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Yin Ren
Key Laboratory of Urban Environment and Health, Fujian Key Laboratory of Watershed Ecology, Key Laboratory of Urban Metabolism of Xiamen, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China

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Journal article
Published: 18 August 2021 in Science of The Total Environment
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Sampling design in soil science is critical because the lack of reliable methods and collecting samples requires tremendous work and resources. The aims were to obtain an optimal sampling design for assessing potentially toxic elements pollution using pilot Pb soil samples from the urban green space area of Shanghai, China. Two general steps have been used. The first step is to determine the optimum sample size against improving the prediction accuracy and monitoring costs using the spatial simulated annealing (SSA) algorithm. Secondly, we evaluated their likely placement of new extra sampling points by integrated SSA with k-means (SSA+ k-means) and expert-based (SSA+ expert-based) sampling methods. The improvement of sampling design by the integrated sampling approaches was evaluated using mean kriging variance (MKV), root mean square error (RMSE), and mean absolute percentage error (MAPE). The findings indicated that adding and placing 350 new monitoring points upon the existing sampling design by SSA increased the prediction accuracy by 64.35%. The MKV for the optimized SSA+ k-means sample was lower than by 4.12 mg/kg, 9.46 mg/kg compared with locations optimized by SSA and SSA+ expert-based method, respectively. Optimizing new sampling locations by SSA+ k-means sampling method was reduced MAPE by 9.26% and RMSE by 7.13 mg/kg compared to optimizing by SSA alone. However, there was no improvement in placing the new sampling points in SSA+ expert-based sampling method; instead, it increased the error by 8.11%. This paper shows integrating optimization approaches to evaluate the existing sampling design and optimize a new optimal sampling design.

ACS Style

Abiot Molla; Shudi Zuo; Weiwei Zhang; Yue Qiu; Yin Ren; Jigang Han. Optimal spatial sampling design for monitoring potentially toxic elements pollution on urban green space soil: A spatial simulated annealing and k-means integrated approach. Science of The Total Environment 2021, 802, 149728 .

AMA Style

Abiot Molla, Shudi Zuo, Weiwei Zhang, Yue Qiu, Yin Ren, Jigang Han. Optimal spatial sampling design for monitoring potentially toxic elements pollution on urban green space soil: A spatial simulated annealing and k-means integrated approach. Science of The Total Environment. 2021; 802 ():149728.

Chicago/Turabian Style

Abiot Molla; Shudi Zuo; Weiwei Zhang; Yue Qiu; Yin Ren; Jigang Han. 2021. "Optimal spatial sampling design for monitoring potentially toxic elements pollution on urban green space soil: A spatial simulated annealing and k-means integrated approach." Science of The Total Environment 802, no. : 149728.

Review
Published: 27 May 2021 in Atmosphere
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Air pollutant forecasting can be used to quantitatively estimate pollutant reduction trends. Combining bibliometrics with the evolutionary tree and Markov chain methods can achieve a superior quantitative analysis of research hotspots and trends. In this work, we adopted a bibliometric method to review the research status of statistical prediction methods for air pollution, used evolutionary trees to analyze the development trend of such research, and applied the Markov chain to predict future research trends for major air pollutants. The results indicate that papers mainly focused on the effects of air pollution on human diseases, urban pollution exposure models, and land use regression (LUR) methods. Particulate matter (PM), nitrogen oxides (NOx), and ozone (O3) were the most investigated pollutants. Artificial neural network (ANN) methods were preferred in studies of PM and O3, while LUR were more widely used in studies of NOx. Additionally, multi-method hybrid techniques gradually became the most widely used approach between 2010 and 2018. In the future, the statistical prediction of air pollution is expected to be based on a mixed method to simultaneously predict multiple pollutants, and the interaction between pollutants will be the most challenging aspect of research on air pollution prediction. The research results summarized in this paper provide technical support for the accurate prediction of atmospheric pollution and the emergency management of regional air quality.

ACS Style

Kuo Liao; Xiaohui Huang; Haofei Dang; Yin Ren; Shudi Zuo; Chensong Duan. Statistical Approaches for Forecasting Primary Air Pollutants: A Review. Atmosphere 2021, 12, 686 .

AMA Style

Kuo Liao, Xiaohui Huang, Haofei Dang, Yin Ren, Shudi Zuo, Chensong Duan. Statistical Approaches for Forecasting Primary Air Pollutants: A Review. Atmosphere. 2021; 12 (6):686.

Chicago/Turabian Style

Kuo Liao; Xiaohui Huang; Haofei Dang; Yin Ren; Shudi Zuo; Chensong Duan. 2021. "Statistical Approaches for Forecasting Primary Air Pollutants: A Review." Atmosphere 12, no. 6: 686.

Journal article
Published: 30 April 2021 in International Journal of Environmental Research and Public Health
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High concentrations of potentially toxic elements (PTE) create global environmental stress due to the crucial threat of their impacts on the environment and human health. Therefore, determining the concentration levels of PTE and improving their prediction accuracy by sampling optimization strategy is necessary for making sustainable environmental decisions. The concentrations of five PTEs (Pb, Cd, Cr, Cu, and Zn) were compared with reference values for Shanghai and China. The prediction of PTE in soil was undertaken using a geostatistical and spatial simulated annealing algorithm. Compared to Shanghai’s background values, the five PTE mean concentrations are much higher, except for Cd and Cr. However, all measured values exceeded the reference values for China. Pb, Cu, and Zn levels were 1.45, 1.20, and 1.56 times the background value of Shanghai, respectively, and 1.57, 1.66, 1.91 times the background values in China, respectively. The optimization approach resulted in an increased prediction accuracy (22.4% higher) for non-sampled locations compared to the initial sampling design. The higher concentration of PTE compared to background values indicates a soil pollution issue in the study area. The optimization approach allows a soil pollution map to be generated without deleting or adding additional monitoring points. This approach is also crucial for filling the sampling strategy gap.

