Grid Cell Level Research Articles

A novel 3D opto-electro-thermal coupling numerical model for photovoltaic/thermal systems

Photovoltaic/thermal (PV/T) systems offer a promising solution for enhancing energy conversion performance by capturing waste heat and reducing the operating temperature of photovoltaic cells. The performance of these PV/T systems could be further optimized by numerical simulation. However, currently reported PV/T models utilize simplified models and and fail to consider the electro-thermal coupling in local areas of solar cells, thereby inadequately representing the underlying physical mechanisms. To address this issue, a novel opto-electro-thermal coupling numerical model of perovskite PV/T systems based on drift–diffusion equations is developed in this work. This model enables the investigation of the energy conversion mechanism of optical, electrical and thermal energy at grid cell level. Building on the developed model, the energy conversion performance at system level under various operational conditions is analyzed via a modified weighted total efficiency evaluation indicator. The results indicate that: the total efficiency is significantly compromised by elevated environmental temperature, with an efficiency loss of 8.45 % observed when the temperature increases by 40 °C. Meanwhile, as irradiation intensity rises, the efficiency gained from utilizing waste heat would become increasingly significant in PV/T systems. More importantly, the optimal flow rate for various irradiation intensities is also derived. This work could offer theoretical guidance for investigating more comprehensive energy conversion mechanisms and optimizing energy conversion performance for PV/T systems.

Global warming level indicators of climate change and hotspots of exposure

Abstract In the 21st century, a growing population will be exposed to various hazards caused by a warming climate. Here we present a new database of 12 climate change indicators with a total of 42 variants at different global warming levels (GWLs) (1.2 °C–3.5 °C), which is based on global climate and hydrological model data from the Coupled Model Intercomparison Project round 6 and the Inter-Sectoral Impact Model Intercomparison Project round 3b at 0.5° spatial resolution. It comprises of indicators relating to temperature and precipitation extremes, heatwave events, and hydrological variability. To facilitate the comparison of hazards from different indicators, including for an audience without a scientific background, we have developed a bivariate hazard score which is applied on the grid cell level and incorporates statistics on both the absolute hazard (e.g. low or high precipitation) and the relative change under global warming compared to the reference period (e.g. a large change from low to high precipitation). Additionally, we calculate exposed land area and population through the 21st century for a large set of countries and regions by combining this score with gridded projections of population from the Shared Socioeconomic Pathways (SSPs). The datasets are intended for use by the wider research community and analysts seeking digestible climate hazard and exposure data summarized by GWLs. To illustrate potential uses of the data, in a preliminary analysis we find that even at 1.5 °C large parts of the land area and population face substantial unavoidable risks from multiple indicators, with 86% of the world’s population exposed to at least three indicators with at least medium hazard using the population projections for SSP2 in the year 2050. This picture only worsens with increasing warming, as the land areas facing the highest number of impacts coincide with some of the most densely populated parts of the world.

Identifying power outage hotspots to support risk management planning.

Secure and reliable power systems are vital for modern societies and economies. While there is a focus in the literature on predicting power outages caused by severe weather events, relatively little literature exists on identifying hot spots, locations where outages occur repeatedly and at a higher rate than expected. Reliably identifying hotspots can provide critical input for risk management efforts by power utilities, helping them to focus scarce resources on the most problematic portions of their system. In this article, we show how existing work on Moran's I spatial statistic can be adapted to identify power outage hotspots based on the types and quantities of data available to utilities in practice. The local Moran's I statistic was calculated on a grid cell level and a set of criteria were used to filter out which grid cells are considered hotspots. The hotspot identification approach utilized in this article is an easy method for utilities to use in practice, and it provides the type of information needed to directly support utility decisions about prioritizing areas of a power system to inspect and potentially reinforce.

