The research, conducted by Ajay D. Sarange of Swami Ramanand Teerth Marathwada University and Kishor Y. Ingale of Netaji Subhashchandra Bose Arts, Commerce, and Science College in Nanded, India, is notable less for any single technique than for the disciplined way it connects every stage of a data-to-decision pipeline. Seventeen city-zone load series from Laayoune, Boujdour, Foum El Oued, and Marrakech were converted to a common unit, aggregated into four city-level series, and aligned on shared thirty-minute timestamps spanning a full year, from January 2023 to January 2024. The result is an experimental aggregate demand portfolio of nearly half a million observations that the authors stress is methodological rather than physical: the cities are not electrically interconnected, and no claim is made that a single retailer serves them all.

The forecasting stage is deliberately conservative. Three candidate models, a persistence forecast, a seasonal-naive forecast lagged by one day, and a Random Forest, were compared using only chronological training and validation splits, with no shuffling that could leak future information. The Random Forest, fed nothing but the previous twelve hours of half-hourly loads, won selection on validation error and was then asked to produce recursive 96-period forecasts, equivalent to 48-hour horizons, in which its own predictions feed back into subsequent steps. Crucially, the held-out test data were never touched during model choice or scenario construction. Interestingly, the seasonal-naive method later achieved lower test-period error than the selected Random Forest, an outcome the authors report candidly; the scheduling contribution, they emphasise, is not forecasting superiority but the disciplined treatment of uncertainty.

That uncertainty enters through a moving-block bootstrap applied to validation-period forecast residuals. Rather than resampling isolated errors, which would destroy the short-term correlation that matters for scheduling, the method draws contiguous blocks of residuals, 51 half-hours long in this configuration, and adds them to the point forecast to generate 10,000 plausible demand trajectories. A KMeans clustering procedure then compresses this vast pool into one hundred representative scenarios, each an actual generated trajectory selected as nearest to a cluster centroid rather than an averaged profile that would artificially smooth peaks. The authors deliberately compared averaged centroids against these representative selections, finding that representatives better preserved variance and extreme peaks, a detail that matters enormously when the whole point is to prepare for demand spikes.

The decision model itself is a two-stage stochastic programme, a classic operations-research structure with a modern twist. Before demand is known, the scheduler commits to day-ahead procurement, demand-response capacity, and reserve capacity. After a demand scenario materialises, recourse actions kick in: dispatching the committed demand response, deploying reserves, absorbing modelled unmet demand as a penalised slack, or dumping surplus. The mathematics enforces non-anticipativity, meaning first-stage decisions cannot secretly depend on which scenario later occurs. Three variants were compared under identical operational assumptions: a risk-neutral stochastic programme, a Conditional Value-at-Risk formulation that penalises the worst five percent of cost outcomes, and a study-specific formulation dubbed PMAD-SP, which adds a penalty on the maximum periodwise absolute deviation of scenario costs from their mean.

The headline result is striking. Evaluated on a separately generated common set of 10,000 scenarios that played no role in building any policy, the stochastic programme reduced expected normalised cost by 12.72 percent relative to the deterministic benchmark, and slashed the model’s penalised unmet-demand quantity by 87.42 percent. The mechanism is intuitive: the deterministic plan, anchored to one forecast path, committed no reserve capacity at all, leaving it dangerously exposed whenever demand exceeded the forecast. The stochastic plan paid slightly more for day-ahead procurement and for reserves, roughly 71 kilowatts of expected reserve commitment versus zero, but that insurance premium paid for itself many times over when reality diverged from the point forecast.

The risk-aware variants tell more nuanced stories. The CVaR formulation added about 18 percent more reserve commitment than the risk-neutral model and nudged empirical upper-tail cost down by just 0.54 percent, a modest gain the authors attribute to the finite resolution of the hundred-scenario planning tail. PMAD-SP went much further on insurance, boosting reserve commitment by 137 percent, and achieved its real objective: it cut the standard deviation of scenario-total costs by nearly 40 percent while leaving expected cost essentially unchanged. The authors are careful to position PMAD-SP as descriptive cost-dispersion regularisation rather than formal distributionally robust optimisation, since it optimises against a fixed empirical distribution rather than an ambiguity set of possible distributions.

Sensitivity analyses probe the framework’s robustness. Because three of the four cities record current in amperes rather than power, the conversion to kilowatts required assumed values for voltage and power factor; testing nine combinations across 220 to 240 volts and power factors of 0.80 to 1.00 showed the stochastic programme’s advantage persisting in every case, with cost reductions ranging from 12.57 to 12.89 percent. Seven consecutive non-overlapping 48-hour historical windows also saw the stochastic policy win every time, a perfect record confirmed by an exact sign test, though the authors insist this evidence is descriptive because the windows share fitted models, calibration residuals, and adjacent chronology. Formal decision-value calculations reinforce the picture: the value of the stochastic solution amounts to 10.44 percent of the recourse problem’s optimal cost, while perfect foresight would be worth a further 3.67 percent.

The study is equally forthright about its limits. The single-node portfolio carries no network constraints, power flows, storage, renewables, or weather covariates; the cost coefficients are normalised experimental values rather than Moroccan tariffs; and the unmet-demand variable is a penalty-sensitive optimisation slack, not a measure of real blackouts. Reducing the hundred representative scenarios still discards nearly 48 percent of raw periodwise variance, and the risk weights were manually chosen rather than calibrated. Yet the core lesson survives these caveats intact and carries real weight for the energy transition. As smart meters proliferate across Africa, Europe, and beyond, the gap between forecasting demand and scheduling under demand uncertainty is where money is won or lost. This work shows, with unusual methodological transparency, that treating uncertainty as a first-class citizen of the optimisation problem, rather than an afterthought appended to a forecast, changes what a rational retailer buys, reserves, and dispatches, and by amounts large enough to matter.

Subject of Research: Scenario-based stochastic programming for electricity demand scheduling using smart-meter data

Article Title: Scenario based stochastic programming for aggregate electricity demand scheduling using Moroccan smart meter data

Article References: Sarange, A. D., & Ingale, K. Y. (2026). Scenario based stochastic programming for aggregate electricity demand scheduling using Moroccan smart meter data. Discover Informatics, 1(1), Article 19. https://doi.org/10.1007/s44564-026-00021-2

Image Credits: AI Generated

DOI: 10.1007/s44564-026-00021-2

Keywords: stochastic programming, smart meters, load forecasting, electricity demand scheduling, scenario generation, Conditional Value-at-Risk, Random Forest, Morocco, energy management, two-stage optimization, demand response, bootstrap

The research, led by laila TAOUFIQ of Ibn Zohr University and Universiapolis in Agadir, together with Mehdi Harrak, Mimoun Chourak, and Fatima Ezzahra Arrakhiz, set out to identify the root causes of structural failure rather than simply cataloguing the destruction. Earthen buildings constitute a substantial portion of the rural housing stock in the affected High Atlas region, and their vulnerability has long been recognized by earthquake engineers. Morocco does have a seismic regulation specifically for earthen constructions, known as RPCT11, adopted by decree in 2013, but the study’s findings suggest that both the regulation and everyday building practice fell short of what the Al Haouz earthquake demanded. The team frames its work as a direct contribution to seismic risk reduction strategy and to the revision and update of RPCT11 itself.

