Site-specific Data Research Articles

Characterizing the spatial variability of marine soil properties with site-specific sparse data using a Bayesian data fusion approach

Characterizing the spatial variability of marine soil properties with site-specific sparse data using a Bayesian data fusion approach

Bridging the gap: a qualitative process evaluation from the perspectives of healthcare professionals of an audit-and-feedback-based intervention to improve transition to adult care for young people living with type 1 diabetes

BackgroundThe transition from pediatric to adult care is a vulnerable time for young people living with type 1 diabetes (T1D). Bridging the Gap (BTG) is an audit-and-feedback (AF) intervention aimed at improving both transitions-in-care processes and diabetes management in the year following transition. As part of BTG, we conducted a qualitative process evaluation to understand: (a) what was implemented and how; and (b) the contextual factors (micro-, meso- and macro-) that affected implementation, outcomes and study processes.MethodsUsing qualitative descriptive methodology, interviews were conducted with 13 healthcare professionals (HCPs) delivering diabetes care to transitioning youth. Participants were asked about their experiences of BTG study processes and feedback tools, the quality improvement (QI) initiatives implemented at their site, and potential spread and scale. Interviews also explored the impacts of COVID-19 on transition care and study processes and results.ResultsFive key themes were identified. Participants’ reflections on the BTG study design indicated they appreciated its flexible, site-specific approach to QI, which they saw as crucial to the success of their initiatives. Engagement with feedback reports and other study resources provided comparative, site-specific data. Participants described the challenges posed by the COVID-19 pandemic and its impacts on patients, care provision and study implementation. Their site-specific QI initiatives resulted in changes to their transition practices. Finally, participants commented on how BTG and its processes fostered a community of practice (CoP) between sites, resulting in new opportunities to collaborate and share experiences.ConclusionsBTG resulted in a CoP among practitioners delivering transition care to youth with T1D, which could be scaled up to promote a learning health system in pediatric diabetes care. Qualitative process evaluation is a useful tool for understanding how contextual factors affect the implementation and outcomes of complex QI interventions.

Comparative LCA-MCDA of high-strength eco-pervious concrete by using recycled waste glass materials

Sustainable management of construction and demolition (C&D) waste and waste glass (WG) presents a significant challenge in densely populated regions like Hong Kong. While the potential for using WG in concrete is acknowledged, there is limited research on how to optimally use these materials to achieve sustainability, cost-effectiveness, and mechanical integrity. This study addresses these challenges by employing an integrated life cycle assessment and multi-criteria decision analysis (LCA-MCDA) within a cradle-to-gate scope, utilizing site-specific data. The VIKOR method, known for its robust decision-making capabilities in complex environmental assessments, was used to identify optimal concrete mixtures based on environmental, economic, and mechanical performance. The results indicate that replacing 25% of natural aggregate with recycled WG materials provides the best balance across the evaluated criteria. Furthermore, additional environmental benefits can be achieved by substituting natural aggregates with recycled waste concrete aggregates. This research underscores the importance of life cycle thinking in construction materials decision-making, demonstrating that significant use of recycled WG reduces energy consumption and greenhouse gas emissions, offering a sustainable alternative to conventional concrete.

Prediction of deoxynivalenol contamination in spring oats in Sweden using explainable artificial intelligence

Weather conditions and agronomical factors are known to affect Fusarium spp. growth and ultimately deoxynivalenol (DON) contamination in oat. This study aimed to develop predictive models for the contamination of spring oat at harvest with DON on a regional basis in Sweden using machine-learning algorithms. Three models were developed as regional risk-assessment tools for farmers, crop collectors, and food safety inspectors, respectively. Data included: weather data from different oat growing periods, agronomical data, site-specific data, and DON contamination data from the previous year. Results showed that: (1) RF models were able to predict DON contamination at harvest with a total classification accuracy of minimal 0.72; (2) good predictions could already be made in June; (3) rainfall, relative humidity, and wind speed in different oat growing stages, followed by crop variety and elevation were the most important features for predicting DON contamination in spring oats at harvest.

