Environmental Variables Research Articles

Space and spatially structured environmental variables account for variation in blackfly and mayfly communities in sub-tropical South African rivers

Using aquatic invertebrate assemblages for bioassessment of streams and rivers is a fast and cost-effective approach to studying the impact of environmental stressors in aquatic ecosystems. The use of aquatic invertebrates is premised on their predictable responses to environmental stressors. Assemblage structure is not, however, only influenced by environment, but also neutral processes and temporal variation. This study quantified the relative roles of the environment (niche processes), space (neutral processes) and temporal factors in structuring mayfly and blackfly assemblages at family and species levels. Asymmetric eigenvector maps in conjunction with canonical redundancy analysis and variation partitioning were used to explore the relative roles of these three factors in structuring macroinvertebrate assemblages. At a species level, flow and water temperature were more important in explaining assemblage structure than at the family level. At the family level, space was more important than temporal and environmental factors. Unexplained variation at species and family level was more than 50%. This suggests (i) caution in using family-level rapid assessments to infer the role of the environment in structuring aquatic macroinvertebrate assemblages, as neutral processes are also dominant; and (ii) that the importance of stochastic processes, such as dispersal, should not be underestimated in rapid assessments.

Spatially explicit predictions using spatial eigenvector maps

Abstract In this paper, we explain how to obtain sets of descriptors of the spatial variation, which we call “predictive Moran's eigenvector maps” (pMEM), that can be used to make spatially explicit predictions for any environmental variables, biotic or abiotic. It unites features of a method called “Moran's eigenvector maps” (MEM) and those of spatial interpolation, and produces sets of descriptors that can be used with any other modelling method, such as regressions, support vector machines, regression trees, artificial neural networks and so on. The pMEM are the predictive eigenvectors produced by using a distance‐weighting function (DWF) in the construction of MEM. Seven types of pMEM, each associated with one of seven different DWFs, were defined and studied. We performed a simulation study to determine the power of different types of pMEM eigenfunctions at making accurate predictions for spatially structured variables. We exemplified the application of the method to the prediction of the spatial distribution of 35 Oribatid mites living in a peat moss (Sphagnum) mat on the shore of a Laurentian lake. We also provide an R language package called pMEM to make calculations easily available to end users. The results indicate that anyone of the pMEMs obtained from the different DWFs could be the best suited one to predict spatial variability in a given data set. Their application to the prediction of mite distributions highlights the capability of pMEMs for predicting distributions, and for providing spatially explicit estimates of environmental variables that are useful for predicting distributions.

A Global Perspective on Renewable Energy Implementation: Commitment Requires Action

Meeting renewable energy targets is one of the most significant global challenges to achieving SDG 7—Ensure access to affordable, reliable, sustainable, and modern energy for all. This study focuses on the global energy transition to understand the factors that influence success or failure in achieving targets. First, the gap between the stated targets and our predictions was calculated. Next, the roles of economic, political, and environmental variables in determining this gap were analyzed. Data were collected from 63 countries from 2000 to 2022, ensuring the global representativeness and robustness of the results. Many countries may struggle to meet their renewable energy targets. Political stability, regulatory quality, and investment freedom play a remarkable role in helping countries get closer to achieving their targets. More industrialized countries with large populations face greater challenges due to high energy intensity. This paper aims to predict the propensity of countries to meet their energy targets by integrating the forecasting and analysis of the economic, political, and geographical factors that influence a green transition. The results provide new insights into how socioeconomic and geopolitical differences influence the energy transition, offering insights for more effective policies. It is argued that accelerated administrative procedures are needed to reduce investment uncertainty and improve energy systems’ flexibility. In addition, involving local communities in the decision-making process is important to ensure the acceptance of RE projects. Finally, introducing energy markets that reflect the characteristics of renewable sources is recommended to facilitate a more rapid and sustainable transition.