Field observations revealed a consistent pattern of damage across the affected villages. The most common failures included cracks and outright collapse of masonry walls, the fall of roofs and floors, out-of-plane failure in which walls toppled bodily outward or inward perpendicular to their surface, and failures originating at the foundation level. Each of these modes tells a mechanical story. Unreinforced earthen walls are strong in compression but weak in tension and shear, so when ground shaking imposes lateral forces, the walls crack along diagonal planes or separate at corners. Once the box-like integrity of the building is lost, walls acting as independent vertical cantilevers have little resistance left, and out-of-plane collapse follows, often bringing heavy timber-and-earth roofs down with them.

The authors attribute the observed damage to a combination of factors rather than a single cause. Structural deficiencies ranked high: many buildings lacked the wall-to-wall and wall-to-roof connections that allow a structure to behave as a unified three-dimensional frame during shaking. Material limitations compounded the problem, since unfired earth has inherently modest strength, and moisture content, clay mineralogy, and compaction quality all influence how much load an earthen wall can carry. Weak connections between roofing systems and supporting walls meant that roofs could detach and fall as rigid masses, a particularly lethal failure mode. Finally, unfavorable topographical and geotechnical conditions amplified ground motion in some locations, placing certain buildings on ground that shook harder and longer than adjacent sites.

To understand these site effects and soil-structure interactions, the team investigated geotechnical parameters in the most severely affected areas. Such investigations typically involve characterizing the soils on which buildings rest, including particle size distribution, plasticity limits determined through Atterberg tests, and compaction characteristics measured by Proctor testing, all of which appear in the study’s reference framework of Moroccan and international standards. Soft or loose soils can amplify seismic waves and lengthen the duration of strong shaking, while steep mountain slopes in the High Atlas can concentrate motion near ridge crests. When vulnerable earthen structures sit on amplifying ground, the demand placed on already weak walls can exceed their capacity by a wide margin, which helps explain the localized severity of collapse observed in some villages.

The earthquake itself was an unusual and sobering event for Moroccan seismology. Scientific work cited by the study describes the rupture as intraplate reverse faulting within the High Atlas mountain belt, with a deep transpressive fault accommodating the shortening of the African plate’s interior. The event, sometimes referred to as the Adassil earthquake, occurred on a steep reverse fault in the deep crust, and rapid source characterization confirmed its magnitude at 6.8. Morocco’s earthquake catalog, which extends back more than a millennium, records destructive events in the region, but the 2023 earthquake struck a mountainous rural zone where building vulnerability, rather than ground motion alone, controlled the death toll.

What distinguishes the new study is its constructive response to the failure evidence. Based on their findings, the researchers propose a guideline for the construction of buildings made of adobe, rammed earth, and stone, the three dominant earthen and semi-earthen technologies of the region. Adobe involves sun-dried mud bricks laid in mud mortar, rammed earth consists of moist soil compacted in layers within formwork, and stone masonry uses locally quarried rock, often with weak mud mortar in rural practice. The recommendations aim to enhance the seismic resilience of all three building types and to improve protection for both people and their cultural heritage, a dual goal that reflects the region’s extraordinary legacy of earthen architecture, from kasbahs to vernacular village houses.

The engineering logic behind such guidelines draws on a broad international literature that the study situates itself within. Research following earthquakes in Nepal, Turkey, Peru, Italy, and Greece has repeatedly shown that simple, low-cost measures can transform the survival prospects of unreinforced masonry and earthen buildings. Horizontal seismic bands or ring beams at wall tops and floor levels tie the structure together and prevent walls from separating; corner and junction reinforcement resists cracking at the weakest points; and roof lightening reduces the inertial forces and the deadly falling mass. Experimental work on adobe walls strengthened with timber ring beams, on rammed earth reinforced with natural fibers such as coir and bamboo, and on stone masonry retrofitted with reinforced mortar coatings all point toward interventions that remain affordable and culturally acceptable in rural communities.

Material science offers another layer of improvement. Studies of stabilized earth blocks show that small additions of cement or lime can raise strength and durability, though researchers caution that excessive stabilization undermines the environmental advantages that make earthen construction attractive in the first place. Rammed earth’s mechanical behavior is sensitive to moisture content and compaction energy, and modern testing standards now allow engineers to characterize earthen materials with the same rigor applied to conventional construction materials. The Moroccan context is particularly well studied: earlier characterization of soils from the Haouz plain and from oases in southeastern Morocco has documented the suitability of local earth for construction, provided that grading, plasticity, and stabilization are controlled. The new guidelines translate this accumulated knowledge into rules that builders on the ground can actually follow.

The stakes extend beyond Morocco. Earthen construction houses a substantial fraction of the world’s rural population, and as climate change intensifies interest in low-carbon building materials, earth is enjoying renewed attention as a sustainable alternative to fired brick and concrete. The Al Haouz earthquake demonstrated in the starkest terms that sustainability and safety must advance together: a building material with excellent embodied-energy credentials is of little value if it collapses in a moderate earthquake. By documenting failure mechanisms systematically and feeding the results into the revision of RPCT11, the Moroccan team offers a template for other earthquake-prone countries with rich earthen building traditions. The goal, as the authors put it, is to enhance seismic resilience and improve protection for both people and their cultural heritage, ensuring that the architecture of the High Atlas can continue to house generations without becoming their grave.

Subject of Research: Seismic failure mechanisms and resilience improvement of earthen buildings after the 2023 Al Haouz earthquake in Morocco

Article Title: Analysis of failure mechanisms in earthen buildings after the 2023 Al Haouz earthquake in Morocco and improvement solutions

Article References: TAOUFIQ, L., Harrak, M., Chourak, M., & Arrakhiz, F. E. (2026). Analysis of failure mechanisms in earthen buildings after the 2023 Al Haouz earthquake in Morocco and improvement solutions. Bulletin of Earthquake Engineering. https://doi.org/10.1007/s10518-026-02693-5

Image Credits: AI Generated

DOI: 10.1007/s10518-026-02693-5

Keywords: Al Haouz earthquake, Morocco, earthen buildings, adobe, rammed earth, stone masonry, seismic vulnerability, out-of-plane failure, geotechnical investigation, RPCT11, High Atlas, cultural heritage

Engaging leadership is a relatively young construct in organizational psychology. It describes leaders who energize their teams through four interlocking behaviors: inspiring others through strong personal example, empowering employees by giving them autonomy and voice, strengthening the bonds within the team, and fostering a sense of meaning and alignment with organizational goals. The original Engaging Leadership Scale was developed in the Norwegian research tradition, and like most psychological instruments it carries the cultural fingerprints of the population in which it was built. Idioms about what makes a leader inspiring, or what empowerment looks like in daily work, do not translate word for word. That is why the field of cross-cultural adaptation exists: to ensure that when a questionnaire crosses a language border, it still measures the same underlying idea rather than an artifact of translation.

