Spatiotemporal Modeling of Cholera, Uvira, Democratic Republic of the Congo, 2016-2020.

We evaluated the spatiotemporal clustering of rapid diagnostic test-positive cholera cases in Uvira, eastern Democratic Republic of the Congo. We detected spatiotemporal clusters that consistently overlapped with major rivers, and we outlined the extent of zones of increased risk that are compatible with the radii currently used for targeted interventions.

Current status in spatiotemporal analysis of contrast-based perfusion MRI.

In perfusion MRI, image voxels form a spatially organized network of systems, all exchanging indicator with their immediate neighbors. Yet the current paradigm for perfusion MRI analysis treats all voxels or regions-of-interest as isolated systems supplied by a single global source. This simplification not only leads to long-recognized systematic errors but also fails to leverage the embedded spatial structure within the data. Since the early 2000s, a variety of models and implementations have been proposed to analyze systems with between-voxel interactions. In general, this leads to large and connected numerical inverse problems that are intractible with conventional computational methods. With recent advances in machine learning, however, these approaches are becoming practically feasible, opening up the way for a paradigm shift in the approach to perfusion MRI. This paper seeks to review the work in spatiotemporal modelling of perfusion MRI using a coherent, harmonized nomenclature and notation, with clear physical definitions and assumptions. The aim is to introduce clarity in the state-of-the-art of this promising new approach to perfusion MRI, and help to identify gaps of knowledge and priorities for future research.

Integrating Mobile and Fixed-Site Black Carbon Measurements to Bridge Spatiotemporal Gaps in Urban Air Quality.

Urban air pollution can vary sharply in space and time. However, few monitoring strategies can concurrently resolve spatial and temporal variation at fine scales. Here, we present a new measurement-driven spatiotemporal modeling approach that transcends the individual limitations of two complementary sampling paradigms: mobile monitoring and fixed-site sensor networks. We develop, validate, and apply this model to predict black carbon (BC) using data from an intensive, 100-day field study in West Oakland, CA. Our spatiotemporal model exploits coherent spatial patterns derived from a multipollutant mobile monitoring campaign to fill spatial gaps in time-complete BC data from a low-cost sensor network. Our model performs well in reconstructing patterns at fine spatial and temporal resolution (30 m, 15 min), demonstrating strong out-of-sample correlations for both mobile (Pearson's R ∼ 0.77) and fixed-site measurements (R ∼ 0.95) while revealing features that are not effectively captured by a single monitoring approach in isolation. The model reveals sharp concentration gradients near major emission sources while capturing their temporal variability, offering valuable insights into pollution sources and dynamics.

Improved daily estimates of relative humidity at high resolution across Germany: A random forest approach.

The lack of readily available methods for estimating high-resolution near-surface relative humidity (RH) and the incapability of weather stations to fully capture the spatiotemporal variability can lead to exposure misclassification in studies of environmental epidemiology. We therefore aimed to predict German-wide 1 × 1 km daily mean RH during 2000-2021. RH observations, longitude and latitude, modelled air temperature, precipitation and wind speed as well as remote sensing information on topographic elevation, vegetation, and the true color band composite were incorporated in a Random Forest (RF) model, in addition to date for capturing the temporal variations of the response-explanatory variables relationship. The model achieved high accuracy (R2 = 0.83) and low errors (Root Mean Square Error (RMSE) of 5.07%, Mean Absolute Percentage Error (MAPE) of 5.19% and Mean Percentage Error (MPE) of - 0.53%), calculated via ten-fold cross-validation. A comparison of our RH predictions with measurements from a dense monitoring network in the city of Augsburg, South Germany confirmed the good performance (R2 ≥ 0.86, RMSE ≤ 5.45%, MAPE ≤ 5.59%, MPE ≤ 3.11%). The model displayed high German-wide RH (22y-average of 79.00%) and high spatial variability across the country, exceeding 12% on yearly averages. Our findings indicate that the proposed RF model is suitable for estimating RH for a whole country in high-resolution and provide a reliable RH dataset for epidemiological analyses and other environmental research purposes.

