The contrast arose because the study evaluated the same water chemistry against two different toxicological bases. The drinking-water guideline and the toxicological reference values are revised independently, so compliance with a concentration guideline does not necessarily provide a current assessment of health risk. The authors do not characterize the arsenic guideline as flawed. Instead, their results show a structural disconnect between regulatory compliance and contemporary toxicological evidence, demonstrating that a water supply can meet guideline-based quality classifications while producing substantially different risk estimates after arsenic toxicity information is updated.

Trace-element measurements showed that arsenic was the principal contributor to cumulative risk in this system. The researchers quantified 17 elements by inductively coupled plasma mass spectrometry, then assessed health risk deterministically and probabilistically. Arsenic concentrations were correlated with antimony, lithium, and boron, a pattern consistent with a shared geogenic source rather than treatment-related contamination. Municipal and spring samples also had broadly similar trace-element profiles. These observations connect arsenic exposure to the basin’s geothermally influenced geology, although the supplied findings do not establish the precise contribution of any individual geological or treatment process.

Monte Carlo simulation indicated that the difference between the updated and legacy arsenic values was robust to uncertainty in exposure parameters. In other words, varying modeled exposure conditions did not remove the divergence: the revised arsenic toxicity inputs continued to produce higher risk classifications than the older values for the same samples. The result supports the conclusion that updated toxicological information, rather than a change in water quality, drove the reclassification. It also shows why a health-risk assessment based only on a regulatory guideline may not capture the implications of newer dose-response information.

The study is a focused environmental-health assessment rather than a nationwide survey or a clinical investigation. It covers one basin-fed drinking-water system, 42 samples and ten districts in Eskişehir, and its modeled results should not automatically be generalized to other aquifers, cities or water-source types. The reported outcomes are estimates derived from measured water chemistry and toxicological inputs, not observations of diagnosed illness among residents. Even with those limits, the case study has implications beyond arsenic: it demonstrates that unchanged samples can receive a different health-risk interpretation when reference values are revised, and that cumulative assessment of multiple trace elements may reveal risks not apparent from a guideline exceedance count alone.

Subject of Research: Arsenic-based health-risk reassessment of a geothermally influenced drinking-water basin in Eskişehir, Türkiye

Article Title: Updated arsenic toxicity values substantially alter health-risk interpretation in a regulation-compliant, geothermally influenced basin-fed drinking-water system

Article References: Çetinkaya, S., Benkli, A. B., & Erdem, O. (2026). Updated arsenic toxicity values substantially alter health-risk interpretation in a regulation-compliant, geothermally influenced basin-fed drinking-water system. Environmental Geochemistry and Health, 48(16), Article 652. https://doi.org/10.1007/s10653-026-03489-x

Image Credits: AI Generated

DOI: 10.1007/s10653-026-03489-x

Keywords: arsenic, drinking water, health risk, Eskişehir, toxicology, ICP-MS, Monte Carlo simulation, geogenic contamination, Hazard Index, water quality, Updated, toxicity

The framework begins with the physical world. In the proposed architecture, sensing nodes built around ESP32 microcontrollers and Wi-Fi modules would be stationed at every critical stage of a textile effluent treatment plant: the outlet of the equalization tank, the coagulation-flocculation chamber, the aerator basin, the secondary clarifier, the reverse osmosis feed line, and the final permeate outlet. Each node carries sensors for pH, total dissolved solids, dissolved oxygen, oxidation-reduction potential, turbidity, temperature, flow, and trace metals including chromium, lead, and zinc. Readings would stream to a cloud platform such as Firebase or ThingSpeak roughly every thirty seconds, replacing the manual sampling and laboratory testing on which most Indian textile manufacturers still rely, a practice the authors note leads to inconsistent monitoring and costly delays in detecting non-compliance.

That torrent of real-time data feeds the second layer of the system: a stacked ensemble machine learning model designed to predict how efficiently the plant removes dye and other pollutants. The researchers trained five heterogeneous base learners on a literature-derived dataset closely matched to the effluent profile of the Tirupur-Erode industrial belt: support vector regression, random forest, gradient boosting, AdaBoost, and XGBoost. Each was tuned with five-fold cross-validated grid search, with feature scaling fitted independently on each training fold to prevent information leakage. Out-of-fold predictions from the base learners then became training features for a meta-learner, chosen empirically rather than by assumption. Lasso regression won that contest with a cross-validated coefficient of determination of 0.881, beating Ridge, Elastic Net, ordinary linear regression, and even a second XGBoost model, evidence that a simple regularized linear combiner can outperform a complex tree-based one when the meta-training set is small.

