Environmental Monitoring and Assessment最新文献

Remediation of black and odorous water in urban rivers is a key priority for urban water governance, but identifying pollution sources and optimizing strategies remain challenging. This study targeted the 3.2-km Qianjin Canal (Tianjin), integrating the QUAL2Kw model with a self-developed pollution source contribution module (two-step method) to simulate pollutant migration and assess the effectiveness of multiengineering remediation measures. Results showed that the integrated model successfully quantified the contribution of major pollution sources, including point sources (domestic sewage), nonpoint sources (farmland runoff), and source water (rainfall runoff combined with reservoir discharge). Under the combined measures including aeration (20% oxygenation efficiency), water supplementation (0.1 m³/s), and source control, the removal rates of COD, NH₃-N, TN, and TP were 44.0%, 47.5%, 54.1%, and 70.0%, respectively; after treatment, most water quality indicators met GB 3838-2002 Category V. The integrated model effectively supports pollution source identification and remediation strategy optimization for urban black and odorous rivers similar to the Qianjin Canal, providing a targeted scientific framework.

Biodiversity conservation requires rigorous wildlife assessments, and uncrewed aerial vehicles (UAVs) equipped with thermal infrared (TIR) cameras offer a promising tool for surveying large mammals. Despite growing use, formal comparisons of UAV survey methodologies remain limited. We evaluated manual versus programmed flight methods and conducted an orthomosaic trial in Chitwan National Park, Nepal, in May 2022, performing six flights per method across four Terai grassland sites. We compared wildlife counts, survey effort (flight length, duration, number of images, and post-processing requirements), and issues regarding image overlap. Manual flights required 35 min on average, covered 6370 m, and generated 86 images per flight, whereas programmed flights averaged 66 min, 9360 m, and 205 images, representing increases of 57% in flight time, 47% in distance, and 138% in image volume for programmed surveys. There was no significant difference in total mammal counts (P = 0.781) or for specific groups such as deer (P = 0.181) and rhinos (P = 0.515) between the manual and programmed flights. However, manual flights yielded imagery that was better suited for species identification. Both approaches were influenced by observer bias, either in real-time species identification during manual flights or post-processing for programmed flights. Our results highlight that for our study area and species of interest, manual UAV flights were able to reduce survey effort while maintaining comparable detection rates and improving species identification. Orthomosaic processing, using both direct georeferencing and Structure-from-Motion, proved largely ineffective for thermal imagery of mobile mammals, as moving animals were often excluded due to image overlap requirements. The study also offers guidance for designing UAV-based wildlife monitoring programs, highlighting the potential of AI, video, and advanced sensors, as well as important limitations to consider before conducting surveys.

Understanding long-term changes in the spatial distribution and habitat suitability of wildlife is critical for effective conservation planning. This study assessed the spatio-temporal distribution of the southern giraffe (Giraffa giraffa) in Hwange National Park (HNP) and examined how environmental variability has influenced habitat suitability over the past two decades. Giraffe occurrence data were obtained from road-count surveys conducted in 2002, 2012, and 2022 and analyzed alongside key environmental variables, including temperature, Normalized Difference Vegetation Index, rainfall, elevation, terrain ruggedness, and distance to water. To ensure model stability, collinearity among variables was tested using the variance inflation factor. Habitat suitability was modelled using an ensemble approach combining support vector machine (SVM), random forest (Sutton et al.), and maximum entropy (MaxEnt) algorithms, implemented in the flexSDM package in R. Results revealed marked spatial and temporal variations in giraffe habitat suitability, with the highest concentrations consistently recorded in the Main Camp management area. Alarmingly, suitable habitat within HNP declined by approximately 60% over the study period, a trend likely driven by both environmental changes and anthropogenic pressures. Habitat preference analyses further indicated that southern giraffes consistently selected mixed woodland–bushland mosaics, which likely provide access to diverse forage resources, predator avoidance opportunities, and thermoregulatory benefits. These findings highlight the vulnerability of giraffe populations to habitat loss and underscore the importance of integrating long-term environmental dynamics into conservation planning. The study provides essential insights to guide targeted conservation interventions for giraffes in HNP, particularly in light of escalating climate variability and human disturbances across the landscape.

