The analysis drew on unit-level data from the fourth and fifth rounds of the National Family Health Survey, conducted in 2015-16 and 2019-21 respectively, covering a total of 14,542 tribal households across Odisha. This period is analytically valuable because it straddles the launch of the Ujjwala scheme, allowing researchers to compare clean fuel adoption before and after the policy intervention. The headline number is stark: clean cooking fuel use among tribal households rose from just 5.3 percent in the fourth round to 12.6 percent in the fifth. That is a substantial relative improvement, yet it means that nearly nine out of ten tribal households in the state still relied on traditional biomass such as firewood, dung and crop residues for their daily cooking, with all the health and environmental burdens that entails.

To understand why some households made the switch and others did not, the researchers estimated binary probit and logit models, standard econometric techniques for modelling a binary outcome such as whether or not a household uses a clean fuel. Because these models produce coefficients that are not directly interpretable as probabilities, the team also calculated marginal effects, the change in the probability of adoption associated with each factor. The models were estimated separately for the two survey rounds and for rural and urban subsamples, and the results were statistically robust, with Wald chi-square tests significant at the one percent level and pseudo R-squared values indicating strong explanatory power.

The determinants that emerged tell a consistent story. Urban residence substantially increased the likelihood of clean fuel use: in the fifth round, the marginal effect of urban living was 8.2 percentage points, meaning urban tribal households were considerably more likely to cook with LPG than their rural counterparts, reflecting better supply chains, distribution networks and market access. Female-headed households were also more likely to adopt clean fuels, an effect that grew stronger between the two survey rounds, consistent with the idea that women, who bear the primary burden of cooking and fuel collection, are powerful agents of transition when they control household decisions. Education mattered in a graded fashion: primary education raised adoption modestly, secondary education more strongly, and higher education most strongly of all, with a marginal effect of 6.2 percentage points in the recent round.

Wealth was the single most powerful predictor. Households classified as rich had a marginal effect of 47.6 percentage points relative to poor households in the fifth round, while middle-wealth households showed an effect of 16.4 points. This gradient underscores a central tension in clean cooking policy: a free LPG connection does not by itself overcome the recurring costs of refills, which many poor households find prohibitive. The finding that larger family size reduced the probability of adoption, with a marginal effect of minus 2.4 percentage points per additional member, likely reflects the economics of cooking volume, since bigger families need more fuel and biomass remains effectively free even when its true cost, measured in smoke and labour, is enormous.

Two other negative determinants deserve attention because they speak to the specific livelihoods of tribal communities. Ownership of agricultural land reduced adoption probability by 1.7 percentage points, and ownership of livestock or farm animals reduced it by 2.2 points. These assets tie households to agrarian routines in which biomass is readily available and often collected as a by-product of farm work, lowering the perceived need to purchase fuel. Even more striking was the effect of cooking location: households that cooked outdoors were 11 percentage points less likely to use clean fuel than those with a separate kitchen room. Outdoor cooking, common in many tribal settlements, appears to reduce the perceived health benefits of switching, since smoke disperses in open air, and it also reflects housing conditions that limit the practical use of LPG stoves.

To isolate the effect of the Pradhan Mantri Ujjwala Yojana itself, the researchers employed a Propensity Score Matching combined with a Difference-in-Differences design, a quasi-experimental approach that compares below-poverty-line households eligible for the scheme with comparable non-eligible households before and after the intervention. The matching step constructs a counterfactual group of households statistically similar in observable characteristics, while the difference-in-differences step nets out time trends that would have occurred regardless of the policy. The results indicated a significantly higher increase in LPG adoption among below-poverty-line households during the PMUY period, providing credible evidence that the scheme did accelerate the transition among its intended beneficiaries rather than merely subsidising households that would have switched anyway.

The study also examined LPG adoption before and after the COVID-19 lockdown using the fifth-round data, with a treatment variable identifying PMUY households and an interaction term capturing the differential post-lockdown change. The interaction was positive and significant, with a marginal effect of 12.9 percentage points in the pre-COVID specification and 14.4 points in the post-COVID specification, again suggesting that the scheme’s beneficiaries gained ground relative to others. Yet the authors caution that connection-based targets are not the same as sustained use. A household that receives a free cylinder and regulator may revert to biomass once the first refills must be purchased at market prices, a pattern of partial or stacked fuel use that has been documented across the Global South and that limits the health dividends of LPG programmes.

The health stakes of this transition are considerable. Burning solid biomass indoors releases fine particulate matter, carbon monoxide and other pollutants at concentrations far exceeding safe guidelines, and the burden falls disproportionately on the women and children who spend the most time near the cookstove. Respiratory infections, chronic obstructive pulmonary disease, cardiovascular strain and low birth weight are among the documented consequences of household air pollution. For tribal communities, which already face elevated poverty and reduced access to healthcare, the persistence of biomass cooking represents a compounding disadvantage, which is precisely why the slow pace of transition documented in Odisha matters beyond the state’s borders.

The authors conclude that the path forward requires integrated, context-sensitive policy interventions that extend beyond connection-based targets. Affordability of refills, reliability of supply in remote and forested districts, behavioural change campaigns that address cooking habits and cultural preferences, and women’s empowerment within tribal communities all emerge as necessary ingredients of a genuine transition. The Odisha evidence suggests that the Ujjwala scheme opened a door that had long been closed to the poorest tribal households, but that walking through it, cylinder after cylinder, year after year, depends on incomes, infrastructure and social dynamics that a single subsidy cannot supply. As India pursues its broader clean energy ambitions, the kitchen, the study reminds us, remains one of the hardest places to change.

Subject of Research: Determinants of clean cooking fuel adoption among tribal households in Odisha under India's Pradhan Mantri Ujjwala Yojana

Article Title: Clean cooking fuel transitions among tribal households in Odisha under the Pradhan Mantri Ujjwala Yojana

Article References: Naik, D., Bera, R., Mohapatra, M., Behera, R. R., & Behera, R. R. (2026). Clean cooking fuel transitions among tribal households in Odisha under the Pradhan Mantri Ujjwala Yojana. Discover Sustainability. https://doi.org/10.1007/s43621-026-04857-4

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04857-4

Keywords: clean cooking fuel, LPG, Pradhan Mantri Ujjwala Yojana, tribal households, Odisha, energy transition, NFHS, household air pollution, energy access, probit model, propensity score matching, women's empowerment

The genus Hemidactylus, established in 1820, is one of the most species-rich groups among geckos, with 198 described species worldwide, 60 of which occur in India. These medium to large geckos occupy a remarkable range of habitats, from forest floors and tree trunks to caves and rock crevices, and some species live as human commensals on the walls of houses. Since 2008, intensive studies of the systematics and phylogeny of the genus have unravelled a great deal of cryptic diversity, leading to the description of 39 new species, and the pace of discovery suggests that many more await formal recognition.

Within the Indian radiation of the genus, the H. prashadi group is the most species-rich, currently comprising 23 described species. The group is divided into major clades, including a northern clade with species distributed mainly north of 14°N and a southern clade largely south of 13°N. The northern clade includes a northern Eastern Ghats group with two described species, H. kangerensis from Chhattisgarh and H. sushilduttai from northern Andhra Pradesh. Earlier work by Mohapatra and colleagues in 2023 had already flagged a distinct, undescribed large-bodied, tuberculate lineage from Kalinga Ghati in Odisha’s Kandhamal district, identified on the basis of the mitochondrial ND2 gene, and the new study formally describes that lineage as a novel species.

The researchers based the description on six specimens collected from outside protected areas in the Kandhamal, Khordha, Ganjam, and Angul districts of Odisha. The animals were collected by hand, euthanised with halothane following ethical guidelines, fixed in 8% formaldehyde, and stored in 70% ethanol. All specimens were deposited in the Reptilia section of the Zoological Survey of India in Kolkata. Morphological and meristic data were collected using a stereo dissecting microscope, with measurements taken using digital vernier callipers to the nearest 0.01 mm and converted to the nearest 0.1 mm. The characters recorded included snout-vent length, head dimensions, limb lengths, counts of lamellae beneath the digits, numbers of dorsal tubercle rows, supralabials and infralabials, and the number of femoral pores in males.

