Significant Spatial Variation Research Articles

Unimproved source of drinking water and its associated factors: a spatial and multilevel analysis of Ethiopian demographic and health survey

BackgroundDrinking water quality has been a major public health concern in lower and middle income countries where access to improved water supplies is limited. Ethiopia is thought to have one of the worst drinking water infrastructures in the world. This study aimed to assess the spatial variation and determinants of using unimproved sources of drinking water in Ethiopia using recent nationally representative data.MethodsA population-based cross-sectional study was employed with the recent EDHS data of 2019. A total of 8663 households were sampled using a stratified two-stage cluster sampling method. Kuldorff’s SaTScan version 9.6 software was used to generate spatial scan statistics. ArcGIS version 10.7 software was used to visualize the spatial patterns of unimproved drinking water sources. A multilevel multivariable mixed-effect logistic regression was used to identify factors associated with the use of an unimproved drinking water source. In the multivariable multilevel analysis, those variables with a p-value < 0.05 were considered to be significant predictors of using an unimproved source of drinking water.ResultAround 31% (95% CI: 30%, 32%) of the population in Ethiopia uses unimproved sources of drinking water with significant spatial variation across the country. Households aged 41–60 [AOR = 0.69; 95%CI; 0.53, 0.89] as compared to the households aged 10–25, households having middle wealth index [AOR = 0.48; 95%CI; 0.40, 0.59], and households having a rich wealth index [AOR = 0.31; 95%CI; 0.25, 0.39] as compared to the poor households, living in high community literacy level [AOR = 0.36; 95%CI; 0.16, 0.80], living in high-level community poverty [AOR = 3.03; 95%CI; 1.32, 6.98], rural residence [AOR = 7.88; 95%CI; 2.74, 22.67] were significant predictors of use of unimproved source of drinking water. Hot spot areas of use of unimproved drinking water sources were observed in Amhara, Afar, and Somalia regions and some parts of SNNPR and Oromia regions in Ethiopia. The primary clusters were found in Ethiopia’s Somalia and Oromia regions.ConclusionAround one third of the Ethiopian population utilizes unimproved source of drinking water and it was distributed non-randomly across regions of Ethiopia. The age of the household head, wealth status of the household, residence, community poverty level, and community literacy level were found to be significantly associated with utilizing unimproved drinking water source. State authorities, non-governmental organizations and local health administrators should work to improve the quality of drinking water particularly for high risk groups such as communities living in high poverty and low literacy, poor households, rural residents, and hot spot areas to decrease the adverse consequences of using unimproved drinking water source.

Characterization and spatiotemporal dynamics of the ichthyofauna of two tidal containment zones on the Brazilian Amazon coast

This study aimed to characterize spatiotemporal variations in the ichthyofauna of two tidal containment zones in Maranhão state, Brazil. Monthly, collections were carried out in the containment zones of São Luís and São José de Ribamar, with samplings performed after the peak of the lowest tide (transition between low tide and high tide).Physicochemical parameters such as water salinity, temperature, transparency, and precipitation were measured monthly. A gill net (25-mm mesh) was used to capture fish. A total of 169 ichthyic individuals were captured and identified in the coastal containment areas of São Luís and São José de Ribamar. The orders Perciformes (41%) and Siluriformes (13.6%) were the most representative in terms of number of species. According to analysis of the t-test, there were significant seasonal and spatial variations in the number of organisms (p &lt; 0.05), with the highest catches in rainy and transitional months. It was possible to identify a variation in niches in both study areas, the influence of physicochemical parameters on ichthyofaunal communities of São Luís and São José de Ribamar, and the effects of the containment structures on fish biomass and diversity, mainly in São Luís.

A MeerKAT-meets-LOFAR study of Abell 1413: a moderately disturbed non-cool-core cluster hosting a ~500 kpc ‘mini’-halo

