Analysis of air pollution over Hanoi, Vietnam using multi-satellite and MERRA reanalysis datasets

Lasko, Kristofer; Vadrevu, Krishna Prasad; Nguyen, Thanh Thi

doi:10.1371/journal.pone.0196629

Cited by 82 publications

(18 citation statements)

References 64 publications

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“…It is important to note that the aerosol species' concentration properties are not fully constrained by the observations and are principally determined by the underlying GOCART model (Gelaro et al, 2017). Notwithstanding this fact, MERRA-2 has shown considerable skill in simulating numerous observable aerosol properties worldwide (Buchard et al, 2017) and has been used to estimate PM10 and PM2.5 in regions like Europe, the Middle East, and Asia (e.g, Provençal et al 2017;Lasko et al 2018;Song et al, 2018;Xiao et al, 2018;Mahesh et al, 2019;Ukhov et al, 2020;Navinya et al, 2020).…”

Section: Pm10 Compositionmentioning

confidence: 99%

The Negative Impact of Biomass Burning and the Orinoco Low-Level Jet on the Air Quality of the Orinoco River Basin

Rodríguez-Gómez

Echeverry

Jaramillo

et al. 2021

ATM

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Biomass burning (BB) is a common activity in developing countries and has been identified as a serious air pollution threat. The present work evaluates the air quality of the largest town in the extensive Colombian savannas (250,000 km2) for the first time, from measurements over three consecutive years (2017-2020). Although the air quality in Villavicencio is good in terms of PM10 and O3 for most of the year, the pollution levels for both pollutants exceed the World Health Organization recommended limits during the dry season (February to April). The combination of the Orinoco low-level jet (OLLJ) with the BB emissions from the Venezuelan and the Colombian savannas was identified as the main cause of the poor air quality episodes during the dry season in this city. Organic carbon derived from reanalysis was identified as the main component of the high PM10 concentrations during the dry season. However, mineral dust and sea salt particles were also found to play an important role in the poor air quality observed in Villavicencio and likely along the Orinoco river basin region. Finally, between November and March, the OLLJ was found to be an efficient mechanism for the transport of air pollutants from the Atlantic Ocean and the savannas regions in the Orinoco River Basin in Venezuela and Colombia towards southern regions of Colombia, and in some cases, Ecuador.

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Section: Pm10 Compositionmentioning

confidence: 99%

The Negative Impact of Biomass Burning and the Orinoco Low-Level Jet on the Air Quality of the Orinoco River Basin

Rodríguez-Gómez

Echeverry

Jaramillo

et al. 2021

ATM

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“…Datasets from sensors such as the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) and reanalysis data such as the Modern-Era Retrospective analysis for Research and Applications-Version 2 (MERRA-2) have been instrumental in characterising the Spatio-temporal and vertical patterns of smoke, BC and CO. For example, using CALIPSO data, Shikwambana [33] showed that biomass burning is responsible for a substantial amount of smoke aerosols in central Africa during the June-July-August (JJA) season. On the other hand, Lasko et al [34] used the MERRA-2 reanalysis BC concentration data from 2012-2016 to explore the pollution trends over time. Their results show that forest biomass burning from Northwest Vietnam and Laos impacts on the air quality of Hanoi during the peak Ultraviolet Aerosol Index (UVAI) months of March and April.…”

Section: Introductionmentioning

confidence: 99%

Assessing Spatio-Temporal Variability of Wildfires and their Impact on Sub-Saharan Ecosystems and Air Quality Using Multisource Remotely Sensed Data and Trend Analysis

