The study, published in Theoretical and Applied Climatology, pitted the Weather Research and Forecasting model, known universally as WRF, against its younger rival, the Model for Prediction Across Scales–Atmosphere, or MPAS-A. Both were developed at the National Center for Atmospheric Research in collaboration with the U.S. Department of Energy, but they represent fundamentally different philosophies of how to chop the atmosphere into computable pieces. WRF, the long-reigning workhorse of operational forecasting in Brazil and worldwide, divides space into a regular grid of quadrilateral cells using finite-difference mathematics. MPAS-A instead wraps the planet in an unstructured Voronoi mesh — a honeycomb of hexagonal cells solved with finite-volume methods — that can transition smoothly from coarse global coverage to razor-sharp regional detail without the awkward seams of nested grids.

That architectural difference matters because the São Paulo storm was a textbook case of scale interaction. Brazilian Navy synoptic charts from the day show a high-pressure system of 1026 hPa parked over the South Atlantic, east of the state, while a deepening low-pressure area associated with a trough near southern Brazil and Paraguay intensified rapidly. The clash between warm pre-frontal air and cold post-frontal air, supercharged by daytime heating and oceanic moisture, ignited a cluster of cumulonimbus clouds. GOES-16 satellite imagery shows isolated convective cores forming between 5:30 and 6:30 in the evening, organizing into a full-blown convective system by 8:30, and then sweeping out to sea after 9:00. A pre-frontal low-level jet, channeled by valleys and hills toward the coast, added fuel to the fire.

To see which model could capture this chaos, the team, led by Rosales Aylas Georgynio Yossimar of SENAI CIMATEC’s Supercomputing Center in Salvador, ran both models at 3-kilometer resolution over a domain covering southeastern Brazil and a generous slice of the South Atlantic. WRF used its classic two-way nested setup at 9 and 3 kilometers, while MPAS-A employed a quasi-uniform mesh at roughly 3 kilometers across the entire domain. Both simulations shared 41 vertical levels and equivalent physics suites — the tropical package in WRF and the mesoscale reference package in MPAS-A — and both were initialized and driven by the ERA5 reanalysis from the European Centre for Medium-Range Weather Forecasts. Three separate experiments, initialized at different times before the event, tested how forecast skill decayed with lead time.

The verdict on the big picture was encouraging: both models successfully reproduced the synoptic skeleton of the event, including the trough, the frontal organization, and the intensification of low-level winds. At the 850 hPa level, roughly one to two kilometers above the surface, both simulations captured wind speeds exceeding 25 meters per second — Beaufort force 10, a strong gale — sweeping over the central and northern parts of São Paulo state near Bauru before the wind corridor shifted southeastward and weakened after 8:00 in the evening. WRF painted broader wind fields, while MPAS-A drew more compact, concentrated cores of extreme wind, a signature the authors link to its finite-volume formulation on the Voronoi mesh.

Beneath that broad agreement, however, the models diverged in revealing ways. Against ERA5, MPAS-A showed stronger spatial correlation over most of the analyzed period, particularly over the ocean, suggesting its unstructured grid handles mesoscale gradients and frontal features with more numerical grace. Yet higher spatial correlation did not translate into smaller errors: during the storm’s intensification phase, MPAS-A’s mean absolute error and root mean square error actually grew, hinting that the model may have over-intensified wind maxima or displaced the storm’s core. WRF, meanwhile, showed a more heterogeneous bias pattern, overestimating winds over land while underestimating them over the southern ocean sector. The team is careful to note that these differences cannot be pinned on mesh geometry alone — dynamics, physics coupling, and land-surface representation all conspire.

One unexpected confound emerged from the ground up. Because of limitations in MPAS-A’s preprocessing tools, the two models were fed different land-use datasets: WRF received the current 2019 MapBiomas classification of Brazil, while MPAS-A relied on the older MODIS-IGBP dataset from the early 2000s. Grasslands covered about 35 percent of WRF’s domain but only 7 percent of MPAS-A’s, with corresponding shifts in savanna and cropland fractions. Since vegetation categories control roughness length, albedo, and evapotranspiration — and therefore the surface energy balance that drives boundary-layer turbulence — this discrepancy adds a layer of ambiguity to any model-versus-model comparison, and the authors flag it as a caveat that future studies must untangle.

