Atmospheric chemistry keeps pollutants in the air

New study from Hokkaido University details processes that keep pollutants aloft despite a drop in emissions

Hokkaido University

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The ice core used in the study (Photo: Mai Matsumoto)

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Credit: Photo: Mai Matsumoto

Nitrates in the atmosphere reduce air quality and play an important role in climate change. An international team led by Hokkaido University researchers has revealed how chemical processes in the atmosphere have led to persistently high nitrate levels despite a reduction in emissions over the past few decades. These findings, published in Nature Communications, will help improve climate modelling by refining our ability to assess and predict atmospheric nitrate levels.

Atmospheric nitrate levels peaked between 1970 and 2000. The levels decreased somewhat with the decrease in the emission of nitrate precursors since the 1990s, but the drop in nitrate levels is smaller than the drop in the emission of precursors—something is keeping nitrates in the atmosphere.

Nitrates can exist in either a gaseous or particulate form in the atmosphere. Gaseous nitrate is more easily deposited out of the atmosphere, while the particulate form—particularly finer particles—can be transported over long distances. Understanding the balance between gaseous and particulate nitrates is therefore important in getting a picture of the atmospheric dynamics and persistence of nitrates.

The persistence of atmospheric nitrates in source regions is explained by a buffering effect, where gaseous nitrates are converted to particulate nitrates, contributing to their persistence. The impact of this buffering over long timescales and at long ranges is unclear, but nitrates deposited in Arctic ice cores show the same patterns as atmospheric nitrates. These sites are far from the sources, so the continuing high deposition rates don’t reflect local processes near the source but must be due to atmospheric transport and other processes in the atmosphere.

To understand these dynamics, a research team led by Professor Yoshinori Iizuka at the Institute of Low Temperature Science, Hokkaido University, examined the nitrate deposition history from 1800 to 2020 in an ice core taken from southeastern Greenland. As expected, nitrate levels within the core increased from the 1850s, peaking between the 1970s and 2000s before declining somewhat but remaining high. Overall, the increase in nitrates up to the 1970s happened more gradually than the increase in precursors, and the decrease after the 1990s was also slower and smaller than the decrease in precursor emission.

The delayed effect and persistence of nitrates indicates factors other than the emission of precursors are affecting nitrate levels. The researchers investigated these factors with a global chemical transport model and found that the difference between nitrate and precursor levels correlated with atmospheric acidity and not with other meteorological factors, such as air temperature.

In other words, the persistence of nitrates has been driven by chemical processes happening in the atmosphere rather than meteorological conditions or atmospheric dynamics. Changes in atmospheric acidity altered the proportion of nitrate that was gaseous or particulate. This affects the lifetime of nitrates in the atmosphere. Atmospheric acidity has increased the fraction of nitrates in particulate form, enabling this pollutant to persist longer and travel farther.

“Ours is the first study to present accurate information for records of particulate nitrates in ice cores, which has been a very challenging problem,” says Iizuka. “As it is more difficult to reduce anthropogenic emissions of substances that lead to increased nitrates, accurate measurements of particulate nitrates in the ice cores provides data for increasing the accuracy of predicting the amplification of Arctic warming in the future.”

“It was difficult to present accurate nitrate from ice cores, but our team was able to do so this time.” says Iizuka. “In the future, nitrate will replace sulfate as the primary aerosol in the Arctic, suggesting this result leads to the higher accuracy of future predictions of Arctic warming amplification.”

Since the 1990s, although nitrate emissions have decreased greatly (graph, orange line), the Greenland SE-Dome ice core shows that particulate nitrates have persisted (graph, black line). This is due to a buffering effect where gaseous nitrates are converted to particulate nitrates even though emissions have decreased; these particulate nitrates were transported to the Arctic and recorded in the Arctic ice. (Illustration: Sakiko Ishino)

Credit

Illustration: Sakiko Ishino

Sumito Matoba (left) and Yoshinori Iizuka (right) drilling the ice core in Greenland. (Photo: Tetsuhide Yamasaki) 

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Photo: Tetsuhide Yamasaki

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Journal

Nature Communications

DOI

10.1038/s41467-025-59208-0 

Method of Research

Experimental study

Subject of Research

Not applicable

Article Title

Acidity-driven gas-particle partitioning of nitrate regulates its transport to Arctic through the industrial era

 

How do multi-scale features and attention mechanisms optimize apple disease identification?

