Significant Spatial Variation Research Articles

Could persistent organic pollutants affect future generations of sea turtles by maternal transfer? First results for Caretta caretta nests along the North-Western coast of Italy

Since 2013, loggerhead sea turtle (Caretta caretta, Linnaeus 1758) nesting has been observed further north along the Italian coast, reaching the Tuscan coast (NW Mediterranean Sea). The four nesting events that occurred in Tuscany in the summer of 2019 spurred the scientific community to monitor these occurrences more carefully, following them from egg deposition to hatching. This provided an opportunity to collect samples for conducting multidisciplinary investigations, including the toxicological investigations of the biological material collected from the four nests. The aim of this study was to conduct an initial assessment of persistent organic pollutants in the eggs laid in Tuscany, aiming to establish a baseline on this topic for subsequent nesting events that have occurred until today. Organochlorine compounds (OCs), specifically polychlorinated biphenyls (PCBs), dichlorodiphenyltrichloroethane (DDT) and its metabolites, and hexachlorobenzene (HCB), were analyzed and detected in unhatched eggs, embryos, and chorio-allantoic membranes (CAMs). OCs were detected in all samples, with PCBs > DDTs ≫ HCB. A significant spatial variation in pollutant levels and profiles among sea turtle nesting locations was found. Embryos showed higher levels of contamination than egg contents regardless of the developmental stages. Depth of the laying chamber and egg mass were not significant factors in OC bioaccumulation. For the first time in the Mediterranean Sea, this study assessed the role of CAM in the transfer of contaminants to the embryo. Overall, the OC levels found were lower compared to the results from other studies conducted worldwide on loggerhead sea turtle biological material. This was the first assessment of nest biological material for the North-Western coast of Italy.

Evaluating and Analyzing the Efficiency and Influencing Factors of Cold Chain Logistics in China’s Major Urban Agglomerations under Carbon Constraints

With environmental degradation and energy shortages, green and low-carbon development has become an industry trend, especially in regards to cold chain logistics (CCL), where energy consumption and emissions are substantial. In this context, determining how to scientifically evaluate the cold chain logistics efficiency (CCLE) under carbon emission constraints is of great significance for achieving sustainable development. This study uses the three-stage data envelopment analysis (DEA) and the Malmquist index model to analyze the overall level and regional differences regarding CCLE in China’s four major urban agglomerations, under carbon constraints, from 2010 to 2020. Then, the influencing factors of CCLE are identified through Tobit regression. The results reveal that: (1) the CCLE in the four urban agglomerations is overestimated when carbon constraints are not considered; (2) the CCLE in the four urban agglomerations shows an upward trend from 2010 to 2020, with an average annual growth rate of 1.25% in regards to total factor productivity. However, there are significant spatial and temporal variations, with low-scale efficiency being the primary constraint. (3) Different influencing factors have different directions and exert different effects on CCLE in different urban agglomerations, and the improvement of economic development levels positively affects all regions.

TOD typologies for metro stations based on the butterfly model

As a promising planning strategy for urban sustainability, transit-oriented development (TOD) has been widely encouraged and adopted to integrate land use and transport systems. Because of the different applications in form, function and impact, it is necessary to explore context-based TOD typologies to better map the TOD specificities and focus on strategic planning. As the best-known method, the node-place model provides a useful analytical framework to evaluate TOD typologies. However, by using two indexes to aggregate the node and place value, details of some important TOD characteristics are disregarded in this model. Proposing a multi-axial indexes system, the butterfly model can perform a more detailed evaluation of node and place. In this research, we have revised and optimized the butterfly model and taken Tianjin Metro Line 1 as a case study. We find that all stations can be divided into five TOD types with significant spatial variations. TOD principles have already been implemented in Tianjin with good integration between metro system and land use. The method demonstrated in this paper equips urban planners with a useful tool for evaluating TOD performance and adopting more targeted strategies.

