The research, led by Dongyu Wang and colleagues, was conducted in a commercial-style solar greenhouse at Baiwang Plantation in Beijing’s Haidian District. Solar greenhouses are a distinctive form of protected agriculture widely adopted in northern China, built around an asymmetric single-slope design: a light-transmitting south-facing roof, a massive heat-storing north wall, and insulated sidewalls. This architecture allows vegetables to be grown through cold seasons with minimal external energy input, but it comes with a thermodynamic price. The enclosure’s uneven heating produces strong nonlinear fluctuations, pronounced time-lag effects, and sharp three-dimensional spatial heterogeneity in temperature, humidity, and radiation, all of which complicate any attempt to characterize the environment from a single measurement point.

To capture that heterogeneity directly, the team instrumented the greenhouse with 27 temperature and humidity sensors arranged in three experimental zones along the structure’s 80-meter length. In each zone, sensors formed a 3-by-3 horizontal grid with 2-meter spacing, mounted at three heights: 0.5, 1.0, and 1.5 meters above the ground. An outdoor weather station recorded air temperature, humidity, pressure, solar radiation, wind, and rainfall. Everything was logged at 15-minute intervals from April through July 2024, spanning a full cucumber growth cycle from transplanting to maturity, and yielding more than 386,000 valid observation records. The crop was grown in north-south double rows on large ridges with drip irrigation under plastic sheeting, a standard commercial configuration that makes the findings relevant to real production settings.

The monitoring data revealed just how dramatically the microclimate changes as the crop develops. During the seedling stage, when plants were short, temperature curves at different sensor nodes were nearly synchronized, with daytime peak differences of only about 1.4 to 1.8 degrees Celsius. At that point, a single central sensor was still a reasonably fair representative of the whole space. But as the plants grew to roughly 1.22 meters, vertical stratification emerged between the lower and middle layers, and daytime temperature differences among nodes widened to about 4 degrees. By the maturity stage, with plants reaching approximately 1.85 meters, the maximum instantaneous vertical temperature difference hit 8 degrees Celsius, while relative humidity at the canopy bottom frequently stayed above 85 percent. The lower canopy had effectively become a separate, humid, disease-friendly microclimate that a central sensor could not see.

That observation motivated the core of the study: a neural architecture the authors call CNN-BiLSTM-SA, which combines three complementary components. A multi-scale convolutional module first scans the raw sensor sequences with parallel one-dimensional kernels of different sizes. Small kernels capture high-frequency pulses caused by instantaneous ventilation events, while larger kernels track slow trends dominated by the diurnal cycle. A neighborhood enhancement step at the input stage lets each sensor’s signal incorporate information from its spatial neighbors, mitigating the context loss that comes from treating sensors as isolated points. The convolutional features are then passed to a bidirectional long short-term memory network, or BiLSTM, whose forward chain learns historical dynamics such as cooling after ventilation and whose backward chain incorporates future influences such as heat released by the north wall, addressing the thermal inertia and time lags inherent in greenhouse physics.

The most distinctive element is the sensor-level gated attention mechanism. Instead of averaging all measurement points equally, the model computes a dynamic weight for each sensor at each time step, based on both that sensor’s temporal feature vector and global meteorological context such as solar radiation and outdoor conditions. A Softplus mapping generates the weights, which are normalized and used to blend all sensor features into a single representation of the greenhouse’s instantaneous physical state. In effect, the network learns when and where to listen: during periods of strong environmental fluctuation, it can up-weight sensors in high-gradient boundary zones or humid canopy cores, and relax that focus when conditions are uniform. The authors also added physics-informed weak constraints to the training loss, penalizing implausible spatial jumps between adjacent sensors and unrealistic temperature inversions between vertical layers, so that predictions remain consistent with fluid-dynamical continuity rather than merely fitting the numbers.

The performance results are striking. On the held-out test set, CNN-BiLSTM-SA achieved a mean absolute error of 0.886 degrees Celsius, a root mean square error of 1.236 degrees, and a coefficient of determination of 0.92 for temperature prediction, along with a mean absolute error of 2.619 percent, a root mean square error of 3.735 percent, and an R-squared of 0.93 for humidity. Removing the attention mechanism degraded performance substantially: compared with an otherwise identical CNN-BiLSTM model, the full architecture cut temperature root mean square error by 26.6 percent and humidity error by 37.9 percent. The model also degraded gracefully with longer forecast horizons, with temperature error rising only from 1.236 to 1.439 degrees between 1-hour and 24-hour predictions, and humidity error from 3.735 to 4.548 percent, indicating stable day-ahead forecasting without sudden failure. Notably, an ablation with an overly strong physics constraint, at a regularization weight of one, caused humidity predictions to collapse, a cautionary demonstration that physical priors must be balanced against data fitting rather than imposed at full strength.

