Environmental Monitoring Applications Research Articles

Ammonia and temperature sensing applications using fluorometric and colorimetric microparticles and polymeric films doped with BODIPY-emitters.

Fourfunctionalized BODIPY derivatives(BDP1 to BDP4) were synthesizedand their optical properties investigatedboth in solution and when incorporated into a solid matrix. Recognizing the versatility of BODIPY derivatives and the increasing interest in developing new luminescent organic dyes embedded in polymers, the BODIPY derivatives were dispersed into two types of polymeric matrices: Poly(methyl methacrylate) (PMMA) and Thermoplastic Polyurethane (TPU), both as films and microparticles. This resulted in eight new BODIPY-doped polymer films and eight types of BODIPY-doped polymeric microparticles for use in aqueous solutions. The integration of the BODIPY dyes into the polymeric matrices combines the unique properties of the polymer films, such as porosity, flexibility, and elasticity, with the excellent photophysical characteristics of the BODIPYs. Importantly, the dispersion minimized issues such as aggregation-caused quenching commonly observed in solid-state luminescent materials. The thermometric responses of all polymer films were evaluated by studying their solid-state emission spectra in the 25-200°C temperature range. The reversibility of these temperature-induced changes was also assessed, revealing excellent recovery of luminescence. These promising results suggest these materials could have applications as fluorescent thermometric sensors. Furthermore, we explored the potential of the brominated (BDP3) and chalcogenated (BDP4) BODIPY derivatives as ammonia sensors. The two derivatives produced yellow fluorescent products upon interaction with the analyte. Kinetic studies using solid-state emission spectra of BDP4@TPU and BDP4@PMMA showed significant differences in reaction rates (minutes for BDP4@TPU and hours in the case of BDP4@PMMA) attributable to the higher permeability of TPU when compared with PMMA. Detection and quantification of ammonia concentration were conducted by means of simple photographic analysis, measuring the "R" (red) and "G" (green) components of RGB color parameters. Theresults from the photographic method correlated well with the results from fluorimetric spectroscopy studies. The photographic analysis is straightforward, portable, and does not require expensive equipment. Finally, we successfully applied polymeric microparticles doped with BODIPYs to detect ammonia in water, demonstrating their effectiveness without the need for organic solvents. This highlights their potential for environmental monitoring and other applications requiring sensitive and selective detection methods.

Causality-Driven Feature Selection for Calibrating Low-Cost Airborne Particulate Sensors Using Machine Learning

With escalating global environmental challenges and worsening air quality, there is an urgent need for enhanced environmental monitoring capabilities. Low-cost sensor networks are emerging as a vital solution, enabling widespread and affordable deployment at fine spatial resolutions. In this context, machine learning for the calibration of low-cost sensors is particularly valuable. However, traditional machine learning models often lack interpretability and generalizability when applied to complex, dynamic environmental data. To address this, we propose a causal feature selection approach based on convergent cross mapping within the machine learning pipeline to build more robustly calibrated sensor networks. This approach is applied in the calibration of a low-cost optical particle counter OPC-N3, effectively reproducing the measurements of PM1 and PM2.5 as recorded by research-grade spectrometers. We evaluated the predictive performance and generalizability of these causally optimized models, observing improvements in both while reducing the number of input features, thus adhering to the Occam’s razor principle. For the PM1 calibration model, the proposed feature selection reduced the mean squared error on the test set by 43.2% compared to the model with all input features, while the SHAP value-based selection only achieved a reduction of 29.6%. Similarly, for the PM2.5 model, the proposed feature selection led to a 33.2% reduction in the mean squared error, outperforming the 30.2% reduction achieved by the SHAP value-based selection. By integrating sensors with advanced machine learning techniques, this approach advances urban air quality monitoring, fostering a deeper scientific understanding of microenvironments. Beyond the current test cases, this feature selection method holds potential for broader applications in other environmental monitoring applications, contributing to the development of interpretable and robust environmental models.

