To analyze the spatial distribution patterns, risks, and sources of heavy metals (As, Cd, Cr, Cu, Hg, Mn, Ni, Pb, Zn, Fe), 36 road dust samples were collected from an urbanized area of Beijing in ...June 2016. The mean concentration of most metals, except As and Mn, exceeded their corresponding background values, with the mean concentration of Cd being 8 times that of its background. Spatially, for most heavy metals, except As and Mn, the high concentration areas were mainly within the 5th ring road, especially the northern area. The geo-accumulation index of Cd and Cu indicated moderate contamination at many sites. The entire study area was prone to potential ecological risks, with higher risks within the 4th ring road. Cd caused high potential ecological risk at most sites. According to the health risk assessment results, the non-carcinogenic risks that human beings suffered from heavy metals were insignificant. However, the carcinogenic risks due to Ni and Cr exceeded the acceptable level. Based on the source apportionment using positive matrix factorization, four factors were defined for the heavy metals. Factor 1, which was traffic-related exhaust, accounted for 34.47% of the concentration of heavy metals. The contributions of Factors 2 and 3 were approximately 25% each. Factor 2 was potentially related to coal combustion, while Factor 3 could be related to the manufacture and use of metal components. Factor 4, which could be related to the use of pesticides, fertilizers, and medical devices, accounted for 14.88%, which was the lowest.
Display omitted
•Methods were used to explore contamination and sources of metals in Beijing.•Beijing suffered potential ecological risks, especially risks related to Cd and Hg.•Four factors influenced metal content, with traffic-related exhaust contributed most.•The contribution of pesticides and fertilizers should be paid attention to.
Display omitted
•A new method amended existing widely used risk assessment methods was developed.•Critical sources and critical source area for road dust were introduced and identified.•Fuel ...combustion contributed the largest to heavy metal but less to ecological risks.•Pesticide and fertilizers contributed half of risks, posing no less than moderate risks.•Traffic exhaust was critical in all seasons, with higher impact within smaller ring roads.
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.
Due to significant human activity, road dust is becoming contaminated by heavy metals in many cities. To comprehensively investigate the variation of contamination level and sources of heavy metals ...in road dust, 10 heavy metals in road dust samples from Beijing, China, in both summer and winter, were evaluated by spatial analysis using geographic information system (GIS) mapping technology and the positive matrix factorization (PMF) Model. Although the concentrations of some heavy metals between summer and winter had similarities, the differences of others and spatial distributions of heavy metals between summer and winter were considerable. The mean concentrations of As, Cd, Cr, Cu, and Fe were lower in winter, while those of Hg, Mn, Ni, Pb, and Zn were higher. According to the values of the Pollution Index (PI) and Nemerow Integrated Pollution Index (NIPI), there were no obvious differences between summer and winter, but the range between different sites in winter was nearly twice that of summer. Based on the PMF model, four sources of heavy metals in the dust samples were identified. Although the types of sources were consistent, the relative contributions of each source differed between summer and winter. Non-exhaust vehicle emissions was the most important source in summer (34.47 wt%), while fuel combustion contributed the largest proportion to the total heavy metals in winter (32.40 wt%). The impact of each source also showed spatial variation different trends in summer and winter. With the alteration of seasons, intensity of human activities also changed, such as the number of tourists, energy needs for building temperature regulation, construction, and the amount of pesticides and fertilizer. That might be the reason for the variation of heavy metal concentrations and relative contribution of their sources between summer and winter.
Display omitted
•Impact of human activity on heavy metals in road dusts was studied in two seasons.•Pollution levels and source types were similar, while source contribution varied.•Dominant source was traffic exhaust in summer, while fuel combustion in winter.•Source contribution and impact area changed with the variation of human activities.
Fecal coliform bacteria are a key indicator of human health risks; however, the spatiotemporal variability and key influencing factors of river fecal coliform have yet to be explored in a ...rural-suburban-urban watershed with multiple land uses. In this study, the fecal coliform concentrations in 21 river sections were monitored for 20 months, and 441 samples were analyzed. Multivariable regressions were used to evaluate the spatiotemporal dynamics of fecal coliform. The results showed that spatial differences were mainly dominated by urbanization level, and environmental factors could explain the temporal dynamics of fecal coliform in different urban patterns except in areas with high urbanization levels. Reducing suspended solids is a direct way to manage fecal coliform in the Beiyun River when the natural factors are difficulty to change, such as temperature and solar radiation. The export of fecal coliform from urban areas showed a quick and sensitive response to rainfall events and increased dozens of times in the short term. Landscape patterns, such as the fragmentation of impervious surfaces and the overall landscape, were identified as key factors influencing urban non-point source bacteria. The results obtained from this study will provide insight into the management of river fecal pollution.
