Pollution Monitoring Research Articles (Page 1)

Deep learning-based hyperspectral oil spill detection for marine pollution monitoring in the Gulf of Mexico: A step toward marine pollution monitoring and SDG 14 compliance.

Beaver dam–pond systems reshape the hydrology of lowland landscapes by slowing water flow and trapping sediments, thereby reducing the movement of pollutants. This study examined how such beaver-engineered wetlands can naturally filter and signal contamination risks associated with lead (Pb). We combined data from three matrices—bottom sediments, riparian vegetation, and non-invasively collected beaver fur—across three Lithuanian sites (2022–2024). Previously published datasets on plants and sediments were complemented with new information from beaver fur to explore seasonal and age-related effects as well as differences inside and outside dam zones. Lead levels were consistently higher in sediments than in plants, while beaver fur reflected variable, site-specific exposures. These results show that beaver activity contributes to the capture and redistribution of sediment-bound Pb in wetland buffers. The approach demonstrates how beaver habitats can serve as low-cost, nature-based sentinels for pollutant monitoring. Using beaver fur as a non-invasive bioindicator and managing dam stability can improve the ecological and policy relevance of buffer zones. Overall, the findings support the integration of beaver-engineered wetlands into environmental management and EU water policy, contributing to SDG 6 goals for clean water and sustainable wetland use.

OIL Terminal Constanța is one of the largest petroleum terminals in Eastern Europe, playing a strategic role in the transportation and storage of petroleum products for the European market. Its economic and operational importance is increasing, particularly in the context of the upcoming exploitation of the Neptun Deep gas field in the Black Sea, which will transform the terminal into a key logistics hub for exports and the distribution of natural gas and its derivatives. This development underscores the need for enhanced pollution prevention measures to protect the region's soil and water resources. This study develops an integrated hydrogeophysical model for characterizing the subsurface extent of pollution by correlating advanced geophysical methods with pre-existing hydrogeological and geological data. Based on the geomorphological analysis of the area, magnetometry, electrometry, ground-penetrating radar (GPR), and photogrammetry are applied, alongside seismic refraction, which is used for the first time in this region. The studied area features a coastal landscape dominated by recent sedimentary deposits and complex aquifer structures that influence the dynamics of subsurface pollution. Natural coastal erosion and fluctuations in the water table contribute to increased vulnerability to contamination, necessitating close monitoring of interactions between industrial activities and the geological environment. Magnetometry is employed to detect buried metallic structures, such as historical pipelines that may be fractured and serve as potential pollution sources. Electrometry assesses variations in soil resistivity, enabling the identification of the water table level and sedimentary succession, both critical factors in evaluating pollutant migration potential. Ground-penetrating radar (GPR) provides high-resolution imaging of contaminated areas, while seismic refraction helps identify geological discontinuities that may influence contaminant spread. Aerial photogrammetry supports detailed terrain mapping and long-term pollution monitoring. The correlation of geophysical data with historical borehole records in the area allows for the reconstruction of geological structures and stratigraphic sequences. These data are integrated into the hydrogeophysical model, which estimates the migration pathways of the pollution plume and identifies essential measures for protecting soil and groundwater resources. The study results offer a comprehensive understanding of hydrocarbon dispersion mechanisms in the subsurface and support authorities and industrial operators in making informed decisions regarding pollution risk management. This research demonstrates the applicability of modern geophysical methods in environmental protection and proposes a replicable methodological framework for other industrial areas exposed to contamination risks.

Abstract. Elevated surface concentrations of nitrogen dioxide (NO2) are associated with poor air quality, making its detection and monitoring important for human health and the environment. Existing instruments such as the TROPOMI satellite currently deliver daily global maps of NO2 tropospheric columns, and the future Sentinel-4 instrument will return hourly maps. While areas of strong concentrations (cities, large industries) can be detected in these satellite observations, their spatiotemporal resolution remains too coarse to capture local hot spots and quick variations. In the context of urban air quality monitoring, we present a new type of remote sensing instrument capable of observing spatial and temporal gradients in the NO2 field, which is not currently possible with either space instruments or from the routine operations of conventional diffraction grating and other ground-based remote sensing instruments. This novel instrument is based on an acousto-optical tunable filter (AOTF) located at the heart of a telecentric imaging system. The instrument acquires spectral images in the region 430–455 nm, where NO2 exhibits strong absorption features. A dense spectral sampling was commanded in order to enable the application of the DOAS (differential optical absorption spectroscopy) method in the processing of the spectra measured by each detector pixel. In March 2024, the instrument was deployed at the BAQUNIN supersite for atmospheric research, located in the center of Rome. In order to validate the NO2 camera measurements, coincident acquisitions by a MAX-DOAS and a Pandora spectrometer were performed. The results show very good agreement among the three instruments. They also illustrate the additional capabilities of the NO2 camera in observing the spatial and temporal variability of the urban NO2 field.

