The research, published on 20 October 2026 in Frontiers in Sustainability, modeled waste moving backward through the healthcare system. The case study assumed that infectious waste generated at diagnostic centers would be transported to treatment plants within 24 hours, after which recyclable material could proceed to recovery facilities and non-recyclable or residual waste could go to final disposal. The model minimized two competing objectives: total system cost, including facilities, transport, processing, disposal, energy and recovery revenue, and environmental risk from transportation and treatment. Energy consumption entered the economic objective through treatment costs rather than forming a separate third environmental objective.

The proposed framework was designed for infectious waste arising at laboratories and diagnostic centers, an area the authors identified as comparatively underexplored in earlier medical-waste network research. Its four principal network components were waste-generating sources, processing facilities, recovery facilities and a centralized disposal site. Flow constraints connected each stage, facility-opening variables represented operating decisions, binary variables constrained treatment technologies to waste categories they could handle, and capacity limits constrained processing, recovery and disposal. Such structure turned the general objective into a testable mathematical design problem for the selected Vellore data rather than a direct evaluation of an operating waste system.

Uncertainty was represented with trapezoidal fuzzy numbers for waste generation and transportation costs. Each fuzzy parameter was described by lower and upper bounds and a central range of more plausible values. A necessity-based transformation converted the fuzzy constraints into deterministic equivalents, after which optimality and feasibility robustness terms penalized poor objective performance and constraint violations under unfavorable conditions. The researchers used a fuzzy confidence value of 0.5 and a robustness parameter of 0.1 for the main experiments, a combination they selected as balanced rather than maximally conservative. Other model parameters were treated as deterministic, so the modeled uncertainty did not encompass every potential source of operational variation.

Solutions were generated with the Non-dominated Sorting Genetic Algorithm II, or NSGA-II, an evolutionary algorithm that searches for trade-offs among conflicting objectives. It combines selection, crossover and mutation with elitism, fast non-dominated sorting and a crowding-distance mechanism intended to preserve diversity along the Pareto front. The implementation ran in Python through the pymoo package and Google Colab, using a population of 500, simulated binary crossover with probability 0.95, polynomial mutation with probability 0.20 and 1,000 generations. Each optimization run required roughly 34 to 36 minutes, while a scalability test found that average runtime rose from 9.92 minutes for the smallest benchmark network to 48.88 minutes for the largest.

Because NSGA-II returns a set of non-dominated alternatives rather than one definitive network, the researchers applied the Technique for Order Preference by Similarity to Ideal Solution, or TOPSIS. This method normalized the cost and risk values, compared every Pareto solution with the ideal and least desirable alternatives and ranked them by their relative closeness. The final reported solution averaged the highest-ranked TOPSIS results across repeated runs, reducing the influence of the algorithm’s random initialization and stochastic search. Equal weights for cost and risk produced a compromise rather than privileging either objective. Tests using cost-risk weight combinations of 0.3 and 0.7, 0.5 and 0.5, and 0.7 and 0.3 produced only small changes in the selected cost and risk values.

The Vellore network covered 60 administrative wards. One diagnostic center was assigned to approximately every four wards using a P-median model, while the treatment network included one existing facility and two candidates selected through the same location method. Road-network shortest-path distances were calculated in ArcGIS. Four treatment technologies—autoclaving, incineration, microwave treatment and chemical disinfection—were matched to five waste categories according to applicability and regulatory constraints. Two waste streams were assumed to be recyclable, and 20% of material sent to recycling was modeled as residue requiring disposal. The treatment facilities had respective capacities of 3,600, 3,240 and 2,880 kg for autoclaving, while the two recovery facilities had capacities of 3,500 and 2,800 kg and the disposal facility had a total capacity of 12,000 kg.

Several inputs came from operating information in Vellore, including treatment capacities and distances, while missing cost, operational and exposure parameters were represented by synthetic values chosen to resemble realistic conditions. Ward population data came from the 2011 Census of India. Route exposure estimates ranged from 700 to 2,800 people, and facility exposure estimates ranged from 1,400 to 2,500 people. These assumptions mean the numerical results should be interpreted as a case-study model rather than a direct measurement of contamination incidents, clinical outcomes or carbon emissions. The authors stated that detailed route-level exposure and facility-specific environmental risk data were unavailable, and they did not conduct a field implementation of the optimized network.

Across ten independent comparisons, NSGA-II produced the strongest overall Pareto-front metrics among the three tested algorithms. Its hypervolume was 0.0323, compared with 0.0318 for NSGA-III and 0.0283 for MOEA/D; its inverted generational distance was 0.0105, lower than NSGA-III’s 0.0247 and MOEA/D’s 0.0376. NSGA-II also had the lowest spacing value, 0.0137, and a mean TOPSIS closeness coefficient of 0.9647, the highest of the three. The authors attributed NSGA-III’s relative weakness to the test problem having only two objectives, whereas its reference-point diversity mechanism was designed especially for many-objective cases. MOEA/D attained the greatest individual closeness coefficient in one run, but the authors reported greater fluctuation across the ten runs.

Sensitivity analysis showed a clear but limited robustness trade-off. Raising the robustness parameter from 0 to 1 increased average cost by about 16.63%, from 11,023,496 to 12,855,670, while average risk fell by only about 0.51%, from 273,680,193 to 272,290,679. By contrast, changing the recycling-residue fraction from 0 to 0.5 kept average cost within roughly 0.2% and produced only small changes in risk. Joint variation in fuzzy confidence and robustness parameters also left solution quality broadly stable, although cost generally rose as robustness increased. The selected main setting of fuzzy confidence 0.5 and robustness 0.1 was retained as a compromise that avoided an overly conservative solution.