Beyond point predictions, the framework reconstructs continuous three-dimensional fields by combining inverse distance weighting interpolation with parabolic geometric masking. The resulting visualizations recovered physically meaningful structures: an east-west temperature gradient of up to 6.4 degrees aligned with solar radiation incidence and heat accumulation, a north-south contrast driven by differential light transmission through the covering materials, and a vertical stratification pattern with maximum differences of 7 degrees reflecting buoyancy-driven warm-air ascent. On the humidity side, the model correctly located concentrated high-humidity cores in the middle and lower canopy, where crop transpiration and restricted airflow trap water vapor, and identified drier zones near ventilation boundaries. Two-dimensional slice comparisons showed that adding the attention mechanism sharply reduced residual errors, which without it frequently exceeded 2 degrees and 6 percentage points near the southern film and in densely planted central regions. The authors acknowledge some remaining smoothing of local humidity extremes, a reminder that fields governed by tightly coupled transpiration, airflow, and vapor retention are harder to resolve than temperature alone.

The practical implications extend to sensor economics and the emerging concept of greenhouse digital twins. The full 27-sensor network served as a dense reference for model calibration, but the team tested reduced configurations: keeping only the nine lower-layer sensors increased temperature and humidity errors by 78.8 and 45.5 percent respectively, proving that vertical gradients cannot be captured from below the canopy alone. A optimized 24-sensor arrangement, however, raised errors by only 1.38 and 3.59 percent relative to the full network, suggesting that operational deployments can trim costs after site- and season-specific calibration. Looking forward, the authors propose integrating LiDAR-derived canopy structure and traits such as leaf area index, coupling the framework with radiation and carbon dioxide variables through physics-informed neural networks, and feeding the reconstructed fields into crop growth and disease-risk models. In such a system, ventilation and heating would no longer respond to an average number from a central sensor, but to the actual geography of risk inside the greenhouse, targeting the humid canopy cores where fungal pathogens take hold before any single-point alarm would ever sound.

Subject of Research: Deep learning-based spatiotemporal prediction and 3D reconstruction of temperature and humidity fields in solar greenhouses

Article Title: A multi-sensor deep learning framework for spatiotemporal prediction and three-dimensional reconstruction of temperature and humidity fields in solar greenhouses

Article References: A multi-sensor deep learning framework for spatiotemporal prediction and three-dimensional reconstruction of temperature and humidity fields in solar greenhouses. (n.d.). https://doi.org/10.1016/j.atech.2026.102517

Image Credits: AI Generated

DOI: 10.1016/j.atech.2026.102517

Keywords: solar greenhouse, deep learning, microclimate, temperature prediction, humidity prediction, attention mechanism, BiLSTM, convolutional neural network, precision agriculture, sensor networks, digital twin, spatial heterogeneity

The review, published on 3 September 2026, does not claim that radiomics should replace tissue-based biomarkers. Its central argument is more subtle and, arguably, more clinically useful: radiomics should serve as a spatial context that tells clinicians when a biopsy result can be trusted and when it might be dangerously misleading. Head and neck squamous cell carcinoma, or HNSCC, is an ideal testing ground for this idea. These tumors arise in anatomically complex regions — the larynx, oropharynx, oral cavity, hypopharynx — where treatment decisions carry enormous consequences for speech, swallowing, and quality of life, and where the stakes of both overtreatment and undertreatment are exceptionally high.

To understand why spatial context matters, it helps to distinguish between biomarkers that vary across a tumor and those that do not. The authors organize tissue-based biomarkers along a spectrum of spatial stability. HPV and p16 status, which define a biologically distinct and generally more treatable subtype of oropharyngeal cancer, tend to be uniform throughout the tumor, so a single biopsy usually captures them reliably. Certain genomic alterations behave similarly. But other clinically decisive biomarkers are far less cooperative. PD-L1 expression, the density and location of immune-cell infiltration, necrosis, and immune exclusion — the phenomenon where immune cells are kept at the tumor’s periphery — can all vary dramatically from one region of a tumor to another. A core needle sample taken from a well-perfused, immune-inflamed edge may paint a completely different picture than a hypoxic, necrotic core just a few millimeters away.