Since the 2000s, Burkina Faso has experienced a rapid mining expansion with more than one hundred sites established, leading to increased waste generation often discharged untreated into the environment. Assessing water quality in these areas is therefore critical to mitigate environmental degradation and public health risks. This study develops a site-specific water quality index (WQI) for a gold mining area in Bam Province, Burkina Faso, with the objective of improving pollution monitoring and management in relation to tailing dams. Surface and groundwater samples were collected between 2021 and 2024. Physico-chemical and bacteriological analyses of groundwater sources including wells, piezometers and boreholes revealed that several parameters such as pH, turbidity, nitrates, sulphates, total iron, aluminium, arsenic, cadmium, cyanide and total and faecal coliforms exceeded international drinking water standards. Geospatial techniques were employed to identify the main contamination sources: domestic wastewater, industrial and artisanal mining and agricultural runoff. The evolution of these parameters in relation to the dynamics of soil occupation and the influence of geological structure has enabled the distinction of key parameters associated with discharges. Although individual contaminant levels were mostly moderate, their combined effects pose a significant long-term risk to ecosystems and human health. The tailored WQI is suitable for both surface water and groundwater. It provides an integrated tool for classifying and monitoring water quality in mining environments, supporting evidence-based decision making in the management of tailing dams, environmental protection and public health.

Water pollution monitoring data typically exhibit characteristics of complexity, high dimensionality, and non-linearity. However, traditional receptor models-including PCA, PMF, and APCS-MLR-struggle to handle nonlinear high-dimensional water quality data and are susceptible to interference from outliers and missing values, resulting in inaccurate pollution source apportionment. To address this issue, this study aimed to establish a precise pollution source apportionment model to determine the quantity of potential pollution sources, identify their types, and quantify their contribution rates. The proposed method follows three key steps: first, PCA is applied to determine the number of potential pollution sources based on the cumulative variance contribution rate of principal components; second, an AE model is used for dimensionality reduction and pollution source identification; third, a CatBoost model is employed to quantify the contribution rate of each identified source. Taking the Qinhuai New River as a case study, four types of pollution sources were identified: organic pollution or domestic sewage discharge sources, industrial pollution sources, urban runoff or soil erosion sources, and agricultural pollution sources, with their respective contribution rates being 31.1%, 21.5%, 21.7%, and 25.7%. Model evaluation results demonstrate that the AE model achieves a reconstruction 0.95 and a MSE 0.05; Meanwhile, the CatBoost model for contribution rate quantification also yields an 0.95 and MSE 0.05, indicating high fitting accuracy. Overall, the PCA-AE-CatBoost model outperforms the PCA-APCS-MLR and PCA-CatBoost models, providing a more accurate technical basis for pollution control.

Accurate estimation of particulate matter (PM₁₀) concentrations is critical for assessing air quality and mitigating public health risks. Traditional monitoring data processing methods, such as simple moving averages (MA), often struggle to capture rapid fluctuations in pollutant levels due to their uniform weighting of historical data, potentially compromising real-time decision-making. This study evaluates the efficiency of the Exponential Moving Average (EMA) algorithm, which prioritizes recent observations through exponential weighting, to improve PM₁₀ concentration estimates. Using data from urban air quality monitoring stations, EMA was applied across varying time windows and compared against conventional MA approaches. Performance was assessed against ground-truth measurements. Results demonstrated that EMA significantly reduced estimation errors. The algorithm exhibited enhanced responsiveness to abrupt PM₁₀ spikes, attributed to its dynamic weighting mechanism. Sensitivity analysis revealed that optimal smoothing factors depended on the selected time window, balancing noise reduction and trend detection. These findings underscore EMA’s potential as a robust tool for air pollution monitoring data analyses, offering superior adaptability to temporal variability. Implementation of EMA in regulatory and public health frameworks could enhance early warning systems and pollution control strategies. Future research should explore integrating EMA with machine learning models and low-cost sensor networks to further optimize real-time air quality management.

Assessment of historical heavy metal(loid)s contamination records reveals dominant control of grain size effect on the sedimentary profiles from the North Yellow Sea.

Non-contact hyperspectral monitoring of urban wastewater quality: Optimization of model calibration and performance.

Machine learning-assisted magnetic nanomotors for the identification and degradation of organic pollutants.

Trace elements and POPs in baitfish from Madagascar: Implications for whale shark and human exposure.

Low ppt residue monitoring of persistent organic pollutants in groundwater samples using direct immersion solid phase micro extraction arrow (DI-SPME) with gas chromatography-mass spectrometry

Molecularly imprinted electrochemical sensor based on nitrogen-doped hollow mesoporous carbon spheres with PEDOT for selective and ultrasensitive detection of chlorpromazine.

Analysis of source regions and transport pathways of sub-micron aerosol components in Europe.

High microplastic exposure affects survival and health of Dreissena spp. mussels: implications for freshwater pollution monitoring.

Dual Z-scheme In2S3/Bi2S3/ZnS heterojunction with broad-spectrum response as a photoactive material for ultrasensitive detection of environmental Pollutant tetracycline.

Pb, Cd, and Cu concentrations in Scyliorhinus canicula (Linnaeus, 1758) and M. merluccius (Linnaeus, 1758): Accumulation trends and interspecific biomonitor potential.

Multi-heterostructure and surface free radicals induced enhanced ammonia gas sensing performance of polyaniline/tin phosphide/nitrogen-doped reduced graphene oxide at room temperature.

Mn-doped carbon dots-based fluorescent-colorimetric dual-mode probes for selective and sensitive detection of Cr(VI) ions and l-ascorbic acid via smartphone-integrated analytical platform.

Zooplankton community structure as determinant of toxic substances content along the European coast.