[Distribution, Respiratory Exposure, and Traceability of Atmospheric Microplastics in Yichang City].

As an emerging environmental pollutant, microplastics have attracted much attention, but the sources and health hazards of atmospheric microplastics (AMPs) remain unclear. In order to explore the distribution characteristics, assess the risk of human respiratory exposure, and analyze the sources of AMPs in different functional areas of Yichang City, AMPs samples from 16 observation points were collected and analyzed, and the HYSPLIT model was used. The results showed that the main shapes of AMPs in Yichang City were fiber, fragment, and film, and six colors were observed including transparent, red, black, green, yellow, and purple. The smallest size was 10.42 µm, and the largest was 4761.42 µm. The deposition flux of AMPs was (4400±474) n·(m2·d)-1. The types of APMs were polyester fiber (PET), acrylonitrile-butadiene-styrene copolymer (ABS), polyamide (PA), rubber (Rubber), polyethylene (PE), cellulose acetate (CA), and polyacrylonitrile (PAN). The order of the subsidence flux in each functional area was as follows:urban residential area>agricultural production area>landfill>chemical industrial park>town residential area. The human respiratory exposure risk assessment models showed that the daily intake of AMPs (EDI) for adults and children in urban residential areas was higher than in town residential areas. The atmospheric backward trajectory simulation showed that the AMPs in the districts and counties of Yichang City mainly came from the surrounding areas via short-distance transportation. This study provided basic data support for the research on AMPs in the middle reaches of the Yangtze River and was of great significance for the traceability and health risk research of AMPs pollution.

[Levels, Characteristics, and Potential Source of Micro(meso)plastic Pollution of Soil in Liaohe River Basin].

With the Liaohe River basin as the research object, the morphology, abundance, and distribution characteristics of micro(meso) plastics of soil in Liaohe River basin were studied based on the density flotation principle and the technique of stereo microscope and micro-FTIR and the significant factors affecting the distribution of the micro(meso)plastics and their potential sources were reasonably speculated. The results indicated that the average abundance of the soil micro(meso)plastic is (145.83±211.46) n·kg-1. The most abundant types of micro(meso)plastics are debris (46.00%), <1000 µm (39.57%), PP (41.71%), and white (46.86%). Among them, Rayon, PES, and PET micro(meso)plastics are primarily in the shape of fibrous (>85%), PE is mostly in the shape of film and fragments (96.91%), PP is mainly in the shape of fragments (85.62%), and PS is primarily in the shape of foam. Industrial activities (express delivery companies, plastic factories, and clothing factories), agricultural activities (plastic mulch, sewage irrigation, and sludge composting), population density, and sewage treatment plants, etc, may lead to higher levels of micro(meso)plastic abundance in the soil of the study area. Plastic products (pesticide bottles, chemical fertilizer packaging bags, woven bags, and plastic agricultural films), sewage discharge and irrigation, and foam materials used for packaging and decoration are the potential sources of soil micro(meso)plastic in the region.

The Characteristic Spectral Selection Method Based on Forward and Backward Interval Partial Least Squares.

In the near-infrared spectroscopy, the Forward Interval Partial Least Squares (FiPLS) and Backward Interval Partial Least Squares (BiPLS) are commonly used modeling methods, which are based on the wavelength variable selection. These methods are usually of high prediction accuracy, but are strongly characteristic of greedy search, which causes that the intervals selected are not good enough to indicate the analyte information. To solve the problem, a spectral characteristic intervals selection strategy (FB-iPLS) based on the combination of FiPLS and BiPLS is proposed. On the basis of spectral segmentation, both FiPLSs are used to select useful intervals, and BiPLS is used to delete useless intervals, so as to perform the selection and deletion of the characteristic variables alternatively, which conducts a two-way choice of the target characteristic variables, and is used to improve the robustness of the model. The experiments on determining the ethanol concentration in pure water are conducted by modeling with FiPLS, BiPLS and the proposed method. Since different size of intervals will affect the result of the model, the experiments here will also examine the model results with different intervals of these three models. When the spectrum is divided into 60 segments, the FB-iPLS method obtains the best prediction performance. The correlation coefficients (r) of the calibration set and validation set are 0.967 7 and 0.967 0 respectively, and the cross-validation root mean square errors (RMSECV) are 0.088 8 and 0.057 1, respectively. Compared with FiPLS and BiPLS, the overall prediction performance of the proposed model is better. The experiments show that the proposed method can further improve the predictive performance of the model by resolving the greedy search feature against BiPLS and FiPLS, which is more efficient for and representative of the selection of characteristic intervals.

[Establishment and Improvement of Portable X-Ray Fluorescence Spectrometer Detection Model Based on Wavelet Transform].

The concentration of Cr, Cu, Zn, As and Pb in soil was tested by portable X-ray fluorescence spectrometer. Each sample was tested for 3 times, then after using wavelet threshold noise filtering method for denoising and smoothing the spectra, a standard curve for each heavy metal was established according to the standard values of heavy metals in soil and the corresponding counts which was the average of the 3 processed spectra. The signal to noise ratio (SNR), mean square error (MSE) and information entropy (H) were taken to assess the effects of denoising when using wavelet threshold noise filtering method for determining the best wavelet basis and wavelet decomposition level. Some samples with different concentrations and H3 B03 (blank) were chosen to retest this instrument to verify its stability. The results show that: the best denoising result was obtained with the coif3 wavelet basis at the decomposition level of 3 when using the wavelet transform method. The determination coefficient (R2) range of the instrument is 0.990-0.996, indicating that a high degree of linearity was found between the contents of heavy metals in soil and each X-ray fluorescence spectral characteristic peak intensity with the instrument measurement within the range (0-1,500 mg · kg(-1)). After retesting and calculating, the results indicate that all the detection limits of the instrument are below the soil standards at national level. The accuracy of the model has been effectively improved, and the instrument also shows good precision with the practical application of wavelet transform to the establishment and improvement of X-ray fluorescence spectrometer detection model. Thus the instrument can be applied in on-site rapid screening of heavy metal in contaminated soil.