The authors interpreted the optimized architecture as a way to shorten modeled routes, move infectious waste promptly to compatible treatment, recover recyclable material and reduce the quantity sent to final disposal. These are model outputs, not evidence that the framework reduced disease transmission or greenhouse-gas emissions in Vellore. The study did not test the network in operation, and its six-day horizon, synthetic inputs, assumed energy and transport parameters, and uncertainty limited to selected variables constrain generalization. Future work could add fluctuating energy prices, treatment capacities, vehicle fleets, recovery revenues, stochastic or robust multi-period models and additional waste categories. Within those limitations, the analysis provides a transparent computational framework for comparing cost and contamination risk when medical-waste flows and operating conditions are uncertain.

Subject of Research: Case-study optimization of a medical-waste reverse supply chain for cost, environmental risk, energy use and uncertainty

Article Title: Integrated cost–risk optimization of sustainable reverse supply chain networks for medical waste using artificial intelligence with energy consumption and uncertainty considerations

Article References: S., R., & K., K. (2026). Integrated cost–risk optimization of sustainable reverse supply chain networks for medical waste using artificial intelligence with energy consumption and uncertainty considerations. Frontiers in Sustainability, 7, Article 1927379. https://doi.org/10.3389/frsus.2026.1927379

Image Credits: AI Generated

DOI: 10.3389/frsus.2026.1927379

Keywords: medical waste, reverse supply chain, NSGA-II, TOPSIS, fuzzy robust optimization, environmental risk, energy consumption, Vellore, diagnostic centers, network design, Integrated, cost

The centerpiece of the analysis is the prevalence of undernourishment, the standard indicator used to track progress toward SDG 2. Rather than treating all countries as a single homogeneous sample, the study first builds composite indices that capture four risk dimensions: institutional risk, demographic risk, environmental risk, and economic risk. These indices aggregate multiple underlying variables into coherent measures, allowing the researcher to test whether broad systemic pressures—not just isolated factors such as conflict or drought—are statistically associated with hunger. This multidimensional framing reflects a growing consensus in the food security literature that undernourishment is rarely the product of a single cause. Instead, it emerges from the interaction of fragile governance, population pressure, climate exposure, and volatile economic conditions, each capable of amplifying the others.

The methodological innovation lies in a two-stage empirical strategy. In the first stage, a K-means clustering algorithm—an unsupervised machine learning technique that partitions observations into groups based on similarity—classifies the 120 countries into three food insecurity profiles: high, moderate, and low. The high-insecurity cluster includes countries such as Afghanistan, the Democratic Republic of the Congo, Kenya, Liberia, Madagascar, Sierra Leone, Uganda, and Zambia. The moderate cluster contains a broad group including Bangladesh, Ethiopia, India, Nigeria, and Pakistan, among others. The low-insecurity cluster spans most of Europe, North America, parts of Asia and Latin America, and several African countries such as Ghana, Senegal, and South Africa. This data-driven grouping avoids imposing arbitrary regional or income-based categories and lets the underlying risk structure define the comparison groups.

In the second stage, the study applies pooled quantile regression within each cluster. Unlike ordinary least squares regression, which estimates the effect of a variable on the mean of the outcome, quantile regression estimates effects at different points of the outcome distribution—the 10th, 50th, and 90th percentiles, for example. This distinction matters enormously for food insecurity research. A country sitting at the 90th percentile of undernourishment is in a fundamentally different situation from one at the median, and the factors pushing it deeper into crisis may differ from those that matter for countries closer to the average. Mean-based studies, the paper argues, obscure exactly this kind of heterogeneity, potentially leading policymakers to design uniform interventions that fail where they are needed most.

The headline finding is stark: environmental risk is the most consistent factor associated with undernourishment across all three clusters. In the quantile estimates, the environmental composite index shows positive and statistically significant coefficients across most quantiles in the moderate and low-insecurity groups, with particularly strong associations in the low-risk cluster at the median and upper quantiles. When the composite is decomposed into its components, carbon dioxide emissions show positive and significant associations with undernourishment across nearly all quantiles in the high and moderate clusters, and across the upper half of the distribution in the low-insecurity cluster, where the coefficient at the 90th percentile reaches 1.20. Agricultural land use shows negative associations in the moderate cluster but positive ones in the low cluster, suggesting that the environmental dimension operates through different channels at different stages of development.

Demographic risk tells a more complicated, and arguably more surprising, story. In countries with high and moderate food insecurity, the demographic composite index is positively associated with undernourishment at the lower quantiles, indicating that population pressure adds to hunger risk where food systems are already fragile. But the sign flips at the upper quantiles and in the more secure cluster, where demographic risk is negatively associated with food insecurity. Life expectancy, one of the demographic components, shows negative and significant coefficients at the upper quantiles in both the high and moderate clusters, consistent with the idea that longer-lived, healthier populations are better positioned to secure adequate nutrition. The lesson is that demographic dynamics are not inherently good or bad for food security; their effect depends on the vulnerability context in which they unfold.

Institutional quality, captured by the institutional composite index, emerges as a protective factor—but only in certain settings. In the moderate and low-insecurity clusters, the index shows negative and significant coefficients at the median and upper quantiles, with the strongest effects in the low-risk cluster, where the coefficient reaches −0.63 at the 90th percentile. In other words, better institutions are most strongly associated with lower undernourishment precisely in the countries that are already relatively secure, while in the most vulnerable group the institutional variable shows no significant association at any quantile. This asymmetry carries a sobering implication: governance improvements alone may not deliver measurable hunger reductions in the most fragile states, where environmental and demographic pressures may overwhelm institutional gains, or where institutions may be too weak for the index to register meaningful variation.