Groundwaterscapes: A Global Classification and Mapping of Groundwater's Large‐Scale Socioeconomic, Ecological, and Earth System Functions

AbstractGroundwater is a dynamic component of the global water cycle with important social, economic, ecological, and Earth system functions. We present a new global classification and mapping of groundwater systems, which we call groundwaterscapes, that represent predominant configurations of large‐scale groundwater system functions. We identify and map 15 groundwaterscapes which offer a new lens to conceptualize, study, model, and manage groundwater. Groundwaterscapes are derived using a novel application of sequenced self‐organizing maps that capture patterns in groundwater system functions at the grid cell level (∼10 km), including groundwater‐dependent ecosystem type and density, storage capacity, irrigation, safe drinking water access, and national governance. All large aquifer systems of the world are characterized by multiple groundwaterscapes, highlighting the pitfalls of treating these groundwater bodies as lumped systems in global assessments. We evaluate the distribution of Global Groundwater Monitoring Network wells across groundwaterscapes and find that industrial agricultural regions are disproportionately monitored, while several groundwaterscapes have next to no monitoring wells. This disparity undermines the ability to understand system dynamics across the full range of settings that characterize groundwater systems globally. We argue that groundwaterscapes offer a conceptual and spatial tool to guide model development, hypothesis testing, and future data collection initiatives to better understand groundwater's embeddedness within social‐ecological systems at the global scale.

How well can satellite altimetry and firn models resolve Antarctic firn thickness variations?

Abstract. Elevation changes of the Antarctic Ice Sheet (AIS) related to surface mass balance and firn processes vary strongly in space and time. Their subdecadal natural variability is large and hampers the detection of long-term climate trends. Firn models or satellite altimetry observations are typically used to investigate such firn thickness changes. However, there is a large spread among firn models. Further, they do not fully explain observed firn thickness changes, especially on smaller spatial scales. Reconciled firn thickness variations will facilitate the detection of long-term trends from satellite altimetry; the resolution of the spatial patterns of such trends; and, hence, their attribution to the underlying mechanisms. This study has two objectives. First, we quantify interannual Antarctic firn thickness variations on a 10 km grid scale. Second, we characterise errors in both the altimetry products and firn models. To achieve this, we jointly analyse satellite altimetry and firn modelling results in time and space. We use the timing of firn thickness variations from firn models and the satellite-observed amplitude of these variations to generate a combined product (“adjusted firn thickness variations”) over the AIS for 1992–2017. The combined product characterises spatially resolved variations better than either firn models alone or altimetry alone. It provides a higher resolution and a more precise spatial distribution of the variations compared to model-only solutions and eliminates most of the altimetry errors compared to altimetry-only solutions. Relative uncertainties in basin-mean time series of the adjusted firn thickness variations range from 20 % to 108 %. At the grid cell level, relative uncertainties are higher, with median values per basin in the range of 54 % to 186 %. This is due to the uncertainties in the large and very dry areas of central East Antarctica, especially over large megadune fields, where the low signal-to-noise ratio poses a challenge for both models and altimetry to resolve firn thickness variations. A large part of the variance in the altimetric time series is not explained by the adjusted firn thickness variations. Analysis of the altimetric residuals indicate that they contain firn model errors, such as firn signals not captured by the models, and altimetry errors, such as time-variable radar penetration effects and errors in intermission calibration. This highlights the need for improvements in firn modelling and altimetry analysis.

Reduced floating-point precision in regional climate simulations: an ensemble-based statistical verification

Abstract. The use of single precision in floating-point representation has become increasingly common in operational weather prediction. Meanwhile, climate simulations are still typically run in double precision. The reasons for this are likely manifold and range from concerns about compliance and conservation laws to the unknown effect of single precision on slow processes or simply the less frequent opportunity and higher computational costs of validation. Using an ensemble-based statistical methodology, Zeman and Schär (2022) could detect differences between double- and single-precision simulations from the regional weather and climate model COSMO. However, these differences are minimal and often only detectable during the first few hours or days of the simulation. To evaluate whether these differences are relevant for regional climate simulations, we have conducted 10-year-long ensemble simulations over the European domain of the Coordinated Regional Climate Downscaling Experiment (EURO-CORDEX) in single and double precision with 100 ensemble members. By applying the statistical testing at a grid-cell level for 47 output variables every 12 or 24 h, we only detected a marginally increased rejection rate for the single-precision climate simulations compared to the double-precision reference based on the differences in distribution for all tested variables. This increase in the rejection rate is much smaller than that arising from minor variations of the horizontal diffusion coefficient in the model. Therefore, we deem it negligible as it is masked by model uncertainty. To our knowledge, this study represents the most comprehensive analysis so far on the effects of reduced precision in a climate simulation for a realistic setting, namely with a fully fledged regional climate model in a configuration that has already been used for climate change impact and adaptation studies. The ensemble-based verification of model output at a grid-cell level and high temporal resolution is very sensitive and suitable for verifying climate models. Furthermore, the verification methodology is model-agnostic, meaning it can be applied to any model. Our findings encourage exploiting the reduction of computational costs (∼30 % for COSMO) obtained from reduced precision for regional climate simulations.