ABSTRACT Many relaxed cool-core clusters host diffuse radio emission on scales of hundreds of kiloparsecs: mini-haloes. However, the mechanism responsible for generating them, as well as their connection with central active galactic nuclei, is elusive and many questions related to their physical properties and origins remain unanswered. This paper presents new radio observations of the galaxy cluster Abell 1413 performed with MeerKAT (L-band; 872 to 1712 MHz) and LOFAR HBA (120 to 168 MHz) as part of a statistical and homogeneous census of mini-haloes. Abell 1413 is unique among mini-halo clusters as it is a moderately disturbed non-cool-core cluster. Our study reveals an asymmetric mini-halo up to 584 kpc in size at 1283 MHz, twice as large as first reported at similar frequencies. The spectral index is flatter than previously reported, with an integrated value of α = −1.01 ± 0.06, shows significant spatial variation, and a tentative radial steepening. We studied the point-to-point X-ray/radio surface brightness correlation to investigate the thermal/non-thermal connection: our results show a strong connection between these components, with a superlinear slope of $b = 1.63^{+0.10}_{-0.10}$ at 1283 MHz and $b = 1.20^{+0.13}_{-0.11}$ at 145 MHz. We also explore the X-ray surface brightness/radio spectral index correlation, finding a slope of $b = 0.59^{+0.11}_{-0.11}$. Both investigations support the evidence of spectral steepening. Finally, in the context of understanding the particle acceleration mechanism, we present a simple theoretical model which demonstrates that hybrid scenarios – secondary electrons (re-)accelerated by turbulence – reproduce a superlinear correlation slope.

Rural revitalization of China: A new framework, measurement and forecast

Recent years have witnessed an increasing focus on rural revitalization (RR). In order to explore the current status and the coordinated growth of different aspects of RR, this paper first constructs a new 5E framework with a comprehensive index system; then analyzes the overall development status (ODS) and the coordinated development status of 5 dimensions (CDS) of 30 regions in China from 2006 to 2020 temporally and spatially with entropy weight and the technique for order preference by similarity to the ideal solution method; thirdly forecasts the CDS in the next four years and proposed targeted and differentiated development suggestions. Some key results are: (1) ODS was relatively stable with significant spatial variations. (2) CDS of the five dimensions fluctuated relatively mildly, and villages of the coastal areas were higher than those in the inland areas. (3) CDS will be generally similar to the past temporally and spatially in the future. (4) Different regions should take targeted and feasible measures to achieve benign coordinated growth among dimensions of RR, and the national authority should take supportive actions comprehensively.

Identifying interactions of linked irrigated lake-groundwater system by combining hydrodynamic and hydrochemical method.

During the irrigation period, the interactions between the linked lake-groundwater systems are complicated and change. This is because natural and human activities are happening at the same time, which makes it harder to identify the interactions. This study uses data on water level, hydrochemistry, and hydrogen-oxygen stable isotopes to analyze the hydrodynamics, electrical conductivity (EC), isotopic characteristics, and spatial distribution of lake water and groundwater to reveal lake-groundwater interactions. The results indicate that the hydrochemical type of Chagan Lake and groundwater is dominated by the HCO3-Na type. The key hydrochemical indicator EC obtained by principal component analysis (PCA) can be used to reveal the lake-groundwater interaction, and the interaction should be identified by location according to the significant correlation between hierarchical clustering results and regional distribution. The lake body's geographic coefficient of variation for EC and δ18O is small, and irrigation return flow is one factor in the region's surface water's significant spatial variation for EC and δ18O. The three study methods indicate that the groundwater supplies the lake in the vicinity of the Huoling River-Hongzi Pool, while in other sections, the lake water leaks and replenishes the groundwater, exhibiting geographic inconsistency. The isotope method was employed as a support tool to determine that groundwater might recharge the lake at Xinmiao Pool. According to the calculations of the Mix SIAR model, the groundwater recharge contribution rate in the Xinmiao Pool section is approximately 51%, while in the remaining sections, the contribution rate of lake water to groundwater ranges from approximately 25% to 52%. Therefore, the identification of the interaction is crucial for the linked irrigated lake-groundwater system where water sources are scarce and threatened by agricultural pollution.

