Spatially explicit downscaling and projection of population in mainland China.

Spatially explicit population data is critical to investigating human-nature interactions, identifying at-risk populations, and informing sustainable management and policy decisions. Most long-term global population data have three main limitations: 1) they were estimated with simple scaling or trend extrapolation methods which are not able to capture detailed population variation spatially and temporally; 2) the rate of urbanization and the spatial patterns of settlement changes were not fully considered; and 3) the spatial resolution is generally coarse. To address these limitations, we proposed a framework for large-scale spatially explicit downscaling of populations from census data and projecting future population distributions under different Shared Socio-economic Pathways (SSP) scenarios with the consideration of distinctive changes in urban extent. We downscaled urban and rural population separately and considered urban spatial sprawl in downscaling and projection. Treating urban and rural populations as distinct but interconnected entities, we constructed a random forest model to downscale historical populations and designed a gravity-based population potential model to project future population changes at the grid level. This work built a new capacity for understanding spatially explicit demographic change with a combination of temporal, spatial, and SSP scenario dimensions, paving the way for cross-disciplinary studies on long-term socio-environmental interactions.

Tracking COVID-19 urban activity changes in the Middle East from nighttime lights.

In response to the COVID-19 pandemic, governments around the world have enacted widespread physical distancing measures to prevent and control virus transmission. Quantitative, spatially-disaggregated information about the population-scale shifts in activity that have resulted from these measures is extremely scarce, particularly for regions outside of Europe and the US. Public health institutions often must make decisions about control measures with limited region-specific data about how they will affect societal behavior, patterns of exposure, and infection outcomes. The Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS DNB), a new-generation space-borne low-light imager, has the potential to track changes in human activity, but the capability has not yet been applied to a cross-country analysis of COVID-19 responses. Here, we examine multi-year (2015-2020) daily time-series data derived from NASA's Black Marble VIIRS nighttime lights product (VNP46A2) covering 584 urban areas, in 17 countries in the Middle East to understand how communities have adhered to COVID-19 measures in the first 4 months of the pandemic. Nighttime lights capture the onset of national curfews and lockdowns well, but also expose the inconsistent response to control measures both across and within countries. In conflict-afflicted countries, low adherence to lockdowns and curfews was observed, highlighting the compound health and security threats that fragile states face. Our findings show how satellite measurements can aid in assessing the public response to physical distancing policies and the socio-cultural factors that shape their success, especially in fragile and data-sparse regions.

Artificial light at night: an underappreciated effect on phenology of deciduous woody plants.

Artificial light at night (ALAN), an increasing anthropogenic driver, is widespread and shows rapid expansion with potential adverse impact on the terrestrial ecosystem. However, whether and to what extent does ALAN affect plant phenology, a critical factor influencing the timing of terrestrial ecosystem processes, remains unexplored due to limited ALAN observation. Here, we used the Black Marble ALAN product and phenology observations from USA National Phenology Network to investigate the impact of ALAN on deciduous woody plants phenology in the conterminous United States. We found that (1) ALAN significantly advanced the date of breaking leaf buds by 8.9 ± 6.9 days (mean ± SD) and delayed the coloring of leaves by 6.0 ± 11.9 days on average; (2) the magnitude of phenological changes was significantly correlated with the intensity of ALAN (P < 0.001); and (3) there was an interaction between ALAN and temperature on the coloring of leaves, but not on breaking leaf buds. We further showed that under future climate warming scenarios, ALAN will accelerate the advance in breaking leaf buds but exert a more complex effect on the coloring of leaves. This study suggests intensified ALAN may have far-reaching but underappreciated consequences in disrupting key ecosystem functions and services, which requires an interdisciplinary approach to investigate. Developing lighting strategies that minimize the impact of ALAN on ecosystems, especially those embedded and surrounding major cities, is challenging but must be pursued.

Satellite-based assessment of electricity restoration efforts in Puerto Rico after Hurricane Maria.

A real-time understanding of the distribution and duration of power outages after a major disaster is a precursor to minimizing their harmful consequences. Here, we develop an approach for using daily satellite nighttime lights data to create spatially disaggregated power outage estimates, tracking electricity restoration efforts after disasters strike. In contrast to existing utility data, these estimates are independent, open, and publicly-available, consistently measured across regions that may be serviced by several different power companies, and inclusive of distributed power supply (off-grid systems). We apply the methodology in Puerto Rico following Hurricane Maria, which caused the longest blackout in US history. Within all of the island's settlements, we track outages and recovery times, and link these measures to census-based demographic characteristics of residents. Our results show an 80% decrease in lights, in total, immediately after Hurricane Maria. During the recovery, a disproportionate share of long-duration power failures (> 120 days) occurred in rural municipalities (41% of rural municipalities vs. 29% of urban municipalities), and in the northern and eastern districts. Unexpectedly, we also identify large disparities in electricity recovery between neighborhoods within the same urban area, based primarily on the density of housing. For many urban areas, poor residents, the most vulnerable to increased mortality and morbidity risks from power losses, shouldered the longest outages because they lived in less dense, detached housing where electricity restoration lagged. The approach developed in this study demonstrates the potential of satellite-based estimates of power recovery to improve the real-time monitoring of disaster impacts, globally, at a spatial resolution that is actionable for the disaster response community.

