Why it's high time we stopped anthropomorphising ants We have long drawn parallels between ants and humans. Now we are comparing the insects to computers. Pollution is making many cities unlivable for their human inhabitants, but it is also tearing ant families and communities apart. Ants recognise each other by sniffing a thin layer of hydrocarbons on the outside of their exoskeletons; each colony has a specific "smell". But a new study reveals that ozone emissions can change the structure of these hydrocarbons.

Ensembles of geophysical models improve prediction accuracy and express uncertainties. We develop a novel data-driven ensembling strategy for combining geophysical models using Bayesian Neural Networks, which infers spatiotem-porally varying model weights and bias, while accounting for heteroscedastic uncertainties in the observations. This produces more accurate and uncertainty-aware predictions without sacrificing interpretability.

Ensembles of geophysical models improve prediction accuracy and express uncertainties. We develop a novel data-driven ensembling strategy for combining geophysical models using Bayesian Neural Networks, which infers spatiotem-porally varying model weights and bias, while accounting for heteroscedastic uncertainties in the observations. This produces more accurate and uncertainty-aware predictions without sacrificing interpretability.

Atmospheric ozone is a crucial absorber of solar radiation and an important greenhouse gas. However, most climate models participating in the Coupled Model Intercomparison Project (CMIP) still lack an interactive representation of ozone due to the high computational costs of atmospheric chemistry schemes. Here, we introduce a machine learning parameterization (mloz) to interactively model daily ozone variability and trends across the troposphere and stratosphere in standard climate sensitivity simulations, including two-way interactions of ozone with the Quasi-Biennial Oscillation. We demonstrate its high fidelity on decadal timescales and its flexible use online across two different climate models -- the UK Earth System Model (UKESM) and the German ICOsahedral Nonhydrostatic (ICON) model. With atmospheric temperature profile information as the only input, mloz produces stable ozone predictions around 31 times faster than the chemistry scheme in UKESM, contributing less than 4 percent of the respective total climate model runtimes. In particular, we also demonstrate its transferability to different climate models without chemistry schemes by transferring the parameterization from UKESM to ICON. This highlights the potential for widespread adoption in CMIP-level climate models that lack interactive chemistry for future climate change assessments, particularly when focusing on climate sensitivity simulations, where ozone trends and variability are known to significantly modulate atmospheric feedback processes.

In our project WatchPlant, we propose to use a decentralized network of living plants as air-quality sensors by measuring their electrophysiology to infer the environmental state, also called phytosensing. We conducted in-lab experiments exposing ivy (Hedera helix) plants to ozone, an important pollutant to monitor, and measured their electrophysiological response. However, there is no well established automated way of detecting ozone exposure in plants. We propose a generic automatic toolchain to select a high-performance subset of features and highly accurate models for plant electrophysiology. Our approach derives plant- and stimulus-generic features from the electrophysiological signal using the tsfresh library. Based on these features, we automatically select and optimize machine learning models using AutoML. We use forward feature selection to increase model performance. We show that our approach successfully classifies plant ozone exposure with accuracies of up to 94.6% on unseen data. We also show that our approach can be used for other plant species and stimuli. Our toolchain automates the development of monitoring algorithms for plants as pollutant monitors. Our results help implement significant advancements for phytosensing devices contributing to the development of cost-effective, high-density urban air monitoring systems in the future.

Tropospheric ozone, known as a concerning air pollutant, has been associated with health issues including asthma, bronchitis, and impaired lung function. The rates at which peroxy radicals react with NO play a critical role in the overall formation and depletion of tropospheric ozone. However, obtaining comprehensive kinetic data for these reactions remains challenging. Traditional approaches to determine rate constants are costly and technically intricate. Fortunately, the emergence of machine learning-based models offers a less resource and time-intensive alternative for acquiring kinetics information. In this study, we leveraged deep reinforcement learning to predict ranges of rate constants (\textit{k}) with exceptional accuracy, achieving a testing set accuracy of 100%. To analyze reactivity trends based on the molecular structure of peroxy radicals, we employed 51 global descriptors as input parameters. These descriptors were derived from optimized minimum energy geometries of peroxy radicals using the quantum composite G3B3 method. Through the application of Integrated Gradients (IGs), we gained valuable insights into the significance of the various descriptors in relation to reaction rates. We successfully validated and contextualized our findings by conducting cross-comparisons with established trends in the existing literature. These results establish a solid foundation for pioneering advancements in chemistry, where computer analysis serves as an inspirational source driving innovation.