Comparisons against Brazil’s INMET weather stations exposed the models’ Achilles’ heel: complex topography. At Campos do Jordão, perched at 1,628 meters in the Serra da Mantiqueira, WRF produced its worst errors, with root mean square errors and mean biases exceeding 6 meters per second. The reason is almost cartographic: WRF’s nearest grid point sat closer to the mountain slope, where terrain-induced flow acceleration inflated its wind estimates, while MPAS-A’s representative point fell in the calmer urban core. At Cachoeira Paulista, tucked into the Paraíba Valley at the foot of the same range, similar problems appeared. Near the storm center around Bauru, by contrast, both models performed respectably, with errors under 3 meters per second and biases below 2.

Temperature told a story of mirror-image biases. WRF tended to overestimate 2-meter air temperature, sometimes by more than 1.5 degrees Celsius, while MPAS-A generally underestimated it, especially during daytime heating. Yet both models faithfully reproduced the diurnal cycle — the pre-storm warming on October 10, the abrupt frontal cooling on the 11th, and the recovery on the 12th — though they sharpened the storm-driven temperature drop more than observations showed. The most troubling statistic came from hourly wind correlations: no model exceeded 0.78 at any station, roughly 63 percent of stations fell between 0.2 and 0.6, and some correlations were near zero or negative, exposing a fundamental struggle to capture hour-by-hour wind variability during the event.

The authors attribute these timing failures to a familiar culprit: the sparse observational network of the Southern Hemisphere, which degrades the initial conditions fed into every model, compounded by uncertainties in turbulence parameterization and the use of hourly mean winds rather than true gust measurements. Their prescription is clear — better physics, denser observations, and complementary techniques such as data assimilation and statistical post-processing. As forecasting agencies worldwide weigh a transition from WRF’s rigid squares to MPAS-A’s flexible hexagons, this storm-soaked Brazilian case study offers a sobering reminder: the grid may be getting smarter, but the atmosphere still keeps some of its wildest moments just beyond the model’s grasp.

Subject of Research: Comparative simulation of an extreme wind gust event in São Paulo using the WRF and MPAS-A atmospheric models

Article Title: Simulation of an anomalously high wind gust event in São Paulo: A comparison of the MPAS-A and WRF models

Article References: Yossimar, R. A. G., da Silveira, P. W. M., dos, S. T. S., Duarte, J. W., de, L. F. J. L., Cotta, W. A. L., Cavalcante, A. A., Rodrigues, S. A., da Silva, R. D. N., & Martins, M. D. (2026). Simulation of an anomalously high wind gust event in São Paulo: A comparison of the MPAS-A and WRF models. Theoretical and Applied Climatology, 157(10), Article 644. https://doi.org/10.1007/s00704-026-06537-9

Image Credits: AI Generated

DOI: 10.1007/s00704-026-06537-9

Keywords: WRF, MPAS-A, wind gusts, extratropical cyclone, São Paulo, numerical weather prediction, mesoscale modeling, Voronoi mesh, ERA5 reanalysis, boundary layer, severe weather, Brazil

Verification of measles elimination is a retrospective judgment. It requires documented evidence of high vaccination coverage and of the interruption of endemic transmission for at least twelve consecutive months, confirmed through epidemiological, laboratory and molecular data reviewed by the commission. As such, it is an accurate statement about what has already happened in a country. It says nothing, however, about how much viral exposure a country is currently absorbing from the outside world. For most of the past two decades, that distinction was largely immaterial, because verified countries were surrounded by other verified countries and the regional firewall held. The loss of regional verification has now made the difference consequential, and a rapidly evolving situation in Brazil illustrates exactly why.

Between January and early August 2026, the state of São Paulo confirmed 23 measles cases, compared with just two in the whole of 2025. The cases clustered in the state capital, Guarulhos and São Bernardo do Campo, along the corridor served by the largest international air hub in South America. The first two cases, which occurred in March and April, were imported and affected unvaccinated individuals; the remainder were classified as import-related, with the source of infection unidentified at the time of writing. The response, it must be stressed, has been prompt and competent. A supplementary infant dose was recommended in the affected municipalities in June 2026, before the transmission chain was formally recognized. Brazil had been reverificated as measles-free in November 2024, five years after losing that status, having sustained interruption of endemic transmission since June 2022. Yet a capable programme, holding verified status, nonetheless found itself confronting a chain of transmission whose origin it could not trace.

The letter argues that two quantities, neither of which is captured by the current verification framework, would add prospective information at low cost. The first is importation pressure: a measure of how much measles virus is being introduced into a population by international travel. The concept builds on prior modeling work showing that an inflow variable, defined as the sum across origin countries of air passenger volume multiplied by measles incidence in the origin country, predicted imported measles cases in United States states with a correlation of 0.84 and an area under the receiver operating characteristic curve of 0.78. Crucially, that study found that both air travel data and international surveillance data contributed to the predictive performance, meaning that the signal emerges from the combination of connectivity and epidemiology rather than from either alone.

