Exploring the effects of landscape structure on aerosol optical depth (AOD) patterns using GIS and HJ-1B images

Ye, Luping; Fang, Linchuan; Tan, Wenfeng; Wang, Yunqiang; Huang, Yu

doi:10.1039/c5em00538h

Cited by 7 publications

(3 citation statements)

References 43 publications

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“…Haze is affected by pollution sources (Wang et al 2015), meteorological conditions (Bei et al 2016) and vegetation coverage (Ye et al 2016;Zhang 2019). Reducing pollutant emissions is accomplished by actions such as reducing vehicle pollution and dust, controlling industrial pollution and the emission of NH 3 in agricultural areas, reducing the unorganized combustion of biomass and concentration of air pollutants, and coping with haze pollution (An et al 2019;Yang et al 2016).…”

Section: Evaluation and Sensitivity Analysis Of The Ecosystem Servicementioning

confidence: 99%

Evaluation and sensitivity analysis of the ecosystem service functions of haze absorption by green space based on its quality in China

Ping¹,

Wang²,

Yang³

et al. 2020

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Evaluation of the ecosystem service functions of haze absorption by green space is important for controlling haze. In this study, the ecosystem service functions of haze absorption by green space in China in 2001, 2004, 2007, 2010, 2013, 2016 and 2018 are analyzed based on green space quality and sensitivity using a geographic information system (GIS) and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. The results showed that the benchmark ecosystem service functions of haze absorption by green space when considering only the area of green space showed a trend that increases first and then decreases in 2001–2018, with 9000458.55 million Kg, 9145110.75 million Kg and 7734526.75 million Kg in 2001, 2013 and 2018, respectively. However, the corrected functions based on green space quality were 7724215.34 million Kg, 8320301.79 million Kg and 6510132.55 million Kg in the corresponding years. This indicated large differences between ecosystem service functions of haze absorption based on the quality and area of green space; only considering the area of green space to evaluate ecosystem service functions will result in overestimation. In terms of the spatial distribution of the ecosystem service functions of haze absorption by green space, there were greater differences in the benchmark and corrected functions, and the spatial distributions of the maximum, intermediate and minimum ecosystem service functions were notably different. However, the benchmark and corrected functions all showed a consistent trend in the rank of their contribution rates and ecosystem service functions as well as consistent distribution trends: the spatial distribution of ecosystem service functions of haze absorption by green space was very different in the same year, but there was little difference among different years. The change coefficients for the ecosystem service functions of haze absorption by arable land and grass land remained stable, whereas the coefficient of sensitivity for forest cover was elastic. Patch density (PD) and the ecosystem service functions of SO2 absorption, NOx absorption, dust retention and total ecosystem services showed a significant negative correlation, with correlation coefficients of -0.407, -0.511, -.330 and -0.332, respectively. In contrast, the area-weighted mean shape index (SAPE_AM) and ecosystem service functions exhibited significant positive relationships with correlation coefficients of 0.650, 0.634, 0.568 and 0.570, respectively. The results provide an improved method for evaluating the ecosystem service functions of haze absorption by green space as well as a reference for the prevention and control of haze and the coordinated development of regional societies, the economy and the environment.

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Section: Evaluation and Sensitivity Analysis Of The Ecosystem Servicementioning

confidence: 99%

Evaluation and sensitivity analysis of the ecosystem service functions of haze absorption by green space based on its quality in China

Ping¹,

Wang²,

Yang³

et al. 2020

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“…Recently, studies on how to optimize the landscape pattern of urban green spaces to reduce air pollution concentrations at the macro-level have increasingly garnered considerable attention, and the current research results primarily focused on exploring the correlation between the concentrations of PM 2.5 , PM 10 , and other pollutants, including land use or the landscape patterns of land cover. Ye et al [ 16 ] explored the relationship between PM 2.5 growth and land use changes in China from 1998–2015 and inferred that PM 2.5 concentrations were higher in the eastern plains and Taklamakan Desert in China, and higher PM 2.5 concentrations existed on artificial land surfaces, croplands, and deserts, while forests, grasslands, and unused land usually contained lower PM 2.5 concentrations. Simultaneously, the average annual increase in PM 2.5 concentrations in a desert land and artificial land surfaces was higher than that of other land types.…”

Section: Introductionmentioning

confidence: 99%

Does the Spatial Pattern of Plants and Green Space Affect Air Pollutant Concentrations? Evidence from 37 Garden Cities in China

