Assessing outdoor air quality vertically in an urban street canyon and its response to microclimatic factors.

The vertical distribution of air pollutants in urban street canyons is closely related to residents' health. However, the vertical air quality in urban street canyons has rarely been assessed using field observations obtained throughout the year. Therefore, this study investigated the seasonal and annual concentrations of particulate matter (PM2.5 and PM10), CO, NO2, SO2, O3, air quality index, and their responses to microclimatic factors at three height levels (1.5, 27, and 69 m above street level) in an urban street canyon. The PM concentration was higher at 27 m than at 1.5 m in winter, whereas the situation was reversed in other seasons. It was found that photochemical pollutants such as NO2 and O3 were the primary pollutants in the urban street canyon. The days on which O3 was the primary pollutant at the height of 1.5 m accounted for 81.07% of the entire year. The days on which NO2 was the primary pollutant at the height of 27 and 69 m accounted for 82.49% and 72.33% of the entire year, respectively. Substantially higher concentrations of NO2 and O3 were found at the height of 27 m than at 69 m. In-canyon concentrations of NO2 and O3 were strongly correlated with air temperature, wind speed, and wind direction, which played important roles in photochemical reactions and pollutant dispersion.

Natural and human factors influencing urban particulate matter concentrations in central heating areas with long-term wearable monitoring devices.

In northern China, central heating, as an important source of urban particulate matter (UPM), causes more than half of the air pollution during the heating season and has significant spatial-temporal heterogeneity. Owing to the limitations of stationary air monitoring networks, few studies distinguish between heating/non-heating seasons and few have been conducted in urban areas. However, fixed monitoring cannot accurately capture the dynamic exposure of residents to UPM, and there is a lack of comprehensive evaluation of the factors affecting UPM. Therefore, this study used wearable Sniffer 4D equipment to monitor the concentrations of UPM (PM1, PM2.5, and PM10) in selected typical areas of Shenyang City from March 2019 to February 2020. A random forest model was combined with land use and point-of-interest data to analyze the contributions and marginal effects of multiple influences on UPM, in both heating and non-heating seasons. The results showed that in the eastern part of the study area, UPM showed completely opposite spatial distribution characteristics during the two seasons. The concentrations of UPM were higher during the heating season than during the non-heating season. The results indicated that temperature and humidity were important factors in diffusing UPM. The production and operation of boilers were important for the production of UPM. In two-dimensional landscape pattern indices, the percentage of forest and Shannon diversity index were the first and second most important factors, respectively. The three-dimensional pattern of buildings had important effects on the transport and diffusion of UPM (landscape height range >100, floor area ratio >1.3, and landscape volume density >5). Wearable devices could monitor the real situation of residents' exposure to UPM and quantify the factors influencing the spatial-temporal distribution of UPM in an ecological sense. These results provide a scientific basis for urban planning and for health risk reduction for residents.

Characterization and first flush analysis in road and roof runoff in Shenyang, China.

As urbanization increases, urban runoff is an increasingly important component of total urban non-point source pollution. In this study, the properties of urban runoff were examined in Shenyang, in northeastern China. Runoff samples from a tiled roof, a concrete roof and a main road were analyzed for key pollutants (total suspended solids (TSS), total nitrogen (TN), total phosphorus (TP), chemical oxygen demand (COD), Pb, Cd, Cr, Cu, Ni, and Zn). The event mean concentration, site mean concentration, M(V) curves (dimensionless cumulative curve of pollutant load with runoff volume), and mass first flush ratio (MFF30) were used to analyze the characteristics of pollutant discharge and first flush (FF) effect. For all events, the pollutant concentration peaks occurred in the first half-hour after the runoff appeared and preceded the flow peaks. TN is the main pollutant in roof runoff. TSS, TN, TP, Pb, and Cr are the main pollutants in road runoff in Shenyang. There was a significant correlation between TSS and other pollutants except TN in runoff, which illustrated that TSS was an important carrier of organic matter and heavy metals. TN had strong positive correlations with total rainfall (Pearson's r = 0.927), average rainfall (Pearson's r = 0.995), and maximum rainfall intensity (Pearson's r = 0.991). TP had a strong correlation with rainfall intensity (Pearson's r = 0.940). A significant positive correlation between COD and rainfall duration (Pearson's r = 0.902, significance level = 0.05) was found. The order of FF intensity in different surfaces was concrete roof > tile roof > road. Rainfall duration and the length of the antecedent dry period were positively correlated with the FF. TN tended to exhibit strong flush for some events. Heavy metals showed a substantially stronger FF than other pollutant.

