China faces a contradictory period of economic development and environmental protection, with it being essential to control total emissions within the limit of atmospheric environmental capacity (AEC) by promoting atmospheric environmental carrying capacity (AECC). This implies that well-calculated AEC and AECC values are the key macro-criteria for improving environmental quality and supporting the challenging coordinated development of economy and environment. When considering compound air pollution characterised as fine particulate matter (PM 2.5), conventional methods are not capable of calculating AEC and AECC, but the system dynamics (SD) method retains the advantage of simplicity in resolving complex problems. In the present study, we first describe the background and definitions of AEC and AECC, which are different from Western concepts, and their dialectical relationships. Then, with the statistical data from Wuhan city in 2014, we establish an 'economy-energy-atmospheric environment' dynamic model using the SD method, which does not need to simulate the complicated physicochemical processes of atmospheric transmission and diffusion. Instead, it uses the pollutants' proportionality factors and conversion rates to establish quantitative connections among different types of variables. Finally, we simulate the dynamic trends of gross domestic production (GDP), PM 2.5 , and six air pollutant emissions between 2015 and 2030 in four different scenarios and calculate the results of AEC and AECC constrained by GDP and PM 2.5 .

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Within the scope of ecological development planning in China, afforestation is highly valued. However, the scientific planning of afforestation still has inadequacies. There are few studies on the spatial distribution of urban forests targeted at air quality improvement. Here, we implemented a virtual experiment to evaluate whether different tree planting distribution plans with the same afforestation scale would have a significant effect on fine particulate matter (PM2.5) removal. As a case study of Wuhan, this paper identified the statistical regularity between PM2.5 concentration and adsorption of representative trees through field sampling and measurement, simulated the influence of different afforestation plans on PM2.5 concentration based on Geographic Information System (GIS), judged the significance of the difference of the plans, and proposed a greening distribution strategy. The results show that different forest layouts had no significant impact on PM2.5 in the administrative region, and the concentration reduction rate was only 1%–2%. Targeted planting of trees in heavily polluted areas in the city center would have achieved better air quality improvement, with a reduction rate of 3%–5%. In Wuhan construction areas, trees should be planted to increase the forest coverage rate to 30%. The edge of the urban metropolitan development zone needs to be strengthened with trees to form a forest belt 10 km–20 km wide, with a forest coverage rate of at least 60%. In general, the capability of trees to reduce PM2.5 concentration is weak. The fundamental way to improve air quality is to reduce emissions; planting trees is only an auxiliary measure. More ecological forest functions should be considered in city-wide afforestation distribution.