Search Results (112)

Stormwater carries significant amounts of pollutants—including metals, microorganisms, organic micropollutants, and nutrients—from land surfaces into nearby water bodies, leading to water quality deterioration and threats to both human health and ecosystems. The removal of these contaminants is essential not only for environmental protection, but also to enable the reuse of treated water for various beneficial applications. Common treatment methods include bioretention systems, biofiltration, constructed wetlands, rain gardens, swales, and permeable pavements. To improve pollutant removal efficiency, adsorbent materials are often incorporated into the soil substrate of these treatment devices. However, most research on adsorbents has focused on their effectiveness against one or two specific pollutants and has been conducted under static, short-term laboratory conditions rather than dynamic, field-relevant scenarios. Column-based dynamic filtration type studies, which are more informative for field applications, are limited. In one study, a combination of two or more adsorbents with contrasting properties that matched the affinity preferences of the different pollutants to the substrate media removed 77–100% of several heavy metals that occur in real stormwater compared to 38–73% removal with only one adsorbent. In another study, polycyclic aromatic hydrocarbon removal with zeolite was only 30–50%, but increased to >99% with 0.3% granular activated carbon addition. Long-term dynamic column-based filtration experiments and field studies using real stormwater, which contains a wide range of pollutants, are recommended to better evaluate the performances of the combined adsorbent systems. Full article

Nitrate contamination poses a significant global environmental threat, impacting the water quality in surface and groundwater systems. Despite its considerable impact, there remains a lack of comprehensive understanding of nitrate sources and discharge patterns, particularly in the Lake Victoria basin of East Africa. To address this gap, a study was conducted in the Kagera River basin, responsible for 33% of Lake Victoria’s surface inflow. This study utilized δ¹⁵N and δ¹⁸O isotope analysis in nitrate, hydrochemistry, and the Bayesian mixing model (MixSIAR) to identify and quantify nitrate sources. Spatiotemporal data were collected across three seasons: long rains, dry season, and short rains, in areas with diverse land uses. Nitrate isotopic data from water and potential sources were integrated into a Bayesian mixing model to determine the relative contributions of various nitrate sources. Notable spatial variations were observed at sampling sites with concentrations ranging from 0.004 to 3.31 mg L⁻¹. Spatially and temporally, δ¹⁵N-NO₃⁻ values ranged from +6.0% to +10.2‰, whereas δ¹⁸O-NO₃⁻ displayed significant spatial differences with mean ranges from −1% to +7‰. MixSIAR analysis revealed important contributions from manure and sewage sources ranging between 49% and 73%. A boron analysis revealed manure was the main source of nitrates in the manure and sewage. These results show that it is necessary to implement improved manure and sewage management practices, especially through proper waste treatment and disposal systems, to enable informed policy decisions to enhance nitrogen management strategies in riparian East Africa, and to safeguard the region’s water resources and ecosystems. Full article

Water film depth (WFD) on runways is a key factor contributing to aircraft hydroplaning during takeoff and landing. Thus, the early measurement or prediction of WFD during rain is critical for reducing accidents. Most existing WFD prediction models are derived from experiments conducted on road surfaces. However, an accurate prediction of WFD on runways and reduced hydroplaning risk require a precise empirical prediction model. This study conducted experiments involving four parameters—rainfall intensity, pavement mean texture depth, drainage length, and transverse slope—to develop a WFD dataset specific to different runway conditions. The multiple linear regression method is employed to establish a model for WFD predictions. The proposed National Taiwan University (NTU) model’s predictability is compared with three existing empirical models using NTU and Gallaway datasets. The results clearly demonstrate the superior accuracy and robustness of the NTU model compared to the other evaluated models. The NTU model offers a precise and practical predictive formula, making it highly suitable for integration into contaminated runway warning and management systems. This study employed a laser displacement sensor and a programmable logic controller to obtain high-accuracy, high-sampling-rate WFD data. Modern automated data acquisition enables simultaneous measurement at multiple points and captures the complete WFD curve from zero to a stable depth, which was previously difficult to obtain. Full article

