Key Pollution Sources Research Articles

Air quality in different urban hotspots in a metropolitan city in India and the environmental implication.

This research study investigates hourly data on concentrations of five major air pollutants such as particulate matter (PM10, PM2.5) and gaseous pollutants (SO2, NO2, CO) measured during 2022 at four hotspot sites (industrial site, traffic site, commercial site, harbour, and one residential site) in Chennai, India. The analysis encompasses temporal variations spanning annual, seasonal, and diurnal variations in the pollutants. Notably, PM10 and CO emerge as the predominant pollutants, with the highest concentrations at industrial and traffic sites (PM10: 67.64 ± 40.77µg/m3, CO: 1.41 ± 0.84mg/m3; traffic site: PM10: 58.67 ± 20.05µg/m3, CO: 0.99 ± 0.57mg/m3). Seasonal dynamics reveal prominent winter spikes in particulate matter (PM10, PM2.5) and carbon monoxide (CO) concentrations, while nitrogen dioxide (NO2) and sulphur dioxide (SO2) levels peak during the summer season, particularly in the harbour area. The proximity to roadways exerts a discernible influence on diurnal patterns, with traffic sites showcasing broader rush hour peaks compared to sharper spikes observed at other sites. Furthermore, distinct bimodal patterns are evident for PM10 and PM2.5 concentrations in residential and harbour areas. A common lognormal distribution pattern is identified across the studied sites, suggesting consistent air quality trends despite contrasting locations. The conditional probability function (CPF) is used in conjunction with local meteorological conditions for identifying key pollution sources in each location. The implementation of polar plots emphasizes industries as principal local sources of pollution, at industrial sites significantly contributing to PM10, SO2, and NO2 concentrations under specific wind conditions. The main objective of the present study is to facilitate a good understanding of pollutant dynamics, pollution sources, and their intricate interplay with meteorological factors, thereby contributing to the formulation and implementation of effective air pollution control and mitigation strategies.

A novel hybrid features grey incidence model and its application in identifying key factors influencing air pollution in Jiangsu Province

Accurately assessing the key factors influencing air pollution is crucial for effective air pollution control. To address this need, we propose a novel Hybrid Features Grey Incidence Model (HFGIM), which integrates geometric feature differences from both proximity and similarity perspectives. Firstly, we extract geometric feature difference vectors of proximity and similarity from time series data and measure the overall feature difference degree by calculating vector norms. Secondly, we calculate the relative feature differences and information contribution rates of proximity and similarity to derive the hybrid feature differences coefficient between sequences, thereby obtaining the hybrid features incidence degree. After detailing the model’s properties and modelling steps, we introduce the Cross-sectional Data Hybrid Features Grey Incidence Model (C-HFGIM) and the Panel Data Hybrid Features Grey Incidence Model (P-HFGIM) for handling cross-sectional and panel data, respectively. Applying HFGIM, we identified the key pollutants and primary pollution source indicators of air pollution in Jiangsu Province. We also compared HFGIM with other classical grey incidence models to verify the proposed model’s effectiveness. Based on the analysis results, we propose policy recommendations for air pollution control in Jiangsu Province.

Quality evaluation and health risk assessment of karst groundwater in Southwest China

