A multi-strategy improved rime optimization algorithm for three-dimensional USV path planning and global optimization.

The RIME optimization algorithm (RIME) represents an advanced optimization technique. However, it suffers from issues such as slow convergence speed and susceptibility to falling into local optima. In response to these shortcomings, we propose a multi-strategy enhanced version known as the multi-strategy improved RIME optimization algorithm (MIRIME). Firstly, the Tent chaotic map is utilized to initialize the population, laying the groundwork for global optimization. Secondly, we introduce an adaptive update strategy based on leadership and the dynamic centroid, facilitating the swarm's exploitation in a more favorable direction. To address the problem of population scarcity in later iterations, the lens imaging opposition-based learning control strategy is introduced to enhance population diversity and ensure convergence accuracy. The proposed centroid boundary control strategy not only limits the search boundaries of individuals but also effectively enhances the algorithm's search focus and efficiency. Finally, to demonstrate the performance of MIRIME, we employ CEC 2017 and CEC 2022 test suites to compare it with 11 popular algorithms across different dimensions, verifying its effectiveness. Additionally, to assess the method's practical feasibility, we apply MIRIME to solve the three-dimensional path planning problem for unmanned surface vehicles. Experimental results indicate that MIRIME outperforms other competing algorithms in terms of solution quality and stability, highlighting its superior application potential.

Coking exhaust contributes to airborne particulate matter in the Beijing-Tianjin-Hebei region.

Coking was regarded as a predominant source of air pollution. Despite the adoption of more environmentally friendly equipment, whether the coking enterprises in the Beijing-Tianjin-Hebei (BTH) region are still causing regional air pollution is worthy of study, which is essential for the control of coking enterprises in this area. To improve the prediction accuracy of large-scale air pollutant distribution, the air particle distribution in the BTH region was simulated via land use regression (LUR) combined with Bayesian maximum entropy (BME); then, the distribution was correlated with the exhaust gas emitted from coking enterprises. Results indicated that the R2 of the "LUR + BME" method reached 0.95, higher than 0.82 using LUR alone. The air quality distribution presented a pattern of "low in the northern mountains and high in the southern plains", similar to the distribution of coking enterprises in BTH region. A significant correlation was found between exhaust emissions from coking enterprises and air quality in the BTH region, confirming the contribution of coking emissions to air pollution in this region, and the necessity to continue the strict control on coking enterprises in BTH area.

An interpolation method incorporating the pollution diffusion characteristics for soil heavy metals - taking a coke plant as an example.

The existing spatial interpolation methods in the prediction of soil heavy metal distribution are generally based on spatial auto correlation theory, rarely considering the pollution patterns. By contrast, in polluted sites, heavy metals have a strong heterogeneity even within a very small area, which is not exactly in line with auto correlation theory. This contradiction may lead to inaccuracy in spatial prediction. Atmospheric diffusion and deposition are one of the main sources of soil heavy metal pollution caused by coal-related production activities. To improve the prediction accuracy, the diffusion patterns of pollutants were considered in this paper by integrating Geodetector, Co-Kriging (COK), and partition interpolation. Geodetector was used to identify the main driving factors of soil pollution, based on which, the main driving factors were used as covariates introduced into the interpolation method (COK). Specifically, the amount of particulate matter deposition obtained by a pollutant diffusion model (AERMOD) was used as a covariate. For comparison, the distances to quenching, coke oven, and ammonium sulfate section were also used as covariates. Compared with the Ordinary Kriging method, the method COK-AERMOD established here decreased the root mean square error values of As (2.05 reduced to 1.89), Cd (0.18 reduced to 0.16), Cr (19.07 reduced to 12.97), Cu (6.92 reduced to 4.72), Hg (0.32 reduced to 0.28), Ni (16.92 reduced to 16.10), Pb (18.29 reduced to 16.62), and Zn (159.68 reduced to 153.66). This method in this paper is informative for the interpolation of soil elements in contaminated areas with known pollution source and diffusion patterns.

Apportionment and location of heavy metal(loid)s pollution sources for soil and dust using the combination of principal component analysis, Geodetector, and multiple linear regression of distance.

