Fine Spatial Temporal Resolution Research Articles

Estimating morning and evening commute period O3 concentration in Taiwan using a fine spatial-temporal resolution ensemble mixed spatial model with Geo-AI technology

Elevated levels of ground-level ozone (O3) can have harmful effects on health. While previous studies have focused mainly on daily averages and daytime patterns, it's crucial to consider the effects of air pollution during daily commutes, as this can significantly contribute to overall exposure. This study is also the first to employ an ensemble mixed spatial model (EMSM) that integrates multiple machine learning algorithms and predictor variables selected using Shapley Additive exExplanations (SHAP) values to predict spatial-temporal fluctuations in O3 concentrations across the entire island of Taiwan. We utilized geospatial-artificial intelligence (Geo-AI), incorporating kriging, land use regression (LUR), machine learning (random forest (RF), categorical boosting (CatBoost), gradient boosting (GBM), extreme gradient boosting (XGBoost), and light gradient boosting (LightGBM)), and ensemble learning techniques to develop ensemble mixed spatial models (EMSMs) for morning and evening commute periods. The EMSMs were used to estimate long-term spatiotemporal variations of O3 levels, accounting for in-situ measurements, meteorological factors, geospatial predictors, and social and seasonal influences over a 26-year period. Compared to conventional LUR-based approaches, the EMSMs improved performance by 58% for both commute periods, with high explanatory power and an adjusted R2 of 0.91. Internal and external validation procedures and verification of O3 concentrations at the upper percentile ranges (in 1%, 5%, 10%, 15%, 20%, and 25%) and other conditions (including rain, no rain, weekday, weekend, festival, and no festival) have demonstrated that the models are stable and free from overfitting issues. Estimation maps were generated to examine changes in O3 levels before and during the implementation of COVID-19 restrictions. These findings provide accurate variations of O3 levels in commute period with high spatiotemporal resolution of daily and 50m * 50m grid, which can support control pollution efforts and aid in epidemiological studies.

Understanding the spatiotemporal evolution of opioid overdose events using a regionalized sequence alignment analysis

BackgroundOpioid overdose events and deaths have become a serious public health crisis in the United States, and understanding the spatiotemporal evolution of the disease occurrences is crucial for developing effective prevention strategies, informing health systems policy and planning, and guiding local responses. However, current research lacks the capability to observe the dynamics of the opioid crisis at a fine spatial-temporal resolution over a long period, leading to ineffective policies and interventions at the local level. MethodsThis paper proposes a novel regionalized sequential alignment analysis using opioid overdose events data to assess the spatiotemporal similarity of opioid overdose evolutionary trajectories within regions that share similar socioeconomic status. The model synthesizes the shape and correlation of space-time trajectories to assist space-time pattern mining in different neighborhoods, identifying trajectories that exhibit similar spatiotemporal characteristics for further analysis. ResultsBy adopting this methodology, we can better understand the spatiotemporal evolution of opioid overdose events and identify regions with similar patterns of evolution. This enables policymakers and health researchers to develop effective interventions and policies to address the opioid crisis at the local level. ConclusionsThe proposed methodology provides a new framework for understanding the spatiotemporal evolution of opioid overdose events, enabling policymakers and health researchers to develop effective interventions and policies to address this growing public health crisis.

A Geo-AI-based ensemble mixed spatial prediction model with fine spatial-temporal resolution for estimating daytime/nighttime/daily average ozone concentrations variations in Taiwan

High levels of ground level ozone (O3) are associated with detrimental health concerns. Most of the studies only focused on daily average and daytime trends due to the presence of sunlight that initiates its formation. However, atmospheric chemical reactions occur all day, thus, nighttime concentrations should be given equal importance. In this study, geospatial-artificial intelligence (Geo-AI) which combined kriging, land use regression (LUR), machine learning, an ensemble learning, was applied to develop ensemble mixed spatial models (EMSMs) for daily, daytime, and nighttime periods. These models were used to estimate the long-term O3 spatio-temporal variations using a two-decade worth of in-situ measurements, meteorological parameters, geospatial predictors, and social and season-dependent factors. From the traditional LUR approach, the performance of EMSMs improved by 60% (daytime), 49% (nighttime), and 57% (daily). The resulting daily, daytime, and nighttime EMSMs had a high explanatory power with and adjusted R2 of 0.91, 0.91, and 0.88, respectively. Estimation maps were produced to examine the changes before and during the implementation of nationwide COVID-19 restrictions. These results provide accurate estimates and its diurnal variation that will support pollution control measure and epidemiological studies.

