Effect of soil moisture and its correction method for quantitative analysis of hazardous metals in polluted soil for the on‐site XRF analysis

In on–stream X–ray fluorescence analysis of ore slurries, the effects due to variation in the particle-size of solids can cause appreciable and sometimes major errors in the measured concentration of an element to be determined. Weight percentage of slurry ore grains with diameter smaller than 75 um (called later W75) is commonly used as measure of ore fineness and can be determined using particle-size analyzers. A method of correcting for W75 variaition is highly desireable, particularly in a case when high analysis accuracy is required for economical reasons.

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Particle Size Effects in Geological Analyses by X-Ray Fluorescence

Advances in X-ray Analysis ◽

10.1154/s0376030800010752 ◽

1985 ◽

Vol 29 ◽

pp. 587-592

Author(s):

K.K. Nielson ◽

V.C. Rogers

Keyword(s):

Particle Size ◽

Quantitative Analysis ◽

Size Effects ◽

Light Element ◽

New Method ◽

X Ray ◽

Particle Size Effects ◽

Sample Matrix ◽

Xrf Analysis ◽

The Matrix

Particle-size effects can cause significant errors in x-ray fluorescence (XRF) analysis of particulate materials. The effects are usually removed when samples are fused or dissolved to standardize the matrix for quantitative analysis. Recent improvements in numerical matrix corrections reduce the need to standardize the sample matrix via fusion or dissolution, particularly when the CEMAS method is used to estimate unmeasured light-element components of undefined materials for matrix calculations. A new method to correct for particle-size effects has therefore been examined to potentially avoid the need for destructive preparation of homogeneous samples.

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Use of SMAP Soil Moisture and Fitting Methods in Improving GPM Estimation in Near Real Time

Remote Sensing ◽

10.3390/rs11030368 ◽

2019 ◽

Vol 11 (3) ◽

pp. 368 ◽

Cited By ~ 4

Author(s):

Zhi Zhang ◽

Dagang Wang ◽

Guiling Wang ◽

Jianxiu Qiu ◽

Weilin Liao

Keyword(s):

Soil Moisture ◽

Real Time ◽

Mainland China ◽

Correction Method ◽

Cumulative Distribution ◽

Area Index ◽

Soil Moisture Data ◽

Precipitation Product ◽

Antecedent Precipitation ◽

Potential Factors

Satellite-based precipitation products have been widely used in a variety of fields. However, near real time products still contain substantial biases compared with the ground data. Recent studies showed that surface soil moisture can be utilized in improving rainfall estimation as it reflects recent precipitation. In this study, soil moisture data from Soil Moisture Active Passive (SMAP) satellite and observation-based fitting are used to correct near real time satellite-based precipitation product Global Precipitation Measurement (GPM) in mainland China. The particle filter is adopted to assimilate the SMAP soil moisture into a simple hydrological model, the antecedent precipitation index (API) model; three fitting methods—i.e., linear, nonlinear, and cumulative distribution function (CDF) fitting corrections—both separately and in combination with the SMAP soil moisture data, are then used to correct GPM. The results show that the soil moisture-based correction significantly reduces the root mean square error (RMSE) and mean absolute errors (BIAS) of the original GPM product in most areas of China. The median RMSE value for daily precipitation over China is decreased by approximately 18% from 5.25 mm/day for the GPM estimates to 4.32 mm/day for the soil moisture corrected estimates, and the median BIAS value is decreased by approximately 13% from 2.03 mm/day to 1.76 mm/day. The fitting correction method alone also improves GPM, although to a lesser extent. The best performance is found when the SMAP soil moisture assimilation is combined with the linear fitting of observed precipitation, with a median RMSE of 4.00 mm/day and a BIAS of 1.69 mm/day. Despite significant reductions to the biases of the satellite precipitation product, none of these methods is effective in improving the correlation between the satellite product and observational reference. Leaf area index and the frequency of the SMAP overpasses are among the potential factors influencing the correction effect. This study highlights that combining soil moisture and historical precipitation information can effectively improve satellite-based precipitation products in near real time.

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