Taking Advantage of the Improved Availability of Census Data: A First Look at the Gridded Population of the World, Version 4

Global population datasets overestimate flood exposure in Sweden

Accurate population data is crucial for assessing exposure in disaster risk assessments. In recent years, there has been a significant increase in the development of spatially gridded population datasets. Despite these datasets often using similar input data to derive population figures, notable differences arise when comparing them with direct ground-level observations. This study evaluates the precision and accuracy of flood exposure assessments using both known and generated gridded population datasets in Sweden. Specifically focusing on WorldPop and GHSPop, we compare these datasets against official national statistics at a 100 m grid cell resolution to assess their reliability in flood exposure analyses. Our objectives include quantifying the reliability of these datasets and examining the impact of data aggregation on estimated flood exposure across different administrative levels. The analysis reveals significant discrepancies in flood exposure estimates, underscoring the challenges associated with relying on generated gridded population data for precise flood risk assessments. Our findings emphasize the importance of careful dataset selection and highlight the potential for overestimation in flood risk analysis. This emphasises the critical need for validations against ground population data to ensure accurate flood risk management strategies.

Rethinking the country-level percentage of population residing in urban area with a global harmonized urban definition

The UN (United Nations) collects global data on the country-level Percentage of Population Residing in Urban Area (PPRUA). However, variations in urban definitions make these data incomparable across countries. This study assesses national defined PPRUA within UN statistics against estimates we derived using global comparable definitions. Refer to the UN's Degree of Urbanization framework, we propose 90 global harmonized methods for estimating PPRUA by combining different configurations of three global population datasets, six urban total population thresholds, and five urban population density thresholds. This approach demonstrated significant variations in country-level PPRUA estimations, with wide 95% confidence intervals using the Z score method. Most national defined PPRUA fall between the upper 95% CI and the median of the estimations, underscoring the need for globally harmonious PPRUA estimates. This study advocates for a reassessment of datasets and thresholds in the future and for investigating urbanization on a scale beyond the country level.

Water Scarcity Assessment of Hydropower Plants in China under Climate Change, Sectoral Competition, and Energy Expansion

Hydropower plays a pivotal role in low-carbon electricity generation, yet many projects are situated in regions facing heightened water scarcity risks. This research devised a plant-level Hydropower Water Scarcity Index (HWSI), derived from the ratio of water demand for electricity generation to basin-scale available runoff water. We assessed the water scarcity of 1736 hydropower plants in China for the baseline year 2018 and projected into the future from 2025 to 2060. The results indicate a notable increase in hydropower generation facing moderate to severe water scarcity (HWSI >0.05), rising from 10% in 2018 to 24-34% of the national total (430-630 TWh), with a projected peak in the 2030s-2040s under the most pessimistic scenarios. Hotspots of risk are situated in the southwest and northern regions, primarily driven by decreased river basin runoff and intensified sectoral water use, rather than by hydropower demand expansion. Comparative analysis of four adaptation strategies revealed that sectoral water savings and enhancing power generation efficiency are the most effective, potentially mitigating a high of 16% of hydropower risks in China. This study provides insights for formulating region-specific adaptation strategies and assessing energy-water security in the face of evolving environmental and societal challenges.

Organic fertilization co-selects genetically linked antibiotic and metal(loid) resistance genes in global soil microbiome

Antibiotic resistance genes (ARGs) and metal(loid) resistance genes (MRGs) coexist in organic fertilized agroecosystems based on their correlations in abundance, yet evidence for the genetic linkage of ARG-MRGs co-selected by organic fertilization remains elusive. Here, an analysis of 511 global agricultural soil metagenomes reveals that organic fertilization correlates with a threefold increase in the number of diverse types of ARG-MRG-carrying contigs (AMCCs) in the microbiome (63 types) compared to non-organic fertilized soils (22 types). Metatranscriptomic data indicates increased expression of AMCCs under higher arsenic stress, with co-regulation of the ARG-MRG pairs. Organic fertilization heightens the coexistence of ARG-MRG in genomic elements through impacting soil properties and ARG and MRG abundances. Accordingly, a comprehensive global map was constructed to delineate the distribution of coexistent ARG-MRGs with virulence factors and mobile genes in metagenome-assembled genomes from agricultural lands. The map unveils a heightened relative abundance and potential pathogenicity risks (range of 4-6) for the spread of coexistent ARG-MRGs in Central North America, Eastern Europe, Western Asia, and Northeast China compared to other regions, which acquire a risk range of 1-3. Our findings highlight that organic fertilization co-selects genetically linked ARGs and MRGs in the global soil microbiome, and underscore the need to mitigate the spread of these co-resistant genes to safeguard public health.

Unraveling near real-time spatial dynamics of population using geographical ensemble learning

Dynamic gridded population data are crucial in fields such as disaster reduction, public health, urban planning, and global change studies. Despite the use of multi-source geospatial data and advanced machine learning models, current frameworks for population spatialization often struggle with spatial non-stationarity, temporal generalizability, and fine temporal resolution. To address these issues, we introduce a framework for dynamic gridded population mapping using open-source geospatial data and machine learning. The framework consists of (i) delineation of human footprint zones, (ii) construction of muliti-scale population prediction models using automated machine learning (AutoML) framework and geographical ensemble learning strategy, and (iii) hierarchical population spatial disaggregation with pycnophylactic constraint-based corrections. Employing this framework, we generated hourly time-series gridded population maps for China in 2016 with a 1-km spatial resolution. The average accuracy evaluated by root mean square deviation (RMSD) is 325, surpassing datasets like LandScan, WorldPop, GPW, and GHSL. The generated seamless maps reveal the temporal dynamic of population distribution at fine spatial scales from hourly to monthly. This framework demonstrates the potential of integrating spatial statistics, machine learning, and geospatial big data in enhancing our understanding of spatio-temporal heterogeneity in population distribution, which is essential for urban planning, environmental management, and public health.

A unified dataset for pre-processed climate indicators weighted by gridded economic activity

Although high-resolution gridded climate variables are provided by multiple sources, the need for country and region-specific climate data weighted by indicators of economic activity is becoming increasingly common in environmental and economic research. We process available information from different climate data sources to provide spatially aggregated data with global coverage for both countries (GADM0 resolution) and regions (GADM1 resolution) and for a variety of climate indicators (total precipitations, average temperatures, average SPEI). We weigh gridded climate data by population density, night-time light intensity, cropland, and concurrent population count - all proxies of economic activity - before aggregation. Climate variables are measured daily, monthly, and annually, covering (depending on the data source) a time window from 1900 (at the earliest) to 2023. We pipeline all the preprocessing procedures in a unified framework, and we validate our data through a systematic comparison with those employed in leading climate impact studies.

The effect of explicit convection on simulated malaria transmission across Africa

Malaria transmission across sub-Saharan Africa is sensitive to rainfall and temperature. Whilst different malaria modelling techniques and climate simulations have been used to predict malaria transmission risk, most of these studies use coarse-resolution climate models. In these models convection, atmospheric vertical motion driven by instability gradients and responsible for heavy rainfall, is parameterised. Over the past decade enhanced computational capabilities have enabled the simulation of high-resolution continental-scale climates with an explicit representation of convection. In this study we use two malaria models, the Liverpool Malaria Model (LMM) and Vector-Borne Disease Community Model of the International Centre for Theoretical Physics (VECTRI), to investigate the effect of explicitly representing convection on simulated malaria transmission. The concluded impact of explicitly representing convection on simulated malaria transmission depends on the chosen malaria model and local climatic conditions. For instance, in the East African highlands, cooler temperatures when explicitly representing convection decreases LMM-predicted malaria transmission risk by approximately 55%, but has a negligible effect in VECTRI simulations. Even though explicitly representing convection improves rainfall characteristics, concluding that explicit convection improves simulated malaria transmission depends on the chosen metric and malaria model. For example, whilst we conclude improvements of 45% and 23% in root mean squared differences of the annual-mean reproduction number and entomological inoculation rate for VECTRI and the LMM respectively, bias-correcting mean climate conditions minimises these improvements. The projected impact of anthropogenic climate change on malaria incidence is also sensitive to the chosen malaria model and representation of convection. The LMM is relatively insensitive to future changes in precipitation intensity, whilst VECTRI predicts increased risk across the Sahel due to enhanced rainfall. We postulate that VECTRI's enhanced sensitivity to precipitation changes compared to the LMM is due to the inclusion of surface hydrology. Future research should continue assessing the effect of high-resolution climate modelling in impact-based forecasting.

