Application of best subset method for river water quality modeling: A study on Godavari River,India

The utilization of water quality analysis to inform optimal decision-making is imperative to achieve sustainable management of river water quality. A multitude of research works in the past has focused on river water quality modeling. Despite being a precise statistical regression technique that allows for fitting separate models for all potential combinations of predictors and selecting the optimal subset model, the application of best subset method in river water quality modeling is not widely adopted. The current research aims to validate the use of best subset method in evaluating the water quality parameters of the Godavari River, one of the largest rivers in India, by developing regression equations for different combinations of its physicochemical parameters. The study involves in formulating best subset regression equations to estimate the concentrations of river water quality parameters while also identifying and quantifying their variations. A total of 17 water quality parameters are analyzed at 13 monitoring sites using 13 years (1993–2005) of observed data for the monsoon (June–October) period and post-monsoon (November–February) period. The final subset model is selected among model combinations that are developed for each year's dataset through widely used statistical criteria such as R², F value, adjusted R²_a, AIC_c, and RSS. The final best subset model across all parameters exhibits R² values surpassing 0.8, indicating that the models possess the ability to account for over 80% of the variations in the concentrations of dependent parameters. Therefore, the findings demonstrated the appropriateness of this method in evaluating the water quality parameters in extensive rivers. This work is very useful for decision-making and in the management of river water quality for its sustainable use in the study area.     

Effects of sludge characteristics on electrical resistance and energy consumption during electro-dewatering process

The increase of electrical resistance (ER) and energy consumption (EC) during the later stage of dewatering is a major problem hindering the development of electro-dewatering (EDW) technology. As the variations of sludge characteristics are significant during the EDW process, the relationships between sludge characteristics and ER and EC during EDW remain unclear. In this study, the effects of moisture content (MC), thickness, pH, conductivity, zeta potential, temperature, and gas volume on the ER and EC during the EDW process were statistically investigated using correlation and multiple linear regression analyses. Herein, the results showed that the ER of the sludge near the anode was primarily affected by pH, whereas the sludge near the cathode was primarily affected by the MC and conductivity. Further, sludge temperature and conductivity were the most reliable indicators to predict the EC near the anode and cathode, respectively. The results of this study provide theoretical guidance useful for solving the increase of ER and EC during the later stage of the EDW process.     

一种基于Logistic回归的全损飞行事故分析与预测

运用Logistic回归方法分析全损飞行事故的影响因素,并构建预测模型,然后利用飞行事故案例进行了实证检验。通过对样本数据过离散、空单元、多元共线性和特异值、特殊影响案例的诊断,以及对模型的拟合程度和预测效果的检验,表明所构建的模型比较符合现实情况,而且具有较好的拟合程度和预测效果。模型指出航班类型和机龄因素具有统计学意义,国内航班比国际航班的全损概率要高;机龄越老,全损概率越高。     

工伤事故灾害空间分布特征及其与经济增长的关联性

以经济区域为基本单元,在对工伤事故灾害和经济增长空间分布特征描述的基础上,采用面板回归工具分析两者的空间关联性。研究结果证明:工伤事故灾害与经济增长规模之间存在梯度空间差异规律,经济增长对工伤事故灾害有重要影响,并且不同经济增长对工伤事故灾害的影响存在较大差异性,工业化、经济一体化程度和人力资本素质的区域不均衡是造成工伤事故灾害空间分布差异的重要变量。     

Bicyclist injury severity in traffic crashes: A spatial approach for geo-referenced crash data to uncover non-stationary correlates