This is not a theoretical concern. The landmark multiregion sequencing work of Gerlinger and colleagues, published in the New England Journal of Medicine in 2012, demonstrated that intratumor heterogeneity and branched evolution are fundamental features of cancer biology. In HNSCC, the clinical consequences are concrete. When a tumor board decides whether a patient with HPV-positive oropharyngeal cancer can safely receive reduced-intensity treatment — so-called de-escalation — the decision often hinges on biomarkers measured in a single sample. If that sample happens to come from an unrepresentative region, the de-escalation decision rests on sand. Similarly, eligibility for immunotherapy frequently depends on PD-L1 scoring, and the review points out that localized sampling may incompletely capture heterogeneity in exactly these selected clinical contexts.

Radiomics offers a fundamentally different vantage point. The technique extracts hundreds of quantitative features from routinely acquired medical images — computed tomography, magnetic resonance imaging, and FDG-PET — converting the visual texture, shape, and intensity patterns of a tumor into high-dimensional data. First articulated in a widely cited 2014 Nature Communications paper by Aerts and colleagues, the premise is that image features reflect underlying biological processes: coarse heterogeneity on CT may mirror necrosis and hypoxia; specific texture patterns on diffusion-weighted MRI, measured through the apparent diffusion coefficient, correlate with cell density and oxygenation; FDG-PET textural features track metabolic activity and its spatial distribution. Because imaging captures the entire tumor volume — and often the surrounding peritumoral tissue — it sees what the biopsy cannot.

The review synthesizes a substantial body of evidence linking radiomic features to the biological processes that matter most in HNSCC. Tumor hypoxia is a classic example: it has been known since Brizel and colleagues’ 1997 work to worsen prognosis in head and neck cancer, largely because oxygen-deprived cells resist radiation. Hypoxia is also profoundly spatial — it develops in regions distant from functional blood vessels — which makes it a natural target for imaging. Studies have shown that textural features of hypoxia PET predict survival during chemoradiotherapy, and recent work on apparent diffusion coefficient MRI suggests it can act as a predictive biomarker for hypoxia, treatment de-escalation, and recurrence in HPV-associated oropharyngeal cancer. On the immune side, radiomic signatures have been associated with PD-L1 expression, CD8-positive T-cell infiltration, T cell-inflamed gene expression profiles, and even gamma-delta T-cell abundance, while radiogenomic analyses have connected imaging heterogeneity to somatic mutations in genes such as TP53, FAT1, and KMT2D.

What sets the Keio review apart from much of the radiomics literature is its insistence on mapping these features to specific clinical decision points rather than treating them as generic prognostic scores. The authors walk through three major scenarios. The first is larynx preservation, where the choice between organ-sparing chemoradiotherapy — a strategy established by the landmark 2003 trial of concurrent chemotherapy and radiotherapy — and primary surgery depends on predicting response. Radiomic features reflecting hypoxia and necrosis could flag tumors whose biopsy-based profiles look favorable but whose whole-tumor biology suggests resistance. The second is immunotherapy stratification, where combining a PD-L1 score from a biopsy with imaging evidence of immune exclusion or spatially restricted immune activity could refine patient selection for checkpoint inhibitors such as pembrolizumab and nivolumab, agents whose benefit in recurrent or metastatic HNSCC was established in the KEYNOTE-048 and CheckMate 141 trials. The third is recurrence assessment, where peritumoral radiomic signatures and subregion-based models have shown promise in predicting locoregional failure after chemoradiotherapy and even the site of recurrence after reirradiation.

The review also embraces a dynamic dimension of imaging that tissue sampling cannot match: delta radiomics, the analysis of how radiomic features change over time. Serial multiparametric MRI and FDG-PET acquired during the course of radiation therapy have been shown to predict treatment response, and FDG-PET can identify pathological response early during neoadjuvant immune checkpoint blockade. In principle, a clinician could watch the spatial biology of a tumor evolve week by week during treatment and adjust course accordingly — something no biopsy workflow could realistically achieve. Combined with deep-learning segmentation tools that automate tumor delineation on CT and MRI, and with harmonization methods such as ComBat that correct for differences between scanners and institutions, the technical pipeline for deploying these approaches at scale is maturing rapidly.

The authors are careful about the limits of the evidence. Nearly all of the studies they synthesize are retrospective, and the field has long struggled with reproducibility: radiomic features can be sensitive to image acquisition parameters, reconstruction algorithms, and segmentation choices, which is why standardization initiatives such as the Image Biomarker Standardisation Initiative have become essential infrastructure. Multi-institutional modeling studies have begun to evaluate how well radiomics and deep-learning models generalize beyond their development cohorts, and systematic reviews of machine-learning models for predicting radiation toxicity have taken a sober look at actual versus claimed performance. The review’s framing — radiomics as context rather than standalone predictor — is partly a response to this uncertainty. A model that claims to replace PD-L1 testing must clear an enormous evidentiary bar; a tool that flags when a biopsy result deserves a second look needs to be merely reliable enough to prompt caution.

