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Among the critical global crises curbing world development and sustainability, air quality degradation has been a long-lasting and increasingly urgent one and it has been sufficiently proven to pose severe threats to human health and social welfare. A higher level of model prediction accuracy can play a fundamental role in air quality assessment and enhancing human well-being. In this paper, four types of machine learning models—random forest model, ridge regression model, support vector machine model, extremely randomized trees model—were adopted to predict PM2.5 concentration in ten cities in the Jing-Jin-Ji region of north China based on multi-sources spatiotemporal data including air quality and meteorological data in time series. Data were fed into the model by using the rolling prediction method which is proven to improve prediction accuracy in our experiments. Lastly, the comparative experiments show that at the city level, RF and ExtraTrees models have better predictive results with lower mean absolute error (MAE), root mean square error (RMSE), and higher index of agreement (IA) compared to other selected models. For seasonality, level four models all have the best prediction performances in winter time and the worst in summer time, and RF models have the best prediction performance with the IA ranging from 0.93 to 0.98 with an MAE of 5.91 to 11.68 μg/m3. Consequently, the demonstration of how each model performs differently in each city and each season is expected to shed light on environmental policy implications. [ABSTRACT FROM AUTHOR]

Background and Objectives: Health outcomes such as survival, minimal disability and well-being are presumptively dependent on food and ecosystems. An integral measure of the critical urban food system linkages to health problems is needed. Much of the current health change in cities could be attributed to short-comings in food systems which can pose threats to food security and food safety. Health problems have needed a reconceptualisation of present medical and nutritional constructs.Methods and Study Design: The present study is based on a situational analysis of food and the related ecosystems presumptively affected by rapid urbanisation in China. With an ecological information matrix, an Urban Food System Index with ten indicators which could influence food system outcomes and promote health and well-being has been developed. It uses sixteen data sets from the National Bureau of Statistics for all 31 provinces in China. The indicators were Locality, Climate, Biodiversity, Infrastructure, Transport, Population structure, Livelihood, Recreation and Socialisation, Personal security and Communication. The indicators for each province, scored between 1 (severe) and 5 (best), were used to predict life expectancy for China as a whole by multivariable regression analysis.Results: The best model explained 70% of the variance and had significant beta coefficients for population structure (proportion of juveniles) (-0.52, p<0.0001) and livelihood (food expenditure) (0.31, p<0.05).Conclusions: Population characteristics and livelihoods related to food systems can account for much of life expectancy as a health outcome. An index which captured this in-formation is provided and could evaluate concurrently as well as prospectively food system-related health with urbanisation. [ABSTRACT FROM AUTHOR]

For most of its history, China has supported a growing population through food systems which have been mutually inclusive of people and their locality. This trajectory has required adequate ecosystem maintenance or humanised reformulation and a high degree of recyclable nutrient flow. The 'tipping point' in habitat sustainability has come with the size and demographic structure of China's population to one that is ageing, with modernisation of its infrastructure and increased expectations of better livelihoods, standards of living and health. In order to meet these expectations, China has embarked on rapid urbanisation for upwards of 300 million people over the next 15-20 years and to do so taking account of the environmental limitations. The process will radically change rural as well as urban China and the systems which connect them. Chief among these will be ecosystems in number and type along with the food and health systems integral to them. To minimise ecological damage and optimise the benefits to people and place, describing, monitoring and managing the process will be paramount. The present paper is a situational analysis of health as it may be ecologically favoured or disordered (Ecosystem Health Disorders) and of the food systems on which the environment and health depend. An effort is made to enumerate the current situation in China in a way that might enable the optimisation of humanised ecosystems. [ABSTRACT FROM AUTHOR]