Derivation of site-specific soil-water characteristic curve (SWCC) from extremely sparse experimental data by hierarchical Bayesian method with consideration of geotechnical sites similarity

Soil-water characteristic curves (SWCCs) play a crucial role in understanding soil behavior related to water movement and soil moisture effects, rendering them an essential tool in engineering geology and geotechnical engineering applications. Traditionally, SWCCs are determined through labor-intensive laboratory experiments involving varying levels of suction, a process that can take several months. Moreover, obtaining a high-quality SWCC from numerous measurements becomes particularly challenging when immediate site-specific data are required for design and analysis. This paper introduces a hierarchical Bayesian method for deriving site-specific SWCCs by integrating extremely sparse data (e.g., one or two measurements) for the site of interest with existing data from sites with similar geological and sedimentary characteristics. The SWCC parameters are estimated using a Bayesian framework and Markov chain Monte Carlo simulations. This approach not only enables the derivation of accurate SWCCs but also helps quantify the associated uncertainties. The effectiveness of the proposed method is demonstrated using both numerical and real-world data from different types of loess and unsaturated soils in the unsaturated soil database (UNSODA). The results show that site-specific SWCCs of unsaturated soils can be accurately estimated from sparse measurements by incorporating information from similar sites. This work offers an efficient and reasonably accurate approach for deriving SWCCs of unsaturated soils for geotechnical applications, especially when the number of site-specific SWCC measurement is extremely sparse and limited.

A multi-objective optimization decision-making methodology for fostering synergies in the water-energy-food nexus

Current decision-making methods within the water-energy-food nexus (WEFN) encounter challenges in practicality, portability, scalability, and accuracy. Optimization methods, integrating site-specific data, offer promise for achieving desired outcomes and enhancing practical decision implementation within WEFN systems. However, these methods still struggle with solving multi-objective and multi-constraint problems, poor performance, and decision-making difficulties. This study developed an optimization method for WEFN systems, which integrates an evolutionary algorithm, swarm intelligence algorithm, multistage evolutionary algorithm, and post-optimization theories to address these issues. Performance results demonstrated that the proposed algorithm outperformed traditional metaheuristic optimization algorithms. Specifically, it excelled in tackling high-dimensional constrained problems, surpassing the classical NSGA series algorithms with a 1.34-fold improvement in the comprehensive performance metric HV. Subsequently, this algorithm combined with knee point post-optimization theory was applied to a typical arid region known for food and energy production. Compared with the business-as-usual scenario, the optimized schemes enhance agricultural benefits, saving 13.3 billion m3 of water over the entire planning period. Meanwhile, sustainable energy implementation would yield potential carbon emission reduction benefits of around 2.8 billion CNY. In summary, the proposed method would successfully provide a paradigm for the synergetic decision-making of WEFN systems.

Mapping the climate impact of construction processes: a case study from Denmark

Abstract Construction processes comprise a significant proportion of a building’s embodied carbon and resource consumption. Life cycle assessments (LCAs) allow for calculating impacts from embedded materials throughout a building’s life cycle. However, research on life cycle modules related to transport (A4) and the construction installation process (A5) is limited. Current studies suggest that construction processes contribute between 5 and 18.5% of the climate impact but lack consistent methodological approaches and exhibit variable background data, hindering clear comparisons. This study aims to establish a basis for comparison by conducting a comprehensive LCA for a multifamily residential from Denmark, including the construction phases. Site-specific data for the case study, including transportation details, grid-based energy metering, and hauling tickets, were directly provided by the building contractor, ensuring the accuracy and completeness of data collection. The study shows that construction processes contribute 12.3% to the studied residential building’s whole-life carbon emissions (WLC). Approximately 62.5% of the total carbon emissions are attributable to upfront emissions and, thus, are emitted before the building’s occupancy, indicating significant potential for immediate emission mitigation measures. The study’s insights into carbon emissions related to construction activities highlight transportation and waste management as substantial contributors. This offers pathways for emission monitoring and facilitates a framework for further exploration to mitigate carbon emissions during the construction phase.

