Environmental Monitoring and Assessment最新文献

This study is an attempt to determine natural radioactivity levels in soils collected from the mountains surrounding the Koya District in the Kurdistan Region, Iraq. A portable radioisotope identifier was used for gamma-ray detection from the sampling points to avoid random sample collection. Surface soil samples were taken based on the level of gamma ray detection from the sample locations. A high-resolution detector was used to measure radionuclide content of the samples. The obtained results ranged from 11.49 to 90.32 Bq kg⁻¹ for ²²⁶Ra, 18.15 to 106.8 Bq kg⁻¹ for ²³²Th, and 51.59 to 817.9 Bq kg⁻¹ for ⁴⁰ K. Additionally, the radiation hazard parameters were calculated. The average values of absorbed dose rate (D), annual effective dose equivalent (AEDE), and excess lifetime cancer risk (ELCR) were found to be higher than the permissible limits. The distribution of ²²⁶Ra, ²³²Th, and ⁴⁰ K in the soils of the study area was visually depicted by spatial distribution maps. In addition, the correlation between radionuclides and radiological risk metrics was investigated using a multivariate statistical approach. The results showed that the soils of the Bawajy and Bnabawe mountains present a moderate radiation risk due to moderately high levels of ²²⁶Ra, ²³²Th, and ⁴⁰ K.

Streams in agricultural areas are susceptible to contamination by various compounds, including pesticides. Although spot water sampling is traditionally used for monitoring pesticide contamination of surface waters, this method may underestimate the presence and bioavailability of certain compounds. To better assess environmental exposure, this study investigates the potential of epilithic biofilm (periphyton) as a biomonitor of pesticide contamination. Biofilms were sampled from several streams in the province of Quebec (East Canada) subjected to “moderate” and “intense” agricultural pressure to determine pesticide accumulation of selected herbicides (atrazine, S-metolachlor, imazethapyr) and insecticides (chlorantraniliprole, clothianidin, thiamethoxam, chlorpyrifos) using an optimized QuEChERS extraction method. Benthic macroinvertebrates were also sampled at certain sites to assess their potential as indicators of pesticide exposure using the same approach as biofilms. High detection frequencies and elevated concentrations of atrazine, S-metolachlor, chlorantraniliprole and chlorpyrifos were observed in periphyton from areas under intense agricultural pressure. Pesticides were more frequently detected in water than in periphyton, except for chlorpyrifos. Relationships between concentrations in water and in periphyton followed a linear model for atrazine, S-metolachlor and chlorantraniliprole. This study highlights the potential of periphytic biofilms as complementary tools for pesticide monitoring, although further laboratory and in situ validation is needed. Macroinvertebrates also accumulated pesticides under intense agricultural pressure, particularly the insecticide chlorantraniliprole, raising concerns about potential risks to invertebrate communities. These findings illustrate the affinity of pesticides for different matrices (water versus periphyton versus invertebrates) and the need for monitoring programs not to rely solely on spot water measurements.

Organic pollution in the lake water bodies poses a serious threat to the stability of aquatic ecosystems and human health. Dissolved organic matter (DOM) is a key component of organic pollution. The analysis of its sources is crucial for pollution control. In order to accurately trace the source of organic pollution in the Changdang Lake basin, this study proposes a traceability method combining three-dimensional excitation-emission matrix (3D-EEM) fluorescence spectroscopy technology with a deep learning model. First, water quality samples were collected from the rivers connected to Changdang Lake and surrounding industrial wastewater, agriculture, and domestic pollution sources. Raw data were obtained by 3D-EEM spectroscopy. Parallel factor analysis (PARAFAC) was used to analyze the fluorescence data. Obtain fluorescence spectral images that characterize different pollution sources. Industrial wastewater, agricultural, and domestic pollution sources were used as training data (labeled 0, 1, and 2, respectively) to build and pre-train a deep learning model. Four deep learning models (Transformer, GoogLeNet, VGG, and AlexNet) were selected for comparison. Transformer model performs better in terms of both recognition efficiency and accuracy. The fluorescence spectrum images of the rivers connected to Changdang Lake and Lake body were input into the trained Transformer model to identify the sources of pollution. Tucker Congruence (TC) coefficients are introduced to quantify and verify the recognition results. The results show that for 40 fluorescent components in the Changdang Lake basin, the identification results of 38 components are consistent with the TC coefficients, with an identification accuracy rate of 95%. Compared with the traditional manual tracing method that relies on TC coefficients, this method significantly reduces the workload. The time required has been reduced from hours to minutes. This study provides efficient and reliable technical support for tracing the source of organic pollution in lake basins.

