Modeling the contribution of point sources and non-point sources to Thachin River water pollution

Major rivers in developing and emerging countries suffer increasingly of severe degradation of water quality. The current study uses a mathematical Material Flow Analysis (MMFA) as a complementary approach to address the degradation of river water quality due to nutrient pollution in the Thachin River Basin in Central Thailand. This paper gives an overview of the origins and flow paths of the various point- and non-point pollution sources in the Thachin River Basin (in terms of nitrogen and phosphorus) and quantifies their relative importance within the system. The key parameters influencing the main nutrient flows are determined and possible mitigation measures discussed.The results show that aquaculture (as a point source) and rice farming (as a non-point source) are the key nutrient sources in the Thachin River Basin. Other point sources such as pig farms, households and industries, which were previously cited as the most relevant pollution sources in terms of organic pollution, play less significant roles in comparison. This order of importance shifts when considering the model results for the provincial level. Crosschecks with secondary data and field studies confirm the plausibility of our simulations. Specific nutrient loads for the pollution sources are derived; these can be used for a first broad quantification of nutrient pollution in comparable river basins. Based on an identification of the sensitive model parameters, possible mitigation scenarios are determined and their potential to reduce the nutrient load evaluated.A comparison of simulated nutrient loads with measured nutrient concentrations shows that nutrient retention in the river system may be significant. Sedimentation in the slow flowing surface water network as well as nitrogen emission to the air from the warm oxygen deficient waters are certainly partly responsible, but also wetlands along the river banks could play an important role as nutrient sinks.     

Modelling air pollution for epidemiologic research--part II: predicting temporal variation through land use regression

Over recent years land use regression (LUR) has become a frequently used method in air pollution exposure studies, as it can model intra-urban variation in pollutant concentrations at a fine spatial scale. However, very few studies have used the LUR methodology to also model the temporal variation in air pollution exposure. The aim of this study is to estimate annual mean NO₂ and PM₁₀ concentrations from 1996 to 2008 for Greater Manchester using land use regression models. The results from these models will be used in the Manchester Asthma and Allergy Study (MAAS) birth cohort to determine health effects of air pollution exposure.The Greater Manchester LUR model for 2005 was recalibrated using interpolated and adjusted NO₂ and PM₁₀ concentrations as dependent variables for 1996-2008. In addition, temporally resolved variables were available for traffic intensity and PM₁₀ emissions. To validate the resulting LUR models, they were applied to the locations of automatic monitoring stations and the estimated concentrations were compared against measured concentrations.The 2005 LUR models were successfully recalibrated, providing individual models for each year from 1996 to 2008. When applied to the monitoring stations the mean prediction error (MPE) for NO₂ concentrations for all stations and years was -0.8 μg/m³ and the root mean squared error (RMSE) was 6.7 μg/m³. For PM₁₀ concentrations the MPE was 0.8 μg/m³ and the RMSE was 3.4 μg/m³.These results indicate that it is possible to model temporal variation in air pollution through LUR with relatively small prediction errors. It is likely that most previous LUR studies did not include temporal variation, because they were based on short term monitoring campaigns and did not have historic pollution data. The advantage of this study is that it uses data from an air dispersion model, which provided concentrations for 2005 and 2010, and therefore allowed extrapolation over a longer time period.     

Spatial analysis and land use regression of VOCs and NO2 from school-based urban air monitoring in Detroit/Dearborn, USA

Passive ambient air sampling for nitrogen dioxide (NO₂) and volatile organic compounds (VOCs) was conducted at 25 school and two compliance sites in Detroit and Dearborn, Michigan, USA during the summer of 2005. Geographic Information System (GIS) data were calculated at each of 116 schools. The 25 selected schools were monitored to assess and model intra-urban gradients of air pollutants to evaluate impact of traffic and urban emissions on pollutant levels. Schools were chosen to be statistically representative of urban land use variables such as distance to major roadways, traffic intensity around the schools, distance to nearest point sources, population density, and distance to nearest border crossing. Two approaches were used to investigate spatial variability. First, Kruskal-Wallis analyses and pairwise comparisons on data from the schools examined coarse spatial differences based on city section and distance from heavily trafficked roads. Secondly, spatial variation on a finer scale and as a response to multiple factors was evaluated through land use regression (LUR) models via multiple linear regression. For weeklong exposures, VOCs did not exhibit spatial variability by city section or distance from major roads; NO₂ was significantly elevated in a section dominated by traffic and industrial influence versus a residential section. Somewhat in contrast to coarse spatial analyses, LUR results revealed spatial gradients in NO₂ and selected VOCs across the area. The process used to select spatially representative sites for air sampling and the results of coarse and fine spatial variability of air pollutants provide insights that may guide future air quality studies in assessing intra-urban gradients.     