Groundwater in karst regions is of immense value due to its vital support for regional ecosystems and residents' livelihoods. However, it is simultaneously threatened by multi-source pollution from agricultural non-point sources, industrial and domestic point sources, and mining activities. This study focuses on the Guangxi of China, which features typical karst topography, aiming to thoroughly assess the groundwater quality and related health risks in Guangxi, especially identifying the impacts of various key pollution sources on the groundwater environment. A total of 1912 groundwater samples were collected, covering an area of approximately 237,600 km2. The spatial distribution of pollutants was analysed using the Nemeroww index method and Kriging interpolation, while multivariate statistical and cluster analysis methods were employed to identify the main types of pollution sources. Furthermore, based on the human health risk assessment model of the U.S. Environmental Protection Agency (US EPA), a risk assessment was conducted for key pollutants. The results revealed widespread heavy metal contamination in Guangxi's groundwater, particularly with concentrations of Mn, As, Al, Pb reaching up to 9.4 mg/L, 2.483 mg/L, 37.95 mg/L, 4.761 mg/L, respectively, significantly exceeding China's national Class III groundwater quality standards. Cluster analysis indicated that mining and industrial activities are the primary sources of pollution. The health risk assessment demonstrated that these activities pose a significant risk to public health. The aim of this study is to provide a scientific basis for the protection of the groundwater environment in Guangxi and other karst areas, the formulation of pollution prevention and control strategies, and the optimization of urban and industrial land use layouts. Future research should focus on advanced isotopic and molecular biological techniques to trace pollution sources more precisely and evaluate the effectiveness of pollution control measures.

Analysis of total nitrogen pollution load and identification of key pollution sources in Futuan River basin based on SWAT model

In order to identify the key source area of the Futuan River basin, the Futuan River basin is taken as the target area, establishes the Soil Water Assessment Tool (SWAT) model, simules the runoff and total nitrogen pollution process in the basin, and analyzes the spatiotemporal characteristics of total nitrogen pollution. The key source areas of total nitrogen pollution in the basin were identified by the cumulative load curve method. The results show that the total nitrogen pollution load in the Futuan River basin presents obvious seasonal distribution characteristics. The subbasins with high load intensity of total nitrogen into the river are mainly in the middle and upper reaches of the Futuan River.

Disentangling impacts of multiple pollutants on acute cardiovascular events in New York city: A case-crossover analysis

BackgroundAmbient air pollution contributes to an estimated 6.67 million deaths annually, and has been linked to cardiovascular disease (CVD), the leading cause of death. Short-term increases in air pollution have been associated with increased risk of CVD event, though relatively few studies have directly compared effects of multiple pollutants using fine-scale spatio-temporal data, thoroughly adjusting for co-pollutants and temperature, in an exhaustive citywide hospitals dataset, towards identifying key pollution sources within the urban environment to most reduce, and reduce disparities in, the leading cause of death worldwide. ObjectivesWe aimed to examine multiple pollutants against multiple CVD diagnoses, across lag days, in models adjusted for co-pollutants and meteorology, and inherently adjusted by design for non-time-varying individual and aggregate-level covariates, using fine-scale space-time exposure estimates, in an exhaustive dataset of emergency department visits and hospitalizations across an entire city, thereby capturing the full population-at-risk. MethodsWe used conditional logistic regression in a case-crossover design – inherently controlling for all confounders not varying within case month – to examine associations between spatio-temporal nitrogen dioxide (NO2), fine particulate matter (PM2.5), sulfur dioxide (SO2), and ozone (O3) in New York City, 2005–2011, on individual risk of acute CVD event (n = 837,523), by sub-diagnosis [ischemic heart disease (IHD), heart failure (HF), stroke, ischemic stroke, acute myocardial infarction]. ResultsWe found significant same-day associations between NO2 and risk of overall CVD, IHD, and HF – and between PM2.5 and overall CVD or HF event risk – robust to all adjustments and multiple comparisons. Results were comparable by sex and race – though median age at CVD was 10 years younger for Black New Yorkers than White New Yorkers. Associations for NO2 were comparable for adults younger or older than 69 years, though PM2.5 associations were stronger among older adults.Discussion: Our results indicate immediate, robust effects of combustion-related pollution on CVD risk, by sub-diagnosis. Though acute impacts differed minimally by age, sex, or race, the much younger age-at-event for Black New Yorkers calls attention to cumulative social susceptibility.

