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• Proposed high-performance concrete and developed a regression model to predict HMRAC's compressive strength. Recycled Coarse Aggregate Concrete (RAC) is a type of concrete that uses recycled coarse aggregate (RCA) instead of natural coarse aggregate (NCA), which helps to reduce the negative environmental impact of construction waste. In order to improve the low compressive strength of RAC and solve the problem of shortage of natural aggregates, this paper develops a high-performance concrete called recycled coarse aggregate concrete with silica fume-slag-fly ash hybrid micro-powders (HMRAC) by replacing part of the cement with a mixture of micronized silica fume (SF), slag (SG) and fly ash (FA) and using RCA of 50% mass instead of NCA. The optimum combination parameters and prediction model for the compressive strength of HMRAC were also proposed by orthogonal experimental design. A constant water-cement ratio of 0.45 and a 50% mass replacement of natural coarse aggregate (NCA) by recycled coarse aggregate (RCA) were adopted in the experiment, and the mass replacement ratios of silica fume (SF), slag (SG), and fly ash (FA) for ordinary Portland cement (OPC) were used as the test variables. In total, 16 combinations were tested, including a control group where 50% of the NCA mass was replaced by RCA. First, we investigated the degree and significance of the effect of the mass substitution ratio of SF, SG, and FA on the compressive strength of HMRAC using analysis of variance (ANOVA) and extreme difference analysis. Then, we determined the optimal combination ratio of SF, SG, and FA. Secondly, multiple regression analysis was used to propose a multiple regression model for predicting the compressive strength of HMRAC. Finally, we computed and analyzed the carbon emissions of HMRAC. The results indicated that the mass substitution rates of FA and SG had a greater effect on the compressive strength, and the mass substitution rate of SF had a lesser effect on the compressive strength. The interaction of SF, SG, and FA can significantly enhance the compressive strength of RAC. The optimal compressive strength performance of HMRAC was observed when the proportions were as follows: SF constituted 10%, SG made up 15%, and FA accounted for 5%. The regression model has reasonable accuracy and a small standard deviation of residuals. It can effectively predict the compressive strength value of HMRAC, aligning well with the experimental results. It exhibits a superior carbon reduction rate of 21.90%, compared to conventional concrete. [ABSTRACT FROM AUTHOR]

Eliminating all forms of poverty in the world is the first United Nations Sustainable Development Goal (SDG). Developing a scientific and feasible method for monitoring and evaluating local poverty is important for the implementation of the SDG agenda. Based on the 2030 United Nations SDGs, in this paper, a quantitative evaluation model is built and applied to all poverty-stricken counties in Hunan Province. First, based on the SDG global index framework and local index system of China, a local SDG index system for poverty-related goals is designed, and the weights of the indexes are derived using an entropy method. The scores obtained for counties and districts with data available are then taken as the true value for the poverty assessment. Second, using National Polar-orbiting Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime light images and land use and digital elevation model data, six factors, including socioeconomic, land cover, terrain and traffic factors, are extracted. Third, we then construct multiple linear evaluation models of poverty targets defined by the SDGs and machine learning evaluation models, including regression trees, support vector machines, Gaussian process regressions and ensemble trees. Last, combined with statistical data of poverty-stricken counties in Hunan Province, model validation and accuracy evaluation are carried out. The results show that the R2 and relative error of the localized, multiple linear evaluation model, including all six factors, are 0.76 and 19.12%, respectively. The poverty-stricken counties in Hunan Province were spatially aggregated and distributed mainly in the southeastern and northwestern regions. The proposed method for regional poverty assessment based on multisource geographic data provides an effective poverty monitoring reference scheme for the implementation of the poverty eradication goals in the 2030 agenda.

In this paper statistical analysis of residential electricity demand in Nigeria is presented. Multiple linear regression model and quadratic regression model with interactions are applied to estimate residential electricity consumption and to forecast long-term residential electricity demand in Nigeria. Population and temperature are used as explanatory variables. The results show the Quadratic Regression with interaction has RMSE of 52.77 and r-square value of 0.9389 which indicates that 93.89% of the variation in residential electricity consumption is explained by the model. On the other hand , the multiple linear regression model has RMSE of 69.97 and r-square value of 0.873 which indicates that 87.3% of the variation in residential electricity consumption is explained by the model. Essentially, the quadratic regression model with interaction with lower RMSE and higher r-square value is selected and then used to forecast the residential electricity demand in Nigeria from 2015 to 2029. From the results, the Residential Electricity Consumption in Nigeria will reach 6521.09 MW/h in the year 2029. Furthermore, the results show that population has a positive sign and it is significant in the short run and in the long run forecasting. On the other hand, the result also revealed insignificant moderately weak relationship between residential electricity consumption and temperature.