A distance-decay variable selection strategy for land use regression modeling of ambient air pollution exposures

Land use regression (LUR) has emerged as an effective and economical means of estimating air pollution exposures for epidemiological studies. To date, no systematic method has been developed for optimizing the variable selection process. Traditionally, a limited number of buffer distances assumed having the highest correlations with measured pollutant concentrations are used in the manual stepwise selection process or a model transferred from another urban area.In this paper we propose a novel and systematic way of modeling long-term average air pollutant concentrations through “A Distance Decay REgression Selection Strategy” (ADDRESS). The selection process includes multiple steps and, at each step, a full spectrum of correlation coefficients and buffer distance decay curves are used to select a spatial covariate of the highest correlation (compared to other variables) at its optimized buffer distance. At the first step, the series of distance decay curves is constructed using the measured concentrations against the chosen spatial covariates. A variable with the highest correlation to pollutant levels at its optimized buffer distance is chosen as the first predictor of the LUR model from all the distance decay curves. Starting from the second step, the prediction residuals are used to construct new series of distance decay curves and the variable of the highest correlation at its optimized buffer distance is chosen to be added to the model. This process continues until a variable being added does not contribute significantly (p > 0.10) to the model performance. The distance decay curve yields a visualization of change and trend of correlation between the spatial covariates and air pollution concentrations or their prediction residuals, providing a transparent and efficient means of selecting optimized buffer distances. Empirical comparisons suggested that the ADDRESS method produced better results than a manual stepwise selection process of limited buffer distances. The method also enables researchers to understand the likely scale of variables that influence pollution levels, which has potentially important ramifications for planning and epidemiological studies.     

Parameter uncertainty analysis of the non-point source pollution in the Daning River watershed of the Three Gorges Reservoir Region, China

The generation and formation of non-point source pollution involves great uncertainty, and this uncertainty makes monitoring and controlling pollution very difficult. Understanding the main parameters that affect non-point source pollution uncertainty is necessary to provide the basis for the planning and design of control measures. In this study, three methods were adopted to do the parameter uncertainty analysis with the Soil and Water Assessment Tool (SWAT). Based on the results of parameter sensitivity analysis by the Morris screening method, the ten parameters that most affect runoff, sediment, organic N, nitrate, and total phosphorous (TP) were chosen for further uncertainty analysis. First-order error analysis (FOEA) and the Monte Carlo method (MC) were used to analyze the effect of parameter uncertainty on model outputs. FOEA results showed that only a few parameters had significantly affected the uncertainty of the final simulation results, and many parameters had little or no effect. The SCS curve number was the parameter with significant uncertainty impact on runoff, sediment, organic N, nitrate and TP, and it showed that the runoff process was mainly responsible for the uncertainty of non-point source pollution load. The uncertainty of sediment was the biggest among the five model output results described above. MC results indicated that neglecting the parameter uncertainty of the model would underestimate the non-point source pollution load, and that the relationship between model input and output was non-linear. The uncertainty of non-point source pollution exhibited a temporal pattern: It was greater in summer than in winter. The uncertainty of runoff was smaller compared to that of sediment, organic N, nitrate, and TP, and the source of uncertainty was mainly affected by parameters associated with runoff.     

