Application of land use regression to map environmental noise in Shanghai, China

Geospatial analysis for environmental noise mapping: A land use regression approach in a metropolitan city

Environmental noise can lead to adverse health outcomes. Understanding the spatial variability of environmental noise is crucial for mitigating potential health risks and developing influential urban strategies for reducing noise levels. This study aimed to measure noise levels and develop a land use regression (LUR) model to determine the spatial variability of environmental noise in Shiraz, Iran. A grid-based technique was used to establish 191 noise measurement sites (summer) across the city to generate the LUR model based on two noise metrics: Lden and Lnight. Leave-one-out cross-validation (LOOCV) and 38 additional measurement sites (winter) were used for the LUR model assessment. The mean values of Lden and Lnight during summer were 68.20 (±8.05) and 58.95 (±9.55), respectively, while during winter, the corresponding values were 69.46 (±5.46) and 58.81 (±6.79). The LUR models explained 67% and 65% of the spatial variability in Lden and Lnight, respectively. LOOCV analysis demonstrated R2 values of 0.64 and 0.61. Moreover, findings indicated mean absolute error (MAE) values of 3.96 dB(A) for Lden and 4.74 dB(A) for Lnight. Validation based on an additional set of 38 measurement sites revealed R2 values of 0.62 for both Lden and Lnight, with MAE of 2.78 and 3.31, respectively. In addition, the adjusted R2 values were 0.54 and 0.53. The results indicated no significant temporal variations between summer and winter. The results revealed that road-related variables significantly influenced noise levels. Moreover, the results indicated that Lden and Lnight levels were higher than the World Health Organization recommendations for exposure to road traffic noise. The results of our study showed that the LUR modeling approach based on geographical predictors is an effective tool for assessing changes in ambient noise levels in other cities in Iran and around the globe.

The impact of residential environment on stroke onset and its spatial heterogeneity: A multiscale exploration in Shanghai

BACKGROUND

Stroke is a worldwide concern due to its high disability and mortality rates, especially in many countries entering ageing societies. This study aims to understand the spatial heterogeneity of stroke onset and residential environment influence scopes from multiscale.

METHODS

The 2013 to 2022 spatiotemporal distribution pattern of stroke onset was obtained via out-patient data from a hospital in Shanghai. Then nine residential environmental factors were selected to estimate the association of stroke onset by multiscale geographically weighted regression (MGWR), in three scenarios.

RESULTS

Accessibility to pubs/bars (PUB) and building density (BD) were the top two residential environmental factors both for the entire sample and by gender. Stress-related environmental factors have a greater impact on the onset of stroke in men but are limited in scope. The population of elderly people have relevance to environmental variables heterogeneity. The indicators relating to unhealthy food and alcohol suggest that habit-inducing environmental factors have a limited impact on stroke onset, but rather that pre-existing habits play a greater role.

CONCLUSIONS

MGWR analyses individual components across multiple bandwidths, revealing geographical disparities in the impact of elements that would otherwise be undetected on a global scale. Environmental factors have a limited impact on the onset of stroke. When society is faced with both heavy ageing and fiscal constraints, some of the blue-green space budgets can be scaled back to invest in more secure facilities.

A study on identifying synergistic prevention and control regions for PM2.5 and O3 and exploring their spatiotemporal dynamic in China

Air pollutants, notably ozone (O3) and fine particulate matter (PM2.5) give rise to evident adverse impacts on public health and the ecotope, prompting extensive global apprehension. Though PM2.5 has been effectively mitigated in China, O3 has been emerging as a primary pollutant, especially in summer. Currently, alleviating PM2.5 and O3 synergistically faces huge challenges. The synergistic prevention and control (SPC) regions of PM2.5 and O3 and their spatiotemporal patterns were still unclear. To address the above issues, this study utilized ground monitoring station data, meteorological data, and auxiliary data to predict the China High-Resolution O3 Dataset (CHROD) via a two-stage model. Furthermore, SPC regions were identified based on a spatial overlay analysis using a Geographic Information System (GIS). The standard deviation ellipse was employed to investigate the spatiotemporal dynamic characteristics of SPC regions. Some outcomes were obtained. The two-stage model significantly improved the accuracy of O3 concentration prediction with acceptable R2 (0.86), and our CHROD presented higher spatiotemporal resolution compared with existing products. SPC regions exhibited significant spatiotemporal variations during the Blue Sky Protection Campaign (BSPC) in China. SPC regions were dominant in spring and autumn, and O3-controlled and PM2.5-dominated zones were detected in summer and winter, respectively. SPC regions were primarily located in the northwest, north, east, and central regions of China, specifically in the Beijing-Tianjin-Hebei urban agglomeration (BTH), Shanxi, Shaanxi, Shandong, Henan, Jiangsu, Xinjiang, and Anhui provinces. The gravity center of SPC regions was distributed in the BTH in winter, and in Xinjiang during spring, summer, and autumn. This study can supply scientific references for the collaborative management of PM2.5 and O3.

Effects of built and natural environments on leisure physical activity in residential and workplace neighborhoods

Few studies have investigated relative contributions of the built and natural environments to and their nonlinear associations with leisure physical activity (PA) in different spatial contexts. Applying gradient boosting decision tree models to data comprising 1049 adults collected in Shanghai, we investigated the associations between built and natural environments and leisure PA in residential and workplace neighborhoods. Results show that the built environment is more important than the natural environment to leisure PA in both residences and workplaces. Environmental attributes have nonlinear and threshold effects. Within certain ranges, land use mix and population density have opposite associations with leisure PA in residences and workplaces, whereas the distance to the city center and the area of water are associated with leisure PA in residences and workplaces with the same direction. These findings help urban planners design context-specific environmental interventions for supporting leisure PA.

Association of residential noise exposure with maternal anxiety and depression in late pregnancy

BACKGROUND

Noise is one of the most important environmental risk factors that adversely affects human health. Residential noise exposure has been associated with increased risk of anxiety and depression in the general population. However, limited study has been conducted in pregnant women.

OBJECTIVE

To examine the associations of residential noise exposure with prenatal anxiety and depression.

METHODS

Self-Rating Anxiety Scale (SAS) and Center for Epidemiological Survey Scale (CES-D) were used to assess the status of prenatal anxiety and depression for 2,018 pregnant women in Shanghai, China. Residential noise exposure was represented by a land use regression model. Multivariate logistic regression model was used to estimate the associations of noise exposure with prenatal anxiety and depression.

RESULTS

The prevalence rates of prenatal anxiety and depression were 7.5 % and 8.1 %, respectively. The mean (±standard deviation) residential noise exposure during the whole pregnancy was 60.69 (±3.31) dB (A). Higher residential noise exposure was associated with increased odds of both prenatal anxiety and depression. Compared with low level of noise exposure group (<65 dB(A)), the odds of prenatal anxiety and depression increased 69 % (OR = 1.69, 95 % CI, 1.01-2.82) and 71 % (OR = 1.71, 95 % CI, 1.05-2.80) in higher noise exposure group (≥65 dB(A)), respectively. Stratified analyses showed that the associations were stronger among pregnant women with lower socioeconomic status.

CONCLUSION

Residential noise exposure during pregnancy might be a risk factor for prenatal anxiety and depression.