Soil gas gradient method for estimating natural source zone depletion rates of LNAPL and specific chemicals of concern

This paper presents a simplified approach for the soil gas gradient method for estimating natural source zone depletion (NSZD) rates of specific contaminants of concern (COCs) at sites contaminated by light non-aqueous phase liquids (LNAPL). Traditional approaches to quantify COC-specific NSZD rates often rely on numerical or analytical reaction-transport models that require detailed site-specific data. In contrast, the proposed method employs simple analytical solutions, making it more accessible to practitioners. Specifically, it requires only the maximum soil gas concentration, the effective diffusion coefficient, and the diffusive reaction length calculated from vertical soil gas concentration profiles. The simplified approach was validated against a reactive transport numerical model reported in the literature, showing consistent results within the same order of magnitude for BTEX NSZD rates at a gasoline spill site in South Carolina. Further validation using a larger dataset involved comparing NSZD rate estimates for benzene and total petroleum hydrocarbons (TPH) against those obtained using BioVapor, utilizing empirical soil gas data from the USEPA Petroleum Vapor Intrusion Database. Results demonstrated a strong correlation between NSZD rates and maximum soil gas concentrations, allowing the development of a rapid screening approach based only on the measured soil gas concentrations and literature values for diffusion coefficients and diffusive reaction lengths. This approach aligned well with previous modeling studies and was consistent with literature values for TPH NSZD rates. Overall, both the simplified and screening approaches offer practical, easy-to-use tools for evaluating temporal variability in natural attenuation rates, supporting baseline assessments and ongoing performance evaluations of remediation at LNAPL sites.

Occurrence and Distribution of Organophosphate Flame Retardants in Tap Water System-Implications for Human Exposure from Shanghai, China.

The pollution of organophosphate flame retardants (OPFRs) is of global concern, but the site-specific data of OPFR concentrations in drinking water are scarce for many areas of the world outside of Europe and the US. This study aimed to investigate the occurrence and profiles of OPFRs in the tap water treatment and delivery process in Shanghai. In total, 106 samples were analyzed for 10 OPFRs, which were collected periodically from monitoring points of drinking water treatment plants and piped water between November 2021 and July 2023. The average daily doses of OPFRs through the ingestion of tap water were calculated by multiplying nominal volumes of water ingestion rates with the measured concentrations of OPFRs. Hazard quotients, the hazard index, and the carcinogenic risks of OPFRs via drinking water were used to estimate the health risks. Tributyl phosphate (TBP), tris(2-chloroethyl) phosphate (TCEP), and tris (1-chloro-2-propyl) phosphate (TCIPP) were found in >90% of the tap water samples, whereas triethyl phosphate (TEP) and tris (2,3-dibromopropyl) phosphate (TDBPP) were not found in any samples. The concentrations of Σ10OPFRs were found at part-per-trillion ranges, with average concentrations that ranged from 86.0 ng/L in February 2023 (dry season) to 218 ng/L in July 2022 (wet season). TCIPP was the most abundant compound among the investigated OPFRs. The average daily dose of Σ10OPFRs via the ingestion of tap water was up to 20.4 ng/kg body weight/day. The hazard quotients of OPFRs through drinking water were in the range of 10-5-10-4, indicating low risk levels. Moreover, the hazard index of OPFRs indicated that the risk for children (2 × 10-4) was higher than adults (7 × 10-5). Tap water intake may be an important source of OPFRs exposure. But the risk of OPFRs for local residents is at a low level through drinking water.

Cross-site Validation of AI Segmentation and Harmonization in Breast MRI.

This work aims to perform a cross-site validation of automated segmentation for breast cancers in MRI and to compare the performance to radiologists. A three-dimensional (3D) U-Net was trained to segment cancers in dynamic contrast-enhanced axial MRIs using a large dataset from Site 1 (n = 15,266; 449 malignant and 14,817 benign). Performance was validated on site-specific test data from this and two additional sites, and common publicly available testing data. Four radiologists from each of the three clinical sites provided two-dimensional (2D) segmentations as ground truth. Segmentation performance did not differ between the network and radiologists on the test data from Sites 1 and 2 or the common public data (median Dice score Site 1, network 0.86 vs. radiologist 0.85, n = 114; Site 2, 0.91 vs. 0.91, n = 50; common: 0.93 vs. 0.90). For Site 3, an affine input layer was fine-tuned using segmentation labels, resulting in comparable performance between the network and radiologist (0.88 vs. 0.89, n = 42). Radiologist performance differed on the common test data, and the network numerically outperformed 11 of the 12 radiologists (median Dice: 0.85-0.94, n = 20). In conclusion, a deep network with a novel supervised harmonization technique matches radiologists' performance in MRI tumor segmentation across clinical sites. We make code and weights publicly available to promote reproducible AI in radiology.

