Economic losses due to ozone impacts on human health, forest productivity and crop yield across China

China's economic growth has significantly increased emissions of tropospheric ozone (O3) precursors, resulting in increased regional O3 pollution. We analyzed data from >1400 monitoring stations and estimated the exposure of population and vegetation (crops and forests) to O3 pollution across China in 2015. Based on WHO metrics for human health protection, the current O3 level leads to +0.9% premature mortality (59,844 additional cases a year) with 96% of populated areas showing O3–induced premature death. For vegetation, O3 reduces annual forest tree biomass growth by 11–13% and yield of rice and wheat by 8% and 6%, respectively, relative to conditions below the respective AOT40 critical levels (CL). These CLs are exceeded over 98%, 75% and 83% of the areas of forests, rice and wheat, respectively. Using O3 exposure–response functions, we evaluated the costs of O3-induced losses in rice (7.5 billion US$), wheat (11.1 billion US$) and forest production (52.2 billion US$) and SOMO35–based morbidity for respiratory diseases (690.9 billion US$) and non–accidental mortality (7.5 billion US$), i.e. a total O3-related cost representing 7% of the China Gross Domestic Product in 2015. Keywords: Surface ozone, Human health, Wheat, Rice, Forests, Crops, Risk assessment, Impacts, Economic valuatio

Biophysical Assessment of Single Cell Cytotoxicity: Diesel Exhaust Particle-Treated Human Aortic Endothelial Cells

Exposure to diesel exhaust particles (DEPs), a major source of traffic-related air pollution, has become a serious health concern due to its adverse influences on human health including cardiovascular and respiratory disorders. To elucidate the relationship between biophysical properties (cell topography, cytoskeleton organizations, and cell mechanics) and functions of endothelial cells exposed to DEPs, atomic force microscope (AFM) was applied to analyze the toxic effects of DEPs on a model cell line from human aortic endothelial cells (HAECs). Fluorescence microscopy and flow cytometry were also applied to further explore DEP-induced cytotoxicity in HAECs. Results revealed that DEPs could negatively impair cell viability and alter membrane nanostructures and cytoskeleton components in a dosage- and a time-dependent manner; and analyses suggested that DEPs-induced hyperpolarization in HAECs appeared in a time-dependent manner, implying DEP treatment would lead to vasodilation, which could be supported by down-regulation of cell biophysical properties (e.g., cell elasticity). These findings are consistent with the conclusion that DEP exposure triggers important biochemical and biophysical changes that would negatively impact the pathological development of cardiovascular diseases. For example, DEP intervention would be one cause of vasodilation, which will expand understanding of biophysical aspects associated with DEP cytotoxicity in HAECs

Fractional Modeling for Quantitative Inversion of Soil-Available Phosphorus Content

The study of field spectra based on fractional-order differentials has rarely been reported, and traditional integer-order differentials only perform the derivative calculation for 1st-order or 2nd-order spectrum signals, ignoring the spectral transformation details between 0th-order to 1st-order and 1st-order to 2nd-order, resulting in the problem of low-prediction accuracy. In this paper, a spectral quantitative analysis model of soil-available phosphorus content based on a fractional-order differential is proposed. Firstly, a fractional-order differential was used to perform a derivative calculation of original spectral data from 0th-order to 2nd-order using 0.2-order intervals, to obtain 11 fractional-order spectrum data. Afterwards, seven bands with absolute correlation coefficient greater than 0.5 were selected as sensitive bands. Finally, a stepwise multiple linear regression algorithm was used to establish a spectral estimation model of soil-available phosphorus content under different orders, then the prediction effect of the model under different orders was compared and analyzed. Simulation results show that the best order for a soil-available phosphorus content regression model is a 0.6 fractional-order, the coefficient of determination ( R 2 ), root mean square error (RMSE), and ratio of performance to deviation (RPD) of the best model are 0.7888, 3.348878, and 2.001142, respectively. Since the RPD value is greater than 2, the optimal fractional model established in this study has good quantitative predictive ability for soil-available phosphorus content

