Dr. Zhiyong Hu

This chapter assesses the association between adult stroke mortality prevalence rate and long-term exposure to PM2.5 (ambient air pollution of particulate matter with an aerodynamic diameter of 2.5 m or less) adjusting for unhealthy lifestyles and other health conditions. Health data, based on 2017 or 2016 model-based small area estimates of chronic disease, was obtained from the “500 Cities Project” 2019 release. PM2.5 data for the year 2016 was acquired from the “The Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD), V4.03 (1998-2019)” datasets. Average PM2.5 was calculated for each city using a GIS zonal statistics function. First, classic ordinary least (OLS) square regression modeling (univariate linear regression, and a multivariate linear regression model fitted using a generalized linear model via penalized maximum likelihood). However, Moran’s I test found significant positive spatial autocorrelation with stroke data, PM2.5, and the residues of the OLS multilinear regression model, which makes the OLS modeling unreliable. Therefore, two spatial regression models (spatial lag and spatial error) were further run to account for the spatial dependence. Both models have successfully explained away spatial dependency. The spatial error model has much better goodness-of-fit than the spatial lag model as indicated by the higher log-likelihood, lower Akaike info criterion (AIC), and lower Schwartz criterion. The spatial error model found that long-term exposure to ambient PM2.5 may increase the risk of stroke and that increasing physical activity, reducing smoking and body weight, enough sleep, and controlling diseases such as blood pressure, coronary heart disease, diabetes, and cholesterol, may mitigate the effect.

Cadmium (Cd) contamination in rice threats food safety and human health. Control of Cd pollution has become an urgent need. Most existing studies on heavy metal pollution control have focused on industrial wastewater and few on irrigation water. Some researchers have found ecological ditches, plant ponds and constructed wetlands have the potential of treating heavy metal contaminated irrigation water, but they examined only one of the methods and the validity needs to be verified by field studies. Our study has filled the gap by combining the methods and using field experiments. We examined efficiencies of removal of Cadmium from irrigation water by 14 different combinations of ecological ditches, plant ponds, and constructed wetlands using field experiments. The effects of the purification on Cd concentration in paddy soil and rice grains were also examined. Results showed that there were significant differences among efficiencies of purification of Cd contaminated irrigation water using different systems and that pH, chemical form of Cd in irrigation water, vegetation coverage and biomass of aquatic plants significantly affect the efficiency. Of the 14 purification systems, seven resulted in the concentration of Cd in the effluent water meeting the National Standard for Irrigation Water Quality (GB5084–2021) for all days of the experiment period. The highest amount and rate of Cd removal were achieved by the combination of two-stage ecological ditch, two-stage plant pond, and one-stage constructed wetland, while the highest removal amount and rate per 100 m2 was achieved by the combination of one-stage plant pond and one-stage constructed wetland. Considering purification efficiency, area of coverage, and cost of construction and maintenance, we suggest that combination of plant pond and constructed wetland be a priority choice for purification of Cd pollution in irrigation water. Compared to the control data collected from rice grain and paddy soil irrigated by unpurified water, Cd concentration in rice grain and paddy soil irrigated by purified water declined by 5.08–19.42 % and 30.93–77.15 % respectively. All results showed that removal of Cd contamination from irrigation water effectively controlled cadmium pollution in rice grain and paddy soil. Our study not only contributes to pollution control practice, but also warrants further investigation of the mechanisms of how the treatment systems work. The most efficient method we identified could be applied locally, regionally and in areas of similar topography, climate, soil, vegetation, agriculture, and heavy metal pollution.
The highest purification efficiency was achieved by the combination of two-stage ecological ditch, two-stage plant pond, and one-stage constructed wetland, while the highest purification efficiency per 100 m 2 was achieved by the combination of one-stage plant pond and one-stage constructed wetland. [Display omitted]
•The comparative study examined efficiencies of removal of Cd from rice irrigation water using field experiments.•Fourteen combinations of ecological ditches, plant ponds, and constructed wetlands were tested and compared.•The most efficient purification system was the combination of “one-stage plant pond + one-stage constructed wetland”.•The purification systems effectively reduced Cd concentration in rice grain and paddy soil.