Since 2003, the scan line corrector (SLC) of the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) sensor has failed permanently, inhibiting the retrieval or scanning of 22% of the pixels in each Landsat 7 SLC‐off image. This utter failure has seriously limited the scientific applications and usability of ETM+ data. Precise and complete recovery of the missing pixels for the Landsat 7 SLC‐off images is a challenging issue and developing an efficient gap‐fill algorithm with improved ETM+ data usability has been ever‐demanding. In this study, a new gap filling method has been introduced to reconstruct the SLC‐off images via multi‐temporal SLC‐off auxiliary fill images. A correlation is established between the corresponding pixels in the target SLC‐off image and two auxiliary fill images in parallel using the multiple linear regressions model. Both simulated and actual defective Landsat 7 images were tested to assess the performance of the proposed model by comparing with two multi‐temporal data based methods, the local linear histogram matching method and Neighbourhood Similar Pixel Interpolator method. The quantitative evaluations indicate that the proposed method makes an accurate estimate of the missing values even for more temporally distant fill images.

The company’s social responsibility, also called corporate social responsibility (CSR), is considered a key strategic factor in management. The purpose of this paper is to analyze the existing relationships between the CSR principles and the profitability of companies in the construction sector in Galicia. To this end, a review of the concept of CSR and its evolution is made; then, an analysis of the relationship between CSR and business results for Galician companies of the construction sector is carried out; finally, quantitative indicators are developed to evaluate the management and performance of the company and the theoretical approaches are compared with the results obtained through econometric modeling for a sample of 100 companies. This comparative analysis is done through return on assets (ROA,) or economic profitability, and return on equity (ROE), or financial profitability. We conclude that there is a positive relationship between financial returns and CSR, so the latter is consolidated as a source of competitive advantage.

Recently, in the automobile industry, the use of zinc-plated high-strength steels has been increasing to lighten vehicles and improve safety. In this scenario, gas metal arc welding (GMAW) processes are applied to automobile bodies and chassis parts. However, porosity defects occur in the welds because of the zinc vapor formed in the zinc coating layer during the GMAW process. This causes a decrease in the strength of the welded portion. These porosity defects have internal porosity and external pits, but in the actual production line, the quality of the welds is assessed by the occurrence of defects in external pits. In this study, using arc voltage and a system based on the waveform of the welding current, feature variables were extracted to characterize the sizes of external pits formed in high tensile strength galvanized steel during the GMAW process. Subsequently, a size prediction model was applied to predict the sizes of the external defects in the pits, and the results were verified using a multiple linear regression model and an artificial neural network. [ABSTRACT FROM AUTHOR]

