Pollutant Concentrations Research Articles

A Hybrid Model High-Order Hesitant Fuzzy Time Series and Multilayer Perceptron with Mean Aggregated Membership Value for Enhanced Air Pollutant Concentration Forecasting

A Hybrid Model High-Order Hesitant Fuzzy Time Series and Multilayer Perceptron with Mean Aggregated Membership Value for Enhanced Air Pollutant Concentration Forecasting

Spatiotemporal correlations of PM2.5 and O3 variations: A street-scale perspective on synergistic regulation.

PM2.5 and O3 are major pollutants affecting air quality and posing serious health risks in China. While many studies focus on their control at urban and regional scales, their co-regulation at the street level-closely tied to traffic emissions and commuting patterns-remains unexplored. This study addressed the gap by using nonlinear statistical methods to analyze the spatiotemporal evolution of PM2.5 and O3 from street-scale mobile measurements in Fuzhou, China. A random forest (RF) model was applied to elucidate factors influencing PM2.5-O3 synchronicity. Key findings revealed that street-scale variations in PM2.5 and O3 exhibited multifractality and long-term persistence. Co-directional changes between PM2.5 and O3 peaked at noon, compared to traffic peak hours and midnight. An 800 m threshold was identified for analyzing PM2.5-O3 synchronicity-below this spatial scale, local factors weaken their concordance, while beyond it, the concordance strengthened. RF models showed that PM2.5 was primarily influenced by precursor substances in winter and meteorological conditions in summer, while O3 was consistently affected by meteorological conditions across both seasons. Road traffic and construction disrupted the co-directional changes of PM2.5 and O3, whereas high humidity partially mitigated high concentrations of both pollutants but enhanced their synchronicity.

Applying Bayesian statistics and MCMC to ozone reaction kinetics: Implications for water treatment models.

Ozonation is an advanced oxidation process widely used for water and wastewater treatment. The pseudo-first-order kinetic model is frequently applied for designing and scaling this treatment, offering insights into oxidation pathways through ozone reaction rate constants (kO3,P and kOH,P) and the exposure ratio term (Rt). This work evaluates the kinetic constants for ozonation reactions involving three pharmaceuticals, caffeine (CAF), atenolol (ATL), and propranolol (PRO), classified as emerging contaminants (ECs), with the aim of guiding future research and industrial applications for various pollutants. Two estimation methods were compared: the conventional indirect linearization method and a novel direct Bayesian approach employing the Monte Carlo Markov Chain (MCMC) method with the Metropolis-Hastings (MH) algorithm. Additionally, measurement uncertainties (σmed - 1, 2 and 5%) were incorporated into the direct method, enabled by Bayesian inference, to assess the impact of noise on the estimation of kinetic constants. This consideration is critical for ensuring safety, efficiency, and reproducibility in industrial applications. Results indicated that the direct Bayesian method was both accurate and promising. It effectively estimated the pollutant concentration curves despite the presence of noise, providing improved parameter adjustments compared to the indirect method. While, σmed influenced the parameter estimation, the Bayesian approach maintained a superior fit to the curves even with measurement noises, showing applicability of the proposed method. The obtained Markov chains exhibited good convergence, particularly for the direct oxidation parameter (kO3,P), suggesting the method's effectiveness and sensitivity to the molecular structure of each compound. Furthermore, the use of direct residual ozone measurement (sensors) in this study was found to influence parameter prediction, leading to variations in rate constants compared to values reported in the literature. Therefore, this study recommends using the direct Bayesian method with the exposure ratio term for more reliable industrial-scale ozonation applications, offering a significant advancement over traditional approaches.