The ensemble’s headline number is impressive: a purification prediction accuracy, expressed as a coefficient of determination, of 0.978 under the study’s evaluation protocol. But accuracy alone is not the innovation. The third layer of the framework borrows a concept from manufacturing logistics: the economic production quantity inventory model. The authors observed that the volume of purified water awaiting reuse behaves exactly like inventory in a factory system. It builds up during active treatment, depletes during valorization discharge to farms, and accumulates as a backlog when inflow exceeds plant capacity. Each of these four phases, from backlog clearance through overload accumulation, is governed by a differential equation, and solving them with their boundary conditions yields closed-form expressions for the cycle’s transition times. In this analogy, IoT sensor investment plays the role of preservation technology, slowing the decay of treatment efficiency, while investment in machine learning performance plays the role of green technology, cutting the residual pollution load.

On top of this dynamic model sits the genuinely novel element: three independently optimized objectives rather than the customary one or two. The economic objective maximizes profit from selling purified irrigation water and fertilizer produced from treatment sludge, net of treatment, IoT infrastructure, and machine learning operational costs. The environmental objective minimizes the net residual pollution load, with a cap-and-trade-style penalty for exceeding discharge caps and a subsidy for staying under. The social objective quantifies health risk to downstream agricultural communities directly from measured pollutant concentrations relative to regulatory limits, rising in proportion to the severity of any exceedance. Health risk here is not folded into cost as an afterthought, nor proxied by prediction accuracy; it is a first-class goal with its own mathematical standing.

Because these goals conflict, the researchers resolved them using weighted maximum-minimum fuzzy goal programming, an approach rooted in Zadeh’s fuzzy logic and Zimmermann’s membership functions. Each objective is mapped to a satisfaction value between zero and one, then the method maximizes the minimum satisfaction across all three, so no goal is sacrificed entirely. Crucially, the optimization treats machine learning performance level as a decision variable that operators can adjust in real time, alongside treatment volume and sensor investment. Four numerical scenarios spanning inflow rates of 300 to 800 cubic meters per day and model accuracies from 0.765 to 0.991 were each verified against an exhaustive grid search, with all reported optima converging to within 0.002 of the true optimum.

The results carry an encouraging economic message. Every scenario landed on a profitable operating point, with daily profits ranging from about 162 to 1,461 rupees and overall satisfaction levels between 66.15 and 73.34 percent. Optimal treatment volumes stayed between 90 and 100 percent of plant capacity in all cases, and the concave profit surface confirmed that pushing throughput to the limit yields diminishing returns rather than unbounded gains. The scenario with the highest model accuracy and largest scale achieved the best overall satisfaction of 73.34 percent, earning 1,461.22 rupees per day while cutting net pollution load and keeping the health-risk index low.

The most consequential finding, however, concerns the low-accuracy scenario. The configuration with the weakest baseline model, a prediction accuracy of just 0.765, still achieved the second-highest satisfaction level, 73.28 percent, by adopting the highest optimal machine learning performance level of any scenario, 0.852. In other words, sustained operational investment in the model’s running performance can compensate substantially for a modest underlying accuracy, giving plant operators a real-time lever for improving sustainability outcomes without replacing their algorithms. Sensitivity analysis reinforced this hierarchy: prediction accuracy had the largest influence on overall satisfaction, followed by fertilizer price, with treatment cost and inflow rate exerting moderate effects.

The authors are candid about the framework’s limits. The IoT architecture remains conceptual, validated by simulation rather than physical deployment, the numerical examples use illustrative parameters awaiting field calibration, and, tellingly, none of the four scenarios achieved full regulatory compliance at its optimum. Pollutant concentrations exceeded prescribed limits in three of the four examples, revealing that the fuzzy compromise balances health risk against profitability rather than enforcing compliance as a hard constraint. The team flags compliance-based constraints, deep learning architectures trained on larger field datasets, pilot-scale validation, and life cycle assessment as priorities for future work, along with neutrosophic fuzzy modeling for deeper uncertainty.

