Stochastic Environmental Research and Risk Assessment最新文献

Time series of traffic flows, extracted from mobile phone origin–destination data, are employed for monitoring people crowding and mobility in areas subject to flooding risk. By applying a vector autoregressive model with exogenous covariates combined with dynamic harmonic regression to such time series, we detected the presence of many extreme events in the residuals, which exhibit heavy-tailed distribution. For this reason, we propose a time series clustering procedure based on tail dependence which is suitable for data characterized by a spatial dimension, since objects’ geographical proximity is taken into account. The final aim is to obtain clusters of areas characterized by the common tendency to the manifestation of extreme events, that in this case study are represented by extremely high incoming traffic flows. The proposed method is applied to the Mandolossa, a strongly urbanized area located on the western outskirts of Brescia (northern Italy) which is subject to frequent flooding.

The accuracy of pollution source identification significantly depends on the amount of effective information derived from monitoring data. Currently, most of the comprehensive studies on groundwater contamination source identification and optimal design of monitoring solutions are based on hypothetical cases, whereas relevant studies on actual cases only consider one characteristic of the pollution source (either locations or fluxes). An optimal monitoring network (OMN)-based pollution source characterisation framework that takes source locations and source fluxes into account is presented to enhance the accuracy of pollution source identification. The genetic algorithm (GA) and particle swarm optimization (PSO) were used to solve the optimization model of pollution source characteristics identification. The framework is applied to a landfill for waste located in Baicheng City, China. The results showed that the identification results based on OMN has a smaller mean relative error and higher accuracy, compared with those based on random monitoring network (RMN). This study shows that OMNs can provide more effective information for pollution source identification and effectively enhance the accuracy of the groundwater sources characteristics identification.

Selecting appropriate Global Climate Models (GCMs) presents a significant challenge for accurate climate projections. To address this, a novel framework based on information theory based minimum redundancy and maximum relevancy (MRMR) method identifies top-performing GCMs across the entire study region using multicriteria decision analysis methodology. A subset of the ten best-performing models out of twenty-two GCMs is chosen for multi-model ensemble analysis. Five MME methods are selected to assess the ensemble performance of the ten selected GCMs, categorized into simple, regression-based, geometric-based, and machine learning ensembles. This study evaluates the effectiveness of the MME method based on a comprehensive index called the extended distance between indices of simulation and observation. An Adaptive Multimodel Standardized Drought Index (AMSDI) has been developed based on the optimal MME method. For the application of the framework and the proposed index, historical precipitation data from 1950 to 2014 were utilized from 28 grid points in the Punjab province of Pakistan as the reference dataset. Additionally, simulations from 22 models of the Coupled Model Intercomparison Project phase 6, both past and future, were employed for the estimation procedure. In AMSDI indicator, we used improved multimodel ensemble of precipitation for future drought characterization under various future scenarios. Outcome associated with this research show that AMSDI effectively have ability to effectively identifiy extreme drought events for all three future scenarios. In conclusion, the AMSDI method is shown to be effective and flexible, improving accuracy in monitoring droughts.

Atlantic tropical cyclones often associate with heavy rainfall, which causes inland- and coastal-flooding in the United States, and the storm-induced rainfall is closely related to its storm scale, movement, and location. For a better performance in flood or risk analysis in a region, understanding the characteristics and distribution of tropical storm (TS) induced extreme rainfall is essential. This study proposes dimensionless rainfall-duration curves for designated four-quartile storms that represents the temporal distribution of TS induced extreme rainfall in the Gulf of Mexico from 1979 to 2021. Our study employs spatiotemporal analysis to compute rainfall while TSs are located overseas and inland from satellite based climate forcing data and hurricane track records, annual maximum approach to define TS induced extreme rainfall events, and designated track types to categorize events based on their trajectories. As a result, extreme rainfall relating to TSs in the Gulf of Mexico are found to be considerably higher in inland than overseas. For inland, majority of the TSs was found to be the 1st- and 2nd-quartile storms. However, the 3rd-quartile storms, which case are rare, were found to have the overall largest amount of rainfall per duration compared to the other quartile storms. As for overseas, more than half of the TSs were found to be the 4th-quartile storm while the 2nd-quartile storm has higher overall rainfall per duration. Spatial analysis shows that Texas, Louisiana, Mississippi, Florida, and South Carolina are determined as high-threatened areas by TS induced extreme rainfall.

