Environmental Monitoring and Assessment最新文献

Bioindicators add valuable understanding of biological impacts to contaminant monitoring programs. However, attributing effects (e.g., mortality and growth impairment) to contaminant exposures is challenging because of potential confounding by environmental variables. We assessed the influence of four water quality variables (temperature, dissolved oxygen, pH, and conductivity) on contaminant effects assessments during in situ exposures of the amphipod Hyalella azteca in six agricultural and urban watersheds in southern Ontario, Canada (2005–06, 2008–10). We further tested whether sampling in specific months of the growing season would minimize confounding effects. While high toxicity from organophosphate and carbamate pesticides increased mortality and reduced growth in caged amphipods, warmer stream temperatures also affected endpoints, increasing mortality and improving growth. Seasonal patterns indicated early summer (June) as optimal for detecting impacts when: (1) stream pesticide concentrations were highest, (2) acetylcholinesterase (AChE) inhibition (a biomarker of organophosphate/carbamate exposure) was highest, and (3) stream temperature was below its seasonal peak. Specifically, higher correlations among organophosphate pesticide concentrations, AChE inhibition, and mortality indicated better attribution of cause in this month (r = 0.53—0.76, p < 0.05). Ability to discriminate between pesticide-impacted sites and reference sites was also greater than other months (June = 100% correct assignment as high-impact or reference site). Considering sampling times of stream bioindicators that maximize pesticide responses and limit confounding effects of water quality may improve the accuracy and resource-efficiency of biological monitoring programs.

Drought remains a menace in the Horn of Africa; as a result, the Ethiopia’s Genale Dawa River Basin is one of the most vulnerable to agricultural drought. Hence, this study integrates remote sensing and machine learning algorithm for early warning identification through assessment and prediction of index-based agricultural drought over the basin. To track the severity of the drought in the basin from 2003 to 2023, a range of high-resolution satellite imagery output indexes were used, including the Vegetation Condition Index (VCI), Thermal Condition Index (TCI), and Vegetation Health Index (VHI). Additionally, the Artificial Neural Network machine learning technique was used to predict agricultural drought VHI for the period of 2028 and 2033. Results depict that during the 2023 period, 25% of severe drought and 18% of extreme drought countered at the lower part of the basin at Dolo ado and Chereti regions. A high TCI value was found that around 23.24% under extreme drought and low precipitation countered in areas of Moyale, Dolo ado, Dolobay, Afder, and Bure lower than 3.57 mm per month. Similarly, increment of severe drought from 24.26% to 24.58% and 16.53% to 16.58% of extreme drought value of VHI might be experienced during the 2028 and 2033 period respectively in the area of Mada Wolabu, Dolo ado, Dodola, Gore, Gidir, and Rayitu. The findings of this study are significantly essential for the institutes located particularly in the basin as they will allow them to adapt drought-coping mechanisms and decision-making easily.

Due to their resilience, various biological systems under environmental changes typically exhibit nonlinear responses with sudden, abrupt shifts. Although such shifts are theoretically expected, few studies traced state-and-transition dynamics in nature (Liu et al., Science 317:1513–1516, 2007). We analysed 18 years’ data to trace biomass patterns, species assemblages and small mammals’ population trajectories in spontaneously growing forest on formerly ploughed field, hereafter, the postagrogenic forest, and in unmanaged former pasture, hereafter, the grassland. The clear response at individual, populational and ecosystem scales triggered by extraordinary 2010 drought was observed. In the postagrogenic forest, transitioning to the historical ecosystem state, we found a shift from the grassland type of the small mammals’ biomass pattern to the forest type with the abrupt reorganisation of the small mammals’ community. In the grassland, a relatively steady novel ecosystem, we revealed only a long-term diminishing of total small mammals’ biomass, i.e. a regime shift, while maintaining the same functional structure. The changes were based on population response. The bank vole did not show any population reaction, which testifies the ability of individuals to tolerate the drought. The common shrew experienced a population depression, which in postagrogenic forest resulted in the regimen shift after recovery, but in the grassland in only temporal decline with following return to the initial state. The root vole showed a delayed population response with the general decline of population in the grassland, and population collapse in the postagrogenic forest. Therefore, the same impact triggered various responses among different species and resulted in different effects in the successional and steady ecosystems.

