In this study, 56 volatile organic compounds species (VOCs) and other pollutants (NO, NO₂, SO₂, O₃, CO and PM_2.5) were measured in the northern suburbs of Nanjing from September 2014 to August 2015. The total volatile organic compound (TVOC) concentrations were higher in the autumn (40.6 ± 23.8 ppbv) and winter (41.1 ± 21.7 ppbv) and alkanes were the most abundant species among the VOCs (18.4 ± 10.0 ppbv). According to the positive matrix factorization (PMF) model, the VOCs were found to be from seven sources in the northern suburbs of Nanjing, including liquefied petroleum gas (LPG) sources, gasoline vehicle emissions, iron and steel industry sources, industrial refining coke sources, solvent sources and petrochemical industry sources. One of the sources was influenced by seasonal variations: it was a diesel vehicle emission source in the spring, while it was a coal combustion source in the winter. According to the conditional probability function (CPF) method, it was found that the main contribution areas of each source were located in the easterly direction (mainly residential areas, industrial areas, major traffic routes, etc.). There were also seasonal differences in concentration, ozone formation potential (OFP), OH radical loss rate (L^OH) and secondary organic aerosols potential (SOAP) for each source due to the high volatility of the summer and autumn temperatures, while combustion increases in the winter. Finally, the time series of O₃ and OFP was compared to that PM_2.5 and SOAP and then they were combined with the wind rose figure. It was found that O₃ corresponded poorly to the OFP, while PM_2.5 corresponded well to the SOAP. The reason for this was that the O₃ generation was influenced by several factors (NO_x concentration, solar radiation and non-local transport), among which the influence of non-local transport could not be ignored.

Most terrestrial carbon is stored in forest biomass, which plays an important role in local, regional, and global climate change. Monitoring of forests and their status, and accurate estimation of forest biomass are important in mitigating the impacts of climate change. Empirical models developed using remote-sensing and field-measured forest data are commonly used to estimate forest biomass. In the present study, we used a mechanistic model to estimate height and biomass in the Three Gorges reservoir region (China) based on the allometric scale and resource limits (ASRL) model. The forests in the Three Gorges reservoir region are important and unique in view of the vertical distribution of vegetation and mixed needleleaf. Detailed information about the forest in this region is available from the Geoscience Laser Altimeter System (GLAS) and field measurements from 714 forest plots. The ASRL model parameters were adjusted using GLAS-derived forest tree height to reduce the deviation between modelled and observed forest height. The predicted maximum forest tree height from the optimized ASRL model was compared to measured tree heights, and a good correlation (R² = 0.566) was found. The allometric scale function between forest height and diameter at breast height (DBH) is developed and the maximum forest tree height from the optimized ASRL model transferred to DBH. Moreover, the forest biomass was estimated from DBH according to the allometric scale function that was determined using DBH and biomass data. The results of maximum forest biomass using the ASRL model and the allometric scale function show a good accuracy (R² = 0.887) in the Three Gorges reservoir region. Here, we present the forest biomass estimation approach following allometric theory for accurate estimation of maximum forest tree height and biomass. The proposed approach can be applied to forest species in all types of environmental conditions.