Monitoring Stations Research Articles

Feasibility of low-cost particulate matter sensors for long-term environmental monitoring: Field evaluation and calibration

Low-cost sensor networks offer the potential to reduce monitoring costs while providing high-resolution spatiotemporal data on pollutant levels. However, these sensors come with limitations, and many aspects of their field performance remain underexplored. During October to December 2023, this study deployed two identical low-cost sensor systems near an urban standard monitoring station to record PM2.5 and PM10 concentrations, along with environmental temperature and humidity. Our evaluation of the monitoring performance of these sensors revealed a broad data distribution with a systematic overestimation; this overestimation was more pronounced in PM10 readings. The sensors showed good consistency (R2 > 0.9, NRMSE<5 %), and normalization residuals were tracked to assess stability, which, despite occasional environmental influences, remained generally stable. A lateral comparison of four calibration models (MLR, SVR, RF, XGBoost) demonstrated superior performance of RF and XGBoost over others, particularly with RF showing enhanced effectiveness on the test set. SHAP analysis identified sensor readings as the most critical variable, underscoring their pivotal role in predictive modeling. Relative humidity consistently proved more significant than dew point and temperature, with higher RH levels typically having a positive impact on model outputs. The study indicates that, with appropriate calibration, sensors can supplement the sparse networks of regulatory-grade instruments, enabling dense neighborhood-scale monitoring and a better understanding of temporal air quality trends.

Spatiotemporal characteristics and potential pollution factors of water quality in the eastern route of the South-to-North Water Diversion Project in China

The eastern route of the South-to-North Water Diversion Project (SNWDP-ER) in China, an open-channel cross-basin project, is exposed to multiple potential pollution sources and has changing hydrodynamic conditions between diversion and non-diversion periods. After a decade of pollution control efforts, it is imperative to perceive the water quality conditions and identify potential pollution factors for further maintenance. Combining the water quality identification index (WQII) and multivariate statistical techniques (MSTs), this study conducted an exploratory analysis in SNWDP-ER, based on the monitoring data in 2020–2021. The monitoring and WQII results show that the overall water quality in SNWDP-ER is satisfactory and meets the Class III requirements in China Environmental Quality Standard for Surface Water, except for total nitrogen (TN) which falls into Class V or even worse. The periodic alternations of flow direction between diversion and non-diversion periods tend to deteriorate water quality, particularly in the initial stage of alternations. Water quality appears more stable during the diversion period, and impounding lakes along the project are crucial to water environment restoration in this period. Due to the positive influence brought by the self-purification and environmental carrying in the Luoma Lake, Nansi Lake, and Dongping Lake, water quality exhibits an enhancing trend from upstream to downstream in the Lianghu and Jiaodong reaches. Besides, the monitoring stations in Shandong reach exhibit significant divisional heterogeneity under hierarchical cluster analysis (HCA) with the Nansi Lake and Dongping Lake as the demarcation points, and the results of factor analysis/principal component analysis (FA/PCA) further identified the contributions of mining, shipping, and land use to each section. Based on the findings, the study recommends implementing wastewater discharge control, abandoned mines remediation, and continued water quality monitoring for further maintenance and improvement in SNWDP-ER.

Feeding en route: Prey availability and traits influence prey selection by an avian predator on migration.