The proposed refinement is a modest extension and, more importantly, a change of use. Rather than confining the metric to predictive studies, the author suggests weighting the inflow by the susceptible fraction of the destination population and reporting the resulting quantity routinely alongside elimination status. For operational specification, the letter proposes a rolling three-month window; counting arriving passengers rather than scheduled flights, since seat capacity and passenger load differ substantially between routes; using incidence per million rather than absolute case counts, so that origin countries of different sizes are comparable; estimating susceptibility from two-dose rather than first-dose coverage; and stratifying by age band where subnational coverage data permit, since susceptibility is not uniformly distributed across age groups. A verified country could then state not only that endemic transmission has been absent, but how much introduction pressure it is presently absorbing.

The second proposed indicator is surveillance traceability, operationalized as the proportion of confirmed cases for which no epidemiological link to a known source can be established. This figure is already generated by routine case investigation in every elimination setting, but it is typically reported as a descriptive detail rather than monitored as an indicator in its own right. A case without an identifiable source may indicate unrecognised transmission occurring upstream, although it may equally reflect incomplete interview data, unrecognised exposure to a known case, population mobility, or insufficiently discriminatory viral sequencing. The letter is careful to frame the measure narrowly: it is an indicator of investigation and linkage capacity rather than a direct measurement of surveillance sensitivity. Its practical merit is availability. It can be computed continuously from data already collected, whereas the conventional criterion, the absence of endemic transmission over twelve months, resolves only in retrospect.

Considered jointly, the two quantities describe four distinct epidemiological situations. Low importation pressure combined with high traceability corresponds to a stable elimination setting, the condition most verified countries aspired to for years. High pressure with high traceability describes a heavily exposed system that is nonetheless tracking introductions successfully, the condition under which imported cases are contained without secondary spread. Low pressure with low traceability corresponds to a system whose investigative weakness is masked by the absence of challenge, a hidden fragility that would only become apparent once the virus arrived. High pressure with low traceability corresponds to a state of increased vulnerability to sustained transmission. The São Paulo metropolitan region in mid-2026, the letter argues, illustrates precisely this last category, and its defining features were in principle recognizable prospectively, from connectivity and linkage data, rather than only in hindsight from the case count.

The value of this framing, the author contends, is that it renders elimination continuous and prospective rather than binary and retrospective. Under current practice, supplementary campaigns, supplementary infant doses and reinforcement of laboratory capacity are triggered by confirmed cases. Indicators of exposure and traceability would allow such interventions to be triggered by exposure itself, potentially earlier, before sustained transmission becomes established. In an era when a single untraced importation in a susceptible pocket can reignite endemic transmission across an entire region, that shift in timing could prove decisive.

The letter is explicit that this is a proposal rather than a validated instrument, and the limitations are substantial. Neither quantity has been calibrated against real-world outcomes. The unlinked-case proportion is sensitive to investigation capacity and unstable at small case numbers, which is precisely the situation in most elimination settings where cases are rare. Importation pressure estimated from aggregate air connectivity ignores heterogeneity in traveller susceptibility and omits land and maritime movement, a limitation acknowledged within the air travel literature itself and one that is material in several countries of the region, including Brazil, which shares extensive overland borders. The case figures cited derive from official state communications rather than from a published epidemiological bulletin, and the outbreak was still ongoing at the data cut-off of 9 August 2026. The claim, therefore, is deliberately narrower than a method: that the status a country holds and the exposure it faces have become distinguishable quantities; that the distinction is measurable from data already being collected; and that measuring it is a more useful response to regional resurgence than enumerating cases once they appear. As the Americas work toward regaining regional verification, the letter suggests that sustaining elimination will require not just counting what has happened, but continuously measuring what is coming.