Semiautomated generation of species-specific training data from large, unlabeled acoustic datasets for deep supervised birdsong isolation.

Bioacoustic monitoring is an effective and minimally invasive method to study wildlife ecology. However, even the state-of-the-art techniques for analyzing birdsongs decrease in accuracy in the presence of extraneous signals such as anthropogenic noise and vocalizations of non-target species. Deep supervised source separation (DSSS) algorithms have been shown to effectively separate mixtures of animal vocalizations. However, in practice, recording sites also have site-specific variations and unique background audio that need to be removed, warranting the need for site-specific data. Here, we test the potential of training DSSS models on site-specific bird vocalizations and background audio. We used a semiautomated workflow using deep supervised classification and statistical cleaning to label and generate a site-specific source separation dataset by mixing birdsongs and background audio segments. Then, we trained a deep supervised source separation (DSSS) model with this generated dataset. Because most data is passively-recorded and consequently noisy, the true isolated birdsongs are unavailable which makes evaluation challenging. Therefore, in addition to using traditional source separation (SS) metrics, we also show the effectiveness of our site-specific approach using metrics commonly used in ornithological analyses such as automated feature labeling and species-specific trilateration accuracy. Our approach of training on site-specific data boosts the source-to-distortion, source-to-interference, and source-to-artifact ratios (SDR, SIR, and SAR) by 9.33 dB, 24.07 dB, and 3.60 dB respectively. We also find our approach allows for automated feature labeling with single-digit mean absolute percent error and birdsong trilateration accuracy with a mean simulated trilateration error of 2.58m. Overall, we show that site-specific DSSS is a promising upstream solution for wildlife audio analysis tools that break down in the presence of background noise. By training on site-specific data, our method is robust to unique, site-specific interference that caused previous methods to fail.

Are the Horizontal-to-Vertical Spectral Ratios of Earthquakes and Microtremors the Same?

ABSTRACT We consider the similarities and differences between earthquake and microtremor horizontal-to-vertical spectral ratios (eHVSR and mHVSR, respectively) using a dataset of 161 sites in southern California. Quantitative comparisons are made in terms of the eHVSR and mHVSR lognormal median curves, as well as the frequencies and amplitudes associated with the fundamental- and higher-mode resonances where present. The results show only 58% of the eHVSR–mHVSR pairs agree in terms of their median curve and only 25% of the eHVSR–mHVSR pairs agree in terms of shared resonances, which increases to 68% if flat HVSRs are considered equivalent. Furthermore, while the shared resonances match very well in terms of frequency (root mean square error, RMSE, <0.11 Hz), the amplitudes of those resonances do not agree (RMSE >1.6). These findings demonstrate that while eHVSR and mHVSR agree at some sites, they are not equivalent at all sites. To investigate if the agreement between eHVSR and mHVSR could be related to features of the microtremor data, earthquake recordings, and/or the site conditions, three machine learning (ML) models at varying levels of interpretability are presented. The ML models—which include multivariate logistic regression, gradient-boosted trees, and support vector machines—show only partial success at using site-specific data to predict whether eHVSR and mHVSR will likely agree in terms of their median curve (accuracy of 78%) and number of resonances (accuracy of 84%). Therefore, we conclude that while eHVSR and mHVSR can be quite similar in terms of resonant frequencies at some sites, they are not identical at all sites. Furthermore, preliminary evidence shows that the agreement of eHVSR and mHVSR can be predicted a priori given features of the microtremor measurements, earthquake recordings, and site conditions, although a larger dataset will be necessary for developing a robust predictive model.

Spatial Variability in Soil Water-Physical Properties in Southern Subtropical Forests of China