Cross-Country Assessment of Socio-Ecological Drivers of COVID-19 Dynamics in Africa: A Spatial Modelling Approach

Understanding how countries’ socio-economic, environmental, health status, and climate factors have influenced the dynamics of COVID-19 is essential for public health, particularly in Africa. This study explored the relationships between African countries’ COVID-19 cases and deaths and their socio-economic, environmental, health, clinical, and climate variables. It compared the performance of Ordinary Least Square (OLS) regression, the spatial lag model (SLM), the spatial error model (SEM), and the conditional autoregressive model (CAR) using statistics such as the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Root Mean Square Error (RMSE), and coefficient of determination (R2). Results showed that the SEM with the 10-nearest neighbours matrix weights performed better for the number of cases, while the SEM with the maximum distance matrix weights performed better for the number of deaths. For the cases, the number of tests followed by the adjusted savings, Gross Domestic Product (GDP) per capita, dependence ratio, and annual temperature were the strongest covariates. For deaths, the number of tests followed by malaria prevalence, prevalence of communicable diseases, adjusted savings, GDP, dependence ratio, Human Immunodeficiency Virus (HIV) prevalence, and moisture index of the moistest quarter play a critical role in explaining disparities across countries. This study illustrates the importance of accounting for spatial autocorrelation in modelling the dynamics of the disease while highlighting the role of countries’ specific factors in driving its dynamics.

Estimating and Modeling Pinus contorta Transpiration in a Montane Meadow Using Sap-Flow Measurements

This study quantifies the transpiration of encroached lodgepole pine (Pinus contorta var. murryana (Grev. & Balf.) Engelm.) in a montane meadow using pre-restoration sap-flow measurements. Lodgepole pine transpiration and its response to environmental variables were examined in Rock Creek Meadow (RCM), Southern Cascade Range, CA, USA. Sap-flow data from lodgepole pines were scaled to the meadow using tree survey data and then validated with MODIS evapotranspiration estimates for the 2019 and 2020 growing seasons. A modified Jarvis–Stewart model calibrated to 2020 sap-flow data analyzed lodgepole pine transpiration’s correlation with solar radiation, air temperature, vapor pressure deficit, and soil volumetric water content. Model validation utilized 2021 growing season sap-flow data. Calibration and validation employed a Markov Chain Monte Carlo (MCMC) approach through the DREAM(ZS) algorithm with a generalized likelihood (GL) function, enabling parameter and total uncertainty assessment. The model’s scaling was compared with simple scaling estimates. Average lodgepole pine transpiration at RCM ranged between 220.6 ± 25.3 and 393.4 ± 45.7 mm for the campaign (mid-July 2019 to mid-August 2020) and 100.2 ± 11.5 to 178.8 ± 20.7 mm for the 2020 partial growing season (April to mid-August), akin to MODIS ET. The model aligned well with observed normalized sap-velocity during the 2020 growing season (RMSE = 0.087). However, sap-velocity, on average, was underpredicted by the model (PBIAS = −6.579%). Model validation mirrored calibration in performance metrics (RMSE = 0.1233; PBIAS = −2.873%). The 95% total predictive uncertainty confidence intervals generated by GL-DREAM(ZS) enveloped close to the theoretically expected 95% of total observations for the calibration (94.5%) and validation (81.8%) periods. The performance of the GL-DREAM(ZS) approach and uncertainty assessment in this study shows promise for future MJS model applications, and the model-derived 2020 transpiration estimates highlight the MJS model utility for scaling sap-flow measurements from individual trees to stands of trees.

