Characteristics and meteorological mechanisms of transboundary air pollution in a persistent heavy PM2.5 pollution episode in Central-East China

Shi, Chong; Nduka, Ifeanyichukwu C.; Yang, Yibo; Huang, Yong; Yao, Risheng; Zhang, Hao; He, Bingfang; Xie, Chengbo; Wang, Zhenzhu; Yim, Steve Hung Lam

doi:10.1016/j.atmosenv.2019.117239

Cited by 51 publications

(25 citation statements)

References 48 publications

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“…Cohort studies focusing on the long-term effect on specific diseases of exposure to air pollution require accurate exposure estimates for a large group of participants (e.g., thousands or more) over a defined time period (Brokamp et al, 2019;Morley and Gulliver, 2018;Zhou et al, 2020). Different air quality prediction methods, such as air dispersion models, atmospheric chemical transport models, satellite remote sensing and various statistical methods, have been developed and applied to estimate air pollution (Yim et al, 2019a, b;Tong et al, 2018a, b;Luo et al, 2018;Shi et al, 2020a) and population exposure (Gu and Yim 2016;Gu et al, 2018;Hao et al, 2016;Li et al, 2020;Hou et al, 2019;Michanowicz et al, 2016;Wang et al, , 2020Yim et al, 2019c). Among these exposure assessment methods, land-use regression (LUR) is a widely used modeling approach to characterize long-term-average air pollutant concentrations at a fine spatial scale, which provides high-spatial-resolution estimates of exposure for use in epidemiological studies (Bertazzon et al, 2015;Jones et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…The LUR method is based on the principle that ambient air pollutant concentrations at fixed-site measurement stations are linearly associated with different environmental features (e.g., land use, population density, road network and meteorological conditions) surrounding the stations (Anand and Monks, 2017;Lu et al, 2020;Naughton et al, 2018;Wu et al, 2017). In a city or even at a smaller-spatial-scale area, the LUR method is comparable to or sometimes even better than the approaches of satellite-remote-sensing-based air quality retrievals and air dispersion models in characterizing spatiotemporal variation in air pollution (Marshall et al, 2008;Shi et al, 2020b). Following feasible procedures of data processing and analysis, established air pollution LUR models can be applied to predict concentrations of air pollutants at locations without measurements at multiple spatial scales or at residential locations of participants in epidemiological studies (Li et al, 2021;Liu et al, 2016;Shi et al, 2020b).…”

Section: Introductionmentioning

confidence: 99%

“…In a city or even at a smaller-spatial-scale area, the LUR method is comparable to or sometimes even better than the approaches of satellite-remote-sensing-based air quality retrievals and air dispersion models in characterizing spatiotemporal variation in air pollution (Marshall et al, 2008;Shi et al, 2020b). Following feasible procedures of data processing and analysis, established air pollution LUR models can be applied to predict concentrations of air pollutants at locations without measurements at multiple spatial scales or at residential locations of participants in epidemiological studies (Li et al, 2021;Liu et al, 2016;Shi et al, 2020b).…”

Section: Introductionmentioning

confidence: 99%

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Development and intercity transferability of land-use regression models for predicting ambient PM10, PM2.5, NO2 and O3 concentrations in northern Taiwan

Chuang

et al. 2021

Atmos. Chem. Phys.

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Abstract. To provide long-term air pollutant exposure estimates for epidemiological studies, it is essential to test the feasibility of developing land-use regression (LUR) models using only routine air quality measurement data and to evaluate the transferability of LUR models between nearby cities. In this study, we developed and evaluated the intercity transferability of annual-average LUR models for ambient respirable suspended particulates (PM10), fine suspended particulates (PM2.5), nitrogen dioxide (NO2) and ozone (O3) in the Taipei–Keelung metropolitan area of northern Taiwan in 2019. Ambient PM10, PM2.5, NO2 and O3 measurements at 30 fixed-site stations were used as the dependent variables, and a total of 156 potential predictor variables in six categories (i.e., population density, road network, land-use type, normalized difference vegetation index, meteorology and elevation) were extracted using buffer spatial analysis. The LUR models were developed using the supervised forward linear regression approach. The LUR models for ambient PM10, PM2.5, NO2 and O3 achieved relatively high prediction performance, with R2 values of > 0.72 and leave-one-out cross-validation (LOOCV) R2 values of > 0.53. The intercity transferability of LUR models varied among the air pollutants, with transfer-predictive R2 values of > 0.62 for NO2 and < 0.56 for the other three pollutants. The LUR-model-based 500 m × 500 m spatial-distribution maps of these air pollutants illustrated pollution hot spots and the heterogeneity of population exposure, which provide valuable information for policymakers in designing effective air pollution control strategies. The LUR-model-based air pollution exposure estimates captured the spatial variability in exposure for participants in a cohort study. This study highlights that LUR models can be reasonably established upon a routine monitoring network, but there exist uncertainties when transferring LUR models between nearby cities. To the best of our knowledge, this study is the first to evaluate the intercity transferability of LUR models in Asia.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Development and intercity transferability of land-use regression models for predicting ambient PM10, PM2.5, NO2 and O3 concentrations in northern Taiwan

Chuang

et al. 2021

Atmos. Chem. Phys.

