This page has only limited features, please log in for full access.
Land use change has an important influence on the spatial and temporal distribution of PM2.5 concentration. Therefore, based on the particulate matter (PM2.5) data from remote sensing instruments and land use change data in long time series, the Getis-Ord Gi* statistic and SP-SDM are employed to analyze the spatial distribution pattern of PM2.5 and its response to land use change in China. It is found that the average PM2.5 increased from 25.49 μg/m3 to 31.23 μg/m3 during 2000-2016, showing an annual average growth rate of 0.97%. It is still greater than 35 μg/m3 in nearly half of all cities. The spatial distribution pattern of PM2.5 presents the characteristics of concentrated regional convergence. PM2.5 is positively correlated with urban land and farmland, negatively correlated with forest land, grassland, and unused land. Furthermore, the average PM2.5 concentrations show the highest values for urban land and decrease in the order of farmland > unused land > water body > forest > grassland. The impact of land use change on PM2.5 is a non-linear process, and there are obvious differences and spillover effects for different land types. Thus, reasonably controlling the scale of urban land and farmland, optimizing the spatial distribution pattern and development intensity, and expanding forest land and grassland are conducive to curbing PM2.5 pollution. The research conclusions provide a theoretical basis for the management of PM2.5 pollution from the perspective of optimizing land use.
Debin Lu; Wanliu Mao; Wu Xiao; Liang Zhang. Non-Linear Response of PM2.5 Pollution to Land Use Change in China. Remote Sensing 2021, 13, 1612 .
AMA StyleDebin Lu, Wanliu Mao, Wu Xiao, Liang Zhang. Non-Linear Response of PM2.5 Pollution to Land Use Change in China. Remote Sensing. 2021; 13 (9):1612.
Chicago/Turabian StyleDebin Lu; Wanliu Mao; Wu Xiao; Liang Zhang. 2021. "Non-Linear Response of PM2.5 Pollution to Land Use Change in China." Remote Sensing 13, no. 9: 1612.
The lockdown of cities in the Yangtze River Delta (YRD) during COVID-19 has provided many natural and typical test sites for estimating the potential of air pollution control and reduction. To evaluate the reduction of PM2.5 concentration in the YRD region by the epidemic lockdown policy, this study employs big data, including PM2.5 observations and 29 independent variables regarding Aerosol Optical Depth (AOD), climate, terrain, population, road density, and Gaode map Point of interesting (POI) data, to build regression models and retrieve spatially continuous distributions of PM2.5 during COVID-19. Simulation accuracy of multiple machine learning regression models, i.e., random forest (RF), support vector regression (SVR), and artificial neural network (ANN) were compared. The results showed that the RF model outperformed the SVR and ANN models in the inversion of PM2.5 in the YRD region, with the model-fitting and cross-validation coefficients of determination R2 reached 0.917 and 0.691, mean absolute error (MAE) values were 1.026 μg m−3 and 2.353 μg m−3, and root mean square error (RMSE) values were 1.413 μg m−3, and 3.144 μg m−3, respectively. PM2.5 concentrations during COVID-19 in 2020 have decreased by 3.61 μg m−3 compared to that during the same period of 2019 in the YRD region. The results of this study provide a cost-effective method of air pollution exposure assessment and help provide insight into the atmospheric changes under strong government controlling strategies.
Debin Lu; Wanliu Mao; Lilin Zheng; Wu Xiao; Liang Zhang; Jing Wei. Ambient PM2.5 Estimates and Variations during COVID-19 Pandemic in the Yangtze River Delta Using Machine Learning and Big Data. Remote Sensing 2021, 13, 1423 .
AMA StyleDebin Lu, Wanliu Mao, Lilin Zheng, Wu Xiao, Liang Zhang, Jing Wei. Ambient PM2.5 Estimates and Variations during COVID-19 Pandemic in the Yangtze River Delta Using Machine Learning and Big Data. Remote Sensing. 2021; 13 (8):1423.
