Seasonal Variation Research Articles

Estimating the efficacy of solar photovoltaic panels in Lebanon using a digital surface model: A geospatial approach

With the escalating need for alternative energy sources due to economic crises and fossil fuel shortages in Lebanon, solar photovoltaic (PV) panels have emerged as an attractive solution. This study examines the capacity and efficacy of rooftop-installed PV solar panels. Using geospatial technologies, including Digital Surface Models drone-based photogrammetry, the study assesses geometric and solar characteristics, seasonal solar radiation, solar duration, and power for 40 PV units installed in the study area. This research presents specific quantitative values for optimal orientations that result in high solar radiation across various seasons and identifies varying slopes influencing the performance of PV solar panels. Employing the Agglomerative Hierarchical Clustering (AHC) technique, PV units are systematically classified into clusters labeled as Moderate, High, Low, and Very Low solar power, offering quantitative metrics regarding the effectiveness of distinct panels. The high-efficiency Cluster exhibits an average solar power of 1868.114 kWh/m² during the summer season, whereas the Very Low Cluster, comprising panels with minimal solar power output, averages 150.578 kWh/m² in the same season. In conclusion, the most effective PV solar panels within the study area are those oriented between 195 and 225 degrees, with shallow inclination angles and larger surface areas contributing to enhanced performance in capturing solar radiation and generating power. These precise quantitative insights contribute to informed decision-making for optimizing the placement of PV panels to enhance energy generation. The study's recommendations are substantiated by specific numerical data, guiding future solar installations to maximize solar energy generation.

Differences in soil water movement between the dip and anti-dip slopes of a karst trough valley

Exploring temporal and spatial variations in soil water processes is crucial for identifying the ecological water supply capacity of soil and understanding hydrologic cycles within karst catchments. However, the existing body of knowledge related to soil water dynamics across dip and anti-dip slopes in karst landscapes remains limited. To address this gap, this study investigated soil water dynamics in a karst trough valley (KTV) in southwest China, using stable isotopes (δD and δ18O) from eight sampling sites and precipitation data from April 2017 to November 2018. Our findings suggest that seasonal variations in soil water isotopes are primarily influenced by climatic factors, with soil water lagging behind rainwater isotopes by one to four months due to differences in the residence time of precipitation in different soil layers. Spatially, our results reveal the coexistence of diffuse and preferential flows on both dip and anti-dip slopes. Specifically, on the dip slope, preferential flow dominates above 20 cm, while diffuse flow prevails below this depth. In contrast, on the anti-dip slope, preferential flow is prominent above 40 cm depth, while diffuse flow dominates beneath this threshold. These spatial patterns not only impact the ecological water and nutrient supply capacity of soil but they also contribute to distinct patterns in vegetative growth on both slopes. In turn, vegetation growth facilitates preferential flow pathways, establishing a positive feedback loop between soil water movement and vegetative cover. These findings may lead to a deeper understanding of soil water processes on the dip and anti-dip slopes of KTVs and provide crucial insights into the relationship between soil water and vegetative growth in similar karst landforms.

A novel dynamic scale factor designed for recovering global TWS changes

For time-variable satellite gravity solutions of GRACE and GRACE-FO in terms of spherical harmonics coefficients, the Scale Factor (SF) is often used to recover the close to “true” signal of Terrestrial Water Storage (TWS) anomalies. However, the conventional SF method has some limitations that may hinder its effectiveness, including: (1) their dependency on input hydrological models that may lead to divergent estimations of SFs; (2) the arbitrary choice of filter strength, which may not be representative in different regions; (3) limited consideration of SF in the temporal dynamics (the conventional SF was fixed value) for monthly varied TWS. Here, we propose a new Dynamic SF method to overcome these limitations and increase accuracy of the restored global TWS changes. This method involves: (1) the Bayesian Three-Cornered Hat (BTCH) method is applied to merge three sets of hydrological products into an optimal hydrological dataset to be used for estimating a unique SF, (2) the anisotropic DDK3 filter (found to be numerically optimal) is applied to suppress the correlated noise, and (3) an iterative Kalman filter process is formulated and implemented to estimate monthly Dynamic SF corresponding to monthly global TWS fields. The Dynamic SF outperformed an ordinary SF method in terms of the Root Mean Squared Error (RMSE), the Mean Absolute Error (MAE), and the Signal to Noise Ratio (SNR), which were found to be improved by 30.8%, 32.2%, and 41.3%, respectively. Moreover, the recovered TWS using the Dynamic SF method showed a good agreement with the GRACE/GRACE-FO RL06 mascon solutions of the CSR and that of JPL regarding the long-term trend, seasonal, and interannual variations.

