Surface modeling of soil antibiotics

Shi, Wenjiao; Yue, Tianxiang; Du, Zhengping; Zong, Wang; Li, Xuewen

doi:10.1016/j.scitotenv.2015.11.077

Cited by 22 publications

(18 citation statements)

References 23 publications

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“…High accuracy surface modeling can simulate continuous attribute variations in three-dimensional space, and has been successfully used in simulating climate change [17,32,33], constructing DEMs [28,29], interpolating soil properties [34,35,36] and terrestrial land cover change [19]. For this study, HASM took the approximate XCO 2 surface as its driving field and the flight retrieved XCO 2 as its optimum control constraints as shown in Equation (15):

Xsim = HASM (Xinitial, Xsam)

where

Xsim

is the final XCO 2 surface which is calculated by HASM,

Xinitial

is the approximate XCO 2 surface which is obtained by regression modeling, and

Xsam

is the retrieval XCO 2 .…”

Section: Methodsmentioning

confidence: 99%

Modelling of XCO2 Surfaces Based on Flight Tests of TanSat Instruments

Zhang

Tian

Wilson

et al. 2016

Sensors

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The TanSat carbon satellite is to be launched at the end of 2016. In order to verify the performance of its instruments, a flight test of TanSat instruments was conducted in Jilin Province in September, 2015. The flight test area covered a total area of about 11,000 km2 and the underlying surface cover included several lakes, forest land, grassland, wetland, farmland, a thermal power plant and numerous cities and villages. We modeled the column-average dry-air mole fraction of atmospheric carbon dioxide (XCO2) surface based on flight test data which measured the near- and short-wave infrared (NIR) reflected solar radiation in the absorption bands at around 760 and 1610 nm. However, it is difficult to directly analyze the spatial distribution of XCO2 in the flight area using the limited flight test data and the approximate surface of XCO2, which was obtained by regression modeling, which is not very accurate either. We therefore used the high accuracy surface modeling (HASM) platform to fill the gaps where there is no information on XCO2 in the flight test area, which takes the approximate surface of XCO2 as its driving field and the XCO2 observations retrieved from the flight test as its optimum control constraints. High accuracy surfaces of XCO2 were constructed with HASM based on the flight’s observations. The results showed that the mean XCO2 in the flight test area is about 400 ppm and that XCO2 over urban areas is much higher than in other places. Compared with OCO-2’s XCO2, the mean difference is 0.7 ppm and the standard deviation is 0.95 ppm. Therefore, the modelling of the XCO2 surface based on the flight test of the TanSat instruments fell within an expected and acceptable range.

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Xsim = HASM (Xinitial, Xsam)

where

Xsim

is the final XCO 2 surface which is calculated by HASM,

Xinitial

is the approximate XCO 2 surface which is obtained by regression modeling, and

Xsam

is the retrieval XCO 2 .…”

Section: Methodsmentioning

confidence: 99%

Modelling of XCO2 Surfaces Based on Flight Tests of TanSat Instruments

Zhang

Tian

Wilson

et al. 2016

Sensors

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“…Antibiotics are disseminated mainly through direct and indirect emissions such as excrement, sewage irrigation and sludge compost, getting into the environment negatively impacting soil ecosystems Shi, Yue, Du, Wang, & Li, 2016) and Agua (Vo et al, 2016), as it is shown in graph 1, the interaction of antibiotics and the environment. …”

Section: A N T I B I O T I C S O U R C E S I N T H E Environmentmentioning

confidence: 99%

“…This manure is applied to the soil as fertilizer; however, the active constituents of antibiotics are not biodegraded Shi et al, 2016) since they are persistent in the environment (Koba et al, 2017), this generates negative impacts on the micro biota of the soil, which meet important processes (Jechalke et al,…”

Section: Impact On Soilmentioning

confidence: 99%

Antibiotics and their environmental implications

Vilca

Ángeles

Palma

et al. 2018

Rev. investig. altoandin.

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“…To evaluate the potential risks to humans and the environment, there is a growing concern about the spatial distributions of soil heavy metals in the environment because an inaccurate estimation of soil heavy metal distributions will result in considerable bias in risk assessment 1 . Soil sampling analysis can provide highly accurate data of soil heavy metals at sampling sites, but these sampling points are sparse because of the laborious sampling process and the expensive costs for sample analysis in the lab 5 . Therefore, methods of spatial distribution modelling are required to obtain accurate spatial distribution maps of soil heavy metals from limited point observations for risk control.…”

Section: Introductionmentioning

confidence: 99%

“…Several classical methods, such as kriging, inverse distance weighting and splines, are extensively used to estimate the spatial distributions of soil heavy metals in soil pollution investigations 1 ; nevertheless, each of these methods has its own limitations 1, 5 . These classical methods, which predict the soil heavy metal contents of untested locations based on the neighbouring soil samples and the spatial autocorrelation of soil sampling data 6 , all have a smoothing effect that tends to underestimate the local high values and overestimate the local low values 7 .…”

Section: Introductionmentioning

confidence: 99%

Prediction of soil cadmium distribution across a typical area of Chengdu Plain, China

Wang

Dai

et al. 2017

Sci Rep

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A suitable method and appropriate environmental variables are important for accurately predicting heavy metal distribution in soils. However, the classical methods (e.g., ordinary kriging (OK)) have a smoothing effect that results in a tendency to neglect local variability, and the commonly used environmental variables (e.g., terrain factors) are ineffective for improving predictions across plains. Here, variables were derived from the obvious factors affecting soil cadmium (Cd), such as road traffic, and were used as auxiliary variables for a combined method (HASM_RBFNN) that was developed using high accuracy surface modelling (HASM) and radial basis function neural network (RBFNN) model. This combined method was then used to predict soil Cd distribution in a typical area of Chengdu Plain in China, considering the spatial non-stationarity of the relationships between soil Cd and the derived variables based on 339 surface soil samples. The results showed that HASM_RBFNN had lower prediction errors than OK, regression kriging (RK) and HASM_RBFNNs, which didn’t consider the spatial non-stationarity of the soil Cd-derived variables relationships. Furthermore, HASM_RBFNN provided improved detail on local variations. The better performance suggested that the derived environmental variables were effective and HASM_RBFNN was appropriate for improving the prediction of soil Cd distribution across plains.

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Surface modeling of soil antibiotics

Cited by 22 publications

References 23 publications

Modelling of XCO2 Surfaces Based on Flight Tests of TanSat Instruments

Modelling of XCO2 Surfaces Based on Flight Tests of TanSat Instruments

Antibiotics and their environmental implications

Prediction of soil cadmium distribution across a typical area of Chengdu Plain, China

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