Springer Science and Business Media LLC

This study focused on the development of Bayesian bivariate semiparametric models for ozone and PM2.5 emissions. The semiparametric models rely on a Dirichlet mixture, which accounts for unobserved heterogeneity by employing random distribution for the intercept of each entity. The models with and without spatially structured random effects were also evaluated. Overall, four models were developed: three semiparametric with a flexible intercept and one parametric to serve as a reference. The significance of simultaneous estimation of PM2.5 and ozone was demonstrated by selection of common contributing factors as well as the statistically significant bivariate error term. In terms of relationship between dependent and independent variables, the areas with higher population and poverty, along with lesser educated residents, were observed to experience higher ozone and PM2.5 concentration. Also, the entities with higher vehicular traffic density and larger geographic areas were observed to be positively correlated with ozone and PM2.5 concentration. The underlying influential factor for both such variables may be the vehicular emissions, which is directly associated with traffic density and indirectly with land area as larger entities may tend to have higher traffic activity. The superiority of the semiparametric models, compared with parametric, signified the advantage associated with the flexible Dirichlet approach. The models with and without spatial correlation structures illustrated the mixed performance across the cases of parametric and semiparametric bivariate ones. The lack of consistent advantages associated with the inclusion of spatial effects may be due to the fact that the spatial correlation was not observed to be significant for the current dataset. Other spatial structure specifications may lead to different performance results.

Stormwater Control Measures (SCMs) are widely used to control and treat stormwater runoff pollution. The first step in SCM design is to evaluate the precipitation patterns at a site. SCMs are normally designed using a storm with a specific return period. A robust design process that uses frequency distribution of precipitation and monitoring of performance could improve our understanding of the behavior and limitations of a particular design. This is not the current norm in the design of SCMs. In this research, frequency analyses (FA) of precipitation events was conducted using hourly precipitation data from 1948 to 2010 for eight sites representing the four major physiographic regions of Virginia. The available data were treated using an inverse distance method to eliminate missing gaps before processing into events determined by minimum inter-event times. FA was at each site to develop frequency plots of precipitation and dry duration. FA of the eight locations indicates a range of rainfall depths from 22.9 mm in Bristol to 35.6 mm in Montebello, compared to the nominal “water quality storm” of 25.4 mm (i.e., 1 in.). Similarly, for dry duration, for a 10 % exceedance probability, the range is from 16.8 days in Richmond and Norfolk to 19.5 days in Montebello. Dry duration provides guidance for vegetation selection, which is important for some SCMs. The degree of variability in both parameters argues for consideration of site-specific information in design. FA was also used to provide guidance to improve monitoring programs. Monte Carlo simulations demonstrated that performance monitoring programs applied in different regions would likely encounter more than 30 % of precipitation events less than 6.35 mm, and 10 % over 25.4 mm under various sampling regimes. The percentages of precipitation events encountered in the Coastal Plain and Piedmont regions are not impacted by sampling regimes, however the Blue Ridge Mountains and Valley and Ridge regions are likely impacted. Anticipating event occurrences improves the chances of implementing a successful monitoring program. The use of these results could enhance the performance of SCMs with consideration of local conditions for both monitoring SCMs and their design basis.

Các khu ngập nước được xây dựng ngày càng được sử dụng để xử lý các dòng thải nước thải phát sinh không liên tục từ các ao nuôi thủy sản. Hầu hết các quy trình thiết kế khu ngập nước đều dựa trên các dòng thải ổn định và do đó không thể xác định được sự phân bố chất ô nhiễm tạm thời xảy ra từ các dòng thải không liên tục. Với tính chất theo mùa của các hoạt động nuôi thủy sản, khu ngập nước phải được phát triển bằng cách sử dụng nước từ các nguồn gần đó (được gọi là nước bổ sung trong nghiên cứu này). Trong một số trường hợp, nồng độ chất ô nhiễm trong nước bổ sung được ghi nhận là cao hơn so với mức đo được trong các ao nuôi thủy sản. Thêm vào đó, các chất ô nhiễm trong khu ngập nước có thể thể hiện sự biến đổi không gian do các tương tác địa hóa với đất ngập nước. Những ảnh hưởng này không thể được ghi nhận bằng các kế hoạch thiết kế ổn định hiện có. Một mô hình toán học dựa trên khái niệm các bình theo chuỗi được phát triển để vượt qua những hạn chế đã nêu. Một giải pháp phân tích cho các dòng thải không liên tục nhiều lần và nồng độ ban đầu thay đổi theo không gian được thu được bằng cách sử dụng biến đổi Laplace và các kỹ thuật chồng chất. Tính hữu ích của mô hình được phát triển được minh họa bằng một ví dụ dựa trên khu ngập nước tại Cơ sở Nuôi tôm Loma Alta (LASAF) ở miền Nam Texas. Mô hình được phát triển nắm bắt mối quan hệ phi tuyến tính cực kỳ giữa nồng độ xả tối đa và thời gian cư trú thủy lực. Đối với các điều kiện được giả định trong nghiên cứu này, mức độ trộn lẫn không phải là vấn đề chính khi khoảng cách giữa các tải trọng lớn hơn 0.75 lần thời gian cư trú thủy lực (HRT). Kết quả mô hình cũng gợi ý rằng khu ngập nước nên được định hướng theo cách mà sự sản xuất địa hóa của chất ô nhiễm bên trong, nếu có, gần với lối vào.