[Analysis of heavy metal in soil with portable X-ray fluorescence spectrometry].

The concentrations of main heavy metal pollutants (Cu, Zn, Pb, Cr and As) in soil were tested by NITON XL3 600 portable X-ray fluorescence spectrometry (PXRF). The results showed that the minimum? detection limits for elements Cu, Zn, Pb, Cr and As were 23.96, 11.69, 8.58, 19.23, 6.24 mg x kg(-1), respectively. The soil composition standard material GSS-5 was detected by PXRF for 5 replicate measurements, The accuracy was 96%-102% and relative standard deviations (RSD) was 1.0%-7.6%. In term of field situ testing, RSD was less than 20%, and accuracy was 55%-119%. The performance was assessed by laboratory testing and field situ detection, and the results indicated that PXRF is effective for rapid, quantitative monitoring of soil metal contamination.

[FTIR microspectroscopy and its progress in application].

FTIR microspectroscopy technique was born in the mid-nineties. The research on this technique has just began abroad, and this technology has not yet been widely recognized in China. It is a rapid, nondestructive testing technology, has the advantages of microdomain, visualization, high precision and high sensitivity. In the present study, the composition, operational principle and working mode of FTIR microspectroscopy were summarized. The progress in application of FTIR microspectroscopy technique was investigated in some fields, including biomedicine, microbiology, forensic science, materials science, nutrition and feed science and agricultural products. The difficulty of FTIR microspectroscopy research and the prospects of this technique were also discussed.

[Determination of Cr, Cu, Zn, Pb and As in soil by field portable X-ray fluorescence spectrometry].

Total concentrations of Cr, Cu, Zn, Pb and As were determined in soil samples from Beijing, Xinjiang, Heilongjiang, Yunnan, and Jiangsu provinces, using field portable X-ray fluorescence spectrometry (XRF). The relationship between XRF analysis results and the concentration of heavy metals in soils was established. The influence of soil particle size and humidity was also considered. Experiments showed that the particle size of soil affected XRF performance. While particle size decreased from 420 to 180 microm, the relative standard deviation (RSD) of XRF detect results reduced from 15.6% to 6.9%. Soil humidity mainly affected the counts of XRF measured. As the soil water content increased from 5% to 252, the analysis result's relative ratio of humid soil samples to oven dried soil samples decreased from 86% to 69%, according with the equation I = 100e(0.015c), where I means relative ratio, and c means water content (R2 = 0.83, n=30). A high degree of linearity was found for all the five heavy metals with the XRF measurement in the range of 0 to 1500 mg x kg(-1). But the linearity equation was not the same among these soils. The linearity equation established with Yunnan soil has a small slope because of higher Fe concentration in soil. The performance of instrument was assessed by comparing XRF analysis result with the standard sample reference, and the result showed that XRF is an effective tool for rapid, quantitative monitoring of soil metal contamination.

[Real-time analysis of soil N and P with near infrared diffuse reflectance spectroscopy].

Improving the efficiency of fertilization is an effective method of enhancing income for farmers, but it depends on measuring the soil nutrients accurately and rapidly. Near infrared reflectance spectroscopy (NIRS) is a fast method to detect the soil nutrients. In order to evaluate the feasibility of using NIRS to determine the soil N and P contents, the soil samples were collected from different LULC (land use and land cover) types in Daxing district, Beijing, and their biochemical parameters were determined by traditional chemical method. Then, the near infrared reflectance spectra of the samples were acquired, and NIRS models were built using partial least square regression (PLS) and Fourier transform technology for the total N and total P. The determination coefficients of cross validation for the total N and total P were 0.862 6 and 0.668 5 respectively. Ten samples were used to test the performance of the models. The coefficients of correlation between the chemically determined value and the NIRS predicted one were 0.969 8(N) and 0.830 7(P) respectively. The root mean standard error of prediction for N and P were 0.009 5(%) and 0.008 6(%), respectively. The ratios of RMSEP to SD(RPD) were 3.78(N) and 1.69(P). The results showed that the NIRS could be used to evaluate the soil N accurately and the soil P roughly.

[Multivariate geostatistical and GIS-based approach to study the spatial distribution of soil heavy metals in field scale].

One hundred and two surface soil samples were collected from four fields in a farm in Daxing district of Beijing, and analyzed for concentration of Cu, Zn, Pb, Cr and Cd. Soil pH, OM, the concentration of Fe and Mn was also analyzed. The results of statistical analysis indicated there was no significant difference among three wheat fields, but the difference between wheat field and vegetable field was evident. In the wheat fields, the concentration of heavy metals in soil was all below the critical value of first level in environmental quality standard for soils. On the contrary, the concentration of Cd in some plots of vegetable field exceeded the critical value of second first level in environmental quality standard for soils. Principal component analysis was applied to estimate the sources of heavy metals. The results grouped Cu, Cd, and Zn in factor 1, Cr in factor 2, Pb in factor 3. Semivariogram of factors in the whole farm was fitted well by Gausssian model. Further analysis showed good spatial correlation of factor 1, and poor spatial correlation of factor 2 and factor 3. The approach of ordinary kriging interpolation approved the Cu, Cd, Zn in the soil was associated with the agricultural landuse. The distribution of heavy metals was mainly attributed to the excessive input of fertilizer and pesticide in vegetable product practice.