The relationship between extreme ambient temperature and small for gestational age: A cohort study of 1,436,480 singleton term births in China.

Studies have shown that exposure to extreme ambient temperature can contribute to adverse pregnancy outcomes, however, results across studies have been inconsistent. We aimed to evaluate the relationships between trimester-specific extreme temperature exposures and fetal growth restriction indicated by small for gestational age (SGA) in term pregnancies, and to assess whether and to what extent this relationship varies between different geographic regions. We linked 1,436,480 singleton term newborns (2014-2016) in Hubei Province, China, with a sub-district-level temperature exposures estimated by a generalized additive spatio-temporal model. Mixed-effects logistic regression models were employed to estimate the effects of extreme cold (temperature ≤5th percentile) and heat exposures (temperature >95th percentile) on term SGA in three different geographic regions, while adjusting for the effects of maternal age, infant sex, the frequency of health checks, parity, educational level, season of birth, area-level income, and PM2.5 exposure. We also stratified our analyses by infant sex, maternal age, urban‒rural type, income categories and PM2.5 exposure for robustness analyses. We found that both cold (OR:1.32, 95% CI: 1.25-1.39) and heat (OR:1.17, 95% CI: 1.13-1.22) exposures during the third trimester significantly increased the risk of SGA in the East region. Only extreme heat exposure (OR:1.29, 95% CI: 1.21-1.37) during the third trimester was significantly related to SGA in the Middle region. Our findings suggest that extreme ambient temperature exposure during pregnancy can lead to fetal growth restriction. Governments and public health institutions should pay more attention to environmental stresses during gestation, especially in the late stage of the pregnancy.

Exploring the effects of multi-dimensional geographic environment on daily sleep and physical activity based on the Actigraph data.

Understanding the interplay between people's daily sleep and physical activity and how geographic environment influences them are important for developing healthy cities. However, such research has been limited. This study aims to explore the bidirectional and nonlinear relationship between daily sleep and physical activity, and further investigate the comprehensive influences of multi-dimensional geographic environment on these health behaviors. Based on the objective data on sleep and physical activity over seven consecutive days using wrist-based accelerometers in Beijing, China, we developed a series of models to analyze the mutual influences between people's daily sleep and physical activity, and employed the generalized additive model (GAM) to examine their potential nonlinear relationships and how geographic environment - including meteorological conditions, built environment, and social environment - influences them. The results show that sleep and physical activity exhibit notable bidirectional relationship. Moderate-to-vigorous physical activity (MVPA) is observed to improve sleep quality, but it decreases sleep duration. In contrast, total sleep time (TST) exhibits an inverted U-shaped pattern with both MVPA and total step counts, with the optimal sleep duration at 5 h. Furthermore, meteorological factors, built environment characteristics, and social environment have significant linear or nonlinear effects on people's daily sleep and physical activity. The outcomes of this study offer valuable insights for enhancing residents' health and developing healthy cities.

Impact assessment of recent climate change on rice yields in the Heilongjiang Reclamation Area of north-east China.

BACKGROUND: Investigating the degree to which climate change may have impacted on rice yields can provide an insight into how to adapt to climate change in the future. Meteorological and rice yield data over the period 1960-2009 from the Heilongjiang Reclamation Area of north-east China (HRANC) were used to explore the possible impacts of climate change on rice yields at sub-regional scale. RESULTS: Results showed that a warming trend was obvious in the HRANC and discernible climate fluctuations and yield variations on inter-annual scale were detected to have occurred in the 1980s and 1990s, respectively. Statistically positive correlation was observed between growing season temperature and rice yields, with an increase rate by approximately 3.60% for each 1°C rise in the minimum temperature during growing season. Such findings are consistent with the current mainstream view that warming climate may exert positive impacts on crop yields in the middle and higher latitude regions. CONCLUSION: Our study indicated that the growing season minimum temperature was a major driver of all the climatic factors to the recent increase trends in rice yield in HRANC over the last five decades.

PM2.5 Concentrations Variability in North China Explored with a Multi-Scale Spatial Random Effect Model.

