Pollution source identification methods and remediation technologies of groundwater: A review

Groundwater is an important source of drinking water.Groundwater pollution severely endangers drinking water safety and sustainable social development.In the case of groundwater pollution,the top priority is to identify pollution sources,and accurate information on pollution sources is the premise of efficient remediation.Then,an appropriate pollution remediation scheme should be developed according to information on pollution sources,site conditions,and economic costs.The methods for identifying pollution sources mainly include geophysical exploration,geochemistry,isotopic tracing,and numerical modeling.Among these identification methods,only the numerical modeling can recognize various information on pollution sources,while other methods can only identify a certain aspect of pollution sources.The remediation technologies of groundwater can be divided into in-situ and ex-situ remediation technologies according to the remediation location.The in-situ remediation technologies enjoy low costs and a wide remediation range,but their remediation performance is prone to be affected by environmental conditions and cause secondary pollution.The ex-situ remediation technologies boast high remediation efficiency,high processing capacity,and high treatment concentration but suffer high costs.Different methods for pollution source identification and remediation technologies are applicable to different conditions.To achieve the expected identification and remediation results,it is feasible to combine several methods and technologies according to the actual hydrogeological conditions of contaminated sites and the nature of pollutants.Additionally,detailed knowledge about the hydrogeological conditions and stratigraphic structure of the contaminated site is the basis of all work regardless of the adopted identification methods or remediation technologies.

A new approach to spatial source apportionment of haze pollution in large scale and its application in China

Long-lasting expansion of haze pollution in China has already presented a stern challenge to regional joint prevention and control. There is an urgent need to enlarge and reconstruct the coverage of joint prevention and control of air pollution in key area. Air quality models can identify and quantify the regional contribution of haze pollution and its key components with the help of numerical simulation, but it is difficult to be applied to larger spatial scale due to the complexity of model parameters. The time series analysis can recognize the existence of spatial interaction of haze pollution between cities, but it has not yet been used to further identify the spatial sources of haze pollution in large scale. Using econometric framework of time series analysis, this paper developed a new approach to perform spatial source apportionment. We applied this approach to calculate the contribution from spatial sources of haze pollution in China, using the monitoring data of particulate matter(PM_(2.5)) across 161 Chinese cities. This approach overcame the limitation of numerical simulation that the model complexity increases at excess with the expansion of sample range, and could effectively deal with severe large-scale haze episodes.

Study on the Current Situation and Source Apportionment of PM_(2.5) Pollution in China

In this paper,using concentration data of PM2. 5in 2013 in China and referring to a lot of literature,we preliminary studied the pollution of fine particulate matter and summarized PM2. 5source apportionment in the key cities in China. Our results showed that PM2. 5showed significant spatial and temporal distribution; high surface concentrations of PM2. 5concentrated mainly in the North China Plain,the Sichuan Basin,Yangtze River Delta and other regions; the average annual concentration of PM2. 5was about 80μg / m3 in North China Plain; Seasonal changes in the concentration of PM2. 5was winter > spring > autumn > summer; fired sources,industrial sources,vehicle exhaust were the major sources of PM2. 5; motor vehicle exhaust mostly contributed 10%- 30% to PM2. 5. This review provides a fundamental understanding of PM2. 5source apportionment and serves as an important reference for future source apportionment studies to be widely conducted in China.

Sources and health risks of nitrate pollution in surface water in the Weihe River watershed,China

Owing to the significant reductions in streamflow and an increase in human activities in recent years,the quality of surface water in Weihe River continues to pose environmental health concerns.We utilized hydrochemistry and nitrogen and oxygen isotopes to elucidate the status and identify sources of nitrate pollution in the south and north banks for three seasons(flood,dry,and mean-flow periods)in the Weihe River watershed.A Bayesian isotope mixing model was applied to estimate the contributions of four potential NO_(3)-sources to river pollution(manure and sewage,soil nitrogen,inorganic fertilizer,and nitrate in precipitation).The U.S.Environmental Protection Agency(USEPA)evaluation model was implemented to evaluate the health risks associated with nitrate pollution in the surface water.Nitrate pollution was most severe during the dry period because the river flow was small.Due to the influence of the topography and land use type of the Weihe River,the pollution in the main stream was greater than that of the tributaries,and the pollution of the south bank was greater than that of the north bank.During the flood and mean-flow periods,δ^(15)N and δ^(18)O were mainly distributed in the NH_(4)^(+) of the fertilizer and soil nitrogen.During the dry period,δ^(15)N and δ^(18)O were mainly distributed in domestic sewage and manure regions.According to the Stable Isotope Analysis in R(SIAR)model,manure and sewage were the major nitrate sources during the dry period(73%).However,a decrease in the contribution from domestic sewage and manure was observed during the flood period(45%)compared to the dry period,but with a significantly increased contribution from soil nitrogen(23%)and inorganic fertilizer(21%).The health risk value in the dry period was higher than that in the wet and mean flow periods,and children are more susceptible to nitrate pollution than adults.Therefore,reducing the discharge of domestic sewage and manure and improving the utilization rate of nitrogen fertilizers may be effective measures to improve water quality in the watershed.