During animal migration, ephemeral communities of taxa at all trophic levels co-occur over space and time. The interactions between predators and prey along migration corridors are ecologically and evolutionarily significant. However, these interactions remain understudied in terrestrial systems and warrant further investigations using novel approaches. We investigated the predator-prey interactions between a migrating avivorous predator and ephemeral avian prey community in the fall migration season. We tested for associations between avian traits and prey selection and hypothesized that prey traits (i.e. relative size, flocking behaviour, habitat, migration tendency and availability) would influence prey selection by a sexually dimorphic raptor on migration. To document prey consumption, we sampled trace prey DNA from beaks and talons of migrating sharp-shinned hawks Accipiter striatus (n = 588). We determined prey availability in the ephemeral avian community by extracting weekly abundance indices from eBird Status and Trends data. We used discrete choice models to assess prey selection and visualized the frequency of prey in diet and availability on the landscape over the fall migration season. Using eDNA metabarcoding, we detected prey species on 94.1% of the hawks sampled (n = 525/588) comprising 1396 prey species detections from 65 prey species. Prey frequency in diet and eBird relative abundance of prey species were correlated over the migration season for top-selected prey species, suggesting prey availability is an important component of raptor-songbird interactions during fall. Prey size, flocking behaviour and non-breeding habitat association were prey traits that significantly influenced predator choice. We found differences between female and male hawk prey selection, suggesting that sexual size dimorphism has led to distinct foraging strategies on migration. This research integrated field data collected by a volunteer-powered raptor migration monitoring station and public-generated data from eBird to reveal elusive predator-prey dynamics occurring in an ephemeral raptor-songbird community during fall migration. Understanding dynamic raptor-songbird interactions along migration routes remains a relatively unexplored frontier in animal ecology and is necessary for the conservation and management efforts of migratory and resident communities.

Spatio-Temporal Dynamics Coupling between Land Use/Cover Change and Water Quality in Dongjiang Lake Watershed Using Satellite Remote Sensing

With rapid social and economic development, land use/land cover change (LUCC) has intensified with serious impacts on water quality in the watershed. In this study, we took Dongjiang Lake watershed as the study area and obtained measured data on water quality parameters from the watershed’s water quality monitoring stations. Based on Landsat-5, Landsat-8, or Sentinel-2 remote sensing data for multiple periods per year between 1992 and 2022, the sensitive satellite bands or band combinations of each water quality parameter were determined. The Random Forest method was used to classify the land use types in the watershed into six categories, and the area proportion of each type was calculated. We established machine learning regression models and polynomial regression models with WQI as the dependent variable and the area proportion of each land use type as the independent variable. Accuracy test results showed that, among them, the quadratic cubic polynomial regression model with grassland, forest land, construction land, and unused land as its independent variables was the best model for coupling watershed water quality with LUCC. This study’s results provide a scientific basis for monitoring spatial and temporal changes in water quality caused by LUCC in the Dongjiang Lake watershed.

Tidal variation and flow dynamics in Indian Sundarban based on field observation and numerical models

Sundarban is made up of a mesh of macrotidal estuarine systems that contain the world's largest mangroves, the eco-geomorphic environment of which is heavily influenced by tidal behaviour and flow characteristics. The present study aimed to identify the tidal attributes and document the flow character of Indian Sundarban throughout a fortnightly tidal cycle. As such, seven tide monitoring stations were installed across the Saptamukhi-Hariabhanga and connected river system to monitor tidal water levels. Based on this data, the Institute of Ocean Sciences' (IOS) method was used to extract 17-major tidal harmonic components to study the links between various oceanic and shallow-water tidal constituents that may contribute to tidal asymmetry. A Mike-21-flow model was also run to simulate the tidal flow of the regime. Result shows that the progression and convergence of tidal waves in the funnel-shaped estuarine system causes notable lag between the southern and northern limits (avg. 45 min over a 52 km-long stretch of Thakuran estuary). The harmonic components indicate that semi-diurnal M2, S2; over tide 2MS6; and diurnal O1, K1 are the major forces, shaping the tidal amplitude in this region. The third, fourth, and sixth diurnal constituents represent additional tidal components, although with relatively minor amplitudes. The transition from coastal to headward stations sees a shift in the relative phase from 93.73° to 96.17°, suggesting a flood dominance and minimal dissipation of energy within the system. Mike-21FM found that the spring flood-current speed with an average value of 0.42–0.56 m/s is significantly higher than the ebb current of averaging 0.25–0.32 m/s. The nature of neap tidal current speed in the Sundarban region generally mirrors that of the spring condition, although with much less intensity. We assume that since any bi-weekly hydraulic analysis has not yet been conducted in Sundarban, it may be highly valuable for future studies.