Economic risk, by contrast, is the least consistent of the four dimensions. Across most clusters and quantiles, the economic composite index fails to reach statistical significance. The exception is telling: in the most vulnerable group, at the 90th percentile, the index shows a negative and significant coefficient. This means that economic risk matters only at the extreme upper tail of undernourishment in the hardest-hit countries—exactly the situations of acute crisis that mean-based analyses tend to smooth away. The finding underscores the paper’s central methodological claim that quantile-specific, asymmetric associations are invisible to average-effect estimation, and that policies calibrated to the average country may miss the dynamics driving the worst outcomes.

What does this mean for the pursuit of SDG 2? The study’s answer is that interventions should be severity-specific rather than uniform. For countries in the high-insecurity cluster, environmental pressures and demographic stress dominate the picture, suggesting that climate adaptation, emissions mitigation, and investments in health and human capital are inseparable from food policy. For moderate-risk countries, strengthening institutions appears to be a viable pathway to reducing hunger, particularly for populations at the upper end of the undernourishment distribution. For relatively secure countries, the strong environmental signal—especially the carbon emissions component—suggests that even wealthy nations’ food security trajectories are tied to their environmental footprints. The clustering approach itself offers a practical template: by grouping countries according to their risk profiles rather than their geography or income alone, policymakers can identify peer countries facing similar systemic pressures and adapt strategies that have worked elsewhere in the same cluster.

The research also carries broader lessons for how the development community measures progress. Global hunger statistics are typically reported as averages, and international targets are set against those averages. But if the drivers of undernourishment differ systematically across the outcome distribution—as this study demonstrates—then average-based monitoring can mask deterioration among the most vulnerable even as headline numbers improve. The combination of unsupervised clustering and pooled quantile regression offers a replicable framework for other SDG indicators, from poverty to health access, wherever heterogeneity across vulnerability contexts is suspected. As climate change intensifies and geopolitical instability persists, the study’s core message grows more urgent: ending hunger will require understanding not just which risks matter, but for whom, and at what point of the crisis spectrum they bite hardest.

Subject of Research: Heterogeneous associations between multidimensional systemic risks and food insecurity across 120 countries

Article Title: Assessing food insecurity under global risks using a clustered pooled quantile framework for sustainable development goal 2

Article References: Essadik, E. (2026). Assessing food insecurity under global risks using a clustered pooled quantile framework for sustainable development goal 2. Discover Sustainability. https://doi.org/10.1007/s43621-026-04811-4

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04811-4

Keywords: food insecurity, Sustainable Development Goal 2, undernourishment, systemic risk, quantile regression, K-means clustering, environmental risk, institutional quality, demographic risk, economic risk, food security policy, Discover Sustainability

The scale of the problem begins with the sheer abundance of plastic in landfill systems. The dominant polymers recovered from landfill waste and leachate are polyethylene, polypropylene, and polystyrene, the same materials that make up the bulk of consumer packaging. Crucially, the review notes that microplastic abundance and fragmentation increase with depth in landfill bodies, meaning that the deeper and older the waste, the more broken-down plastic particles it contains. Mechanical stress, chemical attack, and microbial activity all conspire to shred larger items into ever smaller fragments. As particles shrink, their surface area relative to their volume grows dramatically, and weathering introduces oxygen-containing functional groups onto their surfaces, making them more hydrophilic and more chemically reactive than the pristine plastic that entered the site.

This aging process is not a footnote; it is the engine of the problem. Fresh plastic is relatively hostile to microbial colonization, but weathered plastic is a different story. The oxidized surfaces and large specific surface areas of aged microplastics provide ideal attachment points for biofilms, dense microbial communities that researchers have dubbed the plastisphere. Within landfill leachate, a nutrient-rich and microbially crowded fluid, these biofilms can harbor not only ordinary bacteria but also pathogens and bacteria carrying antibiotic resistance genes. Studies cited in the review found that polystyrene microplastics in landfill leachate selectively enrich certain resistance genes and pathogens, and that the long-term aging of particles enhances their role as vectors for resistance genes, effectively turning plastic debris into microscopic ferries for some of medicine’s most feared genetic cargo.

The mechanism by which microplastics promote the spread of resistance centers on horizontal gene transfer, the process by which bacteria swap genetic material across species boundaries rather than passing it only to their offspring. Microplastic-associated biofilms pack diverse microbes into intimate contact on a shared surface, multiplying the opportunities for conjugation, the bacterial equivalent of mating through which plasmids carrying resistance genes move between cells. Under some experimental conditions, microplastics also promote horizontal transfer by triggering oxidative stress responses and altering bacterial membrane permeability, changes that make cells more willing to take up foreign DNA. Size matters too: research on micro- and nanoplastics shows that smaller particles can enhance the conjugative transfer of resistance genes in a size-dependent manner, with nanoplastics exerting particularly strong effects. In leachate treatment systems, the presence of microplastics has been shown to alter the migration trajectory of resistance genes, redirecting their spread through the microbial community.

