Nitrate prediction in groundwater of data scarce regions: The futuristic fresh-water management outlook

Nitrate contamination in groundwater poses a significant threat to water quality and public health, especially in regions with limited data availability. This study addresses this challenge by employing machine learning (ML) techniques to predict nitrate (NO3--N) concentrations in Mexico's groundwater. Four ML algorithms-Extreme Gradient Boosting (XGB), Boosted Regression Trees (BRT), Random Forest (RF), and Support Vector Machines (SVM)-were executed to model NO3--N concentrations across the country. Despite data limitations, the ML models achieved robust predictive performances. XGB and BRT algorithms demonstrated superior accuracy (0.80 and 0.78, respectively). Notably, this was achieved using ∼10 times less information than previous large-scale assessments. The novelty lies in the first-ever implementation of the 'Support Points-based Split Approach' during data pre-processing. The models considered initially 68 covariates and identified 13-19 significant predictors of NO3--N concentration spanning from climate, geomorphology, soil, hydrogeology, and human factors. Rainfall, elevation, and slope emerged as key predictors. A validation incorporated nationwide waste disposal sites, yielding an encouraging correlation. Spatial risk mapping unveiled significant pollution hotspots across Mexico. Regions with elevated NO3--N concentrations (>10 mg/L) were identified, particularly in the north-central and northeast parts of the country, associated with agricultural and industrial activities. Approximately 21 million people, accounting for 10 % of Mexico's population, are potentially exposed to elevated NO3--N levels in groundwater. Moreover, the NO3--N hotspots align with reported NO3--N health implications such as gastric and colorectal cancer. This study not only demonstrates the potential of ML in data-scarce regions but also offers actionable insights for policy and management strategies. Our research underscores the urgency of implementing sustainable agricultural practices and comprehensive domestic waste management measures to mitigate NO3--N contamination. Moreover, it advocates for the establishment of effective policies based on real-time monitoring and collaboration among stakeholders.

Large-scale assessment of PFAS compounds in drinking water sources using machine learning

The monitoring of Per and Polyfluoroalkyl substances (PFAS) in drinking water sources has significantly increased due to their recognition as a major public health concern. This information has been utilized to assess the importance of potential explanatory variables in determining the presence and concentration of PFAS in different regions. Nevertheless, the significance of these variables and the reliability of the methods in regions beyond where they were initially tested is still uncertain. Hence, our research pursues two main objectives: 1) to evaluate the validity of the aforementioned variables and methods for several PFAS species in a different area and 2) to build on existing modeling work; a new PFAS predictive model is introduced which is more reliable in determining the presence and concentration of PFAS at a regional level. To achieve these goals, we reconstructed four state-of-the-art models using a statewide dataset available for Michigan. These models involve spatial regression techniques, classification and regression random forest algorithms, and boosted regression trees. They also include numerous explanatory variables, such as features of local soil and hydrology and the number of nearby contamination sources. Then, we use a Bayesian selection approach to find the most relevant among these variables. Finally, we employ the most relevant covariates to assess PFAS occurrence and estimate their concentration using a novel combination of machine learning algorithms and conditional autoregressive (CAR) modeling. In the first case, PFAS occurrence was assessed with an accuracy comparable to the reconstructed models (>90%) while using significantly fewer variables. In the second case, by maintaining low data requirements, the estimated concentrations of most PFAS compounds were more closely aligned with available observations compared to previous methods, with correlation coefficients ρ > 0.90 and R2 > 0.77.

Drivers of Solidago species invasion in Central Europe-Case study in the landscape of the Carpathian Mountains and their foreground

AIM

The invasion process is a complex, context-dependent phenomenon; nevertheless, it can be described using the PAB framework. This framework encompasses the joint effect of propagule pressure (P), abiotic characteristics of the environment (A), and biotic characteristics of both the invader and recipient vegetation (B). We analyzed the effectiveness of proxies of PAB factors to explain the spatial pattern of Solidago canadensis and S. gigantea invasion using invasive species distribution models.

LOCATION

Carpathian Mountains and their foreground, Central Europe.

METHODS

The data on species presence or absence were from an atlas of neophyte distribution based on a 2 × 2 km grid, covering approximately 31,200 km2 (7,752 grid cells). Proxies of PAB factors, along with data on historical distribution of invaders, were used as explanatory variables in Boosted Regression Trees models to explain the distribution of invasive Solidago. The areas with potentially lower sampling effort were excluded from analysis based on a target species approach.

RESULTS

Proxies of the PAB factors helped to explain the distribution of both S. canadensis and S. gigantea. Distributions of both species were limited climatically because a mountain climate is not conducive to their growth; however, the S. canadensis distribution pattern was correlated with proxies of human pressure, whereas S. gigantea distribution was connected with environmental characteristics. The varied responses of species with regard to distance from their historical distribution sites indicated differences in their invasion drivers.

