Search Results (56)

Rainfall is an essential component of boreal forest ecosystems. Aerotechnogenic pollution significantly affects the composition of rainfall. To predict the dynamics of biogeochemical cycles and develop strategies to enhance forest resilience in the Arctic zone, it is necessary to study the composition and characteristics of rainfall. The objective of this study is to evaluate the variation in the chemical composition of stemflow in the most typical pine and spruce forests of Fennoscandia under conditions of aerotechnogenic pollution based on long-term monitoring data from 1999 to 2022. The research was carried out in forests exposed to atmospheric industrial pollution from the largest copper–nickel smelter in northern Europe (Murmansk Region, Russia). The study of rainwater composition was conducted in four microsites: open areas (OA), between crowns (BWC), below crowns (BC) and stemflow (SF). A significant influence of the tree canopy on the rainfall composition was noted. Stemflow was found to have the highest concentration of pollutants, indicating a significant biochemical role of this type of precipitation. The results showed an increase in the concentrations of heavy metals and sulfates in rainwater as we moved closer to the pollution source. Below crowns and in the stemflow of spruce forests, element concentrations are higher compared to pine forests. The highest concentrations of major pollutants in stemflow (Ni, Cu and SO₄²⁻) are observed in June—at the beginning of the growing season. Long-term dynamics reveal a decrease in the concentrations of Cu, Cd and Cr in defoliated forests and technogenic sparse forests. Stemflow volume rises from background to technogenic sparse forests due to deteriorating tree-crown conditions. This is associated with the deteriorating condition of tree stands, as manifested by reductions in tree height, diameter and needle cover. It has been established that under pollution conditions, trees’ assimilating organs actively accumulate heavy metals, thereby altering the composition of precipitation passing through the canopy. Full article

Accurate nitrogen dioxide (NO₂) forecasting is crucial for proactive emission control and issuing public health warnings. This study provides the first evaluation of the China Meteorological Administration’s (CMA) operational CUACE/Haze-Fog V3.0 numerical prediction system, assessing its daily NO₂ forecast accuracy against independent satellite measurements and in situ observations. We compare model forecasts with TROPOspheric Monitoring Instrument (TROPOMI) satellite column data and observations from 1677 Chinese ground monitoring stations, focusing on four key regions: the Yangtze River Delta, Pearl River Delta, Beijing–Tianjin–Hebei, and Urumqi. An optimal spatial resolution of 0.15° × 0.15° was determined for TROPOMI data processing. The results indicate a strong seasonal dependency in model performance. The model systematically underestimates NO₂ concentrations in winter but performs significantly better in summer. This systematic bias is confirmed by a Normalized Mean Bias (NMB) consistently below −20% in northern regions during the winter. In the Beijing–Tianjin–Hebei region, the Root Mean Square Error (RMSE) reached 3.57 × 10¹⁵ molec/cm² (vs. TROPOMI) and 1.09 × 10¹⁵ molec/cm³ (vs. ground stations) in winter, decreasing to 0.95 and 0.91, respectively, in summer. Critically, this winter bias pertains to pollution magnitude rather than temporal correlation; the model captures pollution trends but underestimates peak severity. Our study reveals a ‘vertical decoupling’ in the operational forecasting system. While the model utilizes surface data assimilation to correct surface pollutants, this study demonstrates that these corrections fail to propagate vertically to the total NO₂ column during winter stable boundary layer conditions. This finding has broader implications for chemical transport models (CTMs): relying solely on surface data assimilation is insufficient for constraining column burdens in regions with complex vertical stratification. We propose that future operational systems integrate satellite-based vertical constraints to resolve the systematic winter bias identified here. Full article

