Predicting Ground Level PM<sub>2.5</sub> Concentration Over Delhi Using Landsat 8 Satellite Data

Mishra, R. S.; Agarwal, Aviral; Shukla, Ankita

doi:10.1080/2150704x.2020.1832279

Cited by 11 publications

(8 citation statements)

References 28 publications

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“…This is especially important in data-scare regions such as Belize. For example, Landsat-8, Sentinel-2, Sentinel-3, and moderate resolution imaging spectroradiometer (MODIS) have been used to evaluate changes in air quality emissions (Wang and Christopher, 2003;Gupta et al, 2006;Mishra et al, 2021), water clarity (Barnes et al, 2013;Zheng et al, 2016;Kuhn et al, 2019), and coastal/ocean productivity (Ho et al, 2017;Astuti et al, 2018;Caballero et al, 2020). A variety of satellites have been used for impact assessment such as Landsat-8 (Nanda et al, 2020;Patel et al, 2020;Yunus et al, 2020), PlanetScope (Niroumand-Jadidi et al, 2020), Sentinel-2 (Braga et al, 2020;Garg et al, 2020), Sentinel-3 (Cherif et al, 2020;Mishra et al, 2020), and MODIS (Gaiser et al, under revision).…”

Section: Introductionmentioning

confidence: 99%

Effect of COVID-19 Anthropause on Water Clarity in the Belize Coastal Lagoon

et al. 2021

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The Coronavirus disease 2019 (COVID-19) pandemic halted human activities globally in multiple sectors including tourism. As a result, nations with heavy tourism, such as Belize, experienced improvements in water quality. Remote sensing technologies can detect impacts of “anthropauses” on coastal water quality. In this study, moderate resolution imaging spectroradiometer (MODIS) satellite data were employed along the Belizean coast to investigate impacts of the COVID-19 shutdown on water quality. The attenuation coefficient at 490 nm, Kd(490), was used as an indicator of water quality, with a lower Kd(490) indicating increased water clarity. Four Coastal Management Zones were characterized by marine traffic as high traffic areas (HTAs) and two as low traffic areas (LTAs). Monthly composites for two periods, 2002–2019 (baseline) and 2020 were examined for Kd(490). For months prior to the COVID-19 shutdown in Belize, there was generally no significant difference in Kd(490) (p > 0.05) between 2020 and baseline period in HTAs and LTAs. Through the shutdown, Kd was lower in 2020 at HTAs, but not for LTAs. At the LTAs, the Kd(490)s observed in 2020 were similar to previous years through October. In November, an unusually active hurricane season in 2020 was associated with decreased water clarity along the entire coast of Belize. This study provides proof of concept that satellite-based monitoring of water quality can complement in situ data and provide evidence of significant water quality improvements due to the COVID-19 shutdown, likely due to reduced marine traffic. However, these improvements were no longer observed following an active hurricane season.

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Section: Introductionmentioning

confidence: 99%

Effect of COVID-19 Anthropause on Water Clarity in the Belize Coastal Lagoon

et al. 2021

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“…Some studies recently, explored, has been the retrieval of ground PM 2.5 directly from the top‐of‐atmosphere (TOA) reflectance data of MODIS, while omitting the intermediate process of avoiding AOD retrieval (J. Liu et al., 2019 ; Yan et al., 2020 ; Yang et al., 2020 ). A multispectral empirical model has also, been proposed to estimate the concentration of PM 2.5 using Landsat 8 OLI and by taking into account the corresponding meteorological parameters (Mishra et al., 2021 ). The results have shown a high degree of accuracy.…”

Section: Introductionmentioning

confidence: 99%

Estimation of On‐Road PM_2.5 Distributions by Combining Satellite Top‐of‐Atmosphere With Microscale Geographic Predictors for Healthy Route Planning