Strategic control of combustion-induced ammonia emissions: A key initiative for substantial PM2.5 reduction in Tianjin, North China Plain

Information on the temporal and spatial variations in the sources of ammonium salts (NH4+), a crucial alkaline component in PM2.5, is limited. Here, we simultaneously collected PM2.5 and gaseous ammonia (NH3) samples in both summer and winter from two sites in Tianjin: an urban site (Tianjin University, TJU) and a suburban site (Binhai New-region, BH). NH3 concentrations, the contents of major water-soluble inorganic ions in PM2.5, and the compositions of ammonium‑nitrogen isotopes (δ15N-NH4+) were measured. As a result, (NH4)2SO4 and NH4NO3 were the predominant forms of NH4+ in PM2.5 during summer and winter, respectively. However, the NH4NO3 concentrations were notably greater at TJU (6.2 ± 7.3 μg m−3) than at BH (3.8 ± 4.7 μg m−3) in summer, with no regional differences observed in winter. Both sites displayed almost half the contribution of c-NH3 (combustion-related NH3) to NH4+, differing from the finding of previous isotope-based studies. This discrepancy could be attributed to the combined effects of NHx isotope fractionation and seasonal δ15N value variations in NH3 sources. The contribution fractions of v-NH3 (volatile NH3) and c-NH3 exhibited similar patterns at both sites seasonally, probably caused by coal combustion for heating in winter and temperature fluctuations. However, the contribution fraction of c-NH3 was lower at BH than at TJU in summer but greater in winter than at TJU. In summer, NH4NO3 was unstable and limited its delivery to TJU from BH, and the high contribution of c-NH3 to NH4+ at TJU could be attributed to local vehicle emissions. In winter, the stable particulate NH4NO3 that formed from the c-NH3 in the upwind area could be transported to the downwind area, increasing the NH4+ concentration at BH. Our study provides valuable insights for devising emission mitigation strategies to alleviate the increasing burden of NH3 in the local atmosphere.

Kudzu invasion and its influential factors in the southeastern United States

As an invasive plant species with wide ecological tolerance and fast infestation, kudzu has posed persistent challenges to the ecosystem and economy in the southeastern United States. However, current studies on kudzu mapping and invasive mechanisms are inadequate to unveil the characteristics of kudzu’s spatiotemporal dynamics. In this study, we investigate kudzu distribution and its influential factors based on continuous Sentinel-2 satellite images and a multi-temporal mapping strategy. The study was conducted in Knox County, Tennessee, a representative region highly impacted by kudzu in the southeastern United States. Our results revealed a steady increase in the kudzu invasion area from 2020 to 2022, with a phenology of a growth season from May to September (peak in August) and a rapid defoliation in the fall. We found precipitation is not a limiting factor for kudzu growth, while temperature has strong correlations with kudzu growth, exhibiting seasonal variations in this humid subtropical region. Human activities play an important role in kudzu distribution with a sharp decrease in kudzu density away from road networks, residential clusters, and the edges of urban forests. These findings provide valuable insights into the complex nature of the kudzu invasion in Knox County, as well as other similar areas in the southeastern United States. The observed unique growth patterns of kudzu and its interactions with various environmental factors in this study are also of importance for future initiatives related to mapping the distribution and devising strategies for the prevention and control of kudzu invasion.

Insight Study of Trace Elements in PM2.5 During Nine Years in Delhi, India: Seasonal Variation, Source Apportionment, and Health Risks Assessment.