Silicon (Si) may benefit the growth and physiology of various cultivated species, especially under stress conditions. Here, we hypothesized that soil Si supplying as Ca₂SiO₄ would increase the drought tolerance and water use efficiency of young Coffea arabica L. (Arabica coffee) plants, by maintaining shoot water status and photosynthesis under low water availability. To test such a hypothesis, morphological and physiological (leaf water potential, leaf gas exchange, photochemical activity, chlorophyll content) traits of coffee plants were evaluated under varying soil Ca₂SiO₄ applications (0, 3000, 6000 kg ha⁻¹) and water availability. The chemical composition of plant tissues was evaluated under well-watered conditions after six months of Ca₂SiO₄ application, with fertilized plants showing higher concentrations of Ca (leaves and roots) and B (all plant organs) as compared to plants not supplied with Ca₂SiO₄ (control treatment). As there were no changes in Si concentration in plant organs under Ca₂SiO₄ application, our data indicate that the coffee species is a Si non-accumulator, or at least the cultivar ‘Catuaí Vermelho’ evaluated herein. Additionally, the photosynthetic capacity of coffee plants increased with 6000 kg Ca₂SiO₄ ha⁻¹ compared to the control under well-watered conditions, as given by increases in gross and net photosynthesis under light saturation, light saturation point, maximum RuBisCO carboxylation rate, maximum electron transport-dependent RuBP regeneration, and maximum rate of triose phosphate use. Such photosynthetic improvements underlined high leaf CO₂ assimilation, transpiration, carboxylation efficiency, and chlorophyll content in plants grown under Si supplying and well-watered conditions. The negative impact of water deficit on leaf gas exchange was alleviated by Ca₂SiO₄ application, but the instantaneous water use efficiency was maintained as similar in both water regimes, as expected for Si non-accumulator species. Morphologically, coffee stem diameter was increased under Ca₂SiO₄ application, regardless of water regime. In conclusion, our data revealed that high Ca₂SiO₄ doses benefit coffee performance and also suggest that the use of steel slag—an industrial byproduct rich in Ca₂SiO₄—can be considered as a sustainable practice for residue recycling in agriculture while improving C. arabica growth and physiology under varying water availability. Full article

This study was conducted to investigate the effect of hydrogen peroxide (H₂O₂) pretreatment on the anaerobic digestion performance of dairy wastewater. Initial physicochemical characterization revealed that the substrate is highly enriched in volatile solids (approximately 90.67%), indicating its strong potential for anaerobic biodegradation. Given this favorable composition, biochemical methane potential (BMP) assays were performed under mesophilic conditions (37 °C) to quantify biogas and methane generation from the untreated and pretreated dairy effluent. To enhance substrate biodegradability and increase methane yield, an oxidative pretreatment using various doses of H₂O₂ was applied. This pretreatment aimed to disrupt the complex organic matter and promote the solubilization of chemical oxygen demand (COD), especially in its soluble form (sCOD), which is more readily assimilated by methanogenic microorganisms. The experimental results demonstrated a significant improvement in biogas production efficiency. While the untreated sample yielded approximately 100 mL CH₄/g VS, the pretreated substrate achieved a maximum of 168 mL CH₄/g VS, marking a substantial enhancement. Gas composition analysis further revealed that methane accounted for nearly 45% of the total biogas produced under optimal conditions. The dosage of 0.2 g H₂O₂ per g of volatile solids (VS) resulted in the highest improvement in methane production after thermal treatment C1, followed by 1.35 g H₂O₂/g VS, and then 0.5 g H₂O₂/g VS. Furthermore, the kinetics of methane production were assessed by fitting the experimental data to the modified Gompertz model. This model enabled the determination of key parameters, such as the maximum specific methane production rate and the duration of the lag phase. The high coefficient of determination (R²) values obtained confirmed the excellent agreement between the experimental data and the model predictions, highlighting the robustness and reliability of the modified Gompertz model in describing the anaerobic digestion process of dairy waste subjected to oxidative pretreatment. Full article