That pragmatic framing may prove to be the review’s most lasting contribution. Rather than promising a revolution, Kono and colleagues describe an incremental integration: radiomics as a layer of spatial intelligence woven into existing biomarker-guided workflows, identifying the patients in which hypoxia, necrosis, stromal architecture, or immune exclusion make a small sample untrustworthy. For a disease where the difference between de-escalation and full-dose treatment can mean preserving a voice, and where immunotherapy decisions hinge on biomarkers that flicker across the tumor landscape, that kind of whole-tumor perspective could change how multidisciplinary teams weigh the evidence in front of them. The next step, the authors imply, is prospective validation — testing whether spatially informed caution actually improves outcomes. If it does, the humble biopsy may finally gain the companion it has always needed: a map of everything it cannot see.

Subject of Research: Use of radiomic imaging features as spatial context for interpreting biopsy biomarkers in head and neck squamous cell carcinoma treatment decisions

Article Title: Radiomics as a spatial context for treatment decision-making in head and neck cancer

Article References: Kono, T., Kasahara, K., Ogawa, R., & Ozawa, H. (2026). Radiomics as a spatial context for treatment decision-making in head and neck cancer. British Journal of Cancer. https://doi.org/10.1038/s41416-026-03600-0

Image Credits: AI Generated

DOI: 10.1038/s41416-026-03600-0

Keywords: radiomics, head and neck cancer, HNSCC, spatial heterogeneity, PD-L1, HPV, hypoxia, immunotherapy, biomarkers, de-escalation, medical imaging, tumor microenvironment

The research, led by Hai Sun of the Ocean University of China together with colleagues at Qingdao University of Technology, Clemson University, and Western Sydney University, tackles one of the most persistent blind spots in flood management: the pathways linking land-use patterns to hydrological responses. Urbanization reshapes basin hydrology by replacing soils and vegetation with impervious surfaces such as roads, rooftops, and parking lots. These surfaces prevent infiltration, accelerate runoff generation, and amplify flood peaks, sending more water into rivers faster and overwhelming channels and drainage systems. Green infrastructure — permeable pavements, rain gardens, wetlands, and vegetated storage areas — counteracts this by improving infiltration, storage, and surface roughness. But until now, planners have lacked a rigorous, basin-scale framework for measuring how these benefits vary across space and under different storm conditions.

To close that gap, the team constructed three urban expansion scenarios for the year 2044 in the Poyang Lake Basin: a no-green-infrastructure baseline, a storage-oriented green infrastructure scenario, and a permeability-oriented scenario. The Poyang Lake Basin, China’s largest freshwater lake system and a critical node in the Yangtze River’s hydrology, is a natural laboratory for this question. Its low-lying floodplains, dense tributary network, and rapidly expanding urban platforms make it acutely sensitive to changes in land cover.

Land-use changes under each scenario were simulated using a cellular automaton–Markov (CA–Markov) model, a technique that combines transition probabilities derived from historical land-change data with spatial neighborhood constraints to project how urban footprints evolve. This allowed the researchers to generate realistic maps of where impervious surfaces would spread by 2044 and where green infrastructure would be deployed under each strategy. The hydrological consequences were then evaluated through a coupled one-dimensional and two-dimensional modeling framework, chaining HEC-HMS, a rainfall-runoff model, to HEC-RAS, a river hydraulics and flood inundation model. The coupling is significant: it enables a continuous simulation chain from land-use evolution to runoff generation to flood dynamics, rather than treating each link in isolation.

The baseline results are sobering. Under unconstrained urban expansion, impervious surface coverage in the basin rises from 3.93 percent to 7.37 percent — nearly a doubling of sealed ground. Correspondingly, the basin-averaged curve number, a standard parameter in the Soil Conservation Service runoff method that encapsulates how readily a landscape converts rainfall into runoff, increases from 68 to 72. That seemingly modest shift carries heavy consequences: the researchers calculate an approximately 18 percent decrease in potential maximum retention, the landscape’s capacity to absorb and store rainfall before it becomes floodwater. In plain terms, by mid-century the basin could surrender nearly a fifth of its natural buffering capacity to concrete and asphalt.

Green infrastructure partially blunts this trajectory, but the two strategies do so very differently. Permeability-oriented green infrastructure — designed to restore infiltration across the urban surface — achieves the strongest reduction in impervious coverage, limiting the rise to 6.68 percent, and effectively reverses the degradation of infiltration and storage capacity captured by the curve number. Storage-oriented green infrastructure, which concentrates water retention in discrete facilities, shows only limited improvement in these landscape-scale parameters. The reason is structural: storage works locally, behind berms and inside basins, while permeability works everywhere, beneath every street and rooftop it touches.