Multivariate Hydrological Modeling Based on Long Short-Term Memory Networks for Water Level Forecasting

In the Department of Chocó, flooding poses a recurrent and significant challenge due to heavy rainfall and the dense network of rivers characterizing the region. However, the lack of adequate infrastructure to prevent and predict floods exacerbates this situation. The absence of early warning systems, the scarcity of meteorological and hydrological monitoring stations, and deficiencies in urban planning contribute to the vulnerability of communities to these phenomena. It is imperative to invest in flood prediction and prevention infrastructure, including advanced monitoring systems, the development of hydrological prediction models, and the construction of hydraulic infrastructure, to reduce risk and protect vulnerable communities in Chocó. Additionally, raising public awareness of the associated risks and encouraging the adoption of mitigation and preparedness measures throughout the population are essential. This study introduces a novel approach for the multivariate prediction of hydrological variables, specifically focusing on water level forecasts for two hydrological stations along the Atrato River in Colombia. The model, utilizing a specialized type of recurrent neural network (RNN) called the long short-term memory (LSTM) network, integrates data from hydrological variables, such as the flow, precipitation, and level. With a model architecture featuring four inputs and two outputs, where flow and precipitation serve as inputs and the level serves as the output for each station, the LSTM model is adept at capturing the complex dynamics and cross-correlations among these variables. Validation involves comparing the LSTM model’s performance with linear and nonlinear Autoregressive with Exogenous Input (NARX) models, considering factors such as the estimation error and computational time. Furthermore, this study explores different scenarios for water level prediction, aiming to utilize the proposed approach as an effective flood early warning system.

Assessing the accuracy of MUR high resolution satellite sea surface temperature data

Marine bivalve mollusks are valuable proxies for El Niño-Southern Oscillation (ENSO) events in paleoclimate research, reflecting changes in sea surface temperature (SST) and other ambient marine conditions in their shell chemistry. However, the use of several Peruvian species of bivalves as paleoENSO proxies needs to be established by comparing their shells’ δ18O-derived SST records with instrumental SST measurements. Along the Peruvian coast, the limited spatial coverage of in-situ coastal monitoring stations hinders such comparisons. This study aims to assess the accuracy of the Multi-scale Ultra-high Resolution (MUR) satellite-derived SST dataset as an alternative source of SST information for paleoENSO studies in Peru, which could advance research on Peruvian bivalve species. This study compares MUR satellite data with in-situ measured SST from ten coastal monitoring stations operated by the Instituto del Mar del Perú (IMARPE). The correlation between the datasets is evaluated, and systematic and non-systematic variations are examined. Environmental factors that may impact the accuracy of satellite SST measurements, such as clouds and aerosols, are also investigated. Results show that MUR data generally correlate with overall trends in SST variability captured by in-situ measurements. However, there are regional variations in the accuracy of MUR data, with some areas showing consistent temperature offsets. Differences in measurement methodologies and environmental variables are explored as potential causes of variations between the datasets. Overall, the study demonstrates the potential of using high-resolution satellite SST data for assessing δ18O SST records from bivalves in locations without local SST records. It provides insights into the accuracy and limitations of using MUR data as an alternative source of SST information for paleoENSO studies in coastal Peru.