Wang

Guo

Jin

et al. 2022

Plants

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Relevant studies have demonstrated that urban green spaces composed of various types of plants are able to alleviate the morbidity and mortality of respiratory diseases, by reducing air pollution levels. In order to explore the relationship between the spatial pattern of urban green spaces and air pollutant concentrations, this study takes 37 garden cities with subtropical monsoon climate in China as the research object and selects the urban air quality monitoring data and land use type data in 2019 to analyze the relationship between the spatial pattern and the air pollutant concentration through the landscape metrics model and spatial regression model. Moreover, the threshold effect of the impact of green space on air pollutant concentrations is estimated, as well. The results showed that the spatial pattern of urban green space was significantly correlated with the concentrations of PM2.5 (PM with aerodynamic diameters of 2.5 mmor less), NO2 (Nitrogen Dioxide), and SO2 (Sulfur dioxide) pollutants in the air, while the concentrations of PM10 (PM with aerodynamic diameters of 10 mmor less) pollutants were not significantly affected by the green space pattern. Among them, the patch shape index (LSI), patch density (PD) and patch proportion in landscape area (PLAND) of forest land can affect the concentration of PM2.5, NO2, and SO2, respectively. The PLAND, PD, and LSI of grassland and farmland can also have an additional impact on the concentration of SO2 pollutants. The study also found that there was a significant threshold effect within the impact mechanism of urban green space landscape pattern indicators (LSI, PD, PLAND) on the concentrations of PM2.5, NO2, and SO2 air pollutants. The results of this study not only clarified the impact mechanism of the spatial pattern of urban green space on air pollutant concentrations but also provided quantitative reference and scientific basis for the optimization and updating of urban green space to promote public health.

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“…In addition, although other studies have focused on cities in China, Germany, and the US, to our knowledge, no study has examined the association of air pollution with urban characteristics such as city size, growth rate and built environment features in LAC ( Wu et al, 2015 ; McCarty and Kaza, 2015 ; Liu et al, 2018 ). These characteristics of the urban environment have been shown to be particularly helpful in explaining pollution ( Ye et al, 2016 ), particularly in contexts where air quality data are limited or non-existent ( Weber et al, 2014 ). Thus, understanding the relation between policy-amenable features of cities and air pollution levels in urban areas can be critical to the development and promotion of urban policies to protect both population health and the environment.…”

Section: Introductionmentioning

confidence: 99%

Ambient fine particulate matter in Latin American cities: Levels, population exposure, and associated urban factors

Kephart¹,

Dronova

McClure

et al. 2021

Science of The Total Environment

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Background Exposure to particulate matter (PM 2.5 ) is a major risk factor for morbidity and mortality. Yet few studies have examined patterns of population exposure and investigated the predictors of PM 2.5 across the rapidly growing cities in lower- and middle-income countries. Objectives Characterize PM 2.5 levels, describe patterns of population exposure, and investigate urban factors as predictors of PM 2.5 levels. Methods We used data from the Salud Urbana en America Latina/Urban Health in Latin America (SALURBAL) study, a multi-country assessment of the determinants of urban health in Latin America, to characterize PM 2.5 levels in 366 cities comprising over 100,000 residents using satellite-derived estimates. Factors related to urban form and transportation were explored. Results We found that about 172 million or 58% of the population studied lived in areas with air pollution levels above the defined WHO-AQG of 10 μg/m 3 annual average. We also found that larger cities, cities with higher GDP, higher motorization rate and higher congestion tended to have higher PM 2.5 . In contrast cities with higher population density had lower levels of PM 2.5 . In addition, at the sub-city level, higher intersection density was associated with higher PM 2.5 and more green space was associated with lower PM 2.5 . When all exposures were examined adjusted for each other, higher city per capita GDP and higher sub-city intersection density remained associated with higher PM 2.5 levels, while higher city population density remained associated with lower levels. The presence of mass transit was also associated with lower PM 2.5 after adjustment. The motorization rate also remained associated with PM 2.5 and its inclusion attenuated the effect of population density. Discussion These results show that PM 2.5 exposures remain a major health risk in Latin American cities and suggest that urban planning and transportation policies could have a major impact on ambient levels.

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Exploring the effects of landscape structure on aerosol optical depth (AOD) patterns using GIS and HJ-1B images

Cited by 7 publications

References 43 publications

Evaluation and sensitivity analysis of the ecosystem service functions of haze absorption by green space based on its quality in China

Evaluation and sensitivity analysis of the ecosystem service functions of haze absorption by green space based on its quality in China

Does the Spatial Pattern of Plants and Green Space Affect Air Pollutant Concentrations? Evidence from 37 Garden Cities in China

Ambient fine particulate matter in Latin American cities: Levels, population exposure, and associated urban factors

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