Integrating solar-induced chlorophyll fluorescence with traditional remote sensing and environmental variables for enhanced rice yield prediction in Nepal using machine learning

Rice is a vital cereal crop providing essential food for Nepal. To mitigate climate risks and ensure food security, especially with climate change and increasing extreme weather events, it is imperative and a prerequisite to have timely and reliable rice yield prediction. While traditional vegetation indices (VIs) and environmental variables are commonly used in rice yield prediction, the potential of solar-induced chlorophyll fluorescence (SIF), despite its greater sensitivity, has been explored in limited studies. Furthermore, integrating SIF with other remote sensing and environmental data for regional crop yield modeling still needs to be explored. This study aims to address these gaps by incorporating SIF to enhance rice yield prediction in Nepal, offering a novel approach to improving model accuracy. This study uses multi-source data and machine learning algorithms to study 22 major rice-growing districts in the Terai region of Nepal. Random forest (RF), support vector machine (SVR), gradient boosting regressor (GBR), and least absolute shrinkage and selection operator (LASSO) algorithms were employed to predict crop yield using various combinations of input features. Machine learning models were trained and tested over four different time windows within the growing period of rice, using 15 years of rice yield data from 2003 to 2017. Our analysis reveals that GBR out-performed other machine learning algorithms when using input features from July to September, achieving root mean square error (RMSE) and the ratio of RMSE (RRMSE) to observed mean values of 250 kg/ha and 8.6%, respectively. The findings also reveal that incorporating SIF alongside traditional remote sensing and environmental variables significantly enhances the model's prediction accuracy. These findings are valuable for ensuring food security, optimizing agricultural resource management, and supporting decision-making for farmers, policymakers, and supply chain stakeholders.

EVALUATION OF BITUMINOUS MIXES WITH EPOXY-MODIFIED ASPHALT BINDERS

Road surfaces have long been made with bituminous mix because of its low cost and versatile applications. However, environmental variables, traffic loads, and ageing frequently affect its endurance. Many additives have been introduced to overcome these problems, and epoxy resin has shown great promise. This study evaluates bituminous binder and bituminous mixes with different percentages of epoxy modification. Epoxy asphalt binder's (EAB) rheological properties were described. Following that, an evaluation was conducted on the engineering properties of an epoxy asphalt mixture (EAM). The results demonstrated that the rheological properties of EAB outperform those of neat binders. Furthermore, the outcomes demonstrated that EAM had better moisture damage performance in terms of static immersion test and retained Marshall stability test than the conventional hot dense graded bituminous mixes. According to the results, the ideal content for both VG 30 and VG 40 binders is 2% epoxy modification. Out of all the mix permutations, bituminous mixes with 2% epoxy modification yielded the best results. Comparing blends with control mixes or unmodified bituminous mixes, on the other hand, revealed a lower resistance to moisture damage than mixes with 3% epoxy modification with both binders. The study concludes that epoxy resin can significantly improve the performance and longevity of asphalt concrete roadways, offering benefits to the road construction industry.

Assessing the influence of environmental variables on energy efficiency changes in the provision of drinking water services

Within the context of the water-energy nexus in drinking water provision, it is crucial to evaluate temporal shifts in energy efficiency. Utilizing the Stochastic Nonparametric Envelopment of Data (StoNED) approach, this research estimates longitudinal variations in the energy efficiency of drinking water services integrating multiple environmental variables. The analysis, conducted in England and Wales from 2008 to 2020, indicates that the source and quality of raw water, as well as population density, influenced the energy performance of water utilities. Quantitative findings evidenced marked disparity in energy efficiency among different companies, with an average efficiency of 0.617 and a range from 0.437 to 0.944. The dynamic assessment indicates an average annual enhancement of 1.1% in the sector, predominantly propelled by technical advancements (0.9%) rather than by improvements in efficiency (0.2%). This study emphasizes the critical need for bespoke policies and incentives to boost energy efficiency and support sustainable urban water management.

