We evaluated a transcriptome using high-throughput Illumina HiSeq sequencing and related it to the morphology, leaf anatomy, and physiological parameters of Carpinus putoensis putoensis under NO2 stress. The molecular mechanism of the C. putoensis NO2 stress response was evaluated using sequencing data. NO2 stress adversely affected the morphology, leaf anatomy, and total peroxidase (POD) activity. Through RNA-seq analysis, we used NCBI to compare the transcripts with nine databases and obtained their functional annotations. We annotated up to 2255 million clean Illumina paired-end RNA-seq reads, and 250,200 unigene sequences were assembled based on the resulting transcriptome data. More than 89% of the C. putoensis transcripts were functionally annotated. Under NO2 stress, 1119 genes were upregulated and 1240 were downregulated. According to the KEGG pathway and GO analyses, photosynthesis, chloroplasts, plastids, and the stimulus response are related to NO2 stress. Additionally, NO2 stress changed the expression of POD families, and the HPL2, HPL1, and POD genes exhibited high expression. The transcriptome analysis of C. putoensis leaves under NO2 stress supplies a reference for studying the molecular mechanism of C. putoensis resistance to NO2 stress. The given transcriptome data represent a valuable resource for studies on plant genes, which will contribute towards genome annotations during future genome projects.

Determining the relationships between the structure and species of plant communities and their impact on ambient particulate matter (PM) is an important topic in city road greenbelt planning and design. The correlation between the distribution of plant communities and ambient PM concentrations in a city road greenbelt has specific spatial patterns. In this study, we selected 14 plant-community-monitoring sites on seven roads in Nanjing as research targets and monitored these roads in January 2022 for various parameters such as PM with aerodynamic diameters ≤ 10 µm (PM10) and PM with aerodynamic diameters ≤ 2.5 µm (PM2.5). We used a spatial model to analyze the relationship between the concentrations of ambient PM10 and PM2.5 and the spatial heterogeneity of plant communities. The consequences revealed that the composition and species of plant communities directly affected the concentrations of ambient PM. However, upon comparing the PM concentration patterns in the green community on the urban road, we found that the ability of the plant community structures to reduce ambient PM is in the order: trees + shrubs + grasses > trees + shrubs > trees + grasses > pure trees. Regarding the reduction in ambient PM by tree species in the plant community (conifer trees > deciduous trees > evergreen broad-leaved trees) and the result of the mixed forest abatement rate, coniferous + broad-leaved trees in mixed forests have the best reduction ability. The rates of reduction in PM10 and PM2.5 were 14.29% and 22.39%, respectively. We also found that the environmental climate indices of the road community, temperature, and traffic flow were positively correlated with ambient PM, but relative humidity was negatively correlated with ambient PM. Among them, PM2.5 and PM10 were significantly related to temperature and humidity, and the more open the green space on the road, the higher the correlation degree. PM10 is also related to light and atmospheric radiation. These characteristics of plant communities and the meteorological factors on urban roads are the foundation of urban greenery ecological services, and our research showed that the adjustment of plant communities could improve greenbelt ecological services by reducing the concentration of ambient PM.

As an important part of urban ecosystems, plants can reduce NO2 concentrations in the air. However, there is little evidence of the effects of different plant communities on NO2 concentrations in street-scale green spaces. We used a multifunctional lifting environmental detector to investigate the impact of environmental factors and small plant communities on NO2 concentrations in street green spaces during the summer and winter in Nanjing, China. The results showed that temperature, atmospheric pressure, and noise were significantly (P < 0.05) correlated with seasonal changes, temperature and humidity significantly (P < 0.01) influenced NO2 concentrations in winter and summer, and the average NO2 concentration in summer was generally higher than in winter. By comparing NO2 concentrations in different plant community structures and their internal spaces, we found that the plant community structure with tree-shrub-grass was more effective in reducing pollution. These findings will help predict the impact of plant communities on NO2 concentrations in urban streets and help city managers and planners effectively reduce NO2 pollution.

To study the effects of species diversity of different urban road green space on PM2.5 reduction, and to provide a theoretical basis for the optimal design of urban road plantings. Different combinations of road plantings in Xianlin Avenue of Nanjing were used as sample areas, and 3–6 PM2.5 monitoring points were set up in each sample area. The monitoring points were setup at 10, 20, 30, 40, 50, and 60 m from the roadbed for detecting PM2.5 concentrations in different sample areas. Moreover, the living vegetation volume of each sample area was calculated. The coupling relationship between the living vegetation volumes and PM2.5 concentrations in different sample areas was evaluated by regression fitting and other methods. PM2.5 concentrations among different sample areas were significantly different. PM2.5 concentrations were higher in the morning than in the afternoon, while the differences were not significant. The living vegetation volumes of the eight sample areas varied from 2038.73 m3 to 15,032.55 m3. Affected by different plant configurations, the living vegetation volumes in the sample areas showed obvious differences. The S2 and S6 sample area, which was consisted a large number of shrubshave better PM2.5 reduction capability. The fitting curve of living vegetation volumes and PM2.5 concentrations in sample areas of S1 and S3–S8 can explain 76.4% of the change in PM2.5 concentrations, which showed significant fitting. The fitting relationship between living vegetation volumes and PM2.5 concentrations in different road green space is different owing to different compositions of plantings. With the increase in living vegetation volumes, their fitting functions first increase and then decrease in a certain range. It is speculated that only when the living vegetation volume exceeds a certain range, it will promote PM2.5 reduction.

The proportional complex integral (PCI) controller can realize zero steady-state error control of AC signals with specific frequencies in a static coordinate system. The frequency of the traditional power grid is relatively fixed, so the zero steady-state error control of the AC signal is realized by adopting the PCI controller. With the increasing proportion of renewable energy generation, the frequency characteristics in the power system become uncertain. When the microgrid operates in island mode, the frequency of the fundamental wave and harmonics fluctuates greatly, which causes the PCI controller to fail to achieve zero steady-state error control. This paper proposes two controllers: positive and negative sequence decoupling quasi-proportional complex integral (PNDQPCI) controller and high-order positive and negative sequence decoupling quasi-proportional complex integral (HPNDQPCI) controller. The PNDQPCI controller can effectively control the positive and negative sequence voltage when the system frequency drifts; The HPNDQPCI controller can effectively suppress multiple harmonics. Finally, simulation and experiment are presented to verify the theoretical analysis as well as the feasibility and the superiority of the proposed controller.