ACS Style

Weiwei Zhang; Jigang Han; Abiot Molla; Shudi Zuo; Yin Ren. The Optimization Strategy of the Existing Urban Green Space Soil Monitoring System in Shanghai, China. International Journal of Environmental Research and Public Health 2021, 18, 4820 .

AMA Style

Weiwei Zhang, Jigang Han, Abiot Molla, Shudi Zuo, Yin Ren. The Optimization Strategy of the Existing Urban Green Space Soil Monitoring System in Shanghai, China. International Journal of Environmental Research and Public Health. 2021; 18 (9):4820.

Chicago/Turabian Style

Weiwei Zhang; Jigang Han; Abiot Molla; Shudi Zuo; Yin Ren. 2021. "The Optimization Strategy of the Existing Urban Green Space Soil Monitoring System in Shanghai, China." International Journal of Environmental Research and Public Health 18, no. 9: 4820.

Journal article
Published: 24 April 2021 in Journal of Environmental Management
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Water security represents ecological security and a policy priority for sustainable development; however, un-gridded assessment results cannot be used to support urban environmental management decisions. This study proposes a systematic framework to obtain a gridded regional water security assessment, which reflects the regional natural resource, based on the index system derived from the Pressure-State-Response (PSR) model and the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model. The results were applied to sustainable water management. Using 15 key cities in the Yangtze River Delta (YRD) region as a case study to apply the methodology, we found that the comprehensive water security was relatively high and high-value areas were widely distributed, accounting for about two-thirds of the study area. Low-value areas were mainly distributed in central and eastern regions, such as Shanghai, Suzhou, and Nanjing. There was evidence of a water resource shortage during the twelve-month period studied, particularly in August. The proportions of comprehensive water security in each administrative unit and the differences between simulated and target water quality could be used in the spatial planning and the exploration of payments for ecosystem services (PES) mechanism in county-level or smaller administrative units. Despite the premise requirement and the grid resolution problems of the InVEST model, it can be concluded that our assessment method proves capable of matching spatial and temporal differences in water supply and demand at a fine scale, and results can be used to supply useful information for urban management decision making.

ACS Style

Panfeng Dou; Shudi Zuo; Yin Ren; Manuel J. Rodriguez; Shaoqing Dai. Refined water security assessment for sustainable water management: A case study of 15 key cities in the Yangtze River Delta, China. Journal of Environmental Management 2021, 290, 112588 .

AMA Style

Panfeng Dou, Shudi Zuo, Yin Ren, Manuel J. Rodriguez, Shaoqing Dai. Refined water security assessment for sustainable water management: A case study of 15 key cities in the Yangtze River Delta, China. Journal of Environmental Management. 2021; 290 ():112588.

Chicago/Turabian Style

Panfeng Dou; Shudi Zuo; Yin Ren; Manuel J. Rodriguez; Shaoqing Dai. 2021. "Refined water security assessment for sustainable water management: A case study of 15 key cities in the Yangtze River Delta, China." Journal of Environmental Management 290, no. : 112588.

Journal article
Published: 01 April 2021 in Environment International
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Urban green space has been implicated in shaping airborne microbes, but there is an only rudimentary understanding of the key factors of urban green space affecting the composition and structures of airborne microbes. Here, we selected 40 urban sites based on stratified random sampling design and investigated the effects of multiple factors including landscapes, plant, soil, and anthropogenic factors on airborne microbial communities, especially bacterial and fungal pathogens. Bacterial and fungal communities in the control area with lower greenness were significantly (P < 0.05) different from those in other areas with a gradient of green space. The relative abundance of bacterial and fungal pathogens significantly (P < 0.05) decreased with increasing greenness. Other than soil thickness, soil type, slope position, and population density, plant species considerably contributed to the shift in the composition and abundance of potential bacterial and fungal pathogens. A significantly (P < 0.05) reduced abundance of bacterial and fungal pathogens was observed in areas with >30% masson pine. Together, these results provide insights into the importance of green space for providing health benefits for city dwellers by reducing pathogens in air, as well as providing support for the inclusion of plant species in the management of urban green space to reduce exposure risk of airborne pathogens.

ACS Style

Hu Li; Zhi-Feng Wu; Xiao-Ru Yang; Xin-Li An; Yin Ren; Jian-Qiang Su. Urban greenness and plant species are key factors in shaping air microbiomes and reducing airborne pathogens. Environment International 2021, 153, 106539 .

AMA Style

Hu Li, Zhi-Feng Wu, Xiao-Ru Yang, Xin-Li An, Yin Ren, Jian-Qiang Su. Urban greenness and plant species are key factors in shaping air microbiomes and reducing airborne pathogens. Environment International. 2021; 153 ():106539.

Chicago/Turabian Style

Hu Li; Zhi-Feng Wu; Xiao-Ru Yang; Xin-Li An; Yin Ren; Jian-Qiang Su. 2021. "Urban greenness and plant species are key factors in shaping air microbiomes and reducing airborne pathogens." Environment International 153, no. : 106539.