Reliable, high-resolution emission inventories are essential for accurately simulating air quality and for designing evidence-based mitigation policies. Yet their performance over Japan—where transboundary inflow, strict fuel regulations, and complex source mixes coexist—remains poorly quantified. This study therefore benchmarks four widely used anthropogenic inventories—REAS v3.2.1, CAMS-GLOB-ANT v6.2, ECLIPSE v6b, and HTAP v3—by coupling each to WRF-Chem (10 km grid) and comparing simulated surface PM₂.₅, SO₂, CO, and NO_x with observations from >900 stations across eight Japanese regions for the years 2010 and 2015. All simulations shared identical meteorology, chemistry, and natural-source inputs (MEGAN 2.1 biogenic VOCs; FINN v1.5 biomass burning) so that differences in model output isolate the influence of anthropogenic emissions. HTAP delivered the most balanced SO₂ and CO fields (regional mean biases mostly within ±25%), whereas ECLIPSE reproduced NO_x spatial gradients best, albeit with a negative overall bias. REAS captured industrial SO₂ reliably but over-estimated PM₂.₅ and NO_x in western conurbations while under-estimating them in rural prefectures. CAMS-GLOB-ANT showed systematic biases—under-estimating PM₂.₅ and CO yet markedly over-estimating SO₂—highlighting the need for Japan-specific sulfur-fuel adjustments. For several pollutant–region combinations, absolute errors exceeded 100%, confirming that emissions uncertainty, not model physics, dominates regional air quality error even under identical dynamical and chemical settings. These findings underscore the importance of inventory-specific and pollutant-specific selection—or better, multi-inventory ensemble approaches—when assessing Japanese air quality and formulating policy. Routine assimilation of ground and satellite data, together with inverse modeling, is recommended to narrow residual biases and improve future inventories. Full article

Spatial-temporal optimal interpolation (STOI) is a data assimilation method based on minimizing the error in an estimate. Error correlations can be modeled with analytical functions. We investigated the effect of the form of the error correlation functions on the uncertainty in an estimate. We applied STOI to the estimation of aerosol distribution over Europe using the results of GEOS-Chem chemical transport model simulations and observations from a ground-based radiometric Aerosol Robotic Network. We used exponential functions to model correlation curves. The results show that a ±15–25% change in the argument of the constructed exponential functions has little effect on the mean square error in the estimate in regions where observations are dense. STOI estimates are sensitive to the form of the correlation curves in regions where observations are sparse. Full article

This study evaluated the effects of a plant pathogenic bacterium Pectobacterium carotovorum strain BD163 inoculation and nutrient solution (CaCO₃ (2 mM), NaCl (1 mM) and K₂Cr₂O₇ (0.001 mM)) on the growth, photosynthesis, nutrient uptake and metabolomics of tomato seedlings. The experiment had four experimental treatments (1. solution + BD163 inoculation, 2. solution alone, 3. BD163 inoculation, 4. control). Plant growth and photosynthesis responses were minimal, and differences in nutrient assimilation and metabolite profiles were clear-cut. Of the photosynthesis parameters, only water use efficiency was impacted; it was higher in the bacterium-only treatment and unchanged in the other treatments. The quantities of boron, bismuth and nickel were affected, accumulating mostly in the “solution + BD163 inoculation” experimental set. Principal component analysis of metabolomics data separated the treatments into three groupings; group 1 was the double treatment, group 2 was the nutrient solution treatment and, finally, group 3 was the P. carotovorum and control treatments. Correlation analysis of the data showed an assumed interdependence of several plant factors. The authors concluded that the interaction between the bacterium, the plant and the nutrient solution is complex and more pronounced at the chemical and metabolite level than at the growth and photosynthesis level. Full article

