The relationships between PM2.5 and meteorological factors in China: Seasonal and regional variations

The interactions between PM2.5 and meteorological factors play a crucial role in air pollution analysis. However, previous studies that have researched the relationships between PM2.5 concentration and meteorological conditions have been mainly confined to a certain city or district, and the correlation over the whole of China remains unclear. Whether or not spatial and seasonal variations exit deserves further research. In this study, the relationships between PM2.5 concentration and meteorological factors were investigated in 74 major cities in China for a continuous period of 22 months from February 2013 to November 2014, at season, year, city, and regional scales, and the spatial and seasonal variations were analyzed. The meteorological factors were relative humidity (RH), temperature (TEM), wind speed (WS), and surface pressure (PS). We found that spatial and seasonal variations of their relationships with PM2.5 do exist. Spatially, RH is positively correlated with PM2.5 concentration in North China and Urumqi, but the relationship turns to negative in other areas of China. WS is negatively correlated with PM2.5 everywhere expect for Hainan Island. PS has a strong positive relationship with PM2.5 concentration in Northeast China and Mid-south China, and in other areas the correlation is weak. Seasonally, the positive correlation between PM2.5 concentration and RH is stronger in winter and spring. TEM has a negative relationship with PM2.5 in autumn and the opposite in winter. PS is more positively correlated with PM2.5 in autumn than in other seasons. Our study investigated the relationships between PM2.5 and meteorological factors in terms of spatial and seasonal variations, and the conclusions about the relationships between PM2.5 and meteorological factors are more comprehensive and precise than before.Comment: 3 tables, 13 figure

Relation between urban volume and land surface temperature: A comparative study of planned and traditional cities in Japan

The horizontal two-dimensional (2D) urban land use approach is not sufficient to trace rapid changes in urban environment. Hence, a three-dimensional (3D) approach that is different from the traditional geographical method is necessary to understand the mechanism of compound urban diversity. Using remote sensing data captured in 2010/2011 and geospatial tools and techniques, we quantified the urban volume (UV, consisting of urban built volume (UBV) and urban green volume (UGV)) and retrieved and mapped the land surface temperature (LST) of two cities in Japan (Tsukuba, a planned city, and Tsuchiura, a traditional city). We compared these two cities in terms of (1) UBV and UGV and their relationships with mean LST; and (2) the relationship of the UGV–UBV ratio with mean LST. Tsukuba had a total UBV of 74 million m3, while Tsuchiura had a total of 89 million m3. In terms of UGV, Tsukuba had a total of 52 million m3, while Tsuchiura had a total of 29 million m3. In both cities, UBV had a positive relationship with mean LST (Tsukuba: R2 = 0.31, p < 0.001; Tsuchiura: R2 = 0.42, p < 0.001), and UGV had a negative relationship with mean LST (Tsukuba: R2 = 0.53, p < 0.001; Tsuchiura: R2 = 0.19, p < 0.001). Tsukuba also had a higher UGV–UBV ratio of 54.9% in comparison with Tsuchiura, with 28.7%. Overall, the results indicate that mean LST was more intense in the traditional city (Tsuchiura). This could have been due to the difference in urban spatial structure. As a planned city, Tsukuba is still a relatively young city that has more dispersed green spaces and a well-spread (so far) built-up area

Feature selection of various land cover indices for monitoring surface heat island in Tehran city using Landsat 8 imagery

Recently, scientists have been taking a great interest in Global warming issue, since the global surface temperature has been significantly increased all through last century. The surface heat island (SHI) refers to an urban area that has higher surface temperatures than its surrounding rural areas due to urbanization. In this paper, Tehran city is used as case study area. This paper tries to employ a quantitative approach to explore the relationship between land surface temperature and the most widespread land cover indices, and select proper (urban and vegetation) indices by incorporating supervised feature selection procedures using Landsat 8 imageries. In this regards, genetic algorithm is incorporated to choose best indices by employing kernel base one, support vector regression and linear regression methods. The proposed method revealed that there is a high degree of consistency between affected information and SHI dataset (RMSE&nbsp;= 0.9324,&nbsp;NRMSE&nbsp;= 0.2695 and R2&nbsp;= 0.9315). First published online: 30 May 201

Detection of unfavourable urban areas with higher temperatures and lack of green spaces using satellite imagery in sixteen Spanish cities.

