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Phosphorus-solubilizing microorganisms convert insoluble phosphorus in the soil into phosphorus that can be absorbed by plants. Soluble phosphate combines with heavy metals to form precipitation, reducing the content of available heavy metals, thereby reducing the absorption of heavy metals by crops, which plays an important role in the remediation of heavy metal-contaminated soil. The effects of the immobilization of Cd and Pb and the release of PO 4 3- by the phosphorus-solubilizing bacterium Klebsiella sp. M2 were studied through solution culture experiments. In addition, the effects of strain M2 on wheat uptake of Cd and Pb and its microbiological mechanism were also explored through pot experiments. The results showed that strain M2 reduced the concentrations of Cd and Pb and increased the concentration of PO 4 3- in the solution through cell wall adsorption and induced phosphate precipitation. Pot experiments showed that compared to those in the CK group and inactivated strain M2 group, inoculation with live strain M2 significantly increased （123%-293%） the contents of Ca 2 -P and Ca 8 -P in rhizosphere soil, decreased the content of DTPA-Cd （34.48%） and DTPA-Pb （36.72%） in wheat rhizosphere soil, and thus hindered the accumulation of Cd and Pb in wheat grains. Moreover, high-throughput sequencing results showed that strain M2 significantly increased the diversity of wheat rhizosphere bacterial communities； increased the relative abundance of Proteobacteria, Gemmatimonadetes, and Bacteroidota in wheat rhizosphere soil； and increased the proportion of heavy metal-immobilizing and phosphorus-promoting bacteria in wheat rhizosphere soil （mainly Sphingomonas , Nocardioides , Bacillus , Gemmatimonas , and Enterobacter ）. These bacterial genera played an important role in immobilizing heavy metals and preventing wheat from absorbing heavy metals. These results provide bacterial resources and theoretical basis for the bioremediation of heavy metal-contaminated farmland.

Meteorological conditions play a key role in the occurrence and evolution of atmospheric complex pollution. Considering the different pollution formation mechanisms of PM 2.5 and O 3 , statistical calculation and in-depth learning methods were used to construct the PM 2.5 and O 3 meteorological condition indexes based on long-term pollution meteorological observation data. A research method was developed to study the meteorological characteristics and impact contribution of atmospheric complex pollution by using the meteorological condition index, and quantitative analysis of the distribution and variation of pollution excluding the influence of regional meteorological differences was also conducted. The results showed that in the summer of 2021, the pollution meteorological conditions in the key regions in central and eastern China were generally worse in the north and better in the south(index:"2+26" cities>the border area of Jiangsu, Anhui, Shandong, and Henan>the Yangtze River Delta) and the worst in June and the best in July. The "double high" pollution began to appear when the PM 2.5 meteorological condition index>30 and O 3 meteorological condition index>100; meanwhile, the unfavorable meteorological conditions for O 3 also promoted the increase in PM 2.5 concentration, resulting in the frequency of "double high" increases with the increase in O 3 meteorological condition index. Compared with that during the same period last year, ρ (PM 2.5 ) of each region decreased by 3.9 μg·m -3 , 3.3 μg·m -3 , and 1.4 μg·m -3 due to the contribution of the improvement in the pollution meteorological conditions, which is nearly 58.5% on average of the total decrease in PM 2.5 concentration. However, the change in O 3 pollution meteorological conditions was better in the north and worse in the south, and the overall deterioration in the Yangtze River Delta Region led to approximately 2.8 μg·m -3 growth for the O 3 concentration. The PM 2.5 and O 3 concentrations after excluding the impact of meteorological differences showed different distribution characteristics from the air quality monitoring, in which the high concentrations of PM 2.5 were distributed along the Bohai Sea, the inter-provincial border, and the south of the region, whereas the high concentrations of O 3 were concentrated along the Taihang Mountains, around Mount Tai, and in parts of the Yangtze River Delta. The daily concentration variations in a single city during a specific pollution control period could be used as a basis for evaluating the effectiveness of local supervision and control, which will provide a reference for the dynamic supervision and daily scheduling of local control management.

A large-scale arsenic slag-contaminated site was selected to determine soil pollution characteristics and the degree of ecological risk of a large-scale contaminated site. In this study, 184 site soil samples, 101 agricultural soil samples, and 14 arsenic slag samples were collected and the pollution characteristics and ecological risk of As were evaluated using multivariate statistics, geostatistics, and the geoaccumulation and potential ecological risk index methods. The results show that the average As contents in site soil, agricultural soil, and arsenic slag are 1333.0, 358.1, and 17316.1 mg·kg -1 , respectively. The degree of contamination of As exceeds the national standard Ⅲof the environmental soil quality. The results of the vertical and three-dimensional distributions of contamination show that As is accumulated to different extents in different strata. The accumulation in the site surface soil is the most serious. The As concentrations in site soil samples are higher than that in agricultural soil samples. The potential ecological risk index method shows that all mean potential ecological risk indexes exceed 100 in different strata. Moreover, the percentage of samples in site and agricultural soils at medium pollution level ratios is 58.21% and 61.39%, respectively, illustrating that the As pollution of soil samples from this contaminated site and surrounding farm areas is serious.

In order to explore input pathways and pollution characteristics of Cd contamination in paddy soil in Youxian, Hunan Province, Cd contents in paddy soils, sediments of irrigation canals, typical mineral and industrial products such as coal, gangue and cement were analyzed. It was suggested that the average contents of Cd both in surface paddy soil and the corresponding natural soil were higher than the soil quality standard 0.3 mg x kg(-1). Cd contents in gangue and cement were similar as those in the corresponding natural soils. The atmosphere deposition of Cd was the highest in factory area. The profiles of Cd in 0-100 cm paddy soil and 0-40 cm in natural soils varied significantly from the upper to the lower layer. Cd contents in 0-40 cm layer in paddy soil were much higher than those in corresponding natural soils in mineral and mineral-factory areas. The potentiality for downward movement of Cd in soils in mineral area was the highest among the three studied areas. It suggested Cd contents in surface paddy soil were higher in upwind areas than those in downwind areas in mineral-factory and factory areas. It could be concluded that the main input pathways of Cd in mineral and mineral-factory. areas were from irrigation water, while contribution of atmosphere deposition in mineral-factory and factory areas was also very significant.

In order to enhance the reliability of risk estimation and to improve the accuracy of pollution scope determination in a battery contaminated site with the soil characteristic pollutant Pb, four spatial interpolation models, including Combination Prediction Model (OK(LG) + TIN), kriging model (OK(BC)), Inverse Distance Weighting model (IDW), and Spline model were employed to compare their effects on the spatial distribution and pollution assessment of soil Pb. The results showed that Pb concentration varied significantly and the data was severely skewed. The variation coefficient of the site was higher in the local region. OK(LG) + TIN was found to be more accurate than the other three models in predicting the actual pollution situations of the contaminated site. The prediction accuracy of other models was lower, due to the effect of the principle of different models and datum feature. The interpolation results of OK(BC), IDW and Spline could not reflect the detailed characteristics of seriously contaminated areas, and were not suitable for mapping and spatial distribution prediction of soil Pb in this site. This study gives great contributions and provides useful references for defining the remediation boundary and making remediation decision of contaminated sites.