Search Results (206)

The reduction of environmental pollutant emissions—including greenhouse gases, particulate matter, and other harmful substances—represents one of the foremost challenges in climate policy, economics, and industrial management today. Excessive emissions of CO₂, NO_X, and suspended particulates exert significant impacts on climate change as well as human health and welfare. Consequently, numerous studies and regulatory and technological initiatives are underway to mitigate these emissions. One critical area is intra-plant transport within manufacturing facilities, which, despite its localized scope, can substantially contribute to a company’s total emissions. This paper aims to assess the potential of computer simulation using FlexSim software as a decision-support tool for planning inter-operational transport, with a particular focus on environmental aspects. The study analyzes real operational data from a selected production plant (case study), concentrating on the optimization of the number of transport units, their routing, and the layout of workstations. It is hypothesized that reducing the number of trips, shortening transport routes, and efficiently utilizing transport resources can lead to lower emissions of carbon dioxide (CO₂) and nitrogen oxides (NO_X). The findings provide a basis for a broader adoption of digital tools in sustainable production planning, emphasizing the integration of environmental criteria into decision-making processes. Furthermore, the results offer a foundation for future analyses that consider the development of green transport technologies—such as electric and hydrogen-powered vehicles—in the context of their implementation in the internal logistics of manufacturing enterprises. Full article

Fixed automated (unmanned) above-water radiometric measurements are subject to unavoidable sky conditions and surface perturbations, leading to significant uncertainties in retrieved water surface remote sensing reflectances (R_rs(λ), sr⁻¹). This study evaluates various above-water R_rs(λ) glint correction methods using a comprehensive dataset collected at the Royal Netherlands Institute for Sea Research (NIOZ) Jetty Station located in the Marsdiep tidal inlet of the Dutch Wadden Sea, the Netherlands. The dataset includes in-situ water constituent concentrations (2006–2020), inherent optical properties (IOPs) (2006–2007), and above-water hyperspectral (ir)radiance observations collected every 10 min (2006–2023). The bio-optical models were validated using in-situ IOPs and utilized to generate glint-free remote sensing reflectances, R_rs,ref(λ), using a robust IOP-to-R_rs forward model. The R_rs,ref(λ) spectra were used as a benchmark to assess the accuracy of glint correction methods under various environmental conditions, including different sun positions, wind speeds, cloudiness, and aerosol loads. The results indicate that the three-component reflectance model (3C) outperforms other methods across all conditions, producing the highest percentage of high-quality R_rs(λ) spectra with minimal errors. Methods relying on fixed or lookup-table-based glint correction factors exhibited significant errors under overcast skies, high wind speeds, and varying aerosol optical thickness. The study highlights the critical importance of surface-reflected skylight corrections and wavelength-dependent glint estimations for accurate above-water R_rs(λ) retrievals. Two showcases on chlorophyll-a and total suspended matter retrieval further demonstrate the superiority of the 3C model in minimizing uncertainties. The findings highlight the importance of adaptable correction models that account for environmental variability to ensure accurate R_rs(λ) retrieval and reliable long-term water quality monitoring from hyperspectral radiometric measurements. Full article

Monitoring chlorophyll-a concentration (Chl-a) is essential for assessing aquatic ecosystem health, yet its retrieval using remote sensing remains challenging in turbid coastal waters because of the intricate optical characteristics of these environments. Elevated levels of colored (chromophoric) dissolved organic matter (CDOM) and suspended sediments (aka total suspended solids, TSS) interfere with satellite-based Chl-a estimates, necessitating alternative approaches. One potential solution is machine learning, indirectly including non-Chl-a signals into the models. In this research, we develop machine learning models to predict Chl-a concentrations in the Chesapeake Bay, one of the largest estuaries on North America’s East Coast. Our approach leverages the Extra-Trees (ET) algorithm, a tree-based ensemble method that offers predictive accuracy comparable to that of other ensemble models, while significantly improving computational efficiency. Using the entire ocean color datasets acquired by the satellite sensors MODIS-Aqua (>20 years) and VIIRS-SNPP (>10 years), we generated long-term Chl-a estimates covering the entire Chesapeake Bay area. The models achieve a multiplicative absolute error of approximately 1.40, demonstrating reliable performance. The predicted spatiotemporal Chl-a patterns align with known ecological processes in the Chesapeake Bay, particularly those influenced by riverine inputs and seasonal variability. This research emphasizes the potential of machine learning to enhance satellite-based water quality monitoring in optically complex coastal waters, providing valuable insights for ecosystem management and conservation. Full article

