Per- and polyfluoroalkyl substances (PFAS) are persistent contaminants that are widely detected in surface waters and are of growing global concern. However, the factors associated with variations in PFAS contamination across space and time at a continental scale remain poorly understood. Here, we combine results from a harmonised pan-European river monitoring study with partial least squares path modelling (PLS-PM) to identify the key factors associated with PFAS contamination in European rivers. Seven high-priority PFAS were monitored at 280 sites across 93 rivers in 31 European countries, representing the PFAS footprint of approximately 309 million people. At least one PFAS was detected at 93% of monitoring locations. Highest concentrations were observed in parts of southern, southeastern and western Europe and were associated with landfills, wastewater discharges and industrial activity. Higher PFAS concentrations occurred in summer, potentially reflecting dilution-related effects associated with lower flow conditions. PLS-PM revealed that PFAS concentrations were associated with socioeconomic pressures and environmental conditions. The Human Footprint Index showed the strongest association among the socioeconomic variables examined, while electrical conductivity was the environmental variable most strongly associated with PFAS concentrations, possibly reflecting shared transport pathways with wastewater and industrial effluents. An indicative, screening-level assessment against existing regulatory threshold values suggested that PFAS concentrations at 61% of sites are of potential concern. These findings support the use of combined human pressure and hydrochemical indicators to inform more targeted, risk-based monitoring and regulatory prioritisation at large spatial scales.

Copper (Cu) is a widely used industrial metal and a common contaminant in industrial wastewaters, posing risks for aquatic ecosystems. While microalgae have been widely studied for heavy-metal phycoremediation, research has largely focused on metal adsorption and removal performance, with comparatively little attention to the metabolic adaptations that allow microalgae to maintain function under sub-inhibitory exposure. In this study, we selected a Cu-tolerant strain of Tetraselmis suecica (IC50: 5.7 mg L-1) and evaluated its physiological, biochemical, and transcriptomic responses to 1 mg L-1 Cu2+ exposure, an environmentally relevant concentration found in industrial effluents. Growth, chlorophyll fluorescence, and total protein and carbohydrate levels remained unaffected, indicating physiological stability under 1 mg L-1 Cu2+. Over 90% of Cu remained sorbed to biomass by 24 h. Sequential chemical extraction revealed that over 70% of sorbed Cu was bound to soluble organic acids and insoluble phosphates, indicating the involvement of multiple detoxification strategies. The evidence from RNA-seq analysis suggested that the increased expression of ZIP, MTP, and MATE family transporters played key roles in the Cu assimilation mechanisms and the management of excess Cu in the cytosol. Transcriptomic analysis also revealed upregulation of starch catabolism and glycolysis enzymes, increased fatty acid synthesis, and elevated nitrogen assimilation, providing insights into how green microalgae potentially allocate photosynthetic energy into different forms of fixed carbon under subinhibitory Cu exposure.

Water pollution policies have been enacted across the globe to minimize the environmental risks posed by micropollutants (MPs). For regulative institutions to be able to ensure the realization of environmental objectives, they need information on the environmental fate of MPs. Furthermore, there is an urgent need to further improve environmental decision-making, which heavily relies on scientific data. Use of mathematical and computational modeling in environmental permit processes for water construction activities has increased. Uncertainty of input data considers several steps from sampling and analysis to physico-chemical characteristics of MP. Machine learning (ML) methods are an emerging technique in this field. ML techniques might become more crucial for MP modeling as the amount of data is constantly increasing and the emerging new ML approaches and applications are developed. It seems that both modeling strategies, traditional and ML, use quite similar methods to obtain uncertainties. Process based models cannot consider all known and relevant processes, making the comprehensive estimation of uncertainty challenging. Problems in a comprehensive uncertainty analysis within ML approach are even greater. For both approaches generic and common method seems to be more useful in a practice than those emerging from ab initio. The implementation of the modeling results, including uncertainty and the precautionary principle, should be researched more deeply to achieve a reliable estimation of the effect of an action on the chemical and ecological status of an environment without underestimating or overestimating the risk. The prevailing uncertainties need to be identified and acknowledged and if possible, reduced. This paper provides an overview of different aspects that concern the topic of uncertainty in MP modeling.