This paper seeks to identify the most unfavourable areas of a city in terms of high temperatures and the absence of green infrastructure. An automatic methodology based on remote sensing and data analysis has been devel oped and applied in sixteen Spanish cities with different characteristics. Landsat-8 satellite images were selected for each city from the July-August period of 2019 and 2020 to calculate the spatial variation of land surface temperature (LST). The Normalized Difference Vegetation Index (NDVI) was used to determine the abundance of vegetation across the city. Based on the NDVI and LST maps created, a k-means unsupervised classification clustering was performed to automatically identify the different clusters according to how favourable these areas were in terms of temperature and presence of vegetation. A Disadvantaged Area Index (DAI), combining both variables, was developed to produce a map showing the most unfavourable areas for each city. Overall, the percentage of the area susceptible to improvement with more vegetation in the cities studied ranged from 13 % in Huesca to 64–65 % in Bilbao and Valencia. The influence of several factors, such as the presence of water bodies or large buildings, is discussed. Detecting unfavourable areas is a very interesting tool for defining future planning strategy for green spaces

Advances in remote sensing applications for urban sustainability

Abstract: It is essential to monitor urban evolution at spatial and temporal scales to improve our understanding of the changes in cities and their impact on natural resources and environmental systems. Various aspects of remote sensing are routinely used to detect and map features and changes on land and sea surfaces, and in the atmosphere that affect urban sustainability. We provide a critical and comprehensive review of the characteristics of remote sensing systems, and in particular the trade-offs between various system parameters, as well as their use in two key research areas: (a) issues resulting from the expansion of urban environments, and (b) sustainable urban development. The analysis identifies three key trends in the existing literature: (a) the integration of heterogeneous remote sensing data, primarily for investigating or modelling urban environments as a complex system, (b) the development of new algorithms for effective extraction of urban features, and (c) the improvement in the accuracy of traditional spectral-based classification algorithms for addressing the spectral heterogeneity within urban areas. Growing interests in renewable energy have also resulted in the increased use of remote sensing—for planning, operation, and maintenance of energy infrastructures, in particular the ones with spatial variability, such as solar, wind, and geothermal energy. The proliferation of sustainability thinking in all facets of urban development and management also acts as a catalyst for the increased use of, and advances in, remote sensing for urban applications

EXAMINATION OF PHOTOCHEMISTRY AND METEOROLOGY OF ATMOSPHERIC POLLUTANTS FROM THE NORTH CHINA PLAIN

Increasingly severe air pollution over metropolitan regions in China has raised attention in light of its local and regional impacts on health and climate. Computer models can simulate complex interactions between photochemistry and meteorology to inform policy decisions in reducing ground-level pollution. However, models rely on an accurate portrayal of emissions that often possess large uncertainties over regions with evolving pollution characteristics. This work is comprised of a quantitative analysis of air pollutants in the North China Plain that strives to improve such uncertainties by identification of important sources and meteorological conditions for pollution through the combination of observations and models. Measurements used in this dissertation focus on in situ observations from the Spring 2016 Air chemistry Research in Asia (ARIAs) campaign, which sampled atmospheric composition across the heavily populated and industrialized Hebei Province in the North China Plain. High amounts of ozone (O3) precursors were found throughout and even above the planetary boundary layer, continuing to generate O3 at high rates to be potentially transported downwind. Evidence for the importance of anthropogenic VOCs on O3 production is presented. Concentrations of NOx and VOCs even in the rural areas of this highly industrialized province promote widespread O3 production and in order to improve air quality over Hebei, both NOx and VOCs should be regulated. The ARIAs airborne measurements also provide a critical opportunity to characterize chlorofluorocarbons (CFCs) over a suspected CFC-11 source region in China, finding mixing ratios were well above 2016 global background levels. Based on correlations of CFCs with compounds used in their manufacture, I identify likely source regions of new CFCs production and release, in violation of the Montreal Protocol. Finally, I examine the influence of meteorology on surface and aloft measurements during ARIAs. A multiday persistent high pressure episode is presented as a case study to examine the influence of regional transport on air quality measured during ARIAs. This dissertation provides valuable information for understanding one of the most polluted regions in China. Coordinated field and modeling efforts can together provide scientific guidance to inform pollution control measures to meet air quality targets in China

中国と日本における都市発展及びその環境への影響の総合評価に関する研究

This study mainly focused on the spatial effect on city development. Spatial analysis was conducted to explore the characteristics and correlates of city development, and its impact on environment for cities in China and Japan. The issue of city development was investigated from multiple perspectives. The history of urban development process in China and Japan was summarized, and the correlates with urban development were compared. Meanwhile, the urban heat island of cities in China and Japan were compared北九州市立大

Impact of land use and land cover change on land surface temperature in Iskandar Malaysia using remote sensing technique

