The Optimization Strategy of the Existing Urban Green Space Soil Monitoring System in Shanghai, China

Association between residential greenspace and mental health among cancer survivors in Shanghai, China

BACKGROUND

Living near and enjoying visually green landscapes is associated with better mental health, but evidence focusing on vulnerable populations (such as cancer survivors) is sparse. The purpose of this study was to explore the association between residential greenspace and anxiety and depressive symptoms among cancer survivors in Shanghai, China.

METHODS

In total, 4195 cancer survivors participated in this study from the 2022 Shanghai Cancer Patient Needs Survey. The estimation of residential greenspaces was based on Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). The presence and severity of depressive and anxiety symptoms were assessed by using the Patient Health Questionnaire-2 (PHQ-2) and Generalized Anxiety Disorder-2 (GAD-2). The relation between mental health and green space was assessed using the Generalized Additive Model (GAM) after controlling for relevant individual covariates and contextual characteristics.

RESULTS

The prevalence of anxiety and depression in cancer survivors was 36.2% and 28.3% respectively. After multivariate adjustment, each increase in inter-quartile range (IQR) for NDVI in the 250 m buffer (NDVI-250m) was associated with a decrease in PHQ-2 score (△score (95%CI): 0.018 (-0.034, -0.002)) and GAD-2 score (△score (95%CI): 0.018 (-0.034, -0.002)), respectively. We observed that an increase in IQR for NDVI-250m was associated with a 3.3% (Odds ratio (OR) (95%CI):0.967 (0.943, 0.991)) reduction in anxiety symptoms. More pronounced greenspace-mental health effects were found among young adults (18-65 years) and participants living in suburban areas, compared to young people over 65 and those living in urban areas (P-interaction < 0.05).

CONCLUSIONS

Higher levels of residential green space are associated with lower risk of depression and anxiety disorders. Our findings will fill the gap in the relationship between green space and mental health among cancer survivors in urban China, and provide new evidence for garden afforestation, community planning and policy-making. To better understand this association, more longitudinal studies are necessary to investigate the mechanisms involved.

Evaluating sample sizes and design for monitoring and characterizing the spatial variations of potentially toxic elements in the soil

Cost-effective, representative and spatial coverage sampling designs are required to monitor the effects of potentially toxic elements (PTEs) in the soil. This study aims to evaluate the minimum sample sizes and placement of soil sampling designs to monitor and characterize the spatial variation of the PTEs (Cu, Zn, Cd, Cr, Pb, and Ni) in the soils. However, there is no standardized approach for evaluating the optimum soil sample size and monitoring location because of the spatial heterogeneity of PTEs in the soil. As a result, three broad techniques were applied. The first step was to use Global Moran's I and q-statistic values to describe the variability of soil PTEs and select appropriate evaluation methods. Second, using simple random sampling (SRS), ordinary kriging (OK), and Mean of Surface with Non-homogeneity (MSN), we estimated and evaluated soil PTEs in the current soil sampling schemes. Finally, MSN and spatial simulated annealing (SSA) optimization techniques were used to assess the required sample sizes and placements in the existing designs. Method performance was evaluated using a standard error (SE) and a relative standard error of the mean (RSE). Except for Zn and Cd, all PTEs tested showed heterogeneous distributions over the area. The MSN lowered the predicted SE by 79-86 % compared with SRS. The OK approach also outperformed the SRS method regarding mean estimated values of soil PTEs by 42-57 %. After SSA refined the initial design, the predicted SE by MSN of Cr and Zn was lowered by 13 % and 39 %, respectively. The MSN was effective with small sample sizes, reducing sample sizes and surveying costs by 39 % after SSA optimized the existing sample numbers. Thus, integrating various sampling strategies may be efficient for building optimal sample designs to monitor PTEs in the soil.

Optimal spatial sampling design for monitoring potentially toxic elements pollution on urban green space soil: A spatial simulated annealing and k-means integrated approach

Sampling design in soil science is critical because the lack of reliable methods and collecting samples requires tremendous work and resources. The aims were to obtain an optimal sampling design for assessing potentially toxic elements pollution using pilot Pb soil samples from the urban green space area of Shanghai, China. Two general steps have been used. The first step is to determine the optimum sample size against improving the prediction accuracy and monitoring costs using the spatial simulated annealing (SSA) algorithm. Secondly, we evaluated their likely placement of new extra sampling points by integrated SSA with k-means (SSA+ k-means) and expert-based (SSA+ expert-based) sampling methods. The improvement of sampling design by the integrated sampling approaches was evaluated using mean kriging variance (MKV), root mean square error (RMSE), and mean absolute percentage error (MAPE). The findings indicated that adding and placing 350 new monitoring points upon the existing sampling design by SSA increased the prediction accuracy by 64.35%. The MKV for the optimized SSA+ k-means sample was lower than by 4.12 mg/kg, 9.46 mg/kg compared with locations optimized by SSA and SSA+ expert-based method, respectively. Optimizing new sampling locations by SSA+ k-means sampling method was reduced MAPE by 9.26% and RMSE by 7.13 mg/kg compared to optimizing by SSA alone. However, there was no improvement in placing the new sampling points in SSA+ expert-based sampling method; instead, it increased the error by 8.11%. This paper shows integrating optimization approaches to evaluate the existing sampling design and optimize a new optimal sampling design.