Anisotropic molecular diffusion in confinement I: Transport of small particles in potential and density gradients

HypothesisDiffusion in confinement is an important fundamental problem with significant implications for applications of supported liquid phases. However, resolving the spatially dependent diffusion coefficient, parallel and perpendicular to interfaces, has been a standing issue. In the vicinity of interfaces, density fluctuations as a consequence of layering locally impose statistical drift, which impedes the analysis of spatially dependent diffusion coefficients even further. We hypothesise, that we can derive a model to spatially resolve interface-perpendicular diffusion coefficients based on local lifetime statistics with an extension to explicitly account for the effect of local drift using the Smoluchowski equation, that allows us to resolve anisotropic and spatially dependent diffusivity landscapes at interfaces. Methods and simulationsAn analytic relation between local crossing times in system slices and diffusivity as well as an explicit term for calculating drift-induced systematic errors is presented. The method is validated on Molecular Dynamics simulations of bulk water and applied to simulations of water in slit pores. FindingsAfter validation on bulk liquids, we clearly demonstrate the anisotropic nature of diffusion coefficients at interfaces. Significant spatial variations in the diffusivities correlate with interface-induced structuring but cannot be solely attributed to the drift induced by local density fluctuations.

Identifying coal mining subsidence impacts by soil moisture based on optical trapezoid model in Google Earth Engine

AbstractMany coal mining areas overlapped with agricultural land in the world. However, surface subsidence and waterlogging brought on by coal mining inexorably harm the agricultural land. Soil moisture can reflect the variation of mining impact significantly according to the geographic features of the high underground water table, which shows significant spatial gradient variation within the subsidence basin. Previous studies have used site survey and unmanned aerial vehicle (UAV), which are not applicable to large‐scale assessments of the ecological impacts and defining the scale of impacts. Here, we used the Google Earth Engine (GEE) platform and an optical trapezoid model (OPTRAM) to estimate the soil moisture distribution around 50 mining subsidence waterbody samples in eastern China. Based on the spatial variation in soil moisture, the impact boundary of coal mining was determined using buffer analysis and trend fitting. The results showed that: (1) The OPTRAM can well reflect the soil moisture distribution which shows a good correlation between estimated and measured values, with R = 0.83 and RMSE = 0.054. (2) The spatial trend fitting of soil moisture is suitable for evaluating mining impact, and the impact distances are in the range of 96–762 m, 80% of which are located at 128–436 m. We also find that 28% of subsidence waterbody buffer basins were heavily influenced by human activities, and the coal mining impact boundaries cannot be effectively identified. The study provides a new idea to quantify the ecological and agricultural impact boundary of coal mining, which could be applied to other similar regions across the globe.

Variations of Remote-Sensed Forel-Ule Index in the Bohai and Yellow Seas during 1997–2019

Water color, often quantified using the Forel-Ule Index (FUI), is a crucial parameter for assessing the water quality and ecological health of coastal waters. However, there is limited research on the spatiotemporal variations of FUI and the associated influencing factors in the Bohai and Yellow Seas. In this study, we utilized multi-sensor satellite datasets to retrieve monthly FUI products for the Bohai and Yellow Seas spanning the period from September 1997 to December 2019. Subsequently, we examined significant spatial disparities and variations across multiple timescales in the remotely sensed FUI time series. The climatological annual mean FUI map reveals a decreasing trend from nearshore to offshore regions, with similar spatial patterns observed in terms of overall and interannual FUI variability. The annual variations in wind field, sea surface temperature (SST), and ocean stratification play a key role in the seasonal dynamics of FUI by modulating the sediment resuspension process, resulting in low FUI values in summer and high FUI values in winter. Linear regression analysis of FUI anomaly indicates a long-term decreasing trend in FUI for the three bays of the Bohai Sea, while upward trends in FUI predominantly prevail in the central Yellow Sea. Factors related to interannual FUI variations, such as surface winds, SST, river outflow, rainfall, and anthropogenic activities, are qualitatively discussed. The findings of this study provide the first comprehensive evaluation of water color variations and their underlying mechanisms in the Bohai and Yellow Seas.

A Spatially Explicit Evaluation of the Economic Performance of a Perennial Energy Crop on the Marginal Land of the Loess Plateau and China

