Identification Of Pollution Sources Research Articles

Pollution Source Identification for River Chemical Spills by Modular-Bayesian Approach: A Retrospective Study on the ‘Landmark’ Spill Incident in China

It is important to identify source information after a river chemical spill incident occurs. Among various source inversion approaches, a Bayesian-based framework is able to directly characterize inverse uncertainty using a probability distribution and has recently become of interest. However, the literature has not reported its application to actual spill incidents, and many aspects in practical use have not yet been clearly illustrated, e.g., feasibility for large scale pollution incidents, algorithm parameters, and likelihood functions. This work deduced a complete modular-Bayesian approach for river chemical spills, which combined variance assumptions on a pollutant concentration time series with Adaptive-Metropolis sampling. A retrospective case study was conducted based on the ‘landmark’ spill incident in China, the Songhua River nitrobenzene spill of 2005. The results show that release mass, place, and moment were identified with biases of −26.9%, −7.9%, and 16.9%, respectively. Inverse uncertainty statistics were also quantified for each source parameter. Performance, uncertainty sources, and future work are discussed. This study provides an important real-life case to demonstrate the usefulness of the modular-Bayesian approach in practice and provides valuable references for the setting of parameters for the sampling algorithm and variance assumptions.

An effective method for point pollution source identification in rivers with performance-improved ensemble Kalman filter

To manage a river sudden pollution incident, a fundamental task is to quickly identify when, where, and how much pollutant was released. The source identification of this kind of pollution incident due to instantaneously spilled pollutant from point source remains a challenging problem because of the limited observations and the ill-posedness. In this paper, the ensemble Kalman filter (EnKF) is coupled with the backward location probability (BLP) to improve its performance in identifying the river pollution source. When applied in practice, the proposed BLP-EnKF method has two important advantages. First, it supports on-line identification and can mitigate the limitation from shortage of observations in the sudden river pollution case. Second, BLP-EnKF performs much better than conventional EnKF in terms of computation time, because it can identify the pollution source without restart forecast process. The effectiveness and efficiency of BLP-EnKF is tested and demonstrated by a synthetic case study. The case results show that the BLP-EnKF can identify all the source parameters with a relative error approximately 1.00% or smaller. Its performance can be further improved with more accurate estimation of observational error or dispersion coefficient. A real-world case in Ganjiang River further demonstrates the applicability of BLP-EnKF in practice. The pollution source is identified successfully with relative errors smaller than 3.0% for all the source parameters, while the computation time is sharply shorted from 8.0 h of conventional EnKF to 22.0 s of BLP-EnKF.

Surveillance efficiency evaluation of air quality monitoring networks for air pollution episodes in industrial parks: Pollution detection and source identification

Both air pollution detection and source identification for air pollution episodes are highly desirable for detecting and controlling industrial air pollution. Surveillance of air pollution episodes in industrial parks is the focus of this article. The surveillance in this study consists of air pollution detection and subsequent source identification. The Gaussian puff model is applied to simulate the dispersion of air pollution, and the source area analysis method is used to reconstruct unknown source terms. A case study involving hydrogen sulfide emissions in a typical chemical industrial park is presented. The long-term efficiencies of both pollution detection and source identification of a developing planning of boundary-type air quality monitoring network (AQMN) are evaluated. Five typical scenarios are identified for the evaluation. Moreover, several key factors for the surveillance efficiency variation (i.e., meteorological conditions, monitor number and distance between sources) are discussed. The efficiency of pollution detection increases with the number of monitors. The efficiency of source identification increases with the number of monitors and the distance between sources.