This study investigated the concentrations, seasonal variations, sources, and human health risks associated with exposure to heavy elements (As, Al, Pb, Cr, Mn, Cu, Zn, and Ni) of PM2.5 at an urban location of Delhi (28° 38' N, 77° 10' E; 218m amsl), India, from January 2013 to December 2021. The average mass concentration of PM2.5 throughout the study period was estimated as 127 ± 77µgm-3, which is exceeding the National Ambient Air Quality Standards (NAAQS) limit (annual: 40µgm-3; 24h: 60µgm-3). The seasonal mass concentrations of PM2.5 exhibited at the order of post-monsoon (192 ± 110 µgm-3) > winter (158 ± 70 µgm-3) > summer (92 ± 44 µgm-3) and> monsoon (67 ± 32 µgm-3). The heavy elements, Al (1.19µgm-3), Zn (0.49µgm-3), Pb (0.43µgm-3), Cr (0.21µgm-3), Cu (0.21µgm-3), Mn (0.07µgm-3), and Ni (0.14µgm-3) exhibited varying concentrations in PM2.5, with the highest levels observed in the post-monsoon season, followed by winter, summer, and monsoon seasons. Six primary sources throughout the study period, contributing to PM2.5 were identified by positive matrix factorization (PMF), such as dust (paved/crustal/soil dust: 29.9%), vehicular emissions (17.2%), biomass burning (15.4%), combustion (14%), industrial emissions (14.2%), and Br-rich sources (9.2%). Health risk assessments, including hazard quotient(HQ), hazard index(HI), and carcinogenic risk(CR), were computed based on heavy elements concentrations in PM2.5. Elevated HQvalues for Cr and Mn linked with adverse health impacts in both adults and children. High carcinogenic risk values were observed for Cr in both adults and children during the winter and post-monsoon seasons, as well as in adults during the summer and monsoon seasons. The combined HIvalue exceeding one suggests appreciable non-carcinogenic risks associated with the examined elements. The findings of this study provide valuable insights into the behaviour and risk mitigation of heavy elements in PM2.5, contributing to the understanding of air quality and public health in the urban environment of Delhi.

One year of active moss biomonitoring in the identification of PAHs in an urbanized area-prospects and implications.

Classical monitoring of air pollution provides information on environmental quality but involves high costs. An alternative to this method is the use of bioindicators. The purpose of our work was to evaluate atmospheric aerosol pollution by selected polycyclic aromatic hydrocarbons conducted as part of annual active biomonitoring ("moss-bag" technique) with the use of three moss species: Pleurozium schreberi, Sphagnum fallax, and Dicranum polysetum. The gas chromatography-mass spectrometry (GC-MS) was utilized to determine certain 13 polycyclic aromatic hydrocarbons (PAHs). Three seasonal variations in PAH concentrations have been observed as a result of the study. A fire on the toilet paper plant caused an increase of five new compounds: benzo(k)fluoranthene (BkF), benzo(a)pyrene (BaP), indeno(1.2.3)-cd_pyrene (IP), dibenzo(a.h)anthracene (Dah), and benzo(g.h.i)perylene (Bghi) in proximity after 8months of exposure compared to previous months. The effect of meteorological conditions on the deposition of PAHs (mainly wind direction) in mosses was confirmed by principal component analysis (PCA). Dicranum polysetum moss accumulated on average 26.5% more PAHs than the other species, which allows considering its broader use in active biomonitoring. The "moss-bag" technique demonstrates its feasibility in assessing the source of PAH air pollution in a long-term study. It is recommended to use this biological method as a valuable tool in air quality monitoring.

Effect of meteorological factors on the incidence of Mycoplasma pneumoniae pneumonia in Japan: a time series analysis.

Mycoplasma pneumoniae (M. pneumoniae) is a major cause of upper and lower respiratory tract infections and respiratory tract disease in humans. While accumulated pieces of epidemiological evidence suggest an association between meteorological factors and the risk of M. pneumoniae pneumonia, comprehensive nationwide studies on this topic are lacking. We aimed to systematically assess the effect of meteorological factors such as mean temperature and relative humidity on the incidence of M. pneumoniae pneumonia in Japan over a 15-year period from 2005 to 2019. The exposure - response relationships between incidence of M. pneumoniae pneumonia, mean temperature, and relative humidity in all 47 Japanese prefectures (covering whole country) for 2005 - 2019 were quantified by using a distributed lag non-linear model for each prefecture and the estimates from all the prefectures were then pooled using a multivariate mete-regression model to derive nationwide average associations. The study encompassed a total of 162,845 M. pneumoniae pneumonia cases. Our findings indicate that seasonal variations in weekly mean temperature and relative humidity were positively associated with the incidence of M. pneumoniae pneumonia. Specifically, when considering - 1.3°C as the reference, the relative risk (RR) peaked at 16.8°C (with RRs of 1.50, 95% confidence interval (CI): 1.32-1.70). Similarly, when using 45.5% relative humidity as the reference, the RR reached its peak at 87.7% (with RRs of 1.49, 95% CI: 1.33-1.67). These results emphasize the necessity of implementing climate change adaptation strategies and public health interventions in regions vulnerable to M. pneumoniae pneumonia.

