[Distribution Prediction of Soil Heavy Metals Based on Remote Sensing Temporal-Spatial-Spectral Features and Random Forest Model]

Obtaining soil heavy metal content characteristics and spatial distribution is crucial for preventing soil pollution and formulating environmental protection policies. We collected 304 surface soil samples (0-20 cm) in the Changqing district. At the same time, the spectral, temporal, and spatial features of soil heavy metals were derived from multi-remote sensing data; the temporal-spatial-spectral features closely related to soil heavy metals were selected via correlation analysis and used as input independent variables. The measured soil arsenic (As) content was used as the dependent variable to establish a spatial prediction model based on the random forest (RF) algorithm. The results showed the following:the As content in the soils exceeded the background value by 43.17% but did not exceed the risk screening values and intervention values, indicating slight heavy metal pollution in the soil. The accuracy ranking of the spatial prediction models with one feature type from high to low was spatial features (ratio of performance to inter-quartile range (RPIQ)=3.87)>temporal features (RPIQ=2.57)>spectral features (RPIQ=2.50). The spatial features were the most informative for predicting soil heavy metals. The models using temporal-spatial, temporal-spectral, and spatial-spectral features were superior to those using only one feature type, and the RPIQ values were 4.81, 4.21, and 4.70, respectively. The RF model with temporal-spatial-spectral features achieved the highest spatial prediction accuracy (R2=0.90; root mean square error (RMSE)=0.77; RPIQ=5.68). The As content decreased from the northwest to the southeast due to Yellow River erosion and industrial activities. The spatial prediction of soil heavy metals incorporating remote sensing temporal-spatial-spectral features and the random forest model provides effective support for soil pollution prevention and environmental risk control.

[A Comparison Study on Multiple Modeling Approaches for Air Pollutant Geographic Model Development in Shanghai]

Geostatistical models have been widely used in the exposure assessment of ambient air pollutants. However, few studies have focused on comparisons of modeling approaches and their prediction results. Here, we collected the NO2 and PM2.5 monitoring data from 55 sites in Shanghai from 2016 to 2019 and the geographic variables, such as road network, points of interest of emission locations, and satellite data were included. We used partial least squares regression (PLS), supervised linear regression (SLR), and random forest (RF) algorithms to develop spatial models and used ordinary kriging (OK) to develop a two-step model. We evaluated the models using a 5-fold cross validation method and selected the best model structure for each modeling approach between one-or two-step models that had been developed with or without OK. The results revealed that the best NO2 models were the RF-OK (Rmse2 was 0.70-0.82) and PLS-OK (Rmse2 was 0.78-0.84) models; the PLS model for PM2.5(Rmse2 was 0.62-0.71) outperformed the other PM2.5 models. We used the best models to predict annual exposures in Shanghai at a 1 km spatial scale and conducted the correlation analysis among the predictions of the best models. The results demonstrated that the NO2 predictions had higher correlation coefficients (r was 0.82-0.91) compared with those of the PM2.5 models (r was 0.66-0.96). Based on the exposure results predicted using the three models in 2019, we evaluated the cumulative population exposure concentrations for NO2 and PM2.5 in Shanghai.

[Source Apportionment and Pollution Assessment of Soil Heavy Metal Pollution Using PMF and RF Model: A Case Study of a Typical Industrial Park in Northwest China]

Based on the concentration data of seven heavy metal elements[As, Cd, Cu, Pb, Hg, Ni, and Cr(Ⅵ)] in the surface soil of a typical industrial park in northwest China, the characteristics of heavy metal pollution in the industrial park were analyzed, and the ecological risk and pollution were evaluated using the potential ecological risk index and the index of geo-accumulation. The positive matrix factorization (PMF) model and random forest (RF) model were used for quantitative source analysis, and the emission data of sampling enterprises and empirical data of the source emission component spectrum were combined to identify the characteristic elements and determine the emission source category. The results showed that the heavy metals at all sampling points in the park did not exceed the second-class screening value of construction land in the soil pollution risk control standard for construction land (GB 36600-2018). However, compared with the local soil background values, five elements, excluding As and Cr, were enriched in different degrees, presenting slight pollution and moderate ecological risk (RI=250.04). Cd and Hg were the main risk elements of the park. The results of source analysis showed that the five main sources of pollution were fossil fuel combustion and chemical production sources (33.73%, 9.71%, total source contribution rate of PMF and RF, respectively; the same below), natural sources and waste residue landfill (32.40%, 40.80%), traffic emissions (24.49%, 48.08%), coal burning and non-ferrous metal smelting (5.43%, 0.11%), and electroplating and ore smelting (3.95%, 1.30%). The simulation R² of the total variable of the two models were above 0.96, indicating that the models could predict heavy metals well. However, considering the actual situation of the number of enterprises in the park and roading density, the main pollution sources of soil heavy metals in the park should be industrial sources, and the simulation results of the PMF model were closer to the actual situation in the park.

