Feasibility of using Vis-NIR spectroscopy and PXRF spectrometry to estimate regional soil cadmium concentration.

Conventionally, soil cadmium (Cd) measurements in the laboratory are expensive and time-consuming, involving complex processes of sample preparation and chemical analysis. This study aimed to identify the feasibility of using sensor data of visible near-infrared reflectance (Vis-NIR) spectroscopy and portable X-ray fluorescence spectrometry (PXRF) to estimate regional soil Cd concentration in a time- and cost-saving manner. The sensor data of Vis-NIR and PXRF, and Cd concentrations of 128 surface soils from Yunnan Province, China, were measured. Outer-product analysis (OPA) was used for synthesizing the sensor data and Granger-Ramanathan averaging (GRA) was applied to fuse the model results. Artificial neural network (ANN) models were built using Vis-NIR data, PXRF data, and OPA data, respectively. Results showed that: (1) ANN model based on PXRF data performed better than that based on Vis-NIR data for soil Cd estimation; (2) Fusion methods of both OPA and GRA had higher predictive power (R2) = 0.89, ratios of performance to interquartile range (RPIQ) = 4.14, and lower root mean squared error (RMSE) = 0.06, in ANN model based on OPA fusion; R2 = 0.88, RMSE = 0.06, and RPIQ = 3.53 in GRA model) than those based on either Vis-NIR data or PXRF data. In conclusion, there exists a great potential for the combination of OPA fusion and ANN to estimate soil Cd concentration rapidly and accurately.

Very high-resolution remote sensing-based mapping of urban residential districts to help combat COVID-19.

Urban residential districts (URDs) are a major element in the formation of cities that are essential for urban planning. Regarding the COVID-19 virus, which remains variable in aerosols for several hours, airborne transmission tends to occur in areas of poor ventilation and high occupant density. Thus, ventilation capacity is an important factor influencing airborne transmission in URDs, which should be evaluated as part of efforts to fight COVID-19 and guide healthy city planning and implementation. Here, we develop and test systematic methods to map URDs in a typical city in northern China and quantify their ventilation capacity using very high-resolution remote sensing images. Four fundamental spatial forms of URD are identified in the research area: the point-group form, parallel form, enclosed form, and hybrid form. Our analyses indicate that the integrated ventilation capacities for well-designed URDs are nearly twice those of poorly designed URDs. Large variations in ventilation capacity are also observed within URDs, with up to 13.42 times difference between the buildings. Therefore, very high-resolution remote sensing data are fundamental for extracting building height and generating precise spatial forms, which can improve the micro-scale URD ventilation planning for the prevention of COVID-19.

Using Deep Learning to Identify Utility Poles with Crossarms and Estimate Their Locations from Google Street View Images.

Traditional methods of detecting and mapping utility poles are inefficient and costly because of the demand for visual interpretation with quality data sources or intense field inspection. The advent of deep learning for object detection provides an opportunity for detecting utility poles from side-view optical images. In this study, we proposed using a deep learning-based method for automatically mapping roadside utility poles with crossarms (UPCs) from Google Street View (GSV) images. The method combines the state-of-the-art DL object detection algorithm (i.e., the RetinaNet object detection algorithm) and a modified brute-force-based line-of-bearing (LOB, a LOB stands for the ray towards the location of the target [UPC at here] from the original location of the sensor [GSV mobile platform]) measurement method to estimate the locations of detected roadside UPCs from GSV. Experimental results indicate that: (1) both the average precision (AP) and the overall accuracy (OA) are around 0.78 when the intersection-over-union (IoU) threshold is greater than 0.3, based on the testing of 500 GSV images with a total number of 937 objects; and (2) around 2.6%, 47%, and 79% of estimated locations of utility poles are within 1 m, 5 m, and 10 m buffer zones, respectively, around the referenced locations of utility poles. In general, this study indicates that even in a complex background, most utility poles can be detected with the use of DL, and the LOB measurement method can estimate the locations of most UPCs.

Reoperation for recurrent hepatolithiasis: laparotomy versus laparoscopy.

