Asthma, rhinitis and eczema (allergic or non-allergic) have increased throughout the world during the last decades, especially among children. Changes in the indoor environment are suspected to be important causes. China has experienced a dramatic change in indoor environmental exposures during the past two decades. However, such changes and their associations with children’s asthma and other health aspects have not been thoroughly studied. China, Children, Homes, Health (CCHH), Phase I, was a cross-sectional questionnaire survey of 48219 children 1–8 years old in 10 Chinese cities during 2010–2012. The questionnaire includes the International Study of Asthma and Allergies in Childhood (ISAAC) core health questions and additional questions regarding housing, life habits and outdoor environment. In health analyses, children aged 3–6 years old were included. The prevalences of doctor diagnosed asthma varied from 1.7% to 9.8% (mean 6.8%), a large increase from 0.91% in 1999 and 1.50% in 2000. The prevalence of wheeze, rhinitis and atopic eczema (last 12 months) varied from 13.9% to 23.7%, 24.0% to 50.8% and 4.8% to 15.8%, respectively. Taiyuan had the lowest prevalences of all illnesses and Shanghai the highest, except for wheezewhere the highest value was for Urumqi. We found (1) no obvious association between disease prevalences and ambient PM₁₀ concentrations and (2) higher prevalences of disease in humid climates with hot summers and cold winters, but with no centrally heated buildings. Associations between the diseases and economic status as indexed by Gross Domestic Product (GDP) requires further study.

Machine learning has proven to be a powerful technique during the past decades. Artificial neural network (ANN), as one of the most popular machine learning algorithms, has been widely applied to various areas. However, their applications for catalysis were not well-studied until recent decades. In this review, we aim to summarize the applications of ANNs for catalysis research reported in the literature. We show how this powerful technique helps people address the highly complicated problems and accelerate the progress of the catalysis community. From the perspectives of both experiment and theory, this review shows how ANNs can be effectively applied for catalysis prediction, the design of new catalysts, and the understanding of catalytic structures.