Impact of Climate-Driven Land-Use Change on O3 and PM Pollution by Driving BVOC Emissions in China in 2050

Predicting the distribution pattern changes of dye plant habitats caused by climate change

Climate change has accelerated the habitat loss and fragmentation of wildlife. Dye plants of "Fengxiang dyeing" are important indigenous natural resources for traditional printing and dyeing craft in southwest China, is of practical and cultural importance for dozens of ethnic minorities. However, lack of the spatial distribution information of these plants has hampered holistic and efficient conservation management measures. We analyzed the potentially suitable areas of four dye plants (Liquidambar formosana, Strobilanthes cusia, Persicaria tinctoria and Indigofera tinctoria) necessary for "Fengxiang dyeing" based on their geographical distribution sites under different climatic situations using the maximum entropy (MaxEnt) model. The results showed that temperature, precipitation and elevation were the most important factors affecting the suitable geographical areas of the four dye plants. Under the current climate conditions, the overlapping suitable habitat areas of the four plants were mainly in the four southern provinces of China, including Guizhou, Guangxi, Guangdong and Hainan. L. formosana was used as the base plant for combination with the other three plants under the two future climate scenarios (SSP126 and SSP585), and the overlapping suitable habitat areas of the obtained seven combination patterns were considered suitable for potential craft development. Five patterns showed an increase, while two patterns showed a decreasing trend with the increasing carbon emission. The prediction results showed that the overlapping suitable habitat center of the four plants will gradually move to the northeast, indicating that the overlapping suitable habitat area and craft distribution area will be changed. These results provide the basis for understanding the spatial distribution pattern changes of dye plants caused by climate change and establishing measures for protecting and developing printing and dyeing craft.

Localized biogenic volatile organic compound emission inventory in China: A comprehensive review

Volatile organic compounds (VOCs) are the precursors of forming ozone (O3) and fine particulate matter (PM2.5). Accurate estimates of biogenic VOC (BVOC) emissions is essential for understanding the formation mechanism of O3 and PM2.5 pollution and precise reduction on anthropogenic emissions and thereby mitigating O3 and PM2.5 pollution. To gain comprehensive knowledge of BVOC emissions and improve the accuracy of their estimation, this study reviewed localized national, regional, and municipal emission estimations in China. From their comparisons, BVOC emission characteristics and deficiencies in the inventory compilation methodology were also investigated. The estimated BVOC emissions in China ranged between 10 and 58.9 Tg yr-1 and 10.9-18.9 Tg C yr-1, with diverse contributions for different BVOC categories. The simulated historical and future BVOC emissions exhibited an increasing trend. The uncertainty of the BVOC estimates was mainly from the applications of incomplete emission models, less localized accurate emission factors, deficient vegetation cover information, and low-resolution meteorological data in the inventory compilation. The regional and municipal BVOC emission inventories mainly focused on the Beijing-Tianjin-Hebei, Pearl River Delta, Sichuan Basin, and Yangtze River Delta regions, as well as the cities therein. For the same area, different studies reported diverse BVOC emissions by a maximum of two orders of magnitude. There is usually a lack of basic data with more detailed investigations and higher precision for estimation of BVOC emissions. By summarizing the measurements on terrestrial and marine BVOC emission fluxes, they are mainly focused on the Guangdong, Zhejiang and Jiangxi provinces, and Yellow Sea, East China Sea, and South China Sea, respectively. Expanding the temporal and spatial scales of observations is encouraged to enhance our understanding on the emissions and improve the emission estimates.