Nadifi and Zenjari followed the established, multi-stage protocol for this kind of work. The process began with forward translation, in which bilingual experts rendered the English items into French with attention to meaning rather than literal wording. A back-translation step then converted the French version into English again, ideally by translators blind to the original, so that discrepancies between the back-translation and the source could reveal ambiguities or cultural mismatches. An expert panel reviewed the material and refined the wording, and a pilot test with members of the target population checked that the items were comprehensible and natural in a Moroccan workplace setting. Only after this iterative polishing was the final French-Moroccan version administered in a cross-sectional online survey of 298 participants working in Moroccan public and private organizations.

The choice of knowledge workers as the study population is significant. These are employees whose primary capital is expertise—analysts, engineers, managers, educators, and professionals whose output depends on judgment and creativity rather than routine physical labor. Knowledge workers are precisely the group for which engaging leadership is hypothesized to matter most, because their motivation and discretionary effort are difficult to monitor and depend heavily on intrinsic drivers. Morocco’s economy, like many others, is shifting toward such knowledge-intensive roles, yet most validated leadership instruments in the country were imported without formal adaptation. A rigorously tested French-language measure fills a practical gap for human resource practitioners and organizational scientists working in Morocco and other Francophone settings.

The statistical heart of the study lies in confirmatory factor analysis, a technique that tests whether the pattern of correlations among questionnaire items matches a theoretical model of how the construct is structured. The researchers compared competing models, including a second-order hierarchical model in which the four leadership facets are treated as manifestations of one overarching engaging leadership construct. That hierarchical model fit the data reasonably well, with a chi-square to degrees-of-freedom ratio of 2.27, a Comparative Fit Index of 0.978, a Tucker-Lewis Index of 0.971, a Root Mean Square Error of Approximation of 0.065, and a Standardized Root Mean Square Residual of 0.029. For readers outside psychometrics, values of CFI and TLI above roughly 0.95 and an RMSEA below about 0.08 are conventionally considered indicators of good model fit, so these numbers describe a measurement model that reproduces the observed data closely.

Reliability, the degree to which the scale produces consistent measurements, was equally impressive. The overall twelve-item scale achieved a Cronbach’s alpha of 0.950, a figure that sits well within the range conventionally described as excellent. The four subscales each showed internal consistency coefficients between 0.821 and 0.903, spanning acceptable to strong. In practical terms, this means that respondents who answered one inspiring-leadership item positively tended to answer the other items in that facet consistently, and that the total score behaves as a coherent summary of a person’s perception of their leader. Reliability of this magnitude is a prerequisite for any instrument intended to be used in real organizations, whether for research surveys, leadership development programs, or internal diagnostics.

The study also examined measurement invariance across gender, a technical but consequential question. Invariance testing asks whether the questionnaire functions the same way for different groups: configural invariance checks that the same factor structure holds, metric invariance checks that items relate to the construct with equal strength across groups, and scalar invariance checks that items behave similarly at the same underlying level of the trait. All three levels were supported in this sample, meaning that men and women in the study interpreted and responded to the items in comparable ways. Without such evidence, comparisons of average leadership scores between genders could reflect measurement bias rather than genuine differences, so the invariance findings license meaningful group comparisons in future research.

Convergent validity was assessed by testing whether the new scale correlates with a related construct in the expected direction. The global ELS score correlated positively with work engagement, the well-studied state of vigor, dedication, and absorption in one’s job, with a correlation coefficient of 0.46. This is a moderate, theoretically sensible association: employees who perceive their leaders as engaging tend to report higher engagement with their own work. The finding does not prove that engaging leadership causes engagement—the cross-sectional design cannot establish causal direction—but it demonstrates that the scale captures something meaningfully connected to outcomes that organizations care about, rather than measuring statistical noise.

The authors are notably transparent about one limitation. The four factors of the scale correlated relatively highly with one another, which raises concerns about discriminant validity, the ability of the subscales to measure distinct facets rather than one undifferentiated construct. When facets overlap this heavily, a respondent’s score on one dimension is nearly redundant with their scores on the others, and facet-level interpretations—claiming, for instance, that a leader is strong on empowerment but weak on inspiration—become statistically fragile. The researchers conclude that the French-Moroccan ELS is more convincing as a global measure of engaging leadership than as a set of separate facet scores, and they advise that facet-level conclusions be approached with caution. This kind of honest boundary-setting is a hallmark of careful psychometric work, and it gives future users a clear map of what the instrument can and cannot support.

The broader significance of the study extends beyond Morocco. Psychological measurement has a replication and generalizability problem, and instruments validated in Northern European or North American samples often fail silently when exported to other contexts. By documenting every step of the adaptation pipeline and reporting fit statistics, reliability coefficients, invariance tests, and validity correlations in the open-access literature, Nadifi and Zenjari provide both a usable tool and a template for similar efforts across the Francophone organizational world, from North Africa to sub-Saharan Africa and beyond. For Moroccan organizations, the scale offers a scientifically grounded way to assess a leadership style increasingly linked to employee motivation and retention. For the international research community, it adds a new population to the growing map of engaging leadership evidence, helping to determine whether the construct is a universal feature of effective management or one whose expression varies with culture, language, and the structure of work itself.

Subject of Research: Cross-cultural adaptation and psychometric validation of the Engaging Leadership Scale in Morocco

Article Title: Cross-cultural adaptation and psychometric evaluation of the French-Moroccan version of the Engaging Leadership Scale among knowledge workers in Morocco

Article References: Nadifi, M., & Zenjari, A. (2026). Cross-cultural adaptation and psychometric evaluation of the French-Moroccan version of the Engaging Leadership Scale among knowledge workers in Morocco. BMC Psychology. https://doi.org/10.1186/s40359-026-05654-4

Image Credits: AI Generated

DOI: 10.1186/s40359-026-05654-4

Keywords: engaging leadership, psychometrics, cross-cultural adaptation, scale validation, Morocco, knowledge workers, measurement invariance, confirmatory factor analysis, work engagement, organizational psychology, BMC Psychology, French translation

The research, led by Cristina González-Martín and colleagues at the Universidade da Coruña in Spain, was conducted through international cooperation initiatives in two very different settings: the town of Sangmélima in the South Region of Cameroon, and a partnership with the Complexe Régional des Personnes en Situation d’Handicap in Azrou, Morocco. Rather than importing a short-lived medical mission, the team designed a model built on four complementary pillars: podiatric clinical assessment, health education, capacity building of local healthcare personnel, and referral and care coordination for complex cases. The aim was not only to treat feet but to leave behind a local system capable of continuing the work.