Journal article
Published: 21 December 2020 in Forests
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Sensitivity analysis and parameter optimization of stand models can improve their efficiency and accuracy, and increase their applicability. In this study, the sensitivity analysis, screening, and optimization of 63 model parameters of the Physiological Principles in Predicting Growth (3PG) model were performed by combining a sensitivity analysis method and the Markov chain Monte Carlo (MCMC) method of Bayesian posterior estimation theory. Additionally, a nine-year observational dataset of Chinese fir trees felled in the Shunchang Forest Farm, Nanping, was used to analyze, screen, and optimize the 63 model parameters of the 3PG model. The results showed the following: (1) The parameters that are most sensitive to stand stocking and diameter at breast height (DBH) are nWs(power in stem mass vs. diameter relationship), aWs(constant in stem mass vs. diameter relationship), alphaCx(maximum canopy quantum efficiency), k(extinction coefficient for PAR absorption by canopy), pRx(maximum fraction of NPP to roots), pRn(minimum fraction of NPP to roots), and CoeffCond(defines stomatal response to VPD); (2) MCMC can be used to optimize the parameters of the 3PG model, in which the posterior probability distributions of nWs, aWs, alphaCx, pRx, pRn, and CoeffCond conform to approximately normal or skewed distributions, and the peak value is prominent; and (3) compared with the accuracy before sensitivity analysis and a Bayesian method, the biomass simulation accuracy of the stand model was increased by 13.92%, and all indicators show that the accuracy of the improved model is superior. This method can be used to calibrate the parameters and analyze the uncertainty of multi-parameter complex stand growth models, which are important for the improvement of parameter estimation and simulation accuracy.

ACS Style

Chenjian Liu; Xiaoman Zheng; Yin Ren. Parameter Optimization of the 3PG Model Based on Sensitivity Analysis and a Bayesian Method. Forests 2020, 11, 1369 .

AMA Style

Chenjian Liu, Xiaoman Zheng, Yin Ren. Parameter Optimization of the 3PG Model Based on Sensitivity Analysis and a Bayesian Method. Forests. 2020; 11 (12):1369.

Chicago/Turabian Style

Chenjian Liu; Xiaoman Zheng; Yin Ren. 2020. "Parameter Optimization of the 3PG Model Based on Sensitivity Analysis and a Bayesian Method." Forests 11, no. 12: 1369.

Journal article
Published: 30 November 2020 in Remote Sensing
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Gridded CO2 emission maps at the urban scale can aid the design of low-carbon development strategies. However, the large uncertainties associated with such maps increase policy-related risks. Therefore, an investigation of the uncertainties in gridded maps at the urban scale is essential. This study proposed an analytic workflow to assess uncertainty propagation during the gridding process. Gridded CO2 emission maps were produced using two resolutions of geospatial datasets (e.g., remote sensing satellite-derived products) for Jinjiang City, China, and a workflow was applied to analyze uncertainties. The workflow involved four submodules that can be used to evaluate the uncertainties of CO2 emissions in gridded maps, caused by the gridded model and input. Fine-resolution (30 m) maps have a larger spatial variation in CO2 emissions, which gives the fine-resolution maps a higher degree of uncertainty propagation. Furthermore, the uncertainties of gridded CO2 emission maps, caused by inserting a random error into spatial proxies, were found to decrease after the gridding process. This can be explained by the “compensation of error” phenomenon, which may be attributed to the cancellation of the overestimated and underestimated values among the different sectors at the same grid. This indicates a nonlinear change between the sum of the uncertainties for different sectors and the actual uncertainties in the gridded maps. In conclusion, the present workflow determined uncertainties were caused by the gridded model and input. These results may aid decision-makers in establishing emission reduction targets, and in developing both low-carbon cities and community policies.

ACS Style

Shaoqing Dai; Yin Ren; Shudi Zuo; Chengyi Lai; Jiajia Li; Shengyu Xie; Bingchu Chen. Investigating the Uncertainties Propagation Analysis of CO2 Emissions Gridded Maps at the Urban Scale: A Case Study of Jinjiang City, China. Remote Sensing 2020, 12, 3932 .

AMA Style

Shaoqing Dai, Yin Ren, Shudi Zuo, Chengyi Lai, Jiajia Li, Shengyu Xie, Bingchu Chen. Investigating the Uncertainties Propagation Analysis of CO2 Emissions Gridded Maps at the Urban Scale: A Case Study of Jinjiang City, China. Remote Sensing. 2020; 12 (23):3932.

Chicago/Turabian Style

Shaoqing Dai; Yin Ren; Shudi Zuo; Chengyi Lai; Jiajia Li; Shengyu Xie; Bingchu Chen. 2020. "Investigating the Uncertainties Propagation Analysis of CO2 Emissions Gridded Maps at the Urban Scale: A Case Study of Jinjiang City, China." Remote Sensing 12, no. 23: 3932.

Journal article
Published: 18 November 2020 in Remote Sensing
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Aerosol optical depth (AOD) is a key parameter that reflects the characteristics of aerosols, and is of great help in predicting the concentration of pollutants in the atmosphere. At present, remote sensing inversion has become an important method for obtaining the AOD on a large scale. However, AOD data acquired by satellites are often missing, and this has gradually become a popular topic. In recent years, a large number of AOD recovery algorithms have been proposed. Many AOD recovery methods are not application-oriented. These methods focus mainly on to the accuracy of AOD recovery and neglect the AOD recovery ratio. As a result, the AOD recovery accuracy and recovery ratio cannot be balanced. To solve these problems, a two-step model (TWS) that combines multisource AOD data and AOD spatiotemporal relationships is proposed. We used the light gradient boosting (LightGBM) model under the framework of the gradient boosting machine (GBM) to fit the multisource AOD data to fill in the missing AOD between data sources. Spatial interpolation and spatiotemporal interpolation methods are limited by buffer factors. We recovered the missing AOD in a moving window. We used TWS to recover AOD from Terra Satellite's 2018 AOD product (MOD AOD). The results show that the MOD AOD, after a 3 × 3 moving window TWS recovery, was closely related to the AOD of the Aerosol Robotic Network (AERONET) (R = 0.87, RMSE = 0.23). In addition, the MOD AOD missing rate after a 3 × 3 window TWS recovery was greatly reduced (from 0.88 to 0.1). In addition, the spatial distribution characteristics of the monthly and annual averages of the recovered MOD AOD were consistent with the original MOD AOD. The results show that TWS is reliable. This study provides a new method for the restoration of MOD AOD, and is of great significance for studying the spatial distribution of atmospheric pollutants.