ПРИМЕНЕНИЕ МИКРОБНЫХ ТЕХНОЛОГИЙ ПРИ ПЕРЕРАБОТКЕ ОТХОДОВ ПТИЦЕВОДСТВА, ИСПОЛЬЗУЕМЫХ ДЛЯ ПОВЫШЕНИЯ ПЛОДОРОДИЯ ПОЧВЫ И УРОЖАЙНОСТИ ЯРОВОЙ ПШЕНИЦЫ

The aim of research is to study the environmental safety and effectiveness of the influence of the product of enzymatic processing of biowaste of a poultry farm (biofertilizer) on individual elements of soil fertility and the yield of spring soft wheat. Objectives: to study the chemical composition of the fertilizer; to measure the number of microorganisms; to determine the content of nitrate nitrogen and phytotoxicity when adding microfertilizer to the soil; to evaluate its effectiveness in a field experiment when cultivating spring wheat. Field trials of the biofertilizer were carried out at the experimental site of the agrochemistry laboratory in the southern forest-steppe zone of the Omsk Region. The soil of the experimental site is meadow-chernozem, medium-deep, medium-humus, heavy loamy. The scheme of the field experiment was developed taking into account the dynamics of mineralization (20 %) of organic fertilizers in the year of their application: 1 – control (without fertilizers); 2 – application of nitrogen-phosphorus mineral fertilizers at a dose of N60P60 (equivalent in terms of N, P, K content in 10 tons of biofertilizer); application of biofertilizer at doses: 3 – 10 t/ha; 4 – 15 t/ha; 5 – 20 t/ha. The experiment was repeated three times. The area of the experimental plot was 18 m2. The experiment was set up, records were taken and observations were carried out using generally accepted methods. The laboratory experiment was set up using the accelerated composting method according to the following scheme: 1 – control (0); application of biofertilizer to the soil in doses of: 2 – 10 t/ha; 3 – 20 t/ha; 4 – 30 t/ha. A qualitative analysis of the biofertilizer showed that the concentration of pollutants in it did not exceed the permissible limits. It did not contain any pathogenic or disease-causing microorganisms. In terms of macronutrient content (NPK), it is not inferior to the main bioresources (cattle manure, sapropel, green manure). The narrow nitrogen to carbon ratio (3.6) in it ensures a short period of its biodegradation in the soil. The application of biofertilizer in doses of 10–20 t/ha has a positive effect on microbial homeostasis, while the toxicity of the soil substrate was not manifested, the nitrogen regime of plant nutrition improved, and the yield of spring wheat increased to 11 %.

A Review on PM2.5 Sources, Mass Prediction, and Association Analysis: Research Opportunities and Challenges

Air pollution has long been one of the most life-threatening issues which has led to massive amounts of premature human death due to fatal diseases and environmental disasters. Several Sustainable Development Goals (SDGs) set up by the United Nations coincide with the solutions for air pollution reduction. To reach a sustainable future, researchers have conducted many theoretical analyses or case studies of air pollution at different places on the globe and proposed prudent strategies for obtaining an equilibrium between socioeconomic development and air pollution reduction. This research selected a substantial number of articles and existing review papers published between 2013 and 2024 and organized these publications into subfields. This research was focused on filling the gap between existing reviews and the state-of-the-art technologies in the last decade. To be informative and contextual, this review presented a pathway for readers to comprehend the research in three contiguous phases of air pollution analysis, from compositional apportionment and mass prediction of pollution to disclosing associations between pollution concentration and natural or anthropogenic factors. At the end of this review, the author highlighted several research fields which have been overlooked in previous reviews but will be increasingly important in the future.

Concentration of traffic air pollutants and influencing metrological factors in Hawassa City roadways, Ethiopia