Fine particulate matter concentration prediction based on hybrid convolutional network with aggregated local and global spatiotemporal information: A case study in Beijing and Chongqing

Air pollution is a highly concerned environmental issue that have serious impacts on human health and the ecological environment. Accurate air quality prediction can help people effectively deal with the threats posed by air pollution. Most previous work mainly focused on temporal modeling of air quality data at monitoring stations. In recent years, a number of works have used graph convolution to model the spatial dependencies between neighboring sites to extract spatial features and combined temporal model to extract temporal features for predicting PM2.5. However, these models only considered the local spatial relationships of adjacent sites and ignored sites with longer geographic distance. Therefore, this study proposes a spatio-temporal hybrid model based on convolution and attention (named Attentive Graph Convolution and 1D Convolution Network, AGCC) to predict multi-site and multi-step PM2.5 concentration. This method not only models the local spatial relationships of adjacent sites, but also combines spatial attention and graph convolutional network (GCN) to model the global spatial relationships of sites. The local and global spatial features are obtained respectively and are fused to obtain spatial features with richer semantic information. Meanwhile, the temporal dependency relationship is modeled through a temporal module composed of temporal attention and one-dimensional convolutional neural network (Conv 1D). AGCC was compared to six baseline models at different prediction horizon based on the air quality datasets of Beijing and Chongqing. The experimental results demonstrate that the model proposed by our achieves the best performance and verifies the feasibility of the model.

Exploring groundwater depletion and land subsidence dynamics in Taiwan’s Choushui river alluvial fan: insights from integrated GNSS and hydrogeological data analysis

The Choushui River Alluvial Fan (CRAF) is a major agricultural area in Taiwan with heavy groundwater usage. The extraction of groundwater here has caused land subsidence, which is now a significant global environmental issue. This study analyzes land subsidence in the CRAF by integrating hydrogeological data from 233 groundwater monitoring stations across four aquifers (CRAF Groundwater_NET) and data from 50 continuous GNSS stations (CRAF GNSS_NET). We developed an automated processing flow for GNSS static surveying within CRAF GNSS_NET, and further employed a time-series fitting method to examine the long-term trends and annual changes for both GNSS and groundwater level data. Our analysis of the time-series data from the past decade identifies areas of significant groundwater level depletion and subsidence hotspots. We explore the relationship between groundwater level variations and surface displacements within CRAF, utilizing GNSS data to analyze horizontal and vertical displacement trends, as well as annual changes. We integrate these findings with hydrogeological data to understand regional subsidence patterns. Our results indicate that CRAF is characterized by distinct hydrogeological features. The study finds that the amplitudes of annual changes in both groundwater level and vertical displacement generally increase from northeast to southwest in the analyzed region. One particular area shows significant groundwater level decline, with the most severe rate recorded at 0.54 m/year. Similarly, GNSS analysis indicates pronounced subsidence trends in the same area, with rates ranging from 4.2 to 5.2 cm/year. These findings highlight the critical need for the development of effective groundwater management strategies to ensure sustainable use of groundwater resources and to implement mitigation measures against land subsidence in similar multiple-aquifer settings.

Air pollution and survival in patients with malignant mesothelioma and asbestos-related lung cancer: a follow-up study of 1591 patients in South Korea

BackgroundDespite significant advancements in treatments such as surgery, radiotherapy, and chemotherapy, the survival rate for patients with asbestos-related cancers remains low. Numerous studies have provided evidence suggesting that air pollution induces oxidative stress and inflammation, affecting acute respiratory diseases, lung cancer, and overall mortality. However, because of the high case fatality rate, there is limited knowledge regarding the effects of air pollution exposures on survival following a diagnosis of asbestos-related cancers. This study aimed to determine the effect of air pollution on the survival of patients with malignant mesothelioma and asbestos-related lung cancer.MethodsWe followed up with 593 patients with malignant mesothelioma and 998 patients with lung cancer identified as asbestos victims between 2009 and 2022. Data on five air pollutants—sulfur dioxide, carbon monoxide, nitrogen dioxide, fine particulate matter with a diameter < 10 μm, and fine particulate matter with a diameter < 2.5 μm—were obtained from nationwide atmospheric monitoring stations. Cox proportional hazard models were used to estimate the association of cumulative air pollutant exposure with patient mortality, while adjusting for potential confounders. Quantile-based g-computation was used to assess the combined effect of the air pollutant mixture on mortality.ResultsThe 1-, 3-, and 5-year survival rates for both cancer types decreased with increasing exposure to all air pollutants. The estimated hazard ratios rose significantly with a 1-standard deviation increase in each pollutant exposure level. A quartile increase in the pollutant mixture was associated with a 1.99-fold increase in the risk of malignant mesothelioma-related mortality (95% confidence interval: 1.62, 2.44). For lung cancer, a quartile increase in the pollutant mixture triggered a 1.87-fold increase in the mortality risk (95% confidence interval: 1.53, 2.30).ConclusionThese findings support the hypothesis that air pollution exposure after an asbestos-related cancer diagnosis can negatively affect patient survival.