ACS Style

Yufeng Chi; Zhifeng Wu; Kuo Liao; Yin Ren. Handling Missing Data in Large-Scale MODIS AOD Products Using a Two-Step Model. Remote Sensing 2020, 12, 3786 .

AMA Style

Yufeng Chi, Zhifeng Wu, Kuo Liao, Yin Ren. Handling Missing Data in Large-Scale MODIS AOD Products Using a Two-Step Model. Remote Sensing. 2020; 12 (22):3786.

Chicago/Turabian Style

Yufeng Chi; Zhifeng Wu; Kuo Liao; Yin Ren. 2020. "Handling Missing Data in Large-Scale MODIS AOD Products Using a Two-Step Model." Remote Sensing 12, no. 22: 3786.

Journal article
Published: 18 August 2020 in Sustainable Cities and Society
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Exploring the driving forces responsible for heterogeneous surface temperatures is important for selecting proper mitigation strategies to improve the urban thermal environment. Many previous studies have examined the relationship between Land Surface Temperature (LST) and landscape composition and configuration of greenspace. However, investigations focused on the influence of artificial surface on LST are limited. This study develops a method for exploring the driving forces of LST heterogeneity at various spatial scales in Beijing, China. We classified LSTs into six sequential temperature classes to which we applied stepwise regression analysis to detect factors controlling LST variation. We found that: (1) high and low temperature patches were unevenly distributed across the city, with high temperature patches often forming high temperature belts that might negate the expected environmental benefits of planned urban greenspaces, (2) the explanatory power of parameters significantly associated with LST variation was noticeably reduced at finer spatial scales, and (3) variations in significant parameters in low temperature areas can serve as reference target for improving the thermal environment of high temperature areas. Our findings provide insights that can be used to mitigate the impact of urbanization on the thermal environments of cities and achieve the sustainable urban development.

ACS Style

Zhifeng Wu; Lei Yao; Mazhan Zhuang; Yin Ren. Detecting factors controlling spatial patterns in urban land surface temperatures: A case study of Beijing. Sustainable Cities and Society 2020, 63, 102454 .

AMA Style

Zhifeng Wu, Lei Yao, Mazhan Zhuang, Yin Ren. Detecting factors controlling spatial patterns in urban land surface temperatures: A case study of Beijing. Sustainable Cities and Society. 2020; 63 ():102454.

Chicago/Turabian Style

Zhifeng Wu; Lei Yao; Mazhan Zhuang; Yin Ren. 2020. "Detecting factors controlling spatial patterns in urban land surface temperatures: A case study of Beijing." Sustainable Cities and Society 63, no. : 102454.

Preprint content
Published: 23 March 2020
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Identifying factors that influence the land surface temperature (LST) of urban forests can help improve simulations and predictions of spatial patterns of urban cool islands. This requires a quantitative analytical method that combines spatial statistical analysis with multi-source observational data. The purpose of this study was to reveal how human activities and ecological factors jointly influence LST in clustering regions (hot or cool spots) of urban forests. Using Xiamen City, China from 1996 to 2006 as a case study, we explored the interactions between human activities and ecological factors, as well as their influences on urban forest LST. Population density was selected as a proxy for human activity. We integrated multi-source data (forest inventory, digital elevation models (DEM), population, and remote sensing imagery) to develop a database on a unified urban scale. The driving mechanism of urban forest LST was revealed through a combination of multi-source spatial data and spatial statistical analysis of clustering regions. The results showed that the main factors contributing to urban forest LST were dominant tree species and elevation. The interactions between human activity and specific ecological factors linearly or nonlinearly increased LST in urban forests. Strong interactions between elevation and dominant species were generally observed and were prevalent in either hot or cold spots areas in different years. In conclusion, quantitative studies based on spatial statistics and GeogDetector models should be conducted in urban areas to reveal interactions between human activities, ecological factors, and LST.

ACS Style

Yin Ren; Luying Deng; Shudi Zuo; Xiaodong Song; Yinlan Liao; Chengdu Xu; Qi Chen; Lizhong Hua; Zhengwei Li. Quantifying the Influences of Various Ecological Factors on Land Surface Temperature of Urban Forests. 2020, 1 .

AMA Style

Yin Ren, Luying Deng, Shudi Zuo, Xiaodong Song, Yinlan Liao, Chengdu Xu, Qi Chen, Lizhong Hua, Zhengwei Li. Quantifying the Influences of Various Ecological Factors on Land Surface Temperature of Urban Forests. . 2020; ():1.

Chicago/Turabian Style

Yin Ren; Luying Deng; Shudi Zuo; Xiaodong Song; Yinlan Liao; Chengdu Xu; Qi Chen; Lizhong Hua; Zhengwei Li. 2020. "Quantifying the Influences of Various Ecological Factors on Land Surface Temperature of Urban Forests." , no. : 1.