Microwave-Assisted Synthesis of N, S Co-Doped Carbon Quantum Dots for Fluorescent Sensing of Fe(III) and Hydroquinone in Water and Cell Imaging

The detection of heavy metal ions and organic pollutants from water sources remains critical challenges due to their detrimental effects on human health and the environment. Herein, a nitrogen and sulfur co-doped carbon quantum dot (NS-CQDs) fluorescent sensor was developed using a microwave-assisted carbonization method for the detection of Fe3+ ions and hydroquinone (HQ) in aqueous solutions. NS-CQDs exhibit excellent optical properties, enabling sensitive detection of Fe3+ and HQ, with detection limits as low as 3.40 and 0.96 μM. Notably, with the alternating introduction of Fe3+ and HQ, NS-CQDs exhibit significant fluorescence (FL) quenching and recovery properties. Based on this property, a reliable “on-off-on” detection mechanism was established, enabling continuous and reversible detection of Fe3+ and HQ. Furthermore, the low cytotoxicity of NS-CQDs was confirmed through successful imaging of HeLa cells, indicating their potential for real-time intracellular detection of Fe3+ and HQ. This work not only provides a green and rapid synthesis strategy for CQDs but also highlights their versatility as fluorescent probes for environmental monitoring and bioimaging applications.

Mamba-UAV-SegNet: A Multi-Scale Adaptive Feature Fusion Network for Real-Time Semantic Segmentation of UAV Aerial Imagery

Accurate semantic segmentation of high-resolution images captured by unmanned aerial vehicles (UAVs) is crucial for applications in environmental monitoring, urban planning, and precision agriculture. However, challenges such as class imbalance, small-object detection, and intricate boundary details complicate the analysis of UAV imagery. To address these issues, we propose Mamba-UAV-SegNet, a novel real-time semantic segmentation network specifically designed for UAV images. The network integrates a Multi-Head Mamba Block (MH-Mamba Block) for enhanced multi-scale feature representation, an Adaptive Boundary Enhancement Fusion Module (ABEFM) for improved boundary-aware feature fusion, and an edge-detail auxiliary training branch to capture fine-grained details. The practical utility of our method is demonstrated through its application to farmland segmentation. Extensive experiments on the UAV-City, VDD, and UAVid datasets show that our model outperforms state-of-the-art methods, achieving mean Intersection over Union (mIoU) scores of 71.2%, 77.5%, and 69.3%, respectively. Ablation studies confirm the effectiveness of each component and their combined contributions to overall performance. The proposed method balances segmentation accuracy and computational efficiency, maintaining real-time inference speeds suitable for practical UAV applications.

Deep-Eutectic-Solvent-Decorated Metal–Organic Framework for Food and Environmental Sample Preparation

Deep eutectic solvent (DES) is distinguished by its unique solvent properties, chemical stability, and eco-friendly nature, which are pivotal in a spectrum of chemical processes. It enhances the sample preparation process by increasing efficiency and minimizing the environmental impact. Metal–organic frameworks (MOFs), which are porous structures formed through coordination bonds between metal ions and organic ligands, are defined by their adjustable pore dimensions, extensive surface areas, and customizable architectures. The integration of DES within MOF to create DES@MOF capitalizes on the beneficial attributes of both materials, augmenting MOFs’ stability and versatility while providing a multifunctional carrier for DES. This composite material is both highly stable and readily tunable, establishing it as a leading contender for applications in sample preparation for food and environmental samples. This comprehensive review explores the application of DES-decorated MOF in food and environmental sample preparation and highlights the expansive potential of DES@MOF in diverse fields. We provide a detailed analysis of the characteristics of DES@MOF and its individual components, methods for decorating MOFs with DES, the advantages of these composite materials in sample pretreatment, and their specific applications in food safety and environmental monitoring. DESs are employed to modify MOFs, offering a multitude of benefits that can substantially improve the overall performance and applicability of MOFs. The review also discusses current challenges and future directions in this field, offering valuable insights for further research and development. The synergistic effects of DES and MOFs offer new opportunities for applications in food safety and other areas, leading to the development of more efficient, sensitive, and environmentally friendly analytical methods. This collaboration paves the way for sustainable technologies and innovative solutions to complex challenges.

Novel Approach in Vegetation Detection Using Multi-Scale Convolutional Neural Network