BACKGROUND: Laparoscopy has been proposed for the management of recurrent hepatolithiasis, but no comparative study of its relative efficacy versus laparotomy has been performed, and the patient selection criteria for laparoscopy are not clear. This study aimed to investigate the therapeutic effect of laparoscopy versus laparotomy for repeated hepatolithiasis and to highlight how to select patients best suited for laparoscopy. METHODS: We performed a cohort study of 94 patients who underwent laparotomy or laparoscopy for recurrent hepatolithiasis between January 2010 and May 2014. The clinical data of 53 patients who underwent open biliary exploration (laparotomy group) and 41 patients who underwent laparoscopic biliary exploration (laparoscopy group) for recurrent hepatolithiasis were retrospectively analyzed and compared. RESULTS: Intestinal adhesions to the porta hepatis occurred in 62 (66%) patients. There was no difference in operating time between the two groups. In comparing the laparoscopic group versus the laparotomy group, the intraoperative blood loss was less (P = .001), the incidence of postoperative ascites (9.8 vs. 30.2%, P = .016) and/or pleural effusion (7.3 vs. 28.3%, P = .010) was lower, and the stone clearance rates were comparable. Wound morbidity appeared peculiarly in 15 (28.3%) patients among the laparotomy group. The postoperative hospital stay in the laparoscopy group was shorter than that in the laparotomy group (P = .000). CONCLUSION: Laparoscopy is a safe and effective treatment for recurrent hepatolithiasis patients who are scheduled for bile duct exploration.

Spatial distribution and ecological risk assessment of trace metals in urban soils in Wuhan, central China.

Surface soil samples from 467 sample sites were collected in urban area of Wuhan City in 2013, and total concentrations of five trace metals (Pb, Zn, Cu, Cr, and Cd) were measured. Multivariate and geostatistical analyses showed that concentrations of Pb, Zn, and Cu are higher along Yangtze River in the northern area of Wuhan, gradually decrease from city center to suburbs, and are mainly controlled by anthropogenic activities, while those of Cr and Cd are relatively spatially homogenous and mainly controlled by soil parent materials. Pb, Zn, Cu, and Cd have generally higher concentrations in roadsides, industrial areas, and residential areas than in school areas, greenbelts, and agricultural areas. Areas with higher road and population densities and longer urban construction history usually have higher trace metal concentrations. According to estimated results of the potential ecological risk index and Nemero synthesis pollution index, almost the whole urban area of Wuhan is facing considerable potential ecological risk caused by soil trace metals. These results reveal obvious trends of trace metal pollution, and an important impact of anthropogenic activities on the accumulation of trace metals in soil in Wuhan. Vehicular emission, industrial activities, and household wastes may be the three main sources for trace metal accumulation. Increasing vegetation cover may reduce this threat. It should be pointed out that Cd, which is strongly accumulated in soil, could be the largest soil pollution factor in Wuhan. Effective measures should be taken as soon as possible to deal with Cd enrichment, and other trace metals in soil should also be reduced, so as to protect human health in this important large city.

Estimating the pollution risk of cadmium in soil using a composite soil environmental quality standard.

Estimating standard-exceeding probabilities of toxic metals in soil is crucial for environmental evaluation. Because soil pH and land use types have strong effects on the bioavailability of trace metals in soil, they were taken into account by some environmental protection agencies in making composite soil environmental quality standards (SEQSs) that contain multiple metal thresholds under different pH and land use conditions. This study proposed a method for estimating the standard-exceeding probability map of soil cadmium using a composite SEQS. The spatial variability and uncertainty of soil pH and site-specific land use type were incorporated through simulated realizations by sequential Gaussian simulation. A case study was conducted using a sample data set from a 150 km(2) area in Wuhan City and the composite SEQS for cadmium, recently set by the State Environmental Protection Administration of China. The method may be useful for evaluating the pollution risks of trace metals in soil with composite SEQSs.

Updating categorical soil maps using limited survey data by Bayesian Markov chain cosimulation.

Updating categorical soil maps is necessary for providing current, higher-quality soil data to agricultural and environmental management but may not require a costly thorough field survey because latest legacy maps may only need limited corrections. This study suggests a Markov chain random field (MCRF) sequential cosimulation (Co-MCSS) method for updating categorical soil maps using limited survey data provided that qualified legacy maps are available. A case study using synthetic data demonstrates that Co-MCSS can appreciably improve simulation accuracy of soil types with both contributions from a legacy map and limited sample data. The method indicates the following characteristics: (1) if a soil type indicates no change in an update survey or it has been reclassified into another type that similarly evinces no change, it will be simply reproduced in the updated map; (2) if a soil type has changes in some places, it will be simulated with uncertainty quantified by occurrence probability maps; (3) if a soil type has no change in an area but evinces changes in other distant areas, it still can be captured in the area with unobvious uncertainty. We concluded that Co-MCSS might be a practical method for updating categorical soil maps with limited survey data.