Data article
Published: 11 February 2020 in Data in Brief
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This paper presented the spatial database collected in 2013 for mitigating the urban carbon emissions of Jinjiang city, China. The database included the high-resolution CO2 emissions gridded maps, urban form fragmentation evaluation maps, and city-scale effect related impact factors distribution maps at 30 m and 500 m. We collected the multi-sources data including statistical, vector, and raster data from open-access websites and local governments. We used a general hybrid approach based on global downscaled and bottom-up elements to produce the CO2 emissions gridded maps. The urban fragmentation was measured by the landscape fragmentation metrics under the feature scale and the accurate identification of the urban functional districts. The percentage of the urban area and the points of interest (POI) density representing the city-scale effect related impact factors were calculated in each grid by the land use and POI data. Our database could be used for the validation of urban CO2 emissions estimation at the city scale. The landscape metrics and city-scale effect related impact factors maps can also be used for evaluating the socio-economic status in order to solve the other urban spatial planning problems.

ACS Style

Shaoqing Dai; Shudi Zuo; Yin Ren. A spatial database of CO2 emissions, urban form fragmentation and city-scale effect related impact factors for the low carbon urban system in Jinjiang city, China. Data in Brief 2020, 29, 105274 .

AMA Style

Shaoqing Dai, Shudi Zuo, Yin Ren. A spatial database of CO2 emissions, urban form fragmentation and city-scale effect related impact factors for the low carbon urban system in Jinjiang city, China. Data in Brief. 2020; 29 ():105274.

Chicago/Turabian Style

Shaoqing Dai; Shudi Zuo; Yin Ren. 2020. "A spatial database of CO2 emissions, urban form fragmentation and city-scale effect related impact factors for the low carbon urban system in Jinjiang city, China." Data in Brief 29, no. : 105274.

Articles
Published: 04 February 2020 in International Journal of Environmental Health Research
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Understanding the spatio-temporal characteristics of air pollutants is essential to improving air quality. One aspect is the question of whether green spaces can reduce air pollutant concentrations. However, previous studies on this issue have reported mixed results. This study analyzed the spatio-temporal characteristics of NO2, PM2.5 and O3 in Fujian Province, Southeast China in 2015. In order to reduce uncertainties in the conclusions drawn, the effects landscape metrics describing green spaces have on air pollutants have been analyzed using Pearson correlation analysis at six different spatial scales for the four seasons, considering the influence of meteorological conditions. The results show that PM2.5 and O3 are major pollutants whose relative importance varies with the seasons. Significant differences in pollutant concentrations were observed in suburban and urban areas, highlighting the importance of ensuring a reasonable spatial distribution of monitoring stations. Moreover, significant correlations between air pollutants and green space landscape patterns during the four seasons were found, revealing increased air pollutant concentrations with increasing landscape fragmentation and reduced connectivity and aggregation. This probably indicates that interconnected green spaces have the potential to improve air quality. Utilizing green space function regulations can alleviate NO2 and PM2.5 pollution effectively, but it is still difficult to reduce O3 concentrations because green spaces are likely to not only serve as sinks for O3, but can also promote O3 formation.

ACS Style

Longyan Cai; Mazhan Zhuang; Yin Ren. Spatiotemporal characteristics of NO2, PM2.5 and O3 in a coastal region of southeastern China and their removal by green spaces. International Journal of Environmental Health Research 2020, 1 -17.

AMA Style

Longyan Cai, Mazhan Zhuang, Yin Ren. Spatiotemporal characteristics of NO2, PM2.5 and O3 in a coastal region of southeastern China and their removal by green spaces. International Journal of Environmental Health Research. 2020; ():1-17.

Chicago/Turabian Style

Longyan Cai; Mazhan Zhuang; Yin Ren. 2020. "Spatiotemporal characteristics of NO2, PM2.5 and O3 in a coastal region of southeastern China and their removal by green spaces." International Journal of Environmental Health Research , no. : 1-17.

Journal article
Published: 22 January 2020 in Urban Forestry & Urban Greening
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It has been established that green space is capable of reducing atmospheric PM2.5 concentrations. However, the differing effects of landscape patterns from various types of green spaces on PM2.5 are still not well understood. In addition, little information is available on individual and interactive effects of different green spaces and environmental factors on PM2.5. In this study, we analyzed the relationships between different green spaces, environmental factors, and PM2.5 in mid-winter (January) in southeastern China using redundancy analysis (RDA) and simple correlation analyses at landscape scales. PM2.5 distribution was dominated by meteorological factors (an average of 46.21 %), and green space showed immense potential for PM2.5 reduction, as indicated by strong interaction between green space–non green space on PM2.5 (33.66 % at the 5000 scale). The individual and interactive effects of forestland on PM2.5 were, on average, 1.6 times higher than those of grassland. But obvious interaction between grassland–non grassland on PM2.5 was also observed at large scales (19.16 % at the 5000 scale). This suggests that grassland has the ability to reduce atmospheric PM2.5 concentrations. Unfortunately, the effect of grassland on PM2.5 was overlooked among previous small–scale studies owing to neglecting the scale effect. In reality, the effects of different green spaces on PM2.5 are more accurately assessed at broader scales. Additionally, significant negative correlation existed between green space area and PM2.5, indicating that more green space can directly absorb and adsorb more PM2.5. The results demonstrate that planting more trees and grasses to improve urban air conditions is beneficial, effective, and recommended. Given the cost and benefit analysis, planting grasses is a more affordable option for PM2.5 reduction, particularly at the 1000–3000 m scales. Moreover, correlation analyses further indicated that as green space and forestland landscape fragmentation increased and aggregation was reduced, PM2.5 concentrations increased. Our results provide useful input for green space planning in this area.