Vegetation segmentation plays a crucial role in accurately monitoring and analyzing vegetation cover, growth patterns, and changes over time, which in turn contributes to environmental studies, land management, and assessing the impact of climate change. This study explores the potential of a multi-scale convolutional neural network (MSCNN) design for object classification, specifically focusing on vegetation detection. The MSCNN is designed to integrate multi-scale feature extraction and attention mechanisms, enabling the model to capture both fine and coarse vegetation patterns effectively. Moreover, the MSCNN architecture integrates multiple convolutional layers with varying kernel sizes (3 × 3, 5 × 5, and 7 × 7), enabling the model to extract features at different scales, which is vital for identifying diverse vegetation patterns across various landscapes. Vegetation detection is demonstrated using three diverse datasets: the CamVid dataset, the FloodNet dataset, and the multispectral RIT-18 dataset. These datasets present a range of challenges, including variations in illumination, the presence of shadows, occlusion, scale differences, and cluttered backgrounds, which are common in real-world scenarios. The MSCNN architecture allows for the integration of information from multiple scales, facilitating the detection of diverse vegetation types under varying conditions. The performance of the proposed MSCNN method is rigorously evaluated and compared against state-of-the-art techniques in the field. Comprehensive experiments showcase the effectiveness of the approach, highlighting its robustness in accurately segmenting and classifying vegetation even in complex environments. The results indicate that the MSCNN design significantly outperforms traditional methods, achieving a remarkable global accuracy and boundary F1 score (BF score) of up to 98%. This superior performance underscores the MSCNN’s capability to enhance vegetation detection in imagery, making it a promising tool for applications in environmental monitoring and land use management.

The Evolution and Impact of GIS in Environmental Science

This article examines the transformative role of Geographic Information Systems (GIS) in environmental science, focusing on its applications in environmental monitoring, climate change research, and disaster management. It highlights how GIS integration with advanced technologies like remote sensing, machine learning, and big data analytics has enhanced our understanding and management of complex environmental systems. The article discusses specific case studies, including global forest cover analysis, coral reef monitoring, climate change modeling, and disaster response efforts. It also explores the future prospects of GIS, including its potential integration with artificial intelligence and improved satellite technology. The article underscores GIS's critical role in providing accurate, timely, and actionable environmental insights, enabling more effective decision-making and problem-solving in addressing global environmental challenges.

Revolutionizing agri-food technology: Development and validation of the Portable Intelligent Oil Recognition System (PIORS)

The increasing demand for high-quality vegetable oils has amplified the need for efficient, and accurate, oil recognition systems. This paper introduces the Portable Intelligent Oil Recognition System (PIORS), a pioneering advancement in the field. PIORS stands as the first-ever portable, AI-powered device tailored for rapid and accurate oil type identification, capable of delivering results within a mere five seconds. Using advanced machine learning, PIORS can distinguish between various oil types such as sesame, black seed, flaxseed, and almond oils, ensuring the quality and safety of the final product. The device's innovative design integrates an advanced sensor array with seamless Bluetooth connectivity, offering real-time data synchronization with mobile applications. Several Machine learning models, including Support Vector Machines (SVM), AdaBoost, Random Forest, K-Nearest Neighbors (K-NN), Gradient-Boosting Decision Tree (GBDT), and Extreme Gradient Boosting (XGBoost) were implemented and thoroughly validated to obtain correct categorization. The results show that the XGBoost instantaneous classification algorithm continuously beat the competition, obtaining an astounding success rate of 97 percent in predicting and differentiating between the four oil classes. PIORS not only sets a new standard for speed and efficiency in oil quality assessment but also paves the way for groundbreaking applications in food safety, environmental monitoring, and quality control processes. This paper details the development, implementation, and validation of PIORS, showcasing its potential to revolutionize the agri-food industry with its cutting-edge, AI-driven approach to oil recognition.

Frequency Selective Surfaces: Design, Analysis, and Applications

This paper aims to provide a general review of the fundamental ideas, varieties, methods, and experimental research of the most advanced frequency selective surfaces available today. Frequency-selective surfaces are periodic structures engineered to work as spatial filters in interaction with electromagnetic (EM) waves with different frequencies, polarization, and incident angles in a desired and controlled way. They are usually made of periodic elements with dimensions less than the operational wavelength. The primary issue examined is the need for more efficient, compact, and adaptable electromagnetic filtering solutions. The research method involved a comprehensive review of recent advancements in FSS design, focusing on structural diversity, miniaturization, multiband operations, and the integration of active components for tunability and reconfigurability. Key findings include the development of highly selective miniaturized FSSs, innovative applications on flexible and textile substrates, and the exploration of FSSs for liquid and strain sensing. The conclusions emphasize the significant potential of FSS technology to enhance wireless communication, environmental monitoring, and defense applications. This study provides valuable insights into the design and application of FSSs, aiming to guide future research and development in this dynamic field.