Intercomparison of global foliar trait maps reveals fundamental differences and limitations of upscaling approaches

Foliar traits such as specific leaf area (SLA), leaf nitrogen (N), and phosphorus (P) concentrations play important roles in plant economic strategies and ecosystem functioning. Various global maps of these foliar traits have been generated using statistical upscaling approaches based on in-situ trait observations. Here, we intercompare such global upscaled foliar trait maps at 0.5° spatial resolution (six maps for SLA, five for N, three for P), categorize the upscaling approaches used to generate them, and evaluate the maps with trait estimates from a global database of vegetation plots (sPlotOpen). We disentangled the contributions from different plant functional types (PFTs) to the upscaled maps and quantified the impacts of using different plot-level trait metrics on the evaluation with sPlotOpen: community weighted mean (CWM) and top-of-canopy weighted mean (TWM). We found that the global foliar trait maps of SLA and N differ drastically and fall into two groups that are almost uncorrelated (for P only maps from one group were available). The primary factor explaining the differences between these groups is the use of PFT information combined with remote sensing-derived land cover products in one group while the other group mostly relied on environmental predictors alone. The maps that used PFT and corresponding land cover information exhibit considerable similarities in spatial patterns that are strongly driven by land cover. The maps not using PFTs show a lower level of similarity and tend to be strongly driven by individual environmental variables. Upscaled maps of both groups were moderately correlated to sPlotOpen data aggregated to the grid-cell level (R = 0.2–0.6) when processing sPlotOpen in a way that is consistent with the respective trait upscaling approaches, including the plot-level trait metric (CWM or TWM) and the scaling to the grid cells with or without accounting for fractional land cover. The impact of using TWM or CWM was relevant, but considerably smaller than that of the PFT and land cover information. The maps using PFT and land cover information better reproduce the between-PFT trait differences of sPlotOpen data, while the two groups performed similarly in capturing within-PFT trait variation.Our findings highlight the importance of explicitly accounting for within-grid-cell trait variation, which has important implications for applications using existing maps and future upscaling efforts. Remote sensing information has great potential to reduce uncertainties related to scaling from in-situ observations to grid cells and the regression-based mapping steps involved in the upscaling.

Insights into the spatial distribution of global, national, and subnational greenhouse gas emissions in the Emissions Database for Global Atmospheric Research (EDGAR v8.0)

Abstract. To mitigate the impact of greenhouse gas (GHG) and air pollutant emissions, it is of utmost importance to understand where emissions occur. In the real world, atmospheric pollutants are produced by various human activities from point sources (e.g. power plants and industrial facilities) but also from diffuse sources (e.g. residential activities and agriculture). However, as tracking all these single sources of emissions is practically impossible, emission inventories are typically compiled using national-level statistics by sector, which are then downscaled at the grid-cell level using spatial information. In this work, we develop high-spatial-resolution proxies for use in downscaling the national emission totals for all world countries provided by the Emissions Database for Global Atmospheric Research (EDGAR). In particular, in this paper, we present the latest EDGAR v8.0 GHG, which provides readily available emission data at different levels of spatial granularity, obtained from a consistently developed GHG emission database. This has been achieved through the improvement and development of high-resolution spatial proxies that allow for a more precise allocation of emissions over the globe. A key novelty of this work is the potential to analyse subnational GHG emissions over the European territory and also over the United States, China, India, and other high-emitting countries. These data not only meet the needs of atmospheric modellers but can also inform policymakers working in the field of climate change mitigation. For example, the EDGAR GHG emissions at the NUTS 2 level (Nomenclature of Territorial Units for Statistics level 2) over Europe contribute to the development of EU cohesion policies, identifying the progress of each region towards achieving the carbon neutrality target and providing insights into the highest-emitting sectors. The data can be accessed at https://doi.org/10.2905/b54d8149-2864-4fb9-96b9-5fd3a020c224 specifically for EDGAR v8.0 (Crippa et al., 2023a) and https://doi.org/10.2905/D67EEDA8-C03E-4421-95D0-0ADC460B9658 for the subnational dataset (Crippa et al., 2023b).