Those parameter-level differences propagate directly into flood behavior. Under small to moderate rainfall events, permeability-oriented green infrastructure reduces peak discharge by 6.2 percent and total runoff volume by 3.54 percent, and — critically — it maintains its effectiveness even under extreme conditions, because infiltration capacity does not fill up the way a storage basin does. Storage-oriented measures, by contrast, remain constrained by finite capacity: once a retention facility fills, additional rainfall passes through unattenuated. Yet the picture is not one-sided. The analysis reveals clear spatial heterogeneity: in areas with sufficient storage capacity, storage-based strategies actually outperform infiltration-based measures during large rainfall events, when rainfall intensity outpaces the soil’s ability to absorb water and detention volume becomes the deciding factor. The catch is that this advantage is limited by spatial and capacity constraints at the basin scale — there is simply not enough suitable land and storage volume to deploy it everywhere.

The two-dimensional hydraulic component of the framework sharpens this spatial story further. Permeability-oriented green infrastructure reduces the extent of high-depth and high-velocity flood hotspots, with the strongest benefits concentrated along river corridors and urban platforms — precisely the locations where people and assets cluster. This leads the authors to a practical siting logic built around the curve number itself: boost permeability and roughness on the slopes, maintain them through the channels, and add storage capacity at confluences where flows converge and backwater effects amplify. In other words, read the landscape’s hydrological fingerprints and match the intervention to the terrain.

The study’s most consequential recommendation is that no single strategy suffices. Because green infrastructure performance varies with location, terrain, and storm magnitude, the authors argue for differentiated strategies aimed at residual high-risk areas: infiltration-dominated measures for moderate rainfall, enhanced storage and conveyance capacity for extreme events, and spatially targeted deployment that combines infiltration and storage synergies in low-lying zones, storage-control combinations along flow corridors, and strict land-use regulation in high-risk areas. This is a departure from the one-size-fits-all deployments that have characterized much green infrastructure planning, including China’s high-profile sponge city program, and it provides theoretical support and decision-making guidance for coordinated planning and refined flood risk management at the basin scale.

The timing could hardly be more urgent. Recent global research has documented rapid urban growth inside flood zones since 1985 and projected substantial increases in future fluvial flood risk across China’s major urban agglomerations, with the Global South bearing disproportionately higher exposure. As climate change intensifies extreme rainfall and cities continue to seal their surfaces, the Poyang Lake findings offer a template that travels: simulate the land, couple it to the water, and let the spatial heterogeneity of performance — not generic best practice — dictate where every permeable meter and every storage basin goes. The difference, this research suggests, may be measured not just in percentage points of peak discharge, but in neighborhoods that stay dry.

Subject of Research: Spatial variability in the flood mitigation performance of green infrastructure under future urban expansion in the Poyang Lake Basin

Article Title: Spatial heterogeneity of green infrastructure performance in flood mitigation under urban expansion

Article References: Sun, H., Wang, H., Chu, Y., Yao, W., Fan, C., Chu, Z., & Liang, B. (2026). Spatial heterogeneity of green infrastructure performance in flood mitigation under urban expansion. Natural Hazards, 122(19), Article 637. https://doi.org/10.1007/s11069-026-08312-5

Image Credits: AI Generated

DOI: 10.1007/s11069-026-08312-5

Keywords: green infrastructure, flood mitigation, urban expansion, Poyang Lake Basin, CA-Markov model, HEC-HMS, HEC-RAS, curve number, impervious surfaces, runoff reduction, spatial heterogeneity, flood risk management

The research team, led by Xu Yuhui with Batunacun and colleagues, focused on Jiziwan, a region spanning roughly 790,000 square kilometres across Inner Mongolia, Gansu, Ningxia, Shaanxi and Shanxi. Home to about 48 million permanent residents, the area drew approximately 627.8 million tourists and 53.4 billion yuan in tourism revenue in 2023 alone. Yet it is also one of the most ecologically fragile corners of the Yellow River Basin, beset by soil erosion, land desertification and acute water scarcity. That combination of rich natural and cultural attractions and a delicate ecosystem makes Jiziwan an ideal natural laboratory for asking a question that has long dogged tourism scholarship: when a destination’s tourism economy bounces back from shocks, is that resilience anchored primarily in the environment, or in a broader constellation of economic, social and technological forces?