Evaluating the relative influence of climate and human activities on recent vegetation dynamics in West Bengal, India

Assessing the relative importance of climate change and human activities is important in developing sustainable management policies for regional land use. In this study, multiple remote sensing datasets, i.e. CHIRPS (Climate Hazard Group InfraRed Precipitation with Station Data) precipitation, MODIS Land Surface Temperature (LST), Enhanced Vegetation Index (EVI), Potential Evapotranspiration (PET), Soil Moisture (SM), WorldPop, and nighttime light have been analyzed to investigate the effect that climate change (CC) and regional human activities (HA) have on vegetation dynamics in eastern India for the period 2000 to 2022. The relative influence of climate and anthropogenic factors is evaluated on the basis of non-parametric statistics i.e., Mann-Kendall and Sen's slope estimator. Significant spatial and elevation-dependent variations in precipitation and LST are evident. Areas at higher elevations exhibit increased mean annual temperatures (0.22 °C/year, p < 0.05) and reduced winter precipitation over the last two decades, while the northern and southwest parts of West Bengal witnessed increased mean annual precipitation (17.3 mm/year, p < 0.05) and a slight cooling trend. Temperature and precipitation trends are shown to collectively impact EVI distribution. While there is a negative spatial correlation between LST and EVI, the relationship between precipitation and EVI is positive and stronger (R2 = 0.83, p < 0.05). Associated hydroclimatic parameters are potent drivers of EVI, whereby PET in the southwestern regions leads to markedly lower SM. The relative importance of CC and HA on EVI also varies spatially. Near the major conurbation of Kolkata, and confirmed by nighttime light and population density data, changes in vegetation cover are very clearly dominated by HA (87%). In contrast, CC emerges as the dominant driver of EVI (70–85%) in the higher elevation northern regions of the state but also in the southeast. Our findings inform policy regarding the future sustainability of vulnerable socio-hydroclimatic systems across the entire state.

Understanding Rainfall Distribution Characteristics over the Vietnamese Mekong Delta: A Comparison between Coastal and Inland Localities

This study examines the changing rainfall patterns in the Vietnamese Mekong Delta (VMD) utilizing observational data spanning from 1978 to 2022. We employ the Mann–Kendall test, the sequential Mann–Kendall test, and innovative trend analysis to investigate trends in annual, wet, and dry season rainfall, as well as daily rainfall events. Our results show significant spatial variations. Ca Mau, a coastal province, consistently showed higher mean annual and seasonal rainfall compared to the further inland stations of Can Tho and Moc Hoa. Interestingly, Ca Mau experienced a notable decrease in annual rainfall. Conversely, Can Tho, showed an overall decrease in some months of the wet season and an increase in dry season rainfall. Furthermore, Moc Hoa showed an increase in the number of rainy days, especially during the dry season. Principal component analysis (PCA) further revealed strong correlations between annual rainfall and extreme weather events, particularly for Ca Mau, emphasizing the complex interplay of geographic and climatic factors within the region. Our findings offer insights for policymakers and planners, thus aiding the development of targeted interventions to manage water resources and prepare for changing climate conditions.

Active Galactic Nuclei and Host Galaxies in COSMOS-Web. I. NIRCam Images, Point-spread-function Models and Initial Results on X-Ray-selected Broad-line AGNs at 0.35 ≲ z ≲ 3.5

We present detailed and comprehensive data reduction and point-spread-function (PSF) model construction for all public JWST NIRCam imaging data from the COSMOS-Web treasury program (up to 2023 June, totaling 0.28 deg2). We show that the NIRCam PSF has significant short-timescale temporal variations and random spatial variations in all four filters (F115W, F150W, F277W, and F444W). Combining NIRCam with archival Hubble Space Telescope imaging, we perform multiwavelength active galactic nucleus (AGN)+host image decomposition to study the properties of 143 X-ray-selected (L bol = 1043.6–47.2 erg s−1) broad-line AGNs at 0.35 ≲ z ≲ 3.5. Leveraging the superb resolution, wavelength coverage, and sensitivity of NIRCam, we successfully detect host stellar emission after decomposing the central AGN point source in 142 objects. ∼2/3 AGNs are in star-forming galaxies based on the UVJ diagram, suggesting that there is no instantaneous negative AGN feedback. X-ray-selected broad-line AGN hosts follow a similar stellar mass–size relation as inactive galaxies, albeit with slightly smaller galaxy sizes. We find that although major mergers are rare (∼7%–22%) among the sample, more subtle nonaxisymmetric features from stellar bars, spiral arms, and minor mergers are ubiquitous, highlighting the importance of secular processes and minor mergers in triggering AGN activity. For a subsample of 30 AGNs at 1 < z < 2.5 with black hole mass measurements from single epoch spectra, they follow a similar black hole mass-stellar mass relation as local inactive early-type galaxies but reside preferentially near the upper envelope of nearby AGNs. We caution that selection biases and the intrinsic differences of AGN populations at different redshifts may significantly affect their location on the black hole mass-stellar mass plane.

