Do green spaces affect the spatiotemporal changes of PM2.5 in Nanjing?

Association of residential greenness, air pollution with adverse birth outcomes: Results from 61,762 mother‑neonatal pairs in project ELEFANT (2011-2021)

BACKGROUND

Emerging evidence has demonstrated the benefits of greenness exposure on human health, while conflicts remain unsolved in issue of adverse birth outcomes.

METHODS

Utilizing data from project ELEFANT spanning the years 2011 to 2021, we assessed residential greenness using the NDVI from MODIS data and residential PM2.5 exposure level from CHAP data. Our primary concerns were PTD, LBW, LGA, and SGA. Cox proportional hazard regression model was used to examine the association of residential greenness and air pollution exposure with risk of adverse birth outcomes. We performed mediation and modification effect analyses between greenness and air pollutant.

RESULTS

We identified 61,762 mother‑neonatal pairs in final analysis. For per 10 μg/m3 increase in PM2.5 concentration during entire pregnancy was associated with 19.8 % and 20.7 % increased risk of PTD and LGA. In contrast, we identified that an 0.1 unit increment in NDVI were associated with 24 %, 43 %, 26.5 %, and 39.5 % lower risk for PTD, LBW, LGA, and SGA, respectively. According to mediation analysis, NDVI mediated 7.70 % and 7.89 % of the associations between PM2.5 and PTD and LGA. Residential greenness could reduce the risk of PTD among mothers under 35 years old, living in rural areas, primigravidae and primiparity..

CONCLUSIONS

In summary, our results highlighted the potential of residential greenness to mitigate the risk of adverse birth outcomes, while also pointing to the adverse impact of PM2.5 on increased risk of multiple adverse birth outcomes (PTD and LGA). The significant mediation effect of NDVI emphasizes its potential as an important protective factor of PM2.5 exposure. Additionally, the identification of susceptible subgroups can inform targeted interventions to reduce adverse birth outcomes related to air pollution and lack of green spaces. Further research and understanding of these associations can contribute to better public health strategies aimed at promoting healthier pregnancies and birth outcomes.

Particulate matter and inflammatory skin diseases: From epidemiological and mechanistic studies

Epidemiological and toxicological studies have confirmed that exposure to atmospheric particulate matter (PM) could affect our cardiovascular and respiratory systems. Recent studies have shown that PM can penetrate the skin and cause skin inflammation, but the evidence is limited and contradictory. As the largest outermost surface of the human body, the skin is constantly exposed to the environment. The aim of this study was to assess the relationship between PM and inflammatory skin diseases. Most epidemiological studies have provided positive evidence for outdoor, indoor, and wildfire PM and inflammatory skin diseases. The effects of PM exposure during pregnancy and inflammatory skin diseases in offspring are heterogeneous. Skin barrier dysfunction, Oxidative stress, and inflammation may play a critical role in the underlying mechanisms. Finally, we summarize some interventions to alleviate PM-induced inflammatory skin diseases, which may contribute to public health welfare. Overall, PM is related to inflammatory skin diseases via skin barrier dysfunction, oxidative stress, and inflammation. Appropriate government interventions are beneficial.

The effects of surface vegetation coverage on the spatial distribution of PM2.5 in the central area of Nanchang City, China

The frequent occurrence of haze has caused widespread concern in China, and PM2.5 is thought to be the main cause. Previous research showed that PM2.5 was not only influenced by meteorological conditions but also by land cover especially surface vegetation. It was concluded that PM2.5 concentration is significantly influenced by surface vegetation, but spatially how and in what manner are still unanswered. Taking the central area of Nanchang City, China, as a case, this study firstly applied land use regression (LUR) model to simulate the distribution of PM2.5 in 2020. Then, the dichotomous model was used to determine vegetation coverage. A statistical regression model was used to analyze the influence of vegetation cover on PM2.5 and the scale effects. The results showed that (1) vegetation coverage and PM2.5 concentration were both significantly negatively correlated at the spatial scales selected for this study. (2) The effect of vegetation coverage on PM2.5 varied with season and the 500 m had the best correlation. (3) The non-linear regression model fits better than the linear model, and the effect of vegetation coverage on PM2.5 was complex. (4) The effect of vegetation coverage on PM2.5 concentration was different with PM2.5 concentration level. The higher the PM2.5 concentration, the more pronounced the effect of vegetation coverage on it. This study proposed the idea and method of coupling vegetation coverage with PM2.5 concentration at the regional scale from gradient landscape's point of view and provided some references for mitigating PM2.5 pollution through optimizing urban vegetation patterns.