Participants were identified through collaborating healthcare, educational, and disability-support institutions, combined with community outreach activities that brought screening directly to the populations least likely to reach a clinic on their own. This institution-based identification plus outreach approach matters, because foot disorders disproportionately affect people who are already marginalized: children with congenital deformities, people living with neurological conditions, and individuals with disabilities who face physical, financial, and geographic barriers to care. In total, 545 participants were assessed over the programme period, 102 in Morocco and 443 in Cameroon, making this one of the larger descriptive datasets on community podiatric needs in these regions.

The clinical findings reveal two distinct epidemiological profiles. In the Moroccan cohort, neurological disorders were the most common comorbidity, affecting 43.1 percent of participants, a figure that reflects the partnership with a regional disability complex in Azrou. The most frequent podiatric findings were Achilles tendon contracture in 29.4 percent of participants, equinus foot in 28.4 percent, hindfoot valgus in 19.6 percent, and clubfoot in 9.8 percent. These are conditions in which the soft tissues and bony architecture of the foot and ankle become fixed in abnormal positions, often after years without intervention. An Achilles tendon contracture limits the ankle’s ability to dorsiflex, turning every step into a mechanical compromise; equinus foot, in which the foot is held in a downward position, forces compensations at the knee, hip, and spine.

Clubfoot, or congenital talipes equinovarus, deserves particular attention. It is one of the most common congenital musculoskeletal deformities worldwide, and when treated early with serial casting techniques such as the Ponseti method, most children walk normally. Untreated, however, the foot becomes rigidly inverted and adducted, and adults with untreated clubfoot often experience pain, callosities, difficulty with footwear, and profound social stigma. The fact that nearly one in ten assessed participants in Morocco presented with clubfoot underscores how a condition that is largely correctable in infancy continues to produce disability when access to early diagnosis and treatment is missing, a textbook illustration of health inequity expressed in the architecture of the foot.

The Cameroonian cohort in Sangmélima showed a different pattern. The most prevalent podiatric findings were metatarsus adductus in 12.9 percent of participants, a congenital or developmental inward deviation of the forefoot; claw toes in 7.0 percent, in which the toes curl downward at both joints and are prone to pressure lesions; gait adduction in 5.2 percent; clubfoot in 4.5 percent; and hindfoot valgus in 4.5 percent. Hindfoot valgus, an outward tilting of the heel, alters weight distribution across the foot and can lead to flatfoot deformity, plantar pain, and progressive joint wear. Claw toes frequently complicate conditions such as diabetes and neurological disease, where loss of intrinsic muscle balance deforms the toes and creates ulcer-prone pressure points.

From a biomechanical standpoint, the conditions documented in both countries share a common thread: they are progressive and time-sensitive. Soft tissue contractures respond to stretching, casting, orthoses, and minor procedures when addressed early, but become fixed deformities that require complex surgery when neglected. This is precisely why the study’s authors emphasize that insufficient healthcare infrastructure, shortages of trained professionals, and limited rehabilitation resources contribute to preventable disability and reduced quality of life. The clinical assessment component of the programme therefore functioned not merely as a diagnostic exercise but as a triage system, channeling complex cases into a referral and care coordination pathway while simpler problems could be managed through education and conservative measures.

The education and capacity-building components are arguably the most strategically important. The programme trained local healthcare personnel in podiatric assessment and basic management, aiming to embed foot health competencies within institutions that will remain in the communities long after international teams depart. Health education delivered to participants and families addressed hygiene, footwear, early warning signs, and the importance of seeking care before deformities become fixed. This dual investment in human capital and community knowledge is what distinguishes a sustainable health system intervention from a one-off surgical camp, and the authors argue that the model is potentially sustainable and replicable in similar resource-limited settings.

What makes the study notable for the global health community is its framing. Foot disorders are rarely counted among headline health inequities, which tend to focus on infectious disease, maternal mortality, or major non-communicable diseases. Yet mobility is the infrastructure of daily life, and the data from 545 individuals in Morocco and Cameroon give quantitative weight to a burden that has historically been invisible in national health statistics. The researchers used descriptive analyses to characterize participant demographics, comorbidities, and podiatric disorders, and the resulting profiles provide an evidence base that health ministries and non-governmental organizations can use to plan services, allocate rehabilitation resources, and justify investment in training programmes.

The authors conclude that the community-based podiatric health model was feasible to implement in resource-limited settings and provided an integrated approach combining clinical assessment, health education, workforce development, and referral coordination. No specific funding from public, commercial, or not-for-profit grant agencies supported the research, and the authors declare no competing interests. As with any descriptive implementation study, the findings document needs and demonstrate feasibility rather than proving long-term outcomes, and future work will need to track whether trained personnel retain skills, whether referred cases complete treatment, and whether disability outcomes improve. But the central message is clear and urgent: with modest, well-organized investment, the silent epidemic of untreated foot disease in underserved communities can be seen, measured, and treated, one community at a time.

Subject of Research: Community-based podiatric care and foot health equity in low-resource settings in Morocco and Cameroon

Article Title: Community-based podiatric health programme to improve access to foot care and strengthen local health capacity in Morocco and Cameroon

Article References: González-Martín, C., Barreiro, V. B., Garcia-Rodriguez, T., Fernandez-López, U., & Grela-Fariña, M. (2026). Community-based podiatric health programme to improve access to foot care and strengthen local health capacity in Morocco and Cameroon. International Journal for Equity in Health. https://doi.org/10.1186/s12939-026-03030-7

Image Credits: AI Generated

DOI: 10.1186/s12939-026-03030-7

Keywords: podiatry, health equity, global health, community health, capacity building, rehabilitation, clubfoot, Morocco, Cameroon, low-resource settings, foot care, disability

The study was conducted by Mohamed Boullouz, Mohamed Louay Metougui, and Ngonidzashe Chirinda at the Agricultural Innovation and Technology Transfer Center of Mohammed VI Polytechnic University in Ben Guerir, Morocco. Their experimental site sits in a semiarid zone where wheat, the region’s staple cereal, is grown under drip irrigation, a water-saving technique that delivers water and dissolved nutrients directly to the root zone. Drip systems are often promoted as environmentally friendly because they reduce water waste and can improve nitrogen use efficiency. But as this study demonstrates, the climate accounting of such systems depends heavily on which fertilizer goes into the irrigation lines.

