Wastewater Discharge Transports Riverine Microplastics over Long Distances.

Wastewater discharge from wastewater treatment plants continuously pumps microplastics into rivers, yet their transport distances within these waterways remain unknown. Herein, we developed a conceptual framework by synthesizing the microplastic data from the Yangtze River Basin to evaluate its transport distances, quantifying a significant spatial dependence between large-scale wastewater discharge and riverine microplastics (p < 0.05). The presence of microplastics at a specific sampling site could be attributed to wastewater discharge within a large-scale range spanning >1000 km upstream, encompassing a substantial portion equivalent to one-third of the Yangtze River Basin. The dominance analysis indicated that the contribution of wastewater discharge in rivers with higher discharge (>100 m3/s) to riverine microplastic pollution exceeded 65% within the Yangtze River Basin. The spatial dependence framework of riverine microplastics on wastewater discharge advances our prior understanding of the prevention and control of riverine microplastics by demonstrating that such pollution is not limited to nearby environmental factors.

Correlation of powers of Hüsler-Reiss vectors and Brown-Resnick fields, and application to insured wind losses.

Hüsler-Reiss vectors and Brown-Resnick fields are popular models in multivariate and spatial extreme-value theory, respectively, and are widely used in applications. We provide analytical formulas for the correlation between powers of the components of the bivariate Hüsler-Reiss vector, extend these to the case of the Brown-Resnick field, and thoroughly study the properties of the resulting dependence measure. The use of correlation is justified by spatial risk theory, while power transforms are insightful when taking correlation as dependence measure, and are moreover very suited damage functions for weather events such as wind extremes or floods. This makes our theoretical results worthwhile for, e.g., actuarial applications. We finally perform a case study involving insured losses from extreme wind speeds in Germany, and obtain valuable conclusions for the insurance industry.

Bayesian design for minimizing prediction uncertainty in bivariate spatial responses with applications to air quality monitoring.

Model-based geostatistical design involves the selection of locations to collect data to minimize an expected loss function over a set of all possible locations. The loss function is specified to reflect the aim of data collection, which, for geostatistical studies, could be to minimize the prediction uncertainty at unobserved locations. In this paper, we propose a new approach to design such studies via a loss function derived through considering the entropy about the model predictions and the parameters of the model. The approach includes a multivariate extension to generalized linear spatial models, and thus can be used to design experiments with more than one response. Unfortunately, evaluating our proposed loss function is computationally expensive so we provide an approximation such that our approach can be adopted to design realistically sized geostatistical studies. This is demonstrated through a simulated study and through designing an air quality monitoring program in Queensland, Australia. The results show that our designs remain highly efficient in achieving each experimental objective individually, providing an ideal compromise between the two objectives. Accordingly, we advocate that our approach could be adopted more generally in model-based geostatistical design.

Reclaiming independence in spatial-clustering datasets: A series of data-driven spatial weights matrices.

Most spatial models include a spatial weights matrix (W) derived from the first law of geography to adjust the spatial dependence to fulfill the independence assumption. In various fields such as epidemiological and environmental studies, the spatial dependence often shows clustering (or geographic discontinuity) due to natural or social factors. In such cases, adjustment using the first-law-of-geography-based W might be inappropriate and leads to inaccuracy estimations and loss of statistical power. In this work, we propose a series of data-driven Ws (DDWs) built following the spatial pattern identified by the scan statistic, which can be easily carried out using existing tools such as SaTScan software. The DDWs take both the clustering (or discontinuous) and the intuitive first-law-of-geographic-based spatial dependence into consideration. Aiming at two common purposes in epidemiology studies (ie, estimating the effect value of explanatory variable X and estimating the risk of each spatial unit in disease mapping), the common spatial autoregressive models and the Leroux-prior-based conditional autoregressive (CAR) models were selected to evaluate performance of DDWs, respectively. Both simulation and case studies show that our DDWs achieve considerably better performance than the classic W in datasets with clustering (or discontinuous) spatial dependence. Furthermore, the latest published density-based spatial clustering models, aiming at dealing with such clustering (or discontinuity) spatial dependence in disease mapping, were also compared as references. The DDWs, incorporated into the CAR models, still show considerable advantage, especially in the datasets for common diseases.