Storm Surges and Sea Level Rise Cluster Hydrological Variables Across a Coastal Forest Bordering a Salt Marsh

AbstractSea level rise and storm surges drive coastal forest retreat and salt marsh expansion. Both salinization and flooding control ecological zonation and ecosystem transition in coastal areas. Hydrological variables, if coupled with ecological surveys, can explain the different stages of coastal forest retreat and marsh encroachment. In this research, long‐term data of a host of hydrological variables collected along transects from marsh to inner forest were analyzed. Linear discriminant analysis (LDA) was used to identify the primary hydrological variables responsible for the forest‐marsh gradient and their seasonal patterns. Water content (WC) in the soil (WC) and groundwater electrical conductivity (EC) were found to be the main variables responsible for the hydrological differences among the sites. Higher values of WC and EC were found in the low‐forest area near the salt marsh, with hydrological differences between forest levels reflected in ecological community structure. In particular, some sites were characterized by high EC while others by high WC values, suggesting significant spatial variations within hundreds of meters. The forested area, relatively flat in elevation, was characterized by limited hydraulic gradients and consequently lateral discharges. These characteristics made the role of groundwater level negligible in driving the hydrological clustering. Seasonal LDA data suggest that the sites are hydrologically different during winter (higher distance among clusters of variables) and similar during summer (low distance among clusters). In the study area, higher rainfall occurs during summer, decreasing groundwater EC in areas characterized by low canopy cover (dying forest). Rainfall moved low forest sites closer to the pristine high forest in the LDA analysis. During storm surge events, the distance between clusters decreased, indicating uniform salinization and flooding across the forest. Therefore, we conclude that ecological zonation in a coastal forest is reflected in seasonal hydrological differences in the absence of storm surges. Storm surges do not produce contrasting hydrological conditions and might not be responsible for ecological differences in the short‐term. On the contrary, differences in hydrological recovery are responsible for forest zonation. An additional analysis carried out using a binary Marsh‐Healthy forest LDA classifier indicates when each site switches from a forest hydrological state to a salt‐marsh hydrological state. Our results are useful for long‐term predictions of the ecological evolution of the forest–salt marsh ecotone.

Variaciones fisicoquímicas del agua de una gran presa Hidroeléctrica Mexicana, Región Centro semi-árida

The Zimapan hydropower plant built in 1995 was the first project to take advantage of Mexico's City wastewater for electric power generation. This tropical reservoir receives pollutants that affect water quality from municipal, agricultural, and industrial wastewater through the Tula and San Juan rivers. The study considered the changes in the physicochemical parameters (surface to 20 m depth) for May (wet warm), November (wet cold-after rains), and February (dry cold) in seven dam sites: two lotic, two transitional, and three lentic sites. Mean values ranges were temperature 20.71-24.38 °C, 1.59-7.56 mg/L dissolved oxygen, Secchi disk transparency 2.26-3.98 m, pH 7.12-8.40, electrical conductivity 48-1334 µS/cm, total nitrogen 14.20-30.74 mg/L, total phosphorus 10.45-15.83 mg/L, and dissolved organic carbon 9.11-20.63 mg/L. In November, all the reservoir surface water was hypoxic due to increased dissolved organic carbon by river input. Even though dissolved oxygen has a seasonal variation, this reservoir has a sustainable fishery of tilapia and black bass (~400 tons/y). The dam is meromictic and hypereutrophic, with seasonal water quality variations with no significant spatial variations. Water quality variation observed in this study can be useful to Mexican decision-makers on water and fisheries management, prevent massive fish kills, and be a support guide for commercial and sport fishers.

Synergistic impacts of Landscape, Soil, and environmental factors on the spatial distribution of soil aggregates stability in the Danjiangkou reservoir area