IntroductionThe traffic air pollution caused by transportation is a growing global problem that contributes to millions of deaths each year. Despite its importance, information on pollutant concentration is limited in many developing cities, especially in Ethiopia. This study aimed to determine the concentration levels and spatial and temporal variations of traffic air pollutants in Hawassa and to investigate the influence of metrological parameters on the concentration of traffic air pollutants.MethodsA real-time monitoring system of Aero-Qual Series 300/500 was used to monitor pollutants, and 24 monitoring sites were included on both heavy and low-traffic volume roads. The study monitored morning and afternoon times over 24 days to comprehensively characterize the temporal variations.ResultsThe results showed that the mean PM2.5 concentration on heavy- and low-traffic volume roads was 161.6 ± 26.1 μg/m3 and 95 ± 14.2 μg/m3, respectively, whereas the PM10 concentration was 178.7 ± 20.3 μg/ m3 and 102.3 ± 17.6 μg/m3, respectively. Similarly, the mean NO2 concentrations on roads with heavy and low traffic volumes were 86.4 ± 14.4 μg/m3 and 61.7 ± 14.2 μg/m3, respectively. Significantly higher, concentrations were recorded on traffic light roads, followed by main asphalt roads, for both types of traffic air pollutants. The ratio of PM2.5/PM10 was higher (0.924), in which the pollution sources attributed to anthropogenic sources. Kendall’s tau-b correlation analysis suggested that Meteorological parameters (temperature and relative humidity) were positively correlated with traffic air pollutants. Likewise, stepwise multiple linear regression analysis confirms that the concentrations of traffic air pollutants had a positive relationship with metrological parameters.ImplicationsThe findings of this study therefore showed the need for regular air quality monitoring of the urban areas to copping out the adverse public health impacts. And, it highlighted an urgent need for long-term monitoring of traffic air pollution and the development of emission control programs that can be readily implemented to decrease the emissions from anthropogenic sources. Also, it brings a sense of collaboration among stakeholders to tackle the effects of air pollution by providing an inclusive and sustainable development agenda for Hawassa.

Selection of sampling sites in Germany for the International Moss Survey 2020 using statistics and decision modelling

BackgroundAfter 1990, 1995, 2000, 2005 and 2015, Germany participated in the International Moss Monitoring 2020 (MM2020). The German contribution to MM2020 aimed at pilot studies on the suitability of bioindication with mosses for recording the atmospheric deposition of persistent organic pollutants and microplastics.ResultsThis investigation was based on moss samples collected at 25 sites in Germany: Eight sites at which concentrations of persistent organic pollutants were determined in the Moss Survey 2015 were included. In addition, twelve sites were selected from the pool of the total of 400 moss collection sites in 2015. Further five sites of the German moss monitoring network 2015 were added, at which moss samples were collected in 2020 for developing the sample preparation and for preliminary investigations. The selection of the five test sites was based on the same criteria as for the 20 target sites of the 2020 monitoring to make the analysis data of the test phase usable for later evaluations. To ensure methodological transparency and objectivity, a procedure based on statistical methods and decision modelling was developed for this purpose. The decision algorithm enabled taking into account a large number of technical criteria. Selected features of the three subsamples comprising 8, 20 and 25 sites were compared with those of the full sample (n = 400 sites of Moss Survey 2015) and inferentially tested whether the thinning of the 2015 sampling network (n = 400) to 8, 20 and 25 sites, respectively, leads to significant changes in its information quality or not.ConclusionsMethods of decision modelling and inferential statistics have proven their worth for transparently restructuring the moss monitoring network.

Study on the Biofilm Kinetics in Micro-Electrolysis Biological Reactors

The kinetic study of micro-electrolysis biotechnology not only determines the removal efficiency of a micro-electrolysis process but also influences the optimal design of a system. This paper investigates the relationship between electric field strength, pollutant degradation rate, and biofilm thickness by constructing a microporous biofilm model for pollutant removal. Additionally, the study derives equations linking electric field strength to reaction rate, pollutant effluent concentration, and biofilm thickness under both high and low pollutant influent concentrations. This work bridges the gap between macroscopic processes and periplasmic mechanisms, enhancing our understanding of pollutant removal mechanisms and facilitating process optimization. It also provides theoretical support for the sustainable development of micro-electrolysis biotechnology. Future research will focus on experimental validation and the optimization of model accuracy and flexibility to accommodate diverse treatment conditions.

Advanced Hybrid Models for Air Pollution Forecasting: Combining SARIMA and BiLSTM Architectures

