The Ventilation Efficiency of Urban Built Intensity and Ventilation Path Identification: A Case Study of Wuhan.

Urban ventilation is being hampered by rough surfaces in dense urban areas, and the microclimate and air quality of the urban built environment are not ideal. Identifying urban ventilation paths is helpful to save energy, reduce emissions, and improve the urban ecological environment. Wuhan is the capital city of Hubei, and it has a high urban built intensity and hot summers. Taking Wuhan city, with a size of 35 km ×50 km, as an example, the built environment was divided into grids of 100 m × 100 m and included the building density, floor area ratio, and average building height. The ventilation mechanism of the urban built intensity index has previously been explained. The decrease in building density is not the sole factor causing an increase in wind speed; the enclosure and width of the ventilation path and the height of the front building are also influential. Twelve urban built units were selected for CFD numerical simulation. The ventilation efficiency of each grid was evaluated by calculating the wind speed ratio, maximum wind speed, average wind speed, and area ratio of strong wind. The relationship between the urban built intensity index and ventilation efficiency index was established using the factor analysis method and the Pearson correlation coefficient; building density and average building height are the most critical indexes of ventilation potential. In addition, the layout of the building also has an important impact on ventilation. A suitable built environment is that in which the building density is less than 30%, the average building height is greater than 15 m, and the floor area ratio is greater than 1.5. The urban built intensity map was weighted to identify urban ventilation paths. The paper provides a quantitative reference for scientific planning and design of the urban spatial form to improve ventilation.

A cooled city? Comparing human activity changes on the impact of urban thermal environment before and after city-wide lockdown.

The outbreak of the COVID-19 epidemic in early 2020 reduced human outdoor activities and changed the spatial-temporal distribution of the population. To find its changes on the impact of urban thermal environment, we applied Pearson correlation analysis and OLS linear regression model from the perspective of urban land use and the local climate zone (LCZ) scheme, and selected Wuhan City in China as a case study. The results showed that the population size decreased in most urban land use and LCZ classes due to the Spring Festival and epidemic effects, which caused residents to leave Wuhan City. As a result, the normalized surface urban heat island changes (SUHInc) decreased by 9.41% at the city level, and a larger SUHInc occurred in commercial and industrial land. Among the LCZ classes, the built-up classes also tended to have a larger SUHInc than the natural land cover classes. However, the population size and human outdoor activity changes did not modify the spatial distribution of the urban thermal environment, because the same trends were observed for various urban land use and LCZ classes, which illustrated that the contribution of anthropogenic heat discharge on the urban thermal environment is relatively weaker. The above findings imply that it is necessary to apply different methods for various urban land uses and alleviate urban heat island.

The Diversified Impacts of Urban Morphology on Land Surface Temperature among Urban Functional Zones.

Local warming induced by rapid urbanization has been threatening residents' health, raising significant concerns among urban planners. Local climate zone (LCZ), a widely accepted approach to reclassify the urban area, which is helpful to propose planning strategies for mitigating local warming, has been well documented in recent years. Based on the LCZ framework, many scholars have carried out diversified extensions in urban zoning research in recent years, in which urban functional zone (UFZ) is a typical perspective because it directly takes into account the impacts of human activities. UFZs, widely used in urban planning and management, were chosen as the basic unit of this study to explore the spatial heterogeneity in the relationship between landscape composition, urban morphology, urban functions, and land surface temperature (LST). Global regression including ordinary least square regression (OLS) and random forest regression (RF) were used to model the landscape-LST correlations to screen indicators to participate in following spatial regression. The spatial regression including semi-parametric geographically weighted regression (SGWR) and multiscale geographically weighted regression (MGWR) were applied to investigate the spatial heterogeneity in landscape-LST among different types of UFZ and within each UFZ. Urban two-dimensional (2D) morphology indicators including building density (BD); three-dimensional (3D) morphology indicators including building height (BH), building volume density (BVD), and sky view factor (SVF); and other indicators including albedo and normalized difference vegetation index (NDVI) and impervious surface fraction (ISF) were used as potential landscape drivers for LST. The results show significant spatial heterogeneity in the Landscape-LST relationship across UFZs, but the spatial heterogeneity is not obvious within specific UFZs. The significant impact of urban morphology on LST was observed in six types of UFZs representing urban built up areas including Residential (R), Urban village (UV), Administration and Public Services (APS), Commercial and Business Facilities (CBF), Industrial and Manufacturing (IM), and Logistics and Warehouse (LW). Specifically, a significant correlation between urban 3D morphology indicators and LST in CBF was discovered. Based on the results, we propose different planning strategies to settle the local warming problems for each UFZ. In general, this research reveals UFZs to be an appropriate operational scale for analyzing LST on an urban scale.