Soil aggregate stability (SAS) plays a crucial role in determining the soil's resistance to erosion. Conducting a spatial variability analysis of SAS in the reservoir area, which is highly susceptible to soil degradation and erosion, can yield valuable insights for agricultural practices, landscape planning, and environmental management in this region. Nevertheless, existing research remains constrained in its capacity to accurately map and forecast soil aggregates at a spatial level. This study aims to investigate the spatial distribution of SAS in the Danjiangkou reservoir area, as well as to explore the intrinsic linkage mechanisms between changes in soil properties, environmental factors and landscape patterns and SAS. Results revealed significant spatial variations in SAS within the vicinity of the Danjiangkou reservoir area. Notably, soil properties (including clay, available potassium, soil organic matter, and available phosphorus), environmental factors (such as digital elevation model, slope, land surface temperature difference between seasons (ΔLST)), and landscape pattern (largest patch index (LPI), shannon’s diversity index (SHDI), edge density (ED)) significantly affected the spatial variability of SAS, explaining 18.91%, 8.22%, and 8.17% of the total variability of soil SAS, respectively. Structural equation modeling results indicated that altering soil properties serves as the primary approach to enhance SAS. Furthermore, interventions targeting landscape factors such as LPI, SHDI, and ED, alongside environmental factors like ΔLST and slope, exert direct influences on SAS, while also indirectly impacting it through their effects on soil properties. These findings contribute to the design of land planning and soil and water conservation measures in the reservoir watershed, promoting its protection of soil structure and its ecological service function.

Investigating of Spatiotemporal Correlation between Urban Spatial Form and Urban Ecological Resilience: A Case Study of the City Cluster in the Yangzi River Midstream, China

The anthropogenic disturbance caused by irrational urbanization impacts the ecological security of cities. The study of the relationship between urban spatial form (USF) and urban ecological resilience (UER) can offer guidance for sustainable urban development. We select the UER evaluation indexes and construct a DRSIR model based on the causal relationship among the natural, social, and economic dimensions of cities. We also carried out empirical research on the impact of USF on UER in the city cluster located in the Yangtze River midstream (YRM) through the geographical and temporal weighted regression (GTWR) model and geographical detector for four time cross-sections: 2005, 2010, 2015, and 2020. The following findings were obtained: (1) The computing results for UERIs of cities in the YRM noticeably and consistently increased during the study period, exhibiting a spatial distribution pattern with high values in the southeast, second-highest in the northwest, and low values in the center. (2) The regression coefficients of USFIs and UERIs displayed significant spatial and temporal variations in the YRM during the study period. (3) CA, AWMPFD, PD, and AI showed a positive correlation with UERIs, while PD exhibited a negative correlation with UERIs. (4) LPI and AI emerged as the primary drivers of spatial heterogeneity in UERIs, and the interaction between these two factors significantly enhanced their impact on UERIs compared to individual influencing factors. Exploring the correlation between UER and USF can be utilized to provide urban management recommendations that are suitable for the level of urban development, which will contribute to the achievement of sustainable development in the YRM.

Genomic prediction for root and yield traits of barley under a water availability gradient: a case study comparing different spatial adjustments

BackgroundIn drought periods, water use efficiency depends on the capacity of roots to extract water from deep soil. A semi-field phenotyping facility (RadiMax) was used to investigate above-ground and root traits in spring barley when grown under a water availability gradient. Above-ground traits included grain yield, grain protein concentration, grain nitrogen removal, and thousand kernel weight. Root traits were obtained through digital images measuring the root length at different depths. Two nearest-neighbor adjustments (M1 and M2) to model spatial variation were used for genetic parameter estimation and genomic prediction (GP). M1 and M2 used (co)variance structures and differed in the distance function to calculate between-neighbor correlations. M2 was the most developed adjustment, as accounted by the Euclidean distance between neighbors.ResultsThe estimated heritabilities (h^2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\widehat{h}}^{2}$$\\end{document}) ranged from low to medium for root and above-ground traits. The genetic coefficient of variation (GCV\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$GCV$$\\end{document}) ranged from 3.2 to 7.0% for above-ground and 4.7 to 10.4% for root traits, indicating good breeding potential for the measured traits. The highest GCV\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$GCV$$\\end{document} observed for root traits revealed that significant genetic change in root development can be achieved through selection. We studied the genotype-by-water availability interaction, but no relevant interaction effects were detected. GP was assessed using leave-one-line-out (LOO) cross-validation. The predictive ability (PA) estimated as the correlation between phenotypes corrected by fixed effects and genomic estimated breeding values ranged from 0.33 to 0.49 for above-ground and 0.15 to 0.27 for root traits, and no substantial variance inflation in predicted genetic effects was observed. Significant differences in PA were observed in favor of M2.ConclusionsThe significant GCV\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$GCV$$\\end{document} and the accurate prediction of breeding values for above-ground and root traits revealed that developing genetically superior barley lines with improved root systems is possible. In addition, we found significant spatial variation in the experiment, highlighting the relevance of correctly accounting for spatial effects in statistical models. In this sense, the proposed nearest-neighbor adjustments are flexible approaches in terms of assumptions that can be useful for semi-field or field experiments.

