To investigate the structure, diversity, and function of different paddy soil fungal communities and the factors affecting them in typical paddy cropping areas in China, five typical Chinese paddy soils were selected in this study, and the composition and diversity of soil fungal communities were comparatively analyzed using high-throughput sequencing technology and functionally predicted using the FUNGuild microecological tool. The results showed that： ① The fungal community diversity of soil samples from Heilongjiang （HLJ） was significantly lower than that of the other four regions （P<0.05）； the highest fungal community richness was found in paddy soils from Yunnan （YN）, which was significantly higher than that of the other regions （P<0.05）； and the soil samples from Hainan （HN）, Jiangxi （JX）, and Shandong （SD） were relatively close to each other. The highest average relative abundance at the level of the five typical paddy phyla was Ascomycota, and the genus with the highest average relative abundance was Tausonia. ② Fungi had the largest proportion of saprophytic trophic types, and their corresponding environmental functions were stronger. ③ The species abundance of soil fungi was highly significantly correlated with soil TP, EC, and BD （P<0.01）, and redundancy analyses also showed that soil TP was the main driver of the fungal community as well as the saprophytic functional taxa. The above results showed that the soil fungal community diversity and structure varied greatly among samples, and the relative abundance of fungal genera was affected by soil physical and chemical properties and altered the fungal community structure in paddy fields. The development of this study will provide theoretical references for the sustainable management based on fungal diversity and function of paddy fields.
In China, atmospheric pollution exhibits a complex pattern, with simultaneous exceedances of fine particulate matter （PM2.5） and ozone （O3） levels becoming evident. To understand the complex pollution characteristics and evolution patterns of PM2.5 and O3 in Bozhou City, various methods such as weather classification, analysis of typical pollution processes, and investigation of precursor sources were employed to explore the pollution and variations of PM2.5 and O3 in Bozhou City from 2017 to 2022 and subsequently analyze their causes and precursor sources. The results indicated that： ① PM2.5-O3 complex pollution in Bozhou City mostly occurred under high-pressure weather conditions, with daytime high temperatures and low humidity promoting the formation of O3 pollution, whereas nighttime high humidity and atmospheric oxidative conditions promoted the generation of secondary components such as nitrates and ammonium salts in PM2.5. ② During the pollution process, PM2.5 in Bozhou City mainly originated from biomass burning, secondary generation, traffic pollution, coal combustion, and dust sources. Volatile organic compounds （VOCs） primarily emerged from plant sources, traffic pollution, oil and gas evaporation, solvent use, fossil fuel combustion, residential emissions, and industrial emissions. Biomass burning and traffic pollution made significant contributions to the pollution process. ③ Analysis of air mass trajectories and regional pollution situations indicated that the overlay of northern and southern air masses, along with local generation, were the main causes of the PM2.5-O3 complex pollution in Bozhou from October 18th to 27th, 2022.
The BCT-7800A PLUS VOC online monitor system was employed to measure ambient volatile organic compounds （VOCs） in a typical solvent-using industrial park in Beijing. From January to June 2023, the pollution characteristics, source apportionment, and ozone formation potential（OFP）of VOCs were studied, and the results of a comparative analysis were also discussed between heating and non-heating periods. The results indicated that VOC concentrations from January to June 2023 were （104.21 ± 91.31） μg·m-3 on average. The concentrations of TVOCs under the influence of southerly and northerly winds were （214.18 ± 202.37） μg·m-3 and （197.56 ± 188.3） μg·m-3, respectively. Alkanes were the species with the highest average concentration and proportion, respectively （45.53 ± 41.43） μg·m-3. The VOC concentration during the heating period was higher than those during the non-heating period, with values of （111.57 ± 83.96） μg·m-3 and （87.92 ± 75.03） μg·m-3, respectively. Propane and ethane were the species with the highest average concentration during the heating period. Compared with those in the non-heating period, the average concentrations of three species （propane, ethane, and n-butane） in the top ten species increased during the heating period, with average concentrations increasing by 51.94%, 54.64%, and 26.32%, respectively. The source apportionment results based on the positive matrix factorization （PMF） model indicated that the major sources of VOCs in the park during the monitoring period were printing emission sources （4.95%）, oil and gas evaporation sources （9.52%）, fuel combustion sources （15.44%）, traffic emissions sources （18.97%）, electronic equipment manufacturing （24.59%）, and industrial painting sources （26.52%）. Therefore, industrial painting sources, electronic equipment manufacturing sources, and traffic emissions sources were the emission sources that the park should focus on controlling. Compared with those during non-heating periods； industrial painting, traffic emission, and fuel combustion sources contributed more during the heating period, with VOC concentrations increasing by 15.02%, 16.53%, and 24.98%, respectively. The average OFP of VOCs from May to June during the monitoring period was 198.51 μg·m-3 and OVOCs, olefins, and aromatic hydrocarbons contributed the most to OFP, which were 47.41%, 22.15%, and 18.41%, respectively. The electronic equipment manufacturing source was the largest contributor to the summer OFP of the park and its contribution rate was 30.11%, which should be strengthened in the future.