How Did Distribution Patterns of Particulate Matter Air Pollution (PM2.5 and PM10) Change in China during the COVID-19 Outbreak: A Spatiotemporal Investigation at Chinese City-Level.

Due to the suspension of traffic mobility and industrial activities during the COVID-19, particulate matter (PM) pollution has decreased in China. However, rarely have research studies discussed the spatiotemporal pattern of this change and related influencing factors at city-scale across the nation. In this research, the clustering patterns of the decline rates of PM2.5 and PM10 during the period from 20 January to 8 April in 2020, compared with the same period of 2019, were investigated using spatial autocorrelation analysis. Four meteorological factors and two socioeconomic factors, i.e., the decline of intra-city mobility intensity (dIMI) representing the effect of traffic mobility and the decline rates of the secondary industrial output values (drSIOV), were adopted in the regression analysis. Then, multi-scale geographically weighted regression (MGWR), a model allowing the particular processing scale for each independent variable, was applied for investigating the relationship between PM pollution reductions and influencing factors. For comparison, ordinary least square (OLS) regression and the classic geographically weighted regression (GWR) were also performed. The research found that there were 16% and 20% reduction of PM2.5 and PM10 concentration across China and significant PM pollution mitigation in central, east, and south regions of China. As for the regression analysis results, MGWR outperformed the other two models, with R2 of 0.711 and 0.732 for PM2.5 and PM10, respectively. The results of MGWR revealed that the two socioeconomic factors had more significant impacts than meteorological factors. It showed that the reduction of traffic mobility caused more relative declines of PM2.5 in east China (e.g., cities in Jiangsu), while it caused more relative declines of PM10 in central China (e.g., cities in Henan). The reduction of industrial operation had a strong relationship with the PM10 drop in northeast China. The results are crucial for understanding how the decline pattern of PM pollution varied spatially during the COVID-19 outbreak, and it also provides a good reference for air pollution control in the future.

Coupling mobile phone data with machine learning: How misclassification errors in ambient PM2.5 exposure estimates are produced?

BACKGROUND: Most studies relying on time-activity diary or traditional air pollution modelling approach are insufficient to suggest the impacts of ignoring individual mobility and air pollution variations on misclassification errors in exposure estimates. Moreover, very few studies have examined whether such impacts differ across socioeconomic groups. OBJECTIVES: We aim to examine how ignoring individual mobility and PM2.5 variations produces misclassification errors in ambient PM2.5 exposure estimates. METHODS: We developed a geo-informed backward propagation neural network model to estimate hourly PM2.5 concentrations in terms of remote sensing and geospatial big data. Combining the estimated PM2.5 concentrations and individual trajectories derived from 755,468 mobile phone users on a weekday in Shenzhen, China, we estimated four types of individual total PM2.5 exposures during weekdays at multi-temporal scales. The estimate ignoring individual mobility, PM2.5 variations or both was compared with the hypothetical error-free estimate using paired sample t-test. We then quantified the exposure misclassification error using Pearson correlation analysis. Moreover, we examined whether the misclassification error differs across different socioeconomic groups. Taking findings of ignoring individual mobility as an example, we further investigated whether such findings are robust to the different selections of time. RESULTS: We found that the estimate ignoring PM2.5 variations, individual mobility or both was statistically different from the hypothetical error-free estimate. Ignoring both factors produced the largest exposure misclassification error. The misclassification error was larger in the estimate ignoring PM2.5 variations than that ignoring individual mobility. People with high economic status suffered from a larger exposure misclassification error. The findings were robust to the different selections of time. CONCLUSIONS: Ignoring individual mobility, PM2.5 variations or both leads to misclassification errors in ambient PM2.5 exposure estimates. A larger misclassification error occurs in the estimate neglecting PM2.5 variations than that ignoring individual mobility, which is seldom reported before.