The effect of postfire regeneration pattern on soil respiration in the boreal forest of China

BackgroundAs the second largest carbon flux between the atmosphere and terrestrial ecosystems, soil respiration involves multiple components of ecosystem production. Revealing soil respiration in forests with different postfire regeneration patterns is critical for determining appropriate restoration strategies in response to increasing wildfire disturbances. Here, we examined the influence of five postfire regeneration patterns (L: Larix gmelinii monocultures, LB: L. gmelinii and Betula platyphylla mixed plantations, P: Pinus sylvestris var. mongolica monocultures, PB: P. sylvestris var. mongolica and B. platyphylla mixed plantations, N: naturally regenerated forests) on soil heterotrophic respiration (Rh) and total respiration (Rs).MethodsTrenching was implemented to monitor soil heterotrophic respiration. We used partial least squares path modeling methods to estimate the different environmental factors regulating soil respiration across forest types.ResultsThe results showed that forest type and season had significant effects on Rs and Rh. Rh was the dominant part of Rs for all forest types (68.84 ~ 90.20%). Compared to naturally regenerated forests, Rs and Rh under L, LB, and PB had higher rates (P < 0.05), while Rs and Rh under P had lower rates (P < 0.05). The temperature sensitivities of Rs under different forest types were 2.316 (L), 1.840 (LB), 1.716 (P), 1.665 (PB), and 2.096 (N).ConclusionsForests regenerated with artificial participation established their plant communities visibly faster than naturally regenerated forests. Mixed species plantation regeneration demonstrated an improvement in soil respiration compared to naturally regenerated forests but had a lower temperature sensitivity of soil respiration than their respective monocultures. Soil temperature and moisture dominated the influence factors on soil respiration throughout the broader seasonal shifts. However, for a single season, forest productivity and soil properties have a greater impact on soil respiration. This study extends our knowledge of the interaction mechanism between soil respiration and environmental variables in boreal forests and contributes to improving confidence in global carbon cycling model predictions.

Utilizing IoMT-Based Smart Gloves for Continuous Vital Sign Monitoring to Safeguard Athlete Health and Optimize Training Protocols.

This paper presents the development of a vital sign monitoring system designed specifically for professional athletes, with a focus on runners. The system aims to enhance athletic performance and mitigate health risks associated with intense training regimens. It comprises a wearable glove that monitors key physiological parameters such as heart rate, blood oxygen saturation (SpO2), body temperature, and gyroscope data used to calculate linear speed, among other relevant metrics. Additionally, environmental variables, including ambient temperature, are tracked. To ensure accuracy, the system incorporates an onboard filtering algorithm to minimize false positives, allowing for timely intervention during instances of physiological abnormalities. The study demonstrates the system's potential to optimize performance and protect athlete well-being by facilitating real-time adjustments to training intensity and duration. The experimental results show that the system adheres to the classical "220-age" formula for calculating maximum heart rate, responds promptly to predefined thresholds, and outperforms a moving average filter in noise reduction, with the Gaussian filter delivering superior performance.

Investigating Respiratory Disease Transmission Patterns Around the Figuil Cement Works

The objective of this research is to examine the dissemination of respiratory illnesses, exacerbated by the airborne pollutants emitted by the Figuil cement works, among the local population residing in the vicinity. The primary objective is to examine the impact of pollution, particularly the emission of fine particles and noxious gases, on the transmission of respiratory diseases such as asthma, chronic bronchitis and other lung disorders. The modified SEIR (Susceptible-Exposed-Infected-Recovered) epidemiological model is employed for the analysis of transmission dynamics within the community. This model incorporates environmental, health and demographic variables, thereby enabling the simulation of disease transmission as a function of varying pollution levels. Particular emphasis is placed on vulnerable groups, such as children and the elderly, due to immunosenescence, who are more likely to suffer from the adverse effects of pollution. The results will facilitate the formulation of efficacious strategies, including the implementation of awareness-raising campaigns and the introduction of sophisticated systems for the filtration and capture of pollutants at their source, such as fine particle filters or devices for the reduction of nitrogen oxides (NOx), with the objective of limiting the spread of respiratory diseases in at-risk areas and the formulation of suitable control measures.