To answer it, the researchers first had to measure resilience itself. They built a comprehensive evaluation system grounded in Martin’s economic resilience theory, capturing four dimensions: resistance ability, the capacity to absorb shocks; recovery ability, the capacity to return to a dynamic equilibrium; reconstruction ability, the capacity to reorganise internal structures under stress; and update ability, the capacity to break path dependence through knowledge and innovation. Twenty indicators fed into this framework, ranging from the density of A-level tourist attractions and tourist arrivals to the number of days of good air quality, green coverage rates, highway passenger volumes, university enrolment and tourism research and development funding. Rather than weighting these indicators subjectively, the team applied a genetic algorithm-optimised projection pursuit model, a technique that projects high-dimensional data into a lower-dimensional space and identifies optimal weightings while sidestepping multicollinearity. An entropy weight method used as a cross-check produced broadly consistent rankings, strengthening confidence in the results.

The measurement revealed a tourism economy on the rise, but an uneven one. The composite resilience index for Jiziwan climbed from 0.175 in 2010 to 0.298 in 2023, dipping in 2020 under the impact of COVID-19 before rebounding to its peak. Kernel density estimation showed the entire distribution shifting rightward over time, but also exposed a persistent double peak: a large peak of cities stuck at low resilience and a smaller peak of high performers, with a right trailing tail signalling entrenched regional disparity. Spatially, resilience declined steadily from the centre of the region to its periphery in every year examined. Cities such as Hohhot, Ordos, Yulin and Taiyuan formed a resilient core, with Taiyuan standing alone at the high-resilience level by 2023, while five western cities, including Alxa, Bayannur, Wuhai, Zhongwei and Baiyin, remained trapped at low levels throughout the study period. Global Moran’s I statistics confirmed weak positive spatial autocorrelation, significant in 2015 and 2020 but not in 2010 or 2023, pointing to localised clustering rather than region-wide spatial dependence.

The methodological heart of the study, and its most novel contribution, lies in how the drivers were identified. Conventional tools each carry well-known limitations: geographic detectors struggle to rank drivers against one another, regression analysis leans on linear assumptions, structural equation models typically yield a single global optimum, and qualitative approaches are subjective and hard to visualise. The team instead turned to partial order theory and the Hasse diagram technique, mathematical tools previously applied to chemical risk assessment, groundwater quality and land degradation, but never before to tourism. Partial order theory ranks comparable elements across multiple criteria without requiring complete ordering, ignores multicollinearity within driver groups, and preserves the information of every indicator. The Hasse diagram technique then renders those partial orders as directed graphs in which maximal elements mark the most strongly influenced cities and chains trace the descending ranking of influence. Fifteen indicators across four driver groups, regional economy, social environment, ecological environment and technological innovation, were compared across 21 cities in three periods: 2010 to 2015, 2015 to 2020 and 2020 to 2023, generating twelve partially ordered sets and twelve Hasse diagrams.

The verdict was unambiguous. In the first period, the ecological environment dominated in 15 of the 21 cities, ahead of social environment with 11, regional economy with 9 and technological innovation with 8. In the second period, the ecological environment again led with 15 cities. By the third period, its reach had expanded to 18 cities, while technological innovation surged from 5 cities in the first period to 11, overtaking the regional economy to claim second place. The regional economy and social environment remained relatively stable influences, with the economy weakening in the northwest and strengthening in the centre-south, and the social environment showing the reverse pattern. The overall picture, the authors conclude, is a multi-dimensional synergistic driving pattern operating under the relative dominance of the ecological environment, a finding confirmed as statistically significant by fixed-effects panel regression in which all four driver groups and nearly all individual indicators showed significant associations with resilience.

Why does the environment loom so large here? The explanation lies in what Jiziwan’s tourism actually sells. Unlike heavily urbanised destinations that trade on cultural landscapes, service infrastructure and urban consumption, this region’s products rest on pleasant climates, vast grasslands, clean rivers, deserts, waterfalls and biodiversity. The quality of the ecological landscape is not one input among many; it is the raw material of the entire tourism economy. Technological innovation, the study argues, functions mainly as a tool for amplifying the value of those ecological resources and improving management efficiency, and cannot substitute for the environmental foundation that shapes attractiveness and resource supply. At the same time, tourism offers ecologically fragile regions a pathway to development that traditional resource-intensive industries cannot, converting ecological advantages into economic benefits through ecotourism, rural tourism and Yellow River cultural tourism, while channelling revenue back into biodiversity protection and environmental education.

Policy has amplified this natural advantage. The researchers identified twelve major ecological policies shaping the region, spanning three tiers: overarching national strategies such as the Three-North shelterbelt project and desertification control; Yellow River Basin-specific programmes linking ecological protection with cultural inheritance and tourism; and local initiatives including wetland restoration, ecological corridors and the development of the Yellow River cultural tourism belt. This cascade of macro-strategic guidance, river-basin support and local implementation has systematically improved environmental quality, raised tourism attractiveness and reinforced the ecological environment’s dominant position in the resilience system. The study also flags the exceptions that prove the rule: in Yulin, Yan’an and Shuozhou, cities at the intersection of the Loess Plateau and the Mu Us Sandy Land whose economies centre on coal and petroleum extraction, mining has so degraded sewage treatment capacity, surface stability and vegetation that the ecological environment exerted no positive influence on resilience in the most recent period.