This study explores a hybrid forecasting framework for air pollutant concentrations (PM10, PM2.5, and NO2) that integrates Seasonal Autoregressive Integrated Moving Average (SARIMA) models with Bidirectional Long Short-Term Memory (BiLSTM) networks. By leveraging SARIMA’s strength in linear and seasonal trend modeling and addressing nonlinear dependencies using BiLSTM, the framework incorporates Box-Cox transformations and Fourier terms to enhance variance stabilization and seasonal representation. Additionally, attention mechanisms are employed to prioritize temporal features, refining forecast accuracy. Using five years of daily pollutant data from Romania’s National Air Quality Monitoring Network, the models were rigorously evaluated across short-term (1-day), medium-term (7-day), and long-term (30-day) horizons. Metrics such as RMSE, MAE, and MAPE revealed the hybrid models’ superior performance in capturing complex pollutant dynamics, particularly for PM2.5 and PM10. The SARIMA combined with BiLSTM, Fourier, and Attention configuration demonstrated consistent improvements in predictive accuracy and interpretability, with attention mechanisms proving effective for extreme values and long-term dependencies. This study highlights the benefits of combining statistical preprocessing with advanced neural architectures, offering a robust and scalable solution for air quality forecasting. The findings provide valuable insights for environmental policymakers and urban planners, emphasizing the potential of hybrid models for improving air quality management and decision-making in dynamic urban environments.

A hybrid deep learning air pollution prediction approach based on neighborhood selection and spatio-temporal attention

Air pollution is a critical global environmental issue, further exacerbated by rapid industrialization and urbanization. Accurate prediction of air pollutant concentrations is essential for effective pollution prevention and control measures. The complex nature of pollutant data is influenced by fluctuating meteorological conditions, diverse pollution sources, and propagation processes, underscores the crucial importance of the spatial and temporal feature extraction for accurately predicting air pollutant concentrations. To address the challenges of data redundancy and diminished long-term prediction accuracy observed in previous studies, this paper presents an innovative approach to predict air pollutant concentrations leveraging advanced data analysis and deep learning methods. The proposed approach, termed KSC-ConvLSTM, integrates the k-nearest neighbors (KNN) algorithm, spatio-temporal attention (STA) mechanism, the residual block, and convolutional long short-term memory (ConvLSTM) neural network. The KNN algorithm adaptively selects highly correlated neighboring domains, while the residual block, enhanced with the STA mechanism, extracts spatial features from the input data. ConvLSTM further processes the output from STA-ConvNet to capture high-dimensional temporal and spatial features. The effectiveness of the KSC-ConvLSTM approach was validated through predictions of PM2.5 concentrations in Beijing and its surrounding urban agglomeration. The experimental results indicate that the KSC-ConvLSTM approach outperforms benchmark approaches in single-step, multi-step, and trend prediction. It demonstrates superior fitting accuracy and predictive performance. Quantitatively, the proposed KSC-ConvLSTM approach reduces the root mean square error (RMSE) by 4.216–8.458 for prediction averages of 1–12 h of PM2.5 in Beijing, compared with the benchmark approach. The findings show that the KSC-ConvLSTM approach shows considerable potential for predicting, preventing, and controlling air pollution.

Deterioration Effects and Microscopic Mechanisms of Solidified/Stabilized Red Mud by CGFPA Binders Under Freeze–Thaw Cycles

Red mud is a kind of solid waste in the production process of the aluminum industry. The long-term stockpiling of red mud not only occupies a large amount of land but also causes environmental pollution. In order to improve the strength, reduce the alkalinity and toxicity of red mud, and study its durability under freeze–thaw cycles, CGFPA binders, whose components were calcium carbide residue, ground granulated blast furnace slag, fly ash, phosphogypsum, and graphene, were adopted to solidify/stabilize red mud in this paper. The effects and the mechanism of freeze–thaw cycling on the unconfined compressive strength, pH value, and toxic leaching of the solidified/stabilized red mud was investigated. The micro-mechanism was analyzed by XRD, SEM-EDS, and FT-IR. The results of the study showed that the mass, unconfined compressive strength, and pH of the solidified/stabilized red mud decreased gradually with an increase in the number of freeze–thaw cycles, while the leaching concentration of pollutants increased gradually. The rate of loss of unconfined compressive strength satisfies an exponential function with the number of cycles, and the logarithm of pollutant concentration satisfies a linear relationship with the number of cycles. The cumulative loss of mass was 6.7%, 5.4%, 3.6%, and 3.3%, and the cumulative loss of unconfined compressive strength was 50.6%, 47.5%, 32.2%, and 25.3%, and the pH value was reduced to 9.42, 9.54, 9.80, and 9.92, respectively, after 10 freeze–thaw cycles at binder mixing ratios of 15%, 20%, 25%, and 30%, while the leaching concentrations of Cu, Zn, Cr, Ni, As, Pb, and Cd increased from 7.4 μg/L, 87.2 μg/L, 5.2 μg/L, 7.0 μg/L, 6.9 μg/L, 3.7 μg/L, and 0.7 μg/L to 17.5 μg/L, 123.5 μg/L, 10.2 μg/L, 15.7 μg/L, 11.4 μg/L, 5.6 μg/L, and 4.9 μg/L, respectively, under the condition of a 30% incorporation ratio. The gelling products generated by the hydration reaction of the binders were mainly C-S-H, C-A-S-H, C-A-H, AFm, etc. Under the action of freeze–thaw cycles, the lattice-like structure of the solidified/stabilized red mud was damaged, resulting in a decrease in its unconfined compressive strength and an increase in pollutant leaching concentration. The research results can provide a theoretical basis for the use of red mud in permafrost regions.