Sea levels are rising globally. To better assess the impacts of sea level rise and coastal inundation on Texas gulf coast, we need to generate coastal marsh substrate as accurate as possible for the coupled hydrodynamic-marsh model (Hydro-MEM) to the region. After marsh is identified and removed from lidar point clouds, we need generate marsh substrate from neighboring points. The experiment is to find robust and fast algorithms for interpolating underlying surface of coastal marsh. First, we evaluated four commonly used geospatial interpolation methods for lidar points. Then we put forward a multiple-scale raster approach which adopts the principle of Voronoi diagram and inverse distance weights interpolation, the experiments show that it can run much fast than point methods with satisfactory accuracy.

This paper outlines the methods, results, and statistical analysis of a model we developed to demonstrate the feasibility of applying remote sensor meteorological data to navigation by using meteorological contour matching (METCOM). Terrain contour matching (TERCOM), a contemporary navigation system, possesses inherent performance flaws that may be resolved and improved by METCOM for subsonic and hypersonic missile or aircraft navigation. Remote sensor imagery data for this model was accessed from the Geostationary Operational Environmental Satellites-R Series operated by the National Oceanic and Atmospheric Administration by using Amazon Web Services through a script we developed in Python. Data processed for the model included imagery data and corresponding geospatial data from the legacy atmospheric profile products: legacy vertical temperature and legacy vertical moisture. Our analysis of the model included an error assessment to determine model accuracy, geostatistical analysis through semivariograms, meteorological signal of model data, and a combinatorial analysis to evaluate navigation performance. We conducted a model assessment which indicated an accuracy of 66.2% in the data used as a combined result of instrument error and interference of cloud formations. Results of the remaining analysis offered methods to evaluate METCOM performance and compare different meteorological data products. These results allowed us to statistically compare METCOM and TERCOM, yielding several indications of improved performance including an increase by a factor of at least 13.5 in data variability and contourability. The analysis we conducted served as a proof of concept to justify further research into the feasibility and application of METCOM.

The use of UAVs inside earth science has been on the rise as of recent. One of the primary uses of drones within the earth science sphere is to create models and maps of large areas that are incredibly accurate. The most basic map application is called an orthomosaic. This map uses a series of photos called Orthophotos which are photos that have been orthorectified, or geometrically corrected to create a uniform scale so that the photo is accurate. This creates a map that is essentially a satellite image, but the distances displayed on the map are extremely precise, combine these photos with a LIDAR software that maps the coordinates for you and then plug them into ArcGIS to create various models. This project will display an Elevation model, a 3D model, an Orthomosaic, and a Map that uses information collected from these to make assumptions about plant health in an area.

Framework-forming scleractinian (FFS) corals provide structurally complex habitats to support abundant and diverse benthic communities but are vulnerable to environmental changes and anthropogenic disturbances. Scientific modeling of suitable habitat provides important insights into the impact of the environmental conditions and fills the gap in the knowledge on habitat suitability. This study presents predictive habitat suitability modeling for deep-sea (depth > 50 in) FFS corals in the GoM. We first conducted a nonparametric estimate of the observed coral point process intensity as a function of each numeric environmental variable. Next, we performed species distribution modeling (SDM) using an assemble of four machine learning models - maximum entropy (ME), support vector machine (SVM), random forest (RF), and deep neural network (DNN). We found that most important variables controlling the coral distribution are super-dominant gravel and rock substrata. SW and SE aspects, slope steepness, salinity, depth, temperature, acidity, dissolved oxygen, and chlorophyll-a. Highly suitable habitats are predicted to be on the continental slope off Texas, Louisiana, and Mississippi and the shelf and slope of the West Florida Escarpment. All the four models have outstanding prediction performances with AUC values over 095. DNN model performs best (AUC - 0987). The study contributes to coral habitat modeling research by presenting unique methods induding nonparametric function of coral point process intensity. DNN and SVM models that have not been used in coral SDM, post-classification model assembling, and percentile approach to determine a threshold value for classifying a suitability score map into a binary map. Our findings would help support conservation prioritization, management and planning, and guide new field exploration. (C) 2020 Elsevier B.V. All rights reserved.