Nitrification is the key process linking the oxidized and reduced forms of reactive nitrogen, playing an important role in the nitrogen biogeochemical cycle. Quantifying the nitrification rate and evaluating its environmental regulators in different aquatic environments at both regional and global scales has received increasing attention. However, the spatiotemporal variations in nitrification rates in coastal waters, particularly with different trophic states, remain unclear. By using the ¹⁵N-labeling technique, here, we quantified the nitrification rates under dark and light conditions in the surface waters of Shenzhen Bay and Dapeng Bay, representing eutrophic and oligotrophic environments, respectively. The nitrification rates were 2–3 orders of magnitude higher in Shenzhen Bay (eutrophic) than those in Dapeng Bay (oligotrophic). The concentrations of ammonium and total suspended matter play key roles in regulating the spatiotemporal distribution and difference in nitrification in these two coastal bays. The nitrification rate under the dark condition (0.13–49.37 nmol N L⁻¹ h⁻¹) was greater than that under light incubation (0–10.15 nmol N L⁻¹ h⁻¹), indicating light inhibition of 33–100% in the surface water. Such results imply that daily integrated nitrification based on the rates under dark incubation may have been overestimated. An environment with high turbidity is preferable for nitrification, as it reduces the damage caused by light to ammonia-oxidizing microbes. Collectively, the differences in nitrification rates further result in a distinct composition of dissolved inorganic nitrogen, with Shenzhen Bay dominated by nitrate and Dapeng Bay dominated by ammonium. Our results provide scientific references for the mitigation of nitrogen pollution in different trophic coastal bays. Full article

The Global Change Observation Mission-Climate (GCOM-C) satellite, launched in December 2017, is equipped with the Second-generation Global Imager (SGLI) sensor, featuring a moderate spatial resolution of 250 m and 19 spectral bands, including the unique 380 nm band. After six years in orbit, a comprehensive evaluation of SGLI products and their temporal consistency is needed. Remote sensing reflectance ($R_{rs}$) is the primary product for monitoring water quality, forming the basis for deriving key oceanic constituents such as chlorophyll-a (Chla) and total suspended matter (TSM). The Japan Aerospace Exploration Agency (JAXA) provides $R_{rs}$ products through two platforms, G-Portal and JASMES, each employing different atmospheric correction methodologies and assumptions. This study aims to evaluate the SGLI full-resolution $R_{rs}$ products from G-Portal and JASMES at regional scales (Japan and East Asia) and assess G-Portal $R_{rs}$ products globally between January 2018 and December 2023. The evaluation employs in situ matchups from NASA’s Aerosol Robotic Network-Ocean Color (AERONET-OC) and cruise measurements. We also assess the retrieval accuracy of two water quality indices, Chla and TSM. The AERONET-OC data analysis reveals that JASMES systematically underestimates $R_{rs}$ values at shorter wavelengths, particularly at 412 nm. While the $R_{rs}$ accuracy at 412 nm is relatively low, G-Portal’s $R_{rs}$ products perform better than JASMES at shorter wavelengths, showing lower errors and stronger correlations with AERONET-OC data. Both G-Portal and JASMES show lower agreement with AERONET-OC and cruise datasets at shorter wavelengths but demonstrate improved agreement at longer wavelengths (530 nm, 565 nm, and 670 nm). JASMES generates approximately 12% more matchup data points than G-Portal, likely due to G-Portal’s stricter atmospheric correction thresholds that exclude pixels with high reflectance. In situ measurements indicate that G-Portal provides better overall agreement, particularly at lower $R_{rs}$ magnitudes and Chla concentrations below 5 mg/m³. This evaluation underscores the complexities and challenges of atmospheric correction, particularly in optically complex coastal waters (Case 2 waters), which may require tailored atmospheric correction methods different from the standard approach. The assessment of temporal consistency and seasonal variations in $R_{rs}$ data shows that both platforms effectively capture interannual trends and maintain temporal stability, particularly from the 490 nm band onward, underscoring the potential of SGLI data for long-term monitoring of coastal and oceanic environments. Full article