Spatiotemporal Variations of Particulate and Gaseous Pollutants and Their Relations to Meteorological Parameters: The Case of Xiangyang, China.

High air pollution levels have become a nationwide problem in China, but limited attention has been paid to prefecture-level cities. Furthermore, different time resolutions between air pollutant level data and meteorological parameters used in many previous studies can lead to biased results. Supported by synchronous measurements of air pollutants and meteorological parameters, including PM2.5, PM10, total suspended particles (TSP), CO, NO2, O3, SO2, temperature, relative humidity, wind speed and direction, at 16 urban sites in Xiangyang, China, from 1 March 2018 to 28 February 2019, this paper: (1) analyzes the overall air quality using an air quality index (AQI); (2) captures spatial dynamics of air pollutants with pollution point source data; (3) characterizes pollution variations at seasonal, day-of-week and diurnal timescales; (4) detects weekend effects and holiday (Chinese New Year and National Day holidays) effects from a statistical point of view; (5) establishes relationships between air pollutants and meteorological parameters. The principal results are as follows: (1) PM2.5 and PM10 act as primary pollutants all year round and O3 loses its primary pollutant position after November; (2) automobile manufacture contributes to more particulate pollutants while chemical plants produce more gaseous pollutants. TSP concentration is related to on-going construction and road sprinkler operations help alleviate it; (3) an unclear weekend effect for all air pollutants is confirmed; (4) celebration activities for the Chinese New Year bring distinctly increased concentrations of SO2 and thereby enhance secondary particulate pollutants; (5) relative humidity and wind speed, respectively, have strong negative correlations with coarse particles and fine particles. Temperature positively correlates with O3.

How Do the Multi-Temporal Centroid Trajectories of Urban Heat Island Correspond to Impervious Surface Changes: A Case Study in Wuhan, China.

Conspicuous expansion and intensification of impervious surfaces accompanied by rapid urbanization are widely recognized to have exerted evident impacts on the urban thermal environment. Investigating the spatially and temporally varying relationships between Land Surface Temperature (LST) and impervious surfaces (IS) at multiple scales is of great significance for steering IS expansion and intensification. This study proposes an analytical framework to investigate the spatiotemporal variations of LST and its responses to IS in Wuhan, China at both city scale and sub-region scale. The summer LST patterns in 2002-2017 are extracted by Multi-Task Gaussian Process (MTGP) model from raw 8-day synthesized MODerate-resolution Imaging Spectroradiometer (MODIS) LST data. At the city scale, the weighted center of LST (LSTWC) and impervious surface fraction (ISFWC), multi-temporal trajectories and coupling indicators are utilized to comprehensively examine the spatial and temporal dynamics of LST and IS within Wuhan. At the sub-region scale, urban heat island ratio index (URI), impervious surfaces contribution index (ISCI) and sprawl rate are introduced for further quantifying the relationships of LST and IS. The results reveal that IS and hot thermal landscapes expanded by 407.43 km2 and 255.82 km2 in Wuhan in 2002-2017 at city scale. The trajectories of LSTWCs and ISFWCs are visually coherent and both heading to southeast direction in general. At the sub-region scale, the specific cardinal directions with the highest ISCI variations are examined to be the exact directions of ISFWC trajectories in 2002-2017. The results reveal that the spatiotemporal variations of LST and IS are highly correlated at both city and sub-region scales within Wuhan, thus testifying the significance of steering IS expansion and renewal for controlling urban thermal environment deterioration.

How to quantify the relationship between spatial distribution of urban waterbodies and land surface temperature?