Modelling air pollution for epidemiologic research — Part I: A novel approach combining land use regression and air dispersion

A common limitation of epidemiological studies on health effects of air pollution is the quality of exposure data available for study participants. Exposure data derived from urban monitoring networks is usually not adequately representative of the spatial variation of pollutants, while personal monitoring campaigns are often not feasible, due to time and cost restrictions. Therefore, many studies now rely on empirical modelling techniques, such as land use regression (LUR), to estimate pollution exposure. However, LUR still requires a quantity of specifically measured data to develop a model, which is usually derived from a dedicated monitoring campaign. A dedicated air dispersion modelling exercise is also possible but is similarly resource and data intensive.This study adopted a novel approach to LUR, which utilised existing data from an air dispersion model rather than monitored data. There are several advantages to such an approach such as a larger number of sites to develop the LUR model compared to monitored data. Furthermore, through this approach the LUR model can be adapted to predict temporal variation as well as spatial variation. The aim of this study was to develop two LUR models for an epidemiologic study based in Greater Manchester by using modelled NO₂ and PM₁₀ concentrations as dependent variables, and traffic intensity, emissions, land use and physical geography as potential predictor variables. The LUR models were validated through a set aside “validation” dataset and data from monitoring stations.The final models for PM₁₀ and NO₂ comprised nine and eight predictor variables respectively and had determination coefficients (R²) of 0.71 (PM₁₀: Adj. R² = 0.70, F = 54.89, p < 0.001, NO₂: Adj. R² = 0.70, F = 62.04, p < 0.001). Validation of the models using the validation data and measured data showed that the R² decreases compared to the final models, except for NO₂ validation in the measured data (validation data: PM₁₀: R² = 0.33, NO₂: R² = 0.62; measured data: PM₁₀: R² = 0.56, NO₂: R² = 0.86). The validation further showed low mean prediction errors and root mean squared errors for both models.     

The impact of urbanization policy on land use change: A scenario analysis

The rapid urbanization has led to extensive land use change particularly in those developing countries. In line with the development of urbanization, arable land is decreasing dramatically, which presents the threat to the food security for human being. It is therefore essential to understand the level of impacts of urbanization on the land use change. This paper introduces a dynamic systems based method for assessing the impacts of urbanization policy on land use change with reference to the urbanization practice in China. Four typical policy scenarios are identified in implementing urbanization in China, including balanced development driven by planning, uneven development driven by planning, balanced development driven by market and uneven development driven by market and their impacts on land use change are analyzed through a dynamic system model. Land use change is considered as a dynamic system model composing five subsystems: urbanization, social, economic, environmental and land use subsystems. The key attributes in these five subsystems are interactive and they are dynamic variables. The assessment on the impacts of urbanization policy to land use change is demonstrated through employing the software iThink to the land use change dynamic model, using the data collected from the Jinyun County in China. The findings suggest that the urban construction land will continue to increase in the foreseeable future in China, whilst the agricultural land will gradually decrease. Nevertheless, different policy scenarios will have different impacts on these land changes. Thus decision makers can adopt different policies to control the rate of land use change.     

运用调查监测数据对北京农业面源污染现状分析与评价

农业面源污染已成为三大环境污染之一,严重制约了农业和社会的可持续发展。本文对几种不同地质类别区域的地下水农田灌溉水质和农用地土壤环境质量污染特征因子开展监测,对调查所得地下水及土壤监测数据进行分析评价。     

An innovative land use regression model incorporating meteorology for exposure analysis

The advent of spatial analysis and geographic information systems (GIS) has led to studies of chronic exposure and health effects based on the rationale that intra-urban variations in ambient air pollution concentrations are as great as inter-urban differences. Such studies typically rely on local spatial covariates (e.g., traffic, land use type) derived from circular areas (buffers) to predict concentrations/exposures at receptor sites, as a means of averaging the annual net effect of meteorological influences (i.e., wind speed, wind direction and insolation). This is the approach taken in the now popular land use regression (LUR) method. However spatial studies of chronic exposures and temporal studies of acute exposures have not been adequately integrated. This paper presents an innovative LUR method implemented in a GIS environment that reflects both temporal and spatial variability and considers the role of meteorology. The new source area LUR integrates wind speed, wind direction and cloud cover/insolation to estimate hourly nitric oxide (NO) and nitrogen dioxide (NO(2)) concentrations from land use types (i.e., road network, commercial land use) and these concentrations are then used as covariates to regress against NO and NO(2) measurements at various receptor sites across the Vancouver region and compared directly with estimates from a regular LUR. The results show that, when variability in seasonal concentration measurements is present, the source area LUR or SA-LUR model is a better option for concentration estimation.     