Simultaneously stabilizing cadmium, lead, and arsenic in contaminated soils is challenging due to their significant differences in physical and chemical properties. This study developed a composite material by modifying hydrochar with iron (Fe), phosphorus (P), and sulfur (S) to address this issue. The iron–phosphorus–thiol-modified Trachycarpus fortunei hydrochar (H-PAL-Fe2-P-T) effectively stabilized these metals. Experimental results showed that the H-PAL-Fe2-P-T achieved over 90% stabilization for DTPA-extracted cadmium, lead, and arsenic. Characterization by XRD, SEM, and FTIR revealed structural and functional changes in the hydrochar. Column leaching tests simulating acid rain showed that the composite material maintained stable stabilization effects, with the fluctuations in the stabilization rates remaining below 20%. Additionally, the composite-modified hydrochar enhanced the stabilization of water-soluble, DTPA-extracted, and TCLP-extracted heavy metals in soil, demonstrating good stability and durability for long-term use. These findings suggest that Fe-, P-, and S-modified hydrochar is a promising and sustainable approach for the remediation of soils contaminated with cadmium, lead, and arsenic. Full article

Rain backscatter corrupts Ku-band scatterometer wind retrieval by mixing with the signatures of the ${\sigma }^{\circ }$ (backscatter measurements) on the sea surface. The measurements are sensitive to rain clouds due to the short wavelength, and the rain-contaminated measurements in a wind vector cell (WVC) deviate from the simulated measurements using the wind geophysical model function (GMF). Therefore, quality control (QC) is essential to guarantee the retrieved winds’ quality and consistency. The normalized maximum likelihood estimator (MLE) residual ($R_n$) is a QC indicator representing the distance between the ${\sigma }^{\circ }$ measurements and the wind GMF; it works locally for one WVC. $J_{OSS}$ is another QC indicator. It is the speed component of the observation cost function, which is sensitive to spatial inconsistencies in the wind field. $R_{n}J$ is a combined indicator, and it takes both local information ($R_n$) and spatial consistency ($J_{OSS}$) into account. This paper focuses on the QC for WindRAD, a dual-frequency (C and Ku band) rotating-fan-beam scatterometer. The $R_n$ and $R_{n}J$ have been established and thoroughly investigated for Ku-band-only and combined C–Ku wind retrieval. An additional 0.4% of WVCs are rejected with $R_{n}J$, as compared to $R_n$ for both Ku-band-only and combined C–Ku wind retrievals. The number of accepted WVCs with high rain rates (>7 mm/h) is reduced by half, and the wind verification with respect to ECMWF winds is generally improved. The C-band measurements are little influenced by rain, so the Ku-based $R_n$ is more effective for the combined C–Ku wind retrieval than the total $R_n$ from both the C and Ku bands. The rejection rate of the combined C–Ku retrievals reduces by about half compared to the Ku-band-only retrieval, with similar wind verification statistics. Therefore, adding the C band into the retrieval suppresses the rain effect, and acceptable QC capabilities can be achieved with fewer rejected winds. Full article

Giardiasis and cryptosporidiosis, caused by Giardia lamblia and Cryptosporidium spp., are parasitic infections transmitted through faecal–oral routes or contaminated water. Although less common in Spain compared to developing countries, they pose a public health concern, particularly for vulnerable groups like children and immunocompromised individuals. This study aims to analyse the cases reported to the Microbiological Information System (MIS) in children between 2012 and 2021, as well as their distribution across sociodemographic variables. Proportions and infectivity rates were determined for epidemiological and sociodemographic data, and the incidence rate for giardiasis and cryptosporidiosis was calculated annually and by health sector. The variables analysed included sex, age, health sector and weather. For both diseases, there was a significant decrease in the number of cases in 2020, suggesting the importance of person-to-person transmission. Children were infected by Giardia in significantly higher proportion (p < 0.001), being the majority in age groups 5–14 years, while the proportion of boys and girls infected by Cryptosporidium was almost identical (1.4% vs. 1.3%), in children aged 2–4 years. Periodically there was a significant increase in cases of cryptosporidiosis, apparently related to the presence of torrential rains. Transmission is related to increased temperature and rainfall. Person-to-person transmission in the paediatric population needs further investigation. This study provides the foundation for future research on the evolution of cases of giardiasis and cryptosporidiosis in Spanish children. The data emphasise the need for informational campaigns on hygienic measures and efforts by public health authorities to maintain water resources in optimal condition to prevent parasite spread. Full article