Accurate identification of sludge contamination sources by classification-based PMF and machine learning with consideration of sewer network distribution differences

The application of source identification such as PMF for large-scale pollution source analysis frequently produces ambiguous outcomes. In this study, we utilized a classification-based method to accurately track key pollution sources in the sludge. In the study, we categorized the wastewater treatment plants into two groups: T1 and T2, according to the pipeline network. T1 sewage treatment plants are the main sewage plants in urban areas, covering a large area and connected to industrial wastewater treatment plants for secondary treatment. T2 sewage treatment plants are typically smaller in size and usually responsible for treating sewage in rural or township areas. The PMF analysis indicates that industrial pollution sources contribute 3.4 times more to T1 sludge than to T2 sludge, making industrial pollution the primary factor causing the disparity. The application of Random Forest and Adaboost based on pollutant concentrations for classification and fitting of sludge resulted in the identification of the main pollutants: Zn, Cu, Ni, and Cyanide, which align with characteristic pollutants from the electroplating industry. The GIS analysis shows a significant correlation between the distance of wastewater treatment plants with abnormal environmental risk and electroplating industrial parks, all within a 20 km radius. Indeed, when conducting large-scale pollution source identification studies, utilizing classification-based analysis can effectively improve the accuracy of pollution source identification, leading to more valuable analysis results.

TP Concentration Inversion and Pollution Sources in Nanyi Lake Based on Landsat 8 Data and InVEST Model

Phosphorus is a limiting nutrient in freshwater ecosystems. Therefore, it is of great significance to use remote sensing technology to estimate the Total phosphorus (TP) concentration in the lake body and identify the contribution of TP inflow load in the surrounding area of the lake body. In this study, two main frameworks (empirical method and machine learning algorithm) for TP estimation are proposed and applied to the development of the Nanyi Lake algorithm. Based on the remote sensing data and ground monitoring data, the results obtained by the two main algorithms are compared to explore whether the machine learning algorithm has better performance than the empirical method in the TP inversion prediction of Nanyi Lake. The Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model was used to simulate the TP inflow load in the Nanyi Lake Basin and determine the key pollution source areas. The results show that the accuracy of the machine learning algorithm is higher than that of the empirical method and has better performance. Among the four machine learning algorithms—support vector machines (SVR), artificial neural network (BP), extreme gradient boosting algorithm (XGBoost) and random forest regression (RF)—the TP concentration inversion model established by the XGBoost algorithm is more accurate and has strong spatiotemporal heterogeneity. The simulation results in the southern and northeastern parts of the Nanyi Lake Basin contribute the most to the pollution load of the lake area, and the simulation results can provide direction for the effective prevention and control of Nanyi Lake, help to further effectively identify the key source areas of TP pollution in the water body of Nanyi Lake, and provide a meaningful scientific reference for water quality monitoring and management, to comprehensively improve the water quality of Nanyi Lake.

Study on the Temporal and Spatial Distribution of Air Pollutants in Typical Cities of China

The present study selects cities such as Zhengzhou, Kaifeng, Anyang, Hebi, Xinxiang, Jiaozuo, and Puyang along the Yellow River Basin in Henan Province. The data of six pollutants, such as PM2.5, PM10, SO2, CO, NO2, and O3, in various cities from 2019 to 2021, and the monthly primary pollutant data of seven cities in the past five years were collected through various channels. The air quality of the above seven cities was analyzed with the spatial-temporal distribution of pollutants as the research objective and geographic information system as the research tool. The results show that affected by the distribution of key pollution sources and meteorological conditions in the urban area, the PM2.5 concentration generally shows a zonal feature of decreasing from northwest to Southeast. The high-value area is located in the north and west of the integrated area of the seven cities, and the low-value area is located in the Southeast of the seven cities.

Wildfire-related smoke inhalation worsens cardiovascular risk in sleep disrupted rats.