Even with those caveats, the study stakes out a position that could reshape how industrial wastewater policy is made. Treating monitoring, prediction, and optimization as separate silos, the authors argue, risks solutions that look green on one ledger but are economically or socially fragile. Their framework, whose entire mathematical structure was independently re-derived and verified with symbolic computer algebra, demonstrates that jointly optimizing profit, pollution, and public health, with machine learning performance as a controllable dial rather than a fixed property, is a necessity rather than a luxury. For a river basin that has absorbed decades of dye-laden discharge, the prospect of treatment plants that pay their own way while safeguarding the communities downstream is a compelling one, and the path there, this research suggests, runs through sensors, ensembles, and a willingness to let mathematics weigh three futures at once.

Subject of Research: IoT-enabled ensemble learning and fuzzy multi-objective optimization for sustainable textile wastewater valorization

Article Title: IoT-enabled ensemble learning for sustainable wastewater valorization: a fuzzy goal programming approach with multi-objective optimization

Article References: John, J. S., & Devarasan, E. (2026). IoT-enabled ensemble learning for sustainable wastewater valorization: a fuzzy goal programming approach with multi-objective optimization. Frontiers in Applied Mathematics and Statistics, 12, Article 1932938. https://doi.org/10.3389/fams.2026.1932938

Image Credits: AI Generated

DOI: 10.3389/fams.2026.1932938

Keywords: IoT, ensemble learning, textile wastewater, wastewater valorization, fuzzy goal programming, multi-objective optimization, machine learning, sustainability, health risk, circular economy, Tirupur, water reuse

The research team, led by Shun Xiao of Shaanxi Normal University together with colleagues from the Chinese Academy of Sciences and partner institutions, focused on fine particulate matter known as PM2.5, particles smaller than 2.5 micrometers that penetrate deep into the lungs. Suspended on these particles are polycyclic aromatic hydrocarbons, or PAHs, a class of organic molecules formed whenever carbon-based fuels burn incompletely. PAHs include compounds such as benzo[a]pyrene that are classified as probable human carcinogens. But the team went a step further and also measured oxygenated polycyclic aromatic hydrocarbons, or OPAHs, the oxidized derivatives produced when parent PAHs react with ozone, nitrogen oxides, and other atmospheric oxidants. These derivatives have received far less regulatory attention, even though laboratory studies suggest many of them are more mutagenic, more persistent, and more soluble than their parent compounds.

The study’s historical comparison delivered the headline result: wintertime PAH concentrations in Xi’an have generally declined during the years of China’s clean air actions. This is a meaningful signal for a city whose geography works against it. Xi’an sits in a basin on the Guanzhong Plain, hemmed in by the Qinling Mountains to the south and the Loess Plateau to the north. In winter, temperature inversions trap cold, dense air near the surface like a lid on a pot, allowing emissions from heating, traffic, and industry to accumulate for days. That a basin city with such unfavorable dispersion conditions has still seen PAH levels fall suggests that emission controls, rather than favorable weather alone, have driven the improvement.

To understand where the remaining pollution comes from, the researchers applied a statistical source-apportionment technique called positive matrix factorization, or PMF. The method disentangles the chemical fingerprints of different emission sources by analyzing the relative abundances of dozens of individual PAH and OPAH species. The results pointed to a mixed picture, with biomass-burning-related mixed combustion emerging as the single largest contributor. In practical terms, the burning of wood, crop residues, and other solid fuels for residential heating and cooking remains the dominant engine of wintertime PAH pollution in the region, alongside contributions from traffic and industrial sources. This matters because residential solid-fuel combustion is notoriously difficult to regulate: millions of households in northern China still rely on it, and emissions occur at low temperatures that favor the formation of both parent PAHs and their oxygenated derivatives.

One of the most technically intriguing findings concerns what happens during haze episodes. When the researchers compared clear days with heavy pollution episodes, they found that absolute concentrations of both PAHs and OPAHs increased, but the increase was statistically significant only for the OPAHs. The parent PAHs rose, but not in a way that stood apart from normal variability. The chemical profiles also told a story: four- and five-ring PAHs dominated the parent compound profile, while a specific OPAH called benzanthrone, abbreviated BcdPQ, dominated the oxidized fraction. This asymmetry suggests that during stagnant, polluted conditions, atmospheric oxidation processes actively transform PAHs into their oxygenated counterparts, so the toxic chemistry of the haze evolves even as emissions stay roughly constant. Secondary formation, in other words, amplifies the oxidized fraction precisely when people are breathing the most polluted air.