The Impact of Land-Use Mix on Technological Innovation: Evidence from a Grid-Cell-Level Analysis of Shanghai, China

While the benefits of land-use mix have been widely analyzed with regard to transportation, public health, and economic development, relatively little attention has been paid to empirically investigating the impact of land-use mix on technological innovation at the intra-urban level. Drawing upon a database of geo-coded patents that are used to reflect the capacity of technological innovation, this paper takes Shanghai as a case study and analyzes how the intra-urban distribution of technological innovation has been associated with land-use mix at the 1 km × 1 km grid cell level. Empirical results, which are robust when the grids are divided at the 2 km × 2 km level, show that the degree of land-use mix is positively associated with the number of patents for a given grid, suggesting that grids with a higher level of land-use mix are likely to have more patents, ceteris paribus. Moreover, the results demonstrate an inverted U-curve relationship between land-use mix and technological innovation, indicating that a too much higher level of land-use mix could lead to a smaller number of patents for a certain grid. In addition, the empirical results suggest the existence of spatial dependence in the effect of land-use mix on technological innovation.

Substantial increase in population exposure to multiple environmental burdens in sub-Saharan Africa (2000-2019)

In the face of increasing global environmental uncertainties, sub-Saharan Africa stands as a highly vulnerable region with a massive population marked with poverty and inequalities. Moreover, different environmental risk factors can coexist simultaneously as multiple environmental burdens (MEBs); however, population exposure to MEB remains unexamined. Here, using open-access spatial data and critical thresholds, we quantify population exposure to four key environmental risk factors: hazardous fine particulate matter (PM2.5) levels, extreme temperature increase, prolonged severe droughts, and green deficit (scarcity of green trees). Further, we explore the concept of MEB, where these risk factors converge. We derive exposure for 2000 and 2019 at the pixel (1 km grid cell) level. We also check how population change, environmental change, and their interaction contribute to the total change in exposure. We found substantial changes in the population exposed from 2000 to 2019, i.e. an increase of ∼460 million people to hazardous PM2.5 levels, ∼16 million to extreme temperature increase, ∼13 million to prolonged severe droughts, and ∼246 million to green deficit. Population exposure to at least three of these four environmental risk factors (3EB) has increased by ∼246 million. In this increase in exposure to 3EB, the contribution of environmental change is higher (48%), than that of interaction and population change (36% and 15%, respectively). Notably, there are striking disparities in population exposure, its change, and the contributing effects among countries and regions of sub-Saharan Africa.

Limited complementarity of functional and taxonomic diversity in Chilean benthic marine invertebrates

AbstractAimPatterns of benthic biodiversity at the macroecological scale remain poorly characterised throughout the Chilean latitudinal gradient, in part due to the lack of integrated databases, uneven sampling effort, and the use of species richness alone to quantify biodiversity. Different diversity measures, encompassing taxonomic and functional components, may give us extra information on biodiversity relevant to conservation planning and management. Thus, evaluating the spatial complementarity of these measures is essential.LocationCoast and continental shelf of Chile.MethodsThe latitudinal gradient of Chile was divided into five ecoregions according to the Marine Ecosystems of the World classification. Using a 55 × 55 km equal area grid, we estimated the incidence coverage‐based estimator (ICE), taxonomic distinctness (Δ+) and three measures of functional diversity: functional richness (FRic), functional evenness (FEve) and functional divergence (FDiv). For each measure, we described spatial patterns, identified hotspots, evaluated hotspot congruence and evaluated complementarity between measures.ResultsDiversity patterns varied between ecoregions and over the latitudinal gradient. ICE and Δ+ peaked in the Chiloense and Channels and Fjords ecoregions. Δ+ and FRic present a similar pattern at mid‐latitudes. FEve showed a contrary pattern, principally with FRic. Areas with high numbers of hotspots differed spatially according to each metric, and three latitudinal bands were observed. ICE, Δ+ and FRic were positively correlated, but the hotspot overlap at the grid cell level was more limited.Main ConclusionsThe complementarity between taxonomic and functional diversity measures is limited when we observe the overlap between grid cells representing hotspots. However, some regions are consistently identified as highly diverse, with the Magellanic Province (Chiloense and Channels and Fjords ecoregions) being the most important for the richness, taxonomic and functional diversity of benthos. Confirmation of the importance of this region can help prioritise conservation efforts.