ACS Style

Longyan Cai; Mazhan Zhuang; Yin Ren. A landscape scale study in Southeast China investigating the effects of varied green space types on atmospheric PM2.5 in mid-winter. Urban Forestry & Urban Greening 2020, 49, 126607 .

AMA Style

Longyan Cai, Mazhan Zhuang, Yin Ren. A landscape scale study in Southeast China investigating the effects of varied green space types on atmospheric PM2.5 in mid-winter. Urban Forestry & Urban Greening. 2020; 49 ():126607.

Chicago/Turabian Style

Longyan Cai; Mazhan Zhuang; Yin Ren. 2020. "A landscape scale study in Southeast China investigating the effects of varied green space types on atmospheric PM2.5 in mid-winter." Urban Forestry & Urban Greening 49, no. : 126607.

Journal article
Published: 18 December 2019 in Building and Environment
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The spatial variation in land surface temperature (LST) is a measure of the exchange and interaction of energy flux between the land surface and near-surface atmosphere. Understanding the connections between LST and complex landscape patterns is vital for selecting proper mitigation strategies to improve the urban thermal environment. However, the high spatial heterogeneity of the urban landscape makes it tricky to accurately characterizing the quantitative contributions from man-made or natural objects. In this study, we first estimated LST with Landsat-8 TIRS by applying a split-window algorithm. We then grouped the urban landscape into 10 urban function zones (UFZs) and classified these UFZs as LST hot or cool spots. We used stepwise regression to examine the relationships between surface temperatures in hot and cool spots and 7 climate-sensitive parameters. We obtained some interesting results: (1) Urban thermal environment is highly heterogeneous at the scale of UFZ. Every UFZ can be partitioned into both hot and cool spots. (2) The prediction accuracy of LST is found to be dependent on a combination of different parameters. The 2-dimensional parameters were more effective in predicting LST than the 3-dimensional parameters according to the stepwise regression analyses. (3) A new 3-dimensional parameter (sun-view factor-SunVF) adopted in this study, provides an alternative option in characterizing the variation of LST of various landscape structures. The results of this study provide insights into how the current spatial pattern of LST formed and into which mitigation strategies can be applied to improve the urban thermal environment.

ACS Style

Zhifeng Wu; Lei Yao; Yin Ren. Characterizing the spatial heterogeneity and controlling factors of land surface temperature clusters: A case study in Beijing. Building and Environment 2019, 169, 106598 .

AMA Style

Zhifeng Wu, Lei Yao, Yin Ren. Characterizing the spatial heterogeneity and controlling factors of land surface temperature clusters: A case study in Beijing. Building and Environment. 2019; 169 ():106598.

Chicago/Turabian Style

Zhifeng Wu; Lei Yao; Yin Ren. 2019. "Characterizing the spatial heterogeneity and controlling factors of land surface temperature clusters: A case study in Beijing." Building and Environment 169, no. : 106598.

Research article
Published: 31 October 2019 in Journal of Environmental Planning and Management
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Urban forests are fundamental components of localized surface energy budgets. Understanding the factors controlling urban forest surface temperatures (UFSTs) should be helpful in mitigating the negative effects of urbanization on urban energy budgets. This study aimed to identify the factors controlling the spatial-temporal pattern of UFSTs by utilizing a variety of data layers and spatial statistical analysis methods. Our results showed that UFST values become more spatially heterogeneous as urbanization progresses. Elevation and degree of slope were the main factors explaining the increase in spatial heterogeneity. Human activities were also significantly related to variations in UFST. Interactions between human activities and almost all environmental factors were related to higher UFST values. Therefore, human activity directly impacts on the spatial heterogeneity of UFST and indirectly affects variations in landscape patterns. Human activities compatible with ecologically sustainable development should be considered for mitigating the deterioration of urban thermal environments.

ACS Style

Zhifeng Wu; Wang Man; Yin Ren. Detection of spatial-temporal variations in forest canopy surface temperature in response to urbanization: a case study from Longyan, China. Journal of Environmental Planning and Management 2019, 63, 1283 -1300.

AMA Style

Zhifeng Wu, Wang Man, Yin Ren. Detection of spatial-temporal variations in forest canopy surface temperature in response to urbanization: a case study from Longyan, China. Journal of Environmental Planning and Management. 2019; 63 (7):1283-1300.

Chicago/Turabian Style

Zhifeng Wu; Wang Man; Yin Ren. 2019. "Detection of spatial-temporal variations in forest canopy surface temperature in response to urbanization: a case study from Longyan, China." Journal of Environmental Planning and Management 63, no. 7: 1283-1300.

Journal article
Published: 03 October 2019 in Journal of Cleaner Production
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Scientifically delineating the spatial heterogeneity of urban landscape fragmentation in relation to CO2 emissions helps the urban carbon mitigation strategy. The combination analysis across spatial resolutions, which is rare, helps explore the comprehensive relationship between urban fragmentation and CO2 emissions. This study compared the relationships between urban form fragmentation and CO2 emissions in an urban system through the analytic framework composed of the Pearson correlation analysis, geographically weighted regression (GWR), and geographical detector methods with the use of multi-source data to construct the CO2 emissions maps. As the result, there was less fragmentation with a 500-m spatial resolution (R500m) than with a 30-m spatial resolution (R30m). In terms of the GWR analysis, the coarse resolution resulted in: 1) positive coefficients of fragmentation metric becoming negative, and 2) greater absolute values of negative coefficients. As to the results of Geographical detector, single factor impact powers and interactions among fragmentation factors showed a weakening effect at R30m, but a strengthening and weakening effect at R500m. However, there were common results observed in low-fragmented areas across different scales. That is, in low-fragmented mixed-function areas and industrial areas, the more fragmented the area was, the less the CO2 emission there would be. However, in low-fragmented residential, administrative and public service areas, the more fragmented the area was, the higher the CO2 emission there would be. Therefore, the government should disperse the mixed function zones and industrial parcels with diverse types of land, and build the contiguous residential and public service land in the low fragmentation area of urban system. The results of this study can provide a reference for the other small and medium towns and cities. The analytical framework can be applied to CO2 emissions research in urban agglomerations, megacities, and small towns.