Chicago/Turabian StyleDebin Lu; Wanliu Mao; Lilin Zheng; Wu Xiao; Liang Zhang; Jing Wei. 2021. "Ambient PM2.5 Estimates and Variations during COVID-19 Pandemic in the Yangtze River Delta Using Machine Learning and Big Data." Remote Sensing 13, no. 8: 1423.
Urban land-use information is important for urban land-resource planning and management. However, current methods using traditional surveys cannot meet the demand for the rapid development of urban land management. There is an urgent need to develop new methods to overcome the shortcomings of conventional methods. To address the issue, this study used the random forest (RF), support vector machine (SVM), and artificial neural network (ANN) models to build machine-leaning methods for urban land-use classification. Taking Hangzhou as an example, these machine-leaning methods could all successfully classify the essential urban land use into 6 Level I classes and 13 Level II classes based on the semantic features extracted from Sentinel-2A images, multi-source features of types of points of interest (POIs), land surface temperature, night lights, and building height. The validation accuracy of the RF model for the Level I and Level II land use was 79.88% and 71.89%, respectively, performing better compared to SVM (78.40% and 68.64%) and ANN models (71.30% and 63.02%). However, the variations of the user accuracy among the methods depended on the urban land-use level. For the Level I land-use classification, the user accuracy was high, except for the transportation land by all methods. In general, the RF and SVM models performed better than the ANN model. For the Level II land-use classification, the user accuracy of different models was quite distinct. With the RF model, the user accuracy of educational and medical land was above 80%. Moreover, with the SVM model, the user accuracy of the business office and educational land classification was above 75%. However, the user accuracy of the ANN model on the Level II land-use classification was poor. Our results showed that the RF model performs best, followed by SVM model, and ANN model was relatively poor in the essential urban land-use classification. The results proved that the use of machine-learning methods can quickly extract land-use types with high accuracy, and provided a better method choice for urban land-use information acquisition.
Wanliu Mao; Debin Lu; Li Hou; Xue Liu; Wenze Yue. Comparison of Machine-Learning Methods for Urban Land-Use Mapping in Hangzhou City, China. Remote Sensing 2020, 12, 2817 .
AMA StyleWanliu Mao, Debin Lu, Li Hou, Xue Liu, Wenze Yue. Comparison of Machine-Learning Methods for Urban Land-Use Mapping in Hangzhou City, China. Remote Sensing. 2020; 12 (17):2817.
Chicago/Turabian StyleWanliu Mao; Debin Lu; Li Hou; Xue Liu; Wenze Yue. 2020. "Comparison of Machine-Learning Methods for Urban Land-Use Mapping in Hangzhou City, China." Remote Sensing 12, no. 17: 2817.
Polycentric urban development has become the buzzword among urban scholars, decision-makers, and planners around the world. From the existing polycentric urban research, a functional approach is increasingly concerned by scholars except for morphological terms. Functional linkages are investigated among the (sub)centers of a polycentric urban system (PUS). However, the (sub)centers are usually pre-defined by a city master plan or identified by a density-sliced approach. However, the definition of the (sub)centers is still dependent on the morphological dimensions rather than functional linkages. To fill the gaps, we proposed a flow-based solution for delineating functional urban regions (FURs). We first built a spatially-embedded network of the entire city within extensive travel flows and then used a community detection method to reveal FURs. The characteristics of the whole PUS and the properties of each FUR are further assessed using complex network analysis. Based on the taxi trajectories of Shanghai, this study shows that the subdivisions of FURs are not necessarily consistent with administrative divisions. The functional linkages are strong among the (sub)centers surrounding the main center, and they are relatively weak among the newly established (sub)centers in the periphery. These findings call for policy interventions to increase the functional linkages of (sub)centers.
Tianyu Wang; Wenze Yue; Xinyue Ye; Yong Liu; Debin Lu. Re-evaluating polycentric urban structure: A functional linkage perspective. Cities 2020, 101, 102672 .
AMA StyleTianyu Wang, Wenze Yue, Xinyue Ye, Yong Liu, Debin Lu. Re-evaluating polycentric urban structure: A functional linkage perspective. Cities. 2020; 101 ():102672.