The practical payoff is a differentiated policy map. The team classified the 21 cities into five strategic categories: ecological restoration-oriented cities such as Alxa, Bayannur, Ulanqab and Linfen, which should prioritise conservation and control development intensity; technology empowerment-oriented cities such as Hohhot, Taiyuan, Yan’an and Luliang, encouraged to deploy virtual and augmented reality and strengthen real-time risk monitoring; economic structure optimisation-oriented cities such as Baiyin, Luliang and Baotou, which can deepen industrial chains and cultivate new tourism business models; infrastructure improvement-oriented cities such as Ordos, Yulin, Baiyin and Wuhai, which need stronger transport, accommodation and public health systems; and regional coordination-oriented cities such as Hohhot, Baotou and Taiyuan, which should build cross-regional governance mechanisms, linked tourism routes and shared markets. Former mining cities, the authors suggest, could pursue green transformation through industrial heritage tourism.

The authors are candid about the limits of their analysis. Because the study area contains only 21 prefecture-level cities, spatial econometric models were not applied, and the results should be read as direct local associations rather than a full account of spatial spillovers between neighbouring cities. Future work with larger samples, longer panels and spatial lag, error or Durbin models could sharpen the picture. Even so, the study delivers a rare quantitative answer to a question usually left to qualitative argument, and its message resonates far beyond the Yellow River bend: in ecologically fragile destinations, protecting the environment is not a constraint on tourism resilience but its very foundation, and the first application of partial order theory to tourism research offers a transferable template for proving it.

Subject of Research: Drivers of tourism economic resilience in the ecologically fragile Jiziwan region of China's Yellow River Basin

Article Title: Ecological environment dominant or multi-dimensional drivers? A study on the drivers of tourism economic resilience in Jiziwan, China

Article References: Yuhui, X., Batunacun, Changan, Kaixin, L., Yufeng, Yongmei, & Dandan, Z. (2026). Ecological environment dominant or multi-dimensional drivers? A study on the drivers of tourism economic resilience in Jiziwan, China. Environmental and Sustainability Indicators, 32, Article 101505. https://doi.org/10.1016/j.indic.2026.101505

Image Credits: AI Generated

DOI: 10.1016/j.indic.2026.101505

Keywords: tourism economic resilience, ecological environment, Jiziwan, Yellow River Basin, partial order theory, Hasse diagram, GA-PP model, ecologically fragile regions, technological innovation, spatial heterogeneity, ecotourism, China

Congenital structural anomalies, which range from heart defects and cleft palates to urinary tract malformations and limb abnormalities, are among the leading causes of infant morbidity, long-term disability, and pediatric surgical intervention worldwide. While genetics plays a central role, growing evidence points to ambient air pollution and broader environmental conditions as contributors to fetal developmental disruption. Most previous studies, however, have relied on global statistical models that implicitly assume the relationship between environmental exposure and health outcome is the same everywhere. The Yunnan study set out to test that assumption in one of China’s most geographically complex provinces.

The research team, led by Cheng-Hao Zhanghuang and colleagues, first painted a detailed epidemiological portrait of the inpatient cohort. Boys accounted for 67.68 percent of admissions, a male-to-female ratio of roughly 2.1 to 1, and cases were concentrated in early childhood, with toddlers aged one to three years forming the largest group at 28.89 percent. Digestive anomalies were the most common category, representing 29.63 percent of patients, followed by urogenital anomalies at 23.30 percent. Other structural anomalies, musculoskeletal anomalies, and circulatory anomalies made up the remainder. The most frequent individual diagnoses included congenital tongue anomalies, cryptorchidism, and polydactyly. Annual admissions rose steadily from 3,568 in 2014 to a peak of 6,237 in 2019, dipped during 2020, and climbed again to 6,017 by 2024.

To enable robust spatial modeling, the investigators filtered the cohort down to the most frequent conditions within each of five anomaly systems: circulatory, digestive, urogenital, musculoskeletal, and other structural anomalies. This yielded a spatial analysis dataset of 41,531 patients, a step designed to reduce statistical instability caused by counties with sparse case counts. Neurological anomalies were excluded because their numbers at the referral center were too small to support reliable spatial estimates. Patients with multiple anomalies were classified by their principal discharge diagnosis to keep categories mutually exclusive and reduce information bias.