Copyright of Revista Espacios is the property of Talleres de Impresos Oma and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract The binding constant (K doc) of organic pollutants (OPs) with dissolved organic matter (DOM) is an important parameter in determining the partitioning of OPs in the aquatic environment. Most estimation models have focused on calculating the K doc of a specific group of OPs but failed to obtain K doc values of different OPs effectively over the last three decades. In this study, we attempted to build a general-applicable K doc model based on various organic compounds' K doc values from the literature since 1973. Two multiple linear regression models, a DOM nonspecific model and an Aldrich HA model, were developed based on two solid and easy to access parameters—molecular connectivity indices (MCI) and polarity correction factors (PCF). In addition, the models' corresponding K ow -K doc models, which were mostly used in previous model studies, were developed for comparison. The adjusted determining coefficient (adj-R2) and standard error of the estimate (SEE) of the DOM nonspecific MCI-PCF-K doc model were 0.815 and 0.579, respectively, whereas the adj-R2 and SEE for the MCI-PCF-K doc model of Aldrich HA reached 0.907 and 0.438, respectively. The Aldrich HA model showed higher pertinence to the nonspecific model. Furthermore, both models exhibited better fit than the K ow -K doc models. The dipole moment modification attempts did not significantly improve either MCI-PCF-K doc models; hence, the two models were not altered with the dipole moment. The robustness tests by a Jackknifed method showed that the two MCI-PCF-K doc models exhibited higher robustness than the K ow -K doc. Of all of the OPs, the phenols contributed the most to their robustness. Furthermore, a sensitivity analysis showed that the two MCI-PCF-K doc models were sensitive to the robust parameters. Graphical abstract Unlabelled Image Highlights • Two types of MCI-PCF-K doc models and the Kow-Kdoc models were developed. • Both MCI-PCF-K doc models exhibited better fit than the Kow-Kdoc models. • The Aldrich HA model showed higher pertinence to the DOM nonspecific MCI-PCF-K doc model. • Both MCI-PCF-K doc models were sensitive to the robust parameters. • Both MCI-PCF-K doc models were not altered with the dipole moment. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Ecology & Rural Environment is the property of Journal of Ecology & Rural Environment Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Highlights • Generate daily COMS Land Surface Temperature (LST) collected every 15 min. • Develop an multiple linear regression (MLR) model using daily COMS LST and MODIS NDVI data in addition to interpolated precipitation (PCP). • Estimate the Surface Soil Moisture (SSM) from January 2013 to May 2015 based on the MLR model. • Compare the quality of this product with in situ measurements and spatial distribution of the soil moisture index (SMI). Abstract This study attempts to estimate the spatial distribution of the soil moisture (SM) in South Korea (99,260 km2) from January 2013 to May 2015 using a multiple linear regression (MLR) model with data from the Korean multipurpose geostationary Communication, Ocean and Meteorological Satellite (COMS), and land surface temperature (LST) data. The normalized distribution vegetation index (NDVI) from the moderate-resolution imaging spectroradiometer (MODIS) on board the Terra satellite was used to reflect the vegetation variations. Observed precipitation data measured by automatic weather stations (AWSs) operated by the Korea Meteorological Administration (KMA) were collected, and SM data were collected from 38 stations operated by various institutions. The regression coefficients of the MLR model were estimated seasonally considering five days of preceding precipitation. The p-values of all regression coefficients were below 0.05, and all coefficients of determination (R2) ranged from 0.17 to 0.63. Specifically, the R2 of loam in the summer was the highest at 0.63, and most of the R2 values were 0.4 or higher. The results of the SM regression showed that the overall R2 was higher than 0.4 and that the root mean square error (RMSE) was less than 5% at all but a few stations. A time series analysis of the simulated SM revealed that the observed SM data ranged from 0 to 20% for sand, 10 to 30% for loam, 20 to 40% for clay and 30 to 50% for silt. The simulated SM followed the volatility of the observed data in most of the soils. The spatial distribution of the simulated SM showed the same trends with the observed data on the monthly spatial precipitation map. Consequently, the estimated map of the soil moisture index (SMI) can be used to better understand the severity of drought and the variability in the SM. [ABSTRACT FROM AUTHOR]

In this study, it is aimed to estimate the regression parameters in case of measurement error in explanatory variables. In the study, data of March, June, September and December between the years 2003 and 2018 were used for the statistical analysis. Dollar values were taken to minimize the inflation effect of the data. To estimate the regression parameters, the least squares method, two group method and three group method were used to calculate the coefficients of the parameters. As a result of the study, it was seen that the three group methods gave better results than the other methods. In the study, the relationship between the multiple regression model and the variables was found to be functional. [ABSTRACT FROM AUTHOR]

Copyright of Galician Journal of Economics / Revista Galega de Economía is the property of Universidad de Santiago de Compostela, Servicio de Publicaciones and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The aim of this study was to find out the predictors for aesthetic tourism patients' satisfaction of the American travelers to Tijuana. The information was obtained by applying a survey to a sample of 385 visitors - patients from clinics in the city. Four dimensions were included in the multiple regression analysis. The results obtained show that the two key factors influence satisfaction level: Medical facilities, services and price dimension, and Geographical and cultural proximity dimensions. Theoretical value of this article is in its contribution to the few body of knowledge on factors that influence the aesthetic tourism satisfaction, as well as, the identification of its main characteristics that allow for the understanding of tourist's behavior in a binational environment. In the same sense, the results allow the owners and managers of clinics in this binational region to develop strategies to attract this market segment. [ABSTRACT FROM AUTHOR]