Fireworks are often used in celebrations and are a known transient source of extreme particulate air pollution, and the particles produced by fireworks are known to contain potentially harmful heavy metals. This study investigated ambient particulate metal concentrations associated with heavy firework use during the United States Independence Day holiday in July 2020 and July 2021 in Fullerton, California, located within the greater Los Angeles metropolitan area. For this study, barium (Ba), chromium (Cr), copper (Cu), lead (Pb), and strontium (Sr) were quantified, with Ba, Cu, and Sr being known tracers for fireworks and Cr and Pb being potentially harmful. Total suspended particulates (TSP) were collected with filters and then extracted and analyzed by graphite furnace atomic absorption spectroscopy. Hourly ambient particulate concentrations at a nearby monitoring station exceeded 500 μg m⁻³ and 300 μg m⁻³ in 2020 and 2021, respectively. Greater concentrations of overall particulate matter and ambient metal concentrations were observed during 2020 when compared to 2021, consistent with studies in the literature that have shown increased firework use in the area, likely due to the COVID-19 restrictions in place in 2020. In 2021, the Ba, Cu, and Sr concentrations peaked overnight on 4–5 July as expected, but the Cr and Pb concentrations peaked in the afternoon on July 5. In 2020, the peak concentrations of Cr and Pb were 510 ± 40 ng m⁻³ and 710 ± 30 ng m⁻³, respectively, while 4900 ± 200 ng m⁻³, 3860 ± 40 ng m⁻³, and 1810 ± 30 ng m⁻³ were observed for Ba, Cu, and Sr, respectively, among the highest ever observed to our knowledge. Full article

Chlorophyll-a (Chla) and total suspended solid (TSS) concentrations are important parameters for water quality assessment, and in recent years, machine learning has been shown to have great potential in this field. However, current water quality parameter inversion models lack interpretability and rarely consider the morphological characteristics of the spectrum. To address this limitation, we used Sentinel-3 OLCI data to construct an interpretable CatBoost model guided by spectral morphological characteristics for remote sensing monitoring of Chla and TSS along the coast of Fujian. The results show that the coastal waters of Fujian Province can be divided into five clusters, and the areas of different clusters will change with the alternation of seasons. Clusters 2 and 4 are the main types of coastal waters. The CatBoost model combined with spectral feature engineering has a high accuracy in predicting Chla and TSS, among which Chla is slightly better than TSS (R² = 0.88, MSE = 8.21, MAPE = 1.10 for Chla predictions; R² = 0.77, MSE = 380.49, MAPE = 2.48 for TSS predictions). We further conducted an interpretability analysis on the model output and found that the combination of BRI and TBI indexes composed of bands such as b8, b9, and b10 and the fluctuation of spectral curves will have a significant impact on the prediction of model output. The interpretable CatBoost model based on spectral morphological features proposed in this study can provide an effective technical means of estimating the chlorophyll-a and total suspended particulate matter concentrations in the coastal areas of Fujian. Full article