As a lifestyle factor, poor sleep status is associated with increased cardiovascular morbidity and mortality and may be influenced by environmental stressors, including air pollution. To determine whether exposure to air pollution modified cardiovascular effects of sleep disruption, we evaluated the effects of single or repeated (twice/wk for 4 wks) inhalation exposure to eucalyptus wood smoke (ES; 964 μg/m3 for 1 h), a key wildland fire air pollution source, on mild sleep loss in the form of gentle handling in rats. Blood pressure (BP) radiotelemetry and echocardiography were evaluated along with assessments of lung and systemic inflammation, cardiac and hypothalamic gene expression, and heart rate variability (HRV), a measure of cardiac autonomic tone. GH alone disrupted sleep, as evidenced by active period-like locomotor activity, and increases in BP, heart rate (HR), and hypothalamic expression of the circadian gene Per2. A single bout of sleep disruption and ES, but neither alone, increased HR and BP as rats transitioned into their active period, a period aligned with a critical early morning window for stroke risk in humans. These responses were immediately preceded by reduced HRV, indicating increased cardiac sympathetic tone. In addition, only sleep disrupted rats exposed to ES had increased HR and BP during the final sleep disruption period. These rats also had increased cardiac output and cardiac expression of genes related to adrenergic function, and regulation of vasoconstriction and systemic blood pressure one day after final ES exposure. There was little evidence of lung or systemic inflammation, except for increases in serum LDL cholesterol and alanine aminotransferase. These results suggest that inhaled air pollution increases sleep perturbation-related cardiovascular risk, potentially in part by increased sympathetic activity.

Influence of Urban and Residential Areas on Surface Water Quality in the Vietnamese Mekong Delta Coastal Province

This study was conducted to assess the impact of residential and urban activities on surface water quality in Ben Tre province in 2021. Water quality data were collected in the dry and rainy seasons at 15 locations for evaluating the parameters of temperature, pH, dissolved oxygen (DO), salinity, turbidity, biochemical oxygen demand (BOD), chemical oxygen demand (COD), total suspended solids (TSS), ammonium (N-NH4+), nitrate (N-NO3−), orthophosphate (P-PO43−), iron (Fe), coliform using national technical regulation on surface water quality (QCVN 08-MT: 2015/BTNMT, A1) and surface water quality index (VN_WQI). Principal component analysis (PCA) and cluster analysis (CA) methods were used to evaluate sampling sites and identify key parameters and potential pollution sources. The results showed that TSS, BOD, COD, N-NH4+, P-PO43−, Fe and coliform are pollutants significantly affect surface water quality in residential and urban areas. Salinity, nutrients, heavy metals and microorganisms had high values in the dry season, while turbidity, suspended solids, dissolved oxygen, and organic matter were higher in the rainy season. Water quality was assessed from moderate to very good indicated by VN_WQI index. PCA results revealed that five key factors explaining 87.2% of the variation and 12 main parameters (temperature, salinity, turbidity, TSS, DO, BOD, COD, N-NH4+, N-NO3−, P-PO43−, Fe and coliform) affecting water quality. CA results showed that the current monitoring locations can be reduced from 15 locations to 12 locations, which could save 20% of monitoring costs. Future study should focus on investigating concrete sources of water pollution for appropriate management measures.

Air pollution risk assessment related to fossil fuel-driven vehicles in megacities in China by employing the Bayesian network coupled with the Fault Tree method