High-resolution spatiotemporal modeling of daily near-surface air temperature in Germany over the period 2000-2020.

The commonly used weather stations cannot fully capture the spatiotemporal variability of near-surface air temperature (Tair), leading to exposure misclassification and biased health effect estimates. We aimed to improve the spatiotemporal coverage of Tair data in Germany by using multi-stage modeling to estimate daily 1 × 1 km minimum (Tmin), mean (Tmean), maximum (Tmax) Tair and diurnal Tair range during 2000-2020. We used weather station Tair observations, satellite-based land surface temperature (LST), elevation, vegetation and various land use predictors. In the first stage, we built a linear mixed model with daily random intercepts and slopes for LST adjusted for several spatial predictors to estimate Tair from cells with both Tair and LST available. In the second stage, we used this model to predict Tair for cells with only LST available. In the third stage, we regressed the second stage predictions against interpolated Tair values to obtain Tair countrywide. All models achieved high accuracy (0.91 ≤ R2 ≤ 0.98) and low errors (1.03 °C ≤ Root Mean Square Error (RMSE) ≤ 2.02 °C). Validation with external data confirmed the good performance, locally, i.e., in Augsburg for all models (0.74 ≤ R2 ≤ 0.99, 0.87 °C ≤ RMSE ≤ 2.05 °C) and countrywide, for the Tmean model (0.71 ≤ R2 ≤ 0.99, 0.79 °C ≤ RMSE ≤ 1.19 °C). Annual Tmean averages ranged from 8.56 °C to 10.42 °C with the years beyond 2016 being constantly hotter than the 21-year average. The spatial variability within Germany exceeded 15 °C annually on average following patterns including mountains, rivers and urbanization. Using a case study, we showed that modeling leads to broader Tair variability representation for exposure assessment of participants in health cohorts. Our results indicate the proposed models as suitable for estimating nationwide Tair at high resolution. Our product is critical for temperature-based epidemiological studies and is also available for other research purposes.

Image segmentation and separation of spectrally similar dyes in fluorescence microscopy by dynamic mode decomposition of photobleaching kinetics.

BACKGROUND: Image segmentation in fluorescence microscopy is often based on spectral separation of fluorescent probes (color-based segmentation) or on significant intensity differences in individual image regions (intensity-based segmentation). These approaches fail, if dye fluorescence shows large spectral overlap with other employed probes or with strong cellular autofluorescence. RESULTS: Here, a novel model-free approach is presented which determines bleaching characteristics based on dynamic mode decomposition (DMD) and uses the inferred photobleaching kinetics to distinguish different probes or dye molecules from autofluorescence. DMD is a data-driven computational method for detecting and quantifying dynamic events in complex spatiotemporal data. Here, DMD is first used on synthetic image data and thereafter used to determine photobleaching characteristics of a fluorescent sterol probe, dehydroergosterol (DHE), compared to that of cellular autofluorescence in the nematode Caenorhabditis elegans. It is shown that decomposition of those dynamic modes allows for separating probe from autofluorescence without invoking a particular model for the bleaching process. In a second application, DMD of dye-specific photobleaching is used to separate two green-fluorescent dyes, an NBD-tagged sphingolipid and Alexa488-transferrin, thereby assigning them to different cellular compartments. CONCLUSIONS: Data-based decomposition of dynamic modes can be employed to analyze spatially varying photobleaching of fluorescent probes in cells and tissues for spatial and temporal image segmentation, discrimination of probe from autofluorescence and image denoising. The new method should find wide application in analysis of dynamic fluorescence imaging data.

Cumulative Erythemal Ultraviolet Radiation and Risk of Cancer in 3 Large US Prospective Cohorts.