Meteorology emerged as a powerful controlling factor. The team found that pollutant concentrations were strongly associated with synoptic-scale pressure patterns, the large-scale weather systems that determine wind speed, boundary-layer height, and atmospheric stability. When high-pressure systems settle over the region, calm winds and suppressed vertical mixing allow locally emitted pollutants to build up. The analysis of regional transport added another layer: pollution in the basin was dominated by accumulation within the basin itself, combined with short-range transport from industrial zones in northern Shaanxi. This finding has direct policy implications, because it means that even aggressive local controls in Xi’an cannot fully protect the city without coordinated management of upwind industrial emissions across the province.

To translate chemistry into health terms, the researchers calculated toxic-equivalent concentrations, a metric that weights each PAH species by its carcinogenic potency relative to benzo[a]pyrene. During haze periods, these toxic-equivalent concentrations were markedly higher, and, strikingly, the OPAHs, particularly BcdPQ, dominated the toxicity-equivalent burden. This is a result that should make regulators pause. Conventional air quality management targets PM2.5 mass and a short list of priority PAHs, yet the compounds contributing the largest share of the calculated toxic load during the worst pollution episodes may fall outside that list entirely. If oxidized derivatives carry much of the risk, then policies that reduce particle mass without curbing the precursors and oxidation conditions that generate OPAHs may overstate the health benefits achieved.

The team also performed screening-level estimates of the incremental lifetime cancer risk, or ILCR, a standard metric that estimates the additional probability of developing cancer over a lifetime of exposure. The estimates generally remained below the commonly used threshold of one in ten thousand, a level often treated as an upper bound of acceptable risk. However, the risks increased during haze episodes, and the elevation was most pronounced for adults and elderly groups, who breathe larger volumes of air and accumulate greater lifetime exposure. While the screening-level nature of the calculation means the absolute numbers should be interpreted with care, the pattern is clear: the episodes that produce the worst visibility also produce the highest estimated risk, and the most vulnerable populations bear the largest share.

The broader context makes the study timely. China’s Clean Air Action plan, launched in 2013 and strengthened in subsequent phases, has achieved documented reductions in PM2.5 concentrations across much of the country, and recent national bulletins report continued improvement in overall air quality. Yet researchers have begun to warn that the easy gains may be over. Studies from other regions, including the Yangtze River Delta, have documented prolonged winter haze events even in the post-action-plan era, and analyses of decadal PAH trends confirm that source profiles are shifting as coal gives way to biomass burning, gas, and electricity in the residential sector. Xi’an’s experience illustrates this transition: the city’s air is cleaner by the measures that policy was designed to improve, but the residual pollution is increasingly shaped by sources that are diffuse, household-level, and chemically transformed in the atmosphere.

What should happen next, according to the study’s conclusions, is a targeted second wave of controls. The authors argue that stronger measures against residential biomass and other solid-fuel combustion, tighter management of traffic emissions, and explicit attention to the atmospheric formation of OPAHs remain necessary in the Fenwei Plain. That would mean accelerating clean heating conversions in rural households around the basin, coordinating industrial emission controls across northern Shaanxi, and expanding the suite of monitored compounds to include oxygenated derivatives whose toxicity may rival or exceed that of the parent PAHs. For a city that has spent a decade clawing back its winter skies, the message is one of progress with a chemical asterisk: the haze is thinner than it used to be, but what remains of it is, in the most toxic sense, not the same haze at all.

Subject of Research: PM2.5-bound PAHs and OPAHs pollution, sources, and health risks in Xi'an under China's Clean Air Action plan

Article Title: Pollution characteristics and health risks of PAHs and OPAHs in PM2.5 of an inland basin city in China under the influence of the clean air action plan

Article References: Xiao, S., Luo, X., Li, J., Zhang, N., Liu, S., Du, W., Liu, J., & Yang, L. (2026). Pollution characteristics and health risks of PAHs and OPAHs in PM2.5 of an inland basin city in China under the influence of the clean air action plan. Environmental Geochemistry and Health, 48(14), Article 566. https://doi.org/10.1007/s10653-026-03449-5

Image Credits: AI Generated

DOI: 10.1007/s10653-026-03449-5

Keywords: PM2.5, PAHs, OPAHs, haze, Clean Air Action, Xi'an, Fenwei Plain, source apportionment, biomass burning, health risk, toxic equivalency, air quality