Compiling fine-resolution geospatial PM2.5 concentrations data is essential for precisely assessing the health risks of PM2.5 pollution exposure as well as for evaluating environmental policy effectiveness. In most previous studies, global and local spatial heterogeneity of PM2.5 is captured by the inclusion of multi-scale covariate effects, while the modelling of genuine scale-dependent variabilities pertaining to the spatial random process of PM2.5 has not yet been much studied. Consequently, this work proposed a multi-scale spatial random effect model (MSSREM), based a recently developed fixed-rank Kriging method, to capture both the scale-dependent variabilities and the spatial dependence effect simultaneously. Furthermore, a small-scale Monte Carlo simulation experiment was conducted to assess the performance of MSSREM against classic geospatial Kriging models. The key results indicated that when the multiple-scale property of local spatial variabilities were exhibited, the MSSREM had greater ability to recover local- or fine-scale variations hidden in a real spatial process. The methodology was applied to the PM2.5 concentrations modelling in North China, a region with the worst air quality in the country. The MSSREM provided high prediction accuracy, 0.917 R-squared, and 3.777 root mean square error (RMSE). In addition, the spatial correlations in PM2.5 concentrations were properly captured by the model as indicated by a statistically insignificant Moran's I statistic (a value of 0.136 with p-value > 0.2). Overall, this study offers another spatial statistical model for investigating and predicting PM2.5 concentration, which would be beneficial for precise health risk assessment of PM2.5 pollution exposure.

The Influence of Air Pollution on Happiness and Willingness to Pay for Clean Air in the Bohai Rim Area of China.

Air pollution imposes detrimental impacts on residents' health and the general quality of life. Quantifying the influential mechanism of air pollution on residents' happiness and the economic value brought by environmental quality improvement could provide a scientific basis for the construction of livable cities. This study estimated urban residents' willingness to pay for air pollution abatement by modeling the spatial relationship between air quality and self-rated happiness with a Bayesian multi-level ordinal categorical response model. Using large-scale geo-referenced survey data, collected in the Bohai Rim area of China (including 43 cities), we found that a standard deviation decrease in the number of polluted days over a year was associated with about a 15 percent increase in the odds of reporting a higher degree of happiness, after controlling for a wide range of individual- and city-scale covariate effects. On average, urban residents in the Bohai Rim region were willing to pay roughly 1.42 percent of their average monthly household income for mitigating marginal reductions in air pollution, although great spatial variability was also presented. Together, we hoped that these results could provide solid empirical evidence for China's regional environmental policies aiming to promote individuals' well-being.

The Evolution Characteristics and Influence Mechanism of Chinese Venture Capital Spatial Agglomeration.

As an emerging financial entity, venture capital has a significant impact on regional development. However, the research on venture capital mainly focuses on the fields of finance, management, and economics, and fewer researchers study venture capital from the perspective of geography and space. This research explored the evolution characteristics and influence mechanism of Chinese venture capital spatial agglomeration. The innovation of this paper lies in including the spatial effect and conducting a spatial econometric analysis of the spatial agglomeration of venture capital in China after the exploratory analysis of the factors affecting the spatial agglomeration of venture capital. Firstly, based on the data of study area, this paper found that the spatial distribution of venture capital in China had an obvious agglomeration characteristic by using multiple measurement methods. Secondly, by constructing the spatial econometric model based on three different spatial weight matrices, we found that the science and technology environment, financial environment, social environment, and entrepreneurial environment levels were the main factors to affect the agglomeration of venture capital. Thirdly, due to the degree of spatial agglomeration of venture capital being divided into three stages in terms of time dimension, after the regression analysis of different periods, we found that the factors which affected spatial agglomeration of venture capital changed significantly with the passage of time. In addition, from the regression results of eastern, central, and western region samples, we can see that the degree of spillover effect was the lowest in the central region, the highest in the western region, and the middle in the eastern region. At last, this paper provided useful policy enlightenment for enterprise innovation, industrial upgrading, and regional economic management.

A Dynamic Spatio-Temporal Analysis of Urban Expansion and Pollutant Emissions in Fujian Province.

Urbanization processes at both global and regional scales are taking place at an unprecedent pace, leading to more than half of the global population living in urbanized areas. This process could exert grand challenges on the human living environment. With the proliferation of remote sensing and satellite data being used in social and environmental studies, fine spatial- and temporal-resolution measures of urban expansion and environmental quality are increasingly available. This, in turn, offers great opportunities to uncover the potential environmental impacts of fast urban expansion. This paper investigated the relationship between urban expansion and pollutant emissions in the Fujian province of China by building a Bayesian spatio-temporal autoregressive model. It drew upon recently compiled pollutant emission data with fine spatio-temporal resolution, long temporal coverage, and multiple sources of remote sensing data. Our results suggest that there was a significant relationship between urban expansion and pollution emission intensity-urban expansion significantly elevated the PM2.5 and NOx emissions intensity in Fujian province during 1995-2015. This finding was robust to different measures of urban expansion and retained after controlling for potential confounding effects. The temporal evolution of pollutant emissions, net of covariate effects, presented a fluctuation pattern rather than a consistent trend of increasing or decreasing. Spatial variability of the pollutant emissions intensity among counties was, however, decreasing steadily with time.