Molecular work was carried out on the mitochondrial ND2 gene, a protein-coding marker widely used in gecko phylogenetics. DNA was extracted using a commercial kit, and the complete ND2 gene was amplified with primers L4437 and H5540, generating sequences of up to 1041 nucleotides. Sequences for the new species were generated from two paratypes from Mandaragiri in Angul district, while the holotype sequence had already been published in earlier phylogenetic trees. The dataset included most named Indian species of the H. prashadi group and sister lineages from the Eastern Ghats, with H. hunae from Sri Lanka and H. vanam from Tamil Nadu used as outgroups. Sequence alignment was performed in MEGA v.12, and phylogenetic analysis was conducted under the GTR + G + I model with nodal support assessed via 1,000 bootstrap replicates.

The phylogenetic analyses recovered three well-supported subclades within the H. prashadi group in the northern Eastern Ghats: the H. sushilduttai, H. triedrus, and H. maculatus subclades, each with bootstrap support above 95. The newly identified lineage falls within the H. sushilduttai subclade and is a sister to a lineage that includes H. kangerensis. Genetic analysis based on uncorrected ND2 p-distances shows that the new species differs from its sister taxon H. sushilduttai by 13–16%, from H. kangerensis by 12.5–15%, and from all other members of the H. prashadi group by more than 18%. Given this substantial genetic divergence, the authors concluded that the lineage warrants recognition as a separate species.

Morphologically, Hemidactylus kalinga is a large, tuberculated gecko reaching a snout-vent length of up to 105.4 mm. Its dorsal scalation is heterogeneous, composed of small granular scales intermixed with large, pointed, strongly keeled, trihedral tubercles that form 15 to 19 fairly regularly arranged longitudinal rows at midbody. Males bear 22 to 26 femoral pores on each side, separated medially by five poreless scales, and there are 9 to 12 lamellae beneath the first digit and 12 to 14 beneath the fourth digit. The species has 11 to 13 supralabials and 9 to 11 infralabials. In life, the body is rusty-brown with four light transverse bands edged with black or dark brown between the occiput and sacrum, and the head is lighter than the body, with a narrow light collar edged by dark brown that is continuous with the post-ocular marking.

The new species can be readily diagnosed from its two closest relatives, H. sushilduttai and H. kangerensis, by a set of characters around the throat and cloaca. In H. kalinga, the second postmental is distinct and roughly 70% of the length of the first postmental, the first postmental touches infralabial I, the second postmental touches infralabials I and II, and there is a single post-cloacal spur on each side. By contrast, H. sushilduttai has two post-cloacal spurs on each side and a different pattern of postmental contacts with the infralabials, while H. kangerensis has three post-cloacal spurs on each side. The number of dorsal tubercle rows also differs among the three species, with 15 to 19 rows in H. kalinga, 16 to 17 in H. sushilduttai, and 18 to 20 in H. kangerensis.

The type series comprises an adult male holotype from Kalinga Ghati in Kandhamal district, collected in May 2016, and five paratypes, two males and three females, from Barbara Reserve Forest in Khordha district, Budhakhol in Ganjam district, and Mandaragiri in Angul district. The specific epithet is a toponym for Kalinga Ghati, the type locality, and also refers to the historical Kalinga Kingdom, which comprised present-day south Odisha, within which the species’ known distribution lies. The paratypes agree with the holotype in most characters, with some variation in the numbers of infralabials, scales across the midbody, dorsal tubercle rows, and femoral pores. One paratype from Budhakhol is lighter in colour without prominent body bands, and uncollected juveniles observed in the field showed darker colouration with more pronounced banding on the body and tail.

Hemidactylus kalinga is endemic to the Eastern Highlands biogeographic province of the Deccan Peninsula biogeographic zone and has been recorded from forest tracts in the northern Eastern Ghats range of Odisha, with its northernmost distribution north of the River Mahanadi. The species inhabits moist and dry deciduous forests with rocky outcrops at elevations of 156 to 746 m above sea level, preferring large boulders in cooler areas covered with large trees or situated in riparian zones. It is sympatric with several other geckos, including the golden gecko Calodactylodes aureus and the nebular bent-toed gecko Cyrtodactylus nebulosus, and occasionally enters human habitations. Field observations show that the species feeds on various arthropods and their larvae; one individual was observed preying on the mole cricket Gryllotalpa africana, apparently tracking the movement of the insect on the ground. The description of H. kalinga makes it the second endemic Hemidactylus from Odisha after H. paucifasciatus and the fourth endemic reptile from the state, underscoring that the Eastern Ghats hill ranges, already home to many endemic lizards, still hold a great deal of undiscovered diversity that will require more intensive taxonomic work to resolve.

Subject of Research: New gecko species Hemidactylus kalinga discovered in Odisha's Eastern Ghats

Article Title: A new species of Hemidactylus Goldfuss, 1820 ( Squamata , Gekkonidae ) from the Northern Eastern Ghats, Odisha, India

Article References: Mohapatra, P. P., Ray, S., Das, A. K., Bhupathi, B., Sarkar, V., Mohalik, R. K., Nair, M. V., & Dutta, S. K. (2025). A new species of Hemidactylus Goldfuss, 1820 (Squamata, Gekkonidae) from the Northern Eastern Ghats, Odisha, India. Herpetozoa, 38, 365-378. https://doi.org/10.3897/herpetozoa.38.e175753

Image Credits: AI Generated

DOI: 10.3897/herpetozoa.38.e175753

Keywords: Hemidactylus kalinga, gecko, Eastern Ghats, Odisha, India, new species, herpetology, taxonomy, ND2 gene, reptiles, species, Hemidactylus

The study, led by Tapas Ranjan Patra of Rajendra University with colleagues from Utkal University and Netaji Subhash Chandra Bose College, drew on daily maximum and minimum temperature records from the India Meteorological Department’s gridded archive covering 1951 to 2024. Rather than averaging the state into a single number, the team preserved spatial detail by working at the 1-degree by 1-degree grid-cell scale. Of 30 grid cells overlapping Odisha’s boundary, 28 had complete enough records to enter the modelling stage, a 93.3 percent spatial coverage that allowed coastal, interior and transitional zones to be assessed within one common framework.

The statistical engine of the study was the generalized extreme value distribution, the standard tool for modelling the tails of annual or seasonal maxima and minima. For each grid cell and each extreme series, the researchers fitted five nested candidate models: a stationary model with a fixed location parameter, a model in which the location shifts linearly through time, and three models that added the El Niño-Southern Oscillation index, the Indian Ocean Dipole index, or both as covariates. Bayesian inference, implemented in PyMC with four Hamiltonian Monte Carlo chains and strict convergence diagnostics, propagated uncertainty from parameter estimation all the way through to the return-level estimates, producing posterior intervals rather than single point values.

Diagnostic discipline was central to the workflow. A fit was deemed clean only if it showed finite parameters, a maximum R-hat of 1.01 or less, effective sample sizes of at least 400, zero divergences and no support violations. Among clean candidates, the model with the highest expected log predictive density was preferred, with a parsimony rule favouring simpler structures when candidates competed within two ELPD units. The team completed 767 fits in total, including rescue refits for difficult cases, and every one of the 140 selected grid-series combinations ultimately yielded numerically valid return levels, with five cold-night cases retained under explicit model cautions.

One of the clearest findings concerns the timing of daytime heat. The annual maximum daily maximum temperature and the March-to-June maximum proved almost indistinguishable: they shared the same mean of 41.4 degrees Celsius, the same median, and the same observed range from roughly 35 to 46.4 degrees. In 99.95 percent of valid grid-years, the annual extreme fell within the extended pre-monsoon heat season. This confirms that Odisha’s hottest days are not scattered through the year but concentrated in a well-defined window when high insolation, dry soils and anticyclonic circulation combine to push daytime temperatures toward their limits.