Quantification of soil water-physical properties and their spatial variation is important to better predict soil structure and functioning, as well as associated spatial patterns in the vegetation. The provision of site-specific soil data further facilitates the implementation of enhanced land use and management practices. Using geostatistical methods, this study quantified the spatial distribution of soil bulk density (SBD), soil moisture (SM), capillary water-holding capacity (CWHC), capillary porosity (CP), non-capillary porosity (NCP), and total porosity (TP) in southern subtropical forests located at the Tropical Forest Research Center in Pingxiang City, China. A topographic map (scale = 1:10,000) was used to create a grid of l km squares across the study area. At the intersections of the grid squares, the described soil water-physical properties were measured. By calculating the coefficient of variation for each soil water-physical property, all measured soil water-physical properties were found to show moderate spatial heterogeneity. Exponential, gaussian, spherical, and linear models were used to fit the semivariograms of the measured soil water-physical properties. Across all soil water-physical properties, the range A0 variable (i.e., the separation distance between the semivariance and the sill value) measured between 3419 m and 14,156 m. The nugget-to-sill ratio ranged from 9 to 41%, indicating variations in the level of spatial autocorrelation among the soil water-physical properties. Many of the soil water-physical properties were strongly correlated (as assessed using Pearson correlation coefficients). Spatial distribution maps of the soil water-physical properties created via ordinary kriging (OK) showed that most water-physical properties had clumped (aggregated) distributions. SBD showed the opposite spatial pattern to SM and CWHC. Meanwhile, CP and TP showed similar distributions.

Conceptualization of territorial resilience potential indicators to coastal hazards through the use of unmanned aerial vehicles

Major natural hazards in recent decades have led to a revisiting of the concept of resilience, particularly in order to analyze traditional models of response to an unforeseen event and post-crisis management mechanisms. For resilience to be an applicable/operational concept to guide management and inform decisions, it must ultimately be characterized for its assessment. This assessment can be done through the identification of indicators specifically contextualized to the study site and object of study. However, today, operationalizing of the concept of resilience tends to draw on annual censuses or aggregated data, providing a generalized large scale view of the territorial resilience potential. The objective of this study is to identify a table of Territorial Resilience Potential (TRP) indicators to coastal hazards applied to coastal island territories, supplied by data from Unmanned Aerial Vehicles (UAVs) to allow for rapid and site specific data acquisition for repetitive surveys. This acquisition method makes it possible to calculate a number of relevant indicators for assessing the resilience potential of coastal areas. In particular, it allows rapid updating of data following major meteorological events and identification of hot and cold spots of resilience potential of a specific study site. To demonstrate the applicability of this method to island territories, the island of Bora Bora in French Polynesia was used as a case study. Finally, these kinds of results can be fed through a spatial decision support system to help decision-makers choose an adaptation and protection strategy in order to move towards resilient territories, over a long period of time.

Individual-based numerical experiment to describe the distribution of floating kelp within the Southern Benguela Upwelling System

Abstract Kelps are resilient organisms, capable of thriving in high-energy wave environments. However, when hydrodynamic drag forces exerted by the wave environment exceed the kelps’ structural limits, individuals become dislodged. Floating kelps generally follow ocean currents, traveling long distances until air-filled structures fail or the epibiont load becomes too great, causing them to sink to the seafloor. The ability of kelp to disperse over vast offshore and nearshore systems makes them important for organic subsidy and as a dispersal vector for marine organisms. Previous research on dislodged macroalgae focused on context-specific rafts, limiting insights into the broader ecological role of floating kelp. This study employed a site-specific Lagrangian trajectory model to describe the spatial distribution of floating Ecklonia maxima along the South African coastline. The model incorporated buoyancy and sinking using site-specific morphological data. Findings revealed that the distribution of floating E. maxima is influenced by oceanographic conditions, and seasonal patterns were also evident. Mesoscale features played a vital role in kelp accumulation on the surface and seafloor and acted as barriers to dispersal. This study offers essential insights into kelp’s role as an organic subsidy and provides numerical evidence for kelp’s potential as a carbon sink, contributing to a better understanding of kelp ecosystems and their ecological functions.

Spatiotemporal variability and trends of intra-seasonal rainfall and temperature in the drought-prone districts of Northwestern Ethiopia

Agriculture in Ethiopia is highly dictated by spatial patterns and temporal distributions of climate variables. The analysis of these climate variables is crucial for understanding the impacts on agricultural productivity. This study aimed to analyze spatiotemporal variability and trends of intra-seasonal rainfall and temperature using site-specific daily data from the Ethiopian Meteorology Institute (1992–2021). Standardized methods explore variability, while Mann–Kendall tests identify trends, using the Modified version for data with autocorrelation. Inverse Distance Weighted interpolation was employed for spatial analysis of rainfall, length of growing season, and temperature. The findings identified that Kiremt dominated the mono-modal rainfall pattern, contributing 72%-86% of total annual rainfall. The study found that the season typically begins early on June 13 in Adiszemen, and July 6 in Arbgebiya and ends between October 6 and October 26. The duration of the season varied across locations, averaging 95 days at Ebenat and 148 days at Adiszemen. The seasonal rainfall anomaly index shows identical patterns between ENSO episodes and seasonal rainfall. These findings inform decision-making and adaptation strategies for ENSO-driven rainfall variability. Temperatures showed predictable seasonal patterns, but have significantly increased over time, with maximum and minimum temperatures rising 0.014 °C to 0.421 °C and 0.027 °C to 0.485 °C per year respectively. This warming trend is negatively impacting water, crops, and livestock, requiring adaptation measures to build regional resilience. This study underscores the critical impact of climate variability on agriculture in the study area. The findings reveal shifts in rainfall patterns and temperature trends, providing essential insights for adapting agricultural practices.