Multivariate Analyses and Human Health Assessments of Heavy Metals for Surface Water Quality in the Xiangjiang River Basin, China

Pollution by heavy metals in river water is becoming a major subject of global drinking water concern, and the Xiangjiang River is one of the most heavily polluted rivers in China. Water samples were collected from 17 sites spanning the entire Xiangjiang watershed from 2005 to 2016 to investigate spatial-temporal distributions and potential human health risks related to 8 metal pollutants (As, Cd, Hg, Cr, Cu, Pb, Zn, and Se). The results of spatial-temporal distribution analyses proved that most metals were below the guideline limits the majority of the time. However, the hazard index and carcinogenic risk analyses indicated that As and Cr were associated with a potential risk of cancer, although noncarcinogenic heavy metals in general and carcinogenic risk declined year by year. A nonparametric seasonal Mann-Kendall's test revealed that there were notable decreasing trends in As, Cd, Zn, Cu, Cr, and Pb for most sites, whereas Se and Hg significantly increased in some areas over the targeted 12 yr. The results of principal component analysis agreed with those of dual hierarchical cluster analysis in the identification of pollution sources, the results of which are as follows: 1) As, Cd, Pb, Hg, and Zn were mainly derived from anthropogenic activities and the smelting industry; 2) Cr and Cu mainly originated from agricultural or industrial activities; and 3) Se was predominantly from natural erosion. The present study will be conducive to optimizing the distribution of water monitoring stations and drafting remediation strategies pertaining to the protection of public health in metal-polluted areas. Environ Toxicol Chem 2019;38:1645-1657. © 2019 SETAC.

Introducing a general framework for pollution source identification in surface water resources (theory and application)

Studies on pollution source identification in water resources date back over three decades. These studies use inverse solution of the transport equation for recovering the release history and/or the location of the pollutant sources. Each of these studies has its own advantages and limitations in accordance with the methods employed and the complexity of the solutions. Moreover, conducted studies on pollution source identification in surface water resources (e.g. rivers) are fewer in number compared to those on groundwater resources. In nearly all previous related studies on rivers, researchers have often developed their own numerical forward models, but these models have never taken into account the complexity of the problem in real conditions, such as river topography, real flow conditions, etc. Therefore, compared to commercial models, these models have many drawbacks and limitations. Thus, it would be desirable to provide a framework that can be employed to solve the transport equation in the inverse model using available software packages. In this study, the inverse solution of the transport equation for recovering release histories of multiple pollutant sources in rivers is achieved by the help of ready software packages via a feasible framework. The proposed framework can be readily applied in one-, two-, and three-dimensional problems. The underlying concept of the proposed framework is the linearity of the governing equation, i.e. transport equation. Furthermore, the present study presents the conditions and rules for the arrangement (number and location) of the measurement points of the pollutant concentration under various conditions. The proper arrangement of the measurement points is significant, since it solves the non-uniqueness problem of the inverse model. The most important factor affecting the arrangement of the measurement points is the flow pattern. In this study, it is suggested that the complexity of the flow pattern may lead to practical arrangements for the control points. The proposed procedure was verified by the use of three series of data in one- and two-dimensional domains under real conditions of flow and topography by employing well-known existing software packages. In each test case, a proper and practical arrangement was proposed for the measurement points of the concentration-time curve. The suggested arrangement resulted in the correct operation of the inverse model. The proposed inverse model showed a good capability with a reasonable percentage of errors of the recovering release rate of pollutant sources. Accordingly, it can be conveniently used in cases where forward models are readily available.

Tracking the contribution of multiple raw and treated wastewater discharges at an urban drinking water supply using near real-time monitoring of β-d-glucuronidase activity