Effects of climate on the distribution and conservation of commonly observed European earthworms

Belowground biodiversity distribution does not necessarily reflect aboveground biodiversity patterns, but maps of soil biodiversity remain scarce because of limited data availability. Earthworms belong to the most thoroughly studied soil organisms and-in their role as ecosystem engineers-have a significant impact on ecosystem functioning. We used species distribution modeling (SDMs) and available data sets to map the spatial distribution of commonly observed (i.e., frequently recorded) earthworm species (Annelida, Oligochaeta) across Europe under current and future climate conditions. First, we predicted potential species distributions with commonly used models (i.e., MaxEnt and Biomod) and estimated total species richness (i.e., number of species in a 5 × 5 km grid cell). Second, we determined how much the different types of protected areas covered predicted earthworm richness and species ranges (i.e., distributions) by estimating the respective proportion of the range area. Earthworm species richness was high in central western Europe and low in northeastern Europe. This pattern was mainly associated with annual mean temperature and precipitation seasonality, but the importance of predictor variables to species occurrences varied among species. The geographical ranges of the majority of the earthworm species were predicted to shift to eastern Europe and partly decrease under future climate scenarios. Predicted current and future ranges were only poorly covered by protected areas, such as national parks. More than 80% of future earthworm ranges were on average not protected at all (mean [SD] = 82.6% [0.04]). Overall, our results emphasize the urgency of considering especially vulnerable earthworm species, as well as other soil organisms, in the design of nature conservation measures.

Global diversity, coexistence and consequences of resistome in inland waters

Human activities have long impacted the health of Earth's rivers and lakes. These inland waters, crucial for our survival and productivity, have suffered from contamination which allows the formation and spread of antibiotic-resistant genes (ARGs) and consequently, ARG-carrying pathogens (APs). Yet, our global understanding of waterborne pathogen antibiotic resistance remains in its infancy. To shed light on this, our study examined 1240 metagenomic samples from both open and closed inland waters. We identified 22 types of ARGs, 19 types of mobile genetic elements (MGEs), and 14 types of virulence factors (VFs). Our findings showed that open waters have a higher average abundance and richness of ARGs, MGEs, and VFs, with more robust co-occurrence network compared to closed waters. Out of the samples studied, 321 APs were detected, representing a 43 % detection rate. Of these, the resistance gene 'bacA' was the most predominant. Notably, AP hotspots were identified in regions including East Asia, India, Western Europe, the eastern United States, and Brazil. Our research underscores how human activities profoundly influence the diversity and spread of resistome. It also emphasizes that both abiotic and biotic factors play pivotal roles in the emergence of ARG-carrying pathogens.

Nationwide estimation of daily ambient PM2.5 from 2008 to 2020 at 1 km2 in India using an ensemble approach

High-resolution assessment of historical levels is essential for assessing the health effects of ambient air pollution in the large Indian population. The diversity of geography, weather patterns, and progressive urbanization, combined with a sparse ground monitoring network makes it challenging to accurately capture the spatiotemporal patterns of ambient fine particulate matter (PM2.5) pollution in India. We developed a model for daily average ambient PM2.5 between 2008 and 2020 based on monitoring data, meteorology, land use, satellite observations, and emissions inventories. Daily average predictions at each 1 km × 1 km grid from each learner were ensembled using a Gaussian process regression with anisotropic smoothing over spatial coordinates, and regression calibration was used to account for exposure error. Cross-validating by leaving monitors out, the ensemble model had an R2 of 0.86 at the daily level in the validation data and outperformed each component learner (by 5-18%). Annual average levels in different zones ranged between 39.7 μg/m3 (interquartile range: 29.8-46.8) in 2008 and 30.4 μg/m3 (interquartile range: 22.7-37.2) in 2020, with a cross-validated (CV)-R2 of 0.94 at the annual level. Overall mean absolute daily errors (MAE) across the 13 years were between 14.4 and 25.4 μg/m3. We obtained high spatial accuracy with spatial R2 greater than 90% and spatial MAE ranging between 7.3-16.5 μg/m3 with relatively better performance in urban areas at low and moderate elevation. We have developed an important validated resource for studying PM2.5 at a very fine spatiotemporal resolution, which allows us to study the health effects of PM2.5 across India and to identify areas with exceedingly high levels.

Assessing vulnerability for future Zika virus outbreaks using seroprevalence data and environmental suitability maps

The 2015-17 Zika virus (ZIKV) epidemic in the Americas subsided faster than expected and evolving population immunity was postulated to be the main reason. Herd immunization is suggested to occur around 60-70% seroprevalence, depending on demographic density and climate suitability. However, herd immunity was only documented for a few cities in South America, meaning a substantial portion of the population might still be vulnerable to a future Zika virus outbreak. The aim of our study was to determine the vulnerability of populations to ZIKV by comparing the environmental suitability of ZIKV transmission to the observed seroprevalence, based on published studies. Using a systematic search, we collected seroprevalence and geospatial data for 119 unique locations from 37 studies. Extracting the environmental suitability at each location and converting to a hypothetical expected seroprevalence, we were able to determine the discrepancy between observed and expected. This discrepancy is an indicator of vulnerability and divided into three categories: high risk, low risk, and very low risk. The vulnerability was used to evaluate the level of risk that each location still has for a ZIKV outbreak to occur. Of the 119 unique locations, 69 locations (58%) fell within the high risk category, 47 locations (39%) fell within the low risk category, and 3 locations (3%) fell within the very low risk category. The considerable heterogeneity between environmental suitability and seroprevalence potentially leaves a large population vulnerable to future infection. Vulnerability seems to be especially pronounced at the fringes of the environmental suitability for ZIKV (e.g. Sao Paulo, Brazil). The discrepancies between observed and expected seroprevalence raise the question: "why did the ZIKV epidemic stop with large populations unaffected?". This lack of understanding also highlights that future ZIKV outbreaks currently cannot be predicted with confidence.

A 31-year (1990-2020) global gridded population dataset generated by cluster analysis and statistical learning

Continuously monitoring global population spatial dynamics is crucial for implementing effective policies related to sustainable development, including epidemiology, urban planning, and global inequality. However, existing global gridded population data products lack consistent population estimates, making them unsuitable for time-series analysis. To address this issue, this study designed a data fusion framework based on cluster analysis and statistical learning approaches, which led to the generation of a continuous global gridded population dataset (GlobPOP). The GlobPOP dataset was evaluated through two-tier spatial and temporal validation to demonstrate its accuracy and applicability. The spatial validation results show that the GlobPOP dataset is highly accurate. The temporal validation results also reveal that the GlobPOP dataset performs consistently well across eight representative countries and cities despite their unique population dynamics. With the availability of GlobPOP datasets in both population count and population density formats, researchers and policymakers can leverage the new dataset to conduct time-series analysis of the population and explore the spatial patterns of population development at global, national, and city levels.