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Heavy metal contamination in drinking water and water resources is one of the problems generated by increasing water demand and growing industrialization. Heavy metals can be toxic to humans and other living beings when their intake surpasses a certain threshold. Generally, heavy metal contamination analysis of water resources requires qualified experts with specialized equipment. In this paper, we introduce a method for citizen-based water-quality monitoring through simple pattern classification of water crystallization using a smartphone and portable microscope. This work is a first step toward the development of a Water Expert System smartphone application that will provide the ability to analyze water resource contamination remotely by sending images to the database and receiving an automatic analysis of the sample via machine learning software. In this study, we show the ability of the method to detect Fe 2 mg/1 L, 5 mg/L,10 mg/L polluted distilled water compared with other heavy metals (Al, Pb) pollution. The experimental results show that the classification used method has an accuracy greater than 90%.

Algae have become one of the most environmentally sustainable feedstocks for several products in energy, food, agriculture, and health sectors driven with a bioeconomy perspective. Meanwhile, algae also carry huge potential of carbon capture and nutrient removal from gaseous or aqueous waste streams, respectively. Once their superior photosynthetic capabilities can be combined with diverse functions of bacteria capable of biotransforming hazardous contaminants, the applications for biological wastewater treatment expand considerably. This chapter addresses the recent developments in algae–bacteria consortia for the treatment of domestic and industrial waste streams. Algae species can tolerate extreme conditions and have the capability to remove both organic and inorganic contaminants under photoautotrophic conditions. Certain species of bacteria also capable of biotransforming key hazardous pollutants can be used to establish mutually beneficial consortia based on carbon-oxygen exchange with algae. As a result, low-cost, high-performance (de)centralized biological treatment systems can be for offered for hard-to-treat industrial and conventional domestic wastewater streams.

Environmental exposure to active pharmaceutical ingredients (APIs) can have negative effects on the health of ecosystems and humans. While numerous studies have monitored APIs in rivers, these employ different analytical methods, measure different APIs, and have ignored many of the countries of the world. This makes it difficult to quantify the scale of the problem from a global perspective. Furthermore, comparison of the existing data, generated for different studies/regions/continents, is challenging due to the vast differences between the analytical methodologies employed. Here, we present a global-scale study of API pollution in 258 of the world's rivers, representing the environmental influence of 471.4 million people across 137 geographic regions. Samples were obtained from 1,052 locations in 104 countries (representing all continents and 36 countries not previously studied for API contamination) and analyzed for 61 APIs. Highest cumulative API concentrations were observed in sub-Saharan Africa, south Asia, and South America. The most contaminated sites were in low- to middle-income countries and were associated with areas with poor wastewater and waste management infrastructure and pharmaceutical manufacturing. The most frequently detected APIs were carbamazepine, metformin, and caffeine (a compound also arising from lifestyle use), which were detected at over half of the sites monitored. Concentrations of at least one API at 25.7% of the sampling sites were greater than concentrations considered safe for aquatic organisms, or which are of concern in terms of selection for antimicrobial resistance. Therefore, pharmaceutical pollution poses a global threat to environmental and human health, as well as to delivery of the United Nations Sustainable Development Goals.

Contamination of agricultural soil with organic contaminants is a global problem due to the risks associated with food security and ecological sustainability. Besides the use of agrochemicals, hundreds of emerging contaminants enter arable lands through polluted irrigation water. In this study, an analytical workflow based on QuEChERS extraction coupled with LC-MS/MS quantification was applied to measure 65 emerging contaminants (42 pesticides and 23 multiclass industrial chemicals) in soil and rice for the first time. The method was validated on paddy and yard soil and rice plants. A recovery efficiency ranging between 70 and 120% (RSD <20%) was achieved for more than 70% of the analytes. Then, the validated method was used to quantify target contaminants in 22 soil and 9 rice samples collected mainly from paddy fields close to the Ergene River (Turkey), which is a highly polluted river used for irrigation in the region. Pesticide residues were present in all soil samples up to 2.4 mg/kg. However, their concentrations were below their maximum residual limits in rice. Azoxystrobin, prochloraz, propiconazole, imidacloprid, and epoxiconazole were the most frequently detected pesticides. In addition, industrial pollutants such as benzyldimethyldodecylammonium and tris(2-butoxyethyl) phosphate were detected in paddy soil samples at concentrations between 0.1 and 691 μg/kg. Benzyldimethyldodecylammonium and 5-methyl-1H benzotriazole were also measured in rice at concentrations up to 0.26 and 2.13 μg/kg, respectively.

