Spatial Autoregressive Models for Stand Top and Stand Mean Height Relationship in Mixed Quercus mongolica Broadleaved Natural Stands of Northeast China

The relationship of stand top and stand mean height is important for forest growth and yield modeling, but it has not been explored for natural mixed forests. Observations of stand top and stand mean height can present spatial dependence or autocorrelation, which should be considered in modeling. Simultaneous autoregressive (SAR) models, including spatial lag model (SLM), spatial Durbin model (SDM) and spatial error model (SEM), within nine spatial weight matrices were utilized to model the stand top and stand mean height relationship in the mixed Quercus mongolica Fisch. ex Ledeb. broadleaved natural stands of Northeast China, using ordinary least squares (OLS) as a benchmark model. The results showed that there was a high linear relationship between stand top and stand mean height and that there was a positive spatial autocorrelation pattern in model residuals of OLS. Moreover, SEM and SDM performed better than OLS in terms of reducing the spatial dependence of model residuals and model fitting, regardless of which spatial weight matrix was used. SEM was better than SDM. SLM scarcely reduced the spatial autocorrelation of model residuals. Among nine spatial matrices in SEM, rook contiguous matrix performed best in model fitting, followed by inverse distances raised to the second power (1/d2) and local statistics model matrix (LSM)

Spatial Autoregressive Models for Stand Top and Stand Mean Height Relationship in Mixed Quercus mongolica Broadleaved Natural Stands of Northeast China

The relationship of stand top and stand mean height is important for forest growth and yield modeling, but it has not been explored for natural mixed forests. Observations of stand top and stand mean height can present spatial dependence or autocorrelation, which should be considered in modeling. Simultaneous autoregressive (SAR) models, including spatial lag model (SLM), spatial Durbin model (SDM) and spatial error model (SEM), within nine spatial weight matrices were utilized to model the stand top and stand mean height relationship in the mixed Quercus mongolica Fisch. ex Ledeb. broadleaved natural stands of Northeast China, using ordinary least squares (OLS) as a benchmark model. The results showed that there was a high linear relationship between stand top and stand mean height and that there was a positive spatial autocorrelation pattern in model residuals of OLS. Moreover, SEM and SDM performed better than OLS in terms of reducing the spatial dependence of model residuals and model fitting, regardless of which spatial weight matrix was used. SEM was better than SDM. SLM scarcely reduced the spatial autocorrelation of model residuals. Among nine spatial matrices in SEM, rook contiguous matrix performed best in model fitting, followed by inverse distances raised to the second power (1/d2) and local statistics model matrix (LSM)

Numerical Simulation of Temperature Field in Rotary Kiln during Limonite Magnetization Roasting

Rotary kiln is an important industrial large-scale heating equipment. Measuring and calculating its internal heat and temperature distribution is of great significance for the reasonable design of rotary kiln and the setting of process parameters such as material flow rate and heating temperature in the process of operation, so as to achieve the purpose of saving energy. This paper analyzes the variation of heat, gas temperature, material temperature and kiln wall temperature with kiln length in the process of limonite reduction roasting in rotary kiln. The calculated results are in good agreement with the measured results. It is of certain significance to understand the distribution law of heat and temperature and reasonably arrange the process parameters of flue gas combustion and material distribution

Precise Measurement of Stem Diameter by Simulating the Path of Diameter Tape from Terrestrial Laser Scanning Data

Accurate measurement of stem diameter is essential to forest inventory. As a millimeter-level measuring tool, terrestrial laser scanning (TLS) has not yet reached millimeter-level accuracy in stem diameter measurements. The objective of this study is to develop an accurate method for deriving the stem diameter from TLS data. The methodology of stem diameter measurement by diameter tape was adopted. The stem cross-section at a given height along the stem was determined. Stem points for stem diameter retrieval were extracted according to the stem cross-section. Convex hull points of the extracted stem points were calculated in a projection plane. Then, a closed smooth curve was interpolated onto the convex hull points to simulate the path of the diameter tape, and stem diameter was calculated based on the length of the simulated path. The stems of different tree species with different properties were selected to verify the presented method. Compared with the field-measured diameter, the RMSE of the method was 0.0909 cm, which satisfies the accuracy requirement for forest inventory. This study provided a method for determining the stem cross-section and an efficient and precise curve fitting method for deriving stem diameter from TLS data. The importance of the stem cross-section and convex hull points in stem diameter retrieval was demonstrated