Urbanization, economic development, and environmental changes in transitional economies in the global south: a case of Yangon

BACKGROUND

Transitional economies in Southeast Asia-a distinct group of developing countries-have experienced rapid urbanization in the past several decades due to the economic transition that fundamentally changed the function of their economies, societies and the environment. Myanmar, one of the least developed transitional economies in Southeast Asia, increased urbanization substantially from 25% in 1990 to 31% in 2019. However, major knowledge gaps exist in understanding the changes in urban land use and land cover and environment and their drivers in its cities.

METHODS

We studied Yangon, the largest city in Myanmar, for the urbanization, environmental changes, and the underlying driving forces in a radically transitioned economy in the developing world. Based on satellite imagery and historic land use maps, we quantified the expansion of urban built-up land and constructed the land conversion matrix from 1990 through 2020. We also used three air pollutants to illustrate the changes in environmental conditions. We analyzed the coupled dynamics among urbanization, economic development, and environmental changes. Through conducting a workshop with 20 local experts, we further analyzed the influence of human systems and natural systems on Yangon's urbanization and sustainability.

RESULTS

The city of Yangon expanded urban built-up land rapidly from 1990 to 2000, slowed down from 2000 to 2010, but gained momentum again from 2010 to 2020, with most newly added urban built-up land appearing to be converted from farmland and green land in both 1990-2000 and 2010-2020. Furthermore, the air pollutant concentration of CO decreased, but that of NO₂ and PM_2.5 increased in recent years. A positive correlation exists between population and economic development and the concentration of PM_2.5 is highly associated with population, the economy, and the number of vehicles. Finally, the expert panel also identified other potential drivers for urbanization, including the extreme climate event of Cyclone Nargis, capital relocation, and globalization.

CONCLUSIONS

Our research highlights the dramatic expansion of urban land and degradation of urban environment measured by air pollutants and interdependent changes between urbanization, economic development, and environmental changes.

The relationship between greenspace and personal exposure to PM2.5 during walking trips in Delhi, India

The presence of urban greenspace may lead to reduced personal exposure to air pollution via several mechanisms, for example, increased dispersion of airborne particulates; however, there is a lack of real-time evidence across different urban contexts. Study participants were 79 adolescents with asthma who lived in Delhi, India and were recruited to the Delhi Air Pollution and Health Effects (DAPHNE) study. Participants were monitored continuously for exposure to PM_2.5 (particulate matter with an aerodynamic diameter of less than 2.5 μm) for 48 h. We isolated normal day-to-day walking journeys (n = 199) from the personal monitoring dataset and assessed the relationship between greenspace and personal PM_2.5 using different spatial scales of the mean Normalised Difference Vegetation Index (NDVI), mean tree cover (TC), and proportion of surrounding green land use (GLU) and parks or forests (PF). The journeys had a mean duration of 12.7 (range 5, 53) min and mean PM_2.5 personal exposure of 133.9 (standard deviation = 114.8) μg/m³. The within-trip analysis showed weak inverse associations between greenspace markers and PM_2.5 concentrations only in the spring/summer/monsoon season, with statistically significant associations for TC at the 25 and 50 m buffers in adjusted models. Between-trip analysis also indicated inverse associations for NDVI and TC, but suggested positive associations for GLU and PF in the spring/summer/monsoon season; no overall patterns of association were evident in the autumn/winter season. Associations between greenspace and personal PM_2.5 during walking trips in Delhi varied across metrics, spatial scales, and season, but were most consistent for TC. These mixed findings may partly relate to journeys being dominated by walking along roads and small effects on PM_2.5 of small pockets of greenspace. Larger areas of greenspace may, however, give rise to observable spatial effects on PM_2.5, which vary by season.

