Which Gridded Population Data Product Is Better? Evidences from Mainland Southeast Asia (MSEA)

The distribution and characterisation of microplastics in air, surface water and sediment within a major river system

Rivers are key pathways for the transfer of microplastics (MP) to marine environments. However, there are considerable uncertainties about the amount of microplastics transported by rivers to the ocean; this results in inaccuracies in our understanding of microplastic quantity and transport by freshwater systems. Additionally, it has been suggested that rivers may represent long-term sinks, with microplastics accumulating in sediment due to their high density or other biological, chemical, and physical factors. The atmosphere is also an important pathway by which airborne microplastics may enter aquatic habitats. Here, we compare for first time microplastics type and concentration in these key environmental mediums (air, water and sediment) along a major river (Ganges), from sea to source to understand 1) the abundance, 2) the spatial distribution, and 3) characteristics. Mean microplastic abundance settling from the atmosphere was 41.12 MP m2 day-1; while concentrations in sediment were 57.00 MP kg-1 and in water were 0.05 MP L-1. Across all sites and environmental mediums, rayon (synthetically altered cellulose) was the dominant polymer (54-82 %), followed by acrylic (6-23 %) and polyester (9-17 %). Fibres were the dominant shape (95-99 %) and blue was the most common colour (48-79 %). Across water and sediment environmental mediums, the number of microplastics per sample increased from the source of the Ganges to the sea. Additionally, higher population densities correlated with increased microplastic abundance for air and water samples. We suggest that clothing is likely to be the prominent source of microplastics to the river system, influenced by atmospheric deposition, wastewater and direct input (e.g. handwashing of clothes in the Ganges), especially in high density population areas. However, we suggest that subsequent microplastic release to the marine environment is strongly influenced by polymer type and shape, with a large proportion of denser microplastics settling in sediment prior to the river discharging to the ocean.

Are open-source hydrodynamic models efficient in quantifying flood risks over mountainous terrains? An exhaustive analysis over the Hindu-Kush-Himalayan region

The Hindu-Kush-Himalaya is abode to numerous severely flood-prone mountainous stretches that distress vulnerable communities and cause massive destruction to physical entities such as hydropower projects. Adopting commercial flood models for replicating the dynamics of flood wave propagation over such regions is a major constraint due to the financial economics threaded to flood management. For the first instance, the present study attempts to investigate whether advanced open-source models are skillful in quantifying flood hazards and population exposure over mountainous terrains. While doing so, the performance of 1D-2D coupled HEC-RAS v6.3 (the most recent version developed by the U.S. Army Corps of Engineers) is reconnoitred for the first time in flood management literature. The most flood-prone region in Bhutan, the Chamkhar Chhu River Basin, housing large groups of communities and airports near its floodplains, is considered. HEC-RAS v6.3 setups are corroborated by comparing them with 2010 flood imagery derived from MODIS through performance metrics. The results indicate a sizable portion of the central part of the basin experiences very-high flood hazards with depth and velocities exceeding 3 m, and 1.6 m/s, respectively, during 50, 100, and 200-year return periods of floods. To affirm HEC-RAS, the flood hazards are compared with TUFLOW at 1D and 1D-2D coupled levels. The hydrological similarity within the channel is reflected at river cross-sections (NSE and KGE > 0.98), while overland inundation and hazard statistics differ, however, very less significant (<10 %). Later, flood hazards extracted from HEC-RAS are fused with the World-Pop population to estimate the degree of population exposure. The study ascertains that HEC-RAS v6.3 is an efficacious option for flood risk mapping over geographically arduous regions and can be preferred in resource-constrained environments ensuring a minimal degree of anomaly.

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.

Small area population denominators for improved disease surveillance and response

The Covid-19 pandemic has highlighted the value of strong surveillance systems in supporting our abilities to respond rapidly and effectively in mitigating the impacts of infectious diseases. A cornerstone of such systems is basic subnational scale data on populations and their demographics, which enable the scale of outbreaks to be assessed, risk to specific groups to be determined and appropriate interventions to be designed. Ongoing weaknesses and gaps in such data have however been highlighted by the pandemic. These can include outdated or inaccurate census data and a lack of administrative and registry systems to update numbers, particularly in low and middle income settings. Efforts to design and implement globally consistent geospatial modelling methods for the production of small area demographic data that can be flexibly integrated into health-focussed surveillance and information systems have been made, but these often remain based on outdated population data or uncertain projections. In recent years, efforts have been made to capitalise on advances in computing power, satellite imagery and new forms of digital data to construct methods for estimating small area population distributions across national and regional scales in the absence of full enumeration. These are starting to be used to complement more traditional data collection approaches, especially in the delivery of health interventions, but barriers remain to their widespread adoption and use in disease surveillance and response. Here an overview of these approaches is presented, together with discussion of future directions and needs.