The team set up a field trial comparing four mineral nitrogen sources at an identical application rate of 125 kilograms of nitrogen per hectare: conventional urea, ammonium nitrate, a urease-inhibited urea product marketed as Duramon, and a blend of ammonium nitrate and urea. An unfertilized control plot completed the design. Urea is by far the most widely used nitrogen fertilizer on Earth, prized for its high nitrogen content and low cost, but it must first be hydrolyzed in soil to ammonium before plant roots can take it up. That hydrolysis step creates a burst of ammonium and a localized rise in soil pH, conditions that can supercharge the microbial processes, nitrification and denitrification, that produce nitrous oxide as a byproduct.

Over the growing season, the researchers measured cumulative nitrous oxide fluxes using standard chamber-based gas sampling. The results were striking. Plots fertilized with conventional urea emitted 1.84 kilograms of nitrogen per hectare as nitrous oxide, while ammonium nitrate and the ammonium nitrate plus urea blend each emitted just 1.02 kilograms. The urease-inhibited urea fell in between at 1.36 kilograms. The unfertilized control produced 0.28 kilograms, establishing the background emission against which fertilizer-induced losses are judged. Expressed per unit of applied nitrogen, urea emitted 1.82 times more nitrous oxide than ammonium nitrate, even though grain yields across the fertilized treatments were statistically comparable.

Those numbers translate directly into emission factors, the percentage of applied nitrogen that escapes into the atmosphere as nitrous oxide. The Intergovernmental Panel on Climate Change currently instructs national inventory compilers to assume a default emission factor of 0.5 percent for dry climates, a figure meant to represent the average behavior of all nitrogen inputs in water-limited regions. The Moroccan measurements tell a different story. Urea’s emission factor was 1.24 percent, with a bootstrap 95 percent confidence interval of 1.10 to 1.38 percent, meaning it exceeded the IPCC default in every one of the thousands of statistical resamples the team performed. The urease-inhibited urea came in at 0.86 percent, also consistently above the default. Only the nitrate-containing fertilizers, at 0.59 percent each, bracketed the IPCC value, with confidence intervals spanning the 0.5 percent line.

The mechanistic explanation lies in soil microbial ecology. When urea hits moist soil, the enzyme urease cleaves it into ammonium within days, temporarily suppressing nitrite-oxidizing bacteria and creating conditions under which nitrifier denitrification and incomplete nitrification release disproportionate amounts of nitrous oxide. Ammonium nitrate, by contrast, delivers nitrogen already split between its ammonium and nitrate forms, bypassing the hydrolysis spike and smoothing out the microbial activity that drives emissions. Urease inhibitors such as the one in Duramon slow the hydrolysis step, and the study confirmed that they do reduce cumulative emissions relative to plain urea. However, the inhibitor did not significantly reduce yield-scaled emissions, the amount of nitrous oxide released per ton of grain produced, suggesting the technology moderates but does not eliminate the urea penalty.

The implications reach well beyond a single wheat field in Morocco. National greenhouse gas inventories under the Paris Agreement rely on IPCC emission factors, and for countries without extensive local measurement networks, the Tier 1 dry-climate default of 0.5 percent is the working assumption. If urea-dominated irrigated systems in semiarid regions actually emit at rates closer to 1.2 percent, those inventories are systematically undercounting agricultural nitrous oxide, and the mitigation policies built on them are aiming at the wrong target. Conversely, the finding that nitrate-based fertilizers hover near the default suggests that the 0.5 percent figure may be reasonable for some fertilizer types but misleading as a blanket value. The study’s authors argue that non-stratified emission factors, ones that ignore fertilizer source, risk underestimating emissions precisely in the urea-heavy systems that dominate global fertilizer consumption.

What makes the result especially compelling is the absence of a trade-off. Climate mitigation in agriculture often asks farmers to accept lower yields, higher costs, or more complex management in exchange for reduced emissions. Here, switching from urea to ammonium nitrate or a urea-ammonium nitrate blend cut nitrous oxide losses by roughly 45 percent while grain yields remained statistically indistinguishable across treatments. In a semiarid environment where every kilogram of grain matters for food security, and where drip irrigation has already been adopted to conserve scarce water, fertilizer substitution is an immediately actionable lever. The caveat is practical: ammonium nitrate can be more expensive or harder to obtain in some markets, and regulatory restrictions on nitrate fertilizers exist in certain countries due to misuse concerns. But where supply chains allow, the agronomic case is straightforward.

The study also adds an important data point to a growing body of work questioning how well global emission factors capture the diversity of real-world cropping systems. Recent research has highlighted that legacy effects of past nitrogen applications, sampling frequency, and irrigation management can all bias emission factor estimates, sometimes causing systematic underestimation. The Moroccan trial, conducted under the precise water control of drip irrigation, isolates fertilizer source as the dominant variable and shows it is powerful enough to double emissions at a fixed nitrogen rate. For semiarid wheat belts stretching from North Africa through the Middle East and Central Asia, where urea is the default nitrogen source and irrigation is expanding, the message is clear: the molecule carrying the nitrogen matters as much as the amount applied, and climate-smart fertilizer policy should treat fertilizer choice as a first-order decision rather than an afterthought.

Subject of Research: Nitrous oxide emissions and emission factors from different mineral nitrogen fertilizers in drip-irrigated semiarid wheat

Article Title: Mineral nitrogen source controls N2O emissions and emission factors in drip-irrigated semiarid wheat

Article References: Boullouz, M., Metougui, M. L., & Chirinda, N. (2026). Mineral nitrogen source controls N2O emissions and emission factors in drip-irrigated semiarid wheat. Environmental Monitoring and Assessment, 198(10), Article 1099. https://doi.org/10.1007/s10661-026-15963-1

Image Credits: AI Generated

DOI: 10.1007/s10661-026-15963-1

Keywords: nitrous oxide, urea, ammonium nitrate, urease inhibitor, emission factors, drip irrigation, semiarid wheat, IPCC Tier 1, greenhouse gas mitigation, nitrogen fertilizer, Morocco, soil microbiology

The study, published in the journal Discover Geoscience, was led by Rachid Ouachoua and Hamid Benssi of Ibn Tofail University in Kenitra, together with Najib Kadiri of Abdelmalek Essaâdi University in Tangier. Their approach combines multi-criteria decision-making, a structured framework for weighing competing factors, with satellite remote sensing to produce a continuous, pixel-by-pixel map of fire susceptibility across an environment where such assessments had never before been carried out. The result is not merely an academic exercise. With Morocco’s oases having lost 1,423 hectares of land and roughly 172,000 date palms to fire between 2009 and 2024, the researchers argue that their index could form the backbone of an early warning system for landscapes that are both ecologically irreplaceable and economically vital to local communities.