Landfills are not passive containers for this activity; they are active generators and distributors of it. Leachate, the liquid that percolates through the waste mass, carries microplastics out of the landfill and into surrounding soil and water systems. Studies from sites in Iran, Indonesia, Thailand, Hong Kong, and Japan have documented microplastics in leachate and underlying soils, and informal, unlined dumps contribute plastic pollution to their immediate surroundings even more readily. Airborne transport adds another route: lichens sampled near an Italian landfill accumulated airborne microplastics, demonstrating that particles can escape the site entirely. Meanwhile, antibiotic resistance genes have been detected migrating from landfill leachate into groundwater, and bioaerosols rising from landfill surfaces carry both pathogens and resistance genes into the air around these facilities. The landfill, in short, functions as a hub from which composite pollution radiates outward through water, soil, and air.

The genetic traffic inside landfills is shaped by more than plastic alone. The review emphasizes a significant association between antibiotic resistance genes and heavy metals, which are abundant in mixed municipal waste. Heavy metals exert a selective pressure that favors microbes carrying resistance genes, and sub-inhibitory concentrations of metals have been shown to facilitate the horizontal transfer of plasmid-mediated resistance genes in water environments. Metal resistance genes can also modulate the expression of antibiotic resistance genes, and the coexistence of microplastics, resistance genes, and heavy metals triggers co-selection pressure that amplifies the composite pollution problem. Landfill age, seasonal variation, and pH all influence resistance gene abundance, with studies showing that genes are enriched as refuse ages, and that seasonal dynamics shift the microbial communities and gene profiles in leachate. Even non-antibiotic pharmaceuticals and personal care products appear capable of influencing resistance gene levels in landfill refuse, adding further layers to an already tangled web of selective forces.

Where does this pollution ultimately go? The review traces potential pathways through soil-water systems and into food webs, where composite pollutants may accumulate in organisms. Trophic transfer of microplastics has been demonstrated in estuarine food chain models, and microplastics are known to adsorb and transport other toxic pollutants, including heavy metals and additive chemicals such as phthalates and bisphenol A, which have been detected alongside microplastics in landfill leachate. Yet the authors are careful about the human health endpoint: while microplastics have been detected in human placenta, urine, and kidney tissue in separate studies, evidence directly linking landfill-derived pollutants to human tissue exposure remains limited. That caveat is important, but it is a statement about the difficulty of tracing exposure pathways, not about the absence of risk. The genes and particles are demonstrably mobile, and the reservoir they occupy is enormous.

What makes the situation particularly difficult to manage is its concealment and complexity. Composite pollution involving microplastics, resistance genes, and heavy metals resists conventional remediation, which is typically designed to handle a single contaminant class. The review also identifies systemic weaknesses in the research itself: a lack of detailed policies, standardized methodologies, and consistent research strategies currently hinders cross-study comparisons, making it hard to build a coherent global picture of the hazard. Detection methods for microplastics in complex matrices like leachate and mineralized refuse remain technically demanding, and the abundance of plastic continues to climb, with global production figures and OECD projections suggesting that plastic pollution will worsen substantially by 2040 without policy intervention. Landfills, as long-term repositories of that plastic, will keep generating microplastics and hosting microbial exchange for decades after closure.

The authors frame their synthesis as a foundation for systematic management and future risk warning, and the implications are clear. Landfills should no longer be viewed merely as engineered burial sites but as active bioreactors where plastic weathering, biofilm formation, horizontal gene transfer, and metal co-selection converge. Monitoring programs that track only conventional contaminants will miss the composite threat entirely. Effective responses will likely require coordinated measures across the plastic lifecycle, from reducing the volume of plastic entering waste streams to improving leachate treatment, controlling informal dumping, and developing standardized methods for detecting microplastics and resistance genes in environmental samples. As the review makes plain, the interaction between microplastics and antibiotic resistance genes in landfills is not a hypothetical concern for the distant future. It is a present-day, self-reinforcing process unfolding beneath our feet, and understanding its mechanisms is the first step toward containing a form of pollution that hides in plain sight.

Subject of Research: Interaction between microplastics and antibiotic resistance genes in landfill environments

Article Title: Microplastics and antibiotic resistance genes in landfills: interaction mechanisms, environmental risks, and composite pollution implications

Article References: Sun, P., Wang, X., Qiu, Y., Liu, H., Long, Y., & Fang, C. (2026). Microplastics and antibiotic resistance genes in landfills: interaction mechanisms, environmental risks, and composite pollution implications. Environmental Monitoring and Assessment, 198(9), Article 1017. https://doi.org/10.1007/s10661-026-15844-7

Image Credits: AI Generated

DOI: 10.1007/s10661-026-15844-7

Keywords: microplastics, antibiotic resistance genes, landfills, leachate, horizontal gene transfer, plastisphere, heavy metals, composite pollution, biofilms, environmental risk, plastic aging, groundwater contamination

The research, published in Frontiers in Geochemistry, analyzed 71 samples spanning nearly every environmental compartment of the Guanajuato River Basin: bedrock, mine tailings, soils, stream sediments, ancient overbank sediments, surface water, and groundwater. The goal was ambitious. Rather than simply measuring how much arsenic or lead was present, the team wanted to determine where those elements came from, whether from the hydrothermal mineralization that formed the district’s famous silver veins, or from the centuries of mining that exploited them. This distinction matters enormously, because elevated concentrations of toxic elements alone cannot discriminate between natural geological enrichment and human contamination, particularly in terrain where both share a common origin.

