The first trans-provincial watershed eco-compensation pilot project has been implemented in Xin'anjiang River watershed, standing between China's Anhui Province and Zhejiang Province. The project purpose is to solve the trans-boundary pollution issue, while ensuring sustainable use of water resources. Models have been proved to be effective tools for identifying major environmental objectives, especially in regard to the watershed management of agricultural non-point source pollution. The regional nutrient management (ReNuMa) model was employed to interpret the main environmental problem (nitrogen contamination) generated within Tunxi catchment, the headstream of Xin'anjiang River watershed. After collecting and processing the available data and parameters, ReNuMa simulated stream flow, sediment yields and dissolved nitrogen loads on a monthly scale from 2000 to 2010 and thus estimated total nitrogen loads. Sensitivity and scenarios analysis were done to infer effective management options. The results showed that source apportionments of N loads emphasized the contribution proportion of each pollution source and characterized the temporal variations caused by natural and anthropogenic factors. Effective countermeasures were elicited by a series of scenarios analysis for water quality improvement. The application of the ReNuMa model showed usefulness to plan N management strategy for policy makers, which in turn helped reduce the N loads draining to the downstream, and gained mutual benefits from the ongoing transprovincial watershed eco-compensation pilot project.

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Based on the localized improved GLOBEIS model, the total amount of biogenic isoprene emissions in Tianjin in 2018 was estimated and the temporal and spatial distribution characteristics were analysed by using the land-use type data interpreted from remote sensing images and the observed hourly meteorological data. The results show that the total amount of biogenic isoprene emission in Tianjin reached 694 t (calculated by C, the same below) in 2018, and the emission intensity was 0.06 t/km2/a. The diurnal, monthly and seasonal variations of biogenic isoprene emissions are obvious: High at noon and low at night; Highest in August and lowest in January; Emissions in summer are the largest while the smallest in winter. The spatial characteristics are closely related to land-use types, and the emissions of biogenic isoprene are mainly concentrated in the forest areas, and the emissions are small in the Urban districts of the city and Binhai New District. Finally, the uncertainty sources of biogenic isoprene emission estimation were analysed.

Based on the activity level data of PM2.5 pollution sources in a certain area around Tianjin City, corresponding emission factors were selected to construct a 0.5km×0.5km small-scale PM2.5 emission inventory for the area in 2017. The results showed that in 2017, an area around the city of Tianjin emitted 3,509.91 tons of PM2.5. According to the emission inventory of PM2.5, in terms of the emission categories of pollution sources, the emission proportion of dust sources, process sources, road mobile sources and fixed combustion sources is 48%, 38%, 6% and 5% respectively. From the perspective of pollution sources, steel emissions accounted for 35% of the overall PM2.5 emissions, and road dust emissions accounted for 25.4% of the overall emissions. In addition, spatial distribution results show that there are pollution sources such as 1 boiler, scattered coal, some enterprises, several main roads, several construction sites and a large number of restaurants within a 3-kilometer radius of a monitoring station in a district around the city of Tianjin. The PM2.5 emitted by these pollution sources will directly affect the PM2.5 concentration in the district. There are still some uncertainties in this list, so the data of activity level, such as road traffic flow, need to be further detailed, and the localization study of emission factors should be further carried out, so as to provide a scientific basis for the formulation of regional pollution prevention strategies.