First Analyses of the TIMELINE AVHRR SST Product: Long-Term Trends of Sea Surface Temperature at 1 km Resolution across European Coastal Zones

Coastal areas are among the most productive areas in the world, ecologically as well as economically. Sea Surface Temperature (SST) has evolved as the major essential climate variable (ECV) and ocean variable (EOV) to monitor land–ocean interactions and oceanic warming trends. SST monitoring can be achieved by means of remote sensing. The current relatively coarse spatial resolution of established SST products limits their potential in small-scale, coastal zones. This study presents the first analysis of the TIMELINE 1 km SST product from AVHRR in four key European regions: The Northern and Baltic Sea, the Adriatic Sea, the Aegean Sea, and the Balearic Sea. The analysis of monthly anomaly trends showed high positive SST trends in all study areas, exceeding the global average SST warming. Seasonal variations reveal peak warming during the spring, early summer, and early autumn, suggesting a potential seasonal shift. The spatial analysis of the monthly anomaly trends revealed significantly higher trends at near-coast areas, which were especially distinct in the Mediterranean study areas. The clearest pattern was visible in the Adriatic Sea in March and May, where the SST trends at the coast were twice as high as that observed at a 40 km distance to the coast. To validate our findings, we compared the TIMELINE monthly anomaly time series with monthly anomalies derived from the Level 4 CCI SST anomaly product. The comparison showed an overall good accordance with correlation coefficients of R > 0.82 for the Mediterranean study areas and R = 0.77 for the North and Baltic Seas. This study highlights the potential of AVHRR Local Area Coverage (LAC) data with 1 km spatial resolution for mapping long-term SST trends in areas with high spatial SST variability, such as coastal regions.

Application of real-time overall equipment efficiency indicator in a medium-sized company

Goal: This research investigated the application of real-time Overall Equipment Efficiency (OEE) at three assembly work centers in a medium-sized company. The objective was to demonstrate the feasibility of integrating Industry 4.0 technologies, such as the Internet of Things, Big Data, and Cloud Computing, in manual work center environments. It aimed to underscore the potential improvements achievable through data-driven actions facilitated by Industry 4.0 technologies, while emphasizing the significance of acquiring real-time OEE data. Design / Methodology / Approach: The research involved theoretical exploration, implementation, data collection (Nov 2022–May 2023), and analysis on assembly workstations in a medium-sized Brazilian eyewear manufacturer. Results: Based on the captured data, the factory implemented a series of corrective actions, leading to a reduction in unplanned stops. The obtained results were significant, as the average efficiency of the studied work centers improved by 12.3% in 7 months, with an increase in performance and in availability. Limitations of the investigation: The analysis faces challenges due time constraints, potentially limiting the full assessment of IoT impact. Seasonal variations in eyeglass production and style-specific demand complicate evaluating the true benefits of Industry 4.0 tools, making effective OEE improvement hard to determine. Practical implications: The study demonstrates a method to gauge manual labor efficiency through Industry 4.0 technologies. Originality / Value: This study shows how Industry 4.0 technologies (IoT, Big Data, Cloud Computing) can be integrated into manual workforces, enhancing efficiency and providing real-time OEE for workers to self-assess.