The study area is geochemically extraordinary. The Guanajuato Mining District hosts low-sulfidation epithermal silver-gold vein systems controlled by the Veta Madre fault zone, a structure that geologists have described as a fossil geothermal system. Hydrothermal activity generated mineral assemblages enriched in silver, gold, lead, zinc, copper, arsenic, antimony, and mercury. In other words, the basin’s rocks were naturally loaded with potentially toxic elements long before the first shovel struck ore. Historical records indicate that Guanajuato was a premier silver-producing region during the eighteenth century, generating substantial volumes of mining waste and profoundly altering the basin’s fluvial architecture. The legacy tailings were derived directly from the same hydrothermal mineralization that naturally enriches the bedrock, creating substantial overlap between natural and mining-related geochemical signatures.

To disentangle these overlapping signals, the researchers deployed a two-pronged analytical strategy. First, they applied multivariate statistics, including Spearman rank correlation, principal component analysis, and hierarchical cluster analysis, to identify elemental associations across the solid and aqueous matrices. Second, they trained an exploratory Random Forest classifier, a machine learning algorithm that builds hundreds of decision trees, using bedrock samples as a geogenic reference class and mine tailings samples as a mining-related reference class. The model achieved an out-of-bag error of 0.12, meaning it correctly classified about 88 percent of training samples it had not seen during model fitting. The researchers then applied the trained model to soils, stream sediments, and overbank sediments to estimate their relative geochemical affinity to each reference class.

The multivariate statistics revealed two principal geochemical associations in the solid samples. The first, comprising aluminum, iron, chromium, vanadium, and nickel, showed strong positive correlations and was interpreted as geogenic, reflecting the natural composition of the basin’s mafic-ultramafic and volcano-sedimentary lithologies. The second association, involving sulfur, arsenic, cadmium, lead, copper, and zinc, was interpreted as mineralized and mining-related. The Random Forest analysis identified sulfur, cadmium, and arsenic as the leading predictors for discriminating between the two reference classes. At the matrix level, median affinity scores for overbank sediments and soils shifted toward the geogenic reference class at 61 and 63 percent respectively, while stream sediments occupied an intermediate position with nearly equal median scores of 49 percent geogenic and 51 percent mining-related affinity.

This intermediate position of stream sediments is geochemically telling. It suggests that modern stream sediments integrate both natural and mining-derived materials, reflecting the long-term influence of weathering, erosion, and sediment transport processes operating throughout the basin. Historical records indicate that large volumes of finely ground wastes were deposited in tailings piles or discharged directly into drainage channels during ore processing. Because these materials are more susceptible to weathering and erosion than the original bedrock, they serve as an efficient mechanism for the downstream redistribution of metal(loid)s and contribute to the persistence of mining-related signatures in modern sediments. Spatially, stream sediment samples located near the historical mining district generally showed lower median geogenic affinity scores and coincided with higher values of a standardized sulfur-plus-cadmium-plus-arsenic index, while elevated index values also occurred at some downstream locations, indicating that this geochemical signature was not restricted to the immediate vicinity of historical tailings deposits.

Perhaps the most striking finding concerns what the preindustrial overbank sediments reveal. These samples were obtained from a roughly two-meter-high floodplain terrace previously dated to pre-industrial depositional periods ranging from 2450 to 9410 years before present. Their concentrations of arsenic, cadmium, copper, and zinc demonstrate that weathering of naturally mineralized lithologies supplied trace elements to the fluvial system long before the onset of intensive mining. The authors conclude that historical mining has amplified and redistributed preexisting hydrothermal geochemical signatures rather than introducing entirely new elemental assemblages. This is a crucial insight for environmental management in naturally mineralized basins worldwide, where elevated elemental concentrations may be misclassified as contamination when they actually reflect geological inheritance.

The water chemistry told a different story. Bicarbonate-type waters dominate the basin, but groundwater is enriched in sodium, silicon, fluoride, arsenic, and zinc, whereas surface waters exhibit higher overall mineralization with calcium-magnesium-sulfate signatures. Most dissolved metal(loid) concentrations remained below the analytical limit of quantification in both surface and groundwater, a contrast the researchers attribute to the carbonate-rich composition of Guanajuato mine tailings, which buffers acidity and limits acid mine drainage generation, favoring metal(loid) retention through interactions with iron-manganese oxyhydroxides and carbonate phases. Arsenic represents an important exception because its mobility may increase under circumneutral to alkaline conditions through desorption from mineral surfaces, making it one of the few elements occurring at measurable dissolved concentrations while also showing enrichment in solid matrices.

The risk assessment revealed spatially heterogeneous environmental concerns. Ecological risk was highest in mine tailings, driven primarily by elevated arsenic, cadmium, and copper, with two specific tailings deposits classified as moderate ecological risk while the remaining deposits were classified as low risk. Water quality and human health risks were restricted to localized groundwater and downstream surface water sites. Among surface waters, particularly high pollution index values coincided with elevated manganese concentrations, whereas high index values in groundwater coincided with arsenic concentrations exceeding drinking-water guideline values. For groundwater samples with quantified arsenic, carcinogenic risk estimates exceeded the upper acceptable level of 1.0E-04, with the highest values observed in two specific production wells. The authors emphasize that these estimates indicate potential carcinogenic concern under the evaluated long-term exposure scenario but require careful interpretation given the high proportion of arsenic measurements below the quantification limit.

