Air Quality over China (original) (raw)
Related papers
Verification of anthropogenic emissions of China by satellite and ground observations
Atmospheric Environment, 2011
An integrated emission inventory of China was validated through comparing the Community Multi-scale Air Quality (CMAQ) simulations with the NO 2 and SO 2 column retrieved from Ozone Monitoring Instrument (OMI), Aerosol Optical Depth (AOD) retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS), and ground observations of SO 2 , NO 2 , PM 10 , PM 2.5 and its components. The model simulations were performed for year 2005. The model generally reproduces both spatial distribution and seasonal variation of tropospheric NO 2 , SO 2 column densities and AOD in China that have been observed by OMI and MODIS. The correlation coefficients between model simulated and satellite observed NO 2 column densities, SO 2 column densities and AOD over east China are 0.90, 0.85 and 0.79, and the normalized mean bias (NMBs) are À8%, 1%, and À8% respectively, which are comparable with the errors from satellite retrievals. The surface concentrations of NO 2 , SO 2 , and PM 10 given by CMAQ model are also comparable with those observed in Beijing, Shanghai, and Guangzhou, with the NMBs ranging from 1% to 18%, À3% to À25%, and À12% to 18%, respectively. The results suggest that the anthropogenic emissions of SO 2 , NOx, and PM 10 used in this study are in line with both the satellite and ground observations therefore are of acceptable accuracy. There is overestimation for SO 2 and underestimation for PM 10 in some industry-intensive areas because of the inaccuracy of spatial and temporal allocations. The CMAQ model also significantly underestimates the PM 2.5 concentration in Beijhjing, mainly due to the limitation of secondary organic aerosol (SOA) formation mechanism used in the model and the underestimation of primary OC and EC emissions. Therefore more efforts shall be made to improve the primary emission estimates of OC and EC, as well as the temporal allocation factor of SO 2 and PM 10 emissions.
Analysis of Air Pollution Trends in Beijing, China
International Journal of Scientific Research in Science and Technology, 2020
Air pollution is a worldwide problem affecting not only the source location, but the globe as a whole. The current study aims to analyse the standard six air pollutants and air quality index (AQI) in Beijing, China. Air quality data was collected from 2014 to 2020 for temporal analysis. The average maximum values of the air pollutants and AQI during the period analysed were, PM2.5: 74.4 µg/m3, PM10: 107.3 µg/m3, SO2: 20.7 µg/m3, CO: 1.5 mg/m3, NO2: 56.3 µg/m3, O3: 173.1 µg/m3 and AQI: 118. Maximum and minimum values of the primary pollutants occurred predominantly during winter and summer months, while O3 exhibited an opposite trend. All air pollutants and AQI declined over the years. Significant reduction of over 50 % was archived for PM2.5, PM10, SO2, CO and less than 5 % for O3. The air pollution trend in Beijing has shown substantial improvement. In 2020, all air pollutants except PM2.5 achieved the national ambient air quality standard. This realisation can be credited to the effective policies implemented by the Chinese government.
Atmospheric Chemistry and Physics Discussions, 2018
APHH-Beijing (Atmospheric Pollution and Human Health in a Chinese Megacity) is an international collaborative project to examine the emissions, processes and health effects of air pollution in Beijing. The four research themes of APHH-China are: (1) sources and emissions of urban atmospheric pollution; (2) processes affecting urban atmospheric pollution; (3) exposure science and impacts on health; and (4) interventions and solutions to reduce health impacts. Themes 1 and 2 are closely integrated and support Theme 3, while Themes 1-3 provide scientific data for Theme 4 on the development of cost-effective solutions. A key activity within APHH-Beijing was the two monthlong intensive field campaigns at two sites: (i) central Beijing, and (ii) rural Pinggu. The coordinated campaigns provided observations of the atmospheric chemistry and physics in and around Beijing during November-December 2016 and May-June 2017. The campaigns were complemented by numerical air quality modelling and air quality and meteorology data at the 12 national monitoring stations in Beijing. This introduction paper provides an overview of (i) APHH-Beijing programme, (ii) the measurement and modelling activities performed as part of it in Beijing, and (iii) the air quality and meteorological conditions during the two field campaigns. The winter campaign was characterized by high PM2.5 pollution events whereas the summer experienced high ozone pollution events. Air quality was poor during the winter campaign, but less severe than in the same period in 2015 when there were a number of major pollution episodes. PM2.5 levels were relatively low during the summer period, matching the cleanest periods over the previous five years. Synoptic scale meteorological analysis suggests that the greater stagnation and weak southerly circulation in November/December 2016 may have contributed to the poor air quality.