Subject of Research: Proposed indicators of measles importation pressure and surveillance traceability for sustaining elimination in the Americas

Article Title: Importation pressure and surveillance traceability: complementary indicators for sustaining measles elimination in the Americas

Article References: Importation pressure and surveillance traceability: complementary indicators for sustaining measles elimination in the Americas. (n.d.). https://doi.org/10.1016/j.nmni.2026.101842

Image Credits: AI Generated

DOI: 10.1016/j.nmni.2026.101842

Keywords: measles, elimination, Americas, importation pressure, surveillance, São Paulo, Brazil, air travel, PAHO, vaccination coverage, public health, epidemiology

The research, conducted in the headwater basin of the Corumbataí River by scientists at the University of Campinas, integrated three independent lines of geochemical evidence—major ion chemistry, stable isotopes of hydrogen and oxygen, and the concentrations of rare earth elements and yttrium, collectively known as REY. The team collected 39 water samples during wet and dry season campaigns in 2022, drawing from springs, rivers, shallow wells, and deep production wells reaching 136 to 200 meters into the aquifer. The goal was ambitious: to rebuild, atom by atom, the conceptual model that governs how water moves, mixes, and ages in this critical recharge zone.

The first surprise came from the deep wells. Groundwater pumped from the Guarani Aquifer System at depth turned out to be strongly alkaline, with a median pH of 9.7, and dominated by sodium and bicarbonate ions—a chemical signature that previous regional models assigned exclusively to confined zones located 20 to 100 kilometers away from the outcrop area. Here, instead, that same facies was found directly beneath the recharge zone, separated from shallower waters not by kilometers of gradual evolution but by thin sheets of diabase, an intrusive volcanic rock from the Serra Geral Formation that sliced into the sandstones roughly 130 million years ago. These sills behave as aquitards, effectively splitting the aquifer into two hydraulically distinct worlds stacked one atop the other.

Stable isotope analysis sharpened the picture dramatically. Springs and surface waters in the basin cluster tightly along the local meteoric water line, with median δ2H values near –40 per mil and δ18O near –6.7 per mil, consistent with modern rainfall in a humid subtropical climate. The deep Guarani groundwater, by contrast, was markedly depleted, with median δ2H of –64.2 per mil and δ18O of –9.7 per mil—values so low that they point to recharge under climatic conditions perhaps 10 to 15 degrees Celsius colder than the present, echoing the last glacial period. In other words, the water supplying public wells in the outcrop area may have entered the ground more than 10,000 years ago and, on any human timescale, is not being replaced.

The rare earth element data added a dimension that conventional hydrochemistry had never captured. Because the lanthanides and yttrium fractionate predictably as water reacts with rocks and travels through an aquifer, their patterns serve as fingerprints of flow paths and residence times. The deep Guarani samples showed extremely low total REY concentrations, with a median of just 0.02 micrograms per liter, strong depletion of light rare earths, and pronounced negative cerium anomalies—hallmarks of geochemically evolved water that has spent a long time underground. Shallow and surface waters carried substantially higher REY loads and less fractionated patterns, marking them as young and chemically immature.

When the researchers superimposed REY concentrations onto the classic Piper diagram, groupings emerged that no major ion analysis alone had ever revealed. One cluster of mixed chloride-nitrate waters carried anomalously high REY loads above 2.8 micrograms per liter, together with elevated nitrate, pointing to anthropogenic contamination of the shallow unconfined Guarani aquifer. Another group bound the springs tightly to the surface waters, confirming their intimate hydraulic connection. A third group captured the sodium-bicarbonate deep waters with REY concentrations below 0.09 micrograms per liter, sealing their identity as isolated, long-residence groundwater sealed off by the diabase.

Ionic ratio analysis told a complementary story. In springs and rivers, the dominant geochemical process is the weathering of feldspar and ferromagnesian minerals in the sandstones and basalts, releasing calcium, sodium, and silica in proportions that match the local geology. In the deep aquifer, the dominant process instead is cation exchange, which strips calcium from solution and loads the water with sodium—explaining the characteristic Na-HCO3 composition. At two of the deep wells, the data even hinted at plagioclase weathering within the diabase sill itself, showing that the volcanic intrusions are not inert barriers but active participants in the water’s chemistry.

The isotope mixing calculations quantified how differently the basin’s two aquifer systems behave. Using the line-conditioned excess, a sensitive indicator of evaporation and recharge seasonality, the team estimated that springs issuing from the Guarani aquifer draw roughly 71 percent of their flow from groundwater with longer residence times, while springs from the overlying Bauru-type sediments receive about 73 percent of their discharge from recent precipitation. This asymmetry reveals that the Guarani outcrop zone is not a simple sponge soaking up rain and releasing it downhill; it is a layered system in which some springs are fed by deep storage and others by rainfall racing through thin soils.