Anthropogenic activities predominantly affect environmental Pb pollution, especially during waste lead-acid battery (LAB) recycling operations. In this study, the presence of Pb and nine other potentially toxic elements (PTEs) in the soil at an abandoned LAB recycling site was investigated. The focus was on spatial and vertical distributions and potential health issues related to PTEs. Average concentrations of Cd, As, Hg, Pb, Al, Zn, Cu, and Sb were elevated at all investigated soil depths, whereas the concentrations of Zn, Cu, and Sb were significant only on the soil surface. Positive matrix factorization, correlation and cluster analyses, as well as self-organizing maps, identified four primary pollution sources: recycling activities (Cd, Hg, Pb, and Sb), mixed anthropogenic sources (Zn and Cu), the soil parent material (As, Cr, and Ni), and surface runoff combined with sand application (Al and pH). While the non-carcinogenic risk results revealed a negligible risk for adults, the hazard index (HI) values for children were greater than one in 26% of the samples. For adults and children, the total carcinogenic risk (TCR) values were acceptable for 98% and 94% of the samples, respectively. Geospatial analysis identified the main hotspot in the battery disposal area. Source-specific non-carcinogenic and carcinogenic risks were most influenced by recycling activities. Monte Carlo simulation (MCS) of total HI for children showed that the risk value exceeded the threshold level (HI > 1) at the 10th percentile, whereas the maximum value of total HI for adults was 0.2. Regarding carcinogenic risk, the TCR values at the 95th percentile of all four sources for adults and children were below the limit value (1 × 10⁻⁴), indicating a low probability of cancer development.

River water quality often follows a long-memory stochastic process with power-type autocorrelation decay, which can only be reproduced using appropriate mathematical models. The selection of a stochastic process model, particularly its memory structure, is often subject to misspecifications owing to low data quality and quantity. Therefore, environmental risk assessment should account for model misspecification through mathematically rigorous and efficiently implementable approaches; however, such approaches have been still rare. We address this issue by first modeling water quality dynamics through the superposition of an affine diffusion process that is stationary and has a long memory. Second, the worst-case upper deviation of the water quality value from a prescribed threshold value under model misspecifications is evaluated using either the divergence risk or Wasserstein risk measure. The divergence risk measure can consistently deal with the misspecification of the memory structure to the worst-case upper deviation. The Wasserstein risk measure is more flexible but fails in this regard, as it does not directly consider the memory structure information. We theoretically compare both approaches to demonstrate that their assumed uncertainties differed substantially. From the application to the 30-year water quality data of a river in Japan, we categorized the water quality indices to be those with truly long memory (Total nitrogen, NO₃-N, NH₄-N, and ({{text{SO}}}_{4}^{2-})), those with moderate power-type memory (NO₂-N, PO₄-P, and Total Organic Carbon), and those with almost exponential memory (Total phosphorus and Chemical Oxygen demand). The risk measures are successfully computed numerically considering the seasonal variations of the water quality indices.

The ecological environment of the Yellow River Delta is undergoing serious degradation due to the pressures of economic development and population growth. To improve and protect the ecological environment, it is crucial to accurately assess and monitor its eco-environmental quality. With consideration of the characteristics of terrestrial salinization in the region and the need for long-term ecological monitoring, we first utilized Google Earth Engine (GEE) to construct the Improved Remote Sensing Ecological Index (IRSEI). The IRSEI is based on the Remote Sensing Ecological Index (RSEI), which consists of the Normalized Difference Vegetation Index (NDVI), WET, Land Surface Temperature (LST), and Normalized Difference Built-Up and Soil Index (NDBSI), as well as the Net Primary Productivity (NPP) index. The entropy weighting method was employed to construct the IRSEI for assessing the eco-environmental quality of the Yellow River Delta. The validity of the index was verified through image entropy and contrast assessment. We then employed the Hurst exponent, Sen's slope estimation, and Coefficient of Variation (CV) to calculate the range of variation of the IRSEI in the Yellow River Delta over a 20-year period to analyze the spatio-temporal evolution of the ecological quality and its distribution pattern. Furthermore, we conducted a comprehensive analysis combining the Geographically and Temporally Weighted Regression (GTWR) model and Geodetector to understand the influence of drivers such as topography, soil, and climate on the IRSEI, considering both the temporal and spatial dimensions. The results indicate that: (1) The proposed IRSEI demonstrates higher reliability, adaptability, and sensitivity compared to RSEI in monitoring the eco-environmental quality of the Yellow River Delta. (2) From 2000 to 2020, the eco-environmental quality of the Yellow River Delta remained generally stable, with a spatial distribution resembling a "Y" shape, showing significant improvement, particularly in Lijin County and its surrounding areas. However, the middle and eastern estuary exhibited a declining trend in eco-environmental quality. (3) The impact of driving factors on the eco-environmental quality varied across the four subordinate regions of the Yellow River Delta, indicating spatial heterogeneity. Factors such as FVC, Soil, LST, JS, and Srad significantly influenced and explained the spatial differentiation of eco-environmental quality in the region. The proposed IRSEI demonstrates better monitoring capabilities in the Yellow River Delta compared to RSEI, providing a scientific basis for land use planning and ecological protection in the area.