Mountain-water-forest-farmland-lake-grassland-sandland holistic protection and restoration engineering and landscape ecology.

Theoretical researches and practices on the life community of mountain-water-forest-farmland-lake-grassland-sandland mosaic and its protection and restoration have been gradually developed in China, which demands the support of a systematic disciplinary theory. Landscape ecology, as an interdisciplinary science of geography and ecology, can meet such demand thanks to its macroscopic spatial theory and technical system. Here, landscape ecology is taken as the supporting discipline of holistic protection and restoration for mountain-water-forest-farmland-lake-grassland-sandland mosaic. Firstly, we clarified that life community of mountain-water-forest-farmland-lake-grassland-sandland is a heterogeneously mosaic landscape, which bears all the characteristics of landscape and thus follows the principles of landscape ecology. Secondly, we expounded how the basic principles of landscape-ecological construction could be applied to the planning and evaluation of holistic protection and restoration for mountain-water-forest-farmland-lake-grassland-sandland mosaic. Finally, we summarized the new trend of landscape-ecological construction research, listed the theoretical and practical problems to be solved, and discussed how the projects of holistic protection and restoration for the mountain-water-forest-farmland-lake-grassland-sandland mosaic can provide a variety of practices for seeking the solutions. The combination of landscape ecology and practical restoration projects would generate effective solutions to realize sustainable development in terms of ecology, economy, and society in China and even the whole world.

Quantitative estimation of the PM2.5 removal capacity and influencing factors of urban green infrastructure.

Long-term exposure to PM2.5 (fine particulate matter with an aerodynamic diameter <2.5 µm) could cause great harm to human health and sustainable development. It remains a challenge to estimate the long-term PM2.5 removal capacity of nature-based green infrastructure in urban areas. In this paper, the annual PM2.5 removal capacity of urban green infrastructure (UGI) from 2000 to 2019 in Shenyang was estimated based on the PM2.5 dry deposition model. The spatial heterogeneity of annual PM2.5 removal capacity were detected Sen-MK test and local spatial autocorrelations analysis. Then the effects of landscape patterns and socioeconomic variables on PM2.5 removal capacity were explored based on linear regression model. The results illustrated that the PM2.5 removal capacity of UGI increased significantly from 2000 to 2019 in Shenyang, with the amount of PM2.5 removal, PM2.5 removal flux and removal rate increasing by 20.64 Mg/a, 0.0258 g/m2/a, and 0.377 %/a, respectively. The PM2.5 removal capacity of UGI exhibited spatial heterogeneity in the study area. Specifically, the regions experiencing the increase in PM2.5 removal capacity of UGI accounted for majority of the old urban area of Shenyang City during the study period; the lower PM2.5 removal capacity clustered in the center urban area, in which high density impervious surfaces distributed, while the higher PM2.5 removal capacity mainly gathered in the area with large scale green space; PM2.5 removal capacity were significantly higher in urban functional zones with a high proportion of green spaces. The landscape metrics representing fragmentation and shape complexity positively affected the annual PM2.5 removal flux and removal rate, while the aggregation metrics had significantly negative correlations with the PM2.5 removal flux and removal rate. Moreover, it was also found that population density and GDP negatively affected the PM2.5 removal capacity of UGI. This study provides a methodological reference and some new insights for future urban landscape planning and air pollution purification.