Past waterborne outbreaks have demonstrated that informed vulnerability assessment of drinking water supplies is paramount for the provision of safe drinking water. Although current monitoring frameworks are not designed to account for short-term peak concentrations of fecal microorganisms in source waters, the recent development of online microbial monitoring technologies is expected to fill this knowledge gap. In this study, online near real-time monitoring of β-d-glucuronidase (GLUC) activity was conducted for 1.5 years at an urban drinking water intake impacted by multiple point sources of fecal pollution. Parallel routine and event-based monitoring of E. coli and online measurement of physico-chemistry were performed at the intake and their dynamics compared over time. GLUC activity fluctuations ranged from seasonal to hourly time scales. All peak contamination episodes occurred between late fall and early spring following intense rainfall and/or snowmelt. In the absence of rainfall, recurrent daily fluctuations in GLUC activity and culturable E. coli were observed at the intake, a pattern otherwise ignored by regulatory monitoring. Cross-correlation analysis of time series retrieved from the drinking water intake and an upstream Water Resource Recovery Facility (WRRF) demonstrated a hydraulic connection between the two sites. Sewage by-passes from the same WRRF were the main drivers of intermittent GLUC activity and E. coli peaks at the drinking water intake following intense precipitation and/or snowmelt. Near real-time monitoring of fecal pollution through GLUC activity enabled a thorough characterization of the frequency, duration and amplitude of peak contamination periods at the urban drinking water intake while providing crucial information for the identification of the dominant upstream fecal pollution sources. To the best of our knowledge, this is the first characterization of a hydraulic connection between a WRRF and a downstream drinking water intake across hourly to seasonal timescales using high frequency microbial monitoring data. Ultimately, this should help improve source water protection through catchment mitigation actions, especially in a context of de facto wastewater reuse.

Identification of pollution sources using artificial neural network (ANN) and multilevel breakthrough curve (BTC) characterization

A pollution source in groundwater may be active at some location for certain periods. There may be multiple potential sources responsible for observed contamination at observation wells. The contamination witnessed in observation wells at different times establishes breakthrough curves (BTCs). These BTCs are usually employed for source identification. In this work, single and multistage artificial neural network (ANN) is employed to identify the potential pollution sources. Temporally varying potential pollution sources are generated using uniform random numbers. These source fluxes are further applied to the simulation of the pollution concentration at observation wells. BTC at an observation well is characterized by statistical parameters and data mining. Characterized BTCs are inputs and source fluxes are outputs of ANN models. Initial stage ANN models are developed at the specified observation well locations, using multilevel BTC characterization. These initial meta models are utilized for the development of intermediate models. Further, the intermediate models are employed for final stage identification. These multi-stage ANN models are found to perform comparatively better than single stage ANN models.

Elemental Composition of Particulate Matter Air Pollution Collected Around Industrial Area in East Java

Air pollution is one of the key environmental and public health concerns in urban areas in Indonesia. The environment impacts are particularly severe in Java region where the cities have a combination of intense industrial activities, large populations, and increase of motor vehicle usage. This paper describes results from a detailed elemental characterization study conducted in Surabaya, East Java. Samples of airborne particulate matter, PM2.5 and PM2.5-10 were collected once a week using a Gent stacked filter sampler during 2012-2015. The samples were analyzed for their elemental composition and black carbon (BC) content by Energy Dispersive X-ray Fluorescence (EDXRF) and light absorption, respectively. The mass concentration of PM2.5 and PM10 ranged from 4.8 to 30.8 μg/m3 and 9.4 to 114.9 μg/m3, respectively. The annual average PM2.5 obtained during over the period of 2012 to 2015 would be in violation of the Indonesian annual average standard concentrations of 15 μg/m3. The distributions of the ratio of PM2.5 to PM10 is less than 0.5. The average ratio of BC concentrations compared to the PM2.5 concentrations was 22.6 %. Several composition of elemental concentration were also determined and the results showed that lead level in PM2.5 and PM2.5-10 was significantly higher with the maximum concentrations were 2779 and 2258 ng/m3 respectively. The Pb concentrations in Surabaya were tens to thousand times higher than in other cities. These results could be used to provide an overview of the measurements of the concentrations and composition of particulate air pollution in Surabaya and as a key in identification and estimation of pollutant sources to design the appropriate and right policies.

Optimal Design of Air Quality Monitoring Network for Pollution Detection and Source Identification in Industrial Parks

Dense air quality monitoring network (AQMN) is one of main ways to surveil industrial air pollution. This paper is concerned with the design of a dense AQMN for H2S for a chemical industrial park in Shanghai, China. An indicator (Surveillance Efficiency, SE) for the long-term performance of AQMN was constructed by averaging pollution detection efficiency (rd) and source identification efficiency (rb). A ranking method was developed by combing Gaussian puff model and Source area analysis for improving calculation efficiency. Candidate combinations with highest score were given priority in the selection of next site. Two existing monitors were suggested to relocate to the west and southwest of this park. SE of optimized AQMN increased quickly with monitor number, and then the growth trend started to flatten when the number reached about 60. The highest SE occurred when the number reached 110. Optimal schemes of AQMNs were suggested which can achieve about 98% of the highest SE, while using only about 60 monitors. Finally, the reason why the highest SE is less than 1 and the variation characteristics of rd and rb were discussed. Overall, the proposed method is an effective tool for designing AQMN with optimal SE in industrial parks.