Dynamical-statistical method for seasonal forecasting of wintertime PM10 concentration in South Korea using multi-model ensemble climate forecasts

Abstract Climate conditions and emissions are among the primary influences on seasonal variations in air quality. Consequently, skillful climate forecasts can greatly enhance the predictability of air quality seasonal forecasts. In this study, we propose a dynamical-statistical method for seasonal forecasting of particulate matter (PM10) concentrations in South Korea in winter using climate forecasts from the Asian Pacific Climate Center (APCC) Multi-Model Ensemble (MME). We identified potential climate predictors that potentially affect the wintertime air quality variability in South Korea in the global domain. From these potential climate predictors, those that can be forecasted skillfully by APCC MME were utilized to establish a multiple-linear regression model to predict the winter PM10 concentration in South Korea. As a result of evaluating the forecast skill through retrospective forecasts for the past 25 winters (1995/96-2019/20), this model showed statistically significant forecast skill at a lead time of a month to a season. The skill of PM10 forecast from the MME was overall better than that from a single model. We also found that it is possible to improve forecast skills through optimal MME combinations.

Sewage Derived Microplastic and Anthropogenic Fibre Retention by Integrated Constructed Wetlands

High loads of microplastics and anthropogenic fibres can be discharged from wastewater treatment plants (WWTPs) into surface water bodies. Integrated Constructed Wetlands (ICWs) are potentially well suited to provide a cost-effective mitigation solution at small WWTPs where conventional treatment is prohibitively expensive. This study aimed to assess the microplastic and anthropogenic fibre retention efficiency of two ICWs (Northrepps and Ingoldisthorpe) in Norfolk (UK) over a 12-month period (2022–2023). Analysing a total of 54 water and 23 sediment samples, the findings revealed that Northrepps ICW received on average 349,920 (± 763,776) anthropogenic fibres day−1, with a retention rate of 99.3%. No seasonal variation was observed in retention efficiency. Ingoldisthorpe ICW intermittently received anthropogenic fibres in low concentrations, with an average of 9504 (± 19,872) day−1 and a retention rate of 100%. Microplastics and anthropogenic fibres were prevalent in sediment samples of the first cell of Northrepps ICW, averaging 10,090 items kg−1 dry sediment, while none were found at concentrations above the limit of detection in the second or third cell. Of the 369 fibres analysed by ATR-FTIR, 55% were plastic (dominated by polyester). Of the 140 suspected microplastic fragments analysed by ATR-FTIR, 73% were confidently identified as plastic (mostly polystyrene, polyethylene, or polypropylene). This study demonstrates how ICWs can effectively retain sewage effluent derived microplastics and anthropogenic fibres. However, the accumulation of plastic waste in ICWs may complicate long term management and their cost-effectiveness.

Long-time exposure to sunlight is associated with the risk of sudden cardiac arrest and coronary heart disease

Abstract Background With ongoing climate change, the duration of sunshine hours will likely increase over the coming decades that escalating air temperature. Little is known about the potential cardiovascular risk from this climate factor. Purpose The aim of this study was to test whether duration of sunlight exposure is associated with incident sudden cardiac arrest (SCA), coronary heart disease (CHD), and hospitalization for heart failure (HF) and whether this association depends on seasonal variation. Methods A total of 308,851 participants from UK Biobank were included and were followed for a median time of 11.8 years. Participants reported how many hours they respectively spent the daylight outdoors on summer and winter typical days. Extreme outliers (>12 hours/day in summer and >8 hours/day in winter) were excluded. Average exposure time was calculated to unify the daylight outdoors time standard. Duration of sunlight exposure (average, summer, and winter) were categorized using quantiles and a restricted cubic spline for outcomes. Multivariate cox proportional hazard regression adjusted by age, sex, demographics, lifestyle, clinical and environmental factors was used for the analysis. Results During a follow-up, 1,572 (0.51%), 11,557 (3.74%) and 2,145 (0.68%) participants were diagnosed with SCA, CHD and hospitalization for HF. Compared with the participants exposed to short-time sunlight (average ≤ 1.5 hours/day), those who exposed to long-term sunlight (average ≥ 3.5 hours/day) had increased risk of SCA (HR 1.22, 95% CI 1.06 to 1.40) and CHD (HR 1.22, 95% CI 1.06 ~1.40). The associations between long-time exposure to sunlight and SCA were largely attributable to summer (HR 1.22, 95% CI 1.06 to 1.39) and not winter (HR 1.02, 95% CI 0.86 to 1.20), respectively. An hour increase of sunlight exposure was associated with increased risk of SCA in summer (HR, 1.03; 95% CI, 1.01 to 1.05), but not in winter (HR, 1.03, 95% CI, 0.99 to 1.06). Subgroup analysis identified that exposure to long-term sunlight was consistently increased risk of SCA, regardless of age, sex, body mass index, previous cardiovascular diseases, total physical activity, types of physical activity in last 4 weeks, use of sun/UV protection, sedentary time, natural environment percentage (buffer 1000m), and PM2.5. Finally, long-time exposure to sunlight was also associated with increased risk of CHD, but not associated with increased risk of HF hospitalization. Conclusions Findings from the present study indicate that daily long-time exposure to sunlight that increase in air temperature is a risk factor for SCA and CHD. These associations revealed graded relationship according to the duration of exposure to sunlight and more pronounced in summer. Our results suggest that public health efforts to reduce cardiovascular risk should take a wide range of exposure time to sunlight into account.

