PCA-Based Identification of Built Environment Factors Reducing PM2.5 Pollution in Neighborhoods of Five Chinese Megacities

Air pollution, especially PM2.5 pollution, still seriously endangers the health of urban residents in China. The built environment is an important factor affecting PM2.5; however, the key factors remain unclear. Based on 37 neighborhoods located in five Chinese megacities, three relative indicators (the range, duration, and rate of change in PM2.5 concentration) at four pollution levels were calculated as dependent variables to exclude the background levels of PM2.5 in different cities. Nineteen built environment factors extracted from green space and gray space and three meteorological factors were used as independent variables. Principal component analysis was adopted to reveal the relationship between built environment factors, meteorological factors, and PM2.5. Accordingly, 24 models were built using 32 training neighborhood samples. The results showed that the adj_R2 of most models was between 0.6 and 0.8, and the highest adj_R2 was 0.813. Four principal factors were the most important factors that significantly affected the growth and reduction of PM2.5, reflecting the differences in green and gray spaces, building height and its differences, relative humidity, openness, and other characteristics of the neighborhood. Furthermore, the relative error was used to test the error of the predicted values of five verification neighborhood samples, finding that these models had a high fitting degree and can better predict the growth and reduction of PM2.5 based on these built environment factors.

Study on optimal adsorption conditions of norfloxacin in water based on response surface methodology

The waste pomelo peel was pyrolyzed at 400 °C to prepare biochar and used as adsorbent to remove norfloxacin (NOR) from simulated wastewater. The adsorption conditions of norfloxacin by biochar were optimized by response surface methodology (RSM). On the basis of single-factor experiment, the adsorption conditions of biochar dosage, solution pH and reaction temperature were optimized by Box-Behnken Design (BBD), and the quadratic polynomial regression model of response value Y1 (NOR removal efficiency) and Y2 (NOR adsorption capacity) were obtained respectively. The results show that the two models are reasonable and reliable. The influence of single factor was as follows: solution pH > biochar dosage > reaction temperature. The interaction between biochar dosage and solution pH was very significant. The optimal adsorption conditions after optimization were as follows: biochar dosage = 0.5 g/L, solution pH = 3, and reaction temperature = 45 °C. The Y1 and Y2 obtained in the verification experiment were 75.68% and 3.0272 mg/g, respectively, which were only 2.38% and 0.0242 mg/g different from the theoretical predicted values of the model. Therefore, the theoretical model constructed by response surface methodology can be used to optimize the adsorption conditions of norfloxacin in water.

Competitive Adsorption of Copper, Zinc, and Chromium from Wastewater Using Corn-Cob Ash: Optimization Approach

Corn Cob ash was used in competitive adsorption of copper, zinc, and chromium from wastewater. The central composite design; a sub-set of response surface methodology was used to optimize the adsorption of the heavy metals. The result of the statistical parameters showed the coefficient of determination (R2) of 1.000, 0.999, and 1.000 for copper, zinc, and chromium respectively. The optimal conditions obtained for adsorbent dosage, initial concentration, temperature, contact time, and particle size were 13.20 mg, 79.72 mg/l, 34.95 °C, 40.38 min, and 1400 µm, respectively with the desirability of 1.000. The predicted and the actual values of metal removal obtained were 69.41%, 76.37%, as well as 70.44%, 72.50%, 77.90 % and 71.00% for copper, zinc, and chromium respectively. The ressult indicated a good conformity between the model predicted values and the actual values, thus having small errors of 3.09%, 1.53 % and 0.56 % for copper, zinc, and chromium respectively.