Identification of groundwater pollution sources in a landfill site using artificial sweeteners, multivariate analysis and transport modeling

In this study, sources of groundwater pollution in a landfill site were identified, using artificial sweeteners as chemical tracers, multivariate statistical analysis and a quantitative analysis of the groundwater flow system through particle tracking and transport modeling. The study area, located in northern Italy, hosts an older unlined landfill and a newer lined municipal solid waste landfill placed downstream of the former. Groundwater, surface water, treated wastewater, and leachate samples were collected in March 2017 for analysis of the artificial sweeteners saccharin, cyclamate, acesulfame and sucralose together with major cations and anions, inorganic nitrogen compounds, total phosphorus, COD and some further parameters. The interpretation of the results suggests that two main leachate leaks/spills are affecting the study area. The first one concerns leachate probably spilling out of the leachate collection system serving the younger lined landfill, the other one involves leachate from the older unlined landfill that also seems to affect an area downstream of the lined landfill. Direct leachate leaks from the lined landfill seem unlikely, although they cannot be definitively excluded.This work underlines the importance of a multi-methods approach, which integrates here chemical tracers, multivariate analysis and transport modeling, for assessing groundwater pollution sources generated from complex landfill sites, where multiple and different sources may exist. In particular, this work highlights how artificial sweeteners can be used for tracing leachate plumes from landfills. The methodology applied in this study can have a broad applicability also in other polluted landfill sites worldwide.

Evidence of mercury sequestration by carbon nanotubes and nanominerals present in agricultural soils from a coal fired power plant exhaust.

Mercury (Hg) in agricultural soils could have negative effects on the environment and the human health. The exposure to high level of Hg through different absorption pathways, such as ingestion and diet through soil-plant system could permanently damage developing foetus of animals and humans. With the aim to assess the potential environmental and health risk and to study the behaviour and fate of Hg from agricultural soils to the environment, 47 soil samples were collected around a thermoelectric power plant in the Santa Catarina (Brazil). The Hg concentration measured by inductively coupled plasma mass spectrometry (ICP-MS) ranged from 0.16 to 0.56 mg kg-1. The distribution obtained by kriging interpolation allowed the identification of the main pollution sources. To see the morphology and composition of soil samples, field emission scanning electron microscopy (FE-SEM) and high resolution transmission electron microscopy (HR-TEM) were used combined with energy dispersive X-ray spectroscopy (EDS), showing that the carbon nanotubes and magnetite as nanomineral contributed to Hg retention. The mentioned molecular characterization, and the low Contamination Factors (CF) values obtained, suggested that there is low risk to the food security of the agro-ecosystems area near to the CFPP in the terms of Hg inputs and contamination.

Large-scale implementation of standardized quantitative real-time PCR fecal source identification procedures in the Tillamook Bay Watershed.

Fecal pollution management remains one of the biggest challenges for water quality authorities worldwide. Advanced fecal pollution source identification technologies are now available that can provide quantitative information from many animal groups. As public interest in these methodologies grows, it is vital to use standardized procedures with clearly defined data acceptance metrics and conduct field studies demonstrating the use of these techniques to help resolve real-world water quality challenges. Here we apply recently standardized human-associated qPCR methods with custom data acceptance metrics (HF183/BacR287 and HumM2), along with established procedures for ruminant (Rum2Bac), cattle (CowM2 and CowM3), canine (DG3 and DG37), and avian (GFD) fecal pollution sources to (i) demonstrate the feasibility of implementing standardized qPCR procedures in a large-scale field study, and (ii) characterize trends in fecal pollution sources in the research area. A total of 602 water samples were collected over a one-year period at 29 sites along the Trask, Kilchis, and Tillamook rivers and tributaries in the Tillamook Bay Watershed (OR, USA). Host-associated qPCR results were combined with high-resolution geographic information system (GIS) land use and general indicator bacteria (E. coli) measurements to elucidate water quality fecal pollution trends. Results demonstrate the feasibility of implementing standardized fecal source identification qPCR methods with established data acceptance metrics in a large-scale field study leading to new investigative leads suggesting that elevated E. coli levels may be linked to specific pollution sources and land use activities in the Tillamook Bay Watershed.