Tradeoffs in environmental and equity gains from job accessibility.

Increasing job accessibility is considered key to urban sustainability progress, both from an environmental and from a social perspective. However, sustainability outcomes depend on the processes contributing to accessibility trends, not just the trends themselves. Here, we ask whether sustainability benefits have followed from accessibility trends in the United States. We measure changes in accessibility from 2002 to 2014 across 909 US urban areas and decompose these changes to understand underlying infrastructure and land use processes. Our results show that job accessibility has increased across 74% of urban areas for the average resident, using both cars and transit. However, most of these accessibility gains were not achieved in ways that are inherently beneficial to environmental or social sustainability. In some urban areas, accessibility increases were conducive to reducing emissions, while in others, accessibility increases were conducive to reducing social inequities. However, accessibility increases almost never created a simultaneous social and environmental "win-win," as is often assumed. Our findings highlight how the spatial patterns of urbanization create tradeoffs between different facets of sustainability. Identifying where social objectives take precedence over environmental objectives (or vice versa) could help determine how accessibility increases can be accomplished to contribute to a more sustainable urban future.

Associations between Greenness, Impervious Surface Area, and Nighttime Lights on Biomarkers of Vascular Aging in Chennai, India.

BACKGROUND: India is undergoing rapid urbanization with simultaneous increases in the prevalence of cardiovascular disease (CVD). As urban areas become home to an increasing share of the world's population, it is important to understand relationships between the built environment and progression towards CVD. OBJECTIVE: We assessed associations between multiple measures of the built environment and biomarkers of early vascular aging (EVA) in the Population Study of Urban, Rural and Semiurban Regions for the Detection of Endovascular Disease and Prevalence of Risk Factors and Holistic Intervention Study (PURSE-HIS) in Chennai, India. METHODS: We performed a cross-sectional analysis of 3,150 study participants. EVA biomarkers included systolic and diastolic blood pressure (SBP and DBP), central pulse pressure (cPP) and flow-mediated dilatation (FMD). Multiple approaches were used to assign residential exposure to factors of the built environment: Moderate Resolution Imaging Spectroradiometer (MODIS)-derived normalized difference vegetation index (NDVI), a measure of vegetation health and greenness; Landsat-derived impervious surface area (ISA); and Visible Infrared Imaging Radiometer Suite (VIIRS)-derived nighttime lights (NTL). Multivariable regression models were used to assess associations between each built environment measure and biomarkers of EVA, adjusting for age, body mass index (BMI), cooking fuel type, energy intake, sex, physical activity, smoking, socioeconomic status, and stress. RESULTS: Residing in areas with higher ISA or NTL, or lower greenness, was significantly associated with elevated SBP, DBP, and cPP, and with lower FMD, adjusting for age, BMI, sex, smoking status, and other CVD risk factors. An interquartile range decrease in greenness had the largest increase in SBP [4.3 mmHg (95% CI: 2.9, 5.6)], DBP [1.2 mmHg (95% CI: 0.4, 2.0)] and cPP [3.1 mmHg (95% CI: 2.0, 4.1)], and the largest decrease in FMD [-1.5% (95%CI: -2.2%, -0.9%]. CONCLUSION: Greenness, ISA, and NTL were associated with increased SBP, DBP, and cPP, and with reduced FMD, suggesting a possible additional EVA pathway for the relationship between urbanization and increased CVD prevalence in urban India. https://doi.org/10.1289/EHP541.

Holidays in lights: Tracking cultural patterns in demand for energy services.

Successful climate change mitigation will involve not only technological innovation, but also innovation in how we understand the societal and individual behaviors that shape the demand for energy services. Traditionally, individual energy behaviors have been described as a function of utility optimization and behavioral economics, with price restructuring as the dominant policy lever. Previous research at the macro-level has identified economic activity, power generation and technology, and economic role as significant factors that shape energy use. However, most demand models lack basic contextual information on how dominant social phenomenon, the changing demographics of cities, and the sociocultural setting within which people operate, affect energy decisions and use patterns. Here we use high-quality Suomi-NPP VIIRS nighttime environmental products to: (1) observe aggregate human behavior through variations in energy service demand patterns during the Christmas and New Year's season and the Holy Month of Ramadan and (2) demonstrate that patterns in energy behaviors closely track sociocultural boundaries at the country, city, and district level. These findings indicate that energy decision making and demand is a sociocultural process as well as an economic process, often involving a combination of individual price-based incentives and societal-level factors. While nighttime satellite imagery has been used to map regional energy infrastructure distribution, tracking daily dynamic lighting demand at three major scales of urbanization is novel. This methodology can enrich research on the relative importance of drivers of energy demand and conservation behaviors at fine scales. Our initial results demonstrate the importance of seating energy demand frameworks in a social context.