The Benchmarking Exercise Programme for Older People (BEPOP): Design, Results and Recommendations from The First Wave of Data Collection.

The Benchmarking Exercise Programme for Older People (BEPOP) service improvement project seeks to determine and promote the exercise training characteristics associated with positive outcomes for resistance exercise for older people living with, or at risk of, sarcopenia or physical frailty. Mixed-methods service improvement project. Ten UK National Health Service physiotherapist-led therapy services delivering exercise interventions for older people submitted anonymized data for up to 20 consecutive patients. A multidisciplinary expert panel generated a report and recommendations with site-specific benchmarking data and feedback. In parallel, participating physiotherapy team members were interviewed to elicit feedback on BEPOP rationale, processes and perceived value. Data from 188 patients were included, mean age 80 years (range 60-101). 115 (61%) received objective assessment of strength-based physical performance. Bodyweight exercises (173 [92%]) and resistance bands (49 [26%]) were the commonest exercise modalities. Exercises progressed predominantly through increased repetitions (163 [87%]) rather than increased load. 50 (30%) had no reassessment of outcomes; only 68 (41%) were signposted to follow-on exercise services. Staff interviews identified themes around knowledge, diagnosis, data collection and practice reflection. BEPOP was feasible to deliver and generated actionable insights for service improvement via improved diagnosis, measurement and progression of resistance exercise.

Estimation of soil organic matter content by combining Zhuhai-1 hyperspectral and Sentinel-2A multispectral images

Hyperspectral satellite imagery has significant advantages in rapidly monitoring soil organic matter (SOM) content over a large area. However, limitations in the timely acquisition of site-specific data may affect its effectiveness due to weather influences and revisit cycles. This paper proposes a novel method for estimating SOM content that combines the high temporal resolution of the Zhuhai-1 hyperspectral satellite image and Sentinel-2A multispectral satellite image to broaden the spectral range of Zhuhai-1 images. Multisource features, including spectral bands, topographic features, textural features, and spectral indexes, are extracted from the combined image and digital elevation model. An improved genetic algorithm (IGA) is proposed to optimize feature selection and extreme gradient boosting (XGBoost) is then applied to estimate SOM content. The proposed method was validated using 197 topsoil samples and satellite images collected from a demonstration area in Lishu County, Jilin Province, China. The results indicate that the estimation accuracy using the combined image was greater than using one single image. Compared with only using the Sentinel-2A and Zhuhai-1 images, the coefficient of determination (R2) values improved from 0.61 and 0.73 to 0.82, the ratio of the prediction to the deviation (RPD) values improved from 1.62 and 1.93 to 2.35, and the ratio of performance to the interquartile distance (RPIQ) values improved from 2.16 and 2.58 to 3.15, respectively. Furthermore, the XGBoost algorithm outperformed the random forest algorithm in terms of model accuracy and mapping reliability. The use of multisource features improved the R2 value from 0.45 to 0.82 compared to only using 31 spectral bands of Zhuhai-1 and Sentinel-2A image, with contribution rates in descending order of spectral indexes (48.2%), topographic features (24.7%), spectral bands (19.9%), and textural features (7.2%). This paper thus presents a promising method for efficient periodic mapping of the SOM content by combining data from a hyperspectral satellite constellation and a multispectral satellite image.