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“…Aerosols play an important role in regional and global climate change through the direct and indirect effects (King et al, 1992;Li et al, 2007;Liu et al, 2019b;Liu et al, 2020;Liu et al, 2018). Fine particulate matter (PM2.5) has attracted loads of concern from scientists, policymaker and the public due to its negative impact on the environment (Li et al, 2020;Yang et al, 2018b;Yim et al, 2019a) and human health (Schwartz, 1996;Pope III et al, 2002;Gu et al, 2018;Gu and Yim, 2016;Gu et al, 2020;Hou et al, 2019;Shi et al, 2020;Yim et al, 2015) . With an extended spatial and temporal coverage, the retrieval of surface PM2.5 concentration from satellite aerosol optical depth (AOD) has become a popular approach to bridging the gap left by ground-level monitoring network, facilitating the detection of the large-scale and long-term aerosol loading and their transboundary transport and the determination of the population exposure level for epidemiological and health study Zou et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

The spatiotemporal relationship between PM2.5 and AOD in China: Influencing factors and Implications for satellite PM2.5 estimations by MAIAC AOD

Wang

Yim

2021

Preprint

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Abstract. Satellite aerosol retrievals have been a popular alternative to monitoring surface PM2.5 concentration due to its extensive spatial and temporal coverage. Satellite-derived PM2.5 estimation strongly relies on an accurate representation of the relationship between ground PM2.5 and satellite aerosol optical depth (AOD). Due to the limitation of satellite AOD data, most studies examined the relationship at a coarse-resolution (i.e., ≥ 10 km) scale; more effort is still needed to better understand the relationship between in-situ PM2.5 and AOD at finer spatial scales. While PM2.5 and AOD could have obvious temporal variations, few studies have examined the diurnal variation in their relationship. Considerable uncertainty therefore still exists in satellite-derived PM2.5 estimation due to these research gaps. Taking advantage of the newly released fine-spatial-resolution satellite AOD data derived by the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm and real-time ground aerosol and PM2.5 measurements, this study explicitly explored the relationship between PM2.5 and AOD and its plausible impact factors including meteorological parameters and topography in mainland China during 2019, at various spatial and temporal scales. Coefficient of variation, Pearson correlation coefficient and slope of linear regression model were used. Spatially, stronger correlations mainly occurred in northern and eastern China and linear slope in northern inland regions was on average larger than those in other areas. Temporally, the PM2.5-AOD correlation peaked in the noon and afternoon and reached the maximum in winter. Simultaneously considering relative humidity (RH) and planetary boundary layer height (PBLH) in the relationship can improve the correlation but the effect of RH and PBLH on the correlation varied spatially and temporally, both in strength and direction. In addition, the largest correlation occurred at 400–600 m primarily in basin terrain such as Sichuan Basin, Shanxi-Shaanxi Basins and Junggar Basin. MAIAC 1-km AOD can better represent the ground-level fine particulate matter in most domains with exceptions such as in very high terrain i.e. Tibetan Plateau and north-central China i.e. Qinghai and Gansu. Findings in this study have useful implications for satellite-based PM2.5 monitoring and will further inform the understanding of the aerosol variation and PM2.5 pollution status in mainland China.

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“…Horvath, 1981), and in respiratory irritants as they are known to have an adverse impact on air quality and health (e.g. Li et al, 2003;Gu et al, 2016;2020;Shi et al, 2019). In terms of scales, satellite observations (e.g.…”

Section: Introductionmentioning

confidence: 99%

Characterizing the performance of a POPS miniaturized optical particle counter when operated on a quadcopter drone