The headline number for heat planners is the recent 50-year return level for pre-monsoon daytime maxima: a spatial median of 43.81 degrees Celsius, with grid-level medians spanning 41.60 to 46.52 degrees. Yet the study is careful about what its zero-change results mean. In 27 of 28 grids, the selected model was stationary, which by construction forces the early-to-recent return-level change to be exactly zero. The authors stress that these zeros are structural properties of the chosen models, not independent evidence that daytime heat has stopped evolving. A separate sensitivity analysis that imposed a time trend uniformly across grids found small positive changes in daytime extremes, though none large enough for the 95 percent credible intervals to exclude zero.

The warm-night story turned out to be strikingly different. Night-time extremes showed much stronger support for time-varying models, and the direction of change was negative: recent return levels for monsoon-season warm-night maxima were lower than early-period values, with the median 50-year return level falling from 30.82 to 30.35 degrees Celsius. In a sensitivity restricted to data through 2007, negative time coefficients persisted in every eligible grid, 12 of 12 for annual warm nights and 19 of 19 for monsoon-season warm nights. The authors caution that this does not mean Odisha is cooling; rather, it reflects seasonally specific behaviour of upper-tail minimum temperatures, consistent with earlier evidence of negative monsoon-season minimum-temperature trends over eastern India, possibly linked to cloud cover, humidity and land-sea influences.

Cold-night extremes in winter behaved differently again, with near-zero and mixed changes and return levels that were more sensitive to how boundary grid cells were handled. Meanwhile, teleconnection covariates earned only a limited role. ENSO appeared in 16 selected models, the Indian Ocean Dipole in 9, and both together in just 6, out of 140 selections, and their coefficient evidence was mixed rather than uniformly directional. The authors interpret this cautiously: large-scale climate modes may modulate Odisha’s extremes only in specific grid cells and seasons, and the data did not justify a state-wide teleconnection narrative.

What emerges is a portrait of thermal risk that refuses to be flattened into a single warming trend. Daytime heat remains anchored in the pre-monsoon season at physically consequential magnitudes, warm-night tails behave according to their own seasonal logic, and cold nights show little coherent change. The authors emphasize that their return levels are conditional summaries of observed tail behaviour, not deterministic forecasts of events arriving once every 50 calendar years. They also note the limits of 1-degree gridded data for urban or district-scale planning, and the fact that their models were fitted independently rather than through a spatial hierarchy.

Even with those caveats, the study offers something regional planners rarely get: a defensible, uncertainty-bounded benchmark of how rare hot days, warm nights and cold nights have actually behaved across Odisha’s landscape. The authors suggest that future work could add spatial hierarchical modelling, compare these observational benchmarks with CMIP6 and CORDEX projections, and test physically grounded covariates such as soil moisture, monsoon onset and humidity-linked heat stress. For a state where heatwaves correlate significantly with recorded mortality, knowing the magnitude and uncertainty of the rarest thermal events, grid cell by grid cell, is a practical foundation for the climate-risk decisions that lie ahead.

Subject of Research: Bayesian extreme-value assessment of observed temperature extremes and return levels across Odisha, India

Article Title: Bayesian assessment of return levels for observed temperature extremes across Odisha in India

Article References: Patra, T. R., Sk, M. M., Pathy, A. C., Meher, B., Naik, D., & Sahoo, D. (2026). Bayesian assessment of return levels for observed temperature extremes across Odisha in India. Discover Geoscience, 4(1), Article 401. https://doi.org/10.1007/s44288-026-00777-y

Image Credits: AI Generated

DOI: 10.1007/s44288-026-00777-y

Keywords: Bayesian inference, extreme value theory, generalized extreme value distribution, temperature extremes, return levels, Odisha, India, heatwaves, ENSO, Indian Ocean Dipole, climate risk, IMD gridded data

The research, led by Susanta Kumar Swain of the Odisha Department of Health and Family Welfare together with colleagues from the Acharya Harihar Post Graduate Institute of Cancer, the Indian Council of Medical Research, and the Norway India Partnership Initiative, set out to test whether a home-based screening model could be delivered through the existing public health system rather than through parallel, donor-driven structures. The team implemented what is known as a screen–triage–treat approach in Thoria Sahi, a settlement in Cuttack, targeting women between the ages of 30 and 65. Data collected between October 2022 and September 2023 were analyzed to measure screening coverage, HPV positivity, follow-up behavior, and treatment outcomes.

The technical logic of the model rests on the biology of the disease itself. Virtually all cases of cervical cancer are caused by persistent infection with high-risk types of human papillomavirus, and detecting the virus’s DNA in cervical cells is a more sensitive screening method than conventional cytology. Crucially, self-collected vaginal samples have been shown in multiple international studies to perform nearly as well as clinician-collected specimens for HPV testing. That property makes self-sampling a powerful tool for communities where cultural norms, modesty concerns, time constraints, and distrust of pelvic examinations keep women away from clinics. In Thoria Sahi, the program leaned on Accredited Social Health Activists, or ASHAs, the frontline community health workers who form the backbone of India’s rural and urban health outreach, to mobilize eligible women and support them in collecting samples at home.

The numbers from the pilot are striking. Of 6,436 women identified as eligible, 4,896 completed screening, a coverage rate of 76 percent. Among those screened, 322 women, or 7 percent, tested positive for high-risk HPV, a prevalence consistent with expectations for a population that had never previously been screened. Each positive result triggered a referral to an Urban Primary Health Centre, where nurses trained in visual inspection with acetic acid, or VIA, performed triage. This two-step design reflects current World Health Organization guidance, which favors HPV-based primary screening followed by a same-day triage test so that women with likely precancerous lesions can be treated immediately rather than lost to follow-up.

Here the pilot encountered the challenge that has undermined many screening programs before it: the follow-up gap. Despite counselling, only 42 percent of HPV-positive women, 135 of 322, returned to a health centre for VIA triage. Of those who did, 38.5 percent were VIA-positive, indicating likely precancerous changes. Among VIA-positive women, 81 percent received thermal ablation on the spot, a low-cost outpatient procedure that uses a heated probe to destroy abnormal cervical tissue. The remainder were referred for colposcopy, biopsy, and further management in line with national guidelines. In total, the program identified and appropriately managed seven cases of precancerous lesions and one case of micro-invasive carcinoma, detections that in an unscreened population would likely have surfaced only years later as advanced, difficult-to-treat disease.

The authors are candid about what the attrition at the triage stage means. A screening program is only as strong as its weakest link, and the drop from 322 HPV-positive women to 135 who completed triage illustrates that testing alone does not guarantee protection. Community-based follow-up by ASHAs confirmed that women who did receive treatment adhered to it and remained well, which suggests the treatment pathway itself functioned smoothly once patients entered it. The bottleneck lay in getting women from a positive home test to the clinic door, a gap shaped by fear, stigma, household obligations, and the practical costs of travel and waiting time. The study’s central lesson, as the authors put it, is that referral and treatment systems must be strengthened from the outset, not retrofitted after a screening campaign has generated a backlog of positive results.

Why does this matter on a global scale? The World Health Organization has set an ambitious goal of eliminating cervical cancer as a public health problem, a target that requires 70 percent of women to be screened with a high-performance test at least twice in their lives by 2030. India, with its enormous population and historically low screening uptake, is pivotal to that effort. National Family Health Survey 5 data showing screening coverage below 2 percent underline how far the country is from the target, and conventional clinic-based approaches have repeatedly failed to close the gap. Self-sampling flips the equation by removing the most intimidating step, the pelvic examination, and by bringing the test to women rather than requiring women to come to the test.

The Odisha pilot is also notable for what it did not do: it did not build a parallel health system. The model was embedded in the public health infrastructure, using government ASHAs for mobilization and follow-up, Urban Primary Health Centres for triage, and designated treatment centres for colposcopy and surgical management. That integration matters for scalability, because vertical programs funded by short-term grants often collapse when the funding ends. By demonstrating that a screen–triage–treat cascade can run through existing public machinery, the study offers a template that state health departments could adapt, provided they invest in supply chains for HPV test kits, laboratory capacity for DNA testing, and training for frontline workers.