Investigation of the relationship between air pollution and gestational diabetes

Background Gestational diabetes mellitus (GDM) can have negative effects on both the pregnancy and perinatal outcomes, as well as the long-term health of the mother and the child. It has been suggested that exposure to air pollution may increase the risk of developing GDM. This study investigated the relationship between exposure to air pollutants with gestational diabetes. Methods The present study is a retrospective cohort study. We used data from a randomised community trial conducted between September 2016 and January 2019 in Iran. During this period, data on air pollutant levels of five cities investigated in the original study, including 6090 pregnant women, were available. Concentrations of ozone (O3), nitric oxide (NO), nitrogen dioxide (NO2), nitrogen oxides (NOx), sulphur dioxide (SO2), carbon monoxide (CO), particulate matter < 2.5 (PM2.5) or <10 μm (PM10) were obtained from air pollution monitoring stations. Exposure to air pollutants during the three months preceding pregnancy and the first, second and third trimesters of pregnancy for each participant was estimated. The odds ratio was calculated based on logistic regression in three adjusted models considering different confounders. Only results that had a p < .05 were considered statistically significant. Results None of the logistic regression models showed any statistically significant relationship between the exposure to any of the pollutants and GDM at different time points (before pregnancy, in the first, second and third trimesters of pregnancy and 12 months in total) (p > .05). Also, none of the adjusted logistic regression models showed any significant association between PM10 exposure and GDM risk at all different time points after adjusting for various confounders (p > .05). Conclusions This study found no association between GDM risk and exposure to various air pollutants before and during the different trimesters of pregnancy. This result should be interpreted cautiously due to the lack of considering all of the potential confounders.

Airborne particle radioactivity during desert dust days in Cyprus

Mediterranean countries are often affected by desert dust storms, which have significant effects on the environment and public health. We compared airborne particle radioactivity levels during desert dust and non-dust days in Cyprus.Gross α- and β-radioactivity from Total Suspended Particle (TSP) samples, collected at two urban routine monitoring stations in Limassol and Nicosia, were available for the period 2017–2020 and 2008–2020, respectively. Radionuclides 137Cs and 40K, from TSP samples, were also available from a semi-industrial monitoring station in Nicosia during 2008–2020. Information on desert dust presence, dust origin, particulate matter (PM) levels, and solar activity (KP index and solar sunspot numbers - SSN) were also obtained. We used linear regression models adjusting for seasonality and long-term trends, and solar activity to assess the effect of dust storms on TSP gross α- and β-, and 137Cs and 40K radioactivity levels.Gross α- and β-radioactivity, and 137Cs and 40K radioactivity levels were significantly higher on days with desert dust compared to days characterized with no influence of desert dust. Levels of gross α- and β-radioactivity during dust days were higher when dust originated from the Middle East deserts than from the Sahara Desert. The same trend was observed for the ratios 137Cs to 40K and 137Cs to PM10. Conversely, ratios of TSP gross α- and β-radioactivity to PM10 were significantly lower during desert dust days in comparison to days without dust influence.This study suggests that desert dust increase both TSP gross α- and β-radioactivity, as well as 137Cs and 40K radioactivity levels. Further studies should clarify the contribution of anthropogenic and other natural sources to the emission or transportation of particles radioactivity, to better mitigate future exposures.

