Spatial Distribution of SDGs Accomplished Under MGNREGA Beyond SDG1

Nations across the world share common responsibility towards achieving Sustainable Development Goals (SDGs). To monitor the progress of individual goals and their global-level comparisons, a set of targets and indicators are developed by the experts. However, systematic methods for assessing spatio-temporal progress towards achieving the SDGs are lacking. This study demonstrates the use of geographically referenced information (GIS) analysis in mapping the SDGs as achieved under the Mahatma Gandhi National Rural Employment Generation Act (MGNREGA) programme in India, taking Uttarakhand state as a case study. Geotagged data of assets representing various work categories permissible under MGNREGA are linked to the targets and indicators of various SDGs. Kernel Density Estimation (KDE) function is used to derive spatially explicit maps. Sub-national-level composite analysis of overall contribution of the MGNREGA to SDGs is carried out district wise for better understanding. Results obtained show significant spatial variation in the distribution of works across the districts, reflecting their varying priorities as MGNREGA is a demand-driven scheme. The future implication of the study is a vastly improved ability to derive latent information based on geographical indicators for targeting interventions and developing informed strategies towards SDGs.

Concentrations, Distribution, and Pollution Assessment of Metals in River Sediments in China

This study conducted a review on the concentrations, spatial distribution and pollution assessment of metals including As, Hg, Cd, Co, Cr, Cu, Mn, Ni, Pb and Zn in 102 river sediments in China between January 2008 and July 2020 based on the online literature. The geo-accumulation index (Igeo) and potential ecological risk index (RI) were used for the pollution assessment of the metals. The results showed that the ranges of metals were: 0.44 to 250.73 mg/kg for As, 0.02 to 8.67 mg/kg for Hg, 0.06 to 40 mg/kg for Cd, 0.81 to 251.58 mg/kg for Co, 4.69 to 460 mg/kg for Cr, 2.13 to 520.42 mg/kg for Cu, 39.76 to 1884 mg/kg for Mn, 1.91 to 203.11 mg/kg for Ni, 1.44 to 1434.25 mg/kg for Pb and 12.76 to 1737.35 mg/kg for Zn, respectively. The median values of these metals were descending in the order: Mn > Zn > Cr > Cu > Pb > Ni > Co > As > Cd > Hg. Compared with the SQGs, As and Cr manifested higher exceeding sites among the metals. Metals of river sediments manifested a significant spatial variation among different regions, which might be attributed to the natural weathering and anthropogenic activity. The mean Igeo values of the metals presented the decreasing trends in the order: Cd > Hg > Zn > Cu > As > Pb > Ni > Co > Cr > Mn. Cd and Hg manifested higher proportions of contaminated sites and contributed most to the RI, which should be listed as priority control of pollutants. Southwest River Basin, Liaohe River Basin, and Huaihe River Basin manifested higher ecological risks than other basins. The study could provide a comprehensive understanding of metals pollution in river sediments in China, and a reference of the control of pollutant discharge in the river basins for the management.

Spatial Variability in COVID-19 Mortality

COVID-19 emerged as a global pandemic in the spring of 2020. Since that time, the disease has resulted in approximately 150 million cases and 3 million deaths worldwide. However, there is significant spatial variation in the rate of mortality from COVID-19. Here, we briefly explore spatial variations in COVID-19 mortality by country groupings and propose possible explanations for the differences observed. Specifically, we find that there is a statistically significant difference in COVID-19 mortality between countries grouped into categories based on (1) developed, primarily western diets and healthcare systems; (2) “Scandinavian” countries with advanced healthcare systems and generally anti-inflammatory diets, and (3) developing countries. We do not infer causality but believe that the observed associations provide hypotheses for future research investigations. Moreover, our results add further evidence to support additional exploration of vitamin D exposure/status and COVID-19 mortality.

Future Risk of Bovine Tuberculosis (Mycobacterium bovis) Breakdown in Cattle Herds 2013–2018: A Dominance Analysis Approach

Bovine tuberculosis (bTB) remains a significant endemic pathogen of cattle herds, despite multi-decadal control programmes being in place in several countries. Understanding the risks of future bTB breakdown (BD) and the associated characteristics of herds and index breakdowns could help inform risk categorisation. Such risk categories could then contribute to tailored management and policies. Here, we estimated the future risk of herd BD for the cohort of herds that were derestricted during 2013 in Ireland using multivariable logit regression models, with a dominance analysis approach. One third of herds that were derestricted in 2013 experienced a breakdown during the follow-up five year period (1469/4459; 33%). BD length was a significant predictor of future risk, primarily driven by long BDs > 230 days relative to short BDs < 130 days (OR 95%CI: 1.157–1.851), as was having had a previous BD (OR 95%CI: 1.012–1.366). Herd-size was the dominant predictor of future risk (accounted for 46% of predicted variance), suggesting significant increase in risk of future breakdown with increasing (log) herd-size (OR 95%CI: 1.378–1.609). There was significant spatial variation in future risk across counties, and it was the second most dominant predictor of future risk (25% of predicted variance). The size of index breakdowns was not a strong predictor of future risk over a 5-year period. These findings can inform a risk-based policy development.