Introduction: Bicyclists are among vulnerable road users with their safety a key concern. This study generates new knowledge about their safety by applying a spatial modeling approach to uncover non-stationary correlates of bicyclist injury severity in traffic crashes. Method: The approach is Geographically Weighted Ordinal Logistic Regression (GWOLR), extended from the regular Ordered Logistic Regression (OLR) by incorporating the spatial perspective of traffic crashes. The GWOLR modeling approach allows the relationships between injury severity and its contributing factors to vary across the spatial domain, to account for the spatial heterogeneity. This approach makes use of geo-referenced data. This study explored more than 7,000 geo-referenced bicycle--motor-vehicle crashes in North Carolina. Results: This study performed a series of non-stationarity tests to identify local relationships that vary substantially across the spatial domain. These local relationships are related to the bicyclist (bicyclist age, bicyclist behavior, bicyclist intoxication, bicycle direction, bicycle position), motorist (driver age, driver intoxication, driver behavior, vehicle speed, vehicle type) and traffic (traffic volume). Conclusions: Results from the regular OLR are in general consistent with previous findings. For example, an increased bicyclist injury severity is associated with older bicyclists, bicyclist being intoxicated, and higher motor-vehicle speeds. Results from the GWOLR show local (rather than global) relationships between contributing factors and bicyclist injury severity. Practical Applications: Researchers and practitioners may use GWOLR to prioritize cycling safety countermeasures for specific regions. For example, GWOLR modeling estimates in the study highlighted the west part (from Charlotte to Asheville) of North Carolina for increased bicyclist injury severity due to the intoxication of road users including both bicyclists and drivers. Therefore, if a countermeasure is concerned with the road user intoxication, there may be a priority for the region from Charlotte to Asheville (relative to other areas in North Carolina).     

Statistical modeling of cargo tank truck crashes: Rollover and release of hazardous materials

Introduction: Cargo Tank Trucks (CTTs) are a primary surface transportation carrier of hazardous materials (hazmat) in the United States and CTT rollover crashes are the leading cause of injuries and fatalities from hazmat transportation incidents. CTTs are susceptible to rollover crashes because of their size, distribution of weight, a higher center of gravity, and the surging and sloshing of liquid cargo during transportation. This study identified and quantified the effects of various factors on the probability of rollover and release of hazmat in traffic crashes where a CTT was involved. Method: Bayesian Model Averaging (BMA)-based logistic regression models were estimated with rollover and hazmat release as the binary response variables, and crash, truck, roadway, environment, and driver characteristics as the explanatory variables. 2010–2016 police-reported CTT-involved crash data from Nebraska and Kansas was utilized. Receiver Operating Characteristic (ROC) curves confirmed appropriateness of the modeling approach for inference and prediction on the crash dataset. Results: CTTs are more likely to rollover in crashes while turning and changing lanes relative to going straight; side impacts (side collisions) and severe crosswinds increased the likelihood of rollovers; tractor and semi-trailer body style decreased the probability of rollover, while truck tractors are more prone to rollovers; collisions with fixed objects and higher posted speeds increased the rollover probability; rollovers and intersection crash locations increased the likelihood of hazmat release. Conclusions: The findings can assist stakeholders (policy-makers, private shippers, and CTT drivers) in restricting CTTs’ operations for safety; scheduling, routing, and fleet planning; and low-level decision-making (e.g., emergency stopping or local routing). Practical Applications: This study identified and quantified the effects of different factors on the conditional probability of rollover and release of hazmat in CTT-involved crashes. The findings may assist stakeholders in decision-making towards safe operations of CTTs for transportation of hazmat.     

Pedestrians under influence (PUI) crashes: Patterns from correspondence regression analysis

Introduction: Alcohol-related impairment is a key contributing factor in traffic crashes. However, only a few studies have focused on pedestrian impairment as a crash characteristic. In Louisiana, pedestrian fatalities have been increasing. From 2010 to 2016, the number of pedestrian fatalities increased by 62%. A total of 128 pedestrians were killed in traffic crashes in 2016, and 34.4% of those fatalities involved pedestrians under the influence (PUI) of drugs or alcohol. Furthermore, alcohol-PUI fatalities have increased by 120% from 2010 to 2016. There is a vital need to examine the key contributing attributes that are associated with a high number of PUI crashes. Method: In this study, the research team analyzed Louisiana’s traffic crash data from 2010 to 2016 by applying correspondence regression analysis to identify the key contributing attributes and association patterns based on PUI involved injury levels. Results: The findings identified five risk clusters: intersection crashes at business/industrial locations, mid-block crashes on undivided roadways at residential and business/residential locations, segment related crashes associated with a pedestrian standing in the road, open country crashes with no lighting at night, and pedestrian violation related crashes on divided roadways. The association maps identified several critical attributes that are more associated with fatal and severe PUI crashes. These attributes are dark to no lighting, open country roadways, and non-intersection locations. Practical Applications: The findings of this study may be used to help design effective mitigation strategies to reduce PUI crashes.     