Display omitted
•Variability of fecal coliform (FC) was studied in a complex rural-urban catchment.•Environmental factors could not explain FC change in urban region due to WWTP.•Quick and sensitive response of FC change to rainfall was found in urban region.•Suspended solids showed direct correlation with FC, especially for upstream areas.•Impervious surface connectivity could be used for predicting NPS-FC variability.
Non-point source (NPS) pollution has become the largest threat to water quality in recent years. Major pollutants, particularly from agricultural activities, which include nitrogen, phosphorus and ...sediment that have been released into aquatic environments, have caused a range of problems in the Three Gorges Reservoir Region (TGRR), China. It is necessary to identify the spatial and temporal distributions of NPS pollutants and the highly polluted areas for the purpose of watershed management. In this study, the NPS pollutant load was simulated using the Soil and Water Assessment Tool (SWAT) and the small-scale watershed extended method (SWEM). The simulation results for four typical small catchments were extended to the entire watershed leading to estimates of the NPS load from 2001 to 2009. The results demonstrated that the NPS pollution load in the western area was the highest and that agricultural land was the primary pollutant source. The similar annual variation trends of runoff and sediment loads demonstrated that the sediment load was closely related to runoff. The loads of total nitrogen (TN) and total phosphorus (TP) were relatively stable from 2001 to 2007, except for high loads in 2006. The increase in pollution source strength was an important reason for the significant upward trend of TN and TP loads from 2008 to 2009. The rainfall from April to October contributed to the largest amount of runoff, sediment and nutrient loads for the year. The NPS load intensities in each sub-basin reveal large variations in the spatial distribution of different pollutants. It was shown that the temporal and spatial distributions of pollutant loads were positively correlated with the annual rainfall amounts and with human activities. Furthermore, this finding illustrates that conservation practices and nutrient management should be implemented in specific sites during special periods for the purpose of NPS pollution control in the TGRR.
•The SWAT was applied in the entire Three Gorges Reservoir Region (TGRR), China.•The small-scale watershed extended method was applied, with the support of the SWAT.•The spatial and temporal distributions of pollutant load were estimated in the TGRR.•The highly polluted areas were identified in the TGRR.•The management efforts were accounted for in specific sites during special periods.
The optimized design of outdoor environment is of utmost importance due to its impact on human health, urban livability and energy consumption inside buildings. The outdoor thermal comfort and its ...spatiotemporal variations were assessed using Universal Thermal Climate Index (UTCI). Annual and seasonal UTCI were calculated using the daily dataset collected from 591 stations in China between 1966 and 2016. A REOF-cluster-EOF hybrid model was developed to optimize regionalization and assess regional-scale variations for UTCI. The results showed the following: (1) UTCI values decreased due to the increase of the latitude in China except for the Qinghai-Tibet Plateau. 69.5% of the total area of China experienced “no thermal stress” conditions in summer, whereas it was only 7.7% in winter. Additionally, the outdoor environment in summer had a wider “thermal comfort zone” than that in other seasons. (2) China was divided into a small number of regions with coherent UTCI changes using REOF analysis and K-means clustering algorithm. Eight homogeneous regions were obtained for annual UTCI. From spring to winter, the numbers of homogeneous regions were eight, nine, ten and seven, respectively. (3) Using EOF analysis, dominant patterns of UTCI in each region were extracted by the first two EOF modes, which accounted for >60% of the total variance. In the first mode, the significant upward trends of UTCI were detected in each region, suggesting the stronger outdoor heat stress. In the second mode, UTCI showed fluctuation between the cold and warm periods with different turning points between regions. Overall, the outdoor thermal comfort seemed to be improved more in high-latitude regions than that in low-latitude regions.
Display omitted
•A REOF-cluster-EOF hybrid model was proposed to examine regional-scale variations.•The thermal comfort zone was the largest in summer and the smallest in winter.•Annual and seasonal UTCI exhibited the significant warming trend in each region.•UTCI fluctuated between cold and warm periods with different turning points.•The outdoor thermal comfort seemed to be improved more in high-latitude regions.
•Environmental and biological hazard risk were assessed by geostatistics and GIS.•Anthropogenic inputs and fine grained sediments might explain for high enrichment.•Arsenic was defined as main ...pollutant in the Yangtze River estuary.•The contamination degree would be assessed by the combination of heavy metals.