Short-Term Increases in NO2 and O3 Concentrations during Pregnancy and Stillbirth Risk in the U.S.: A Time-Stratified Case-Crossover Study

Associations between gaseous pollutant exposure and stillbirth have focused on exposures averaged over trimesters or gestation. We investigated the association between short-term increases in nitrogen dioxide (NO2) and ozone (O3) concentrations and stillbirth risk among a national sample of 116 788 Medicaid enrollees from 2000 to 2014. A time-stratified case-crossover design was used to estimate distributed (lag 0-lag 6) and cumulative lag effects, which were adjusted for PM2.5 concentration and temperature. Effect modification by race/ethnicity and proximity to hydraulic fracturing (fracking) wells was assessed. Short-term increases in the NO2 and O3 concentrations were not associated with stillbirth in the overall sample. Among American Indian individuals (n = 1694), a 10 ppb increase in NO2 concentrations was associated with increased stillbirth odds at lag 0 (5.66%, 95%CI: [0.57%, 11.01%], p = 0.03) and lag 1 (4.08%, 95%CI: [0.22%, 8.09%], p = 0.04) but not lag 0-6 (7.12%, 95%CI: [-9.83%, 27.27%], p = 0.43). Among participants living in zip codes within 15 km of active fracking wells (n = 9486), a 10 ppb increase in NO2 concentration was associated with increased stillbirth odds in single-day lags (2.42%, 95%CI: [0.37%, 4.52%], p = 0.02 for lag 0 and 1.83%, 95%CI: [0.25%, 3.43%], p = 0.03 for lag 1) but not the cumulative lag (lag 0-6) (4.62%, 95%CI: [-2.75%, 12.55%], p = 0.22). Odds ratios were close to the null in zip codes distant from fracking wells. Future studies should investigate the role of air pollutants emitted from fracking and potential racial disparities in the relationship between short-term increases in NO2 concentrations and stillbirth.

Tackling Soil ARG-Carrying Pathogens with Global-Scale Metagenomics

Antibiotic overuse and the subsequent environmental contamination of residual antibiotics poses a public health crisis via an acceleration in the spread of antibiotic resistance genes (ARGs) through horizontal gene transfer. Although the occurrence, distribution, and driving factors of ARGs in soils have been widely investigated, little is known about the antibiotic resistance of soilborne pathogens at a global scale. To explore this gap, contigs from 1643 globally sourced metagnomes are assembled, yielding 407 ARG-carrying pathogens (APs) with at least one ARG; APs are detected in 1443 samples (sample detection rate of 87.8%). The richness of APs is greater in agricultural soils (with a median of 20) than in non-agricultural ecosystems. Agricultural soils possess a high prevalence of clinical APs affiliated with Escherichia, Enterobacter, Streptococcus, and Enterococcus. The APs detected in agricultural soils tend to coexist with multidrug resistance genes and bacA. A global map of soil AP richness is generated, where anthropogenic and climatic factors explained AP hot spots in East Asia, South Asia, and the eastern United States. The results herein advance this understanding of the global distribution of soil APs and determine regions prioritized to control soilborne APs worldwide.

Biogeographic regionalization of human infectious diseases in Brazil based on geographically explicit data

OBJECTIVE

Biogeographic regionalization represents abstractions of the organisation of life on Earth, and can provide a large-scaled framework for health management and planning. We aimed at determining a biogeographic regionalization for human infectious diseases in Brazil, and at investigating non-mutually exclusive hypotheses predicting the observed regions.

METHODS

Based on the spatial distributions of 12 infectious diseases with mandatory notification (SINAN database, 2007-2020, n = 15,839), we identified regions through a clustering procedure based on beta-diversity turnover. The analysis was repeated 1000 times by randomly shuffling the rows (0.5° cells) in the original matrix. We evaluated the relative importance of variables using multinomial logistic regression models: contemporary climate (temperature and precipitation), human activity (population density and geographic accessibility), land cover (11 classes), and the full model (all variables). We refined the geographic boundaries of each cluster by polygonising their kernel densities to identify clusters' core zones.

RESULTS

The two-cluster solution showed the best correspondence between disease ranges and clusters geographic limits. The largest cluster occurred with more density in the central and northeastern regions, while the smaller and complementary cluster occurred in the south and southeastern region. The best model for explaining the regionalization was the full model, supporting the 'complex association hypothesis'. The heatmap showed a NE-S directional display of the cluster's densities, and core zones showed geographic correspondence with tropical + arid (NE) versus temperate (S) climates.

CONCLUSION

Our findings indicate that there is a discernible latitudinal pattern in the turnover of disease in Brazil, and this phenomenon is associated with an intricate interplay between contemporary climate, population activity, and land cover. This generalised biogeographic pattern may offer the earliest insights into the geographic arrangement of diseases in the country. We suggested that the latitudinal pattern could be adopted as a nationwide framework for geographic vaccine allocation.

Access to trauma center care: A statewide system-based approach

BACKGROUND

Timely access to specialized trauma care is a vital element in patient outcome after severe and critical injury requiring the skills of trauma teams in levels I and II trauma centers to avoid preventable mortality. We used system-based models to estimate timely access to care.

METHODS

Trauma system models consisted of ground emergency medical services, helicopter emergency medical services, and designated levels I to V trauma centers were constructed for five states. These models incorporated geographic information systems along with traffic data and census block group data to estimate population access to trauma care within the "golden hour." Trauma systems were further analyzed to identify the optimal location for an additional level I or II trauma center that would provide the greatest increase in access.

RESULTS

The population of the states studied totaled 23 million people, of which 20 million (87%) had access to a level I or II trauma center within 60 minutes. Statewide-specific access ranged from 60% to 100%. Including levels III to V trauma centers, access within 60 minutes increased to 22 million (96%), ranging from 95% to 100%. The addition of a levels I and II trauma center in an optimized location in each state would provide timely access to a higher trauma capability for an additional 1.1 million, increasing total access to approximately 21.1 million people (92%).

CONCLUSION

This analysis demonstrates that nearly universal access to trauma care is present in these states when including levels I to V trauma centers. However, concerning gaps remain in timely access to levels I and II trauma centers. This study provides an approach to determine more robust statewide estimates of access to care. It highlights the need for a national trauma system, one in which all components of state-managed trauma systems are assembled in a national data set to accurately identify gaps in care.

LEVEL OF EVIDENCE

Therapeutic/Care Management; Level IV.

High-resolution gridded population datasets for Latin America and the Caribbean using official statistics

"Leaving no one behind" is the fundamental objective of the 2030 Agenda for Sustainable Development. Latin America and the Caribbean is marked by social inequalities, whilst its total population is projected to increase to almost 760 million by 2050. In this context, contemporary and spatially detailed datasets that accurately capture the distribution of residential population are critical to appropriately inform and support environmental, health, and developmental applications at subnational levels. Existing datasets are under-utilised by governments due to the non-alignment with their own statistics. Therefore, official statistics at the finest level of administrative units available have been implemented to construct an open-access repository of high-resolution gridded population datasets for 40 countries in Latin American and the Caribbean. These datasets are detailed here, alongside the 'top-down' approach and methods to generate and validate them. Population distribution datasets for each country were created at a resolution of 3 arc-seconds (approximately 100 m at the equator), and are all available from the WorldPop Data Repository.