Identification of Socio-Economic Impacts as the Main Drivers of Carbon Stocks in China's Tropical Rainforests: Implications for REDD.

Active incentives or compensation measures plus conservation, sustainable management of forests, and enhancement of forest carbon stocks (denoted together as "REDD+") should be adopted in developing countries to reduce the greenhouse gas emissions caused by deforestation and forest degradation. Identification and analysis of the driving forces behind carbon stocks are crucial for the implementation of REDD+. In this study, the principal component model and the stepwise linear regression model were used to analyze the social and economic driving forces of stocks in three important types of forest change: deforestation, forestland transformation, and forest degradation in China's tropical rainforests of Xishuangbanna, based on the combination of satellite imagery and the normalized difference vegetation index. The findings show that there are different key driving forces that lead to carbon stock changes in the forest land conversion of Xishuangbanna. In particular, the agricultural development level is the main cause of emissions from deforestation, whereas poor performance of protection policies is the main cause of emissions from forest degradation. In contrast, the rural economic development interventions are significantly positive for emissions from forestland transformation. It is crucial to pay attention to distinguishing the driving forces behind carbon stock changes from forest degradation, deforestation, and transformation for optimizing REDD+ implementation and ensuring the effectiveness of REDD+.

High emissions could increase the future risk of maize drought in China by 60-70.

Drought events have considerable direct and indirect economic, environmental, and social impacts, but few studies have analyzed and assessed future changes in drought disasters from a risk perspective to guide responses and adaptations thoroughly. Studying the potential climate-related impacts on future crop yield is therefore urgently needed. Intercomparison of the three Shared Socio-economic Pathway (SSP) scenarios based drought risks and yield loss of China was carried out using the climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6), and the hotspots of high drought risk regions were identified. This study found that the areas affected by severe maize drought (loss ratio larger than 0.2) accounted for 16.13 %, 20.79 %, and 18.87 % of the total national corn areas under three low, medium-to-high and high emission scenarios (SSP1-2.6, SSP3-7.0, SSP5-8.5) respectively. The northwest China maize region, the ecotone between agriculture and animal husbandry, and the western central northern China maize region have relatively high loss risk. Compared with SSP1-2.6, the yield loss rates increased with 70.73 % and 61.52 % of national corn areas for SSP3-7.0 and SSP5-8.5, respectively. There is a decrease in the areas with low-risk and a significant increase in the areas with high-risk for SSP3-7.0 and SSP5-8.5 compared to the SSP1-2.6. These results may provide theoretical support for agricultural drought risk reduction and adaptation planning to ensure food security under climate change.

A comparative time series analysis and modeling of aerosols in the contiguous United States and China.

Long-term trend analysis and modeling of aerosol distribution is of paramount importance to study radiative forcing, climate change, and human health. Previous studies on spatiotemporal trend analysis have not fully considered the impact of spatial and temporal gaps of satellite-retrieved aerosol optical depth (AOD) on precise aerosol characterization. In addition, very few studies analyzed inter-country aerosol variations, trends, driving forces, and predictions at the regional level, which is important to draw lessons from the experiences of one another. This study is focused on comparative time series analyses and modeling of aerosols over the contiguous United States (U.S.) and China during 2003-2015 using MODIS Collection 6 retrievals. An econometric model, namely autoregressive integrated moving average (ARIMA), is employed to reproduce and predict AOD variability over U.S. and China. Results show that high AOD values are observed in the eastern part of U.S. and China. Temporal variations indicate that AODs reach their peak values in summer for both countries. A sustained negative AOD trend is present throughout the U.S. while a distinct spatial variation of AOD trend is exhibited in China. The large differences in variations and trends are closely linked to the energy strategies, economic and urban development, and lifestyle activities of these two countries. Time series modeling reveals that reasonably good performances are found in most parts of these two countries. In particular, the model replicates AOD time series that has clear seasonal variations with much more accuracy. The results suggest that areas most suitable for applying the model for prediction are those with high AOD quality, high completeness of AOD data, and low-AOD values. Overall, the satisfactory predicted results indicate the applicability and feasibility of the ARIMA modeling technique for accurately extracting AOD profiles, predicting future AOD values as well as extrapolating missed AOD values at the regional scale. The retrieved and predicted AOD values may serve as reliable estimates for air quality and epidemiological studies.