Scientific assessment of industrial carbon emissions in the Yellow River Basin and identification of its influencing factors are of great importance for promoting green transformation, ecological protection, and high-quality development of the Yellow River Basin. Considering nine provinces in the Yellow River Basin as the research objects； using relevant data on industrial development and energy consumption in the Yellow River Basin from 2000 to 2019； and with the help of IPCC carbon emission measurement, spatial autocorrelation, and LMDI decomposition, the spatial and temporal evolution characteristics and influencing factors of carbon emissions from industries and industrial sectors in the Yellow River Basin were analyzed. Reasonable suggestions were put forward for reducing the carbon emissions from industries in the Yellow River Basin. The results showed that： ① From 2000 to 2019, industrial carbon emissions in the Yellow River Basin showed a fluctuating growth trend, with a decreasing growth rate. The spatial pattern changed from "low in the upstream and high in the middle and downstream" to "high and low value distribution," and the spatial difference gradually expanded. ② The high carbon industry was the most important source of industrial carbon emissions in the Yellow River Basin, accounting for 96.35% of the carbon emissions between the industries with a continuous growth trend, which was a significant difference. The middle and low carbon industry carbon emissions and the total proportion was low, showing different fluctuations； nine provinces and nine industrial industries had significant spatial variability. ③ Energy structure intensity, economic scale, and population scale promoted the increase in industrial carbon emissions in the Yellow River Basin and energy consumption intensity had an inhibitory effect on the increase in carbon emissions. The economic scale effect was positive and significant, which offset the negative effect of energy consumption intensity. Spatial variability was observed in the contribution value of the influence effect of the factors affecting the carbon emissions of the industry in nine provinces.
This study aimed to explore the relationship between land use landscape pattern and water quality in the upstream of the Gansu water conservation, water and soil erosion, and ecological fragile areas. Based on the land use data and water quality monitoring section in 2020 in the 200 m, 500 m, 1 km, 2 km, 50 km, and 10 km riparian buffer area, the single-factor index evaluation method, random forest regression model, and BP neural network were used to quantify the response relationship between land use and landscape pattern of the upper Yellow River in Gansu province and water quality index and to carry out the basin water quality prediction based on land use landscape index data. The results showed that： ① through the single-factor index method, the major indicators of the total nitrogen （TN） in July and September, dissolved oxygen （DO）, permanganate index, ammonia nitrogen （NH4+ -N）, total phosphorus （TP）, and other surface indexes met the surface water environment class Ⅲ water quality standard. ② The random forest regression model was used to analyze the influence of land use and landscape index on TN, and the difference in TN in different typical areas was obtained. The land use types with the highest influence on the TN index in water conservation areas, soil and soil erosion areas, and ecological fragile areas were cultivated land, grassland, and construction land, respectively. ③ The BP neural network was used to predict the water quality index based on different typical areas of land use landscape index. The result of water conservation areas was good, the error rate between the predicted value and the actual value was below 10%, and the prediction accuracy was high. The study showed that water quality prediction based on land use and landscape index/water quality quantitative relationship model had a good water quality prediction effect.