Copyright of Mugla Journal of Science & Technology is the property of Mugla Journal of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Climate polarization due to global warming has increased the intensity of drought in some regions, and the need for drought estimation studies to help minimize damage is increasing. In this study, we constructed remote sensing and climate data for Boryeong, Chungcheongnam-do, Korea, and developed a model for drought index estimation by classifying data characteristics and applying multiple linear regression analysis. The drought indices estimated in this study include four types of standardized precipitation indices (SPI1, SPI3, SPI6, and SPI9) used as meteorological drought indices and calculated through cumulative precipitation. We then applied statistical analysis to the developed model and assessed its ability as a drought index estimation tool using remote sensing data. Our results showed that its adj.R2 value, achieved using cumulative precipitation for one month, was very low (approximately 0.003), while for the SPI3, SPI6, and SPI9 models, the adj.R2 values were significantly higher than the other models at 0.67, 0.64, and 0.56, respectively, when the same data were used.

Refractory carbonaceous gold ore contains carbonaceous matters that seriously interfere with gold leaching. Therefore, this paper presented an in-depth experimental investigation on the inhibition gold-adsorbing behavior using Trichloroisocyanuric acid (TCCA). Various factors affecting the inhibition were analyzed. Moreover, the kinetics and isotherms associated with the adsorption were established to describe the gold adsorption of carbonaceous matters after TCCA treatment. A variety of analytical techniques were used to clarify the inhibition mechanism. The results showed the average gold adsorption percentage of elemental carbon decreased from 89.79 % to 9.33 % at pH 3.0, TCCA volume 15.17 mL, TCCA concentration 0.104 mol·L−1, 35 ℃ and 3 h. The average adsorption percentage of humic acid decreased from 56.19 % to 3.13 % at pH 5.0, TCCA volume 5.10 mL, TCCA concentration 0.012 mol·L−1, 35 ℃ and 4.5 h. The gold-adsorption behavior of the treated elemental carbon conformed to the pseudo-second-order and Langmuir model, accompanied by an activation energy of 9.42 kJ·mol−1. And the adsorption process of the treated humic acid could be explained by the pseudo-first-order and Langmuir model with an activation energy of 40.11 kJ·mol−1. TCCA molecules and their hydrolysis products covered the elemental carbon surface, resulting in a reduction in the size of the porous structure, which further affected the number and activity of surface-active sites. Treated with TCCA, the surface area of humic acid reduced from 42.84 m2·g−1 to 9.96 m2·g−1. Furthermore, spectroscopic analysis showed that the chemical groups of humic acid were destroyed, suggesting a sharp decrease in chemisorption capability. [Display omitted] • Adsorption percentage of the TCCA-treated elemental carbon decreased by 89.63 %. • Adsorption percentage of the TCCA-treated humic acid decreased by 94.47 %. • TCCA covered elemental carbon's surface to inhibit the surface activity. • TCCA destroyed humic acid's chemical groups of to decrease chemisorption ability. [ABSTRACT FROM AUTHOR]

In this research, the impact of total early-stage entrepreneurial activity and competitiveness of the economy on the real gross domestic product (GDP) per capita is analyzed in a cross-section of world economies using the methods of correlation and multiple regression analysis. In the attempt to select between the linear and the double-logarithmic model, the regression diagnostics and quality of the relationship between the dependent and the independent variables were analyzed. The functional form of the model was tested by the MacKinnon, White and Davidson test. Model selection methods regarding the comparison of coefficients of determination and the Akaike information criterion were used. The results of the analysis show that independent variables have a statistically significant impact on the real GDP per capita, and that the real GDP per capita is elastic to the changes of competitiveness but inelastic to the changes of total early-stage entrepreneurial activity.