Cortical thickness analysis in temporal lobe epilepsy using fully Bayesian spectral method in magnetic resonance imaging.

BACKGROUND: Temporal lobe epilepsy (TLE) is the most common type of epilepsy associated with changes in the cerebral cortex throughout the brain. Magnetic resonance imaging (MRI) is widely used for detecting such anomalies; nevertheless, it produces spatially correlated data that cannot be considered by the usual statistical models. This study aimed to compare cortical thicknesses between patients with TLE and healthy controls by considering the spatial dependencies across different regions of the cerebral cortex in MRI. METHODS: In this study, T1-weighted MRI was performed on 20 healthy controls and 33 TLE patients. Nineteen patients had a left TLE and 14 had a right TLE. Cortical thickness was measured for all individuals in 68 regions of the cerebral cortex based on images. Fully Bayesian spectral method was utilized to compare the cortical thickness of different brain regions between groups. Neural networks model was used to classify the patients using the identified regions. RESULTS: For the left TLE patients, cortical thinning was observed in bilateral caudal anterior cingulate, lateral orbitofrontal (ipsilateral), the bilateral rostral anterior cingulate, frontal pole and temporal pole (ipsilateral), caudal middle frontal and rostral middle frontal (contralateral side). For the right TLE patients, cortical thinning was only observed in the entorhinal area (ipsilateral). The AUCs of the neural networks for classification of left and right TLE patients versus healthy controls were 0.939 and 1.000, respectively. CONCLUSION: Alteration of cortical gray matter thickness was evidenced as common effect of epileptogenicity, as manifested by the patients in this study using the fully Bayesian spectral method by taking into account the complex structure of the data.

Spatial variability of COVID-19 and its risk factors in Nigeria: A spatial regression method.

The novel and unprecedented Coronavirus disease (COVID-19) pandemic has negatively impacted most nations of the world within a short period. While its disproportionate social and spatial variability has been established, the reality in Nigeria is yet to be studied. In this paper, advanced spatial statistical techniques were engaged to study the burden of COVID-19 and its risk factors within the first quarter (March-May) of its incidence in Nigeria. The spatial autocorrelation (Moran's I) test reveals a significant but marginal cluster of COVID-19 occurrence in Nigeria (I = 0.11, p < 0.05). A model comparison between ordinary least square (OLS) and spatial error model (SER) was explored having checked for multicollinearity in the dataset. The OLS model explained about 64% (adjusted R2 = 0.64) of variation in COVID-19 cases, however with significantly clustered residuals. The SER model performed better with randomly distributed residuals. The significant predictors were population density, international airport, and literacy ratio. Furthermore, this study addressed the spatial planning implications of the ongoing disease outbreak while it advocates transdisciplinary approach to urban planning practices in Nigeria.

Spatial regression and spillover effects in cluster randomized trials with count outcomes.

This paper describes methodology for analyzing data from cluster randomized trials with count outcomes, taking indirect effects as well spatial effects into account. Indirect effects are modeled using a novel application of a measure of depth within the intervention arm. Both direct and indirect effects can be estimated accurately even when the proposed model is misspecified. We use spatial regression models with Gaussian random effects, where the individual outcomes have distributions overdispersed with respect to the Poisson, and the corresponding direct and indirect effects have a marginal interpretation. To avoid spatial confounding, we use orthogonal regression, in which random effects represent spatial dependence using a homoscedastic and dimensionally reduced modification of the intrinsic conditional autoregression model. We illustrate the methodology using spatial data from a pair-matched cluster randomized trial against the dengue mosquito vector Aedes aegypti, done in Trujillo, Venezuela.

PySpacell: A Python Package for Spatial Analysis of Cell Images.