To date, the nitrogen metabolism pathways and salt-tolerance mechanisms of halophilic denitrifying bacteria have not been fully studied, and full-scale engineering trials with saline fly ash-washing wastewater have not been reported. In this study, we isolated and screened a halophilic denitrifying bacterium (Marinobacter sp.), GH-1, analyzed its nitrogen metabolism pathways and salt-tolerance mechanisms using whole-genome data, and explored its nitrogen removal characteristics under both aerobic and anaerobic conditions at different salinity levels. GH-1 was then applied in a full-scale engineering project to treat saline fly ash-washing leachate. The main results were as follows: (1) Based on the integration of whole-genome data, it is preliminarily hypothesized that the strain possesses complete nitrogen metabolism pathways, including denitrification, a dissimilatory nitrate reduction to ammonium (DNRA), and ammonium assimilation, as well as the following three synergistic strategies through which to counter hyperosmotic stress: inorganic ion homeostasis, organic osmolyte accumulation, and structural adaptations. (2) The strain demonstrated effective nitrogen removal under aerobic, anaerobic, and saline conditions (3–9%). (3) When applied in a full-scale engineering system treating saline fly ash-washing wastewater, it improved nitrate nitrogen (NO₃⁻-N), total nitrogen (TN), and chemical oxygen demand (COD) removal efficiencies by 31.92%, 25.19%, and 31.8%, respectively. The proportion of Marinobacter sp. increased from 0.73% to 3.41% (aerobic stage) and 2.86% (anoxic stage). Overall, halophilic denitrifying bacterium GH-1 can significantly enhance the nitrogen removal efficiency of saline wastewater systems, providing crucial guidance for biological nitrogen removal treatment. Full article

A common approach to estimating the spatial–temporal distribution of atmospheric species properties is data assimilation. Data assimilation methods provide the best estimate of the required parameter by combining observations with appropriate prior information (background) that can include the model output, climatology data, or some other first guess. One of the relatively simple and computationally cheap data assimilation methods is optimal interpolation (OI). It estimates a value of interest through a weighted linear combination of observational data and background that is defined only once for the whole time interval of interest. Spatial–temporal OI (STOI) utilizes both spatial and temporal observational error covariance and background error covariance. This allows for filling in not only spatial, but also temporal gaps in observations. We applied STOI to daily mean aerosol optical depth (AOD) observations obtained at the European AERONET (Aerosol Robotic Network) sites with the use of the GEOS-Chem chemical transport model simulations and the AOD climatology data as backgrounds. We found that mean square errors in the estimate when using modeled data are comparable with those when using climatology data. Based on these results, we merged estimates obtained using modeled and climatology data according to their mean square errors. This allows for improving the AOD estimates in areas where observations are limited in space and time. Full article

The Silvermines Pb-Zn-Ag-Ba orebodies comprise vein, replacement, cross-cutting and stratiform mineralization mostly hosted in Lower Carboniferous limestones in the vicinity of a major ENE and E-W trending normal fault array and represent a classic example of Irish-Type Zn-Pb mineralization. Historically the deposits have been exploited at various times, but the major limestone-hosted Zn-Pb-Ba mineralization was not discovered until the 1960s. Structurally controlled crosscutting vein and breccia mineralization represent pathways of hydrothermal fluids escaping from the Silvermines fault at depth that exhaled and replaced shallowly buried Waulsortian limestones creating the larger stratiform orebodies such as the Upper G and B-Zones. The B-Zone, comprising a pre-mining resource of 4.64 Mt of 4.53% Zn, 3.58% Pb, 30 g/t Ag has a locally highly variable host mineralogy dominated by pyrite, barite, siderite, within dolomitic and limestone breccias with local silica-haematite alteration. A small, highly unusual pod of very high-grade Ag-rich mineralization in the B-Zone, the 4611 Pod, discovered in 1978, has not been previously documented. Unpublished records, field notes, and mineralogical and chemical data from consultant reports have been assimilated to document this interesting and unusual occurrence. The pod, representing an irregular lens of mineralization ca 2 m thick and representing 500 t, occurs within the B-Zone orebody and comprises high grade Zn and Pb sulfides with significant patches of proustite-pyrargyrite (ruby silvers) and a host of associated Pb, Ag, Sb, As, Cu, Ge sulfide minerals, including significant argyrodite. Although evidence of any distinct feeder below the pod is lacking, the nature of the pod, its unusual mineralogy and its paragenesis suggests that it represents a small, possibly late source of exotic hydrothermal fluid where it entered the B-Zone stratiform mineralizing system. Full article