Effects of idealized land cover and land management changes on the atmospheric water cycle

Abstract. Land cover and land management changes (LCLMCs) play an important role in achieving low-end warming scenarios through land-based mitigation. However, their effects on moisture fluxes and recycling remain uncertain, although they have important implications for the future viability of such strategies. Here, we analyse the impact of idealized LCLMC scenarios on atmospheric moisture transport in three different Earth system model (ESMs): the Community Earth System Model (CESM), the Max Planck Institute Earth System Model (MPI-ESM), and the European Consortium Earth System Model (EC-EARTH). The LCLMC scenarios comprise of a full cropland world, a fully afforested world, and a cropland world with unlimited irrigation expansion. The effects of these LCLMC in the different ESMs are analysed for precipitation, evaporation, and vertically integrated moisture flux convergence to understand the LCLMC-induced changes in the atmospheric moisture cycle. Then, a moisture tracking algorithm is applied to assess the effects of LCLMC on moisture recycling at the local (grid cell level) and the global scale (continental moisture recycling). By applying a moisture tracking algorithm on fully coupled ESM simulations we are able to quantify the complete effects of LCLMC on moisture recycling (including circulation changes), which are generally not considered in moisture recycling studies. Our results indicate that cropland expansion is generally causing a drying and reduced local moisture recycling, while afforestation and irrigation expansion generally cause wetting and increased local moisture recycling. However, the strength of this effect varies across ESMs and shows a large dependency on the dominant driver. Some ESMs show a dominance of large-scale atmospheric circulation changes while other ESMs show a dominance of local to regional changes in the atmospheric water cycle only within the vicinity of the LCLMC. Overall, these results corroborate that LCLMC can induce substantial effects on the atmospheric water cycle and moisture recycling, both through local effects and changes in atmospheric circulation. However, more research is needed to constrain the uncertainty of these effects within ESMs to better inform future land-based mitigation strategies.

A New Framework for Estimating and Decomposing the Uncertainty of Climate Projections

Abstract Climate projections obtained by running global climate models (GCMs) are subject to multisource uncertainties. The existing framework based on analysis of variance (ANOVA) for decomposing such uncertainties is unable to include the interaction effect between GCM and internal climate variability, which ranks only second to the main effect of GCM in significance. In this study, a three-way ANOVA framework is presented, and all main effects and interaction effects are investigated. The results show that, although the overall uncertainty (O) is mainly contributed by main effects, interaction effects are considerable. Specifically, in the twenty-first century, the global mean (calculated at the grid-cell level and then averaged, and likewise below) relative contributions of all main effects are 54% for precipitation and 82% for temperature; those of all interaction effects are, respectively, 46% and 18%. As the three-way ANOVA cannot investigate the uncertainty components resulting from uncertainty sources, it is improved by deducing the relationship between uncertainty components resulting from uncertainty sources and those resulting from the main effects and interaction effects. By the improved three-way ANOVA, O is decomposed into uncertainty components resulting from the emission scenario (S), GCM (M), and internal climate variability (V). The results reveal that O is mainly contributed by M in the twenty-first century for precipitation, and by M before the 2060s whereas by S thereafter for temperature. The robustness of the V characterization is explored by investigating the variation of V on the number of included ensemble members. The extent of the underestimation of the V contribution is roughly an average of 4% for precipitation and 1% for temperature.

Introducing a global dataset on conflict forecasts and news topics

Abstract This article provides a structured description of openly available news topics and forecasts for armed conflict at the national and grid cell level starting January 2010. The news topics, as well as the forecasts, are updated monthly at conflictforecast.org and provide coverage for more than 170 countries and about 65,000 grid cells of size 55 × 55 km worldwide. The forecasts rely on natural language processing (NLP) and machine learning techniques to leverage a large corpus of newspaper text for predicting sudden onsets of violence in peaceful countries. Our goals are a) to support conflict prevention efforts by making our risk forecasts available to practitioners and research teams worldwide, b) to facilitate additional research that can utilize risk forecasts for causal identification, and c) to provide an overview of the news landscape.

