Du Yunxia, Song Kaishan, Wang Qiang, Li Sijia, Wen Zhidan, Liu Ge, Tao Hui, Shang Yingxin, Hou Junbin, Lyu Lili, Zhang Bai
Northeast Institute of Geography and Agroecology, CAS, Changchun 130102, China; Hainan Normal University, Haikou 571158, China.
Northeast Institute of Geography and Agroecology, CAS, Changchun 130102, China; School of Environment and Planning, Liaocheng University, Liaocheng, 252000, China.
Sci Total Environ. 2022 Feb 1;806(Pt 4):151374. doi: 10.1016/j.scitotenv.2021.151374. Epub 2021 Nov 2.
In this study, we empirically developed a robust model (the Root Mean Square Error (RMSE), bias, NSE and RE were 26.63 mg/L, -4.86 mg/L, 0.47 and 16.47%, respectively) for estimating the total suspended solids (TSS) concentrations in lakes and reservoirs (Hereinafter referred to as lakes) across the Eastern Plain Lake (EPL) Zone. The model was based on 700 in-situ TSS samples collected during 2007-2020 and logarithmic transformed red band reflectance of Landsat data. Based on the Google Earth Engine (GEE), the TSS concentrations in 16,804 lakes were mapped from 1984 to 2019. The results demonstrated a decreasing tendency of TSS in 82.2% of the examined lakes (72.5% of the basins) indicating that the pollutants carried by TSS flowing into the lakes were decreasing. Statistically significant variation (p < 0.05) was found in half of these lakes (28.6% of the basins). High TSS level (>100 mg/L) was observed in 0.31% of lakes (1.1% of the basins). The changing rates of TSS in 47.8% of the lakes (52.7% of the basins) ranged between -50 mg/L/yr and 0. We found high and significantly increased relative spatial heterogeneity of TSS in 4.6% and 6.5% of lakes, respectively. Likewise, the environmental factors, i.e., fertilizer usage, domestic wastewater, industrial wastewater, precipitation, wind speed and Normalized Difference Vegetation Index (NDVI) exhibited a significant correlation with interannual TSS in 38, 21, 20, 11, 17 and 15 of the 91 basins, respectively. This analysis indicated that only precipitation and fertilizer usage were significantly (p < 0.05) related to the spatial distribution of TSS. The relative contributions of the six factors to the interannual TSS changes were varied in different basins. Overall, the NDVI (the representation of vegetation cover) had a high mean contribution to the interannual TSS changes with an average contribution of 7.2%, and contributions of fertilizer were varied greatly among the basins (0.01%-68%). Human activities (fertilizer usage, domestic wastewater, industrial wastewater) and natural factors (precipitation, wind speed and NDVI) played relatively important roles to TSS changes in 14 and 15 of the 91 basins, respectively. Beyond the six factors in this study, other unanalyzed factors (such as lake depth and soil texture) also had some impacts on the distribution of TSS in the study area.
在本研究中，我们通过实证开发了一个稳健的模型（均方根误差（RMSE）、偏差、NSE和RE分别为26.63mg/L、-4.86mg/L、0.47和16.47%），用于估算东部平原湖区（以下简称湖泊）中湖泊和水库的总悬浮固体（TSS）浓度。该模型基于2007 - 2020年期间收集的700个现场TSS样本以及陆地卫星数据的对数变换红波段反射率。基于谷歌地球引擎（GEE），绘制了1984年至2019年期间16804个湖泊的TSS浓度图。结果表明，82.2%的受检湖泊（72.5%的流域）中TSS呈下降趋势，这表明流入湖泊的TSS携带的污染物在减少。在这些湖泊的一半（28.6%的流域）中发现了具有统计学意义的变化（p < 0.05）。在0.31%的湖泊（1.1%的流域）中观察到高TSS水平（>100mg/L）。47.8%的湖泊（52.7%的流域）中TSS的变化率在-50mg/L/年至0之间。我们分别在4.6%和6.5%的湖泊中发现了TSS相对较高且显著增加的空间异质性。同样，环境因素，即化肥使用、生活污水、工业废水、降水、风速和归一化植被指数（NDVI），分别在91个流域中的38、21、20、11、17和15个流域中与年际TSS呈现出显著相关性。该分析表明，只有降水和化肥使用与TSS的空间分布显著相关（p < 0.05）。六个因素对年际TSS变化的相对贡献在不同流域中各不相同。总体而言，NDVI（植被覆盖的代表）对年际TSS变化的平均贡献较高，平均贡献为7.2%，化肥的贡献在各流域之间差异很大（0.01% - 68%）。人类活动（化肥使用、生活污水、工业废水）和自然因素（降水、风速和NDVI）分别在91个流域中的14个和第15个流域中对TSS变化起到了相对重要的作用。在本研究的六个因素之外，其他未分析的因素（如湖泊深度和土壤质地）也对研究区域内TSS的分布产生了一些影响。