RFSDAF: A New Spatiotemporal Fusion Method Robust to Registration Errors

Spatiotemporal fusion is commonly used in remote sensing to generate fine spatial–temporal resolution images. Among most present spatiotemporal fusion methods, the registration error results in a mismatch in the reflectance and class abundance information within the MODIS pixel to be analyzed during the spatiotemporal fusion. In this article, a new spatiotemporal fusion model, robust flexible spatiotemporal data fusion (RFSDAF), is proposed; in other words, this method is robust to registration errors. The RFSDAF method uses a multiscale fusion strategy that is adaptive to different degrees of coregistration error. It incorporates multiscale information, which extends the analysis of the reflectance and class fraction from per-MODIS pixel to inter-MODIS pixels, and it is robust to coregistration errors. This method does not require high-accuracy preprocession coregistration algorithms and can automatically reduce the effect of the registration error to a great extent. The RFSDAF method is compared with four spatiotemporal image fusion algorithms, and the effectiveness of the method in resolving the registration error is demonstrated using both a simulated dataset and two actual satellite datasets. The experimental results show that the RFSDAF method can better reduce the impact of the registration error than the spatial and temporal adaptive reflectance fusion model (STARFM) and FSDAF, which adopts the phase cross-correlation algorithm to preprocess and coregister the MODIS–Landsat image through real image experiments. Consequently, the RFSDAF method has a great robustness for real spatiotemporal image fusion with registration errors and has the potential for monitoring land surface dynamics.

Characterizing environmental geographic inequalities using an integrated exposure assessment

BackgroundAt a regional or continental scale, the characterization of environmental health inequities (EHI) expresses the idea that populations are not equal in the face of pollution. It implies an analysis be conducted in order to identify and manage the areas at risk of overexposure where an increasing risk to human health is suspected. The development of methods is a prerequisite for implementing public health activities aimed at protecting populations.MethodsThis paper presents the methodological framework developed by INERIS (French National Institute for Industrial Environment and Risks) to identify a common framework for a structured and operationalized assessment of human exposure. An integrated exposure assessment approach has been developed to integrate the multiplicity of exposure pathways from various sources, through a series of models enabling the final exposure of a population to be defined.ResultsMeasured data from environmental networks reflecting the actual contamination of the environment are used to gauge the population’s exposure. Sophisticated methods of spatial analysis are applied to include additional information and take benefit of spatial and inter-variable correlation to improve data representativeness and characterize the associated uncertainty. Integrated approaches bring together all the information available for assessing the source-to-human-dose continuum using a Geographic Information System, multimedia exposure and toxicokinetic model.DiscussionOne of the objectives of the integrated approach was to demonstrate the feasibility of building complex realistic exposure scenarios satisfying the needs of stakeholders and the accuracy of the modelling predictions at a fine spatial-temporal resolution. A case study is presented to provide a specific application of the proposed framework and how the results could be used to identify an overexposed population.ConclusionThis framework could be used for many purposes, such as mapping EHI, identifying vulnerable populations and providing determinants of exposure to manage and plan remedial actions and to assess the spatial relationships between health and the environment to identify factors that influence the variability of disease patterns.