Characterization of the nonpoint source pollution into river at different spatial scales

The objectives of this study were to investigate the effects of rainfall and underlying surface conditions on nonpoint source (NPS) pollution loads and to identify the uncertainty in NPS pollution loads at different spatial scales in the Fuxi River basin, China. Data on monitored daily flow rates and concentrations of ammonium nitrogen, total nitrogen, total phosphorus and permanganate index at the sub‐basin and basin scales were collected for a period from 2013 to 2015. Dynamic time warping distance and information measures were used to characterize pollution loads and determine the uncertainties. The results indicate that, at both sub‐basin and basin scales, NPS pollution loads increased nonlinearly with rainfall until it reached 38.4 mm, and subsequently, the NPS pollution loads stabilized. The underlying surface conditions affected the NPS pollution loads more profoundly than rainfall. Additionally, the uncertainty in NPS pollution loads increased with the spatial scales.     

Accuracy of approximate methods of uncertainty propagation in seismic loss estimation

In this paper the efficacy of an approximate method of uncertainty propagation, known as the first-order second-moment (FOSM) method, for use in seismic loss estimation is investigated. The governing probabilistic equations which define the Pacific Earthquake Engineering Research (PEER)-based loss estimation methodology used are discussed, and the proposed locations to use the FOSM approximations identified. The justification for the use of these approximations is based on a significant reduction in computational time by not requiring direct numerical integration, and the fact that only the first two moments of the distribution are known. Via various examples it is shown that great care should be taken in the use of such approximations, particularly considering the large uncertainties that must be propagated in a seismic loss assessment. Finally, a complete loss assessment of a structure is considered to investigate in detail the location where significant approximation errors are incurred, where caution must be taken in the interpretation of the results, and the computational demand of the various alternatives.     

我国非点源污染的现状及其治理前景浅析

从实际出发,综述了我国非点源污染的特征及其主要危害,根据非点源污染的特殊性,探讨了其主要防治方法,并分析了我国非点源污染治理的难点,通过从“源”和“汇”两方面进行控制,从而使非点源污染在进入地表水体之前得到治理。     

Uncertainty analysis of parameters in non‐point source pollution simulation: case study of the application of the Soil and Water Assessment Tool model to Yitong River watershed in northeast China

Uncertainty analysis of the model parameters in non‐point source pollution (NPSP) simulation is important because of its great effects on predictions and decision‐making. Understanding the main parameters that effect the uncertainty of NPSP is necessary to provide the basis for formulating control measures. In this study, two methods were applied to conduct parameter uncertainty analysis for Soil and Water Assessment Tool (SWAT). Sobol’ method was used to screen out the model parameters with great effects on the runoff, sediment, total nitrogen (TN) and total phosphorus (TP). The results obtained by sensitivity analysis were used subsequent model calibration and further uncertainty analysis. Monte Carlo (MC) method was employed to analyse the effects of parameter uncertainty on the model outputs. However, such problems are time‐consuming because the MC method required to invoke simulation model thousands of times. To address this challenge, a kriging surrogate model was developed to improve the overall calculation efficiency. The results obtained by sensitivity analysis showed that curve number value (CN2), soil evaporation compensation factor (ESCO), universal soil loss equation support practice factor (USLE_P) and initial organic nitrogen concentration in soil layer (SOL_ORGN) had significant effects on the SWAT outputs. The uncertainty analysis results showed that the uncertainty of runoff is the lowest, followed by TP and TN, and the uncertainty of sediment was the greatest. The kriging surrogate model has the ability to solve this time‐consuming problem rapidly with a high degree of accuracy, and thus it is very robust.     