Do We Need More Urban Green Space to Alleviate PM2.5 Pollution? A Case Study in Wuhan, China

Urban green space can help to reduce PM2.5 concentration by absorption and deposition processes. However, few studies have focused on the historical influence of green space on PM2.5 at a fine grid scale. Taking the central city of Wuhan as an example, this study has analyzed the spatiotemporal trend and the relationship between green space and PM2.5 in the last two decades. The results have shown that: (1) PM2.5 concentration reached a maximum value (139 μg/m3) in 2010 and decreased thereafter. Moran’s I index values of PM2.5 were in a downward trend, which indicates a sparser distribution; (2) from 2000 to 2019, the total area of green space decreased by 25.83%. The reduction in larger patches, increment in land cover diversity, and less connectivity led to fragmented spatial patterns of green space; and (3) the regression results showed that large patches of green space significantly correlated with PM2.5 concentration. The land use/cover diversity negatively correlated with the PM2.5 concentration in the ordinary linear regression. In conclusion, preserving large native natural habitats can be a supplemental measure to enlarge the air purification function of the green space. For cities in the process of PM2.5 reduction, enhancing the landscape patterns of green space provides a win-win solution to handle air pollution and raise human well-being.

Research Framework Built Natural-Based Solutions (NBSs) as Green Hotels

In this study, value-belief-norm (VBN) theory and the social exchange theory (SET) were applied to predict hotel customers’ pro-environmental responsibility behavior intention (PRBI) for the characteristics of NBSs in green hotels—specifically, to investigate the relationship between NBSs as green hotel and PRBI, and to test its mediating effect on pro-environmental perceived (PPV), pro-environmental perceived belief (PPVBE), personal pro-environmental norms (PPN), attitude toward environmental behavior (ATEB), mental health (MH), well-being (WB), and satisfaction (SA) and the moderating effect of locations (urban, rural) among these variables toward pro-environmental responsibility behavior intention (PRBI). Data were collected using a survey of 440 customers who had visited green hotels in the Republic of Korea within the last 12 months. We used to test the research hypotheses by structural equation modeling (SEM). The findings generally supported the hypothesized associations between variables within our proposed theoretical framework and confirmed the moderating effect of location. The study’s results have important theoretical and practical implications for the environment. We investigated the relationship between the characteristics of NBSs and PRBI of green hotels, and we investigated the relationship between psychological state, attitude, and behavior of green hotel customers by applying variables suitable for ART, SET, and VBN. In addition, we verified the moderating effect of customers’ green behavior and attitudes toward green hotels located in urban and rural areas. Moreover, these findings herein may encourage green hotels to participate in preventing environmental problems. It provides primary data on customers’ perception of ecofriendliness in establishing corporate management strategies.

Can Nature-Based Solutions (NBSs) for Stress Recovery in Green Hotels Affect Re-Patronage Intention?

Our research framework in this paper investigated natural-based solutions (NBSs) at green hotels. We employed attention restoration theory (ART) to test the mediating effect of perceived stress (PS), psychological wellness (PW), satisfaction (SA), and the moderating effect of health consciousness (HC) on re-patronage intentions (RI). Data were collected through a survey of 544 customers who frequently visited green hotels in Korea, and structural equation modeling (SEM) was used to test the research hypotheses. The findings generally supported the hypothesized associations of the study variables within our proposed theoretical framework (PS, PW, SF) in order of the mediating effect on RI and confirmed the moderating effect of HC. In addition, the study’s results have important theoretical and practical implications for the environment. In the former case, our results demonstrate the application of ART and NBS by explaining the effect of the relationship among PS, PW, and SF on RI and confirm the mediating effect of the ART (PS, PW, SF) on RI, as demonstrated in previous studies. Moreover, in the latter case our results may encourage green hotels to participate in the prevention of environmental problems.

A Geospatial Platform for Crowdsourcing Green Space Area Management Using GIS and Deep Learning Classification

Green space areas are one of the key factors in people’s livelihoods. Their number and size have a significant impact on both the environment and people’s quality of life, including their health. Accordingly, government agencies often rely on information relating to green space areas when devising suitable plans and mandating necessary regulations. At present, obtaining information on green space areas using conventional ground surveys faces a number of limitations. This approach not only requires a lengthy period, but also tremendous human and financial resources. Given such restrictions, the status of a green space is not always up to date. Although software applications, especially those based on geographical information systems and remote sensing, have increasingly been applied to these tasks, the capability to use crowdsourcing data and produce real-time reports is lacking. This is partly because the quantity of data required has, to date, prohibited effective verification by human operators. To address this issue, this paper proposes a novel geospatial platform for green space area management by means of GIS and artificial intelligence. In the proposed system, all user-submitted data are automatically verified by deep learning classification and analyses of the greenness areas on satellite imagery. The experimental results showed that the classification and analyses can identify green space areas at accuracies of 93.50% and 97.50%, respectively. To elucidate the merits of the proposed approach, web-based application software was implemented to demonstrate multimodal data management, cleansing, and reporting. This geospatial system was thus proven to be a viable tool for assisting governmental agencies to devise appropriate plans toward sustainable development goals.