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Atmospheric environment in urban built-up area is severely influenced by the surrounding landscape pattern. Understanding the relationship between air pollution and surrounding landscape pattern at small scale has great significance for mitigating air pollution from the perspective of urban construction. The annual average concentrations of NO2, SO2, PM2.5 and PM10 from 266 air pollution monitoring stations in 30 provincial capitals of China in 2017 were chosen as dependent variables. Ten two-dimensional and three-dimensional landscape pattern indices (number of buildings, building aggregation, building density, impervious water ratio, quantitative density of catering, building footprint area, high building ratio, floor area ratio, total building area and building type Shannon diversity index) within the 3 km area around the monitoring stations were used as independent variables. The effects of landscape pattern on the concentration of four air pollutants were analyzed using the boosted regression trees model. The results showed that the concentration of four air pollutants in the central and northern cities were significantly higher than that in the southeast coastal cities and southwest cities. The most important factor affecting the concentrations of NO2, SO2, PM2.5 and PM10 was the impervious ratio, with relative contribution rates of 40.7%, 36.3%, 51.0% and 51.8% respectively. The results of sub-region analysis showed that the most important influencing factor differed in different regions, including the impervious ratio in the East and Central China; the number and density of buildings in South China; the impervious ratio and diversity of building types in North China; the impervious ratio and the number of buildings in Northeast China, the density of buildings in Northwest China. Such differences were mainly caused by climate, topography, urban planning, and other factors.

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Urban street canyon is one of the most important characteristics and spatial forms of cities. It is one of the most frequently used public spaces in cities, with the most serious automobile exhaust pollution and the largest population density. The unreasonable space configuration and internal composition might decrease self-purification of urban ventilation but increase local air pollutant concentration. Here, we reviewed the impacts of street canyon morphology, street trees, vehicle flow and meteorological factors on the distribution of air pollutants in street canyons. We scrutinized the relevant methods of numerical simulation, wind tunnel experiments, and field monitoring on the distribution and diffusion of air pollutants in street canyons. We recommended that future research should concentrate on the impacts of various parameters on the distribution and diffusion of air pollutants based on the field monitoring data. Meanwhile, further research should develop optimization strategies for street canyon design which is conducive to the dispersion of air pollutants, and put forward scientific support and optimization scheme for the controlling of air pollutants from the perspective of urban planning and pattern optimization.

Disentangling Responses of the Subsurface Microbiome to Wetland Status and Implications for Indicating Ecosystem Functions.

In this study, we analyzed microbial community composition and the functional capacities of degraded sites and restored/natural sites in two typical wetlands of Northeast China-the Phragmites marsh and the Carex marsh, respectively. The degradation of these wetlands, caused by grazing or land drainage for irrigation, alters microbial community components and functional structures, in addition to changing the aboveground vegetation and soil geochemical properties. Bacterial and fungal diversity at the degraded sites were significantly lower than those at restored/natural sites, indicating that soil microbial groups were sensitive to disturbances in wetland ecosystems. Further, a combined analysis using high-throughput sequencing and GeoChip arrays showed that the abundance of carbon fixation and degradation, and ~95% genes involved in nitrogen cycling were increased in abundance at grazed Phragmites sites, likely due to the stimulating impact of urine and dung deposition. In contrast, the abundance of genes involved in methane cycling was significantly increased in restored wetlands. Particularly, we found that microbial composition and activity gradually shifts according to the hierarchical marsh sites. Altogether, this study demonstrated that microbial communities as a whole could respond to wetland changes and revealed the functional potential of microbes in regulating biogeochemical cycles.

Effect of temperature and moisture on soil organic carbon mineralization of predominantly permafrost peatland in the Great Hing'an Mountains, northeastern China.