The technical details of the cascade deserve attention for anyone designing similar programs. HPV-DNA testing detects the genetic material of high-risk virus types, identifying women at risk before any cellular abnormality appears, which gives clinicians a longer window for intervention than cytology-based screening. VIA, the triage step used here, involves applying dilute acetic acid to the cervix and looking for acetowhite areas that suggest precancerous change; it is inexpensive, requires minimal equipment, and allows immediate see-and-treat decisions. Thermal ablation is appropriate for lesions that meet size and location criteria and is far simpler than excisional procedures, making it feasible at primary care level. The 38.5 percent VIA positivity among HPV-positive women in this pilot is within the range reported in comparable Indian studies, reflecting the fact that most HPV infections clear spontaneously and only a minority progress to lesions.

The study received no external funding and was approved by the State Research and Ethical Committee of the Odisha Department of Health and Family Welfare, with written informed consent obtained from all participants. Its findings arrive at a moment when HPV vaccination is expanding and molecular diagnostics are becoming cheaper, creating a rare window in which elimination of cervical cancer in low-resource settings looks technically achievable. The Odisha experience shows that when the test comes to the woman, coverage can soar; the unfinished work lies in ensuring that a positive result is followed swiftly by triage and treatment. If health systems can close that loop, home-based self-sampling could become one of the most consequential public health tools of the decade, turning a disease that kills quietly into one that is caught early, treated cheaply, and ultimately prevented.

Subject of Research: Home-based HPV-DNA self-sampling for cervical cancer screening in underserved urban communities in India

Article Title: Improving coverage of cervical cancer screening for hard-to-reach women: lessons learned from piloting an innovative home-based screening model in Odisha, India

Article References: Swain, S. K., Nayak, B., Hariprasad, R., Rattan, R., Srivastava, A. K., Bhargava, S., & Biswal, D. K. (2026). Improving coverage of cervical cancer screening for hard-to-reach women: lessons learned from piloting an innovative home-based screening model in Odisha, India. BMC Health Services Research. https://doi.org/10.1186/s12913-026-15839-7

Image Credits: AI Generated

DOI: 10.1186/s12913-026-15839-7

Keywords: cervical cancer, HPV-DNA testing, self-sampling, cancer screening, Odisha, India, ASHA health workers, screen-triage-treat, thermal ablation, VIA, public health, urban slums

The research team carried out their trial across ten higher education institutions in the eastern Indian state of Odisha, a setting chosen in part because smartphone penetration there is high: ownership and use among young people aged 15 to 29 exceeds 70 percent. In total, 16,683 students participated, making the study an order of magnitude larger than most previous experiments on the topic. Institutions were randomized so that some classrooms operated under phone restrictions while others continued with unrestricted use, allowing the researchers to isolate the causal effect of the bans rather than merely correlating phone habits with achievement. The breadth of the sample also meant the team could examine how effects varied across disciplines, degree levels, and baseline academic standing.

The outcome measures went well beyond test scores. The researchers tracked academic performance and grade point averages, but they also surveyed students on their attitudes toward the restrictions, their well-being, their motivation, their digital habits, and their experiences of online harassment. Classroom observations supplemented the survey data, giving the team a direct window into how the bans changed day-to-day behavior in lectures and tutorials. This combination of administrative records, self-reports, and observational evidence is what gives the study its unusual depth, since each type of measurement can compensate for the weaknesses of the others.

The headline result is that classroom phone bans improved academic performance, but the distribution of those gains is what makes the findings politically and scientifically significant. The improvements were largest among lower-performing students and first-year undergraduates, the very groups most at risk of falling behind. Among higher-performing students, postgraduates, and those in STEM fields, effects were minimal. This pattern suggests that phones function primarily as a distraction for students whose attentional resources or study skills are already stretched, while students with stronger self-regulation or more demanding engagement with material may be less susceptible. In practical terms, a blanket policy that appears to do little for top students may still deliver substantial benefits by lifting the bottom of the distribution.

That distributional point is reinforced by one of the study’s most consequential findings: removing phones reduced disparities in GPA across the student body. If phone restrictions compress achievement gaps, they could serve as a low-cost equity tool for educational systems in low- and middle-income countries, where smartphones have spread faster than the infrastructure or pedagogy needed to manage them in classrooms. The authors suggest that restrictions may help close gaps in learning outcomes, a framing that shifts the debate from whether bans are merely harmless to whether they can actively narrow inequalities that have proven resistant to more expensive interventions.

Crucially, the researchers found that attendance rates did not change under the bans. This null result is informative in itself. It indicates that the academic gains were not driven by students simply showing up to class more often; the students who attended were the same students who would have attended anyway. Instead, the evidence points to what happened inside the classroom: greater attention and more productive classroom dynamics. The improvement, in other words, reflects a change in the quality of instructional time rather than the quantity of it, which is exactly the mechanism that proponents of phone bans have long hypothesized but rarely been able to demonstrate experimentally at scale.

The behavioral and attitudinal data add a further layer. Classroom observations recorded less disruptive behavior and fewer off-topic conversations in phone-free sessions, along with greater teacher engagement. Instructors, freed from competing with a screen for every student’s attention, apparently taught differently, and students responded in kind. Meanwhile, students who experienced the bans became more supportive of the restrictions and more likely to view them as beneficial, while showing less preference for unrestricted phone use. Sungu and colleagues interpret this as evidence that students may become more receptive to phone restrictions after experiencing a more focused, phone-free classroom environment, a finding with direct implications for how such policies should be introduced and communicated.

This last point deserves emphasis because resistance from students is one of the most common objections to phone bans worldwide. Critics argue that restrictions are paternalistic, that they provoke backlash, or that students will simply find workarounds. The Odisha trial suggests the opposite dynamic: experience with the policy appears to convert skeptics. If students’ revealed preferences shift toward supporting the restrictions once they have lived under them, then the political sustainability of bans may be much stronger than surveys of students’ stated preferences would predict. Attitude change of this kind, driven by lived experience rather than persuasion campaigns, is often the most durable kind.

The study also speaks to a broader scientific debate about attention and technology in learning environments. Laboratory studies have long shown that the mere presence of a phone can reduce cognitive capacity, and observational studies have linked classroom phone use to lower achievement. But such findings have been difficult to translate into policy because they cannot establish that banning phones in real institutions produces real gains. By randomizing at scale in ordinary higher education settings, Sungu and colleagues close that inferential gap. The results align with the mechanistic picture from smaller studies: phones impose a continuous tax on attention, and removing that tax yields measurable returns, especially for students with the least cognitive slack.

There are, of course, boundaries to what the trial can claim. The effects were minimal among higher-performing, postgraduate, and STEM students, so the findings should not be read as a universal prescription. The study was conducted in higher education institutions in a single Indian state, and extrapolation to primary and secondary schools, or to very different cultural and technological contexts, requires caution. The researchers measured well-being, motivation, digital use, and online harassment alongside achievement, and the published report will be the authoritative source for how those secondary outcomes shifted. Still, the scale of the experiment, the rigor of its design, and the consistency of its behavioral, attitudinal, and academic results make it a landmark contribution. As governments from Europe to Asia weigh statewide phone restrictions, the Odisha trial offers the clearest experimental answer yet to the central question: in the classroom, the phone is not a neutral tool but an active impediment to learning, and removing it pays off most for the students who can least afford to lose focus.

Subject of Research: Effects of classroom smartphone bans on academic performance and student engagement in higher education in India

Article Title: Classroom phone bans improve learning and student engagement in India

Article References: Classroom phone bans improve learning and student engagement in India. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: smartphone bans, classroom policy, randomized controlled trial, India, student engagement, academic performance, education equity, attention, higher education, Odisha, GPA disparities, Science journal

The study arrives at a moment when AI adoption in higher education has become one of the most intensively researched topics in educational technology. Bibliometric analyses show an explosion of publications on generative AI in universities, and systematic reviews have catalogued factors influencing student readiness, from technological self-efficacy to prior digital literacy. Yet the authors note a conspicuous gap: most of this evidence comes from urban, well-resourced institutions in North America, Europe and East Asia, or from major Indian metropolitan centers. Rural contexts, where infrastructure is thinner and digital divides run deeper, remain empirically underexplored. India’s own census classifications define rural areas in ways that shape access to electricity, broadband and institutional funding, making the rural-urban distinction far more than a demographic label. It is a structural boundary condition for technology adoption.

