Bioremediation of Petroleum-Contaminated Soil

Petroleum is not only an important energy resource to boost the economic development, but also a major pollutant of the soil. The toxicity of petroleum can cause a negative impact on ecosystem, as well as the negative effects related to its carcinogenic for both animals and humans. In the present study, bioremediation as an alternative tool for restoration petroleum-contaminated soils was set forth, and focusing on the phytoremediatior plants, petroleum-biodegradable microorganism are responsible for the biodegradation of petroleum. In the present chapter, the bioremediation of petroleum-contaminated soil, as well as the influence factors of bioremediation are elaborated based on the recently studies. This will provide a novel understanding on bioremediation and help improve strategies for petroleum-contaminated soils remediation

A customized early warning score enhanced emergency department patient flow process and clinical outcomes in a COVID-19 pandemic.

Objective: Patient crowding and boarding in the emergency department (ED) is associated with adverse outcomes and has become increasingly problematic in recent years. We investigated the impact of an ED patient flow countermeasure using an early warning score. Methods: We conducted a cross-sectional analysis of observational data from patients who presented to the ED of a Level 1 Trauma Center in Pennsylvania. We implemented a modified version of the Modified Early Warning Score (MEWS), called mMEWS, to address patient flow. Patients aged ≥18 years old admitted to the adult hospital medicine service were included in the study. We compared the pre-mMEWS (February 19, 2017-February 18, 2019) to the post-mMEWS implementation period (February 19, 2019-June 30, 2020). During the intervention, low MEWS (0-1) scoring admissions went directly to the inpatient floor with expedited orders, the remainder waited in the ED until the hospital medicine admitting team evaluated the patient and then placed orders. We investigated the association between mMEWS, ED length of stay (LOS), and 24-hour rapid response team (24 hour-RRT) activation. RRT activation rates were used as a measure of adverse outcome for the new process and are a network team response for admitted patients who are rapidly decompensating. The association between mMEWS and the outcomes of ED length of stay in minutes and 24 hour-RRT activation was assessed using linear and logistic regression adjusting for a priori selected confounders, respectively. Results: Of the total 43,892 patients admitted, 19,962 (45.5%) were in the pre-mMEWS and 23,930 (54.5%) in the post-mMEWS implementation period. The median post-mMEWS ED LOS was shorter than the pre-mMEWS (376 vs 415 minutes; Conclusion: The use of a modified MEWS enhanced admission process to the hospital medicine service, even during the COVID-19 pandemic, was associated with a significant decrease in ED LOS without a significant increase in 24 hour-RRT activation

A Semi-Empirical Inversion Model for Assessing Surface Soil Moisture using AMSR-E Brightness Temperatures

In 2004-2005, 2007 and 2009, three major drought disasters occurred in Guangdong Province of southern China, which caused serious economic losses. Hence, it has recently become an important research subject in China to monitor surface soil moisture (SSM) and the drought disaster quickly and accurately. SSM is an effective indicator for characterizing the degree of drought. First, using the brightness temperatures (T[subscript b]) of the Advanced Microwave Scanning Radiometer on the EOS Aqua Satellite (AMSR-E), a modified surface roughness index was developed to map the land surface roughness. Then by combining microwave polarization difference indices (MPDI)-based vegetation cover classification and the modified surface roughness index, a simple semi-empirical model of SSM was derived from the passive microwave radiative transfer equation using AMSR-E C-band T[subscript b] and observed surface soil temperature (T[subscript s]). The model was inverted to calculate SSM. The results show the ability to discriminate over a broad range of SSM (7%~73%) with an accuracy of 2.11% in bare ground and flat areas (R² =0.87), 2.89% in sparse vegetation and flat surface areas (R²=0.85), about 6%~9% in dense vegetation areas and rough surface areas (0.80≤R²≤0.83). The simulation results were also validated using in-situ SSM data (R²=0.87, RMSE=6.36%). Time series mapping of SSM from AMSR-E imageries further demonstrated that the presented method was effective to detect the initiation, duration and recovery of the drought events.This is the author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by Elsevier and can be found at: http://www.journals.elsevier.com/journal-of-hydrology/.Keywords: Drought disaster, Brightness temperature (T[subscript b]), South China, Semi-Empirical model, Surface soil moisture (SSM), AMSR-E, Passive microwave remote sensin

Estimating the Leaf Area Index of Winter Wheat Based on Unmanned Aerial Vehicle RGB-Image Parameters

The leaf area index (LAI) is not only an important parameter for monitoring crop growth, but also an important input parameter for crop yield prediction models and hydrological and climatic models. Several studies have recently been conducted to estimate crop LAI using unmanned aerial vehicle (UAV) multispectral and hyperspectral data. However, there are few studies on estimating the LAI of winter wheat using unmanned aerial vehicle (UAV) RGB images. In this study, we estimated the LAI of winter wheat at the jointing stage on simple farmland in Xinjiang, China, using parameters derived from UAV RGB images. According to gray correlation analysis, UAV RGB-image parameters such as the Visible Atmospherically Resistant Index (VARI), the Red Green Blue Vegetation Index (RGBVI), the Digital Number (DN) of Blue Channel (B) and the Green Leaf Algorithm (GLA) were selected to develop models for estimating the LAI of winter wheat. The results showed that it is feasible to use UAV RGB images for inverting and mapping the LAI of winter wheat at the jointing stage on the field scale, and the partial least squares regression (PLSR) model based on the VARI, RGBVI, B and GLA had the best prediction accuracy (R2 = 0.776, root mean square error (RMSE) = 0.468, residual prediction deviation (RPD) = 1.838) among all the regression models. To conclude, UAV RGB images not only have great potential in estimating the LAI of winter wheat, but also can provide more reliable and accurate data for precision agriculture management