Pyrolysis characteristics of fat from Nannochloropsis sp. and its effect on pyrolysis of all components

The pyrolysis of microalgae to produce bio-oil is one of the most promising technologies on renewable energy resources. Bio-oil is a kind of clean biological energy resource with high energy density and similar chemical composition to the diesel fuel. During the process of pyrolysis, microalgae produces great amount of nitrogen-containing compound, which causes NOx emissions during combustion. However, pyrolysis of microalgae was still remained in its all components investigation at present. Therefore, the pyrolysis of a single component will make full use of microalgae resources. The crude fat was employed as raw material, which was extracted from Nannochloropsis sp. by using acid hydrolyzation method. Two kinds of feedstocks, namely crude fat and all components, were pyrolyzed in a fixed bed reactor, and the effect of temperature and heating rate on the yield and properties of the two groups of bio-oil were investigated. It showed that the fat in the Nannochloropsis sp. increased the yield and properties of bio-oil when pyrolyze all components. Moreover, with the increase in temperature and heating rate, the yield and properties of bio-oil from crude fat and all components followed the same varying trend, and their best properties were obtained at 20℃/min,600℃. Compared with pyrolysis of all components, the deoxidizing ratio and the content of carbon, hydrogen elements in crude fat after being pyrolyzed were higher, therefore the properties could be further improved with the increase of fat in the Nannochloropsis sp.

Impact of Fractional Calculus on Correlation Coefficient between Available Potassium and Spectrum Data in Ground Hyperspectral and Landsat 8 Image

As the level of potassium can interfere with the normal circulation process of biosphere materials, the available potassium is an important index to measure the ability of soil to supply potassium to crops. There are rarely studies on the inversion of available potassium content using ground hyperspectral remote sensing and Landsat 8 multispectral satellite data. Pretreatment of saline soil field hyperspectral data based on fractional differential has rarely been reported, and the corresponding relationship between spectrum and available potassium content has not yet been reported. Because traditional integer-order differential preprocessing methods ignore important spectral information at fractional-order, it is easy to reduce the accuracy of inversion model. This paper explores spectral preprocessing effect based on Grünwald−Letnikov fractional differential (order interval is 0.2) between zero-order and second-order. Field spectra of saline soil were collected in Fukang City of Xinjiang. The maximum absolute of correlation coefficient between ground hyperspectral reflectance and available potassium content for five mathematical transformations appears in the fractional-order. We also studied the tendency of correlation coefficient under different fractional-order based on seven bands corresponding to the Landsat 8 image. We found that fractional derivative can significantly improve the correlation, and the maximum absolute of correlation coefficient under five spectral transformations is in Band 2, which is 0.715766 for the band at 467 nm. This study deeply mined the potential information of spectra and made up for the gap of fractional differential for field hyperspectral data, providing a new perspective for field hyperspectral technology to monitor the content of soil available potassium

Determination of Soil Salt Content Using a Probability Neural Network Model Based on Particle Swarm Optimization in Areas Affected and Non-Affected by Human Activities

Traditional partial least squares regression (PLSR) and artificial neural networks (ANN) have been widely applied to estimate salt content from spectral reflectance in many different saline environments around the world. However, these methods entail a great amount of calculation, and their accuracy is low. To overcome these problems, a probability neural network (PNN) model based on particle swarm optimization was used in this study to build soil salt content models. Furthermore, there is a clear correlation between the level of human activities and the degree of salinization of an environment. This paper is the first to discuss this matter. Here, the performance of the PNN model to estimate soil salt content from reflectance data was investigated in areas non-affected (Area A) and affected (Area B) by human activities. The study area is located in Xingjinag, China. Different mathematical procedures, five wave band intervals, and two types of signal input sources were used for cross analysis. The coefficient of determination (R2), root mean square error (RMSE), and ratio of performance to deviation (RPD) index values were compared to verify the reliability of the model. Particle swarm optimization was used to adjust the optimal smoothing parameters of the PNN model and to avoid the long training processes required by the traditional ANN. The results show that the optimal wave band interval of the PNN is between 1000 nm and 1350 nm in Area A and between 400 nm and 700 nm in Area B. The reciprocal (1/R) transformation after Savitzky-Golay (SG) smoothing of the signal source is optimal for both areas. The RPD for both is greater than 30, which shows that the PNN model is applicable to areas with and without human activities and the prediction results are very good. The results indicated that the optimal wave band intervals for PNN modeling differed in areas affected and non-affected by human activities. The optimal interval of the artificial activities region falls in the visible light portion of the spectrum, and the optimized wave band region without human activities falls in the near-infrared short-wave portion of the spectrum