Tong

Shi

et al. 2022

GeoHealth

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How to reduce the health risks for commuters, caused by air pollution such as PM 2.5 has always been an urgent issue needing to be solved. Proposed in this study, is a novel framework which enables greater avoidance of pollution and hence assists the provision of healthy travel. This framework is based on the estimation of on‐road PM 2.5 throughout the whole city. First, the micro‐scale PM 2.5 is predicted by land use regression (LUR) modeling enhanced by the use of the Landsat‐8 top‐of‐atmosphere (TOA) data and microscale geographic predictors. In particular, the green view index (GVI) factor derived, the sky view factor, and the index‐based built‐up index, are incorporated within the TOA‐LUR modeling. On‐road PM 2.5 distributions are then mapped in high‐spatial‐resolution. The maps obtained can be used to find healthy travel routes with less PM 2.5 . The proposed framework was applied in high‐density Hong Kong by Landsat 8 images. External testing was based on mobile measurements. The results showed that the estimation performance of the proposed seasonal TOA‐LUR Geographical and Temporal Weighted Regression models is at a high‐level with an R 2 of 0.70–0.90. The newly introduced GVI index played an important role in these estimations. The PM 2.5 distribution maps at high‐spatial‐resolution were then used to develop an application providing Hong Kong residents with healthy route planning services. The proposed framework can, likewise, be applied in other cities to better ensure people's health when traveling, especially those in high‐density cities.

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“…Sorek-Hamer et al [34] proposed the strategy of using satellite products to simulate PM2.5 and short-term pollution events in 2020. Mishra, R.K. et al [35] uses Landsat 8 satellite data to predict the ground PM2.5 concentration in Delhi.…”

Section: Introductionmentioning

confidence: 99%

“…In future research, we will try to use the technology of combining PM2.5 and satellite data [31][32][33][34][35][36]40] to obtain data, and study the correlation of more factors with PM2.5.…”

mentioning

confidence: 99%

Grey Correlation Analysis of Haze Impact Factor PM2.5

Liu

Yin

et al. 2021

Atmosphere

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In recent years, frequent severe haze weather has formed in China, including some of the most populated areas. We found that these smog-prone areas are often relatively a “local climate” and aim to explore this series of scientific problems. This paper uses remote sensing and data mining methods to study the correlation between haze weather and local climate. First, we select Beijing, China and its surrounding areas (East longitude 115°20′11″–117°40′35″, North latitude 39°21′11″–41°7′51″) as the study area. We collected data from meteorological stations in Beijing and Xianghe from March 2014 to February 2015, and analyzed the meteorological parameters through correlation analysis and a grey correlation model. We study the correlation between the six influencing factors of temperature, dew point, humidity, wind speed, air pressure and visibility and PM2.5, so as to analyze the correlation between haze weather and local climate more comprehensively. The results show that the influence of each index on PM2.5 in descending order is air pressure, wind speed, humidity, dew point, temperature and visibility. The qualitative analysis results confirm each other. Among them, air pressure (correlation 0.771) has the greatest impact on haze weather, and visibility (correlation 0.511) is the weakest.

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Predicting Ground Level PM_2.5 Concentration Over Delhi Using Landsat 8 Satellite Data

Cited by 11 publications

References 28 publications

Effect of COVID-19 Anthropause on Water Clarity in the Belize Coastal Lagoon

Effect of COVID-19 Anthropause on Water Clarity in the Belize Coastal Lagoon

Estimation of On‐Road PM_2.5 Distributions by Combining Satellite Top‐of‐Atmosphere With Microscale Geographic Predictors for Healthy Route Planning

Grey Correlation Analysis of Haze Impact Factor PM2.5

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Predicting Ground Level PM2.5 Concentration Over Delhi Using Landsat 8 Satellite Data

Cited by 11 publications

References 28 publications

Effect of COVID-19 Anthropause on Water Clarity in the Belize Coastal Lagoon

Effect of COVID-19 Anthropause on Water Clarity in the Belize Coastal Lagoon

Estimation of On‐Road PM2.5 Distributions by Combining Satellite Top‐of‐Atmosphere With Microscale Geographic Predictors for Healthy Route Planning

Grey Correlation Analysis of Haze Impact Factor PM2.5

Contact Info

Product

Resources

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Predicting Ground Level PM_2.5 Concentration Over Delhi Using Landsat 8 Satellite Data

Estimation of On‐Road PM_2.5 Distributions by Combining Satellite Top‐of‐Atmosphere With Microscale Geographic Predictors for Healthy Route Planning