A Research Challenging Vision Concerning Waste of Agricultural Management in a Bio-Based Circular Economy: A Review Article

Agricultural waste represents an enormous reservoir of underutilized biomass resources, which may even pose environmental and economic risks. Residual resources of this nature can be transformed into bioenergy and bio-based products through cascading conversion processes, thereby fitting the criteria of a circular economy. Significant challenges are examined through a transdisciplinary lens, with an emphasis on the European context. Due to the seasonality, regionality, and complexity of agricultural residue management chains, environmental and economic repercussions are challenging to quantify. It is discussed how to develop multi-criteria decision support instruments that can be implemented in the earliest phases of research. The technological advancement of Anaerobic Digestion (AD), a highly developed conversion technology, is examined in the context of seasonal and geographical variations in refuse feedstock. Utilizing agricultural byproducts to manufacture high-value compounds is a significant challenge that is examined in this article, with innovative cascading conversion processes that are both eco-efficient and cost-effective (bio-refinery concept) taken into consideration. Furthermore, industrial ecology examines the promotion of businesses based on agricultural residues in order to foster local synergy between various industrial and agricultural value chains. In order to optimize the management of materials and knowledge fluxes and facilitate a holistic approach, the connection of stakeholders to encourage resource exchange and cross-sector collaboration at appropriate geographic scales is discussed.

Multispecies deep learning using citizen science data produces more informative plant community models

In the age of big data, scientific progress is fundamentally limited by our capacity to extract critical information. Here, we map fine-grained spatiotemporal distributions for thousands of species, using deep neural networks (DNNs) and ubiquitous citizen science data. Based on 6.7 M observations, we jointly model the distributions of 2477 plant species and species aggregates across Switzerland with an ensemble of DNNs built with different cost functions. We find that, compared to commonly-used approaches, multispecies DNNs predict species distributions and especially community composition more accurately. Moreover, their design allows investigation of understudied aspects of ecology. Including seasonal variations of observation probability explicitly allows approximating flowering phenology; reweighting predictions to mirror cover-abundance allows mapping potentially canopy-dominant tree species nationwide; and projecting DNNs into the future allows assessing how distributions, phenology, and dominance may change. Given their skill and their versatility, multispecies DNNs can refine our understanding of the distribution of plants and well-sampled taxa in general.

#294 Pragmatic literature reviews to populate a patient-level model to project the future clinical burden of CKD in low-and middle-income countries