Correction of in-situ portable X-ray fluorescence (PXRF) data of soil heavy metal for enhancing spatial prediction.

Heavy metal data measured by portable X-ray fluorescence (PXRF), especially by in-situ PXRF, are usually affected by multiple soil factors, such as soil moisture (SM), soil organic matter (SOM), and soil particle size (SPZ). Thus, a correction may be needed. However, traditionally-used correction methods, such as non-spatial linear regression (LR), cannot effectively correct the spatially non-stationary influences of the related soil factors on PXRF analysis. Moreover, these correction methods are not robust to outliers. In this study, robust geographically weighted regression (RGWR) was used to correct in-situ and ex-situ PXRF data of soil Pb in a peri-urban agricultural area of Wuhan City, China. The accuracy of the corrected PXRF data by RGWR was compared with those by non-spatial and spatial but non-robust methods (i.e., LR and GWR). In addition, to find an appropriate method of using the corrected PXRF data for more accurate spatial prediction, we compared robust ordinary kriging with the corrected PXRF data as part of hard data (ROK-CPXRF) and robust ordinary cokriging with the corrected PXRF data as auxiliary soft data (RCoK-CPXRF). Results showed that (i) RGWR obtained higher correction accuracy than LR and GWR on both the in-situ and ex-situ PXRF data; (ii) the accuracy of the RGWR-corrected in-situ PXRF data was increased nearly to that of the RGWR-corrected ex-situ PXRF data; (iii) given the same amount of sample data, ROK-CPXRF obtained higher prediction accuracy than RCoK-CPXRF. It is concluded that the methods suggested in this study may largely promote the application of in-situ PXRF technique for rapid and accurate soil heavy metal investigation in large-scale areas.

Pre-evacuation Time Estimation Based Emergency Evacuation Simulation in Urban Residential Communities.

The timely and secure evacuation of an urban residential community is crucial to residents' safety when emergency events happen. This is different to evacuation of office spaces or schools, emergency evacuation in residential communities must consider the pre-evacuation time. The importance of estimating evacuation time components has been recognized for approximately 40 years. However, pre-evacuation time is rarely discussed in previous community-scale emergency evacuation studies. This paper proposes a new method that estimates the pre-evacuation time, which makes the evacuation simulation in urban residential communities more realistic. This method integrates the residents' pre-evacuation behavior data obtained by surveys to explore the influencing factors of pre-evacuation time and builds a predictive model to forecast pre-evacuation times based on the Random Forest algorithm. A sensitivity analysis is also conducted to find the critical parameters in evacuation simulations. The results of evacuation simulations in different scenarios can be compared to identify potential evacuation problems. A case study in Luoshanqicun Community, Pudong New District, Shanghai, China, was conducted to demonstrate the feasibility of the proposed method. The simulation results showed that the pre-evacuation times have significant impacts on the simulation procedure, including the total evacuation time, the congestion time and the congestion degree. This study can help to gain a deeper understanding of residents' behaviors under emergencies and improve emergency managements of urban communities.

Urban Ecological Security Simulation and Prediction Using an Improved Cellular Automata (CA) Approach-A Case Study for the City of Wuhan in China.

Ecological security is an important research topic, especially urban ecological security. As highly populated eco-systems, cities always have more fragile ecological environments. However, most of the research on urban ecological security in literature has focused on evaluating current or past status of the ecological environment. Very little literature has carried out simulation or prediction of future ecological security. In addition, there is even less literature exploring the urban ecological environment at a fine scale. To fill-in the literature gap, in this study we simulated and predicted urban ecological security at a fine scale (district level) using an improved Cellular Automata (CA) approach. First we used the pressure-state-response (PSR) method based on grid-scale data to evaluate urban ecological security. Then, based on the evaluation results, we imported the geographically weighted regression (GWR) concept into the CA model to simulate and predict urban ecological security. We applied the improved CA approach in a case study-simulating and predicting urban ecological security for the city of Wuhan in Central China. By comparing the simulated ecological security values from 2010 using the improved CA model to the actual ecological security values of 2010, we got a relatively high value of the kappa coefficient, which indicates that this CA model can simulate or predict well future development of ecological security in Wuhan. Based on the prediction results for 2020, we made some policy recommendations for each district in Wuhan.