ACS Style

Shudi Zuo; Shaoqing Dai; Yin Ren. More fragmentized urban form more CO2 emissions? A comprehensive relationship from the combination analysis across different scales. Journal of Cleaner Production 2019, 244, 118659 .

AMA Style

Shudi Zuo, Shaoqing Dai, Yin Ren. More fragmentized urban form more CO2 emissions? A comprehensive relationship from the combination analysis across different scales. Journal of Cleaner Production. 2019; 244 ():118659.

Chicago/Turabian Style

Shudi Zuo; Shaoqing Dai; Yin Ren. 2019. "More fragmentized urban form more CO2 emissions? A comprehensive relationship from the combination analysis across different scales." Journal of Cleaner Production 244, no. : 118659.

Review
Published: 01 June 2019 in Environmental Reviews
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The urban heat island (UHI) phenomenon is among the most evident features of human impact on the Earth’s system. This phenomenon has been widely observed and documented in many cities around the world. UHI-related publications have increased rapidly over the last three decades. However, because of a refined methodology and widening scope, a holistic understanding of research patterns and issues related to UHI research is lacking. Although others have summarized developments in UHI studies, these publications have focused on describing the current state of research rather than uncovering research trends and prospects. In the present study, we examined the evolution of UHI-related research from 1990 to 2017 and applied a scientometrics approach to identify research trends. The characteristics of publication outputs, key scientific disciplines, and cooperation between countries and institutions were determined by a citation analysis. We also discuss research trends, including future directions, approaches, and expected data. We identified two potential directions for UHI research through the results of key co-word clustering and discriminant analyses: negative impacts of UHI on public health and strategies to mitigate and adapt to UHI effects. We provide a broad review of the development of UHI research that may inspire future studies on the UHI phenomenon by new researchers in this field.

ACS Style

Zhifeng Wu; Yin Ren. A bibliometric review of past trends and future prospects in urban heat island research from 1990 to 2017. Environmental Reviews 2019, 27, 241 -251.

AMA Style

Zhifeng Wu, Yin Ren. A bibliometric review of past trends and future prospects in urban heat island research from 1990 to 2017. Environmental Reviews. 2019; 27 (2):241-251.

Chicago/Turabian Style

Zhifeng Wu; Yin Ren. 2019. "A bibliometric review of past trends and future prospects in urban heat island research from 1990 to 2017." Environmental Reviews 27, no. 2: 241-251.

Journal article
Published: 16 May 2019 in Ecological Indicators
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Urbanization has significant effects to ecosystems in watersheds, but the link between surface water quality and the dissolved organic matter (DOM) composition is poorly understood. We investigated the fluorescent intensities (Fmax) of DOM components and examined their correlation with the water quality parameters in peri-urban (Zhangxi River) and urban (Lu River) watersheds in Ningbo, East China. DOM quality was measured by fluorescent excitation-emission matrices (EEMs) coupled with parallel factor analysis (PARAFAC). Terrestrial humic-like components (C1 and C2) and protein-like component (C3) were derived by the PARAFAC model. We found more serious water pollution (significantly higher values for most water quality parameters, i.e. chemical oxygen demand (COD), chlorophyll a (Chl-a), dissolved organic carbon (DOC), ammonia and total suspended solids (TSS)) in urban than peri-urban watersheds. However, there were no significant differences in the levels of total nitrogen (TN) and total phosphorus (TP) between the urban and peri-urban watersheds. The results showed that the urban watershed had higher terrestrial humic-like C1 (39%) and lower protein-like C3 (30%) than the peri-urban watershed, while the peri-urban watershed showed an inverse trend (33% and 37%, respectively). The results also revealed that the DOM fluorescent indices were significantly different between the peri-urban and the urban watersheds. Redundancy analysis (RDA) was applied to evaluate the correlation between DOM fluorescent indices and water quality parameters (i.e., COD, TN, TP, DOC). It revealed that the pollution sources and water quality correlated to the fluorescent indices and the C1-C3 DOM fluorescent components. A significant linear relationship between COD and C2 was found in both watersheds, suggesting that C2 might be a good COD indicator. Our results suggest that the distinctive DOM composition between the watersheds could be attributed to different human activities at both sites. The correlation between DOM fluorescent components and water quality can be assessed by the EEM-PARAFAC method, indicating considerable potential for the use of this technique to monitor surface water quality.

ACS Style

Jianfeng Tang; Xinhu Li; Changli Cao; Meixia Lin; Qianlinglin Qiu; Yaoyang Xu; Yin Ren. Compositional variety of dissolved organic matter and its correlation with water quality in peri-urban and urban river watersheds. Ecological Indicators 2019, 104, 459 -469.

AMA Style

Jianfeng Tang, Xinhu Li, Changli Cao, Meixia Lin, Qianlinglin Qiu, Yaoyang Xu, Yin Ren. Compositional variety of dissolved organic matter and its correlation with water quality in peri-urban and urban river watersheds. Ecological Indicators. 2019; 104 ():459-469.