Basil is best known as an aromatic and medicinal herb due to its rich profile of bioactive compounds. While secondary metabolite production, coupled with growth, has been well studied, photosynthesis has often been overlooked in this regard. In this study, we investigate the effect of light intensities of blue, green, red, and white light of semiconductor LEDs up to 10000 µmol m⁻² s⁻¹ on photosynthetic efficiency and primary and secondary metabolism. Chlorophyll fluorescence data indicate that the conversion of light into chemical energy is the same under green, red, and white light, and 35% increased under blue light. Primary metabolism, represented by assimilation rate, shows that blue light has the lowest assimilation, whereas red and (surprisingly) green light have the highest. Light saturation is reached at 1500 µmol m⁻² s⁻¹, while assimilation under green light is maintained up to 5000 µmol m⁻² s⁻¹. The earliest photoinhibition occurred under blue light in comparison to the other light wavelength under investigation. Blue light also enhances the production of phenolic and flavonoid concentrations up to 40% or 100%, respectively. These results show that photosynthesis, photoinhibition, and secondary metabolite production are wavelength-dependent and indicate how energy fluxes between these processes are related. Full article

The metavolcanic rocks around Maroua in the Far North Region of Cameroon are located at the northern margin of the Central African Fold Belt (CAFB) and have not been studied to date. The petrographic and whole-rock geochemical data presented in this paper highlight their magma genesis and geodynamic evolution. The lavas are characterized by basaltic, andesitic, and dacitic compositions and belong to the calc-alkaline medium-K and low-K tholeiite series. The mafic samples are essentially magnesian, while the felsic samples are ferroan. On a chondrite-normalized REE diagram, mafic and felsic rocks display fractionated patterns, with light REE enrichment and heavy REE depletion (La_N/Yb_N = 1.41–5.38). The felsic samples display a negative Eu anomaly (Eu/Eu* = 0.59–0.87), while the mafic lavas are characterized by a positive Eu anomaly (Eu/Eu* = 1.03–1.35) or an absence thereof. On a primitive mantle-normalized trace element diagram, the majority of the samples exhibit negative Ti and Nb–Ta anomalies (0.08–0.9 and 0.54–0.74, respectively). These characteristic features exhibited by the metavolcanic rocks of Maroua are similar to those of subduction-zone melts. This subduction would have taken place after the convergence between the Congo craton (Adamawa-Yadé domain) and the Saharan craton (Western Cameroonian domain). Petrological modelling using major and trace elements suggests a derivation of the Maroua volcanics from primitive parental melts generated by the 5–10% partial melting of a source containing garnet peridotite, probably generated during the interaction between the subducted continental crust and the lithospheric mantle and evolved chemically through fractional crystallization and assimilation. Full article

Oceanic intraplate volcanoes sometimes experience late-stage eruptive activity known as rejuvenated volcanism, and contrasting interpretations for its petrogenesis depend on the compositional characteristics. In the Juan Fernández Ridge (JFR), a volcanic chain approximately 800 km in length emplaced on the Nazca Plate, some subaerial occurrences of rejuvenated volcanism have been recognized on the Robinson Crusoe and Santa Clara Islands, both part of the same deeply eroded shield volcano complex. This study aims to understand the origin and magmatic evolution of rejuvenated volcanism on Santa Clara Island, emplaced after ~2.15 Ma of quiescence above the shield sequence, mainly via the analysis of unpublished geochemical and isotopic data. Field reconnaissance identified two nearly coeval rejuvenated sequences on Santa Clara Island: Bahía W (BW) and Morro Spartan (MS), both formed by basanitic and picro-basaltic lava flows with brecciated levels and local intercalations of sedimentary and pyroclastic deposits. In comparison to the chemical signature of the preceding shield-building stage (comprised mainly of basalts and picrites), the two rejuvenated sequences exhibit a notable enrichment in incompatible elements, but the Sr, Nd, and Pb isotopes are very similar to the FOZO mantle endmember, with an apparent additional contribution of HIMU and EM1 components. The geochemistry of lavas revealed the involvement of various processes, including contamination by ultramafic xenoliths, high-pressure fractional crystallization of olivine and clinopyroxene, and potential partial assimilation of oceanic lithospheric components. While the oceanic lithosphere has been considered as a potential source, the isotopic data from Santa Clara lies outside of the mixing curve between depleted mantle (DM, here represented by the North Chile Rise and the East Pacific Rise) and the previous shield stage, suggesting that a lithospheric mantle is not the primary source for the rejuvenated stage volcanism. Therefore, we favor an origin of the rejuvenated volcanism from the mantle plume forming the JFR, supported by similarities in isotopic signatures with the shield stage and high values of ²⁰⁸Pb/²⁰⁴Pb (only comparable to San Félix—San Ambrosio in the vicinity of JFR), implying the presence of a regional source with radiogenic ²⁰⁸Pb/²⁰⁴Pb isotope ratios. In addition, isotopic variations are subparallel to the mixing line between HIMU and EM1 components, whose participation in different proportions might explain the observed trends. In conclusion, we propose that the source of the rejuvenated volcanism on Santa Clara Island is a heterogeneous mantle plume, the same one that fed the shield stage. The rejuvenated volcanism is derived from a secondary melting zone away from the main axis of the plume. Full article