Regional geographies and public health lessons of the COVID-19 pandemic in the Arctic.

This study examines the COVID-19 pandemic's spatiotemporal dynamics in 52 sub-regions in eight Arctic states. This study further investigates the potential impact of early vaccination coverage on subsequent COVID-19 outcomes within these regions, potentially revealing public health insights of global significance. We assessed the outcomes of the COVID-19 pandemic in Arctic sub-regions using three key epidemiological variables: confirmed cases, confirmed deaths, and case fatality ratio (CFR), along with vaccination rates to evaluate the effectiveness of the early vaccination campaign on the later dynamics of COVID-19 outcomes in these regions. From February 2020 to February 2023, the Arctic experienced five distinct waves of COVID-19 infections and fatalities. However, most Arctic regions consistently maintained Case Fatality Ratios (CFRs) below their respective national levels throughout these waves. Further, the regression analysis indicated that the impact of initial vaccination coverage on subsequent cumulative mortality rates and Case Fatality Ratio (CFR) was inverse and statistically significant. A common trend was the delayed onset of the pandemic in the Arctic due to its remoteness. A few regions, including Greenland, Iceland, the Faroe Islands, Northern Canada, Finland, and Norway, experienced isolated spikes in cases at the beginning of the pandemic with minimal or no fatalities. In contrast, Alaska, Northern Sweden, and Russia had generally high death rates, with surges in cases and fatalities. Analyzing COVID-19 data from 52 Arctic subregions shows significant spatial and temporal variations in the pandemic's severity. Greenland, Iceland, the Faroe Islands, Northern Canada, Finland, and Norway exemplify successful pandemic management models characterized by low cases and deaths. These outcomes can be attributed to successful vaccination campaigns, and proactive public health initiatives along the delayed onset of the pandemic, which reduced the impact of COVID-19, given structural and population vulnerabilities. Thus, the Arctic experience of COVID-19 informs preparedness for future pandemic-like public health emergencies in remote regions and marginalized communities worldwide that share similar contexts.

Spatiotemporal Distribution of Malaria in the Kingdom of Saudi Arabia.

Malaria is a significant public health concern in the Kingdom of Saudi Arabia (KSA). This study aimed to investigate the spatiotemporal distribution of malaria in the KSA between 2017 and 2021. A spatial analysis was conducted using data for malaria cases stratified by Plasmodium species reported by the Ministry of Health for the period 2017-2021. Covariate data such as environmental, socioeconomic, and demographic factors were assembled from different publicly available sources. A total of 13,852 cases were reported from 20 regions in the KSA during the study period. The study indicated a decline in the overall number of reported cases from 2715 in 2017 to 2616 in 2021, primarily driven by a decrease in Plasmodium falciparum infections. However, the number of Plasmodium vivax cases increased in 2021. Southern regions of the KSA remained at higher risk due to imported cases from neighboring Yemen. Socioeconomic and demographic factors, such as access to healthcare and education, were found to affect malaria transmission. Environmental factors, such as temperature and rainfall, were also identified as determinants of malaria risk. This study showed significant spatial variation in malaria cases in the KSA that was related to underlying socioeconomic status and environmental factors. The findings of this study highlight the need for continued efforts to control and eliminate malaria in the KSA, particularly in regions with higher risk of malaria.

The Impact of Spatial and Locational Characteristics on Airbnb Prices in London

This research delves into the intricate relationship between spatial and locational attributes and Airbnb pricing in London. Utilizing data encompassing Airbnb listings in London collected over one year concluding on December 10, 2022, and employing advanced geospatial statistical techniques, including a geographically weighted regression (GWR) model equipped with ten select explanatory variables, this study reveals multifaceted spatial patterns underlying Airbnb pricing. The study underscores the paramount significance of dissecting the multifaceted determinants of Airbnb pricing, encompassing property characteristics, location-specific variables, host-related attributes, and customer feedback. Through empirical analyses, this research illuminates pronounced spatial heterogeneity within Airbnb pricing, with notable variations discerned across different room types. Interpretation of model coefficients reveals the multifaceted influence of factors, such as proximity to subway stations, volume of customer reviews, and specific scores, on pricing dynamics. Additionally, the GWR model exposes significant spatial variations in the impact of location and neighborhood-related variables on pricing, with particularly marked effects in the realms of entire homes and private rooms. This study aims to illustrate the intricate interplay between spatial and locational characteristics and Airbnb pricing dynamics, offering invaluable insights for researchers and industry practitioners.