Applicability of calibrated diffuse reflectance spectroscopy models across spatial and temporal boundaries

Diffuse reflectance spectroscopy (DRS) is an emerging soil testing approach. Although several studies have validated the DRS approach, limited efforts are made to assess the applicability of calibrated DRS models on new samples collected at different locations and/or time. To test such spatio-temporal applicability of calibrated DRS models, we collected surface soil samples from 1,112 smallholder farms during 2018 (T2018) and 607 farms during 2021 (T2021) covering seven districts of the Bundelkhand region of central India. The T2018 samples covered 7 development blocks; the T2021 samples were also collected from these blocks but from different sampling locations. Additionally, a new sampling site (Jhansi-Bamour block) was added during 2021 to create an independent test dataset. Collected samples were analysed for 17 soil parameters (basic soil properties, macronutrients, and micronutrients) and spectral reflectance over the visible to near-infrared region. Corresponding soil test crop response (STCR) ratings were also estimated. The Cubist model was calibrated in the T2018 dataset and tested against the T2021 dataset using the coefficient of determination (R2), root-mean-squared error (RMSE), and percentage relative error deviation (PRED) at 30% error threshold as performance statistics. Model applicability was assessed at each block level (site-specific), by dividing the study site into their two geology-specific regions, and by treating the entire dataset as a regional-scale spectral library. Results showed that DRS models calibrated on a finer scale (site-specific) are less efficient in estimating soil parameters in broader scale (geology-specific and regional-scale) test T2021 samples although their STCR ratings may safely be estimated at local scales. When site-specific data were aggregated to broader scales and T2018 dataset was spiked with 20% samples from the T2021 dataset, model performance improved for critical soil parameters such as soil organic carbon (SOC) contents and several plant nutrients and their ratings; application of such large-scale models also improved the estimation accuracy when applied to site-specific datasets. Exchangeable Ca and Mg, clay and SOC contents were frequently well-estimated with R2 values ranging from 0.54 to 0.93. Fine sand was the next best estimated soil property with R2 values in the range of 0.40–0.75. The STCR ratings estimated in the DRS approach matched the wet chemistry-based STCR ratings to the tune of 43 to 100%. Overall, as many as 60% of all new samples could be estimated with more than 70% accuracy for 8 out of 17 parameters. With the DRS approach tested on both spatially- and temporally-independent test datasets and, specifically, with high estimation accuracy of STCR ratings, our results suggest that the DRS approach may safely be used as a viable alternative to conventional soil testing in smallholder farms.

Assessing Greenhouse Gas Emissions and Methane Production During Post-Landfill Maintenance in Kish Island: A LandGEM Simulation Approach

Background: Municipal solid waste (MSW) landfills are significant contributors to the emission of greenhouse gases, particularly methane, during the breakdown of waste materials. Researchers have undertaken extensive investigations into quantifying methane emissions from landfills, utilizing various methodologies beyond reliance on the LandGEM model. Numerous studies have underscored the efficacy of this model in predicting methane generation and exploring its utility in activities such as energy generation and emission mitigation strategies. Objectives: Municipal solid waste landfills are vital contributors to greenhouse gas emissions, notably methane, during waste decomposition. Significant research has focused on estimating methane output from landfills, utilizing various approaches beyond the LandGEM model. Studies highlight the model's potential in assessing methane generation and its relevance to activities like energy generation and emission management. Methods: The LandGEM simulation model is employed to forecast future waste generation and provide valuable insights into the environmental consequences of landfill management on the island. By analyzing population data and waste generation statistics from 2023 to 2042, the study utilizes the LandGEM software to estimate methane emissions, incorporating site-specific data and waste composition analysis. Results: The findings reveal that 36% of the total waste on Kish Island consists of putrescible material, with food waste being a significant component. Over the study period, approximately 1,282,637 tons of waste are projected to be disposed of in the Kish landfill. The LandGEM model predicts the highest landfill gas emission in 2043, primarily methane, along with carbon dioxide and non-metallic organic compounds. The estimated quantities for these gases in 2043 are 5.415E+03, 1.486E+04, and 2.327E+02 metric tons, respectively. Conclusions: This research highlights the importance of tailored waste management strategies to mitigate environmental impacts and emphasizes the need for efficient gas collection systems. A proposed gas collection system combining vertical wells, horizontal trenches, and under-membrane pipes is discussed to enhance efficiency and reduce environmental risks. The study provides valuable insights into waste management and landfill gas emissions on Kish Island, underscoring the significance of accurate emission prediction and effective management to minimize environmental consequences and optimize the utilization of renewable energy resources.