Carbon Monitor Europe near-real-time daily CO2 emissions for 27 EU countries and the United Kingdom

With the urgent need to implement the EU countries pledges and to monitor the effectiveness of Green Deal plan, Monitoring Reporting and Verification tools are needed to track how emissions are changing for all the sectors. Current official inventories only provide annual estimates of national CO₂ emissions with a lag of 1+ year which do not capture the variations of emissions due to recent shocks including COVID lockdowns and economic rebounds, war in Ukraine. Here we present a near-real-time country-level dataset of daily fossil fuel and cement emissions from January 2019 through December 2021 for 27 EU countries and UK, which called Carbon Monitor Europe. The data are calculated separately for six sectors: power, industry, ground transportation, domestic aviation, international aviation and residential. Daily CO₂ emissions are estimated from a large set of activity data compiled from different sources. The goal of this dataset is to improve the timeliness and temporal resolution of emissions for European countries, to inform the public and decision makers about current emissions changes in Europe.

Spatially Explicit Wastewater Generation and Tracking (SEWAGE-TRACK) in the Middle East and North Africa region

This study developed the SEWAGE-TRACK model for disaggregating lumped national wastewater generation estimates using population datasets and quantifying rural and urban wastewater generation and fate. The model allocates wastewater into riparian, coastal, and inland components and summarizes the fate of wastewater into productive (direct and indirect reuse) and unproductive components for 19 countries in the Middle East and North Africa (MENA) region. As per the national estimates, 18.4 km³ of municipal wastewater generated in 2015, was disaggregated over the MENA region. Results from this study revealed urban and rural areas to contribute to 79 % and 21 % of municipal wastewater generation respectively. Within the rural context, inland areas generated 61 % of the total wastewater. The riparian and coastal regions produced 27 % and 12 %, respectively. Within the urban settings, riparian areas produced 48 %, while inland and coastal regions generated 34 % and 18 % of the total wastewater, respectively. Results indicate that 46 % of the wastewater is productively used (direct reuse and indirect use), while 54 % is lost unproductively. Of the total wastewater generated, the most direct use was observed in the coastal areas (7 %), the most indirect reuse in the riparian regions (31 %), and the most unproductive losses in inland areas (27 %). The potential of unproductive wastewater as a non-conventional freshwater source was also analyzed. Our results indicate that wastewater is an excellent alternative water source and has high potential to reduce pressure on non-renewable sources for some countries in the MENA region. The motivation of this study is to disaggregate wastewater generation and track wastewater fate using a simple but robust approach that is portable, scalable and repeatable. Similar analysis can be done for other regions to produce information on disaggregated wastewater and its fate. Such information is highly critical for efficient wastewater resource management.

A synthesis of hydroclimatic, ecological, and socioeconomic data for transdisciplinary research in the Mekong

The Mekong River basin (MRB) is a transboundary basin that supports livelihoods of over 70 million inhabitants and diverse terrestrial-aquatic ecosystems. This critical lifeline for people and ecosystems is under transformation due to climatic stressors and human activities (e.g., land use change and dam construction). Thus, there is an urgent need to better understand the changing hydrological and ecological systems in the MRB and develop improved adaptation strategies. This, however, is hampered partly by lack of sufficient, reliable, and accessible observational data across the basin. Here, we fill this long-standing gap for MRB by synthesizing climate, hydrological, ecological, and socioeconomic data from various disparate sources. The data- including groundwater records digitized from the literature-provide crucial insights into surface water systems, groundwater dynamics, land use patterns, and socioeconomic changes. The analyses presented also shed light on uncertainties associated with various datasets and the most appropriate choices. These datasets are expected to advance socio-hydrological research and inform science-based management decisions and policymaking for sustainable food-energy-water, livelihood, and ecological systems in the MRB.

CLUMondo-BNU for simulating land system changes based on many-to-many demand-supply relationships with adaptive conversion orders

Land resources are fundamentally important to human society, and their transition from one macroscopic state to another is a vital driving force of environment and climate change locally and globally. Thus, many efforts have been devoted to the simulations of land changes. Among all spatially explicit simulation models, CLUMondo is the only one that simulates land changes by incorporating the multifunctionality of a land system and allows the establishment of many-to-many demand-supply relationships. In this study, we first investigated the source code of CLUMondo, providing a complete, detailed mechanism of this model. We found that the featured function of CLUMondo-balancing demands and supplies in a many-to-many mode-relies on a parameter called conversion order. The setting of this parameter is a manual process and requires expert knowledge, which is not feasible for users without an understanding of the whole, detailed mechanism. Therefore, the second contribution of this study is the development of an automatic method for adaptively determining conversion orders. Comparative experiments demonstrated the validity and effectiveness of the proposed automated method. We revised the source code of CLUMondo to incorporate the proposed automated method, resulting in CLUMondo-BNU v1.0. This study facilitates the application of CLUMondo and helps to exploit its full potential.

European fluxes of medical gadolinium to the ocean: A model based on healthcare databases

Marine ecosystems are exposed to a multitude of stresses, including emerging metals as Rare Earth Elements. The management of these emerging contaminants represents a significant environmental issue. For the past three decades, the increasing medical use of gadolinium-based contrast agents (GBCAs) has contributed to their widespread dispersion in hydrosystems, raising concerns for ocean conservation. In order to control GBCA contamination pathways, a better understanding of the cycle of these elements is needed, based on the reliable characterization of fluxes from watersheds. Our study proposes an unprecedented annual flux model for anthropogenic gadolinium (Gd_anth) based on GBCA consumption, demographics and medical uses. This model enabled the mapping of Gd_anth fluxes for 48 European countries. The results show that 43 % of Gd_anth is exported to the Atlantic Ocean, 24 % to the Black Sea, 23 % to the Mediterranean Sea and 9 % to the Baltic Sea. Together, Germany, France and Italy contribute 40 % of Europe's annual flux. Our study was therefore able to identify the current and future major contributors to Gd_anth flux in Europe and identify abrupt changes related to the COVID-19 pandemic.

Non-monotonic changes in Asian Water Towers' streamflow at increasing warming levels

Previous projections show consistent increases in river flows of Asian Water Towers under future climate change. Here we find non-monotonic changes in river flows for seven major rivers originating from the Tibetan Plateau at the warming levels of 1.5 °C, 2.0 °C, and 3.0 °C based on an observation-constrained hydrological model. The annual mean streamflow for seven rivers at 1.5 °C warming level decreases by 0.1-3.2% relative to the present-day climate condition, and increases by 1.5-12% at 3.0 °C warming level. The shifting river flows for the Yellow, Yangtze, Brahmaputra, and Ganges are mostly influenced by projected increases in rainfall, but those for the Mekong, Salween, and Indus are dictated by the relative changes in rainfall, snowmelt and glacier melt. Reduced river flows in a moderately warmed climate threaten water security in riparian countries, while elevated flood risks are expected with further temperature increases over the Tibetan Plateau.

Mortality Attributable to Ambient Air Pollution: A Review of Global Estimates

Since the publication of the first epidemiological study to establish the connection between long-term exposure to atmospheric pollution and effects on human health, major efforts have been dedicated to estimate the attributable mortality burden, especially in the context of the Global Burden of Disease (GBD). In this work, we review the estimates of excess mortality attributable to outdoor air pollution at the global scale, by comparing studies available in the literature. We find large differences between the estimates, which are related to the exposure response functions as well as the number of health outcomes included in the calculations, aspects where further improvements are necessary. Furthermore, we show that despite the considerable advancements in our understanding of health impacts of air pollution and the consequent improvement in the accuracy of the global estimates, their precision has not increased in the last decades. We offer recommendations for future measurements and research directions, which will help to improve our understanding and quantification of air pollution-health relationships.