Phytoremediation of Brackish Waters Polluted by Petroleum Hydrocarbon by Using Azolla microphylla

The research aimed to measure the effectiveness of Azolla microphylla in reducing petroleum hydrocarbon in brackish water. A completely randomized design was used with two factors: pollutant concentration (factor A) and A. microphylla addition (factor B). A consisted of A1 (10 ppm), A2 (20 ppm), A3 (30 ppm), A41 (40 ppm), and A5 (50 ppm). B consisted of B1 (50 g) and B0 (0 g of A. mycrophilla). The experimental units contained 10 L of water planted with fern (50 g) and control (B0). Analysis of variance shows that A. microphylla has a significant effect on the reduction of petroleum hydrocarbons in brackish water. The plant reduced levels of this pollutant by up to 36%. Without A. microphylla plants, the pollutant was decreased to 14-22% only. The pollutant reduced the leaf chlorophyll of the fern to 39.5%. The higher the pollutant level, the lower the chlorophyll content.

The effect of air pollution exposure on foetal growth restriction in pregnant women who conceived by in vitro fertilisation a cross-sectional study

Studies investigating the relationship between exposure to air pollutants during pregnancy and foetal growth restriction (FGR) in women who conceive by in vitro fertilisation (IVF) are lacking. The objective was to investigate the effect of air pollutant exposure in pregnancy on FGR in pregnant women who conceive by IVF. We included pregnant women who conceived by IVF and delivered healthy singleton babies in Guangzhou from October 2018 to September 2023. We also collected data on air pollutant concentrations in Guangzhou during the same period. We analysed the impact of air pollution exposure during pregnancy on FGR. After adjusting for confounders, our analysis showed that in the first trimester, high concentrations of PM10 and NO2 in the fourth quartile significantly increased the risk of FGR. Specifically, the odds ratios were 6.430 (95% confidence interval [CI]: 1.035–39.96) for PM10 and 10.73 (95% CI: 1.230–93.48) for NO2. In the second trimester, exposure to PM2.5, PM10, and NO2 was associated with an increased risk of FGR. In addition, subgroup analyses showed that exposure to NO2 during pregnancy increased the risk of FGR in women aged 35 years and older. The results of this cross-sectional study suggest that exposure to PM2.5, PM10, and NO2 in pregnant women who conceive by IVF is associated with the occurrence of FGR.

Dynamics of water pollution concentration with uniform and exponential increment of pollutants

The study focuses on the one dimensional Advection-Dispersion Equation (ADE) to study the dynamics of concentration of water pollution when the pollutants at the source is increasing uniformly and exponentially. Analytical solutions are obtained by using Laplace transform and numerical solutions are by Finite Difference Method (FDM). The steady state case is studied. The dynamics of pollution concentration along the length of river channel are studied through two dimensional plots by varying the rate of added pollutants, cross sectional area of river and water flow velocity. The pollution concentration decreases along the length of river (downstream) for each analysis. The analytical and numerical solutions are shown in three dimensional plots. The analytical and numerical solutions are compared with the help of relative error. The relative errors calculated for uniform and exponential increment of pollutants at the source are compared while studying the dynamics of the concentration. The environmental and chemical engineering may substantially benefit from such studies.