Spatial Variation of Airborne Pollen Concentrations Locally around Brussels City, Belgium, during a Field Campaign in 2022–2023, Using the Automatic Sensor Beenose

As a growing part of the world population is suffering from pollen-induced allergies, increasing the number of pollen monitoring stations and developing new dedicated measurement networks has become a necessity. To this purpose, Beenose, a new automatic and relatively low-cost sensor, was developed to characterize and quantify the pollinic content of the air using multiangle light scattering. A field campaign was conducted at four locations around Brussels, Belgium, during summer 2022 and winter–spring 2023. First, the consistency was assessed between the automatic sensor and a collocated reference Hirst-type trap deployed at Ixelles, south-east of Brussels. Daily average total pollen concentrations provided by the two instruments showed a mean error of about 15%. Daily average pollen concentrations were also checked for a selection of pollen species and revealed Pearson and Spearman correlation coefficients ranging from 0.71 to 0.93. Subsequently, a study on the spatial variability of the pollen content around Brussels was conducted with Beenose sensors. The temporal evolution of daily average total pollen concentrations recorded at four sites were compared and showed strong variations from one location to another, up to a factor 10 over no more than a few kilometers apart. This variation is a consequence of multiple factors such as the local vegetation, the wind directions, the altitude of the measurement station, and the topology of the city. It is therefore highly necessary to multiply the number of measurement stations per city for a better evaluation of human exposure to pollen allergens and for more enhanced pollen allergy management.

Decomposing PM2.5 concentrations in urban environments into meaningful factors 2. Extracting the contribution of traffic-related exhaust emissions

Vehicle-emitted fine particulate matter (PM2.5) has been associated with significant health outcomes and environmental risks. This study estimates the contribution of traffic-related exhaust emissions (TREE) to observed PM2.5 using a novel factorization framework. Specifically, co-measured nitrogen oxides (NOx) concentrations served as a marker of vehicle-tailpipe emissions and were integrated into the optimization of a Non-negative Matrix Factorization (NMF) analysis to guide the factor extraction. The novel TREE-NMF approach was applied to long-term (2012–2019) PM2.5 observations from air quality monitoring (AQM) stations in two urban areas. The extracted TREE factor was evaluated against co-measured black carbon (BC) and PM2.5 species to which the TREE-NMF optimization was blind. The contribution of the TREE factor to the observed PM2.5 concentrations at an AQM station from the first location showed close agreement (R2=0.79) with monitored BC data. In the second location, a comparison of the extracted TREE factor with measurements at a nearby Surface PARTiculate mAtter Network (SPARTAN) station revealed moderate correlations with PM2.5 species commonly associated with fuel combustion, and a good linear regression fit with measured equivalent BC concentrations. The estimated concentrations of the TREE factor at the second location accounted for 7–11 % of the observed PM2.5 in the AQM stations. Moreover, analysis of specific days known to be characterized by little traffic emissions suggested that approximately 60–78 % of the traffic-related PM2.5 concentrations could be attributed to particulate traffic-exhaust emissions. The methodology applied in this study holds great potential in areas with limited monitoring of PM2.5 speciation, in particular BC, and its results could be valuable for both future environmental health research, regional radiative forcing estimates, and promulgation of tailored regulations for traffic-related air pollution abatement.

Factor-Augmented Autoregressive Neural Network to forecast NOx in the city of Madrid

Air pollution poses a significant threat to public health and the environment in urban areas worldwide. In the context of urban air quality, nitrogen oxides (NOx), comprising nitrogen dioxide (NO2) and nitric oxide (NO), stand out as key pollutants with well-documented adverse effects. The city of Madrid, as the capital and largest urban center of Spain and the third largest of Europe, is no exception to the challenges posed by NOx pollution. Most of the recent literature on forecasting air pollution, and specifically on NOx, is based on the use of Neural Networks (NN). Little is known about the forecasting ability of factor models in this context. The main aim of this paper is to use Factor-Augmented Autoregressive Neural Networks (FA-ARNN-X) to predict future patterns of NOx pollutants in the territorial monitoring stations of Madrid, using lagged NOx values, meteorological variables and latent factors. The main results indicate that the proposed forecasting model provides statistically more accurate predictions of air pollution than its competing benchmarks and should be used by policymakers for more accurate air pollution monitoring.