A molecular-chemistry-manipulation strategy toward functional lignin-based porous carbon nanospheres with tunable-pore structure

Porous carbon nanospheres derived from biomass materials have inspired numerous interests in various areas. Nevertheless, their fabrications mainly depend on template-assisted or chemical activation routes. Here, a molecular-chemistry-manipulation strategy was proposed innovatively by taking advantage of the heterogeneous self-condensation characters of lignin molecules under thermal stimulation, opening a practical and versatile synthetic platform for the construction of lignin-based hierarchical porous carbon nanospheres (LHPCS). We can manipulate the condensed kinetics by tuning the environmental variables and forming designated nanospheres with inhomogeneous molecular chemistry. This intraparticle difference could provide a possibility to tune its refined structure for synthesizing hierarchical porous carbon nanospheres. Such LHPCS behaved the specific surface area of 962 m2 g−1 and pore volume of 0.76 cm3 g−1, as well as regulable pore ratios (15.5–22.9 % of macropore, 12.7–37 % for mesopore and 45–68.9 % for micropore). Besides, the monodisperse spherical morphology with hierarchical interleaved channels affords them excellent performance in optical management and energy storage, including pore-determined photothermal properties, extremely low reflectance of ca. 1.9 %, stable multi-angles specular reflectance, and anti-self-discharge induced by charge rearrangement. Our proposed molecular chemistry strategy provides an alternative pathway for constructing gradient and adjustable pore structures.

First Detection of Hepatitis E Virus RNA in Ovine Raw Milk from Herds in Central Italy.

HEV mainly enters animal and human hosts through the orofecal route, which presents a critical health concern alongside the associated environmental variable. Among products of animal origin, milk (both ovine and bovine) can harbor HEV RNA, which can potentially be transmitted to consumers. In this study, a total of 220 raw ovine milk samples were collected from Apennine breed subjects farmed (transhumance method) in three different Italian provinces, L'Aquila, Pescara, and Teramo, located in the Abruzzo region (Central Italy). All the specimens were screened using one-step real-time RT-qPCR and nested RT-PCR assays. Among them, 5/220 or 2.27% harbored HEV RNA fragments belonging to the ORF1 and ORF2 codifying regions of the genotype 3c. The average viral amount discovered was 102 GE/mL. These subjects represented 2/57 or 3.51% of the Pescara herd, and 3/105 or 2.86% of the Teramo herd. Although HEV RNA was discovered in sheep fecal samples, the original data obtained in the present study represent the first HEV RNA detection in ovine raw milk from Italy.

Xanthium strumarium L., an invasive species in the subtropics: prediction of potential distribution areas and climate adaptability in Pakistan

Invasive species such as Xanthium strumarium L., can disrupt ecosystems, reduce crop yields, and degrade pastures, leading to economic losses and jeopardizing food security and biodiversity. To address the challenges posed by invasive species such as X. strumarium, this study uses species distribution modeling (SDM) to map its potential distribution in Pakistan and assess how it might respond to climate change. This addresses the urgent need for proactive conservation and management strategies amidst escalating ecological threats. SDM forecasts a species’ potential dispersion across various geographies in both space and time by correlating known species occurrences to environmental variables. SDMs have the potential to help address the challenges posed by invasive species by predicting the future habitat suitability of species distributions and identifying the environmental factors influencing these distributions. Our study shows that seasonal temperature dependence, mean temperature of wettest quarter and total nitrogen content of soil are important climatic factors influencing habitat suitability of X. strumarium. The potential habitat of this invasive species is likely to expand beyond the areas it currently colonizes, with a notable presence in the Punjab and Khyber Pakhtunkhwa regions. These areas are particularly vulnerable due to threats to agriculture and biodiversity. Under current conditions, an estimated 21% of Pakistan’s land area is infested by X. strumarium, mainly in upper Punjab, central Punjab and Khyber Pakhtunkhwa. The range is expected to expand in most regions except Sindh. The central and northeastern parts of the country are proving to be particularly suitable habitats for X. strumarium. Effective strategies are crucial to contain the spread of X. strumarium. The MaxEnt modeling approach generates invasion risk maps by identifying potential risk zones based on a species’ climate adaptability. These maps can aid in early detection, allowing authorities to prioritize surveillance and management strategies for controlling the spread of invasive species in suitable habitats. However, further research is recommended to understand the adaptability of species to unexplored environments.

