Comparison of remotely sensed PM2.5 concentrations between developed and developing countries: Results from the US, Europe, China, and India

Cao, Shisong; Zhao, Wenji; Guan, Hao; Hu, Deyong; Mo, You; Zhao, Wei; Li, Shanshan

doi:10.1016/j.jclepro.2018.02.096

Cited by 38 publications

(10 citation statements)

References 19 publications

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“…Biomass energy use is key to economic growth and reducing imports. Shisong et al (2018) employed a quantile regression panel approach to estimate CO2 emissions. According to their analysis, nonrenewable energy reduces CO2 emissions the greatest.…”

Section: Literature Reviewmentioning

confidence: 99%

Electricity Production Sources and CO2 Emission in OECD countries: Static and Dynamic Panel Analysis

Voumik¹,

Islam²,

Raihan³

2022

GSSR

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Industrialization, urbanization, population growth, and changes in lifestyle have all contributed to a rise in the OECD countries' risk of global warming. The amount of carbon dioxide (CO2) generated from heat and power sources put out is directly related to how much electricity they make. Finding out which sources are bad for the environment, and which are not is the primary motivation behind this study. The impact of different approaches to energy production on carbon dioxide emissions is analyzed using OECD data. The data is analyzed using Quantile Regression (QR), Generalized Method of Moments (GMM), and Pooled Ordinary Least Squares (OLS). The study found that CO2 emissions were significantly impacted in a positive direction when electricity was generated using coal, oil, or gas. The emissions from coal-fired power plants are the most detrimental. The generation of hydroelectricity and other forms of renewable energy can reduce CO2 emissions in all regression models. The most compelling evidence of a correlation between CO2 emissions and energy sources was uncovered in this study. In order to produce credible findings, the paper used both QR and GMM methods. Important implications for environmental policy are drawn from this article's findings. Both are required to lessen our reliance on fossil fuels and promote the development of renewable energy sources like solar, wind, and hydroelectricity.

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Section: Literature Reviewmentioning

confidence: 99%

Electricity Production Sources and CO2 Emission in OECD countries: Static and Dynamic Panel Analysis

Voumik¹,

Islam²,

Raihan³

2022

GSSR

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“…and have revealed a significant decline in aerosol loading over several major industrial regions due to anthropogenic emission controls (Cao et al, 2018;Chin et al, 2014;Yu et al, 2020;Zhai et al, 2019). The East Coast of the United States (ECUS) (Figure 1a), located in the outflow regions of Eastern United States, has been found to experience significant declines in cloud droplet number concentrations (N d ) in recent decades, in response to decline in aerosol loading (Figure 1d and 1e) (Bai et al, 2020;Bennartz & Rausch, 2017;Cherian & Quaas, 2020;Li et al, 2018).…”

mentioning

confidence: 99%

Strong Aerosol Effects on Cloud Amount Based on Long‐Term Satellite Observations Over the East Coast of the United States

Yang

Wang

Zhu

et al. 2021

Geophysical Research Letters

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The radiative forcing associated with aerosol-cloud interactions, traditionally referred to as aerosol indirect effects, indirectly by modifying the microphysical properties of clouds, affecting their reflectivity and persistence, contributes the largest uncertainty to total radiative forcing estimates (Boucher et al., 2013). For liquid clouds, reducing droplet size and increasing reflectance of clouds due to increased droplet number for a constant liquid water path, namely the "Twomey" effect (Twomey, 1977), is relatively well understood (Christensen et al., 2020; Diamond et al., 2020; Liu & Li, 2019). However, aerosol effects on the amount of boundary layer clouds that cover large areas of the oceans and strongly reflect incoming solar radiation are still not well-documented (Bellouin et al., 2020), especially the magnitude of the aerosol influence on cloud fraction (CF) (Ghan et al., 2016; Gryspeerdt et al., 2016). Understanding how aerosols affect cloud cover helps to reduce the considerable uncertainty of the radiative forcing associated with aerosol-cloud interactions (Fan et al., 2016) because of the strong correlation of CF to other cloud properties and their large impact on radiation. The long-term satellite observations provide excellent opportunities for quantifying cloud-mediated aerosol radiative effects (

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“…Based on previous research, uncertainty may exist in the global PM 2.5 data as a result of the satellite retrieval method (van Donkelaar et al, 2015). Existing studies have resolved this by applying a three-year average as an annual average (Han, Zhou and Li, 2015;Peng et al, 2016;Shisong et al, 2018). For this research, three-year moving averages were applied to the satellite retrievals from the period 2009 to 2016.…”

Section: Satellite-derived Datamentioning

confidence: 99%

Evaluating the potential of remote sensing imagery in mapping ground-level fine particulate matter (PM2.5) for the Vaal Triangle Priority Area

2020

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The quality of air breathed in South Africa is of great concern, especially in industrialised regions where PM2.5 concentrations are high. Long term exposure to PM2.5 is associated with serious adverse health impacts. Traditionally, PM2.5 is monitored by a network of ground-based instruments. However, the coverage of monitoring networks in South Africa is not dense enough to fully capture the spatial variability of PM2.5 concentrations. This study explored whether satellite remote sensing could offer a viable alternative to ground-based monitoring. Using an eight-year record (2009 to 2016) of satellite retrievals (MODIS, MISR and SeaWIFS) for PM2.5 concentrations, spatial variations and temporal trends for PM2.5 are evaluated for the Vaal Triangle Airshed Priority Area (VTAPA). Results are compared to corresponding measurements from the VTAPA surface monitoring stations. High PM2.5 concentrations were clustered around the centre and towards the south-west of the VTAPA over the highly industrialised cities of Vanderbijlpark and Sasolburg. Satellite retrievals tended to overestimate PM2.5 concentrations. Overall, there was a poor spatial agreement between satellite-retrieved PM2.5 estimates and ground-level PM2.5 measurements. Root mean square error values ranged from 6 to 11 µg/m3 and from -0.89 to 0.32 for the correlation coefficient. For satellite remote sensing to be effectively exploited for air quality assessments in the VTAPA and elsewhere, further research to improve the precision and accuracy of satellite-retrieved PM2.5 is required.

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Comparison of remotely sensed PM2.5 concentrations between developed and developing countries: Results from the US, Europe, China, and India

Cited by 38 publications

References 19 publications

Electricity Production Sources and CO2 Emission in OECD countries: Static and Dynamic Panel Analysis

Electricity Production Sources and CO2 Emission in OECD countries: Static and Dynamic Panel Analysis

Strong Aerosol Effects on Cloud Amount Based on Long‐Term Satellite Observations Over the East Coast of the United States

Evaluating the potential of remote sensing imagery in mapping ground-level fine particulate matter (PM2.5) for the Vaal Triangle Priority Area

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