Throughout the world, river basins are directly or indirectly affected by human activities, reducing local and global biodiversity and preventing the ecosystem from properly functioning. Our research focused on the Irpin River basin (Ukraine), whose water bodies have experienced various impacts due to human activities, including the unexpected extremes caused by military operations in the catchment area: long-term flooding, disturbance of free flow, significant water level fluctuations, etc. The study hypothesized that the primary factors determining the structural and spatial distribution of quantitative indicators of microalgae and aquatic macrophytes are the result of various hydromorphological changes, that lead to changes in the physical and chemical parameters of the aquatic environment. Very high values of chlorophyll a in the water column (59–106 µg · L⁻¹), an increase in the abundance (number of cells) and biomass of algae (due to the predominance of certain groups in the transformed sections), as well as saprobic index were recorded in the sections of the Irpin River basin that underwent significant hydromorphological changes. Our results revealed a strong correlation between phytoplanktonic (in the water column) chlorophyll a levels and water temperature (r = 0.76, p < 0.001), as well as organic phosphorus and polyphosphate concentrations (r = 0.61, p < 0.01). ANOVA and Monte Carlo permutation tests in a Canonical Correspondence Analysis (CCA) showed that the abundance of different divisions of phytoplankton and phytobenthos were significantly and similarly related to several environmental variables. We observed a positive correlation between the number of cyanobacteria and the concentration of ammonium nitrogen, nitrites, and phosphorus compounds. An increase in dissolved organic matter in the water can explain the increase in the biomass of Dinoflagellata and Euglenophyta. Species richness and the cover values of the macrophytes also clearly reflected changes in vegetation activity in sections of the Irpin River caused by hydromorphological changes. The results indicated that long-term flooding had the most negative impact on macrophyte communities. At some sites, the impact was so severe that the number of macrophyte species was very low. The total number of macrophyte species showed a significant negative correlation with total suspended solids (r = −0.51, p < 0.05) and phytoplankton chlorophyll a concentration (r = −0.73, p < 0.001). Our results provide a scientific basis for predicting changes in riverine microalgal and aquatic macrophyte communities due to extreme hydrological events. Full article

As the scale of shrimp aquaculture continues to expand, the environmental impacts of shrimp effluents have become increasingly severe. The purification of aquaculture effluents can no longer be overlooked. Effectively reducing the discharge of aquaculture wastewater and mitigating its potential pollution risks to the surrounding aquatic ecological environment are key issues that need to be addressed to promote the industry’s development towards a greener, more environmentally friendly, and sustainable path. This study explored the purification effect of the integration of tilapia and Spirulina on tail water from a zero-water-exchange aquaculture of whiteleg shrimp (Litopenaeus vannamei) in seawater, with the aim of assessing the growth performance of tilapia and the efficacy of the fish–algae integration in purifying tail water from the perspective of tail water resource utilisation. The study found that the removal rates of the biofloc sedimentation volume and total suspended particle concentration in the fish–algae group were 42.6% and 29.6%, respectively. The removal rates of phosphate and total phosphorus in the fish–algae group were 26.3% and 20.8%, respectively. Research indicates that tilapia effectively removes suspended organic matter from water. Introducing Spirulina into this water body aids in the removal of soluble nitrogen and phosphorus from the effluent, and tilapia exhibit a favourable feeding response to Spirulina. Full article

The wastewater generated by the pharmaceutical industry poses a risk to the environment due to undesirable characteristics such as low biodegradability, high levels of contaminants, and the presence of suspended solids, in addition to the high load of organic matter due to the presence of drugs and other emerging products in the effluent. This study aims to reduce the impact of wastewater pollution by removing amoxicillin (AMO) antibiotics as an organic pollutant. In this concept, two synthesized catalysts, NiAl₂O₄ and ZnO, are sensitive oxides to light energy. The prepared materials were then characterized using X-ray diffraction, UV–vis solid reflectance diffuse, Raman spectroscopy, scanning electron microscopy, BET, and ATR-FTIR spectroscopy. The effects of principal operating parameters under sunlight, namely, the percentage of the mixture of NiAl₂O₄ and ZnO, the pH of the medium, and the initial concentration of the antibiotic were studied experimentally to determine the optimal conditions for achieving a high degradation rate. The results showed that photodegradation is higher at a pH of 6, with a weight percentage of the mixture of 50% for both catalysts in 1 g/L of the total catalyst dose. Then, the effect of the initial concentration of AMO on the photodegradation reaction showed an important influence on the photodegradation process; as the degradation rate decreases, the initial AMO concentration increases. A high degradation rate of 92% was obtained for an initial AMO concentration of 10 mg/L and a pH of 6. The kinetic study of degradation established that the first-order model and the Langmuir–Hinshelwood (LH) mechanism fit the experimental data perfectly. The study showed the success of using heterosystem photocatalysts and sustainable energy for effective pharmaceutical removal, which can be extended to treat wastewater with other organic emerging pollutants. On the other hand, modeling was introduced using Gaussian process regression (GPR) to predict the degradation rate of AMO under sunlight in the presence of heterogeneous ZnO and NiAl₂O₄ systems. The model evaluation criteria of GPR in terms of statistical coefficients and errors show very interesting results and the performance of the model used. Where statistical coefficients were close to one (R = 0.9981), statistical errors were very small (RMSE = 0.1943 and MAE = 0.0518). The results suggest that the model has a strong predictive power and can be used to optimize the process of AMO removal from wastewater. Full article