The accurate identification of sources for soil heavy metal(loid) is difficult, especially for multi-functional parks, which include multiple pollution sources. Aiming to identify the apportionment and location of heavy metal(loid)s pollution sources, this study established a method combining principal component analysis (PCA), Geodetector, and multiple linear regression of distance (MLRD) in soil and dust, taking a multi-functional industrial park in Anhui Province, China, as an example. PCA and Geodetector were used to determine the type and possible location of the source. Source apportionment of individual elements is achieved by MLRD. The detection results quantified the spatial explanatory power (0.21 ≤ q ≤ 0.51) of the potential source targets (e.g., river and mining area) for the PCA factors. A comparative analysis of the regression equation (Model 1 and Model 3) indicated that the river (0.50 ≤ R2 ≤0.78), main road (0.47 ≤ R2 ≤ 0.81), and mine (0.14 ≤ R2 ≤ 0.92) (p < 0.01) were the main sources. Different from the traditional source apportionment methods, the current method could obtain the exact contributing sources, not just the type of source (e.g., industrial activities), which could be useful for pollution control in areas with multiple sources.

Discharge Patterns of Potentially Harmful Elements (PHEs) from Coking Plants and Its Relationship with Soil PHE Contents in the Beijing-Tianjin-Hebei Region, China.

The Beijing-Tianjin-Hebei (BTH) region in China is a rapid development area with a dense population and high-pollution, high-energy-consumption industries. Despite the general idea that the coking industry contributes greatly to the total emission of potentially harmful elements (PHEs) in BTH, quantitative analysis on the PHE pollution caused by coking is rare. This study collected the pollutant discharge data of coking enterprises and assessed the risks of coking plants in BTH using the soil accumulation model and ecological risk index. The average contribution rate of coking emissions to the total emissions of PHEs in BTH was ~7.73%. Cross table analysis indicated that there was a close relationship between PHEs discharged by coking plants and PHEs in soil. The accumulation of PHEs in soil and their associated risks were calculated, indicating that nearly 70% of the coking plants posed a significant ecological risk. Mercury, arsenic, and cadmium were the main PHEs leading to ecological risks. Scenario analysis indicated that the percentage of coking plants with high ecological risk might rise from 8.50% to 20.00% as time progresses. Therefore, the control of PHEs discharged from coking plants in BTH should be strengthened. Furthermore, regionalized strategies should be applied to different areas due to the spatial heterogeneity of risk levels.

Influencing factors and prediction of arsenic concentration in Pteris vittata: A combination of geodetector and empirical models.

Phytoextraction using hyperaccumulator, Pteris vittata, to extract arsenic (As) from soil has been applied to large areas to achieve an As removal rate of 18% per year. However, remarkable difference among different studies and field practices has led to difficulties in the standardization of phytoextraction technology. In this study, data on As concentration in P. vittata and related environmental conditions were collected through literature search. A conceptual framework was proposed to guide the improvement of phytoextraction efficiency in the field. The following influencing factors of As concentration in this hyperaccumulator were identified: total As concentration in soil, soil available As, organic matter in soil, total potassium (K) concentration in soil, and annual rainfall. The geodetection results show that the main factors that affect As concentration in P. vittata include soil organic matter (q = 0.75), soil available As (q = 0.67), total K (q = 0.54), and rainfall (q = 0.42). The predictive models of As concentration in P. vittata were established separately for greenhouse and field conditions through multivariate linear stepwise regression method. Under greenhouse condition, soil available As was the most important influencing factor and could explain 41.4% of As concentration in P. vittata. Two dominant factors were detected in the field: soil available As concentration and average annual rainfall. The combination of these two factors gave better prediction results with R2 = 0.762. The establishment of the model might help predict phytoextraction efficiency and contribute to technological standardization. The strategies that were used to promote As removal from soil by P. vittata were summarized and analyzed. Intercropping with suitable plants or a combination of different measures (e.g., phosphate fertilizer and water retention) was recommended in practice to increase As concentration in P. vittata.

[Analysis of the Spatial Distribution of Heavy Metals in Soil from a Coking Plant and Its Driving Factors].