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

Understanding the spatio-temporal characteristics of air pollutants is essential to improving air quality. One aspect is the question of whether green spaces can reduce air pollutant concentrations. However, previous studies on this issue have reported mixed results. This study analyzed the spatio-temporal characteristics of NO₂, PM_2.5 and O₃ in Fujian Province, Southeast China in 2015. In order to reduce uncertainties in the conclusions drawn, the effects landscape metrics describing green spaces have on air pollutants have been analyzed using Pearson correlation analysis at six different spatial scales for the four seasons, considering the influence of meteorological conditions. The results show that PM_2.5 and O₃ are major pollutants whose relative importance varies with the seasons. Significant differences in pollutant concentrations were observed in suburban and urban areas, highlighting the importance of ensuring a reasonable spatial distribution of monitoring stations. Moreover, significant correlations between air pollutants and green space landscape patterns during the four seasons were found, revealing increased air pollutant concentrations with increasing landscape fragmentation and reduced connectivity and aggregation. This probably indicates that interconnected green spaces have the potential to improve air quality. Utilizing green space function regulations can alleviate NO₂ and PM_2.5 pollution effectively, but it is still difficult to reduce O₃ concentrations because green spaces are likely to not only serve as sinks for O_3, but can also promote O₃ formation.

Estimating the Impact of COVID-19 on the PM2.5 Levels in China with a Satellite-Driven Machine Learning Model

China implemented an aggressive nationwide lockdown procedure immediately after the COVID-19 outbreak in January 2020. As China emerges from the impact of COVID-19 on national economic and industrial activities, it has become the site of a large-scale natural experiment to evaluate the impact of COVID-19 on regional air quality. However, ground measurements of fine particulate matters (PM2.5) concentrations do not offer comprehensive spatial coverage, especially in suburban and rural regions. In this study, we developed a machine learning method with satellite aerosol remote sensing data, meteorological fields and land use parameters as major predictor variables to estimate spatiotemporally resolved daily PM2.5 concentrations in China. Our study period consists of a reference semester (1 November 2018-30 April 2019) and a pandemic semester (1 November 2019-30 April 2020), with six modeling months in each semester. Each period was then divided into subperiod 1 (November and December), subperiod 2 (January and February) and subperiod 3 (March and April). The reference semester model obtained a 10-fold cross-validated R2 (RMSE) of 0.79 (17.55 μg/m3) and the pandemic semester model obtained a 10-fold cross-validated R2 (RMSE) of 0.83 (13.48 μg/m3) for daily PM2.5 predictions. Our prediction results showed high PM2.5 concentrations in the North China Plain, Yangtze River Delta, Sichuan Basin and Xinjiang Autonomous Region during the reference semester. PM2.5 levels were lowered by 4.8 μg/m3 during the pandemic semester compared to the reference semester and PM2.5 levels during subperiod 2 decreased most, by 18%. The southeast region was affected most by the COVID-19 outbreak with PM2.5 levels during subperiod 2 decreasing by 31%, followed by the Northern Yangtze River Delta (29%) and Pearl River Delta (24%).