Estimation of Salinity Content in Different Saline-Alkali Zones Based on Machine Learning Model Using FOD Pretreatment Method

Soil salinization is a global ecological and environmental problem in arid and semi-arid areas that can be ameliorated via soil management, visible-near infrared-shortwave infrared (VNIR-SWIR) spectroscopy can be adapted to rapidly monitor soil salinity content. This study explored the potential of Grünwald–Letnikov fractional-order derivative (FOD), feature band selection methods, nonlinear partial least squares regression (PLSR), and four machine learning models to estimate the soil salinity content using VNIR-SWIR spectra. Ninety sample points were field scanned with VNIR-SWR and soil samples (0–20 cm) were obtained at the time of scanning. The samples points come from three zones representing different intensities of human interference (I, II, and III Zones) in Fukang, Xinjiang, China. Each zone contained thirty sample points. For modeling, we firstly adopted FOD (with intervals of 0.1 and range of 0–2) as a preprocessing method to analyze soil hyperspectral data. Then, four sets of spectral bands (R-FOD-FULL indicates full band range, R-FOD-CC5 bands that met a 0.05 significance test, R-FOD-CC1 bands that met a 0.01 significance test, and R-FOD-CC1-CARS represents CC1 combined with competitive adaptive reweighted sampling) were selected as spectral input variables to develop the estimation model. Finally, four machine learning models, namely, generalized regression neural network (GRNN), extreme learning machine (ELM), random forest (RF), and PLSR, to estimate soil salinity. Study results showed that (1) the heat map of correlation coefficient matrix between hyperspectral data and salinity indicated that FOD significantly improved the correlation. (2) The characteristic band variables extracted and used by R-FOD-CC1 were fewer in number, and redundancy between bands smaller than R-FOD-FULL and R-FOD-CC5, thus estimation accuracy of R-FOD-CC1 was higher than R-FOD-CC5 or R-FOD-FULL. A high prediction accuracy was achieved with a less complex calculation. (3) The GRNN model yielded the best salinity estimation in all three zones compared to ELM, BPNN, RF, and PLSR on the whole, whereas, the RF model had the worst estimation effect. The R-FOD-CC1-CARS-GRNN model yielded the best salinity estimation in I Zone with R2, RMSE and RPD of 0.7784, 1.8762, and 2.0568, respectively. The fractional order was 1.5 and estimation performance was great. The optimal model for predicting soil salinity in II and III Zone was, also, R-FOD-CC1-CARS-GRNN (R2 = 0.7912, RMSE = 3.4001, and RPD = 1.8985 in II Zone; R2 = 0.8192, RMSE = 6.6260, and RPD = 1.8190 in III Zone), with the fractional order of 1.7- and 1.6-, respectively, and the estimation performance were all fine. (4) The characteristic bands selected by the best model in I, II, and III Zones were 8, 9, and 11, respectively, which account for 0.45%, 0.51%, and 0.63%% of the full bands. This approach reduces the number of modeled band variables and simplifies the model structure

Application of Fractional Differential Calculation in Pretreatment of Saline Soil Hyperspectral Reflectance Data