Introduction: Soil depth is defined as the depth from the surface to more-or-less consolidated material and can be considered as the most crucial soil indicator, affecting desertification and degradation in disturbed ecosystems. Soil depth varies as a function of many different factors, including slope, land use, curvature, parent material, weathering rate, climate, vegetation cover, upslope contributing area, and lithology. Topography, one of the major soil forming factors, controls various soil properties. Thus, quantitative information on the topographic attributes has been applied in the form of digital terrain models (DTMs). The prediction of soil depth by topographic attributes depends mainly on: i) the spatial scale of topographic variation in the area, ii) the nature of the processes that are responsible for spatial variation in soil depth, and iii) the degree to which terrain-soil relationships have been disturbed by human activities. This study was conducted to explore the relationships of soil depth with topographic attributes in a hilly region of western Iran. Materials and Methods: The study area is located at Koohrang district between 32°20′ to 32°30′ N latitudes and 50°14′ to 50°24′ E longitudes, in Charmahal and Bakhtiari province, western Iran. The field sites with an area of 30,000 ha are located on the hillslopes at about 20% transversal slope. The soils at the site are classified as Typic Calcixerepts, Typic Xerorthents and Calcic Haploxerepts for the representative excavated profiles in summit, shoulder and backslope, respectively. The soils located at footslope and toeslope were classified as Chromic Calcixererts. Measurements were made in twenty representative hillslopes of the studied area. At the selected site, one hundred points were selected using randomly stratified methodology, considering all geomorphic surfaces including summit, shoulder, backslope, footslope and toeslope during sampling. Overall, 100 profiles were dug and described; and the solum thickness was measured for each profile. DEM data were created by using a 1:2,5000 topographic map. Topographical indices were generated from the DEM using TAS software. Terrain attributes in two categories, primary and secondary (compound) attributes; primary attributes are included elevation, slope, aspect, catchment area, dispersal area, plan curvature, profile curvature, tangential curvature, shaded relief. Secondary or compound attributes such as soil water content or the potential for sheet erosion, stream power index, wetness index, and sediment transport index. Correlation coefficients to define relationships between soil depth and terrain attributes, and analysis of variance by Duncan test were done using the SPSS software. The statistical software SPSS was used for developing multiple linear regression models. Terrain attributes were selected as the independent variables and soil depth was employed as dependent variable in the model. Thirty sampling sites were used to validate the developed soil-landscape model. In testing soil-landscape model, we calculated two indices from the observed and predicted values included mean error (ME) and root mean square error (RMSE). Results and Discussion: The soil depth in the studied profiles varied from 30 cm to 150 cm with an average of 108.6 cm. Relatively high variability (CV = 76%) was obtained for soil depth in the study area. The linear correlation analysis of the 12 topographic attributes and one soil property (soil depth), showed that there was a significant correlation among 36 of the 77 attribute pairs. Soil depth showed high positive significant correlations with catchment area, plan curvature, and wetness index, and showed high negative correlation with sediment transport index, sediment power index and slope. Low positive significant correlations of soil depth were identified with tangential curvature, and profile curvature. Moreover, soil depth was negatively correlated with elevation. The rest of the topographic attributes including aspect, shaded relief, and dispersal area were not significantly correlated with soil depth. Many of these relationships are similar to those found in other landscapes. The results of analysis of variance showed that there are significant differences for soil depth among the selected slope positions in the studied area. The highest values of soil depth were observed in the downslope positions including footslope and toeslope. The lowest soil depth was observed in shoulder position with the highest rate of soil erosion. Conclusions: It seems that the high variability for soil depth depends on topography of the field, and the landscape position, causing differential accumulation of water at different positions on the landscape; and moreover the soil erosion and deposition processes, resulting in high variability in the soil depth. We found relatively high correlation coefficients of soil depth with two groups of topographic attributes (erosional processes and water accumulation). Empirical model (MLR) using selected terrain attributes explains 76% of the variation of soil depth in the studied area. The terrain attributes that best predicted soil depth variability in the selected site were mainly the attributes that had significant relationships with soil depth. The dominant attributes in the MLR model included slope, wetness index, catchment area and sediment transport index.

In today's global competitive environment, it is important to be able to evaluate the efficient use of a firms' resources. The aim of this study is to predict the discard rate for headlight frames before the project of an automotive sub-industry firm in Bursa. For this prediction, the multilayer perceptron model, the radial basis function network model and multiple linear regression models were used. Matlab R2010b software was used for the multilayer perceptron model and radial basis function network solutions, and SPSS 13 packet software was used to solve the multiple linear regressions. Comparing the three models, the multilayer perceptron model was identified as the best predictive model.