Application of multivariate statistics in the source identification of heavy-metal pollution in roadside soils of Bursa, Turkey

Bursa located within the western part of Turkey is a rapidly growing and heavily industrialized city, hence a considerable amount of pollutions reaching intolerable levels around the city caused especially by anthropogenic activities. This study aims at finding out causes, intensity, and extent of pollution around the D-90 highway and O-33 motorway. Soil sampling was invoked as the first method to identify and discuss the nature of the pollution. A total of 91 soil samples from 10 different locations were collected away from the roadsides. The samples were analyzed for As, Cd, Cr, Co, Cu, Hg, Mn, Ni, Pb, and Zn. The analytical results were evaluated to understand chemical variability of soil compositions using several multivariate statistical methods such as correlation analysis, principal component analysis (PCA), and cluster analysis (CA). Cluster analysis and PCA clearly reveal three distinct groups of elements as (a) Pb, As, and Cd; (b) Cu, Zn, and Hg; and (c) Ni, Cr, Co, and Mn. Two of these groups (Cd, Pb, and As and Cu, Zn, and Hg) are originated from anthropogenic sources (traffic and industrial activities). Ni, Co, Cr, and Mn elements are reflecting underlying lithology. Soil pollution was investigated with the use of enrichment factor (EF), pollution index (PI), and integrated pollution index (IPI) values. The results consistent with the multivariate statistical evaluations demonstrate that soils along the Bursa highway are seriously polluted by toxic trace elements. Lead, As, Cd, Zn, Hg, and Cu are ascertained to be of anthropogenic origin and derived from traffic and industrial activities.

REGULATED AIR POLLUTANT EMISSIONS FROM HIGHER EMITTERS STATIONARY SOURCES IN BELO HORIZONTE, MINAS GERAIS, BRAZIL

Abstract Belo Horizonte, capital of Minas Gerais state, is a highly urbanized city located in the third largest metropolitan area of Brazil, being one of the most important cities in the country. There are several potential air pollutant emission sources in the studied area, such as industry and the vehicular fleet (1,669,884 vehicles in 2015). These can affect the air quality, which can have an impact on population health. Despite this critical scenario, few studies have been developed with the objective of evaluating the air quality in this city, especially regarding the identification of pollutant emission sources. Thus, the aim of this study was to identify and quantify atmospheric emissions by stationary sources of significant environmental impact in Belo Horizonte. The quantification of emissions was preferably performed based on chimneys monitoring data. However, in the absence of these data, estimates were made based on AP-42 guidelines. As a result, 75 chimneys were identified, belonging to 28 companies. Pollutant emission rates of 305, 235, 234, 224, 206, 180 and 63 t year-1 were observed for NOX, CO, TSP, PM10, PM2.5, SO2 and VOC, respectively. The results obtained can contribute to the diagnosis, modeling and management of air quality in Belo Horizonte.

Laser Systems for the Pollutants Control in the Oil and Gas Industry

The questions of the laser technologies application in oil and gas industry and the scope of laser systems for the atmosphere, sea and underground waters control are considered in this article. Options of creation and using of the lidar for environmental monitoring, technological control and search of the hydrocarbons fields in various natural and technogenic conditions are offered. The main attention is paid to the Raman lidar for the sensing of the oil hydrocarbon molecules in the atmosphere. It is shown that such a lidar measurement time for the 1015 cm−3 concentration of the studied molecules at the ranging distance up to 200 m doesn’t exceed 265 microseconds. Creation of the fluorescent lidar for the sea water and the underground waters of the coastal area monitoring will allow to solve a problem of their pollution. Recording of the pollutants concentration spatial distribution can serve as the basic data for the solution of the inverse problem of the identification of pollution sources. The suggested methods have simple hardware realization and allow to diagnose the high-concentrated flows of the weighed particles and also hydrocarbons in the sea and underground waters.