30 samples of eight heavy metals were collected in February 2011 within Yangtze River estuary (YRE). The mean concentrations met the primary standard criteria based on Marine Sediments Quality of China. The spatial distribution showed that a gradient concentration decreased gradually from inner-estuary to river mouth. Anthropogenic inputs might be the main contributor, and fine grained sediments might also aggravate the heavy metal contamination. The assessment results indicated that the YRE was in low risk of contamination caused by every single heavy metal. However, it was in considerable degree of contamination considering combination of all the heavy metals. The toxicities of heavy metals might be elevated when heavy metals were in combination. Arsenic should be of primary concern due to its higher assessment values and the potential of adverse biological effects. And the concentration of As in the YRE had a trend to increase because of anthropogenic activities nearby.
The increase in extreme climate events such as flooding and droughts predicted by the general circulation models (GCMs) is expected to significantly affect hydrological processes, erosive dynamics, ...and their associated nonpoint source (NPS) pollution, resulting in a major challenge to water availability for human life and ecosystems. Using the Hydrological Simulation Program-Fortran model, we evaluated the synergistic effects of droughts and rainfall events on hydrology and water quality in an upstream catchment of the Miyun Reservoir based on the outputs of five GCMs. It showed substantial increases in air temperature, precipitation intensity, frequency of heavy rains and rainstorms, and drought duration, as well as sediment and nutrient loads in the RCP 8.5 scenario. Sustained droughts followed by intense precipitation could cause complex interactions and mobilize accumulated sediment, nutrients and other pollutants into surface water that pose substantial risks to the drinking water security, with the comprehensive effects of soil water content, antecedent drought duration, precipitation amount and intensity, and other climate characteristics, although the effects varied greatly under different rainfall patterns. The Methods and findings of this study evidence the synergistic impacts of droughts and heavy rainfall on watershed system and the significant effects of initial soil moisture conditions on water quantity and quality, and help to guide a robust adaptive management system for future drinking water supply.
Display omitted
•An integrated method is proposed for studying the parameter scaling effect.•Sensitivity parameters of the SWAT were explored at different scales.•Key parameters of hydrological/NPS ...prediction at varying scales were compared.•Temporal and spatial scaling effects of parameter sensitivity were explored.•Suggestions were given for model construction and NPS management.
Scale represents an important concept in all scientific disciplines, but the scaling effect related to non-point source (NPS) pollution simulation and sensitivity parameters has not yet been reported. In this study, the sliding window, the Fourier amplitude sensitivity test and the nested watershed idea were used with the Soil and Water Assessment Tool (SWAT) model to explore the temporal and spatial scaling effects of parameter sensitivity in a typical watershed of the Three Gorges Reservoir Region, China. The results indicated that a great scaling effect could be observed at varying spatio-temporal scales, while the scaling effect would be transferred and amplified from the hydrological modelling to the NPS simulation. Soil properties such as SOL_K and SOL_BD were identified as key parameters under smaller spatial and temporal scales, while channel-related parameters in terms of ALPHA_BF, CH_K and CH_N showed greater sensitivity at larger scales. Specifically, some parameters, such as CH_N, USLE_K, USLE_P and ERORGP, were always identified as key sensitive parameters for the sediment and NPS-TP simulations, but some parameters, such as CH_K, showed sensitivity only above a specific spatial scale (778 km2 in this study). These results could be used as a reference for studying the scaling effect of model parameter sensitivity and provide important information for model construction and the management of NPS pollution at different scales.
► Compared existing exotic and native NPS models, discussed their advantages and disadvantages, proposed further improvements. ► Discussed the limited applications, inefficient calibration and ...validation, and incomplete mechanism description in China's NPS modelling. ► Recommend future studies to investigate the model mechanism, as well as methods of improvement, causes of uncertainty and integration.
With the development of technology for controlling point source pollution, non-point source (NPS) pollution issues have become increasingly prominent worldwide. Because of the wide range, difficult control and complex uncertainties involved in simulation processes, NPS pollution control has become a hotspot in the area of water pollution control. In China, NPS pollution control will be one of the most important issues in water environmental protection in the next several decades. To control NPS pollution, it is important to know how much there is. In this paper, the authors provide an overview of the current NPS pollution modelling technology in China. We first compared several methods used for estimation of the NPS pollution load in China. We next discussed the advantages and disadvantages of these methods in detail, both from the method itself and the simulation results. We found that most of these methods are derived directly from models developed by several developed countries, especially the USA. Although these models may be suitable to the situation of the country they were designed in, they may not be suitable to the actual situation of China. Other methods have been developed by scholars in China, but these are all very simple and may not provide a good estimation. Finally, we point out that we can only determine if a NPS model is good or bad according to the actual conditions of the study area and the available data for this area. Overall, the results of this study indicated that digesting and absorbing foreign NPS models, modifying the related processes and using related key parameters with Chinese characteristics are the future research direction for NPS pollution modelling in China.
PDF