Soil available phosphorus and pH are key factors affecting the site index of Larix kaempferi plantations in China

Assessing the quality of forest sites is crucial for evaluating the potential productivity of forests and formulating effective management strategies. Therefore, it is essential to understand how environmental variables affect the site quality. This study focuses on quantifying the effects of 44 different environmental variables including climate, topography, and soil properties on the site index of Larix kaempferi plantations in three different climate regions in China, utilizing the random forest algorithm. L. kaempferi site index was determined from stem analysis data by felling dominant trees from 51 even-aged stands. The results indicated that the proposed random forest model explained ~59.47% of site index variations. Among many environmental variables, available phosphorus, pH, degree-days above 5°C (DD5), and spring mean maximum temperature (Tmax_MAM) had significant effects on the site index (P &amp;lt; 0.05), and the importance of soil chemical properties generally exhibits relatively larger effects on the site index than climate variables and topography variables. The partial dependence analysis revealed that the L. kaempferi plantations had maximum values at ~30 mg/kg of available phosphorus in the first soil layers, 30 mg/kg of available phosphorus in the second soil layers, 20 mg/kg of available phosphorus in the third soil layers, the DD5 between 2,600and 3,000°C, and Tmax_MAM ~15°C. Our findings attempt to provide a better understanding of the site–growth relationship.

A GIS-MCDA approach to map environmental suitability of Posidonia oceanica meadows as blue nature-based solutions in the Mediterranean eco-region

The growing environmental risks induced by interacting climate and human-induced pressures threaten the survival and growth of marine coastal ecosystems (MCEs) and the ecosystem services they provide. Nature-based solutions (NBS), consisting of ecosystem-based approaches, have emerged as vital tools for climate adaptation and mitigation facing biodiversity loss and societal challenges. Identifying suitable environmental conditions for implementing Blue-NBS in marine coastal areas is a key priority to drive robust and cost-effective nature-based adaptation pathways. This study developed a suitability model for Blue-NBS, with a specific focus on Posidonia oceanica meadows in the Mediterranean Sea under a baseline scenario. GIS-based Multiple Criteria Decision Analysis (MCDA) was applied for data integration and prioritization of different environmental variables in geomorphological (e.g., depth), water quality (WQ) (e.g., salinity), and climatic (e.g., thermal stress) sub-groups. Suitability classes and scores for each variable were determined using statistical distributions, ensuring a data-driven approach to defining environmental suitability. Variables' weights were derived from the Analytic Hierarchy Process (AHP) based on expert judgment and then combined with scores to generate suitability maps for managing Blue-NBS on seagrasses. Depth was found to be the most dominant environmental variable, with shallow areas (e.g., Northern Adriatic, Gulf of Gabés) showing higher suitability. The southern part of the Mediterranean (e.g., Egypt) reported relatively low scores for both climate and WQ, while the Northern Adriatic had the lowest WQ scores. This study represents the first attempt to evaluate Blue-NBS suitability for seagrass meadows at the eco-regional scale with geomorphologic, WQ, and climatic variables, providing decision support for the selection and allocation of Blue-NBS in different environmental settings. The resulting environmental suitability maps represent a basis for the integration of socio-economic and governance-related indicators into a more complex, multi-tier approach to support NBS mainstreaming.

Population density of plant-parasitic nematodes in rice fields associated with environmental variables in Malang, Indonesia

Population density of plant-parasitic nematodes in rice fields associated with environmental variables in Malang, Indonesia

Relationship between COVID-19 Cases and Environmental Contaminants in Quito, Ecuador.