Back-Propagation Neural Network and ARIMA Algorithm for GDP Trend Analysis

GDP (gross domestic product) is a key indicator for assessing a country’s or region’s macroeconomic situation, as well as a foundation for the government to develop economic development strategies and macroeconomic policies. Currently, the majority of methods for forecasting GDP are linear methods, which only take into account the linear factors that affect GDP. GDP (gross domestic product) is widely regarded as the most accurate indicator of a country’s economic health. GDP not only reflects a country’s economic development over time but can also reflect its national strength and wealth. As a result, the GDP trend forecast partially reflects China’s transformation and future development. The time series ARIMA (Autoregressive Integrated Moving Average) model and the BPNN (BP neural network) model are combined in this article to create the ARIMA-BPNN fusion prediction model. The predicted values of the two models were then weighted averaged to obtain the predicted values of the linear part of the improved fusion model. To get the predicted values of the improved fusion model, we weighted average the residual parts of the two models, predict the nonlinear residual with BPNN, and add the predicted values of the two parts. It is applied to the actual GDP forecast in H province from 2019 to 2022, and the actual forecast verifies the effectiveness of the fusion forecast model in the actual forecast.

Statistical Study Based on the Kriging Method and Geographic Mapping in Rigid Pavement Defects in Southern Chile

ASTM D6433 is used to assess the need for maintenance of pavement sections. Although the Pavement Condition Index (PCI) factor calculation method provides reliable values, this method analyzes sections and defects individually and indicates current maintenance needs, but it cannot be used to predict the occurrence of new defects. Therefore, it is necessary to complement this method by considering variables that influence the occurrence of faults, among which are the geospatial distribution and the specific characteristics of the slabs. This research focuses on the identification of multiple types of disturbances that exist in Portland Cement Pavements (PCC), located in a high traffic area in the city of Valdivia (Chile). A spatial geostatistical relationship is established through visual inspection using geographical maps, as well as distribution, using the kriging method. This technique makes use of variograms that allow quantifying the parameters used in this study, thus expressing the spatial autocorrelation of the faults analyzed. From the results obtained by spatial geostatistics and kriging, it is possible to generate a data correlation for the distribution and characteristics of the streets considered. In addition, a co-kriging method is established instead of an ordinary kriging method. The relationship between observed and predicted values improved from 0.3327 to 0.5770. The width of the slabs, as well as some streets, is shown in our analysis to be unimportant. For better model accuracy, the number of covariates associated with the type of vehicle traffic, the age and shape of the slabs, and the construction techniques used for the pavement needs to increase.

Cross Second Virial Coefficient of the H2O–CO System from a New Ab Initio Pair Potential

The cross second virial coefficient $$B_{12}$$ B 12 for the interaction between water (H2O) and carbon monoxide (CO) was obtained with low uncertainty at temperatures from 200 K to 2000 K employing a new intermolecular potential energy surface (PES) for the H2O–CO system. This PES was fitted to interaction energies determined for about 58 000 H2O–CO configurations using high-level quantum-chemical ab initio methods up to coupled cluster with single, double, and perturbative triple excitations [CCSD(T)]. The cross second virial coefficient $$B_{12}$$ B 12 was extracted from the PES using a semiclassical approach. An accurate correlation of the calculated $$B_{12}$$ B 12 values was used to determine the dilute gas cross isothermal Joule–Thomson coefficient, $$\phi _{12}=B_{12}-T(\mathrm {d}B_{12}/\mathrm {d}T)$$ ϕ 12 = B 12 - T ( d B 12 / d T ) . The predicted values for both $$B_{12}$$ B 12 and $$\phi _{12}$$ ϕ 12 agree reasonably well with the few existing experimental data and older calculated values and should be the most accurate estimates of these quantities to date.

Impact of Cutting Parameters on Cutting Force of AISI 410 and AISI 420 MSS during CNC Dry Milling

A CNC dry milling experiment was conducted for the machining parameter optimization of two grades of Martensitic Stainless steel (MSS). Optimization is done by employing Taguchi method (S/N ratio and ANOVA). The specimens used are MSS grades 410 and 420.The experiments were designed by employing L9 orthogonal array for 3 levels of feed and spindle speeds. The impact of these parameters on cutting force was analyzed. The analysis reveals that spindle speed constitute the maximum impact on cutting force for both MSS grades. Optimum cutting parameters are obtained at 30 mm/min (feed rate) and 1500 rpm (spindle speed). Due to higher Chromium and Carbon content in AISI 420 MSS resulted higher cutting force values compared with AISI 410 MSS. Optimum values of cutting parameters are estimated for improving productivity and quality. The predicted values at optimal conditions are estimated. The results indicate a good conformity with the outcome of experiment.