Liu¹,

Osborne²,

Haywood³

et al. 2021

Preprint

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Abstract. The Printed Optical Particle Spectrometer (POPS) is an advanced and small low-cost, light-weight, and high-sensitivity optical particle counter (OPC), particularly designed for deployed on unpiloted aerial vehicles (UAVs) and balloon sondes. We report the performance of the POPS against a reference scanning mobility particle sizer (SMPS) and an airborne passive cavity aerosol spectrometer probe (PCASP) while the POPS is operated on the ground and also while operated on a quadcopter drone, a DJI Matrice 200 V2. This is the first such documented test of the performance of a POPS instrument on a UAV. We investigate the root mean square difference (RMSD) and mean absolute difference (MAD) in particle number concentrations (PNCs) when operating on the ground and on the Matrice 200. When windspeeds are less than 2.6 m/s, we find only modest differences in the RMSDs and MADs of 2.4 % and 2.3 % respectively when operating on the ground, and to 5 % and 3 % when operating at 10m altitude. When windspeeds are greater than 2.6 m/s but less than 7.7 m/s the RMSDs and MADs increase to 10.2 % and 7.8 % respectively when operating on the ground, and 26.2 % and 19.1 %, respectively when operating at 10m altitude. No statistical difference in PNCs was detected when operating on the UAV in either ascent or descent. We also find size distributions of aerosols in the accumulation mode (here defined by diameter, d, where 0.1 ≤ d ≤ 1 µm) are relatively consistent between measurements at the surface and measurements at 10m altitude with RMSD and MAD of less than 21.6 % and 15.7 %, respectively. However, the differences between coarse mode (here defined by d > 1 µm) are universally larger than those measured at the surface with a RMSD and MAD approaching 49.5 % and 40.4 %. Our results suggest that the impact of the UAV rotors on the POPS does not unduly affect the performance of the POPS for wind speed less than 2.6 m/s, but when operating under higher wind speed of up to 7.6 m/s, larger discrepancies are noted. In addition to this, it appears that the POPS measures sub-micron aerosol particles more accurately than super-micron aerosol particles when airborne on the UAV. These measurements lay the foundations for determining the magnitude of potential errors that might be introduced into measured aerosol particle size distributions and concentrations owing to the turbulence created by the rotors on the UAV.

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Characteristics and meteorological mechanisms of transboundary air pollution in a persistent heavy PM2.5 pollution episode in Central-East China

Cited by 51 publications

References 48 publications

Development and intercity transferability of land-use regression models for predicting ambient PM<sub>10</sub>, PM<sub>2.5</sub>, NO<sub>2</sub> and O<sub>3</sub> concentrations in northern Taiwan

Development and intercity transferability of land-use regression models for predicting ambient PM<sub>10</sub>, PM<sub>2.5</sub>, NO<sub>2</sub> and O<sub>3</sub> concentrations in northern Taiwan

The spatiotemporal relationship between PM<sub>2.5</sub> and AOD in China: Influencing factors and Implications for satellite PM<sub>2.5</sub> estimations by MAIAC AOD

Characterizing the performance of a POPS miniaturized optical particle counter when operated on a quadcopter drone

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Characteristics and meteorological mechanisms of transboundary air pollution in a persistent heavy PM2.5 pollution episode in Central-East China

Cited by 51 publications

References 48 publications

Development and intercity transferability of land-use regression models for predicting ambient PM&lt;sub&gt;10&lt;/sub&gt;, PM&lt;sub&gt;2.5&lt;/sub&gt;, NO&lt;sub&gt;2&lt;/sub&gt; and O&lt;sub&gt;3&lt;/sub&gt; concentrations in northern Taiwan

Development and intercity transferability of land-use regression models for predicting ambient PM&lt;sub&gt;10&lt;/sub&gt;, PM&lt;sub&gt;2.5&lt;/sub&gt;, NO&lt;sub&gt;2&lt;/sub&gt; and O&lt;sub&gt;3&lt;/sub&gt; concentrations in northern Taiwan

The spatiotemporal relationship between PM&lt;sub&gt;2.5&lt;/sub&gt; and AOD in China: Influencing factors and Implications for satellite PM&lt;sub&gt;2.5&lt;/sub&gt; estimations by MAIAC AOD

Characterizing the performance of a POPS miniaturized optical particle counter when operated on a quadcopter drone

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Development and intercity transferability of land-use regression models for predicting ambient PM<sub>10</sub>, PM<sub>2.5</sub>, NO<sub>2</sub> and O<sub>3</sub> concentrations in northern Taiwan

Development and intercity transferability of land-use regression models for predicting ambient PM<sub>10</sub>, PM<sub>2.5</sub>, NO<sub>2</sub> and O<sub>3</sub> concentrations in northern Taiwan

The spatiotemporal relationship between PM<sub>2.5</sub> and AOD in China: Influencing factors and Implications for satellite PM<sub>2.5</sub> estimations by MAIAC AOD