Categorisation of air quality monitoring stations by evaluation of PM10 variability

Barrero, M. A.; Orza, J.A.G.; Cabello, M.; Cantón, L.

doi:10.1016/j.scitotenv.2015.03.138

Cited by 73 publications

(29 citation statements)

References 40 publications

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“…SO 2 and CO are respectively measured using ultraviolet fluorescence and infrared absorption (MEE, 2012;Zhang and Cao, 2015). We applied quality control to the hourly CNEMC data following Barrero et al (2015) to exclude severe outliers . There are also occasional consecutive repeats of data that may be caused by faulty instruments or reporting (Rohde and Muller, 2015;Silver et al, 2018).…”

Section: Observationsmentioning

confidence: 99%

“…gov/reanalysis/MERRA-2, last access: 20 March 2019). We choose these meteorological variables for their strong correlations with PM 2.5 identified in previous studies (Wang et al, 2014;Cai et al, 2017;Shen et al, 2017;Leung et al, 2018;Song et al, 2019;Zou et al, 2017). V850 in particular is a strong predictor of PM 2.5 wintertime pollution events in the North China Plain because northerly winds (negative V850) ventilate the region with clean dry air (Cai et al, 2017;Pendergrass et al, 2019).…”

Section: Observationsmentioning

confidence: 99%

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Fine particulate matter (PM<sub>2.5</sub>) trends in China, 2013–2018: separating contributions from anthropogenic emissions and meteorology

et al. 2019

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Abstract. Fine particulate matter (PM2.5) is a severe air pollution problem in China. Observations of PM2.5 have been available since 2013 from a large network operated by the China National Environmental Monitoring Center (CNEMC). The data show a general 30 %–50 % decrease in annual mean PM2.5 across China over the 2013–2018 period, averaging at −5.2 µg m−3 a−1. Trends in the five megacity cluster regions targeted by the government for air quality control are -9.3±1.8 µg m−3 a−1 (±95 % confidence interval) for Beijing–Tianjin–Hebei, -6.1±1.1 µg m−3 a−1 for the Yangtze River Delta, -2.7±0.8 µg m−3 a−1 for the Pearl River Delta, -6.7±1.3 µg m−3 a−1 for the Sichuan Basin, and -6.5±2.5 µg m−3 a−1 for the Fenwei Plain (Xi'an). Concurrent 2013–2018 observations of sulfur dioxide (SO2) and carbon monoxide (CO) show that the declines in PM2.5 are qualitatively consistent with drastic controls of emissions from coal combustion. However, there is also a large meteorologically driven interannual variability in PM2.5 that complicates trend attribution. We used a stepwise multiple linear regression (MLR) model to quantify this meteorological contribution to the PM2.5 trends across China. The MLR model correlates the 10 d PM2.5 anomalies to wind speed, precipitation, relative humidity, temperature, and 850 hPa meridional wind velocity (V850). The meteorology-corrected PM2.5 trends after removal of the MLR meteorological contribution can be viewed as being driven by trends in anthropogenic emissions. The mean PM2.5 decrease across China is −4.6 µg m−3 a−1 in the meteorology-corrected data, 12 % weaker than in the original data, meaning that 12 % of the PM2.5 decrease in the original data is attributable to meteorology. The trends in the meteorology-corrected data for the five megacity clusters are -8.0±1.1 µg m−3 a−1 for Beijing–Tianjin–Hebei (14 % weaker than in the original data), -6.3±0.9 µg m−3 a−1 for the Yangtze River Delta (3 % stronger), -2.2±0.5 µg m−3 a−1 for the Pearl River Delta (19 % weaker), -4.9±0.9 µg m−3 a−1 for the Sichuan Basin (27 % weaker), and -5.0±1.9 µg m−3 a−1 for the Fenwei Plain (Xi'an; 23 % weaker); 2015–2017 observations of flattening PM2.5 in the Pearl River Delta and increases in the Fenwei Plain can be attributed to meteorology rather than to relaxation of emission controls.

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Section: Observationsmentioning

confidence: 99%

Section: Observationsmentioning

confidence: 99%

Fine particulate matter (PM<sub>2.5</sub>) trends in China, 2013–2018: separating contributions from anthropogenic emissions and meteorology

et al. 2019

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“…Nevertheless, it is difficult to perform long-term observations of the HCHO concentration over a wide area using ground-based measurements. However, long-term information regarding ambient HCHO on a global scale can be accessibly obtained from spacebased remote sensors such as Global Ozone Monitoring Experiment (GOME; Martin et al, 2004), the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY; Bovensmann et al, 1999), the GOME-2 instruments (De Smedt et al, 2012, the Ozone Monitoring Instrument (OMI; González Abad et al, 2015), the Ozone Mapping and Profiler Suite (OMPS; C. González Abad et al, 2016) and the TROPOspheric Monitoring Instrument (TROPOMI;De Smedt et al, 2018). However, HCHO observations from SCIAMACHY and TROPOMI are only available until 2011 (Shah et al, 2018) and after October 2017, respectively; the GOME-2A instrument suffers degradation issues (De Smedt et al, 2012); OMI observations are easily affected by the instrumental "row anomaly" (González Abad et al, 2015); and the spatial (80 km× 40 km) and temporal (within 1.5 d) resolution of GOME-2B is even lower than OMPS (Munro et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Primary and secondary sources of ambient formaldehyde in the Yangtze River Delta based on Ozone Mapping and Profiler Suite (OMPS) observations

Liu

et al. 2019

Atmos. Chem. Phys.