Hydrological models are widely used to investigate practical issues of water resources. Parametric uncertainty is considered as one of the most important sources of uncertainty in environmental researches. Generally, it is assumed that the parameters are independent mutually, but correlation within the parameter space is an important factor having the potential to cause uncertainty. The objective and innovation of this study was to address the effects of parameters correlation on a continuous hydrological model uncertainty. HEC-HMS with soil moisture accounting (SMA) infiltration method was used to model daily flows and simulate certainty bounds for Karoon III basin, southwest of IRAN, in two scenarios, independent and correlated parameters using 2-copula. The parameters were represented by probability distributions, and the effect on prediction error were evaluated using Latin hypercube sampling (LHS) on Monte Carlo simulation (MCS). Saturated hydraulic conductivity (K), Clark storage-coefficient (R), and time of concentration (tc) were chosen for investigation, based on observed sensitivity analysis of simulated peak over threshold (POT). One hundred runs were randomly generated from 100 parameter sets captured from LHS of parameters distributions in each sub-basin. Using generated parameter sets, 100 continuous hydrographs were simulated and values of certainty bounds calculated. Results showed that when 2-copula correlated R and tc, with 0.656 Kendall’s Tau and 0.818 Spearman’s Rho coefficients, were propagated, decreasing of outputs’ sharpness was more than when considering K and R (K-R), with 0.166 and 0.262; therefore, incorporation of correlations in the MCS is important, especially when the correlation coefficients exceed 0.65. The model was evaluated at the outlet of the basin using daily stream flow data. Model reliability was better for above-normal flows than normal and below-normal. Reliability increases of simulated flow when considering correlated R-tc was more than K-R because of the correlation values. Incorporation of copula for K-tc not only did not improve the model reliability but also decreased it. Results showed improvement of model reliability, by decreasing predicted error of hydrologic modeling, when dealing with correlated parameters in the system.

The main aim of this study was to construct several regression models of air quality using techniques based on the statistical learning, in the metropolitan area of Oviedo, in northern Spain. In this research, a hybrid particle swarm optimization-based evolutionary support vector regression is implemented to predict the air quality from the experimental dataset (specifically, nitrogen oxides, carbon monoxide, sulfur dioxide, ozone, and dust) collected from 2013 to 2015 in the metropolitan area of Oviedo. Furthermore, a multilayer perceptron network (MLP) and the M5 model tree were also fitted to the experimental dataset for comparison purposes. Finally, the predicted results show that the hybrid proposed model is more robust than the MLP and M5 model tree prediction methods in terms of statistical estimators and testing performances.

This article aims to discuss the influence of input data on the simulation model when designing Strategic Noise Maps. The studied noise map was made in the Macrocenter of the Independent City of Buenos Aires (Argentina), which has an approximated extension of 20 km2 and about 500,000 inhabitants. The several input data for the model are analyzed, for their quality and the lack of some of them could affect the final result. Also, the evolution and validity of experimental measurements are analyzed when validating a simulated map. Finally, a study of the uncertainty of the map based on the input data is made, comparing it with the recommendations internationally adopted.

In this paper, a novel approach of modelling time series of nitrate concentrations in drinking water by nonlinear growth functions is proposed. An almost constant increase in the average values of nitrate concentrations occurred at the Vinokovščak wellfield in the county of Varaždin, Croatia, in the period between 1998 and 2013. Our hypothesis was that the time series would be well adjusted to the nonlinear growth function with asymptotic properties after smoothing performed by the moving average method. We used a general growth model which does not have asymptotic properties and the logistic, Gompertz and Richards functions which do have asymptotic properties. Last three growth functions were modified based on the knowledge of a constant presence of basic nitrate concentrations in the drinking water before growth. The fit of growth curves to experimental data was assessed using R 2 and Aikake’s information criterion. The modified growth functions gave the best fit to the smoothed time series of nitrate concentrations with R 2 ranging from 0.9960 to 0.9986.

This paper is based on the perception that the inertia of climate and socio-economic systems are key parameters in the climate change issue. In a first part, it develops and implements a new approach based on a simple integrated model with a particular focus on an innovative transient impact and adaptation modeling. In a second part, a climate–economy feedback is defined and characterized. The following results were found. 1) It has a long characteristic time, which lies between 50 and 100 years depending on the hypotheses; this time scale is long when compared to the system's other time scales, and the feedback cannot act as a natural damping process of climate change. 2) Mitigation has to be anticipated since the feedback of an emission reduction on the economy can be significant only after a 20-year delay and is really efficient only after at least 50 years. 3) Even discounted, production changes due to an action on emissions are significant over more than one century. 4) The methodology of the Intergovernmental Panel on Climate Change (IPCC), which neglects the feedback from impacts to emissions, is acceptable up to 2100, whatever is the level of impacts. This analysis allows also to define a climatic cost of growth as the additional climate change damages due to the additional emissions linked to economic growth.

Interactions among atmospheric parameters exist at different scales. The pristine approach for observational or model data analysis involves changing the input parameters one at a time (OAT) and studying the effect on the system. Limitations of this approach for atmospheric applications are discussed. A fractional factorial (FF) based study is evolved and a methodology is outlined involving dynamic graphical analysis. Observational data from the FIFE and HAPEX‐MOBILHY experiments are utilized with a vegetation and soil moisture scheme dynamically coupled in a planetary boundary layer model to demonstrate the robustness of this approach. Both low‐resolution and high‐resolution designs are considered. Various aspects of the vegetation‐atmosphere interactions are delineated. Results obtained from the interaction‐based FF approach differ considerably from the earlier OAT‐type studies.