Contribution of non-point source pollution that migrated with underground runoff process based on the SWAT model and a digital filter algorithm.

Non-point source (NPS) pollution has always been the focus of research worldwide, and understanding the migration process is the basis for effective control of NPS pollution. In this study, the SWAT model and digital filtering algorithm were combined to explore the contribution of NPS pollution that migrated with underground runoff (UR) process to the Xiangxi River watershed. The results showed that the surface runoff (SR) was the main migration process of NPS pollution, while the contribution of NPS pollution that migrated with the UR process only accounted for 30.9%. With the decrease in annual precipitation among the three selected hydrological years, the proportion of NPS pollution that migrated with the UR process for TN decreased, whereas the proportion for TP increased. The contribution of NPS pollution migrated with UR process varied remarkably during different months. Although the maximum total load and the load of NPS pollution that migrated with the UR process for TN and TP all appeared in the wet season, due to the hysteresis effect, the load of NPS pollution that migrated with the UR process for TP appeared 1 month later than the total load of NPS pollution. With an increase in precipitation from the dry season to the wet season, the proportion of NPS pollution that migrated with the UR process for TN and TP decreased gradually, and the degree of decrease in NPS pollution that migrated with the UR process for TP was more evident than that for TN. Besides, being affected by topography, land use, and other factors, the proportion of NPS pollution that migrated with the UR process for TN decreased from 80% in upstream areas to 9% in downstream areas, while that for TP reached a maximum of 20% in downstream areas. Based on the research results, the contribution of soil and groundwater cumulative nitrogen and phosphorus should be considered, and different managements and control measures for different migration routes should be adopted in controlling pollution.

Study of uncertainty of satellite and reanalysis precipitation products and their impact on hydrological simulation.

Satellite and reanalysis precipitation products are potential alternatives in hydrological studies, and it is very important to evaluate their accuracy and potential use for reliable simulations. In this study, three precipitation products (Tropical Rainfall Measuring Mission 3B43 Version 7 (TRMM 3B43), spatial interpolation grid data based on 2472 national meteorological observation stations in China (GRID_0.5), and National Centers for Environmental Prediction-Climate Forecast System Reanalysis (NCEP-CFSR)) were evaluated against gauge observations in the Xiangxi River watershed of Hubei Province. The performance results indicated that the results of the three precipitation products were correlated with those of the rain gauges; however, there were differences among the three products. TRMM 3B43 tended to overestimate precipitation with the highest correlation coefficient, while NCEP-CFSR tended to underestimate precipitation with the least satisfactory performance, and the performance of GRID_0.5 ranked between them. However, the annual and monthly mean errors differed, as the errors of most of the results driven by NCEP-CFSR were lowest. The errors varied at different time scales. During years with high precipitation, the results were often underestimated, while the results are often overestimated during years with low precipitation. According to the average monthly results, the GRID_0.5 results were closest to the gauge observations for most months. During the wet season, TRMM 3B43 performed better, while NCEP-CFSR precipitation performed better during the dry season. The errors from precipitation to streamflow, NPS pollution, and water environmental capacity (WEC) driven by the three precipitation products increased gradually, ranging from 10% for precipitation to over 20% for NPS pollution and almost 100% for WEC. The error increase for NCEP-CFSR was lower than that of the other two products. Although the simulation error from precipitation to the WEC results driven by the three precipitation products gradually increased, the degree of overestimation and underestimation became smaller.

Multiscale spatiotemporal characteristics of landscape patterns, hotspots, and influencing factors for soil erosion.

Soil erosion is an increasingly serious eco-environmental problem, and effective control of soil erosion is an important part of soil resource protection and ecological restoration. In this study, the multi-scale characteristics and influencing factors of soil erosion were analyzed in the Beijing-Tianjin-Hebei (BTH) region from 2000 to 2015. The results showed that the average soil erosion in the study area was 3500 t/(km2·a), in which the severe erosion areas accounted for 10% of the total area. Although the total soil erosion rate decreased by 60% from 2000 to 2015, the rate of current soil erosion was higher than the soil loss tolerance. The severe erosion area had the highest aggregation index, making it the most suitable for centralized treatment. Meanwhile, the fractal dimension index of severe erosion showed a downward trend from 2000 to 2015. This decrease in complexity led to a more optimistic conservation situation. The hotspot areas overlapped with the relatively high erosion zones and were aggregated as three large patches in the northern, southwestern, and southern BTH regions. Soil erosion distribution depends on both anthropogenic activities and natural conditions. The slope factor, which reflects the impact of natural factors on soil erosion, was the most dominant factor on soil erosion from 2000 to 2010. Conversely, the land use factor, which is mainly controlled by humans, became the dominant factor in 2015. The distribution characteristics and influencing factors of soil erosion both had scale effects. As the scale decreased from city to town, the patches of high and severe erosion classes became more regular and aggregated, the hotspot area had the most concentrated and severe soil erosion rate at the town scale, and human impacts became dominant. Conservation targeting hotspot areas measured at the town scale, which was 20% of the total area, could reduce the total soil loss by 38%. For a region with a complex structure, the main influencing factors showed strong spatial dependence.