Top-down observational constraints on emissions flux estimates from satellite observations of chemical composition are subject to biases and errors stemming from transport, chemistry and prior emissions estimates. In this context, we developed an ensemble data assimilation system to optimize the initial conditions for carbon monoxide (CO) and aerosols, while also quantifying the respective emission fluxes with a distinct attribution of anthropogenic and wildfire sources. We present the separate assimilation of CO profile v9 retrievals from the Measurements of Pollution in the Troposphere (MOPITT) instrument and Aerosol Optical Depth (AOD), collection 6.1, from the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments. This assimilation system is built on the Data Assimilation Research Testbed (DART) and includes a meteorological ensemble to assimilate weather observations within the online Community Atmosphere Model with Chemistry (CAM-chem). Inversions indicate an underestimation of CO emissions in CAMS-GLOB-ANT_v5.1 in China for 2015 and an overestimation of CO emissions in the Fire INventory from NCAR (FINN) version 2.2, especially in the tropics. These emissions increments are consistent between the MODIS AOD and the MOPITT CO-based inversions. Additional simulations and comparison with in situ observations from the NASA Atmospheric Tomography Mission (ATom) show that biases in hydroxyl radical (OH) chemistry dominate the CO errors. Full article

We previously introduced the parametric variance Kalman filter (PvKF) assimilation as a cost-efficient system to estimate the dynamics of methane analysis concentrations. As an extension of our development, this study demonstrates the linking of PvKF to a 4D-Var inversion aiming to improve on methane emissions estimation in comparison with the traditional 4D-Var. Using the proposed assimilation–inversion framework, we revisit fundamental assumptions of the perfect and already optimal model state that is typically made in the 4D-Var inversion algorithm. In addition, the new system objectively accounts for error correlations and the evolution of analysis error variances, which are non-trivial or computationally prohibitive to maintain otherwise. We perform observing system simulation experiments (OSSEs) aiming to isolate and explore various effects of the assimilation analysis on the source inversion. The effect of the initial field of analysis, forecast of analysis error covariance, and model error is examined through modified 4D-Var cost functions, while different types of perturbations of the prior emissions are considered. Our results show that using PvKF optimal analysis instead of the model forecast to initialize the inversion improves posterior emissions estimate (~35% reduction in the normalized mean bias, NMB) across the domain. The propagation of analysis error variance using the PvKF formulation also tends to retain the effect of background correlation structures within the observation space and, thus, results in a more reliable estimate of the posterior emissions in most cases (~50% reduction in the normalized mean error, NME). Our sectoral analysis of four main emission categories indicates how the additional information of assimilation analysis enhances the constraints of each emissions sector. Lastly, we found that adding the PvKF optimal analysis field to the cost function benefits the 4D-Var inversion by reducing its computational time (~65%), while including only the error covariance in the cost function has a negligible impact on the inversion time (10–20% reduction). Full article