The dust generated during tunnel construction poses serious health risks to workers, as it not only causes respiratory obstruction but also leads to pneumoconiosis and respiratory failure after prolonged exposure. However, most existing studies focus on specific construction stages or particular particle sizes and often assume an ideal airflow, neglecting the complex flow fields, vortex effects, and dust composition variations at different stages in tunnel and mine construction. This study systematically analyzes the spatiotemporal distribution characteristics of dust at various stages of tunnel construction and proposes targeted prevention and control strategies. On the basis of measured data from three construction stages—the working face, initial support, and secondary lining stages—and SPSS 27 statistical analysis, a dynamic analysis was conducted on the concentration and distribution patterns of total suspended particulates (TSPs) and particulate matter of different sizes (PM10, PM4, PM2.5, and PM1). The results show that coarse particles dominate during the working face stage, whereas fine particles gradually accumulate during the initial support and secondary lining stages. Finally, this work establishes a dust concentration–excavation time/tunnel depth equation and proposes targeted dust control measures. These findings offer important practical value for enhancing construction safety and air quality. Full article

The vertical distribution of the marine total suspended matter (TSM) concentration significantly influences marine material transport, sedimentation processes, and biogeochemical cycles. Traditional field observations are constrained by limited spatial and temporal coverage, necessitating the use of remote-sensing technology to comprehensively understand TSM variations over extensive areas and periods. This study proposes a remote-sensing approach to estimate the vertical distribution of TSM concentrations using MODIS satellite data, with the Bohai Sea and Yellow Sea (BSYS) as a case study. Extensive field measurements across various hydrological conditions and seasons enabled accurate reconstruction of in situ TSM vertical distributions from bio-optical parameters, including the attenuation coefficient, particle backscattering coefficient, particle size, and number concentration, achieving a determination coefficient of 0.90 and a mean absolute percentage error of 26.5%. In situ measurements revealed two distinct TSM vertical profile types (vertically uniform and increasing) and significant variation in TSM profiles in the BSYS. Using surface TSM concentrations, wind speed, and water depth, we developed and validated a remote-sensing approach to classify TSM vertical profile types, achieving an accuracy of 84.3%. Combining this classification with a layer-to-layer regression model, we successfully estimated TSM vertical profiles from MODIS observation. Long-term MODIS product analysis revealed significant spatiotemporal variations in TSM vertical distributions and column-integrated TSM concentrations, particularly in nearshore regions. These findings provide valuable insights for studying marine sedimentation and biological processes and offer a reference for the remote-sensing estimation of the TSM vertical distribution in other marine regions. Full article