Association of greenspace exposure with telomere length in preschool children

Exposure to greenspace has been associated with a wide range of health benefits; however, the available evidence on the association of this exposure with telomere length (TL), an early marker of ageing, is still scarce. We investigated the association of greenspace exposure with TL in a sample of 200 preschool children (aged 5-7 years) residing in Sabzevar, Iran (2017). We comprehensively characterized different aspects of greenspace exposure encompassing residential, kindergarten, and total (including both residential and kindergarten) surrounding greenspace (using satellite-derived Normalized Difference Vegetation Index), residential and kindergarten distance to green spaces, time spent in private gardens and public green spaces, and the number of plant pots at home. Relative leukocyte TL (LTL) in blood samples of the study participants was measured using quantitative polymerase chain reaction (qPCR). We applied mixed effects linear regression models with kindergarten and qPCR plate as random effects, to estimate the association of indicators of greenspace exposure (one at a time) with LTL, controlled for relevant covariates. We observed an inverse association between distance from home and kindergarten to green spaces larger than 5000 m² and LTL. Moreover, higher total surrounding greenspace at 300m and 500m buffers and higher surrounding greenspace at 300m buffer around kindergarten and home were associated with longer LTL. Furthermore, longer time spent (h/week) in the public green spaces was associated with longer LTL. Our findings for residential and kindergarten distance to any green space (regardless of the size), residential surrounding greenspace at 100m and 500m buffers, kindergarten surrounding greenspace at 100m buffer, time spent in private gardens (h/week) and the number of plant pots at home were not conclusive. Our findings were generally suggestive for a positive association between greenspace exposure and LTL in preschool children. More studies are needed to confirm these findings in other settings with different climates and populations.

Hotels' Eco-Friendly Physical Environment as Nature-Based Solutions for Decreasing Burnout and Increasing Job Satisfaction and Performance

This study investigates the effect of the hotel’s nature-friendly environment on burnout, job satisfaction and job performance of hotel employees. A total of 11 hypotheses were set up to achieve the purpose of this study, and an empirical analysis was conducted based on 309 surveys collected from hotel employees. A total of 11 hypotheses were set to achieve the research goals, and an empirical analysis was conducted based on a total of 309 pieces of data collected from 320 hotel employees who are currently working in 11 hotels in South Korea. As a result, eight hypotheses were accepted and three were rejected. Specifically, it was found that the hotel’s nature-friendly environment reduced burnout in employees, and indirectly had a significant effect on job satisfaction and job performance. Therefore, the relationship between the variables presented was clearly demonstrated through the research results, and the purpose of this study was satisfactorily explained. The results are expected to be of great help to hotel employees and researchers in developing strategies to efficiently manage hotel employees through nature-based solutions (NBSs). Based on the results, the proposed theoretical and practical implications are discussed in detail in the discussion section.

Spatial Accessibility of Urban Forests in the Pearl River Delta (PRD), China

The Pearl River Delta (PRD) is one of the most important economic zones both in China and in the world. Its rapid economic development has been associated with many environmental problems such as the loss of forests in urban areas. We estimated the accessibility of forests in the PRD by quantifying spatial proximity and travel time. We found that distances from a large proportion of the points of interest (POIs) (~45%) and urban lands (~38%, where ~49 urban residents live) to the nearest forests were greater than 1000 m; suggesting a low spatial proximity to forests. Urban parks—important outdoor recreational areas—appeared to have insufficient forest coverage within their 1000 m buffer zones. When forest accessibility was measured by travel time under optimal modes of transport; it was less than 15 min for most urban lands (~95%), which accommodates 98% of the total urban population. More importantly; the travel time to the nearest forest was negatively correlated with gross domestic product density (GDPd), but not with population density (POPd). The GDPd and POPd; however; increased log-linearly with the Euclidean distance to the nearest forest. In addition to the low proximity to forests; there existed inequalities among urban residents who live in areas with different levels of GDPd and POPd. Future urban planning needs not only to increase the total coverage of urban forests; but also to improve their spatial evenness across the urban landscapes in the PRD.

Improving estimates of built-up area from night time light across globally distributed cities through hierarchical modeling

Built-up area has become an important indicator for studying urban environments, but mapping built-up area at the regional/global scale remains challenging due to the complexity of impervious surface features. Nighttime light data (NTL) is one of the major remote sensing data sources for regional/global built-up or impervious surface mapping. A single regression relationship between fractional built-up/impervious area and NTL or various indices derived based on NTL and vegetation index (e.g., NDVI) data had been established in many previous studies. However, due to the varying geographical, climatic, and socio-economic characteristics of cities, the same regression relationship may vary significantly across cities. In this study, we examined the regression relationship between percentage of built-up area (pBUA) and vegetation adjusted nighttime light urban index (VANUI) for 120 randomly selected cities around the world with a hierarchical hockey-stick regression model. We found that there is a substantial variability in the slope (0.658 ± 0.318), the threshold VANUI (-1.92 ± 0.769, log scale) after which the linear relationship holds, and the coefficient of determination R² (0.71 ± 0.14) among globally distributed cities. A small proportion of this substantial variability can be attributed to socio-economic status (e.g., total population, GDP per capita) and landscape structures (e.g., compactness and fragmentation). Due to these variations, our hierarchical model or no-pooling model (i.e., fit each city individually) can significantly improve model prediction accuracy (17% in terms of root mean squared error) over a complete-pooling model. We, however, recommend hierarchical models as they can provide meaningful priors for future modeling under a Bayesian framework, and achieve higher prediction accuracy than no-pooling models when sample size is small.

