Abiotic and biotic releases of nitrous acid (HONO) from soils contribute substantially to the missing source of tropospheric HONO and hydroxyl radicals (OH). However, global and regional patterns of soil HONO emissions are rarely quantified, and the contributions of such emissions to atmospheric oxidization capacity are unclear. Here, we present that the best estimate of global soil HONO emissions in 2017 was 9.67 with a range of 7.36–11.99 Tg N yr−1, and cropland soils accounting for ∼79%. The analyses also indicate that regional soil HONO emissions enhanced ground OH concentrations by 10%–60% and ozone concentrations by 0.5–1.5 ppb in daytime in the ambient area of Shanghai, China. The impact of soil HONO emissions on OH budgets was more significant in rural than urban areas. These findings suggest that the soil HONO emissions, especially from cropland, could quicken photochemical reactions, and aggravate air pollution in rural areas.

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The majority of children with Autism Spectrum Disorders (ASD) have faced difficulties in sensory processing, which affect their ability of effective attention and stress management. Children with ASD also have unique patterns of sensory processing when responding to the stimuli in the environment. In this study, a real-time monitoring system has been designed and developed for attention and stress detection. Comprehensive sensory information, including environmental, physiological, and sensory profile data can be collected by the system using sensors, smart devices, and a standard sensory profiling questionnaire. Data acquisition with 35 ASD children using the system prototype was successfully conducted. With the acquired data set, different machine learning models were trained to predict attentional and stress level. Among all the investigated models, Gradient Boosting Decision Tree and Random Forest obtained the best prediction accuracies of 86.67% and 99.05% on attention and stress detection respectively. The two models were then implemented into the system for automatic detection. Future work could be focusing on exploring more supportive features to improve the prediction accuracy for attention detection. Such an easily-accessed monitoring system tailored for children with ASD could be widely-used in daily life to assist ASD users with their attention and stress management. CCS CONCEPTS •Human-centered computing~Accessibility~Accessibility technologies • Applied computing~Life and medical sciences~Health informatics • Social and professional topics~User characteristics~People with disabilities

Sensory processing issues are one of the most common issues observed in autism spectrum disorders (ASD). Technologies that could address the issue serve a more and more important role in interventions for ASD individuals nowadays. In this study, a sensory management recommendation system was developed and tested to help ASD children deal with atypical sensory responses in class. The system employed sensor fusion and machine learning techniques to identify distractions, anxious situations, and the potential causes of these in the surroundings. Another novelty of the system included a sensory management strategy making a module based on fuzzy logic, which generated alerts to inform teachers and caregivers about children’s states and risky environmental factors. Sensory management strategies were recommended to help improve children’s attention or calm children down. The evaluation results suggested that the use of the system had a positive impact on children’s performance and its design was user-friendly. The sensory management recommendation system could work as an intelligent companion for ASD children that helps with their in-class performance by recommending management strategies in relation to the real-time information about the children’s environment.