A comprehensive assessment of the anthropogenic transformation of soils in the Rostov agglomeration was carried out using principal component analysis. Based on data from 45 chemical and physical parameters, natural (Ah, A, B), buried (Ab, Bb), and anthropogenic (UR) soil horizons were analyzed. The first four principal components were found to explain 78.3% of the total data variance. Two dominant factors of transformation were identified, which are primarily reflected in the first two principal components: PC1 (36.94% of variance), reflecting processes of physical degradation due to technogenic sand input and associated carbonate pollution (high loadings for sand, inorganic carbon (IC), and Ca content), and PC2 (21.41% of variance), associated with toxic pollution by heavy metals and phosphorus (high negative loadings for Pb, As, Zn, Sr, and P). Analysis of the contribution of individual parameters to the total variance revealed the most significant indicators: Mg, Pb, As, Si, Ca, P, Sr, and TOC. A smaller but statistically significant contribution was made by PC3 (14.25%, carbon and alkaline element balance—Ca and Mg) and PC4 (5.67%, which probably reflects the processes of soil acidification and deterioration of its structure). Clustering in the principal component analysis space confirmed a clear separation of horizons by type and degree of anthropogenic impact, mainly for the first two principal components. The results demonstrate that urbanization leads to a complex transformation of the soil cover, expressed in three main processes: physical degradation (technogenic sand input), chemical pollution (heavy metals), and disruption of the carbon balance (decrease in organic and increase in inorganic carbon). The obtained data allow for the ranking of risk factors and form the basis for developing priority measures for monitoring and remediation of soils in large agglomerations of the European South of Russia.

Nitrogen dioxide (NO₂) is a major atmospheric pollutant that threatens human health and environmental quality amid rapid urbanization and industrialization. The Yellow River Basin is a heavily populated and economically significant area that is essential to China’s industrial and agricultural sectors. For this reason, it is especially critical to accurately measure NO₂ concentrations and related dry deposition fluxes. To estimate near-surface NO₂ concentrations throughout the Yellow River Basin from 2015 to 2023, a Random Forest (RF) machine learning model was created using tropospheric NO₂ column data from the Ozone Monitoring Instrument (OMI), ground-based station observations, and auxiliary environmental variables. With an R² (coefficient of determination) of 0.884 and RMSE (root mean square error) of 4.626 µg/m³ for the training set and an R² of 0.777 with RMSE of 6.447 µg/m³ for the test set, the model demonstrated strong predictive ability. NO₂ levels showed a little downward trend from 2015 to 2021, followed by a modest uptick in 2021–2023, according to spatial and temporal analysis. Seasonally, there was a clear U-shaped pattern with winter peaks and summer troughs in NO₂ concentrations and the associated dry deposition fluxes. Upstream regions like Sichuan and Qinghai had consistently low levels, while industrialized downstream provinces like Shandong, Henan, and Shanxi had high rates. These results offer scientific support for nitrogen load mitigation and air quality management in the Yellow River Basin, as well as crucial insights into the spatial dynamics of NO₂ pollution.

Past studies on Himalayan lakes provide limited understanding of the effectiveness of destratification aeration in improving different water quality characteristics of eutrophicated lakes. Scientific literature lacks a comprehensive understanding of factors influencing artificial aeration’s impact on thermal regimes and water quality in subtropical Himalayan lakes. Addressing the knowledge gap, here, we investigate the effects of bubble plume–type artificial aeration on physicochemical parameters and cyanobacterial blooms in Nainital lake, a eutrophic freshwater lake in the Himalayas. Results show a substantial reduction but not complete elimination of thermal stratification and anoxic conditions that prevailed before aeration. Observed temperature profiles reveal weak thermal stratification during summer and an early (October) lake overturn causing a well-mixed system till late spring. Adequate dissolved oxygen (DO) levels are found for most of the year except for summer hypoxia (< 2 mg/L) limited within 5–7 m of the lake bottom. Significant concentrations (0.5–2.2 µg/L) of phycocyanin only emerge between spring and early summer. Study results highlight a prominent influence of seasonal variability in air temperature on lake temperature; local wind patterns, and rainfall (through nutrient-laden inflows) on DO levels; and solar radiation, mixing intensity, and nutrient levels on cyanobacterial blooms in the aerated system. These factors can be critical in defining the effectiveness of artificial mixing in a subtropical lake, especially in the Himalayas. Thus, the influencing factors should be adequately considered in the design and planning of destratification aeration systems for other eutrophicated lakes in the region.

Climate change and biological invasions are major drivers of global biodiversity loss. Ageratum conyzoides L. is a highly aggressive invader, yet its ecological risks and potential range dynamics in India remain insufficiently quantified. To assess its future invasion potential, we applied an ensemble species distribution modelling approach (BIOMOD2 in R), integrating random forest, artificial neural networks, and generalized linear models. Bioclimatic predictors were obtained from CMIP6-based climate projections across four SSP pathways (WorldClim v2.1). Model performance was evaluated using multiple evaluation metrics including TSS, ROC, and Kappa to ensure robustness. Precipitation-related predictors, including precipitation of the wettest month (BIO13; 500–1000 mm), and precipitation seasonality (BIO15; 40–60%) were identified as dominant drivers of distribution. High-suitability areas (≥ 70% probability), the potential invasion-risk zones, are projected to concentrate in the Western Ghats and the Himalayan foothills, with marked upslope expansion, and to extend into the Eastern Ghats and Central Highlands. Least-suitable habitats (climate refugial zones, ~ 2.40 million km² during 1970–2000) are projected to shrink substantially by 2100, to ~ 1.82 million km² (SSP1-2.6), ~ 1.45 million km² (SSP2-4.5), ~ 1.23 million km² (SSP3-7.0), and ~ 1.04 million km² (SSP5-8.5). These contractions indicate a broad climatic shift toward conditions favorable for the spread of the species. Overall, climate change is projected to markedly enhance the potential spread of A. conyzoides across India. The findings underscore the need for proactive, region-specific management in biodiversity hotspots such as the Western Ghats and Himalayas, the protection of climatically stable refugia, and the integration of predictive modeling into national invasive-species management policies.