Understanding influential factors for fecal contamination in groundwater is critical for ensuring water safety and public health. The objective of this study is to identify key factors for fecal contamination of shallow tubewells using machine learning methods. Three methods, including recursive feature elimination (RFE) with XGBoost, Random Forest, and mutual information, were implemented to examine E. coli presence and concentration in 1495 tubewell water samples in Matlab, Bangladesh. For E. coli presence, climatic variables, including average rainfall and temperature over the 30, 15, and 7 days preceding sampling, as well as ambient temperature and rainfall on the sampling day, emerged as critical predictors. Land cover characteristics, such as the percentages of urban and agricultural areas within 100 m of a tubewell, were also significant. For E. coli concentration, land cover characteristics within 100 m, the number of hot and heavy-rain days in the 30 days preceding sampling, average rainfall and temperature in the 3 days preceding sampling, and ambient temperature on the sampling day were identified as key drivers. Random Forest and mutual information yielded results that were more similar to each other than to those of RFE with XGBoost. The findings highlight the interplay between climatic factors, land use, and population density in determining fecal contamination in shallow well water and demonstrate the power of machine learning algorithms in ranking these factors. Full article

Due to its structure and properties, diatomite from a deposit in Jawornik Ruski (Subcarpathian Voivodeship) can be used as a sorbent in rain gardens. The purpose of the current research is to analyze how enriching the substrate used in a rain garden with diatomite can affect the removal of biogenic pollutants. This study was carried out under laboratory conditions using retention columns, two experimental columns with different contents of diatomite, and a control column without the addition of diatomite. Analyses of the materials used included studies of the characteristics of the rain garden layers (water permeability and granulometric analysis) and characterization of the diatomite (SEM images, oxide and phase composition, leachability, and BET). The effects of diatomite on pollutant removal were studied for NH₄⁺, PO₄³⁻, NO₃⁻. The results showed approximately 3-fold higher reductions in the concentration of NH₄⁺ and PO₄³⁻ in the columns with the addition of diatomite than in the control one (reduction in the concentration of NH₄⁺ by 93 and 94% and of PO₄³⁻ by 94 and 98% with the addition of 20 and 30% diatomite contents, respectively). The study results confirmed the possibility of removing contaminants using diatomite, thus reducing their entry into the aquatic environment. Full article

The chemistry of atmospheric precipitation serves as an important proxy for discriminating the source(s) of air contaminants in urban environments as well as to discuss the dynamic of atmospheric chemistry exchanges. This approach can be undertaken at time scales varying from single events to seasonal and yearly time frames. Here, we characterized the chemical composition of two single rain episodes (18 July 2018 and 21 February 2019) collected in Wrocław (SW Poland). Our results demonstrated inner variations and seasonality (within the rain event as well as between summer and winter), both in ion concentrations as well as in their potential relations with local air contaminants and scavenging processes. Coupling statistical analysis of chemical parameters with meteorological/synoptic conditions and HYSPLIT back trajectories allowed us to identify three main factors (i.e., principal components; PC) controlling the chemical composition of precipitation, and that these fluctuated during each event: (i) PC1 (40%) was interpreted as reflecting the long-range transport and/or anthropogenic influences of emission sources that included biomass burning, fossil fuel combustion, industrial processes, and inputs of crustal origin; (ii) PC2 (20%) represents the dissolution of atmospheric CO₂ and HF into ionic forms; and (iii) PC3 (20%) originates from agricultural activities and/or biomass burning. Time variations during the rain events showed that each factor was more important at the start of the event. The study of both SO₄²⁻ and Ca²⁺ concentrations showed that while sea spray inputs fluctuated during both rain events, their overall impact was relatively low. Finally, below-cloud particle scavenging processes were only observed for PM₁₀ at the start of the winter rain episode, which was probably explained by the corresponding low rain intensity and an overlap from local aerosol emissions. Our study demonstrates the importance of multi-time scale approaches to explain the chemical variability in rainwater and both its relation to emission sources and the atmosphere operating processes. Full article