Global biogeography and projection of soil antibiotic resistance genes

Although edaphic antibiotic resistance genes (ARGs) pose serious threats to human well-being, their spatially explicit patterns and responses to environmental constraints at the global scale are not well understood. This knowledge gap is hindering the global action plan on antibiotic resistance launched by the World Health Organization. Here, a global analysis of 1088 soil metagenomic samples detected 558 ARGs in soils, where ARG abundance in agricultural habitats was higher than that in nonagricultural habitats. Soil ARGs were mostly carried by clinical pathogens and gut microbes that mediated the control of climatic and anthropogenic factors to ARGs. We generated a global map of soil ARG abundance, where the identified microbial hosts, agricultural activities, and anthropogenic factors explained ARG hot spots in India, East Asia, Western Europe, and the United States. Our results highlight health threats from soil clinical pathogens carrying ARGs and determine regions prioritized to control soil antibiotic resistance worldwide.

Analysing the relationship between human modification and land surface temperature fluctuation in the Ramganga basin, India

In many regions across the world, including river basins, population growth and land development have enhanced the demand for land and other natural resources. The anthropogenic activities can be detrimental to the vital ecosystems that sustain the river basin region. This work assessed the impact of human modification on land surface temperature (LST) for the Ramganga basin in India. It has been hypothesised that the footprints of anthropogenic activities in the region have been connected to the LST fluctuation for the region, which could indicate environmental degradation. The LST variation between 2000 and 2016 has been estimated to test this hypothesis. The spatio-temporal correlation between human modification and LST has been computed. LST has been calculated with MODIS satellite data in the Google earth engine (GEE) platform, and anthropogenic activities can be visualised using an LU/LC map of the basin created by the Classification and Regression (CART) technique. The statistical parameters (average, maximum and standard deviation) of annual temperature for each pixel in 17 years (2000-2016) have been assessed to establish the links with human modification. The result of this work portrays a positive correlation of 0.705 between maximum LST and human modification. The forest class in the basin region has the lowest average human modification value (0.37), and it also possesses the lowest mean LST of 26.72 °C. Similarly, the settlement class has the highest average human modification value (0.85), and the mean LST temperature of this class has been on the higher side, having a value of 31.07 °C.

Spatial modeling of vaccine deserts as barriers to controlling SARS-CoV-2

Background

COVID-19 vaccine distribution is at risk of further propagating the inequities of COVID-19, which in the United States (US) has disproportionately impacted the elderly, people of color, and the medically vulnerable. We sought to measure if the disparities seen in the geographic distribution of other COVID-19 healthcare resources were also present during the initial rollout of the COVID-19 vaccine.

Methods

Using a comprehensive COVID-19 vaccine database (VaccineFinder), we built an empirically parameterized spatial model of access to essential resources that incorporated vaccine supply, time-willing-to-travel for vaccination, and previous vaccination across the US. We then identified vaccine deserts—US Census tracts with localized, geographic barriers to vaccine-associated herd immunity. We link our model results with Census data and two high-resolution surveys to understand the distribution and determinates of spatially accessibility to the COVID-19 vaccine.

Results

We find that in early 2021, vaccine deserts were home to over 30 million people, >10% of the US population. Vaccine deserts were concentrated in rural locations and communities with a higher percentage of medically vulnerable populations. We also find that in locations of similar urbanicity, early vaccination distribution disadvantaged neighborhoods with more people of color and older aged residents.

Conclusion

Given sufficient vaccine supply, data-driven vaccine distribution to vaccine deserts may improve immunization rates and help control COVID-19.

Characterizing COVID-19 waves in urban and rural districts of India

Understanding spatial determinants, i.e., social, infrastructural, and environmental features of a place, which shape infectious disease is critically important for public health. We present an exploration of the spatial determinants of reported COVID-19 incidence across India's 641 urban and rural districts, comparing two waves (2020-2021). Three key results emerge using three COVID-19 incidence metrics: cumulative incidence proportion (aggregate risk), cumulative temporal incidence rate, and severity ratio. First, in the same district, characteristics of COVID-19 incidences are similar across waves, with the second wave over four times more severe than the first. Second, after controlling for state-level effects, urbanization (urban population share), living standards, and population age emerge as positive determinants of both risk and rates across waves. Third, keeping all else constant, lower shares of workers working from home correlate with greater infection risk during the second wave. While much attention has focused on intra-urban disease spread, our findings suggest that understanding spatial determinants across human settlements is also important for managing current and future pandemics.

A data set of distributed global population and water withdrawal from 1960 to 2020

Population and water withdrawal data sets are currently faced with difficulties in collecting, processing and verifying multi-source time series, and the spatial distribution characteristics of long series are also relatively lacking. Time series is the basic guarantee for the accuracy of data sets, and the production of long series spatial distribution is a realistic requirement to expand the application scope of data sets. Through the time-consuming and laborious basic processing work, this research focuses on the population and water intake time series, and interpolates and extends them to specific land uses to ensure the accuracy of the time series and the demand of spatially distributed data sets. This research provides a set of population density and water intensity products from 1960 to 2020 distributed to the administrative units or the corresponding regions. The data set fills the gaps in the multi-year data set for the accuracy of population density and the intensity of water withdrawal.

Measurement(s)	distributed global population and water withdrawal
Technology Type(s)	mathematical statistics and analysis

Projecting 1 km-grid population distributions from 2020 to 2100 globally under shared socioeconomic pathways

Spatially explicit population grid can play an important role in climate change, resource management, sustainable development and other fields. Several gridded datasets already exist, but global data, especially high-resolution data on future populations are largely lacking. Based on the WorldPop dataset, we present a global gridded population dataset covering 248 countries or areas at 30 arc-seconds (approximately 1 km) spatial resolution with 5-year intervals for the period 2020-2100 by implementing Random Forest (RF) algorithm. Our dataset is quantitatively consistent with the Shared Socioeconomic Pathways' (SSPs) national population. The spatially explicit population dataset we predicted in this research is validated by comparing it with the WorldPop dataset both at the sub-national and grid level. 3569 provinces (almost all provinces on the globe) and more than 480 thousand grids are taken into verification, and the results show that our dataset can serve as an input for predictive research in various fields.

Carbon Monitor Cities near-real-time daily estimates of CO2 emissions from 1500 cities worldwide

Building on near-real-time and spatially explicit estimates of daily carbon dioxide (CO2) emissions, here we present and analyze a new city-level dataset of fossil fuel and cement emissions, Carbon Monitor Cities, which provides daily estimates of emissions from January 2019 through December 2021 for 1500 cities in 46 countries, and disaggregates five sectors: power generation, residential (buildings), industry, ground transportation, and aviation. The goal of this dataset is to improve the timeliness and temporal resolution of city-level emission inventories and includes estimates for both functional urban areas and city administrative areas that are consistent with global and regional totals. Comparisons with other datasets (i.e. CEADs, MEIC, Vulcan, and CDP-ICLEI Track) were performed, and we estimate the overall annual uncertainty range to be ±21.7%. Carbon Monitor Cities is a near-real-time, city-level emission dataset that includes cities around the world, including the first estimates for many cities in low-income countries.