Identification of Contaminant Source Characteristics and Monitoring Network Design in Groundwater Aquifers: An Overview

The groundwater system is often polluted by different sources of contamination where the sources are difficult to detect. The presence of contamination in groundwater poses significant challenges to its delineation and quantification. The remediation of a contaminated site requires an optimal decision making system to identify the pollutant source characteristics accurately and efficiently. The source characteristics are generally identified using contaminant concentration measurements from arbitrary or planned monitoring locations. To effectively characterize the sources of pollution, the monitoring locations should be selected appropriately. An efficient monitoring network will result in satisfactory characterization of contaminant sources. On the other hand, an appropriate design of monitoring network requires reliable source characteristics. A coupled iterative sequential source identification and dynamic monitoring network design, improves substantially the accuracy of source identification model. This paper reviews different source identification and monitoring network design methods in groundwater contaminant sites. Further, the models for sequential integration of these two models are presented. The effective integration of source identification and dedicated monitoring network design models, distributed sources, parameter uncertainty, and pollutant geo-chemistry are some of the issues which need to be addressed in efficient, accurate and widely applicable methodologies for identification of unknown pollutant sources in contaminated aquifers.

Source identification and global implications of black carbon

Black carbon(BC)is one of the short-lived air pollutants that contributes significantly to aerosol radiative forcing and global climate change.It is emitted by the incomplete combustion of fossil fuels,biofuels,and biomass.Urban environments are quite complex and thus,the use of mobile jointly with fixed monitoring provides a better understanding of the dynamics of BC distribution in such areas.The present study addresses the measurement of BC concentration using real-time mobile and ambient monitoring in Barranquilla,an industrialized urban area of the Colombian Caribbean.A microaethalometer(MA200)and an aethalometer(AE33)were used for measuring the BC concentration.The absorption Ångström exponent(AAE)values were determined for the study area,for identifying the BC emission sources.The results of the ambient sampling show that vehicle traffic emissions prevail;however,the influence of biomass burning was also observed.The mean ambient BC concentration was found to be 1.04±1.03μg/m^(3) and varied between 0.5 and 4.0μg/m^(3).From the mobile measurements obtained in real traffic conditions on the road,a much higher average value of 16.1±16.5μg/m^(3) was measured.Many parts of the city showed BC concentrations higher than 20μg/m^(3).The spatial distribution of BC concentration shows that vehicle emissions and traffic jams,a consequence of road and transport infrastructure,are the factors that most affect the BC concentration.A comparison of results obtained from two aethalometers indicates that the concentrations measured by MA200 are 9%lower than those measured by AE33.The AAE obtained was found to vary between 1.1 and 1.6,indicating vehicular emissions as the most crucial source.In addition,it was observed that the BC concentration on working days was 2.5 times higher than on the weekends in the case of mobile monitoring and 1.5 times higher in the case of ambient monitoring.

