Impact of Energy Consumption on Air Quality in Jiangsu Province of China

He, Ling‐Yun; Zhong, Zhong; Yin, Fang; Wang, Deqing

doi:10.3390/su10010094

Cited by 14 publications

(10 citation statements)

References 44 publications

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“…Secondly, studies performed at the regional/provincial level were all resourceful, including those on Northwest China [20], Yunnan [21], Guangdong [22][23][24], Jiangsu [25,26], Shanghai [27], Chongqing [28,29], and Xinjiang [30]. The factors found to have the greatest effect on carbon emissions were economic activity [20], investment [21], energy density [21,23,24,26], economic growth [23,24], export [21], population scale [24], energy efficiency [20], technical progress [24], industrial structure [22,23,26], and energy Sustainability 2019, 11,7008 3 of 20 mix [24,26]. For example, Dong et al [21] analyzed the energy-related carbon emissions of five provinces in Northwest China from 1995 to 2012 (Shaanxi, Ningxia, Qinghai, Gansu, and Xinjiang) and found that the effect of economic activity produced a significant impact on increased carbon emission.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of Energy-Related Carbon Emissions in Inner Mongolia, China

Tseng

2019

Sustainability

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Inner Mongolia has shown both rapid economic growth and a large renewable energy base, this has come about by the introduction of the "Western Development" strategy and renewable energy policy of the Chinese Government. However, this has led to a contradictory situation where both high carbon emission and reduction exist together. The average economic growth of Inner Mongolia reached 15.76% between 2006 and 2016, which caused huge CO 2 emissions. However, promotion of the renewable energy policy (since 2005) resulted in an energy self-sufficiency rate that reached 270.80% by 2016. In this study of the Inner Mongolia carbon emission situation, the logarithmic mean divisia index (LMDI) model was used to analyze the factors affecting carbon emission fluctuations from 2005 to 2016. The decoupling elasticity index was then used to measure the decoupling effect of the economic growth and carbon emissions. The results of this research show that: firstly, CO 2 emissions increased rapidly from 651.03 million tons in 2006 to 1723.24 million tons in 2013. Despite a slight decline in CO 2 emissions, a level above 1600 million tons was maintained between 2014 and 2016. Secondly, the industry sector was the main source of CO 2 emissions in Inner Mongolia, and coal-based fuel played a determining role. Thirdly, in this study, two important contributions were made, including the discovery of two new drivers: labor and emission intensity factors. Further, findings about the effect of the six industrial sectors, economic structure, energy density, and emission intensity factors were also decomposed. It was found that during research period, the population factor, labor factor, and labor productivity factor all had a positive influence on CO 2 emissions, whereas the economic structure factor and emission intensity factor had different impacts on the CO 2 emissions depending on the particular industrial sector. Furthermore, the energy intensity of six industrial sectors contributed to the decrease in aggregate CO 2 emissions. Finally, in this study, it was also found that economic growth and CO 2 growth in Inner Mongolia presented a weak decoupling state. Policy recommendations based on these results have been presented.

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Section: Literature Reviewmentioning

confidence: 99%

Analysis of Energy-Related Carbon Emissions in Inner Mongolia, China

Tseng

2019

Sustainability

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“…In 2016, Yale University published the Environmental Performance Index (EPI) report, which ranked China as of last in the world, just before Bangladesh, based on air quality assessment [52,53]. Jiangsu Province is an economically developed province in eastern China, having dense metropolises and large rural areas, with urban constructions, high traffic volumes, industrial production, and crop residue burning, which all add significant amounts of pollutants into the atmosphere, resulting in noticeable air pollution in the province [1].…”

Section: Introductionmentioning

confidence: 99%

Identification of NO2 and SO2 Pollution Hotspots and Sources in Jiangsu Province of China

