China's rapid urbanization has led to an increasing level of exposure to air pollution and a decreasing level of exposure to vegetation among urban populations. Both trends may pose threats to psychological well-being. Previous studies on the interrelationships among greenness, air pollution and psychological well-being rely on exposure measures from remote sensing data, which may fail to accurately capture how people perceive vegetation on the ground. To address this research gap, this study aimed to explore relationships among neighbourhood greenness, air pollution exposure and psychological wellbeing, using survey data on 1029 adults residing in 35 neighbourhoods in Guangzhou, China. We used the Normalized Difference Vegetation Index (NDVI) and streetscape greenery (SVG) to assess greenery exposure at the neighbourhood level, and we distinguished between trees (SVG-tree) and grasses (SVG-grass) when generating streetscape greenery exposure metrics. We used two objective (PM2.5 and NO2 concentrations) measures and one subjective (perceived air pollution) measure to quantify air pollution exposure. We quantified psychological well-being using the World Health Organization Well-Being Index (WHO-5). Results from multilevel structural equation models (SEM) showed that, for parallel mediation models, while the association between SVG-grass and psychological well-being was completely mediated by perceived air pollution and NO2, the relationship between SVG-tree and psychological well-being was completely mediated by ambient PM2.5, NO2 and perceived air pollution. None of three air pollution indicators mediated the association between psychological well-being and NDVI. For serial mediation models, measures of air pollution did not mediate the relationship between NDVI and psychological well-being. While the linkage between SVG-grass and psychological well-being scores was partially mediated by NO2-perceived air pollution, SVG-tree was partially mediated by both ambient PM2.5-perceived air pollution and NO2-perceived air pollution. Our results suggest that street trees may be more related to lower air pollution levels and better mental health than grasses are. Response to Reviewers: Responses to Reviewer #1 Reviewer comment 1: I don't want to be a stickler, but I cannot agree with your reasoning that specifying both serial and parallel mediation components is a bad thing because it would decrease the degrees of freedom. First of all, the epidemiological reality is complex and probably involves all sorts of mediation components working together. Second, more complex models with fewer df may actually suffer from an artificial increase in model fit measures, hence the parsimony-adjusted model fit measures like the PNFI (
Nitrogen removal processing in different constructed wetlands treating different kinds of wastewater often varies, and the contribution to nitrogen removal by various pathways remains unclear. In this study, the seasonal nitrogen removal and transformations as well as nitrogen balance in wetland microcosms treating slightly polluted river water was investigated. The results showed that the average total nitrogen removal rates varied in different seasons. According to the mass balance approach, plant uptake removed 8.4-34.3 % of the total nitrogen input, while sediment storage and N(2)O emission contributed 20.5-34.4 % and 0.6-1.9 % of nitrogen removal, respectively. However, the percentage of other nitrogen loss such as N(2) emission due to nitrification and denitrification was estimated to be 2.0-23.5 %. The results indicated that plant uptake and sediment storage were the key factors limiting nitrogen removal besides microbial processes in surface constructed wetland for treating slightly polluted river water.
Consider a continuous-time renewal risk model with a constant force of interest. We assume that claim sizes and inter-arrival times correspondingly form a sequence of independent and identically distributed random pairs and that each pair obeys a dependence structure described via the conditional tail probability of a claim size given the inter-arrival time before the claim. We focus on determination of the impact of this dependence structure on the asymptotic tail probability of discounted aggregate claims. Assuming that the claim-size distribution is subexponential, we derive an exact locally uniform asymptotic formula, which quantitatively captures the impact of the dependence structure. When the claim-size distribution is extended-regularly-varying tailed, we show that this asymptotic formula is globally uniform.
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