Low-power and cost-effective readout circuit design for compact semiconductor gas sensor systems

This study introduces a novel readout circuit architecture that enhances semiconductor gas sensor systems by reducing power consumption, enabling miniaturization, and improving economic viability. Validated at the PCB level, the design shows strong commercial potential by addressing power efficiency and signal accuracy challenges. The technology is adaptable for applications in environmental monitoring, industrial safety, and medical diagnostics, where efficient and reliable gas sensing is essential.

Fabrication of an electrochemical sensor based on Zinc Vanadate anchored with multi-walled carbon nanotubes modified glassy carbon electrode for the determination of Carbendazim

The detection of Carbendazim (CBZ) insecticides has prompted concerns regarding their potential impacts on both human health and the environment. To address this issue, a novel nanocomposite comprising Zinc Vanadate (ZVO) was prepared using a common hydrothermal technique and incorporated with multi-walled carbon nanotube (MWCNT) to form ZVO/MWCNT nanocomposite. Thus, a glassy carbon electrode (GCE) has been modified using this ZVO/MWCNT nanocomposite, which offering a simple and affordable platform for the electrochemcial detection of CBZ. The structure and composition of the nanomaterial were confirmed through various microscopic and spectroscopic techniques, while its electrochemical characteristics were investigated using electrochemical impedance spectroscopy (EIS), differential pulse voltammetry (DPV), and cyclic voltammetry (CV) techniques. The GCE/ZVO/MWCNT modified electrode exhibited a broad linear response ranging from 0.05 to 110μM, with a low detection limit of 0.005μM and high sensitivity (2.93 μA μM−1 cm−2), with recoveries ranging from 98% to 99.93%, respectively. Moreover, GCE/ZVO/MWCNT electrode demonstrated excellent stability, repeatability, and practical utility for CBZ detection. Based on electrochemical studies that indicate the formation of oxidized products consistent with a 2-electron transfer process. Therefore, this innovative GCE/ZVO/MWCNT modified electrode offers promising prospects for sensitive and selective CBZ determination, with potential applications in environmental monitoring and health protection.

Detection of E. coli using bacteriophage T7 and analysis of excitation‑emission matrix fluorescence spectroscopy

Conventional detection methods require the isolation and enrichment of bacteria, followed by molecular, biochemical, or culture-based analysis. To address some of the limitations of conventional methods, this study develops a machine learning (ML) approach to analyze the excitation-emission matrix (EEM) fluorescence data generated based on bacteriophage T7 and E. coli interactions for in-situ detection of live bacteria in the presence of fresh produce homogenate. We trained classification models using various ML algorithms based on the 3-D EEM data generated with bacteria and their interactions with a T7 phage. These ML algorithms, including linear Support Vector Classifier and Random Forest, demonstrate high accuracy (>0.85) for detecting E. coli at 102 CFU/ml concentration within 6 hours. Additionally, these ML models can differentiate among different E. coli concentration levels. For example, the Gaussian Process model achieved an accuracy of 92% in detecting different concentration levels of live E. coli. Application of these ML methods to detect E. coli in spinach homogenate yielded an accuracy of 89% using the linear-SVC model. Furthermore, feature selection techniques were employed to reduce the dimensionality of the data, revealing that only six features were necessary for achieving classification accuracy (>0.85) of spinach homogenate samples containing 102 CFU/ml of E. coli. These findings highlight the potential of this novel bacterial detection methodologies, offering rapid, specific, and efficient solutions for applications in food safety and environmental monitoring.

High-performance flexible and broadband photodetectors on paper substrates using FeSnS bimetallic sulfide nanosheets

Advancements in technology have made it easier to construct flexible broadband photodetectors for wearable devices, improving light harvesting and detecting capabilities. This work described a new study that used bimetallic sulfide nanosheets (FeSnS) to produce flexible and broadband photodetectors on paper substrates using hand-print method. A simple, economical, one-step hydrothermal technique was employed to synthesise FeSnS materials, which were characterised using XRD, UV–Vis Spectroscopy, FESEM, EDS, XPS, and UPS. Photodetection tests found that 2-FeSnS (Fe at 2 wt%) outperformed similar work on paper-based devices, with a responsivity of 32.4 mA/W under a 470 nm incident wavelength, high specific detectivity, and a response time of 1.16 s. Durability and flexibility testing validated the robustness of the device. The use of a simple hydrothermal synthesis and hand-print fabrication method to create high-performance, eco-friendly, and flexible photodetectors is novel, expanding their potential applications in wearable electronics, environmental monitoring, and low-cost disposable sensing platforms.