How accurate are WorldPop-Global-Unconstrained gridded population data at the cell-level?: A simulation analysis in urban Namibia

Disaggregated population counts are needed to calculate health, economic, and development indicators in Low- and Middle-Income Countries (LMICs), especially in settings of rapid urbanisation. Censuses are often outdated and inaccurate in LMIC settings, and rarely disaggregated at fine geographic scale. Modelled gridded population datasets derived from census data have become widely used by development researchers and practitioners; however, accuracy in these datasets are evaluated at the spatial scale of model input data which is generally courser than the neighbourhood or cell-level scale of many applications. We simulate a realistic synthetic 2016 population in Khomas, Namibia, a majority urban region, and introduce several realistic levels of outdatedness (over 15 years) and inaccuracy in slum, non-slum, and rural areas. We aggregate the synthetic populations by census and administrative boundaries (to mimic census data), resulting in 32 gridded population datasets that are typical of LMIC settings using the WorldPop-Global-Unconstrained gridded population approach. We evaluate the cell-level accuracy of these gridded population datasets using the original synthetic population as a reference. In our simulation, we found large cell-level errors, particularly in slum cells. These were driven by the averaging of population densities in large areal units before model training. Age, accuracy, and aggregation of the input data also played a role in these errors. We suggest incorporating finer-scale training data into gridded population models generally, and WorldPop-Global-Unconstrained in particular (e.g., from routine household surveys or slum community population counts), and use of new building footprint datasets as a covariate to improve cell-level accuracy (as done in some new WorldPop-Global-Constrained datasets). It is important to measure accuracy of gridded population datasets at spatial scales more consistent with how the data are being applied, especially if they are to be used for monitoring key development indicators at neighbourhood scales within cities.

The Missing Millions in Maps: Exploring Causes of Uncertainties in Global Gridded Population Datasets

Gridded population datasets model the population at a relatively high spatial and temporal granularity by reallocating official population data from irregular administrative units to regular grids (e.g., 1 km grid cells). Such population data are vital for understanding human–environmental relationships and responding to many socioeconomic and environmental problems. We analyzed one very broadly used gridded population layer (GHS-POP) to assess its capacity to capture the distribution of population counts in several urban areas, spread across the major world regions. This analysis was performed to assess its suitability for global population modelling. We acquired the most detailed local population data available for several cities and compared this with the GHS-POP layer. Results showed diverse error rates and degrees depending on the geographic context. In general, cities in High-Income (HIC) and Upper-Middle-Income Countries (UMIC) had fewer model errors as compared to cities in Low- and Middle-Income Countries (LMIC). On a global average, 75% of all urban spaces were wrongly estimated. Generally, in central mixed or non-residential areas, the population was overestimated, while in high-density residential areas (e.g., informal areas and high-rise areas), the population was underestimated. Moreover, high model uncertainties were found in low-density or sparsely populated outskirts of cities. These geographic patterns of errors should be well understood when using population models as an input for urban growth models, as they introduce geographic biases.

High-resolution gridded estimates of population sociodemographics from the 2020 census in California

This paper introduces a series of high resolution (100-meter) population grids for eight different sociodemographic variables across the state of California using data from the 2020 census. These layers constitute the ‘CA-POP’ dataset, and were produced using dasymetric mapping methods to downscale census block populations using fine-scale residential tax parcel boundaries and Microsoft’s remotely-sensed building footprint layer as ancillary datasets. In comparison to a number of existing gridded population products, CA-POP shows good concordance and offers a number of benefits, including more recent data vintage, higher resolution, more accurate building footprint data, and in some cases more sophisticated but parsimonious and transparent dasymetric mapping methodologies. A general accuracy assessment of the CA-POP dasymetric mapping methodology was conducted by producing a population grid that was constrained by population observations within block groups instead of blocks, enabling a comparison of this grid’s population apportionment to block-level census values, yielding a median absolute relative error of approximately 30% for block group-to-block apportionment. However, the final CA-POP grids are constrained by higher-resolution census block-level observations, likely making them even more accurate than these block group-constrained grids over a given region, but for which error assessments of population disaggregation is not possible due to the absence of observational data at the sub-block scale. The CA-POP grids are freely available as GeoTIFF rasters online at github.com/njdepsky/CA-POP, for total population, Hispanic/Latinx population of any race, and non-Hispanic populations for the following groups: American Indian/Alaska Native, Asian, Black/African-American, Native Hawaiian and other Pacific Islander, White, other race or multiracial (two or more races) and residents under 18 years old (i.e. minors).

Influence of Spatial Resolution on Satellite-Based PM2.5 Estimation: Implications for Health Assessment

Satellite-based PM2.5 estimation has been widely used to assess health impact associated with PM2.5 exposure and might be affected by spatial resolutions of satellite input data, e.g., aerosol optical depth (AOD). Here, based on Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD in 2020 over the Yangtze River Delta (YRD) and three PM2.5 retrieval models, i.e., the mixed effects model (ME), the land-use regression model (LUR) and the Random Forest model (RF), we compare these model performances at different spatial resolutions (1, 3, 5 and 10 km). The PM2.5 estimations are further used to investigate the impact of spatial resolution on health assessment. Our cross-validated results show that the model performance is not sensitive to spatial resolution change for the ME and LUR models. By contrast, the RF model can create a more accurate PM2.5 prediction with a finer AOD spatial resolution. Additionally, we find that annual population-weighted mean (PWM) PM2.5 concentration and attributable mortality strongly depend on spatial resolution, with larger values estimated from coarser resolution. Specifically, compared to PWM PM2.5 at 1 km resolution, the estimation at 10 km resolution increases by 7.8%, 22.9%, and 9.7% for ME, LUR, and RF models, respectively. The corresponding increases in mortality are 7.3%, 18.3%, and 8.4%. Our results also show that PWM PM2.5 at 10 km resolution from the three models fails to meet the national air quality standard, whereas the estimations at 1, 3 and 5 km resolutions generally meet the standard. These findings suggest that satellite-based health assessment should consider the spatial resolution effect.

Spatio-temporal spread of COVID-19: Comparison of the inhomogeneous SEPIR model and data from South Carolina

During the COVID-19 pandemic authorities have been striving to obtain reliable predictions for the spreading dynamics of the disease. We recently developed a multi-“sub-populations” (multi-compartments: susceptible, exposed, pre-symptomatic, infectious, recovered) model, that accounts for the spatial in-homogeneous spreading of the infection and shown, for a variety of examples, how the epidemic curves are highly sensitive to location of epicenters, non-uniform population density, and local restrictions. In the present work we test our model against real-life data from South Carolina during the period May 22 to July 22 (2020). During this period, minimal restrictions have been employed, which allowed us to assume that the local basic reproduction number is constant in time. We account for the non-uniform population density in South Carolina using data from NASA’s Socioeconomic Data and Applications Center (SEDAC), and predict the evolution of infection heat-maps during the studied period. Comparing the predicted heat-maps with those observed, we find high qualitative resemblance. Moreover, the Pearson’s correlation coefficient is relatively high thus validating our model against real-world data. We conclude that the model accounts for the major effects controlling spatial in-homogeneous spreading of the disease. Inclusion of additional sub-populations (compartments), in the spirit of several recently developed models for COVID-19, can be easily performed within our mathematical framework.

Response of Ecohydrological Variables to Meteorological Drought under Climate Change

Drought is the most widespread climatic extreme that has negative impacts on ecohydrology. Studies have shown that drought can cause certain degrees of disturbances to different ecohydrological variables, but the duration and severity thresholds of drought that are sufficient to cause changes in ecohydrological variables remain largely unknown. At the same time, we should not ignore the dynamic variation of drought’s effect on ecohydrological variables under the condition of climate change. Here, we derived the thresholds of several ecohydrological variables in response to drought in a historical period (1982–2015), including evapotranspiration (ET), soil moisture (SM), the vapor pressure deficit (VPD) and the normalized difference vegetation index (NDVI), and we projected the occurrence probability’s change trend of drought events that cause changes in ecohydrological variables under future climate change. The results show that the impact of drought on ecohydrological variables is not dependent on drought indicators. ET and NDVI were expected to decrease in most parts of the world due to increases in radiation (RAD) and temperature (TEMP) and decreases in precipitation (PRE) during drought periods. SM decreased in most regions of the world (93.47%) during the drought period, while VPD increased in 85.41% of the globe. The response thresholds for different ecohydrological variables to drought in the same area did not differ significantly (especially for ET, SM and VPD). When a drought lasted for 8 to 15 months and the corresponding drought severity reached 10 to 15 (the inverse of the cumulative values of the drought index when the drought occurs), the drought caused changes in the ecohydrological variables in most regions of the world. Compared with arid and semiarid regions, ecohydrological variables are more sensitive to drought in humid and semihumid regions (p < 0.05), and high-intensity human activities in different climatic conditions increased significantly the severity of drought processes. Between 2071 and 2100, more than half of the world’s ecohydrological variables are expected to be more susceptible to drought disturbances (regions with shorter return periods of drought events that cause significant changes in ET, SM, VPD and NDVI account for 60.1%, 64.4%, 59.6% and 54.5% of the global land area, respectively).