Vehicle emissions have become one of the key pollution sources affecting air quality and human health in China's megacities. How to curb excess vehicle emissions has become a key pain point of urban air pollution prevention and control. This work tries to explore an effective integrated approach/framework to quantitatively assess the risk factors of excess vehicle emissions (EVE) and their impact on air quality for China's typical megacities. Bayesian Network is employed as the assessment tool by coupling with the Fault Tree method to curb the above problem for the first time. Four megacities (Beijing, Tianjin, Hangzhou, and Guangzhou) in China are selected as case studies, and the risk factors leading to EVE are identified to construct the Bayesian Network model. At the same time, some accurate quantisation algorithms of the occurrence probability of root nodes were proposed for the target megacities from 2014 to 2019. The analysis results show that the variation trend of the probability of EVE has a good positive correlation with the variation trend of air quality in some megacities. From 2014 to 2019, the no-occurrence probability of EVE in Beijing, Tianjin, and Hangzhou increased from 0.4972, 0.4973, and 0.6314 to 0.6491, 0.6846, and 0.7564; the good air quality rate increased from 47.1%, 47.9%, and 59.2%–65.8%, 60%, and 78.6%. Based on the developing trend of the historical data/information and considering the impact of new energy vehicles, the no-occurrence probability of EVE in Beijing and Tianjin is predicted to be increased from 0.6888 to 0.7929 in 2020 to 0.8561 and 0.8645 in 2025. This work may provide a novel approach and perspective that can realise accurate traceability of key risk factors, quantitative risk assessment and prediction function of urban vehicle emissions for sustainable development of China's megacities.

Chemical Reaction Activity and Source Apportionment of Atmospheric VOCs in Summer in Dalian

To investigate the characteristics and source apportionment of atmospheric volatile organic compounds (VOCs) in Dalian, the concentration level, composition characteristics, reaction activity, and source apportionment of atmospheric VOCs in Dalian were analyzed based on high-resolution online observation VOCs data from June to August 2020. The results showed that the average φ(VOCs) was (10.21±5.71)×10-9, in which alkanes accounted for 66.35%, alkenes for 11.89%, alkynes for 7.75%, and aromatics for 14.01%. VOCs and NOx were high at night and low during the day, whereas the change trend of O3 was opposite. Considering the species activity, it was determined that toluene, ethylene, m/p-xylene, 1-hexene, n-butane, isopentane, n-pentane, and isoprene were the key species affecting atmospheric VOCs in Dalian. Priority control of olefin and aromatic hydrocarbon emissions is the key to improve O3 pollution in summer in Dalian. By applying the positive matrix factorization (PMF) model, six major VOCs sources were extracted, namely traffic sources (26.38%), combustion sources (22.75%), industrial emission sources (17.09%), solvent usage sources (14.59%), natural sources (11.72%), and others (7.47%). The emissions of traffic sources and combustion sources are the key pollution sources for O3 prevention and control in Dalian in summer.

Analysis of non-point source nitrogen pollution in watersheds based on SWAT model

By simulating and estimating non-point source pollution in Luoyang, the sources and spatial distribution of nitrogen pollution sources in this region were revealed, which provided a decision-making basis for further promoting non-point source pollution control and governance. Based on the SWAT model and different scenarios, the non-point nitrogen pollution sources in Luoyang from 2009 to 2018 were quantitatively identified, and the reasons for their changes under different pollution sources and different land uses were analyzed. The results show: From the analysis of different pollution source types, the main sources of non-point source pollution total nitrogen (TN) are nitrogen fertilizer application, atmospheric deposition and soil nitrogen reservoirs, and the pollution brought by livestock and poultry breeding and rural life is relatively low; From the analysis of different land use types, the entry coefficient of each non-point source TN pollution in Luoyang region is ranked as follows: atmospheric deposition (18.1%) > nitrogen fertilizer application (14.7%) > rural life (12.5%) > livestock manure (2.9%) > soil nitrogen pool (0.2%); Different pollution sources have different critical control periods, the key period for controlling natural sources (soil nitrogen pools and atmospheric deposition) is the rainy season, while the focus for controlling anthropogenic sources (rural living, livestock and poultry farming and fertilizer application) is the period of pollution cast emissions. In terms of time, the key period for controlling natural sources (soil nitrogen pool and atmospheric deposition) is the rainy season, while the key period for controlling anthropogenic sources (rural life, livestock and poultry breeding and application of chemical fertilizers) is the period of pollution input and emission; The key pollution sources of different land uses are also different. The key pollution sources of non-point source pollution of garden land and human settlements are nitrogen fertilizer application and domestic sewage, while the three major pollution sources of paddy field and dry land are nitrogen fertilizer application, atmospheric deposition and soil nitrogen pool.