To probe this boundary, the team employed a quantitative survey design targeting rural higher education students in Odisha. Data were collected from 111 students using a ten-item instrument built on a Likert scale, with each item probing a different facet of perceived barriers. The researchers organized these facets into five conceptual clusters: access constraints, meaning unreliable connectivity and inadequate devices; knowledge gaps, meaning insufficient training in how AI systems work; attitudinal resistance, meaning skepticism or discomfort toward AI; cognitive concerns, meaning worries about overreliance and the erosion of independent thinking; and ethical apprehensions, meaning anxieties about privacy, bias and academic integrity. This multidimensional structure matters because treating AI barriers as a single blob obscures the different levers policymakers might pull to address them.

The psychometric quality of the instrument was strong. The scale achieved a Cronbach’s alpha of 0.912, a measure of internal consistency that indicates the ten items reliably tapped a coherent underlying construct of perceived barriers. Values above 0.9 are generally considered excellent in psychometric theory, lending credibility to the composite scores the researchers analyzed. Rather than relying solely on classical parametric statistics, which can be fragile with modest sample sizes and ordinal Likert data, the team used non-parametric tests for group comparisons and reported effect sizes alongside p-values. This dual reporting is important because statistical significance in a small sample can be misleading without a sense of how large the differences actually are.

One of the most methodologically notable features of the study is its handling of the multiple comparisons problem. When researchers test many items across many demographic groups simultaneously, the probability of finding at least one spurious statistically significant result inflates rapidly. To guard against this, the authors applied the Benjamini-Hochberg procedure, a widely used technique that controls the false discovery rate, the expected proportion of false positives among all declared significant findings. This correction is often skipped in educational survey research, which means many published group differences may not survive scrutiny. By applying it here, the Odisha team set a higher evidentiary bar, and the results they report are more trustworthy as a consequence.

What did the corrected analyses reveal? In short, demographic segmentation largely dissolved. Gender, age, field of study and family type did not demonstrate statistically robust differences in perceived barriers after the false discovery rate correction, and the effect sizes were generally small, indicating negligible practical differences between groups even where nominal differences appeared. The one demographic variable that showed any differentiation was academic level, and even there the signal was limited and marginal. It surfaced as a single item-level difference and a small but statistically significant difference in the composite summary index, suggesting that students at different stages of their programs may experience slightly different intensities of barrier, perhaps reflecting growing familiarity with academic technology demands as students progress.

The interpretive weight of the study rests on what this null-heavy pattern means. The authors conclude that perceived barriers to AI integration in rural higher education are shaped more by shared contextual conditions than by demographic characteristics. In other words, a female undergraduate in the humanities and a male postgraduate in the sciences at the same rural institution are likely to report similar obstacles: the same patchy internet, the same lack of formal AI training, the same institutional culture of limited exposure. This finding aligns with a broader literature on digital divides, which has increasingly emphasized that structural and situational factors, rather than individual attributes alone, determine how people experience and adopt new technologies. It also echoes work on low socioeconomic digitalization, where citizens’ difficulties with digital services were traced to situated conditions rather than personal deficits.

The theoretical scaffolding of the study draws on the technology acceptance tradition. Fred Davis’s Technology Acceptance Model, introduced in the 1980s, proposed that perceived usefulness and perceived ease of use govern whether people embrace new information systems, and the later Unified Theory of Acceptance and Use of Technology extended this framework with additional moderators. Studies applying these models to AI chatbots and educational AI have repeatedly found that prior AI experience and digital literacy moderate acceptance. The Odisha findings add a rural inflection to this tradition: when the environment supplies little prior exposure and little infrastructure, the usual demographic moderators that animate urban studies may be swamped by the sheer weight of shared disadvantage. The barrier profile becomes a property of the setting, not of the population within it.

For policymakers and university administrators, the practical implication is that institution-wide strategies, not targeted demographic interventions, are the appropriate response in rural settings. If barriers are common across gender, age and discipline, then programs aimed narrowly at, say, women students or science majors would miss the bulk of the problem. Instead, the authors underscore the importance of strategies that operate at the level of the whole institution: improving connectivity and device access, embedding AI literacy across curricula, addressing attitudinal and ethical concerns through transparent institutional policies, and normalizing critical engagement with generative AI in classrooms. Related research on pedagogical approaches to generative AI has argued that fostering critical engagement, rather than mere technical skill, is key to preparing students, and the rural context makes this doubly urgent because students there often lack informal exposure that urban peers acquire organically.

The study’s authors are candid about its preliminary nature. A sample of 111 students from one Indian state cannot settle questions about AI adoption everywhere, and self-reported perceptions are not the same as observed usage behavior. The composite summary index difference by academic level hints at dynamics that larger, longitudinal designs could unpack, tracking whether barriers attenuate as AI tools proliferate or harden into durable inequalities. The dataset and instrument are available from the corresponding author upon reasonable request, enabling replication. Still, the core contribution stands: rare context-specific empirical evidence from rural higher education, gathered with unusually careful statistics, showing that the AI divide in places like Odisha is not a mosaic of demographic micro-divides but a single shared landscape of constraint. Bridging it will require acting on the landscape itself, not on the demographic variations that cross it.

Subject of Research: Perceived barriers to artificial intelligence adoption among rural higher education students in Odisha, India

Article Title: Perceived barriers to adoption of artificial intelligence among rural higher education students

Article References: Satapathy, D., Divya, V. S., Pradhan, D. K., & Nayak, A. K. (2026). Perceived barriers to adoption of artificial intelligence among rural higher education students. SN Social Sciences, 6(10), Article 500. https://doi.org/10.1007/s43545-026-01801-1

Image Credits: AI Generated

DOI: 10.1007/s43545-026-01801-1

Keywords: artificial intelligence, higher education, rural students, digital divide, Odisha, AI adoption, technology acceptance, survey research, perceived barriers, educational technology, India, Benjamini-Hochberg

The scale of the human stakes is enormous. India is the world’s second-largest fish producer, with a record 175.45 lakh tonnes of fish production in 2023, and its fisheries sector has grown by 10.87 percent since 2014–15. Odisha alone produces roughly 8.73 lakh metric tonnes annually, drawing on a 480 kilometre coastline and nearly 11 lakh hectares of inland and brackish waters. Globally, small-scale fisheries account for more than 90 percent of active fishers and support nearly two-thirds of the global fish catch, yet they remain chronically underreported in statistics and overlooked in policy. Chilika exemplifies this blind spot: more than 400,000 fishers live in nearly 150 settlements around the lagoon, many operating under a unique governance system that grants exclusive fishing rights administered through cooperatives and supported by the Chilika Development Authority.

Salinity in Chilika is governed by a delicate tug-of-war between seawater pushing in through tidal inlets from the Bay of Bengal and freshwater discharging from inland rivers. The lagoon spans nearly 1,100 square kilometres during the monsoon and contracts to about 906 square kilometres in the dry months, with an average depth of just 1.8 metres. This shallow, hydrologically dynamic system is divided into four ecological sectors: the freshwater-dominated Northern Sector, the Central and Southern Sectors, and the saline-rich Outer Channel. Seasonal salinity ranges from nearly zero during the rains to around 17 parts per thousand in winter and roughly 35 parts per thousand in summer, creating one of the steepest estuarine gradients in Asia and a natural laboratory for studying how salt shapes biodiversity.

The historical record reveals dramatic swings. Average salinity fell from 22.3 parts per thousand in 1957–1958 to between 9.4 and 11.8 parts per thousand by the early 1960s, and dropped to a critical low of 8.3 parts per thousand by 1999. The culprit was partly geomorphological: the commissioning of the Hirakud Dam on the Mahanadi River in 1957 unexpectedly increased the silt load entering the lagoon, driving progressive sedimentation, choking of the Arakhakuda mouth, reduced tidal exchange, salinity decline, and rampant weed overgrowth. In response, an artificial sea mouth was opened near Sipakuda village on 23 September 2000, restoring seawater ingress. Natural inlets subsequently formed at Gabakunda in 2008, Dhalabali in 2012, and during Cyclone Phailin in October 2013, and research by Panda and Mohanty showed that the shift from a single to a multiple inlet system between 2006 and 2009 produced pronounced changes in annual mean salinity, with cascading effects on sedimentation, marine species intrusion, and ecological balance.