Urban waterbodies can effectively mitigate the increasing UHI effects and thus enhance climate resilience of urban areas. To contribute to our limited understanding in cooling effect of waterbodies on surrounding thermal environments, we examine the quantitative relationship between the spatial distribution of urban waterbodies and the land surface temperature (LST) in Wuhan, China. This paper 1) applies two indicators, the fractional water cover and the gravity water index, for measuring the spatial distribution of urban waterbodies; 2) conducts simple linear regression and spatial regression analyses to explore the LST-water relationship at multiple scales; and 3) compares the individual regression results from different land use types. The results show that the spatial distribution of urban waterbodies affects the LST significantly, and the gravity water index sufficiently explains the LST variation at various scales. Furthermore, the impact of urban waterbody distribution on the LST does vary across different land use types. Conclusions from this study provide insights of the cooling effect of urban waterbodies, which can further assist city planners and decision makers in utilizing cooling effects of waterbodies to improve the thermal environment of urban areas.

The multi-timescale temporal patterns and dynamics of land surface temperature using Ensemble Empirical Mode Decomposition.

Temporal variation patterns of Land Surface Temperature (LST) under different time scales are crucial in understanding the response of urban thermal environment to different forcings. However, there is no integrated toolset to extract such patterns from satellite remotely sensed time series LST (TSLST) data. This paper presents a workflow to extract the multi-timescale temporal patterns and dynamics from nonlinear and non-stationary TSLST data by taking Wuhan, China as case study. The 8-day MODerate-resolution Imaging Spectroradiometer (MODIS) satellite image products spanning the 2003-2017 period are used to generate a TSLST dataset with continuous and smooth surfaces on the monthly basis through the non-parametric Multi-Task Gaussian Process Modeling (MTGP). The study area is segmented into multiple time series clusters by k-means to bridge with urban planning in terms of research and implementation scale. Then, temporal patterns including annual, interannual components, and overall trends are reconstructed based on the components with characteristic time scales decomposed by the adaptive Ensemble Empirical Mode Decomposition (EEMD) method. The generated patterns of the 17 time series clusters are interpreted from the perspective of earth revolution, meteorological cycles and urbanization. Specifically, the annual components which are mainly generated by earth revolution reveal consistent rhythmic patterns among the time series. The interannual components preserve similar shapes although they differ in amplitudes. The overall shape is basically consistent with that of air temperature of Central China, which may be mainly induced by the El Niño-Southern Oscillation (ENSO) phenomenon. The overall trends which exert considerable differences are grouped into three types by shape. Such differences may be potentially caused by the inconsistent levels of localized urbanization, afforestation or circular economy development. This study facilitates the understanding of TSLST patterns and human-environment interactions. The proposed workflow can be utilized for other cities and potentially used for comparison among different cities.

An IHS-Based Pan-Sharpening Method for Spectral Fidelity Improvement Using Ripplet Transform and Compressed Sensing.

Pan-sharpening aims at integrating spectral information from a multi-spectral (MS) image and spatial information from a panchromatic (PAN) image in a fused image with both high spectral and spatial resolutions. Numerous pan-sharpening methods are based on intensity-hue-saturation (IHS) transform, which may cause evident spectral distortion. To address this problem, an IHS-based pan-sharpening method using ripplet transform and compressed sensing is proposed. Firstly, the IHS transform is applied to the MS image to separate intensity components. Secondly, discrete ripplet transform (DRT) is implemented on the intensity component and the PAN image to obtain multi-scale sub-images. High-frequency sub-images are fused by a local variance algorithm and, for low-frequency sub-images, compressed sensing is introduced for the reconstruction of the intensity component so as to integrate the local information from both the intensity component and the PAN image. The specific fusion rule is defined by local difference. Finally, the inverse ripplet transform and inverse IHS transform are coupled to generate the pan-sharpened image. The proposed method is compared with five state-of-the-art pan-sharpening methods and also the Gram-Schmidt (GS) method through visual and quantitative analysis of WorldView-2, Pleiades and Triplesat datasets. The experimental results reveal that the proposed method achieves relatively higher spatial resolution and more desirable spectral fidelity.