Ergene River is heavily utilized for irrigation of fields to grow the main stocks of rice, wheat, and sunflower of Turkey also exported to Europe; therefore, monitoring the river's water quality is crucial for public health. Although the river quality is routinely monitored, the evaluation of pollution based on micropollutants is limited. In this study, we measured 222 organic micropollutants in 300 samples collected from 75 different locations on the Ergene River between August 2017 and May 2018 using direct injection liquid chromatography-tandem spectrometry with optimized scheduled multiple reaction monitoring. In total, 165 micropollutants were detected at a range of concentrations between 1.90 ng/L and 1824.55 μg/L. Sixty-three chemical substances were recurrent micropollutants that were detected at least one location in all seasons. Among them, 41 chemical substances were identified as the core micropollutants of the Ergene River using data-driven clustering methods. Hexa(methoxymethyl)melamine, benzotriazoles, and benzalkonium chlorides were frequently detected core micropollutants with an industrial origin. Besides, diuron, carbendazim, and cadusafos were common pesticides in the river. Core micropollutants were further categorized based on their type of source and environmental behavior using Kurtosis of concentration and load data obtained for each micropollutant. As a result, the majority of the core micropollutants are recalcitrant chemicals either released from a specific source located upstream of the river or have urban and agricultural sources dispersed on the watershed. In this study, we assessed the current state of pollution in the Ergene River at the micropollutant level with a very high spatial resolution and developed a statistical approach to categorize micropollutants that can be used to monitor the extent of pollution and track pollution sources in the river.

Quaternary ammonium compounds (QACs) are active ingredients of many disinfectants used against SARS-CoV-2 to control the transmission of the virus through human-contact surfaces. As a result, QAC consumption has increased more than twice during the pandemic. Consequently, the concentration of QACs in wastewater and receiving environments may increase. Due to their antimicrobial activity, high levels of QACs in wastewater may cause malfunctioning of biological treatment systems resulting in inadequate treatment of wastewater. In this study, a biocatalyst was produced by entrapping Pseudomonas sp. BIOMIG1 capable of degrading QACs in calcium alginate. Bioactive 3-mm alginate beads degraded benzalkonium chlorides (BACs), a group of QACs, with a rate of 0.47 mu M-BACs/h in shake flasks. A bench-scale continuous up-flow reactor packed with BIOMIG1-beads was operated over one and a half months with either synthetic wastewater or secondary effluent containing 2-20 mu M BACs at an empty bed contact time (EBCT) ranging between 0.6 and 4.7 h. Almost complete BAC removal was achieved from synthetic and real wastewater at and above 1.2 h EBCT without aeration and effluent recirculation. The microbial community in beads dominantly composed of BIOMIG1 with trace number of Achromobacter spp. after the operation of the reactor with the real wastewater, suggesting that BIOMIG1 over-competed native wastewater bacteria during the operation. This reactor system offers a low cost and robust treatment of QACs in wastewater. It can be integrated to conventional treatment systems for efficient removal of QACs from the wastewater, especially during the pandemic period.

Pseudomonas sp. strain BIOMIG1BAC is an antibiotic-resistant gammaproteobacterium that can completely mineralize different homologs of benzalkonium chloride disinfectants. Here, we report the annotated complete genome sequence of this microorganism, which includes one circular chromosome with a length of 7,675,262 bp.