The effect of natural and anthropogenic factors on PM2.5: Empirical evidence from Chinese cities with different income levels.

The aim of this paper is to estimate the effects of natural conditions and anthropogenic factors on PM2.5 concentrations, taking into consideration differences in the income levels, and thus the development stages, of the cities studied. To achieve this goal, a balanced dataset of 287 Chinese cities was divided into different income-based panels for the period 1998-2015. The empirical estimation results indicated that meteorological conditions exerted varied effects on PM2.5 concentrations across different income-based panels. The results show that the coefficients of temperature were positive and significant in all panels, with the exception of upper-middle-income cities. Whilst wind speed and precipitation were found to be conducive to reducing PM2.5 concentrations, no such significant correlation was found in relation to relative humidity (except in high-income cities). In terms of the anthropogenic factors addressed in the study, we found an inverted U-shaped relationship between economic development and PM2.5 concentrations, confirming the Environmental Kuznets Curve hypothesis. In addition, the industrial structure and road density were observed to exert significant positive impacts on PM2.5 concentrations. The empirical analysis of the effects of FDI on PM2.5 concentrations indicate that FDI aggravated PM2.5 pollutions in the total cities and lower-middle-income cities panels, supporting the Pollution Haven Hypothesis. The empirical results for population density suggested that it does not significantly influence PM2.5 concentrations. Moreover, we found that built-up area exerts mixed effects on PM2.5 concentrations. These results cast a new light on the issue of PM2.5 pollution for government policy makers tasked with formulating measures to mitigate the concentration of such pollutants, encouraging that consideration be given to the differences between cities with different income levels.

Reconsidering the Relationship between Air Pollution and Deprivation.

This paper critically examines the relationship between air pollution and deprivation. We argue that focusing on a particular economic or social model of urban development might lead one to erroneously expect all cities to converge towards a particular universal norm. A naive market sorting model, for example, would predict that poor households will eventually be sorted into high pollution areas, leading to a positive relationship between air pollution and deprivation. If, however, one considers a wider set of theoretical perspectives, the anticipated relationship between air pollution and deprivation becomes more complex and idiosyncratic. Specifically, we argue the relationship between pollution and deprivation can only be made sense of by considering processes of risk perception, path dependency, gentrification and urbanization. Rather than expecting all areas to eventually converge to some universal norm, we should expect the differences in the relationship between air pollution and deprivation across localities to persist. Mindful of these insights, we propose an approach to modeling which does not impose a geographically fixed relationship. Results for Scotland reveal substantial variations in the observed relationships over space and time, supporting our argument.

Multilevel Modelling with Spatial Interaction Effects with Application to an Emerging Land Market in Beijing, China.

This paper develops a methodology for extending multilevel modelling to incorporate spatial interaction effects. The motivation is that classic multilevel models are not specifically spatial. Lower level units may be nested into higher level ones based on a geographical hierarchy (or a membership structure--for example, census zones into regions) but the actual locations of the units and the distances between them are not directly considered: what matters is the groupings but not how close together any two units are within those groupings. As a consequence, spatial interaction effects are neither modelled nor measured, confounding group effects (understood as some sort of contextual effect that acts 'top down' upon members of a group) with proximity effects (some sort of joint dependency that emerges between neighbours). To deal with this, we incorporate spatial simultaneous autoregressive processes into both the outcome variable and the higher level residuals. To assess the performance of the proposed method and the classic multilevel model, a series of Monte Carlo simulations are conducted. The results show that the proposed method performs well in retrieving the true model parameters whereas the classic multilevel model provides biased and inefficient parameter estimation in the presence of spatial interactions. An important implication of the study is to be cautious of an apparent neighbourhood effect in terms of both its magnitude and statistical significance if spatial interaction effects at a lower level are suspected. Applying the new approach to a two-level land price data set for Beijing, China, we find significant spatial interactions at both the land parcel and district levels.