Spatial-temporal characteristics, source-specific variation and uncertainty analysis of health risks associated with heavy metals in road dust in Beijing, China.

Based on the concentrations of ten heavy metals (As, Cd, Cr, Cu, Hg, Mn, Ni, Pb, Zn, Fe) in 144 road dust samples collected from 36 sites across 4 seasons from 2016 to 2017 in Beijing, this study systematically analyzed the levels and main sources of health risks in terms of their temporal and spatial variations. A combination of receptor models (positive matrix factorization and multilinear engine-2), human health risk assessment models, and Monte Carlo simulations were used to apportion the seasonal variation of the health risks associated with these heavy metals. While non-carcinogenic risks were generally acceptable, Cr and Ni induced cautionary carcinogenic risks (CR) to children (confidence levels was approximately 80% and 95%, respectively).. Additionally, fuel combustion posed cautionary CR to children in all seasons, while the level of CR from other sources varied, depending on the seasons. Heavy metal concentrations were the most influential variables for uncertainties, followed by ingestion rate and skin adherence factor. The values and spatial patterns of health risks were influenced by the spatial pattern of risks from each source.

Temporal variations of levels and sources of health risk associated with heavy metals in road dust in Beijing from May 2016 to April 2018.

To analyze the temporal variations of heavy metals, health risk, and source-specific health risk, 24 road dust samples were collected from Beijing in each month in two years. The temporal variations of Hg, Pb, and Ni were higher than other heavy metals. Most heavy metals reached their highest concentrations either in winter or in spring, then the concentrations decreased and reached the lowest values in autumn. Human health risk assessment (HHRA) model showed that As, Cr, and Ni might pose cautionary carcinogenic risk (CR) to children (CR > 10-6). CR for adults were only 0.15 to 0.19 times of that for children. Four sources were identified based on positive matrix factorization model and HHRA model, they were traffic exhaust, fuel combustion, construction, and use of pesticides and fertilizers. Influenced by the difference of carcinogenicity of heavy metals, traffic exhaust contributed the largest to heavy metals (36.02%, over 42.24% higher than other sources), while contributions of fuel combustion to CR (36.95%) was similar to traffic exhaust (37.17%). Monte-Carlo simulation showed that the 95th percentile of probability density functions of CR posed by Cr and Ni from each source were 9.90 × 10-5 to 2.64 × 10-4, posing cautionary carcinogenic risk to children. The seasonal change of CR varied among different sources. CR from use of pesticides and fertilizers in spring was 35.06 times of that in winter, and that from fuel combustion in winter was 1.15-2.40 times of that in other seasons. CR from each source was sensitive to ingestion rate and skin adherence factor.

Identification and uncertainty analysis of high-risk areas of heavy metals in sediments of the Yangtze River estuary, China.

In this study, ordinary kriging (OK) and indicator kriging (IK) were used to analyze the uncertainty associated with high-risk areas of seven heavy metals (As, Cd, Cr, Cu, Hg, Pb, and Zn) in sediments of the Yangtze River estuary during four seasons. The OK results showed that the high-risk areas of Cd, Cr, Cu, Hg, and Pb had a high proportion, with the highest corresponding to Cr pollution (up to 60%). Predictions based on IK revealed that the proportion of high-risk areas of Cr, Cd, and Hg pollution were high, especially that of Cr was higher than 90%. However, there were uncertainties between the OK and IK results. The uncertainty results revealed that the uncertainty areas of Cr pollution were relatively large, accounting for about 30%, while those of Cd, Cu, and Hg pollution were lower than 10%.

Source-specific ecological risk analysis and critical source identification of heavy metals in road dust in Beijing, China.

To explore the spatial variation of source-specific ecological risks and identify critical sources of heavy metals in road dust, 36 road dust samples collected in Beijing in March 2017 were analyzed for heavy metals. A new method that takes into consideration the heavy-metal toxic response and is flexible to changes in the number of calculated heavy metals, called the Nemerow integrated risk index (NIRI), was developed for ecological risk assessment. The NIRI indicated that heavy metals posed considerable to high risks at the majority of sites, and 22 % of the sites suffered extreme risk in spring (NIRI > 320). Four main sources were identified based on positive matrix factorization (PMF): traffic exhaust, fuel combustion, construction, and use of pesticides and fertilizers. Owing to the lower toxic response factors of representative heavy metals of fuel combustion than those of other sources, although fuel combustion had the highest contribution (34.21 %) to heavy metals in spring, it only contributed 5.57 % to ecological risks. Critical sources and critical source areas were determined by considering the contributions to both heavy metals and ecological risks. The use of pesticide and fertilizer and traffic-related exhaust were identified as critical sources of heavy metals in spring. Source-specific ecological risks and critical sources of heavy metals changed with the changing seasons, which suggests that different strategies should be adopted in different seasons.