Ultraviolet radiation (UVR) exposure is the major risk factor for melanoma. However, epidemiologic studies on UVR and noncutaneous cancers have reported inconsistent results, with some suggesting an inverse relationship potentially mediated by vitamin D. To address this, we examined 3 US prospective cohorts, the Health Professionals Follow-up Study (HPFS) (1986) and Nurses' Health Study (NHS) I and II (1976 and 1989), for associations between cumulative erythemal UVR and incident cancer risk, excluding nonmelanoma skin cancer. We used a validated spatiotemporal model to calculate erythemal UVR. Participants (47,714 men; 212,449 women) were stratified into quintiles by cumulative average erythemal UVR, using the first quintile as referent, for Cox proportional hazards regression analysis. In the multivariable-adjusted meta-analysis of all cohorts, compared with the lowest quintile, risk of any cancer was slightly increased across all other quintiles (highest quintile hazard ratio (HR) = 1.04, 95% confidence interval (CI): 1.01, 1.07; P for heterogeneity = 0.41). All UVR quintiles were associated with similarly increased risk of any cancer excluding melanoma. As expected, erythemal UVR was positively associated with risk of melanoma (highest quintile HR = 1.17, 95% CI: 1.04, 1.31; P for heterogeneity = 0.83). These findings suggest that elevated UVR is associated with increased risk of both melanoma and noncutaneous cancers.

Modeling Seasonal Variations in Vertical GPS Coordinate Time Series Using Independent Component Analysis and Varying Coefficient Regression.

Common seasonal variations in Global Positioning System (GPS) coordinate time series always exist, and the modeling and correction of the seasonal signals are helpful for many geodetic studies using GPS observations. A spatiotemporal model was proposed to model the common seasonal variations in vertical GPS coordinate time series, based on independent component analysis and varying coefficient regression method. In the model, independent component analysis (ICA) is used to separate the common seasonal signals in the vertical GPS coordinate time series. Considering that the periodic signals in GPS coordinate time series change with time, a varying coefficient regression method is used to fit the separated independent components. The spatiotemporal model was then used to fit the vertical GPS coordinate time series of 262 global International GPS Service for Geodynamics (IGS) GPS sites. The results show that compared with least squares regression, the varying coefficient method can achieve a more reliable fitting result for the seasonal variation of the separated independent components. The proposed method can accurately model the common seasonal variations in the vertical GPS coordinate time series, with an average root mean square (RMS) reduction of 41.6% after the model correction.

Transmission dynamics of Ebola virus disease and intervention effectiveness in Sierra Leone.

Sierra Leone is the most severely affected country by an unprecedented outbreak of Ebola virus disease (EVD) in West Africa. Although successfully contained, the transmission dynamics of EVD and the impact of interventions in the country remain unclear. We established a database of confirmed and suspected EVD cases from May 2014 to September 2015 in Sierra Leone and mapped the spatiotemporal distribution of cases at the chiefdom level. A Poisson transmission model revealed that the transmissibility at the chiefdom level, estimated as the average number of secondary infections caused by a patient per week, was reduced by 43% [95% confidence interval (CI): 30%, 52%] after October 2014, when the strategic plan of the United Nations Mission for Emergency Ebola Response was initiated, and by 65% (95% CI: 57%, 71%) after the end of December 2014, when 100% case isolation and safe burials were essentially achieved, both compared with before October 2014. Population density, proximity to Ebola treatment centers, cropland coverage, and atmospheric temperature were associated with EVD transmission. The household secondary attack rate (SAR) was estimated to be 0.059 (95% CI: 0.050, 0.070) for the overall outbreak. The household SAR was reduced by 82%, from 0.093 to 0.017, after the nationwide campaign to achieve 100% case isolation and safe burials had been conducted. This study provides a complete overview of the transmission dynamics of the 2014-2015 EVD outbreak in Sierra Leone at both chiefdom and household levels. The interventions implemented in Sierra Leone seem effective in containing the epidemic, particularly in interrupting household transmission.

Phenology-adjusted dynamic curve number for improved hydrologic modeling.