The researchers are transparent about the limitations of their approach. The Random Forest model was trained on a limited dataset of only 12 lithological and 13 mine tailings samples, making the findings exploratory rather than definitive. Variable importance measures based on mean decrease in impurity may be biased when predictors are highly correlated, and the Random Forest is primarily a predictive classification algorithm rather than a mechanistic source-apportionment model. Water sampling was restricted to the dry season, so the hydrochemical patterns represent low-flow conditions and may not capture seasonal variations in metal(loid) mobility. Nevertheless, the integrated evaluation of multiple environmental matrices provides a screening framework for interpreting mining-related impacts in naturally mineralized basins, identifying priority areas for continued monitoring, particularly specific groundwater sites and tailings deposits with elevated ecological risk. For a basin shaped by five centuries of mining, the message is clear: the past is not buried, it is flowing downstream, one sediment particle at a time.

Subject of Research: Geochemical connectivity among mine tailings, soils, sediments, and waters in a historical mining basin integrating multivariate statistics and exploratory random forest modeling

Article Title: Geochemical connectivity among mine tailings, soils, sediments, and waters in a historical mining basin integrating multivariate statistics and exploratory random forest modeling

Article References: Rueda-Garzon, L. F., Carrillo-Chávez, A., Miranda-Avilés, R., Calvo-Ramos, D., Morales-Martínez, J. L., Muñoz-Torres, C., & García-Martínez, R. (2026). Geochemical connectivity among mine tailings, soils, sediments, and waters in a historical mining basin integrating multivariate statistics and exploratory random forest modeling. Frontiers in Geochemistry, 4, Article 1919883. https://doi.org/10.3389/fgeoc.2026.1919883

Image Credits: AI Generated

DOI: 10.3389/fgeoc.2026.1919883

Keywords: geochemistry, mining, machine learning, arsenic, Guanajuato, random forest, environmental risk, source apportionment, Geochemical, connectivity, mine, tailings

The timing of this warning could hardly be more significant. Wastewater-based surveillance earned its reputation during the COVID-19 pandemic, when measurements of SARS-CoV-2 RNA in sewage proved to be a leading indicator of clinical case waves across cities and continents. That success spawned an entire discipline, and models such as the wastewater-based epidemic model developed for SARS-CoV-2 in three Canadian cities demonstrated that, under the right conditions, gene copy concentrations in influent sewage could be translated into estimates of infected individuals in a catchment. The European Union has since embedded wastewater surveillance into law through the recast Urban Wastewater Treatment Directive of 2024, and the European Centre for Disease Prevention and Control has published a framework for integrating wastewater data into infectious disease surveillance at the EU level. Antimicrobial resistance is now a priority target of these systems.

Yet the Cyprus-based authors argue that the epidemiological machinery that made viral surveillance interpretable does not yet exist for resistance. For viruses, researchers have invested heavily in quantifying shedding dynamics: how many genome copies an infected person excretes per gram of faeces, how those rates change over the course of infection, and how they differ between variants. Longitudinal studies of SARS-CoV-2, pepper mild mottle virus and crAssphage shedding, together with time-varying shedding models revisiting the 2013 poliovirus outbreak in Israel, have provided the conversion factors that turn a wastewater concentration into an approximate count of infected people. For antimicrobial resistance genes and resistant bacteria, no equivalent body of calibration data exists. The authors point out that faecal carriage of extended-spectrum beta-lactamase-producing Enterobacteriaceae in the general population, the duration of colonisation after international travel, and the effects of antibiotic exposure on the gut resistome are all documented in clinical studies, but these threads have never been woven into transfer functions that connect a gene abundance measured in sewage to a prevalence of carriage in the community above.

The technical reasons for this gap are formidable. A wastewater concentration is the product of a long and noisy chain of processes. It begins with the heterogeneous shedding of resistance determinants by carriers, which depends on the species of bacterium, the type of gene, whether the gene is carried on a chromosome or a plasmid, and whether it resides inside intact cells, in faecal particles, or as free extracellular DNA. It continues with in-sewer decay and partitioning, processes that have been characterised for pathogens such as Campylobacter jejuni and Campylobacter coli but that differ for every gene and microbe of interest. Hydraulic conditions, temperature, sewer biofilms and sediment resuspension all modulate what arrives at the sampling point. Sampling itself, whether flow-weighted composite or grab, introduces further uncertainty. By the time a sample reaches the laboratory, the signal is a composite whose relationship to the original biological state in the population is mediated by dozens of parameters, most of which are unknown for antimicrobial resistance.

Measurement technology compounds the problem. Quantitative metagenomics, which promises absolute quantification of resistance genes across thousands of loci simultaneously, is advancing quickly, and recent work has begun to establish detection limits for environmental monitoring of antibiotic resistance and for viruses. But the Cyprus team emphasises that analytical quantification is not the same as epidemiological calibration. A measurement can be perfectly reproducible and still be uninterpretable if nobody knows what concentration corresponds to what burden of carriage, what change in concentration corresponds to what change in incidence, or what effluent concentration corresponds to what environmental exposure risk. The authors describe this as quantifying variation without resolving biological states, a phrase that captures the epistemological core of their argument: surveillance data are being collected and compared, but the mapping from data to meaning remains undefined.

The downstream half of the problem, the effluent side, is equally under-calibrated. Wastewater treatment plants are known to substantially reduce the load of resistance genes and resistant bacteria leaving a catchment, with studies showing that extended-spectrum beta-lactamase and carbapenemase genes are substantially and sequentially reduced during conveyance and treatment of urban sewage. But removal efficiency varies enormously with treatment technology, capacity overloading, and the fate of intracellular versus extracellular DNA during processes such as ultrafiltration, ultraviolet disinfection and chlorination. Paradoxically, some disinfection steps may promote rather than reduce resistance spread: chlorine disinfection has been shown to facilitate natural transformation of bacteria through reactive oxygen species-mediated oxidative stress, and disinfection byproducts can increase transformation rates of environmental DNA in model organisms such as Acinetobacter baylyi. Without calibrated models of these processes, an effluent measurement cannot be translated into a statement about the resistance burden delivered to a receiving river, coastal water or irrigation system.