Dezenfektan aktif maddeleri kentsel ve endüstriyel atıksularda oldukça sık rastlanan kirleticilerdir. Bu kirleticilerin biyolojik sistemlerde giderimi oldukça zordur. Yüzeysel sularda da sıkça karşılaşılan bu kirleticiler hem doğal hayatı hem de insan sağlığını tehdit etmektedir. Bu çalışmanın amacı, atıksuda en çok karşılaşılan kirleticilerden biri olan benzalkonyum klorürlerin (BAK’ler) biyolojik sistemlerde en verimli şekilde giderimini sağlayacak koşulların belirlenmesidir. Bu amaçla, atıksudan izole edilmiş BAK gideren bir bakteri olan Pseudomanas sp. BIOMIG1’in farklı koşullarda BAK biyotransformasyon kinetiği belirlenmiştir. Elde edilen veriler ve Michaelis-Menten modeli kullanılarak, bu mikroorganizmanın BAK biotransformasyon kinetiği parametreleri hesaplanmış ve uygulanan koşulların kinetiğe etkisi belirlenmiştir. BIOMIG1, BAK’leri 1.4 mg/L-saat hızında giderebilmekte ve bu kirleticileri amonyak ve karbon dioksite dönüştürmektedir. Mililitrede yüz bin adet bakteri yoğunluğu gibi düşük bakteri yoğunluklarında bile BAK gecikmeli de olsa yüksek hızda giderilebilmektedir. BAK homologlarının biyotransformasyon hızı karşılaştırıldığında, 14 karbon alkil zincir uzunluğuna sahip BAK en hızlı, 16 karbonlu BAK ise en yavaş biyotransformasyon hızına sahiptir. BAK giderim hızının en yüksek olduğu sıcaklık 35°, bu sıcaklık üstündeki sıcaklıklarda BIOMIG1 yaşayamamaktadır. Dolayısıyla yüksek sıcaklıklarda BAK parçalanması ya benzildimetilamin birikmesiyle sonlanmış ya da hiç gerçekleşmemiştir. Bu çalışmanın sonuçları, özellikle BAK gibi mikrokirleticilerin arıtımını hedefleyen ileri arıtma sistemlerinin tasarlanması ve işletilmesinde faydalı olacaktır.

Acetaminophen (APAP), which is an active ingredient of many analgesic drugs, is one of the contaminants of emerging concern in the environment. Although APAP is biodegradable, it is frequently detected in treatment plant effluents, surface water and soil suggesting that there are factors affecting the fate of APAP in the environment. In this study, four strains of bacteria that can degrade APAP were isolated from soil. Those strains belonged to Rhodococcus, Pseudomonas, Flavobacterium, and Sphingobium genera of Bacteria. A series of kinetic experiments were performed on the isolates in shake-flasks to determine biodegradation rate constant as well as the effect of temperature, APAP concentration and cell density on the biodegradation rates. APAP biodegradation follows the first order reaction kinetics which is coupled with cell growth. The specific APAP biodegradation rate constant (k) for all strains was similar and equal to 0.19 ± 0.01 h-1. The temperature, at which APAP biodegradation rate was maximum, was 35 °C. APAP biodegradation rate was linearly correlated with both the initial APAP concentration and the cell density. Initial step of the APAP biodegradation was hydrolysis of the amide bond which resulted in formation and accumulation of p-aminophenol suggesting that aryl acylamidase enzyme is responsible for the biotransformation. In addition, free and immobilized crude enzymes of the isolates transformed APAP at similar rates, comparable to the intact cells. This study showed that APAP biodegradation is achieved by a diverse group of bacteria having a similar enzyme operating at a constant kinetics which is very slow at environmentally relevant APAP concentrations. Natural removal of APAP in the environment is limited by kinetics, therefore APAP-bearing waste streams should be treated in adsorption enhanced biological systems before discharged into the environment.

Active ingredients of disinfectants are very common pollutants in urban and industrial wastewater. Removal of these contaminants is very difficult in biological treatment systems. As a result; these pollutants, which are also frequently detected in surface waters, threaten both nature and human health. The objective of this study is to determine the optimum conditions that will provide the most efficient removal of benzalkonium chlorides (BACs), a common contaminant, in biological treatment systems. For this purpose, BAC biotransformation kinetics were determined under different conditions using Pseudomanas sp. BIOMIG1, a bacterium that is the key BAC degrader in the environment. Using the data collected and the Michaelis-Menten growth model, BAC biotransformation kinetic parameters were calculated and the effect of the applied conditions on kinetics was determined. BIOMIG1 can transform BACs at a rate of 1.4 mg/L-hour and converts these pollutants into ammonia and carbon dioxide. BAC mineralization can be achieved even at low bacterial densities such as 100000 cells/mL after a short delay. When biotransformation rate of BAC homologs was compared, BAC with 14 carbon alkyl chain length had the fastest and BAC with 16 carbons had the slowest rate of biotransformation. The temperature at which the BAC biotransformation rate was the highest was 35 degrees. BAC was converted to benzyldimethylamine at all temperatures above 35 degrees since BIOMIG1 does not survive above this temperature. The outcomes presented in this study would be used for the design and operation of advanced treatment systems targetting the removal of micropollutants like BACs.