Pretreatment of spectrum data is a necessary and effective method for improving the accuracy of hyperspectral model building. Traditional differential calculation pretreatment only considers the integer-order differential, such as the 1st order and 2nd order, and overlooks important spectrum information located at fractional order. Since fractional differential (FD) has rarely been applied to spectrum field measurement, there are few reports on the spectrum features of saline soils under different degrees of human interference. We used FD to analyze field spectrum data of saline soil collected from Xinjiang’s Fukang City. Order interval of 0.2 was adopted to divide 0–2 orders into 11-order differentials. MATLAB programming was used to convert the raw spectral reflectance and its four common types of mathematics and to conduct FD calculation. Spectrum data for area A (no human interference area) and area B (human interference area) was separately processed. From the statistical analysis of soil salinization characteristics, the salinization degree and type for area B were more diverse and complicated than area A. MATLAB simulation results showed that FD calculation could depict the minute differences between different FD order spectra under different human interference areas. The overall differential value trend appeared to move towards 0 reflectance value. After differential processing, the trend of bands that passed the 0.05 significance test of correlation coefficient (CC) showed an increase first then decrease later. The maximum CC absolute value for five spectrum transformations all appeared in the fractional order. FD calculation could significantly increase the correlation between spectral reflectance and soil salt content both for full-band and single-band spectra. Results of this study can serve as a reference for the application of FD in field hyperspectral monitoring of soil salinization for different human interference areas

Estimation of Salinity Content in Different Saline-Alkali Zones Based on Machine Learning Model Using FOD Pretreatment Method

Soil salinization is a global ecological and environmental problem in arid and semi-arid areas that can be ameliorated via soil management, visible-near infrared-shortwave infrared (VNIR-SWIR) spectroscopy can be adapted to rapidly monitor soil salinity content. This study explored the potential of Grünwald–Letnikov fractional-order derivative (FOD), feature band selection methods, nonlinear partial least squares regression (PLSR), and four machine learning models to estimate the soil salinity content using VNIR-SWIR spectra. Ninety sample points were field scanned with VNIR-SWR and soil samples (0–20 cm) were obtained at the time of scanning. The samples points come from three zones representing different intensities of human interference (I, II, and III Zones) in Fukang, Xinjiang, China. Each zone contained thirty sample points. For modeling, we firstly adopted FOD (with intervals of 0.1 and range of 0–2) as a preprocessing method to analyze soil hyperspectral data. Then, four sets of spectral bands (R-FOD-FULL indicates full band range, R-FOD-CC5 bands that met a 0.05 significance test, R-FOD-CC1 bands that met a 0.01 significance test, and R-FOD-CC1-CARS represents CC1 combined with competitive adaptive reweighted sampling) were selected as spectral input variables to develop the estimation model. Finally, four machine learning models, namely, generalized regression neural network (GRNN), extreme learning machine (ELM), random forest (RF), and PLSR, to estimate soil salinity. Study results showed that (1) the heat map of correlation coefficient matrix between hyperspectral data and salinity indicated that FOD significantly improved the correlation. (2) The characteristic band variables extracted and used by R-FOD-CC1 were fewer in number, and redundancy between bands smaller than R-FOD-FULL and R-FOD-CC5, thus estimation accuracy of R-FOD-CC1 was higher than R-FOD-CC5 or R-FOD-FULL. A high prediction accuracy was achieved with a less complex calculation. (3) The GRNN model yielded the best salinity estimation in all three zones compared to ELM, BPNN, RF, and PLSR on the whole, whereas, the RF model had the worst estimation effect. The R-FOD-CC1-CARS-GRNN model yielded the best salinity estimation in I Zone with R2, RMSE and RPD of 0.7784, 1.8762, and 2.0568, respectively. The fractional order was 1.5 and estimation performance was great. The optimal model for predicting soil salinity in II and III Zone was, also, R-FOD-CC1-CARS-GRNN (R2 = 0.7912, RMSE = 3.4001, and RPD = 1.8985 in II Zone; R2 = 0.8192, RMSE = 6.6260, and RPD = 1.8190 in III Zone), with the fractional order of 1.7- and 1.6-, respectively, and the estimation performance were all fine. (4) The characteristic bands selected by the best model in I, II, and III Zones were 8, 9, and 11, respectively, which account for 0.45%, 0.51%, and 0.63%% of the full bands. This approach reduces the number of modeled band variables and simplifies the model structure