Environmental Monitoring and Assessment最新文献

The sources of atmospheric microplastics (AMPs) are complex and widely distributed. Microplastic pollution is particularly severe in urban areas. In this study, the abundance of AMPs was investigated at ten representative sampling points, with three points at an experimental building, and seven sample points at a residential district, an industrial area, a park, a farmland, a roadside, a river, and a seaside, respectively. The results show that the average abundance of AMPs is 2.22 n/m³, with a range from 1.31 to 4.5 n/m³. Human activities significantly contribute to the release of MPs. Furthermore, the abundance of AMPs decreases with increasing altitude. The predominant colors of AMPs are black and transparent, and particle sizes predominantly range from 50 to 200 µm. The micro-Fourier transform infrared spectrometer (µ-FTIR) analysis indicates that AMPs are primarily composed of polyethylene (PE), polypropylene (PP), and polyethylene terephthalate (PET), with fibrous shapes being predominant. In the principal component analysis (PCA), it was observed that AMPs exhibit a positive correlation with temperature and a negative correlation with humidity. This research may shed new light on future policy-making in microplastic control.

This pioneering study evaluates the prevalence of aeroallergens reactivity among atopic populations living in the Vaal Triangle Airshed Priority Area (VTAPA), South Africa. A total of 138 volunteers (51 males and 87 females), of African, colored, white, and Asian ethnicity, and with a mean (range) age of 22 (18–56) years were participating in the study. The study was conducted on the North-West University (NWU) campus in Vanderbijlpark/VTAPA. The International Study of Asthma and Allergies in Childhood questionnaire was utilized for pre-screening to identify individuals with probable allergic dispositions. Subsequently, skin prick testing was conducted using commercial aeroallergen extracts for all confirmed participants with allergy symptoms. One hundred six participants were clinically diagnosed with pollen and fungal spore allergies. The highest allergy prevalence was attributed to Cynodon dactylon ((L.) Pers) (Bermuda grass) (41.5%), followed by Lolium perenne (L.) (ryegrass), grass mix, and Zea mays (L.) (maize) (31.1%), respectively. Moreover, among the tree allergens, Olea (L.) (olive tree) was the most prevalent allergen (20; 18.8%), followed by Platanus (L.) (plane tree) (18; 16.9%). Among the weeds, 16 (15.1%) participants were allergic to the weed mix (Artemisia (L.) (wormwood), Chenopodium (Link) (goosefoot), Salsola (L.) (saltwort), Plantago (L.) (plantain), and 11 (10.3%) to Ambrosia (L.) (ragweed)). Regarding the fungal spores, Alternaria (Fr.) (9; 8.5%) followed by Cladosporium (Link) (5; 4.7%) had the highest skin sensitivity. In this pilot study, our findings provide insights into the prevalence of allergic responses in the study population—underlining the strong impact of allergens of exotic plants—and contribute to the existing aerobiological data in South Africa.

The Shitalakshya River, vital to the Dhaka district, faces severe pollution challenges due to industrial discharges, urban runoff, and other anthropogenic activities. This study investigated the concentration of polycyclic aromatic hydrocarbons (PAHs) and heavy metals in the river water, utilizing GC–MS/MS and ICP-MS techniques. The results revealed a total PAH concentration ranging from 4.97 to 5.87 ng/mL, with 3-ring PAHs being the most prevalent. Heavy metals such as Fe, As, Ni, and Zn were found in significant concentrations, exceeding international standards for drinking water and aquatic life. The ecological risk assessment identified benzo(b)fluoranthene, benzo(k)fluoranthene, and indeno(1,2,3-cd)pyrene as the highest threats to aquatic organisms. Health risk assessments indicated substantial risks from dermal and ingestion exposures, particularly due to arsenic, highlighting potential long-term health implications for local residents. The study underscores the urgent need for comprehensive monitoring, pollution source identification, and stringent regulatory measures to mitigate these risks.

