Estimating PM2.5 Concentrations Based on MODIS AOD and NAQPMS Data over Beijing–Tianjin–Hebei

Wang, Qingxin; Zeng, Qiaolin; Tao, Jianhua; Sun, Lin; Zhang, Liang; Gu, Tianyu; Wang, Zifeng; Chen, Liangfu

doi:10.3390/s19051207

Cited by 39 publications

(21 citation statements)

References 44 publications

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“… 97 There are several factors needed to be considered in developing and calibrating the PM levels, including geographical location, meteorological/climate conditions and aerosol optical depth. 97 , 98 Most studies employed a spatiotemporal prediction model by adjusting the subjects’ geographical or residential location. However, some studies did not describe how individual PM exposure estimation was performed or PM modelling was developed.…”

Section: Discussionmentioning

confidence: 99%

Particulate Air Pollution and Osteoporosis: A Systematic Review

Pang

Ekeuku

Chin

2021

RMHP

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Air pollution is associated with inflammation and oxidative stress, which predispose to several chronic diseases in human. Emerging evidence suggests that the severity and progression of osteoporosis are directly associated with inflammation induced by air pollutants like particulate matter (PM). This systematic review examined the relationship between PM and bone health or fractures. A comprehensive literature search was conducted from January until February 2021 using the PubMed, Scopus, Web of Science, Google Scholar and Cochrane Library databases. Human cross-sectional, cohort and case–control studies were considered. Of the 1500 papers identified, 14 articles were included based on the inclusion and exclusion criteria. The air pollution index investigated by most studies were PM 2.5 and PM 10 . Current studies demonstrated inconsistent associations between PM and osteoporosis risk or fractures, which may partly due to the heterogeneity in subjects’ characteristics, study design and analysis. In conclusion, there is an inconclusive relationship between osteoporosis risk and fracture and PM exposures which require further validation.

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Section: Discussionmentioning

confidence: 99%

Particulate Air Pollution and Osteoporosis: A Systematic Review

Pang

Ekeuku

Chin

2021

RMHP

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“…In total, the correlation between estimated and measured PM 2.5 concentration is more higher in spring and autumn. The In our study, even though the complicated PM 2.5 concentration distribution in Beijing, the modified corrected AOD-PM 2.5 models achieved higher model fitting R 2 values than those in previous studies, e.g., an observation-based algorithm that considers the effect of the main aerosol characteristics applied to the Beijing-Tianjin-Hebei region with R 2 of 0.70 [3], the GWR model applied to the whole China mainland with an overall R 2 of 0.64 [5], an improved model applied to the Beijing-Tianjin-Hebei region with an R 2 of 0.77 [15], satellite-driven PM 2.5 models with VIIRS nighttime data applied to the Beijing-Tianjin-Hebei region with R 2 of 0.75 [19], and NAQPMS data incorporated to MODIS data applied to the Beijing-Tianjin-Hebei region from January to December 2017 with seasonal R 2 values of 0.75, 0.62, 0.80, and 0.78 in the spring, summer, autumn, and winter, respectively [22]. Table 3 is comparison between estimated and measured seasonal average PM 2.5 concentration of 15 monitoring stations from 2014 to 2016.…”

Section: Resultsmentioning

confidence: 99%

“…However, it performs unsatisfactorily in urban regions. The DB algorithm performs better than the DT algorithm over urban areas, and it can provide better land coverage over both dark and bright surfaces, which is more reasonable for PM 2.5 concentration distribution research in Beijing [ 20 – 22 ].…”

Section: Methodsmentioning

confidence: 99%

Estimation of ground-level PM2.5 concentration using MODIS AOD and corrected regression model over Beijing, China

Zhang

2020

PLoS ONE

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To establish a new model for estimating ground-level PM 2.5 concentration over Beijing, China, the relationship between aerosol optical depth (AOD) and ground-level PM 2.5 concentration was derived and analysed firstly. Boundary layer height (BLH) and relative humidity (RH) were shown to be two major factors influencing the relationship between AOD and ground-level PM 2.5 concentration. Thus, they are used to correct MODIS AOD to enhance the correlation between MODIS AOD and PM 2.5. When using corrected MODIS AOD for modelling, the correlation between MODIS AOD and PM 2.5 was improved significantly. Then, normalized difference vegetation index (NDVI), surface temperature (ST) and surface wind speed (SPD) were introduced as auxiliary variables to further improve the performance of the corrected regression model. The seasonal and annual average distribution of PM 2.5 concentration over Beijing from 2014 to 2016 were mapped for intuitively analysing. Those can be regarded as important references for monitoring the ground-level PM 2.5 concentration distribution. It is obviously that the PM 2.5 concentration distribution over Beijing revealed the trend of "southeast high and northwest low", and showed a significant decrease in annual average PM 2.5 concentration from 2014 to 2016.

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“…In other words, AOD gives information about how much direct sunlight is prevented from reaching the ground by aerosols. Furthermore, it gives an estimation of PM2.5 surface concentration [56]. Measurements using satelliteborne observation and aerosol reanalysis products revealed that during lockdown (24th Mar -22nd Apr 2020) maximum decrement in AOD value is observed in eastern IGP ($40% of pre-lockdown; 20th Feb-20th Mar 2020) followed by northwest (27%) and south-India (13%) [4].…”

Section: Aerosol Optical Depth (Aod)mentioning

confidence: 99%

COVID-19 Lockdown and the Aerosphere in India: Lessons Learned on How to Reduce Air Pollution

Panda¹,

Satpathy²,

Das³

et al. 2022

Biotechnology to Combat COVID-19

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The giant increase in COVID-19 infection across India forced the government to impose strict lockdown in order to curb the pandemic. Although the stringent restrictions crippled India’s economy and poor people’s livelihood, it significantly improved the air quality of most of the polluted cities of India and rejuvenated the atmosphere. Thus, the major objective of this study is to provide a comprehensive overview of lockdown on pollutants prevailing in the atmosphere. A prominent decline in primary pollutants such as Particulate matter (PM), Black carbon (BC), Oxides of nitrogen (NOx), Carbon monoxide (CO) is observed across the country. However, lockdown had a trifling impact on Sulphur dioxide (SO2) concentration over some parts of India due to the constant operation of coal-fired thermal plants as a part of essential service. Furthermore, the sudden decline in NOx concentration disturbed the complex atmospheric chemistry and lead to an enhancement of surface ozone (O3) (secondary pollutant) in many cities of India. Thus, lockdown emerged as a unique opportunity for the atmospheric researchers, policymakers as well as stakeholders to collect baseline data of pollutants and their major sources. This will help to set new targets of air quality standards and to develop various mitigation processes to combat air pollution.

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Estimating PM2.5 Concentrations Based on MODIS AOD and NAQPMS Data over Beijing–Tianjin–Hebei

Cited by 39 publications

References 44 publications

Particulate Air Pollution and Osteoporosis: A Systematic Review

Particulate Air Pollution and Osteoporosis: A Systematic Review

Estimation of ground-level PM2.5 concentration using MODIS AOD and corrected regression model over Beijing, China

COVID-19 Lockdown and the Aerosphere in India: Lessons Learned on How to Reduce Air Pollution

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