To construct the Fire Susceptibility Index, the team selected six conditioning factors grounded in established wildfire literature: land surface temperature, the Normalized Difference Vegetation Index, the Normalized Difference Moisture Index, proximity to roads, proximity to settlements, and land cover. Each factor was derived from satellite imagery processed at a resolution of 10 meters. Sentinel-2 surface reflectance data from 16 August 2021, ten days before the region’s largest recorded fire, supplied the vegetation indices and served as input for land cover classification, while Landsat 8 thermal imagery from 17 August 2021 was used to calculate land surface temperatures, which ranged from 32.7 to 50.5 degrees Celsius across the oasis.

A notable methodological choice was the exclusion of topographic variables such as elevation, slope, and aspect, which feature prominently in most wildfire susceptibility studies worldwide. Analysis of the shuttle radar topography data revealed that the oasis floor is almost perfectly flat, with a mean slope of just 0.59 degrees, a maximum of 5.4 degrees, and only 8 percent of the area exceeding 5 degrees of inclination. In such terrain, the researchers concluded, topography contributes essentially no spatial discrimination of fire risk, and including it would have added complexity without adding predictive power. The decision illustrates a broader principle in susceptibility mapping: the relevant drivers of fire depend on the landscape in question, and a framework designed for mountain forests must be adapted before it can serve a hyper-arid valley.

The land cover layer, arguably the most important input, was produced using a U-Net convolutional neural network, a deep learning architecture originally developed for biomedical image segmentation. The model, built on a ResNet-34 backbone with pre-trained ImageNet weights, was trained on 247 digitized training polygons spanning five classes: palms and trees, cropland, settlements, bare soil, and water. After 50 epochs of training with class balancing to correct for uneven sample sizes, the network achieved 95 percent overall accuracy, with F1 scores ranging from 93.5 percent for the palms and trees class to 100 percent for water, and a Kappa coefficient of 0.9375 indicating near-perfect agreement with reference data. The classification revealed that palms and trees dominate the oasis, covering about 2,100 hectares or 52.5 percent of the area, followed by cropland at 950 hectares, settlements at 520 hectares, bare soil at 330 hectares, and water at 100 hectares.

Weighing these factors fell to the Analytic Hierarchy Process, a technique introduced by Thomas Saaty in 1980 that converts expert judgment into numerical weights through pairwise comparisons on a one-to-nine scale. The resulting matrix, with a consistency ratio of 0.09, just under the accepted threshold of 0.10, assigned the highest weight of 0.30 to land cover, reflecting the primacy of fuel type in determining flammability. NDVI received 0.25, capturing the dense woody biomass and accumulated dry palm fronds that constitute the primary fuel in Moroccan oases. Together, fuel-related factors accounted for 55 percent of the total index weight. Proximity to roads and settlements jointly carried 31 percent, an acknowledgment that roughly 90 percent of wildfire ignitions in populated arid regions are human-caused. Moisture and temperature received smaller weights of 0.08 and 0.06 respectively, because in a landscape where summer temperatures routinely exceed 40 degrees Celsius and vegetation is consistently dry, these variables vary little and discriminate less between risky and safe locations.

Combining the weighted layers through weighted linear combination produced FSI values ranging from 0.30 to 0.91, with a mean of 0.67 and a standard deviation of only 0.07. That narrow spread is itself telling: the entire oasis system carries an elevated baseline of fire risk, a consequence of continuous dense vegetation, pervasive heat, and human infrastructure threaded through every part of the valley. Using a threshold of 0.7, selected by comparing candidate values against the known extent of the August 2021 fire, the researchers classified 42.7 percent of the oasis, approximately 1,707 hectares, as high or very high susceptibility. The spatial pattern was unmistakable, with risk climbing from the river margins toward settlements, wherever dense palm groves pressed close against roads and villages.

The validation against the August 2021 fire, which consumed 318 hectares, is the study’s most striking result. Of 28,963 burned pixels identified through the Burned Area Index calculated from post-fire Sentinel-2 imagery, 96.5 percent fell within zones the model had classified as high or very high susceptibility. Mean FSI was significantly higher for burned pixels, 0.72, than for the unburned pixels inside the same fire perimeter, 0.67, a difference that was statistically significant, though the authors caution that spatial autocorrelation between adjacent pixels means the effective sample size is smaller than the raw pixel count suggests. Receiver operating characteristic analysis yielded an area under the curve of 0.703, a figure comparable to other multi-criteria decision-making studies in Mediterranean and arid environments, and the researchers are careful to note that a susceptibility map identifies where fire conditions favor burning, not where ignition will inevitably occur.

Independent corroboration came from 60 historical fire records spanning 2009 to 2024. The mean FSI at fire points was 0.72, and no fire ever occurred in a low-risk zone. Forty-eight percent of events fell in the extreme class, 30 percent in the very high class, and 20 percent in the high class, meaning 98 percent of all recorded fires occurred in high, very high, or extreme susceptibility zones. The highest concentration of events was observed in the Aoufous sector, which recorded three fires per square kilometer over the 15-year period, confirming it as the valley’s most critical fire hotspot. This spatial agreement, the researchers argue, demonstrates that the fire risk structure identified by the index is robust and persistent, dominated as it is by relatively stable factors such as land cover and proximity to human infrastructure.

The practical implications extend well beyond the map itself. The researchers recommend systematic removal of dry palm fronds and agricultural residues, which accumulated after the region’s transition from traditional wood fuel to bottled gas, along with green firebreaks around settlements, strategic vegetation reduction in extreme-risk areas, public awareness campaigns, regulation of agricultural burning, and improved surveillance along road corridors. They also emphasize that the August 2021 fire followed extended high temperatures, underscoring the need to integrate real-time weather data with the static susceptibility map. The team acknowledges limitations, including reliance on a single large validation fire, the absence of dynamic meteorological variables such as wind speed and humidity, and the potential subjectivity of a single-author pairwise comparison matrix. Future work should incorporate multi-expert weight elicitation, higher-resolution thermal data, and additional burn indices. Even so, the study demonstrates that a transparent, interpretable framework can deliver actionable fire intelligence for data-sparse arid regions, and its integration into a dynamic geodatabase could support an operational early warning system for some of North Africa’s most vulnerable landscapes.