The management implications are stark. The semi-confined groundwater being tapped by public supply wells in the outcrop area is, according to the isotope evidence, old to very old—likely non-renewable on human timescales. Every liter extracted is, in effect, mined from a paleowater reserve recharged under ice-age climates. The study’s authors argue that groundwater management in the region must be urgently improved and regulated, prioritizing surface water sources and modern springs where possible, while recognizing that those same springs are vulnerable to the urban and agricultural contamination already signaled by nitrate and anomalous REY signatures in the shallow aquifer.

Scientifically, the work demonstrates that rare earth elements, long underused in hydrogeology, can expose aquifer compartmentalization that conventional tools miss entirely. By combining REY fingerprints with stable isotopes and classical hydrochemistry, the researchers showed that hydrochemical facies previously thought to be separated by tens of kilometers can coexist at a single location, divided only by a diabase sill. The finding echoes recent work in Uruguay showing that the Guarani Aquifer System behaves far more complexly than gradual, eastward-evolving flow models predict. For the millions of people who depend on this vast reservoir, the message is clear: the map of their water has just been redrawn, and the deepest layer of it is older, more fragile, and more finite than anyone managing it had assumed.

Subject of Research: Hydrogeological conceptual modeling of the Guarani Aquifer System outcrop area in São Paulo, Brazil, using stable isotopes and rare earth element tracers

Article Title: Hydrogeological conceptual model for the Guarani Aquifer System outcrop area in Brazil: Insights from stable isotopes and rare earth elements

Article References: Bassetto-Ferreira, R., Enzweiler, J., & de Abreu, A. E. S. (2026). Hydrogeological conceptual model for the Guarani Aquifer System outcrop area in Brazil: Insights from stable isotopes and rare earth elements. Hydrogeology Journal. https://doi.org/10.1007/s10040-026-03164-6

Image Credits: AI Generated

DOI: 10.1007/s10040-026-03164-6

Keywords: Guarani Aquifer System, hydrogeology, stable isotopes, rare earth elements, groundwater recharge, diabase sills, paleowater, São Paulo, aquifer connectivity, water management, hydrochemistry, springs

The research, published in the journal Clean Technologies and Environmental Policy, focused on two adjacent but contrasting metropolitan areas in São Paulo State: Campinas and Piracicaba. Campinas is a densely urbanized hub of industry, technology and logistics, where vehicle and factory emissions dominate the air quality picture. Piracicaba, by contrast, is shaped by agro-industry, particularly sugarcane cultivation and processing, with cropland covering much of its landscape. Both regions sit within the Atlantic Forest biome, one of the world’s great biodiversity hotspots, now heavily fragmented by urban expansion and agriculture. The researchers, led by Ana Laura Fragoso Favoreti of the Universidade Estadual de Campinas, reasoned that these two very different landscapes would provide a natural experiment in how land use modulates the relationship between greenness and pollution.

The team assembled an unusually rich dataset. Monthly concentrations of coarse particulate matter (PM10), fine particulate matter (PM2.5) and nitrogen dioxide (NO2) came from the automated monitoring stations operated by CETESB, the São Paulo State environmental agency, covering January 2019 through December 2025. Hourly measurements passed through strict quality control: physically implausible values were discarded, and daily and monthly averages were only accepted when at least 75 percent of the expected observations were valid. Vegetation greenness was measured using the Normalized Difference Vegetation Index, or NDVI, computed from cloud-masked Sentinel-2 satellite imagery at ten-meter resolution within Google Earth Engine. Crucially, the team did not rely on a single greenness figure per station; instead, they calculated average NDVI within concentric buffers of 100, 250, 500 and 1000 meters around each monitoring station, explicitly testing whether the vegetation-pollution relationship changes with spatial scale.

The headline finding is a consistent negative correlation between greenness and pollution. Spearman rank correlations between monthly NDVI and pollutant concentrations were predominantly negative across both regions, ranging from −0.24 to −0.83 for PM10, −0.17 to −0.82 for PM2.5 and −0.31 to −0.75 for NO2. In plain terms, months and places with lusher vegetation tended to record dirtier air less often. Some of the strongest relationships emerged at stations embedded in agro-industrial landscapes: Rio Claro and Santa Gertrudes showed correlations approaching −0.83 for PM10 and −0.82 for PM2.5, while the central Campinas station, hemmed in by dense traffic, produced the weakest and least consistent signals.

