Aging Population, Balanced Diet and China's Grain Demand.

The need to make more accurate grain demand (GD) forecasting has become a major topic in the current international grain security discussion. Our research aims to improve short-term GD prediction by establishing a multi-factor model that integrates the key factors: shifts in dietary structures, population size and age structure, urbanization, food waste, and the impact of COVID-19. These factors were not considered simultaneously in previous research. To illustrate the model, we projected China's annual GDP from 2022 to 2025. We calibrated key parameters such as conversion coefficients from animal foods to feed grain, standard person consumption ratios, and population size using the latest surveys and statistical data that were either out of date or missing in previous research. Results indicate that if the change in diets continued at the rate as observed during 2013-2019 (scenario 1), China's GD is projected to be 629.35 million tons in 2022 and 658.16 million tons in 2025. However, if diets shift to align with the recommendations in the Dietary Guideline for Chinese Residents 2022 (scenario 2), GD would be lower by 5.9-11.1% annually compared to scenario 1. A reduction in feed grain accounts for 68% of this change. Furthermore, for every 1 percentage point increase in the population adopting a balanced diet, GD would fall by 0.44-0.73 million tons annually during that period. Overlooking changes in the population age structure could lead to an overprediction of annual GDP by 3.8% from 2022 to 2025. With an aging population, China's GD would fall slightly, and adopting a balanced diet would not lead to an increase in GD but would have positive impacts on human health and the environment. Our sensitivity analysis indicated that reducing food waste, particularly cereal, livestock, and poultry waste, would have significant effects on reducing GD, offsetting the higher demand due to rising urbanization and higher incomes. These results underscore the significance of simultaneous consideration of multiple factors, particularly the dietary structure and demographic composition, resulting in a more accurate prediction of GD. Our findings should be useful for policymakers concerning grain security, health, and environmental protection.

Intake fractions of industrial air pollutants in China: estimation and application.

Intake fractions, an emissions-intake relationship for primary pollutants, are defined and are estimated in order to make simple estimates of health damages from air pollution. The sulfur dioxide (SO2) and total suspended particles (TSP) intake fractions for five cities of China are estimated for the four main polluting industries-electric power generation, mineral (mostly cement) products industry, chemical process industry and metallurgical industry (mainly iron and steel smelting). The Industrial Source Complex Long Term (ISTLT3) model is used to simulate the spatial distribution of incremental ambient concentrations due to emissions from a large sample of site-specific sources. Detailed population distribution information is used for each city. The average intake fractions within 50 km of these sources are 4.4x10(-6) for TSP, and 4.2x10(-6) for SO2, with standard deviations of 8.15x10(-6) and 9.16x10(-6), respectively. They vary over a wide range, from 10(-7) to 10(-5). Although the electric power generation has been the focus of much of the air pollution research in China, our results show that it has the lowest average intake fraction for a local range among the four industries, which highlights the importance of pollutant emissions from other industrial sources. Sensitivity analyses show how the intake fractions are affected by the source and pollutant characteristics, the most important parameter being the size of the domain. However, the intake fraction estimates are robust enough to be useful for evaluating the local impacts on human health of primary SO2 and TSP emissions. An application of intake fractions is given to demonstrate how this approach provides a rapid population risk estimate if the dose-response function is linear without threshold, and hence can help in prioritizing pollution control efforts.