Based on the principles design of hydrofoil weirs, Modified Semi-Cylindrical Weirs (MSCWs) incorporate an innovative tangential ramp along the downstream crest contour, thereby significantly enhancing their performance compared to conventional semi-cylindrical weirs. A pivotal parameter in the calculation of flow discharge over the weir is the discharge coefficient (C_d). This study involves a comprehensive comparative analysis of various C_d estimation methodologies for MSCWs, employing a range of machine learning-based models, notably including Artificial Neural Network (ANN), Multivariate Adaptive Regression Splines (MARS), M5 tree, Locally Weighted Polynomial Regression (LWPR), and Support Vector Machine (SVM) models. To begin, a feature selection analysis utilizing the Gamma Test (GT) method was conducted to identify the optimal input configuration for modeling the discharge of MSCWs. The results of the feature selection revealed that the C_d of the MSCWs is primarily influenced by the ratio of upstream flow depth (y_up) to crest radius (R), while showing negligible sensitivity to the slope of the downstream ramp (θ). The dataset was partitioned into two segments: 70% were assigned to the training stage, while the remaining 30% were allocated to the testing stage. The precision of C_d predictions is evaluated through four key statistical metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Square Error (RMSE), Correlation Coefficient (R²), and Nash –Sutcliff Efficiency (NSE). The outcomes reveal that, for the training and testing phases, the R² values for the ANN, MARS, M5 tree, LWPR and SVM models are respectively 0.967, 0.931, 0.974, 0.937, and 0.933, and 0.925, 0.953, 0.953, 0.980, and 0.954. Notably, the LWPR model outperforms the ANN, MARS, M5 tree, and SVM models, boasting MAE, MSE, RMSE, and NSE values of 0.0167, 0.0005, 0.0217, and 0.942 during training, and 0.0107, 0.0002, 0.0136, and 0.949 during testing. As a result, the LWPR model clearly emerges as the superior model, followed by the M5 model tree.

Anthropogenic factors such as over-pumping and natural events such as earthquakes impact coastal aquifers by reducing freshwater recharge, aquifer water budgets, and increasing saltwater intrusion (SWI). This study investigates the impact of hydrodynamic forces induced by earthquakes on SWI in one hypothetical case, namely, the Henry problem, and a real case of the Biscayne aquifer located in Florida, USA. The analysis was carried out using the analytical solution of estimating the earthquake’s induced hydrodynamic pressure and applying the SEAWAT code to investigate the SWI for the base case and three scenarios, namely for the horizontal acceleration (α_h) by 0.10 g, 0.20 g, and 0.30 g. The results show that earthquakes might considerably increase the SWI in coastal aquifers. Moreover, the rise in salinity across expansive land areas significantly threatens agricultural productivity and jeopardizes food security. Namely, in the case of Biscayne aquifer, salinity was increased by 12.10%, 21.90%, and 45.70% for the horizontal seismic acceleration of 0.1 g, 0.20 g, and 0.30 g, respectively. Hence, the conclusions drawn from this study underscore the need for carefull consideration of earthquake impacts in future planning and water management strategies for coastal regions. This proactive approach is crucial to preemptively address and mitigate the groundwater salinization hazard associated with SWI fluctuations due to earthquakes.

Air pollution is one of the grave concerns of the modern era, claiming millions of lives and adversely impacting the economy. The primary objective of this study was to develop advanced forecast models for PM_2.5 levels in the Hyderabad district of India using artificial intelligence techniques. This study presents a novel approach to PM_2.5 modelling, leveraging the power of artificial intelligence (AI) and data-driven insights for Hyderabad District. Factor analysis was performed to check for correlations of PM_2.5 and aerosol optical depth (AOD) with various meteorological and pollutant variables, based on which it was observed that except temperature and solar radiation, all the variables showed considerable correlations with aerosols. The hybrid deep learning-based CNN – LSTM model was identified as the best-fit model for predicting PM_2.5 with an R² = 0.88, MSE = 68.93 (µg/m³)², RMSE = 8.30 µg/m³, and MAE = 6.45 µg/m³ as against the MLP – ARIMA and MLP models. A study on feature importance showed that AOD is a significant contributor to PM_2.5 prediction with a factor importance of 6.8%, ranking second only to meteorological factors. Wind direction and relative humidity exhibited factor importance values of 10.94% and 8.69%, respectively. The AI-driven PM_2.5 modelling approach offers a more comprehensive understanding of pollution patterns and their relationship with meteorological conditions and geographical characteristics. These results highlight the strong predictive power of the CNN – LSTM model and the significant influence of AOD and meteorological factors on PM_2.5 levels. These insights can inform policymakers, urban planners, and environmental agencies in formulating effective pollution control strategies and mitigation measures, leading to improved air quality and public health in the Hyderabad district and beyond.