Technologies such as microscopy, sequential hybridization, and mass spectrometry enable quantitative single-cell phenotypic and molecular measurements in situ. Deciphering spatial phenotypic and molecular effects on the single-cell level is one of the grand challenges and a key to understanding the effects of cell-cell interactions and microenvironment. However, spatial information is usually overlooked by downstream data analyses, which usually consider single-cell read-out values as independent measurements for further averaging or clustering, thus disregarding spatial locations. With this work, we attempt to fill this gap. We developed a toolbox that allows one to test for the presence of a spatial effect in microscopy images of adherent cells and estimate the spatial scale of this effect. The proposed Python module can be used for any light microscopy images of cells as well as other types of single-cell data such as in situ transcriptomics or metabolomics. The input format of our package matches standard output formats from image analysis tools such as CellProfiler, Fiji, or Icy and thus makes our toolbox easy and straightforward to use, yet offering a powerful statistical approach for a wide range of applications. © 2019 International Society for Advancement of Cytometry.

Assessing the effect of containment measures on the spatio-temporal dynamic of COVID-19 in Italy.

This paper aims at investigating empirically whether and to what extent the containment measures adopted in Italy had an impact in reducing the diffusion of the COVID-19 disease across provinces. For this purpose, we extend the multivariate time-series model for infection counts proposed in Paul and Held (Stat Med 30(10):118-1136, 2011) by augmenting the model specification with B-spline regressors in order to account for complex nonlinear spatio-temporal dynamics in the propagation of the disease. The results of the model estimated on the time series of the number of infections for the Italian provinces show that the containment measures, despite being globally effective in reducing both the spread of contagion and its self-sustaining dynamics, have had nonlinear impacts across provinces. The impact has been relatively stronger in the northern local areas, where the disease occurred earlier and with a greater incidence. This evidence may be explained by the shared popular belief that the contagion was not a close-to-home problem but rather restricted to a few distant northern areas, which, in turn, might have led individuals to adhere less strictly to containment measures and lockdown rules.

Modeling the spatio-temporal dynamics of air pollution index based on spatial Markov chain model.

An environmental problem which is of concern across the globe nowadays is air pollution. The extent of air pollution is often studied based on data on the observed level of air pollution. Although the analysis of air pollution data that is available in the literature is numerous, studies on the dynamics of air pollution with the allowance for spatial interaction effects through the use of the Markov chain model are very limited. Accordingly, this study aims to explore the potential impact of spatial dependence over time and space on the distribution of air pollution based on the spatial Markov chain (SMC) model using the longitudinal air pollution index (API) data. This SMC model is pertinent to be applied since the daily data of API from 2012 to 2014 that have been gathered from 37 different air quality stations in Peninsular Malaysia is found to exhibit the property of spatial autocorrelation. Based on the spatial transition probability matrices found from the SMC model, specific characteristics of air pollution are studied in the regional context. These characteristics are the long-run proportion and the mean first passage time for each state of air pollution. It is found that the probability for a particular station's state to remain good is 0.814 if its neighbors are in a good state of air pollution and 0.7082 if its neighbors are in a moderate state. For a particular station having neighbors in a good state of air pollution, the proportion of time for it to continue being in a good state is 0.6. This proportion reduces to 0.4, 0.01, and 0 for the cell of moderate, unhealthy, and very unhealthy states, respectively. In addition, there exists a significant spatial dependence of API, indicating that air pollution for a particular station is dependent on the states of the neighboring stations.

On spatial conditional extremes for ocean storm severity.

We describe a model for the conditional dependence of a spatial process measured at one or more remote locations given extreme values of the process at a conditioning location, motivated by the conditional extremes methodology of Heffernan and Tawn. Compared to alternative descriptions in terms of max-stable spatial processes, the model is advantageous because it is conceptually straightforward and admits different forms of extremal dependence (including asymptotic dependence and asymptotic independence). We use the model within a Bayesian framework to estimate the extremal dependence of ocean storm severity (quantified using significant wave height, H S ) for locations on spatial transects with approximate east-west (E-W) and north-south (N-S) orientations in the northern North Sea (NNS) and central North Sea (CNS). For H S on the standard Laplace marginal scale, the conditional extremes "linear slope" parameter α decays approximately exponentially with distance for all transects. Furthermore, the decay of mean dependence with distance is found to be faster in CNS than NNS. The persistence of mean dependence is greatest for the E-W transect in NNS, potentially because this transect is approximately aligned with the direction of propagation of the most severe storms in the region.