Chicago/Turabian StyleTianyu Wang; Wenze Yue; Xinyue Ye; Yong Liu; Debin Lu. 2020. "Re-evaluating polycentric urban structure: A functional linkage perspective." Cities 101, no. : 102672.
As the spatial carrier of the emission sources and influencing factors of PM2.5, land use and its changes can inevitably affect local and regional PM2.5 concentrations. The relationship between the growth of PM2.5 and the changes of land use in China during 1998-2015 was explored in this paper using the Theil-Sen median trend analysis, Mann-Kendall and spatial econometric model. The results showed that the area where PM2.5 concentration was less than 10 μg/m3 accounted for a small portion (18.33%) of the land area in China, and the area where PM2.5 concentration was more than 35 μg/m3 accounted for 31.30% of the land area. High PM2.5 concentration was found in the East China Plain and Taklimakan desert; artificial surfaces, cultivated land and deserts were coated with high PM2.5 concentration more frequently, while the forest, grassland and unused land were usually covered with low PM2.5 concentration. PM2.5 concentration in desert land and artificial surfaces respectively increased at a pace of 1.07 μg/m3 and 0.80 μg/m3 per year during 1998-2015, higher than those in other land use types. They mainly came from the sand dust aerosol in northwest China, while those in the other areas mainly came from emissions in the human activities. Therefore, reasonable coordinating the proportion of construction land, cultivated land, forest land and grassland in eastern China, and strengthening desert governance in northwest China, are suggested to reduce PM2.5 concentration in China.
Debin Lu; Jianhua Xu; Wenze Yue; Wanliu Mao; Dongyang Yang; Jinzhu Wang. Response of PM2.5 pollution to land use in China. Journal of Cleaner Production 2019, 244, 118741 .
AMA StyleDebin Lu, Jianhua Xu, Wenze Yue, Wanliu Mao, Dongyang Yang, Jinzhu Wang. Response of PM2.5 pollution to land use in China. Journal of Cleaner Production. 2019; 244 ():118741.
Chicago/Turabian StyleDebin Lu; Jianhua Xu; Wenze Yue; Wanliu Mao; Dongyang Yang; Jinzhu Wang. 2019. "Response of PM2.5 pollution to land use in China." Journal of Cleaner Production 244, no. : 118741.
Rapid urbanization and economic development caused serious environmental pollution burden in China. This study explored the spatiotemporal profile of PM2.5 concentrations in China from 1998 to 2016, examined its relationship with urbanization and other socioeconomic factors, including industry, abatement investment, and clean energy consumption by constructing the Environmental Kuznets Curve (EKC) model, and interpreted its responses to urbanization and these factors using the Generalized Additive Model (GAM). The results showed that PM2.5 pollution generally presented a worsening situation in most provinces during the study period. PM2.5-urbanization relationship approved an inverted U-shape EKC pattern in whole China and the central and eastern region, but presented an N-shape EKC pattern in the developed eastern region. Industry and its interaction with urbanization drove increasing PM2.5 concentrations. The interactions of urbanization with abatement investment and clean energy consumption had negative effects on PM2.5 concentrations nationally, but showed different impacts across regions. The GAM's results further verified that PM2.5 concentrations increased along with urbanization and industry, but enhancing abatement investment and clean energy consumption can reverse the increased trend. The major findings and policy implications can contribute to successful policy-making aimed at successful PM2.5 pollution abatement.
Xiaomin Wang; Guanghui Tian; Dongyang Yang; Wenxin Zhang; Debin Lu; Zhongmei Liu. Responses of PM2.5 pollution to urbanization in China. Energy Policy 2018, 123, 602 -610.
AMA StyleXiaomin Wang, Guanghui Tian, Dongyang Yang, Wenxin Zhang, Debin Lu, Zhongmei Liu. Responses of PM2.5 pollution to urbanization in China. Energy Policy. 2018; 123 ():602-610.