Local exposure misclassification in national models: relationships with urban infrastructure and demographics

BackgroundNational-scale linear regression-based modeling may mischaracterize localized patterns, including hyperlocal peaks and neighborhood- to regional-scale gradients. For studies focused on within-city differences, this mischaracterization poses a risk of exposure misclassification, affecting epidemiological and environmental justice conclusions.ObjectiveCharacterize the difference between intraurban pollution patterns predicted by national-scale land use regression modeling and observation-based estimates within a localized domain and examine the relationship between that difference and urban infrastructure and demographics.MethodsWe compare highly resolved (0.01 km2) observations of NO2 mixing ratio and ultrafine particle (UFP) count obtained via mobile monitoring with national model predictions in thirteen neighborhoods in the San Francisco Bay Area. Grid cell-level divergence between modeled and observed concentrations is termed “localized difference.” We use a flexible machine learning modeling technique, Bayesian Additive Regression Trees, to investigate potentially nonlinear relationships between discrepancy between localized difference and known local emission sources as well as census block group racial/ethnic composition.ResultsWe find that observed local pollution extremes are not represented by land use regression predictions and that observed UFP count significantly exceeds regression predictions. Machine learning models show significant nonlinear relationships among localized differences between predictions and observations and the density of several types of pollution-related infrastructure (roadways, commercial and industrial operations). In addition, localized difference was greater in areas with higher population density and a lower share of white non-Hispanic residents, indicating that exposure misclassification by national models differs among subpopulations.ImpactComparing national-scale pollution predictions with hyperlocal observations in the San Francisco Bay Area, we find greater discrepancies near major roadways and food service locations and systematic underestimation of concentrations in neighborhoods with a lower share of non-Hispanic white residents. These findings carry implications for using national-scale models in intraurban epidemiological and environmental justice applications and establish the potential utility of supplementing large-scale estimates with publicly available urban infrastructure and pollution source information.

Becoming neighbors with refugees and voting for the far-right? The impact of refugee inflows at the small-scale level

We investigate the effect of the refugee inflow between 2014 and 2017 on the electoral support for far-right parties in the 2017 German federal election. Leveraging unique, fine-grained data, we differentiate between the impact of refugee inflow at two distinct geographic scales: the immediate neighborhood (1km x 1km) and the county level. To address potential endogeneity concerns, we employ past settlement patterns as instrumental variables. Our results reveal noteworthy variations across geographic scales and regions. We find that refugee inflows lead to an increase in far-right vote shares at the county level, while we observe a negative but statistically insignificant effect at the grid cell level. This negative neighborhood effect attains statistical significance in West Germany, particularly in urban counties, aligning with the contact hypothesis. We find no significant effect in rural areas, and a positive impact of the inflow rate at the county level in West Germany, suggesting a contrasting effect beyond the immediate local vicinity. Despite the fact that support for far-right parties is most pronounced in East Germany, our analysis does not yield robust evidence of a statistically significant relationship in this region.

Future land use land cover changes in El-Fayoum governorate: a simulation study using satellite data and CA-Markov model

This study aims to monitor the changes in land use land cover (LULC) in El-Fayoum governorate over time (past, present, and future) to provide current information for stakeholders involved in land use planning. The study utilized Landsat satellite images and applied the Support Vector Machine algorithm using ArcGIS Pro 2.8.3 to classify the images into four major LULC classes: water, desert, built-up, and agricultural. To evaluate the accuracy of the LULC maps, the study used kappa statistical parameters, which ranged from 0.91 to 0.94, indicating acceptable results for further analysis. To predict spatio-temporal LULC changes, the study considered biophysical and socioeconomic factors such as distance to canals, distance to roads, distance to urban areas, a digital elevation model, and slope. A combination of Multi-Criteria Evaluation, a Fuzzy Membership Function, and the Analytic Hierarchy Process were employed to develop a land cover suitability map. The Hybrid CA-Markov model of the IDRISI-TerrSet software was used to simulate LULC changes, and the accuracy of the simulation was validated using 2020 imagery data. The values gained from the kappa indices for agreement (standard) = 0.9006, kappa for lack of information (no) = 0.916, and kappa for location at grid cell level (location) = 0.9572 demonstrate that the results of the simulation of the LULC changes were deemed satisfactory. The future scenarios modeled in LULC indicate a significant change in the LULC classes over time, specifically for 2030. The change rates of agriculture, desert, built-up, and water areas in El-Fayoum in 2030 compared to 2020 are estimated to be 9.68%, − 17.58%, 133.62%, and 6.06%, respectively. These findings establish both past and future LULC trends and provide crucial data useful for planning and sustainable land use management.