Identification of diverse air pollution sources in a complex urban area of Croatia

Pinpointing the contribution of sources in complex urban areas, affected by large point sources such as oil refineries, is important for developing emission control strategies. Receptor models based on the chemical composition of particulate matter (PM), such as chemical mass balance (CMB) and positive matrix factorization (PMF), are useful means for source apportionment, but the inclusion of other gaseous pollutants need further consideration. The results of the multipollutant analyses using temporal variations in pollutant concentrations, chemical PM speciation and receptor modeling, PMF and conditional bivariate polar plots (CBPF), were used for determination of major pollutant sources of fine particulate matter (PM2.5) and less represented pollutants – hydrogen sulfide (H2S), nitrogen dioxide (NO2) and sulfur dioxide (SO2) in an urban area in Slavonski Brod, Croatia influenced by a large point source (an oil refinery) in Brod, Bosnia and Herzegovina. It is found that the composition of PM2.5 is dominated by carbonaceous combustion particles, mainly organic carbon (OC), with maximum values appearing during winter. Summer PM2.5 levels were dominated by sulfate and ammonium, which can be related to the industrial activities i.e., oil refinery. According to PMF analysis, the majority of OC is coming from biomass burning with ∼50% contribution to observed species concentration followed by ∼30% from industry/refinery and ∼10% from traffic. CPBF model showed that urban and highway traffic was the main source of NO2 concentrations while oil refinery was identified as the dominant source of SO2 and H2S. The CBPF receptor model combines concentrations of pollutants and meteorological parameters and emerged as a reliable complementary tool for the identification of sources for considered gaseous pollutants. Limitations of the CBPF method are in the application in stable atmospheric boundary layer conditions (SABL) as wind direction is not representative. Also, larger uncertainty is related to the representation of peak concentrations transported with higher wind speeds (>8 m/s) due to the lower number of events. This work uses various source apportionment methods in the assessment of PM but also for gaseous pollutants, such as NO2, SO2 and H2S that are less represented in the source apportionment studies and can be used for future scientific applications to assure more efficient air quality management.

Hydrogeochemical and multivariate statistical appraisal of pollution sources in the groundwater of the lower Bhavani River basin in Tamil Nadu

ABSTRACTMultivariate statistical techniques have emerged as one of the most effective tools in hydrochemical characterization and the identification of pollution sources in groundwater. Hydrogeochemical data of the 36 wells in the Lower Bhavani River basin in Tamil Nadu are used in this study. Hierarchical cluster analysis(HCA) derived three major clusters, in which cluster 1 has high concentration of Ions (n = 14; avg TDS = 1259 mg/L), followed by cluster 2 (n = 13; avg TDS = 775 mg/L) and cluster 3 (n = 8; avg TDS = 357mg/L). The hydrochemical facies also agree with the cluster hydrochemistry with Na-Cl type (cluster 1), Ca-Mg-Cl (cluster 2), and Ca-HCO3 (cluster 3) showing the influences of anthropogenic and the natural (rock–water interaction related) geochemical patterns. Aqueous Speciation Modeling suggests that the undersaturated Halite (Na-Cl), Gypsum, and Anhydrate minerals in all three clusters, Indicating the possibility for the dissolution of Na, Ca, SO4, and Cl. As in HCA, Principle Component Analysis (PCA) also delivered three major components showing the impact of textile industries and agricultural fertilizers, leakage of sewages and the natural interaction of water with fluorite rich minerals. It is observed that both natural and anthropogenic processes are controlling the variations in the hydrochemical parameters and correlated with land use patterns.