Spatiotemporal Changes in PM2.5 and Their Relationships with Land-Use and People in Hangzhou

Increases in the extent and level of air pollution in Chinese cities have become a major concern of the public and burden on the government. While ample literature has focused on the status, changes and causes of air pollution (particularly on PM_2.5 and PM₁₀), significantly less is known on their effects on people. In this study we used Hangzhou, China, as our testbed to assess the direct impact of PM_2.5 on youth populations that are more vulnerable to pollution. We used the ground monitoring data of air quality and Aerosol optical thickness (AOT) product from the Moderate Resolution Imaging Spectroradiometer (MODIS) for the spatiotemporal changes of PM_2.5 by season in 2015. We further explored these distributions with land cover, population density and schools (kindergarten, primary school and middle school) to explore the potential impacts in seeking potential mitigation solutions. We found that the seasonal variation of PM_2.5 concentration was winter > spring > autumn > summer. In Hangzhou, the percentage of land area exposed to PM_2.5 > 50 µg m⁻³ accounted for 59.86% in winter, 56.62% in spring, 40.44% in autumn and 0% in summer, whereas these figures for PM_2.5 of <35 µg m⁻³ were 70.01%, 5.28%, 5.17%, 4.16% in summer, winter, autumn and spring, respectively. As for land cover, forest experienced PM_2.5 of 35–50 µg m⁻³ (i.e., lower than those of other cover types), likely due to the potential filtering and absorption function of the forests. More importantly, a quantitative index based on population-weighted exposure level (pwel) indicated that only 9.06% of the population lived in areas that met the national air quality standards. Only 1.66% (14,055) of infants and juveniles lived in areas with PM_2.5 of <35 µg m⁻³. Considering the legacy effects of PM_2.5 over the long-term, we highly recommend improving the monitoring systems for both air quality and people (i.e., their health conditions), with special attention paid to infants and juveniles.

Spatiotemporal variability of the near-surface CO2 concentration across an industrial-urban-rural transect, Nanjing, China

Urban lands are CO₂ emission hotspots. In this paper, we report the CO₂ concentration observations along an industrial-urban-rural transect and in a network of sites in the urban center, in Nanjing, China. The mean CO₂ concentration was highest at the industrial site, not at the densely populated urban center (urban: 429.2±8.7ppm, rural: 421.2±10.0ppm, industrial: 443.88±18.3ppm), based on four sampling periods in four different seasons in 2014 and 2015. At the urban sites, a reversed weekend effect was observed, whereby the weekend CO₂ concentration was higher than the weekday concentration by a mean of 0.9ppm over the four measurement periods and by 8.1ppm in the spring, suggesting higher traffic volume on weekends than on weekdays. The vertical CO₂ gradient was weak above the urban canopy layer, with a mean difference of only 1.1ppm between the 60-m and 110-m measurement heights, reflecting efficient mixing in both daytime and nighttime periods. The average along-wind concentration gradient was 0.25±0.87ppmkm^-1 at the height of 110m according to the observations made at five urban sites. Based on a simple box model, we estimated an anthropogenic surface flux of about 0.4mgCO₂m^-2s^-1 for the urban center.

The relationship between urban form and air pollution depends on seasonality and city size

Understanding how urban form is related to air pollution is important to urban planning and sustainability, but the urban form-air pollution relationship is currently muddled by inconsistent findings. In this study, we investigated how the compositional and configurational attributes of urban form were related to different air pollution measures (PM_2.5, API, and exceedance) in 83 Chinese cities, with explicit consideration of city size and seasonality. Ten landscape metrics were selected to quantify urban form attributes, and Spearman's correlation was used to quantify the urban form-air pollution relationship. Our results show that the urban form and air pollution relationship was dominated by city size and moderated by seasonality. Specifically, urban air pollution levels increased consistently and substantially from small to medium, large, and megacities. The urban form-air pollution relationship depended greatly on seasonality and monsoons. That is, the relationship was more pronounced in spring and summer than fall and winter, as well as in cities affected by monsoons. Urban air pollution was correlated more strongly with landscape composition metrics than landscape configuration metrics which seemed to affect only PM_2.5 concentrations. Our study suggests that, to understand how air pollution levels are related to urban form, city size and seasonality must be explicitly considered (or controlled). Also, in order to mitigate urban air pollution problems, regional urban planning is needed to curb the spatial extent of built-up areas, reduce the degree of urban fragmentation, and increase urban compactness and contiguity, especially for large and megacities.