MAIN CONCLUSIONS

Proxies of PAB are helpful in the choice of explanatory variables as well as the ecological interpretation of species distribution models. The results underline that human activity can cause variation in the invasion of ecologically similar species.

Understanding multiple stressors in a Mediterranean basin: Combined effects of land use, water scarcity and nutrient enrichment

River basins are extremely complex hierarchical and directional systems that are affected by a multitude of interacting stressors. This complexity hampers effective management and conservation planning to be effectively implemented, especially under climate change. The objective of this work is to provide a wide scale approach to basin management by interpreting the effect of isolated and interacting factors in several biotic elements (fish, macroinvertebrates, phytobenthos and macrophytes). For that, a case study in the Sorraia basin (Central Portugal), a Mediterranean system mainly facing water scarcity and diffuse pollution problems, was chosen. To develop the proposed framework, a combination of process-based modelling to simulate hydrological and nutrient enrichment stressors and empirical modelling to relate these stressors - along with land use and natural background - with biotic indicators, was applied. Biotic indicators based on ecological quality ratios from WFD biomonitoring data were used as response variables. Temperature, river slope, % of agriculture in the upstream catchment and total N were the variables more frequently ranked as the most relevant. Both the two significant interactions found between single hydrological and nutrient enrichment stressors indicated antagonistic effects. This study demonstrates the potentialities of coupling process-based modelling with empirical modelling within a single framework, allowing relationships among different ecosystem states to be hierarchized, interpreted and predicted at multiple spatial and temporal scales. It also demonstrates how isolated and interacting stressors can have a different impact on biotic quality. When performing conservation or management plans, the stressor hierarchy should be considered as a way of prioritizing actions in a cost-effective perspective.

Additive effects prevail: The response of biota to multiple stressors in an intensively monitored watershed

Freshwater ecosystems are impacted by a range of stressors arising from diverse human-caused land and water uses. Identifying the relative importance of single stressors and understanding how multiple stressors interact and jointly affect biology is crucial for River Basin Management. This study addressed multiple human-induced stressors and their effects on the aquatic flora and fauna based on data from standard WFD monitoring schemes. For altogether 1095 sites within a mountainous catchment, we used 12 stressor variables covering three different stressor groups: riparian land use, physical habitat quality and nutrient enrichment. Twenty-one biological metrics calculated from taxa lists of three organism groups (fish, benthic invertebrates and aquatic macrophytes) served as response variables. Stressor and response variables were subjected to Boosted Regression Tree (BRT) analysis to identify stressor hierarchy and stressor interactions and subsequently to Generalised Linear Regression Modelling (GLM) to quantify the stressors standardised effect size. Our results show that riverine habitat degradation was the dominant stressor group for the river fauna, notably the bed physical habitat structure. Overall, the explained variation in benthic invertebrate metrics was higher than it was in fish and macrophyte metrics. In particular, general integrative (aggregate) metrics such as % Ephemeroptera, Plecoptera and Trichoptera (EPT) taxa performed better than ecological traits (e.g. % feeding types). Overall, additive stressor effects dominated, while significant and meaningful stressor interactions were generally rare and weak. We concluded that given the type of stressor and ecological response variables addressed in this study, river basin managers do not need to bother much about complex stressor interactions, but can focus on the prevailing stressors according to the hierarchy identified.

Dissolved organic carbon and its potential predictors in eutrophic lakes

Understanding of the true role of lakes in the global carbon cycle requires reliable estimates of dissolved organic carbon (DOC) and there is a strong need to develop remote sensing methods for mapping lake carbon content at larger regional and global scales. Part of DOC is optically inactive. Therefore, lake DOC content cannot be mapped directly. The objectives of the current study were to estimate the relationships of DOC and other water and environmental variables in order to find the best proxy for remote sensing mapping of lake DOC. The Boosted Regression Trees approach was used to clarify in which relative proportions different water and environmental variables determine DOC. In a studied large and shallow eutrophic lake the concentrations of DOC and coloured dissolved organic matter (CDOM) were rather high while the seasonal and interannual variability of DOC concentrations was small. The relationships between DOC and other water and environmental variables varied seasonally and interannually and it was challenging to find proxies for describing seasonal cycle of DOC. Chlorophyll a (Chl a), total suspended matter and Secchi depth were correlated with DOC and therefore are possible proxies for remote sensing of seasonal changes of DOC in ice free period, while for long term interannual changes transparency-related variables are relevant as DOC proxies. CDOM did not appear to be a good predictor of the seasonality of DOC concentration in Lake Võrtsjärv since the CDOM-DOC coupling varied seasonally. However, combining the data from Võrtsjärv with the published data from six other eutrophic lakes in the world showed that CDOM was the most powerful predictor of DOC and can be used in remote sensing of DOC concentrations in eutrophic lakes.