This study characterized the contamination, sources, and associated risks of heavy metals in the soil–rice system of the Nandu River Basin, a typical subtropical red soil region in China. Concentrations of eight heavy metals and metalloids (Cr, Ni, Cu, Zn, Pb, Cd, Hg, As) were determined in forty paired soil–rice samples. Pollution status and ecological risk were evaluated using the Nemerow integrated pollution index and Hakanson’s potential ecological risk index, respectively. Human health risks were assessed via a multi-pathway exposure model, and source apportionment was performed by integrating correlation analysis, principal component analysis (PCA), and the absolute principal component score–multiple linear regression (APCS-MLR) receptor model. Results showed that although most soil metals did not exceed national risk screening thresholds, Hg was severely enriched (6.44 times the local background level), posing a high ecological risk. In rice grains, Cr exceeded the food safety standard in 12% of samples. Rice consumption posed unacceptable carcinogenic risks from As and Cd, and unacceptable non-carcinogenic risks from As and Cr. The high spatial variability of Hg and Pb in rice (coefficient of variation, CV > 140%) indicated strong anthropogenic influence. Source analysis identified four principal components. PC1 (47.3%) represented a mixed source of volcanic weathering and traffic/agricultural activities (Pb, Zn, Ni). PC2 (25.66%) reflected combined geogenic and agricultural inputs (As, Cu, Cr). PC3 (10.86%) was linked to electronics and traffic emissions (Hg). PC4 (7.32%) was attributed to fertilizer application (Cd). Health risk assessment showed children faced a 78% higher carcinogenic risk from oral soil As intake than adults. Dermal contact with soil Cr also posed a notable non-carcinogenic risk for adults. In conclusion, contamination in the basin originates from composite natural–anthropogenic sources. Hg is the primary driver of ecological risk, while Cr, As, and Cd in rice constitute key health concerns, especially for children. These findings provide a critical scientific basis for developing targeted risk management strategies for heavy metals in high‑background red soil agricultural regions.

Global climate change presents numerous changes to terrestrial ecosystems, including warming, species extinction, habitat shrinkage and shift, invasion of weed species, and biodiversity loss. Mountain regions such as the Himalayas are witnessing species alterations and habitat shifts. Invasive species, such as Lantana camara, are among the species most affected by climate change-induced warming. Multiple studies focused on the negative impact of this weed, its physiology, medicinal properties, and growth in the tropics under climate change. A gap remained in understanding how this weed interacts and grows in different ecosystems across the altitudinal gradient of mountainous landscapes under climate change. The Central Himalayas provide a platform for studying climate-induced altitudinal habitat change, with a decadal rate of warming ranging from 0.3 to 0.9 °C and high variations in elevation (213 to 7500 m). The distribution of Lantana camara in 2000, 2024, and 2050 has been estimated in response to changing climatic setups using a random forest algorithm. Findings revealed a strong correlation between habitat shifts of Lantana camara and changes in thermal zones, resulting in a 173% expansion of its habitat between 2000 and 2050. Lantana camara is climbing to middle altitudes from the lowlands, invading present-day temperate ecosystems. Without immediate intervention, it can disrupt the availability of medicinal herbs and species composition in middle and high altitudes. Thus, management of this weed using a combination of manual, mechanical, chemical, and biological methods needs to be implemented.