Boreal peatlands represent a large global carbon pool. The relationships between carbon mineralization, soil temperature and moisture in the permafrost peatlands of the Great Hing'an Mountains, China, were examined. The CO2 emissions were measured during laboratory incubations of samples from four sites under different temperatures (5, 10, 15, and 20 degrees C) and moisture contents (0%, 30%, 60%, 100% water holding capacity (WHC) and completely water saturated). Total carbon mineralization ranged from 15.51 to 112.92 mg C under the treatments for all sites. Carbon mineralization rates decreased with soil depth, increased with temperature, and reached the highest at 60% WHC at the same temperature. The calculated temperature coefficient (Q10) values ranged from 1.84 to 2.51 with the soil depths and moisture. However, the values were not significantly affected by soil moisture and depth for all sites due to the different peat properties (P > 0.05). We found that the carbon mineralization could be successfully predicted as a two-compartment function with temperature and moisture (R2 > 0.96) and total carbon mineralization was significantly affected by temperature and moisture (P < 0.05). Thus, temperature and moisture would play important roles in carbon mineralization of permafrost peatlands in the Great Hing'an Mountains, indicating that the permafrost peatlands would be sensitive to the environment change, and the permafrost peatlands would be potentially mineralized under future climate change.

Land use regression modelling of PM2.5 spatial variations in different seasons in urban areas.

As one of the principal components of haze, fine particulate matter (PM2.5) has potential negative health effects, causing widespread concern. Identification of the pollutant spatial variation is a prerequisite of understanding ambient air pollution exposure and further improving air quality. Seven urban built-up areas in Liaoning central urban agglomeration (LCUA) were used for land use regression (LUR) modelling of PM2.5 concentrations using small amounts of spatially aggregated data and to assess the model's seasonal consistency. LUR models explained 52-61% of the variation in the PM2.5 concentrations at urban scales. The average building floor area was the key predictor in each model, and the percent water area was predictor with a negative coefficient. Good seasonal consistency was observed between the heating-seasonal model and annual average model, showing that the annual average PM2.5 pollution in the LCUA was mainly influenced by pollution during the heating season. Extending the linear LUR model with regression kriging improved the model's explanatory ability and predictive performance. The predicted PM2.5 concentrations in Shenyang and Anshan were the highest and that in Yingkou was the lowest. The building three-dimensional variables played important roles in the urban spatial modelling of air pollution.

How Do Economic Growth, Urbanization, and Industrialization Affect Fine Particulate Matter Concentrations? An Assessment in Liaoning Province, China.

With China's rapid development, urban air pollution problems occur frequently. As one of the principal components of haze, fine particulate matter (PM2.5) has potential negative health effects, causing widespread concern. However, the causal interactions and dynamic relationships between socioeconomic factors and ambient air pollution are still unclear, especially in specific regions. As an important industrial base in Northeast China, Liaoning Province is a representative mode of social and economic development. Panel data including PM2.5 concentration and three socio-economic indicators of Liaoning Province from 2000 to 2015 were built. The data were first-difference stationary and the variables were cointegrated. The Granger causality test was used as the main method to test the causality. In the results, in terms of the causal interactions, economic activities, industrialization and urbanization processes all showed positive long-term impacts on changes of PM2.5 concentration. Economic growth and industrialization also significantly affected the variations in PM2.5 concentration in the short term. In terms of the contributions, industrialization contributed the most to the variations of PM2.5 concentration in the sixteen years, followed by economic growth. Though Liaoning Province, an industry-oriented region, has shown characteristics of economic and industrial transformation, policy makers still need to explore more targeted policies to address the regional air pollution issue.

Effects of Landscape Pattern Change on Water Yield and Nonpoint Source Pollution in the Hun-Taizi River Watershed, China.