Encapsulating the Role of Solution Response Space Roughness on Global Optimal Solution: Application in Identification of Unknown Groundwater Pollution Sources

A major challenge of any optimization problem is to find the global optimum solution. In a multi-dimensional solution space which is highly non-linear, often the optimization algorithm gets trapped around some local optima. Optimal Identification of unknown groundwater pollution sources poses similar challenges. Optimization based methodology is often applied to identify the unknown source characteristics such as location and flux release history over time, in a polluted aquifer. Optimization based models for identification of these characteristics of unknown ground-water pollution sources rely on comparing the simulated effects of candidate solutions to the observed effects in terms of pollutant concentration at specified sparse spatiotemporal locations. The optimization model minimizes the difference between the observed pollutant concentration measurements and simulated pollutant concentration measurements. This essentially constitutes the objective function of the optimization model. However, the mathematical formulation of the objective function can significantly affect the accuracy of the results by altering the response contour of the solution space. In this study, two separate mathematical formulations of the objective function are compared for accuracy, by incorporating different scenarios of unknown groundwater pollution source identification problem. Simulated Annealing (SA) is used as the solution algorithm for the optimization model. Different mathematical formulations of the objective function for minimizing the difference between the observed and simulated pollutant concentration measurements show different levels of accuracy in source identification results. These evaluation results demonstrate the impact of objective function formulation on the optimal identification, and provide a basis for choosing an appropriate mathematical formulation for unknown pollution source identification in contaminated aquifers.

Inversion method for multi-point source pollution identification:Sensitivity analysis and application to European Tracer Experiment data

快速并且准确地识别未知污染源,在大气污染应急响应和源头控制过程中起着至关重要的作用.本文利用敏感性试验及欧洲示踪物测场试验(ETEX-1)数据研究了新提出的两步反演算法的实用性,并将其反演结果与现有的两种算法进行了对比分析.敏感试验表明,在观测数据较为丰富的情况下,三种算法对观测误差和风场误差的敏感性均较低;而当观测数据较为稀疏时,所有算法的估计精度都将随着误差的增加而下降,但与其他两种算法相比,两步反演算法具有更高的估计精度以及更稳定的估计性能.此外,欧洲示踪物测场试验的源项估计结果也表明,在三个算法中,两步反演算法具有最小的位置估计误差.

Status, Distribution and Ecological Risk Assessment of Persistent Toxic Substances in Suburban Agricultural Soils in Hohhot City, North China

A total of 52 soil samples of Hohhot City were collected and four heavy metals and eight organochlorine pesticides were analyzed. The results showed that Cr, Cu, Zn, and Pb in soil were in range of 20.54 - 48.15 mg/kg, 40.10 - 94.60 mg/kg, 35.14 - 110.48 mg/kg, 38.86g - 245.36 mg/kg, with a mean value of 37.24 mg/kg, 60.76 mg/kg, 80.49 mg/kg, and 145.99 mg/kg, respectively. The high degree of variation of Pb, Zn, and Cu, reflected that the heavy metals were mainly interfered by human factors. The content values of ∑DDTs ranged from 5.01 ng/g to 105.08 ng/g with a mean of 36.94 ng/g, while the ∑HCHs ranged from 6.52 ng/g to 48.65 ng/g with an average of 23.29 ng/g, indicated that DDTs were highly used than HCHs in the study area. The relatively low α-HCH/γ-HCH ratio and relatively high o,p’-DDT/p,p’-DDT ratio indicated the application of lindane and dicofol on regional agricultural soil. The mean Igeo values was Pb (2.9) > Cu (1.5) > Cr (-0.31) > Zn (-0.35), revealing that the soil in Hohhot City was not polluted by Cr and Zn (Igeo 0), moderate polluted by Cu (1 Igeo 2), moderate to heavy pollution by Pb (2 Igeo 3). The range of potential ecological risk index of metals was from 35.02 to 132.96, indicating low to moderate potential ecological risk. HCHs in all soil samples were less than 50 ng/g, which could be considered as unpolluted, while DDTs in several samples were between 50 and 500 ng/g, which could be regarded as having low pollution.

A new method for multi-point pollution source identification

对于污染源源项特征(数量,位置及排放强度)的快速且精确估计是污染事件应急响应的关键.与单点源估计相比,多点源的重建更具挑战性.本文提出了一种新的针对于多点源污染事件的两步反演算法,该算法通过结合大气化学模式与有限的浓度观测数据以实现对于未知数量的多点源的准确估计.在其计算过程中,无需任何未知量的先验信息,且可以自动识别污染源的数量,并确定每一个污染源的位置及强度.本文通过使用若干组理想试验验证了算法的适用性,试验结果表明,该算法可准确估计单点源,两点源和三点源的个数及位置,强度的估计误差可基本忽略,且该算法的估计精度和计算效率不会随着点源个数的增加而减弱.