Differences between gridded population data impact measures of geographic access to healthcare in sub-Saharan Africa

Background

Access to healthcare is imperative to health equity and well-being. Geographic access to healthcare can be modeled using spatial datasets on local context, together with the distribution of existing health facilities and populations. Several population datasets are currently available, but their impact on accessibility analyses is unknown. In this study, we model the geographic accessibility of public health facilities at 100-meter resolution in sub-Saharan Africa and evaluate six of the most popular gridded population datasets for their impact on coverage statistics at different administrative levels.

Methods

Travel time to nearest health facilities was calculated by overlaying health facility coordinates on top of a friction raster accounting for roads, landcover, and physical barriers. We then intersected six different gridded population datasets with our travel time estimates to determine accessibility coverages within various travel time thresholds (i.e., 30, 60, 90, 120, 150, and 180-min).

Results

Here we show that differences in accessibility coverage can exceed 70% at the sub-national level, based on a one-hour travel time threshold. The differences are most notable in large and sparsely populated administrative units and dramatically shape patterns of healthcare accessibility at national and sub-national levels.

Conclusions

The results of this study show how valuable and critical a comparative analysis between population datasets is for the derivation of coverage statistics that inform local policies and monitor global targets. Large differences exist between the datasets and the results underscore an essential source of uncertainty in accessibility analyses that should be systematically assessed.

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.

Spatio-Temporal Patterns of Land-Use Changes and Conflicts between Cropland and Forest in the Mekong River Basin during 1990–2020

The Mekong River Basin (MRB) has experienced drastic and extensive land-use and land-cover changes (LULCCs) since the 1990s, including the conflicts between cropland and forest, yet remain quantitatively uninvestigated. With three decades (1990–2020) of land-use products, here we reveal the characteristics of LULCCs and the conflicts between cropland and forest in the MRB and its three sub-basins, i.e., upstream area (UA), midstream area (MA), and downstream area (DA). The four main results are as follows: (1) Since 1990, the dominated features are forest loss and cropland expansion in the MRB and show obvious sub-basin differences. (2) The LULCC was most active before 2000, with a comprehensive dynamic degree of almost 2%. Among them, construction land has the highest single dynamic degree (5%), especially in the DA, reaching 12%. (3) The key features of land-use transfer are the interconversions of forest and cropland, as well as cropland converted into construction land. About 18% (63,940 km2) of forest was reclaimed as cropland, and 17% (45,967 km2) of cropland was returned to forest in the past 31 years. (4) The conflict between cropland and forest was the most dominant LULCC, accounting for 86% of the MRB area. Overall, cropland expansion and forest loss (CEFL) were more dominant in the DA, while cropland fallow and forest restoration (CFFR) had an advantage in the MA. Indeed, CEFL was mainly seen in the plains below a 200 m elevation level, while CFFR tended to occur in the highlands. Our basin-scale study can enrich the existing pan-regional results of LULCCs, and facilitates the understanding of the dynamics and related mechanisms of CFER and CFFR in the tropics.

Small area population denominators for improved disease surveillance and response

The Covid-19 pandemic has highlighted the value of strong surveillance systems in supporting our abilities to respond rapidly and effectively in mitigating the impacts of infectious diseases. A cornerstone of such systems is basic subnational scale data on populations and their demographics, which enable the scale of outbreaks to be assessed, risk to specific groups to be determined and appropriate interventions to be designed. Ongoing weaknesses and gaps in such data have however been highlighted by the pandemic. These can include outdated or inaccurate census data and a lack of administrative and registry systems to update numbers, particularly in low and middle income settings. Efforts to design and implement globally consistent geospatial modelling methods for the production of small area demographic data that can be flexibly integrated into health-focussed surveillance and information systems have been made, but these often remain based on outdated population data or uncertain projections. In recent years, efforts have been made to capitalise on advances in computing power, satellite imagery and new forms of digital data to construct methods for estimating small area population distributions across national and regional scales in the absence of full enumeration. These are starting to be used to complement more traditional data collection approaches, especially in the delivery of health interventions, but barriers remain to their widespread adoption and use in disease surveillance and response. Here an overview of these approaches is presented, together with discussion of future directions and needs.