The Loess Plateau, with a large area of marginal land, holds the potential to produce 62–106 Tg per year of switchgrass biomass; however, the economic feasibility of producing bioenergy in the region is unclear. The farm-gate feedstock production (FGFP) cost of switchgrass was calculated in a spatially explicit way by taking the geographic variation in crop yield, soil properties, land quality, and input costs into consideration in order to evaluate the economic performance of bioenergy production. Cost–supply curves were constructed to explore the energy supply potential of switchgrass feedstock. The calculations were conducted using ArcGIS in a 1 km grid and all the evaluations were conducted under different agricultural management practice (AMP) scenarios in parallel. The FGFP costs showed significant spatial variation ranging from 95 to 7373 CNY (Chinese Yuan) per tonne−1 and that the most economically desirable areas are scattered in the south and southeast region. The weighted average FGFP costs are 710, 1125, and 1596 CNY per tonne−1 for small bale (SB), large bale (LB), and chipping (CP) harvest methods, respectively. The projected energy supply potential is 1927 PJ (Petajoules) per year−1, of which 30–93% can be supplied below the market prices of different fossil fuels according to feedstock formats. Compared to current biomass residual pricing, 50–66 Tg (Teragrams) switchgrass feedstock is competitive. The results demonstrated that the Loess Plateau holds the potential to produce bioenergy that is economically feasible. This study provides a methodological framework for spatially explicit evaluation of the economic performance of perennial energy crops. Detailed information obtained from this study can be used to select the optimal locations and AMPs to produce feedstock production at minimum cost.

Occurrence and risk associated with urban road-deposited microplastics

An in-depth understanding of urban road-deposited MPs is important for the accurate prediction of the risk posed by MPs in different exposure scenarios. This study provides new insights into the intrinsic/extrinsic factors in terms of the variability of concentration and species in urban road-deposited MPs. The study results confirmed that a considerable abundance of road-deposited MPs can be identified with the average concentration ranging from 0.33 to 3.64 g m-2. Land use types and sediment particle size are the important factors that contribute to MPs abundance. The majority of detected MPs including polyethylene (PE), polypropylene (PP), polystyrene (PS) and polyethylene terephthalate (PET) are mainly derived from anthropogenic activities in commercial and residential land uses while rubber MP particles in urban road surfaces are mainly derived from tyre wear. The significant correlation (p < 0.05) between MPs and fine dust particles (< 150 µm) indicated the high affinity of small dust particles for MPs. The risk scores from MPs varied greatly from 10 to 11,000 among the study sites, which indicated the significant spatial variation of potential environmental risks posed by road-deposited MPs. The hotspots of risks posed by MPs were in areas with a high fraction of industrial, commercial and residential land uses. Specifically, the highest risk from MPs was found in mixed industrial and residential areas.

Evaluation of Methane Emission Reduction Potential of Water Management and Chinese Milk Vetch Planting in Hunan Paddy Rice Fields

In order to explore the methane reduction potential with two scenarios of water management and Chinese Milk Vetch return, we calculated the methane emissions of Hunan Province rice fields in 2019 using the SECTOR tool based on Excel and released by the International Rice Research Institute. Thus, we preliminarily established an agricultural carbon emissions monitoring, reporting, and verification (MRV) system. The results showed that: (1) There was significant spatial variation in methane emissions in Hunan rice fields, with higher emissions in both the south and north and lower emissions in the east and west. Late rice was the main contributor to methane emissions, and the cities of Changde, Hengyang, Yueyang, and Shaoyang were high-emission areas due to differences in rice planting types and areas. Compared with flooding (1275.75 Gg), optimized water management measures (mid-drainage and AWD irrigation) reduced methane emissions by 29~45% (905.79 and 701.66 Gg, respectively). (2) Under the same nitrogen input conditions, compared with a solely straw return (375.24 Gg), combining green manure with straw return could partially reduce methane emissions from Hunan super hybrid rice (327.63 Gg). Compared with the control fertilizers (404.28 Gg), the reduction rates of winter-planted Chinese Milk Vetch, the return of rice straw, and the incorporation of both Chinese Milk Vetch and straw were 7.19%, 13.01%, and 18.96%, respectively. Based on scientific accounting tools, a preliminary MRV system for rice field carbon emissions was established. Under the national demand for reducing fertilizer use and increasing efficiency, equal nitrogen organic amendments could effectively contribute to the development of green, low-carbon, and high-quality agriculture.