Implications of the steady-state assumption for the global vegetation carbon turnover

Vegetation carbon turnover time (τ) is a central ecosystem property to quantify the global vegetation carbon dynamics. However, our understanding of vegetation dynamics is hampered by the lack of long-term observations of the changes in vegetation biomass. Here we challenge the steady state assumption of τ by using annual changes in vegetation biomass that derived from remote-sensing observations. We evaluate the changes in magnitude, spatial patterns, and uncertainties in vegetation carbon turnover times from 1992 to 2016. We found the robustness in the steady state assumption for forest ecosystems at large spatial scales, contrasting with local larger differences at the grid cell level between τ under steady state and τ under non-steady state conditions. The observation that terrestrial ecosystems are not in a steady state locally is deemed crucial when studying vegetation dynamics and the potential response of biomass to disturbance and climatic changes.

Environmental health impacts and inequalities in green space and air pollution in six medium-sized European cities

Background: The GoGreenRoutes project aims to introduce co-created nature-based solutions (NBS) to enhance environmental quality in six medium-sized cities (Burgas, Lahti, Limerick, Tallinn, Umeå, and Versailles). We estimated the mortality and economic impacts attributed to suboptimal exposure to green space and air pollution, economic impacts, and the distribution thereof the adult population by socioeconomic status.Methods: We retrieved data from publicly accessible databases on green space (NDVI and % Green Area), air pollution (NO2 and PM2.5) and population (≥20 years, n = 804,975) at a 250m × 250m grid-cell level, and mortality for each city for 2015. We compared baseline exposures at the grid-cell to World Health Organization's recommendations and guidelines. We applied a comparative risk assessment to estimate the mortality burden attributable to not achieving the recommendations and guidelines. We estimated attributable mortality distributions and the association with income levels.Results: We found high variability in air pollution and green spaces levels. Around 60% of the population lacked green space and 90% were exposed to harmful air pollution. Overall, we estimated age-standardized mortality rates varying from 10 (Umeå) to 92 (Burgas) deaths per 100,000 persons attributable to low NDVI levels; 3 (Lahti) to 38 (Burgas) per 100,000 persons to lack of % Green Area; 1 (Umeå) to 88 (Tallinn) per 100,000 persons to exceedances of NO2 guidelines; and 1 (Umeå) to 206 (Burgas) per 100,000 persons to exceedances of PM2.5 guidelines. Lower income associated with higher or lower mortality impacts depending on whether deprived populations lived in the densely constructed, highly-trafficked city centre or greener, less polluted outskirts.Conclusions: We attributed a considerable mortality burden to lack of green spaces and higher air pollution, which was unevenly distributed across different social groups. NBS and health-promoting initiatives should consider socioeconomic aspects to regenerate urban areas while providing equally good environments.

Spatial patterns in pollution discharges from livestock and poultry farm and the linkage between manure nutrients load and the carrying capacity of croplands in China

The rapid development of livestock and poultry farming in China has resulted in an increasing threat of water pollution. In particular, mitigating livestock-related pollutant discharges is a key issue for environmental sustainability, especially for inland surface water bodies. In order to ensure the effective control of pollution and the efficient utilization management of livestock manure, spatially explicit surveys of pollutant generation and discharge from the livestock sector must be performed. In the present study, we estimated the grid cell-level distributions in the generation and discharge of four typical pollutants (chemical oxygen demand, ammonium nitrogen, total nitrogen and total phosphorus) from the livestock sector across the country with a spatial resolution of 30 arc-seconds. The distributions were estimated using the most recent pollution source census data and multi-sourced ancillary materials by a dasymetric mapping approach. We further investigated the feasibility of the resource utilization of livestock manure by comparing manure-source nutrients with the carrying capacity of adjacent croplands. Our results show that low-intensive farming generated and discharged the majority of livestock farming pollution, with other cattle and pigs breeding identified as the two major sources of pollution from the livestock sector. Southwest, Central and East China suffered the highly densified pollutants generation and discharges. Furthermore, cropland exceeding its carrying capacity was concentrated in these regions. Our findings provide additional insights into livestock and poultry farming in the context of relocation, strengthening regulation, transforming breeding operations, and rationalizing the resource use of manure, all of which are important measures for the sustainable development of both agriculture and the environment.