Chicago/Turabian Style

Jianfeng Tang; Xinhu Li; Changli Cao; Meixia Lin; Qianlinglin Qiu; Yaoyang Xu; Yin Ren. 2019. "Compositional variety of dissolved organic matter and its correlation with water quality in peri-urban and urban river watersheds." Ecological Indicators 104, no. : 459-469.

Journal article
Published: 15 November 2018 in Remote Sensing
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The spatiotemporal distribution pattern of the surface temperatures of urban forest canopies (STUFC) is influenced by many environmental factors, and the identification of interactions between these factors can improve simulations and predictions of spatial patterns of urban cool islands. This quantitative research uses an integrated method that combines remote sensing, ground surveys, and spatial statistical models to elucidate the mechanisms that influence the STUFC and considers the interaction of multiple environmental factors. This case study uses Jinjiang, China as a representative of a city experiencing rapid urbanization. We build up a multisource database (forest inventory, digital elevation models, population, and remote sensing imagery) on a uniform coordinate system to support research into the interactions that influence the STUFC. Landsat-5/8 Thermal Mapper images and meteorological data were used to retrieve the temporal and spatial distributions of land surface temperature. Ground observations, which included the forest management planning inventory and population density data, provided the factors that determine the STUFC spatial distribution on an urban scale. The use of a spatial statistical model (GeogDetector model) reveals the interaction mechanisms of STUFC. Although different environmental factors exert different influences on STUFC, in two periods with different hot spots and cold spots, the patch area and dominant tree species proved to be the main factors contributing to STUFC. The interaction between multiple environmental factors increased the STUFC, both linearly and nonlinearly. Strong interactions tended to occur between elevation and dominant species and were prevalent in either hot or cold spots in different years. In conclusion, the combining of multidisciplinary methods (e.g., remote sensing images, ground observations, and spatial statistical models) helps reveal the mechanism of STUFC on an urban scale.

ACS Style

Shudi Zuo; Shaoqing Dai; Xiaodong Song; Chengdong Xu; Yilan Liao; Weiyin Chang; Qi Chen; Yaying Li; Jianfeng Tang; Wang Man; Yin Ren. Determining the Mechanisms that Influence the Surface Temperature of Urban Forest Canopies by Combining Remote Sensing Methods, Ground Observations, and Spatial Statistical Models. Remote Sensing 2018, 10, 1814 .

AMA Style

Shudi Zuo, Shaoqing Dai, Xiaodong Song, Chengdong Xu, Yilan Liao, Weiyin Chang, Qi Chen, Yaying Li, Jianfeng Tang, Wang Man, Yin Ren. Determining the Mechanisms that Influence the Surface Temperature of Urban Forest Canopies by Combining Remote Sensing Methods, Ground Observations, and Spatial Statistical Models. Remote Sensing. 2018; 10 (11):1814.

Chicago/Turabian Style

Shudi Zuo; Shaoqing Dai; Xiaodong Song; Chengdong Xu; Yilan Liao; Weiyin Chang; Qi Chen; Yaying Li; Jianfeng Tang; Wang Man; Yin Ren. 2018. "Determining the Mechanisms that Influence the Surface Temperature of Urban Forest Canopies by Combining Remote Sensing Methods, Ground Observations, and Spatial Statistical Models." Remote Sensing 10, no. 11: 1814.

Journal article
Published: 04 October 2018 in International Journal of Environmental Research and Public Health
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Regional soil quality issues arising from rapid urbanization have received extensive attention. The riverbank that runs through a city is representative of urbanization gradient transformation. Thirty soil samples in the Yangtze River Delta urban agglomeration were collected and analyzed for the concentrations of seven analytes. Correlation, principle component analysis, cluster analysis and GeoDetector models suggested that the four groups (Cr-Ni-Cu, Cu-Zn-As-Sb, Cd and Pb) shared the same sources in the core urban region; five groups (Cr-Ni-Cu-Zn, As, Cd, Sb and Pb) in the suburbs and three groups (Cr-Ni, Cu-Zn-Cd-Sb-Pb and As) in the exurbs. GeoDetector methods not only validated the results of the three other methods, but also provided more possible impact factors. Besides the direct influences, the interaction effects among factors were quantified. Interactive combination with strong nonlinear increment changed from between-two-weak factors in the central region to between-strong-and-weak factors in the suburbs. In the exurbs, the stronger interaction effects were observed between strong and weak factors. Therefore, the GeoDetector model, which provided more detailed information of artificial sources could be used as a tool for identifying the potential factors of toxic elements and offering scientific basis for the development of subsequent pollution reduction strategies.

ACS Style

Shudi Zuo; Shaoqing Dai; Yaying Li; Jianfeng Tang; Yin Ren; Shaoqing Dai. Analysis of Heavy Metal Sources in the Soil of Riverbanks Across an Urbanization Gradient. International Journal of Environmental Research and Public Health 2018, 15, 2175 .

AMA Style

Shudi Zuo, Shaoqing Dai, Yaying Li, Jianfeng Tang, Yin Ren, Shaoqing Dai. Analysis of Heavy Metal Sources in the Soil of Riverbanks Across an Urbanization Gradient. International Journal of Environmental Research and Public Health. 2018; 15 (10):2175.

Chicago/Turabian Style

Shudi Zuo; Shaoqing Dai; Yaying Li; Jianfeng Tang; Yin Ren; Shaoqing Dai. 2018. "Analysis of Heavy Metal Sources in the Soil of Riverbanks Across an Urbanization Gradient." International Journal of Environmental Research and Public Health 15, no. 10: 2175.