Representing responses to climate change in spatial land system models

Modelling future change to land use and land cover is done as part of many local and global scenario environmental assessments. Nevertheless, there are still considerable challenges related to simulating land-use responses to climate change. Mostly, climate change is considered by changing the temperature and precipitation, affecting the spatial distribution and productivity of future land use and land cover as result of differential changes in growing conditions. Other climate change effects, such as changes in the water resources needed to support future cropland expansion and intensification, are often neglected. In this study, we demonstrate how including different types of responses to climate change influences the simulation of future changes to land use and land cover, and land management. We study the influence of including different climate change effects in land system modeling step by step. The results show that land system models need to include numerous simultaneous climate change effects, particularly when looking at adaptation options such as implementing irrigation. Otherwise, there is a risk of biased impact estimates leading either to under- or overestimation of the consequences of land use change, including land degradation. Spatial land system models therefore need to be developed accounting for a multitude of climate change impacts, uncertainties related to climate data, and an assessment of the sensitivity of the outcomes toward the decisions of modellers on representing climate change impacts.

[Emission Inventory and Characteristics of Anthropogenic Air Pollutant Sources in the Sichuan Province]

Based on anthropogenic source activity data and emission factors for the Sichuan Province, the 1 km×1 km-gridded atmospheric air pollutant emission inventory of 2015 was developed in combination with GIS technology and the combined "bottom-up" and "top-down" construction method. The results show that the total emission of SO₂, NO_x, CO, PM₁₀, PM_2.5, BC, OC, VOCs, and NH₃ in Chengdu is 444.9×10³, 820.0×10³, 3773.1×10³, 1371.6×10³, 537.5×10³, 28.7×10³, 53.1×10³, 923.6×10³, and 988.0×10³ t, respectively. Power plants and other industrial combustion boilers contribute more than 95% of the SO₂ emissions. Mobile, fossil fuel combustion, and industrial process sources contribute 54%, 23%, and 20% of the NO_x emissions, respectively. The industrial process of steel production and building materials manufacturing contribute 20% PM₁₀ of the emissions and take up 34% PM_2.5 of the emissions. Fugitive dust and road fugitive dust contributes 60% PM₁₀ and 35% PM_2.5 of the emissions, respectively. Biomass combustion contributes 33% BC and 51% OC of the emissions, respectively. The solvent use of mechanical processing, building decoration, electronic equipment manufacturing, and printing and furniture industry contribute 46% of the VOCs of the emissions. The NH₃ emissions mainly orginate from the sources of livestock feeding and nitrogen fertilizers, accounting for 70% and 25% of the NH₃ emissions, respectively. The spatial distribution of the emissions shows that high emissions are mainly distributed in the most densely populated, agricultural, and industrial more developed areas in Panzhihua and the Sichuan Basin. The urban agglomerations of the Chengdu Plain, represented by Chengdu, Deyang, and Mianyang, are the areas with emission concentration in the Sichuan Basin. The emissions inventory in this study has uncertainties. More fundamental studies on activity data should be conducted and the emission factors of typical emission sources should be further localized to improve the emission inventory and prevention and control of complex air pollution in the Sichuan Province and provide scientific support.

[Emission Inventory and Pollution Characteristics of Industrial VOCs in Hangzhou, China]

For the 2022 Asian Games, a series of major events were held in Hangzhou City in 2016 at the international summit, where research on VOC emission inventories, especially on industrial VOCs, has attracted the attention of local residents and management departments. By taking a bottom-up approach, 3518 enterprises in more than 30 industries in Hangzhou were investigated and an industrial VOCs emission inventory for Hangzhou was established. Based on the data on regional emissions, emissions intensity, and spatial distribution, the industrial VOCs emissions in Hangzhou were systematically analyzed. The VOC emissions from industrial sources in Hangzhou amounted to 36839.5 tons in 2015. Printing and reproduction of recorded media; chemical raw materials and chemical products manufacturing; and the metal, textile, rubber, and plastics products industries were the most important sources of industrial VOC emissions in Hangzhou. The largest industrial VOC emissions were measured in Xiaoshan District, followed by Fuyang District and Dajiangdong Industrial Agglomeration Area. The highest VOC emissions intensity was detected in Fuyang District, Jiande City, and Lin'an City. Industrial VOC emissions were mainly concentrated in more intensive industrial areas in Xiaoshan, Fuyang, Yuhang, and Dajiangdong Districts.

[Air Pollutant Emission Inventory and Impact of Typical Industries on PM2.5 in Chengde]

In this study, detailed activity level of typical sector in Chengde in 2013 was obtained through a full-coverage investigation. A comprehensive emission inventory with country-level resolution in 2013 was developed based on guide of atmospheric pollutant emission inventory and updated emission factors. Then, the emission inventory within 1 km×1 km grid was generated using source-based spastial surrogates including population, road network and landuse date. Furthemore, meteorology-air quality modeling system (WRF-CMAx) including Particulate Source Apportionment Technology (PSAT) module was established in order to evaluate the impact of topical sector (e. g., electric power, the production of construction materials, the metallurgical industry, etc.) on PM_2.5 concentration in January, April, July and October which were considered as the representative months of winter, spring, summer and autumn. The results showed the total emission of SO₂, NO_x, TSP, PM₁₀, PM_2.5, CO, VOCs and NH₃ in Chengde in 2013 was respectively 81134 t, 72556 t, 368750 t, 119974 t, 51152 t, 1281371 t, 170642 t and 81742 t. Industrial source was the main emission contributor of SO₂, NO_x, CO, VOCs, accounting for 89.5%, 51.9%, 82.5% and 45.6% of total emissions, respectively. The major emission source of NO_x also included on-road and non-road mobile source, respectively accounting for 26.7% and 10.8%. The major emission source of TSP, PM₁₀ and PM_2.5 was fugitive dust, accounting for 76.7%, 65.6% and 46.54%, respectively. Ammonia emissions from animals and farm accounted for 67.1% and 15.8% of total emissions, respectively. The numerical simulation result showed that the fugitive dust, the others, the metallurgical industry and boilers industry had relatively higher contributions to PM_2.5 concentration, accounting for 23.1%, 20.6%, 13.3% and 11.2%, respectively. These emission sources should be paid more attention during the decision-making with respect to control strategies.