et al. 2021

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Nitrogen dioxide (NO2) and sulfur dioxide (SO2) are important atmospheric trace gases for determining air quality, human health, climate change, and ecological conditions both regionally and globally. In this study, the Ozone Monitoring Instrument (OMI), total column nitrogen dioxide (NO2), and sulfur dioxide (SO2) were used from 2005 to 2020 to identify pollution hotspots and potential source areas responsible for air pollution in Jiangsu Province. The study investigated the spatiotemporal distribution and variability of NO2 and SO2, the SO2/NO2 ratio, and their trends, and potential source contribution function (PSCF) analysis was performed to identify potential source areas. The spatial distributions showed higher values (>0.60 DU) of annual mean NO2 and SO2 for most cities of Jiangsu Province except for Yancheng City (<0.50 DU). The seasonal analyses showed the highest NO2 and SO2 in winter, followed by spring, autumn, and summer. Coal-fire-based room heating and stable meteorological conditions during the cold season may cause higher NO2 and SO2 in winter. Notably, the occurrence frequency of NO2 and SO2 of >1.2 was highest in winter, which varied between 9.14~32.46% for NO2 and 7.84~21.67% for SO2, indicating a high level of pollution across Jiangsu Province. The high SO2/NO2 ratio (>0.60) indicated that industry is the dominant source, with significant annual and seasonal variations. Trends in NO2 and SO2 were calculated for 2005–2020, 2006–2010 (when China introduced strict air pollution control policies during the 11th Five Year Plan (FYP)), 2011–2015 (during the 12th FYP), and 2013–2017 (the Action Plan of Air Pollution Prevention and Control (APPC-AC)). Annually, decreasing trends in NO2 were more prominent during the 12th FYP period (2011–2015: −0.024~−0.052 DU/year) than in the APPC-AC period (2013–2017: −0.007~−0.043 DU/year) and 2005–2020 (−0.002 to −0.012 DU/year). However, no prevention and control policies for NO2 were included during the 11th FYP period (2006–2010), resulting in an increasing trend in NO2 (0.015 to 0.031) observed throughout the study area. Furthermore, the implementation of China’s strict air pollution control policies caused a larger decrease in SO2 (per year) during the 12th FYP period (−0.002~−0.075 DU/year) than in the 11th FYP period (−0.014~−0.071 DU/year), the APPC-AC period (−0.007~−0.043 DU/year), and 2005–2020 (−0.015~−0.032 DU/year). PSCF analysis indicated that the air quality of Jiangsu Province is mainly influenced by local pollution sources.

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“…Recently, aerosol products have been widely evaluated over land, including the American continents [54,55] and South Asia [56], using the MODIS MAIAC algorithm. Although a few validation studies have also been conducted over China [57][58][59], the performance of the MAIAC algorithm is still unclear in China due to high levels of aerosol loading, and heterogeneity. Due to China's rapid social and economic development in recent years, the country is in line with serious aerosol pollution.…”

Section: Introductionmentioning

confidence: 99%

Identification of Aerosol Pollution Hotspots in Jiangsu Province of China

et al. 2021

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Aerosol optical depth (AOD) is an important atmospheric parameter for climate change assessment, human health, and for total ecological situation studies both regionally and globally. This study used 21-year (2000–2020) high-resolution (1 km) Multiangle Implementation of Atmospheric Correction (MAIAC) algorithm-based AOD from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the Terra and Aqua satellites. MAIAC AOD was evaluated against Aerosol Robotic Network (AERONET) data across three sites (Xuzhou-CUMT, NUIST, and Taihu) located in Jiangsu Province. The study also investigated the spatiotemporal distributions and variations in AOD, with associated trends, and measured the impact of meteorology on AOD in the 13 cities of Jiangsu Province. The evaluation results demonstrated a high correlation (r = 0.867~0.929) between MAIAC AOD and AERONET data, with lower root mean squared error (RMSE = 0.130~0.287) and mean absolute error (MAE = 0.091~0.198). In addition, the spatial distribution of AOD was higher (>0.60) in most cities except the southeast of Nantong City (AOD < 0.4). Seasonally, higher AOD was seen in summer (>0.70) than in spring, autumn, and winter, whereas monthly AOD peaked in June (>0.9) and had a minimum in December (<0.4) for all the cities. Frequencies of 0.3 ≤ AOD < 0.4 and 0.4 ≤ AOD < 0.5 were relatively common, indicating a turbid atmosphere, which may be associated with anthropogenic activities, increased emissions, and changes in meteorological circumstances. Trend analysis showed significant increases in AOD during 2000–2009 for all the cities, perhaps reflecting a booming economy and industrial development, with significant emissions of sulfur dioxide (SO2), and primary aerosols. China’s strict air pollution control policies and control of vehicular emissions helped to decrease AOD from 2010 to 2019, enhancing air quality throughout the study area. A notably similar pattern was observed for AOD and meteorological parameters (LST: land surface temperature, WV: water vapor, and P: precipitation), signifying that meteorology plays a role in terms of increasing and decreasing AOD.

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Impact of Energy Consumption on Air Quality in Jiangsu Province of China

Cited by 14 publications

References 44 publications

Analysis of Energy-Related Carbon Emissions in Inner Mongolia, China

Analysis of Energy-Related Carbon Emissions in Inner Mongolia, China

Identification of NO2 and SO2 Pollution Hotspots and Sources in Jiangsu Province of China

Identification of Aerosol Pollution Hotspots in Jiangsu Province of China

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