It’s possible that I shall make an ass of myself. But in that case one can always get out of it with a little dialectic. I have, of course, so worded my proposition as to be right either way (K.Marx, Letter to F.Engels on the Indian Mutiny)
Thursday, September 25, 2025
DOGGIE VEGANISM
Reducing the environmental ‘paw-print’ - plant-based dog foods are better for the environment than red meat options
A new study, led by experts at the University of Nottingham, has found that the production of meat-based pet foods has a much greater impact on the environment in comparison to plant-based alternatives.
The study, led by Rebecca Brociek from the School of Veterinary Medicine and Science, and published in Frontiers in Nutrition - Nutrition and Sustainable Diets, showed that plant-based diets for pets had the lowest impact across all measures of environmental impact. This included the land needed to produce them, greenhouse gas emissions, the polluting effects of production on soil and water reservoirs, and freshwater withdrawal.
Pet food production contributes substantially to global environmental issues, driven largely by animal-derived ingredients.
In this study, experts quantified the environmental impact of 31 commercially available dry dog foods purchased in the UK, categorised as plant-based, red-meat based and veterinary-renal diets.
The environmental metrics were estimated using life cycle assessment datasets and adjusted for ingredient makeup, energy density and differences in moisture content, which were mostly taken from the dog food packaging.
The results showed that plant-based diets had the lowest impact across all measures of environmental impact. Poultry-based and veterinary diets were intermediate, while beef and lamb-based foods had substantially higher impact compared to all other foods.
For example, over nine years of adult life, a 20kg dog fed a beef-based diet was estimated to require 57 football fields worth of land to grow their food (versus 1.4 fields for plant-based).
This latest study comes after a previous study where the team showed that plant-based pet food sold in the UK provided similar nutrition to meat-based food.
“Our findings show that there is a much greater environmental impact when producing meat-based pet food.
“We have already show in our previous work that plant-based diets at the point of purchase are roughly equivalent to others. This next paper is a case study of 31 supermarket-available dog foods, giving dog owners who factor sustainability into their purchases, guidance on how to also reduce their environmental pawprint,” says Rebecca, the lead author on the study.
A research team led by Prof. CHU Yannan at the Hefei Institutes of Physical Science, Chinese Academy of Sciences, has uncovered a new way to detect cancer early—by analyzing the invisible chemical "scents" that the body gives off.
Their study, published in the Journal of Proteome Research, shows that volatile organic compounds (VOCs) could serve as reliable markers for multi-cancer, or pan-cancer, screening.
Early diagnosis is critical for improving survival rates, but most screening methods today focus on single types of cancer and often require invasive procedures. By contrast, pan-cancer screening aims to detect tumors in multiple organs at once, opening a path toward simpler and more effective early detection.
In this study, the team established a pan-cancer mouse model by chemically inducing tumors in organs such as the lungs, stomach, liver, and esophagus. Over a 21-week tumor development period, the researchers collected urine, feces, and odor samples from both tumor-bearing and healthy mice at six time points. Using headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC-MS), they conducted non-targeted detection and analysis of VOCs.
The results revealed three sets of tumor-associated VOCs that not only reflected metabolic changes during cancer progression but also distinguished tumor-bearing mice from healthy controls.
Importantly, early tumor signals were detectable in urine at week 5, in odor at week 13, and in feces at week 17, well before advanced tumor development.
This study provides a valuable experimental foundation for exploring VOC biomarkers in pan-cancer research, according to the team.
A team led by Prof. YIN Huajie from the Hefei Institute of Physical Science of the Chinese Academy of Sciences has developed a solar-powered system that produces green hydrogen directly from atmospheric moisture without relying on external water or energy sources.
The results were published in Advanced Materials.
Proton Exchange Membrane Water Electrolysis (PEMWE) technology is one of the primary routes for producing green hydrogen, drawing significant attention due to its high efficiency and high-purity hydrogen output. However, the PEMWE process heavily relies on high-purity water as the reaction raw material, limiting its application in water-scarce regions. Atmospheric Water Harvesting (AWH), as an emerging approach to obtaining pure water, holds promise as a viable solution to the water shortage issue in the production of green hydrogen.