The desertification in the Qaidam Basin has significantly impacted the ecological environment and human livelihood. Amidst the backdrop of anomalous climate warming, predicting the dynamic changes and future trends of desertification within the basin is imperative. In this study, we employ a variety of spatio-temporal statistical analyses to examine the evolutionary trend and driving forces of desertification from 2000 to 2021, integrating vegetation coverage (FVC) indices with climatic factors. Furthermore, a predictive model for desertification was developed, utilizing 6th international coupled model comparison programme (CMIP6) model data coupled with a multivariate pixel-based regression approach. The results indicate a 13% reduction, equivalent to 35,766 km², in the area of severe desertification in the Qaidam Basin from 2000 to 2021. Both non-desertification and mild desertification increased by 7%, indicating a notable reduction in the severity of desertification processes. However, compared to the period from 2000 to 2010, the pace of desertification reversal slowed down between 2011 and 2021, corresponding to the waning upward trend in temperature and precipitation in the upper basin. The desertification prediction model revealed that under the SSP1-26, SSP3-70, SSP2-45, and SSP5-85 scenarios, the vegetation coverage is projected to decline at rates of 0.004/10a, 0.003/10a, 0.002/10a, and 0.002/10a, respectively, from 2015 to 2100. This suggests that desertification in the basin is likely to worsen over time, with greater radiative forcing leading to more pronounced desertification effects. Future FVC projections suggest that desertification mitigation in the Qaidam Basin will plateau around 2040 and then worsen, particularly in the northeast Qilian Mountains. This trend may be due to glacier melting from ongoing climate warming, leading to reduced regional water resources.

It is important to quantify the emissions from livestock to adapt mitigation practices for the rural communities where the livestock populations lie. This study reviewed the existing empirical models and selected a simple model that requires only one input, i.e., the dry matter intake (DMI), to estimate methane emissions from livestock. This input can be easily recorded by the rural communities to quantify the emissions from their livestock. The data required to estimate the methane emissions is collected from selected rural communities in the northern part of India. It is observed from the pilot study that based on the quantity of feed, the emissions are highest for buffaloes (133.65–275.63 g/d/livestock) followed by cows (109.2–217.42 g/d/livestock) and sheep (41.81–58.93 g/d/livestock). The study also recommends the necessity to focus on quality feeds, feed additives such as coconut oil and seaweed, using improved forage varieties, technological innovations for breeding, manure management, and sustainable integrated livestock farming systems. Policies and schemes are also required to mainstream research on livestock and issues leading to emissions, such as scaling up the production of low-emission species like poultry, sheep, and pigs. Policies promoting mixed farming and advanced breeding research, improved feed quality and accessibility, and policies to support incentives that can drive behavioral changes among producers and consumers should also be analyzed and updated. Livestock are mainly in rural communities, and hence it is an important task for researchers and academicians to train the rural communities to quantify the emissions, and the adaptation and mitigation practices to overcome them. The outcome of the study can be used as resource material to empower rural communities.

Mercury (Hg) is a highly toxic heavy metal that presents a notable and worldwide threat to human health and the environment. The most direct method to evaluate the potential effects on human health due to Hg exposure is to monitor biological samples. When biological samples are limited, predictive models are valuable tools to estimate levels of Hg exposure. In this study, fish consumption data was used to compare two toxicokinetic models to predict Hg exposure in a coastal population in northwestern Mexico. To calculate daily Hg intake, 15 children, 42 women, and 18 men were surveyed regarding their fish consumption habits. The data were analyzed using deterministic and probabilistic models, and the results were validated by comparing them with the Hg levels in their hair. Fish consumption varied from 46 to 219 g·day⁻¹. Notably, 6.7% of participants exhibited Hg levels that exceeded the oral reference dose (RfD) of 0.1 μg·kg⁻¹ bw·day⁻¹ and were thus considered to be at risk of adverse health effects. The average Hg concentration in hair among the sampled groups ranged from 1.59 to 4.42 μg·g⁻¹ (with two outlier values of 16.96 and 54.07 μg·g⁻¹). The Hg levels in 86.85% of the population surpassed the reference value of 1 μg·g⁻¹. The predictions generated by the deterministic and probabilistic models based on the ingestion rate (CRj) closely mirrored the actual Hg levels in hair. We highlight the importance of mathematical models to predict the body burden of Hg, particularly when sampling resources are limited.

Ensuring global food security in the face of growing population, climate change, and resource limitations is a critical challenge. Hyperspectral imaging (HSI), particularly when combined with drone technology, offers innovative solutions to enhance agricultural productivity and food quality by providing detailed, real-time data on crop health, disease detection, water and nutrient management, and post-harvest quality control. This review highlights the applications of drone-based HSI in precision agriculture, where it enables early detection of crop stress, accurate yield prediction, and soil health assessment. In post-harvest management, HSI is utilized to monitor food freshness and ripeness and detect potential contaminants, improving food safety and reducing waste. While the benefits of HSI are significant, challenges such as managing large volumes of data, translating spectral information into actionable insights, and ensuring cost-effective access for smallholder farmers remain barriers to its widespread adoption. Looking forward, future directions include advancements in miniaturized sensors, integration with Internet of Things (IoT) devices and satellite data for comprehensive agricultural monitoring, and expanding HSI applications to precision animal sciences. Collaboration among researchers, policymakers, and industry will be crucial to scaling the impact of HSI on global food systems, ensuring sustainable and equitable access to technology.