New Insights for Tracking Global and Local Trends in Exposure to Air Pollutants

Over six million people die prematurely each year from exposure to air pollution. Current air quality metrics insufficiently monitor exposure to air pollutants. This gap hinders the ability of decisionmakers to address the public health impacts of air pollution. To spur new emissions control policies and ensure implemented solutions realize meaningful gains in environmental health, we develop a framework of public-health-focused air quality indicators that quantifies over 200 countries' trends in exposure to particulate matter, ozone, nitrogen oxides, sulfur dioxide, carbon monoxide, and volatile organic compounds. We couple population density to ground-level pollutant concentrations to derive population-weighted exposure metrics that quantify the pollutant levels experienced by the average resident in each country. Our analyses demonstrate that most residents in 171 countries experience pollutant levels exceeding international health guidelines. In addition, we find a negative correlation between temporal trends in ozone and nitrogen oxide concentrations, which─when qualitatively interpreted with a simple atmospheric chemistry box model─can help describe the apparent tradeoff between the mitigation of these two pollutants on local scales. These novel indicators and their applications enable regulators to identify their most critical pollutant exposure trends and allow countries to track the performance of their emission control policies over time.

Changes in Long-Term PM2.5 Pollution in the Urban and Suburban Areas of China’s Three Largest Urban Agglomerations from 2000 to 2020

Particulate matter (PM2.5) is a significant public health concern in China, and the Chinese government has implemented a series of laws, policies, regulations, and standards to improve air quality. This study documents the changes in PM2.5 and evaluates the effects of industrial transformation and clean air policies on PM2.5 levels in urban and suburban areas of China’s three largest urban agglomerations, Beijing–Tianjin–Hebei (BTH), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD) based on a new degree of urbanization classification method. We used high-resolution PM2.5 concentration and population datasets to quantify the differences in PM2.5 concentrations in urban and suburban areas of these three urban agglomerations. From 2000 to 2020, the urban areas have expanded while the suburban areas have shrunk. PM2.5 concentrations in urban areas were approximately 32, 10, and 7 μg/m3 higher than those in suburban areas from 2000 to 2020 in BTH, YRD, and PRD, respectively. Since 2013, the PM2.5 concentrations in the urban regions of BTH, YRD, and PRD have declined at average annual rates of 7.30, 5.50, and 5.03 μg/m3/year, respectively, while PM2.5 concentrations in suburban areas have declined at average annual rates of 3.11, 4.23 and 4.69 μg/m3/year, respectively. By 2018, all of the urban and suburban areas of BTH, YRD, and PRD satisfied their specific targets in the Air Pollution and Control Action Plan. By 2020, the PM2.5 declines of BTH, YRD, and PRD exceeded the targets by two, three, and four times, respectively. However, the PM2.5 exposure risks in urban areas are 10–20 times higher than those in suburban areas. China will need to implement more robust air pollution mitigation policies to achieve the World Health Organization’s Air Quality Guideline (WHO-AQG) and reduce long-term PM2.5 exposure health risks.

Exploring agricultural land-use and childhood malaria associations in sub-Saharan Africa

Agriculture in Africa is rapidly expanding but with this comes potential disbenefits for the environment and human health. Here, we retrospectively assess whether childhood malaria in sub-Saharan Africa varies across differing agricultural land uses after controlling for socio-economic and environmental confounders. Using a multi-model inference hierarchical modelling framework, we found that rainfed cropland was associated with increased malaria in rural (OR 1.10, CI 1.03-1.18) but not urban areas, while irrigated or post flooding cropland was associated with malaria in urban (OR 1.09, CI 1.00-1.18) but not rural areas. In contrast, although malaria was associated with complete forest cover (OR 1.35, CI 1.24-1.47), the presence of natural vegetation in agricultural lands potentially reduces the odds of malaria depending on rural-urban context. In contrast, no associations with malaria were observed for natural vegetation interspersed with cropland (veg-dominant mosaic). Agricultural expansion through rainfed or irrigated cropland may increase childhood malaria in rural or urban contexts in sub-Saharan Africa but retaining some natural vegetation within croplands could help mitigate this risk and provide environmental co-benefits.

Predicted wind and solar energy expansion has minimal overlap with multiple conservation priorities across global regions

Protected areas and renewable energy generation are critical tools to combat biodiversity loss and climate change, respectively. Over the coming decades, expansion of the protected area network to meet conservation objectives will be occurring alongside rapid deployment of renewable energy infrastructure to meet climate targets, driving potential conflict for a finite land resource. Renewable energy infrastructure can have negative effects on wildlife, and co-occurrence may mean emissions targets are met at the expense of conservation objectives. Here, we assess current and projected overlaps of wind and solar photovoltaic installations and important conservation areas across nine global regions using spatially explicit wind and solar data and methods for predicting future renewable expansion. We show similar levels of co-occurrence as previous studies but demonstrate that once area is accounted for, previous concerns about overlaps in the Northern Hemisphere may be largely unfounded, although they are high in some biodiverse countries (e.g., Brazil). Future projections of overlap between the two land uses presented here are generally dependent on priority threshold and region and suggest the risk of future conflict can be low. We use the best available data on protected area degradation to corroborate this level of risk. Together, our findings indicate that while conflicts between renewables and protected areas inevitably do occur, renewables represent an important option for decarbonization of the energy sector that would not significantly affect area-based conservation targets if deployed with appropriate policy and regulatory controls.

Exposure to Unconventional Oil and Gas Development and All-cause Mortality in Medicare Beneficiaries

Little is known about whether exposure to unconventional oil and gas development is associated with higher mortality risks in the elderly and whether related air pollutants are exposure pathways. We studied a cohort of 15,198,496 Medicare beneficiaries (136,215,059 person-years) in all major U.S. unconventional exploration regions from 2001 to 2015. We gathered data from records of more than 2.5 million oil and gas wells. For each beneficiary's ZIP code of residence and year in the cohort, we calculated a proximity-based and a downwind-based pollutant exposure. We analyzed the data using two methods: Cox proportional hazards model and Difference-in-Differences. We found evidence of statistically significant higher mortality risk associated with living in proximity to and downwind of unconventional oil and gas wells. Our results suggest that primary air pollutants sourced from unconventional oil and gas exploration can be a major exposure pathway with adverse health effects in the elderly.

A crowdsourced global data set for validating built-up surface layers

Several global high-resolution built-up surface products have emerged over the last five years, taking full advantage of open sources of satellite data such as Landsat and Sentinel. However, these data sets require validation that is independent of the producers of these products. To fill this gap, we designed a validation sample set of 50 K locations using a stratified sampling approach independent of any existing global built-up surface products. We launched a crowdsourcing campaign using Geo-Wiki ( https://www.geo-wiki.org/ ) to visually interpret this sample set for built-up surfaces using very high-resolution satellite images as a source of reference data for labelling the samples, with a minimum of five validations per sample location. Data were collected for 10 m sub-pixels in an 80 × 80 m grid to allow for geo-registration errors as well as the application of different validation modes including exact pixel matching to majority or percentage agreement. The data set presented in this paper is suitable for the validation and inter-comparison of multiple products of built-up areas.

