Spatial patterns and temporal variations of six criteria air pollutants during 2015 to 2017 in the city clusters of Sichuan Basin, China

Zhao, Suping; Yu, Ye; Yin, Daiying; Qin, Dahe; He, Jianjun; Dong, Lijun

doi:10.1016/j.scitotenv.2017.12.172

Cited by 177 publications

(76 citation statements)

References 51 publications

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“…According to regression model analysis (RMA) for seasonal and monthly variations, all ambient air pollutants showed statistically remarkable seasonal and monthly variations ( Figure S1(a,b), Figure 2 www.nature.com/scientificreports www.nature.com/scientificreports/ ppb), when SR ( Figure S2a), temperature ( Figure S2a), hydroxyl radical as the most important oxidant species for the formation of O 3 , VOCs and photochemical reactions are higher 26,27 . Observed seasonal and monthly patterns for PM 2.5 , NO 2 , SO 2 and CO can be attributed to a combination of unfavorable meteorological conditions, including stagnant weather, reduced horizontal and vertical WS, higher nebulosity, reduced sunshine time, lower SR, temperature inversion and lower the boundary layer during the coldest seasons and months compared to summer and spring months ( Figure S2) [28][29][30][31] . In relation to PM 10 , exactly similar to Ahvaz city, Middle East dust storm was responsible for the peak concentration of PM 10 during summer in Tehran 11,[32][33][34][35] .…”

Section: Seasonal and Monthly Patterns Of Ambient Air Pollutants To mentioning

confidence: 99%

Temporal variations of ambient air pollutants and meteorological influences on their concentrations in Tehran during 2012–2017

Yousefian

Faridi

Azimi

et al. 2020

Sci Rep

128

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We investigated temporal variations of ambient air pollutants and the influences of meteorological parameters on their concentrations using a robust method; convergent cross mapping; in Tehran (2012-2017). Tehran citizens were consistently exposed to annual PM 2.5 , PM 10 and no 2 approximately 3.0-4.5, 3.5-4.5 and 1.5-2.5 times higher than the World Health Organization air quality guideline levels during the period. Except for O 3 , all air pollutants demonstrated the lowest and highest concentrations in summertime and wintertime, respectively. The highest O 3 concentrations were found on weekend (weekend effect), whereas other ambient air pollutants had statistically significant (P < 0.05) daily variations in which higher concentrations were observed on weekdays compared to weekend (holiday effect). Hourly O 3 concentration reached its peak at 3.00 p.m., though other air pollutants displayed two peaks; morning and late night. Approximately 45% to 65% of AQI values were in the subcategory of unhealthy for sensitive groups and PM 2.5 was the responsible air pollutant in Tehran. Amongst meteorological factors, temperature was the key influencing factor for PM 2.5 and PM 10 concentrations, while nebulosity and solar radiation exerted major influences on ambient SO 2 and o 3 concentrations. Additionally, there is a moderate coupling between wind speed and NO 2 and CO concentrations.

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Section: Seasonal and Monthly Patterns Of Ambient Air Pollutants To mentioning

confidence: 99%

Temporal variations of ambient air pollutants and meteorological influences on their concentrations in Tehran during 2012–2017

Yousefian

Faridi

Azimi

et al. 2020

Sci Rep

128

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“…Anthropogenic activities associated with rapidly developed industrialization and urbanization have been leading to a sustained increase in the amounts of atmospheric pollutants, especially in the fast-developing countries (IPCC, 2013). As one of the largest emission sources of aerosols and their precursors, China has been suffering from serious air pollution for years (Lei et al, 2011;Li et al, 2011), with severe haze events frequently occurring in winter, especially over large urban agglomerations, such as the North China Plain (NCP) (Han et al, 2014;Gao et al, 2015), the Yangtze River Delta area (YRD) (Ding et al, 2016;Wang et al, 2016a), the Pearl River Delta area (PRD) (Fan et al, 2015;Liu et al, 2018b), and the Sichuan Basin (SCB) (Zhao et al, 2018;Zhang et al, 2019). During severe haze events, the observed maximum hourly surface-layer PM 2.5 (fine particulate matter with aerodynamic diameter of 2.5 µm or less) concentration exceeded 1000 µg m -3 (Wang et al, 2013b;Sun et al, 2016;Li et al, 2017a), which could significantly influence visibility (Li et al, 2014), radiation budget (Steiner et al, 2013), atmospheric circulation (Jiang et al, 2017), cloud properties (Unger et al, 2009), and even human health (Guo et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Assessing the formation and evolution mechanisms of severe haze pollution in Beijing−Tianjin−Hebei region by using process analysis