The identification of different air pollution sources is essential to effectively control atmospheric pollution, particularly in megacities of emerging countries with rapid economic development, such as the Metropolitan Area of Lima-Callao (MALC). The objective of this research was to identify the main sources of particulate matter pollution by applying bivariate polar plots and the k-means clustering algorithm. These statistical techniques were applied to hourly in situ data of four variables collected over a 5-year period (2015–2019) by the Automatic Air Quality Monitoring Network of the MALC: wind direction, wind speed, PM₁₀, and PM_2.5 concentrations. Average PM₁₀ concentrations ranged from 34 μg m⁻³ (CDM station) to 126.7 μg m⁻³ (VMT station), while average PM_2.5 concentrations ranged from 16.8 μg m⁻³ (CDM station) to 41.2 μg m⁻³ (ATE station). The diurnal variation of PM presented two peaks, one in the morning (from 0800 to 1000 h) and the other at night (from 1900 to 2300 h), with the highest concentrations of PM₁₀ recorded at the ATE (0800 h: 155.8 μg m⁻³) and VMT (2100 h: 154.6 μg m⁻³) stations, and PM_2.5 at the ATE station (0800 h: 60.3 μg m⁻³ and 2300 h: 37.5 μg m⁻³). The results showed that the contributions of PM₁₀ are directly related to emissions from industrial activities, automotive fleet, construction, demolition, wind erosion, and the suspension and resuspension of particulates from unpaved roads. Meanwhile, high concentrations of PM_2.5 are mainly attributed to vehicle exhaust emissions, industrial emissions, secondary particulate formation, and drag by the action of the winds. The major source of particulate matter contamination is the vehicle fleet, and within this, automobiles, station wagons, combi vans, and 2 and 3-wheel motorcycles are those that have the greatest contribution. These results were supported by non-parametric statistical tests such as Kruskal–Wallis and Mann–Whitney U and validated by the conditional bivariate probability function. The findings of this work may help to implement pollution prevention and control strategies in the future within this South American megacity.

Water systems globally are declining in water quality, largely due to anthropogenic activities, with freshwater contamination reaching unprecedented levels. The Brazilian Legal Amazon (BLA), holding 13.56% of the world’s fresh water, is affected due to the lack of basic sanitation. This study assesses the quality of the Lontra River, in southeastern BLA, by monitoring physicochemical and microbiological parameters and detecting DEC E. coli and Salmonella spp. over 16 months, following current regulations. The data were analyzed using Pearson’s chi-square and Fisher’s exact test (p ≤ 0.05), Kruskal–Wallis test (p ≤ 0.05), with Tukey post hoc (α = 0.05), and Spearman’s correlation (p ≤ 0.05). Results showed pH levels were below the optimal range established by Brazilian water quality indices and legislation, with seasonal variations, and dissolved oxygen also below acceptable limits. Thermotolerant coliforms (TtC) exceeded thresholds, signaling microbiological contamination. DEC E. coli and Salmonella spp. were detected in 32.14% and 67.85% of samples, respectively, even in those with low TtC levels. No correlation was found between TtC and pathogen presence, suggesting that meeting minimum water quality standards does not guarantee the absence of pathogenic microorganisms. These findings reveal significant environmental and public health risks related to surface water quality in the Lontra River. Also highlight discrepancies between regulatory water quality standards and the actual conditions observed, underscoring the need for regionally adapted water monitoring and management practices to protect both environmental and public health in the BLA.

The primary objective of this study is to evaluate the content of heavy metals, including cadmium, lead, zinc, arsenic, copper and nickel, in cereal bars intended for consumption by children and adolescents. The study also aims to identify factors that differentiate the content of these products. The study material comprised 50 cereal bars, which were consumed by a population group of children and adolescents. The determination of zinc, arsenic, copper and nickel was accomplished through the utilisation of optical emission spectrometry with inductively coupled plasma excitation on a spectrometer, while cadmium and lead were ascertained by atomic absorption spectrometry with electrothermal atomization (ET-AAS) on an atomic absorption spectrometer. In the heavy metal analysis of the cereal bars, lead was not detected in contents above the limit of quantification (LOQ) of 0.10 mg/kg. However, three samples exceeded the maximum permissible levels of cadmium, with contents of 0.046 mg/kg in two cases and 0.043 mg/kg in one case, while the minimum determined cadmium content was 0.011 mg/kg. The arsenic content of the samples oscillated between 0.56 and 4.16 mg/kg. Although there are no established standards for nickel, zinc and copper, their contents are of potential health significance in the context of chronic exposure, requiring detailed assessment. The results of the statistical analysis showed significant differences in copper and zinc depending on product composition (p ≤ 0.0001), suggesting higher contents of these metals in oatmeal-dominant products, possibly due to specific raw material sources or variation in the production process. The homogeneity of nickel, cadmium and arsenic contents regardless of product composition was confirmed by the absence of significant statistical differences (p > 0.05). The analysis of geographical variation showed that products of Polish origin were characterised by higher copper and cadmium contents compared to products from Germany, suggesting a potential influence of local environmental and technological conditions. Hazard quotient (HQ and HI) calculations for the analysed scenarios showed that the limits were exceeded, especially for products with a predominance of oatmeal and Polish production, highlighting the need for further research into the sources of these contaminants and their health implications.