Coking plants are typical industrial pollution sites and may release heavy metals into the environment, posing a threat to human health. Scholars have discovered that different types of heavy metals are released during different coking production processes and lead to spatial differences in heavy metals. Research on the spatial distribution and driving factors of pollutants in the soil inside and outside coking plants is important for sampling design, risk assessment, pollution prevention and control, etc.. Inverse distance weight was used to analyze the spatial distribution of As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn inside and outside of the coking plant. A geo-detector was used to find out the difference in the driving factors for the spatial distribution of heavy metals between soil from inside and outside the coking plant. The results showed that except As, Ni, and Zn, the overall background value rate of other heavy metals was above 50%, and the continuity of the spatial distribution of heavy metals in the soil was poor. The coefficient of variation (CV) exceeded 30%, representing a moderate variation. The average degree of CV inside the coking plant was Hg > Cd > As > Cu > Zn > Cr > Pb > Ni, and the external average degree of CV was Hg > Cu > Cd > As > Zn > Pb > Cr > Ni. An analysis of heavy metal content showed that the content of As, Cd, Cr, Pb, and Zn outside the coking plant was bigger than inside. According to geo-detector results, the physicochemical properties factors with a large contribution rate to the spatial distribution of heavy metals inside and outside the coking plant was the soil's total nitrogen, organic matter, and available medium-micro element content. Pollution source factors that contributed the most to the spatial distribution of heavy metals inside were the crude benzol and cold drum section, while the coke oven and quench section determined the outside spatial distribution of heavy metals. The q value of the strongest factor inside the coking plant was more than 0.5 while outside the coking plant it was less than 0.5. According to the interaction detector result, the interaction factors values of pollution sources and soil physicochemical properties to the inside spatial distribution of heavy metals was higher than outside. According to the distribution and geo-detector results, the strongest physicochemical properties driving factors that determined the inside and outside spatial distribution of heavy metals were relatively consistent. These factors were soil nutrient factors, which mainly influenced the availability of heavy metals. The differences in the production processes led to the difference between the inside and outside spatial distribution of heavy metals. The content of heavy metals outside the coking plant was higher than inside because the heavy metals came from various pollution sources. The driving forces for the distribution of heavy metals inside the plant were higher than outside and showed that the heavy metals inside of the plant were mainly from the coking plant. Heavy metal distribution inside the coking plant was mainly driven by the pollution source factor of the coking refining process and coking water, while heavy metal distribution outside the coking plant was mainly driven by the coking gas production process and other emission pollution source factors.

[Heavy Metal Contents of Soil and Surface Dust and Its Ecological Risk Analysis in a Multifunctional Industrial Park].

A multifunctional industrial park can perform both producing and living functions. The smelting and processing of non-ferrous metals may lead to soil pollution, posing risks to human beings. In this study, an industrial park located in central Anhui Province, China, with copper (Cu) processing and mechanical components as the main industries, was selected as the study object. By collecting and testing soil and dust samples, the horizontal and vertical distribution characteristics of heavy metals in soil and dust in the park were analyzed. The ecological risk index is used to identify areas with higher risks and correlation and principal component analysis are used to disclose the potential source of heavy metals. Results showed that the contents of Cu, Zn, As, Pb, and Cd in the soil were 2.65, 1.76, 1.56, 2.14, and 3.87 times that of the background value, respectively. The heavy metal content of dust was significantly higher than that of soil, with contents of Cr, Ni, Cu, Zn, Hg, As, Pb, and Cd of 1.93, 1.05, 7.57, 4.63, 6.08, 5.39, 2.58, and 5.50 times that of the background value, respectively. Horizontally, the areas with higher ecological risks concentrated in the western part of the park, while vertically there was no significant trend with increases in soil depth. For the dust samples, areas with high ecological risks were closer to the main traffic arteries. Principal component analysis indicated that the main source of heavy metal in western soils was probably irrigation with contaminated river water. Road traffic, on the other hand, is more likely to be the main contributor to high dust heavy metal levels. This result is important for the park to control the potential health risks caused by heavy metals through zoning management according to the functions of different areas.

Bioaccessibility of metals/metalloids in willow catkins collected in urban parks of Beijing and their health risks to human beings.

Air pollution and its resulting health risks in Beijing City have been widely investigated by scientists and administrators. However, the health risks caused by willow and poplar catkins in April and May (known as "spring snow") have been rarely reported. Poplar and willow are the two common trees in Beijing City that generate many whirling catkins in the air. The chemical composition of catkins remains unknown. In this study, catkins and dust samples were collected in several parks in Beijing. The total concentrations of metals/metalloids in catkins measured through inductively coupled plasma mass spectrometry were generally lower than those of the corresponding dust samples, and they were lower than the risk control standard for soil contamination of development land. The simulated rain and lung fluid extraction rates of catkin samples were significantly higher than those of the dust samples. The concentration of extracted Pb and Zn using simulated rainwater exceeded the environmental quality standards for surface water (0.1 and 2.0 mg/L for Pb and Zn, respectively), indicating the possibility of runoff pollution. Scanning electron microscopy images showed that fine particles (<10 µm) are attached to the surface of catkins. Therefore, the metals/metalloids in fine particles adsorbed by the catkin samples possess higher bioaccessibility than that in the dust samples based on different sizes of particles. A significant correlation is found between Pb in catkin and Pb in dust. Therefore, attention should be paid to the possible increase in metal/metalloid concentrations in catkins planted in contaminated areas.