Spatial-temporal patterns and influence of land-use and socioeconomic factors on fine particulate matter pollution in Accra, Ghana

Background: Air pollution levels in cities in Sub-Saharan Africa (SSA), the world’s most rapidly urbanizing region, are among the highest globally due to influences from diverse local and regional sources. However, very little is known about within-city disparities at fine spatial-temporal resolution. We examined the space-time patterns of fine particle pollution (PM2.5) in relation to land-use variables and socioeconomic factors in Accra, Ghana, and evaluated trends over a decade.Methods: We measured and analysed weekly gravimetric and minute-by-minute PM2.5 (optical sensor) concentrations from 150 unique locations (10 fixed [1-year] and 140 weekly [7-day] sites) covering a range of land-use, socioeconomic features and source influences.Results: PM2.5 concentrations were highest between December - February, during the “Harmattan” when transported dust and changes in regional and local meteorology affect Accra. In this period, levels were as high as 267 μg/m3 (mean 94.1 μg/m3), compared to annual PM2.5 concentrations ranging from 14.4 to 31.3 μg/m3 at fixed sites. Across all 150 sites, weekly concentrations ranged from <10 μg/m3 to >250 μg/m3. Sites near roadsides had the highest overall mean PM2.5 (43 μg/m3), followed by high-density (poor) and low-density (affluent) residential sites; peri-urban background sites had the lowest levels. By time of day, daily PM2.5 at all sites rose at dawn and peaked at daybreak, followed by a gradual decline till afternoon and a modest evening peak. There were indications of decreased PM2.5 concentrations at residential sites when compared to matching data from a decade ago (39 vs 25 μg/m3), suggesting likely reductions in biomass emissions.Conclusion: Fine particle pollution in Accra may be decreasing over time but remains 2 to 4-fold higher than the WHO guideline with significant variations in relation to spatial, socioeconomic, and land-use factors. Equitable local policies and interventions are needed to reduce air pollution levels and protect the vulnerable.

Estimation of water volume in ungauged, dynamic floodplain lakes

Lakes play a crucial role in retaining water and altering biogeochemical processes on floodplains. Existing strategies and algorithms for estimation of water storage are insufficient for dynamic floodplain lakes due to the scarcity of available observations. Combining a time series of open water area with a fine spatial-temporal resolution by integrating Landsat and MODIS observations of Poyang Lake (China) with digital elevation models, and limited gauge data, generated water storage estimates as a function of surface hydrological connectivity. Despite possessing a relatively small portion of Poyang Lake’s water volume, the floodplain lakes occupy a large part of the surface water area, especially in the low water period. Floodplain lakes, in particular, those distributed in the upper delta contribute to relieving drought conditions in Poyang Lake.

Dynamic response of East Asian Greater White-fronted Geese to changes of environment during migration: Use of multi-temporal species distribution model

Understanding how migratory species select habitats is essential for applied ecology and biological conservation. Although migratory species move across a wide range of environments during migration, their dynamic response to environments has rarely been considered. Taking advantage of the fine spatial-temporal resolution of satellite tracking data, we studied habitat selection of East Asian greater white-fronted geese (Anser albifrons) along their spring migration route from Yangtze River Basin to Lena Delta and Yana Bay. We developed a novel methodology to improve dynamic species distribution models (SDMs) by incorporating environmental variables derived from remotely sensed data precisely corresponding to migration time. Our results demonstrate that distance to the nearest water body, elevation, human population density and temperature contribute greatly to the models. Water-related and topographic factors (e.g., elevation, slope and distance to the nearest water body) were consistently associated with habitat selection of the geese from wintering area to breeding area, while the varied influences of temperature and human population density in different migration periods are closely related to their adaptation to local environments. In addition, response curves of vegetation index indicate that the geese are more strongly associated with food quality than quantity in wintering area and stopover sites. By building SDMs in different periods, we provide a unique dynamic perspective on how a long-distance migrant responds to different environments. The methodology proposed here could be integrated to future conservation management plans for predicting species relationship with fast changing environmental conditions.

Generating a series of fine spatial and temporal resolution land cover maps by fusing coarse spatial resolution remotely sensed images and fine spatial resolution land cover maps

