Spatiotemporal characteristics of NO2, PM2.5 and O3 in a coastal region of southeastern China and their removal by green spaces

Synergistic control of urban heat island and urban pollution island effects using green infrastructure

Urban heat island (UHI) and urban pollution island (UPI) effects are two major challenges that affect the liveability and sustainability of cities under the circumstance of climate change. However, existing studies mostly addressed them separately. Urban green infrastructure offers nature-based solutions to alleviate urban heat, enhance air quality and promote sustainability. This review paper provides a comprehensive synthesis of the roles of urban green spaces, street trees, street hedges, green roofs and vertical greenery in mitigating UHI and UPI effects. These types of green infrastructure can promote the thermal environment and air quality, but also potentially lead to conflicting impacts. Medium-sized urban green spaces are recommended for heat mitigation because they can provide a balance between cooling efficiency and magnitude. Conversely, street trees pose a complex challenge since they can provide cooling through shading and evapotranspiration while hindering pollutant dispersion due to reduced air ventilation. Integrated research that considers simultaneous UHI and UPI mitigation using green infrastructure, their interaction with building features, and the urban geographical environment is crucial to inform urban planning and maximize the benefits of green infrastructure installations.

Linking joint exposures to residential greenness and air pollution with adults' social health in dense Hong Kong

Despite the growing recognition of the impact of urban environments on social health, limited research explores the combined associations of multiple urban exposures, particularly in dense cities. This study examines the interplay between greenspace, air pollution, and social health as well as the underlying pathways and population heterogeneity in Hong Kong using cross-sectional survey data from 1977 adults and residential environmental data. Social health includes social contacts, relations, and support. Greenspace used street-view greenness (SVG), park density, and the normalized difference vegetation index (NDVI). 100-m daily ground NO2 and O3, indicative of air pollution, were derived using a spatiotemporal deep learning model. Mediators involved physical activity and negative emotions. Main analyses were performed in a 1000-m buffer with multivariate logistical regressions, stratification, interaction, and Partial Lease Square - Structural Equation Modelling (PLS-SEM). Multi-exposure models revealed positive associations between park density/SVG and social contacts, as well as between SVG and social relations, while O3 was negatively associated with social relations/support. Significant moderators included age, birthplace, employment, and education. PLS-SEM indicated direct positive associations between SVG and social contacts/relations and significant indirect negative associations between NO2/O3 and social health via negative emotions. This study adds to urban health research by exploring complex relationships between greenspace, air pollution, and social health, highlighting the role of the environment in fostering social restoration.

Does the morphology of residential greenspaces contribute to the development of a cardiovascular-healthy city?

BACKGROUNDS

Greenspaces are indispensable for the construction of a healthy city. Research has shown that greenspaces contribute to the reduction of cardiovascular risks. However, the role of greenspace morphology in the development of a healthy city is not well understood.

METHODS

Our study utilized data from a cardiovascular disease screening cohort comprising 106,238 residents in Anhui Province, China, aged between 35 and 75 years. We calculated landscape indices of each participant using high-resolution land cover data to measure the greenness, fragmentation, connectivity, aggregation, and shape of greenspaces. We used a multivariate linear regression model to assess the associations between these landscape indices and triglyceride risk, and employed a structural equation model to explore the potential contributions of heatwaves and fine particulate matter (PM2.5) to this association.

RESULTS

Overall, triglyceride was expected to increase by 0.046% (95% CI: 0.040%, 0.052%) with a 1% increase in the percentage of built-up area. Conversely, an increase in the percentage of greenspace was associated with a 0.270% (95% CI: 0.337%, -0.202%) decrease in triglyceride levels. Furthermore, when the total greenspace was held constant, the shape, connectedness, and aggregation of greenspace were inversely correlated with triglyceride levels, with effects of -0.605% (95% CI: 1.012%, -0.198%), -0.031% (95% CI: 0.039%, -0.022%), and -0.049% (95% CI: 0.058%, -0.039%), respectively. Likewise, the protective effect of the area-weighed mean shape index was higher than that of the total amount of greenspace. The stratification results showed that urban residents benefited more from greenspace exposure. Greenspace morphology can minimize triglyceride risk by reducing pollutant and heatwaves, with aggregation having the greatest effect on reducing pollutants whereas fragmentation is more efficient at reducing heatwaves.

