Machine learning-based prediction and assessment of recent dynamics of forest net primary productivity in Romania

While the analysis of spatio-temporal changes in the net primary productivity (NPP) of forests can provide critical information on carbon cycle and climate change, these ecological trends have remained unclear in many countries worldwide, including Romania. By using complex (satellite, forest and climate) data, many sophisticated (machine learning) algorithms and some widely applied (the Mann-Kendall test and Sen's slope estimator) statistical procedures, this study investigates, for the first time, recent forest NPP trends (1987-2018) that occurred in Romania, in relation to climate change that affected the country over the past decades. Following the modelling, mapping and assessment of NPP dynamics, results showed almost exclusively positive trends for this ecological parameter, which accounts for ∼99% of all forest NPP changes that occurred throughout the country, after 1987. Interestingly, almost three quarters (∼73%) of all NPP increasing trends are statistically significant, which indicates that Romania's forests have recently experienced a large-scale improvement in carbon fluxes and stocks. Investigations of eco-climatic relationships suggest that climate change has partially contributed to these surprising NPP dynamics observed in recent decades. All these findings can provide valuable information for forest management and for many stakeholders and policymakers who operate in the forestry and climate fields in Romania.

Spatio-temporal geostatistical modelling of sulphate concentration in the area of the Reocín Mine (Spain) as an indicator of water quality

Water stored in open-pit lakes can be a water resource when the mine is closed. This study aimed to develop a reliable model to evaluate the water quality, based on the sulphate concentration, in the Reocín Mine area (Spain) by using geostatistical algorithms. To this end, water samples were taken from the beginning of the flooding period in November 2004 until August 2020. The model showed that the sulphate concentration was highest between February 2009 and February 2012 and decreased as the flooding process progressed. The area with the highest concentration (2000 mg L^-1) was the central part of the study area, where the mine is located, while in the northeast and southwest, the values from the beginning of the flooding period were lower, below 500 mg L^-1. In the last obtained model, the values decreased considerably to 1300 mg L^-1 in the central area and below 250 mg L^-1 in the northeast and southwest areas. The modelling conducted to assess the water quality in the area of influence of the mine determined that the flooding process has little influence on the water in the rivers and streams in the area, since the sulphate concentration measured in the adjacent rivers and streams was less than 250 mg L^-1, indicating that anomalous concentrations were only found in the open-pit area. It was shown that geostatistical algorithms are useful tools that can be used to model the intensity and extension of water pollutants in space over time.

A spatial model for nutrient mitigation potential of blue mussel farms in the western Baltic Sea

Worldwide, coastal and marine policies are increasingly aiming for environmental protection, and eutrophication is a global challenge, particularly impairing near-coastal marine water bodies. In this context, mussel mitigation aquaculture is currently considered an effective tool to extract nutrients from such water bodies. Mussel mitigation farming using longline systems with loops of collector material is a well-developed technology and considered promising in the western Baltic Sea. Besides several spatially limited field studies, a suitable spatial model for site-specific implementation is still lacking. In this study, we present a modular spatial model, consisting of a spatial and temporal habitat factor model (Module 1), blue mussel growth model (Module 2), mussel farm model (Module 3), and an avoidance of food limitation model (Module 4). The modules integrate data from in situ monitoring, mussel growth experiments, and eco-physiological modelling for the western Baltic Sea, to estimate spatially explicit nutrient reduction potentials. The model is flexible with respect to farm setups and harvest times and considers natural variability, model uncertainty, and required hydrodynamics. Modelling results proved valid at all scales and modules, and point out key areas for efficient mussel mitigation farms in Danish, German and Swedish areas. Modelled long-term mean mitigation potentials for harvest in November reach up to 0.88 t_N/ha and 0.05 t_P/ha for a farm setup using 2 m depth-range of the water column and 3.0 t_N/ha and 0.17 t_P/ha using up to 8 m, respectively. For Danish water bodies, we demonstrate that in efficient areas, mitigation farms (18.8 ha, 90 km collector substrate in loops with 2 m depth-range) required <3.6% of the space to extract the target nitrogen loads for good ecological status. The developed approach could prove valuable for implementing environmental policies in aquatic systems, e.g. in situ nutrient mitigation, aquaculture spatial planning, and habitat suitability mapping.