Studies of land cover dynamics would benefit greatly from the generation of land cover maps at both fine spatial and temporal resolutions. Fine spatial resolution images are usually acquired relatively infrequently, whereas coarse spatial resolution images may be acquired with a high repetition rate but may not capture the spatial detail of the land cover mosaic of the region of interest. Traditional image spatial–temporal fusion methods focus on the blending of pixel spectra reflectance values and do not directly provide land cover maps or information on land cover dynamics. In this research, a novel Spatial–Temporal remotely sensed Images and land cover Maps Fusion Model (STIMFM) is proposed to produce land cover maps at both fine spatial and temporal resolutions using a series of coarse spatial resolution images together with a few fine spatial resolution land cover maps that pre- and post-date the series of coarse spatial resolution images. STIMFM integrates both the spatial and temporal dependences of fine spatial resolution pixels and outputs a series of fine spatial–temporal resolution land cover maps instead of reflectance images, which can be used directly for studies of land cover dynamics. Here, three experiments based on simulated and real remotely sensed images were undertaken to evaluate the STIMFM for studies of land cover change. These experiments included comparative assessment of methods based on single-date image such as the super-resolution approaches (e.g., pixel swapping-based super-resolution mapping) and the state-of-the-art spatial–temporal fusion approach that used the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) and the Flexible Spatiotemporal DAta Fusion model (FSDAF) to predict the fine-resolution images, in which the maximum likelihood classifier and the automated land cover updating approach based on integrated change detection and classification method were then applied to generate the fine-resolution land cover maps. Results show that the methods based on single-date image failed to predict the pixels of changed and unchanged land cover with high accuracy. The land cover maps that were obtained by classification of the reflectance images outputted from ESTARFM and FSDAF contained substantial misclassification, and the classification accuracy was lower for pixels of changed land cover than for pixels of unchanged land cover. In addition, STIMFM predicted fine spatial–temporal resolution land cover maps from a series of Landsat images and a few Google Earth images, to which ESTARFM and FSDAF that require correlation in reflectance bands in coarse and fine images cannot be applied. Notably, STIMFM generated higher accuracy for pixels of both changed and unchanged land cover in comparison with other methods.

Near-source grid-based measurement of CO and PM2.5 concentration during a full-scale fire experiment in southern European shrubland

There is limited research on the exposure of wildland firefighters to smoke because of the operational obstacles when monitoring air pollutants in the field. In this work, a grid of portable sensors was used to measure PM2.5 and CO concentrations in the near-source region during the burn of two shrubland research blocks in Central Portugal. Strong spatial variability of smoke levels was observed in the analysis of the ratios between mean concentrations of neighbouring sensors, with values as high as 4.4 for PM2.5 and 7.4 for CO. These large gradients were registered at a distance of only 5 m suggesting that considerable differences on individual exposure can occur depending on the location of that individual in relation to the smoke plume trajectory. Also, peak events of 2–3 times the mean were observed in periods exceeding 6 min. In the two experiments, the average concentrations of both PM2.5 and CO were higher during smouldering, which represents a risk of acute exposure due to the closer proximity of firefighters to the emission source during mop-up, stressing the importance of wearing portable gas detectors for managing critical exposure. The collected data constitutes a step forward in the effort to understand the mechanisms controlling the exposure during firefighting operations, by providing a source of information on near-ground concentration fluctuations within a biomass-burning smoke plume at a fine spatial-temporal resolution.

The Different Impact of a Half-Separated Gravel and Vegetated Bed in Open Channels

This paper investigates the different impact of a half-separated gravel and vegetated bed in open channels. This experimental investigation is based on the 2D Particle Image Velocimetry (PIV) method, which allows data acquisition at fine spatial-temporal resolution. For the simulation of the two porous beds : a) a grass-like vegetation and b) gravel bed of 2 cm height were used to represent the permeable bed. Velocity is measured : a) at the central line of 3.75 cm above the interface of vegetated and gravel bed, b) over the vegetated bed and c) over the gravel bed for different discharges and for the same different total heights. In total, 27 experiments were carried out. Results show that the presence of a half-separated gravel and vegetated bed influences significantly the velocity distribution in comparison with a simple permeable bed (vegetated or gravel). This is due to the fact that the presence of gravel bed in the half-separated bed decreases the velocity of the flow in regard to the simple vegetated or gravel bed due to the great roughness which is observed near the surface gravel-vegetated bed. This fact increases the turbulence and reduces the velocity. Also, the presence of half-separated gravel and vegetated bed influences the values of velocity distribution in a different way in comparison with situations over of half-separated impermeable and permeable bed.