Characteristics of visibility and particulate matter (PM) in an urban area of Northeast China

Zhao, Huiling; Che, Huizheng; Zhang, Xiaoye; Ma, Yanhui; Wang, Yangfeng; Wang, Hong; Wang, Yaqiang

doi:10.5094/apr.2013.049

Cited by 117 publications

(47 citation statements)

References 36 publications

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“…Categorical or time variables (e.g., peak cooking hour) could be used as a proxy of these sources to improve spatiotemporal LUR models. The spatial pattern was remarkably higher in six downtown districts, even though no traffic-related variables were involved in the model, which indicates human activities contribute a lot to the increment in PM 2.5 concentrations diurnally (Dons et al 2013;Zhao et al 2013a). Employing incremental concentration to build LUR models may be a novel approach and calls for further research.…”

Section: Diurnal Variationsmentioning

confidence: 99%

Applying land use regression model to estimate spatial variation of PM2.5 in Beijing, China

Peng

et al. 2014

Environ Sci Pollut Res

128

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Fine particulate matter (PM 2.5 ) is the major air pollutant in Beijing, posing serious threats to human health. Land use regression (LUR) has been widely used in predicting spatiotemporal variation of ambient air-pollutant concentrations, though restricted to the European and North American context. We aimed to estimate spatiotemporal variations of PM 2.5 by building separate LUR models in Beijing. Hourly routine PM 2.5 measurements were collected at 35 sites from 4th March 2013 to 5th March 2014. Seventy-seven predictor variables were generated in GIS, including street network, land cover, population density, catering services distribution, bus stop density, intersection density, and others. Eight LUR models were developed on annual, seasonal, peak/non-peak, and incremental concentration subsets. The annual mean concentration across all sites is 90.7 μg/m 3 (SD=13.7). PM 2.5 shows more temporal variation than spatial variation, indicating the necessity of building different models to capture spatiotemporal trends. The adjusted R 2 of these models range between 0.43 and 0.65. Most LUR models are driven by significant predictors including major road length, vegetation, and water land use. Annual outdoor exposure in Beijing is as high as 96.5 μg/m 3 . This is among the first LUR studies implemented in a seriously air-polluted Chinese context, which generally produce acceptable results and reliable spatial air-pollution maps. Apart from the models for winter and incremental concentration, LUR models are driven by similar variables, suggesting that the spatial variations of PM 2.5 remain steady for most of the time. Temporal variations are explained by the intercepts, and spatial variations in the measurements determine the strength of variable coefficients in our models.

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Section: Diurnal Variationsmentioning

confidence: 99%

Applying land use regression model to estimate spatial variation of PM2.5 in Beijing, China

Peng

et al. 2014

Environ Sci Pollut Res

128

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“…In particular, the PM 10 concentration in Shenyang reached 5.33 times China's national ambient air quality standards (150 µg/m 3 ) (GB3095-2012:http://kjs.mep.gov.cn/hjbhbz/bzwb/dqhjbh/ dqhjzlbz/201203/t20120302_224165.htm). This result can be attributed to human activities (Zhao et al, 2013b).…”

Section: Site Description Instruments and Data Measurementmentioning

confidence: 93%

“…2013a, Xia et al (2007) and Wang et al (2010) have analyzed aerosol optical properties and direct radiative forcing in northern China. In addition, Zhao et al (2013b) have analyzed the characteristics of visibility and particulate matter in an urban area of Northeast China.…”

Section: Introductionmentioning

confidence: 99%

Characteristics of Particulate Matter during Haze and Fog (Pollution) Episodes over Northeast China, Autumn 2013

Yang

Zhao

2015

Aerosol Air Qual. Res.

Self Cite

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Mass concentrations of particulate matter (PM), including PM 1.0 , PM 2.5 and PM 10 , were measured from October 13 th to November 30 th 2013 at eight sites in Northeast China to evaluate their variations during pollution periods. Five major pollution periods were identified during the autumn of 2013. The maximum daily average PM 2.5 concentrations were 437 ± 85 µg/m 3 and 322 ± 50 µg/m 3 in Harbin and Shenyang, respectively. The minimum was 75 ± 28 µg/m 3 in Dandong. The presence of finer particles was significantly related to visibility degradation during pollution periods. Wind speeds had a negative correlation with PM concentrations, while high relative humidity (RH) favored the formation of haze in Northeast China.Visibility on non-hazy days was approximately 2.5-3.0 times greater than that on hazy days. During hazy days, the PM 1.0 :PM 2.5 ratios were 0.89 ± 0.04, 0.85 ± 0.04 and 0.91 ± 0.04 at Anshan, Shenyang and Dandong, respectively. These results show that PM 1.0 was the dominant particle pollutant in Northeast China during periods of pollution. High RH and low wind speeds during hazy days may favor the accumulation of atmospheric pollutants. The results of this study provide useful information toward recognizing air pollution episode characteristics in Northeast China.

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“…The more 5000_residence_CA, the more sources of pollution. It can stimulate liveness of the surrounding businesses, transportation, and production activities so as to release more pollution and cause environmental damage to a certain extent [36,37]. The variables negatively correlated with the PM 2.5 were the annual average relative humidity, 5000_CONTAG, and 4000_waters_LPI.…”

Section: Lur Model Constructionmentioning

confidence: 99%

Optimization of PM2.5 Estimation Using Landscape Pattern Information and Land Use Regression Model in Zhejiang, China

Yang

Chen

et al. 2018

Atmosphere

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Abstract:The motivation of this paper is that the effect of landscape pattern information on the accuracy of particulate matter estimation is seldom reported. The landscape pattern indexes were incorporated in a land use regression (LUR) model to investigate the performance of PM 2.5 simulation over Zhejiang Province. The study results show that the prediction accuracy of the model has been improved significantly after the incorporation of the landscape pattern indexes. At class-level, waters and residential areas were clearly landscape components influencing decreasing or increasing PM 2.5 concentration. At landscape-level, CONTAG (contagion index) played a huge negative role in pollutant concentrations. Latitude and relative humidity are key factors affecting the PM 2.5 concentration at province level. If the land use regression model incorporating landscape pattern indexes was used to simulate distribution of PM 2.5 , the accuracy of ordinary kriging for the LUR-based data mining was higher than the accuracy of LUR-based ordinary kriging, especially in the area of low pollution concentration.

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Characteristics of visibility and particulate matter (PM) in an urban area of Northeast China

Cited by 117 publications

References 36 publications

Applying land use regression model to estimate spatial variation of PM2.5 in Beijing, China

Applying land use regression model to estimate spatial variation of PM2.5 in Beijing, China

Characteristics of Particulate Matter during Haze and Fog (Pollution) Episodes over Northeast China, Autumn 2013

Optimization of PM2.5 Estimation Using Landscape Pattern Information and Land Use Regression Model in Zhejiang, China

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