The relationship between COVID-19 infections and environmental contaminants provides insight into how environmental factors can influence the spread of infectious diseases. By integrating epidemiological and environmental variables into a mathematical framework, the interaction between virus spread and the environment can be determined. The aim of this study was to evaluate the impact of atmospheric contaminants on the increase in COVID-19 infections in the city of Quito through the application of statistical tests. The data on infections and deaths allowed to identify the periods of greatest contagion and their relationship with the contaminants O3, SO2, CO, PM2.5, and PM10. A validated database was used, and statistical analysis was applied through five models based on simple linear regression. The models showed a significant relationship between SO2 and the increase in infections. In addition, a moderate correlation was shown with PM2.5, O3, and CO, and a low relationship was shown for PM10. These findings highlight the importance of having policies that guarantee air quality as a key factor in maintaining people's health and preventing the proliferation of viral and infectious diseases.

Defective Pennywort Leaf Detection Using Machine Vision and Mask R-CNN Model

Demand and market value for pennywort largely depend on the quality of the leaves, which can be affected by various ambient environment or fertigation variables during cultivation. Although early detection of defects in pennywort leaves would enable growers to take quick action, conventional manual detection is laborious and time consuming as well as subjective. Therefore, the objective of this study was to develop an automatic leaf defect detection algorithm for pennywort plants grown under controlled environment conditions, using machine vision and deep learning techniques. Leaf images were captured from pennywort plants grown in an ebb-and-flow hydroponic system under fluorescent light conditions in a controlled plant factory environment. Physically or biologically damaged leaves (e.g., curled, creased, discolored, misshapen, or brown spotted) were classified as defective leaves. Images were annotated using an online tool, and Mask R-CNN models were implemented with the integrated attention mechanisms, convolutional block attention module (CBAM) and coordinate attention (CA) and compared for improved image feature extraction. Transfer learning was employed to train the model with a smaller dataset, effectively reducing processing time. The improved models demonstrated significant advancements in accuracy and precision, with the CA-augmented model achieving the highest metrics, including a mean average precision (mAP) of 0.931 and an accuracy of 0.937. These enhancements enabled more precise localization and classification of leaf defects, outperforming the baseline Mask R-CNN model in complex visual recognition tasks. The final model was robust, effectively distinguishing defective leaves in challenging scenarios, making it highly suitable for applications in precision agriculture. Future research can build on this modeling framework, exploring additional variables to identify specific leaf abnormalities at earlier growth stages, which is crucial for production quality assurance.

First use of Chironomid Pupal Exuvial Technique in freshwater biomonitoring in Cameroon: Ecological aspect and morphological description of Chironomidae (Insecta: Diptera)

The biodiversity of urban streams is gradually degrading as a result of anthropogenic pollution. In this study, chironomid pupal exuviae were used as surrogates to assess urban streams and a lake in Douala Cameroon. We compared these assessments with samples collected from a suburban forest stream. Based on their highly adaptive capability, we predicted that chironomid communities would present high diversity and abundance in urban habitats. Chironomid pupal exuviae were collected using the chironomid pupal exuvial technique in 14 urban and 2 forest reference stations during dry and rainy seasons. Measurements of the environmental variables were done simultaneously. Physicochemical analysis revealed that all urban sites have had very poor ecological health status whereas forest site had good water quality as shown by the hierarchical cluster analysis. This ecological survey identified 8 species/morphotypes of chironomids all belonging to the subfamily Chironominae, tribe Chironomini. These are Chironomus holomelas(?), Chironomus sp.1, Chironomus sp.2, Chironomus sp.3, Chironomus (Lobochironomus) sp., Stenochironomus sp., Dicrotendipes sp. and Dicrotendipes pulsus. No midge pupal exuviae were recorded at the forest stations (S1 & S2) and the stations S6 and S7 located downstream industrial mill effluents. The species richness of the study sites was significantly very low as compare to other Afrotropical studies. Canonical redundancy analysis revealed that high values of water temperature, ammonium, nitrates and phosphates, and low oxygen concentration were significant predictors of the distribution and high abundance of Chironomidae. This survey highlighted the impacts of anthropogenic activities on chironomid diversity and distribution, with great decrease of their diversity due to industrial pollution.