Untreated Rainfall Runoff Water Quality Characteristics of Different Land Uses in Infilled Lake Areas—The Case of Wuhan Shahu

Fast urbanization in developing countries contributes to heavier pollution in urban water environments, as urbanization enhances land development and even requires lake filling to meet urban land needs, which produces significant water runoff pollution. Centralized construction brings heavier non-point source pollution, which is one of the most crucial types of pollution in urban areas. However, the pollution caused by urbanization in infilled lake areas is lack of attention. To reduce the negative impact of pollution brought on by urbanization, it is important to monitor the characteristics of runoff water qualities and their relationship with rainfall features. In this study, runoff water quality was monitored from 10 rainfall events, totaling 157 samples in the Wuhan Shahu area, an infilled lake area, to characterize the pollutant event mean concentrations (EMCs) and to explore the different effects of land-use types. COD (chemical oxygen demand), TN (total nitrogen), and TP (total phosphorus) were selected as water quality indicators. The results show that the pollutants have a significant spatial variation. Residential runoff had the largest COD (averaged EMC = 54.02 (mg/L)) and TN (averaged EMC = 2.69 (mg/L)) pollution, while road runoff had the second largest COD (averaged EMC = 48.05 (mg/L)) and TN (2.572 (mg/L)) pollution. The TP pollution level is opposite to COD and TN, as commercial and green spaces have heavier pollution, while the differences in TP pollution levels between the four types of land are not significant. The release of organic matter and nitrogen is closely related to human activities on land types, while phosphorus emission is relatively stable, indicating that it is not easily affected or controlled. In addition, the variation in pollutants between land types is also related to a certain extent. The correlation analysis shows that parameters like antecedent dry days (ADDs), rainfall intensity, and rainfall duration most significantly affect the EMCs of commercial and green spaces. Phosphorus pollutants on roads are harder to reduce. These results may help researchers to identify the specific pollutant source and find an effective method to reduce pollution in infilled lake areas and other areas.

Active dark carbon fixation evidenced by 14C isotope assimilation and metagenomic data across the estuarine-coastal continuum

Estuaries, as important land-ocean transitional zones across the Earth's surface, are hotspots of microbially driven dark carbon fixation (DCF), yet understanding of DCF process remains limited across the estuarine-coastal continuum. This study explored DCF activities and associated chemoautotrophs along the estuarine and coastal environmental gradients, using radiocarbon labelling and molecular techniques. Significantly higher DCF rates were observed at middle- and high-salinity regions (0.65–2.31 and 0.66–2.82 mmol C m−2 d−1, respectively), compared to low-salinity zone (0.07–0.19 mmol C m−2 d−1). Metagenomic analysis revealed relatively stable DCF pathways along the estuarine-coastal continuum, primarily dominated by Calvin-Benson-Bassham (CBB) cycle and Wood-Ljungdahl (WL) pathway. Nevertheless, chemoautotrophic communities driving DCF exhibited significant spatial variations. It is worth noting that although CBB cycle played an important role in DCF in estuarine sediments, WL pathway might play a more significant role, which has not been previously recognized. Overall, this study highlights that DCF activities coincide with the genetic potential of chemoautotrophy and the availability of reductive substrates across the estuarine-coastal continuum, and provides an important scientific basis for accurate quantitative assessment of global estuarine carbon sink.

Atmospheric deposition of mineral dust and associated nutrients over the Equatorial Indian Ocean