某金矿尾矿库土壤重金属污染特征及来源分析

尾矿中含有的重金属通过介质向环境缓慢释放,重金属进入土壤后,会对周围土壤、生态环境造成影响,危害人类身体健康和安全。本文以某金矿尾矿库为研究对象,对尾矿库流域内土壤重金属进行取样调查,确定尾矿库及周边土壤重金属污染现状,分析重金属来源及污染特征。对尾矿库及周边的土壤进行pH、氰化物、锑、砷、镉、铜、铅、锌、汞等元素含量测试,采用单项污染指数与内梅罗指数法进行重金属污染程度分析与评价;同时运用主成分分析法,探究尾矿库及周边区域土壤重金属形成原因和污染特征。研究结果表明：尾矿库流域内地表土样处于重污染状态,污染区主要集中在尾矿库东北侧,污染范围较大;氰化物、锑、镉、铜、铅、锌存在着不同程度的相关性,表明研究区内存在这6种元素不同程度的复合污染或这些元素具有同源性,重金属污染主要来源为矿石开采和工业活动等人为来源;土壤中砷的吸附、迁移与土壤pH值关系密切。调查和分析尾矿库土壤污染情况,查明尾矿库污染形成原因和污染特征,对尾矿库的综合治理具有重要意义。     

Assessment of water quality management with a systematic qualitative uncertainty analysis

Uncertainty is an inevitable source of noise in water quality management and will weaken the adequacy of decisions. Uncertainty is derived from imperfect information, natural variability, and knowledge-based inconsistency. To make better decisions, it is necessary to reduce uncertainty. Conventional uncertainty analyses have focused on quantifying the uncertainty of parameters and variables in a probabilistic framework. However, the foundational properties and basic constraints might influence the entire system more than the quantifiable elements and have to be considered in initial analysis steps. According to binary classification, uncertainty includes quantitative uncertainty and non-quantitative uncertainty, which is also called qualitative uncertainty. Qualitative uncertainty originates from human subjective and biased beliefs. This study provides an understanding of qualitative uncertainty in terms of its conceptual definitions and practical applications. A systematic process of qualitative uncertainty analysis is developed for assisting complete uncertainty analysis, in which a qualitative network could then be built with qualitative relationship and quantifiable functions. In the proposed framework, a knowledge elicitation procedure is required to identify influential factors and their interrelationship. To limit biased information, a checklist is helpful to construct the qualitative network. The checklist helps one to ponder arbitrary assumptions that have often been taken for granted and may yield an incomplete or inappropriate decision analysis. The total maximum daily loads (TMDL) program is used as a surrogate for water quality management in this study. 15 uncertainty causes of TMDL programs are elicited by reviewing an influence diagram, and a checklist is formed with tabular interrogations corresponding to each uncertainty cause. The checklist enables decision makers to gain insight on the uncertainty level of the system at early steps as a convenient tool to review the adequacy of a TMDL program. Following the instruction of the checklist, an appropriate algorithm in a form of probability, possibility, or belief may then be assigned for the network. Consequently, the risk or evidence of the success of outcomes will be obtained. The incorporation of the systematic consideration of qualitative uncertainty into water quality management is expected to refine the decision-making process.     

Estimation of source contribution to nitrate loading in road runoff using stable isotope analysis

The nitrogen load was determined in road runoff during rainfall events. Moreover, nitrate isotopes analysis was conducted to determine the contribution of nitrates from atmospheric deposition and leaching from road dust. The concentrations of NO₃-N in road runoff were higher than those in atmospheric deposits for each rainfall event, except one event with a long antecedent dry weather period. The δ¹⁸O-NO₃ in road runoff was lower than in atmospheric deposits and higher than in leachate from road dust; however, no difference in δ¹⁵N-NO₃ was observed. By using δ¹⁸O-NO₃ as an indicator for evaluating NO₃-N sources in road runoff, contribution ratios of NO₃-N from road dust were estimated to be 14–22%, 23–25%, and 22–34% for Event 1 to Event 3, respectively. These results indicated that the NO₃-N from the atmosphere accounts for more than half of the NO₃-N in road runoff.     

Detection and remediation of human-origin pollution at two public beaches in Virginia using multiple source tracking methods

Two public beaches (Anderson and Hilton) in Newport News, Virginia, were frequently closed to swimming in 2004 due to high Enterococcus spp. counts that exceeded the regulatory standard. The microbial source tracking (MST) methods of antibiotic resistance analysis (ARA) and fluorometry (to detect optical brighteners) were used in the summer of 2004 to determine the origins of fecal pollution at the two beaches. Both MST methods detected substantial human-origin pollution at the two beaches, in locations producing consistently high levels of Enterococcus spp. Investigations by municipal officials led to the fluorometric detection and subsequent repair of sewage infrastructure problems at both beaches. The success of the mitigation efforts was confirmed during the summer of 2005 using ARA and fluorometry, with the results cross-validated by pulsed-field gel electrophoresis (PFGE).     