But raw correlations in environmental data can mislead, because both vegetation and pollution follow strong seasonal cycles. The study’s statistical core therefore consisted of multiple linear regression models in which pollutant concentrations were log-transformed and adjusted for temperature, relative humidity, wind speed, and categorical fixed effects for month and year, with autocorrelation-robust standard errors to guard against the serial dependence that plagues monthly time series. These models explained between 75 and 96 percent of the variance in concentrations, and within them, the vegetation signal sharpened in one region and faded in the other. In Piracicaba, NDVI retained statistically significant negative associations across most buffer scales for all three pollutants, with standardized coefficients exceeding −1.0 at the 100-meter buffer. In Campinas, by contrast, nearly all adjusted associations were statistically indistinguishable from zero, a result the authors attribute to smaller sample sizes, reduced statistical power, and strong collinearity between NDVI and meteorological predictors driven by shared seasonality.

The spatial scale of the analysis proved decisive. Vegetation effects in Piracicaba were strongest at the smallest buffers, within 100 to 500 meters of the sensors, and weakened or vanished at 1000 meters, suggesting that nearby canopy exerts a genuinely local influence on measured air quality. One striking exception hinted at a cautionary tale: at the 1000-meter buffer for PM10 in Piracicaba, the coefficient reversed sign, becoming strongly positive. The likely explanation is that broad-scale greenness in an agricultural landscape can flag croplands, unpaved surfaces and fire-prone areas that actually generate coarse particles. In other words, at larger scales, the satellite index may be measuring the geography of emissions rather than the geography of pollution removal.

Seasonality dominated the temporal record. Pollutant concentrations rose sharply during the dry season from May to August, when reduced rainfall suppresses wet deposition and weak winds limit dispersion. The seasonal contrast was especially dramatic for PM10 in Piracicaba, where dry-season median concentrations of 53.59 micrograms per cubic meter were 2.5 times the wet-season value. Peak particulate concentrations arrived in September in both regions, likely reflecting long-range transport of biomass burning aerosols from central Brazil, where fire activity intensifies through August and September. Vegetation greenness moved in exact anti-phase, peaking in the January-to-March wet season and bottoming out in September precisely when particulate pollution peaked. The authors are candid that this synchronized seasonality means part of the observed correlation may reflect shared seasonal dynamics rather than a direct cleansing effect of leaves.

Regional comparisons added further nuance. PM10 and NO2 concentrations were significantly higher in Piracicaba than in Campinas, consistent with the influence of agro-industrial sources, soil resuspension and combustion, while PM2.5 showed no significant difference between regions, hinting that fine particles are more regionally mixed and shaped by secondary formation and long-distance transport. Inter-annual variability told its own story: 2024 stood out as an anomalously polluted year in both regions, coinciding with Brazil’s exceptional drought and intensified fire activity, while Campinas recorded a conspicuous drop in NO2 in 2020 during COVID-19 lockdowns, before concentrations rebounded to their highest level in 2023.

What should planners take away? The authors emphasize that NDVI is an indirect structural proxy for vegetation vigor, not a direct measure of pollutant filtration. It cannot capture canopy height, leaf area, species composition or street geometry, all of which determine whether vegetation intercepts particles or, in dense street canyons, traps them by blocking wind. The study’s observational design likewise rules out causal claims: the results describe statistical associations between surrounding greenness and pollutant concentrations, adjusted for weather and time, not proof that trees remove the pollution. Still, the message for green infrastructure policy is clear. Greening works best as one component of integrated air quality management, deployed with attention to local emission sources, urban morphology and spatial scale, rather than as a standalone cure. In rapidly urbanizing, agro-industrial landscapes like Campinas and Piracicaba, the authors conclude, pairing vegetation planning with air quality monitoring and land use management will deliver far more environmental benefit than simply increasing greenness alone.

Subject of Research: The spatiotemporal relationship between urban vegetation greenness measured by NDVI and air pollutant concentrations in two São Paulo metropolitan regions from 2019 to 2025.

Article Title: Urban green spaces and air quality in the State of São Paulo: a spatiotemporal analysis of the metropolitan regions of Campinas and Piracicaba

Article References: Favoreti, A. L. F., Rodrigues, B. N., Emiliano, W. M., Canteras, F. B., & Molina Junior, V. E. (2026). Urban green spaces and air quality in the State of São Paulo: a spatiotemporal analysis of the metropolitan regions of Campinas and Piracicaba. Clean Technologies and Environmental Policy, 28(10), Article 255. https://doi.org/10.1007/s10098-026-03604-7

Image Credits: AI Generated

DOI: 10.1007/s10098-026-03604-7

Keywords: urban green spaces, air quality, NDVI, particulate matter, nitrogen dioxide, green infrastructure, remote sensing, Sentinel-2, Google Earth Engine, São Paulo, Campinas, Piracicaba