The Fengfeng mining area is an important coal-producing area in China and crucial environmental problems have been caused by large-scale exploitation of coal mines. The spatio-temporal evolution and driving factors of the ecological environment quality in this area must be explored for promoting the transformation of coal-based cities. Based on Landsat data of the Google earth engine （GEE） platform, this study constructed a new remote sensing-based ecological index （RSEInew） for the Fengfeng mining area from 2000 to 2020. The spatial and temporal evolution of RSEInew and its driving factors were evaluated by using trend analysis and geographic detector methods. The results showed that： ① From 2000 to 2020, the RSEInew of the Fengfeng mining area presented a fluctuating increasing trend （trend = 0.002 2）, and the proportion of good and excellent ecological environmental quality showed an increasing trend, rising from 24.80% in 2000 to 65.54% in 2020. ② The change in the RSEInew grade indicated that the proportion of significant improvement （3 and 4） of ecological environment quality grade in the Fengfeng Mining area from 2000 to 2020 was 10.21%, which was mainly distributed in Hexun town and Yijing town in the northwest of the Fengfeng mining area. The proportion of significant degradation （-3 and -4） was only 1.58%, mainly scattered in Linshui town and Dashe town. ③ RSEInew values increased significantly during 2000-2020 in the area accounting for 18.29%, mainly distributed in the central and northern areas and the western fringe of the Fengfeng mining area. The significantly reduced area accounted for 9.25%, mainly concentrated in the eastern area of the Fengfeng mining area. The coefficient of variation results showed that the areas with high fluctuation of RSEInew were mainly concentrated in Pengcheng town and Linshui town in the middle and eastern Fengfeng mining area. ④ From the perspective of influencing factors, the average q value of land use type （X6） during 2000-2020 was 0.290, which was much higher than other factors. The q value of social and economic factors showed an increasing trend, indicating that the spatial distribution of ecological environment quality in this region was increasingly strongly influenced by human activities. The interaction results showed that land use change was the key factor influencing ecological environment quality in the Fengfeng mining area.
According to the data sets of fine particulate matter （PM2.5） and its components in 35 cities in the Huaihe River Basin from 2015 to 2021, the temporal and spatial distribution patterns of pollutants were analyzed. The influence of meteorological factors on PM2.5 concentrations was examined using a random forest model. The original series of PM2.5, sulfate （SO42-）, nitrate （NO3-）, ammonium salt （NH4+）, organic matter （OM）, and black carbon （BC） were rebuilt using KZ （Kolmogorov-Zurbenko） filtering and multiple linear regression （MLR） to quantify the effects of meteorological conditions. The results demonstrated that from 2015 to 2021, the declining rates of PM2.5, SO42-, NO3-, NH4+, OM, and BC in the Huaihe River Basin were 4.71, 0.99, 1.05, 0.77, 1.01, and 0.19 μg·（m3·a）-1, respectively. The high mass concentrations of PM2.5 and its components were concentrated in the central and western regions of the HRB, whereas those in coastal and southern cities were lower. The variance contributions of the short-term, seasonal, and long-term components of PM2.5 to the original PM2.5 sequences in 35 cities were 51.6%, 35.9%, and 7.0%, respectively. The PM2.5 in coastal cities were more affected by the short-term components. The meteorological conditions were unfavorable for PM2.5 reduction in the HRB from 2015 to 2018, whereas the meteorological conditions supported the PM2.5 decrease from 2019 to 2021. From 2015 to 2021, the contribution rates of meteorological conditions to the long-term component reductions of PM2.5, SO42-, NO3-, NH4+, OM, and BC were 28.3%, 29.1%, 31.0%, 29.3%, 27.8%, and 28.6%, respectively. The contribution rates of meteorological conditions to the long-term PM2.5 reduction were 43.4%, 25.6%, 25.5%, and 20.6% in the HRB cities in Anhui, Shandong, Jiangsu, and Henan Provinces, respectively. With the decrease in PM2.5 concentration in the HRB, the sulfur oxidation rate （SOR） increased significantly, while the nitrogen oxide oxidation rate （NOR） changed little.
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