Source apportionment of indoor residential fine particulate matter using land use regression and constrained factor analysis

Source contributions to urban fine particulate matter (PM(2.5) ) have been modelled using land use regression (LUR) and factor analysis (FA). However, people spend more time indoors, where these methods are less explored. We collected 3-4- day samples of nitrogen dioxide and PM(2.5) inside and outside of 43 homes in summer and winter, 2003-2005, in and around Boston, Massachusetts. Particle filters were analysed for black carbon and trace element concentrations using reflectometry, X-ray fluorescence (XRF), and high-resolution inductively coupled mass spectrometry (ICP-MS). We regressed indoor against outdoor concentrations modified by ventilation, isolating the indoor-attributable fraction, and then applied constrained FA to identify source factors in indoor concentrations and residuals. Finally, we developed LUR predictive models using GIS-based outdoor source indicators and questionnaire data on indoor sources. FA using concentrations and residuals reasonably separated outdoor (long-range transport/meteorology, fuel oil/diesel, road dust) from indoor sources (combustion, smoking, cleaning). Multivariate LUR regression models for factors from concentrations and indoor residuals showed limited predictive power, but corroborated some indoor and outdoor factor interpretations. Our approach to validating source interpretations using LUR methods provides direction for studies characterizing indoor and outdoor source contributions to indoor cocentrations. PRACTICAL IMPLICATIONS: By merging indoor-outdoor modeling, factor analysis, and LUR-style predictive regression modeling, we have added to previous source apportionment studies by attempting to corroborate factor interpretations. Our methods and results support the possibility that indoor exposures may be modeled for epidemiologic studies, provided adequate sample size and variability to identify indoor and outdoor source contributions. Using these techniques, epidemiologic studies can more clearly examine exposures to indoor sources and indoor penetration of source-specific components, reduce exposure misclassification, and improve the characterization of the relationship between particle constituents and health effects.     

Sustainable reduction in the flux of microbial compliance parameters from urban and arable land use to coastal bathing waters by a wetland ecosystem produced by a marine flood defence structure

'Natural' treatment systems such as wetlands and reed beds have been proposed as sustainable means of reducing fluxes of faecal indicator organisms (FIOs) to recreational and shellfish harvesting waters. This is because FIO fluxes to coastal waters from both point (effluent) and diffuse (catchment) sources can cause non-compliance with microbiological standards for bathing and shellfish harvesting waters. The Water Framework Directive requires competent authorities in the member states to manage both point and diffuse sources of FIOs in an integrated manner to achieve compliance with 'good' water quality as defined in a series of daughter Directives. This study was undertaken to investigate the relative sources of FIOs to the popular bathing waters around Clacton, UK. In this predominantly arable (mainly cereal cropping) farming area, the principal land use predictor, explaining 76% of the variance in geometric mean presumptive Escherichia coli concentration at sub-catchment outlets during the bathing season, was the proportion of built-up (i.e. urbanised) land in each sub-catchment. This new finding contrasts with earlier studies in livestock farming regions where the proportion of improved grassland has proven to be the strongest predictor of microbial concentration. Also novel in this investigation, a flood defence wall has been built creating a wetland area which discharges every tidal cycle. The wetland produces over 97% reduction in the flux and concentrations of FIOs to the marine recreational waters. Also, FIO concentrations in water draining through the wetland to the sea were similar to concentrations measured in six UK sewage treatment plant effluents subject to secondary (biological) treatment followed by UV disinfection.     

建筑排水用硬聚氯乙烯(PVC-U)管材拉伸屈服强度检测结果的不确定度分析

对建筑排水用硬聚氯乙烯(PVC-U)管材拉伸屈服强度检测结果的不确定度进行评定,分析不确定度产生的来源,影响因素及过程计算,得到不确定度范围,使之在检测工作中进行质量控制,确保测量结果更为准确。