"Late-stage" deforestation enhances storm trends in coastal West Africa

Deforestation affects local and regional hydroclimate through changes in heating and moistening of the atmosphere. In the tropics, deforestation leads to warming, but its impact on rainfall is more complex, as it depends on spatial scale and synoptic forcing. Most studies have focused on Amazonia, highlighting that forest edges locally enhance convective rainfall, whereas rainfall decreases over drier, more extensive, deforested regions. Here, we examine Southern West Africa (SWA), an example of "late-stage" deforestation, ongoing since 1900 within a 300-km coastal belt. From three decades of satellite data, we demonstrate that the upward trend in convective activity is strongly modulated by deforestation patterns. The frequency of afternoon storms is enhanced over and downstream of deforested patches on length scales from 16 to 196 km, with greater increases for larger patches. The results are consistent with the triggering of storms by mesoscale circulations due to landscape heterogeneity. Near the coast, where sea breeze convection dominates the diurnal cycle, storm frequency has doubled in deforested areas, attributable to enhanced land-sea thermal contrast. These areas include fast-growing cities such as Freetown and Monrovia, where enhanced storm frequency coincides with high vulnerability to flash flooding. The proximity of the ocean likely explains why ongoing deforestation across SWA continues to increase storminess, as it favors the impact of mesoscale dynamics over moisture availability. The coastal location of deforestation in SWA is typical of many tropical deforestation hotspots, and the processes highlighted here are likely to be of wider global relevance.

Towards an Improved Large-Scale Gridded Population Dataset: A Pan-European Study on the Integration of 3D Settlement Data into Population Modelling

Large-scale gridded population datasets available at the global or continental scale have become an important source of information in applications related to sustainable development. In recent years, the emergence of new population models has leveraged the inclusion of more accurate and spatially detailed proxy layers describing the built-up environment (e.g., built-area and building footprint datasets), enhancing the quality, accuracy and spatial resolution of existing products. However, due to the consistent lack of vertical and functional information on the built-up environment, large-scale gridded population datasets that rely on existing built-up land proxies still report large errors of under- and overestimation, especially in areas with predominantly high-rise buildings or industrial/commercial areas, respectively. This research investigates, for the first time, the potential contributions of the new World Settlement Footprint—3D (WSF3D) dataset in the field of large-scale population modelling. First, we combined a Random Forest classifier with spatial metrics derived from the WSF3D to predict the industrial versus non-industrial use of settlement pixels at the Pan-European scale. We then examined the effects of including volume and settlement use information into frameworks of dasymetric population modelling. We found that the proposed classification method can predict industrial and non-industrial areas with overall accuracies and a kappa-coefficient of ~84% and 0.68, respectively. Additionally, we found that both, integrating volume and settlement use information considerably increased the accuracy of population estimates between 10% and 30% over commonly employed models (e.g., based on a binary settlement mask as input), mainly by eliminating systematic large overestimations in industrial/commercial areas. While the proposed method shows strong promise for overcoming some of the main limitations in large-scale population modelling, future research should focus on improving the quality of the WFS3D dataset and the classification method alike, to avoid the false detection of built-up settlements and to reduce misclassification errors of industrial and high-rise buildings.

The pesticide health risk index - An application to the world's countries

While the use of pesticides continues to rise worldwide, our understanding of the pervasiveness of associated contamination and the health risks humans may be exposed to remain limited to small samples size, and based on small geographic scales, the exposed population, or the pesticide types. Using our recent mapping of global pesticide use, we quantify three complementary health risk metrics for 92 active ingredients: (i) the pesticide hazard load (PHL); (ii) the population exposure (PE); and (iii) the human intake relative to the acceptable dose (INTR). We integrated these metrics into the pesticide health risk index (PHRI) to assess the standing of 133 nations against the global averages of PHL and PE and the acceptable levels of INTR using data of 2015 (PHRI > 1 indicates a concern). We found that some low-toxicity ingredients have PHL values equivalent to high-toxicity ones, and hence neglecting low-toxicity ingredients may cause biases in risk assessments. The geography of PHL, PE, and INTR show hotspots across the Americas, East and South Asia, and Europe, but with the EU27 countries generally showing lower PHL than other countries possibly due to strict governance on pesticide use. By our measure, about 1.7 billion people (24% of the world population) reside in close proximity to where pesticide applications are greater than 100 kg-a.i. km^-2 year^-1; about 2.3 billion people (32% of the world population) may exceed the acceptable pesticide intake and about 1.1 billion (15% of the world population) may exceed this by 10 fold. We identified 36 countries with PHRI > 1 and 6 countries with PHRI > 5; of these countries, 10 belong to lower-middle and low income economies. Our analyses show that proximity exposure to pesticides may be more widespread than revealed in occupational studies, and therefore assessments of potential health effects over wider scales may be needed.

Ingestion of plastics and other debris by coastal and pelagic birds along the coast of Espírito Santo, Eastern Brazil

Although the ingestion of plastics and other anthropogenic debris by seabirds is a global problem, few studies have employed standardized protocols to quantify and classify the debris ingested by seabirds in the Southwest Atlantic. We evaluated the ingestion of marine debris (items >0.1 mm) by 126 coastal and pelagic birds (19 species) along the coast of Espírito Santo, Eastern Brazil. Debris were found in 30% of birds examined (11 species). Particles <1 mm accounted for 35% of all debris items. Most ingested debris were plastics (97%). Ingestion of >0.1 g of plastic debris was recorded in five species: Atlantic yellow-nosed albatrosses (Thalassarche chlororhynchos), Cory's shearwaters (Calonectris borealis), Manx shearwaters (Puffinus puffinus), brown boobies (Sula leucogaster), and Magellanic penguins (Spheniscus magellanicus). Our findings suggest that the ingestion of marine debris, especially plastics, is a common problem for coastal and pelagic birds in tropical Southwest Atlantic waters.

Estimates of country level temperature-related mortality damage functions

Many studies project that climate change is expected to cause a significant number of excess deaths. Yet, in integrated assessment models that determine the social cost of carbon (SCC), human mortality impacts do not reflect the latest scientific understanding. We address this issue by estimating country-level mortality damage functions for temperature-related mortality with global spatial coverage. We rely on projections from the most comprehensive published study in the epidemiology literature of future temperature impacts on mortality (Gasparrini et al. in Lancet Planet Health 1:e360–e367, 2017), which estimated changes in heat- and cold-related mortality for 23 countries over the twenty-first century. We model variation in these mortality projections as a function of baseline climate, future temperature change, and income variables and then project future changes in mortality for every country. We find significant spatial heterogeneity in projected mortality impacts, with hotter and poorer places more adversely affected than colder and richer places. In the absence of income-based adaptation, the global mortality rate in 2080–2099 is expected to increase by 1.8% [95% CI 0.8–2.8%] under a lower-emissions RCP 4.5 scenario and by 6.2% [95% CI 2.5–10.0%] in the very high-emissions RCP 8.5 scenario relative to 2001–2020. When the reduced sensitivity to heat associated with rising incomes, such as greater ability to invest in air conditioning, is accounted for, the expected end-of-century increase in the global mortality rate is 1.1% [95% CI 0.4–1.9%] in RCP 4.5 and 4.2% [95% CI 1.8–6.7%] in RCP 8.5. In addition, we compare recent estimates of climate-change induced excess mortality from diarrheal disease, malaria and dengue fever in 2030 and 2050 with current estimates used in SCC calculations and show these are likely underestimated in current SCC estimates, but are also small compared to more direct temperature effects.