1-km2 Resolution Research Articles

Assessment of geographical accessibility to COVID-19 testing facilities in Nepal (2021)

Ensuring equitable physical access to SARS-CoV-2 testing has proven to be crucial for controlling the COVID-19 epidemic, especially in countries like Nepal with its challenging terrain. During the second wave of the pandemic in May 2021, there was immense pressure to expand the laboratory network in Nepal to ensure calibration of epidemic response. The expansion led to an increase in the number of testing facilities from 69 laboratories in May 2021 to 89 laboratories by November 2021. We assessed the equity of physical access to COVID-19 testing facilities in Nepal during 2021. Furthermore, we investigated the potential of mathematical optimisation in improving accessibility to COVID-19 testing facilities. Based on up-to-date publicly available data sets and on the COVID-19-related daily reports published by Nepal's Ministry of Health and Population from May 1 to November 15, 2021, we measured the disparities in geographical accessibility to COVID-19 testing across Nepal at a resolution of 1km2. In addition, we proposed an optimisation model to prescribe the best possible locations to set up testing laboratories maximizing access, and tested its potential impact in Nepal. The analysis identified vulnerable districts where, despite ramping up efforts, physical accessibility to testing facilities remains low under two modes of travel-walking and motorized driving. Both geographical accessibility and its equality were better under the motorised mode compared with the walking mode. If motorised transportation were available to everyone, the population coverage within 60min of any testing facility (public and private) would be close to threefold the coverage for pedestrians within the same hour: 61.4% motorised against 22.2% pedestrian access within the hour, considering the whole population of Nepal. Very low accessibility was found in most areas except those with private test centres concentrated in the capital city of Kathmandu. The hypothetical use of mathematical optimisation to select 20 laboratories to add to the original 69 could have improved access from the observed 61.4% offered by the laboratories operating in November to 71.4%, if those 20 could be chosen optimally from all existing healthcare facilities in Nepal. In mountainous terrain, accessibility is very low and could not be improved, even considering all existing healthcare facilities as potential testing locations. The findings related to geographical accessibility to COVID-19 testing facilities should provide valuable information for health-related planning in Nepal, especially in emergencies where data might be limited and decisions time-sensitive. The potential use of publicly available data and mathematical optimisation could be considered in the future. WHO Special Programme for Research and Training in Tropical Diseases (TDR).

Mapping the charging demand for electric vehicles in 2050 from mobility habits

This paper proposes a method to spatially model and compare charging needs on the European scale, considering local disparities in population density, distance to city centres, car ownership and mobility habits. Mobility habits are modelled across Europe in terms of distance and time frame to elaborate scenarios of charging behaviour. The first step of the method is to calculate the density of electric vehicles with a resolution of 1km2, according to the progressive electrification of the fleet each year between 2020 and 2050. The second step is to quantify the mobility of commuters using their driving distance to work areas and mobility statistics. The model is then applied in a case study in Switzerland to plan the public charging infrastructure required to satisfy the charging needs of the local population. Despite lower motorization rates and driving distances, the results show a stronger need for charging in cities. With 50% of commuters charging at work and 20% at home during the workday, the demand in the evening can be reduced by 50% in the suburban areas compared to the baseline scenario in which all commuters are charging at home in the evening. This model can be used to quantify the energy needs of commuters, plan the deployment of the charging infrastructure, or simulate the effect of policies.

Associations between traffic-related air pollutants and preterm birth in a North Carolina birth cohort, 2003-2015

Background and Aim: Preterm birth (PTB; <37 weeks completed gestation) is associated with exposure to fine particles and ozone, but less is known about associations with nitrogen dioxide (NO₂) and other traffic-related air pollutants, such as benzene, toluene, ethylbenzene, and xylene (BTEX). We estimated the associations between PTB and NO₂, with and without adjustment for BTEX. Methods: We examined the associations between PTB and gestational exposure to NO₂ in a North Carolina singleton birth cohort (N=1,367,507) from 2003-2015. Daily 8-hour max NO₂ concentrations from a hybrid-model with a spatial resolution of 1km² were aggregated to census tracts and linked to residential address at delivery, then averaged across each week of pregnancy. We obtained annual average ambient concentrations for BTEX for census tracts from the 2011 National Air Toxics Assessment. Modified Poisson regression models with robust errors were used to estimate risk differences (RD(95% CIs)) per 10-ppb increase in NO₂, adjusted for gestational parent marital status, race/ethnicity, age at delivery, Medicaid status, month of conception, and BTEX. Results: The median (IQR) NO₂ across each day of pregnancy was 15.0(10.2) ppb and 1.6(1.1) ppb for BTEX. The associations between NO₂ and PTB were of similar magnitude across each week of gestation. RDs with and without adjustment for BTEX varied. RDs per 10-ppb increase NO₂ exposure during each week of gestation ranged from -7(95% CI: -14,1) in weeks 17 and 23 to 0(-6,5) in week 36 per 10,000 births, respectively. Adjusting for BTEX, RDs ranged from 19(13,25) in week 36 to 36(27,44) in week 24 per 10,000 births, respectively. Conclusions: While the association between NO₂ exposure and PTB were generally null across each week of gestation, when adjusting for BTEX, we observed a consistent pattern of increased risk of PTB with NO₂ exposure. Keywords: air pollution, preterm birth, nitrogen dioxide, reproductive outcomes