That environmental endpoint matters because the receiving environments are not passive sinks. Research along the wastewater-river continuum has documented the dissemination and persistence of antimicrobial resistance far downstream of discharge points. Treated wastewater irrigation has been shown to alter the resistance profiles of agricultural soils, subsoil pore-water and rhizospheres, while biofilms in reclaimed water distribution systems act as hot spots of horizontal gene transfer. Heavy metals such as copper accelerate the conjugative transfer of resistance genes in freshwater microcosms, and even herbicide selection can promote antibiotic resistance in soil microbiomes. Sludge and compost application to land introduces further pathways. Each of these environmental compartments hosts its own ecology of gene exchange, shaped by pH, soil type, moisture and microbial community composition. The authors argue that a calibrated surveillance framework must therefore serve two distinct interpretive goals simultaneously: translating influent signals into population-level epidemiological burden, and translating effluent signals into quantified risk of environmental introduction and subsequent exposure of humans, animals and crops.

The Perspective, illustrated with a conceptual overview of the calibration gap and a proposed calibration solution for interpreting epidemiological data and environmental introduction risk, does not merely diagnose the problem; it sketches what a solution would require. Drawing on the authors’ experience validating the national-scale SARS-CoV-2 surveillance system in Cyprus, and on emerging methods such as metagenomic absolute quantification with cellular internal standards and microbiome-based microbial risk assessment across multiple environments, they call for systematic studies that establish the missing transfer functions. These include quantifying shedding of resistance determinants by carriers in the community, characterising in-sewer and treatment-plant fate for priority genes and bacteria, and linking measured effluent concentrations to quantified exposure in receiving environments through models that account for dilution, decay, regrowth and gene transfer. Global comparative work, such as the exploration of human faecal and sewage resistomes as a function of socio-economic status, and metagenomic source attribution of resistance in wastewater, provides the kind of comparative baseline that calibration will ultimately require.

The stakes of getting this right are considerable. Antimicrobial resistance is one of the leading global threats to public health, and wastewater offers something clinical surveillance cannot: a continuous, catchment-wide, pathogen-agnostic signal that does not depend on patients seeking care or on laboratories reporting results. Wastewater surveillance has already revealed potential underreporting of disease cases in the Detroit area for Salmonella enterica, Campylobacter jejuni and norovirus, and has detected early introductions of the emerging fungal pathogen Candida auris in Utah. Small-scale wastewater-based epidemiology studies of antibiotic resistance are proliferating worldwide. But the Cyprus authors warn that without calibration, these signals risk being over-interpreted, with policy decisions made on the basis of numbers whose epidemiological and environmental meaning is unknown. Conversely, a properly calibrated system could convert routine sewage measurements into actionable estimates of community carriage prevalence and quantified environmental release, transforming wastewater plants from passive sentinels into genuine early-warning infrastructure.

The message of the Perspective is ultimately one of disciplined optimism. The raw ingredients for calibration already exist, scattered across clinical cohort studies of gut colonisation, environmental microbiology of sewers and treatment plants, and risk assessment frameworks developed for water reuse under EU Regulation 2020/741. What is missing is the deliberate, coordinated effort to assemble these ingredients into validated models, much as the viral surveillance community did in the years after 2020. The authors, writing with support from the SPORE-MED project under the PRIMA Programme and the EU-WISH joint action on integrated wastewater surveillance for public health, frame calibration not as an academic luxury but as the precondition for the next phase of the field. As regulators across Europe and beyond write antimicrobial resistance monitoring into wastewater law, the question they pose is blunt: a signal is only as valuable as the model that gives it meaning, and for antimicrobial resistance, that model has yet to be built.

Subject of Research: Calibration of wastewater-based antimicrobial resistance surveillance signals for epidemiological and environmental risk interpretation

Article Title: Calibrating antimicrobial resistance influent and effluent signals in wastewater systems

Article References: Naziri, Α., Chatzimichail, S., Karaolia, P., & Fatta-Kassinos, D. (2026). Calibrating antimicrobial resistance influent and effluent signals in wastewater systems. Nature Water. https://doi.org/10.1038/s44221-026-00701-1

Image Credits: AI Generated

DOI: 10.1038/s44221-026-00701-1

Keywords: antimicrobial resistance, wastewater surveillance, wastewater-based epidemiology, resistance genes, sewage, wastewater treatment plants, environmental risk, public health, metagenomics, horizontal gene transfer, effluent, Nature Water

Hydraulic fracturing has become a cornerstone of the United States domestic energy economy, and the Appalachian Basin, which stretches across parts of Pennsylvania, Ohio, West Virginia, and New York, hosts some of the most intensive shale gas development in the country. Operators drill horizontally into organic-rich shale formations thousands of feet below the surface and inject large volumes of water, sand, and chemical additives under high pressure to create networks of fractures that release natural gas. Once flowback begins, the water returning to the surface, along with water that is co-produced from the formation over the life of a well, carries dissolved salts, metals, naturally occurring radioactive materials, and hydrocarbons in concentrations that reflect the deep subsurface environment.