Optimizing NO2 monitoring network using a background map for spatial heterogeneity stratification

Nitrogen dioxide (NO2) is a critical air pollutant affecting health and the environment. However, existing monitoring station networks often fail to adequately capture the regional distribution of NO2, indicating a need for enhanced sampling strategies. This study focuses on Southwest Fujian in China and introduces a high-precision NO2 background map to delineate spatial stratified heterogeneity. Subsequently, the Mean of Surface with Non-homogeneity (MSN) method was employed to optimize the design of the NO2 monitoring network, proposing the addition of 125 new stations. The Bayesian Kriging analysis, utilized to evaluate the optimized network, resulted in a coefficient of determination (R2) of 0.87, a mean absolute percentage error (MAPE) of 9.61%, a mean absolute error (MAE) of 2.75 μg/m3 and a root mean square error (RMSE) of 2.47 μg/m3. The improved accuracy and efficiency of the NO2 monitoring network were validated against the background map. This research underscores the effectiveness of integrating precise mapping techniques with strategic network optimization for superior environmental monitoring outcomes.

Assessing groundwater quality and its association with child undernutrition in India

Background and objectivesGroundwater contamination poses a significant health challenge in India, particularly impacting children. Despite its importance, limited research has explored the nexus between groundwater quality and child nutrition outcomes. This study addresses this gap, examining the association between groundwater quality and child undernutrition, offering pertinent insights for policymakers. Data and methodsThe study uses data from the fifth round of the National Family Health Survey (NFHS) and the Central Groundwater Board (CGWB) to analyze the association between groundwater quality and child nutritional status. The groundwater quality data were collected by nationwide monitoring stations programmed by CGWB, and the child undernutrition data were obtained from the NFHS-5, 2019–21. The analysis included descriptive and logistic regression model. The study also considers various demographic and socio-economic factors as potential moderators of the relationship between groundwater quality and child undernutrition. FindingsSignificant variation in groundwater quality was observed across India, with numerous regions displaying poor performance. Approximately 26.53 % of geographical areas were deemed unfit for consuming groundwater. Environmental factors such as high temperatures, low precipitation, and arid, alluvial, laterite-type soils are linked to poorer groundwater quality. Unfit-for-consumption groundwater quality increased the odds of undernutrition, revealing a 35 %, 38 %, and 11 % higher likelihood of stunting, underweight, and wasting in children, with higher pH, Magnesium, Sulphate, Nitrate, Total Dissolved Solids, and Arsenic, levels associated with increased odds of stunting, underweight, and wasting. Higher temperatures (>25 °C), high elevations (>1000 m), and proximity to cultivated or industrial areas all contribute to heightened risks of child undernutrition. Children consuming groundwater, lacking access to improved toilets, or living in rural areas are more likely to be undernourished, while females, higher-income households, and those consuming dairy, vegetables, and fruits daily exhibit lower odds of undernutrition. Policy implicationsPolicy implications highlight the urgent need for investment in piped water supply systems. Additionally, focused efforts are required to monitor and improve groundwater quality in regions with poor water quality. Policies should emphasize safe sanitation practices and enhance public awareness about the critical role of safe drinking water in improving child health.