Tropical rainforests are critical biodiversity hotspots but are increasingly threatened by anthropogenic pressures that degrade their structure and ecological integrity. Monitoring environmental indicators of forest health is essential for assessing the effectiveness of management interventions. This study evaluated the effects of contrasting forest management regimes on stand structure, regeneration, and biodiversity in Kakamega Forest. Two regimes were assessed: the strictly protected Buyangu block under government management and the participatory forest management (PFM) block, subdivided into Isecheno (low disturbance), Ikuywa (medium disturbance), and Ileho (high disturbance), representing a clear gradient of management intensity and human pressure. We employed systematic transect surveys and plot-based sampling to quantify structural, regeneration and biodiversity indicators. Significant differences emerged across management regimes and zones. The Isecheno zone under PFM exhibited stronger forest structural condition, with a basal area (BA) of 89 ± 9 m² ha⁻¹ and tree species richness of 50 ± 2 species, compared to Buyangu’s lower richness (42 ± 3 species). In contrast, Ileho showed greater anthropogenic pressure, with logging levels far exceeding those in other sites (150 ± 20 stumps ha⁻¹), open canopy (50% closure), and reduced species richness (40 ± 1 species). Buyangu displayed the highest canopy closure (85%) and dead tree density (87 ± 15 ha⁻¹), reflecting minimal human disturbance and a closed-canopy stand structure. These results demonstrate that integrating community engagement, robust environmental monitoring, and adaptive, site-specific strategies can improve forest health indicators while supporting conservation and livelihoods. The findings provide transferable lessons for tropical forests worldwide facing similar conservation challenges.

The assemblage of moss communities depends on substrata, climatic variables, and vegetation types. This study explored moss species richness across four vegetation types in the North-Western Himalayas—Moist Temperate Deciduous Forest, Dwarf Rhododendron Scrub, Alpine Pastures, and Dry Deodar Forest—investigating the roles of substrata, vegetation types, and microclimatic factors in shaping moss assemblages and ecological functions. Using the floristic habitat sampling method, we recorded a total of 174 moss taxa, with 51 new records in these vegetation types and 3 species, i.e., Bucklandiella nitidula, Trachypodopsis formosana, and Entosthodon rubiginosus, reported for the first time in India. We estimated the species richness and relative richness (at species, genus, and family level) across all vegetation types. Among the studied vegetation types, we found that Moist Temperate Deciduous Forest showed the highest species richness (87 taxa). The Dry Deodar Forest had the highest family relative richness (24.39%) relative to all the other studied vegetation types. Based on the evaluation of different substrata, we found phorophytes, particularly Rhododendron arboreum, and rocks as most moss-rich substrates, the former supporting 20 distinct epiphytic taxa. Beta diversity was found to be relatively high (2.34). While CCA ordination revealed discernible gradients relating elevation and climatic variables to moss assembly patterns, subsequent Monte Carlo permutation tests indicated these patterns were not statistically significant, highlighting the exploratory nature of these associations. Selective distribution of mosses was observed on the basis of climate patterns and substratum. Our findings underscore the ecological importance of phorophytes and undisturbed natural topography, highlighting their role in moss species richness and the need for their conservation.

Open-pit mining disrupts landscape structure and ecological functions, directly affecting habitat quality (HQ) and landscape ecological risk (LER). While balancing these factors is critical for sustainable mining management, integrated approaches remain limited. To address this gap, we propose a two-dimensional framework that integrates habitat quality and landscape ecological risk, offering a more detailed, tree-level assessment compared to conventional land-use-based approaches. The results indicate that (1) low/lower-quality habitats persistently exceeded 69% across mining stages, with degradation dominating initial/developmental phases (1990–2010) and improvement emerging in the stable phase (2010–2020). (2) High LER areas correlated with forest/grassland fragmentation, whereas low LER zones linked to construction/bare land continuity. Notably, forest and farmland expansion in stable stages increased LER, requiring targeted land-use strategies to mitigate risks. (3) The key transitions in ecosystem coordination zones included the conversion of bare land and construction land to forestland_UP-RP (Ulmus pumila–Robinia pseudoacacia, UP-RP), forestland_PT (Pinus tabuliformis, PT), and grassland. Although transitions (e.g., construction land to UP-RP, bare land to PT) improved HQ, they still posed landscape ecological risks. These findings strengthen land-use planning’s scientific basis and provide actionable ecological governance insights for mining areas, fragile cities, and resource-based regions, while their enhanced detail improves assessment accuracy and enables precise restoration strategies.