Spatiotemporal distribution characteristics and key sources of nitrogen pollution in a typical agricultural watershed based on SWAT model

水资源恶化、水体富营养化严重威胁生态环境健康，农业活动所产生的氮是造成水污染的主要原因之一.本研究以句容水库农业流域为研究对象，基于实地监测数据验证了SWAT模型模拟当地氮污染的适用性，并分析了氮素负荷的时空分布特征及其关键源.结果表明：硝态氮（NO₃^--N）和总氮（TN）的年均入库量分别为9.98和27.22 t.时间尺度上，入库氮素负荷量年际呈现“M”型波动趋势；年内主要集中在汛期（6—9月），汛期氮素负荷量占到全年的84%以上.空间尺度上，径流NO₃^--N负荷量和TN负荷量均呈现“局部集中，临近水体”的分布特征，主要集中在农田占比高的地区.流域入库TN负荷量主要来源是农田施肥，贡献率达49.60%.研究结果可为当地流域氮污染控制提供理论依据.;The deterioration of water resources and surface water eutrophication threaten the health of the ecological environment seriously. Nitrogen (N) pollution from agricultural activities is an important source of water pollution. In this study, the Jurong Reservoir agricultural watershed was taken as the research object, and the local applicability of the SWAT model for N pollution was validated based on field monitoring data. In addition, the spatial and temporal distribution characteristics of N load and key pollution sources were analyzed. During the monitoring period, the annual N load showed an M type fluctuation trend, with a high N load occurred in the flood season from June to September, which accounted for more than 84% of the total N load of the whole year. Moreover, the spatial distribution of nitrate nitrogen (NO₃^--N) and total nitrogen(TN) load showed a trend of local concentration, adjacent to water body, mainly concentrated in the area with a high proportion of farmland. Farmland fertilizer is the main source of TN load in this watershed, contributing to 49.60% of total N load and the average annual storage capacity of NO₃^--N and TN was 9.98 t and 27.22 t, respectively. Our results can serve as a theoretical basis for the control of N pollution at the watershed scale.

Emission estimates and inventories of non-methane volatile organic compounds from anthropogenic burning sources in India

Comprehensive, spatially disaggregated emission inventories are required for many developing regions to evaluate the relative impacts of different sources and to develop mitigation strategies which can lead to effective emission controls. This study developed a 1 km 2 non-methane volatile organic compound (NMVOC) emission model for the combustion of fuel wood, cow dung cake, municipal solid waste (MSW), charcoal, coal and liquefied petroleum gas (LPG) in India from 1993 to 2016. Inputs were selected from a range of detailed fuel consumption surveys and recent emission factors measured during comprehensive studies of local burning sources. For the census year of 2011, we estimated around 13 (5–47) Tg of NMVOCs were emitted from biomass and MSW combustion in India. Around 54% of these emissions were from residential solid biofuel combustion, 23% from open burning of MSW, 23% from crop residue burning on fields and <1% from LPG for cooking. NMVOC emissions from residential combustion were shown to be highly sensitive to the amount of cow dung cake combusted and this acted as a key pollution source across the Indo-Gangetic Plain. The results of this study indicate that multiple mitigation strategies are required across several different categories of burning source to achieve effective NMVOC emission reduction. • New emission inventories developed for burning sources in India. • Estimates made for non-methane volatile organic compounds. • Estimates made for polycyclic aromatic hydrocarbons. • Pollution from cow dung cake combustion significant to North of India. • Waste and crop residue burning also significant pollution sources.