Minute Cu2+ coupling with HCO3− for efficient degradation of acetaminophen via H2O2 activation

Homogeneous Cu2+-mediated activation of H2O2 has been widely applied for the removal of organic contaminants, but fairly high dosage of Cu2+ is generally required and may cause secondary pollution. In the present study, minute Cu2+ (2.5 μM) catalyzed H2O2 exhibited excellent efficiency in degradation of organic pollutants with the assistant of naturally occurring level HCO3− (1 mM). In a typical case, acetaminophen (ACE) was completely eliminated within 10 min which followed the pseudo-first-order kinetics. Singlet oxygen and superoxide radical rather than traditionally identified hydroxyl radical were the predominant reactive oxygen species (ROS) responsible for ACE degradation. Meanwhile, Cu3+ was deduced through Cu+ and p-hydroxybenzoic acid formation analysis. CuCO3(aq) was the main complex with high reactivity for the activation of H2O2 to form ROS and Cu3+. The removal efficiency of ACE depended on the operating parameters, such as Cu2+, HCO3− and H2O2 dosage, solution initial pH. The presence of Cl−, HPO42−, humic acid were found to retard ACE removal while other anions such as SO42− and NO3− had no obvious effect. ACE exhibited lower degradation efficiency in real water matrices than that in ultra-pure water. Nevertheless, 58–100% of ACE was removed from domestic wastewater, lake water and tap water within 60 min. Moreover, eight intermediate products were identified and the possible degradation pathways of ACE were proposed. Additionally, other typical organic pollutants including bisphenol A, norfloxacin, lomefloxacin hydrochloride and sulfadiazine, exhibited great removal efficiency in the Cu2+/H2O2/HCO3− system

Existing tree crown ratio (CR) models.

<p>Note: MHT: merchantable height, TSC: tree slenderness coefficient, D: diameter at breast height, H: total tree height, BA: stand basal area, CCF: stand crown competition factor, PCT: The percentile in the stand basal area distribution, CCR: compacted crown ratio, BAL: basal area per ha for trees larger than the subject tree, ELEV: elevation, SL: slope, ASPECT: aspect, SD: stand density, DH: dominant tree height, AZ: azimuth of aspect in radians, Age: tree age, <i>a</i>, <i>b</i>, <i>c</i>: model parameters, <b>β</b>: parameter vector, x: vector of stand or tree variables.</p><p>Existing tree crown ratio (CR) models.</p

A total of 14 candidate variables used for developing crown ratio model.

<p>A total of 14 candidate variables used for developing crown ratio model.</p

Performance assessment of mixed-effect model Eq (14) using measurements of crown ratio with different variance models.

<p>Note: D: diameter at breast height, DD: dominant tree diameter, DH: dominant tree height, AIC: Akaike information criterion, Loglik: log-likelihood, LR: likelihood ratio, Variance model 1 means that the variances are homogeneous, PF: power model—<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0133294#pone.0133294.e015" target="_blank">Eq (8)</a>, EF: exponential model—<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0133294#pone.0133294.e016" target="_blank">Eq (9)</a>, CPF: constant plus power model—<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0133294#pone.0133294.e017" target="_blank">Eq (10)</a>, F denotes a model failing to converge.</p><p>Performance assessment of mixed-effect model <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0133294#pone.0133294.e045" target="_blank">Eq (14)</a> using measurements of crown ratio with different variance models.</p