Evacuation Priority Method in Tsunami Hazard Based on DMSP/OLS Population Mapping in the Pearl River Estuary, China

Evacuation plans are critical in case of natural disaster to save people’s lives. The priority of population evacuation on coastal areas could be useful to reduce the death toll in case of tsunami hazard. In this study, the population density remote sensing mapping approach was developed using population records in 2013 and Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) night-time light (NTL) image of the same year for defining the coastal densest resident areas in Pearl River Estuary (PRE), China. Two pixel-based saturation correction methods were evaluated for application of population density mapping to enhance DMSP/OLS NTL image. The Vegetation Adjusted NTL Urban Index (VANUI) correction method (R2 (original/corrected): 0.504, Std. error: 0.0069) was found to be the better-fit correction method of NTL image saturation for the study area compared to Human Settlement Index (HSI) correction method (R2 (original/corrected): 0.219, Std. error: 0.1676). The study also gained a better dynamic range of HSI correction (0~25 vs. 0.1~5.07) compared to the previous one [27]. The town-level’s population NTL simulation model is built (R2 = 0.43, N = 47) for the first time in PRE with mean relative error (MSE) of 32% (N = 24, town level), On the other side, the tsunami hazard map was produced based on numerical modeling of potential tsunami wave height and velocity, combining with the river net system, elevation, slope, and vegetation cover factors. Both results were combined to produce an evacuation map in PRE. The simulation of tsunami exposure on density of population showed that the highest evacuation priority was found to be in most of Zhuhai city area and the coastal area of Shenzhen City under wave height of nine meters, while lowest evacuation priority was defined in Panyu and Nansha Districts of Guangzhou City, eastern and western parts of Zhongshan City, and northeast and northwest parts of Dongguan City. The method of tsunami risk simulation and the result of mapped tsunami exposure are of significance for direction to tsunami disaster-risk reduction or evacuation traffic arrangement in PRE or other coastal areas in the world

Coupling and Coordination Relationships between Urban Expansion and Ecosystem Service Value in Kashgar City

The growing urbanization of oasis cities in arid and semi-arid regions of Northwest China has an adverse influence on the fragile local ecological system. Therefore, improved understanding of the coupling and coordination between urban expansion (UE) and ecosystem services value (ESv) is critical to long term sustainable development. Here, we study the urbanization trend of a typical oasis city of Northwest China (Kashgar) using Landsat TM/ETM+/OLI imagery from 1990 to 2015. Land use types are classified and the spatio-temporal features of UE are analyzed; ESv of each land use types and the ecosystem services function (ESf) are determined; the driving factors of UE and the spatio-temporal change of ESv are analyzed; and the coupling and coordination relationship between UE and ESv is quantitatively determined. Results show that: (1) The land use structure has changed significantly between 1990 and 2015, with construction land (40.51 km2) showing the highest growth and farmland (28.42 km2). (2) UE values during 2000–2005 (16.65 km2) and 2010–2015 (21.09 km2) are relatively large, and during 1990–2015, the city extended from the center to the outskirts at a dynamic growth rate of 13.17% and a comprehensive expansion index of 1.54‰. (3) The total ESv was reduced by CNY 35.76 million (USD ~ 5.26 million), ranked from high to low as: waste treatment (CNY 9.94 million, USD ~1.46 million), water source conservation (CNY 7.95 million, USD ~ 1.17 million), soil formation (CNY 4.60 million, USD ~ 0.68 million), biodiversity protection (CNY 3.37 million, USD ~ 0.5 million), climate regulation (CNY 3.15 million, USD ~ 0.46 million), food production (CNY 2.83 million, USD ~ 0.42 million), gas regulation (CNY 1.96 million, USD ~ 0.29 million), entertainment and leisure (CNY 1.26 million, USD ~ 0.19 million), and raw materials (CNY 0.68 million, USD ~ 0.1 million). (4) The coupling degree between UE and ESv is relatively small (UE and ESv is relatively low, indicating that UE already poses a serious danger to the ecological environment. (5) The rapid growth of the population and economy and government policies are the main driving factors of intensive UE. Increasing climatic factors such as precipitation, temperature, and runoff impact ESv in some positive ways whereas UE leads to a reduction of ESv. Our results here can help to guide long-term sustainable development of arid regions, reasonable urban planning of oasis cities, and protection of the local ecological environment

Papaya Tree Detection with UAV Images Using a GPU-Accelerated Scale-Space Filtering Method

The use of unmanned aerial vehicles (UAV) can allow individual tree detection for forest inventories in a cost-effective way. The scale-space filtering (SSF) algorithm is commonly used and has the capability of detecting trees of different crown sizes. In this study, we made two improvements with regard to the existing method and implementations. First, we incorporated SSF with a Lab color transformation to reduce over-detection problems associated with the original luminance image. Second, we ported four of the most time-consuming processes to the graphics processing unit (GPU) to improve computational efficiency. The proposed method was implemented using PyCUDA, which enabled access to NVIDIA’s compute unified device architecture (CUDA) through high-level scripting of the Python language. Our experiments were conducted using two images captured by the DJI Phantom 3 Professional and a most recent NVIDIA GPU GTX1080. The resulting accuracy was high, with an F-measure larger than 0.94. The speedup achieved by our parallel implementation was 44.77 and 28.54 for the first and second test image, respectively. For each 4000 × 3000 image, the total runtime was less than 1 s, which was sufficient for real-time performance and interactive application