Towards continuous mass and size distributions for beach plastic litter: Spatiotemporal analyses of abundance and composition

Beaches are known as hotspots for the accumulation of plastic debris and are widely used for monitoring marine litter on a global scale. However, there is a significant knowledge gap regarding temporal trends in marine plastic pollution. Moreover, existing studies on beach plastics and popular monitoring protocols only provide count data. Consequently, it is not possible to monitor marine litter based on weights, which hampers the further application of beach plastic data. To address these gaps, we conducted an analysis of spatial and temporal trends in plastic abundance and composition using OSPAR beach litter monitoring data from 2001 to 2020. We established size and weight ranges for 75 (macro-)plastic categories to estimate the total plastic weight, enabling us to investigate plastic compositions. While the amount of plastic litter exhibits significant spatial variation, most individual beaches displayed notable temporal trends. The spatial variation in composition is primarily attributed to differences in total plastic abundance. We describe the compositions of beach plastics using generic probability density functions (PDFs) for item size and weight. Our trend analysis, method for estimating plastic weight from count data, and PDFs for beached plastic debris represent novel contributions to the field of plastic pollution science.

Spatiotemporal variation in soil methane uptake in a cool-temperate immature deciduous forest

Atmospheric methane (CH4) concentration has been increasing recently, contributing to global warming. As natural sinks, forest soils are expected to mitigate the atmospheric CH4 rise. However, it had been difficult to measure soil CH4 flux continuously and accurately because of the limited stability and precision of CH4 analyzers in the field. In this study, we measured hourly CH4 flux with plant roots (Root) and without roots (Trench) during the growing season in a regenerating deciduous forest using an automated chamber system with an up-to-date analyzer. Combined with a Random Forest (RF) approach, we studied the spatiotemporal variation and control of soil CH4 uptake rates. The results showed that the soil was a CH4 sink throughout the experimental period, and the existence of roots significantly enhanced CH4 uptake, mainly through improving soil aeration. The CH4 uptake rate varied seasonally according to the variations in soil gaseous diffusion caused by soil moisture and temperature differences. In addition, soil CH4 uptake showed a significant spatial variation, mainly resulting from spatial difference in soil porosity, soil carbon and nitrogen contents, and fine root biomass. The RF models showed high performance in soil CH4 flux prediction using the soil O2 diffusion coefficient and soil temperature as explanatory variables. The performance of RF models using ordinary variables of soil water content or water-filled pore space (WFPS) was equal to or slightly better than that of models using the diffusion coefficient. The higher importance of ambient CH4 concentration in Trench chambers indicates an increase in soil CH4 uptake at higher CH4 concentrations, which is predicted in the future. Although there are limitations, we believe that a machine learning approach, such as RF, using a large amount of continuous data with high temporal resolution, has great potential for investigating the dynamic variation in soil CH4 flux.

Adaptive Robotic Information Gathering via non-stationary Gaussian processes

Robotic Information Gathering (RIG) is a foundational research topic that answers how a robot (team) collects informative data to efficiently build an accurate model of an unknown target function under robot embodiment constraints. RIG has many applications, including but not limited to autonomous exploration and mapping, 3D reconstruction or inspection, search and rescue, and environmental monitoring. A RIG system relies on a probabilistic model’s prediction uncertainty to identify critical areas for informative data collection. Gaussian processes (GPs) with stationary kernels have been widely adopted for spatial modeling. However, real-world spatial data is typically non-stationary—different locations do not have the same degree of variability. As a result, the prediction uncertainty does not accurately reveal prediction error, limiting the success of RIG algorithms. We propose a family of non-stationary kernels named Attentive Kernel (AK), which is simple and robust and can extend any existing kernel to a non-stationary one. We evaluate the new kernel in elevation mapping tasks, where AK provides better accuracy and uncertainty quantification over the commonly used stationary kernels and the leading non-stationary kernels. The improved uncertainty quantification guides the downstream informative planner to collect more valuable data around the high-error area, further increasing prediction accuracy. A field experiment demonstrates that the proposed method can guide an Autonomous Surface Vehicle (ASV) to prioritize data collection in locations with significant spatial variations, enabling the model to characterize salient environmental features.

Hydrological and Precipitation Extremes and Trends over the Paraiba do Sul River Basin, Brazil