Extreme weather adds another layer of volatility. The Super Cyclone of 1999 devastated fisheries resources and infrastructure. Cyclone Phailin in 2013 triggered a dramatic post-monsoon salinity decline and altered nutrient concentrations, lowering phosphate and nitrate while raising silicate and ammonia. Cyclone Fani in 2019, striking in May during the lagoon’s peak salinity period, instead elevated salinity relative to other storms, while Hudhud in 2014 had minimal impact. Data from 1999 to 2021 show average salinity of 10.69 parts per thousand, ranging from 0.1 to 36.9, with the Northern Sector experiencing sharp fluctuations and the Southern Sector remaining more stable. Most recently, a deep depression over the northwestern Bay of Bengal on 13 July 2022 opened a new inlet, driving Outer Channel salinity from 21 to 34 parts per thousand in June down to zero to 2 parts per thousand by August. Broader climate phenomena, including the El Niño–Southern Oscillation, modulate these patterns by influencing precipitation, river discharge, and lagoon–sea exchange.

The biological consequences are striking. Fish diversity in Chilika is structured along the salinity gradient: freshwater species dominate the north, brackish species the centre and south, and the tidal Outer Channel supports both brackish and marine fishes. Species counts have climbed from 138 aquatic species recorded in 1918 to 336 by 2008, including 261 finfish, 28 prawns, and 34 crabs, before slipping slightly to 317 in 2023 across 207 genera, 88 families, and 23 orders. The lagoon also shelters around 700 plant and 800 animal species, including the endangered Irrawaddy dolphin, and serves as a critical stopover on the Central Asian Flyway for migratory birds. Salinity shifts impose oxidative stress on aquatic organisms, altering survival, reproduction, and distribution, and declining salinity has stressed brackish biota such as prawns, which form the backbone of the local fishery.

The 2000 hydrological intervention, however, demonstrates that salinity management can also drive recovery. Enhanced seawater exchange improved recruitment of juvenile marine fish, lifting fish landings from 11,989 metric tonnes in 2001–02 to 14,228 metric tonnes in 2011–12, documenting 56 new species, and coinciding with the reappearance of the Irrawaddy dolphin. Phytoplankton communities responded too: diatoms replaced cyanobacteria and came to dominate up to 72 percent of phytoplankton biomass, benefiting fish production. After the new inlet opened in July 2022, landings rose to 223.62 metric tonnes, with mullets and other marine species featuring prominently. Yet fishers also reported disturbances, including sand infestation, fluctuating water depths, and the arrival of unfamiliar marine organisms such as stingrays, octopuses, and jellyfish, which reshaped fishing seasons and species composition.

Habitat degradation compounds the problem. Declining salinity converts brackish habitats into freshwater-dominated systems, eliminating niches for estuarine species such as mullets and anchovies, narrowing tidal channels, and degrading seagrass meadows that serve as critical spawning and feeding grounds for prawns and crabs. Long-term desalination has fuelled invasive weed growth, particularly in the northern sector, while excessive sedimentation and intensive prawn aquaculture, locally known as gheri or bund fishing, have altered hydrology and sediment transport, impeding fish migration and nutrient flow. Embankments and dams further disrupt natural flow, fragmenting habitats and reducing species resilience. The socioeconomic fallout is cascading: declining catches and unstable incomes exacerbate poverty, malnutrition, and occupational migration, and in some cases entire villages have shifted away from fishing altogether, while shrimp aquaculture introduced in the 1980s disrupted traditional access rights and intensified competition.

The review’s authors frame salinity not merely as a physical parameter but as a socio-ecological determinant linking ecological balance with human well-being, and they chart a path forward. Key strategies include restoring ecological buffers such as mangroves, seagrass beds, and wetlands that stabilize salinity and provide fish nurseries; promoting livelihood diversification through eco-tourism, aquaculture, and small enterprises; enforcing fisheries laws such as the Marine Fishing Regulation Acts in collaboration with local communities; and building fisher capacity in adaptive practices like seasonal restrictions, spatial management, and catch diversification. Regular monitoring of salinity and hydrological parameters is deemed indispensable for guiding adaptive management, and economic valuation shows fishery resources account for over 71 percent of the lagoon’s total economic value, underscoring what is at stake.

Ultimately, the study lays the groundwork for future econometric analyses quantifying how salinity variation cascades through biodiversity and artisanal livelihoods. As climate change accelerates sea-level rise, saltwater intrusion, and cyclone intensity across low-lying Asian coasts, Chilika’s experience offers a cautionary tale and a template: salinity management, if pursued inclusively and grounded in fishers’ rights, can allow Asia’s largest brackish water lagoon to thrive both as an ecological jewel and as a lifeline for the communities whose fortunes rise and fall with its salt.

Subject of Research: Salinity dynamics and their ecological and socioeconomic impacts on artisanal fisheries in Chilika Lagoon, India

Article Title: Salinity changes and their environmental impacts on artisanal fisheries in Chilika Lagoon

Article References: Nandy, A., Rout, D. S., & Tripathy, P. (2026). Salinity changes and their environmental impacts on artisanal fisheries in Chilika Lagoon. Discover Oceans, 3(1), Article 24. https://doi.org/10.1007/s44289-026-00137-1

Image Credits: AI Generated

DOI: 10.1007/s44289-026-00137-1

Keywords: Chilika Lagoon, salinity, artisanal fisheries, brackish water, climate change, biodiversity, tidal inlets, cyclones, Odisha, small-scale fisheries, habitat degradation, hydrology

The elements in question are an unusual cast of characters. Instead of the familiar roster of lead, cadmium and arsenic that dominates most contamination studies, the researchers focused on aluminium, molybdenum, rubidium, strontium, titanium, barium, gallium, tin and vanadium. These elements are naturally present in the Earth’s crust, and several of them, including gallium and vanadium, are increasingly economically significant as critical raw materials for electronics and energy technologies. That very fact makes them understudied environmental contaminants: regulatory frameworks and monitoring programmes have historically prioritized the classic toxic metals, leaving elements such as tin and gallium comparatively invisible in soil pollution assessments.

The sampling design was deliberately simple but effective. Soil cores were collected at 20, 50, 300 and 700 metres from the mine, at two depths, from 0 to 15 centimetres and from 15 to 30 centimetres below the surface. This gradient approach allowed the team to test a basic prediction: if mining is the dominant source of contamination, concentrations and contamination indices should generally decline with distance from the pit. That is broadly what the data showed. Contamination factors and geo-accumulation indices, two standard measures that compare measured concentrations against local background values, tended to decrease as distance from the mine increased, indicating a spatial pattern of relative enrichment centred on the mining operations.

Among the nine elements examined, tin and gallium stood out with the highest relative enrichment relative to the selected local background. This finding is intriguing because both elements are associated with coal and coal-bearing strata. Coal deposits are known hosts for gallium, which can accumulate to economically interesting levels in some coal seams and coal ashes, and trace elements in general are partitioned into the mineral and organic fractions of coal during formation. When coal is excavated, crushed, transported and burned or stockpiled, these elements can be redistributed into surrounding soils through dust deposition, runoff from spoil heaps and the weathering of mine waste.

The researchers were careful, however, not to over-interpret their data. Aluminium and titanium, which also showed elevated values at some sites, were treated cautiously because of their predominantly lithogenic origin. Both elements are major constituents of common rock-forming minerals, particularly aluminosilicates and titanium-bearing oxides, so high concentrations in soil may simply reflect the composition of the local parent material rather than any anthropogenic input. Distinguishing between a natural geochemical baseline and mining-induced enrichment is one of the central challenges of environmental geochemistry, and the study’s authors explicitly flagged this ambiguity as a reason for careful interpretation rather than alarm.