Chicago/Turabian StyleXiaomin Wang; Guanghui Tian; Dongyang Yang; Wenxin Zhang; Debin Lu; Zhongmei Liu. 2018. "Responses of PM2.5 pollution to urbanization in China." Energy Policy 123, no. : 602-610.
Urbanization has led to an obvious urban heat island (UHI) effect in the Yangtze River Delta (YRD), China. The ozone (O) pollution in the YRD is getting worse. The UHI effect is a key factor that affects the O level. Understanding the influences of the UHI effect on O concentrations is necessary for improving air quality. In this study, the temporal and spatial relationship between UHI and O in the YRD during 2015 was investigated. The influence factors of UHI effect and O are both natural and artificial. Multi-source remote sensing data, which include land cover, land surface temperature (LST), Normalization Difference Vegetation Index (NDVI), and digital elevation model (DEM) data, were used to extract surface landscape elements. The results showed that: (1) the average hourly O concentration was 61.83 μg/m (30.92 ppb), the highest value was 105.32 μg/m (52.66 ppb) at 15:00 and the O peak was 82.50 μg/m (41.25 ppb) in September. The O concentrations and temperature have a similar variation trend both in diurnal and monthly. The O concentrations in coastal stations are higher than those inland. (2) The average daytime UHI intensity was 1.24 °C, and the daytime O concentration was 80.66 μg/m (40.33 ppb). There is a positive relationship between UHI and O in the YRD. The relationship in the central developed cities is higher than that in the northern and southern cities. (3) The related factors influencing UHI and O include surface landscape, topography and population. The LST and NDVI are most important among these factors. (4) Due to various geographical backgrounds, the UHI intensities and O concentrations show obvious spatial differences. This study provides a reference with which to better understand the relationship among UHI, O and related factors. Furthermore, the issues of atmospheric and energy transmission in this region deserve further study.
Yuanyuan Wang; Hongyu Du; Yanqing Xu; Debin Lu; Xiyuan Wang; Zhongyang Guo. Temporal and spatial variation relationship and influence factors on surface urban heat island and ozone pollution in the Yangtze River Delta, China. Science of The Total Environment 2018, 631-632, 921 -933.
AMA StyleYuanyuan Wang, Hongyu Du, Yanqing Xu, Debin Lu, Xiyuan Wang, Zhongyang Guo. Temporal and spatial variation relationship and influence factors on surface urban heat island and ozone pollution in the Yangtze River Delta, China. Science of The Total Environment. 2018; 631-632 ():921-933.
Chicago/Turabian StyleYuanyuan Wang; Hongyu Du; Yanqing Xu; Debin Lu; Xiyuan Wang; Zhongyang Guo. 2018. "Temporal and spatial variation relationship and influence factors on surface urban heat island and ozone pollution in the Yangtze River Delta, China." Science of The Total Environment 631-632, no. : 921-933.
Land cover-landscape pattern affects the atmospheric environment directly or indirectly, and the understanding of the atmospheric environment response to land cover - landscape pattern is of great significance to the maintenance and improvement of ecological environment. In this paper, such 9 landscape metrics as PLAND, PD, LPI, ED, MPS, AWMSI, CONTAG, SHDI and SHEI were selected by remote sensing inversion of PM2.5 data and land use data in long time series. The correlation analysis and multiple stepwise regression analysis were also used to analyze the effect of land use and landscape pattern on PM2.5 in Yangtze River Delta. The results showed that: (1) PM2.5 concentration was increasing in Yangtze River Delta from 1998 to 2015; (2) PM2.5 concentration was negatively correlated with the forest land and grassland, while positively correlated with the urban construction land; (3) At the level of landscape type, the urban construction land, water body and farm land PLAND, LPI, ED, MPS, AWMSI were positively correlated, the urban construction land and water body PD were positively correlated with PM2.5, the farm land PD was negatively correlated with PM2.5, and the forest land and grassland PLAND, PD, LPI, ED, MPS and AWMSI were negatively correlated with PM2.5. (4) In the integral landscape of land use, AWMSI was negatively correlated with PM2.5 concentration. It is of great significance to control PM2.5 pollution from the perspective of land use planning and contributed to an estimation methods of PM2.5 concentrations using land use type and land use landscape metrics in the absence of missing PM2.5 monitoring data.