Representing the Connectivity of Upland Areas to Floodplains and Streams in SWAT+

AbstractIn recent years, watershed modelers have put increasing emphasis on capturing the interaction of landscape hydrologic processes instead of focusing on streamflow at the watershed outlet alone. Understanding the hydrologic connectivity between landscape elements is important to explain the hydrologic response of a watershed to rainfall events. The Soil and Water Assessment Tool+ (SWAT+) is a new version of SWAT with improved runoff routing capabilities. Subbasins may be divided into landscape units (LSUs), e.g., upland areas and floodplains, and flow can be routed between these LSUs. We ran three scenarios representing different extents of connectivity between uplands, floodplains, and streams. In the first and second scenarios, the ratio of channelized flow from the upland to the stream and sheet flow from the upland to the floodplain was 70/30 and 30/70, respectively, for all upland/floodplain pairs. In the third scenario, the ratio was calculated for each upland/floodplain pair based on the upland/floodplain area ratio. Results indicate differences in streamflow were small, but the relative importance of flow components and upland areas and floodplains as sources of surface runoff changed. Also, the soil moisture in the floodplains was impacted. The third scenario was found to provide more realistic results than the other two. A realistic representation of connectivity in watershed models has important implications for the identification of pollution sources and sinks.

Investigation and Source Apportionment of Air Pollutants in a Large Oceangoing Ship during Voyage.

The aims of this study were to determine compartmental air pollution during navigation of a large oceangoing ship and to identify preliminarily the major pollution sources. During the voyage of a bulk carrier ship, air samples were collected at 18 selected sites using a stratified sampling method. The concentrations of 15 pollutants were determined using gas chromatography. Results showed the concentrations of these pollutants varied significantly among the sampling sites, indicating major pollution sources at or nearby those locations. Five common factors extracted using factor analysis explained 89.092% of the total variance. Multivariate linear regression analysis showed the contributions to air pollution of these five common factors, i.e., the volatilization of ship paint, volatilization of ship-based oil, cooking activities, high-temperature release of rubber components on the ship and daily use of chemical products, and the application of deodorant and insecticide, were 41.07%, 25.14%, 14.37%, 11.78%, and 7.63%, respectively. Three significant groups were determined using cluster analysis based on their similarity, i.e., high, medium, and low pollution of sampling sites. This study established that the air of the bulk carrier ship was heavily polluted, and that effective identification of pollution sources could provide a scientific basis for its control.

Comparison of two receptor models PCA-MLR and PMF for source identification and apportionment of pollution carried by runoff from catchment and sub-watershed areas with mixed land cover in South Korea

The application and comparison of receptor modeling techniques based on ambient air quality and particulate matter increasingly being studied. However, less is known about the comparison of receptor modeling techniques using spatial runoff quality data to identify and quantify the stormwater runoff pollution. This study compared the performance of principal component analysis-multiple linear regressions (PCA-MLR) and positive matrix factorization (PMF) models on stormwater runoff data collected from a small catchment (Site 1) with urban development activity and a sub-watershed outlet (Site 2). In both sites, the PCA-MLR model identified three pollution sources, whereas PMF identified five with a detailed source mechanism including two additional sources. Furthermore, the spatial land-use land-cover (LULC) analysis results indicate that the Site 1 exhibited a rapid conversion of the native area into a built-up area over the monitoring period compared to Site 2. Based on the modeling results, domestic wastewater and soil erosion were the major source of pollution at Site 1 and Site 2, respectively. The performance evaluation statistics including Nash coefficient (0.86–0.99), % error (<−14 to 2), and coefficient of determination (R2 ≤ 0.99) showed better performance for the PMF model than the PCA-MLR model. Overall, the PMF receptor modeling approach was found to be more robust for the current study sites with different land use types. The findings of this study could provide a basis for further application of these receptor models and their comparison using spatial-temporal ionic and sediment related runoff monitoring data. Also, the models from this research could be combined with other receptor models on runoff quality data (e.g. CMB or UNMIX) to explore and inter-compare the outcomes, and to determine how the model results are affected by modifications to input data and model parameters. Therefore, further research is required to precisely assess the accuracy of both receptor models.