BneR: A collaborative workflow for air pollution exposure modeling and uncertainty characterization using the Bayesian Nonparametric Ensemble.

Air pollution is a major public health threat globally. Health studies, regulatory actions, and policy evaluations typically rely on air pollutant concentrations from single exposure models, assuming accurate estimations and ignoring related uncertainty. We developed a modeling framework, bneR, to apply the Bayesian Nonparametric Ensemble (BNE) prediction model that combines existing exposure models as inputs to provide air pollution estimates and their spatio-temporal uncertainty. The bneR modeling framework (1) harmonizes air pollutant datasets to use standardized inputs for the BNE algorithm; (2) applies the BNE algorithm to obtain the posterior predictive distribution of pollutant concentrations; and (3) generates visualizations. We applied bneR to estimate NO2 concentrations and characterize uncertainty levels at high spatio-temporal resolution (daily, 1 km2) over New York State (NYS) for 2015. We met with stakeholders and modelers to discuss bneR user-friendliness and interpretation of its estimates. Using bneR, we harmonized the spatial scale of four input NO2 models (using the finer resolution, 1 km2 for BNE estimations), applied BNE to obtain the NO2 daily posterior predictive distribution, and visualized the results. Over NYS, the daily average NO2 concentration was 6.0 (interquartile range, IQR: 4.6-6.8) pbb with daily average uncertainty (as SD) of 1.2 (IQR: 1.0-1.3) ppb. BNE performed well with cross-validated RMSE=2.84 ppb and R2=0.80. Meeting stakeholders and modelers allowed us to understand that efficient communication on how uncertainty is estimated and interpreted is a key feature for these communities to engage in using bneR and its data products.

Variation of Surface Pollutants and Their Influence on Lightning Development in the Urban Cities of Northern India

ABSTRACTLightning development and its variation with surface pollutants like particulate matter (PM10), sulfur dioxide (SO2), and nitrogen dioxide (NO2) were investigated in two urban cities of northern India, one from the high‐altitude region Haldwani (Uttarakhand) and the other from Indo‐Gangetic plain region, Agra (Uttar Pradesh). Lightning data is retrieved from Lightning Imaging Sensor (LIS) aboard the International Space Station (ISS) satellite for the period (2018–2022). Surface pollutants data is obtained from Uttar Pradesh Pollution Control Board for the same period. In the high altitude Haldwani region, it is observed that SO2 is very well correlated (CC = 0.95) with lightning flash frequency compared to other surface pollutants PM10 and NO2 in the pre‐monsoon season (March–May). Annual mean values of lightning and concentration of surface pollutants in both study regions indicate that lightning frequency is higher in the high‐altitude region of Haldwani than Agra, which is situated in the Indo‐Gangetic plain region of Uttar Pradesh. No significant correlation is observed between lightning and meteorological variables in Haldwani region. However, a significant correlation is observed between lightning and 2‐m air temperature in Agra. A distinct relation is observed between lightning and surface pollutants in the high‐altitude region of Haldwani and the Indo‐Gangetic plain region of Agra. The present study shows that high concentrations of SO2, associated with intense convection, are one of the reasons for the high lightning frequency in the high‐altitude region of Haldwani.

Impact of Ambient Air Pollution on Reduced Visual Acuity Among Children and Adolescents

ABSTRACT Purpose Previous studies have assessed the impact of air pollution on myopia from the individual level, while none of them have explored the role of air pollution in visual health disparities between different regions from the area level. This ecological study aimed to investigate the impact of ambient air pollution on reduced visual acuity (VA). Methods The data were derived from the Chinese National Survey on Students’ Constitution and Health (CNSSCH) conducted in 2014 and 2019, which involved 261,833 and 267,106 students respectively. The participants were 7–22 years old randomly selected from 30 mainland provinces in China. Locally weighted scatterplot smoothing (LOESS) regression models and fixed-effects panel regression models were used to explore the associations of provincial-level prevalence of reduced VA with air quality index (AQI), fine particulate matter ;(PM2.5), PM10, sulfur dioxide (SO2), carbon monoxide (CO), nitrogen dioxide (NO2) and ozone (O3) concentrations. Results There were nearly linear positive dose-response relationships between AQI, air pollutant concentrations and the prevalence of reduced VA. After adjusting for covariates, an interquartile range increase in PM2.5 exposure was significantly associated with a 5.0% (95% confidence interval, 0.7%-9.3%) increase in the prevalence of reduced VA, whereas no significant associations were observed between AQI, the other five pollutants and the prevalence of reduced VA. Conclusion Regions with more polluted air tend to have a higher prevalence of reduced VA. Exposure to PM2.5 might be an important risk factor for myopia among children and adolescents.