The heart of the study lay in its environmental data assembly. The team compiled eleven county-level environmental and contextual variables averaged over 2014 to 2023, including carbon monoxide, sulfur dioxide, nitrogen dioxide, PM2.5, PM10, ozone, carbon dioxide, land surface temperature, elevation, population density, and the normalized difference vegetation index, a satellite-derived measure of green vegetation cover. Data came from sources such as the National Tibetan Plateau Data Center, NASA Earthdata, the LandScan population dataset, and the Emissions Database for Global Atmospheric Research. Variables with high multicollinearity were removed to ensure that each remaining predictor contributed independent information to the models.

Rather than relying solely on ordinary least squares regression, which produces a single average coefficient for the entire province, the researchers employed geographically weighted regression, or GWR. This technique allows regression coefficients to vary across space, estimating a separate local relationship for each county. Across all five anomaly systems, GWR consistently outperformed the global models, delivering higher coefficients of determination and lower corrected Akaike information criterion and cross-validation values. The authors interpret this as clear evidence of spatial non-stationarity: the strength and even the direction of environmental associations with hospital-based anomaly burden shift across the provincial landscape.

The specific patterns were striking. Carbon monoxide showed predominantly positive associations with referral-weighted institutional burden across anomaly systems, suggesting that counties with higher long-term CO levels tended to contribute more anomaly cases to the referral center. Sulfur dioxide, by contrast, exhibited pronounced spatial heterogeneity, with local coefficients flipping in both magnitude and direction depending on location. Vegetation coverage displayed a consistent negative association across all five systems, hinting that greener counties carried lower institutional anomaly burden, while population density showed positive but geographically variable relationships. The authors emphasize that these are contextual, area-level patterns rather than proof of individual-level causal effects.

Importantly, the researchers are careful about what their data can and cannot show. Because the study draws on a single referral center, the measured burden reflects healthcare-seeking behavior, referral pathways, transportation access, and institutional admission practices, not province-wide prevalence. Remote counties with poor road links or limited referral connections may be underrepresented even if their true anomaly burden is substantial. The lack of individual maternal residential histories also prevented trimester-specific prenatal exposure assessment, and genetic etiologies could not be reliably excluded. The authors explicitly frame their findings as descriptive and hypothesis-generating, requiring validation through population-based registries and multi-center studies before any policy conclusions are drawn.

Nevertheless, the methodological message is clear and potentially far-reaching. In regions marked by complex terrain, uneven economic development, and sharp urban-rural contrasts, one-size-fits-all environmental health models may obscure localized vulnerability. Spatially explicit approaches such as GWR can reveal where environmental associations are strongest, where they weaken, and where they reverse, offering surveillance programs a sharper tool for targeting resources. Proposed biological mechanisms linking prenatal air pollution exposure to congenital anomalies, including oxidative stress, placental dysfunction, and inflammatory disruption of embryonic signaling, remain speculative in this ecological context, but the mapped heterogeneity provides a concrete starting point for future mechanism-oriented investigation.

As congenital anomalies continue to impose a heavy surgical and developmental burden on pediatric health systems worldwide, the Yunnan study adds an important dimension to the evidence base: geography matters. The same pollutant may carry different weight in a densely populated basin than on a remote highland plateau, and greener landscapes may buffer contextual risk in ways that global models cannot capture. Whether these spatial patterns hold up in population-based data from other provinces and countries will determine whether geographically weighted thinking becomes a standard feature of environmental epidemiology for birth defects research.

Subject of Research: Spatial heterogeneity in area-level environmental associations with hospital-based congenital structural anomaly burden in Southwest China

Article Title: Spatial heterogeneity in area-level environmental context of hospital-based congenital structural anomaly burden in Southwest China: a retrospective study from a provincial pediatric referral center

Article References: Zhanghuang, C.-H., Ma, Y.-Y., Zheng, C.-L., Hu, X., Zhang, M.-X., Gao, Y.-P., Chen, J.-R., Yang, S.-W., Zhang, H., Dai, R.-T., Zhang, X.-C., Shen, J., Yan, B., & Wu, J. (2026). Spatial heterogeneity in area-level environmental context of hospital-based congenital structural anomaly burden in Southwest China: a retrospective study from a provincial pediatric referral center. World Journal of Pediatrics. https://doi.org/10.1007/s12519-026-01059-w

Image Credits: AI Generated

DOI: 10.1007/s12519-026-01059-w

Keywords: congenital anomalies, spatial epidemiology, geographically weighted regression, air pollution, Yunnan Province, pediatrics, birth defects, environmental health, vegetation index, population density, referral center, spatial heterogeneity