The use of high resolution temperature exposure data to estimate the short-term effects on cause specific mortality at municipal level in 5 Italian regions.

TPS 661: Climate change: temperature effects 1, Exhibition Hall, Ground floor, August 26, 2019, 3:00 PM - 4:30 PM Introduction: Health effects of high and low temperatures in urban areas are well known, however evidence in suburban and rural areas is scarce. Aim: To estimate the short-term effects of heat and cold on cause-specific mortality at municipal level for the period 2001-2010 in 5 Italian regions. Methods: Daily mean air temperature with a spatial resolution of 1km2 was derived using satellite land surface temperature (LST), observed temperature data and spatio-temporal land use and land cover predictors. Non-linear distributed lag (DLNM) models, adjusted for temporal trends, PM10 and influenza epidemics, were used to estimate the association between temperature and cause-specific mortality at municipal level. The percent change in risk (IR%), for temperature increases between the 75th and 99th percentile (heat) and decreases from the 25th to the 1st percentile (cold) are reported. Risk estimates by age, gender and urbanization (high, medium, low) were also calculated. Results: A significant effect of both heat and cold was observed, with some heterogeneity among regions. The pooled effect estimates for heat and cold on total mortality were 1.33 (95%IC: 1.21-1.47) and 1.18 (95%IC: 1.12-1.25) respectively. In summer, significant effects were observed for cardiac, cerebrovascular, ischemic, respiratory, metabolic and mental disease, diabetes, and disease of the central nervous system. Estimates range from 1.21% (95%IC: 1.04-1.43) for ischemic heart disease to 1.97% (95%IC: 1.65-2.34) for disease of the central nervous system. In winter, significant effects were observed only for cardiovascular (1.21% CI95%:1.17-1.30) and respiratory disease (1.43% CI95%:1.24-1.65). A greater risk in mortality was found among the very old (75+ years) with higher risk estimates among females for heat and among males for cold. Risks were comparable between urban, suburban and rural areas. Conclusions: Extreme temperatures are an important risk factor in both urban and rural areas.

The impact of urbanization on air stagnation: Shenzhen as case study

One of the most concerning consequences arising from the dramatic urbanization in cities is air stagnation and the related high concentration of air pollutants. Many studies have investigated the impact of urbanization on air stagnation, but few have systematically evaluated such impact and its spatial-temporal variances at the municipal scale. This study proposed an approach based on high-resolution urban climate simulations for evaluating the impact of urbanization on air stagnation. We took the city of Shenzhen in south-eastern China, a city that grew from a small fishing and farming village to a highly urbanized city in the past thirty years, as a compelling case study. Using the WRF/Noah LSM/SLUCM model, we simulated and evaluated the probability of 6-hourly air stagnation cases (ASCs) in 1979 and 2010 at the spatial resolution of 1-km2 to demonstrate the change over a thirty-year period. Comparison results show that urbanization worsened the problem of air stagnation in Shenzhen. The number of 6-hourly ASCs has increased by 21,700 for the entire Shenzhen, and by 11.4 on average for each grid with a 1 km2 size. A maximum increase of 458 ASCs in a grid was also observed.