Oil Palm’s Empty Fruit Bunch as a Sorbent Material in Filter System for Oil-Spill Clean Up

Oil pollution such as diesel poses a significant threat to the environment. Due to this, there is increasing interest in using natural materials mainly from agricultural waste as organic oil spill sorbents. Oil palm’s empty fruit bunch (EFB), a cost-effective material, non-toxic, renewable resource, and abundantly available in Malaysia, contains cellulosic materials that have been proven to show a good result in pollution treatment. This study evaluated the optimum screening part of EFB that efficiently absorbs oil and the physicochemical characterisation of untreated and treated EFB fibre using Fourier Transform Infrared Spectroscopy (FTIR) and Scanning Electron Microscopy (SEM). The treatment conditions were optimised using one-factor-at-a-time (OFAT), which identified optimal treatment conditions of 170 °C, 20 min, 0.1 g/cm3, and 10% diesel, resulting in 23 mL of oil absorbed. The predicted model was highly significant in statistical Response Surface Methodology (RSM) and confirmed that all the parameters (temperature, time, packing density, and diesel concentration) significantly influenced the oil absorbed. The predicted values in RSM were 175 °C, 22.5 min, 0.095 g/cm3, and 10%, which resulted in 24 mL of oil absorbed. Using the experimental values generated by RSM, 175 °C, 22.5 min, 0.095 g/cm3, and 10%, the highest oil absorption achieved was 24.33 mL. This study provides further evidence, as the data suggested that RSM provided a better approach to obtain a high efficiency of oil absorbed.

Dead Sea Water Levels Analysis Using Artificial Neural Networks and Firefly Algorithm

In this study, the performance of adaptive multilayer perceptron neural network (MLPNN) for predicting the Dead Sea water level is discussed. Firefly Algorithm (FFA), as an optimization algorithm is used for training the neural networks. To propose the MLPNN-FFA model, Dead Sea water levels over the period 1810–2005 are applied to train MLPNN. Statistical tests evaluate the accuracy of the hybrid MLPNN-FFA model. The predicted values of the proposed model were compared with the results obtained by another method. The results reveal that the artificial neural network (ANN) models exhibit high accuracy and reliability for the prediction of the Dead Sea water levels. The results also reveal that the Dead Sea water level would be around -450 until 2050.

Sustainable deployment of crushed concrete aggregates strengthened with cement and sand

Purpose: Paper assessed the feasibility of crushed concrete aggregates (CCA), a subsidiary of construction and demolition (C&D) waste, blended with cement and sand to form a composite for civil engineering field applications. Design/methodology/approach: The compaction and strength characteristics of CCA were observed by conducting Proctor compaction and California Bearing Ratio (CBR) tests. Different proportions of CCA, sand and cement were used. Moreover, the effect of curing period (0, 4, 7, 14 and 28 days) was also studied. In addition, regression analyses were performed to develop empirical expressions to predict the compaction and strength characteristics of the CCA composite. Findings: Increasing the CCA content up to 50% increases the maximum dry unit weight (MDUW) and decreases the optimum moisture content (OMC). However, on further increasing its content the MDUW decreases and OMC increases. Percent increase in the CBR value can go up to 412% if the CCA content is increased up to 50%. However, the percent reduction in CBR of about 20% can take place if 100% CCA content is used. Moreover, multiple regression shows that the experimental results are in good agreement with the predicted values. Research limitations/implications: The results obtained are purely dependent on the type of material. However, they are in favour of the used material as a probable option for road sub-base layer, and also for reducing burden on available natural resources. Therefore, it is recommended to conduct some initial tests to confirm the feasibility of the material. Practical implications: The proposed study will guide the design Engineers to choose CCA as one of the potential materials for road construction. Originality/value: It was observed that there is a need to maximize the utilization of C&D waste without making any compromise with its mechanical properties. So keeping that in view, the present study was conducted.