Higher Education Press

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Credit: Dandan DAI , Hui LIU

Apple, as a globally vital economic crop, often suffers severe yield reductions due to leaf diseases such as rust, powdery mildew, and brown spot. Traditional disease identification methods heavily rely on manual expertise, requiring professional technicians to visually inspect leaf morphology, color, and other characteristics for diagnosis. However, this approach is time-consuming, labor-intensive, and prone to errors, as visual differences in early disease stages are often subtle and easily confused. In recent years, machine learning algorithm-based disease recognition technologies have enabled automated analysis of leaf images, rapidly locating lesions and classifying disease types with high accuracy in laboratory settings. However, in complex field environments, factors like varying lighting conditions and background interference often degrade accuracy. The critical question arises: How can algorithms achieve both “clear vision” (high precision) and “fast computation” (efficiency) in such scenarios?

Professor Hui Liu’s team from school of traffic and transportation engineering, central south university, addressed this challenge by developing the Incept_EMA_DenseNet model. By integrating multi-scale feature analysis and an attention mechanism, this study elevated the accuracy of apple leaf disease identification to 96.76%, significantly outperforming mainstream models. This innovation effectively balances algorithmic precision and practicality. The study has been published in Frontiers of Agricultural Science and Engineering (DOI: 10.15302/J-FASE-2024583).

Starting with the limitations of traditional models, this study found that single-scale feature extraction struggles to capture both global distribution and local details of diseases. For instance, while rust’s yellow spots and gray spot’s brown patches may share similar local textures, their overall patterns differ. To address this, they embedded a multi-scale fusion module into the model’s shallow layers, enabling simultaneous analysis of fine textures and overall leaf morphology.

To further prioritize critical information, they introduced an Efficient Multi-scale Attention (EMA) mechanism, which automatically identifies disease regions and assigns higher weights. For example, when analyzing powdery mildew, the algorithm focuses on the density of white powdery substances rather than healthy green areas. Compared to conventional attention methods, EMA simplifies computations, reduces parameters by 50%, and improves classification accuracy by an additional 1.38%, achieving true “intelligent focusing”.

To ensure field applicability, this study optimized the classic DenseNet_121 network through lightweight modifications. The refined model can run smoothly on standard smartphones, allowing farmers to diagnose diseases in real time by simply photographing leaves, eliminating reliance on expensive equipment.

The technology’s efficacy was validated on a dataset of 15,000 images. In mixed tests involving eight common diseases and healthy leaves, the model achieved over 94% accuracy in distinguishing easily confused diseases (e.g., brown spot vs. gray spot) and adapted to varying lighting and camera angles. This breakthrough enables farmers to swiftly implement targeted treatments via mobile apps, reducing pesticide misuse and economic losses—all without specialized expertise.

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Journal

Frontiers of Agricultural Science and Engineering

DOI

10.15302/J-FASE-2024583 

Method of Research

Experimental study

Subject of Research

Not applicable

Article Title

An improved multiscale fusion dense network with efficient multiscale attention mechanism for apple leaf disease identification

 

Picking fruit with just a wave? New robot makes harvesting more efficient

Higher Education Press

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Credit: Ziwen CHEN , Yuhang CHEN , Hui LI , Pei WANG

In the wave of agricultural automation, fruit picking has long been a technical challenge. Traditional manual harvesting is inefficient and costly, while fully automated robots often struggle with inaccurate recognition and clumsy operation in complex environments. How can machines adapt more flexibly to orchard conditions while lowering operational barriers and achieving “human-robot synergy”?

A research team led by Associate Professor Pei Wang from Southwest University has addressed this question with an innovative study. They developed a gesture-controlled human-robot collaborative harvesting robot that can precisely locate and pick fruit with a simple wave. This technology not only significantly improves efficiency but also offers a new approach for small-scale orchards to transition toward intelligent operations. The study has been published in Frontiers of Agricultural Science and Engineering (DOI: 10.15302/J-FASE-2024588).

The core innovation of this robot lies in its “human-machine division of labor”. Researchers found that humans excel at identifying fruit locations and determining picking paths, while robotic arms outperform in repetitive motions and force control. Based on this insight, they designed a motion-sensing interaction system: the operator uses a Leap Motion sensor to capture hand movements in real time, directing the robotic arm to the target position, and then triggers the automated picking process with a double-tap gesture. This design combines human “eyes” with machine “arms”, retaining human flexibility while leveraging mechanical stability.