Preparation, characterization and evaluation of a styrene-based molecularly imprinted polymer for capturing pyridine and pyrrole from crude oil

The rapid increase in global energy demand met with fossil fuel exploration and use as an energy source has not been achieved without its impacts on human and environmental health. Consequently, the imposition of stricter measures on key pollution sources in the effort to reduce the emission of contaminants into the environment became necessary. In this work, a new molecularly imprinted polymer (MIP) targeting pyridine (pyd) and pyrrole (pyr) was synthesized using styrene as a functional monomer. MIP and the non-imprinted polymer (NIP) were characterized by infrared spectroscopy, x-ray spectroscopy, and scanning electron microscopy. The polymer had spongy surfaces and porous network structures. The effects of several factors, such as initial analyte concentration, contact time, and adsorbent dose on pyd and pyr removal efficiencies from crude oil were evaluated. Quantitative analyses indicated that the removal of pyd and pyr occurred at fast kinetics, with a removal efficiency of 15.12 and 15.33, respectively, after 10 min of adsorption time. Optimum adsorption for both analytes was however not reached until 420 min, indicating slow kinetics. The uptake of the analytes increased with a rise in concentration. The highest removal percentages were recorded at 99.9% and 99.7% for pyd and pyr respectively, at 0.06 mol dm−3 adsorbate concentration of the modeling organic media. Two adsorption isotherm models were applied to analyze the equilibrium data, obtaining the best description by the Freundlich isotherm model. The accuracy of the MIP materials was validated using Bonny crude oil from Nigeria. The results showed that the MIP was effective for the adsorption of pyd and pyr in both modeling organic media and real crude oil.

Spatiotemporal Analysis of Surface Water Quality in Dong Thap Province, Vietnam Using Water Quality Index and Statistical Approaches

The study was conducted to spatiotemporally analyze the quality, location and critical water variables influencing water quality using water monitoring data from the Department of Environment and Natural Resources, Dong Thap province in 2019. The water quality parameters including turbidity, pH, temperature, dissolved oxygen (DO), total suspended solids (TSS), biological oxygen demand (BOD), chemical oxygen demand (COD), nitrite (N-NO2−), nitrate (N-NO3−), ammonium (N-NH4+), total nitrogen (TN), orthophosphate (P-PO43−), chloride (Cl−), oil and grease, sulfate (SO42−), coliforms, and Escherichia coli (E. coli) were collected at 58 locations with the frequency of four times per year (February, May, August, and November). These parameters were compared with national technical regulation on surface water quality—QCVN 08-MT: 2015/BTNMT. Water quality index (WQI) was calculated and spatially presented by geographical information system (GIS) tool. Pearson correlation analysis, cluster analysis (CA), and principal component analysis (PCA) were used to evaluate the correlation among water quality parameters, group and reduce the sampling sites, and identify key parameters and potential water pollution sources. The results showed that TSS, BOD, COD, N-NH4+, P-PO43−, coliforms, and E. coli were the significant concerns impairing the water quality. Water quality was assessed from poor to medium levels by WQI analysis. CA suggested that the current monitoring locations could be reduced from 58 sites to 43 sites which can be saved the total monitoring budget up to 25.85%. PCA showed that temperature, pH, TSS, DO, BOD, COD, N-NH4+, N-NO2−, TN, P-PO43−, coliforms, and E. coli were the key water parameters influencing water quality in Dong Thap province’s canals and rivers; thus, these parameters should be monitored annually. The water pollution sources were possibly hydrological conditions, water runoff, riverbank erosion, domestic and urban activities, and industrial and agricultural discharges. Significantly, the municipal and agricultural wastes could be decisive factors to the change of surface water quality in the study area. Further studies need to focus on identifying sources of water pollution for implementing appropriate water management strategies.