CONCLUSION

Exposure to the greenspaces morphology is associated with a reduction in triglyceride risk. The study has important practical and policy implications for early health monitoring and the spatial layout of greenspace and will provide scientific information for healthy urban planning by reducing unfavorable health consequences.

Where and how to invest in greenspace for optimal health benefits: a systematic review of greenspace morphology and human health relationships

Research on the relationship between greenspace morphology and health is a growing field that informs the spatial design of greenspace to enhance health outcomes. This study reviews the current progress, methodologies, and knowledge gaps in this area. From a database search of 272 940 English articles and 39 053 Chinese articles up to April 18, 2024, we identified 22 and 7 studies on the topic for further evaluation. Predominantly cross-sectional and neighbourhood-scale analyses were conducted using land cover maps ranging from 0·25 to 100 meters in resolution. Six primary characteristics of greenspace morphology have been studied, including size, shape, fragmentation, connectedness, aggregation, and diversity. While associations between greenspace morphology and health outcomes have been observed, both their reliability and generalisability remain suggestive due to ecological study designs and heterogeneity among studies. Future research should prioritise individual-level prospective cohorts and intervention studies. Exploring mechanisms linking greenspace morphology and health, determining optimal map resolution, and distinguishing it from greenness magnitude in statistical analysis is essential. This evidence is crucial for health-promoting greenspace planning and should be routinely integrated into urban epidemiological research.

The spatial distribution mechanism of PM2.5 and NO2 on the eastern coast of China

The spatial distribution characteristics of multi-air pollutants and their impacts are difficult to quantify effectively. As PM2.5 and NO2 are the main air pollutants, it is of great significance to explore the spatial causes of their pollution and their interaction mechanism. This study used machine learning (LightGBM) and hot spot analysis to map the spatial distribution of PM2.5 and NO2 in Southwest Fujian (SWFJ) in 2018 and their key pollution areas. Then, the factors and interactive detection of geographical detectors were used to conduct a detailed analysis of the quantitative impact of potential factors such as human activities, terrain, air pollutants, and meteorology on PM2.5 and NO2 pollution. From this we can learn that 1. LightGBM has good stability for drawing the spatial distribution of PM2.5 and NO2. 2. The spatial mechanism of PM2.5 and NO2 can be effectively interpreted from a massive data and macro perspective. 3. A large amount of evidence shows that potential factors such as human activities, topography, air pollutants and meteorology have direct or indirect effects on PM2.5 and NO2 pollution in the SWFJ area. This includes the direct impact of local road traffic emissions on the distribution of PM2.5 and NO2 pollution, the digestion of both by vegetation, the mutual transformation of atmospheric pollutants themselves, and the impact of meteorological conditions. This study not only confirms the effectiveness of machine learning combined with geographical detectors to promote the study of regional air pollution mechanisms, but also confirms the feasibility of exploring the spatial distribution mechanisms of various air pollutants. Therefore, this study is of great significance for explaining the spatial distribution of PM2.5 and NO2, and can also provide reference for policy formulation to reduce regional PM2.5 and NO2 concentrations.

Effect of urban form on PM2.5 concentrations in urban agglomerations of China: Insights from different urbanization levels and seasons

Planned urban form has become an important strategy to improve air quality in urban agglomerations (UAs), especially pollution due to PM_2.5, but the influencing mechanisms are not yet clear. This study explores the relationship between four metrics of urban form (size, fragmentation, shape, and dispersion) as determined by analysis of remotely sensed images at 30-m resolution and PM_2.5 concentrations in 19 Chinese UAs. The influence of level of urban development and season is examined. Five control variables, including population density, temperature, precipitation, wind speed, and the normalized difference vegetation index (NDVI) are selected for use in multiple linear regression models. Size, fragmentation, and shape of urban form, but not dispersion, were found to have significant effects on PM_2.5 concentrations of different urbanization-level UAs. Urban size and fragmentation have stronger impacts on PM_2.5 concentrations in UAs with lower urbanization levels while urban shape has a greater impact in higher-level UAs. In terms of seasonal variation in all UAs, urban form is more pronouncedly associated with PM_2.5 concentrations during spring and autumn than summer and winter. Urban size and fragmentation are positively associated with PM_2.5 concentrations whereas urban shape and dispersion are on the contrary. The relationships between urban form and PM_2.5 uncovered here underscore the importance of urban planning as a tool to minimize PM_2.5 pollution. Specifically, local government should encourage polycentric urban form with lower fragmentation in urban agglomerations. UAs with lower urbanization levels should control the disordered expansion of construction land and higher-level UAs should promote the mix of green land and construction land. Moreover, measures to control air pollution from anthropogenic activities in spring, autumn and winter are likely to be more effective in decreasing PM_2.5 concentrations in UAs.