Tourism still represents a means of generating revenues in the coastal areas in the Mexican Caribbean, despite the growing concern about the social and environmental impacts. The Nichupte Lagoon System (NLS), the most representative lagoon of Quintana Roo State for being in the middle of Cancun’s hotel development, has experienced a continuous drop-off in its water quality due to several factors, including dredging and wastewater discharges from different anthropogenic activities, which modify the flux of nutrients, increase the number of pathogenic microorganisms, and promote physicochemical changes in this ecosystem. Three sampling campaigns (2018, 2019, and 2020) were carried out in the NLS in August, which is the month of greatest tourist occupancy. To evidence the presence of anthropogenic wastewater in the NLS, the caffeine tracer was used, and to determine the water quality, 43 sampling stations were monitored for “in situ” physicochemical parameters (salinity and dissolved oxygen), and water samples were collected for the quantification of nutrients (NO₂⁻ + NO₃⁻, NH₄⁺, SRP and SRSi) and chlorophyll-a (Chl-a). For data analysis, the lagoon was subdivided into five zones (ZI, ZII, ZIII, ZIV, and ZV). Caffeine spatial and time variation evidence (1) the presence of anthropogenic wastewater in all areas of the NLS probably resulting from the tourist activity, and (2) wastewater presence is directly influenced by the coupling of the hydrological changes driven by anomalous rain events and the number of tourists. This same tendency was observed for nutrients that increased from 2018 to 2019 and the trophic state changed from oligotrophic to hypertrophic in all areas, as a result of previous anomalous precipitations in 2018, followed by normal precipitations in 2019. From 2019 to 2020, the nutrients decreased due to the drop in tourism due to COVID-19, promoting fewer nutrients in the lagoon, but, also coupled with an anomalous precipitation event (Cristobal storm), resulted in a dilution phenomenon and an oligotrophic state. The cluster analysis indicated that the least similar zones in the lagoon were the ZI and ZV due to their geomorphology that restricts the connection with the rest of the system. Principal component analysis revealed that wastewater presence evidenced by the caffeine tracer had a positive association with dissolved oxygen and chlorophyll-a, indicating that the arrival of nutrients from wastewater amongst other sources promotes algal growth, but this could develop into an eutrophic or hypertrophic state under normal precipitation conditions as seen in 2019. This study shows the relevance of monitoring in time of vulnerable karstic systems that could be affected by anthropogenic contamination from wastewater inputs, stressing the urgent need for efficient wastewater treatment in the area. The tourist industry in coastal karstic lagoons such as the NLS must have a Wastewater Treatment Program as a compensation measure for the anthropic pressure that is negatively changing the water quality of this highly relevant socio-environmental system. Full article

Groundwater, widely used for supplying drinking water to populations, is a vital resource that must be managed sustainably, which requires a thorough understanding of its diverse physico-chemical and bacteriological characteristics. This study, based on a 27-year extraction from the Sise-Eaux database (1993–2020), focused on the island of Corsica (72,000 km²), which is diverse in terms of altitude and slopes and features a strong lithological contrast between crystalline Corsica and metamorphic and sedimentary Corsica. Following logarithmic conditioning of the data (662 water catchments, 2830 samples, and 15 parameters) and distinguishing between spatial and spatiotemporal variances, a principal component analysis was conducted to achieve dimensionality reduction and to identify the processes driving water diversity. In addition, the spatial structure of the parameters was studied. The analysis notably distinguishes a seasonal determinism for bacterial contamination (rain, runoff, bacterial transport, and contamination of catchments) and a more strictly spatial determinism (geographic, lithological, and land use factors). The behavior of each parameter allowed for their classification into seven distinct groups based on their average coordinates on the factorial axes, accounting for 95% of the dataset’s total variance. Several strategies can be considered for the inventory and mapping of groundwater, namely, (1) establishing quality parameter distribution maps, (2) dimensionality reduction through principal component analysis followed by two sub-options: (2a) mapping factorial axes or (2b) establishing a typology of parameters based on their behavior and mapping a representative for each group. The advantages and disadvantages of each of these strategies are discussed. Full article

Dry riverbeds can transport mining waste during torrential rain events, disseminating pollutants from mining areas to natural ecosystems. This study evaluates the impact of these mine wastes on soils, sediments, and runoff/pore water in the La Carrasquilla dry riverbed (southeastern Spain). An integrated approach utilizing geochemical and mineralogical techniques was employed, analyzing water, soil, and sediment samples from both the headwater and mouth of the riverbed. Soil profiles and pore water were collected at 30 cm, 60 cm, and 90 cm deep, alongside sediment and runoff water samples. The assessment of metal(loid) contamination focused on arsenic, cadmium, chromium, copper, iron, nickel, manganese, zinc, and lead, utilizing sequential extraction to evaluate metal partitioning across soil phases. Various pollution indices, including the contamination factor (Cf), pollution load index (PLI), potential ecological risk index (RI), and metal(loid) evaluation index (MEI), were employed to classify contamination levels. The highest level of contamination was reported in the headwater, which suggested anthropogenic activities linked to the presence of mining residues as the major source of metal(loid)s. However, an active deposition of As, Cd, Cu, Fe, Mn, and Zn was reported in the topsoil at the mouth. In the headwater, a quartz and muscovite-rich zone exhibited the highest Cf for Pb (1022), primarily bound to the soil residual fraction (62.8%). At the headwater and mouth, pore water showed higher concentrations of sulfate, Ca, Na, Cl, Mg, and Mn and higher salinity than acceptable limits for drinking water or irrigation established by the World Health Organization. Runoff-water metal concentrations surpassed established guidelines, with MEI values indicating significant contamination by cadmium (36.1) and manganese (19.0). These findings highlight the considerable ecological risk of Pb and underscore the need for targeted remediation strategies to mitigate environmental impacts in the Mar Menor coastal lagoon. Full article