Characterization of Groundwater Pollution Sources with Unknown Release Time History

Characterizations of unknown groundwater pollution sources in terms of source location, source flux release history and sources activity initiation times, from sparse observation concentration measurements are a challenging task. Optimization-based methods are often applied to solve groundwater pollution source characterization problem. These methods are effective only when the starting times of activity of the sources are precisely known, or the possible time window within which the sources activity actually start is known with a fair degree of certainty. However, in real life scenarios, the starting time of the activity of the sources is either unknown or can lie anywhere within a time window of years or decades. Absence of any prior information about the span of time window, within which the sources become active, makes existing source identification methodologies inefficient. As an alternative, an optimization-based source identification model is proposed, to simultaneously estimate source flux release history and sources activity initiation times. The method considers source flux release history and sources activity initiation times as explicit decision variables, optimally estimated by the decision model. Performance of the developed methodology is evaluated for an illustrative study area having multiple sources with different source activity initiation times, missing observation data and transient flow conditions. These evaluation results demonstrate the potential applicability of the proposed methodology and its capability to correctly estimate the unknown source flux releasing history and sources activity initiation times.

Source Apportionment of Air Particulates in South Africa: A Review

Source apportionment studies are useful in understanding sources of pollution and can be used in health risk assessments to evaluate the human health impacts from air pollutants. This study reviewed and analysed available source apportionment studies of air particulate in South Africa in October 2016. Searches were performed using different databases for peer reviewed articles including Google scholar, Scopus, EbscoHost, Science Direct and National Research Foundation database. Source categories were identified and these varied depending on the sites where the research was conducted (rural, urban or remote) but biomass burning dominated. A total of 35 source apportionment records were found with the majority of studies in urban areas (60%) while industrial sites had the least number of records (17.1%). The period 2011-2016 had the highest number of records while 1990-1995 had only three publicly available studies. There is limited research on source apportionment studies of air particulate in South Africa, calling for more research in this area.

Identification of pollution sources in rivers using a hydrodynamic diffusion wave model and improved Bayesian-Markov chain Monte Carlo algorithm

Water quality restoration in rivers requires identification of the locations and discharges of pollution sources,and a reliable mathematical model to accomplish this identification is essential.In this paper,an innovative framework is presented to inversely estimate pollution sources for both accident preparedness and normal management of the allowable pollutant discharge.The proposed model integrates the concepts of the hydrodynamic diffusion wave equation and an improved Bayesian-Markov chain Monte Carlo method(MCMC).The methodological framework is tested using a designed case of a sudden wastewater spill incident(i.e.,source location,flow rate,and starting and ending times of the discharge)and a real case of multiple sewage inputs into a river(i.e.,locations and daily flows of sewage sources).The proposed modeling based on the improved Bayesian-MCMC method can effectively solve high-dimensional search and optimization problems according to known river water levels at pre-set monitoring sites.It can adequately provide accurate source estimation parameters using only one simulation through exploration of the full parameter space.In comparison,the inverse models based on the popular random walk Metropolis(RWM)algorithm and microbial genetic algorithm(MGA)do not produce reliable estimates for the two scenarios even after multiple simulation runs,and they fall into locally optimal solutions.Since much more water level data are available than water quality data,the proposed approach also provides a cost-effective solution for identifying pollution sources in rivers with the support of high-frequency water level data,especially for rivers receiving significant sewage discharges.

Sources of Particular Pollutants inAmbient Air at a Petrochemical Enterprise

The study on source apportionment of particular pollutants in ambient air at a petrochemical enterprise is the basis of the control over air pollution.Through analyzing particular pollutants in the samples collected from one petrochemical enterprise in northwestern China,the sources of particular pollutants were discussed.The test results showed that concentrations of particular pollutants in different sites were remarkably different.Results showed that the sampling sites with higher concentrations of particular pollutants,including toluene,xylenes,NH3and H2S,were located at the boundary of the petrochemical enterprise.Instead,the concentrations of NMHC in the ambient air sampling sites were higher than those at the boundary of the petrochemical enterprise.The sampling sites with higher concentrations of particular pollutants were located in the area that was close to the petrochemical enterprise.The results obtained from the Pearson correlation coefficients analyses,the factor analyses,andχ2-tests of the particular pollutants had revealed that NH3,H2S,toluene and xylenes at all sampling sites came from the same source,while NMHC might come from some other sources besides the petrochemical enterprise.