Tiller angle is an important influencing factor in rice plant architecture that affects planting density and yield per unit area. Molecular tools to predict tiller angle contribute to breeding programs, which aim at optimizing rice plant architecture. In this study, several single-nucleotide polymorphism (SNP) markers related to tiller angle were developed and used with a model population to define a linear regression model for the prediction of tiller angle in rice. The resulting linear regression model, consisting of eight SNP markers as independent variables, was assessed using an independent test population. Overall, the regression model achieved an adjusted R2 of 0.51 and exhibited consistent predictive accuracy with an R2 of 0.61. Three of the eight independent variables, namely, PIN2-1, LIC1-1, and TAC1, contributed substantially to the linear regression model. These three major effect markers were also major determinants of tiller angle in the independent test population. Allelic combinations of the three major effect markers modulated tiller angle in the range of 5.6–19°. The DNA markers and linear regression model developed in this study will facilitate rice breeding programs for improving plant architecture.

Canopy closure (CC) is an important parameter in forest ecosystems and has diverse applications in a wide variety of fields. Canopy closure estimation models, using a combination of measured data and remote sensing data, can largely replace traditional survey methods for CC. However, it is difficult to estimate the forest CC based on high spatial resolution remote sensing images. This study used China Gaofen-1 satellite (GF-1) images, and selected China’s north temperate Wangyedian Forest Farm (WYD) and subtropical Gaofeng Forest Farm (GF) as experimental areas. A parametric model (multiple linear regression (MLR)), non-parametric model (random forest (RF)), and semi-parametric model (generalized additive model (GAM)) were developed. The ability of the three models to estimate the CC of plantations based on high spatial resolution remote sensing GF-1 images and their performance in the two experimental areas was analyzed and compared. The results showed that the decision coefficient (R2), root mean square error (RMSE), and relative root mean square error (rRMSE) values of the parametric model (MLR), semi-parametric model (GAM), and non-parametric model (RF) for the WYD forest ranged from 0.45 to 0.69, 0.0632 to 0.0953, and 9.98% to 15.05%, respectively, and in the GF forest the R2, RMSE, and rRMSE values ranged from 0.40 to 0.59, 0.0967 to 0.1152, and 16.73% to 19.93%, respectively. The best model in the two study areas was the GAM and the worst was the RF. The accuracy of the three models established in the WYD was higher than that in the GF area. The RMSE and rRMSE values for the MLR, GAM, and RF established using high spatial resolution GF-1 remote sensing images in the two test areas were within the scope of existing studies, indicating the three CC estimation models achieved satisfactory results.

Low-cost sensors have become an increasingly important supplement to air quality monitoring networks at the ground level, yet their performances have not been evaluated at high-elevation areas, where the weather conditions are complex and characterized by low air pressure, low temperatures, and high wind speed. To address this research gap, a seven-month-long inter-comparison campaign was carried out at Mt. Tai (1534 m a.s.l.) from 20 April to 30 November 2018, covering a wide range of air temperatures, relative humidities (RHs), and wind speeds. The performance of three commonly used sensors for carbon monoxide (CO), ozone (O3), and particulate matter (PM2.5) was evaluated against the reference instruments. Strong positive linear relationships between sensors and the reference data were found for CO (r = 0.83) and O3 (r = 0.79), while the PM2.5 sensor tended to overestimate PM2.5 under high RH conditions. When the data at RH >95% were removed, a strong non-linear relationship could be well fitted for PM2.5 between the sensor and reference data (r = 0.91). The impacts of temperature, RH, wind speed, and pressure on the sensor measurements were comprehensively assessed. Temperature showed a positive effect on the CO and O3 sensors, RH showed a positive effect on the PM sensor, and the influence of wind speed and air pressure on all three sensors was relatively minor. Two methods, namely a multiple linear regression model and a random forest model, were adopted to minimize the influence of meteorological factors on the sensor data. The multi-linear regression (MLR) model showed a better performance than the random forest (RF) model in correcting the sensors’ data, especially for O3 and PM2.5. Our results demonstrate the capability and potential of the low-cost sensors for the measurement of trace gases and aerosols at high mountain sites with complex weather conditions.