Subject of Research: Wildfire susceptibility assessment of the Lower Ziz Valley oases in Morocco using multi-criteria decision-making and remote sensing

Article Title: Integrating multi-criteria decision-making and remote sensing for wildfire susceptibility assessment of the Lower Ziz Valley oases in Morocco

Article References: Ouachoua, R., Benssi, H., & Kadiri, N. (2026). Integrating multi-criteria decision-making and remote sensing for wildfire susceptibility assessment of the Lower Ziz Valley oases in Morocco. Discover Geoscience, 4(1), Article 373. https://doi.org/10.1007/s44288-026-00735-8

Image Credits: AI Generated

DOI: 10.1007/s44288-026-00735-8

Keywords: wildfire susceptibility, Fire Susceptibility Index, oasis ecosystems, multi-criteria decision-making, Analytic Hierarchy Process, remote sensing, GIS, U-Net deep learning, Land Surface Temperature, NDVI, Lower Ziz Valley, Morocco

The Essaouira Basin sits along Morocco’s Atlantic margin, where sedimentary layers deposited over hundreds of millions of years record the opening of the central Atlantic and the subsequent uplift of the High Atlas mountains. Groundwater in the region is under mounting stress from climate change and growing demand, yet the deep structural controls on aquifer geometry have remained poorly constrained. Understanding where water-bearing layers thicken, where faults cut them apart, and where impermeable bodies seal them off is essential for managing a resource that recharges slowly in a semi-arid climate.

To build that understanding, the team processed Bouguer gravity anomaly data ranging from −65 to +35 milligals, using polynomial regression to strip away deep regional trends and isolate a residual gravity field between −38 and +19 milligals. These residual anomalies reflect density contrasts in the subsurface caused by the topography of the Paleozoic basement, the rise of Triassic salt diapirs, and lateral variations in the thickness of the sedimentary cover. Gravity data of this kind act as a large-scale density map: masses of dense basement rock pull slightly harder on a gravimeter than lighter sediments or salt, and the resulting patterns can be inverted into structural information.

Extracting usable structure from gravity data requires sharpening the edges of buried bodies. The researchers applied an improved logistic filter, a total horizontal gradient transform and the UC technique to the residual field, and together these methods resolved 13 gravity fault contacts, labelled FG1 through FG13. The mapped faults follow dominant northeast-southwest, east-west and north-south trends, echoing the tectonic history of the Atlantic rift and the later Atlas compression. Each contact marks a place where rock density changes abruptly, typically across a fault plane, and their orientations provide a first-order template for how the basin is fractured.

Gravity alone, however, cannot tell which layer is which. For that, the team turned to five seismic reflection profiles, which image subsurface layering by recording the echoes of sound waves sent into the ground. Calibrated against four boreholes, the seismic data allowed the researchers to constrain five key regional reflectors: the top of the Paleozoic basement, the Triassic-Jurassic boundary, the top of the Jurassic aquifer of Oxfordian-Kimmeridgian age, the top of the Lower Cretaceous aquifer of Barremian-Albian age, and the base of the Upper Cretaceous aquifer at the Albian-Cenomanian transition. These five surfaces define the main water-bearing packages of the basin and anchor the structural interpretation in real stratigraphy.

Integrating the gravity and seismic results revealed five negative gravity anomalies, designated N1 to N5, and three structural sub-basins, SB1 to SB3, bounded by steeply dipping faults. These sub-basins are the deep depressions where sedimentary layers accumulated to their greatest thicknesses, and they correspond closely to the zones where the Upper Jurassic, Lower Cretaceous and Upper Cretaceous aquifer units reach their maximum development. In other words, the places where the basin sank deepest over geological time are precisely the places where the largest volumes of groundwater storage now reside.

One of the most striking findings concerns salt. Seismic profiles imaged salt diapirs, walls of Triassic evaporites that have flowed upward through the overlying sediments, and these structures connect with the well-known Jbel Hadid and Jbel Amsittene salt anticlines at the surface. Because salt is essentially impermeable to water, these diapirs and anticlines act as lateral seals that compartmentalize the aquifer units into hydraulically discrete cells. Water in one cell cannot easily flow into its neighbour, which means that pumping in one compartment will not be buffered by storage in another. For water managers, this is a crucial insight: the basin is not a single connected reservoir but a mosaic of partially isolated blocks.

The structural framework also points to where the resource can best be replenished. Fault intersections, where fracture networks are densest, enhance permeability and maximize recharge potential, and the study identifies these zones as priority targets for artificial recharge schemes and strategic borehole siting. Rather than drilling blindly, planners can now use the structural synthesis map to place wells where fault-bounded structural lows coincide with thick aquifer sections and fracture-enhanced connectivity to the surface. The authors emphasize that this gravity-seismic integrated approach is transferable to comparable semi-arid basins across Morocco and North Africa, where similar structural controls likely govern hidden groundwater systems.

The significance of the work extends beyond the Essaouira Basin itself. Across North Africa, sedimentary basins host deep aquifers that sustain agriculture and urban supply, yet their structural frameworks are often known only from sparse wells. By demonstrating how residual gravity processing, edge-detection filters, seismic stratigraphy and borehole calibration can be woven into a quantitative hydrostructural model, the study offers a template for groundwater exploration under data scarcity. As climate pressures intensify across the semi-arid belt of the southern Mediterranean, mapping the deep structures that store, seal and channel groundwater may prove as important to regional water security as any new dam or desalination plant.

Subject of Research: Integrated geological and geophysical characterization of deep aquifer systems in the Essaouira Basin, western Morocco

Article Title: Hydrogeological Characterization of Deep Aquifer Systems in the Essaouira Basin, Western Morocco: Insights from Integrated Geological and Geophysical Approaches

Article References: Zbiri, A., Kchikach, A., Jaffal, M., Mrlina, J., Zappalá, S., Guernouche, M., Radwan, A. E., & Azaroual, M. (2026). Hydrogeological Characterization of Deep Aquifer Systems in the Essaouira Basin, Western Morocco: Insights from Integrated Geological and Geophysical Approaches. Natural Resources Research. https://doi.org/10.1007/s11053-026-10766-0

Image Credits: AI Generated

DOI: 10.1007/s11053-026-10766-0

Keywords: Essaouira Basin, hydrogeology, groundwater, gravity data, seismic reflection, salt diapirs, faults, aquifer geometry, Morocco, semi-arid regions, structural geology, artificial recharge

The four sites examined in the study are far more than arbitrary sampling points. Each receives effluent from a distinct urban and industrial catchment, and each has developed its own chemical fingerprint over the quarter-century of observation. Oued Moghogha has been consistently enriched in chromium, manganese, and nickel, a pattern the researchers link to industrial discharges typical of metal-processing and tanning activities in its watershed. Oued Negro stands out for copper, while Oued Souani is dominated by lead. The Rejet Fnideq site, near the coastal town of Fnideq, shows a signature enriched in cadmium and zinc, metals commonly associated with urban runoff, galvanized surfaces, and mixed domestic and commercial waste streams. The persistence of these distinct signatures across twenty-five years of sampling demonstrates how tightly sediment chemistry mirrors the character of the land use feeding each outfall.