​Parcel feature data derived from Google Street View images for urban land use classification in Brooklyn, New York Cityfor urban land use classification in Brooklyn, New York Cityretain-->.

Google Street View (GSV) was used for urban land use classification, together with airborne light detection and ranging (LiDAR) data and high resolution orthoimagery, by a parcel-based method. In this data article, we present the input raw GSV images, intermediate products of GSV images, and final urban land use classification data that are related to our research article "Parcel-based urban land use classification in megacity using airborne LiDAR, high resolution orthoimagery, and Google Street View" (Zhang et al., 2017) [1]. More detail about other used data and our findings can be found in Zhang et al. (2017) [1].

Spatial assessment of soil nitrogen availability and varying effects of related main soil factors on soil available nitrogen.

To effectively understand the availability of soil nitrogen and assist in soil nitrogen control at the regional scale, it is essential to understand the accurate spatial distribution patterns of the three soil nitrogen parameters [i.e., total nitrogen (TN), available nitrogen (AN) and nitrogen availability ratio (NAR)] and explore the spatially varying influences of major impact factors on soil AN. Land use affects the spatial distributions of soil TN, AN and NAR (i.e., AN/TN). To explore the effects of different land use types and improve mapping accuracy, residual kriging with land use information and ordinary kriging (without land use information) were compared based on the sample data of soil TN and AN in Hanchuan county, China. A local regression technique, geographically weighted regression (GWR), was adopted to explore the varying relationships between soil AN and its major impact factors in soil (i.e., soil TN and soil pH), due to the advantages of GWR over the traditional ordinary least squares regression (OLS) model. The results showed that (1) land use types as auxiliary information obviously improved the prediction accuracies of the three soil nitrogen parameters; (2) GWR performed much better than OLS in terms of fitting accuracy; and (3) GWR effectively revealed the spatially varying influences of the impact factors on soil AN, which were ignored by OLS. Based on the results, suggestions for soil nitrogen control measures in different subareas were proposed.

Choledochoscopic lithotripsy is a useful adjunct to laparoscopic common bile duct exploration for hepatolithiasis: a cohort study.

BACKGROUND: This study aims to investigate the role of combining choledochoscopic lithotripsy with laparoscopic common bile duct exploration for hepatolithiasis in patients who are not suitable for hepatectomy. METHODS: From March 2009 to March 2013, 86 patients with hepatolithiasis irrespective of whether they underwent a choledochoscopic plasma shock wave lithotripsy or not were analyzed. RESULTS: Sixty-two patients underwent lithotripsy and 24 patients underwent basket lithoextraction intraoperatively. Plasma shock wave lithotripsy did not lengthen the operating time, but decreased the postoperative residual stone rate and reduced the frequency of postoperative choledochoscopic lithotomy for patients with remnant stones. The overall final stone clearance rate was 98.8%. During a mean follow-up of 26.2 months, recurrent stones and cholangiocarcinoma developed in 1 patient, respectively. CONCLUSION: Laparoscopic common bile duct exploration combined with choledochoscopic lithotripsy is a definitive procedure for hepatolithiasis in patients who are not candidates for hepatectomy.

Spatial uncertainty of joint health risk of multiple trace metals in rice grain in Jiaxing city, China.

Trace metals in rice may affect human health if their concentrations reach risk levels in the human body due to long-term ingestion. In this study, concentrations of five trace metals (Cd, Cr, Cu, Pb and Zn) in field-sampled rice grains were measured at 285 sampling sites in Jiaxing city, China. The objective was to map the spatial distribution and uncertainty of the potential human health risk of trace metals in local rice grains at a regional scale. The probability map of multiple trace metals that tend to be hazardous was produced based on the permissible limits in Chinese National Standards. It showed higher probabilities, which exceeded the national standards, for one or more of trace metals in rice grains were mainly located in several small subareas around the center of the study area. We used the Target Hazard Quotient (THQ) index and the Total THQ (TTHQ) index to represent the potential human health risks caused by individual and multiple trace metals in local rice grains. The TTHQ was essentially regarded as an integrative uniform rice quality index in this study. Stochastic simulation was then conducted to simulate the TTHQ for mapping the spatial distribution and uncertainty of the total potential health risk. Probability maps of different risk grades for TTHQ indicated that most places were in unsafe grades. In general, maps of site-specific health risks posed by trace metals in local rice grains and associated uncertainty information are valuable to spatial decision making in agricultural planning, rice uses, and environmental management.