Max-convolution processes with random shape indicator kernels

In this paper, we introduce a new class of models for spatial data obtained from max-convolution processes based on indicator kernels with random shape. We show that these models have appealing dependence properties including tail dependence at short distances and independence at long distances. We further consider max-convolutions between such processes and processes with tail independence, in order to separately control the bulk and tail dependence behaviors, and to increase flexibility of the model at longer distances, in particular, to capture intermediate tail dependence. We show how parameters can be estimated using a weighted pairwise likelihood approach, and we conduct an extensive simulation study to show that the proposed inference approach is feasible in relatively high dimensions and it yields accurate parameter estimates in most cases. We apply the proposed methodology to analyze daily temperature maxima measured at 100 monitoring stations in the state of Oklahoma, US. Our results indicate that our proposed model provides a good fit to the data, and that it captures both the bulk and the tail dependence structures accurately.

Historic water quality patterns in endemic schistosomiasis regions of southern Africa: Implications for disease vector distribution

Alterations in atmospheric temperature and rainfall have a major influence on the distribution patterns of vector-borne diseases, including schistosomiasis, by changing the optimum habitat of the intermediate hosts (vectors) that transmit these diseases. Water quality degradation and the creation of artificial freshwater habitats may widen or restrict the distribution of disease vectors. Understanding the influence of water quality on ecosystems is essential in determining the environments that vectors thrive in and where potential outbreaks of the disease they carry will occur. This study aims to analyse the changes in various water quality variables between 1977 and 2009 in the Mbombela river systems and how this may have altered the presence of schistosomiasis snail vectors in these areas. Historical water quality data for the Sabie, Kaap, Komati and Crocodile rivers were obtained from the Department of Water and Sanitation monitoring stations and separated into three decades (1977–1987; 1988–1998 and 1999–2009). The Physicochemical Driver Assessment Index (PAI) was used to determine the ecological category of the rivers in each decade. A Principal Component Analysis was also created for the different river systems to identify possible changes in water quality over the 3 decades. According to the PAI, the Sabie River remained in a relatively good state while the Crocodile, Komati and Kaap rivers had a low water quality to begin with and became further degraded over the three decades. The PCAs indicated the Sabie and Crocodile rivers had increased levels of salts, nutrients and pH between 1988-1998 and 1999–2009 while the Kaap River had high salt content and pH between 1977 and 1998 and high nutrients during the decade of 1999–2009. These results indicate that there has been a change in the water quality of Mbombela river systems with notable increases in salt, pH and nutrient levels. As a result, the aquatic biota have likely been negatively affected and this poses severe impacts on the presence and abundance of schistosomiasis vectors as the snails are sensitive to changes in salinity, nutrients and pH.

Improving river water quality prediction with hybrid machine learning and temporal analysis

River systems provide multiple ecosystem services to society globally, but these are already degraded or threatened in many areas of the world due to water quality issues linked to diffuse and point-source pollutant inputs. Water quality evaluation is essential to develop remediation and management strategies. Computational tools such as machine learning based predictive models have been developed to improve monitoring network capabilities. The model's performance is reduced when datasets composed of reductant information are used for training, on the other hand, the selection of most representative and variable water quality scenarios could result in higher precision. This study analyzed historical water quality behavior in the Santiago River, Mexico, to identify the most variable and representative data available to train machine learning models (Adaptive Neuro Fuzzy Inference System – ANFIS, Artificial Neural Network – ANN, and Support Vector Machine - SVM). Thirteen monitoring sites were clustered according to their water quality variability from 2009 to 2022. Subsequently, a Time Series Analysis (TSA) was used to select the most representative monitoring station from each cluster. Data for 6/13 monitoring sites were retained for the Best Training Subset (BTS) used to train restricted models that performed with similar (ANN and SMV) or higher (ANFIS) prediction accuracy (in terms of RMSE, MAE, MSE and R2) for both training and testing. This study provides evidence of water quality data containing redundant information that is not useful to improve machine learning model performance, in turn leading to overtraining. Combined analytical approaches can maximize the representativeness and variability of data selected for machine learning applications, leading to improved prediction.