Analysis of Pollution Characteristics and Emissions Reduction Measures in the Main Cotton Area of Xinjiang

With cotton production in Xinjiang increasing annually, the impact on the environment of agricultural waste produced to improve production has been reflected. This study selected Bozhou of Xinjiang, the main cotton producing region in northern Xinjiang, as the research object, and collected hourly concentration data of six pollutants from 2017 to 2021, and analyzed the spatial and temporal distribution characteristics of each pollutant. At the same time, Morlet wavelet analysis was used to further analyze the variation period of PM_2.5 (PM particles with aerodynamic diameters less than 2.5 μm) concentration. The Weather Research and Forecasting model coupled with the Community Multiscale Air Quality (WRF-CMAQ) model was used to evaluate the emissions reduction measures for the most polluted month. The results showed that the concentration of particulate matter (PM particles with aerodynamic diameters less than 2.5 μm and 10 μm) decreased from the southern mountains to the north; moreover, the concentrations of CO (carbon monoxide), NO₂ (nitrogen dioxide), and SO₂ (sulfur dioxide) in the suburbs were higher than those in the urban center. The concentration of O₃ (Ozone) was the highest in summer, while the concentrations of other pollutants were high in autumn and winter. Under the time scale of a = 13, 24, PM_2.5 had significant periodic fluctuation. The health risk values of PM_2.5 and PM₁₀ in this study were within the scope of the United States Environmental Protection Agency (USEPA) criteria, but it is still necessary to keep a close watch on them. In the context of emissions reduction measures, agricultural sources reduced by 20%, residential sources by 40%, industrial sources by 20%, and transportation sources by 20%; no change in the power source remains. Under these conditions, the daily average value of each pollutant met the first level of the national ambient air quality standard. The research results provide a reference for the local government to formulate heavy pollution emissions reduction policies.

Effect of COVID-19 Response Policy on Air Quality: A Study in South China Context

Mass suspension of anthropogenic activities is extremely rare, the quarantine due to the coronavirus disease 2019 (COVID-19) represents a natural experiment to investigate the impact of anthropogenic activities on air quality. The mitigation of air pollution during the COVID-19 lockdown has been reported from a global perspective; however, the air pollution levels vary in different regions. This study initiated a novel synthesis of multiple-year satellite observations, national ground measurements towards SO2, NO2 and O3 and meteorological conditions to evaluate the impact of the COVID-19 lockdown in Beihai, a specific city in a less developed area in southwest China, to reveal the potential implications of control strategies for air pollution. The levels of the major air pollutants during the COVID-19 lockdown (LP) and during the same period of previous years (SP) were compared and a series of statistical tools were applied to analyze the sources of air pollution in Beihai. The results show that air pollutant levels decreased with substantial diversity during the LP. Satellite-retrieved NO2 and SO2 levels during the LP decreased by 5.26% and 22.06%, while NO2, SO2, PM2.5 and PM10 from ground measurements during the LP were 25.6%, 2.7%, 22.2% and 22.2% lower than during SP, respectively. Ground measured SO2 concentrations during the LP were only 2.7% lower than during the SP, which may be attributed to uninterrupted essential industrial activities, such as power plants. Polar plots analysis shows that NO2 concentrations were strongly associated with local emission sources, such as automobiles and local industry. Additionally, the much lower levels of NO2 concentrations during the LP and the absence of an evening peak may highlight the significant impact of the traffic sector on NO2. The decrease in daily mean O3 concentrations during the LP may be associated with the reduction in NO2 concentrations. Indications in this study could be beneficial for the formulation of atmospheric protection policies.