Currently about 110,000 chemical substances are present in the European market. The fate of most of those chemicals in the environment is not known. However, biodegradability of those chemicals should be tested before they are registered to the Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) System. Current protocols offered by The Organisation for Economic Co-operation and Development (OECD) for testing the biodegradability of the chemicals are limited mainly due to they are low throughput and do not reflect real-world conditions. In OECD protocols, the biodegradability of a single chemical is tested. However, many chemicals coexist in the environment. In addition, experiments are set at a very high initial chemical concentration that is not expected in the environment. Both limitations are due to the lack of an analytical method which can measure multiple compounds simultaneously at very low concentrations. In this study, we coupled OECD 314 Simulation Tests to Assess the Biodegradability of Chemicals Discharged in Wastewater protocol with a powerful liquid chromatography mass spectrometry with scheduled multiple reaction monitoring and tested the biodegradability of 32 priority substances and chemicals with emerging concern. Only seven chemicals were degraded in the test within 28 days. The biodegradation half-lives of those degradable chemicals ranged between 0.6 to 18 days. Acetaminophen was degraded the fastest whereas biodegradation of sulfamethoxazole took longer than the rest of the biodegradable chemicals tested. The novel methodology described here can be applied to test biodegradability of different chemicals as a mixture and adopted as a standard protocol.

Bu çalışmanın amacı, Ergene Nehri’nin Meriç Nehri ile birleştiği noktanın menbasında bulunan E01A012 – Yenicegörüce Akım Gözlem İstasyonu’nun (AGİ) alanı 10,508 m2 olan su toplama havzasındaki yağış-akış ilişkisinin A.B.D. Ordu Mühendisleri Birliği (U.S. Army Corps of Engineers) tarafından geliştirilmiş olan HEC-HMS yazılımı kullanılarak belirlenmesidir. Bu çalışma TÜBİTAK tarafından desteklenen 115Y064 no’lu “Ergene Havzası Su Kalitesi Yönetimi İçin Kirletici Parmak İzine Bağlı Coğrafi Bilgi Sistemi Bazlı Karar Destek Sistemleri Geliştirmesi” başlıklı projenin bir parçası olarak gerçekleştirilmiştir. İlk olarak havza ve civarında ölçülmüş olan günlük yağış ve sıcaklık gibi meteorolojik veriler ile günlük akış verileri toplanmıştır. Ardından havzanın arazi kullanımı, hidrolojik toprak grupları ve sayısal yükseklik verileri gibi havza karakteristiklerini gösteren veriler toplanmış ve Coğrafi Bilgi Sistemi (CBS) ortamında derlenmiştir. CBS ortamında derlenmiş olan sayısal haritalar havzanın özelliklerinin belirlenmesi için WMS’e aktarılmıştır ve ardından havzaya ait HEC-HMS hidrolojik modeli kurulmuştur. Kurulan modelin, 1997-2002 arasındaki günlük veriler kullanılarak kalibrasyonu, 2003-2005 yılları arasındaki günlük veriler kullanılaraksa doğrulaması yapılmıştır. Kurulmuş olan hidrolojik modelin Yenicegörüce AGİ için Nash-Sutcliffe Etkinlik Katsayısı (NSE) kalibrasyon ve doğrulama aşamaları için sırasıyla 0.8 ve 0.75 olarak hesaplanmıştır. Yenicegörüce havzasının D01A008, E01A006 ve E01A012 AGİ’leri ile temsil edilen Hayrabolu, Lüleburgaz ve İnanlı alt-havzaları için de hidrolojik modeller kurulmuş ve kalibre edilmiştir. Model performansları NSE ve korelasyon gibi istatistiksel ölçütler kullanılarak değerlendirilmiştir.

The objective of this study is to develop an artificial neural network (ANN) based solution approach to predict the weekly flows of Ergene River which is the largest river in Thrace region of Turkey. In the developed approach, precipitation – flow data relationships have been investigated in order to establish the best model structure to predict streamflow at the selected basin. The developed relationships are then evaluated using a feed forward neural network where back propagation algorithm is used to determine the associated network weights. The performance of the developed ANN based solution approach is evaluated by using the weekly precipitation and flow data collected from different monitoring sites in Ergene River basin. The model results are also compared with HEC-HMS model outputs which is calibrated using the same precipitation and flow data. Results indicate that the proposed ANN based solution approach can be effectively used to predict the weekly flows of Ergene River.