Stochastic reconstruction of spatio-temporal rainfall patterns by inverse hydrologic modelling

Abstract. Knowledge of spatio-temporal rainfall patterns is required as input for distributed hydrologic models used for tasks such as flood runoff estimation and modelling. Normally, these patterns are generated from point observations on the ground using spatial interpolation methods. However, such methods fail in reproducing the true spatio-temporal rainfall pattern, especially in data-scarce regions with poorly gauged catchments, or for highly dynamic, small-scale rainstorms which are not well recorded by existing monitoring networks. Consequently, uncertainties arise in distributed rainfall–runoff modelling if poorly identified spatio-temporal rainfall patterns are used, since the amount of rainfall received by a catchment as well as the dynamics of the runoff generation of flood waves is underestimated. To address this problem we propose an inverse hydrologic modelling approach for stochastic reconstruction of spatio-temporal rainfall patterns. The methodology combines the stochastic random field simulator Random Mixing and a distributed rainfall–runoff model in a Monte Carlo framework. The simulated spatio-temporal rainfall patterns are conditioned on point rainfall data from ground-based monitoring networks and the observed hydrograph at the catchment outlet and aim to explain measured data at best. Since we infer a three-dimensional input variable from an integral catchment response, several candidates for spatio-temporal rainfall patterns are feasible and allow for an analysis of their uncertainty. The methodology is tested on a synthetic rainfall–runoff event on sub-daily time steps and spatial resolution of 1 km2 for a catchment partly covered by rainfall. A set of plausible spatio-temporal rainfall patterns can be obtained by applying this inverse approach. Furthermore, results of a real-world study for a flash flood event in a mountainous arid region are presented. They underline that knowledge about the spatio-temporal rainfall pattern is crucial for flash flood modelling even in small catchments and arid and semiarid environments.

Spatiotemporal distribution and dynamic modeling of atmospheric gaseous polycyclic aromatic hydrocarbons in a rapidly urbanizing city: Nanjing, China.

Multiple studies have evaluated the concentration and lung cancer risk of polycyclic aromatic hydrocarbons (PAHs). However, the monitoring and dynamic modeling of PAHs with a high resolution were relatively insufficient. We investigated the spatiotemporal distribution of gaseous PAH concentrations using passive air samplers with high sampling density in an industrial city of Nanjing, China (January and October 2015) and found that the gaseous PAH concentrations in western Nanjing were higher than those in eastern Nanjing, mainly because of emission source distribution and wind action. There were notable seasonal changes in PAH concentrations: winter > autumn > spring > summer. We developed an atmospheric PAH dynamic model with a high resolution of 1km2 based on the advection-diffusion equation and coupled with an emissions inventory and atmospheric transportation processes. Acenaphthene was selected as a proxy for gaseous PAHs. The modeled acenaphthene concentrations were similar to the concentrations measured. Moreover, we used the model to identify the impact of meteorological factors on gaseous PAHs via scenario analysis and found that a narrow-range temperature change and even heavy rainfall may not significantly affect atmospheric gaseous PAH concentrations, whereas the wind played an important part in transferring PAHs and changing their geographic distribution.

Identification of landscape character types for trans-regional integration in the Wuling Mountain multi-ethnic area of southwest China

In China, 55 ethnic minority groups, accounting for 8.49% of the total population, live on 64.2% of the total land area. In the context of globalization and modernization, these areas retain a wealth of natural and cultural landscape types. Few studies have been conducted on landscape character on a regional scale in China, and to the best of our knowledge, none in multi-ethnic areas. Our study site was Wuling Mountain, which is located in southwest China and populated by multi-ethnic communities. Nine minorities including the Dong, Gelao, Miao, and Tujia have lived in the area for generations, coexisting with Han Chinese. We analyzed trans-regional data to visualize and identify the landscape character types of Wuling Mountain at two levels. The types were characterized at a resolution of 1km2 based on elevation, relief amplitude, land cover, and ethnic population density. The latter, as a special cultural variable of a multi-ethnic area, indirectly reflects the regional differences in the lives and livelihood activities of different ethnic groups. We used the affinity propagation (AP) algorithm based on a bottom-up approach of a combination of the geographic information system (GIS). The results highlighted the inhabited landscape characteristics of different combinations of minority ethnic populations and the key landscape characteristics that could be used as a basis for the development of trans-regional protection, management, and planning of the natural and cultural landscape in this multi-ethnic area.