Long-Term Effects of Changing Land Use Practices on Surface Water Quality in a Coastal River and Lagoonal Estuary

The watershed of the Neuse River, a major tributary of the largest lagoonal estuary on the U.S. mainland, has sustained rapid growth of human and swine populations. This study integrated a decade of available land cover and water quality data to examine relationships between land use changes and surface water quality. Geographic Information Systems (GIS) analysis was used to characterize 26 subbasins throughout the watershed for changes in land use during 1992–2001, considering urban, agricultural (cropland, animal as pasture, and densities of confined animal feed operations [CAFOs]), forested, grassland, and wetland categories and numbers of wastewater treatment plants (WWTPs). GIS was also used together with longitudinal regression analysis to identify specific land use characteristics that influenced surface water quality. Total phosphorus concentrations were significantly higher during summer in subbasins with high densities of WWTPs and CAFOs. Nitrate was significantly higher during winter in subbasins with high numbers of WWTPs, and organic nitrogen was higher in subbasins with higher agricultural coverage, especially with high coverage of pastures fertilized with animal manure. Ammonium concentrations were elevated after high precipitation. Overall, wastewater discharges in the upper, increasingly urbanized Neuse basin and intensive swine agriculture in the lower basin have been the highest contributors of nitrogen and phosphorus to receiving surface waters. Although nonpoint sources have been emphasized in the eutrophication of rivers and estuaries such as the Neuse, point sources continue to be major nutrient contributors in watersheds sustaining increasing human population growth. The described correlation and regression analyses represent a rapid, reliable method to relate land use patterns to water quality, and they can be adapted to watersheds in any region.     

GIS‐Based Predictive Models of Hillslope Runoff Generation Processes1

Abstract: Successful nonpoint source pollution control using best management practice placement is a complex process that requires in‐depth knowledge of the locations of runoff source areas in a watershed. Currently, very few simulation tools are capable of identifying critical runoff source areas on hillslopes and those available are not directly applicable under all runoff conditions. In this paper, a comparison of two geographic information system (GIS)‐based approaches: a topographic index model and a likelihood indicator model is presented, in predicting likely locations of saturation excess and infiltration excess runoff source areas in a hillslope of the Savoy Experimental Watershed located in northwest Arkansas. Based on intensive data collected from a two‐year field study, the spatial distributions of hydrologic variables were processed using GIS software to develop the models. The likelihood indicator model was used to produce probability surfaces that indicated the likelihood of location of both saturation and infiltration excess runoff mechanisms on the hillslope. Overall accuracies of the likelihood indicator model predictions varied between 81 and 87% for the infiltration excess and saturation excess runoff locations respectively. On the basis of accuracy of prediction, the likelihood indicator models were found to be superior (accuracy 81‐87%) to the predications made by the topographic index model (accuracy 69.5%). By combining statistics with GIS, runoff source areas on a hillslope can be identified by incorporating easily determined hydrologic measurements (such as bulk density, porosity, slope, depth to bed rock, depth to water table) and could serve as a watershed management tool for identifying critical runoff source areas in locations where the topographic index or other similar methods do not provide reliable results.     

关于大气中PM2.5浓度的综合预测

鉴于PM2.5对人体的危害，为了提前预测PM2.5浓度值并及时通过降雨方法降低其浓度，开展了PM2.5浓度综合预测研究。通过对西安市一个空气质量监测点收集到观测数据分析与PM2.5浓度强相关的因素，随后综合考虑天气、温度、风力风向对PM2.5浓度观测值的影响，通过添加虚拟变量及因子分析提取因子的方法，将强相关因素、提取的天气、温度、风力风向因子与PM2.5浓度观测值进行回归预测，最终得到较好预测效果。