A stakeholder workshop about modelled maps of key malaria indicator survey indicators in Madagascar

The Demographic and Health Surveys (DHS) Program has supported three household Malaria Indicator Surveys (MIS) in Madagascar. The results of 13 key malaria indicators from these surveys have been mapped as continuous surfaces using model-based geostatistical methods. The opportunities and limitations of these mapped outputs were discussed during a workshop in Antananarivo, Madagascar in July 2018, attended by 15 representatives from various implementation, policy and research stakeholder institutions in Madagascar. Participants evaluated the findings from the maps, using these to develop figures and narratives to support their work in the control of malaria in Madagascar.

Geo-spatial factors associated with infection risk among young children in rural Ghana: a secondary spatial analysis

Background

Determining the spatial patterns of infection among young children living in a malaria-endemic area may provide a means of locating high-risk populations who could benefit from additional resources for treatment and improved access to healthcare. The objective of this secondary analysis of baseline data from a cluster-randomized trial among 1943 young Ghanaian children (6–35 months of age) was to determine the geo-spatial factors associated with malaria and non-malaria infection status.

Methods

Spatial analyses were conducted using a generalized linear geostatistical model with a Matern spatial correlation function and four definitions of infection status using different combinations of inflammation (C-reactive protein, CRP > 5 mg/L) and malaria parasitaemia (with or without fever). Potentially informative variables were included in a final model through a series of modelling steps, including: individual-level variables (Model 1); household-level variables (Model 2); and, satellite-derived spatial variables (Model 3). A final (Model 4) and maximal model (Model 5) included a set of selected covariates from Models 1 to 3.

Results

The final models indicated that children with inflammation (CRP > 5 mg/L) and/or any evidence of malaria parasitaemia at baseline were more likely to be under 2 years of age, stunted, wasted, live further from a health facility, live at a lower elevation, have less educated mothers, and higher ferritin concentrations (corrected for inflammation) compared to children without inflammation or parasitaemia. Similar results were found when infection was defined as clinical malaria or parasitaemia with/without fever (definitions 3 and 4). Conversely, when infection was defined using CRP only, all covariates were non-significant with the exception of baseline ferritin concentration. In Model 5, all infection definitions that included parasitaemia demonstrated a significant interaction between normalized difference vegetation index and land cover type. Maps of the predicted infection probabilities and spatial random effect showed defined high- and low-risk areas that tended to coincide with elevation and cluster around villages.

Conclusions

The risk of infection among young children in a malaria-endemic area may have a predictable spatial pattern which is associated with geographical characteristics, such as elevation and distance to a health facility.

Original trial registration clinicaltrials.gov (NCT01001871)

Electronic supplementary material

The online version of this article (doi:10.1186/s12936-016-1388-1) contains supplementary material, which is available to authorized users.

Geostatistical modelling of arsenic in drinking water wells and related toenail arsenic concentrations across Nova Scotia, Canada

Arsenic is a naturally occurring class 1 human carcinogen that is widespread in private drinking water wells throughout the province of Nova Scotia in Canada. In this paper we explore the spatial variation in toenail arsenic concentrations (arsenic body burden) in Nova Scotia. We describe the regional distribution of arsenic concentrations in private well water supplies in the province, and evaluate the geological and environmental features associated with higher levels of arsenic in well water. We develop geostatistical process models to predict high toenail arsenic concentrations and high well water arsenic concentrations, which have utility for studies where no direct measurements of arsenic body burden or arsenic exposure are available. 892 men and women who participated in the Atlantic Partnership for Tomorrow's Health Project provided both drinking water and toenail clipping samples. Information on socio-demographic, lifestyle and health factors was obtained with a set of standardized questionnaires. Anthropometric indices and arsenic concentrations in drinking water and toenails were measured. In addition, data on arsenic concentrations in 10,498 private wells were provided by the Nova Scotia Department of Environment. We utilised stepwise multivariable logistic regression modelling to develop separate statistical models to: a) predict high toenail arsenic concentrations (defined as toenail arsenic levels ≥0.12 μg g(-1)) and b) predict high well water arsenic concentrations (defined as well water arsenic levels ≥5.0 μg L(-1)). We found that the geological and environmental information that predicted well water arsenic concentrations can also be used to accurately predict toenail arsenic concentrations. We conclude that geological and environmental factors contributing to arsenic contamination in well water are the major contributing influences on arsenic body burden among Nova Scotia residents. Further studies are warranted to assess appropriate intervention strategies for reducing arsenic body burden among human populations.