, which is regarded as a stochastic process, was applied to predict future urban growth pathways. In addition, a statistical downscaling model driven by possible climate change scenarios was employed to address long-term rainfall variations in Beijing, China. This study differs from previous ones for Beijing in terms of not only the effects of climate change and flooding impact but also the newly-developed economic free trade zone in Xiong’an and the central government’s plan to relocate to the Tongzhou district. Findings indicate that there is no marked difference in LULC over the four key scenarios. Compared to the baseline LULC in 2010, the predicted results indicate that urban expansion is expected to increase more than 6 and 11% in 2030 and 2050, respectively.     

Probabilistic leak detectability assessment via state estimation in water transport networks

Leak detectability or leakage awareness refers to the capability of sensing losses from a water supply system. Several methods exist in the technical literature to tackle this problem, but only few address it with a state estimation approach. The aim of this paper is to present a new methodology that enables probabilistic assessment of the extent to which water loss could be detected using state estimation by only analysing a single hydraulic state, i.e. one time period. Significant leaks are sensed by identifying unusually high normalised state estimation residuals, which can be identified based on the largest normalised residual test. More specifically, the probability of detecting leaks is computed here by working with the multivariate distribution among measurements and estimates to take into account the noisy nature of measurements with an analytical approach rather than with sampling experiments, which are time-consuming. The methodology set out herein also provides a procedure to systematically assess the minimum leak that could be detected in different parts of the network for a specific measurement setting and operating condition. The method has been applied to a water transport network case study to show its potential and to highlight the usefulness of such a tool for practitioners. The limitations of such a methodology are also discussed, including its possible use for on-line leak detection strategies.     

Time-lag effects of vegetation responses to soil moisture evolution: a case study in the Xijiang basin in South China

The time-lag effects of droughts on vegetation responses vary significantly across a large-scale river basin. The spatio-temporal response characteristics obtained are important for decision making processes on the allocation and transportation of regional water resources in mitigating drought impacts. Here we consider the Xijiang (West River) basin in South China as a case study, which has experienced severe drought events since the beginning of the 21st century. A threshold level approach is employed to identify the major drought events over the basin in the first decade of this century. The vegetation responses to land soil water evolution are examined, particularly for the severe drought events occurred. The time-lag effects of the vegetation responses within the basin range within 0–96 days. The lower reaches of the headwater sub-basins in the west part of the Xijiang basin are identified as the regions with short time-lag effects. The enhanced vegetation index (EVI) shows consistent responses to the soil water evolution in conjunction with the climate aridity in this area, which is the drought-vulnerable area in the Xijiang basin.     

A geostatistical protocol to optimize spatial sampling of domestic drinking water supplies in remote environments

This paper deals with the design of optimal spatial sampling of water quality variables in remote regions, where logistics are complicated and the optimization of monitoring networks may be critical to maximize the effectiveness of human and material resources. A methodology that combines the probability of exceeding some particular thresholds with a measurement of the information provided by each pair of experimental points has been introduced. This network optimization concept, where the basic unit of information is not a single spatial location but a pair of spatial locations, is used to emphasize the locations with the greatest information, which are those at the border of the phenomenon (for example contamination or a quality variable exceeding a given threshold), that is, where the variable at one of the locations in the pair is above the threshold value and the other is below the threshold. The methodology is illustrated with a case of optimizing the monitoring network by optimal selection of the subset that best describes the information provided by an exhaustive survey done at a given moment in time but which cannot be repeated systematically due to time or economic constrains.     

Bayesian posterior predictive return levels for environmental extremes

A key aim of most extreme value analyses is the estimation of the r-year return level; the wind speed, or sea-surge, or rainfall level (for example), we might expect to see once (on average) every r years. There are compelling arguments for working within the Bayesian setting here, not least the natural extension to prediction via the posterior predictive distribution. Indeed, for practitioners the posterior predictive return level has been cited as perhaps the most useful point summary from a Bayesian analysis of extremes, and yet little is known of the properties of this statistic. In this paper, we attempt to assess the performance of predictive return levels relative to their estimative counterparts obtained directly from the return level posterior distribution; in particular, we make comparisons with the return level posterior mean, mode and 95% credible upper bound. Differences between the predictive return level and standard summaries from the return level posterior distribution, for wind speed extremes observed in the UK, motivates this work. A large scale simulation study then reveals the superiority of the predictive return level over the other posterior summaries in many cases of practical interest.