The Soil Conservation Service Curve Number (SCS-CN, or CN) is a widely used method to estimate runoff from rainfall events. It has been adapted to many parts of the world with different land uses, land cover types, and climatic conditions and successfully applied to situations ranging from simple runoff calculations and land use change assessment to comprehensive hydrologic/water quality simulations. However, the CN method lacks the ability to incorporate seasonal variations in vegetated surface conditions, and unnoticed landuse/landcover (LULC) change that shape infiltration and storm runoff. Plant phenology is a main determinant of changes in hydrologic processes and water balances across seasons through its influence on surface roughness and evapotranspiration. This study used regression analysis to develop a dynamic CN (CNNDVI) based on seasonal variations in the remotely-sensed Normalized Difference Vegetation Index (NDVI) to monitor intra-annual plant phenological development. A time series of 16-day MODIS NDVI (MOD13Q1 Collection 5) images were used to monitor vegetation development and provide NDVI data necessary for CNNDVI model calibration and validation. Twelve years of rainfall and runoff data (2001-2012) from four small watersheds located in the Konza Prairie Biological Station, Kansas were used to develop, calibrate, and validate the method. Results showed CNNDVI performed significantly better in predicting runoff with calibrated CNNDVI runoff increasing by approximately 0.74 for every unit increase in observed runoff compared to 0.46 for SCS-CN runoff and was more highly correlated to observed runoff (r = 0.78 vs. r = 0.38). In addition, CNNDVI runoff had better NSE (0.53) and PBIAS (4.22) compared to the SCS-CN runoff (-0.87 and -94.86 respectively). In the validated model, CNNDVI runoff increased by approximately 0.96 for every unit of observed runoff, while SCS-CN runoff increased by 0.49. Validated runoff was also better correlated to observed runoff than SCS-CN runoff (r = 0.52 vs. r = 0.33). These findings suggest that the CNNDVI can yield improved estimates of surface runoff from precipitation events, leading to more informed water and land management decisions.

Spatial infectious disease epidemiology: on the cusp.

Infectious diseases continue to pose a significant public health burden despite the great progress achieved in their prevention and control over the last few decades. Our ability to disentangle the factors and mechanisms driving their propagation in space and time has dramatically advanced in recent years. The current era is rich in mathematical and computational tools and detailed geospatial information, including sociodemographic, geographic, and environmental data, which are essential to elucidate key drivers of infectious disease transmission from epidemiological and genetic data. Indeed, this paradigm shift was driven by dramatic advances in complex systems approaches along with substantial improvements in data availability and computational power. The burgeoning output of infectious disease spatial modeling suggests that we are close to a fully integrated approach for early epidemic detection and intervention. This special collection in BMC Medicine aims to bring together a broad range of quantitative investigations that improve our understanding of the spatiotemporal transmission dynamics of infectious diseases in order to mitigate their impact on the human population.

Applying spatiotemporal models to monitoring data to quantify fish population responses to the Deepwater Horizon oil spill in the Gulf of Mexico.

Quantifying the impacts of disturbances such as oil spills on marine species can be challenging. Natural environmental variability, human responses to the disturbance (e.g., fisheries closures), the complex life histories of the species being monitored, and limited pre-spill data can make detection of effects of oil spills difficult. Using long-term monitoring data from the state of Louisiana (USA), we applied novel spatiotemporal approaches to identify anomalies in species occurrence and catch rates. We included covariates (salinity, temperature, turbidity) to help isolate unusual events. While some species showed evidence of unlikely temporal anomalies in occurrence or catch rates, we found that the majority of the observed anomalies were also before the Deepwater Horizon event. Several species-gear combinations suggested upticks in the spatial variability immediately following the spill, but most species indicated no trend. Across species-gear combinations, there was no clear evidence for synchronous or asynchronous responses in occurrence or catch rates across sites following the spill. Our results are in general agreement to other analyses of monitoring data that detected small impacts, but in contrast to recent results from ecological modeling that showed much larger effects of the oil spill on fish and shellfish.