Kganyago

Shikwambana

2019

Sustainability

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Globally, wildfires are considered the most commonly occurring disasters, resulting from natural and anthropogenic ignition sources. Wildfires consist of burning standing biomass at erratic degrees of intensity, severity, and frequency. Consequently, wildfires generate large amounts of smoke and other toxic pollutants that have devastating impacts on ambient air quality and human health. There is, therefore, a need for a comprehensive study that characterizes land-atmosphere interactions with regard to wildfires, critical for understanding the interrelated and multidimensional impacts of wildfires. Current studies have a limited scope and a narrow focus, usually only focusing on one aspect of wildfire impacts, such as air quality without simultaneously considering the impacts on land surface changes and vice versa. In this study, we use several multisource data to determine the spatial distribution, frequency, disturbance characteristics of and variability and distribution of pollutants emitted by wildfires. The specific objectives were to (1) study the sources of wildfires and the period they are prevalent in sub-Saharan Africa over a 9 year period, i.e., 2007-2016, (2) estimate the seasonal disturbance of wildfires on various vegetation types, (3) determine the spatial distribution of black carbon (BC), carbon monoxide (CO) and smoke, and (4) determine the vertical height distribution of smoke. The results show largest burned areas in December-January-February (DJF), June-July-August (JJA) and September-October-November (SON) seasons, and reciprocal high emissions of BC, CO, and smoke, as observed by Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). In addition, the results reveal an increasing trend in the magnitude of BC, and CO concentration driven by meteorological conditions such as low precipitation, low relative humidity, and low latent heat flux. Overall, this study demonstrates the value of multisource remotely sensed data in characterising long-term wildfire patterns and associated emissions. The results in this study are critical for informing better regional fire management and air quality control strategies to preserve endangered species and habitats, promote sustainable land management, and reduce greenhouse gases (GHG) emissions.

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“…The remote sensing data used in the change detection should be obtained from a sensor system that acquires data at approximately the same time of day (Star et al, 1997) or at near anniversary dates to minimize inconsistencies in sun angle or phenology (Jensen, 2009). However, it is not possible to obtain extract (or near) anniversary dates in the HMA because of Landsat orbit changes and monthly average cloud cover of 72.4% (Lasko et al, 2018). Therefore, daytime Landsat images (path 127, row 045/046) with 30-m spatial resolution freely distributed by the U.S. Geological Survey acquired in the wet (summer) season of 1996, 2007 and 2016 were collected (Table 1) in this study.…”

Section: Data Usedmentioning

confidence: 99%

Landsat Time-series Images-based Urban Heat Island Analysis: The Effects of Changes in Vegetation and Built-up Land on Land Surface Temperature in Summer in the Hanoi Metropolitan Area, Vietnam

Nguyễn

2020

Environ. Nat. Resour. J.

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Rapid and unplanned urbanization leads to temperature rise, urban vegetation decrease and built-up land increase, forming an urban heat island (UHI). This study investigated the effects of changes in vegetation and built-up land on land surface temperature (LST) in summer, based on remotely sensed images. LST was first retrieved by means of the Radiative Transfer Model (RTM). Scatterplots and an univariate linear regression model (ULRM) were first employed to independently measure the influence of NDVI on LST, and of NDBI on LST, respectively. In order to assess the effects of changes in vegetation and built-up land on LST, a multivariate linear regression model (MLRM) was finally employed to improve the accuracy of the predicted model in the identification of the joint effect of both the normalized difference vegetation index (NDVI) and the normalized difference built-up index (NDBI) on LST. The result from the case from the Hanoi Metropolitan Area (HMA), Vietnam using Landsat-5 TM and Landsat-8 OLI/TIRS time-series images during the 1996-2016 period shows that there exists a negative effect of built-up land and a positive effect of vegetation on LST. In addition, indications of intensifying UHI effects were detected in the HMA, especially tending to expand faster and wider to the parts of western, northwestern and southwestern HMA during the 1996-2007 period. These findings suggest that vegetation weakens the effect of UHIs, whereas, built-up land greatly strengthens the effect of UHIs in the HMA.

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Analysis of air pollution over Hanoi, Vietnam using multi-satellite and MERRA reanalysis datasets

Cited by 82 publications

References 64 publications

The Negative Impact of Biomass Burning and the Orinoco Low-Level Jet on the Air Quality of the Orinoco River Basin

The Negative Impact of Biomass Burning and the Orinoco Low-Level Jet on the Air Quality of the Orinoco River Basin

Assessing Spatio-Temporal Variability of Wildfires and their Impact on Sub-Saharan Ecosystems and Air Quality Using Multisource Remotely Sensed Data and Trend Analysis

Landsat Time-series Images-based Urban Heat Island Analysis: The Effects of Changes in Vegetation and Built-up Land on Land Surface Temperature in Summer in the Hanoi Metropolitan Area, Vietnam

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