The Association Between Bangladeshi Adults' Demographics, Personal Beliefs, and Nutrition Literacy: Evidence From a Cross-Sectional Survey

Background

Poverty and health illiteracy, combined with inappropriate systems to track disease and infection rates, contribute to children-and-mothers' poor adherence to nutrient-rich foods intake in Bangladesh. Although risk factors for child and pregnant women malnutrition have been explored, the relationship between Bangladeshi adults' nutrition literacy and their demographics and personal beliefs remains unknown. The purpose of this study was to examine the association between adults' nutrition literacy, demographics and personal beliefs in a large sample of Bangladeshi adults.

Methods

Four hundred adults from two districts (Dhaka and Chattogram) of Bangladesh participated in a cross-sectional survey. Data were collected by interviews using a structured questionnaire containing the Nutrition Literacy Scale. Multiple linear regression models were employed to analyze associations between nutrition literacy and related factors.

Results

The mean nutrition literacy score was 21.6 (SD: 3.7; range: 11-32) on a scale of 32. Multiple linear regression revealed that being a businessman (β = 1.66, p = 0.013) or private employee (β = 1.08, p = 0.030), having a higher family income (β = 1.17, p = 0.009), and a higher educational level were positively associated with higher nutrition literacy scores compared to their counterparts. Participants who had ever completed a nutrition-related course (β = 4.95, p < 0.001), and who perceived themselves as having a need for accessing nutrition-related information were positively associated with the higher nutrition literacy compared to their counterparts.

Conclusion

Findings from this study suggest the need for an integrated response plan involving educational interventions and accessible dietary plans targeting adult populations to enhance their nutritional literacy.

PM2.5 Pollutant Concentrations in Greenspaces of Nanjing Are High but Can Be Lowered with Environmental Planning

Small-scale greenspaces in high-density central urban districts serve as important outdoor activity spaces for the surrounding residents, especially the elderly. This study selects six small-scale, popular greenspaces with distinct characteristics that are jointly situated along the same main urban artery in a high-density central urban district. Field investigations and questionnaires are conducted and combined with statistical analyses, to explore the spatial-temporal distribution and influencing factors of PM2.5 concentrations in these greenspaces. The study finds that the air quality conditions in the sites are non-ideal, and this has potential negative impacts on the health of the elderly visitors. Moreover, the difference values of PM2.5 concentrations' spatial-temporal distributions are significantly affected by vehicle-related emissions, which have significant temporal characteristics. PM2.5 concentration is strongly correlated with percentage of green coverage (R = 0.82, p < 0.05), degree of airflow (R = -0.83, p < 0.05), humidity and comfort level (R = 0.54, p < 0.01 and R = -0.40, p < 0.01 respectively). Meanwhile, the sites' "sky view factor" is strongly correlated with degree of airflow (R = 0.82, p < 0.05), and the comfort level plays an indirect role in the process of PM2.5 affecting crowd activities. Based on this analysis, an optimal set of index ranges for greenspace elements which are correlated with the best reduction in PM2.5 concentrations is derived. As such, this research reveals the technical methods to best reduce their concentrations and provides a basis and reference for improving the quality of small-scale greenspaces in high-density urban districts for the benefit of healthy aging.

Testing the Multiple Pathways of Residential Greenness to Pregnancy Outcomes Model in a Sample of Pregnant Women in the Metropolitan Area of Donostia-San Sebastián

Residential greenness may positively impact diverse human health indicators through the reduction of air pollution, the improvement of psychological health, and the promotion of physical activity. Previous studies indicate a weak but positive association with pregnancy outcomes. Our aim was to test the multiple pathways from residential greenness to pregnancy outcomes model, using residential NO₂ concentrations, psychological health, and moderate-to-vigorous physical activity (MVPA) during the first trimester of pregnancy, in a sample of 440 pregnant women residing in Donostia, Spain. Three metrics of residential greenness were calculated around each participant’s home address: normalized difference vegetation index (NDVI) within 300 m, and green space (>5000 m²) availability within 300 and 500 m. Residential NO₂ concentrations, psychological health, and MVPA were explored as mediators of the associations between these metrics and the following pregnancy outcomes: birth weight (BW), low birth weight (LBW), prematurity, small for gestational age (SGA), and large for gestational age (LGA). Educational attainment, parity, and body mass index (BMI) were treated as covariates. Counterfactual mediation analyses showed very low to null statistical support for an association between any of the greenspace metrics and pregnancy outcomes in the full sample. Green space availability (300 m) was associated with lower BW and showed a marginal protective effect against LGA.