Investigating the quality of European silver eels by quantifying contaminants and parasite infestation in a French Mediterranean lagoon complex.

Coastal lagoons are diverse habitats with significant ecological gradients, which provide crucial ecosystem services but face threats from human activities such as invasive species and pollution. Among the species inhabiting the lagoons, the critically endangered European eel (Anguilla anguilla) is an emblematic species strongly impacted by contamination and parasitism. Several indicators were developed to assess the quality of eel at a large geographic scale. Most indicators are based on the concentration of individual pollutant and/or abundance of parasites separately without considering individual variations. This study assessed the quality of 59 eels captured at three different sites inside a Mediterranean lagoon complex (the Camargue, South of France), by integrating multiple degradation factors (POPs, TEs, and A. crassus infestation) and considering individual eel characteristics (length, age, growth rate, and sex). Using multivariate TOPSIS analysis including these degradation factors, this study found that eel quality decreased with age but did not significantly vary between sites. When focusing on each degradation factor, A. crassus infestation rates were lower in older eels, independently to the site; however, the POPs and TEs contaminations were lower in the Grandes Cabanes site compared to the Vaccarès and Fumemorte sites even if smallerand younger eels were more contaminated by POPs. These findings reveal the fine-scale spatial variability in eel quality, with TOPSIS analysis providing a robust method to rank and score scenarios. This approach enhances the understanding of habitat degradation sources affecting eel contamination and parasitic infestation, supporting more effective strategies for sustainable habitat management.

Air Quality-Driven Traffic Management Using High-Resolution Urban Climate Modeling Coupled with a Large Traffic Simulation

This study presents a framework for integrating traffic simulation with high-resolution air pollution modeling to design adaptive traffic management policies aimed at reducing urban air pollution. Building on prior work that establishes the coupling of the MATSim traffic model with the PALM-4U urban climate model, this second part focuses on implementing a feedback loop to inform traffic management decisions based on simulated air pollution concentration levels. The research explores how traffic volumes and atmospheric conditions, such as boundary layer dynamics, influence air quality throughout the day. In an artificial case study of Berlin, a time-based toll is introduced, aimed at mitigating concentration peaks in the morning hours. The toll scheme is tested in two simulation scenarios and evaluated regarding the effectiveness of reducing air pollution levels, particularly NO2 during the morning hours. The case study results serve to illustrate the framework’s capabilities and highlight the potential of integrating traffic and environmental models for adaptive policy design. The presented approach provides a model for responsive urban traffic management, effectively aligning transportation policies with environmental goals to improve air quality in urban settings.

PM2.5 and NOX concentrations decrease as a result of a railway electrification.

This work examines the impact of the electrification of the Holon-Bat Yam passenger train line (central Israel) on air pollutant concentrations using data collected from air quality monitoring stations that operated at the train stations across the electrified train line. We present statistically significant reduction in the annual average NO2, NO and NOX concentrations (29-45%, 79-85% and 65-75%, respectively), attributed to the electrification of the passenger train line. The drop in the NO and NOX concentrations was much stronger than in the NO2 concentrations, since NO is the main nitrogen species emitted by diesel locomotives. PM2.5 concentrations also significantly decreased, but only in two (out of the three) train stations situated along the electrified line. Following various analyses, we conclude that electrification of train lines reduces train locomotive emissions and improves the air quality at the stations, as expected, thus protecting the passengers and reducing their exposure to air pollutants. Although this study presents a specific case, the findings are expected to be applicable, at least quantitatively, to other locations, as railway electrification removes emissions associated with fossil-fuel-powered locomotives. This work supports railway electrification policy, which has the potential to substantially lower air pollution levels and diminish the passengers' exposure to harmful air pollutants.