Abstract Background and Aims Chronic Kidney Disease (CKD) affects approximately one in ten people around the world and is associated with an increased risk of adverse cardiorenal outcomes and mortality. In low- and middle-income countries (LMICs), the situation is aggravated by the paucity of kidney replacement therapies (KRTs). The objective of this study was to review the literature around CKD prevalence to understand the future epidemiology and economic burden of CKD in four LMICs from different continents. Method Inside CKD is a project designed to perform a microsimulation on a representative patient cohort to simulate renal disease progression into the future, and to model the effects of interventions based on population and disease characteristics. A pragmatic literature review was performed to obtain the inputs required to adapt the Inside CKD model to project the epidemiological and economic burden of CKD in four World Bank (2022) classified LMICs (Bolivia, Kenya, Sri Lanka and Uzbekistan). Using a data quality scoring system designed for the Inside CKD programme, a model input template was created to guide the country-specific literature search, to identify country demographics, epidemiological data including CKD staging data, renal registries, comorbidities (type 2 diabetes and hypertension), complications data (stroke, myocardial infarction, heart failure) and economic data including health costs and quality-of-life measures. Additional needs, challenges or country specific risk factors have also been identified. Results Literature reviews identified the best sources of demographic, epidemiological data as well as the main drivers of CKD in the four countries (e.g. hypertension, type 2 diabetes, heart failure). In addition, the reviews identified the best proxy data to use when country-specific data was not available. The UN World Prospects database was selected to derive the current and future population dynamics in the four countries. For Kenya, data from local sources, sub-Saharan African and Uganda were utilised to estimate the primary drivers of CKD: a 2018 meta-analysis reported a CKD prevalence of 18% in sub-Saharan Africa and was used to derive the eGFR and UACR data required to project CKD prevalence. CKD prevalence by stage was also identified for Bolivia. A large-scale study across 12 countries with consistent methodology estimated a CKD prevalence of around 5.5% in Bolivia. CKD data from Kazakhstan was selected as a proxy for Uzbekistan, which had a low prevalence of 1.3%. Country-specific challenges were also identified: in Kenya, results showed the importance of malnutrition, infection and climate as key drivers of CKD. In Sri Lanka and Uzbekistan, environmental factors (air and water pollution) and agricultural activity may contribute to high CKD prevalence in rural areas alongside more common risk factors such as hypertension. In Sri Lanka, CKD has been estimated at around 8.2% in the general population versus 15.0% in areas where groundwater is consumed. For Bolivia, additional parameters including occupation, altitude, seasonal weather variation and extreme temperatures may exacerbate CKD risk. Conclusion CKD is a major public health problem in LMICs but with diverse drivers. Local capacities and healthcare financing priorities vary between regions and depend upon competing demands of acute conditions, infections and non-communicable diseases. Forecasting future CKD burden is helpful for policy and planning purposes. There is a clear need to tailor policies beyond early screening and proactive management to tackle country-specific challenges such as occupation, infection, seasonal weather variation and genetics.

Characteristics and seasonal variations of cirrus clouds from ground-based lidar and satellite observations over Shouxian Area, China

This study investigates the macroscopic and optical properties of cirrus clouds in the 32N region from July 2016 to May 2017, leveraging data from ground-based lidar observations and CALIOP to overcome the inconsistencies in detected cirrus cloud samples. Through extensive data analysis, statistical characteristics of cirrus clouds were discerned, revealing lidar ratio values of 28.5 ± 10.8 from ground-based lidar and 27.4 ± 11.2 from CALIOP. Validation with a decade of CALIOP data (2008-2018) confirmed these findings, presenting a consistent lidar ratio of 27.4 ± 12.0. A significant outcome of the analysis was the identification of a positive correlation between the lidar ratio and cloud centroid temperature, indicating a gradual decrease in the lidar ratio as temperatures dropped. The study established a fundamental consistency in their macroscopic properties, including cloud base height, cloud top height, cloud thickness, cloud centroid height, and cloud centroid temperature. The results for ground-based lidar (CALIOP) are: 10.0 ± 2.1 km (10.0 ± 2.2 km), 11.8 ± 2.1 km (11.5 ± 2.3 km), 1.87 ± 0.83 km (1.52 ± 0.71 km), and 10.5 ± 2.2 km, -46.9 ± 9.7°C (-47.1 ± 10.0°C).These properties exhibited seasonal variations, with cirrus clouds reaching higher altitudes in summer and lower in winter, influenced by the height of the tropopause. The optical properties of cirrus clouds were also analyzed, showing an annual average optical depth of 0.31 ± 0.35 for ground-based lidar and 0.32 ± 0.44 for CALIOP. The study highlighted the distribution of subvisible, thin, and thick cirrus clouds, with a notable prevalence of subvisible clouds during summer, suggesting their frequent formation above 14 km. Furthermore, the study observed linear growth in geometric thickness and optical depth up to 2.5 km from CALIOP and 2.9 km from ground-based lidar. Maximum optical depth was observed at cloud centroid temperatures of -35°C for CALIOP and -40°C for ground-based lidar, with optical depth decreasing as temperatures fell. This suggests that fully glaciated cirrus clouds exhibit the highest optical depth at warmer temperatures, within the complete glaciation temperature range of -35°C to -40°C.