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Formaldehyde (HCHO) in the ambient air not only causes cancer but is also an ideal indicator of volatile organic compounds (VOCs), which are major precursors of ozone (O 3 ) and secondary organic aerosol (SOA) near the surface. It is meaningful to differentiate between the direct emission and the secondary formation of HCHO for HCHO pollution control and sensitivity studies of O 3 production. However, understanding of the sources of HCHO is still poor in China, due to the scarcity of field measurements (both spatially and temporally). In this study, tropospheric HCHO vertical column densities (VCDs) in the Yangtze River Delta (YRD), East China, where HCHO pollution is serious, were retrieved from the Ozone Mapping and Profiler Suite (OMPS) onboard the Suomi National Polar-orbiting Partnership (Suomi-NPP) satellite from 2014 to 2017; these retrievals showed good agreement with the tropospheric HCHO columns measured using ground-based high-resolution Fourier transform infrared spectrometry (FTS) with a correlation coefficient (R) of 0.78. Based on these results, the cancer risk was estimated both nationwide and in the YRD region. It was calculated that at least 7840 people in the YRD region would develop cancer in their lives due to outdoor HCHO exposure, which comprised 23.4 % of total national cancer risk. Furthermore, the contributions of primary and secondary sources were apportioned, in addition to primary and secondary tracers from surface observations. Overall, the HCHO from secondary formation contributed most to ambient HCHO and can be regarded as the indicator of VOC reactivity in Hangzhou and in urban areas of Nanjing and Shanghai from 2015 to 2017, due to the strong correlation between total HCHO and secondary HCHO. At industrial sites in Nanjing, primary emissions more strongly influenced ambient HCHO concentrations in 2015 and showed an obvious decreasing trend. Seasonally, HCHO from secondary formation reached a maximum in summer and a minimum in winter. In the spring, summer, and autumn, secondary formation had a significant effect on the variation of ambient HCHO in urban regions of Nanjing, Hangzhou, and Shanghai, whereas in the winter the contribution from secondary formation became less significant. A more thorough understanding of the variation of the primary and secondary contributions of ambient HCHO is needed to develop a better knowledge regarding the role Published by Copernicus Publications on behalf of the European Geosciences Union. 6718 W. Su et al.: Primary and secondary sources of ambient formaldehyde in the Yangtze River Delta of HCHO in atmospheric chemistry and to formulate effective control measures to decrease HCHO pollution and the associated cancer risk.

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“…Ignaccolo, Ghigo and Giovenali [5] classified the air quality monitoring network in Piemonte (Northern Italy) using functional cluster analysis based on Partitioning around Medoids algorithm and considering three air pollutants, namely NO 2 , PM 10 , and O 3, to classify sites in homogeneous clusters and identify the representative ones. Barrero, Orza, Cabello and Cantón [6] analyzed and made experiments on the variations of PM 10 concentrations at 43 stations in the air quality monitoring network of the Basque Country to group them according to their common characteristics. They implemented the autocorrelation function and K-means clustering.…”

Section: Related Workmentioning

confidence: 99%

Majority Voting Based Multi-Task Clustering of Air Quality Monitoring Network in Turkey

2019

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Air pollution, which is the result of the urbanization brought by modern life, has a dramatic impact on the global scale as well as local and regional scales. Since air pollution has important effects on human health and other living things, the issue of air quality is of great importance all over the world. Accordingly, many studies based on classification, clustering and association rule mining applications for air pollution have been proposed in the field of data mining and machine learning to extract hidden knowledge from environmental parameters. One approach is to model a region in a way that cities having similar characteristics are determined and placed into the same clusters. Instead of using traditional clustering algorithms, a novel algorithm, named Majority Voting based Multi-Task Clustering (MV-MTC), is proposed and utilized to consider multiple air pollutants jointly. Experimental studies showed that the proposed method is superior to five well-known clustering algorithms: K-Means, Expectation Maximization, Canopy, Farthest First and Hierarchical clustering methods.

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Categorisation of air quality monitoring stations by evaluation of PM10 variability

Cited by 73 publications

References 40 publications

Fine particulate matter (PM<sub>2.5</sub>) trends in China, 2013–2018: separating contributions from anthropogenic emissions and meteorology

Fine particulate matter (PM<sub>2.5</sub>) trends in China, 2013–2018: separating contributions from anthropogenic emissions and meteorology

Primary and secondary sources of ambient formaldehyde in the Yangtze River Delta based on Ozone Mapping and Profiler Suite (OMPS) observations

Majority Voting Based Multi-Task Clustering of Air Quality Monitoring Network in Turkey

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Categorisation of air quality monitoring stations by evaluation of PM10 variability

Cited by 73 publications

References 40 publications

Fine particulate matter (PM&lt;sub&gt;2.5&lt;/sub&gt;) trends in China, 2013–2018: separating contributions from anthropogenic emissions and meteorology

Fine particulate matter (PM&lt;sub&gt;2.5&lt;/sub&gt;) trends in China, 2013–2018: separating contributions from anthropogenic emissions and meteorology

Primary and secondary sources of ambient formaldehyde in the Yangtze River Delta based on Ozone Mapping and Profiler Suite (OMPS) observations

Majority Voting Based Multi-Task Clustering of Air Quality Monitoring Network in Turkey

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Fine particulate matter (PM<sub>2.5</sub>) trends in China, 2013–2018: separating contributions from anthropogenic emissions and meteorology

Fine particulate matter (PM<sub>2.5</sub>) trends in China, 2013–2018: separating contributions from anthropogenic emissions and meteorology