Aerosols are potential supplier of nutrients to the surface water of oceans and can impact biogeochemical processes particularly in the remote locations. The nutrient data from atmospheric supply is poorly reported from the Indian Ocean region. In this study, we present atmospheric nutrients such as reactive nitrogen species (Nitrate, Ammonium, Organic nitrogen), micro-nutrients (e.g. Fe, Mn and Cu) concentration along with mineral dust in the aerosol samples collected over meridional transect during summer (April–May 2018) and monsoon (June–July 2019) months. A significant spatial variation of dust was observed during summer (0.6–22.8 μg m−3) and monsoon (2.8–25.1 μg m−3) months with a decreasing trend from north to south. Dust as well as other nutrient species shows a general north to south decreasing trend, however, no such trend was seen in the soluble trace elements (TEs) concentration. Anthropogenic species like NH4+ and nss-K+ were found below detection limit during monsoon campaign. The fractional solubility (in percentage) of Fe, Mn and Cu were estimated by measuring their concentration in ultrapure water leach which averaged around 0.99 ± 1.12, 31.0 ± 14.9 and 31.1 ± 25.4, respectively during summer and 0.09 ± 0.08, 6.0 ± 8.9, 16.7 ± 9.6, respectively, during monsoon period. Correlation of soluble Fe with total Fe and total acidic species suggest varying dust sources is an important controlling factor for the fraction solubility of Fe with negligible contribution from the chemical processing. However, a significant correlation was observed between total acid and fractional solubility of Mn and Cu suggest role of chemical processing in enhancement of their solubility. Dry deposition flux of aeolian dust was estimated for both campaign using Al concentration and relatively higher fluxes were observed for summer (12.6 ± 8.4 mg·m−2·d−1) and monsoon (8.7 ± 8.4 mg·m−2·d−1) months as compared to model based estimates reported in the literature. Contrastingly, estimated deposition flux of soluble Fe from both campaign displays relatively lower values as compared to model based results which underscores a need for re-evaluation of biogeochemical models with real-time data.

Injury severity of police officers involved in traffic crashes: A spatial analysis of Alabama

Traffic crashes are the leading cause of injuries and deaths for police officers in the US. There has been limited research to investigate the characteristics of traffic crashes that involve police officers. This study aims to investigate the relationships between different independent variables and police officer injury severity in traffic crashes. Considering the inherent spatial correlation between traffic crashes and the geographical environment, this study employs the ordered logit regression and the Geographically Weighted Ordered Logistic Regression (GWOLR) approach to identify the contributing factors of police officers’ injuries in traffic crashes and to quantify the correlates of police injury severity in crashes. This study explored traffic crashes of five years (Number of observations = 4,231) that involved police officers in the State of Alabama. The results of the global ordered logit model illustrated that some independent variables, such as pre-crash behaviors, holiday, and road type, are statistically significantly related to police officer crash injury severity. Also, some variables, such as pre-crash behaviors and seatbelt usage, passed the non-stationarity tests. This suggests significant spatial variations in their relationships with the severity of injuries sustained by police officers in traffic crashes. The local correlates of police injury severity are valuable information for law enforcement agencies to localize strategies for improving officer safety on the road. For example, Move-over laws may need to be enhanced in the southeast Alabama area, where failing to yield (to police officers) seems to lead to increased traffic injury severities of police officers compared to other areas in Alabama.

An analysis of the dual burden of childhood stunting and wasting in Myanmar: a copula geoadditive modelling approach.

To analyze the spatial variation and risk factors of the dual burden of childhood stunting and wasting in Myanmar. Analysis was carried out on nationally representative data obtained from the Myan-mar Demographic and Health Survey conducted during 2015-2016. Childhood stunting and wasting are used as proxies of chronic and acute childhood undernutrition. A child with standardized height-for-age Z score (HAZ) below -2 is categorized as stunted while that with a weight-for-height Z score (WHZ) below -2 as wasted. A nationally representative sample of households from the 15 states and regions of Myanmar. Children under the age of five (n 4162). Overall marginal prevalence of childhood stunting and wasting were 28.9% (95% CI 27.5, 30.2) and 7.3% (95% CI 6.5, 8.0) while their concurrent prevalence was 1.6% (95% CI 1.2, 2.0). The study revealed mild positive association between stunting and wasting across Myanmar. Both stunting and wasting had significant spatial variation across the country with eastern regions having higher burden of stunting while southern regions having higher prevalence of wasting. Child age and maternal weight-for-height Z score had significant non- linear association with both stunting and wasting while child gender, ethnicity and household wealth quintile had significant association with stunting. The study provides data-driven evidence about the association between stunting and wasting and their spatial variation across Myanmar. The resulting insights can aid in the formulation and implementation of targeted, region-specific interventions towards improving the state of childhood under-nutrition in Myanmar.

Erosivity index based on climatological norms from 1991 to 2020 for the state of Rio Grande do Sul

Erosivity index based on climatological norms from 1991 to 2020 for the state of Rio Grande do Sul