Study on the association between ambient temperature and mortality using spatially resolved exposure data

There are many studies that have posited an association between extreme temperature and increased mortality. However, most studies use temperature at a single station per city as the reference point to analyze deaths. This leads to exposure misclassification and usually the exclusion of exurban, small town, and rural populations. In addition, few studies control for confounding by PM2.5, which is expected to induce upward bias. The high-resolution temperature and PM2.5 data at a resolution of 1km2 were derived from satellite images and other land use sources.To capture the nonlinear association of temperature with mortality we fit a piecewise linear spline function for temperature, with a change in slope at −1°C and 28°C, the temperature threshold at which mortality in Georgia, North Carolina, and South Carolina increases due to cold and heat, respectively. We conducted stratified analyses by age group, sex, race, education, and urban vs nonurban, as well as sensitivity analyses of different temperature threshold and covariate sets.We found a 0.19% (95% CI=−0.98, 1.34%) increase in mortality for each 1°C decrease in temperature below −1°C and a 2.05% (95% CI=0.87, 3.24%) increase in mortality for each 1°C increase in temperature above 28°C, a 79.8% larger effect size for heat compared to the station-based metric. The effect estimates relying on the monitoring stations were 0.09% (95% CI=−0.79, 0.95%) and 1.14% (95% CI=0.08, 1.57%) for the equivalent temperature changes. The estimates were not confounded by PM2.5. Children under 15 years of age had the largest percentage increase per 1°C increase in temperature (8.19%, 95% CI=−0.38 to 17.49%) followed by Blacks (4.35%, 95% CI=2.22 to 6.53%). Higher education was a protective factor for the effect of extreme temperature on mortality. There was a suggestion that people in less urban areas were more susceptible to extreme temperature. The relationship between temperature and mortality was stronger when using exposure data with more spatial variability than using exposure data based on existing monitors alone.

Grid Based Temperature and Relative Humidity Distribution Map of the Kathmandu Valley

Kathmandu valley has seen visible transformations in its intrinsic climate in recent decades. The atmosphere of the Kathmandu valley has become increasingly uncomfortable, unhealthy and more susceptible to, so far, unanticipated viral and other diseases. The lack of spatial distribution and temporal variation of potential climatic parameters as well as future scenarios have given rise to serious uncertainties with respect to climate change and development of adaptation strategies for sustainable climate of the valley and beyond. An accurate assessment and gridded documentation of prevailing climate at desired resolution is instrumental. Present study has been carried out to develop a high-resolution gridded map of near surface temperature and humidity for Kathmandu valley and its immediate surrounding areas as a starting point to address the foreseen problem of climate change and adaptation. Grid based seasonal and annual distribution map of near surface temperature and humidity have been developed as predicted by numerical simulation at horizontal resolution of 1km2 with the application of WRF Modeling System initializing with NCEP meteorological and USGS land use and terrain elevation data. Model predictions agree well with the available observation data.Journal of Institute of Science and Technology, 2014, 19(1): 7-13

A high-resolution model of bat diversity and endemism for continental Africa

Bats are the second-most species-rich mammal group numbering more than 1270 species globally. Our knowledge of their geographic distributions and diversity patterns however is very limited – possibly the poorest among mammals – mainly due to their nocturnal and volant life history, and challenging fieldwork conditions in the tropics where most bat species occur. This knowledge gap obscures the geographic extent of ecosystem services provided by bats (i.e. pollination, seed dispersal and insect control), translates into inefficient conservation policies, and restricts macroecological analyses to coarse spatial resolutions. In contrast to the currently prevailing method of estimating species distributions using expert-drawn range maps, correlative species distribution models (SDMs) can provide estimates at very fine spatial grains and largely account for widespread sample bias as well as the prevalent Wallacean shortfall in species occurrence data. Very few such studies have hitherto been published that cover a large and complete taxonomic group with fine resolution at continental extent. Using an unparalleled amount of occurrence data, the MaxEnt algorithm and tailored solutions to specific modelling challenges, we created SDMs for nearly all 250 African bat species to explore emerging diversity patterns at a resolution of 1km2. Predicted species richness generally increases towards the equator conforming to expectations. Within the tropical area of elevated richness, several pronounced richness peaks and lows stand out, hinting at a complex interplay of determining factors. Richness gradients are often steep, decreasing strongly away from streams, and especially so in savanna biomes. Species richness also seems positively associated with rugged terrain, in particular at lower elevations. Centres of endemism are found primarily at low latitudes near major elevational ranges. Overlap with hotspots of species richness is rather low, and confined to five or six topodiverse, relatively low lying areas between western Guinea and the East African coast. Several poorly sampled regions are identified that may represent rewarding future survey targets. Our results demonstrate the value of stacking SDMs to infer plausible continent-wide diversity gradients at a spatial resolution fine enough to directly inform conservation policies and to open up new avenues in macroecological research.