This produced water differs sharply in chemistry from ordinary surface water and shallow groundwater. Its telltale signature includes elevated concentrations of chloride, bromide, strontium, barium, and other constituents that accumulate in formation brines over geological timescales. When researchers assess whether shale gas operations have affected a water supply, the appearance of this produced-water-like chemistry is one of the most diagnostic indicators, because few natural processes near the land surface generate the same combination of high salinity and characteristic ion ratios. Detecting such a fingerprint in a drinking water well therefore raises immediate concern that fluids from deep geological formations, or fluids handled at the surface during drilling and production, have reached the aquifer.

The new analysis shifts attention away from the wellbore itself and toward the surface. While much of the public debate over shale gas and water quality has focused on whether fracturing fluids migrate upward through rock to contaminate aquifers, the researchers emphasize that surface spills of produced water, flowback fluids, drilling muds, and stored chemicals represent a far more frequent and better-documented pathway. Tanks overflow, valves fail, pipelines leak, and trucks carrying wastewater are involved in accidents. Each of these events releases fluids at the land surface, and whether those fluids threaten drinking water depends on how quickly they can travel downward through soil and fractured rock toward the water table.

Hydrologic vulnerability, in the framework of the study, is determined by the physical characteristics of the landscape: the permeability of soils and surficial deposits, the depth to the water table, the degree of fracturing in near-surface bedrock, and the connectivity between shallow aquifers and the wells that supply homes and communities. Where these conditions favor rapid infiltration and short travel times, a spill has a greater chance of contaminating groundwater before it can be contained or naturally attenuated. Where clays are thick and the water table is deep and protected, the same spill may pose little risk. By mapping where shale gas infrastructure overlaps with the most vulnerable hydrologic settings, the researchers generated spatial estimates of contamination odds across the Appalachian development footprint.

The central finding is stark in its simplicity: groundwater located in areas of high hydrologic vulnerability shows higher odds of exhibiting produced-water-like chemistry than groundwater in less vulnerable settings where shale gas activity is present. This statistical association does not demonstrate that any individual spill contaminated any individual well, and the authors are careful to frame the result as a probabilistic assessment of risk rather than a forensic reconstruction of specific contamination events. Nevertheless, the pattern is consistent with the hypothesis that surface releases, when they occur in sensitive terrain, can and do leave chemical traces in the aquifers that communities depend on.

For the more than 120,000 residents whose drinking-water sources lie within these vulnerable, actively developed areas, the findings carry practical significance. Many rural households in the Appalachian shale gas region rely on private water wells that are not subject to routine regulatory monitoring, meaning contamination may go undetected unless homeowners independently test their water. The study’s vulnerability mapping offers a way to prioritize both monitoring and spill prevention, directing attention and resources to the places where a release is most likely to translate into a public health exposure. Regulators could use the same framework when deciding where to require enhanced secondary containment, stricter setbacks, or more frequent inspection of tanks, impoundments, and gathering lines.

The research also carries broader implications for how the environmental footprint of shale gas development is evaluated. If surface spills in vulnerable terrain are the dominant pathway by which groundwater acquires produced-water-like chemistry, then mitigation efforts aimed solely at well integrity, while important, address only part of the risk. Wastewater management practices, including the volume of produced water stored at the surface, the routes by which it is trucked, and the integrity of the infrastructure used to move and contain it, become central variables in protecting drinking water. The study suggests that risk is not distributed evenly across the landscape but concentrated where development and hydrologic sensitivity intersect, a spatial reality that targeted policy can exploit.

Appalachia presents a particularly consequential setting for this kind of analysis. The region’s population is dispersed across rural valleys where headwater streams feed public and private water systems, and where shallow aquifers are often the only practical source of household water. The same rugged topography and shallow bedrock that make the area attractive for siting well pads on flat, cleared land can also create rapid pathways for spilled fluids to reach groundwater. Combined with the sheer density of wells and associated infrastructure in the core production counties, these conditions mean that even a modest individual probability of contamination, multiplied across thousands of wells and spill events, can add up to meaningful cumulative exposure for a large population.

The study, published in Nature Sustainability under the title describing the higher odds of produced-water-like chemistry in groundwater vulnerable to shale gas spills, adds a quantitative, spatially explicit dimension to a debate that has often proceeded anecdotally. By linking the chemistry of groundwater to mapped hydrologic vulnerability and the geography of shale gas activity, it provides regulators, operators, and communities with a common evidence base for deciding where the risks are greatest and where protective measures will deliver the most benefit. As horizontal drilling and hydraulic fracturing remain core components of the US energy economy, the researchers’ work underscores that safeguarding the drinking water of more than 120,000 Appalachian residents depends less on any single technology and more on managing, with precision, where and how the industry’s fluids are handled at the surface.

Subject of Research: The risk of shale gas surface spills contaminating vulnerable groundwater with produced-water-like chemistry in the Appalachian region

Article Title: Higher odds of produced-water-like chemistry in groundwater vulnerable to shale gas spills

Article References: Soriano, M. A., Jr., Warren, J. L., Wang, Y. C., van Velden, D., Lin, S., Maxwell, R. M., & Saiers, J. E. (2026). Higher odds of produced-water-like chemistry in groundwater vulnerable to shale gas spills. Nature Sustainability. https://doi.org/10.1038/s41893-026-01933-5

Image Credits: AI Generated

DOI: 10.1038/s41893-026-01933-5

Keywords: shale gas, groundwater contamination, hydraulic fracturing, produced water, Appalachian Basin, surface spills, drinking water, hydrologic vulnerability, Nature Sustainability, horizontal drilling, water quality, environmental risk