BackgroundMany studies have shown that high temperatures or heat waves were associated with mortality and morbidity. However, few studies have examined whether temperature changes between neighboring days have any significant impact on human health.MethodA distributed lag non-linear model was employed to investigate the effect of temperature changes on mortality in summer during 2006–2010 in two subtropical Chinese cities. The temperature change was defined as the difference of the current day’s and the previous day’s mean temperature.ResultsWe found non-linear effects of temperature changes between neighboring days in summer on mortality in both cities. Temperature increase was associated with increased mortality from non-accidental diseases and cardiovascular diseases, while temperature decrease had a protective effect on non-accidental mortality and cardiovascular mortality in both cities. Significant association between temperature changes and respiratory mortality was only found in Guangzhou.ConclusionThis study suggests that temperature changes between neighboring days might be an alternative temperature indicator for studying temperature-mortality relationship.

In this paper, we propose a consistent nonparametric test for linearity in panel data models with interactive fixed effects. To construct the test statistic, we need to estimate the model under the null hypothesis of linearity and then obtain the restricted residuals. We show that after being appropriately centered and standardized, the test statistic is asymptotically normally distributed both under the null hypothesis and a sequence of Pitman local alternatives. To improve the finite sample performance, we propose a bootstrap procedure to obtain the bootstrap p-values. A small set of Monte Carlo simulations illustrates that our test performs well in finite samples. An application to an economic growth data indicates significant nonlinear relationships between economic growth, initial income level and capital accumulation.

China has experienced rapid urban expansion and agricultural land loss, and the land conversion has accelerated in central provinces since the mid-1990s. The goal of this paper is to examine the relative importance of socioeconomic and policy factors on the urban conversion of agricultural land in Henan Province, China. Using panel econometric models, we examine how socioeconomic and policy factors affect agricultural land conversion at the county level across three time periods, 1995-2000, 2000-2005, and 2005-2010. The results show that both urban land rent and urban wages are essential factors that positively contribute to the conversion of agricultural land. It is also found that per capita GDP is correlated with more urban development and agricultural land loss. Consistent with expectations, agricultural financial support is negatively correlated with agricultural land conversion, suggesting a policy success. Finally, the decomposition analysis illustrates that urban wages are the most influential positive factor and agricultural financial support is the most influential negative factor affecting the urban conversion of agricultural land.

A dietary survey was conducted over three consecutive days by using 24-hour dietary recall in the Pearl River Delta of South China to investigate the dietary consumption status. A total of 1702 food samples, 22 food groups, were collected, and aluminium concentrations of foods were determined by using ICP-MS. Weekly dietary exposure to aluminium of the average urban residents of South China was estimated to be 1.5 mg kg⁻¹ body weight, which amounted to 76% of the provisional tolerable weekly intake. Wheat-made products (53.5%) contributed most to the dietary exposure, followed by vegetables (12.2%). The high-level consumers' weekly exposure to aluminium was 11.1 mg kg⁻¹ body weight, which amounted to 407% of the provisional tolerable weekly intake. The results indicated that the general urban residents in South China might be safe from aluminium exposure, but the high-level consumers might be at some risk of aluminium exposure. The foods contributing to aluminium exposure were processed food with aluminium-containing food additives. It is necessary to take effective measures to control the overuse of aluminium-containing food additives.

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