In this study, the researchers developed a self-sustaining system that couples photothermal atmospheric water harvesting with proton exchange membrane electrolysis.
The system uses hierarchically porous carbon as an adsorbent to capture moisture from the air, which is evaporated by solar heat and fed into a custom-built electrolyzer for hydrogen production. The porous material is fabricated through template synthesis and calcination, followed by surface oxidation to improve water affinity.
It demonstrates remarkable performance. Even under low humidity conditions (as low as 20%), it maintains stable water collection and evaporation performance. Under 40% humidity, the system reached a hydrogen production rate of nearly 300 mL per hour with excellent cycle stability and long-term reliability.
Field tests further confirmed that it can continuously produce green hydrogen using only solar energy, with zero carbon emissions and no external energy input.
This work provides a new pathway for sustainable hydrogen production in water-scarce regions, according to the team.
The submersible unmanned vessel (SUV), "Blue Whale," serves as the core observation node within the Intelligent Swift Ocean Observing System (ISOOS), funded by the National Natural Science Foundation of China. It was developed by the marine intelligent unmanned equipment innovation team at the Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) and launched in Zhuhai on April 28, 2025.
Typhoons and their Atlantic counterparts—hurricanes—can develop into massively destructive storms that can take a severe toll on both infrastructure and human life. Climate change is additionally spurring even more intense storms with higher wind speeds and rainfall.
Typhoon-affected countries can reduce some of the devastating impacts of these storms through enhanced preparedness. However, typhoons remain extremely challenging to study in real time, largely due to their extreme winds and heavy rainfall—both over the ocean and on land. Despite this barrier, in situ storm data (gathered directly within the storm) is critical to improving the modeling and prediction of typhoons and other tropical cyclones.
According to researchers, there are three main challenges to obtaining tropical cyclone data directly from the storm: 1) Real-time alignment with a cyclone’s path, 2) Acquisition of upper- ocean data during a cyclone and 3) Integration of satellite and buoy data to enhance observational data. Up to this point, the majority of the field’s in situ storm data relied on remote-sensing satellite data complemented by sparse anchored/drifting buoy networks, making data collected within cyclones scarce.
To address these challenges, a research team from the Marine Intelligent Unmanned Equipment Innovation Team at the Southern Marine Science and Engineering Guangdong Laboratory in Zhuhai, China recently unveiled the “Blue Whale”: an 11-meter submersible unmanned vessel (SUV) developed as a core component of the Intelligent Swift Ocean Observing System (ISOOS). Its design enables more effective measurement of in situ cyclone data while eliminating risks to human life.
The researchers published their study in the July 31 issue of the journal Ocean-Land-Atmosphere Research.
In order to obtain reliable data within tropical cyclones, the team leveraged fully submersible vehicle technology to maximize vehicle stability in high-wind and -rainfall conditions.
“Unlike conventional surface vessels, [semisubmersible or submersible] hulls remain predominantly or entirely submerged during operation. This submerged configuration significantly attenuates wave-induced motions, enables enhanced [vehicle stability] and considerably enhances operational resilience in adverse sea conditions,” said Chao Dong, director of the Key Laboratory of Marine Environmental Survey Technology and Application in Guangzhou, China and first author of the research paper.
The team successfully combined the advantages of both unmanned surface vehicles and unmanned submersibles to develop a vehicle uniquely tailored for collecting data in cyclone conditions. Above water, the Blue Whale utilizes a conventional propulsion system for high-speed surface navigation; underwater, it employs a vector propulsion system, composed of four vector thrusters, for low-speed operations. The SUV is also equipped with a sinking and floating system, an anchoring system and a gravity adjustment system to facilitate fixed-point underwater hovering.
Despite the need for this type of vehicle for data collection in poor sea conditions, this is the first published report of an unmanned, fully submersible vehicle in the literature to date. The team reports that the Blue Whale boasts the following capabilities: 1) A maximum surface velocity of 23 knots and operational range of over 200km in optimal sea conditions, 2) A maximum submerged speed of 3 knots and sustained submerged operation at maximum speed for about 4 hours, 3) The capacity to remain submerged in standby mode for up to 72 hours without surfacing and 4) The ability to carry a 800 kg payload.