A Streamlined Approach by a Combination of Bioindication and Geostatistical Methods for Assessing Air Contaminants and Their Effects on Human Health in Industrialized Areas: A Case Study in Southern Brazil

Industrialization in developing countries associated with urban growth results in a number of economic benefits, especially in small or medium-sized cities, but leads to a number of environmental and public health consequences. This problem is further aggravated when adequate infrastructure is lacking to monitor the environmental impacts left by industries and refineries. In this study, a new protocol was designed combining biomonitoring and geostatistics to evaluate the possible effects of shale industry emissions on human health and wellbeing. Futhermore, the traditional and expensive air quality method based on PM_2.5 measuring was also used to validate the low-cost geostatistical approach. Chemical analysis was performed using Energy Dispersive X-ray Fluorescence Spectrometer (EDXRF) to measure inorganic elements in tree bark and shale retorted samples in São Mateus do Sul city, Southern Brazil. Fe, S, and Si were considered potential pollutants in the study area. Distribution maps of element concentrations were generated from the dataset and used to estimate the spatial behavior of Fe, S, and Si and the range from their hot spot(s), highlighting the regions sorrounding the shale refinery. This evidence was also demonstrated in the measurements of PM_2.5 concentrations, which are in agreement with the information obtained from the biomonitoring and geostatistical model. Factor and descriptive analyses performed on the concentrations of tree bark contaminants suggest that Fe, S, and Si might be used as indicators of industrial emissions. The number of cases of respiratory diseases obtained from local basic health unit were used to assess a possible correlation between shale refinery emissions and cases of repiratory disease. These data are public and may be accessed on the website of the the Brazilian Ministry of Health. Significant associations were found between the health data and refinery activities. The combination of the spatial characterization of air pollution and clinical health data revealed that adverse effects were significant for individuals over 38 years of age. These results also suggest that a protocol designed to monitor urban air quality may be an effective and low-cost strategy in environmentally contaminated cities, especially in low- and middle-income countries.

Nature-based solutions for urban landscapes under post-industrialization and globalization: Barcelona versus Shanghai

Using Barcelona and Shanghai as case studies, we examined the nature-based solutions (NBS) in urban settings-specifically within cities experiencing post-industrialization and globalization. Our specific research questions are: (1) What are the spatiotemporal changes in urban built-up land and green space in Barcelona and Shanghai? (2) What are the relationships between economic development, exemplified by post-industrialization, globalization, and urban green space? Urban land use and green space change were evaluated using data derived from a variety of sources, including satellite images, landscape matrix indicators, and a land conversion matrix. The relationships between economic development, globalization, and environmental quality were analyzed through partial least squares structural equation modeling based on secondary statistical data. Both Barcelona and Shanghai have undergone rapid urbanization, with urban expansion in Barcelona beginning in the 1960s-1970s and in Shanghai in the last decade. While Barcelona's urban green space and green space per capita began declining between the 1950s and 1990s, they increased slightly over the past two decades. Shanghai, however, has consistently and significantly improved urban green space and green space per capita over the past six decades, especially since the economic reform in 1978. Economic development has a direct and significant influence on urban green space for both cities and post-industrialization had served as the main driving force for urban landscape change in Barcelona and Shanghai. Based on secondary statistical and qualitative data from on-site observations and interviews with local experts, we highlighted the institution's role in NBS planning. Furthermore, aspiration to become a global or globalizing city motivated both cities to use NBS planning as a place-making tool to attract global investment, which is reflected in various governing policies and regulations. The cities' effort to achieve a higher status in the global city hierarchy may have contributed to the increase in total green space and urban green per capita. In addition, various institutional shifts, such as land property rights in a market economy vs. a transitional economy, may also have contributed to the differences in efficiency when expanding urban green space in Barcelona and Shanghai.