Mapping intra-urban malaria risk using high resolution satellite imagery: a case study of Dar es Salaam

Background

With more than half of Africa’s population expected to live in urban settlements by 2030, the burden of malaria among urban populations in Africa continues to rise with an increasing number of people at risk of infection. However, malaria intervention across Africa remains focused on rural, highly endemic communities with far fewer strategic policy directions for the control of malaria in rapidly growing African urban settlements. The complex and heterogeneous nature of urban malaria requires a better understanding of the spatial and temporal patterns of urban malaria risk in order to design effective urban malaria control programs. In this study, we use remotely sensed variables and other environmental covariates to examine the predictability of intra-urban variations of malaria infection risk across the rapidly growing city of Dar es Salaam, Tanzania between 2006 and 2014.

Methods

High resolution SPOT satellite imagery was used to identify urban environmental factors associated malaria prevalence in Dar es Salaam. Supervised classification with a random forest classifier was used to develop high resolution land cover classes that were combined with malaria parasite prevalence data to identify environmental factors that influence localized heterogeneity of malaria transmission and develop a high resolution predictive malaria risk map of Dar es Salaam.

Results

Results indicate that the risk of malaria infection varied across the city. The risk of infection increased away from the city centre with lower parasite prevalence predicted in administrative units in the city centre compared to administrative units in the peri-urban suburbs. The variation in malaria risk within Dar es Salaam was shown to be influenced by varying environmental factors. Higher malaria risks were associated with proximity to dense vegetation, inland water and wet/swampy areas while lower risk of infection was predicted in densely built-up areas.

Conclusions

The predictive maps produced can serve as valuable resources for municipal councils aiming to shrink the extents of malaria across cities, target resources for vector control or intensify mosquito and disease surveillance. The semi-automated modelling process developed can be replicated in other urban areas to identify factors that influence heterogeneity in malaria risk patterns and detect vulnerable zones. There is a definite need to expand research into the unique epidemiology of malaria transmission in urban areas for focal elimination and sustained control agendas.

Electronic supplementary material

The online version of this article (doi:10.1186/s12942-016-0051-y) contains supplementary material, which is available to authorized users.

Human and biophysical drivers of fires in Semiarid Chaco mountains of Central Argentina

Fires are a recurrent disturbance in Semiarid Chaco mountains of central Argentina. The interaction of multiple factors generates variable patterns of fire occurrence in space and time. Understanding the dominant fire drivers at different spatial scales is a fundamental goal to minimize the negative impacts of fires. Our aim was to identify the biophysical and human drivers of fires in the Semiarid Chaco mountains of Central Argentina and their individual effects on fire activity, in order to determine the thresholds and/or ranges of the drivers at which fire occurrence is favored or disfavored. We used fire frequency as the response variable and a set of 28 potential predictor variables, which included climatic, human, topographic, biological and hydrological factors. Data were analyzed using Boosted Regression Trees, using data from near 10,500 sampling points. Our model identified the fire drivers accurately (75.6% of deviance explained). Although humans are responsible for most ignitions, climatic variables, such as annual precipitation, annual potential evapotranspiration and temperature seasonality were the most important determiners of fire frequency, followed by human (population density and distance to waste disposals) and biological (NDVI) predictors. In general, fire activity was higher at intermediate levels of precipitation and primary productivity and in the proximity of urban solid waste disposals. Fires were also more prone to occur in areas with greater variability in temperature and productivity. Boosted Regression Trees proved to be a useful and accurate tool to determine fire controls and the ranges at which drivers favor fire activity. Our approach provides a valuable insight into the ecology of fires in our study area and in other landscapes with similar characteristics, and the results will be helpful to develop management policies and predict changes in fire activity in response to different climate changes and development scenarios.

Assessment of land use factors associated with dengue cases in Malaysia using Boosted Regression Trees

The transmission of dengue disease is influenced by complex interactions among vector, host and virus. Land use such as water bodies or certain agricultural practices have been identified as likely risk factors for dengue because of the provision of suitable habitats for the vector. Many studies have focused on the land use factors of dengue vector abundance in small areas but have not yet studied the relationship between land use factors and dengue cases for large regions. This study aims to clarify if land use factors other than human settlements, e.g. different types of agricultural land use, water bodies and forest are associated with reported dengue cases from 2008 to 2010 in the state of Selangor, Malaysia. From the correlative relationship, we aim to generate a prediction risk map. We used Boosted Regression Trees (BRT) to account for nonlinearities and interactions between the factors with high predictive accuracies. Our model with a cross-validated performance score (Area Under the Receiver Operator Characteristic Curve, ROC AUC) of 0.81 showed that the most important land use factors are human settlements (model importance of 39.2%), followed by water bodies (16.1%), mixed horticulture (8.7%), open land (7.5%) and neglected grassland (6.7%). A risk map after 100 model runs with a cross-validated ROC AUC mean of 0.81 (±0.001 s.d.) is presented. Our findings may be an important asset for improving surveillance and control interventions for dengue.