Model-based estimation of intra-cortical connectivity using electrophysiological data.

This paper provides a new method for model-based estimation of intra-cortical connectivity from electrophysiological measurements. A novel closed-form solution for the connectivity function of the Amari neural field equations is derived as a function of electrophysiological observations. The resultant intra-cortical connectivity estimate is driven from experimental data, but constrained by the mesoscopic neurodynamics that are encoded in the computational model. A demonstration is provided to show how the method can be used to image physiological mechanisms that govern cortical dynamics, which are normally hidden in clinical data from epilepsy patients. Accurate estimation performance is demonstrated using synthetic data. Following the computational testing, results from patient data are obtained that indicate a dominant increase in surround inhibition prior to seizure onset that subsides in the cases when the seizures spread.

Systems-level modeling with molecular resolution elucidates the rate-limiting mechanisms of cellulose decomposition by cellobiohydrolases.

Interprotein and enzyme-substrate couplings in interfacial biocatalysis induce spatial correlations beyond the capabilities of classical mass-action principles in modeling reaction kinetics. To understand the impact of spatial constraints on enzyme kinetics, we developed a computational scheme to simulate the reaction network of enzymes with the structures of individual proteins and substrate molecules explicitly resolved in the three-dimensional space. This methodology was applied to elucidate the rate-limiting mechanisms of crystalline cellulose decomposition by cellobiohydrolases. We illustrate that the primary bottlenecks are slow complexation of glucan chains into the enzyme active site and excessive enzyme jamming along the crowded substrate. Jamming could be alleviated by increasing the decomplexation rate constant but at the expense of reduced processivity. We demonstrate that enhancing the apparent reaction rate required a subtle balance between accelerating the complexation driving force and simultaneously avoiding enzyme jamming. Via a spatiotemporal systems analysis, we developed a unified mechanistic framework that delineates the experimental conditions under which different sets of rate-limiting behaviors emerge. We found that optimization of the complexation-exchange kinetics is critical for overcoming the barriers imposed by interfacial confinement and accelerating the apparent rate of enzymatic cellulose decomposition.

Spatiotemporal Modeling of Aedes aegypti Risk: Enhancing Dengue Virus Control through Meteorological and Remote Sensing Data in French Guiana.

French Guiana lacks a dedicated model for developing an early warning system tailored to its entomological contexts. We employed a spatiotemporal modeling approach to predict the risk of Aedes aegypti larvae presence in local households in French Guiana. The model integrated field data on larvae, environmental data obtained from very high-spatial-resolution Pleiades imagery, and meteorological data collected from September 2011 to February 2013 in an urban area of French Guiana. The identified environmental and meteorological factors were used to generate dynamic maps with high spatial and temporal resolution. The study collected larval data from 261 different surveyed houses, with each house being surveyed between one and three times. Of the observations, 41% were positive for the presence of Aedes aegypti larvae. We modeled the Aedes larvae risk within a radius of 50 to 200 m around houses using six explanatory variables and extrapolated the findings to other urban municipalities during the 2020 dengue epidemic in French Guiana. This study highlights the potential of spatiotemporal modeling approaches to predict and monitor the evolution of vector-borne disease transmission risk, representing a major opportunity to monitor the evolution of vector risk and provide valuable information for public health authorities.

Harnessing Few-Shot Learning for EEG signal classification: a survey of state-of-the-art techniques and future directions.