Methodologically, the team layered several complementary analytical techniques. Descriptive statistics established baseline concentrations and variability for each metal at each site. Non-parametric trend analysis using the Mann–Kendall test, paired with Theil–Sen slope estimation, allowed the researchers to detect monotonic increases or decreases in contamination over time without requiring the data to follow a normal distribution—a crucial consideration for environmental monitoring data, which are frequently skewed and censored. Inter-metal correlation analysis, principal component analysis, and hierarchical cluster analysis then served as multivariate lenses, grouping metals that behave together and clustering years and sites that share similar contamination profiles. Finally, five forecasting models—a naive drift method, a linear trend model, the autoregressive integrated moving average approach known as ARIMA, and two machine learning algorithms, random forest and XGBoost—were trained on the historical records to project concentrations through 2030.

One of the most striking findings is the identification of a regime shift around 2016–2017. Before this transition, several metals, most notably lead and zinc, followed one trajectory; after it, they settled into a distinctly different pattern. A regime shift of this kind typically signals a structural change in the sources or transport of contaminants—perhaps altered wastewater treatment, changes in industrial activity, or shifts in sediment transport driven by rainfall and coastal dynamics. In the Tangier–Tetouan corridor, the shift affected lead and zinc most strongly, while chromium and manganese told the opposite story: they declined consistently across the monitoring period, suggesting sustained reductions in the inputs driving their enrichment.

The multivariate analyses sharpened the picture considerably. Principal component analysis revealed three coherent metal associations—chromium–manganese–nickel, cadmium–zinc, and a lead-dominated grouping—that map cleanly onto the site-specific contamination signatures. These associations are more than statistical convenience. Metals that co-vary in sediments often share common sources or similar geochemical behavior, so identifying a chromium–manganese–nickel cluster at one site and a cadmium–zinc cluster at another provides circumstantial evidence about where the contamination originates and how it might respond to changes in land use. Hierarchical cluster analysis of the annual observations similarly grouped years by sector rather than scattering them randomly, confirming that each monitoring location has maintained its own contamination identity throughout the entire record.

The forecasting results carry a clear practical message. Projections for 2026 to 2030 suggest that the post-2017 regime will persist: chromium, copper, manganese, and nickel are expected to continue declining or to stabilize, while lead and zinc are forecast to remain at sustained elevated concentrations at the affected sites. For environmental managers in the Tangier–Tetouan region, this asymmetry matters. It implies that whatever processes reduced the first group of metals are working, while the sources behind lead and zinc contamination either remain active or reflect legacy pollution locked in the sediment that will not dissipate on its own. Forecasting, in this sense, converts a monitoring archive into an early-warning tool.

Equally instructive is the study’s candid assessment of its own models. Counterintuitively, the simplest method—a naive drift forecast that extrapolates recent rates of change—performed best for most of the metals, while random forest was the preferred model for manganese. Iron forecasts proved unreliable across the board, likely because iron behaves largely as a crustal, geogenic element whose concentrations reflect natural sediment mineralogy more than anthropogenic inputs, making its temporal behavior noisy and difficult to predict from short annual series. The lesson echoes a broader finding in the forecasting literature: with limited observations and relatively smooth temporal dynamics, sophisticated algorithms do not automatically outperform simple baselines. Comparing multiple models, as this study did, is essential to avoid placing unwarranted confidence in any single projection.

The broader significance of the work lies in its demonstration that long-term, multi-site monitoring combined with statistical and machine learning tools can disentangle the complex dynamics of coastal pollution. Heavy metals in sediments are persistent by nature: unlike organic pollutants, they do not degrade, and they accumulate in fine-grained particles where benthic organisms can ingest them, entering food webs that extend to fisheries consumed by millions of people along the Mediterranean. Regional reviews of heavy metal pollution in Mediterranean sediments have repeatedly flagged urbanized coastal corridors like Tangier–Tetouan as hotspots, and earlier studies of Tangier Bay and the Tetouan coast documented concerning enrichment decades ago. What the new study adds is temporal resolution—the ability to say not merely that contamination exists, but whether it is improving, worsening, or holding steady, and for which metals and where.

For Morocco, where rapid coastal urbanization, tourism development, and industrial expansion continue to place pressure on the Mediterranean littoral, the findings arrive at a consequential moment. The forecast of persistent lead and zinc elevation suggests that targeted interventions—improved wastewater treatment at the responsible outfalls, control of urban runoff, or remediation of contaminated hotspots—would yield the greatest benefit at the Oued Souani and Rejet Fnideq systems. Meanwhile, the consistent declines in chromium and manganese offer tentative evidence that past management measures or changes in industrial discharge have had measurable effects. Sustaining the annual monitoring program that made these conclusions possible is itself a policy recommendation embedded in the research: without a continuous, standardized record, neither the 2016–2017 regime shift nor the site-specific fingerprints would ever have been detectable, and the machine learning forecasts would have had no foundation on which to stand.

The study also contributes methodologically to a growing global movement that treats environmental monitoring data as a resource for predictive modeling. As climate change alters rainfall patterns and sediment delivery, and as coastal cities across the Mediterranean and beyond continue to grow, the ability to forecast contamination trajectories—even with simple, transparent models—will become an increasingly important component of adaptive environmental management. The Tangier–Tetouan record, spanning a quarter century and four contrasting catchments, stands as a template for how such forecasting can be built from long-term fieldwork, rigorous statistics, and a healthy skepticism about complexity for its own sake.

Subject of Research: Long-term spatiotemporal analysis and machine learning forecasting of heavy metal contamination in Mediterranean riverine sediments in the Tangier–Tetouan region of Morocco

Article Title: Spatiotemporal dynamics and machine learning forecasting of heavy metal contamination in Mediterranean riverine sediments: a case study of the Tangier–Tetouan region, Morocco

Article References: El Hir, S., El Macouti, N. E. H., Maanan, M., Lagrat, I., & Ait Errouhi, A. (2026). Spatiotemporal dynamics and machine learning forecasting of heavy metal contamination in Mediterranean riverine sediments: a case study of the Tangier–Tetouan region, Morocco. Environmental Monitoring and Assessment, 198(10), Article 1065. https://doi.org/10.1007/s10661-026-15913-x

Image Credits: AI Generated

DOI: 10.1007/s10661-026-15913-x

Keywords: heavy metals, sediment contamination, machine learning, forecasting, Mediterranean, Morocco, Tangier, Tetouan, Mann–Kendall trend analysis, random forest, XGBoost, environmental monitoring