The Paraiba do Sul River Basin (PSRB) is a vital source of water resources in Brazil, providing water for human consumption, industry, agriculture, and hydroelectric energy generation. As part of one of the most developed areas of the country, in the Southeast of Brazil, the region is vulnerable to the impacts of climate change, with evidence of extreme events such as droughts and floods affecting the availability and quality of water. Hence, this study analyzes precipitation and streamflow rates data from the PSRB between 1939–2020 to investigate the spatial variability of average patterns and extreme events, trends, and their relationship with urban growth and socioeconomic development. The analysis reveals significant spatial variations in precipitation and runoff rates, with higher altitude areas, such as the Serra da Mantiqueira, exhibiting higher average values. Moreover, the Mann–Kendall trend results showed in most of the sites no significant trend regarding precipitation data; however, about 50% of the sites in the PSRB presented a decreasing trend of runoff rates. Since the precipitation does not explain identified changes in the hydrological patterns, the evaluation of the area’s urban growth and socioeconomic development throughout the decades suggested that human activities, such as those associated with urbanization, have played a significant role in altering the runoff patterns in the basin. These findings highlight the importance of sustainable land-use planning and water resource-management practices in the PSRB to mitigate the negative impacts of urbanization on the hydrological cycle and to enhance the resilience of the region’s water resources.

Untangling the increasing elevation of cropland in China from 1980 to 2020

The redistribution of cropland to areas of higher elevation in China has long affected agricultural development and could seriously threaten national food security. However, there is currently little research reported on this phenomenon, which may limit the improvement of cropland protection policies. To fill this gap, we analyzed the spatiotemporal characteristics and driving mechanisms of increased cropland elevation in China during the period 1980–2020. The average cropland elevation in China increased by 17.38 m from 1980 to 2020. The gravity center of the cropland area and average cropland elevation in China moved to the northwest by 81.00 km and 51.47 km, respectively. The amount of newly added cropland in eastern China was less than that in occupied regions; however, the average elevation of newly added cropland was greater than that of occupied cropland, though the opposite phenomenon was observed in western China. Slope, temperature, land-use intensity, population, economic density, and distance to main roads were the main factors affecting the redistribution of cropland to areas of higher elevation. The effects of these major driving factors exhibited significant spatial and temporal variations in China. This study has important implications for improving existing cropland protection policies and developing more effective cropland management systems in China.

Fluxes in CO2 and CH4 and influencing factors at the sediment-water interface in a eutrophic saline lake

Although saline aquatic ecosystems are significant emitters of greenhouse gases (GHGs), dynamic changes in GHGs at the sediment-water interface remain unclear. The present investigation carried out a total of four sampling campaigns in Daihai Lake, which is a eutrophic saline lake situated in a semi-arid area of northern China. The aim of this study was to investigate the spatio-temporal dynamics of carbon dioxide (CO2) and methane (CH4) fluxes at the sediment-water interface and the influencing factors. The mean concentrations of porewater CO2 and CH4 were 44.98 ± 117.99 μmol L−1 and 124.36 ± 97.00 μmol L−1, far exceeding those in water column of 11.14 ± 2.16 μmol L−1 and 0.33 ± 0.23 μmol L−1, respectively. The CO2 and CH4 fluxes at the sediment-water interface (FS–WCO2 and FS-WCH4) exhibited significant spatial and temporal variations, with mean values of 9.24 ± 13.84 μmol m−2 d−1 and 3.53 ± 4.36 μmol m−2 d−1, respectively, indicating that sediment is the source of CO2 and CH4 in the water column. However, CO2 and CH4 fluxes were much lower than those measured at the water-air interface in a companion study (17.54 ± 14.54 mmol m−2d−1 and 0.50 ± 0.50 mmol m−2d−1, respectively), indicating that the diffusive flux of gases at the sediment-water interface was not the primary source of CO2 and CH4 emissions to the atmosphere. Regression and correlation analyses revealed that salinity (Sal) and nutrients were the most influential factors on porewater gas concentrations, and that gas fluxes increased with increasing gas concentrations and porosity. The microbial activity of sediment is greatly affected by nutrients and Sal. Additionally, Sal has the ability to regulate biogeochemical processes, thereby regulating GHG emissions. The present investigation addresses the research gap concerning GHG emissions from sediments of eutrophic saline lakes. The study suggests that controlling the eutrophication and salinization of lakes could be a viable strategy for reducing carbon emissions from lakes. However, further investigations are required to establish more conclusive results.