Land surface temperature (LST) trends, influenced by climate change, affect vegetation health and productivity, while vegetation, in turn, alters LST by regulating the surface energy balance. These interactions vary by region and vegetation type. In this study, we aimed to (1) examine long-term trends in vegetation conditions and LST over time, and (2) investigate the interactions between vegetation conditions and LST within distinct vegetation types across the Arasbaran Biosphere Reserve. Sentinel-2 spectral-temporal features and the Random Forest model were employed to classify different vegetation types. Time series data for the normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and LST were generated using harmonized Landsat data from 1987 to 2023. Various spatial statistical analyses were applied to address the research questions. The results revealed significant spatial and temporal variations in NDVI, NDWI, and LST among vegetation types. The highest volatility in vegetation conditions occurred in dense and sparse forests, while grasslands exhibited the lowest levels of variability. This variability coincided with an overall increasing trend in NDVI, NDWI, and LST, which was most pronounced in dense forests. Furthermore, a strong negative correlation between NDVI, NDWI, and LST was observed, particularly in croplands. These findings collectively indicate a greening trend in the study area, with forests showing the most pronounced increases. The results also underscore the role of forests and dense vegetation in mitigating projected temperature increases. These insights can inform local land management strategies and decision-making.

Pakistan has a limited forest coverage, with a significant portion, approximately 40%, concentrated in the Khyber Pakhtunkhwa (KP) region. This highlights the regional significance of KP in terms of forest wealth within the country. The substantial utilization and excessive exploitation of forests have negatively affected the ecosystems. This study aimed to focus on the environmental and social variables and their contribution to the onset of forest fires in KP using Maximum Entropy Model (Maxent). MODIS active fire data history from 2000 to 2022 was studied to establish the relation between forest fire likelihood and environmental conditions. The variables under study included raster data of temperature, wind, precipitation, elevation, slope, aspect, and population density with 2.5-min resolution accessed from Worldclim. The area under curve (AUC) fire probability value was determined to be 0.833, suggesting strong performance of the model. The jackknife analysis indicated the highest contribution of wind (34.2%) followed by precipitation (33.7%) and temperature (18.9%). Maxent was also used to study the potential fire risk zones. It was observed that 53% of the study area is under high-risk, 12% under moderate-risk, and 35% under low-risk. High-risk areas include Abbottabad, Mansehra, Battagram, Shangla, and some parts of Buner and Haripur. These results can prove to be helpful insight in developing preventive strategies for more focused fire management plans that can help reduce fire risk by considering environmental and socioeconomic conditions.

In honey bee diet, pollen is the primary source of proteins and essential nutrients. High pollen diversity and protein content support honey bee health, enhancing resistance to different stressors. Agroecosystem simplification, with few dominant species flowering for a limited period, can lead to a shortage of forage and a reduction in the variety and quantity of food. We therefore investigated how agroecosystem landscape characteristics influence pollen collection patterns. We collected beebread from 25 apiaries, located in Emilia-Romagna (Northeastern Italy), in March and June 2021 and 2022. We evaluated their pollen diversity and protein content and assessed the relationship with landscape heterogeneity and composition in a 1500 m radius around each apiary. A total of 138 pollen taxa were identified, predominantly from the Fabaceae, Rosaceae, and Asteraceae families. Pollen richness was significantly higher in June than in March for both years. Protein content, on the other hand, was higher in 2021 compared to 2022 and, for 2022 only, in June compared to March. Cluster analysis of the 25 sites according to their landscape characteristics revealed three distinct groups: Group 1 (mainly arable land), Group 2 (mixed arable land and forest), and Group 3 (arable land and permanent crops). Group 1 had lower landscape heterogeneity. Pollen composition did not differ significantly among groups, suggesting that honey bees might expand their foraging area (over the 1500 m radius that we consider) in response to landscape homogeneity, as observed in Group 1 areas. On the other hand, pollen diversity was highest in Group 3, likely due to the variety of fruit tree species and spontaneous flora.