To address that challenge, the team turned to multivariate statistics, specifically principal component analysis and hierarchical cluster analysis. These techniques group elements according to how their concentrations co-vary across samples, on the logic that elements released by the same source, or controlled by the same geochemical processes, should behave similarly in space. The analyses identified distinct associations among the trace elements, suggesting a combined influence of natural geological factors and possible mining-related inputs. In other words, the statistical structure of the dataset did not point to a single dominant source, but instead revealed a layered signature in which bedrock geology and mining activity both leave their mark on the soil chemistry.

The human health risk assessment portion of the study followed standard screening-level methodologies, estimating average daily intake, hazard quotients and a cumulative hazard index for three exposure pathways: ingestion of soil particles, dermal contact and inhalation of resuspended dust. The results carried a clear message about who is most at risk. Soil ingestion emerged as the dominant exposure pathway, and children exhibited higher average daily intake, hazard quotient and hazard index values than adults across the board. This is a well-established pattern in exposure science, driven by children’s tendency to ingest soil through hand-to-mouth behaviour, their lower body weight and their higher relative doses per kilogram of body mass, but it takes on particular urgency in communities living close to active mining pits.

The element-by-element breakdown of risk added further nuance. Vanadium contributed the largest share of the non-carcinogenic risk through ingestion and dermal exposure, while aluminium was the major contributor through the inhalation pathway. Vanadium is a transition metal that occurs in fossil fuels and can be released during coal handling and combustion, and chronic exposure has been associated with respiratory and other health effects. Aluminium’s prominence in the inhalation route reflects both its abundance in crustal dust and the way inhalation risk calculations weight inhaled particle masses. It is worth emphasizing that these are screening-level estimates designed to flag potential concerns and guide monitoring priorities, not measurements of actual health outcomes in the surrounding population.

What makes the study a useful template is its methodological integration. Pollution indices such as the contamination factor and geo-accumulation index are quick, transparent tools for ranking contamination at individual sites, but they say little about sources. Multivariate statistics can suggest source groupings but depend on the quality and spatial coverage of the underlying data. By applying both approaches to the same dataset, and then layering a health risk assessment on top, the researchers produced a picture that is more robust than any single method could deliver. The gradient design, with sampling points stretching from the mine edge out to 700 metres, adds a quasi-experimental dimension that many contamination surveys lack.

The authors position the work as a baseline study for the Talcher coalfield, one of India’s major coal-producing regions and part of the industrial Talcher-Angul belt, where coal mining, thermal power and heavy industry coexist with agricultural land and growing populations. Baselines of this kind matter because they establish the reference point against which future change can be measured, and because they identify which elements, which pathways and which populations deserve closer attention. The study’s conclusions point toward two priorities for follow-up work: sustained environmental monitoring of the elements identified as most enriched, particularly tin, gallium and vanadium, and detailed source apportionment studies capable of quantitatively separating mining-derived inputs from the natural geological background. As demand for critical minerals grows and mining expands into new regions, understanding how even the less glamorous trace elements move through soils, and into the people who live on them, is becoming an increasingly urgent task for environmental science.

Subject of Research: Heavy and trace element contamination and human health risk assessment in soils around a tropical open-cast coal mine in Talcher, Odisha, India

Article Title: Integrating pollution indices and multivariate statistics to assess heavy and trace element contamination and human health risks in a tropical coal mining region

Article References: Singh, S., Raj, D., Kumari, P., & Maiti, S. K. (2026). Integrating pollution indices and multivariate statistics to assess heavy and trace element contamination and human health risks in a tropical coal mining region. Environmental Geochemistry and Health, 48(15), Article 605. https://doi.org/10.1007/s10653-026-03503-2

Image Credits: AI Generated

DOI: 10.1007/s10653-026-03503-2

Keywords: heavy and trace elements, coal mining, soil contamination, pollution indices, geo-accumulation index, multivariate statistics, principal component analysis, vanadium, gallium, human health risk assessment, Talcher coalfield, Odisha

Researchers from Sambalpur University, Terracon Ecotech, and the Government of Odisha set out to quantify litterfall production, standing litter biomass, decomposition rates, and nutrient fluxes in four sacred forests across the region: Andhari, Dedungri, Medha, and Papanga. Three of the sites are dominated by sal (Shorea robusta), while Papanga is dominated by Cleistanthus collinus. The forests lie within a tropical savanna climate zone, where annual rainfall ranges from 900 to 1,400 millimeters, almost all of it delivered during the June-to-September monsoon, and where pre-monsoon temperatures can exceed 45 degrees Celsius. That pronounced seasonality shapes nearly everything that happens on the forest floor.

Between January and December 2023, the team measured litter inputs monthly using one-square-meter collection pits established at least ten meters inside each forest boundary to avoid edge effects. The collected material was separated into leaves, twigs, branches, and miscellaneous fragments, then oven-dried and weighed. Annual litterfall ranged from 8.59 megagrams per hectare per year at Andhari to 11.32 megagrams per hectare per year at Medha, figures that sit comfortably within the global range for tropical forests and actually exceed values reported for several other tropical dry deciduous forests in India. Leaves dominated the mix, contributing between 80.65 and 83.35 percent of total litterfall, followed by twigs, branches, and other material.

The seasonal signal was unmistakable. Litter production peaked in March at all four sites, coinciding with the height of the dry season, when water stress triggers leaf senescence and abscission in deciduous trees. Statistical analysis confirmed that month-to-month variation within sites was highly significant, while differences among the four forests were not. In other words, climate and phenology, not site identity, are the primary engines driving litterfall in these ecosystems. Standing litter biomass followed the same rhythm, accumulating to a maximum in March and dwindling to a minimum during the rainy and post-rainy months of August through November, with annual means ranging from 2.39 megagrams per hectare at Andhari to 2.68 at Medha.

The speed at which that litter disappeared was the study’s most striking finding. Using the classic litterbag technique, the researchers placed 20-gram samples of mixed-species fresh leaf litter, collected during the February-March peak litterfall period, into 20-by-20-centimeter nylon bags with one-millimeter mesh and laid them on the forest floor in a completely randomized design. Six bags were retrieved from each site every month. Roughly 95 percent of the initial litter mass had vanished within six months at every site. The fitted decay constants ranged from 5.75 to 6.65 per year, corresponding to half-lives of just 38 to 44 days. Model-based extrapolation suggested that 99 percent decomposition would be achieved within 274 to 318 days, although the authors caution that these figures extend beyond the six-month observation window.

Litter turnover rates told a similar story. The ratio of annual litterfall to standing litter biomass, a standard index of how quickly organic matter cycles through the forest floor, ranged from 3.59 to 4.22 per year, equivalent to residence times of only 87 to 102 days. Those turnover rates are higher than values reported for tropical semi-deciduous, tropical dry evergreen, and tropical evergreen forests, indicating that these small sacred groves process organic matter unusually fast. The researchers attribute the rapid decay to favorable temperature and moisture conditions during the decomposition period, which stimulate microbial activity, possibly combined with relatively high litter nutrient quality.

That quality question was addressed through chemical analysis of the mixed-species litter. Initial concentrations of nitrogen, phosphorus, and potassium differed significantly among the four forests, with Papanga showing the highest nutrient levels and the most favorable stoichiometric profile. Correlation analyses revealed that initial nitrogen and phosphorus concentrations were significantly associated with decomposition rates, while carbon content was not. The authors note an important caveat: lignin and cellulose, structural compounds that strongly regulate decomposability, were not measured, so the full biochemical picture of litter quality in these forests remains incomplete.

Nutrient release during decomposition followed a clear hierarchy: potassium was lost fastest, followed by nitrogen, then phosphorus. Potassium, highly soluble and prone to leaching, declined rapidly throughout the experiment, with release reaching 98.72 to 99.20 percent across the sites. Nitrogen loss ranged from 92.26 to 95.08 percent, and phosphorus from 80.54 to 95.34 percent after six months. Papanga recorded the highest total nutrient loss at 96.57 percent. Meanwhile, nitrogen and phosphorus concentrations actually increased in the residual litter, a pattern the researchers attribute to microbial immobilization, in which decomposer organisms accumulate these nutrients in their own biomass before releasing them back to the soil. Carbon concentrations remained relatively stable at Andhari and Medha but declined at Dedungri and Papanga.