Spatial Assessment of the Association between Long-Term Exposure to Environmental Factors and the Occurrence of Amyotrophic Lateral Sclerosis in Catalonia, Spain: A Population-Based Nested Case-Control Study.

BACKGROUND: It is believed that an interaction between genetic and non-genetic factors may be involved in the development of amyotrophic lateral sclerosis (ALS). With the exception of exposure to agricultural chemicals like pesticides, evidence of an association between environmental risk factors and ALS is inconsistent. Our objective here was to investigate the association between long-term exposure to environmental factors and the occurrence of ALS in Catalonia, Spain, and to provide evidence that spatial clusters of ALS related to these environmental factors exist. METHODS: We carried out a nested case-control study constructed from a retrospective population-based cohort, covering the entire region. Environmental variables were the explanatory variables of interest. We controlled for both observed and unobserved confounders. RESULTS: We have found some spatial clusters of ALS. The results from the multivariate model suggest that these clusters could be related to some of the environmental variables, in particular agricultural chemicals. In addition, in high-risk clusters, besides corresponding to agricultural areas, key road infrastructures with a high density of traffic are also located. CONCLUSION: Our results indicate that some environmental factors, in particular those associated with exposure to pesticides and air pollutants as a result of urban traffic, could be associated with the occurrence of ALS.

The role of socioeconomic and climatic factors in the spatio-temporal variation of human rabies in China.

BACKGROUND: Rabies is a significant public health problem in China. Previous spatial epidemiological studies have helped understand the epidemiology of animal and human rabies in China. However, quantification of effects derived from relevant factors was insufficient and complex spatial interactions were not well articulated, which may lead to non-negligible bias. In this study, we aimed to quantify the role of socio-economic and climate factors in the spatial distribution of human rabies to support decision making pertaining to rabies control in China. METHODS: We conducted a multivariate analysis of human rabies in China with explicit consideration for spatial heterogeneity and spatial dependence effects. The panel of 20,368 cases reported between 2005 and 2013 and their socio-economic and climate factors was implemented in regression models. Several significant covariates were extracted, including the longitude, the average temperature, the distance to county center, the distance to the road network and the distance to the nearest rabies case. The GMM was adopted to provide unbiased estimation with respect to heterogeneity and spatial autocorrelation. RESULTS: The analysis explained the inferred relationships between the counts of cases aggregated to 271 spatially-defined cells and the explanatory variables. The results suggested that temperature, longitude, the distance to county centers and the distance to the road network are positively associated with the local incidence of human rabies while the distance to newly occurred rabies cases has a negative correlation. With heterogeneity and spatial autocorrelation taken into consideration, the estimation of regression models performed better. CONCLUSIONS: It was found that climatic and socioeconomic factors have significant influence on the spread of human rabies in China as they continuously affect the living environments of humans and animals, which critically impacts on how timely local citizens can gain access to post-exposure prophylactic services. Moreover, through comparisons between traditional regression models and the aggregation model that allows for heterogeneity and spatial effects, we demonstrated the validity and advantage of the aggregation model. It outperformed the existing models and decreased the estimation bias brought by omission of the spatial heterogeneity and spatial dependence effects. Statistical results are readily translated into public health policy takeaways.

The association between exposure to environmental factors and the occurrence of attention-deficit/hyperactivity disorder (ADHD). A population-based retrospective cohort study.