Spatial-temporal variation of heavy metals' sources in the surface sediments of the Yangtze River Estuary.

In this study, positive matrix factorization, multilinear engine 2, and geographic information systems were used to characterize the spatial-temporal patterns of sources for nine heavy metals in the surface sediments of the Yangtze River Estuary in different seasons. Results showed that six sources were identified: agricultural pesticide, marine transportation, chemical factory wastewater, metal smelter waste, atmospheric deposition, and agricultural fertilizer. The proportions of sources were similar during the entire year but varied among the seasons. The differences in the proportions of agricultural pesticide between winter and other seasons were greater than 12%. Over 40% of the Cd concentration in most seasons was attributed to atmospheric deposition, while less than 5% in autumn. The impact strength of most sources, except marine transportation and metal smelter waste, decreased from the inner regions to the adjacent sea. The difference in the impact strength of agricultural pesticide was the largest throughout the study area.

Uncertainty analysis in source apportionment of heavy metals in road dust based on positive matrix factorization model and geographic information system.

Based on 36 road dust samples from an urbanized area of Beijing in September 2016, the information about sources (types, proportions, and intensity in spatial) of heavy metals and uncertainties were analyzed using positive matrix factorization (PMF) model, bootstrap (BS), geographic information system (GIS) and Kriging. The mean concentration of most heavy metals was higher than the corresponding background, and mean concentration of Cd was six times of its background value. Types and proportions of four sources were identified: fuel combustion (33.64%), vehicle emission (25.46%), manufacture and use of metallic substances (22.63%), and use of pesticides, fertilizers, and medical devices (18.26%). The intensity of vehicle emission and the use of pesticides, fertilizers, and medical devices were more homogeneous in spatial (extents were 1.285 and 0.955), while intensity of fuel combustion and the manufacture and use of metallic substances varied largely (extents were 4.172 and 5.518). Uncertainty analysis contained three aspects: goodness of fit, bias and variability in the PMF solution, and impact of input data size. Goodness of fit was assessed by coefficient of determination (R2) of predicted and measured values, and R2 of most species were higher than 0.56. Influenced by an outlier, R2 of Ni decreased from 0.59 to 0.11. Result of bootstrap (BS) showed good robust of this four-factor configuration in PMF model, and contributions of base run of factors to most species were contained in the small interquartile range and close to median values of bootstrap. Size of input data also had influence on results of PMF model. Residuals changed largely with the increase of number of site, it varied at first and then kept stable after number of site reached 70.

Quantifying and simulating landscape composition and pattern impacts on land surface temperature: A decadal study of the rapidly urbanizing city of Beijing, China.

The increase in impervious surfaces due to the urbanization has caused many adverse effects on urban ecological systems, including the urban heat environmental risk. Revealing the relationship between landscape composition and pattern and land surface temperature (LST) gives insight into how to effectively mitigate the urban heat island (UHI) effect. It is also essential to simulate and optimize the distribution of impervious surfaces in urban planning. In this study, the multi-scale relationship between impervious surface and LST in Beijing was analyzed. Different distributions of land cover types and the corresponding LSTs were simulated under two development scenarios. Various geospatial approaches, including geographic information system (GIS), remote sensing, and the Conversion of Land Use and its Effects at Small regional extent (CLUE-S), were used to facilitate the analysis. The results showed that (1) impervious surfaces increased from 36.76% to 44.95% of the total area between 2005 and 2015 and the mean LST of impervious surfaces was approximately 2 °C higher than that of the areas with vegetation cover; (2) impervious surfaces had a positive logarithmic correlation with LST, while the vegetation coverage had a negative linear correlation with LST; (3) as the grid size increased, the correlation coefficients between the impervious surface density and mean LST increased at different magnitudes, and the correlation coefficients stabilized after the scale of 900 × 900 m; (4) large and contiguous patches of impervious surfaces aggravated the UHI effect when the total percentage of impervious surface remained the same; and (5) to achieve an improved and healthier urban living environment, populations controls should be considered to decrease future impervious surface demands by 7.69%-which corresponds to an average LST decrease of 1.1 °C. Landscape distribution and configuration should also be better integrated into landscape and urban planning.