Industrial emissions pose significant threats to environmental health. Trees serve as biomonitors, with inherent species-specific capacity. Studies on tree species’ responses across varying pollution loads remain poorly understood. This study evaluated the responses of three common tree species (Mangifera indica, Anthocleista vogeli, and Delonix regia) in two industrial sites (Afam and Umuebule) and a non-industrial site (Ohaji-Egbema Forest Reserve) in Southern Nigeria, using Air Pollution Tolerance Index (APTI) and Anticipated Performance Index (API). Trees with diameter at breast height (DBH) > 15 cm were systematically sampled within a 100 m radius. Leaves were collected in triplicates for each species. Structural attributes (DBH, crown diameter, height) were measured in situ; air quality parameters (Pm, CO, NO₂, SO₂, O₃, VOCs) were monitored diurnally for 4 months, while leaf biochemical properties (chlorophyll, relative water content, ascorbic acid, and pH) were determined in the laboratory. Results revealed elevated air pollutants concentrations at the industrial sites, particularly Umuebule. Two-way ANOVA indicated significant effects of site, time, and site × time interaction on air quality parameters and site, species, and site × species interaction effect on the biochemical parameters and APTI (p < 0.001). Pearson correlation revealed significant association between the biochemical parameters, APTI, and air quality parameters, while structural attributes had no significant correlation with biochemical parameters except RWC. APTI and API classified M. indica as “excellent” species in polluted sites, A. vogeli as “moderate” spp. especially in drought-prone areas, and D. regia as “sensitive” spp. in highly polluted areas. These findings demonstrate that environmental conditions and species-specific traits determine tree responses to air pollution.

Atmospheric methane (CH₄), a potent greenhouse gas, has shown a consistent rise since the Industrial Revolution, contributing significantly to global warming and climate change. Understanding the temporal and spatial variability of methane concentrations (XCH₄) and the factors driving these changes is crucial for effective mitigation strategies. However, the complex, multidimensional, and interdependent nature of these factors poses challenges for conventional statistical and geospatial methods, which often struggle with large data volumes and imbalanced datasets. In this study, we integrate multi-source satellite datasets with environmental, meteorological, and socioeconomic variables across Pakistan for the period 2010 to 2020. We employed the random forest machine learning algorithm to analyze complex, nonlinear interactions among these variables and to map the seasonal spatial distribution of dominant CH₄ drivers. The permutation importance metric is used to identify the most influential factors affecting CH₄ concentrations. Our results show that CH₄ concentrations in Pakistan have been increasing at an average annual rate of approximately ~ 13 ppb over the study period. Random forest effectively captures the nonlinear interactions between variables, while the permutation importance metric helps identify the most influential factors. This machine learning framework offers a scalable and efficient method for interpreting complex satellite datasets, providing valuable insights for methane emission monitoring and policy development.

This study examines how canopy closure and vertical vegetation layering within habitat patches in an urban park shape microclimatic conditions and thermal comfort. Habitat patches were identified using a plant-based habitat classification approach. The sky view factor (SVF) was calculated, and microclimate measurements were conducted using fixed and portable sensors. Thermal comfort was assessed using the Universal Thermal Climate Index (UTCI), Humidex, and mean radiant temperature (Tₘᵣₜ), while the park’s contextual cooling effect relative to the surrounding urban fabric was quantified through park cooling intensity (PCI) based on control point comparisons. The results indicate that single-layer patches exhibited the highest maximum temperatures during summer, whereas three- and five-layered structures tended to reduce daytime temperature peaks. Although increased layering in summer reduced daytime temperatures, it was associated with elevated nighttime maxima under certain conditions. In autumn, five-layered structures produced the lowest average temperatures, while permeable three-layered patches composed of tree, shrub, and groundcover combinations. Regarding the radiative environment, multi-layered and evergreen-dominant patches showed reduced Tₘᵣₜ and substantially suppressed midday heat stress, whereas more open and weakly layered patches exhibited increased Tₘᵣₜ and heat stress exposure ≥ 26 °C during periods of intense solar radiation. In winter, higher SVF increased daytime heat gains but amplified nighttime temperature variability through radiative loss and wind exposure. Overall, the findings offer a seasonally and spatially applicable framework for understanding how multi-layered vegetation structures contribute to thermal comfort in urban park environments.