Understanding the influence of landscape pattern changes on water yield (WYLD) and nutrient yield is a key topic for water resource management and nonpoint source (NPS) pollution reduction. The annual WYLD and NPS pollution were estimated in 2004 and 2015 with the calibrated and validated Soil and Water Assessment Tool (SWAT) in the Hun-Taizi River watershed. The impact of land use and landscape pattern changes on the annual WYLD and NPS loading changes were analyzed with a boosted regression tree (BRT) and redundancy analysis (RDA). The results showed that WYLD had a positive correlation with dry farmland and built-up area; however, a negative correlation with paddy field and water area, with the relative contribution of 42.03%, 23.79%, 17.06%, and 13.55%, respectively. The change in nutrient yield was positively correlated with changes in dry farmland, built-up area, and water area but negatively with forestland, according to the BRT model. Landscape patterns had an important influence on WYLD and NPS pollution. A large unfragmented forestland may improve water quality, while a large concentrated dry farmland results in water quality deterioration due to NPS pollution. Water quality is more likely degraded when land uses are complex and scattered with many small patches in a forestland dominated watershed.

Estimating Abundance of Siberian Roe Deer Using Fecal-DNA Capture-Mark-Recapture in Northeast China.

It is necessary to estimate the population abundance of deer for managing their populations. However, most estimates are from high-density populations inhabiting the forests of North America or Europe; there is currently a lack of necessary knowledge regarding low-density deer populations in different forest habitats. In this article, we used fecal DNA based on the capture-mark-recapture method to estimate the population abundance of Siberian roe deer (Capreolus pygargus) in Liangshui National Nature Reserve in the Lesser Xing'an Mountains, northeast China, where the deer population was found to be of a low density by limited studies. We used a robust survey design to collect 422 fecal pellet groups in 2016 and extracted DNA from those samples, generating 265 different genotypes; we thus identified 77 deer individuals based on six microsatellite markers (Roe1, Roe8, Roe9, BM757, MB25 and OarFCB304). With capture and recapture records of these 77 individuals, the abundance of roe deer was estimated to be 87 deer (80-112, 95% CI) using the Program CAPTURE. Using an effective sampling area which resulted from the mean maximum recapture distance (MMRD), we converted the population abundance to a density of 2.9 deer/km2 (2.7-3.7, 95% CI). Our study estimated the roe deer population abundance by a feces-based capture-mark-recapture approach in northeast China, successfully demonstrating the applicability of non-invasive genetic sampling in monitoring populations of deer in this area, which contributes to the development of low-density deer population ecology and management.

Planning for the wetland restoration potential based on the viability of the seed bank and the land-use change trajectory in the Sanjiang Plain of China.

The Sanjiang Plain has the largest marsh wetland area in China. Since the 1950s its size has declined due to land development, between 1986 and 2016 nearly 6072 km2 (57.5% of the area) was lost due to farm land expansion. Since the "Wetland for Grain" project in 2003, efforts have been made to improve marsh area for animal habitat and ecological protection. A key management concern is prioritizing areas for wetland restoration in scientific planning and polices making. In this study, the natural wetland restoration potentials were evaluated based on land-use change trajectory, seed bank viability and watershed sustainability and restorability. The annual land use maps from 1986 to 2016 were reconstructed using CLUE-S model with land use maps in 1995, 2000, 2005, 2010 and 2016, which were interpreted from Landsat TM/ETM images. Seed bank viability was determined by field sampling in wetland and farm land with different reclamation years and germination in lab. Sub-catchment was chosen as sustainability analysis unit, which was quantified by the impacts of wetland on peak flow reduction. The watershed restorability was performed with the factors of wetland degradation degree, seed bank viability, and the percentage of wetland to watershed area (PWW) with different restoration years. The results indicated that reclaimed wetland with a time since last development (TLD) of <15 years had a higher recovery potential and accounted for 39.2% of the lost wetland. Seventeen sub-catchments with a total area of 2177 km2 of farmland could be planned for restoration, which could support more than half of the sub-catchments in the study area. Priority areas were identified for short-, mid- and long-term restoration planning. The results can support the scientific planning demands of various restoration goals in the study area, and provide a new method for wetland restoration.