Atmospheric Chemistry and Physics, 2008
The Chinese capital Beijing is one of the global megacities where the effects of rapid economic growth have led to complex air pollution problems that are not well understood. In this study, ambient particle number size distributions in Beijing between 2004 and 2006 are analysed as a function of regional meteorological transport. An essential result is that the particle size distribution in Beijing depends to large extent on the history of the synoptic scale air masses. A first approach based on manual back trajectory classification yielded differences in particulate matter mass concentration (PM 1 and PM 10) by a factor of two between four different air mass categories, including three main wind directions plus the case of stagnant air masses. A back trajectory cluster analysis refined these results, yielding a total of six trajectory clusters. Besides the large scale wind direction, the transportation speed of an air mass was found to play an essential role on the PM concentrations in Beijing. Slow-moving air masses were shown to be associated with an effective accumulation of surface-based anthropogenic emissions due to both, an increased residence time over densely populated land, and their higher degree of vertical stability. For the six back trajectory clusters, differences in PM 1 mass concentrations by a factor of 3.5, in the mean air mass speed by a factor of 6, and in atmospheric visibility by a factor of 4 were found. The main conclusion is that the air quality in Beijing is not only degraded by anthropogenic aerosol sources from within the megacity, but also by sources across the entire Northwest China plain depending on the meteorological situation.
Characteristics and source distribution of air pollution in winter in Qingdao, eastern China
Environmental pollution (Barking, Essex : 1987), 2017
To characterize air pollution and determine its source distribution in Qingdao, Shandong Province, we analyzed hourly national air quality monitoring network data of normal pollutants at nine sites from 1 November 2015 to 31 January 2016. The average hourly concentrations of particulate matter <2.5 μm (PM2.5) and <10 μm (PM10), SO2, NO2, 8-h O3, and CO in Qingdao were 83, 129, 39, 41, and 41 μg m(-3), and 1.243 mg m(-3), respectively. During the polluted period, 19-26 December 2015, 29 December 2015 to 4 January 2016, and 14-17 January 2016, the mean 24-h PM2.5 concentration was 168 μg m(-3) with maximum of 311 μg m(-3). PM2.5 was the main pollutant to contribute to the pollution during the above time. Heavier pollution and higher contributions of secondary formation to PM2.5 concentration were observed in December and January. Pollution pathways and source distribution were investigated using the HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model and pote...
Record Heavy PM2.5 Air Pollution over China in January 2013: Vertical and Horizontal Dimensions
Sola, 2014
Record heavy PM 2.5 air pollution (maximum concentration of ~1 mg m −3) observed over China in January 2013 was analyzed. The vertical and horizontal scales of the pollution layer are critically important parameters for the analysis of pollution phenomena, but they are difficult to measure. This is because the PM 2.5 aerosol concentration is so high that ordinary remote-sensing methods such as ground-based and space-borne lidar inversion are difficult to apply. First, we showed the detailed time-height structure of aerosol extinction coefficients based on Beijing lidar observation, by assuming a non-zero boundary extinction coefficient and using 3D chemical transport modeling (CTM). The aerosol structure derived from lidar observations and the CTM results were in close agreement. Using ground-based lidar, we also found that a shallow aerosol layer (height of 200−300 m) remained over Beijing for a long time. We also successfully showed that the horizontal extent of the aerosol layer over the China Plain was several hundred km based on CALIOP observations and CTM.
Long-term air pollution data with high temporal and spatial resolutions are needed to support the research of physical and chemical processes that affect the air quality, and the corresponding health risks. However, such datasets were not available in China until recently. For the first time, this study examines the spatial and temporal variations of PM 2.5 , PM 10 , CO, SO 2 , NO 2 , and 8 h O 3 in 31 capital cities in China between March 2013 and February 2014 using hourly data released by the Ministry of Environmental Protection (MEP) of China. The annual mean concentrations of PM 2.5 and PM 10 exceeded the Chinese Ambient Air Quality Standards (CAAQS), Grade I standards (15 and 40 μg/m 3 for PM 2.5 and PM 10 , respectively) for all cities, and only Haikou, Fuzhou and Lasa met the CAAQS Grade II standards (35 and 70 μg/m 3 for PM 2.5 and PM 10 , respectively). Observed PM 2.5 , PM 10 , CO and SO 2 concentrations were higher in cities located in the North region than those in the West and the South-East regions. The number of non-attainment days was highest in the winter, but high pollution days were also frequently observed in the South-East region during the fall and in the West region during the spring. PM 2.5 was the largest contributor to the air pollution in China based on the number of non-attainment days, followed by PM 10 , and O 3 . Strong correlation was found between different pollutants except for O 3 . These results suggest great impacts of coal combustion and biomass burning in the winter, long range transport of windblown dust in the spring, and secondary aerosol formation throughout the year. Current air pollution in China is caused by multiple pollutants, with great variations among different regions and different seasons. Future studies should focus on improving the understanding of the associations between air quality and meteorological conditions, variations of emissions in different regions, and transport and transformation of pollutants in both intra-and inter-regional contexts.
Identification of Aerosol Pollution Hotspots in Jiangsu Province of China
Remote Sensing, 2021
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,...