Lipidomics generates large data that makes manual annotation and interpretation challenging. Lipid chemical and structural diversity with structural isomers further complicates annotation. Although, several commercial and open-source software for targeted lipid identification exists, it lacks automated method generation workflows and integration with statistical and bioinformatics tools. We have developed the Comprehensive Lipidomic Automated Workflow (CLAW) platform with integrated workflow for parsing, detailed statistical analysis and lipid annotations based on custom multiple reaction monitoring (MRM) precursor and product ion pair transitions. CLAW contains several modules including identification of carbon-carbon double bond position(s) in unsaturated lipids when combined with ozone electrospray ionization (OzESI)-MRM methodology. To demonstrate the utility of the automated workflow in CLAW, large-scale lipidomics data was collected with traditional and OzESI-MRM profiling on biological and non-biological samples. Specifically, a total of 1497 transitions organized into 10 MRM-based mass spectrometry methods were used to profile lipid droplets isolated from different brain regions of 18-24 month-old Alzheimer's disease mice and age-matched wild-type controls. Additionally, triacyclglycerols (TGs) profiles with carbon-carbon double bond specificity were generated from canola oil samples using OzESI-MRM profiling. We also developed an integrated language user interface with large language models using artificially intelligent (AI) agents that permits users to interact with the CLAW platform using a chatbot terminal to perform statistical and bioinformatic analyses. We envision CLAW pipeline to be used in high-throughput lipid structural identification tasks aiding users to generate automated lipidomics workflows ranging from data acquisition to AI agent-based bioinformatic analysis.

Air pollution constitutes a global problem of paramount importance that affects not only human health, but also the environment. The existence of spatial and temporal data regarding the concentrations of pollutants is crucial for performing air pollution studies and monitor emissions. However, although observation data presents great temporal coverage, the number of stations is very limited and they are usually built in more populated areas. The main objective of this work is to create models capable of inferring pollutant concentrations in locations where no observation data exists. A machine learning model, more specifically the random forest model, was developed for predicting concentrations in the Iberian Peninsula in 2019 for five selected pollutants: $NO_2$, $O_3$ $SO_2$, $PM10$, and $PM2.5$. Model features include satellite measurements, meteorological variables, land use classification, temporal variables (month, day of year), and spatial variables (latitude, longitude, altitude). The models were evaluated using various methods, including station 10-fold cross-validation, in which in each fold observations from 10\% of the stations are used as testing data and the rest as training data. The $R^2$, RMSE and mean bias were determined for each model. The $NO_2$ and $O_3$ models presented good values of $R^2$, 0.5524 and 0.7462, respectively. However, the $SO_2$, $PM10$, and $PM2.5$ models performed very poorly in this regard, with $R^2$ values of -0.0231, 0.3722, and 0.3303, respectively. All models slightly overestimated the ground concentrations, except the $O_3$ model. All models presented acceptable cross-validation RMSE, except the $O_3$ and $PM10$ models where the mean value was a little higher (12.5934 $\mu g/m^3$ and 10.4737 $\mu g/m^3$, respectively).

The main objective of this study is to model the concentration of ozone in the winter season on air quality through machine learning algorithms, detecting its impact on population health. The study area involves four monitoring stations: Ate, San Borja, Santa Anita and Campo de Marte, all located in Metropolitan Lima during the years 2017, 2018 and 2019. Exploratory, correlational and predictive approaches are presented. The exploratory results showed that ATE is the station with the highest prevalence of ozone pollution. Likewise, in an hourly scale analysis, the pollution peaks were reported at 00:00 and 14:00. Finally, the machine learning models that showed the best predictive capacity for adjusting the ozone concentration were the linear regression and support vector machine.

As air quality plummets across the U.S. this summer, researchers have a glimmer of good news. Artificial intelligence may soon provide advanced warning of future pollution events, which could help hospitals prepare for the uptick in pollution-related illnesses, or even reduce people's exposure entirely. A spike in air pollution often leads to a spike in hospital admissions, as it can exacerbate asthma and other pre-existing respiratory conditions, cause upper respiratory tract infections, or increase the likelihood of stroke. But it's currently impossible to prepare for these spikes due to the constraints of existing air quality forecasts, which are only accurate up to three days in advance, Yunsoo Choi, associate professor of atmospheric chemistry from the University of Houston, told EHN. In that short amount of time, one of the only things we can do to protect ourselves is to limit time spent outdoors. But now, through the use of artificial intelligence (AI) technology, Choi and the University of Houston's Air Quality Forecasting and Modeling Lab created a new model that can predict ozone pollution up to 14 days ahead of time.