To ensure precise execution by the robotic arm, the team overcame multiple technical hurdles. For instance, inverse kinematics calculations for robotic arms often yield multiple solutions, which can cause sudden jerks or freezes. To address this, researchers proposed a “four-step screening method”, evaluating mechanical interference, verifying solution correctness, assessing motion rationality, and optimizing trajectory smoothness to select the safest joint angle combination. Simulation tests showed that the optimized robotic arm exhibited significantly reduced movement paths and joint rotation ranges, resulting in smoother motions.

Unlike traditional robots reliant on camera recognition, this device achieves “intuitive control” through high-precision motion-sensing technology. The Leap Motion controller captures hand movements at a 0.01-millimeter resolution, maintaining stable performance even under uneven lighting or foliage occlusion. Researchers also implemented intelligent filtering algorithms to eliminate “jittery data” caused by hand tremors or environmental interference, ensuring smooth robotic arm movement. Ingeniously, they dynamically mapped Leap Motion's cubic interaction space to the robotic arm's fan-shaped working area, allowing operators to move their hands within a virtual “box” while the robotic arm responds synchronously in the real orchard—as intuitive as playing a motion-sensing game.

Tests revealed an average system response time of 74.4 milliseconds and a 96.7% accuracy rate in gesture recognition. After brief training, operators reduced single-fruit picking time from 8.3 seconds to 6.5 seconds, marking a notable efficiency gain. Particularly in high-altitude harvesting scenarios, the robot eliminates the need for manual climbing, significantly reducing operational risks.

This technology lowers the technical barrier through a “human–robot collaboration” model—eliminating reliance on expensive vision systems and enabling farmers to operate it with minimal training. The modular design of the robotic arm allows for flexible replacement of joint motors, further enhancing maintainability. Tests confirm the system excels in complex terrains and small-scale orchards, adapting well to challenges like foliage occlusion and uneven lighting.

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Journal

Frontiers of Agricultural Science and Engineering

DOI

10.15302/J-FASE-2024588 

Method of Research

Experimental study

Subject of Research

Not applicable

Article Title

Design and control algorithm of a motion sensing-based fruit harvesting robot

 

Possum puzzle solved: ECU study reveals unexpected genetic diversity in WA

Edith Cowan University

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Edith Cowan University (ECU) research has found new insights into the genetic structure of the common brushtail possum, including an additional subspecies in Western Australia.

The study by ECU PhD student Shelby Middleton has revealed that there are genetic differences between possums in the Pilbara and Mid West regions and those in WA’s South West.

“This means the possums in the Pilbara and Mid West are a completely different subspecies to what we previously thought,” Ms Middleton said.

“Previously it was thought that there were two subspecies in WA – those found around Perth and the South West and the northern brushtail possum found around Broome and the Kimberley - but now we know there are three subspecies in different locations across the state.”

Ms Middleton said the study also found that possum populations in the Pilbara and Mid West are genetically closer to those found in the east of Australia, including a subspecies that was presumed locally extinct.

“The possums in the Pilbara and Mid West are more closely related to those found on the east coast, South Australia and central Australia,” Ms Middleton said. “Brushtail possums are presumed to be locally extinct in central Australia, and the Pilbara and Mid West populations are closely related to those that formally inhabited the region.”

Brushtail possums are nocturnal marsupials native to Australia, and while they are common in some areas, other populations have suffered substantial decline in population size and distribution.

Ms Middleton conducted the study alongside the Western Australian Museum and Department of Biodiversity, Conservation and Attractions (DBCA).

Dr Travouillon, Curator of Mammals at the Western Australian Museum and one of Ms Middleton’s supervisors, said museum specimens were critical in this discovery.

“Without the WA Museum’s extensive collection and genetic resources, this work would have not been possible. Museum collections are key to understanding biodiversity,” Dr Travouillon said.

Ms Middleton said the findings have important implications for current conservation strategies.

“Little is known about the common brushtail possum in some regions and with populations declining, it is crucial to understand the genetic relationships,” she said.

“Now we have a better understanding of the relationships within the species, we can be better informed when we’re sourcing the animals for translocation.”

Dr Linette Umbrello, DBCA research scientist and one of Ms Middleton’s supervisors, said this discovery provided insight into the genetic relationships of common brushtail possums in WA and could inform how the species is managed.

“Thanks to this research, we now know that the distribution of subspecies of brushtail possum is slightly different to what was known previously,” Dr Umbrello said.

