Characterizing multi-pollutant air pollution in China: Comparison of three air quality indices

Hu, Jianlin; Ying, Qi; Wang, Yungang; Zhang, Hongliang

doi:10.1016/j.envint.2015.06.014

Cited by 178 publications

(100 citation statements)

References 30 publications

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“…Fig. 6 (2) is commonly used (for example, see Hu et al (2015)), it doesn't consider the possible non-linearities in the exposure-response curve observed by some previous studies (Cohen et al, 2005;Pope et al, 2009;Burnett et al, 2014). Moreover, most of the health studies tend to depict the correlation between PM 2.5 concentrations and specific respiratory or heart diseases.…”

Section: Estimation Of Health Risk Associated With Pollutantsmentioning

confidence: 99%

Significance of PM2.5 Air Quality at the Indian Capital

Sahu

Kota

2017

Aerosol Air Qual. Res.

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In New Delhi, the capital city of India, concentrations of regulated air pollutants often exceed the Indian national ambient air quality standards (INAAQS). As the sources of these pollutants differ, it is of utmost priority to understand the most dangerous air pollutant to formulate better control strategies in the city. In this study, regulated air pollutant concentrations in New Delhi during 2011 to 2014 were collected. Compared to other pollutants, PM 2.5 concentrations exceeded the INAAQS quite often. While PM 2.5 exceeded INAAQS during 85% of the days, NO 2 , O 3 , CO and SO 2 exceeded only on 37, 14, 11 and 0% of the days, respectively. Using air quality index approach, the most dominant pollutant was identified as PM 2.5 , for 75 to 90% of the days. However, a seasonal variation in the percentage dominance of PM 2.5 was observed. For example, PM 2.5 was dominant during 95% of the winter and 68% of monsoon days. In addition to absolute concentrations, pollutants can also be ranked by studying their associated short term mortality impacts. However, such studies are rare in India. For the first time, the short term impact of PM 2.5 concentrations on non-disease specific mortality in New Delhi was assessed using Poisson regression models. Results indicated that the excessive risk associated with PM 2.5 estimated was 0.57, which was higher than the other regulated pollutants. This indicates a projected 6.2 and 6.5% decrease in mortality by meeting the PM 2.5 Indian standards and WHO set limits, respectively.

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Section: Estimation Of Health Risk Associated With Pollutantsmentioning

confidence: 99%

Significance of PM2.5 Air Quality at the Indian Capital

Sahu

Kota

2017

Aerosol Air Qual. Res.

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“…The regional combined pollution characterized by PM 2.5 in Asia is of increasing concern, particularly in China (Hu et al, 2015). High PM 2.5 concentrations are associated with severe haze events, which have occurred in most regions in Central and Eastern China since the beginning of 2013.…”

Section: Introductionmentioning

confidence: 99%

Emission Characteristics and Control Prospects of Primary PM2.5 from Fossil Fuel Power Plants in China

Wang

Cheng

Tian

et al. 2016

Aerosol Air Qual. Res.

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In this study, a unit-based approach was used to establish an integrated emission inventory of primary PM 2.5 from fossil fuel (coal, oil, and natural gas) power plants in China. The inventory was of high spatial and temporal resolution, and composed of detailed chemical speciation. In 2014, the total emissions were estimated to be approximately 669.53 kt. The emissions of primary PM 2.5 from coal-fired power plants (CFPs) were 668.56 kt, making CFPs the largest contributor. The emissions of primary PM 2.5 from oil-fired power plants (OFPs) and natural gas-fired power plants (GFPs) were approximately 17.41 t and 945.60 t, respectively. Spatial distribution features demonstrated that the emissions in the eastern and central provinces of China were much higher than those in the west, except for provinces involved in the "west-to-east power transmission" project. For CFPs, crustal elements and water-soluble inorganic ions were the primary species of PM 2.5 . By contrast, for OFPs and GFPs, carbonaceous components were the predominant species of PM 2.5 . Moreover, this study conducted a scenario analysis of changes in PM 2.5 emissions resulting from technical advancement, penetration, and substitution of CFPs by GFPs for the target years.

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“…Twelve years of daily monitoring data provided a effective data source for the analysis of air pollution level of major Chinese cities [9]. However, a series of studies suggested that PM2.5 in major northern Chinese cities had been an important issue, as although the air pollution index (API) under NAAQS-1996 suggested that air quality was fine, the atmospheric conditions were poor [10][11][12][13][14][15]. In February 2012, China then released new air quality standards (NAAQS-2012) by taking into account six air pollutants: PM2.5, PM10, SO2, O3, NO2 and CO [16].…”

Section: Introductionmentioning

confidence: 99%

Seasonal Variations of Atmospheric Pollution and Air Quality in Beijing

2015

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Abstract:New ambient air quality standards were released in 2012 and implemented in 2013 with real time monitoring data publication of six atmospheric pollutants: particulate matter (PM)2.5, PM10, O3, SO2, NO2 and CO. According to the new standards, Beijing began to publicize real-time monitoring data of 35 monitoring stations in 2013. In this study, real time concentrations of all six atmospheric pollutants of all 35 monitoring stations were collected from September 2014 to August 2015 to investigate the spatial and temporal pattern of the air quality and atmospheric pollutants. By comparing the annual and seasonal variations of all six pollutants' concentrations, it was found that particulate matter, especially PM2.5, is still the major contributor to the deterioration of air quality in Beijing. Although the NO2 and O3 concentrations of some stations were still high under certain circumstances, their contributions to air quality index (AQI) were not comparable to those of PM2.5 and PM10. SO2 and CO concentrations have dropped to well below the qualification standards. Winter and autumn were the most polluted seasons for all pollutants except O3, whose concentrations are higher in summer. South and southeast stations were the most polluted compared with the rest of the stations, especially for particulate matter. Wind profile analysis with heavy pollution situations indicates that low speed southwest or east wind situations have the higher possibility of heavy pollution, suggesting that it is highly possible that long-range transportation of air pollutants from south or east neighboring provinces played an important role in the worsening air conditions in Beijing. OPEN ACCESSAtmosphere 2015, 6 1754

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Characterizing multi-pollutant air pollution in China: Comparison of three air quality indices

Cited by 178 publications

References 30 publications

Significance of PM2.5 Air Quality at the Indian Capital

Significance of PM2.5 Air Quality at the Indian Capital

Emission Characteristics and Control Prospects of Primary PM2.5 from Fossil Fuel Power Plants in China

Seasonal Variations of Atmospheric Pollution and Air Quality in Beijing

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