This paper presents a systematic literature review, providing a comprehensive taxonomy of Data Augmentation (DA), Transfer Learning (TL), and Self-Supervised Learning (SSL) techniques within the context of Few-Shot Learning (FSL) for EEG signal classification. EEG signals have shown significant potential in various paradigms, including Motor Imagery, Emotion Recognition, Visual Evoked Potentials, Steady-State Visually Evoked Potentials, Rapid Serial Visual Presentation, Event-Related Potentials, and Mental Workload. However, challenges such as limited labeled data, noise, and inter/intra-subject variability have impeded the effectiveness of traditional machine learning (ML) and deep learning (DL) models. This review methodically explores how FSL approaches, incorporating DA, TL, and SSL, can address these challenges and enhance classification performance in specific EEG paradigms. It also delves into the open research challenges related to these techniques in EEG signal classification. Specifically, the review examines the identification of DA strategies tailored to various EEG paradigms, the creation of TL architectures for efficient knowledge transfer, and the formulation of SSL methods for unsupervised representation learning from EEG data. Addressing these challenges is crucial for enhancing the efficacy and robustness of FSL-based EEG signal classification. By presenting a structured taxonomy of FSL techniques and discussing the associated research challenges, this systematic review offers valuable insights for future investigations in EEG signal classification. The findings aim to guide and inspire researchers, promoting advancements in applying FSL methodologies for improved EEG signal analysis and classification in real-world settings.

A predictive model for ethylene-mediated auxin and cytokinin patterning in the Arabidopsis root.

The interaction between auxin and cytokinin is important in many aspects of plant development. Experimental measurements of both auxin and cytokinin concentration and reporter gene expression clearly show the coexistence of auxin and cytokinin concentration patterning in Arabidopsis root development. However, in the context of crosstalk among auxin, cytokinin, and ethylene, little is known about how auxin and cytokinin concentration patterns simultaneously emerge and how they regulate each other in the Arabidopsis root. This work utilizes a wide range of experimental observations to propose a mechanism for simultaneous patterning of auxin and cytokinin concentrations. In addition to revealing the regulatory relationships between auxin and cytokinin, this mechanism shows that ethylene signaling is an important factor in achieving simultaneous auxin and cytokinin patterning, while also predicting other experimental observations. Combining the mechanism with a realistic in silico root model reproduces experimental observations of both auxin and cytokinin patterning. Predictions made by the mechanism can be compared with a variety of experimental observations, including those obtained by our group and other independent experiments reported by other groups. Examples of these predictions include patterning of auxin biosynthesis rate, changes in PIN1 and PIN2 patterns in pin3,4,7 mutants, changes in cytokinin patterning in the pls mutant, PLS patterning, and various trends in different mutants. This research reveals a plausible mechanism for simultaneous patterning of auxin and cytokinin concentrations in Arabidopsis root development and suggests a key role for ethylene pattern integration.

A comprehensive review of the development of land use regression approaches for modeling spatiotemporal variations of ambient air pollution: A perspective from 2011 to 2023.

Land use regression (LUR) models are widely used in epidemiological and environmental studies to estimate humans' exposure to air pollution within urban areas. However, the early models, developed using linear regressions and data from fixed monitoring stations and passive sampling, were primarily designed to model traditional and criteria air pollutants and had limitations in capturing high-resolution spatiotemporal variations of air pollution. Over the past decade, there has been a notable development of multi-source observations from low-cost monitors, mobile monitoring, and satellites, in conjunction with the integration of advanced statistical methods and spatially and temporally dynamic predictors, which have facilitated significant expansion and advancement of LUR approaches. This paper reviews and synthesizes the recent advances in LUR approaches from the perspectives of the changes in air quality data acquisition, novel predictor variables, advances in model-developing approaches, improvements in validation methods, model transferability, and modeling software as reported in 155 LUR studies published between 2011 and 2023. We demonstrate that these developments have enabled LUR models to be developed for larger study areas and encompass a wider range of criteria and unregulated air pollutants. LUR models in the conventional spatial structure have been complemented by more complex spatiotemporal structures. Compared with linear models, advanced statistical methods yield better predictions when handling data with complex relationships and interactions. Finally, this study explores new developments, identifies potential pathways for further breakthroughs in LUR methodologies, and proposes future research directions. In this context, LUR approaches have the potential to make a significant contribution to future efforts to model the patterns of long- and short-term exposure of urban populations to air pollution.