This study investigates the potential of utilizing pomegranate peel, a waste material, as an environmentally friendly adsorbent for the removal of methylene blue (MB) from wastewater. The novelty of this study lies in producing a competitive adsorbent (96% removal in a reasonably short contact time), very low cost, from a biowaste material and in providing cost analysis, regeneration method, application, and a mechanism based on a set of results that are in harmony with each other. Characterization of the purified pomegranate (Punicagranatum) peels (PP) inferred remarkable acidic sites. The MB removal was observed to increase with increasing dosages of adsorbent, contact time (93–94% removal took place in the first 30–40 min), agitation speed (the amount of MB removed increased with increasing agitation speed up to 160 rpm), pH (increasing solution pH up to pH 8.0, the removal efficiency of MB by PP increased), and MB initial concentrations. The experimental results of MB adsorption on PP were best fitted to the Temkin isotherm model and the kinetic studies showed that the adsorption rate was best fitted to the pseudo-second-order (PSO) model showing a correlation coefficient R² of 0.969–0.991. Thermodynamic investigations demonstrated that the removal of MB dye utilizing PP may be due to a spontaneous, electrostatic, chemical, and exothermic interaction with a heterogeneous adsorption mechanism. Adsorption is anticipated to begin with attraction between cationic MB and negatively charged active functional groups on adsorbent’s surface, soon to be followed by multisite chemical interaction. The results (up to 96% removal) show that pomegranate peels could be used as a potential eco-friendly, cost-effective, and efficient biosorbent for dye removal from wastewaters. With an average removal effectiveness of 86.8%, PP were successfully utilized to remove MB from MB-spiked actual river water samples.

Graphical Abstract

Chitosan (CHI) was modified with iron and aluminum salts to create ionotropic beads, Fe-CHI and Al-CHI, for the removal of polyethylene terephthalate microplastics (PET-MP) from water. Infrared spectroscopy revealed reduced hydrogen bonding associated with N–H vibration of CHI (3500–3100 cm⁻¹) due to the interaction with the metal ions, and absorption peaks between 500 and 916 cm⁻¹ predominantly due to metal–oxygen stretching vibrations. The swelling behavior of the beads increased with temperature but decreased as pH and metal doping concentration increased. Conductivity and PET-MP removal efficiency improved with higher metal ion concentrations, with Al-CHI exhibiting greater swelling and conductivity compared to Fe-CHI. The highest efficiency for MP remediation was recorded at low pH levels. MP adsorption decreased with rising temperatures and varied with pH changes due to protonation and deprotonation reactions of CHI, along with the various cationic and anionic species formed by the metals. At pH 7, MP removal by Fe-CHI beads declined as the doping concentration increased, attributed to specific Fe species that emerged at this pH. The zeta potential measurements showed that both the beads and the MP were in an unstable range at low pH but shifted towards stability at higher pH levels. Re-adsorption efficiencies exceeded 70% for both low and high-doped Fe-CHI and Al-CHI beads when tested with ~ 40 MP/mL of MP suspension over three different cycles. Overall, the use of ionotropic CHI beads offers a promising, eco-friendly method for effectively reducing PET-MPs in water.

The portable X-ray fluorescence (pXRF) spectrometry has been very useful for the characterization of different earth materials, and its application for foliar analysis is really promising. The performance of pXRF for foliar analysis depends on several factors such as concentration of the elements, fluorescence yield which is influenced by atomic number, spectral interference, and water content. Mn is one of the elements that present a prominent fluorescence peak. In this sense, it was hypothesized that pXRF can directly determine the Mn concentration on foliar samples, even when used on intact leaves (fresh or dry) being a useful tool for agronomic and environmental purposes. Thus, the objective was to assess the performance of a pXRF to determine Mn concentration in two different foliar datasets from Brazil/South America and Mali/Africa. In the Brazilian dataset, leaves from eight crops (common bean, castor plant, coffee, eucalyptus, guava tree, maize, mango, and soybean) were scanned via pXRF at the following conditions: intact and fresh leaves, intact and dry leaves, and powdered samples). In the Malian dataset, powdered samples from cotton and maize were analyzed via pXRF. For comparison, Mn concentration was also determined after nitro-perchloric digestion followed by quantification via inductively coupled plasma optical emission spectroscopy (ICP-OES). After descriptive statistics, linear regressions were performed for all sample preparation conditions in both datasets, using Mn concentrations obtained through pXRF and the acid digestion method. The data quality level of all linear regressions was considered quantitative with high R (0.93 to 0.98) and R² (0.87 to 0.96) values. The direct analysis of Mn via pXRF on intact and fresh leaves yielded R of 0.93, R² of 0.87, and a low relative standard deviation (< 10%). The manufactured pXRF calibration used in this work allowed an accurate direct Mn determination in plant leaves. Considering the importance of Mn as a plant micronutrient and its potential toxicity depending on soil redox conditions, the fast, in situ, non-destructive, and eco-friendly determination via pXRF has a tremendous agronomic and environmental application worldwide.