Interpolation of Mexican soil properties at a scale of 1:1,000,000

México maintains several databases with biotic or abiotic information that enable large scale-studies (for example, at a resolution of 1km2); unfortunately, there is no information at this resolution for soil properties. The goal of this paper was to generate a set of soil variables to address this absence. We evaluated 4400 soil samples taken on Mexican territory. The following nine soil properties were evaluated for each sample: Ca, K, Mg, Na, organic C, organic matter, electrical conductivity, sodium absorption ratio, and pH. With the use of geostatistical methods, a layer was generated for each soil property after six different semivariance models were evaluated. The kriging model (simple, ordinary, or universal) was selected as the best semivariance method with a 10-fold cross-validation approach. Exponential, pentaspherical, and spherical models were selected with ordinary and universal kriging methods to spatially predict the soil properties. Five classes for each soil map were generated. The nine soil properties were classified primarily as slightly low or medium according to their distribution throughout Mexican territory.

Mapping syndromes of land change in Spain with remote sensing time series, demographic and climatic data

The country of Spain is representative of land change processes in Mediterranean member states of the European Union (EU). These land change processes are often triggered by European, national and sub-national policies and include widespread land abandonment and urbanisation trends, as well as an increase in land use intensities accompanied by strong exploitation of water resources. The Mediterranean is part of the dryland ecoregion, which is particularly vulnerable to ecosystem degradation. While remote sensing data permit the characterisation of the temporal dimension of land surface processes, the syndrome-based approach aims to integrate this with information on local/regional socio-economic and physical frameworks. In this study, we incorporated two major drivers of land change, climatic boundary conditions and population density change, to understand the patterns of the assessed land cover changes.We used the Mediterranean Extended Daily One Km AVHRR Data Set (MEDOKADS), which represents the Mediterranean Basin at a resolution of 1km2 for the period of 1989–2004 at 10-day time intervals. The long-term vegetation trend could be characterised based on a linear regression analysis of Normalised Difference Vegetation Index (NDVI) values. Further descriptors of phenology were calculated, such as the amplitude of annual cycles or the date of occurrence of the annual maximum of vegetation cover. Subsequently, trend analysis was applied to these parameters to map phenology shifts representative of changes in land use/cover.Next, a rule-based classification was employed to integrate the individual results and assign syndromes representing specific land change process patterns. For natural and semi-natural areas, locations characterised by increasing biomass are associated with areas of population loss due to migration to urban centres. By contrast, many fertile agricultural areas are associated with intensification processes, including the expansion of irrigation schemes. Furthermore, areas that are dominated by inter-annual variability in precipitation were identified by employing the results of an analysis of NDVI–rainfall relationships. The resulting data set identifies major land change syndromes for the whole of Spain and links the apparent results of change to driving factors and causes, thus serving as an excellent data source to inform policy makers of effects caused by legislative and management actions.

Identifying critical areas for conservation: Biodiversity and climate change in central America, Mexico, and the Dominican Republic

Given the rapidity and intensity of anthropogenic impacts on natural systems, assessing the effectiveness of current protected areas in preserving biodiversity is especially important in Mesoamerica and the Caribbean, which contain a wide array of species and ecosystems. In light of the growing need to consider climate change in policymaking, combining climate change projections with biodiversity maps allows scientists and decision-makers to understand possible climate change impacts on biodiversity. In this study, we use GIS to identify spatial relationships between regional climate change models and species habitat ranges for Mesoamerica and the Caribbean. Evaluating possible effects of climate change in terms of temperature and precipitation involves three factors: historical averages, historical ranges, and future averages. Because different ecosystems and species exist at different temperature and precipitation ranges, we consider “comfort zones” of each area. We develop a quantitative, spatial measurement of climate change intensity of each area by calculating the difference between historical and future averages and dividing that difference by the area's comfort zone, at a spatial resolution of 1km2. The result is a normalized grid of projected climate change severity. According to the modeling results, should worst case scenario conditions prevail, by the 2020s, the Caribbean coasts of Costa Rica, Honduras, Nicaragua, Panama, and the Dominican Republic, will be significantly impacted by climate change. By the 2080s, all of the ecosystems and species of Central America and the Dominican Republic may be subjected to conditions well outside of their traditional comfort zone, while most of Mexico's ecosystems and species are at lower risk of severe climate change impacts. Integrating species richness data with the climate change severity analyses identifies critical areas that may require specific interventions to facilitate the adaptation of species to climate change. The information generated points not only to the utility of current protected areas, but is also useful in guiding the development of new protected areas and biological corridors, for the reduction of the potential impacts of future climate change.