An updated Vegetation Map of China (1:1000000).

Vegetation maps are important sources of information for biodiversity conservation, ecological studies, vegetation management and restoration, and national strategic decision making. The current Vegetation Map of China (1:1000000) was generated by a team of more than 250 scientists in an effort that lasted over 20 years starting in the 1980s. However, the vegetation distribution of China has experienced drastic changes during the rapid development of China in the last three decades, and it urgently needs to be updated to better represent the distribution of current vegetation types. Here, we describe the process of updating the Vegetation Map of China (1:1000000) generated in the 1980s using a "crowdsourcing-change detection-classification-expert knowledge" vegetation mapping strategy. A total of 203,024 field samples were collected, and 50 taxonomists were involved in the updating process. The resulting updated map has 12 vegetation type groups, 55 vegetation types/subtypes, and 866 vegetation formation/sub-formation types. The overall accuracy and kappa coefficient of the updated map are 64.8% and 0.52 at the vegetation type group level, 61% and 0.55 at the vegetation type/subtype level and 40% and 0.38 at the vegetation formation/sub-formation level. When compared to the original map, the updated map showed that 3.3 million km2 of vegetated areas of China have changed their vegetation type group during the past three decades due to anthropogenic activities and climatic change. We expect this updated map to benefit the understanding and management of China's terrestrial ecosystems.

Land use and land cover change analysis and prediction in the upper reaches of the Minjiang River, China.

Scientists have aimed at exploring land use and land cover change (LUCC) and modeling future landscape pattern in order to improve our understanding of the causes and consequences of these phenomena. This study addresses LUCC in the upper reaches of Minjiang River, China, from 1974 to 2000. Based on remotely sensed images, LUCC and landscape pattern change were assessed using cross-tabulation and landscape metrics. Then, using the CLUE-S model, changes in area of four types of land cover were predicted for two scenarios considering forest polices over the next 20 years. Results showed that forestland decreased from 1974 to 2000 due to continuous deforestation, while grassland and shrubland increased correspondingly. At the same time, the farmland and settlement land increased dramatically. Landscape fragmentation in the study area accompanied these changes. Forestland, grassland, and farmland take opposite trajectories in the two scenarios, as does landscape fragmentation. LUCC has led to ecological consequences, such as biodiversity loss and lowering of ecological carrying capacity.

[Research progress on three-dimensional pattern in landscape ecology]. / æ™¯è§‚ç”Ÿæ€å­¦ä¸‰ç»´æ ¼å±€ç ”ç©¶è¿›å±•.

The close interactions between abiotic and biotic components create a variety of three-dimensional (3-D) landscape patterns. Landscape ecology, as a discipline of studying patterns and ecological processes, has made rapid progress in the exploration of 3-D space with the improvement of data acquirement ability, such as lidar technology. The real surface landscape can be described considering 3-D data, which improves the consistency between landscape indices and ecological process, and has overcome the shortage of ecological meanings of traditional researches. However, the lack of universality of methods and conclusions still exist due to different study backgrounds. The sensitivities of research results will increase with the expansion from two-dimensional to 3-D scale because of the increasing data quantity and accuracy. How to select and process the suitable scaled data to get more scientific conclusions need to be discussed in the future. The integration of multi-scale, multi-source and a long time series data will be the study trend with data acquisition becoming more convenient. In addition to the dynamic monitoring and prediction studies, the sustainable and ecological restoration application combined with landscape planning and design will be an important research direction.

Spatiotemporal Pattern of Fine Particulate Matter and Impact of Urban Socioeconomic Factors in China.