Spatiotemporal evolution and influencing factors of land-use emissions in the Guangdong-Hong Kong-Macao Greater Bay Area using integrated nighttime light datasets

Carbon emissions from land-use and land-cover change (together referred to as ‘land-use emissions’) are an important way to influence the regional carbon balance. However, due to the limitations and complexity of obtaining carbon emissions data at spatial scales, previous studies rarely reveal the long-term evolution characteristics of regional land-use emissions. Therefore, we propose a method to integrate DMSP/OLS and NPP/VIIRS nighttime light images to calculate land-use emissions over a long time series. The accuracy validation results show that the integrated nighttime light images and land-use emissions have a good fit and can accurately assess the long-term evolution of regional carbon emissions. In addition, by combining the Exploratory Spatial Analysis (ESTDA) model and the Vector Autoregressive Regression (VAR) model, we found significant spatial variation in carbon emissions in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), with the two regional emission centres spreading outwards between 1995 and 2020, with an increase in construction land area of 3445 km2, resulting in 257 million tons (Mt) of carbon emissions over the same period. The rapid increase in emissions from carbon sources is not offset by a correspondingly large amount of carbon sinks, resulting in a serious imbalance. Controlling the intensity of land use, optimizing the structure of land use and promoting the transformation of the industrial structure are now the keys to achieving carbon reduction in the GBA. Our study demonstrates the enormous potential of long-time-series nighttime light data in regional carbon emission research.

Spatial pattern analysis of zooplankton and surface water of pit lakes (Raniganj coal field, India)

ABSTRACT Open pit technique is often the simplest and most cost effective mining technology. Coal mining, which began in 1774 in Raniganj, West Bengal, India, led to many abandoned pits, which led to the formation of pit lakes. Mining has a tablvariety of environmental ramifications around the world. On the other hand, the pit lakes of the Raniganj coal field (RCF) are being used for recreational purposes. Knowledge of the spatial pattern in the zooplankton population and surface water quality in pit lakes waters of the Raniganj coal field region is rather limited. Sixteen pit lakes in the RCF in West Bengal, India, were investigated from January 2019 to December 2021. In the present study, we analyzed the spatial pattern and mode of distribution of zooplankton communities based on Coefficient of dispersion methods. To perform a comprehensive analysis of the water quality condition of this study area, we apply water quality index (WQI) methods. A total of 51 taxa were identified and the density was primarily dominated by rotifers. The most dominant species were: Brachionus forficula, Paracyclops sp. and Sida sp. Clumped type of zooplankton distribution pattern was observed in the studied pit lakes. The water quality in most of the studied pit lakes was poor. All the water quality parameters showed significant spatial variations as per Kruskal-Wallis test. The clustering technique produces three groups of pit lakes that are particularly convincing as the pit lakes in these groups showed comparable characteristics.

Analysis of Short-Term Drought Episodes Using Sentinel-3 SLSTR Data under a Semi-Arid Climate in Lower Eastern Kenya

This study uses Sentinel-3 SLSTR data to analyze short-term drought events between 2019 and 2021. It investigates the crucial role of vegetation cover, land surface temperature, and water vapor amount associated with drought over Kenya’s lower eastern counties. Therefore, three essential climate variables (ECVs) of interest were derived, namely Land Surface Temperature (LST), Fractional Vegetation Cover (FVC), and Total Column Water Vapor (TCWV). These features were analyzed for four counties between the wettest and driest episodes in 2019 and 2021. The study showed that Makueni and Taita Taveta counties had the highest density of FVC values (60–80%) in April 2019 and 2021. Machakos and Kitui counties had the lowest FVC estimates of 0% to 20% in September for both periods and between 40% and 60% during wet seasons. As FVC is a crucial land parameter for sequestering carbon and detecting soil moisture and vegetation density losses, its variation is strongly related to drought magnitude. The land surface temperature has drastically changed over time, with Kitui and Taita Taveta counties having the highest estimates above 20 °C in 2019. A significant spatial variation of TCWV was observed across different counties, with values less than 26 mm in Machakos county during the dry season of 2019, while Kitui and Taita Taveta counties had the highest estimates, greater than 36 mm during the wet season in 2021. Land surface temperature variation is negatively proportional to vegetation density and soil moisture content, as non-vegetated areas are expected to have lower moisture content. Overall, Sentinel-3 SLSTR products provide an efficient and promising data source for short-term drought monitoring, especially in cases where in situ measurement data are scarce. ECVs-produced maps will assist decision-makers with a better understanding of short-term drought events as well as soil moisture loss episodes that influence agriculture under arid and semi-arid climates. Furthermore, Sentinel-3 data can be used to interpret hydrological, ecological, and environmental changes and their implications under different environmental conditions.