College of Environmental Sciences and Engineering, State Environmental Protection Key Laboratory of All Material Fluxes in River Ecosystems, Peking University, Beijing, 100871, China.
United Center for Eco-Environment in Yangtze River Economic Belt, Chinese Academy for Environmental Planning, Beijing, 100012, China.
J Environ Manage. 2022 Nov 1;321:115847. doi: 10.1016/j.jenvman.2022.115847. Epub 2022 Aug 15.
A high-resolution nutrient emission inventory can provide reliable and accurate identification of priority control areas, which is crucial for efficient decisions on water quality restoration. However, the inventories widely used in large-scale modeling are usually based on provincial inputs, which induce the challenges of lacking localized parameters and missing localized characteristic when provincial scale inputs are converted to finer scales with the down-scale methods. Based on elaborate investigations and statistical data at the county scale with multi-scale data conversion, the China Emission Inventory of Nutrients (CEIN) was developed with a spatial resolution of a 0.1° grid and sub-basin scales. The Yangtze River Basin was used as a case study to illustrate the potential applications of CEIN. The emissions of total nitrogen (TN) and total phosphorus (TP) of Yangtze River Basin is 0.43 Mt and 0.04 Mt for point sources, 11.09 Mt and 4.64 Mt for diffuse sources in 2017. The hotspot analysis for 2606 sub-basins indicated that cropland is the key source of nutrient emissions, accounting for 58.88% and 79.15% of TN and TP, respectively. Industrial sewage and freshwater aquaculture accounted for 27.39% (TN) and 21.98% (TP) of the point sources, which is substantial due to their direct discharge into surface waters. The current results also reveal that, in contrast to CEIN, the previously used common emission factors based on GDP per capita produced considerable overestimations of 2.37 and 2.65 times the actual TN and TP emissions, respectively. Additional advantages of the CEIN have been demonstrated in identifying priority control areas more accurately with reduced bias and quantifying the effects of policies at much smaller scales. For example, the CEIN helps to distinguish hotspots, which was neglected when identifying sources at the level-III sub-basin scale, and indicates that the management of fractional areas (TN: 16.97%; TP: 13.44%) provides the highest nutrient emissions control (TN: 44.34%; TP: 48.65%) for the entire basin. The evaluation of China's toilet revolution policy demonstrates that achieving equitable access to safe sanitation has resulted in a reduction of 7240 t of TN and 833 t of TP, which is extremely critical for rural water quality and health.
高分辨率养分排放清单可以为优先控制区的确定提供可靠、准确的信息，这对于水质恢复的决策至关重要。然而，在大规模建模中广泛使用的清单通常基于省级投入，这在利用降尺度方法将省级投入转化为更精细的尺度时，会导致缺乏本地化参数和丢失本地化特征的挑战。本研究基于县级的详细调查和统计数据，采用多尺度数据转换方法，建立了具有 0.1°格网和子流域尺度的中国养分排放清单（CEIN）。以长江流域为例，说明了 CEIN 的潜在应用。结果表明，2017 年长江流域点源总氮（TN）和总磷（TP）排放量分别为 0.43 Mt 和 0.04 Mt，面源排放量分别为 11.09 Mt 和 4.64 Mt。对 2606 个子流域的热点分析表明，农田是养分排放的关键源，分别占 TN 和 TP 的 58.88%和 79.15%。工业污水和淡水水产养殖分别占点源 TN（27.39%）和 TP（21.98%）的比例较大，这是因为它们直接排入地表水体。研究结果还表明，与 CEIN 相比，之前基于人均 GDP 的常用排放因子分别高估了实际 TN 和 TP 排放的 2.37 倍和 2.65 倍。CEIN 还具有其他优势，可更准确地识别优先控制区，减少偏差，并在更小的尺度上量化政策的效果。例如，CEIN 有助于区分热点，而在三级子流域尺度上识别源时忽略了这些热点，并且表明对分数区域（TN：16.97%；TP：13.44%）的管理可以为整个流域提供最高的养分排放控制（TN：44.34%；TP：48.65%）。对中国厕所革命政策的评估表明，实现安全卫生设施的公平获取导致 TN 减少了 7240 t，TP 减少了 833 t，这对农村水质和健康至关重要。