“This information can be used to help guide management actions for the species as whole, especially for populations that might be considered uncommon.”

The research is published in the Zoological Journal of the Linnean Society.

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Journal

Zoological Journal of the Linnean Society

DOI

10.1093/zoolinnean/zlaf026 

Method of Research

Meta-analysis

Subject of Research

Animals

Article Title

Revised phylogeography of the common brushtail possum (Trichosurus vulpecula) reveals new insights into genetic structure across Australia

 

Young people discover the technologies shaping their future in the World Economic Forum and Frontiers for Young Minds collection

Frontiers

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Frontiers for Young Minds The WEF Collection - Top 10 Emerging Technologies of 2024

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Today's breakthroughs – from interactive smart surfaces to genetically engineered animal organs – that are emerging from laboratories now will be day-to-day realities for tomorrow’s adults and leaders. In this new collection, the next generation takes a driver’s seat in understanding and communicating the technologies that will transform our world.

A new collection from the open-access science journal for kids Frontiers in Young Minds (FYM), published in collaboration with the World Economic Forum, gives curious young people a front-row seat to the innovations that could define their future – and invites them to take active control, ensuring their young peers can understand and enjoy the science, before it is published. 

Reflecting on the collection, young reviewer Hrdaya, age 11, commented: 

“I did not know about these technologies before reviewing the report. I think they are absolutely ingenious and wonderful. Having these technologies would make life easier and more resourceful on earth.” 

From AI-powered science discovery and real-time environmental sensing to privacy-enhancing technologies and new materials to cool buildings, the Top 10 Emerging Technologies of 2024 have been reimagined for young readers and reviewed by kids worldwide. Their feedback, questions, and insights shaped how these cutting-edge ideas are communicated, offering a fresh and often surprising look at technologies that could soon change how we live, work, and care for the planet. 

The collection is based on the Top 10 Emerging Technologies of 2024 report, co-published by the World Economic Forum and Frontiers, which served as the Forum's official knowledge partner. Many of the leading researchers who contributed to the original expert-driven report returned to adapt their work for this new youth-focused collection. 

Each article focuses on one of the ten emerging technologies, such as carbon-capturing microbes, immersive technology for urban planning, or platforms orbiting in the stratosphere which could enable internet everywhere (HAPS). Tailored for a younger audience, the articles – like all FYM articles – are reviewed by young students aged 8-15, who help ensure the content is clear, relevant, and engaging for their peers. 

After reviewing the article, “Feeding Farm Animals While Saving the Planet,” young reviewer Leon, age 13, shared: 

“I was completely unfamiliar with insect- and algae-based animal feeds until reading [the] article. I thought that the idea of more sustainable feeds sounds ingenious and promising as it could address lots of important environmental issues. [If this technology] becomes the norm, [it] would make a big difference in my daily life and the environment all around me as a whole.” 

This peer-review process is the hallmark of Frontiers for Young Minds, where young people play an active role in science communication. They aren't just reading - they're questioning, contributing, and helping scientists explain their work to the next generation. 

When discussing his experience with reviewing the article on digital device overload, Harry, who was 15 at the time of the review, said: 

“I feel that reviewing this article on this technology is a really meaningful task, as it allows me to share my opinions regarding this manuscript so as to improve the reading level and clarity and to play a part in educating more young people my age about this technology which will likely shape our future.” 

Laura Henderson, Head of Program at Frontiers for Young Minds, noted: 

“If our kids are to lead tomorrow’s world, they need to understand the breakthroughs being made today – and that’s what we’re here to do. By involving young reviewers and collaborating with top scientists who shaped the original report, we're connecting the next generation of engaged citizens and scientists directly with key discoveries. We're incredibly proud of this collection, which empowers young minds not just to understand emerging technologies, but to question them, and see themselves as part of the future they'll help build.” 

Ruth Morgan, World Economic Forum Steering Committee member and collection editor, added: 

“These emerging technologies represent exciting capabilities that are going to be part of the future, but that future will be shaped by people. This collection has been crafted with and for the leaders of tomorrow, and we hope that it sparks curiosity, and creates opportunities for younger (and older!) minds to ask questions, to think critically, and to imagine the future and all the possibilities it holds.” 

The full Top 10 Emerging Technologies collection is available for free online and offers educators, parents, and students a powerful new resource for exploring the scientific breakthroughs that are poised to shape the future – all through the eyes of the young people who will live it.