Influence of isochronal heat treatment on spray-deposited mixed phase tin based thin films for ammonia gas sensor applications

The current work applied the spray pyrolysis (SP) approach to coat mixed-phase SnS thin films at 3500C, and then air annealed at 400,450,5000C for use in sensor and solar cells applications. A variety of methods were employed to evaluate the structural, morphological, compositional, optical, and electrical characteristics of synthesized mixed-phase films. Films annealed at 5000C are reported to have bigger crystal sizes, and all deposited SnS films exhibit the orthorhombic phase in addition to tetragonal phase of SnO2, with other visible secondary phases like SnS2,Sn2S3. Fourier transform infrared spectroscopy and Raman analyses verified the molecular structure and vibrational mode of SnS films. The Scanning electron microscopy tests reveal a consistent, size-varying two-dimensional flake like grain structure, while the optical studies show a variation in optical direct band-gap from 1.79-2.75eV. The Hall-effect shows that films subsequently annealed at 4000C have improved electrical properties. An efficient metal oxide semiconductor gas sensor was developed by applying mixed-SnS film on the conducting side of an indium tin oxide coated glass plate. To examine the sensor selectivity at room temperature, a variety of test gases, such as NO2, H2S, C2H5OH, NH3, were purged at different concentrations. The optimum working temperature and concentration for NH3 selective gas have been determined to be 3500C and 5ppm. The sensor sensitivity is found to be maximum at operating temperature of 2000C towards NH3 gas. An inexpensive, fabricated MOS-gas sensor based on mixed-phase SnS can be easily mass produced and may find potential applications in industrial and environmental monitoring.

Dual-functional ratiometric fluorescence sensor based on silica spheres for highly selective detection of Hg2+ and Fe3+

Ratiometric fluorescence sensors, which validate the absence of ambient and excitation light intensity effects, have attracted significant attention. In this study, a novel nanohybrid dual-emission system was fabricated by combining nitrogen-doped carbon quantum dots (NCQDs) with 8-hydroxyquinoline (8-HQ). This system allows for the simultaneous detection of Fe3+ and Hg2+ ions by utilizing distinct signals. A ternary hollow structure nanosensor containing core-shell silica spheres modified by 8-HQ molecules and decorated with carbon quantum dots was fabricated by subsequent solvothermal synthesis. The designed nanohybrid emits dual-emission fluorescence peaks at 480 and 340nm, originating from the intrinsic structure of NCQDs and 8-hydroxyquinoline, respectively. When Hg2+ is present, only the fluorescence intensity at 480nm is noticeably reduced. However, in the presence of Fe3+ ions, the fluorescence intensity at 480nm remains mostly unchanged, but clear fluorescence quenching is detected at 340nm. There was a linear relationship between fluorescence intensity ratios at 340 and 480nm and the concentration of Hg2+ and Fe3+, with detection limits of 9.50nM and 0.96μM, respectively. Furthermore, it demonstrated satisfactory assay performance in quantifying the mercury content in amalgam alloy and the Fe3+ concentration in a water sample. The results of this study offer a new approach to designing multifunctional fluorescent sensors and a potential application for biomedicine and environmental monitoring.

Light-emitting diode-based absorbance detectors for flow-through analysis in analytical chemistry: A tutorial

The development of light-emitting diodes (LEDs) has played a significant role supporting many recent advances made in chemical analysis. Their small size, low power consumption, and semi-monochromaticity make them ideal light sources for portable photometric devices used in field and on-site analysis, environmental monitoring, and industrial applications, such as process analytical technology (PAT) and continuous real-time analysis. This tutorial provides an overview and critical comparison of the key components in LED-based absorbance detectors, including light sources, optical fibres, flow cells, and photodetectors. Fundamental aspects of LEDs relating to their use in such simple detectors are discussed in detail, along with the development of multi-wavelength detectors that incorporate multiple LEDs. The tutorial also details the different methodologies for assessing and characterising the LED-based absorbance detectors. Finally, this tutorial presents some selected applications of LED based photometric detectors in gas sensing, flow injection analysis (FIA), and coupled with separation techniques such as ion chromatography, liquid chromatography, and capillary zone electrophoresis (CZE).