The Blue Whale payload consists of operational equipment for in situ cyclone data collection.
“During submerged operations, its sensor suite—including an acoustic Doppler current profiler, conductivity–temperature–depth sensors, and biochemical sensors for pH, chlorophyll, and turbidity—collects comprehensive water column parameter data,” Dong explained. During surface operations, the SUV can also deploy research rockets to gather atmospheric profile data. Critically, the Blue Whale platform leverages structural optimization and real-time movement and orientation prediction algorithms to improve research rocket success rates in adverse conditions.
While initial testing of the Blue Whale has yielded promising results, the SUV is not yet ready for full operational use. Following internal debugging, mooring trials, dock trials and sea trials, the platform is scheduled for operational deployment in typhoon observation by 2026.
“The Blue Whale’s ability to operate in extreme sea states while maintaining sensor stability represents a significant advancement in marine meteorological observation technology. This innovation directly enhances disaster preparedness by enabling more accurate typhoon intensity forecasts and marine condition warnings,” Dong noted.
Guosong Wu and Yunfei Zhang from the Southern Marine Science and Engineering Guangdong Laboratory and Zhuhai Yunzhou Intelligence Technology Co., Ltd. in Zhuhai, China; Han Zhang from Second Institute of Oceanography at the Ministry of Natural Resources in Hangzhou, China; and Qisen Wang from the Key Laboratory of Marine Environmental Survey Technology and Application at the Ministry of Natural Resources in Guangzhou, China and the Southern Marine Science and Engineering Guangdong Laboratory in Zhuhai, China also contributed to this research.
This research was supported by the National Natural Science Foundation of China (42227901).
In natural ecosystems, soundscapes are made up of animal sounds, environmental noises, and human activity. Because different animals vocalize at different times and frequencies, researchers can study audio recordings to understand local biodiversity. Discriminative machine learning is often used to analyze how biodiversity changes over time and across regions. However, in ecosystems with many species and complex sounds, it is still difficult to accurately identify species and measure biodiversity, which limits the wider use of soundscape analysis in ecological monitoring.
A team led by Prof. LIU Fanglin from the Hefei Institutes of Physical Science of the Chinese Academy of Sciences has developed a novel method based on generative models for ecological soundscape analysis.
The results were recently published in Methods in Ecology and Evolution.
The method uses generative adversarial networks (GANs) to learn the underlying patterns of sound signals from real spectrograms, and then applies this knowledge to reconstruct species-specific vocal components, generating sounds that closely mimic natural soundscapes.
Unlike conventional discriminative models, this generative strategy captures the intrinsic structures and hidden features of the acoustic space such as the frequency ranges, temporal patterns and energy intensities of animal calls, thereby enabling more precise separation of target sound sources, effective removal of environmental noise and clearer restoration of bioacoustic events.
This work expands the theoretical and methodological boundaries of biodiversity and soundscape analysis, providing new ways for automated ecosystem monitoring and ecosystem health assessment.
Recently, a research team in the Hefei Institutes of Physical Science of the Chinese Academy of Sciences has revealed that one-time nitrogen (N) application, a labor-saving practice favored by many farmers, increases both soil and canopy ammonia (NH3) emissions in maize fields, while reducing grain yield and nitrogen use efficiency.
The research results were published in Atmospheric Pollution Research.
The findings are based on a two-year field experiment in Hefei, China.
In the study, the researchers compared plots that received no nitrogen, plots that received all nitrogen as a single early dose, and plots where the nitrogen was split between an early application and a later topdressing. By using portable NH₃ detectors, they measured how much ammonia escaped from the soil and from the maize leaves, which together represent the total field NH₃ loss.
Compared to split nitrogen application, one-time nitrogen application led to higher canopy and soil ammonia emissions, while lowering both grain yield and nitrogen recovery efficiency.
“High soil ammonium (NH4+) concentration and low soil moisture in one-time N application drove more soil NH3 loss, while large leaf area and high leaf apoplast NH4+ concentration boosted canopy emissions,” explained Dr. YANG Yang, a member of the team, “Our results highlight that split N application is a more sustainable choice with lower NH3 emissions and better maize productivity.”
The findings provide practical insights for optimizing fertilizer use and reducing air pollution from agriculture.