Debin Lu; Wanliu Mao; Dongyang Yang; Jianan Zhao; Jianhua Xu. Effects of land use and landscape pattern on PM2.5 in Yangtze River Delta, China. Atmospheric Pollution Research 2018, 9, 705 -713.
AMA StyleDebin Lu, Wanliu Mao, Dongyang Yang, Jianan Zhao, Jianhua Xu. Effects of land use and landscape pattern on PM2.5 in Yangtze River Delta, China. Atmospheric Pollution Research. 2018; 9 (4):705-713.
Chicago/Turabian StyleDebin Lu; Wanliu Mao; Dongyang Yang; Jianan Zhao; Jianhua Xu. 2018. "Effects of land use and landscape pattern on PM2.5 in Yangtze River Delta, China." Atmospheric Pollution Research 9, no. 4: 705-713.
PM2.5 pollution is an environmental issue caused by multiple natural and socioeconomic factors, presenting with significant spatial disparities across mainland China. However, the determinant power of natural and socioeconomic factors and their interactive impact on PM2.5 pollution is still unclear. In the study, the GeogDetector method was used to quantify nonlinear associations between PM2.5 and potential factors. This study found that natural factors, including ecological environments and climate, were more influential than socioeconomic factors, and climate was the predominant factor (q = 0.56) in influencing PM2.5 pollution. Among all interactions of the six influencing factors, the interaction of industry and climate had the largest influence (q = 0.66). Two recognized major contaminated areas were the Tarim Basin in the northwest region and the eastern plain region; the former was mainly influenced by the warm temperate arid climate and desert, and the latter was mainly influenced by the warm temperate semi-humid climate and multiple socioeconomic factors. The findings provided an interpretation of the influencing mechanisms of PM2.5 pollution, which can contribute to more specific policies aimed at successful PM2.5 pollution control and abatement.
Dongyang Yang; Xiaomin Wang; Jianhua Xu; Chengdong Xu; Debin Lu; Chao Ye; Zujing Wang; Ling Bai. Quantifying the influence of natural and socioeconomic factors and their interactive impact on PM2.5 pollution in China. Environmental Pollution 2018, 241, 475 -483.
AMA StyleDongyang Yang, Xiaomin Wang, Jianhua Xu, Chengdong Xu, Debin Lu, Chao Ye, Zujing Wang, Ling Bai. Quantifying the influence of natural and socioeconomic factors and their interactive impact on PM2.5 pollution in China. Environmental Pollution. 2018; 241 ():475-483.
Chicago/Turabian StyleDongyang Yang; Xiaomin Wang; Jianhua Xu; Chengdong Xu; Debin Lu; Chao Ye; Zujing Wang; Ling Bai. 2018. "Quantifying the influence of natural and socioeconomic factors and their interactive impact on PM2.5 pollution in China." Environmental Pollution 241, no. : 475-483.
PM2.5 concentrations increased and have been one of the major social issues along with rapid urbanization in many regions of the world in recent decades. The development of urbanization differed among regions, PM2.5 pollution also presented discrepant distribution across the world. Thus, this paper aimed to grasp the profile of global distribution of urbanization and PM2.5 and their evolutionary relationships. Based on global data for the proportion of the urban population and PM2.5 concentrations in 1998–2015, this paper investigated the spatial distribution, temporal variation, and evolutionary relationships of global urbanization and PM2.5. The results showed PM2.5 presented an increasing trend along with urbanization during the study period, but there was a variety of evolutionary relationships in different countries and regions. Most countries in East Asia, Southeast Asia, South Asia, and some African countries developed with the rapid increase in both urbanization and PM2.5. Under the impact of other socioeconomic factors, such as industry and economic growth, the development of urbanization increased PM2.5 concentrations in most Asian countries and some African countries, but decreased PM2.5 concentrations in most European and American countries. The findings of this study revealed the spatial distributions of global urbanization and PM2.5 pollution and provided an interpretation on the evolution of urbanization-PM2.5 relationships, which can contribute to urbanization policies making aimed at successful PM2.5 pollution control and abatement.