Geopolymer is a sustainable low-carbon cementitious material that is able to incorporate large amounts of solid waste as precursors or activators. As the proportion of municipal solid waste incineration continues to rise in China, the large-scale generation of municipal solid waste incineration fly ash (MSWI FA) has emerged as a significant challenge. The production of geopolymers represents a potential pathway for the comprehensive utilization of MSWI FA. However, most studies have reported that geopolymers containing MSWI FA exhibit low strength, which diminishes their economic value. Furthermore, the unclear environmental risks associated with MSWI FA-based geopolymers have impeded their broader application. This study explores the use of MSWI FA as a substitute for ground granulated blast furnace slag (GGBS) or coal fly ash (CFA) in the production of high-performance geopolymers, achieving compressive strengths exceeding 60 MPa, even when the MSWI FA content reaches 50%. A synergistic effect is observed between MSWI FA and CFA, which enhances the reactivity of CFA. With reasonable formulation, the environmental risks of geopolymers containing MSWI FA are manageable in normal rainfall scenarios. However, there remains a potential risk of soil and groundwater contamination under extreme conditions, such as acid rain. Full article

Semi-volatile organic contaminants (SVOCs) are known for their tendency to evaporate from source regions and undergo atmospheric transport to distant areas. Cold condensation intensifies dry deposition, particle deposition, and scavenging by snow and rain, allowing SVOCs to move from the atmosphere into terrestrial and aquatic ecosystems in alpine and polar regions. However, no standardized methods exist for the sampling, laboratory processing, and instrumental analysis of persistent organic pollutants (POPs) in snow. The lack of reference methods makes these steps highly variable and prone to errors. This study critically reviews the existing literature to highlight the key challenges in the sampling phase, aiming to develop a reliable, consistent, and easily reproducible technique. The goal is to simplify this crucial step of the analysis, allowing data to be shared more effectively through standardized methods, minimizing errors. Additionally, an innovative method for laboratory processing is introduced, which uses activated carbon fibers (ACFs) as adsorbents, streamlining the analysis process. The extraction method is applied to analyze polychlorobiphenyls (PCBs) and chlorinated pesticides (α-HCH, γ-HCH, p,p′-DDE, o,p′-DDT, HCB, and PeCB). The entire procedure, from sampling to instrumental analysis, is subsequently tested on snow samples collected on the Svalbard Islands. To validate the efficiency of the new extraction system, quality control measures based on the EPA methods 1668B and 1699 for aqueous methods are employed. This study presents a new, reliable method that covers both sampling and lab analysis, tailored for detecting POPs in snow. Full article

This research presents a novel fuzzy-logic-based algorithm aimed at detecting and removing interference lines from Micro Rain Radar (MRR-2) data. Interference lines, which are non-meteorological echoes with unknown origins, can severely obscure meteorological signals. Leveraging an understanding of interference line characteristics, such as temporal continuity, we identified and utilized eight key variables to distinguish interference lines from meteorological signals. These variables include radar moments, Doppler spectrum peaks, and the spatial/temporal continuity of Doppler velocity. The algorithm was developed and validated using data from MRR installations at three sites (Seoul, Suwon, and Incheon) in South Korea, from June to September 2021–2023. While there is a slight tendency to eliminate some weak precipitation, results indicate that the algorithm effectively removes interference lines while preserving the majority of genuine precipitation signals, even in complex scenarios where both interference and precipitation signals are present. The developed software, written in Python 3 and available as open-source, outputs in NetCDF4 format, with customizable parameters for user flexibility. This tool offers a significant contribution to the field, facilitating the accurate interpretation of MRR-2 data contaminated by interference. Full article