Atmospheric nitrogen (N) deposition has a significant influence on soil organic carbon (SOC) accumulation in forest ecosystems. Microbial residues, as by-products of microbial anabolism, account for a significant fraction of soil C pools. However, how N deposition affects the accumulation of soil microbial residues in different forest biomes remains unclear. Here, we investigated the effects of six/seven-year N additions on microbial residues (amino sugar biomarkers) in eight forests from tropical to boreal zone in eastern China. Our results showed a minor change in the soil microbial residue concentrations but a significant change in the contribution of microbial residue-C to SOC after N addition. The contribution of fungal residue-C to SOC decreased under low N addition (50 kg N ha⁻¹ yr⁻¹) in the tropical secondary forest (−19%), but increased under high N addition (100 kg N ha⁻¹ yr⁻¹) in the temperate Korean pine mixed forest (+21%). The contribution of bacterial residue-C to SOC increased under the high N addition in the subtropical Castanopsis carlesii forest (+26%) and under the low N addition in the temperate birch forest (+38%), respectively. The responses of microbial residue-C in SOC to N addition depended on the changes in soil total N concentration and fungi to bacteria ratio under N addition and climate. Taken together, these findings provide the experimental evidence that N addition diversely regulates the formation and composition of microbial-derived C in SOC in forest ecosystems.

Soil nitrogen (N) leaching is recognized to have negative effects on the environment. There is a lack of studies on different simultaneously occurring drivers of environmental change, including changing rainfall and N deposition, on soil N leaching. In this study, a two factorial field experiment was conducted in a Korean pine forest with the following four treatments: 30% of throughfall reduction (TR), 50 kg N ha⁻¹ yr⁻¹ of N addition (N+), throughfall reduction plus N addition (TRN+) and natural forest (CK). The zero-tension pan lysimeter method was used to assess the response of soil N leaching loss to manipulated N addition and throughfall reduction. The results showed that the soil N leaching loss in natural forest was 5.0 ± 0.4 kg N ha⁻¹yr⁻¹, of which dissolved organic nitrogen (DON) accounted for 48%. Compared to natural forest, six years of N addition (NH₄NO₃, 50 kg N ha⁻¹ year⁻¹) significantly (P < 0.05) increased soil N leaching losses by 122%, especially in the form of NO₃⁻; a 30% reduction in throughfall slightly decreased N leaching losses by 23%; in combination, N addition and throughfall reduction increased N leaching losses by 48%. There was a strong interaction between N addition and throughfall reduction, which decreased N leaching loss by approximately 2.5 kg N ha⁻¹ yr⁻¹. Our results indicated that drought would diminish the enhancing effect of N deposition on soil N leaching. These findings highlight the importance of incorporating both N deposition and precipitation and their impacts on soil N leaching into future N budget assessments of forest ecosystems under global environmental change.

High concentration particulate matter 2.5 released from forest fires, in addition to direct burns and asphyxia, PM₂.₅ is one of the main pollutants which threaten the safety of forest fire fighter. Therefore, to assess spatial distribution of PM₂.₅, a simulation study was conducted. Fuel beds with different moisture contents and loads were constructed. 144 times burning experiments were carried out under different wind speeds by using wind tunnel device. PM₂.₅ particles at different spatial points were collected and calculated. The results show that, in the two of three variables interaction between wind speed, fuel load, and, except fuel moisture content, wind speed and fuel load are positively correlated with the PM₂.₅ concentrations. From PM₂.₅ concentration which collected at each point in the horizontal and vertical directions, the overall trend is that PM₂.₅ concentration increases along the horizontal downwind direction (C and D higer than A and B) and the vertical upward direction (A and C higer than B and D) Based on BP neural network, the spatial distribution model of PM₂.₅ concentration with single hidden layer was established. The prediction accuracy of modeling samples and validation samples is balanced when hidden layer node is 5. This study will help to make reference for PM₂.₅ occupational exposure standards, forest fire smoke management and forest fire management in China.