Chen¹,

Zhu²,

Liu³

et al. 2019

Preprint

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Abstract. Fine&#8211;particle pollution associated with haze threatens human health, especially in the North China Plain, where extremely high PM2.5 concentrations were frequently observed during winter. In this study, the WRF&#8211;Chem model coupled with an improved integrated process analysis scheme was used to investigate the formation and evolution mechanisms of a haze event happened over Beijing&#8211;Tianjin&#8211;Hebei (BTH) in December 2015, including examining the contributions of local emission and outside transport to the absolute PM2.5 concentration in BTH, and the contributions of each detailed physical or chemical process to the variations in the PM2.5 concentration. The influence mechanisms of aerosol radiative forcing (including aerosol direct and indirect effects) were also examined by using the process analysis. During the aerosol accumulation stage (December 20&#8211;22, Stage_1), the average near&#8211;surface PM2.5 concentration in BTH was 250.0&#8201;&#181;g&#8201;m&#8722;3, which was contributed by local emission of 42.3&#8201;% and outside transport of 36.6&#8201;%. During the aerosol dispersion stage (December 23&#8211;27, Stage_2), the average concentration of PM2.5 was 107.9&#8201;&#181;g&#8201;m&#8722;3. The contribution of local emission increased to 50.9&#8201;%, while the contribution of outside transport decreased to 24.3&#8201;%. The 24&#8211;h change (23:00LST minus 00:00LST) in the near&#8211;surface PM2.5 concentration was +50.4&#8201;&#181;g&#8201;m&#8722;3 during Stage_1 and &#8722;41.5&#8201;&#181;g&#8201;m&#8722;3 during Stage_2. Contributions of aerosol chemistry process and vertical mixing process to the 24&#8211;h change were +43.8 (+17.9) &#181;g&#8201;m&#8722;3 and &#8722;161.6 (&#8722;221.6) &#181;g&#8201;m&#8722;3 for Stage_1 (Stage_2), respectively. Small differences in contributions from other processes were found between Stage_1 and Stage_2, such as advection process, cloud chemistry process, and so on. Therefore, the PM2.5 increase over BTH during haze formation stage (Stage_1) was mainly attributed to strong production by aerosol chemistry process and weak removal by vertical mixing process. When aerosol radiative feedback was considered, the 24&#8211;h PM2.5 increase was enhanced by 9.6 &#181;g&#8201;m&#8722;3 during Stage_1, which could be mainly attributed to the contributions of vertical mixing process (+39.8&#8201;&#181;g&#8201;m&#8722;3), advection process (&#8722;38.6&#8201;&#181;g&#8201;m&#8722;3) and aerosol chemistry process (+5.1&#8201;&#181;g&#8201;m&#8722;3). The restrained vertical mixing could be the primary reason for the enhancement in near&#8211;surface PM2.5 increase when aerosol radiative forcing was considered.

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“…Thus, future studies are suggested to include monitoring NO concentrations. SO2 does not directly involve O3 formation, but it can act as an indicator of industrial emissions contributing to O3 formation [41]. The positive correlations between O3 and PM2.5, as well as PM10, suggested the simultaneous formation of secondary O3 and PM by photochemical reactions in summer [42].…”

Section: Effects Of Air Pollutants and Meteorological Parameters On Mmentioning

confidence: 98%

Characteristics of Surface Ozone in Five Provincial Capital Cities of China during 2014–2015

et al. 2020

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Ozone (O3) pollution has become an increasing concern in China since elevated surface O3 concentrations were observed in recent years. In this study, five provincial cities (Beijing, Shanghai, Guangzhou, Xi’an, and Hefei) located in different regions of China were selected to study the spatiotemporal variations and affecting factors of O3 concentrations during 2014–2015. Beijing, Shanghai, and Guangzhou had suffered more severe O3 pollution, yet Beijing had the highest number of days that exceeded the Chinese MDA8 (maximum daily 8 h average) standard of 160 µg m−3. MDA8 O3 exhibited different seasonal patterns among the five cities. In Beijing and Xi’an, MDA8 O3 showed the highest in summer and lowest in winter. Guangzhou also had the highest O3 concentration in summer, but had similar levels in other three seasons. The O3 levels were similarly high in Shanghai during spring, summer, and autumn, while in Hefei, O3 concentration peaked in autumn. No significant difference between weekend and weekday O3 levels was observed in all the five cities. The diurnal cycle reached a maximum in the afternoon and a minimum in the early morning, which was consistent in the five cities. Correlation analyses showed that the associations between O3 and the other five criteria air pollutants, as well as meteorological parameters, were substantially different among the five cities. Air mass cluster analyses during episodic days revealed that the short-distance transport of O3 and its precursors had a greater impact for high O3 pollution in the five cities. Overall, our results demonstrate that O3 pollution exhibited great divergence among different regions and thus region-oriented control measures are suggested to reduce O3 pollution in China.

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Spatial patterns and temporal variations of six criteria air pollutants during 2015 to 2017 in the city clusters of Sichuan Basin, China

Cited by 177 publications

References 51 publications

Temporal variations of ambient air pollutants and meteorological influences on their concentrations in Tehran during 2012–2017

Temporal variations of ambient air pollutants and meteorological influences on their concentrations in Tehran during 2012–2017

Assessing the formation and evolution mechanisms of severe haze pollution in Beijing−Tianjin−Hebei region by using process analysis

Characteristics of Surface Ozone in Five Provincial Capital Cities of China during 2014–2015

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