BACKGROUND: A number of factors contribute to attention deficit hyperactivity disorder (ADHD) and although they are not fully known, the occurrence of ADHD seems to be a consequence of an interaction between multiple genetic and environmental factors. However, apart from pesticides, the evidence is inadequate and inconsistent as it differs not only in the population and time period analysed, but also in the type of study, the control of the confounding variables and the statistical methods used. In the latter case, the studies also differ in the adjustment of spatial and temporal variability. Our objective here, is to provide evidence on an association between environmental factors and ADHD. METHODS: In our study, we used a population-based retrospective cohort in which we matched cases and controls (children free of the disease) by sex and year of birth (n = 5193, 78.9% boys). The cases were children born between 1998 and 2012 and diagnosed with ADHD (n = 116). To evaluate whether there was a geographical pattern in the incidence of ADHD, we first represented the smoothed standardized incidence rates on a map of the region being studied. We then estimated the probability of being a case by using a generalized liner mixed model with a binomial link. As explanatory variables of interest, we included the following environmental variables: distance to agricultural areas, distance to roads (stratified into three categories according to traffic density and intensity), distance to petrol stations, distance to industrial estates, and land use. We control for both observed (individual and family specific variables and deprivation index) and unobserved confounders (in particular, individual and familial heterogeneity). In addition, we adjusted for spatial extra variability. RESULTS: We found a north-south pattern containing two clusters (one in the centre of the study region and another in the south) in relation to the risk of developing ADHD. The results from the multivariate model suggest that these clusters could be related to some of the environmental variables. Specifically, living within 100 m from an agricultural area or a residential street and/or living fewer than 300 m from a motorway, dual carriageway or one of the industrial estates analysed was associated (statistically significant) with an increased risk of ADHD. CONCLUSION: Our results indicate that some environmental factors could be associated with ADHD occurring, particularly those associated with exposure to pesticides, organochlorine compounds and air pollutants because of traffic.

Spatial Dependence and Determinants of Dairy Farmers' Adoption of Best Management Practices for Water Protection in New Zealand.

This paper analyses spatial dependence and determinants of the New Zealand dairy farmers' adoption of best management practices to protect water quality. A Bayesian spatial durbin probit model is used to survey data collected from farmers in the Waikato region of New Zealand. The results show that farmers located near each other exhibit similar choice behaviour, indicating the importance of farmer interactions in adoption decisions. The results also address that information acquisition is the most important determinant of farmers' adoption of best management practices. Financial problems are considered a significant barrier to adopting best management practices. Overall, the existence of distance decay effect and spatial dependence in farmers' adoption decisions highlights the importance of accounting for spatial effects in farmers' decision-making, which emerges as crucial to the formulation of sustainable agriculture policy.

Predicting responses to climate change using a joint species, spatially dependent physiologically guided abundance model.

Predicting the effects of warming temperatures on the abundance and distribution of organisms under future climate scenarios often requires extrapolating species-environment correlations to climatic conditions not currently experienced by a species, which can result in unrealistic predictions. For poikilotherms, incorporating species' thermal physiology to inform extrapolations under novel thermal conditions can result in more realistic predictions. Furthermore, models that incorporate species and spatial dependencies may improve predictions by capturing correlations present in ecological data that are not accounted for by predictor variables. Here, we present a joint species, spatially dependent physiologically guided abundance (jsPGA) model for predicting multispecies responses to climate warming. The jsPGA model uses a basis function approach to capture both species and spatial dependencies. We apply the jsPGA model to predict the response of eight fish species to projected climate warming in thousands of lakes in Minnesota, USA. By the end of the century, the cold-adapted species was predicted to have high probabilities of extirpation across its current range-with 10% of lakes currently inhabited by this species having an extirpation probability >0.90. The remaining species had varying levels of predicted changes in abundance, reflecting differences in their thermal physiology. Though the model did not identify many strong species dependencies, the variation in estimated spatial dependence across species suggested that accounting for both dependencies was important for predicting the abundance of these fishes. The jsPGA model provides a new tool for predicting changes in the abundance, distribution, and extirpation probability of poikilotherms under novel thermal conditions.

The Spatial Scale and Spread of Child Victimization.