This research addresses a gap in localized air quality assessments by measuring pollution levels in Klaipeda, a Baltic port city, using passive solid particle collectors and nitrogen dioxide (NO₂) diffusion tubes. Passive sampling techniques were employed due to their cost-effectiveness and ease of deployment, allowing for practical monitoring over short-term periods. By targeting diverse functional zones, this study aims to provide a comprehensive analysis of air pollution patterns and seasonal variations in the region. Air pollution, primarily from NO₂ and particulate matter (PM), poses significant risks to public health, especially in densely populated urban areas. Air quality was assessed by measuring total suspended particulates (TSP) and NO₂ concentrations across 19 strategically chosen sites, covering key functional zones, such as industrial areas, green spaces, residential neighborhoods, transport hubs, and the port. Results show elevated pollution levels near major roads and the port area, likely driven by heavy traffic, industrial emissions, and port activities. These patterns correlate with areas of higher population density, highlighting the intersection of air quality challenges with human health risks in urbanized zones. Seasonal data reveal a notable peak in NO₂ concentrations during winter, likely due to increased heating demand and reduced atmospheric dispersion. These findings suggest that air quality management strategies should be adaptive to seasonal fluctuations, particularly by addressing emissions from heating sources in colder months. The study underscores the necessity of integrating sustainable urban planning with targeted air quality interventions. Expanding green spaces, enhancing traffic regulation, and establishing protective zones near industrial areas are critical strategies for mitigating pollution. These insights are essential for guiding both urban development and public health policies in Klaipeda and other coastal cities facing similar environmental challenges. Full article

Dongting Lake is the second largest freshwater lake in China, located in the middle reaches of the Yangtze River. Since the 21st century, it has faced intensified human activities, particularly the Three Gorges Dam impoundment and sand mining. The water quality of Dongting Lake has significantly changed due to human activities and climate change. Currently, quantitative studies on the spatial–temporal variations of total suspended matter (TSM) during Dongting Lake’s dry season and the human impacts on its concentration are lacking. This study utilizes Landsat-5 TM and Landsat-8 OLI data to estimate the changes in TSM concentration during the dry season from 1986 to 2021, analyzing their spatial–temporal variations and driving mechanisms. By evaluating the atmospheric calibration accuracy and model precision metrics, we select a model based on the ratio of red to green band, achieving an $R^2$ of 0.84, $RMSE$ of 18.94 mg/L, and $MRE$ of 27.32%. Applying this model to the images, we map the distribution of the TSM concentration during the dry season from 1986 to 2021, analyzing its spatial pattern and inter-annual variation, and further investigate the impacts of natural factors and human activities on the TSM concentration. Our results show the following: (1) From 1986 to 2021, the TSM concentration during the dry season ranges from 0 to 200 mg/L of Dongting Lake, with an area-wide average value between 41.61 and 75.44 mg/L. (2) The TSM concentration from 1986 to 2021 is significantly correlated with the water level. Before 2006, it correlates positively, but no significant correlation exists from 2006 onward. (3) From 2006 onward, the mean TSM concentration is notably decreased compared to that before 2006, likely due to the Three Gorges Dam, while our analysis indicates a significant positive correlation between the TSM concentration and sand mining intensity during this period. This study highlights the influence of the Three Gorges Dam and sand mining on the TSM concentration in Dongting Lake during the dry season, providing valuable insights for related research on similar lakes. Full article

This experimental study explores the mitigation of membrane fouling in membrane bioreactors (MBRs) through the combined use of granular activated carbon (GAC) and powdered activated carbon (PAC). The research assesses the impact of these materials on the fouling resistance, critical flux, and permeate quality using various mixed liquor suspended solids concentrations and carbon dosages. The results indicate that the GAC-PAC combination significantly reduces the total filtration resistance, particularly the cake layer resistance, by 11.7% to 13.6% compared to setups without activated carbon or with the individual carbon types. The study also reveals that this combination decreased the fouling rate by 15% to 24% at critical flux steps, demonstrating substantial improvements in fouling mitigation and operational efficiency. Furthermore, the GAC-PAC combination, which produces an adsorption process, enhances the permeate quality, achieving the near-complete removal of organic matter, total nitrogen, and turbidity, with total phosphorus removal reaching 99%. These findings demonstrate that the combined use of GAC and PAC not only reduces membrane fouling but also improves the overall MBR performance, making it a viable strategy for enhancing the efficiency of wastewater treatment processes. Full article