Principal Component Multiple Linear Regression Research Articles

Risk Assessment of Heavy Metals in Dust Fall Around Reservoirs in Typical Ecologically Fragile Areas and Traceability Based on APCS-MLR Model

To assess the health risk status and pollution sources of heavy metals in the atmosphere of ecologically vulnerable areas, the surrounding area of Dahekou Reservoir in Xilingol League was selected as the study area. From 2021 to 2022, 12 monitoring points for atmospheric dust fall were collected for a period of one year. A total of 144 samples were collected to determine the contents of eight types of heavy metals, namely Cr, Ni, Pb, Cu, Zn, Mn, As, and Cd. The potential ecological index (Eri) and health risk assessment model were used to assess the risk level of atmospheric heavy metals on ecological security and human health. The analysis of enrichment factors, principal components, and the model of absolute principal component multiple linear regression (APCS-MLR) receptor were used to analyze the sources of heavy metal pollution qualitatively in the atmosphere of the study area. The results showed that:① the mean value of the comprehensive potential ecological risk of heavy metals in the annual atmospheric dust fall in the study area was at a high ecological risk, and only the Cd value was at a very high risk level among the heavy metals, whereas the remaining were at a slight risk. ② The results of the health risk showed that intake by hand, mouth, and skin contact were the main exposure routes, which led to non-carcinogenic and carcinogenic risks. Children were under non-carcinogenic and acceptable carcinogenic risks in different months. During those months, the main source of the risks was As. ③ Through enrichment factor analysis, principal component analysis, and APCS-MLR receptor model calculation, the results revealed that the proportion of wind-blown sources was the largest, accounting for 37.82%, and the contribution rates of coal combustion and traffic sources to Cu, Cd, Pb, and Zn were 73.01%, 40.22%, 70.31%, and 32.82%, respectively. The contribution rate of mining activities to As was 42.59%, while that of industrial sources of Cd was 22.01%; the contributions of other human activity sources of Cd, As, Pb, and Zn were 21.12%, 34.40%, 23.04%, and 32.15%, respectively.

A principal component regression model for predicting phytochemical binding to the H. pylori CagA protein

Helicobater pylori is an important causative factor in the pathogenesis of multiple gastrointestinal diseases. One of the factors responsible for the virulence of H. pylori is the cagA protein, which can interfere with a number of cellular signaling processes once this protein is transferred inside the host cell. Thus, inhibiting the interaction of the cagA protein with the host cell membrane using small molecular inhibitors appears to be a promising pharmacological strategy. In this study, a predictive model for the binding free energy of natural compounds towards the cagA protein is presented. The formulated model which is built on principal component—multiple linear regression demonstrates reliable accuracy (r2test = 0.92, RMSEtest = 0.483), while only requiring five independent variables for the prediction. It was further noted that topological descriptors had the greatest influence on the generated principal components which served as the predictors. The created regression model can help promote and accelerate the discovery of natural compounds as cagA binders for the development of anti-H. pylori agents.

Nondestructive and rapid determination of lignocellulose components of biofuel pellet using online hyperspectral imaging system

BackgroundIn the pursuit of sources of energy, biofuel pellet is emerging as a promising resource because of its easy storage and transport, and lower pollution to the environment. The composition of biomass has important implication for energy conversion processing strategies. Current standard chemical methods for biomass composition are laborious, time-consuming, and unsuitable for high-throughput analysis. Therefore, a reliable and efficient method is needed for determining lignocellulose composition in biomass and so to accelerate biomass utilization. Here, near-infrared hyperspectral imaging (900–1700 nm) together with chemometrics was used to determine the lignocellulose components in different types of biofuel pellets. Partial least-squares regression and principal component multiple linear regression models based on whole wavelengths and optimal wavelengths were employed and compared for predicting lignocellulose composition.ResultsOut of 216 wavelengths, 20, 10 and 17 were selected by the successive projections algorithm for cellulose, hemicellulose and lignin, respectively. Three simple and satisfactory prediction models were constructed, with coefficients of determination of 0.92, 0.84 and 0.71 for cellulose, hemicellulose and lignin, respectively. The relative parameter distributions were quantitatively visualized through prediction maps by transferring the optimal models to all pixels on the hyperspectral image.ConclusionsHence, the overall results indicated that hyperspectral imaging combined with chemometrics offers a non-destructive and low-cost method for determining biomass lignocellulose components, which would help in developing a simple multispectral imaging instrument for biofuel pellets online measurement and improving the production management.

Pacific SST influence on spring precipitation in Addis Ababa, Ethiopia

ABSTRACTRecent periods of drought in Ethiopia and other parts of East Africa have highlighted the growing importance of producing reliable forecasts of seasonal precipitation. Key in deriving such forecasts is a good understanding of the atmospheric and oceanic drivers of different precipitation regimes. In Ethiopia and other parts of East Africa, interannual variability of precipitation depends on variations in sea surface temperature (SST) and atmospheric circulation on both regional and global scales. Links between summer precipitation in Ethiopia and large‐scale modes of climate variability such as El Niño Southern Oscillation (ENSO) have previously been established but the influence of global SST on spring precipitation has not yet been fully explored. Here, we analyse the links between Pacific SST and precipitation in Addis Ababa, Ethiopia for a century‐long period (1900–2004). A tripole correlation pattern between spring precipitation and SST in the Pacific basin is found. We develop regression‐based models to estimate spring precipitation from Pacific SST with a lead time of two to three months. When subject to cross‐validation, models based on principal component multiple linear regression (PC‐MLR) calibrated on Pacific SST during December show substantial skill in reproducing observed temporal variability in Addis Ababa precipitation during February (r = 0.48) and March (r = 0.40), and the period spanning February to April (r = 0.44). Our findings suggest that the inclusion of Pacific SST in predictive models may benefit drought forecasting across Ethiopia.