Iskandar Malaysia is one of the impressive development projects ever undertaken in Malaysia that has been experiencing rapid rate of land use change since 2006. Land use change is due to the urban expansion and reduction in natural green areas resulted from enhanced economic growth. The three objectives of this study are (i) to estimate the land use and land cover changes (LULC) in Iskandar Malaysia from 1989 to 2014, (ii) to investigate the effect of LULC changes on land surface temperature (LST) change in the study area and (iii) to predict the LST by 2025. Remote sensing data namely Landsat (Landsat 5, 7 and 8) and Moderate Resolution Imaging Spectroradiometer (MODIS) of Terra product (MOD11A1) were used to classify various LULC and to calculate the LST in Iskandar Malaysia. There are two digital classification techniques used to classify and test the different LULC in this study area. Maximum Likelihood Classification (MLC) technique provided higher accuracies compared to the Support Vector Machine (SVM) technique. Consequently, the classified satellite images using the MLC technique were used to monitor changes in LULC in Iskandar Malaysia. LST was extracted using mono window. The mean LST using Geographic Information System (GIS) analysis according to LULC shows that water areas recorded the highest night time LST value, while forest recorded the lowest day time LST value. Urban areas are the warmest land use during the day and the second warmest land use during the night time. Moreover, the weighted average used to predict the mean LST of entire Iskandar Malaysia, it was found that if green space increases LST value would decrease by 0.5○C. To predict the effect of LULC changes on mean LST of each LULC types linear curve fitting model was used. According to the results, the mean night LST from 2000 to 2025 will increase in Iskandar Malaysia as urban (20.89°C to 22.39°C±0.45), mangrove (20.88°C to 22.59°C±0.50), forest (20.39°C to 21.04°C±0.18), oil palm (20.39°C to 21.25±0.25), rubber (20.34°C to 22.36°C ± 0.57), and water (21.61 °C to 23.31°C ± 0.51). The results show increment in day time at urban (29.26°C to 32.78°C±1.07), mangrove (26.23°C to 28.82 °C±0.89), forest (25.76°C to 27.54°C±0.49), oil palm (27.02°C to 29.54±0.70), rubber (26.49°C to 27.24°C ±0.29), and water (26.10 °C to 28.77 °C ± 0.8) respectively. Moreover, the relationship between LST and several impervious and vegetation indexes show that there is a strong relationship between impervious indexes and LST, and an inverse relationship between vegetation indexes and LST. Finally, this study concluded that replacing green natural area with improvise surface can increase the land surface temperature and have negative effect on urban thermal comfort

Object-based Urban Building Footprint Extraction and 3D Building Reconstruction from Airborne LiDAR Data

Buildings play an essential role in urban intra-construction, urban planning, climate studies and disaster management. The precise knowledge of buildings not only serves as a primary source for interpreting complex urban characteristics, but also provides decision makers with more realistic and multidimensional scenarios for urban management. In this thesis, the 2D extraction and 3D reconstruction methods are proposed to map and visualize urban buildings. Chapter 2 presents an object-based method for extraction of building footprints using LiDAR derived NDTI (Normalized Difference Tree Index) and intensity data. The overall accuracy of 94.0% and commission error of 6.3% in building extraction is achieved with the Kappa of 0.84. Chapter 3 presents a GIS-based 3D building reconstruction method. The results indicate that the method is effective for generating 3D building models. The 91.4% completeness of roof plane identification is achieved, and the overall accuracy of the flat and pitched roof plane classification is 88.81%, with the user’s accuracy of the flat roof plane 97.75% and pitched roof plane 100%

Quantifying Surface Urban Heat Island Formation in the World Heritage Tropical Mountain City of Sri Lanka

Presently, the urban heat island (UHI) phenomenon, and its adverse impacts, are becoming major research foci in various interrelated fields due to rapid changes in urban ecological environments. Various cities have been investigated in previous studies, and most of the findings have facilitated the introduction of proper mitigation measures to overcome the negative impact of UHI. At present, most of the mountain cities of the world have undergone rapid urban development, and this has resulted in the increasing surface UHI (SUHI) phenomenon. Hence, this study focuses on quantifying SUHI in Kandy City, the world heritage tropical mountain city of Sri Lanka, using Landsat data (1996 and 2017) based on the mean land surface temperature (LST), the difference between the fraction of impervious surfaces (IS), and the fraction of green space (GS). Additionally, we examined the relationship of LST to the green space/impervious surface fraction ratio (GS/IS fraction ratio) and the magnitude of the GS/IS fraction ratio. The SUHI intensity (SUHII) was calculated based on the temperature difference between main land use/cover categories and the temperature difference between urban-rural zones. We demarcated the rural zone based on the fraction of IS recorded, <10%, along with the urban-rural gradient zone. The result shows a SUHII increase from 3.9 °C in 1996 to 6.2 °C in 2017 along the urban-rural gradient between the urban and rural zones (10 < IS). These results relate to the rapid urban expansion of the study areas from 1996 to 2017. Most of the natural surfaces have changed to impervious surfaces, causing an increase of SUHI in Kandy City. The mean LST has a positive relationship with the fraction of IS and a negative relationship with the fraction of GS. Additionally, the GS/IS fraction ratio shows a rapid decline. Thus, the findings of this study can be considered as a proxy indicator for introducing proper landscape and urban planning for the World Heritage tropical mountain city of Kandy in Sri Lanka