Design and Investigation of a High-Performance Quartz-Based SAW Temperature Sensor

In this work, a surface acoustic wave (SAW) temperature sensor based on a quartz substrate was designed and investigated. Employing the Coupling-of-Modes (COM) model, a detailed analysis was conducted on the effects of the number of interdigital transducers (IDTs), the number of reflectors, and their spacing on the performance of the SAW device. The impact of the transversal mode of quartz SAWs on the device was subsequently examined using the finite element method (FEM). The simulation results indicate that optimizing these structural parameters significantly enhances the sensor’s sensitivity and frequency stability. SAW devices with optimal structural parameters were fabricated, and their resonant frequencies were tested across a temperature range of 25–150 °C. Experimental results demonstrate that the SAW temperature sensor maintains high performance stability and data reliability throughout the entire temperature range, achieving a Bode-Q of 7700. Furthermore, the sensor exhibits excellent linearity and repeatability. An analysis of the sensor’s response under varying temperature conditions reveals a significant temperature dependency on its Temperature Coefficient of Frequency (TCF). This feature suggests that the sensor possesses potential advantages for applications in industrial process control and environmental monitoring.

ENVIRONMENTAL RISK ASSESSMENT OF TRANSPORTATION INFRASTRUCTURE DEVELOPMENT USING GIS IN LAGOS STATE

Transportation infrastructure development poses significant environmental risks, including habitat disruption, air and water pollution, and climate change. This study develops a GIS-based framework for ecological risk assessment of transportation infrastructure development. This research identifies high-risk areas and prioritizes mitigation measures by integrating spatial analysis, multi-criteria decision analysis, and expert consultations. The GIS-based approach evaluates environmental sensitivity, proximity to ecosystems, and potential hazards. Results provide valuable insights for sustainable transportation planning, policy-making, and ecological management. This study explored its application in solving Environmental Risk Assessment of Transportation Infrastructure Development GIS application in environmental Risk Assessment monitoring has mainly been divided into three aspects: water, soil, and atmosphere. Based on ArcGIS 10.1 software and ArcGIS 9.3.1 version, GIS has been applied in Transportation system monitoring and soil heavy metal concentration monitoring, respectively. A major function of planners is to promote the best use of a community's land and resources for different construction projects; especially critical are infrastructure projects on which economic development relies. Transportation projects typically involve both environmental and economic issues facing a community as it grows and changes. Considerations of sustainability, as well as the widespread use of collaborative planning, design, and construction, require tools that facilitate long-term impact analysis and easy communication among built-environment professionals. Environmental impact assessment (EIA) is instrumental in studying transportation project impacts, but it is a time-consuming process because of the large number of dependent and independent variables involved. A vulnerability grade map and road distribution map were produced, providing an overall vulnerability score for each of the three planning alternatives considered. A road alignment was indicated as the preferred corridor, which was further recommended for a project development and environmental (PD&E) study.

Data-Driven Environmental Risk Management and Sustainability Analytics

This paper explores the intersection of data-driven approaches and environmental risk management, emphasizing the critical role of technology in enhancing sustainability. It provides a systematic review of current literature on public-private partnerships, data quality challenges, and innovative methodologies such as machine learning and Internet of Things (IoT) applications for environmental monitoring. Key themes include the integration of interoperable data platforms, the implications of big data on climate change, and the importance of fostering government policies that promote data sharing for sustainability initiatives. The analysis highlights best practices and recommendations for leveraging advanced analytics and remote sensing technologies to assess and mitigate environmental risks. Ultimately, this research underscores the necessity of collaborative efforts among stakeholders to develop effective strategies for sustainable resource management.

Identifying freshwater invertebrate taxa susceptible to AChE-acting pesticides

Globally pesticide use has been associated with negative impacts on riverine invertebrate communities, but chronic exposure effects to most specific groups of pesticides are not well understood. In this paper, we sought to identify invertebrate species most vulnerable to effects of AChE-acting pesticides in UK rivers for potential application in environmental monitoring. We did this using a combination of the conservation of molecular target for AChE-acting pesticides (identified using the SeqAPass tool), laboratory-based toxicity data, and both biological traits and life history information. We then applied this information to assess for evidence of impacts on these riverine invertebrate communities in the Anglian region of England where there is high pesticide use. Sensitive genera to AChE-acting pesticides included Aedes, Anopheles, Cloeon, Ischnura, and the tolerant genera were Culex, Daphnia, Lymnaea. Using the UK Environment Agency's biota monitoring data spanning over period of 28 years (1984–2011) we evidence absence of the taxa most sensitive to AChE-acting pesticides (Ischnura, Aedes and Anopheles) but presence of AChE pesticide tolerant taxa (Lymnaea) at riverine sites in the Anglian region with high AChE pesticide use.