Variable fecal source prioritization in recreational waters routinely monitored with viral and bacterial general indicators

Somatic and F+ coliphage methods are under consideration as potential routine surface water quality monitoring tools to identify unsafe levels of fecal pollution in recreational waters. However, little is known about the cooccurrence of these virus-based fecal indicators and host-associated genetic markers used to prioritize key pollution sources for remediation. In this study, paired measurements of cultivated coliphage (somatic and F+) and bacterial (E. coli and enterococci) general fecal indicators and genetic markers indicative of human (HF183/BacR287 and HumM2), ruminant (Rum2Bac), canine (DG3), and avian (GFD) fecal pollution sources were assessed in 365 water samples collected from six Great Lakes Basin beach and river sites over a 15-week recreational season. Water samples were organized into groups based on defined viral and bacterial fecal indicator water quality thresholds and average log10 host-associated genetic marker fecal score ratios were estimated to compare pollutant source inferences based on variable routine water quality monitoring practices. Eligible log10 fecal score ratios ranged from -0.051 (F+ coliphage, GFD) to 2.08 (enterococci, Rum2Bac). Using a fecal score ratio approach, findings suggest that general fecal indicator selection for routine water quality monitoring can influence the interpretation of host-associated genetic marker measurements, in some cases, prioritizing different pollutant sources for remediation. Variable trends were also observed between Great Lake beach and river sites suggesting disparate management practices may be useful for each water type.

Study on spatial distribution of pollutants and total amount reduction in the Duliujian River (Tianjin, China)

Abstract The Duliujian River is the largest river in the downstream area of the Haihe River. In order to further clarify the main pollutant load and load contribution of the basin, and analyze the watershed pollution control unit, key pollution sources and their spatial distribution characteristics, this study divides the control unit of the basin, which is based on the digital elevation model (DEM), and applies the expansion template of Arc Map–Arc Hydro tools software. On the basis of water environment capacity and total maximum daily load (TMDL) theory, water quality standards are set in accordance with the requirements of the water body function. Considering seasonal influence and the pollution load contribution rate, a total pollution load control plan and a load optimization allocation plan are proposed, in order to realize the distribution of pollution load among point source, non-point source and safety margin. According to the calculations, TMDL of chemical oxygen demand (COD) and NH3-N in the flood season (June–September) in the Duliujian River is the largest, about 27,005 kg/d and 2,296 kg/d, respectively. During the dry season (December–March), the reduction of COD and NH3-N is the largest, reaching 50.74% and 90.14%, respectively. And Duliujian River is dominated by non-point source pollution.

Quantifying changes in litter loads in urban stormwater run-off from Cape Town, South Africa, over the last two decades

To implement effective mitigation measures to stop the flow of litter from land-based sources into the sea, it is important to identify key pollution sources and to monitor litter trends over time. We sampled plastic and other anthropogenic debris in urban stormwater run-off in Cape Town, South Africa, from three catchment areas representing different land-use types: residential, industrial and a mixed commercial/residential area. Sampling was conducted in 2018–19 by placing nets over stormwater outlets during rain events. Organic matter constituted 79% of material by dry mass (industrial: 51 ± 22%, commercial/residential: 86 ± 4%, residential: 88 ± 13%). The nets caught 5–576 anthropogenic litter items·ha−1·day−1 (2–377 g·ha−1·day−1) with significantly higher densities in the industrial and commercial/residential areas than the residential area. Among anthropogenic litter items, 40–78% were made of plastic (52–64% by mass). Most plastic items were single-use packaging, but industrial pellets washed out of the industrial outlet during every rain event. Compared to a similar study conducted in 1996, the number of litter items decreased by ~20% in the industrial and residential areas but tripled in the commercial/residential area. The proportion of plastics in the litter stream was broadly similar to 1996. We extrapolate that some 60–570 t of plastic are released from Cape Town stormwater outlets annually, which is orders of magnitude less than predicted by global models. It is nevertheless clear that stormwater outlets are a significant source of litter into the sea. Intercepting this waste before it reaches the sea would greatly reduce litter loads in coastal waters around Cape Town.