Properties of particulate matter and gaseous pollutants in Shandong, China: Daily fluctuation, influencing factors, and spatiotemporal distribution

Yao, Youru; He, Cheng; Li, Shiyin; Ma, Wei; Shu, Li; Yu, Qi; Mi, Na; Yu, Jia; Wang, Wei; Yin, Li; Yong, Zhang

doi:10.1016/j.scitotenv.2019.01.026

Cited by 56 publications

(22 citation statements)

References 49 publications

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“…2B) are Inner Mongolia, Yunnan, Shandong, Hebei, Guangdong, Jiangsu, Guangxi, Hunan, and Jilin. Inner Mongolia (39,40) is famous for its mining industry, desertification, and fragile ecological conditions; Yunnan boasts the highest species richness but suffers from geo-disasters and soil erosion brought about by climate change; and Shandong is a traditional heavy industry province and GHG emissions producer (41), which makes it vulnerable to climate change.…”

Section: Relationships Between Biodiversity and Climate Change-relatementioning

confidence: 99%

Spatial variation in biodiversity loss across China under multiple environmental stressors

et al. 2020

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Biodiversity is essential for the maintenance of ecosystem health and delivery of the Sustainable Development Goals. However, the drivers of biodiversity loss and the spatial variation in their impacts are poorly understood. Here, we explore the spatial-temporal distributions of threatened and declining (“biodiversity-loss”) species and find that these species are affected by multiple stressors, with climate and human activities being the fundamental shaping forces. There has been large spatial variation in the distribution of threatened species over China’s provinces, with the biodiversity of Gansu, Guangdong, Hainan, and Shaanxi provinces severely reduced. With increasing urbanization and industrialization, the expansion of construction and worsening pollution has led to habitat retreat or degradation, and high proportions of amphibians, mammals, and reptiles are threatened. Because distributions of species and stressors vary widely across different climate zones and geographical areas, specific policies and measures are needed for preventing biodiversity loss in different regions.

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Section: Relationships Between Biodiversity and Climate Change-relatementioning

confidence: 99%

Spatial variation in biodiversity loss across China under multiple environmental stressors

et al. 2020

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“…The air quality data used in this study were from routine monitoring data of national and provincial environmental air quality monitoring stations (a total of 154; Figure 1), which are all located in urban areas (built-up area severely affected by humans) and suburban areas (non-built area that is less affected by humans and can be regarded as background points partly) [17]. Meteorological data were obtained from the National Meteorological Information Center (http://data.cma.cn/site/index.html).…”

Section: Data Collectionmentioning

confidence: 99%

“…With a monsoon season and warm temperate climate of middle latitudes, precipitation and heat in Shandong province are concentrated in summer, and their seasonal variations are simultaneous. The prevailing wind directions are southeast in spring and summer, and northwest in autumn and winter [17,18]. As of 2017, the total population of the province exceeds 100 million and the population density was as high as 634 people per square kilometer.…”

mentioning

confidence: 99%

“…With several types of industries, the GDP of Shandong is the third highest in China. Shandong province is generally located in a relatively closed geographical unit surrounded by Yanshan, Taihang, and Dabie mountain, which is not conducive to the diffusion of air pollutants, therefore this region is one of the most heavily polluted areas in China [17]. The main air pollutants in the region are particulate matter (mainly in winter and spring) and ground-level ozone (mainly in summer).…”

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confidence: 99%

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Characteristics of Ozone Pollution, Regional Distribution and Causes during 2014–2018 in Shandong Province, East China

Zhang

Wang

Qu³

et al. 2019

Atmosphere

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The summer ozone pollution of Shandong province has become a severe problem in the period 2014–2018. Affected by the monsoon climate, the monthly average ozone concentrations in most areas were unimodal, with peaks in June, whereas in coastal areas the concentrations were bimodal, with the highest peak in May and the second highest peak in September. Using the empirical orthogonal function method, three main spatial distribution patterns were found. The most important pattern proved the influences of solar radiation, temperature, and industrial structure on ozone. Spatial clustering analysis of the ozone concentration showed Shandong divided into five units, including Peninsula Coastal area (PC), Lunan inland area (LN), Western Bohai area (WB), Luxi plain area (LX), and Luzhong mountain area (LZ). Influenced by air temperature and local circulation, coastal cities had lower daytime and higher nighttime ozone concentrations than inland. Correlation analysis suggested that ozone concentrations were significantly positively correlated with solar radiation. The VOCs from industries or other sources (e.g., traffic emission, petroleum processing, and chemical industries) had high positive correlations with ozone concentrations, whereas NOx emissions had significantly negatively correlation. This study provides a comprehensive understanding of ozone pollution and theoretical reference for regional management of ozone pollution in Shandong province.

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“…In particular, coal consumption for power generation accounted for 35% of the total coal consumption [52]. Numerous studies have shown that fossil fuel burning is closely related to primary particulate matter emissions and secondary aerosol generation [30,[53][54][55]. The high energy consumption industry-intensive structure resulted in the heavy dependence of Shandong Province industry on energy.…”

Section: Study Areamentioning

confidence: 99%

Investigating the Influence of the Implementation of an Energy Development Plan on Air Quality Using WRF-CAMx Modeling Tools: A Case Study of Shandong Province in China

Luo

Chen

et al. 2019

Atmosphere

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In this paper, the Weather Research Forecast (WRF) Comprehensive Air Quality Model with Extensions (CAMx) modeling system with the particulate source apportionment technology (PSAT) module was used to study and analyze the spatial and temporal distribution of atmospheric pollutant concentrations and the source apportionment of fine particles (PM2.5) under the base year and an emission reduction scenario in the Shandong province, China. Our results show that industry is the largest contributor of PM2.5. In addition, the contribution of key energy-related industries was as high as 29.5%, with the thermal power industry being the largest individual contributor. In January, the largest contribution came from residents, reaching 41.3%. Moreover, loose coal burning in rural areas contributed up to 19.4% in winter. Our results also show that the emission reduction scenario had palpable effects on the reduction of air pollution. The more the emissions of SO2, NOx, PM2.5, and PM10 were reduced, the more the average concentration was decreased. The implementation of energy conservation and emission reduction policies by industry and resident is conducive to improving the quality of the atmospheric environment. In particular, a comprehensive control of loose coal burning in winter could significantly improve heavy pollution by particulate matter in winter.

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Properties of particulate matter and gaseous pollutants in Shandong, China: Daily fluctuation, influencing factors, and spatiotemporal distribution

Cited by 56 publications

References 49 publications

Spatial variation in biodiversity loss across China under multiple environmental stressors

Spatial variation in biodiversity loss across China under multiple environmental stressors

Characteristics of Ozone Pollution, Regional Distribution and Causes during 2014–2018 in Shandong Province, East China

Investigating the Influence of the Implementation of an Energy Development Plan on Air Quality Using WRF-CAMx Modeling Tools: A Case Study of Shandong Province in China

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