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Although people may recognize urban vibrancy when they see or sense it, developing direct and comprehensive measures of urban vibrancy remains a challenge. In the context of intense global competition, there is an increased realization that urban vibrancy is vital to the social and economic sustainability of cities. Such vibrancy may be significantly shaped by the urban built environment, yet we know little about the close connections between vibrancy and urban built environments. Empowered by newly available sources of spatial big data, which provide enormous amounts of information on both human dynamics and the built environment, this paper proposes a framework for evaluating and characterizing urban vibrancy. Thus far, vibrancy measures have mostly used single-source data that hardly reflect the multifaceted manifestations of urban vibrancy. Therefore, we propose a more comprehensive measure of urban vibrancy, extracted as the common latent factor from multiple surface attributes. Using the proposed framework, we evaluated and mapped the spatial dynamics of vibrancy in Shanghai, a typical large city in post-reform China, and investigated the associations between vibrancy and various urban built environment indicators. The evidence shows that the horizontal built-up density, rather than vertical height, is the leading generator of vibrancy in Shanghai, followed by the density and mixture of urban functions, accessibility, and walkability. In this vein, we contribute to current debates and future planning practices regarding vibrant spaces in large cities. This proposed evaluation framework, equipped with spatial big data, can benefit future urban studies.

The role of coastal mangrove wetlands in sequestering atmospheric carbon dioxide (CO 2) and mitigating climate change has received increasing attention in recent years. While recent studies have shown that methane (CH 4) emissions can potentially offset the carbon burial rates in low-salinity coastal wetlands, there is hitherto a paucity of direct and year-round measurements of ecosystem-scale CH 4 flux (F CH4) from mangrove ecosystems. In this study, we examined the temporal variations and biophysical drivers of ecosystem-scale F CH4 in a subtropical estuarine mangrove wetland based on 3 years of eddy covariance measurements. Our results showed that daily mangrove F CH4 reached a peak of over 0.1 g CH 4-C m −2 day −1 during the summertime owing to a combination of high temperature and low salinity, while the wintertime F CH4 was negligible. In this mangrove, the mean annual CH 4 emission was 11.7 ± 0.4 g CH 4-C m-2 year −1 while the annual net ecosystem CO 2 exchange ranged between −891 and −690 g CO 2-C m −2 year −1 , indicating a net cooling effect on climate over decadal to centurial timescales. Meanwhile, we showed that mangrove F CH4 could offset the negative radiative forcing caused by CO 2 uptake by 52% and 24% over a time horizon of 20 and 100 years, respectively, based on the corresponding sustained-flux global warming potentials. Moreover, we found that 87% and 69% of the total variance of daily F CH4 could be explained by the random forest machine learning algorithm and traditional linear regression model, respectively, with soil temperature and salinity being the most dominant controls. This study was the first of its kind to characterize ecosystem-scale F CH4 in a mangrove wetland with longterm eddy covariance measurements. Our findings implied that future environmental changes such as climate warming and increasing river discharge might increase CH 4 emissions and hence reduce the net radiative cooling effect of estuarine mangrove forests.

Air pollution exposure characterization has been shaped by many constraints. These include technologies that lead to insufficient coverage across space and/or time in order to characterize individual or community-level exposures with sufficient accuracy and precision. However, there is now capacity for continuous monitoring of many air pollutants using comparatively inexpensive, real-time sensors. Crucial questions remain regarding whether or not these sensors perform adequately for various potential end uses and whether performance varies over time or across ambient conditions. Performance scrutiny of sensors via lab- and field-testing and calibration across their lifetime is necessary for interpretation of data, and has important implications for end users including cost effectiveness and ease of use. We developed a comparatively lower-cost, portable, in-home air sampling platform and a guiding development and maintenance workflow that achieved our goal of characterizing some key indoor pollutants with high sensitivity and reasonable accuracy. Here we describe the process of selecting, validating, calibrating, and maintaining our platform – the Environmental Multi-pollutant Monitoring Assembly (EMMA) – over the course of our study to-date. We highlight necessary resources and consider implications for communities or researchers interested in developing such platforms, focusing on PM2.5, NO, and NO2 sensors. Our findings emphasize that lower-cost sensors should be deployed with caution, given financial and resource costs that greatly exceed sensor costs, but that selected community objectives could be supported at lesser cost and community-based participatory research strategies could be used for more wide-ranging goals.

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