The study’s authors are candid about its limitations. There were no non-sacred control forests for direct comparison, so the work characterizes variation within sacred forests rather than testing the effects of sacred-forest protection itself. Repeated temporal observations were not analyzed with mixed-effects models, and the correlation results should be read as associations rather than causal claims. Still, the baseline data fill a genuine gap: while litterfall and nutrient cycling have been extensively documented in tropical evergreen forests and, to a lesser degree, in sacred groves elsewhere in India, comparable information for the dry deciduous sacred forests of Western Odisha had been essentially absent.

The implications extend beyond ecology into conservation policy. Sacred forests persist as biodiversity hotspots and providers of ecosystem services, including carbon sequestration, water regulation, and soil conservation, precisely because community restrictions limit timber extraction, grazing, and fuelwood collection. Yet pressures such as fuelwood harvesting, livestock grazing, and non-timber forest product extraction persist to varying degrees, with Andhari and Dedungri showing signs of moderate degradation while Medha and Papanga remain relatively well preserved. By demonstrating that these culturally protected patches sustain high litter production, rapid decomposition, and substantial nutrient turnover, the study provides quantitative evidence that sacred forests function as localized reservoirs of soil fertility in human-dominated landscapes. As land-use intensification continues across tropical India, the researchers argue, protecting these living laboratories of biocultural conservation may be one of the most cost-effective strategies for maintaining nutrient cycling and ecological resilience in dry deciduous landscapes.

Subject of Research: Litterfall production, decomposition rates, and nutrient cycling in tropical dry deciduous sacred forests of Western Odisha, India

Article Title: Litterfall dynamics and decomposition-driven nutrient cycling in sacred forests of Eastern India

Article References: Pradhan, A., Mansingh, A., Gopinath, J. S., & Ekka, N. J. (2026). Litterfall dynamics and decomposition-driven nutrient cycling in sacred forests of Eastern India. Discover Plants, 3(1), Article 412. https://doi.org/10.1007/s44372-026-00892-7

Image Credits: AI Generated

DOI: 10.1007/s44372-026-00892-7

Keywords: litterfall, decomposition, nutrient cycling, sacred forests, tropical dry deciduous forest, Odisha, Shorea robusta, litterbags, soil fertility, ecosystem functioning, biocultural conservation, leaf litter

The research, conducted by Tapas Ranjan Patra of the Department of Geography at Rajendra University in Balangir, Odisha, began with an unusually rigorous data-cleaning exercise. Groundwater-quality monitoring programs generate enormous multi-parameter datasets, but raw records are riddled with duplicates, inconsistent sampling locations and measurements that fail basic chemical plausibility checks. Starting from the Central Ground Water Board’s monitoring archive covering 2019 to 2023, the study applied geospatial deduplication to remove repeated observations at the same sites and charge-balance screening to discard samples whose major-ion concentrations did not balance electrically. The result was a curated dataset of 1,268 site-level samples, each carrying measurements of electrical conductivity, total dissolved solids, pH, hardness and the major cations and anions that define groundwater chemistry.

With the cleaned dataset in hand, the analysis turned to the central question of whether Odisha’s groundwater behaves as one continuous chemical spectrum or as a small number of distinct regimes. Using Gaussian mixture modeling, a statistical technique that identifies hidden subpopulations within multivariate data, alongside hierarchical cluster analysis and principal component analysis, the study identified two stable hydrochemical regimes separated along a dominant mineralization gradient. The stability of this two-regime structure was assessed with cluster-stability measures, including the adjusted Rand index, and the number of groups was selected using Bayesian and extended Bayesian information criteria rather than arbitrary thresholds. In other words, the split was not imposed by the analyst but emerged from the data itself and proved reproducible under repeated evaluation.

The two regimes differ in a chemically meaningful way. Regime 1 showed consistently higher electrical conductivity, total dissolved solids and major-ion concentrations than Regime 0, indicating a more heavily mineralized groundwater setting. Such mineralization typically reflects longer residence times of water in the subsurface, greater interaction with host rocks, and in some settings the influence of salinity-related processes such as those documented in coastal aquifers of the region. Regime 0, by contrast, represents a less mineralized, more dilute chemical environment. The study interpreted these contrasts through standard hydrochemical tools, including Gibbs diagrams and ion-ratio analysis, to characterize the processes governing each regime’s major-ion signature.

The most consequential finding concerns the two contaminants that matter most for drinking-water safety in the region: nitrate and fluoride. Both are regulated under Bureau of Indian Standards and World Health Organization guidelines because chronic exposure carries serious health consequences. Excessive nitrate, often linked to agricultural runoff, septic leakage and organic waste, is associated with methemoglobinemia in infants and other adverse outcomes, while elevated fluoride, typically geogenic in origin, causes dental and skeletal fluorosis. When the study cross-tabulated guideline exceedances against the two hydrochemical regimes, a clear pattern emerged. Nitrate exceedance rose from 4.45 percent of samples in Regime 0 to 13.81 percent in Regime 1, roughly a threefold increase, while fluoride exceedance climbed from 0.91 percent to 4.86 percent, more than a fivefold jump.

A critical concern in any observational study of this kind is that such contrasts might simply be artifacts of uneven sampling. If the more mineralized regime happened to be sampled more intensively in heavily contaminated districts, the apparent association could reflect geography rather than chemistry. To rule this out, the study employed district-adjusted statistical models, including generalized linear mixed models with district-level fixed effects, which compare samples within the same district and thereby absorb any district-wide confounding. These models confirmed that the elevated exceedance rates in Regime 1 persisted even after accounting for uneven district-level sampling, indicating that the hydrochemical regime itself carries genuine information about contamination risk.

The analysis went a step further by examining how the chemical parameters within each regime relate to one another. Using conditional-dependence network analysis, a method that maps the partial correlations among variables while controlling for all others, the study found that the higher-mineralization regime was more strongly organized by salinity-related hydrochemical structure. In Regime 1, the major ions moved together in a tightly connected network consistent with shared salinization processes, whereas the network structure in the dilute regime was weaker and differently arranged. This regime-specific organization suggests that the two settings are governed by distinct geochemical processes, not merely different points on a single continuum, and that contamination pathways may therefore differ between them.

The practical implications extend well beyond Odisha. Groundwater supplies the majority of rural drinking water in eastern India, and monitoring agencies face chronic constraints on laboratory capacity and field resources. The study’s regime-based framework offers a way to prioritize that effort: samples drawn from high-mineralization settings deserve more frequent nitrate and fluoride testing, while resources in low-mineralization zones can be allocated more sparingly. Because the underlying data are publicly available through the Central Ground Water Board portal, and the analytical approach relies on standard statistical tools, the framework could be replicated in other spatially heterogeneous groundwater systems across India and beyond, where monitoring networks similarly struggle to convert scattered measurements into actionable priorities.

The study also contributes to a broader scientific conversation about how large environmental datasets should be interpreted. Rather than treating each contaminant measurement in isolation, or collapsing entire aquifers into a single water-quality index, the regime-based approach preserves the natural structure of the data and uses that structure to sharpen surveillance. As the author notes, nitrate and fluoride screening burdens are concentrated in specific hydrochemical settings rather than being uniformly distributed across the monitoring network. Recognizing and mapping those settings transforms routine monitoring from a passive record-keeping exercise into a targeted risk-prioritization tool, one that could help public health authorities intervene before contaminated wells reach the families who depend on them.

Subject of Research: Hydrochemical regimes and nitrate–fluoride contamination surveillance in Odisha's groundwater

Article Title: Hydrochemical regimes and nitrate–fluoride surveillance in groundwater of Odisha, Eastern India

Article References: Patra, T. R. (2026). Hydrochemical regimes and nitrate–fluoride surveillance in groundwater of Odisha, Eastern India. Environmental Monitoring and Assessment, 198(10), Article 1064. https://doi.org/10.1007/s10661-026-15901-1

Image Credits: AI Generated

DOI: 10.1007/s10661-026-15901-1

Keywords: groundwater, hydrochemistry, nitrate, fluoride, Odisha, water quality monitoring, Gaussian mixture model, mineralization gradient, drinking-water safety, Central Ground Water Board, surveillance prioritization, eastern India