Land potential assessment and trend-analysis using 2000-2021 FAPAR monthly time-series at 250 m spatial resolution.

The article presents results of using remote sensing images and machine learning to map and assess land potential based on time-series of potential Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) composites. Land potential here refers to the potential vegetation productivity in the hypothetical absence of short-term anthropogenic influence, such as intensive agriculture and urbanization. Knowledge on this ecological land potential could support the assessment of levels of land degradation as well as restoration potentials. Monthly aggregated FAPAR time-series of three percentiles (0.05, 0.50 and 0.95 probability) at 250 m spatial resolution were derived from the 8-day GLASS FAPAR V6 product for 2000-2021 and used to determine long-term trends in FAPAR, as well as to model potential FAPAR in the absence of human pressure. CCa 3 million training points sampled from 12,500 locations across the globe were overlaid with 68 bio-physical variables representing climate, terrain, landform, and vegetation cover, as well as several variables representing human pressure including: population count, cropland intensity, nightlights and a human footprint index. The training points were used in an ensemble machine learning model that stacks three base learners (extremely randomized trees, gradient descended trees and artificial neural network) using a linear regressor as meta-learner. The potential FAPAR was then projected by removing the impact of urbanization and intensive agriculture in the covariate layers. The results of strict cross-validation show that the global distribution of FAPAR can be explained with an R2 of 0.89, with the most important covariates being growing season length, forest cover indicator and annual precipitation. From this model, a global map of potential monthly FAPAR for the recent year (2021) was produced, and used to predict gaps in actual vs. potential FAPAR. The produced global maps of actual vs. potential FAPAR and long-term trends were each spatially matched with stable and transitional land cover classes. The assessment showed large negative FAPAR gaps (actual lower than potential) for classes: urban, needle-leave deciduous trees, and flooded shrub or herbaceous cover, while strong negative FAPAR trends were found for classes: urban, sparse vegetation and rainfed cropland. On the other hand, classes: irrigated or post-flooded cropland, tree cover mixed leaf type, and broad-leave deciduous showed largely positive trends. The framework allows land managers to assess potential land degradation from two aspects: as an actual declining trend in observed FAPAR and as a difference between actual and potential vegetation FAPAR.

Evaluating low-cost monitoring designs for PM2.5 exposure assessment with a spatiotemporal modeling approach.

Determining the most feasible and cost-effective approaches to improving PM2.5 exposure assessment with low-cost monitors (LCMs) can considerably enhance the quality of its epidemiological inferences. We investigated features of fixed-site LCM designs that most impact PM2.5 exposure estimates to be used in long-term epidemiological inference for the Adult Changes in Thought Air Pollution (ACT-AP) study. We used ACT-AP collected and calibrated LCM PM2.5 measurements at the two-week level from April 2017 to September 2020 (N of monitors [measurements] = 82 [502]). We also acquired reference-grade PM2.5 measurements from January 2010 to September 2020 (N = 78 [6186]). We used a spatiotemporal modeling approach to predict PM2.5 exposures with either all LCM measurements or varying subsets with reduced temporal or spatial coverage. We evaluated the models based on a combination of cross-validation and external validation at locations of LCMs included in the models (N = 82), and also based on an independent external validation with a set of LCMs not used for the modeling (N = 30). We found that the model's performance declined substantially when LCM measurements were entirely excluded (spatiotemporal validation R2 [RMSE] = 0.69 [1.2 µg/m3]) compared to the model with all LCM measurements (0.84 [0.9 µg/m3]). Temporally, using the farthest apart measurements (i.e., the first and last) from each LCM resulted in the closest model's performance (0.79 [1.0 µg/m3]) to the model with all LCM data. The models with only the first or last measurement had decreased performance (0.77 [1.1 µg/m3]). Spatially, the model's performance decreased linearly to 0.74 (1.1 µg/m3) when only 10% of LCMs were included. Our analysis also showed that LCMs located in densely populated, road-proximate areas improved the model more than those placed in moderately populated, road-distant areas.