Frequent hazy weather has been one of the most obvious air problems accompanying China's rapid urbanization. As one of the main components of haze pollution, fine particulate matter (PM2.5), which severely affects environmental quality and people's health, has attracted wide attention. This study investigated the PM2.5 distribution, changing trends and impact of urban factors based on remote-sensing PM2.5 concentration data from 2000 to 2015, combining land-use data and socioeconomic data, and using the least-squares method and structural equation model (SEM). The results showed that the high concentration of PM2.5 in China was mainly concentrated in the eastern part of China and Sichuan Province. The trends of the PM2.5 concentration in eastern part and Northeast China, Sichuan, and Guangxi Provinces were positive. Meanwhile, the ratios of increasing trends were strongest in built-up land and agricultural land, and the decreasing trends were strongest in forest and grassland, but the overall trends were still growing. The SEM results indicated that economic factors contributed most to PM2.5 pollution, followed by demographic factors and spatial factors. Among all observed variables, the secondary industrial GDP had the highest impact on PM2.5 pollution. Based on the above results, PM2.5 pollution remains an important environmental issue in China at present and even in the future. It is necessary for decision-makers to make actions and policies from macroscopic and microscopic, long-term and short-term aspects to reduce pollution.

Effects of urbanization on direct runoff characteristics in urban functional zones.

As urbanization processes, the increasing direct runoff caused by land use change has become a major challenge for urban hydrological system. In this study, the impact of urbanization on direct runoff in the Shenyang urban area was investigated using a modified Soil Conservation Service Curve Number model combined with remote sensing. Urban functional zone (UFZ) was used as the basic unit for hydrological analysis. The hydrological changes in runoff were analyzed by calculating the runoff difference between the current condition and the pre-urbanization condition. Moran's I was used to estimate the spatial autocorrelation of the entire area. Then we assessed the relative influence and marginal effects of factors affecting direct runoff using boosted regression trees (BRT). Our results showed that direct runoff was significantly related to urbanization. Under current conditions, direct runoff increment depth affected by urbanization in the study area was 68.02 mm. For different UFZs, high-density residential, business and industrial zones tended to have large runoff volumes and high runoff coefficients. Through flooding hazard analysis, we found about 6.53% of the study area fell into a significant hazard category. The industrial zone had largest area of significant hazard land (40.97 km2) and the business zone had the largest significant hazard percentage (21.19%). Moran's I results illustrated that the high-high clusters in Shenyang were mainly concentrated in the urban center. BRT analysis indicated that runoff had the strongest correlation with rainfall (52.07%), followed by impervious ratio (27.28%), normalized difference vegetation index (14.31%), antecedent 5-day rainfall (3.02%), and UFZs (1.70%). The industrial zone, business zone and high-density residential zone tend to have greater influence on runoff. Our study could present method for recognizing hotspots of direct runoff in large city, and may provide potential implications for green infrastructure selection and urban planning.

Assessing the Impact of Urbanization on Direct Runoff Using Improved Composite CN Method in a Large Urban Area.

Urbanization is one of the most widespread anthropogenic activities, which brings a range of physical and biochemical changes to hydrological system and processes. Increasing direct runoff caused by land use change has become a major challenge for urban ecological security. Reliable prediction of the quantity and rate of surface runoff is an inherently difficult and time-consuming task for large ungauged urban areas. In this study, we combined Geographic Information System and remote sensing technology with an improved Soil Conservation Service curve number model to evaluate the effects of land use change on direct runoff volume of the four-ring area in Shenyang, China, and analyzed trends of direct runoff at different scales. Through analyzing trends of direct runoff from 1984 to 2015 at different scales, we explored how urbanization and other potential factors affect direct runoff changes. Total direct runoff volume increased over time, and trends varied from the inner urban area to suburban area. Zones 1 and 2 had a tendency toward decreasing direct runoff volume and risks, while Zones 3 and 4 showed gradual increases at both regional and pixel scales. The most important influence on direct runoff change was urban surface change caused by urbanization. This study presents a framework for identifying hotspots of runoff increase, which can provide important guidance to urban managers in future green infrastructure planning, in the hopes of improving the security of urban water ecological patterns.