Evaluation of Local Scale PM Pollution Levels in Typical Street Canyon in Riga

The present study describes long term PM10 and PM2.5 changes in typical street canyon with particular emphasis on seasonal, diurnal variations in context with meteorological data. In order to understand PM10 pollution sources during 28 April 2007-31 December 2007, chemical composition measurements were done with particular emphasis on heavy metals (As, Cd, Ni, and Pb), crustal material (Ca, Mg, Na, and K) and anions (sulphates, nitrates, chlorides). Meteorological data used for this evolutional analysis were measured close to traffic related stations and several meteorological parameters were analyzed in relation to particulate measurements. Keep in mind that atmospheric aerosols are generally hydroscopic. Relative humidity which plays very important role in rain/snow and humidity impact are analyzed.

铅、锌、镉同位素示踪技术在沉积物重金属污染源解析中的应用:综述与展望

沉积物重金属污染防治的关键在于识别其污染源,采用铅、锌、镉多元同位素示踪技术是对其进行污染源解析的最佳手段之一。铅同位素示踪技术较为成熟且应用广泛,锌和镉同位素示踪技术作为新兴技术,在重金属污染源解析研究中可作为有力补充。本文系统概述了Pb-Zn-Cd同位素的示踪机理、组成特征及其在沉积物重金属污染溯源中的应用,提出未来研究工作的重点是采用多元同位素示踪技术,并结合沉积物形态中重金属稳定同位素的组成示踪污染源。

2021年济南市大气PM_(2.5)中PAHs污染特征及其来源解析

目的分析济南市2021年细颗粒物(fine particulate matter,PM_(2.5))中PAHs污染特征及来源分析,为济南市大气环境综合治理提供相关数据支持。方法根据济南市区域分布特点选取2个有代表性监测点,每月的10—16日进行连续监测,使用超高效液相色谱仪对16种PAHs进行检测,分析济南市PM_(2.5)中PAHs变化趋势及特征,使用特征比值和PMF模型对PM_(2.5)中PAHs进行源解析。结果PAHs以4环和6环为主,具有显著的季节性变化趋势,即冬季>秋季>春季>夏季,1月份污染情况尤为突出;秋季和冬季中7种高致癌风险PAHs所占比例高于其他季节,历城区各季节PAHs质量浓度均高于市中区;结合特征比值和PMF对PAHs来源分析,冬季主要污染源是工业和石油产品挥发源、交通源;秋季主要为燃煤燃烧源、工业和石油产品挥发源和交通源。结论济南市PM_(2.5)中PAHs存在一定的污染,需加大清洁能源的使用,同时进一步加强秋、冬季,尤其是采暖季燃煤燃烧、工业和石油产品挥发和交通等污染源的控制。

Characterization and source identification of submicron aerosol dur ing ser ious haze pollution periods in Beijing

Submicron aerosol is of extensive concern not only due to its significant impact on air quality but also because it is detrimental to human health.In this study,we investigated the characteristics,sources and chemical processes of submicron aerosol based on realtime online measurements of submicron aerosols(NR-PM 1)during December 2015 at an urban site in Beijing.The average mass concentration of NR-PM 1 was 92.5±84.9μg/m^(3),the hourly maximum was 459.1μg/m^(3) during the entire observation.The organic aerosol(OA)(55%)was the largest contributor to NR-PM 1.The average mass concentration of PAHs was 0.217±0.247μg/m^(3),exhibiting the highest concentration at night and the lowest levels in the daytime.The average mass concentration of organic nitrate was 2.52±2.36μg/m^(3) and that of inorganic nitrate was 7.62±8.22μg/m^(3),accounting for 36%and 64%,respectively,of the total nitrate mass.Positive matrix factorization(PMF)differentiated the OA into five chemical components including LV-OOA,SV-OOA,COA,HOA and CCOA,accounting for 22%,16%,13%,25%and 24%respectively,of the total OA.The average NR-PM 1 mass concentration on the heavy polluted days(HPD)was 182.8±70.2μg/m^(3),which was approximately 9 times that on clean days(CD).The enhanced secondary formation of SNA was evident on HPD,especially the rapid increase of sulfate(23%)and nitrate(19%).