Previous research shows that large, densely populated urban areas have higher rates of child victimization that have persisted over time. However, few investigations have inquired about the processes that produce and sustain hot and cold spots of child victimization. As a result, the mechanisms that produce the observed spatial clustering of child victimization, and hence "why" harms against children tend to cluster in space, remains unknown. Does the likelihood of being a victim of violence in one location depend on a similar event happening in a nearby location within a specified timeframe? Rather, are child victims of violence more likely to reside in suboptimal neighborhood conditions? This paper aims to present an analytical and theoretical framework for distinguishing between these locational (point) processes to determine whether the empirical spatial patterns undergirding child victimization are more reflective of the "spread" via contagion (i.e., dependency) or whether they are produced by neighborhood structural inequality resulting from spatial heterogeneity. To detect spatial dependence, we applied the inhomogeneous K-function to Los Angeles Medical Examiner data on child homicide victim locations while controlling for regional differences in victimization events (i.e., heterogeneity). Our analysis found strong evidence of spatial clustering in child victimization at small spatial scales but inhibition at larger scales. We further found limited support for the spatiotemporal clustering of child victimization indicative of a contagion effect. Overall, our results support the role of neighborhood structural vulnerability in the underlying mechanisms producing patterns of child victimization across Los Angeles County. We conclude by discussing the policy implications for understanding this spatial patterning in geographical context and for developing effective and targeted preventive interventions.

A framework to evaluate the impact of a hazard chain and geographical covariates on spatial extreme water levels: A case study in the Pearl River Delta.

The interactions and collective impacts of different types of hazards within a compound hazard system, along with the influence of geographical covariates on flooding are presently unclear. Understanding these relationships is crucial for comprehending the formation and dynamic processes of the hazard chain and improving the ability to identify flood warning signals in complex hazard scenarios. In this study, we presented a multivariate spatial extreme value hierarchical (MSEVH) framework to assess the spatial extreme water levels (EWL) at different return levels under the influence of a hazard chain and geographical covariates. The Pearl River Delta (PRD) was selected as a research example to assess the effectiveness of the MSEVH framework. Firstly, we identified a hazard chain (extreme streamflow from the Xijiang River (XR) - extreme streamflow from the Beijiang River (BR) - extreme sea level) and three geographical covariates influencing EWL in the PRD. Then, we compared four hazard scenarios in the MSEVH framework to evaluate the spatial EWL at different return levels under the influence of the hazard chain in the PRD. The final step involves assessing spatial EWL with the effect of the hazard chain and geographical covariates. The results indicate that when extreme streamflow from XR and BR occurs concurrently, the extreme streamflow from BR weakens the influence of extreme streamflow from XR on EWL in the PRD. However, it cannot fully offset the overall impact of extreme streamflow from XR on EWL. In addition, when extreme streamflow from XR, extreme streamflow from BR, and extreme sea level occur simultaneously, the extreme sea level enhances the influence of concurrent extreme streamflow from XR and BR on EWL in the PRD. The proposed MSEVH is not only applicable to the PRD but also shows promising potential for evaluating extreme hydrometeorological variables under the influence of other hazard chains.

Forecasting West Nile Virus With Graph Neural Networks: Harnessing Spatial Dependence in Irregularly Sampled Geospatial Data.

Machine learning methods have seen increased application to geospatial environmental problems, such as precipitation nowcasting, haze forecasting, and crop yield prediction. However, many of the machine learning methods applied to mosquito population and disease forecasting do not inherently take into account the underlying spatial structure of the given data. In our work, we apply a spatially aware graph neural network model consisting of GraphSAGE layers to forecast the presence of West Nile virus in Illinois, to aid mosquito surveillance and abatement efforts within the state. More generally, we show that graph neural networks applied to irregularly sampled geospatial data can exceed the performance of a range of baseline methods including logistic regression, XGBoost, and fully-connected neural networks.

A composite likelihood approach for spatially correlated survival data.

The aim of this paper is to provide a composite likelihood approach to handle spatially correlated survival data using pairwise joint distributions. With e-commerce data, a recent question of interest in marketing research has been to describe spatially clustered purchasing behavior and to assess whether geographic distance is the appropriate metric to describe purchasing dependence. We present a model for the dependence structure of time-to-event data subject to spatial dependence to characterize purchasing behavior from the motivating example from e-commerce data. We assume the Farlie-Gumbel-Morgenstern (FGM) distribution and then model the dependence parameter as a function of geographic and demographic pairwise distances. For estimation of the dependence parameters, we present pairwise composite likelihood equations. We prove that the resulting estimators exhibit key properties of consistency and asymptotic normality under certain regularity conditions in the increasing-domain framework of spatial asymptotic theory.