[Impacts of Emission and Meteorological Conditions on Air Pollutants at Various Sites Around the COVID-19 Lockdown in Wuhan]

The random forest algorithm was used to separate the mass concentrations of six air pollutants (SO₂, NO₂, CO, PM₁₀, PM_2.5, and O₃) contributed by emissions and meteorological conditions. Their variations for five types of sites including Wuhan's central urban, suburb, industrial, the third ring road traffic, and urban background sites were investigated. The results showed that the values of PM_2.5/CO, PM₁₀/CO, and NO₂/CO during the lockdown period decreased by 10.8-21.7, 9.34-24.7, and 14.4-22.1 times compared with the period before the lockdown, indicating that the contributions of emissions to PM_2.5, PM₁₀, and NO₂ were reduced. O₃/CO increased by 50.1-61.5 times, implying that the secondary formation increased obviously. The contributions of emissions to various types of pollutants all increased after the lockdown. During the lockdown period, affected by the operation of some uninterrupted industrial processes, PM_2.5 concentrations in industrial areas dropped the least (20.5%). Compared with the lockdown period, residential activities, transportation, and industrial production were basically restored after the lockdown, resulting in the alleviation of the reduction in PM_2.5 emission-related concentrations. The increase in emission-related O₃ concentrations could be associated with the decreased NO and PM_2.5 concentrations during the lockdown period. The elevated O₃ partially offset the improved air quality brought by the reduced NO₂and PM_2.5 concentrations. After the lockdown, ρ(O₃) related with meteorology at the suburban and urban background sites increased by 16.2 μg·m^-3 and 16.1 μg·m^-3, respectively, which could be attributed to the increased ambient temperature and decreased relative humidity. The decrease in PM_2.5 and increase in O₃ concentrations caused by reduced traffic and industrial emissions at the third ring road traffic and central urban regions can provide reference for the current coordinated and precise control of PM_2.5 and O₃ in subregions.

[Spatial Prediction Method of Farmland Soil Organic Matter in Weibei Dryland of Shaanxi Province]

Accurately predicting the spatial distribution of soil organic matter (SOM) content is of great significance for improving soil quality and improving the level of regional soil management. In order to explore the optimal model for predicting the SOM content of farmland in the Weibei Dryland of Shaanxi Province, the influence factors closely related to SOM content were selected as the modeling covariables, and a geographic detector, the ordinary kriging method (OK), geographic weighted regression model (GWR), partial least squares regression model (PLS), geographically weighted regression extended model (GWR_PLS), and random forest model (RF) were used to predict the spatial distribution of SOM content in training samples. Additionally, the validation set samples were used to compare and analyze the prediction accuracy of the five methods. The results showed:① the main factors affecting the spatial variability of soil SOM were total nitrogen, fertilizer application, available potassium, available phosphorus, and altitude, and the interaction between any two factors was more explanatory for SOM than any single factor. ②ω(SOM) in farmland was between 2.25 and 30.23 g·kg^-1, with an average value of 15.14 g·kg^-1 and a coefficient of variation of 30.00. Although there were local differences in the prediction results of SOM by the five methods, the overall spatial distribution trend was basically the same. In the study area, the content of organic matter was low in the north and northeast and high in the west and southeast. ③ From the perspective of the prediction accuracy of the five methods, the root mean square error (RMSE) and mean absolute error (MAE) of RF were the smallest, and the prediction deviation (RPD) of GWR_PLS was the largest. Compared with the OK method, the correlation coefficients (r) of GWR, PLS, RF, and GWR_PLS increased to 0.907, 0.836, 0.968, and 0.972, respectively. Comprehensive analysis results showed that the random forest model had the highest prediction accuracy.