Using Community Science to Reveal the Global Chemogeography of River Metabolomes

River corridor metabolomes reflect organic matter (OM) processing that drives aquatic biogeochemical cycles. Recent work highlights the power of ultrahigh-resolution mass spectrometry for understanding metabolome composition and river corridor metabolism. However, there have been no studies on the global chemogeography of surface water and sediment metabolomes using ultrahigh-resolution techniques. Here, we describe a community science effort from the Worldwide Hydrobiogeochemistry Observation Network for Dynamic River Systems (WHONDRS) consortium to characterize global metabolomes in surface water and sediment that span multiple stream orders and biomes. We describe the distribution of key aspects of metabolomes including elemental groups, chemical classes, indices, and inferred biochemical transformations. We show that metabolomes significantly differ across surface water and sediment and that surface water metabolomes are more rich and variable. We also use inferred biochemical transformations to identify core metabolic processes shared among surface water and sediment. Finally, we observe significant spatial variation in sediment metabolites between rivers in the eastern and western portions of the contiguous United States. Our work not only provides a basis for understanding global patterns in river corridor biogeochemical cycles but also demonstrates that community science endeavors can enable global research projects that are unfeasible with traditional research models.

Using Community Science to Reveal the Global Chemogeography of River Metabolomes

River corridor metabolomes reflect organic matter (OM) processing that drives aquatic biogeochemical cycles. Recent work highlights the power of ultrahigh-resolution mass spectrometry for understanding metabolome composition and river corridor metabolism. However, there have been no studies on the global chemogeography of surface water and sediment metabolomes using ultrahigh-resolution techniques. Here, we describe a community science effort from the Worldwide Hydrobiogeochemistry Observation Network for Dynamic River Systems (WHONDRS) consortium to characterize global metabolomes in surface water and sediment that span multiple stream orders and biomes. We describe the distribution of key aspects of metabolomes including elemental groups, chemical classes, indices, and inferred biochemical transformations. We show that metabolomes significantly differ across surface water and sediment and that surface water metabolomes are more rich and variable. We also use inferred biochemical transformations to identify core metabolic processes shared among surface water and sediment. Finally, we observe significant spatial variation in sediment metabolites between rivers in the eastern and western portions of the contiguous United States. Our work not only provides a basis for understanding global patterns in river corridor biogeochemical cycles but also demonstrates that community science endeavors can enable global research projects that are unfeasible with traditional research models.

Spatially varying relationships between immigration measures and property crime Types in Vancouver Census Tracts, 2016

We empirically test for spatial heterogeneity or local effects of multiple immigration measures on various property crime classification across Vancouver census tracts, 2016. Using spatially referenced property crime data and census data, we use geographically weighted regression to investigate the neighbourhood-level effects of immigration on crime. We find that estimated parameters vary across space, but these local immigration effects do not always vary significantly at the local level. Overall, significant spatial variation in the effects of immigration on property crime is present. These are important for policy and theory. The identification of varied spatial patterns of immigration effects on crime may help explain some of the inconsistent/disparate results found in neighbourhood-level studies on immigration and crime.

Statistical analysis of long-term trends in UK effective rainfall: implications for deep-seated landsliding

Rainfall and its impact on the water content of a slope are known to be important controls on landslide activity in the UK. This paper presents an analysis of long-term monthly rainfall and temperature records from selected stations throughout the UK, including the application of a non-parametric statistical test to evaluate the significance of any trends in these time series. The results indicate that there is significant spatial variation in the long-term trends. A positive trend of increasing effective rainfall (precipitation minus evapotranspiration) is present at some (but not all) Scottish stations (Eskdalemuir, Lerwick, Paisley, Stornoway and Tiree), but is not present across the rest of the UK.

Characteristics of Water Isotopes and Water Source Identification During the Wet Season in Naqu River Basin, Qinghai-Tibet Plateau

Climate change is affecting the discharge of headstreams from mountainous areas on the Qinghai–Tibet Plateau. To constrain future changes in discharge, it is important to understand the present-day formation mechanism and components of runoff in the basin. Here we explore the sources of runoff and spatial variations in discharge through measurements of δ2H and δ18O in the Naqu River, at the source of the Nu River, on the Qinghai–Tibet plateau, during the month of August from 2016 to 2018. We established thirteen sampling sites on the main stream and tributaries, and collected 39 samples from the river. We examined all the water samples and analyzed them for isotopes. We find a significant spatial variation trend based on one-way analysis of variance (ANOVA) (p < 0.05) between Main stream-2 and tributaries. The local meteoric water-line (LMWL) can be described as: δ2H = 7.9δ18O + 6.29. Isotopic evaporative fractionation in water and mixing of different water sources are responsible for the spatial difference in isotopic values between Main stream-2 and tributaries. Based on isotopic hydrograph separation, the proportion of snowmelt in runoff components ranges from 15% to 47%, and the proportion of rainwater ranges from 3% to 35%. Thus, the main components of runoff in the Naqu River are snowmelt and groundwater.

Global distribution of nearshore slopes with implications for coastal retreat

Nearshore slope, defined as the cross-shore gradient of the subaqueous profile, is an important input parameter which affects hydrodynamic and morphological coastal processes. It is used in both local and large-scale coastal investigations. However, due to unavailability of data, most studies, especially those that focus on continental or global scales, have historically adopted a uniform nearshore slope. This simplifying assumption could however have far-reaching implications for predictions/projections thus obtained. Here, we present the first global dataset of nearshore slopes with a resolution of 1 km at almost 620 000 points along the global coastline. To this end, coastal profiles were constructed using global topo-bathymetric datasets. The results show that the nearshore slopes vary substantially around the world. An assessment of coastline recession driven by sea level rise (SLR) (for an arbitrary 0.5 m SLR) with a globally uniform coastal slope of 1 : 100, as carried out in previous studies, and with the spatially variable coastal slopes computed herein shows that, on average, the former approach would underestimate coastline recession by about 40 %, albeit with significant spatial variation. The final dataset has been made publicly available at https://doi.org/10.4121/uuid:a8297dcd-c34e-4e6d-bf66-9fb8913d983d (Athanasiou, 2019).