Dongyang Yang; Chao Ye; Xiaomin Wang; Debin Lu; Jianhua Xu; Haiqing Yang. Global distribution and evolvement of urbanization and PM2.5 (1998–2015). Atmospheric Environment 2018, 182, 171 -178.
AMA StyleDongyang Yang, Chao Ye, Xiaomin Wang, Debin Lu, Jianhua Xu, Haiqing Yang. Global distribution and evolvement of urbanization and PM2.5 (1998–2015). Atmospheric Environment. 2018; 182 ():171-178.
Chicago/Turabian StyleDongyang Yang; Chao Ye; Xiaomin Wang; Debin Lu; Jianhua Xu; Haiqing Yang. 2018. "Global distribution and evolvement of urbanization and PM2.5 (1998–2015)." Atmospheric Environment 182, no. : 171-178.
The prediction of PM2.5 concentrations with high spatiotemporal resolution has been suggested as a potential method for data collection to assess the health effects of exposure. This work predicted the weekly average PM2.5 concentrations in the Yangtze River Delta, China, by using a spatio-temporal model. Integrating land use data, including the areas of cultivated land, construction land, and forest land, and meteorological data, including precipitation, air pressure, relative humidity, temperature, and wind speed, we used the model to estimate the weekly average PM2.5 concentrations. We validated the estimated effects by using the cross-validated R2 and Root mean square error (RMSE); the results showed that the model performed well in capturing the spatiotemporal variability of PM2.5 concentration, with a reasonably large R2 of 0.86 and a small RMSE of 8.15 (μg/m3). In addition, the predicted values covered 94% of the observed data at the 95% confidence interval. This work provided a dataset of PM2.5 concentration predictions with a spatiotemporal resolution of 3 km × week, which would contribute to accurately assessing the potential health effects of air pollution.
Dongyang Yang; Debin Lu; Jianhua Xu; Chao Ye; Jianan Zhao; Guanghui Tian; Xinge Wang; Nina Zhu. Predicting spatio-temporal concentrations of PM2.5 using land use and meteorological data in Yangtze River Delta, China. Stochastic Environmental Research and Risk Assessment 2017, 32, 2445 -2456.
AMA StyleDongyang Yang, Debin Lu, Jianhua Xu, Chao Ye, Jianan Zhao, Guanghui Tian, Xinge Wang, Nina Zhu. Predicting spatio-temporal concentrations of PM2.5 using land use and meteorological data in Yangtze River Delta, China. Stochastic Environmental Research and Risk Assessment. 2017; 32 (8):2445-2456.
Chicago/Turabian StyleDongyang Yang; Debin Lu; Jianhua Xu; Chao Ye; Jianan Zhao; Guanghui Tian; Xinge Wang; Nina Zhu. 2017. "Predicting spatio-temporal concentrations of PM2.5 using land use and meteorological data in Yangtze River Delta, China." Stochastic Environmental Research and Risk Assessment 32, no. 8: 2445-2456.
Debin Lu; Jianhua Xu; Dongyang Yang; Jianan Zhao. Spatio-temporal variation and influence factors of PM 2.5 concentrations in China from 1998 to 2014. Atmospheric Pollution Research 2017, 8, 1151 -1159.
AMA StyleDebin Lu, Jianhua Xu, Dongyang Yang, Jianan Zhao. Spatio-temporal variation and influence factors of PM 2.5 concentrations in China from 1998 to 2014. Atmospheric Pollution Research. 2017; 8 (6):1151-1159.
Chicago/Turabian StyleDebin Lu; Jianhua Xu; Dongyang Yang; Jianan Zhao. 2017. "Spatio-temporal variation and influence factors of PM 2.5 concentrations in China from 1998 to 2014." Atmospheric Pollution Research 8, no. 6: 1151-1159.