Fully Convolutional Network (FCN), which can adopt various Convolutional Neural Networks (CNN), are now increasingly being used in remote sensing communities. CNN are improved constantly either in accuracy or by reducing parameters for a given equivalent accuracy. This paper investigates five widely used CNNs (AlexNet, VGG16, ResNet, SqueezeNet, and a pruned VGG16) in the context of FCN for coastal beach classification of imagery acquired by Unmanned Aerial Vehicles (UAV). Our experiments show that (1) not every CNN is suitable to FCN for semantic segmentations of images though each CNN approximately achieved an equivalent accuracy for image labeling; (2) band reduced pruning of existing CNN has the least impact on implementation and accuracy. To examine the capability of convolutional layers capturing semantic features, this paper also carries out beach classification experiments using hypercolumn methods with VGG16.

The predicted wind power in coastal waters is an important factor when planning and developing offshore wind farms. The stochastic wind field challenges the accuracy of these predictions. Using single-point wind measurements, most previous studies have focused on the prediction of short-term wind power, ranging from minutes to several days. Longer-term wind power predictions would better support decision-making related to offshore wind power balance management and reserve capacities. In addition, larger-scale wind power predictions, based on gridded wind field data, would provide a more comprehensive understanding of the spatiotemporal variations of wind energy resources. In this study, a spatiotemporal ordinary kriging model was developed to predict the offshore wind power density on a monthly basis using the cross-calibrated multiplatform gridded wind field data. The spatiotemporal variations of wind power density were directly quantified through the development of spatiotemporal variograms that integrated spatial and temporal distances. The proposed model achieved a notable performance with an overall R-2 of 0.94 and a relative prediction error of 16.35% in the validation experiment of predicting the monthly wind power density from 2013 in the coastal waters of China's Guangdong Province. Using this model, the spatial distributions of wind power density along Guangdong's coastal waters at monthly, seasonal, and annual time-scales from 2013 were accurately predicted. The experiment results demonstrated the remarkable potential of the spatiotemporal ordinary kriging model to provide reliable long-term prediction for offshore wind energy resources.

Statistical modeling using ground-based PM2.5 observations and satellite-derived aerosol optical depth (AOD) data is a promising means of obtaining spatially and temporally continuous PM2.5 estimations to assess population exposure to PM2.5. However, the vast amount of AOD data that is missing due to retrieval incapability above bright reflecting surfaces such as cloud/snow cover and urban areas challenge this application. Furthermore, most previous studies cannot directly account for the spatiotemporal autocorrelations in PM2.5 distribution, impacting the associated estimation accuracy. In this study, fixed rank smoothing was adopted to fill the data gaps in a semifinished 3 km AOD dataset, which was a combination of the Moderate Resolution Imaging Spectroradiometer (MODIS) 3 km Dark Target AOD data and MODIS 10 km Deep Blue AOD data from the Terra and Aqua satellites. By matching the gap-filled 3 km AOD data, ground-based PM2.5 observations, and auxiliary variable data, sufficient samples were screened to develop a spatiotemporal regression kriging (STRK) model for PM2.5 estimation. The STRK model achieved notable performance in a cross-validation experiment, with a R square of 0.87 and root-mean-square error of 16.55 μg/m3 when applied to estimate daily ground-level PM2.5 concentrations over East China from March 1, 2015 to February 29, 2016. Using the STRK model, daily PM2.5 concentrations with full spatial coverage at a resolution of 3 km were generated. The PM2.5 distribution pattern over East China can be identified at a relatively fine spatiotemporal scale. Thus, the STRK model with gap-filled high-resolution AOD data can provide reliable full-coverage PM2.5 estimations over large areas for long-term exposure assessment in epidemiological studies.