Spatio-temporal trends in long-term seasonal groundwater level of South-western Punjab using non-parametric statistical tests.

To manage groundwater resources and develop an action plan, it is crucial to understand the long-term behavior of groundwater level (GWL) fluctuations. In this study, Geographic Information System (GIS) and non-parametric statistical tests were applied for detecting long-term (1973 to 2020) spatio-temporal variations and trends in GWL from 137 observation wells evenly distributed across the south-western part of Punjab. This region has experienced significant changes in GWL over the decades. The non-parametric statistical tests included Mann-Kendall (MK), Sens's SlopeEstimator (SSE), and Innovative Trend Analysis (ITA). The study observed significant trends in GWL fluctuations before and after monsoon. The MK and SSE tests showed a statistically increasing trend in observation wells with about 65.7% and 67.2% increase before and after monsoon, respectively. The innovative trend analysis (ITA) also revealed a statistically increasing trend in observation wells with an increase of about 63.5% and 65.7% pre and post-monsoon season, respectively. The results indicate lowering of GWL in the northern districts of southwestern Punjab, while the southern districts experience rising GWLs. This discrepancy can be attributed to diverse agricultural activities and reduced over-exploitation of groundwater in the southern district due to soil salinity and the presence of brackish groundwater. These findings provide valuable insights into the dynamics of GWL in the studied region, highlighting notable trends associated with seasonal variations.

Validation of Vertical Bifacial Agrivoltaic and Other Systems Modelling

In agrivoltaic systems combining solar photovoltaic and agricultural activities, ground albedo is mainly characterized by the crop and its seasonal variations. This study examines the effects of using fixed, satellite-derived, and hourly measured albedo on the performance of a vertical bifacial system and a 1-axis tracking system using a bifacial photovoltaic model (AgriOptiCE). The model is developed with Matlab® and partially based on the open-source package pvlib. AgriOptiCE is firstly validated by comparing estimated front and rear irradiances with on-site measurements for specific periods from a 1-axis tracker site in Golden, USA and a vertical agrivoltaic system in Västerås, Sweden. Furthermore, photovoltaic system power output estimations using AgriOptiCE are also validated for the vertical agrivoltaic system and the conventional ground-mounted fixed-tilt system at the same location. The validations demonstrate the high accuracy of the proposed model in estimating front and rear irradiances and power output, obtaining R2 > 0.85 for all the studied cases. The study results indicate that measured albedo provides the highest accuracy, while satellite-derived albedo has poorer results due to the broader spatial, temporal, and spectral resolution. Fixed albedo is not recommended for yearly assessment of bifacial PV systems because it cannot account for snow events and daily variations, resulting in lower overall accuracy.

Mapping of Groundwater Recharge Zones in Hard Rock Aquifer through Analytic Hierarchy Process in Geospatial Platform

Extensive use of groundwater is a result of the growing population; in relation to this, studies have focused on groundwater conservation measures. This study identified groundwater artificial recharge zones (GWARZs) in the upper Manimuktha sub-basin through the application of remote sensing and GIS. A spatial analysis using the analytical hierarchical process (AHP) and weighted overlay analysis (WOA) was employed by integrating several spatial thematic layers such as geology, geomorphology, aquifer thickness, lineament density (LD), drainage density (DD), soil, slope, rainfall, and land use/land cover (LULC) in order to classify the GWARZs. The geomorphology along with lithology, higher aquifer thickness, low lineament densities, higher drainage densities, and gentle slope regions, were identified as suitable areas for artificial recharge zones. The study area was divided up into five classifications based on the integration analysis: excellent (41.1 km2), good (150.6 km2), moderate (123.9 km2), bad (125.5 km2), and very poor (57.7 km2). Excellent and good GWARZs were identified in the eastern and central regions of the study area. The final outcomes of this research were evaluated with seasonal electrical conductivity (EC) variations. The majority of samples with minor seasonal EC variations were observed in the excellent and good GWARZ categories. The results showed that the spatial analysis tool is useful for GWARZ delineation and sustainably managing groundwater resources.